nitrogen fixing bacteria experiment

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Nitrogen-fixing bacteria free-living in the soil, class practical.

In this activity, students will culture a free-living nitrogen-fixing bacterium (Azotobacter) from the soil. This will reinforce understanding of the role of bacteria in the nitrogen cycle .

Lesson organisation

Make students aware of procedures for safe handling of microbial material when dealing with their incubated plates.

You may be able to set up this investigation alongside the investigation of nitrogen-fixing microbes from legume root nodules in the same lesson. If so, make sure the students do not mix up the plates of different media from the two activities.

Apparatus and Chemicals

For each group of students:.

Nutrient agar plate, 1 (see Standard technique Pouring an agar plate )

Nitrogen-free mineral salts agar plate, 1 (see Standard technique Making up nutrient agars )

Spatula or forceps to dispense soil, 1

Marker pens

Adhesive tape

For the class – set up by technician/ teacher:

Sterile specimen bottles containing samples of a soil that readily forms small crumbs, no more than 1 bottle per group

Nitrogen-free mineral salts agar medium, sterilised in McCartney bottles or poured as plates

Nutrient agar medium, sterilised in McCartney bottles or poured as plates

Nitrogen-free mineral salts agar plate, to be left unopened as a control, 1

Nutrient agar plate, to be left unopened as a control, 1

Disinfectant to clean the work area before and after the investigation ( Note 2 )

Health & Safety and Technical notes

Carry out a full risk assessment before planning any work in microbiology (see note 1 for more details).

Check the standard techniques for more details of Making up nutrient agars , Pouring an agar plate , Aseptic techniques and Incubating and viewing plates .

Read our standard health & safety guidance

2 Suitable disinfectants include sodium chlorate(I) (hypochlorite) at concentrations greater than 1% (refer to CLEAPSS Hazcard 89), or Virkon used according to manufacturer’s instructions. Wear eye protection when handling disinfectants.

SAFETY: Tape plates to keep them closed but not sealed.

Incubate up to but not above 25°C.

Wear eye protection when handling disinfectants.

Preparation a Calculate the quantity required. Prepare just enough nitrogen-free mineral salts, agar medium, and nutrient agar medium for the investigation (12-15 ml for normal depth in a 90 mm Petri dish). Refer to Standard technique: Making up nutrient agars for the details of the recipes. Any surplus will keep for 6-12 months in tightly-sealed screw-top bottles if sterile and stored out of direct sunlight. Or you can buy prepared plates (see Suppliers).

b Distribute the agar into individual McCartney bottles. Sterilise them if you want the students to pour their own plates - see Standard techniques: Pouring an agar plate . Mix to disperse the CaCO 3 in the nitrogen-free mineral salts agar medium before pouring.

c Prepare a suitable solution to disinfect the work area for the investigation and afterwards.

d Collect sterile sample bottles or Petri dishes (or sterilise some) for the soil samples.

Investigation a Leave one plate of each type of culture medium unopened as a control. (See also answers to student notes Q1 for other controls that could be run.)

b Collect one of each type of agar plate. Label one with initials, date and ‘nutrient agar’. Label the other with initials, date, and ‘N-free’.

c Using the spatula or forceps, place 10-20 small crumbs of soil evenly over the surface of each plate.

d Tape the lids closed with adhesive tape and incubate with the lid uppermost at 20-25°C for 2-3 days. Incubating this way up is unusual, but otherwise the soil crumbs will fall off the agar media. See Standard technique Incubating and viewing plates .

e Next lesson, describe the appearance of the colonies on the two types of agar media. Do not open the plates. Mucoid (sticky) colourless growth oozing from soil particles on the nitrogen-free mineral salts agar medium indicates the presence of Azotobacter . Look for any other types of growth you think are likely to be of nitrogen-fixing microorganisms.

Teaching notes

Free-living nitrogen-fixing bacteria fix nitrogen by reducing gaseous nitrogen in the air to ammonia. This is incorporated into organic compounds which can be used by plants. An enzyme complex called nitrogenase catalyses this reaction. Nitrogenase activity is sensitive to the presence of oxygen. Azotobacter (an aerobic nitrogen fixer) has the highest respiratory rate of any organism. This enables it to remove oxygen rapidly from its surroundings through its own respiration. The mucoid slime material produced by the bacteria also aids protection from oxygen.

Nitrogen-fixing bacteria will be unable to compete with other non-nitrogen-fixing soil microbes for nutrients on the nutrient agar medium, but will have an advantage on the selective nitrogen-free mineral salts agar medium. Therefore, colonies of nitrogen-fixing bacteria will grow well on the nitrogen-deficient medium.

Health & Safety checked, June 2008

www.microbiologyonline.org.uk/teachers/resources Society for General Microbiology – source of Basic Practical Microbiology, an excellent manual of laboratory techniques and publishers of Practical Microbiology for Secondary Schools, a selection of tried and tested practicals using microorganisms. These include the original procedure for Nitrogen-fixing bacteria which is also available here.

(Website accessed October 2011)

• Open access
• Published: 16 October 2023

Screening of high-efficiency nitrogen-fixing bacteria from the traditional Chinese medicine plant Astragalus mongolicus and its effect on plant growth promotion and bacterial communities in the rhizosphere

• Zhiyong Shi 1 ,
• Zhenhong Lei 2 ,
• Yuanyuan Wang 1 ,
• Zhenyu Yang 1 ,
• Jingping Niu 1 &
• Jianping Liang 1 , 3  

BMC Microbiology volume  23 , Article number:  292 ( 2023 ) Cite this article

3284 Accesses

5 Citations

Metrics details

Astragalus mongolicus Bunge is used in traditional Chinese medicine and is thus cultivated in bulk. The cultivation of A. mongolicus requires a large amount of nitrogen fertilizer, increasing the planting cost of medicinal materials and polluting the environment. Isolation and screening of plant growth-promoting rhizobacteria (PGPR) and exploring the nitrogen fixation potential of A. mongolicus rhizosphere microorganisms would effectively reduce the production cost of A. mongolicus .

This study used A. mongolicus roots and rhizosphere soil samples from Longxi County of Gansu Province, Jingle County, and Hunyuan County of Shanxi Province, China, to isolate and identify nitrogen-fixing bacteria. Through nitrogen fixation efficiency test, single strain inoculation test, and plant growth-promoting characteristics, three strains, Bacillus sp. J1, Arthrobacter sp. J2, and Bacillus sp. G4 were selected from 86 strains of potential nitrogen-fixing bacteria, which were the most effective in promoting the A. mongolicus growth and increasing the nitrogen, phosphorus, and potassium content in plants. The antagonistic test showed that these bacteria could grow smoothly under the co-culture conditions. The J1, J2, and G4 strains were used in a mixed inoculum and found to enhance the biomass of A. mongolicus plants and the accumulation of the main medicinal components in the field experiment. Mixed bacterial agent inoculation also increased bacterial diversity and changed the structure of the bacterial community in rhizosphere soil. Meanwhile, the relative abundance of Proteobacteria increased significantly after inoculation, suggesting that Proteobacteria play an important role in plant growth promotion.

Conclusions

These findings indicate that specific and efficient PGPRs have a significant promoting effect on the growth of A. mongolicus , while also having a positive impact on the structure of the host rhizosphere bacteria community. This study provides a basis for developing a nitrogen-fixing bacterial fertilizer and improving the ecological planting efficiency of A. mongolicus.

Peer Review reports

Introduction

Traditional agriculture relies heavily on chemical fertilizers and pesticides to improve agricultural productivity to feed the growing world population [ 1 ]. These practices are costly and lead to the degradation of arable land, which harms the environment. This is especially true for the overuse of chemical fertilizer [ 2 ] as, disregarding the balance between land use and nutrition, plants will only absorb 30-40% of the nitrogen fertilizer [ 3 ]. This will result not only in reduced fertility but also in soil acidification, decreased organic matter content [ 4 ], water eutrophication [ 5 ], and nitrate pollution in groundwater and drinking water [ 3 ], causing severe diseases and the proliferation of pests. Therefore, using plant growth-promoting rhizobacteria (PGPR) in agriculture may be a sustainable and environmentally friendly solution to reduce the problems associated with the overuse of chemical fertilizer [ 6 ]. PGPR are microorganisms that live in the plant rhizosphere and have been shown to promote plant growth, control diseases, and increase crop yields [ 7 ]. PGPR can promote plant growth through direct and indirect mechanisms [ 8 ]. Direct mechanisms refer to specific bacterial traits that directly promote plant growth, including the production of auxin, 1-aminocyclopropane-1-carboxylate (ACC), deaminase, cytokinin, gibberellin, nitrogen fixation, phosphorus dissolution, and iron chelation by bacterial iron carriers. Indirect mechanisms are related to bacterial characteristics that inhibit the function of one or more plant pathogenic organisms (fungi and bacteria), including ACC deaminase, antibiotics, cell wall-degrading enzymes, hydrogen cyanide, and induced systemic resistance [ 9 , 10 ]. In addition to these methods for controlling plant pathogens, PGPR can selectively use phages to biocontrol certain bacterial pathogens [ 11 ]. Although PGPR is a common resident in the soil, their numbers are not sufficient to compete with other bacteria established in the rhizosphere. Therefore, it is necessary to inoculate PGPR to increase the number of target microorganisms in the soil and to maximize their beneficial effects on the plant yield.

Bacterial fertilizer using PGPR as the raw material can add a large number of microorganisms to the soil to improve the nutritional environment of crops [ 12 ]. Bacterial fertilizer plays an important role in improving soil fertility and the fertilizer utilization rate as it can rapidly proliferate, resulting in increased numbers of beneficial bacteria that provide nutrition for plants, assist in nutrient absorption, promote growth, and enhance plant resistance to pathogens thus reducing both disease and pests [ 13 ]. However, the application and development of traditional bacterial agents are restricted by poor environmental adaptability, low inoculation efficiency, and unstable inoculation. In recent years, bacterial fertilizers composed of single-strain and single-function microorganisms have been replaced by fertilizers containing a variety of strains and functions [ 14 ].

The efficacy of microbial fertilizers with nitrogen-fixing bacteria as the main constituent is affected by many factors, including crop type, amount of bacterial fertilizer, and soil moisture and organic matter content [ 15 ]. Although nitrogen-fixing bacteria are usually present in traditional biological fertilizers and are effective with certain crop varieties, they have limited efficacy due to low matching with the host plant, weak competition with indigenous customized rhizosphere bacteria, and poor adaptability to the soil environment [ 16 ]. Therefore, it is necessary to isolate specific nitrogen-fixing bacteria from the host as the active ingredient of the inoculant. Rhizosphere colonization is a prerequisite for PGPR to affect plants [ 17 ]. The isolation and study of nitrogen-fixing bacteria and their nitrogen-fixing effect from the host rhizosphere environment will help to improve bacterial fertility and develop specific nitrogen-fixing bacteria fertilizers.

Astragalus mongolicus  Bunge ( A. mongolicus ) is a perennial leguminous herb, growing mainly in Shanxi Province, Gansu Province, and Inner Mongolia Province, amongst other places in China [ 18 ]. A. mongolicus is often used as a traditional Chinese medicine because of its functions: Tonifying Qi and strengthening the exterior, tonifying spleen and Qi, diuresis, and detumescence [ 19 ]. At the same time, it is also widely used in various clinical disciplines as it is efficacious in treating various conditions and disorders. The main secondary metabolites of A. mongolicus include flavonoids, saponins, and polysaccharides, all of which have a wide range of pharmacological effects. In recent years, the market consumption of A. mongolicus has increased, resulting in the gradual depletion of wild A. mongolicus resources [ 20 ]. Thus, A. mongolicus for commercial use is largely artificially cultivated to meet the market demand. The application of chemical fertilizer is generally used in Gansu and Shanxi, the main cultivation areas, to improve the yields, which not only significantly reduces the quality of A. mongolicus but also has adverse effects on the environment. In addition, the nitrogen-fixing bacteria isolated from the other plants bind poorly to A. mongolicus . Therefore, the application of special organic nitrogen-fixing bacteria fertilizer for ecological planting is an appropriate choice to improve the quality of A. mongolicus and implement environmental protection policy.

In the previous study, we isolated two strains of highly efficient rhizobium of A. mongolicus from its authentic producing area. The experiments show that the rhizobium has a strong symbiotic ability with A. mongolicus and high nitrogen fixation efficiency, which can promote the growth of A. mongolicus, and has been applied in field production [ 21 , 22 ]. Meanwhile, efficient non-symbiotic nitrogen fixing bacteria also need to be screened, and then combined with symbiotic rhizobia to form a multi-base combined PGPR bacterial agent, thus promoting the nitrogen utilization of Astragalus more effectively. In this study, we aimed to screen combined nitrogen-fixing bacteria in the rhizosphere of A. mongolicus for plant growth promotion.

In this study, nitrogen-fixing bacteria were isolated from rhizospheres in the A. mongolicus cultivation area, and experiments were conducted to identify high-efficiency nitrogen-fixing bacteria of A. mongolicus . Finally, antagonistic symbiosis experiments were carried out on the isolated high-efficiency nitrogen-fixing bacteria. The symbiotic strains were prepared into multi-species nitrogen-fixing bacteria agents and used in field experiments. The overall objective was to contribute to developing a specific bacterial fertilizer for A. mongolicus to reduce the problems caused by the excessive use of chemical fertilizers.

Materials and methods

Sample collection.

In this study, two-year-old A. mongolicus plants were selected from the major production area, experimental base of Longxi County, Gansu Province, Jingle County, and Hunyuan County, Shanxi Province. The root tissue and rhizosphere soil were collected in sealed bags, numbered, and stored at a low temperature (4 ℃) for later use.

Isolation and purification of potential nitrogen-fixing bacteria

The root tissues were surface-sterilized (washed in 95% ethanol for 1 min, 3% NaOCl for 5 min, a 30s wash in 99% ethanol, and rinsed with sterile water), crushed with tweezers, streaked on Ashby nitrogen-free medium, and cultured at 28 ℃ for 5 d to select a typical single colony. The isolated strains were purified and stored at -80 ℃ for later use.

The fresh rhizosphere soil was diluted with sterile water for 10 4 , 10 5 and 10 6 times respectively to make bacterial suspension. One hundred microliter aliquots of the diluted material were then spread separately on Ashby nitrogen-free medium and cultured at 28 ℃ for 5 d, after which a typical single colony was selected from each inoculum, purified, and stored at -80 ℃ for later use.

Determination of the nitrogen fixation efficiency of strains

The nitrogen fixation efficiency of the stain was measured by the acetylene reduction activity (ARA) assay method. Each strain to be tested was inoculated into 100 mL modified Dobereiner medium and cultured with shaking at 28 ℃ and 120 rpm for 3 days. The ARA assay was conducted following the procedure described in a previous study [ 23 ].

The strains with high nitrogenase activity were preliminarily selected and inoculated into 100 mL Döbereiner nitrogen-free liquid medium respectively and cultured with shaking at 28 °C and 120 rpm for 7 d. The nitrogen content of the supernatant was determined by the micro-Kjeldahl method [ 24 ].

Strain identification

Physiological and biochemical characteristics of strains.

Most physiological and biochemical tests, including citrate hydrolysis, the bromothymol blue (BTB) reaction, ester hydrolysis, Voges-Proskauer (VP) reaction, litmus acidification reaction, and growth at 41 ℃ with 2% sodium chloride, were observed using the methods described by Dong XZ and Cai MY [ 25 ]. Starch hydrolysis and 3-ketolactose hydrolysis were measured by the method described by Smibert and Krieg [ 26 ].

Molecular identification of strains

The strains were inoculated in the enrichment medium and cultured with shaking at 28 ℃ and 180 rpm for 2 days. Then, 3 ml of the culture medium was centrifuged at 10 000 rpm for 3 min to collect the bacteria. Bacterial genomic DNA was extracted according to the instructions of the extraction kit(RTG2401-01 Real-Times Biotechnology Co., Ltd., Beijing, China). The genomic DNA of the tested strain was extracted according to the manual of the reference kit and used for PCR amplification with the bacterial 16S rRNA universal primer 27F (5’-AGAGTTTGATCCTGGCTCAG-3’) /1492R (5’-TACGACTTAACCCCAATCGC-3’). The reaction system and conditions used are shown in Table 1 . The PCR products were detected by 1% agarose gel electrophoresis and sequenced. The sequencing results were compared with bacterial nucleotide sequences using NCBI BLAST. The phylogenetic tree was constructed with Mega-X software to determine the genetic relationships between the strains [ 27 ].

Scanning electron microscope sample preparation

The strains were inoculated in the enrichment medium and cultured with shaking at 28 ℃ and 180 rpm for 2 days. Next, 2 ml of the culture medium was centrifuged at 10 000 rpm for 3 min, and the precipitate was fixed in 2 mL of glutaraldehyde(Solarbio P1126 2.5% glutaraldehyde) fixative solution at 4 ℃ for at least 2 h. The fixed samples were then rinsed with phosphate buffer two to three times and dehydrated in an ethanol gradient of 50%, 70%, 90%, and 100%. After dehydration, the samples were immersed in ethanol-tert-butanol solution for 20 min and replaced in 100% tert-butanol solution twice for 20 min each. The samples were then freeze-dried, sputtered with an ion-sputtering apparatus, and observed and photographed with a scanning electron microscope.

Antagonistic test of strains

The two strains were inoculated on YEM solid plate medium vertically and horizontally, cultured in an incubator at 28 ℃ for 20 min, followed by culturing upside down for 3 days. Bacterial growth at the vertical and horizontal lines intersection was monitored daily. The presence of growth inhibition at the intersection indicates antagonism and that the two strains would not be suitable for mixed culture, while an absence of growth inhibition at the intersection implies no antagonism and suitability for mixed culture.

Strain inoculation test

The strains were inoculated into YEM liquid medium and cultured with shaking at 28 ℃ and 180 rpm for 72 h to obtain the bacterial liquid. At this point, the concentration of bacteria in the medium, measured by spectrophotometry, was greater than 1×1010 CFU.mL-1. The bacterial solution was diluted 1:100 with distilled water, and A. mongolicus seeds were soaked in the bacterial solution (concentration about 108 CFU.mL-1) and then were sown in a sterile seedling substrate, with distilled water treatment as control. The A. mongolicus seedlings were cultured at 26 ℃ and 12 000 lx light intensity with daily light, watered with 2 ml of water daily, transplanted after 15 d, and then watered with 7 ml of water daily. After 30 d, the whole seedlings were dug out, and samples in each group were randomly chosen to determine the biomass and the nitrogen, phosphorus, and potassium contents. The biomass indicators included plant height, root length, and dry weight, both above- and below-ground. The samples were dried and digested to determine the total nitrogen, total phosphorus, and total potassium content using the Kjeldahl method [ 28 ], the molybdenum antimony colorimetric method [ 29 ], and the flame photometer method, respectively [ 30 ].

Preparation of bacterial fertilizer and field inoculation test

The isolated nitrogen-fixing bacteria were individually inoculated into YEM liquid medium and cultured with shaking at 28 ℃ and 180 rpm for 72 h to prepare a mixed bacterial stock solution (the bacterial content was approximately 1×1010 CFU.mL-1). The bacterial stock solution was diluted 100 times into liquid bacterial fertilizer (bacterial content was approximately 108 CFU.mL-1). The field experiment was carried out in May 2019 in the main production fields of A. mongolicus , Hunyuan County, Shanxi Province (39◦516 N, 113◦643 E, altitude: 1238 m). The new A. mongolicus seed from Hunyuan County was used as the test material. The seeds were first soaked with the bacterial solution for an hour and then sown in the experimental field. Distilled water was used as the control treatment. The area of each plot was 167 m2, and the seeding rate was 1.05 kg. Ditches 20 cm wide were made between the rows of A. mongolicus , and the inoculated seeds were spread evenly in the ditch to cover the soil at the base. Each plot test contained three replicates. Random sampling was performed at the June, July, August, September, and October stages of A. mongolicus .

Determination of A. mongolicus biomass and active component content

The soil was cleaned from the surface of the plants, using absorbent paper to absorb the water. After measuring the height and root length of the plants with a ruler, the plants were dried to a constant weight at 70 ℃, and the aerial parts and roots were weighed.

The dry roots were ground and sieved through a 55-mesh sieve. One g of the coarsely ground powder was added to 5% ethanol with a material-to-liquid ratio of 1:20 and flash-extracted for 1 min at 120 V and 4 ℃. After centrifugation at 5000 rpm for 10 min, the supernatant was rotary-evaporated at 55 ℃ and 60 rpm to yield 2 mL of concentrated solution. The concentrated solution was precipitated overnight (12 h) in four times its volume of anhydrous ethanol and centrifuged at 8000 rpm and 4 ℃ for 5 min. Finally, the resultant precipitated and dried polysaccharide was used to determine the total polysaccharide content by the phenol-sulfuric acid method [ 31 ].

After being ground and sieved through a 55-mesh sieve, 1 g of a coarse powder was added to 85% ethanol in a material-to-liquid ratio of 1:20, flash-extracted at 120 V for 1 minute, and centrifuged at 4 ℃ and 5000 rpm for 10 minutes. The supernatant was evaporated at 55 ℃ and 60 rpm to 2 mL of concentrated solution. The concentrated solution was diluted to 10 mL with 30% ethanol and extracted three times with saturated ethyl acetate with water to recover the ethyl acetate layer. The residue was evaporated to dryness in a water bath to obtain the flavonoid sample. The water layer was then extracted with water-saturated n-butanol three times to recover the n-butanol. The residue was then evaporated to dryness in a water bath to obtain a saponin sample. The total flavonoid content was determined by spectrophotometry, and the total saponins were determined by the vanillin-concentrated sulfuric acid method [ 32 ].

High-throughput sequencing and analysis of 16S rRNA

Soil samples before sowing (CK0) and rhizosphere soil samples at 90 days after field experiment conduction (CK: control and T: bacterial fertilizer treatment) were collected in triplicate for bacterial community analysis.

DNA of soil samples was extracted with an Omega E.Z.N.A soil DNA extraction kit. The V3-V4 region of bacterial 16S rRNA genes were amplified using the universal primer pair F338/R806 (F338, 5′-ACTCCTACGGGAGGCAGCAG-3′; R806, 5′-GGACTACHVGGGTWTCTAAT-3′). PCR reactions were performed using TransStart Fastpfu DNA Polymerase 20µl reaction system under the following condition: 95 ℃ for 2 min, followed by 35 cycles at 95 ℃ for 30 s, annealing at 55 ℃ for 1 min, extension at 72 ℃ for 1 min, and a final extension at 72 ℃ for 10 min. Purified amplicons were paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, Inc., Santiago, CA, USA) at Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China).

Illumina raw reads were processed by the QIIME2 pipeline [ 33 ]. The paired-end sequences were merged and renamed according to the sample barcode using Vsearch package. The barcode and primer sequences were trimmed, and then quality filtering, dereplication, clustering, and chimera-removing were conducted in the QIIME2 pipeline. The cleaned sequences were clustered into Operational taxonomic units (OTUs) at a 97% similarity level. Taxonomy annotation of each OTU representative sequence was performed using the blast algorithm and the greengenes database (version 13.5) [ 34 ].

Statistical analysis

Average values and standard deviations were computed according to the experimental data. Using SPSS 25.0 statistical package, a one-way analysis of variance (ANOVA) with Duncan's test was conducted on the data, and a p -value < 0.05 was considered a significant level.

Isolation of nitrogen-fixing bacteria from A. mongolicus

Using YEM solid medium and Ashby nitrogen-free medium, 27 and 59 strains with potential nitrogen fixation ability were isolated from root tissue and rhizosphere soil, respectively. Of these, 30 strains were isolated from Longxi County, Gansu Province, 25 from Jingle County, Shanxi Province, and 31 from Hunyuan County, Shanxi Province.

Screening for nitrogen-fixing efficiency of stains

The results of nitrogen fixation experiments showed that among the 86 strains, 34 strains were able to fix nitrogen (Table 2 ). Among them, 14 strains were isolated from root tissue, and 20 were isolated from soil. Among all the tested strains, 15 strains from Longxi had a nitrogenase activity of 0.45-37.45 nmol C2H4/(mL·h), nine strains from Jingle had a nitrogenase activity of 0.14-67.45 nmol C2H4/(mL·h), and 10 strains from Hunyuan had a nitrogenase activity of 14.32-31.45 nmol C2H4/(mL·h). Among the 34 strains of nitrogen-fixing bacteria, only two strains had a nitrogenase activity higher than 50 nmol C2H4/(mL·h), namely J1 and J2 (collected from Jingle County). The strain J1 (isolated from root tissue) had the highest nitrogenase activity of 67.45 nmol C2H4/(mL·h).

The strains G4, J1, H7 (isolated from root tissue) and strains J2, H3, H13, H16, H20 (isolated from rhizosphere soil) with good growth characteristics and nitrogen fixation efficiency were selected for further analysis (Table 3 ).

Identification of stains

Phenotypic characteristics of strains.

Nitrogen fixation for solid culture, and the phenotypic characteristics of the individual clones of each strain were observed. The results (Table 4 ) showed that although all colonies were white and opaque and the colony surfaces were smooth and moist, no obvious hyphae were visible. The colony characteristics of the eight tested strains varied between species, with the G4 and H3 colonies flowing easily, while the J1 and H7 colonies were raised, flat, and did not flow easily, and the J2, H13, H16, and H20 colonies were raised and flowed easily.

Scanning electron micrographs of strains

The results of the scanning electron microscopy (Fig.  1 ) showed that eight tested strains were all rod-shaped with inconspicuous hyphae. Among them, in J1, the bacilli were clustered with a size range of 0.1-0.15×0.5-0.75 μm, while J2 and G4 were composed of single bacilli in the size range of 0.1-0.15 ×0.75-8 μm, and H3, H7, H13, H16, H20 were visible as cocci in clusters with diameters of 0.2-0.3 μm.

Electron micrograph of 8 tested strains (J1, J2, G4, H3, H7, H13, H16 and H20)

Physiological and biochemical identification of strains

Physiological and biochemical tests were carried out on the eight selected strains. The results are shown in Table 5 . The results showed that the eight experimental strains lacked esterase, had a low rate of utilization of peptone in beef extract, could not use lactose in milk, could not grow in BTB medium, and did not belong to the Agrobacterium genus. All the experimental strains could degrade citric acid, have good heat resistance and salt tolerance, and use citrate. All strains except H7, H13, and H20 contained contact enzymes. All strains except H16 contained amylase. Strains J2, H3, and H16V-P had negative reactions, and strains G4, J1, H7, H13, and H20V-P had positive reactions.

The 16S rDNA sequences of the screened bacteria were compared using NCBI BLAST, and the phylogenetic tree was constructed. As shown in Fig.  2 , all tested strains were phylogenetically distributed with five genera: Agrobacterium, Pseudomonas, Bacillus, Paenarthrobacter , and Arthrobacter . The 16S rRNA sequence analysis showed that Agrobacterium included three genotypes. The H7, H13, and H20 strains were the most similar to the reference strain Agrobacterium fabacearum CNPSo 675, with a similarity of 99.8%. Pseudomonas included one genotype. The similarity between strain H16 and Pseudomonas_silesiensis A3 was 99.3%. Bacillus contained two genotypes. Strains J1 and G4 were the closest to the reference strain Bacillus subtilis JCM 1465, and the similarity was 99.8%. Paenarthrobacter included one genotype. Strain H3 was identical (100% similarity) to Paenarthrobacter nitroguajacolicus G2-1 . Arthrobacter included one genotype, and the similarity between strain J2 and Arthrobacter pascens DSM 20545 was 99.9%.

Molecular identification of 8 tested strains of nitrogen-fixing bacteria

Effects of the different strains on A. mongolicus

Effects of the strains on the biomass of a. mongolicus.

According to the results of the inoculation experiment shown in Figs. 3 and 4 , compared with the control group, the inoculated seedlings showed varying degrees of changes in plant height, root length, above-ground dry weight, and root dry weight. Among them, strains J1, J2, G4, H13, and H20 significantly increased the A. mongolicus plant height. The growth effects of J1, J2, and G4 on the plant roots were obvious. The plants' dry weights of aerial parts were significantly increased by the J1, J2, G4, H7, and H20 strains, while J1, J2, G4, H13, H16, and H20 significantly promoted the root dry weight. Based on the above data, we concluded that the growth of A. mongolicus was most significantly promoted by the J1, J2, and G4 strains.

Effect of the tested strain on the height and root length of A. mongolicus . Different letters indicate the significant difference between treatments according to ANOVA with Duncan's test ( p  < 0.05)

Effect of test strain on the dry weight of A. mongolicus . Different letters indicate the significant difference between treatments according to ANOVA with Duncan's test ( p  < 0.05)

Effects of the strains on nitrogen, phosphorus, and potassium contents of A. mongolicus

The nitrogen, phosphorus, and potassium contents of A. mongolicus seedlings after inoculation with different strains are shown in Fig. 5 . It can be seen from the results that different strains had different effects on these indicators. In the natural growth state (CK), the nitrogen content of the seedlings was 3.68%, the phosphorus content was 5.28%, and the potassium content was 3.71%. Except for strain H16, the treatment indices of the other strains were significantly higher than those of the control. Compared with the control, the seedlings' nitrogen, phosphorus, and potassium contents were significantly increased by 8.07%-46.32%, 38.07%-117.96%, and 7.74%-41.66%, respectively. Strain J2 had the most obvious effect on nitrogen content, with a significant increase of 46.32%, followed by G4, which increased by 38.86%; The effect of strain J2 on the potassium content was the most significant, increasing by 41.66%, followed by J1 which increased by 32.08%. The phosphorus content of seedlings inoculated with strain J1 increased the most by 117.96%, followed by G4 with 91.67%. The growth-promoting effect of the strains was consistent with the increases in nitrogen, phosphorus, and potassium contents. It could be seen that inoculation of the tested strains could significantly promote not only the growth of A. mongolicus seedlings but also the absorption and utilization of nitrogen, phosphorus, and potassium. Moreover, the strains could effectively promote the absorption of phosphorus by the plant. The increase in the phosphorus content indicates an increase in the ATP content in the plants, which provides a material basis for both rapid growth and metabolism.

Effects of the tested strain on nitrogen, phosphorus, and potassium contents of A. mongolicus . Different letters indicate the significant difference between treatments according to ANOVA with Duncan's test ( p  < 0.05)

Antagonism of strains

Experiments to test antagonism were performed with the three J1, J2, and G4. It can be seen from Fig. 6 that the three bacteria did not affect each other's growth at the junction, indicating that the three bacteria are not antagonistic to each other and can thus be co-cultured.

Experimental results of paired antagonism test of strains J1, J2, and G4 (A: J1 and J2, B: J1 and G4, C: J2 and G4)

Growth-promoting Effects of mixed bacterial fertilizer on A. mongolicus

Effects of mixed microbial agents on the biomass of a. mongolicus.

We selected the better-performing J1, J2, and G4 strains to make a mixed bacterial liquid used to dress the A. mongolicus seeds in an artificial climate chamber, after which they were sown in the field. The plant height, root length, and dry weights of the aerial parts and roots of the plants at different growth stages were measured and compared with the plants seeded with distilled water. As shown in Fig. 7 (A, B, C, D), the bacterial fertilizer significantly enhanced the plant height, root length, and dry weights of the above-ground parts and roots of the plants during the different growth periods. Compared with the control group, there are significant differences in plant height in June, July, September, and October. The difference in September is extremely significant. Plant height increased the most in September, an increase of 23.66% compared to the control. Root growth occurred in June, July, and August, and the difference between September and October is extremely significant. The root length increased the most in September, an increase of 56.16% compared to the control. The difference in root dry weight is significant between June and July, August and September, and the difference in October was extremely significant. The root dry weight increased the most in October, 68.06%, compared to the control; the dry leaf weight was extremely different in July, August, and October. The dry weight of the above-ground part increased the most in August, an increase of 60.18% compared to the control.

Effects of bacterial fertilizer on biomass and effective components of A. mongolicus . A is the effect on the plant height, ( B ) is the effect on the root length, ( C ) is the effect on the dry weight of the aerial part of A. mongolicus , ( D ) is the effect on the dry weight of the root, E is the effect on the content of astragalus flavone, ( F ) is the effect on the content of astragaloside, ( G ) is the effect on the content of astragalus polysaccharide. *, **, and *** indicate significant differences between bacterial fertilization and control at p  < 0.05, p  < 0.01, and p  < 0.001, respectively

Effects of mixed microbial agents on the active components of A. mongolicus

As shown in Fig. 7 (E, F, G), the bacterial fertilizer could significantly promote the accumulation of flavonoids, saponins, and polysaccharides, which are the main active components of A. mongolicus . The flavonoid content differed significantly in June, July, and August. The flavonoid content reached the largest difference in June, which increased by 23.41% compared to the control. The saponin content significantly differed in June, August, September, and October. The difference is extremely significant in October. The difference in saponins content reached the largest in September, an increase of 41.21% compared to the control; the polysaccharide content was significantly different in July, September, and October. The polysaccharide content difference reached the largest in July, an increase of 44.87% compared to the control.

Effects of mixed microbial agents on soil bacterial communities of A. mongolicus

The bacterial community of rhizosphere soil samples was analyzed using the Illumina MiSeq PE300 platform and QIIME2 pipeline. A total of 109821 raw reads were sequenced and finally clustered into 2655 OTUs for bacteria after removing singletons. Shannon index showed that the diversity of the bacteria community was significantly improved after planting A. mongolicus ( p < 0.05). However, diversity indices between experimental and control groups showed no significant differences in bacterial diversity ( p > 0.05) (Fig. 8 A). Meanwhile, PCoA analyses were performed to evaluate the structural similarities between bacterial communities in all groups. The result indicated that the bacterial communities in the treatment and control groups had a significant difference (Fig. 8 B).

Effect of bacterial fertilizer on the bacterial diversity and community structure. A  Shannon index of the bacterial community, ( B ): principal coordinate analysis of bacterial communities in different groups. CK0, soil samples before sowing. CK, rhizosphere soil samples at 90 days after planting without fertilizer, T, rhizosphere soil samples at 90 days after planting with bacterial fertilizer treatment. Different letters indicate the significant difference between treatments according to ANOVA with Duncan's test ( p  < 0.05)

The dominant bacterial phyla identified in the special manure group were Actinobacteriota, Proteobacteria, Acidobacteriota, Chloroflexi, Gemmatimonadota, Methylomirabilota, and Bacteroidota. In the treatment group, proteobacteria increased from the relative abundance of 17.88% before sowing (CK0) to 20.48% (T) and relatively decreased to 16.27% in the control group (CK). Compared with that before sowing, the abundance of Acidobacteriota decreased in the treatment group (13.53%) and the control group (16.11%), and the decrease was more significant in the treatment group. The abundance of Actinobacteriota was the highest in all groups and remained stable (average: 33.21%) (Fig. 9 A). At the order level, Rhizobiales, Vicinamibacterales, Gaiellales, Burkholderiales, Solirubrobacterales and Micrococcales were the dominant bacterial order. Rhizobiales, Burkholderiales and Solirubrobacterales were increased in the treatment group and decreased in the control group. Correspondingly, the trend of Vicinamibacterales and Micrococcales is the opposite (Fig. 9 B).

Relative abundance of bacteria at phyla ( A ) and order ( B ) levels of different groups. CK0: soil samples before sowing. CK: rhizosphere soil samples at 90 days after planting without fertilizer, T: rhizosphere soil samples at 90 days after planting with bacterial fertilizer treatment

Lefsee method was used to analyze the statistical differential abundance of rhizosphere bacterial community among groups. The results showed that Rhizobiaceae, Sphingomonadaceae, Xanthobacteraceae, Micromonosporaceae and Hyphomicrobiaceae showed the largest difference in treatment group. Meanwhile, Oxalobacteraceae, Comamonadaceae and Devosiaceae had the largest difference in control group (Fig.  10 ).

Lefsee analysis of significantly different in rhizosphere soil bacteria at family leval. CK: rhizosphere soil samples at 90 days after planting without fertilizer, T: rhizosphere soil samples at 90 days after planting with bacterial fertilizer treatment

In this study, 86 strains of nitrogen-fixing bacteria were isolated from the root tissue and rhizosphere of A. mongolicus in Jingle County, Shanxi Province, Hunyuan County, Shanxi Province, and Longxi County, Gansu Province. Previous studies have shown that it is difficult to isolate high-efficiency nitrogen-fixing bacteria from the rhizosphere of leguminous plants such as Camellia oleifera [ 17 ], and the numbers of high-efficiency nitrogen-fixing bacteria isolated in this study were also less. It is speculated that the population numbers of nitrogen-fixing bacteria in the rhizosphere of A. mongolicus are relatively small, suggesting limited competition with other microorganisms.

Eight species of bacteria isolated from the A. mongolicus rhizosphere were identified by 16S rRNA sequencing. These eight species were distributed over five genera, namely, Agrobacterium [ 35 ], Pseudomonas [ 36 ], Bacillus [ 37 ], Paenarthrobacter [ 38 ], and Arthrobacter [ 39 ] with nitrogen-fixation ability. Some strains that can fix nitrogen also have other functions. For example, Agrobacterium is an important vector in genetic engineering, Paenarthrobacter [ 40 ] and Arthrobacter [ 41 ] are used for treating industrial wastewater and soil pollution, and for promoting plant growth, Bacillus is an important industrial microorganism used in cellulose degradation [ 42 ], while Pseudomonas is one of the most studied bacteria for use in biocontrol applications [ 43 ]. The results further indicated that regional differences influenced microbial species and that strains isolated from the same host in different regions may have relatively distant genetic relationships. The rhizosphere microbial community structure of the same host in the same area is also very complex.

Many studies have described the screening and application of nitrogen-fixing bacteria in legumes, including soybean [ 44 ], chickpea [ 45 ], and alfalfa [ 46 ]. Studies have shown that nitrogen-fixing bacteria can significantly increase yields and improve soil fertility and soil microbial community structure [ 47 ], as well as significantly impacting the protein content [ 48 ]. Studies have shown that nitrogen, phosphorus, and potassium are essential nutrients for the growth and development of higher plants. They are components of various enzymes that participate in many physiological and metabolic processes and significantly impact the development and quality of plants. In this study, after grafting the nitrogen-fixing bacterial strains, it was found that nitrogen absorption by the plant could promote the simultaneous absorption of phosphorus and potassium, thus promoting seedling growth. This may be due to the increase in nitrogen content, which promotes the metabolism of plants, leading to greater absorption of both phosphorus and potassium as well as ATP synthesis, thus promoting plant growth and development [ 49 ]. After inoculation of the three bacterial fertilizers, the A. mongolicus biomass and contents of the three effective components increased compared with the control. After the bacterial fertilizer application, the plant height, root length, and root dry weight of A. mongolicus all increased, and with the fastest growth seen from the July stage to the August stage, the August stage to the September stage. The effects of the nitrogen-fixing bacteria on growth were the most obvious, with the dry weight of the aerial part initially rising and then dropping, reaching a peak during the August period. As the leaves begin to decay after the August period, the dry weight of the above-ground part of the plant decreases more rapidly. The three effective components of A. mongolicus all showed increased accumulation, with the greatest accumulation seen from the growth period to the July period. The accumulation of A. mongolicus flavonoids and saponins decreased slightly from the August stage to the September stage, which may be due to the consumption of material and energy during fruit formation in this period. Plants accumulate energy and secondary metabolites to prepare for the winter. A. mongolicus polysaccharides continued to accumulate before the September period, reaching a maximum during the September period, after which accumulation declined from the September period to the October period. This may be due to the conversion of polysaccharides into other secondary metabolites by plants over the winter.

The mixed inoculation of three kinds of bacteria can significantly change the structure of bacterial flora in rhizosphere soil, which may be the main factor in promoting the growth and accumulation of active components of A. mongolicus . The Shannon index showed that the diversity of bacterial community increased significantly with the growth of plants, and the increase was more significant at bacterial agent inoculation treatment. This is consistent with previous reports [ 50 ]. However, in this experiment, this difference was insignificant between the treatment and control groups. However, the PCoA analysis results showed significant differences in bacterial community structure between the two groups. This suggests that the strains introduced with the inoculation can significantly affect the composition of the local flora.

The relative abundance of rhizosphere soil bacterial community shows four predominant phyla, Actinobacteriota, Proteobacteria, Acidobacteriota, and Chloroflexi. The bacterial community structure was changed after bacterial fertilizer inoculation. In this study, Proteobacteria increased significantly after inoculation compared with the control group. Proteobacteria is considered to play an important role in biological nitrogen fixation, carbon utilization, and promoting plants to resist environmental stress [ 51 ]. Actinobacteriota and Chloroflexi changed little among the groups, indicating that they played an important role in maintaining the stability of bacterial communities in the rhizosphere.

In this study, we isolated three strains of nitrogen-fixing bacteria (J1, J2, and G4) from root tissue and rhizosphere soil of A. mongolicus , which have the advantage of high nitrogen fixation efficiency, good plant growth promotion, and a lack of antagonism to each other. These strains were mixed to form a liquid bacterial fertilizer. Through field experiments, it was found that this bacterial fertilizer significantly promoted plant growth and the main medicinal component accumulation of A. mongolicus. Meanwhile, the inoculation of nitrogen-fixing bacterial fertilizer can change the structure of the rhizosphere microbial community and significantly increase the relative abundance of Proteobacteria. These findings provide a basis to apply these nitrogen-fixing bacteria as a good PGRP agent for future scientific cultivation of A. mongolicus .

Availability of data and materials

The data presented in this study are deposited in NCBI (accession numbers: OP297390-OP297397 and PRJNA873723). Further inquiries can be directed to the corresponding author.

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This research was funded by the project of the National Key Research and Development Program of China (Grant No. 2019YFC1710800), Shanxi NongGu construction and scientific research project (SXNGJSKYZX201905), The earmarked fund for Modern Agro-industry Technology Research System in Shanxi province (Grant No. 2021–11), Hengshan Huangqi Industry Research Institute Project (XDHZHQY2022-04), The science and technology support project for 'Special' and 'Excellent' agricultural high-quality development in Shanxi province (TYGC-47), Science and Technology Innovation Funds of Shanxi Agricultural University (2018YJ31) and Scientific and technological Innovation Project of Colleges and Universities in Shanxi Province (2022L101). The funding body played no role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.

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ZS and JL conceived and designed the study. ZS, ZL, XG, YW performed the experiments. ZS, JN analyzed the data. ZS, JN and JL wrote the manuscript. All the authors have read and approved the final manuscript.

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Shi, Z., Guo, X., Lei, Z. et al. Screening of high-efficiency nitrogen-fixing bacteria from the traditional Chinese medicine plant Astragalus mongolicus and its effect on plant growth promotion and bacterial communities in the rhizosphere. BMC Microbiol 23 , 292 (2023). https://doi.org/10.1186/s12866-023-03026-1

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Nitrogen Fixation Definition and Processes

Nitrogen is an essential component of amino acids, proteins, and DNA, making it fundamental for life. Yet, despite making up approximately 78% of Earth’s atmosphere, atmospheric nitrogen (N 2 ) is not directly usable by most living organisms. This is where the crucial process of nitrogen fixation comes into play.

A Simple Definition of Nitrogen Fixation

Nitrogen fixation converts or ‘fixes’ nitrogen into a form organisms can use. It is the conversion of atmospheric nitrogen (N 2 ) into ammonia (NH 3 ) or related nitrogenous compounds, which are assimilated by plants and subsequently enter the food chain.

Why Is Nitrogen Fixation Important?

Even though nitrogen is abundant in the atmosphere, its triple bond makes it chemically stable and challenging for most organisms to utilize. Only specific bacteria and certain processes break this bond and ‘fix’ nitrogen into a biologically usable form.

So, the primary importance of nitrogen fixation is that it converts nitrogen into a form humans, other animals, plants, and other organisms use. Nitrogen is essential in proteins, nucleic acids, and other molecules.

• Bioavailability: Most organisms cannot directly use atmospheric nitrogen. Nitrogen fixation converts it into compounds like ammonia, which plants can absorb and utilize. Animals get their nitrogen by eating plants or by eating other animals.
• Fertility: In natural ecosystems, the availability of fixed nitrogen often limits plant growth. Nitrogen-fixing organisms thus play a key role in maintaining soil fertility.

Our understanding of nitrogen fixation goes back to the 18th century and continues today:

• 1768: Carlos Linnaeus: This famous Swedish botanist was among the first to note that certain plants, which were later identified as legumes, grew well in soils that were considered too poor to support most other plants.
• 1823: Jean-Baptiste Boussingault: A French agricultural chemist, Boussingault was the first to conclusively demonstrate that plants do not directly absorb free nitrogen from the air. His studies also hinted that legume plants had a unique method of nitrogen acquisition.
• 1886: Hermann Hellriegel and Hermann Wilfarth: These German agronomists discovered the key to how legumes acquire nitrogen. They found that nodules on legume roots fix atmospheric nitrogen. When these nodules were absent or ineffective, the plants couldn’t thrive in nitrogen-deficient soils.
• 1901: Martinus Beijerinck: This Dutch microbiologist identified that the bacteria living within these root nodules were the real heroes of the nitrogen fixation process. He showed that Rhizobium bacteria in the nodules converted atmospheric nitrogen into a form plants could use.
• 1918: Fritz Haber: While his work wasn’t directly related to biological nitrogen fixation, this German chemist’s invention of the Haber Process revolutionized nitrogen fixation on an industrial scale, allowing for large-scale synthesis of ammonia. This had major implications for agriculture, providing an artificial source of fixed nitrogen for fertilizers.

The Role of Nitrogen Fixation in the Nitrogen Cycle

The nitrogen cycle is a series of processes that converts nitrogen in various forms through the environment. Nitrogen fixation plays a key role in the cycle:

• Nitrogen fixation turns nitrogen gas (N 2 ) into ammonia (NH 3 ).
• Nitrification converts ammonia to nitrites (NO 2 ) and then nitrates (NO 3 ).
• Assimilation sees plants absorbing these nitrates.
• Denitrification returns N 2 to the atmosphere, completing the cycle.

Nitrogen fixation involves both biological and physical processes, plus there are commercial processes.

Nitrogen Fixing Bacteria

Bacteria (cyanobacteria or blue-green algae, green sulfur bacteria, purple sulfur bacteria, and anaerobic or methanogenic bacteria) and archaea achieve most of the biological nitrogen fixation. The bacteria are either free-living in soil or in a symbiotic relationships with plants or lichens .

• Free-living Bacteria: Examples include Azotobacter and Clostridium. They fix nitrogen without forming symbiotic relationships.
• Legume-root nodules: Rhizobium and Bradyrhizobium bacteria with legumes like beans and peas.
• Non-leguminous plants: Frankia bacteria with alder trees and some other plants.

The overall chemical reaction for biological nitrogen fixation is:

N 2 + 16 ATP + 16 H 2 O + 8 e – → 2NH 3 + H 2 + 16 ADP + 16 Pi

Most plants do not fix nitrogen. Of those that do, some use all of the nitrogen the bacteria produce. Others leak extra fixed nitrogen into the soil. Nitrogen also enters the soil when plants die. Animals get their nitrogen from plants (indirectly, if they eat other animals).

Nitrogen Fixation by Lightning and UV

During thunderstorms, the energy from lightning breaks nitrogen molecules apart, allowing the atoms to combine with oxygen and form nitrates. Ozone, also formed by lightning, facilitates these reactions. Rainfall carries the nitrates to the ground where it is absorbed by plants.

Ultraviolet light from the Sun also breaks some atmospheric nitrogen so that it forms new compounds.

Industrial Nitrogen Fixation

The Haber process is the prevalent industrial nitrogen fixation process, but there are other methods.

• Frank-Caro Process : Frank and Caro developed a process in 1898 that fixes nitrogen in the form of calcium cyanamide.
• Birkeland-Eyde Process: Invented in 1903 (but based on Henry Cavendish’s 1784 experiments), this process uses electrical arcs to oxidize nitrogen from the air, producing nitrogen oxides which are then converted to nitric acid.
• Haber Process (Haber-Bosch Process): Developed by Fritz Haber in 1909, this process synthesizes ammonia from nitrogen and hydrogen under high temperatures and pressures, using iron as a catalyst.
• Homogeneous Catalysis: A more modern method involves using soluble catalysts to produce ammonia under milder conditions than the Haber process.
• Burris, R.H.; Wilson, P.W. (June 1945). “Biological Nitrogen Fixation”. Annual Review of Biochemistry . 14 (1): 685–708. doi: 10.1146/annurev.bi.14.070145.003345
• Erisman, J.W.; Sutton, M.A.; et al. (October 2008). “How a century of ammonia synthesis changed the world”. Nature Geoscience . 1 (10): 636–639. doi: 10.1038/ngeo325
• Hill, R.D.; Rinker, R.G.; Wilson, H.D. (1979). “Atmospheric Nitrogen Fixation by Lightning”. J. Atmos. Sci . 37 (1): 179–192. doi: 10.1175/1520-0469(1980)037<0179:ANFBL>2.0.CO;2
• Postgate, J. (1998). Nitrogen Fixation (3rd ed.). Cambridge: Cambridge University Press. ISBN 9780521648530.
• Raymond, J.; Siefert, J.L.; Staples, C.R.; Blankenship, R.E. (March 2004). “The natural history of nitrogen fixation”. Molecular Biology and Evolution . 21 (3): 541–554. doi: 10.1093/molbev/msh047

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• Research article
• Open access
• Published: 29 July 2011

Systems biology of bacterial nitrogen fixation: High-throughput technology and its integrative description with constraint-based modeling

• Osbaldo Resendis-Antonio 1 ,
• Magdalena Hernández 1 ,
• Emmanuel Salazar 1 ,
• Sandra Contreras 1 ,
• Gabriel Martínez Batallar 1 ,
• Yolanda Mora 1 &
• Sergio Encarnación 1  

BMC Systems Biology volume  5 , Article number:  120 ( 2011 ) Cite this article

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Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.

In this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris ( bean plant). Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1) extended the metabolic reconstruction reported for R. etli ; 2) simulated the metabolic activity during symbiotic nitrogen fixation; and 3) evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced different effects in nitrogen fixation, all of these in qualitative agreement with observations made in R. etli and other Rhizobiaceas .

Conclusions

In this work we present a genome scale study of the metabolic activity in bacterial nitrogen fixation. This approach leads us to construct a computational model that serves as a guide for 1) integrating high-throughput data, 2) describing and predicting metabolic activity, and 3) designing experiments to explore the genotype-phenotype relationship in bacterial nitrogen fixation.

Biological nitrogen fixation carried out by Rhizobiaceas represents nearly 70 percent of the entire nitrogen transformation required for maintaining life in our biosphere. Simultaneously, nitrogen fixation driven by these bacteria constitutes an appealing and natural strategy for developing sustainable agricultural programs due to its cost-effectiveness in crop improvement and its more environmentally friendly effects in comparison to those produced by chemical fertilizers [ 1 ]. Based on these fundamental and practical issues, the study of bacterial nitrogen fixation is one active line of research that in the post-genomic era demands new paradigms capable of surveying in a systematic fashion the metabolic organization by which this process occurs in nature.

At a molecular level, symbiotic nitrogen fixation arises as a consequence of the coordinated action of a variety of genes, proteins and metabolites that in turn activate signal transduction cascades and transcriptional factors inside bacteroids. At the end of the day, the consequences are the activation and repression of certain metabolic pathways whose end products are required for counteracting the microenvironmental conditions prevailing inside nodules [ 2 – 4 ]. The advent of high-throughput technologies has fostered the genome scale analysis for bacterial nitrogen fixation, and the output data constitute valuable material in deciphering their metabolic organization at different biological layers [ 5 , 6 ]. Although some significant results have been achieved in interpreting the high-throughput data, their overwhelming numbers and heterogeneous composition represent a challenge for inferring biological knowledge in a coherent and systematic fashion. This challenge is, indeed, a central issue in systems biology, and its solution demands integrative efforts among genome scale data, physiological knowledge and computational modeling [ 7 – 11 ].

With the purpose of contributing to this integrative challenge, in this paper we present a systems biology description in bacterial nitrogen fixation. In particular, it integrates high-throughput technology and flux balance analysis in order to explore the metabolic activity of Rhizobium etli bacteroids while they fix nitrogen in symbiotic association with Phaseolus vulgaris (common bean plant) [ 11 ]. To survey the bacterial phenotype and sketch the genetic and metabolic profile during nitrogen fixation, transcriptome and proteome technologies were carried out for R. etli bacteroids selected at 18 days after inoculation with root plants of P. vulgaris (see details in experimental procedure and methods). We selected this interval of time based on experimental knowledge that has indicated it as an average for maximum enzymatic activity of nitrogenase in R etli bacteroids. To identify those genes with a significant role in nitrogen fixation, we accomplished a comparative analysis between the gene expression profile at the nitrogen fixation stage and under free-living conditions in R etli , this last condition mainly defined by succinate and ammonia as carbon and nitrogen sources, respectively (see methods). Simultaneously, the protein profile inside bacteroids was obtained, also at 18 days after plant inoculation. A set of genes with significant participation in bacterial nitrogen fixation was defined by combining those genes differentially expressed in the two physiological conditions--free life and nitrogen fixation-- and those codifying for the proteins detected inside bacteroids. This same set of genes served as our benchmark for extending the metabolic reconstruction for R. etli metabolism ( iOR 363) and evaluating the consistency of the metabolic capacities inferred by the in silico analysis [ 8 ]. To assess the predictive scope of the model, we qualitatively compared the metabolic activity predicted by constraint-based modeling against that which was deduced from the high-throughput data obtained for R etli . Overall, our study represents a significant effort toward the reconstruction of a systems biology platform for studying metabolic activity in bacterial nitrogen fixation. It is characterized by its capacity to integrate and describe high-throughput data and predict the metabolic mechanism underlying bacterial nitrogen fixation.

High-throughput technology to guide the Metabolic Reconstruction

To characterize the gene expression during nitrogen fixation in R.etli , we compared each gene's activity in the free-living condition and in bacteroids driving nitrogen fixation selected at 18 days after inoculation with P. vulgaris . Data from microarray experiments were stored at the data depository GEO ( http://www.ncbi.nlm.nih.gov/geo/ ) with access numbers GPL10081 for R. etli platform and GSE21638 for free life and symbiosis data. Even though a variety of sophisticated regulatory mechanisms may occur at diverse levels of biological organization [ 12 ], we have assumed that those genes with a significant over-expression indicate functional mechanisms for accomplishing nitrogen fixation. Under this criteria, we identified 689 genes (approximately 11% of the R. etli genome) whose transcriptional activity significantly increases during the biological process. To survey the role that these genes have in supporting nitrogen fixation, we classified them in accordance to the functional categories defined for Rhizobiaceas [ 13 , 14 ], see panel (A) in Figure 1 and Additional File 1 . As expected, the majority of the nif and fix genes in bacteroids and other genes required for translation initiation, elongation, and termination were up-regulated inside nodules. Furthermore, our data suggest that the expression of genes forming part of translation initiation, elongation and termination machinery was not absent although it was significantly reduced in the nodule bacteria, a common observation reported in Bradyrhizobium japonicum, Sinorhizobium meliloti and Mesorhizobium loti bacteroids [ 15 – 17 ]. In accordance with the induction of cell-division inhibitor protein minD , a significant number of housekeeping genes down-regulate their expression at nitrogen fixation stages, and from microarray data we concluded that a slower rate of general metabolism, see Additional File 1 .

Schematical view of data from high-throughput technology and constraint-based modeling . (A) Functional distribution of up regulated genes in bacteroids. (B) Functional categories of proteome data. (C) Number of up regulated genes and proteins-coding genes identified by transcriptomics and proteomics. Overlapping region represents the number of genes that were identified by both technologies. (D) Topological properties of the metabolic reconstruction for R.etli ( iOR450) . At the top from left to right: stoichiometric matrix and connectivity distribution (in log-log scale). At the bottom, metabolic pairs and its corresponding number of shared reactions (in log-log scale). (E) Figure in left side depicts the number of enzymes (genes) that were: 1) identified in silico but nor experimentally,(blue); 2) detected by both experimentally and in silico (green); and 3) experimentally detected but not observed in silico (red) along the 22 pathways listed in (F). Blue regions in right pies represent the overall percentage of genes and enzymes that simultaneously appear in silico and in high-throughput data. (F) A set of 22 metabolic pathways were used to assess the agreement between in silico and experimental results. Figure at left shows the activity of gluconeogenesis that emerged from the Flux Balance Analysis (FBA).

To give a broader view of the biological activity inside the bacteroid, proteome analysis was conducted for R. etli bacteroids similarly recollected from nodules selected at 18 days after inoculation in root plants of P. vulgaris [ 18 ], see experimental procedure and methods. In total, proteome studies led us to identify and characterize 415 spot proteins in the bacteroids that suggested the expression of 293 genes during nitrogen fixation, see Figure 1 (B) and Additional File 2 .

Both technologies--transcriptome and proteome--contributed to supply a broader biological landscape regarding bacterial nitrogen fixation. However, it is necessary to be aware of some differences in the experimental design underlying both technologies in order to integrate and interpret this data in a coherent fashion. While microarray technology resulted from a comparative analysis of two physiological conditions (free life and nitrogen fixation stages), proteome data identified the most abundant proteins present exclusively during nitrogen fixation stages. As Figure 1 (C) and Additional File 3 show, a scarce overlapping between the genes identified by both data sets is observed due to the experimental distinctness inherent in each technology. Thus, in order to identify those genes and enzymes with a relevant role in bacteroid metabolism and, in turn, form a set of genes that serve as a benchmark for computational assessment, we followed an integrative, rather than, selective strategy. Taking into account both sources of data, we hypothesized that up-regulated genes identified by microarray data and those genes that codify for the identified proteins potentially reveal those genes with a major role in nitrogen fixation. Under this assumption, both technologies led us to integrate a total of 948 genes that have a role in supporting bacterial nitrogen fixation, see Figure 1 and Additional File 1 and 2 .

Functional classification of this set of genes ranged from enzymes participating in central metabolism and amino acid production to those maintaining specific pathways of nitrogen fixation such as glycogen and poly-β-hydroxybutyrate (PHB) biosynthesis. In addition, we identified enzymes participating in catabolism and anabolism of amino acids, chemotaxis, ribosome composition, RNA polymerase, DNA replication, nucleotide repairs, secretion systems and fatty acids metabolism. Moreover, a significant number of proteins participating as transporters reflects the intense metabolic crosstalk between plant and bacteroid; for instance, proteins participating in transport of small molecules, such as carbon, hydrogen, phosphate and sugar, fall under this classification, see panels (A) and (B) in Figure 1 . We also identified proteins participating in the regulatory mechanism in nitrogen fixation, two components systems, transport and cell surface structure, energy transfer, cellular protection, and the transport and synthesis of polysaccharides. An extended discussion of the functional analysis that emerged from both technologies and its implication at a metabolic level can be reviewed in the Additional File 4 .

Expanding Rhizobium etli metabolic reconstruction and selecting pathways for its experimental assessment

The data generated by high-throughput technology constitutes a cornerstone in moving toward a descriptive analysis of nitrogen fixation. Despite the fact that this top-down scheme represents a valuable contribution to monitoring cell activity at a genome scale, complementary descriptions are required to integrate these data and survey how genetic perturbations affect nitrogen fixation in a systematic and quantitative fashion ( bottom-up scheme). Among these quantitative schemes, constraint-based modeling is an appropriate formalism for exploring the cellular metabolic activity and guiding experiments to improve cellular behavior in a rational, coherent and optimal fashion [ 7 , 8 , 19 , 20 ]. In order to construct a bottom-up scheme for bacterial nitrogen fixation, our strategy consisted of three steps: 1) metabolic reconstruction for R. etli ; 2) in silico modeling of nitrogen fixation, and 3) a cyclic assessment of computational predictions and experimental results.

In terms of metabolic reconstruction, proteome and transcriptome data were used to elaborate on the previous report for R. etli [ 8 ], thereby making some metabolic improvements and including new metabolic pathways absent in the previous version. To visually identify these metabolic reactions, we proceeded to represent the set of genes identified by high-throughput data and those from iOR363 reconstruction into each metabolic pathways defined in KEGG database. A comparative analysis among each pathway led us to visualize and highlight their differences. Consistent with the previous metabolic reconstruction, certain reactions were identified in the experimental set of data, while others led us to postulate the activity of new metabolic pathways that were absent in the previous reconstruction [ 8 ]. Specifically, high-throughput data strongly indicated the biological activity of fatty acid metabolism, and we therefore included this pathway in the metabolic reconstruction, see supplementary material. Overall, a set of 405 reactions and 450 genes made up the new metabolic reconstruction for R. etli ( i0R450 ) with which in silico simulations and analysis were carried out. Topological properties that emerged from the updated metabolic reconstruction are shown in Figure 1 (D) .

To evaluate the concordance between the metabolic activity predicted in silico and that interpreted from high-throughput technology, we selected 22 KEGG metabolic pathways [ 21 ] that had the highest number of genes experimentally detected by high-throughput data see Figure 1 (F) . According to the KEGG database, these 22 metabolic pathways contain 311 genes for R. etli of which 76.7% were included in the metabolic reconstruction iOR450 . This set of genes and their corresponding enzymes constituted the central core for evaluating the coherence between in silico predictions and high-throughput data interpretations. Even though in silico assessment relies on the activity of 22 metabolic pathways, in silico analysis of nitrogen fixation took into account all the reactions included in the metabolic reconstruction. This latter procedure will be valuable especially for exploring and predicting the metabolic role that additional pathways have on nitrogen fixation.

Constraint-based modeling: evaluating the descriptive and predictive capacities of the metabolic reconstruction

Constraint-based modeling is useful for predicting the metabolic phenotype in microorganisms surviving in specific environmental conditions and/or subject to genetic perturbations [ 7 , 22 ]. With the purpose of evaluating the phenotype capacities of the metabolic reconstruction, flux balance analysis (FBA) was carried out for R. etli by imposing physical and chemical constraints to each metabolic reaction and using an objective function that mimics symbiotic nitrogen fixation [ 8 ], see method section. As a result of this analysis, a set of enzymes and genes with a significant role in nitrogen fixation was identified in silico as those that underlie the metabolic fluxes obtained from FBA. To quantify the agreement of experimental and computational interpretations, we defined a consistency coefficient representing the fraction of genes ( η Genes ) or enzymes ( η Enzymes ) predicted active by FBA and detected by high-throughput technology, see methods section. This parameter ranges from 0 to 1, with 1 representing the highest and 0 the lowest consistency between the genes (or enzymes) detected from high-throughput technology and predicted in silico . To evaluate the numerical value of these parameters and estimate the coherence between modeling outputs and high-throughput data during nitrogen fixation, an early metabolic simulation on iOR450 was carried out using the objective function originally suggested in a previous work, Z Fix [ 8 ], i.e.

where glycogen, lysine, poly-hydroxybutyrate, alanine, aspartate and ammonium are denoted as glycogen [c], lys [c], phb [c], ala [e], asp [e] and nh4 [e], respectively. All these metabolites are required to support an effective symbiotic nitrogen fixation [ 8 ], and their spatial location is indicated by [c] and [e] for cytoplasm and external compound. As a result of this simulation, we obtained a consistency coefficient of η Genes = 0.6835 for genes and η Enzymes = 0.702 for enzymes. Notably, this numerical value implied that 68.35% of the genes and 70.2% of the enzymes predicted in silico were consistently identified by high-throughput technology. To evaluate the statistical significance of this correlation, a hypergeometric test was applied in each case. In terms of enzymes, the coefficient reflected that of 74 enzymes predicted in silico , 52 were identified by high-throughput data. Meanwhile, the gene consistency coefficient indicated that of 237 expressed genes, 162 were identified experimentally. In both cases we concluded that these correlations were statistically significant: p-value = 8.59 × 10 -35 and p-value = 4.9 × 10 -64 for genes and enzymes, respectively.

Improving predictability capacity of constraint-based modeling

These results encouraged us to proceed with an analysis of the in silico metabolic phenotype during nitrogen fixation, yet some improvements are desirable for ensuring a model with coherent interpretations and accurate predictions. To raise the qualitative agreement between top-down (high-throughput data) and bottom-up ( in silico modeling) schemes, we therefore explored the possibility of finding an expanded objective function whose in silico phenotype improves the protein consistency coefficient η. To avoid this procedure from becoming a simple computational artifact without a biological foundation, we limited the search to those metabolites whose significant role in the bacterial nitrogen fixation were subject to strong experimental evidence. Thus, guided by a review in the literature, two metabolites were included in the objective function: L-valine and L-histidine both with a biologically meaningful role in nitrogen fixation. Supporting this assumption, mutagenesis made on the biosynthesis of branched chain amino acids, such as L-valine , has been shown to be defective in the initiation of nodule formation on host legumes [ 23 ]. In addition, we found evidence that L-histidine is a central compound participating in the mechanisms for regulating nitrogen fixation [ 12 ], and we noted that its inclusion in the objective function increased the agreement with high-throughput data. We therefore constructed a new objective function to mimic metabolic activity during nitrogen fixation in bacteria, it now integrated by

where boldface letters indicate those metabolites that were added to the previous objective function. Taking into account this implementation and simulating the flux distribution through FBA as described above, we obtained the following results during nitrogen fixation: η Genes = 0.6948 and η Enzymes = 0.7683, see Figure 1 (E). In terms of enzyme activity this numeric value indicates that of the 82 metabolic reactions predicted in silico , 63 of them were consistently justified by high-throughput data ( p-val = 3.05 ×10 -64 ). Meanwhile the gene consistency coefficient indicated that of 249 expressed genes, 173 were identified by high-throughput data ( p-value = 4.9 ×10 -64 ).

Given this improvement, a detailed comparison between computational predictions and high-throughput data of the 22 metabolic pathways defined in Figure 1 (E) led us to distinguish three possible cases: the presence of 1) genes (enzymes) that were predicted in silico but not detected experimentally, 2) genes (enzymes) that were consistently observed in both schemes, and 3) genes (enzymes) that were experimentally detected but not predicted in silico , see Figure 1 (E) . As explained in the methods section, η is related to the fraction of genes (enzymes) that were consistently observed in both schemes and constitutes the backbone of our modeling assessment. However, the biological explanation for the discrepancies described above (in cases 1 and 3) requires feedback assessment between modeling and experiments. For instance, these discrepancies could be reflecting the presence of post-transcriptional and post-translational regulation during nitrogen fixation and the design of proper experiments will be fundamental to discarding or accepting this hypothesis.

A coherent description between in silico modeling and high-throughput data is a primary goal for exploring the fundamental principles governing metabolism in Rhizobiaceas and predicting their phenotype behavior during nitrogen fixation. In this work we present a systems biology framework capable of exploring the metabolic activity of R. etli during nitrogen fixation in symbiosis with P. vulgaris . In particular, we present a genome scale model that integrates high-throughput data for describing, simulating and guiding experiments dealing with metabolic activity in bacterial nitrogen fixation. An important issue in constraint-based optimization analysis is the presence of alternate optimal fluxes, in other words the presence of a set of reactions--or flux distributions--that produce the same quantitative objective function. As a consequence of these alternate fluxes, the metabolic output of one pathway can be substituted by others such that macroscopic phenotype remains constant. Therefore, the distinction of the reactions with and without a range of variability is essential to guess the metabolic activity supporting biological phenotype. Hence, in order to characterize the core metabolic activity and compare our in silico metabolic interpretations with those emerged from high-throughput data, we carried out flux variability analysis (FVA) [ 24 ]. With the purpose to identify those reactions that represent the central core of metabolic activity along the set of alternate solutions, we limited our analysis to those reactions with a range of variability equivalent to zero. This set was such that the minimum and maximum flux variability for each reaction were equivalent and constituted our cornerstone for guiding the metabolic activity during the biological process. As depicted in Figure 2 , the output of this analysis led us to identify some key reactions participating in some metabolic pathways required for sustaining bacterial nitrogen fixation. FVA was carried out with COBRA Toolbox [ 25 ]. As a consequence of this study, some concluding remarks immediately follow.

Flux Variability Analysis (FVA) . In panel (A) we depict the numerical participation of reactions with null variability along seven metabolic pathways included in the metabolic reconstruction. Reactions with null variability were defined as those whose upper and lower limit are equivalent. A fraction of reactions belonging to this classification are shown in (B). The set of reactions obtained by FVA are shown in (C). Here we have used the following abbreviations: PGM (phosphoglucomutase), FBA (fructose-bisphosphate aldolase), TPI (triose-phosphate isomerase), RPI (ribose-5-phosphate isomerase), PUNP1 (purine-nucleoside phosphorylase (Adenosine)), PUNP2 (purine-nucleoside phosphorylase (Deoxyadenosine)), PPCK(phosphoenolpyruvate carboxykinase), PHPB (acetoacetyl-CoA reductase), PHBS (PHB synthase), PGMT(phosphoglucomutase), PGI (glucose-6-phosphate isomerase), PDH (pyruvate dehydrogenase), PC (pyruvate carboxylase), NP1_r (nucleotide phosphatase), INSCR (inositol catabolic reactions (lumped)), INS2D (inositol 2-dehydrogenase), GUAPRTr (guanine phosphoribosyltransferase), GLGC (glucose-1-phosphate adenylyltransferase), GLCS1 (glycogen synthase (ADPGlc)), GAPD(glyceraldehyde-3-phosphate dehydrogenase), G6PDH2(glucose 6-phosphate dehydrogenase), FBP (fructose-bisphosphatase), ENO (enolase), EDD (6-phosphogluconate dehydratase), EDA (2-dehydro-3-deoxy-phosphogluconate aldolase), CS (citrate synthase), ACONTa (aconitase (half-reaction A, Citrate hydro-lyase)), ACONTb (aconitase (half-reaction B, Isocitrate hydro-lyase)), NIT (nitrogenase), NH3t (ammonia reversible transport), NH3e (Ammonium dissociation, extracellular), N2tr (Nitrogen exchange, diffusion) and MMSAD3 (methylmalonate-semialdehyde dehydrogenase (malonic semialdehyde)).

Citric acid cycle

Constraint-based modeling suggested that the TCA cycle is activated during nitrogen fixation by dicarboxylates which constitute the main carbon source in bacteroids [ 26 ], see Additional File 5 panel (B) in supplementary material. Consistent with this finding, eight proteins participating in the TCA cycle were detected in the R. etli bacteroid by proteome technology (FumC, FumB, LpdAch, SucB, SucA, SucC, Mdh and AcnA). To further assess this agreement, we applied gene deletion analysis to explore to what extend the deletion of some enzymes can qualitatively influence the activity of bacterial nitrogen fixation and if the predicted behavior is biologically coherent with knowledge reported in Rhizobiaceas , see method section. Thus, in silico gene deletion analysis accomplished on the metabolic reconstruction leads us to conclude that the aconitase hydratase (AcnA) mutant in R. etli is not lethal in nitrogen fixation. Despite the fact that this result has not been experimentally proven in R. etli , it has been validated in other Rhizobiaceas [ 27 ]. Furthermore, although isocitrate dehydrogenase (Icd) was not detected by high-throughput technology, in silico icd mutants in R. etli suggest a reduced phenotype on nitrogen fixation. This result is qualitatively in agreement with the fact that icd mutants on S. meliloti are symbiotically ineffective [ 28 ]. Similarly, constraint-based modeling concludes that a reduction of enzymatic activity in pyruvate dehydrogenase (PDH) induces a significant reduction in symbiotic nitrogen fixation but does not impair it as occurs in the case of S. meliloti bacteroids [ 29 ], see Figure 3 (B) . This finding suggests that the role of PDH in the production of acetyl-coenzyme A can be replaced by alternative pathways in R. etli bacteroids [ 5 ]. The experimental assessment of this hypothesis for R. etli metabolisms is a central issue to explore in the future.

In silico assessment of gene knockout and phenotype variations on bacterial nitrogen fixation . Panel (A) summarizes the benchmarks used to evaluate the in silico description of nitrogen fixation. Black and blue letter in first column indicates the silenced enzyme its corresponding metabolic pathway respectively. Second column indicates the technology by which the enzymes were identified in this study. Third column indicates the Rhizobiacea used to compare in silico prediction. Forth and fifth columns represent the computational phenotype and the reference supporting the computational result. Sign (+), ( = ) and (-) respectively denotes an increment, invariance and decrement in nitrogen fixation when mutation were accomplished. The in silico phenotype effect carried out by aconitase hydratase (ACONTa), isocitrate dehydrogenase (ICDHx), pyruvate dehydrogenase (PDH), phosphoenolpyruvate carboxykinase (PPCK), biphosphate aldolase (FBA), nitrogenase (NIT) and CTP-synthase (CTPS2) are summarized in left side of panel B. The robustness analysis accomplished for inositol catabolic reaction (INSCT) is shown in panel B.

Glycolysis, gluconeogenesis and pentose phosphate pathways

A common metabolic trait for some Rhizobiaceas is the intense activity of gluconeogenesis pathway [ 3 ]. In agreement with this finding, a significant number of gluconeogenic and glycolytic enzymes were identified by high-throughput technology, and constraint-based modeling consistently concluded that gluconeogenesis pathway was actively participating in nitrogen fixation.

Multiple isoforms of PEP carboxykinase ( pckA ) were detected by proteome technology, see Additional File 2 , mirroring their pivotal role in nitrogen fixation and bacteroid differentiation. Thus, R. etli CE3 pckA mutant produces few nodules into which the infection threads do not appear to penetrate [ 30 ]. In qualitative agreement with this report, in silico mutation suggests that pckA is an essential gene for accomplishing nitrogen fixation in R. etli , see Figure 3 .

In addition, 6-phosphogluconolactonase ( pgl ), glucose 6-phosphate dehydrogenas e (Zwf1), its chromosomal homolog (designated by zwf2) and one transaldolase (Tal) were detected by proteome, supplying evidence that pentose phosphate pathways can be actively participating in nitrogen fixation. Consistent with this finding, in fast-growing Rhizobiaceas , there is evidence that pentose phosphate and Entner-Doudoroff pathways work in coordinate action as the probable major routes for the metabolism of sugars [ 31 ].

As mentioned before, some glycolytic genes were identified by high-throughput data: two triosephosphate isomerases (TpiAch and TpiAf), one glyceraldehyde 3-phosphate dehydrogenase (Gap), one pyruvate kinase II (PykA), one 2-phosphoglycerate dehydratase (enolase), phosphoglycerate mutase (pgm) , and the bisphosphate aldolase ( fbaB ), see Additional File 1 and 2 . Furthermore, there is experimental evidence that the genetic silence of fbaB in R. etli causes the development of sparse, empty nodules on root beans [ 30 ]. Consistent with this fact, computational gene deletion analysis carried out with this gene confirms that fbaB has a crucial role in supporting the metabolism of bacterial nitrogen fixation [ 30 ], see Figure 3 (B). Even though these findings were not enough to postulate an active glycolytic cycle, they may suggest the metabolism of sugar intermediates via other pathways. For example, the presence of a specific transporter for glycerol-3-phosphate ( ugpAch1 , induced 3.88-fold by microarray analysis) indicates that this may be an important source for generating glycolytic intermediates. Similarly, the expression of 6-phosphogluconate dehydrogenase (Gnd) suggests the presence of an active pentose pathway, which is another potential channel for the metabolism of glycolytic intermediates.

Myo-inositol catabolic pathway

Myo-inositol is one of the most abundant compounds in the soybean nodule, and accordingly, high-throughput technology successfully detected the presence of myo-inositol 2-dehydrogenase proteins (IdhA and IolB) encoding a myo-inositol protein in catabolism [ 32 ]. In agreement with this fact, computational analysis of the metabolism in R. etli suggests that a decrease of myo-inositol inside the nodule can reduce its capacity to fix nitrogen, see Figure 3 (B). This result supports the hypothesis that the presence of myo-inositol in the nodule is essential for growth and maturation of the bacteroid and its metabolic inhibition can lead to both a nonfunctional bacteroid and the reduction of nitrogen-fixation activity [ 32 ].

Poly-β-hydroxybutyrate and glycogen accumulation

While most of the bacteroid carbon supplied by the plant is channeled into energy production to fuel nitrogen reduction, in certain types of nodules some carbon is diverted by the bacteroids into the production of intracellular storage polymers composed of either glycogen or poly-β-hydroxybutyrate (PHB). Our simulations produced PHB, and consistent with our predictions, high-throughput analysis led us to identify the presence of three components related to its metabolic pathway: the polymerase PhbC (poly-beta hydroxybutyrate polymerase protein), a putative polyhydroxybutyrate depolymerase protein (detected by transcriptoma, see Additional File 1 ) and the acetyl-CoA acetyltransferase (beta-ketothiolase, phbAch) detected by proteome. Other reports confirm that metabolic fluxes in PHB and glycogen pathways are such that inhibition of one results in accumulation of the other, a property that was consistently observed by in silico modeling [ 8 , 33 , 34 ]. The precise role of PHB and glycogen during infection, nodulation, and nitrogen fixation and the factors that induce their accumulation are not yet determined. Future experiments dealing with these pathways are necessary to elucidate their role in bacterial nitrogen fixation.

• Nitrogen Fixation

To ensure the proper production of the ammonium required to establish an optimal bacterial-plant symbiosis, constraint-based modeling concludes that central genes involved in nitrogen fixation ( nif and fix genes) are required for an optimal activity. Consistent with this fact, NifH, NifD and NifK were identified in proteome data and detected up-regulated in transcriptome analysis. In addition, an up-regulated gene expression was observed for nifE (nitrogenase reductase iron-molibdenum cofactor synthesis truncated protein), nifN (nitrogenase reductase iron-molibdenum cofactor synthesis protein), nifX (iron-molibdenum cofactor processing protein) and nifB (FeMo cofactor biosynthesis).

In R. etli , the iscN gene (Fe-S cofactor nitrogenase synthesis protein) is co-transcribed with nifU and nifS , and in conjunction, these genes were significantly up-regulated in bacteroids in comparison to bacteria under free-life condition (10.82, 3.92 and 1.99-fold, respectively). Furthermore, the iscN mutant in R. etli showed a significant reduction in nitrogen fixation [ 35 ]. Consistent with this report, in silico gene deletion analysis of those genes codifying for nitrogenase mostly reduces nitrogen fixation, see Figure 3 .

Amino acid metabolism and transport

A previous report suggests that Rhizobiaceas require the availability of 20 amino acids to establish an effective symbiosis with legumes [ 36 ]. Some amino acids are synthesized by Rhizobiaceas whereas the remaining are supplied by the host plant, a condition that appears to be plant-type specific. High-throughput analysis led us to identify certain proteins required for the synthesis of arginine, tyrosine, tryptophan, phenylalanine and lysine, the latter participating in the objective function defined in constraint-based modeling. On the other side, from the ABC-transporter proteins founded in nodule bacteria, thirteen were involved in amino acid transport, it strongly suggests that the uptake of amino acid is of particular importance in nitrogen fixation. The general amino acid ABC-transporter protein for AapJ (substrate binding protein) was detected by proteome analysis: the aapJ gene is part of the aapJQMP operon that exists in many Rhizobiaceas and has been described in detail in R. leguminosarum [ 37 ]. BraC1 and braC2, of the branched-chain amino acid ABC transporter, were detected in bacteroid by proteome and transcriptome technologies (2.85 fold). In R. leguminosarum braDEFG is required for alanine, histidine, leucine and arginine uptake [ 38 ] (two of which form part of the objective function associated with the metabolism of nitrogen fixation in our in silico model). Alternately, in R. leguminosarum, braC mutants are effective in alanine uptake (but are lacking in the uptake of the other three amino acids) [ 38 ]. Phenotype behavior for braC mutants has not been studied in R. etli , but there is evidence that braD and braH mutants were found to be deficient in glutamine uptake and respiration but proficient in nodulation and nitrogen fixation [ 30 ].

Nucleotides metabolism

Purine and pyrimidine pathways are important during the nodulation processes given that most purine or pyrimidine auxotrophs in Rhizobiaceas are ineffective in symbiotic nitrogen fixation because they elicit pseudo-nodules devoid of infection threads [ 39 ]. Thus, for instance, the purB and purH gened in Mesorhizobiumi loti are involved in infection thread formation and nodule development in Lotus japonicus [ 40 ]. In addition, purB and purH mutants exhibited purine auxotrophy and nodulation deficiency in L. japonicus [ 40 ]. As Figure 2(A) and Additional File 5 panel (C) shows in the supplementary material, constraint-based modeling concludes that some enzymes in purine and pyrimidine pathways are actively participating in reaching an optimal symbiotic nitrogen fixation. Supporting this finding, several key enzymes were identified in bacteroids by proteome technology. Among them, we identified: phosphoribosylamine-glycine ligase protein (PurD), adenylosuccinate lyase protein (PurB), phosphoribosylformylglycinamidine synthetase protein (PurL), adenylosuccinate synthetase protein (PurA), IMP cyclohydrolase/phospho-ribosylaminoimidazole-carboxami-deformyltransferase protein (PurH), adenylate kinase (ATP-AMP transphosphorylase, Adk) and nucleoside-diphosphate-kinase protein (Ndk).

In the presence of adenine, only the purH mutant induced nodule formation, and the purB mutant produced few infection threads, suggesting that 5-aminoimidazole-4-carboxamide ribonucleotide biosynthesis catalyzed by PurB is required for the establishment of symbiosis. In addition, purL mutants in S. fredii HH103 strain does not grow in minimal medium unless the culture is supplemented with thiamin and adenine or an intermediate of purine biosynthesis [ 41 ]. Furthermore, gene expression of purC1 , phosphoribosylaminoimidazole-succinocarboxamide (SAICAR) synthetase protein, purUch (formyltetrahydrofolate deformylase protein), gmk2 ( guanylate kinase (GMP kinase protein) and pyrE (orotate phosphoribosyltransferase protein) were up-regulated inside bacteroids between 2.3 to 6.35 fold. In S. meliloti , nodule development in the case of pyrE/pyrF mutants did not reach the extent observed in the parental strain. These results suggest that some of the intermediates and/or enzymes of the pyrimidine biosynthetic pathway play a key role in bacteroid transformation and nodule development [ 42 ], information that should be taken into account for constructing an improved objective function and ensuring a proper computational description in future analysis.

Fatty acids metabolisms

According to high-throughput data, metabolism of fatty acid can play a significant role in bacterial nitrogen fixation, this being in contrast to the drastic reduction of lipid biosynthesis observed in B. japonicum [ 43 ]. Thus, a variety of fab genes and proteins participating in fatty acid biosynthesis were detected by both methodologies (proteome and transcriptome). For instance, we detected by proteome the MccB subunit of methylcrotonyl-CoA carboxylase protein, acyl-CoA thiolase protein (FadA), enoyl-CoA hydratase protein (FadB1), enoyl-[acyl-carrier-protein] reductase (NADH) protein (FabI2) and S-malonyltransferase protein (FadD); and by transcriptome fadB2 was induced 3.09-fold. As these findings suggest, fatty acid metabolism could play an important role in bacteroid metabolism given that it can supply a variety of precursors such as components of the rhizobial membrane, lipopolysaccharides and coenzymes required in signal transduction. As opposed to the process in other Rhizobiaceas where fatty acids can be supplied by the host plant [ 43 ], we supply experimental evidence that bacteroids of R. etli synthesize and metabolize their fatty acids. The assessment of this hypothesis and the biological implications on bacterial nitrogen fixation constitute an avenue to experimentally verify in the future.

In this study we present a systemic metabolic description of bacterial nitrogen fixation carried out by R. etli in symbiosis with P. vulgaris , at present the most complete study made in Rhizobiaceas . Collectively, high-throughput data suggest the following significant clues: 1) R. etli bacteroids are capable of synthesizing several amino acids through integrated carbon and nitrogen metabolisms. In addition, we observe the participation of some minor metabolic pathways such as myo-inositol catabolic pathway, degradation and synthesis of poly-b-hydroxybutyrate and glycogen. 2) Gene expression in bacteroids suggests the presence of a specialized transport system for sugars, proteins and ions. 3) An antioxidant defense mechanism based on peroxiredoxine, regulated by nifA , prevails during nitrogen fixation, as opposed to in free-living condition, where the mechanism is rooted in catalases [ 44 ]. 4) R. etli over-expresses genes and enzymes required in fatty acid and nucleic acid metabolism, contrary to other studies in bacteroids. Finally, 5) this study contributes a computational model that serves as a useful framework for integrating data, designing experiments and predicting the phenotype during bacterial nitrogen fixation, see Figure 3 .

This systemic and integrative approach constitutes a valuable effort toward a systems biology description of the metabolism in bacterial nitrogen fixation; however, to increase our understanding and predictive accuracy some issues should be addressed in the future. Thus, particular attention should be directed toward those enzymes that were predicted metabolically active in silico but were not detected experimentally, and conversely, those enzymes that were detected experimentally but not in silico , see Figure 1(E) . We expect that the study of these differences will be fundamental in postulating, verifying and uncovering mechanisms of regulation, while simultaneously confirming or improving hypotheses derived through in silico predictions.

Notably, even though the simulations have been carried out without a detailed numerical description of the coefficients c i in the objective function--see methods section--we have shown that the in silico model is capable of qualitatively predicting the activity of classic metabolic pathways and successfully describing some phenotype behavior in bacterial nitrogen fixation. Even though this represents a significant advance toward a systems biology description of bacterial nitrogen fixation, some improvements should be addressed in future. For instance, additional metabolites with a biological role in nitrogen fixation should be considered in order to obtain a more proper objective function that contributes to uncovering the role that less known metabolic pathways, such as nucleotides and fatty acid metabolisms, have on this biological process. As described here, these improvements will be guided by high-throughput data and the cyclic crosstalk between model and theory, a needed step in integrating, interpreting and generating biological hypotheses in a more accurate fashion.

Overall our study contributes to establishing the bases toward a systems biology platform capable of integrating high-throughput technology and computational simulation of bacterial nitrogen fixation. In particular, we envision that this metabolic reconstruction for R. etli ( iOR450 ) will contribute to the rational design of optimal experiments that help us understand biological principles and identify those molecular mechanisms in order to improve this biological process, all this from a systems biology perspective.

Bacterial strains, growth conditions

The bacterial strain used was R. etli CFN42 wild type [ 11 ]. Culture media and growth conditions for R. etli , and plant experiments were accomplished as previously described in reference [ 45 ].

Plant experiments

Three-day-old Phaseolus vulgaris cv. Negro Jamapa seedlings were inoculated with R. etli CFN42 strains as previously described by Peralta et al . [ 46 ]. After 18 days post-inoculation (dpi), nodules were picked out from the roots, immediately frozen in liquid nitrogen and stored at -70°C until further use. Bacteria were isolated from nodules and their identities verified by their antibiotic resistances.

RNA isolation and microarray hybridization

Microarray experiments were carried out using three independently isolated RNA preparations from independent cultures and set of plants. Approximately 3 g of nodules were immersed in liquid nitrogen and macerated. Total RNA was isolated by acid hot-phenol extraction as described previously by de Vries et al [ 47 ]. For microaerobic free-living conditions, 50 ml of bacterial cell cultures were collected and total RNA isolated using a RNeasy Mini Kit (QIAGEN, Hilden, Germany). RNA concentration was determined by measuring the absorbance at 260 nm. The integrity of RNA was determined by running samples on a 1.3% agarose gel. 10 μg of RNA was differentially labeled with Cy3-dCTP and Cy5-dCTP using a CyScribe First-Strand cDNA labeling kit (Amersham Biosciences). Pairs of Cy3- and Cy5-labeled cDNA samples were mixed and hybridized to a Rhizobium_etli_CFN42_6051_v1.0 DNA microarray as described by Hegde et al. [ 48 , 49 ]. After washing, the arrays were scanned using a pixel size of 10 μm with a Scan Array Lite microarray scanner (Perkin-Elmer, Boston, MA). Three biological replicates with one dye swap were performed. We used real-time quantitative PCR to provide an independent analysis of gene expression for selected genes. Primer sequences and additional experimental protocols are reported in the supplementary material section.

DNA microarray analysis

Spot detection, mean signals, mean local background intensities, image segmentation, and signal quantification were determined for the microarray images using the Array-Pro Analyzer 4.0 software (Media Cybernetics, L.P). Statistical treatment of microarray data was accomplished with bioconductors software ( http://www.bioconductor.org/ ). Specifically, microarray normalization was carried out by applying the maNormMain function in the marray library. MA-plots before and after normalization are depicted in Additional File 5 . Having normalized the gene expressions in the three experimental replicates, differentially expressed genes were identified by the following procedure. First, we calculate the average log-ration for each gene obtained from the three experimental replicates. Then, we obtained the standardized z-score of the log-ratio associated to each gene. The set of genes differentially expressed during nitrogen fixation was selected as those genes with a z-score higher than 1.65, see Additional File 5 . The complete dataset used in the transcriptome analysis can be downloaded from GEO ( http://www.ncbi.nlm.nih.gov/geo ) with accession numbers: GPL10081 for Rhizobium etli platform and GSE21638 for free-life and symbiosis data.

Verification by RT-PCR

We used real-time quantitative PCR to provide an independent assessment of gene expression for selected genes. The cDNA used for microarrays or freshly prepared cDNA was used as a template for Real-time PCR. Primer sequences used were as follows: fabI2 -RECH000938f (5'-GTA TTG CCA AGG CCA TTC AT-3'), fabI2 -RECH000938r (5'-CCC ACA GTT TTT CGA CGT TT-3') for the fabI2 gene. idhA -RECH003170f (5'-TTT CTT CAT GAC CCG CTA CA-3'), idhA -RECH003170r (5'-TTG ATC AGC TTG CCT TCC TT-3') for the idhA gene. ppK -RECH001491f (5'-TCC TGG CAC TGA ACA CTC TG-3'), ppK -RECH001491r (5'-GAG AAG GAA CTG GAC CAC CA- 3') for the ppK gene. hisD -RECH000581f 5'GAT CTG AAG CAA GCC ATT CC 3', hisD -RECH000581r (5'-ACA TAA TCG CCG ATG ACC TC-3') for the hisD gene. nifH -REPD00202f (5'-CCT CGG GCA GAA GAT CCT GA-3'), nifH -REPD00202r (5'-CAT CGC CGA GCA CGT CAT AG-3') for the nifH gene. fixA -REPD00224f (5'-ACA TCA ATG GGC GCG AGA TT-3'), fixA -REPD00224r (5'-TGT CGA TCT GCT CCG CCT TT-3') for the fixA gene. cpxP2 -REPD00252f (5'-TCC GTG CCA TTT CAA AGA CC-3'), cpxP2 -REPD00252f (5'-CCG CCA AAT GAG AAG ATT GC-3') for the cpxP2 gene. hisC -RE1SP0000233f (5'- CGA TGG CGA GAC AGC TAA AT-3'), hisC -RE1SP0000233r (5'-ATC ATC GCA ACG CTA TCT CC-3') for the hisCd gene. Each reaction contained 12.5 μl SYBR green PCR mastermix (Applied Biosystems), 3.5 μl H 2 O, forward and reverse primers in a volume of 5 μl, and template in a volume of 4 μl. PCR reactions were run with the ABI Prism 7700 sequence detection system (Applied Biosystems) using the following steps: 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. The dissociation protocol was 95°C for 15 s, 60°C for 20 s, followed by ramp from 60°C to 95°C for 20 min. The transcript of the histidinol phosphate aminotransferase protein ( hisCd ) was used as an internal (unregulated) reference for relative quantification. This gene was selected as a reference because its expression is constitutive in all tested conditions (free live and symbiosis). Results of RT-PCR in real time were analyzed using the ΔΔCT method [ 49 ] and the data was presented like relative expression. All reactions were done by triplicate.

Proteomics experiments

Bacteroids purification, protein extraction and two dimensional gel electrophoresis were done as previously described in [ 44 ]. Briefly, bacteroids were purified from root nodules by centrifugation through self-generated Percoll gradients. Bacteroid proteins were obtained by sonication at 24 kHz 1 min ON/1 min OFF for 5 cycles at 4°C in a Vibra Cell (Sonics, USA) in the presence of a protease inhibitor (Complete tablets, Roche Diagnostics GmbH, Mannheim, Germany). To further limit proteolysis, protein isolation was performed using phenol extraction. Two dimensional gel electrophoresis (2D-PAGE), was performed like previously described. Gels were stained with Coomasie Blue G-250, scanned with PDI image analysis system, and analyzed with PD-Quest software (Bio-Rad Laboratories, Inc, Hercules, CA.). Selected spots from preparative 2-D gels were excised, digested and the proteins were identified by PMF MALDI-TOF using a Bruker Daltonics Autoflex, following the same methodology mentioned in [ 44 ]. The experiments were performed three times. Selected spots from Coomassie stained preparative 2-D gels were excised and processing automatically using the Proteineer SP spot picker and DP digestion robots (Bruker Daltonics, Billerica MA). Mass spectra were obtained using a Bruker Daltonics Autoflex (Bruker Daltonics Bellerica, Mass. USA) operated in the delayed extraction and reflectron mode. Spectra was externally calibrated using a peptide calibration standard (Bruker Daltonics 206095). Peak lists of the tryptic peptide masses were generated using FlexAnalysis1.2vSD1Patch2 (Bruker Daltonics). The search engine MASCOT server 2.0 was used to compare fingerprints against Rhizobium etli CFN42, NC_007761.1, pA, NC_007762.1, pB, NC_007763.1, pC, NC_007764.1, pD, NC_004041.2, pE, NC_007765.1, pF, NC_007766.1 with the following parameters: one missed cleavage allowed, carbamidomethyl cysteine as the fixed modification and oxidation of methionine as the variable modification. We accepted those proteins with scores greater than 50 and a p < 0.05. Proteome data associated with this manuscript can be downloaded from http://ProteomeCommons.org Tranche using the following hash:

BY/eCcVjwTWN1+m+2ArvJ0QVnesGx5Ekgd4wUOASACfm/ueNl7YI3iLf4xz0lnGsepV5LkpMWOQOrZtjYExlNpQkIBcAAAAAAAABjA = =

High-throughput technology and its use for extending metabolic reconstruction and simulating nitrogen fixation

With the purpose of establishing an integrative description between modeling and experimental data, we extended the metabolic reconstruction for R. etli by including those reactions whose enzyme activity were supported by high-throughput data. Thus, the fatty acids metabolism was included in the metabolic reconstruction, and some metabolic improvements were made along the network. Additional File 6 enlist the main abbreviations used along this paper. Additional File 7 in supplementary material contains a detailed description of the reactions included in this new metabolic version ( iOR450 ). Overall, the updated metabolic reconstruction for R.etli consists of 377 metabolites and 450 genes codifying for enzymes participating in 405 metabolic reactions. The gene-protein reaction association for the entire metabolic reconstruction, lower and upper bounds and reversibility information associated to each reaction are shown in Additional File 7 .

Constraint-based modeling

Metabolic flux distribution supporting nitrogen fixation in Rhizobium etli was predicted in silico by constraint-based modeling [ 8 ]. Briefly, simulations were carried out assuming a steady-state condition for metabolic fluxes and by constructing a mathematical function that mimics nitrogen fixation. This objective function, Z Fix , consists of certain key compounds required for sustaining nitrogen fixation and others required for mimicking the physiological conditions prevailing in the boundaries of the nodules. Thus, objective function was mathematically written as a linear combination of these metabolites ( X i ) and their contribution to nitrogen fixation was weighted by coefficients ( c i ), which for simplicity's sake were all selected as a unit. With the purpose of obtaining a computational profile of metabolic fluxes, we assumed that the metabolic state of the bacteroid during nitrogen fixation is one that optimizes the objective function, Z Fix . This latter issue was solved by taking into account linear programming and considering that the fluxes are constrained by their enzymatic and thermodynamic capacities,

where S i,j represents the entries of the stoichiometric matrix, v j is the metabolic flux of the j-th reaction and α j and β j account for thermodynamic and enzymatic constraints, see Additional File 7 . Linear programming was carried out using the Tomlab optimization package called from COBRA toolbox in Matlab [ 25 ].

External metabolites considered for flux balance analysis

In order to explore the phenotype capacities of the bacteria metabolism, we included in the reconstruction certain exchange and sink reactions for limiting our metabolic modeling and representing the microenvironmental conditions in the plant nodules. In general, these can be classified as one of two categories. Class I includes those metabolites that can be exchanged between the bacteroid membrane and the plant environment. Among them, we included carbon dioxide (CO 2 ), water (H 2 O), oxygen (O 2) , malate (mal-L) and glutamate (glu-L). In addition, exchange reactions for nitrogen (n2), alanine (ala-L), aspartate (asp-L), succinate and ammonium (NH 4) were included in the reconstruction for representing their possible bidirectional exchange from plant to bacteroids. On the other hand, metabolites in class II include those that contribute to the defining of internal frontiers in the bacteroids. Importantly, these sink reactions were included as a representation of metabolites originating from metabolic processes currently absent in the metabolic reconstruction. Thus, phosphate (pi), myo-inositol (inost), L-histidinol phosphate (hisp), palmitoyl-CoA (pmtcoa), dodecanoyl-CoA (dodecoa), decanoyl-CoA (decoa), octanoyl-CoA (otcoa) and hydrogen (h) fall in this classification.

Definition of consistency coefficient

To assess the agreement between in silico predictions and interpretations suggested by high-throughput data, we defined a consistency coefficient, η Genes , that quantifies the fraction of genes that were predicted upregulated in silico and simultaneously detected or induced by proteome or transcriptome technologies. Simultaneously, we defined a consistency coefficient that quantifies the fraction of proteins enzymatically active that were predicted by constraint-base modeling and confirmed by high-throughput technology, η Enzymes . To proceed with this evaluation, we denoted E j kegg ( G j kegg ) as the set of enzymes (genes) that form the j-th metabolic pathways in KEGG database, with j-th ranging from 1 to 22. Similarly, the set of enzymes (genes) that integrates the i-th metabolic pathway in the reconstruction and the set of enzymes detected by high-throughput data are denoted by E j Rec ( G j Rec ) and E j HT ( G j HT ) , respectively. Finally, the sets of enzymes and genes obtained from constraint-based modeling were denoted by E j iModel and G j iModel . More specifically, E j iModel and G j iModel sets were defined as those enzymes and genes participating in the active fluxes obtained from flux balance analysis. In order to evaluate and create a proper framework for comparison between in silico predictions and high-throughput data, we defined the consistency coefficient as the fraction of enzymes (genes) that were actively predicted in silico and were identified by high-throughput technology. This can be summed up in the following equations:

Both ratios range from zero to one and constitute our central parameter to assess and quantify the degree of coherence between constraint-based modeling and experimental data.

In silico gene deletion analysis

Computational gene deletion analysis was used to quantify the effects that gene silencing has in supporting bacterial nitrogen fixation. Thus, once the gene to be switched off was selected, we identified its gene-protein reaction association and selected as zero its upper and lower bound in flux activity. Having made this adjustment, we applied flux balance analysis and obtained the new resulting objective function. In order to quantify the participation of this metabolic reaction in bacterial nitrogen fixation, we calculated the percentage of reduced activity of the mutated strain in comparison to the wild type, see Figure 3 .

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Acknowledgements

The authors thank Prof Jaime Mora Head of the Program of Functional Genomics of Prokaryotes at the Center of Genomic Sciences-UNAM for his support and comments. OR-A also thanks to Prof. B.Ø. Palsson for his guiding and encouraging support during the progress of this study. We thank Oliver Castillo, J. L. Zitlalpopoca, and Hadau Sánchez for plant experiments and greenhouse support, and María del Carmen Vargas for technical assistance. Finally, the authors appreciate the valuable comments and suggestions from two anonymous referees during the review process. This work was supported by combined grants from National Council of Science and Technology CONACyT-Mexico, grants 83461 (OR-A) and 60641 (SE), and from PAPIIT-DGAPA-UNAM through grants IN222707 (SE) and IN203809-3 (OR-A).

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Programa de Genomica Funcional de Procariotes. Centro de Ciencias Genómicas-UNAM, Av. Universidad s/n, Col. Chamilpa, Cuernavaca Morelos, C.P., 62210, Mexico

Osbaldo Resendis-Antonio, Magdalena Hernández, Emmanuel Salazar, Sandra Contreras, Gabriel Martínez Batallar, Yolanda Mora & Sergio Encarnación

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Correspondence to Osbaldo Resendis-Antonio or Sergio Encarnación .

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Authors' contributions.

OR-A conceived the metabolic reconstruction for R. etli and designed the computational analysis to simulate nitrogen fixation. ES realized microarray experiments, and OR-A contributed to their statistical analysis. ME and GM-B prepared the samples and accomplished the identification and processing in spectrometry analysis of all proteome data. YM guided the experimental cultivation for nodule preparation in R etli . SE conceived the study, design and analyzed the experimental data obtained from proteome and microarray technologies. All authors read and approved the final manuscript.

Electronic supplementary material

12918_2011_730_moesm1_esm.xls.

Additional file 1:Microarray Data Analysis. This table shows those genes that were over expressed during bacteroid activity in nitrogen fixation. (XLS 99 KB)

12918_2011_730_MOESM2_ESM.XLS

Additional file 2: Proteome Data . By using mass spectrometry, we identified a set of proteins during bacterial nitrogen fixation for R. etli . In each row, we named the protein and presented some of the parameter utilized for concluding the protein identify. (XLS 226 KB)

12918_2011_730_MOESM3_ESM.XLS

Additional file 3:Intersect between proteome and transcriptome. Genes that were simultaneously identified by proteome and transcriptome technologies. (XLS 18 KB)

12918_2011_730_MOESM4_ESM.DOC

Additional file 4:This file contains an extended descriptive analysis deduced from the genes identified by proteome and transcriptome data. (DOC 310 KB)

12918_2011_730_MOESM5_ESM.TIFF

Additional file 5:(A) MA plot and representation of Metabolic activity. In this figure we show the MA-plot obtained from microarray data and a selected representation of the metabolic activity predicted by FBA in some metabolic pathways: (B) TCA cycle, (C) purine and pyrimidine metabolism. (TIFF 374 KB)

Additional file 6:Abbreviations. This file enlists the main abbreviations used along the paper. (DOC 28 KB)

12918_2011_730_moesm7_esm.xls.

Additional file 7: Metabolic Reconstruction for Rhizobium etli . This table depicts the gene-protein-reaction association for Rhizobium etli metabolic reconstruction, iOR450 . Overall the reconstruction contains 450 genes codifying for a set of enzymes participating in 405 metabolic reactions and 377 metabolites. (XLS 169 KB)

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Resendis-Antonio, O., Hernández, M., Salazar, E. et al. Systems biology of bacterial nitrogen fixation: High-throughput technology and its integrative description with constraint-based modeling. BMC Syst Biol 5 , 120 (2011). https://doi.org/10.1186/1752-0509-5-120

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DOI : https://doi.org/10.1186/1752-0509-5-120

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Isolation and Identification of Nitrogen Fixing Bacteria: Azoarcus Species

Series: Springer Protocols Handbooks > Book: Practical Handbook on Agricultural Microbiology

Protocol | DOI: 10.1007/978-1-0716-1724-3_6

• Department of Life sciences, Hemchandracharya North Gujarat University, Patan, Gujarat, India

Nitrogen is the most important element for all the organisms. It is beneficial for the growth and development of plants. Biological nitrogen fixation is found to be an efficient approach for the availability of nitrogen to the plants using

Nitrogen is the most important element for all the organisms. It is beneficial for the growth and development of plants. Biological nitrogen fixation is found to be an efficient approach for the availability of nitrogen to the plants using diazotrophic bacteria such as Azoarcus species. The present chapter focuses on the methods for the isolation of Azoarcus species on different media and its identification using 16S rDNA sequencing with specific primers for Azoarcus , as the potent nitrogen fixing bacteria from different sources.

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ORIGINAL RESEARCH article

Global diversity and distribution of nitrogen-fixing bacteria in the soil.

• 1 Institute of Ecology and Earth Sciences, University of Tartu, Taru, Estonia
• 2 Zoology Department, College of Science, King Saud University, Riyadh, Saudi Arabia
• 3 Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
• 4 Universidad Nacional de Córdoba, Instituto Multidisciplinario de Biología Vegetal, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
• 5 Universidad Nacional de Río Cuarto, Departamento de Biología Agrícola, Facultad de Agronomía y Veterinaria, Córdoba, Argentina
• 6 Department of Wildlife Management and Ecotourism, University of Namibia, Katima Mulilo, Namibia
• 7 Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN, UMR CNRS 7058), Jules Verne University of Picardie, Amiens, France
• 8 Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia
• 9 Department of Natural Resource Sciences, Thompson Rivers University, Kamloops, BC, Canada
• 10 Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
• 11 Iluka Chair in Vegetation Science and Biogeography, Harry Butler Institute, Murdoch University, Perth, Australia
• 12 Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch, South Africa
• 13 Center of Mycology and Microbiology, University of Tartu, Tartu, Estonia
• 14 Department of Biology, Nakhon Phanom University, Nakhon Phanom, Thailand
• 15 Grupo de Microbiología Ambiental y Grupo BioMicro, Escuela de Microbiología, Universidad de Antioquia UdeA, Medellín, Colombia
• 16 Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

Our knowledge of microbial biogeography has advanced in recent years, yet we lack knowledge of the global diversity of some important functional groups. Here, we used environmental DNA from 327 globally collected soil samples to investigate the biodiversity patterns of nitrogen-fixing bacteria by focusing on the nif H gene but also amplifying the general prokaryotic 16S SSU region. Globally, N-fixing prokaryotic communities are driven mainly by climatic conditions, with most groups being positively correlated with stable hot or seasonally humid climates. Among soil parameters, pH, but also soil N content were most often shown to correlate with the diversity of N-fixer groups. However, specific groups of N-fixing prokaryotes show contrasting responses to the same variables, notably in Cyanobacteria that were negatively correlated with stable hot climates, and showed a U-shaped correlation with soil pH, contrary to other N-fixers. Also, the non-N-fixing prokaryotic community composition was differentially correlated with the diversity and abundance of N-fixer groups, showing the often-neglected impact of biotic interactions among bacteria.

Introduction

Nitrogen limitation to net primary production is widespread in terrestrial and marine ecosystems ( Vitousek & Howarth, 1991 ), and biological nitrogen fixation has a major role in providing nitrogen to the ecosystem. In biological fixation, gaseous N2 is assimilated and transformed only by a select group of microorganisms, either plant symbionts or free-living diazotrophs ( Pajares & Bohannan, 2016 ). These microorganisms are capable of expressing the nitrogenase enzyme codified by nif genes. One of these, the nif H gene coding nitrogenase reductase, has been accessed with molecular techniques for studies on microbial communities’ potential to fix atmospheric N2 ( Gaby & Buckley, 2014 ). The particular microbes include i) bacteria collectively referred to as rhizobia (e.g., Rhizobiaceae, Burkholderiaceae), ii) Actinobacteria from the genus Frankia, iii) Cyanobacteria from the Nostocaceae family, but also iv) free-living Bacteria and Archaea that have obtained nitrogenase via horizontal transfer ( Kuyper & de Goede, 2013 ; Vitousek et al., 2013 ). Rhizobiaceae (α-proteobacteria) and Burkholderiaceae (β-proteobacteria) are the most well-known N-fixing bacterial groups that nodulate mostly on legumes, although a few other plant genera can host rhizobia as well ( Peix et al., 2015 ; Sprent et al., 2017 ; Tedersoo et al., 2018 ). Nitrogen-fixing Frankiaceae inhabit root nodules of eight angiosperm families, and the mutualistic Frankia species fall into three genetic lineages ( Normand et al., 2007 ). In addition, Frankia are considered to be ubiquitous free-living soil organisms because many taxa have been recorded outside the distribution range of their compatible hosts ( Chaia et al., 2010 ). Several groups of Cyanobacteria also possess the ability to fix N2. Although well suited to independent existence in nature, some Cyanobacteria occur in symbiosis with a wide range of hosts, including protists, animals, and plants ( Rai et al., 2000 ).

The nif H gene is the biomarker most widely used to study the ecology and evolution of nitrogen-fixing bacteria ( Gaby & Buckley, 2014 ). Surveys of nif H diversity conducted in a wide range of environments ( Izquierdo & Nüsslein, 2006 ; Põlme et al., 2014 ; Penton et al., 2016 ; Wang et al., 2017 ; Nash et al., 2018 ; Silveira et al., 2021 ; Zhou et al., 2021 ) have demonstrated that the diversity and community composition of N-fixing bacteria vary significantly across habitats and regions. However, we lack a systematic understanding of the global distribution and diversity patterns of these bacteria and the environmental factors underlying them.

According to Tedersoo et al. (2018) and Tamme et al. (2021) , climatic factors significantly drive the diversity of the N-fixing host plants. They found that the absolute diversity of N-fixing plants is highest in warm and wet climates, while the relative richness of N-fixing plants (share in the local flora) is highest in warm and dry climates. We expect the same is true for symbiotic rhizobia. However, symbiotic Actinobacteria and Cyanobacteria, as well as the total N-fixing prokaryotic community, may exhibit different patterns. Plants in symbiosis with Actinobacteria are more abundant at high latitudes ( Menge et al., 2017 ; Tamme et al., 2021 ), while the host plants for N-fixing Cyanobacteria occur both in the tropics as well as in the boreal zone ( DeLuca et al., 2002 ; Sprent et al., 2017 ). Little is known about the global distribution of free-living N fixers, however, although for example in boreal ecosystems, tropical rain forests, temperate grasslands and arctic tundra, free-living nitrogen-fixation might outweigh symbiotic N-fixation ( Reed et al., 2011 ).

The diversity of N-fixing plants varies across biomes: Tedersoo et al. (2018) and Tamme et al. (2021) found that the relative richness of N-fixing plants is highest in tropical and temperate grasslands and semi-deserts. The same pattern may also hold concerning N-fixing bacteria. In addition, the distribution of host plants exhibits regional variation. For instance, the absolute diversity of N-fixing plants is very high in Australia ( Tamme et al., 2021 ). Moreover, Sprent et al. (2017) provide a thorough overview of the biogeographic history and demonstrate that regionally unique evolutionary histories of particular clades of bacteria certainly contribute to the regional turnover of the composition of mutualistic bacterial communities. Part of the regional effect on microbial diversity may therefore be specifically related to the historical distribution of biomes ( Pärtel et al., 2017 ), but there are no data with which to assess its effect on N-fixing organisms. In addition, recent studies have shown that biotic interactions between soil microbes may significantly structure their communities ( Bahram et al., 2018 ; Soliveres et al., 2018 ). However, we are unaware of any work studying the impacts of host plants, historical biome distribution or soil biotic interactions on N-fixing bacteria.

Given the significant effect of soil pH on bacterial communities in general, notably the increase in the abundance and diversity of bacteria with rising soil pH ( Lauber et al., 2009 ; Bahram et al., 2018 ; Delgado-Baquerizo et al., 2018 ), we hypothesised that soil pH also affects the N-fixing bacteria positively. We also expected variation in N-fixing bacterial community composition to associate with nitrogen availability. In experiments, the abundance of nitrogen-fixing bacteria tends to be suppressed by fertilisation with N and increased by fertilisation with P, while both alter the taxon composition of the bacterial communities ( Wang et al., 2017 ). Analogous responses might be expected along natural fertility gradients.

Here, we use environmental DNA (eDNA) extracted from 327 spatially-distinct soil samples from all continents except Antarctica to provide a first overview of the pattern of global biodiversity of N-fixing bacteria. We primarily addressed the distribution of N-fixing bacteria by analysing the nif H gene, but the bacterial 16S SSU rRNA (small subunit ribosomal RNA) gene was also used for complementary analyses.

Materials and methods

Sampling and data collection.

We used a global set of 327 soil samples ( Figure S1 ; Table S1 ) compiled from two similar global sampling efforts (dataset described in Davison et al., 2020 ). Briefly, sampling locations generally experienced little disturbance from human activities, from where topsoil (1-5 cm, to enable a uniform sampling depth considering locations with very shallow soils) samples were collected after removing the litter layer. The sampling was conducted following one of the two approaches: a) about 300 g of topsoil was collected from up to 40 points within about a 50 × 50 m sampling area and then pooled [233 samples]; or b) per sampling area, 5 g topsoil samples were collected from nine points on a regularly spaced 30 × 30 m grid and then pooled [94 samples]. The sampling design was included in statistical models as a covariate. Soil samples were dried within 24 h using silica gel at room temperature. Subsamples [a) 2 g; b) 5 g] of soil were extracted for molecular analysis; the remainder was stored for chemical analysis.

Soil chemical properties were measured from sieved soil samples (2 mm): pH, total N, P, K, Mg, and Ca. Soil pH was measured in 1 M KCl solution following ISO 10390:2005, using a Seven Easy pH meter with an InLab Expert Pro electrode (Mettler Toledo, Malaysia). The content of total N in soil was determined using the Kjeldahl method with a DK-20 digestion block and a UDK-126 distillation unit (Velp Scientifica Srl, Italy). For the determination of P, K, Mg, and Ca, the Mehlich III extraction method was used, with the content of elements determined using an MP-4200 microwave plasma atomic emission spectrometer (Agilent, USA). Chemical analyses were performed at the Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia. In further data analyses, and due to collinearity issues, K, Mg and Ca were ln-transformed and standardised (mean = 0, SD = 1). Then we performed a principal component analysis (PCA) with these macronutrients. In subsequent analyses, we incorporated the first principal component, which described 84.6% of the combined variance, and is negatively correlated to the amount of K, Mg and Ca in soil, referred to as “Other soil macronutrients” henceforth. The rest of soil chemical properties, i.e. total N, P, K, were included and analysed as individual factors.

Bioclimatic variables for each sampling location were accessed from the CHELSA high resolution modelled world climate database ( Karger et al., 2017 ). To incorporate as much of the background climatic information without overparameterizing the model, we performed PCA on the climate variables, standardised before the analysis (precipitation variables were ln -transformed), and included the first three principal components in subsequent analysis. The first principal component (Bioclim PC1) correlated with a stable hot climate, the second (Bioclim PC2) with a warm, arid climate, and the third (Bioclim PC3) with a seasonal humid climate ( Figure S2 ). The first three principal components described approximately 79% of the total variation in bioclimatic factors.

Sampling locations were assigned to biogeographic realms following Olson et al. (2001) . We combined ecologically similar ‘biomes’, resulting in seven biome combinations (following Tamme et al., 2021 ; Table S2 ). The historical stability of biomes at sampling locations was estimated by comparing the Olson et al. (2001) current biome classification with an analogous classification for the Last Glacial Maximum (LGM; approximately 21kyBP; Ray & Adams, 2001 ). Where the biome remained the same at both times, the sampling location was classified as stable; where the biome classification differed, the sampling location was classified as unstable.

The richness of N-fixing plants was estimated using data from Tamme et al. (2021) . In short, GBIF records ( GBIF.org, 2017 ) falling within roughly 7800 km² hexagonal grid cells surrounding the sampling coordinates were checked for quality and identified as N-fixing and non-fixing plant species based on the NodDB database ( Tedersoo et al., 2018 ), and used for richness calculations.

Molecular methods and bioinformatics

DNA was extracted from either a) 2 g or b) 5 g of dried soil using the PowerMax ® Soil DNA Isolation Kit (MoBio Laboratories, Carlsbad, California, USA). Two complementary DNA regions were used to identify N-fixing microorganisms, the dinitrogenase reductase subunit nifH gene and the general prokaryotic 16S rRNA gene.

The specific primer pair 19F (5′-GCIWTYTAYGGIAARGGIGG-3′) and 407R (5′-AAICCRCCRCAIACIACRTC-3′) ( Ueda et al., 1995 ) was used for amplifying the nif H gene. Primers were equipped with Illumina Nextera XT sequencing tags. PCR was carried out in the following thermocycling conditions: an initial 15 min at 95°C, followed by 38 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 1 min, and a final cycle of 10 min at 72°C. The amplicons were ligated using Illumina Nextera XT sample preparation kit (Illumina Inc., San Diego, USA) following the manufacturer’s protocol. Ligation of Illumina adaptors and sequencing using Illumina MiSeq 2x250 bp paired-end mode were performed at the Estonian Genome Centre (Tartu, Estonia).

The data were analysed using the gDat pipeline ( Vasar et al., 2021 ). Demultiplexed paired-end reads were analysed in the following way: primer sequences were matched, allowing 1 mismatch for both pairs. Only pairs where both reads had an average quality score of >30 were retained (after removal of primer sequence). Quality filtered paired-end reads were combined using FLASh (v1.2.10; Magoč & Salzberg, 2011 ) with the default parameters (10-300 bp overlap with at least 75% identity). Orphan reads (paired-end reads that did not meet the conditions for combination) were removed from the analyses, leaving 7,153,685 cleaned combined sequences. The VSEARCH (v2.21.1; Rognes et al., 2016 ) chimera filtering algorithm was used to remove putative chimeric reads in the reference mode using the nif H database ( Gaby & Buckley, 2014 ), yielding 5,083,833 chimera-free sequences that were clustered at 99% identity (following the recommendation of Edgar, 2018 ) into 65,713 OTUs (excluding OTUs with less than 10 sequences). Representative sequences (OTU centroids) were taxonomically classified using a BLAST+ search with ≥90% identity threshold against the nif H database using the best hit, resulting in 14,957 OTU hits (996,027 sequences) which were used in downstream analysis as the total N-fixer community. To enable taxonomic identification of specific groups of N-fixing organisms, we run an additional BLAST+ search against a subset of the nif H database sequences with at least family-level identity known using the best hit, resulting in 9332 OTU hits (565,921 sequences).

Prokaryotic primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′-GGCCGYCAATTYMTTTRAGTTT-3′) were used to amplify the 16S rRNA variable V4 region ( Caporaso et al., 2011 ; Parada et al., 2016 ). Both primers were equipped with unique 12-base Golay barcodes for multiplexing. The PCR amplification program included the following steps: 95°C for 15 min, followed by 25 cycles of 95°C for 30 s, 55°C 30 s and 72°C for 1 min, with a final extension step at 72°C for 10 min. The PCR products were pooled and visualised on 1% agarose gel. Initially, 25 cycles were used for all the samples, and in case the gel band was weak, or there was no PCR product, a higher number of PCR cycles was used (max 30 cycles). Negative and positive controls were included throughout the process. The PCR products were purified using a FavorPrepTM GEL/PCR Purification Kit (Favorgen Biotech Corporation). The libraries were ligated with Illumina adaptors using the TruSeq DNA PCR-free library prep kit (Illumina Inc., San Diego, CA, USA). Libraries were sequenced on the Illumina MiSeq platform, using a 2 × 250 bp paired‐read sequencing approach at Asper Biogene (Tartu, Estonia).

The data were analysed using the gDAT pipeline ( Vasar et al., 2021 ) with the same parameters and thresholds used for the nif H gene analysis. Orphan reads (paired-end reads that did not meet the conditions for combination) were removed from the analyses, leaving 14,593,006 cleaned combined sequences. The VSEARCH chimera filtering algorithm was used to remove putative chimeric reads in de novo mode, yielding 13,960,691 chimera-free sequences clustered at 99% identity into 165,330 OTUs (excluding singletons). Representative sequences (OTU centroids) for each non-singleton OTU were taxonomically classified using a BLAST+ search against the SILVA database (v132; Quast et al., 2013 ), taking the lowest common ancestor using the top 50 best hits (i.e., using the most precise taxonomic resolution at which the top 50 best hits converge), resulting in 141,959 hits.

N-fixer groups

Subsets of N-fixing organisms were defined according to their taxonomy: rhizobia (genus field containing “*rhizobium|Rhizobium”), Cyanobacteria (phylum field containing “Cyanobacteria”) and Frankia (genus field containing “Frankia”). The paraphyletic rhizobia were limited only to the mentioned extent due to the limited availability of species-level taxonomic identification of the OTU-s, precluding the incorporation of nodule-forming species from genera with several different trophic modes ( Willems, 2006 ). For the nif H dataset, we also used the total N-fixers, incorporating all the OTU-s from that primer set that had passed all quality filters and BLAST+ identity thresholds against the full nif H database. OTU-s from the 16S SSU dataset not included in the three N-fixer groups were used in further analyses to represent the non-N-fixing prokaryotic community, referred to as “prokaryotic community composition” henceforth.

Raw reads from this Targeted Locus Study have been deposited in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA; BioProject PRJNA659159). The environmental metadata and community data matrices have been deposited in Mendeley Data (doi: 10.17632/dsvcw24cyc.1).

Statistical analysis

We tested the effects of climate, soil chemical parameters, richness of N-fixing plant species, biome and historical biome stability, and composition of other prokaryotes on the natural logarithm of richness, Shannon diversity index, and relative abundance [ln(abundance of N-fixer group/(abundance of all prokaryotes including the N-fixer group – group abundance))] of different groups of N-fixing bacteria by fitting linear models using Generalised Least Squares (gls() from R package nlme v3.1-157; Pinheiro et al., 2021 ). Model predictors were tested for collinearity using Variance Inflation Factors (vif() from R package car v3.0-13; Fox & Weisberg, 2019 ), following which the biome variable was omitted from further analyses. The sampling design, i.e., from which of the two sampling campaigns the sample originated, was also included in the models as a covariate. To account for potential differences arising from unequal sampling depths (i.e., number of sequences obtained from a sample), we used Hill numbers of order 0 and 1 (richness and Shannon diversity [exp( H )], respectively) that were extrapolated to the asymptote to estimate the diversity at complete sampling coverage, using the R package iNEXT v2.0.20 ( Hsieh et al., 2016 ). After extrapolation, the Shannon diversity [exp( H )] was log-transformed similar to richness, yielding the Shannon diversity index ( H ). For readability, we refer to the extrapolated diversity metrics as “richness” and “Shannon diversity” throughout the paper. The models incorporated a spherical spatial correlation structure with great circle distances. The models were run in parallel with data from both genes, nif H and 16S SSU, except for the N-fixer group relative abundances, which could be calculated only for the 16S SSU dataset. Soil pH, P, N, soil chemistry principal component 1, and bioclimatic principal components were included in the model as linear and quadratic terms to capture unimodal relationships. Drivers of N-fixer communities were identified using distance-based redundancy analysis (dbRDA() from R package vegan v2.6-2; Oksanen et al., 2020 ) on Hellinger-transformed OTU abundances.

To visualise the biogeographical patterns in the richness of N-fixer groups, we generated interpolated maps using weighted categorical k-nearest neighbour (KNN) classification (kknn() in the R package kknn v1.3.1; Schliep & Hechenbichler, 2016 ), using the soil sample richnesses as the training set and a 0.5° × 0.5° map grid as the test set. Grid cell value represents the weighted value of the k nearest training set cells based on great-circle distances. The k-value (k = 18) for KNN was set as the rounded square root of the number of samples, based on the suggestion of ( Duda et al., 2000 ). Greenland and the Sahara region were excluded from the interpolation due to insufficient sampling and differing abiotic conditions.

All statistical analyses were performed in the R language (v4.2.1) in the RStudio IDE (R RStudio Team, 2016 ; Core Team, 2021 ).

In the nif H dataset, we identified a total of 14,957 OTUs as N-fixing microorganisms, and in the taxonomically annotated subset, we identified 5810 rhizobia, 619 Cyanobacteria, and 28 Frankia OTU-s. The 16S SSU dataset yielded 1560 rhizobia, 510 Cyanobacteria, and 27 Frankia OTUs. Due to the very low yield of Frankia sequences and richness in both datasets (e.g., nif H Frankia richness: Figure S3 ), Frankia were not analysed separately but were only included within the total N-fixers group.

N-fixer richness

In the nif H dataset, stable hot climates favoured the richness of total N-fixers and rhizobia but were negatively associated with Cyanobacteria richness ( Figure 1A ; Table 1 ), whereas rhizobia richness was unimodally correlated with warm, arid climates ( Figure 1B ; Table 1 ). Seasonal humidity positively correlated with total N-fixer and rhizobia richness ( Figure 1C ; Table 1 ). Total N-fixers and rhizobia richness were also higher at greater latitudes and in locations where the biome has historically reimained stable when compared to the Last Glacial Maximum ( Table 1 ). Intermediate soil pH levels favoured rhizobia richness ( Figure 1D ; Table 1 ), and intermediate values of soil total N content favoured total N-fixers and Cyanobacteria ( Figure 1F ; Table 1 ); soil P content was positively associated only with Cyanobacteria richness ( Figure 1E ; Table 1 ). N-fixer richness was also associated with the non-N-fixing prokaryotic community, with total N-fixer and Cyanobacteria richness increasing and rhizobia richness declining along the main axis of variation in prokaryotic community composition ( Figure 1G ; Table 1 ). Similar trends were apparent using N-fixer Shannon diversity ( Table S4 ; Figure S4 ) as the response variable, with the exceptions that total N-fixer Shannon diversity was unimodally correlated with soil pH in addition to rhizobia, and rhizobia were unimodally related to soil N content. The 16S SSU dataset also generally exhibited similar patterns ( Table S3 ) but revealed a U-shaped response of Cyanobacteria richness and Shannon diversity ( Table S4 ) to soil pH that was not apparent in the nif H data. The global patterns of N-fixing microorganism richness in the nif H dataset are visualised on Figure 2 .

Figure 1 Factors affecting the natural logarithm of richness of different groups of N-fixing prokaryotes in soil samples in the nitrogenase reductase nifH gene dataset. Lines show predicted values from the GLS model. The effects of (A) Bioclim PC1 [Stable hot climate], (B) Bioclim PC2 [Warm aridity], (C) Bioclim PC3 [Seasonal humidity], (D) soil pH, (E) soil P content, (F) soil total N content, and (G) the community composition gradient of the non-N-fixing prokaryotic community (NMDS axis 1) are shown.

Table 1 Factors affecting the natural logarithm of richness of different groupings of N-fixing prokaryotes in the nitrogenase reductase nif H dataset.

Figure 2 Interpolated (k-nearest-neighbour map cell interpolation based on the values from the collected samples, cell size = 0.5°×0.5°, k = 18) richness maps of total N-fixers (A) , rhizobia (B) , and Cyanobacteria (C) , based on the nifH sequencing dataset.

N-fixer relative abundance

Cyanobacterial relative abundance showed a U-shaped response to a warm, arid climate ( Table 2 ; Figure S5A ). The relative proportion of rhizobia exhibited a unimodal relationship with soil pH ( Table 2 ; Figure S5B ) and a negative relationship with soil P content ( Table 2 ; Figure S5C ). The proportion of Cyanobacteria, on the contrary, demonstrated an overall negative but a U-shaped quadratic response to soil pH ( Table 2 ; Figure S5B ) and soil total N content ( Table 2 ; Figure S5D ). The relative abundance of rhizobia and Cyanobacteria showed contrasting relationships with the community composition of non-N-fixing prokaryotes ( Table 2 ; Figure S5E ).

Table 2 Factors affecting the relative abundance [ln(group/(all prokaryotes – group))] of rhizobia and Cyanobacteria.

N-fixer community composition

Total N-fixer community composition was weakly (dbRDA model R 2 = 0.04) correlated with the bioclimatic variables, the non-N-fixing prokaryotic community, latitude, soil pH, total N and other macroelement content, and the richness of N-fixing plants ( Table S5 ). The community composition of rhizobia was weakly correlated with the bioclimatic variables, the non-N-fixing prokaryotic community, latitude, soil pH and total N and other macroelement content, as well as N-fixing plant richness and the historical biome stability in the nif H dataset (dbRDA model R 2 = 0.03, Table S5 ). In the 16S SSU dataset, rhizobial community composition was correlated with soil P but had no correlations with latitude, other soil macroelements, N-fixing plant richness or the historical stability of the biome (dbRDA model R2 = 0.19; Table S5 ). In the nif H dataset, Cyanobacterial communities (dbRDA model R 2 = 0.03) were weakly associated with soil pH, stable hot climate, seasonal humidity and other soil macroelements ( Table S5 ), but associated with the non-N-fixing prokaryotic community, latitude, soil pH, P and total N, and stable hot and warm arid climates in the 16S SSU dataset (dbRDA model R 2 = 0.05, Table S5 ).

The soil microbial component constitutes the base for terrestrial ecosystem functioning. In particular, biological nitrogen fixation, conducted by specific groups of bacteria, is a crucial component of the terrestrial carbon cycle, providing the nitrogen input into ecosystems that plants require. The extent and global distribution of nitrogen fixation are highly disputed. In their meta-analysis, Davies-Barnard & Friedlingstein (2020) found no evidence for any statistically significant relationship between biological nitrogen fixation and conventionally used climatic and soil parameters. However, the lack of this relationship can partly mirror the limited information about the distribution of N-fixing organisms in general. Here we provide the first estimation of the global distribution of N-fixing bacteria in soil that can serve as background information for further estimating the actual potential of biological N-fixation globally and regionally, as well as in the context of particular ecological conditions.

There is ample information about the distribution and diversity of plants hosting symbiotic nitrogen-fixing bacteria ( Sprent et al., 2017 ; Tedersoo et al., 2018 ; Ardley & Sprent, 2021 ; Tamme et al., 2021 ). Given the recorded correlation between plant and bacterial diversity ( Porazinska et al., 2018 ), we expected that large-scale community patterns of N-fixing bacteria to a certain degree mirror those of their host plants. Indeed, we recorded a degree of similarity. For instance, rhizobia richness was positively correlated to warm and moderately arid climates, with a similar pattern being found for the relative richness of rhizobia-associated plants globally ( Tamme et al., 2021 ) and regionally ( Pellegrini et al., 2016 ). Diazotrophic prokaryotes in general have been also shown to be largely limited by aridity ( Zhao et al., 2020 ), but in this study, only rhizobial richness showed a relationship with the bioclimatic principal component describing aridity. Interestingly, Cyanobacteria-associated plants in Tamme et al. (2021) exhibited similar patterns to rhizobia-associated plants, but Cyanobacteria examined in this study responded negatively to higher temperatures. This decoupling is most likely caused by the fact that the nif H gene sequencing used in this study enabled to detect a large number of Cyanobacteria that are free-living or in symbiosis with other organism groups, such as fungi in cyanolichens ( Rikkinen, 2015 ), termites ( Yamada et al., 2007 ) and various protists ( Nowack & Melkonian, 2010 ). However, the direct comparison of N-fixing plant richness and the richness of N-fixing bacteria yielded no significant associations. Considering that the larger groups of microbial N-fixing bacteria (i.e. total N-fixers and rhizobia) in this study corresponded similarly to the bioclimatic factors that have shown to influence the richness of N-fixing plants ( Tamme et al., 2021 ), the effect of N-fixing plants on N-fixing bacteria can be masked by the environmental co-variation. In addition, the density of observations in the GBIF data used for assessment of N-fixing plant richness varies significantly at the global scale, with much more records in certain regions, such as Europe and North America, thus also possibly contributing to the lack of a direct correlation between N-fixing plant and microbial richness.

Regional biogeographic history can explain some patterns in the diversity of plants and animals ( Mittelbach et al., 2007 ), but it is rarely considered in microbial ecology. Our study revealed that the biogeographic stability of the sampled region is positively associated with the diversity and abundance of N-fixing bacteria, with regions where the biome has been historically stable since the Last Glacial Maximum hosting a greater richness and Shannon diversity. An analogous relationship was described for arbuscular mycorrhizal fungal communities by Pärtel et al. (2017) , who recorded the highest fungal diversity in historically stable tropical grasslands.

Our study revealed that total N-fixer, rhizobial and Cyanobacterial diversity correlated significantly with local non-N-fixing bacterial community composition, highlighting the role of biotic interactions in structuring local microbial communities ( Soliveres et al., 2018 ). Interestingly, rhizobial diversity correlated with the non-N-fixing community composition gradient distinctly from total N-fixing bacteria and Cyanobacteria. These distinct associations between the richness of different groups of N-fixing prokaryotes with the composition of non-N-fixing bacterial community may reflect different levels of dependence on plant hosts within these groups. However, these relationships may also arise from distinct affinities of different microbial groups to environmental factors, and hence the possible causality of the relationships merits further scrutiny. Nevertheless, the often-neglected impact of biotic interactions among bacteria should be considered when addressing the large-scale patterns of N-fixing bacterial communities. Also, further study into the causality and directionality of belowground biotic interactions is direly needed.

The results of this study indicated that the diversity and relative abundance of total N-fixing bacteria and rhizobia are unimodally related to soil pH. At the same time, there is a U-shaped relationship between the diversity and abundance of Cyanobacteria and soil pH. While generally, bacteria exhibit a positive relationship with soil pH ( Lauber et al., 2009 ; Bahram et al., 2018 ; Delgado-Baquerizo et al., 2018 ), unimodal relationships are not uncommon ( Fierer & Jackson, 2006 ). However, a U-shape relationship is unexpected. The maximum diversity of Cyanobacteria at high pH corresponds to the general pattern found for bacteria ( Bahram et al., 2018 ). At the same time, the maximum at low pH may reflect the abundant distribution of important host plants of Cyanobacteria – feather mosses – in the boreal zone ( DeLuca et al., 2007 ). Alternatively, the response of Cyanobacteria resembles the realised niche of an inferior competitor, similar to, e.g. Pinus sylvestris or Saccharomycetales yeasts ( Tedersoo et al., 2020 ), whose abundance or richness maxima also lie at the extremes of the soil pH gradient. Although in the nif H dataset, rhizobial richness in particular was not significantly correlated with soil nitrogen content, in general, the diversity and richness of both total N-fixers and rhizobia across the two amplicons studied were highest at intermediate soil nitrogen contents. Meanwhile, the richness and diversity of Cyanobacteria exhibited a negative correlation with soil nitrogen content. These results are consistent with fertilisation experiments, where nitrogen addition decreased the diversity and abundance of N-fixing bacteria ( Wang et al., 2017 ). At the same time, the positive association between soil P content and the richness and diversity of N-fixing bacteria that could also be expected from previous experiments ( Wang et al., 2017 ) was only demonstrated for Cyanobacteria. This suggests that N is the most important soil nutrient for structuring the composition of N-fixing bacteria.

Overall, global variation in the taxonomic composition of the rhizobial community was driven by multiple climatic, geographic, soil, and biotic factors. Although with fewer significant relationships, similar patterns were revealed for Cyanobacteria. Temperature and soil pH were the strongest drivers of the composition of rhizobial and Cyanobacteria communities, agreeing with the patterns recorded for soil bacteria in general ( Fierer & Jackson, 2006 ; Bahram et al., 2018 ; Karimi et al., 2020 ). At the same time, this is the first study demonstrating the importance of biotic drivers, such as the composition of other prokaryotes (on total N-fixers, rhizobia, and Cyanobacteria) and the plant community (on total N-fixers) in shaping the composition of N-fixing bacteria. While the diversity of N-fixers was correlated with the stability of the biome, the community composition remained largely unaffected (except for a very weak correlation in rhizobia). It might be that environmental parameters are mainly driving the communities, and the historical stability of the environment has allowed for more diversification and hence greater richness without selecting specific indicator taxa for stability.

To conclude, we reported a comprehensive evaluation of the global trends of terrestrial nitrogen fixation via amplification of the nif H gene, supplemented by parallel analysis of similar N-fixer groups in a general 16S SSU prokaryotic dataset. The richness, diversity, relative abundance and community composition of N-fixing prokaryotes are driven mainly by bioclimatic variables, but notably also soil pH. We also demonstrate the interplay of the co-occurring non-N-fixing prokaryotic community composition with the N-fixing community in both composition and diversity. Further research is needed to identify the biotic drivers of N-fixing bacterial communities, e.g. by developing better abilities to distinguish free-living and symbiotic groups of N-fixing bacteria or by elaborating on the potentially facultative nature of N-fixing symbioses.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/ , BioProject PRJNA659159 https://data.mendeley.com/datasets/dsvcw24cyc/1 , doi: 10.17632/dsvcw24cyc.1 .

Author contributions

Conceptualisation, MZ and S-KS. methodology, S-KS, MV, JD, JO. software, MV. formal analysis, S-KS, MV. investigation, MM, MÖ, MZ, MP. resources, SA-Q, MZ, LT. data curation, SA, MB, CB, JC, EF, GD, RD, LF, RG-O, IH, KK, UK, MS, TV, LM, SP, AV-P. writing—original draft preparation, S-KS, MZ, MV. writing—review and editing, JD, MÖ. visualisation, S-KS, MV. project administration, MZ, LT, SA-Q, MM. All authors contributed to the article and approved the submitted version.

This work was supported by the European Regional Development Fund [Centre of Excellence EcolChange], Estonian Research Council [grant numbers PRG609, PRG632, PRG1065, PRG1170, PUT1170, MOBTP105, PSG784], and the University of Tartu [grant number PLTOM20903].

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1100235/full#supplementary-material

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Keywords: 16S SSU, bacterial diversity, biological N fixation, N-fixing bacterial community, biotic interactions, nifH gene

Citation: Sepp S-K, Vasar M, Davison J, Oja J, Anslan S, Al-Quraishy S, Bahram M, Bueno CG, Cantero JJ, Fabiano EC, Decocq G, Drenkhan R, Fraser L, Garibay Oriel R, Hiiesalu I, Koorem K, Kõljalg U, Moora M, Mucina L, Öpik M, Põlme S, Pärtel M, Phosri C, Semchenko M, Vahter T, Vasco Palacios AM, Tedersoo L and Zobel M (2023) Global diversity and distribution of nitrogen-fixing bacteria in the soil. Front. Plant Sci. 14:1100235. doi: 10.3389/fpls.2023.1100235

Received: 16 November 2022; Accepted: 09 January 2023; Published: 20 January 2023.

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Copyright © 2023 Sepp, Vasar, Davison, Oja, Anslan, Al-Quraishy, Bahram, Bueno, Cantero, Fabiano, Decocq, Drenkhan, Fraser, Garibay Oriel, Hiiesalu, Koorem, Kõljalg, Moora, Mucina, Öpik, Põlme, Pärtel, Phosri, Semchenko, Vahter, Vasco Palacios, Tedersoo and Zobel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Siim-Kaarel Sepp, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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nitrogen fixation , any natural or industrial process that causes free nitrogen (N 2 ), which is a relatively inert gas plentiful in air, to combine chemically with other elements to form more-reactive nitrogen compounds such as ammonia , nitrates , or nitrites .

Under ordinary conditions, nitrogen does not react with other elements. Yet nitrogenous compounds are found in all fertile soils , in all living things, in many foodstuffs, in coal , and in such naturally occurring chemicals as sodium nitrate (saltpetre) and ammonia. Nitrogen is also found in the nucleus of every living cell as one of the chemical components of DNA .

Nitrogen is fixed, or combined, in nature as nitric oxide by lightning and ultraviolet rays, but more significant amounts of nitrogen are fixed as ammonia, nitrites, and nitrates by soil microorganisms. More than 90 percent of all nitrogen fixation is effected by them. Two kinds of nitrogen-fixing microorganisms are recognized: free-living (nonsymbiotic) bacteria, including the cyanobacteria (or blue-green algae) Anabaena and Nostoc and genera such as Azotobacter , Beijerinckia , and Clostridium ; and mutualistic (symbiotic) bacteria such as Rhizobium , associated with leguminous plants , and various Azospirillum species, associated with cereal grasses .

The symbiotic nitrogen-fixing bacteria invade the root hairs of host plants , where they multiply and stimulate the formation of root nodules, enlargements of plant cells and bacteria in intimate association. Within the nodules, the bacteria convert free nitrogen to ammonia, which the host plant utilizes for its development. To ensure sufficient nodule formation and optimum growth of legumes (e.g., alfalfa , beans , clovers , peas , and soybeans ), seeds are usually inoculated with commercial cultures of appropriate Rhizobium species, especially in soils poor or lacking in the required bacterium. ( See also nitrogen cycle .)

Nitrogenous materials have long been used in agriculture as fertilizers , and in the course of the 19th century the importance of fixed nitrogen to growing plants was increasingly understood. Accordingly, ammonia released in making coke from coal was recovered and utilized as a fertilizer , as were deposits of sodium nitrate (saltpetre) from Chile. Wherever intensive agriculture was practiced, there arose a demand for nitrogen compounds to supplement the natural supply in the soil. At the same time, the increasing quantity of Chile saltpetre used to make gunpowder led to a worldwide search for natural deposits of this nitrogen compound . By the end of the 19th century it was clear that recoveries from the coal-carbonizing industry and the importation of Chilean nitrates could not meet future demands. Moreover, it was realized that, in the event of a major war, a nation cut off from the Chilean supply would soon be unable to manufacture munitions in adequate amounts.

During the first decade of the 20th century, intensive research efforts culminated in the development of several commercial nitrogen-fixation processes. The three most-productive approaches were the direct combination of nitrogen with oxygen , the reaction of nitrogen with calcium carbide, and the direct combination of nitrogen with hydrogen. In the first approach, air or any other uncombined mixture of oxygen and nitrogen is heated to a very high temperature, and a small portion of the mixture reacts to form the gas nitric oxide. The nitric oxide is then chemically converted to nitrates for use as fertilizers. By 1902 electric generators were in use at Niagara Falls , New York , to combine nitrogen and oxygen in the high temperatures of an electric arc . This venture failed commercially, but in 1904 Christian Birkeland and Samuel Eyde of Norway used an arc method in a small plant that was the forerunner of several larger, commercially successful plants that were built in Norway and other countries.

The arc process, however, was costly and inherently inefficient in its use of energy, and it was soon abandoned for better processes. One such method used the reaction of nitrogen with calcium carbide at high temperatures to form calcium cyanamide , which hydrolyzes to ammonia and urea . The cyanamide process was utilized on a large scale by several countries before and during World War I, but it too was energy-intensive, and by 1918 the Haber-Bosch process had rendered it obsolete.

The Haber-Bosch process directly synthesizes ammonia from nitrogen and hydrogen and is the most economical nitrogen-fixation process known. About 1909 the German chemist Fritz Haber ascertained that nitrogen from the air could be combined with hydrogen under extremely high pressures and moderately high temperatures in the presence of an active catalyst to yield an extremely high proportion of ammonia, which is the starting point for the production of a wide range of nitrogen compounds. This process, made commercially feasible by Carl Bosch , came to be called the Haber-Bosch process or the synthetic ammonia process. Germany’s successful reliance on this process during World War I led to a rapid expansion of the industry and the construction of similar plants in many other countries after the war. The Haber-Bosch method is now one of the largest and most-basic processes of the chemical industry throughout the world.

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• Published: 31 January 2020

Symbiosis of soybean with nitrogen fixing bacteria affected by root lesion nematodes in a density-dependent manner

• Ahmed Elhady 1 , 2 ,
• Johannes Hallmann 1 &
• Holger Heuer   ORCID: orcid.org/0000-0001-6044-8171 1  

Scientific Reports volume  10 , Article number:  1619 ( 2020 ) Cite this article

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• Rhizobial symbiosis

Early maturing varieties of soybean have a high yield potential in Europe, where the main biotic threat to soybean cultivation are root lesion nematodes ( Pratylenchus spp.). Nitrogen fixation in root nodules by highly efficient inoculants of Bradyrhizobium japonicum is an incentive to grow soybean in low-input rotation systems. We investigated density-dependent effects of Pratylenchus penetrans on nitrogen fixation by co-inoculated B. japonicum . Less than 130 inoculated nematodes affected the number and weight of nodules, the density of viable bacteroids in nodules, and nitrogen fixation measured as concentration of ureides in leaves. With more inoculated nematodes, the percentage that invaded the roots increased, and adverse effects on the symbiosis accelerated, leading to non-functional nodules at 4,000 and more nematodes. When P. penetrans invaded roots that had fully established nodules, growth of nodules, density of bacteroids, and nitrogen fixation were affected but not the number of nodules. In contrast, nodulation of already infested roots resulted in a high number of small nodules with decreased densities of bacteroids and nitrogen fixation. P. penetrans invaded and damaged the nodules locally, but they also significantly affected the nodule symbiosis by a plant-mediated mechanism, as shown in an experiment with split-root systems.

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Introduction.

Soybean ( Glycine max (L.) Merrill) is among the economically most important crops worldwide. Its production area is currently increasing in temperate regions. In Germany, the production increased from 1,000 ha in 2003 to 23,900 ha in 2018. On 343,000 ha within Germany, the conditions would allow a potential yield of 3.2 t/ha when growing adapted early maturing varieties 1 , 2 . One of the intentions of farmers to grow soybean is to diversify the crop rotation, to improve the soil quality and take advantage of symbiotic nitrogen fixation in the nodules of soybean roots 3 . High input costs and public pressure to reduce nitrogen use in agriculture in order to reduce environmental contamination increased the incentive to grow legumes. Soybean plants acquire nodulating bacteria, typically strains of the species Bradyrhizobium japonicum 4 or Bradyrhizobium diazoefficiens 5 , from their rhizosphere by specific signalling, and maintain an intimate interaction with the symbionts 6 . The plant controls the nutrient supply and the number of nodules, and can induce senescence of nodules, to balance burden and benefit of the symbiosis 7 . Farmers inoculate the soybean seeds with commercial products containing efficiently nodulating strains to secure high yields 8 , 9 .

However, yield stability is a major concern when growing soybean. This problem is partially caused by pests and diseases. In some years, plant parasitic nematodes decrease soybean yield by more than 30% without visible symptoms aboveground 10 . In a recent survey of German soybean fields, we showed that the root lesion nematodes Pratylenchus penetrans , P. neglectus and P. crenatus are the main biotic threat of soybean production in such temperate regions, while the main threats worldwide, namely Heterodera glycines , Pratylenchus brachyurus or Meloidogyne incognita 11 , were not detected 12 . Early studies reported that plant parasitic nematodes could severely interfere with the soybean-rhizobia symbiosis. Nodules were decreased in number and size 13 , 14 , or increased in number with reduced nodule size 14 due to root invasion by H. glycines , P. penetrans , or Meloidogyne hapla . Nitrogen-fixing capacity as measured by the acetylene reduction assay was significantly reduced by P. penetrans in a phytotron experiment compared to plants without nematodes, but not in a greenhouse experiment 14 . These experiments were performed with high numbers of infective stages of the nematodes that do not reflect normal field densities. In addition, the methods for the measurement of nitrogenase activity by the acetylene reduction assay that were applied in these studies can be highly inaccurate in predicting the rate of nitrogen fixation in nodules 15 , 16 . Thus, the density-dependent effects of root-lesion nematodes on the symbiosis of soybean plants with inoculants of B. japonicum are still unclear, and whether the nodule formation or the functioning of established nodules is affected by the nematodes. Systemic control of nodulation by the plant 17 suggested that the intimate relationship is affected by systemic changes in the plant that are induced by root invasion of lesion nematodes. However, it was occasionally observed that some plant parasitic nematodes fed and reproduced in nodules 18 , 19 , which suggested a local mechanism of damage to the nodules.

The objectives of this study were to investigate whether the root lesion nematode P. penetrans affects the symbiosis of soybean plants with the nitrogen-fixing bacteria, and how this depends on the population density of the nematode in soil and in the roots. We further studied the disturbance of the established symbiosis by invading nematodes, and the effect of lesion nematodes in the roots on nodule formation. We determined as a measure for nitrogen fixation rate the concentration of ureides in the leaves. Ureides are synthesized by soybean plants from fixed nitrogen for transport from the nodules to the shoot 20 . As we observed migration of the nematodes into the nodules, we investigated whether the detrimental effect of the nematodes on nodule functioning is caused by the local damage of the nodules, or by a systemic mechanism.

Density dependent effects of P. penetrans on rhizobial nodulation

The roots of soybean were co-inoculated with B. japonicum and various numbers of P. penetrans to investigate density dependent effects of the nematodes on nodulation, symbiotic N 2 fixation, and plant growth. The nematodes invaded the roots in a density dependent manner (Fig.  1 ). The percentage of inoculated nematodes that entered the roots slightly decreased from 14% with 60 inoculated nematodes to 10% with 375. There was a sharp increase above around 500 inoculated nematodes. With increasing numbers of nematodes in soil, the percentage of invading nematodes rapidly increased to 39%. Nematode counts in roots remained above 31% from 2,000 to 6,000 inoculated nematodes, despite the negative bias of counts at high density in roots. Increasing numbers of lesion nematodes progressively affected the number and weight of nodules per soybean plant, the number of viable bacteroids in the nodules, and the amount of fixed nitrogen measured as concentration of ureides in the leaves (Fig.  2 ). These negative effects, compared to the control plants without inoculated nematodes, started with about 250 infective nematodes. With 4,000 inoculated nematodes, hardly any viable bacteroids were detected and nodules developed only in small number and size. Ureides in the shoots (allantoin and allantoic acid) were near or even below detection limit, showing that no fixed nitrogen was transported to the leaves at these high infection rates. As a consequence of the nitrogen limitation, the shoot weight declined with increasing numbers of inoculated nematodes (Fig.  3A ). The root reacted to low infestation by P. penetrans with slightly increased growth, while 3,000 and more inoculated nematodes affected the root weight (Fig.  3B ). To estimate the threshold densities of inoculated P. penetrans above which the symbiosis was affected, the data were fit to the Seinhorst equation (Table  1 ). The shoot and root weight was affected above 250 or 2,000 nematodes, respectively. However, bacteroids, nodules, or ureides were affected at much lower densities above 0 to 130 inoculated P. penetrans .

Density-dependent invasion of Pratylenchus penetrans into roots of soybean plants. Suspensions of infective stages of P. penetrans were equally inoculated around soybean plants into four holes of 5 cm depth, and invaded nematodes were stained and counted microscopically in the roots after two weeks. The numbers and percentages of nematodes that invaded the root system are shown relative to the number of nematodes that were inoculated to the plant two weeks before.

Density-dependent effects of Pratylenchus penetrans inoculated to soybean roots on nodule number ( A ), nodule weight ( B ), density of Bradyrhizobium japonicum bacteroids ( C ), and nitrogen fixation. ( D ) Viable bacteroids of a rifampicin resistant mutant of B. japonicum 532 C were extracted from 50 mg of nodules and quantified as colony forming units on selective yeast-mannitol agar plates containing vancomycin and rifampicin. Ureides (the transport forms of fixed nitrogen in soybean plants) were determined as allantoin and allantoic acid in 25 mg leaf tissue by a colorimetric assay. Curve fits are based on polynomal regressions using the software PRISM 7.

Density-dependent effects of Pratylenchus penetrans on growth of soybean plants. Root and shoot weights were determined five weeks after inoculation of P. penetrans and B. japonicum to the roots.

Effect of P. penetrans on already nodulated soybean plants

To investigate how an established B. japonicum -soybean symbiosis will be affected by invading lesion nematodes, soybean plants were first inoculated with B. japonicum and allowed to form nodules. Two weeks later, the nodulated plants were infected with 1,000  P. penetrans each. The effect of the root invasion of P. penetrans on the nodules and N 2 fixation was analyzed two and five weeks after incubation of P. penetrans . The number of nodules did not significantly differ between the treatment with or without P. penetrans , and it did not change significantly over time (Fig.  4A ). The mass of the nodules increased over time in both treatments, but was significantly greater in the non-infested control at both time points (Fig.  4B ). The density of viable bacteroids in nodules increased over time and was significantly affected by P. penetrans two and five weeks after inoculation (Fig.  4C ). Concomitantly, the transport forms of fixed nitrogen, the ureides, increased in concentration in the leaves over time. The concentration of ureides was significantly affected by P. penetrans at the 5-weeks sampling, and this trend was also visible at the 2-weeks sampling (Fig.  4D ). In this experiment, the duration of treatments was not long enough to lead to a clear effect of P. penetrans on the growth of nodulated soybean plants (Supplementary Table  S1 ). The shoot fresh weight was significantly decreased two weeks after incubation of P. penetrans compared to non-infected plants, and root weights showed the same trend.

Effects of Pratylenchus penetrans on nodules and nitrogen fixation of soybean plants, which already established symbioses with Bradyrhizobium japonicum . Twelve-day old soybean seedlings were inoculated with B. japonicum and kept for two weeks until nodules have been fully established before inoculation of 1,000  P. penetrans . Two and five weeks thereafter, the number ( A ) and weight ( B ) of nodules, the density of viable bacteroids in nodules ( C ), and the concentration of transported fixed N 2 in leaves ( D ) were determined. Significant differences between plants with and without inoculated nematodes are indicated by stars (Tukey’s test, n = 8, P < 0.05). Boxes indicate lower and upper quartiles separated by the median. Means are shown as (+). Whiskers extend to minimum and maximum values.

Effect of established P. penetrans on nodulation of soybean roots

To investigate how an established infestation with P. penetrans affects the nodule formation and function by B. japonicum in the soybean roots, the nitrogen fixing bacteria were inoculated to soybean roots that were already colonized by P. penetrans , or non-infected roots. Surprisingly, the infested roots formed a significantly higher number of nodules than the non-infested roots in the two weeks after inoculation of B. japonicum (Fig.  5A ). Five weeks after inoculation of B. japonicum , the number of nodules even increased to an extreme value of 128 nodules on average per infested root, while the control plants had an average number of 51 nodules per root. The size of the nodules and the distribution across the root hairs clearly differed between the pre-infected and control roots. In the pre-infected soybean roots, where P. penetrans caused distributed lesions within the roots, the size of nodules was very small and clustered abnormally. In contrast, the nodules were large and well distributed across the root hairs in non-infested soybean roots. The higher number of nodules formed on the pre-infected roots was reflected by a slight trend for increased nodule mass, two and five weeks after inoculation of B. japonicum (Fig.  5B ). However, the pre-infection of soybean roots with nematodes led to a significant decrease of viable bacteroids in nodules compared to the non-infested roots, two and especially five weeks after inoculation of B. japonicum (Fig.  5C ). Consequently, ureide concentrations in leaves of infested plants were significantly lower than in leaves of non-infested plants at both samplings. Plant growth parameters did not significantly differ in this experiment where plants were grown with fertilizer before inoculation of the nodulating bacteria (Supplementary Table  S2 ). The Supplementary Fig.  S1 shows the change in root and nodule morphology caused by P. penetrans . Rhizobial colonization of soybean roots, measured as number of nodules per root mass, was strongly affected by the number of inoculated root lesion nematodes (Supplementary Fig.  S2 ).

Effects of Pratylenchus penetrans already residing in roots on nodulation of soybean plants by Bradyrhizobium japonicum . The 12-day old soybean seedlings were infected with 1,000  P. penetrans . Two weeks later, pots were inoculated with B. japonicum . Two and five weeks after inoculation with B. japonicum , the number ( A ) and weight ( B ) of nodules, the density of viable bacteroids in nodules ( C ), and the concentration of transported fixed N 2 in leaves ( D ) were determined. Significant differences between plants with and without inoculated nematodes are indicated by stars (Tukey’s test, n = 8, *P < 0.05, **P < 0.01). Boxes indicate lower and upper quartiles separated by the median. Means are shown as (+). Whiskers extend to minimum and maximum values.

Plant-mediated effect of P. penetrans on the B. japonicum - soybean symbiosis analyzed in split-root systems

Microscopic analysis of nodules and areas near to nodules showed that P. penetrans invaded the nodules and the area close to nodules (Fig.  6A ). They laid their eggs in such zones, suggesting that nodulation zones are a favorable habitat for these nematodes (Fig.  6B ). The presence of P. penetrans within and close to nodules resulted in brown lesions within nodules and in the cortex and phloem near nodules (Fig.  6C,D ). This looked like a direct effect of the nematodes on the functioning of the nodules. Therefore, we tested in a split-root system whether the effect of P. penetrans on the bacteria-plant symbiosis was partially plant-mediated by spatially separating nematodes from nodules. One side of the root system was either inoculated with P. penetrans , or kept as non-inoculated control. Two weeks later, the other half of the root system was inoculated with B. japonicum , and the roots were sampled after three weeks. As in the previous experiment, the number of nodules was significantly increased in roots of infested plants compared to the non-infested control (Fig.  7A ). Nematodes were not able to migrate to the nodules in the other root system. The mass of nodules and the density of bacteroids was significantly decreased in infested plants compared to the non-infested control (Fig.  7B,C ).

Colonization of nodules in soybean roots by Pratylenchus penetrans . ( A ) P. penetrans invaded the tissues of nodules and the area near to nodules. The nematodes were stained red with acid fuchsin. The arrow indicates a nodule with penetrated nematodes. ( B ) Eggs of P . penetrans laid near a nodule, indicated by the arrow. ( C ) P. penetrans damaged the cortex and phloem near to nodules. The arrow indicates lesions. ( D ) Damage to nodules by lesions (arrow) from migrating P. penetrans .

Systemic effect of Pratylenchus penetrans already residing in roots on the establishment of the symbiosis of soybean and Bradyrhizobium japonicum . In a split-root experiment, one-half of each root system was inoculated with 500  P. penetrans while control plants were not infected. After two weeks, the other half of all root systems was inoculated with B. japonicum . Three weeks after inoculation of B. japonicum , the number ( A ) and weight ( B ) of nodules per plant and the density of bacteroids ( C ) were analyzed. Significant differences between plants with and without inoculated nematodes are indicated by stars (Tukey’s test, n = 6, P < 0.05).

In this study, root invasion by P. penetrans affected the formation and development of nodules and thereby the N 2 fixation by the B. japonicum -soybean symbiosis in a density-dependent manner. The effect on nodulation and N 2 fixation was tested with a range of densities of lesion nematodes that reflected densities observed in the field, as well as with densities of 4,000 and more infective stages per plant that are unusually high at least during the early developmental period of soybean plants 12 . Other studies reporting effects of P. penetrans 14 or Heterodera glycines 21 on soybean nodules only applied very high densities that were largely above damage thresholds. This extreme number of nematodes resulted in severe effects on nodulation, nitrogen fixation and plant growth. In general, these early findings are in line with our results. However, the effect of P. penetrans on nitrogen fixation was not clearly shown in the earlier studies. The applied acetylene-ethylene assay suggested only a 19% reduction in the nitrogen fixing capacity of nodules compared to a control without P. penetrans in a phytotron experiment, and no reduction in a greenhouse experiment 14 . This underestimation of the effects of P. penetrans can be explained by inaccuracy of the assay as it was applied, and because the capacity of nitrogenase activity measured by this assay does not well reflect the amount of fixed nitrogen 15 , 16 . The ureides allantoin and allantoic acid are synthesized in the nodules of of tropical legumes, such as soybean and Phaseolus vulgaris , while asparagine and glutamine are the final products of nitrogen fixation in temperate legumes, such as pea and Faba bean 20 . In our study, we used a differential-colorimetric method 22 to determine allantoin and allantoic acid that were synthesized with fixed atmospheric nitrogen in the nodules, and transported to the shoot tissues 23 , 24 .

The nodulation traits like nodule numbers and mass were negatively correlated with the density of P. penetrans . This might be explained by the competition between parasitic nematodes and rhizobial cells for resources provided by the host plant for the establishment of mutualism 25 , 26 . The soybean roots infected with higher densities of P. penetrans had significantly reduced nitrogen-fixing activity in the nodules as indicated by lower densities of bacteroids and less production of ureides compared to roots infected with lower numbers of nematodes, or uninfected plants. Migration of root lesion nematodes through the roots resulted in a destructive damage of root cells. The basal defense of the plant led to further damage of the root tissue. This might have interrupted the flux and delivery of nutrients that the plant provides to support the bacterial symbiont 27 , 28 . In addition, pathogen induced defense can negatively affect the mutualistic relationship as shown for both rhizobia and arbuscular mycorrhizal fungi 29 .

Our data demonstrated that high densities of P. penetrans reduced the shoot dry mass. The root reacted to low infestation by additional growth at low infection rates, as the plant probably tries to compensate for damaged tissue to sustain nutrient and water uptake. High numbers of 1,000 and more infective stages invading the root significantly affected the root weight. It is apparent that the relation between density and plant growth is complicated 30 . In some cases, damage by nematodes to the root does not result in the reduction of shoot weight because plants may have more roots than needed to support the shoot, or plants compensate the damage to roots by building more side roots. However, their potential to compensate the damage is determined by the availability of nutrients.

Effect of nematodes on the maturation of nodules

We showed that the order of establishment and colonization of roots by P. penetrans and B. japonicum largely determined the effects on the nodules. After initial colonization of roots by rhizobial cells and establishment of the nodules, P. penetrans infection had no effect on the number of nodules compared to non infected roots. Probably the acquisition of B. japonicum by the roots had already been completed. However, the further maturation of the nodules was impaired, as indicated by the slower increase in the weight of nodules and density of bacteroids within the nodules. Both, mutualistic and pathogenic partners induce significant changes in phytohormone levels 31 . With rhizobia, this leads to transportation of the fixed nitrogen and in turn enhances the flux of carbon and amino acids to the bacteroids 32 . This nutrient fluxes can be affected by complex changes in the vascular tissues, sugars transport and phytohormones regulation, that in turn affect the growth of nodules and survival of bacteroids 33 , 34 . In addition, the invasion of nematodes can affect the viability and differentiation of bacteroids by reducing the availability of leghemoglobin, which regulates the supply of oxygen to protect the rhizobial nitrogenase. Accordingly, the concentration of ureides in the leaves was significantly lower in plants infested by P. penetrans .

Interference of lesion nematodes with the regulation of nodulation

Interestingly, P. penetrans that was already established in roots had a very severe impact on nodulation by B. japonicum . A dramatically higher number of nodules developed on infested roots compared to the non-infested roots. This hyper-nodulation resulted in small-sized and aggregated nodules containing low densities of bacteroids. The majority of these clustered small nodules were formed near to the damaged crown of the roots. Nodulation of legume roots is a resource demanding process and therefore tightly regulated 17 . This autoregulation seemed to be disturbed in the infested roots. The nodulation is regulated through systemic mechanisms where the plant coordinates the nodulation and suppresses further formation of nodules by signaling from shoot to root and back again 6 , 35 . We showed in the split-root experiment that the nematodes had a systemic effect on the nodulation, but local damage to the nodules was also observed. Lesions formed by P. penetrans near the nodules releases extracellular ATP, which acts as a damage-associated molecular patterns but likely plays a role in the regulation of nodulation as well 6 , which might result in a local interference of P. penetrans and the soybean - B. japonicum symbiosis. Another key to this interaction might be cytokinin, because cytokinin signaling is important for the control of nodulation by legumes 7 , and cytokinin was also reported to play a role in the parasitism of root knot and cyst nematodes, which produce cytokinin and manipulate cytokinin signaling of the host plant 36 . A local excess of cytokinin likely leads to hyper-nodulation because it acts as an endogenous inducer of nodule primordia formation 7 . However, cytokinin production was not yet shown for root lesion nematodes 27 . In our study, aggregated hyper-nodulation was observed on locally infested roots but much less in the split-root system where P. penetrans was spatially separated from nodules. A similar effect was achieved by removal of nodules, which was suggested as an indication for a locally generated signal of autoregulation of nodulation 37 .The damage of nodules by the migrating nematodes might result in a new round of nodulation by the same mechanism.

It was previously reported that the number of nodules per plant was not well correlated with the total mass of nodules per plant and the amount of fixed nitrogen 38 . It seems that the plant initiated more nodulation processes but the nodules did not become mature. In a Phaseolus vulgaris - Rhizobium leguminosarum symbiosis, the number of nodules was also systemically affected by a fungal leaf pathogen that was inoculated in the initial phase of nodule formation by the nitrogen fixing symbiont, but in contrast to our study the number of nodules was significantly reduced 29 . The fungal pathogen systemically induced higher activity of polyphenol oxidases in the roots, suggesting that plant defense responses interfered with nodulation. In another study, chemically induced defense pathways in soybean systemically reduced the number of nodules and the nitrogen content in leaves and roots after 51 days 39 . The chemical inducer was applied once as foliar spray 5 days after inoculation of seedlings with B. japonicum , while P. penetrans could continuously affect the soybean - B. japonicum symbiosis. In our study, the small sized nodules from pre-infested roots had very low densities of bacteroids compared to those recovered from healthy roots. Limitation of O 2 supply for the bacteroids in the nodules as a response of the plant to stress caused by the nematodes could explain the reduced viability of bacteroids 40 . This was also evidenced by a significant decline in the ureides amount in the infected plants.

In sum, results contribute to a better understanding of the interaction between soybean plants, nitrogen fixing bacteria and Pratylenchus that might lead to strategies to improve N 2 fixation under pathogen pressure. Early nodulation before the nematode population builds up, and control of population densities of root lesion nematodes are important for harnessing the positive effects of symbiotic nitrogen fixation. In the future, it will be of interest to expand our investigation to explore the spatial distribution of plant parasitic nematodes within the nodulated roots and to understand the reasons behind the preference of nematodes to migrate and propagate near to nodule formation zones.

Materials and Methods

Soybean cultivation.

Seeds from soybean cv. Primus were surface sterilized in 1.5% sodium hypochlorite for 15 min and rinsed with sterile distilled water. Seeds were transferred on moist sterilized filter paper in Petri dishes placed in the dark at 20 °C for 4 days to allow germination. Uniformly germinated seedlings were selected and transplanted in 12 cm diameter plastic pots filled with 500 ml growth substrate. Except for the split-root experiment, the growth substrate consisted of two volumes of sand and one volume of a low nitrogen content field soil (7.2 kg N min per ha, loamy sand, braunerde, pH 6.5, 52°17′57″N/10°26′14″E) 41 . Plants were maintained in the greenhouse at 24 °C and 16 h photoperiod and watered as needed every 2–3 days.

B. japonicum inoculum

An isolate of strain B. japonicum 532C was recovered from HiStick Soy (BASF, Ludwigshafen, Germany) by plating the product on a yeast mannitol (YM) agar supplemented with 1 mg/l vancomycin. To support the axenic recovery of the strain from nodules, a rifampicin resistant mutant was isolated. This was done by plating a high-density culture on YM supplemented with 1 mg/ml vancomycin 42 and 50 mg/l rifampicin, and purifying streaks of a single rifampicin-resistant colony on these agar plates. The B. japonicum strain was cultured for 3 days at 28 °C on YM liquid medium supplemented with rifampicin. The B. japonicum cells were spun down at 4,000 g for 10 minutes, and the pellet was washed twice with sterile tap water to remove antibiotic residues, resuspended in sterile tap water and adjusted to OD 600  = 0.2. In the greenhouse experiments, each pot was inoculated with 4 ml of the B. japonicum cell suspension by adding it to four holes of 5 cm depth around the soybean root.

P. penetrans inoculum

The root lesion nematode P. penetrans was extracted from 4 months old carrot disks by the Baermann funnel technique 43 . The densities of nematodes in 1 ml of the suspensions were microscopically determined in a nematode-counting slide. The nematode suspension of mixed infective stages of P. penetrans was equally inoculated around soybean plants into four holes of 5 cm depth. The number of P. penetrans inoculated differed based on the experimental layout.

Greenhouse experiment on density-dependent effects of P. penetrans on nitrogen fixation by co-inoculated B. japonicum

Inocula of P. penetrans were prepared by serial dilution of three independently extracted suspensions of the nematodes. One week after germination, soybean plants were infected with 0, 0, 63, 125, 125, 188, 250, 250, 375, 500, 500, 750, 1000, 1000, 1500, 2000, 2000, 3000, 4000, or 6000 individuals of P. penetrans . Each of the 20 plants was co-inoculated with B. japonicum and randomly distributed in a greenhouse chamber. One month after inoculation, the root weight, shoot weight, concentration of ureides in leaves, number of nematodes in roots, and number and weight of nodules were determined.

Greenhouse experiment on effects of P. penetrans on established nodules

Twelve-day-old soybean seedlings were inoculated with B. japonicum and kept for two weeks until nodules have been established. Sixteen nodulated plants were inoculated with 1,000 infective stages of P. penetrans . Sixteen control plants were not infected with nematodes. Plants were randomly distributed in a greenhouse chamber. Two and five weeks after inoculation, eight plants per treatment and time point were sampled to determine the root weight, shoot weight, ureide concentration in leaves, number of nematodes in roots, and number and weight of nodules.

Greenhouse experiment on effects of established P. penetrans on nodulation

This experiment was implemented to investigate how the process of nodulation and N 2 fixation is affected when the roots are infested by P. penetrans before inoculation of B. japonicum . Sixteen 12-day-old soybean seedlings were infected with 1,000  P. penetrans . Sixteen control plants were not infected with nematodes. Plants were randomly distributed in a greenhouse chamber. Two weeks later, all pots were inoculated with B. japonicum . Eight plants per treatment and time point were sampled two weeks and five weeks after inoculation with B. japonicum to determine the root weight, shoot weight, ureide concentration in leaves, number of nematodes in roots, and number and weight of nodules.

Split-root experiment on plant-mediated effects of P. penetrans on the nodulation by B. japonicum -soybean symbiosis

The root of twelve two-week-old soybean seedlings was split and transplanted into two adjacent square pots (7 cm × 7 cm) filled with two times autoclaved sand. For half of the plants, the split-root system in one pot was inoculated with 500  P. penetrans , the other plants were non-inoculated controls. After two weeks, one-half of each root system without nematodes was inoculated with 2 ml of a B. japonicum suspension (OD 600  = 0.2). The plants were watered and fertilized by a mineral solution 44 supplemented with 0.125 mM NH 4 NO 3 . Three weeks after inoculation of B. japonicum , the plants were sampled to determine the root weight, shoot weight, number of nematodes in roots, and number and weight of nodules.

Analysis of plant samples

The numbers of viable bacteroids of B. japonicum per 0.05 g nodules (3–7 nodules) were determined by surface sterilizing the nodules for 10 min in 1% sodium hypochlorite, washing them with sterilized water, squeezing with the tip of a pipette in 2 ml microtubes, vortexing in 1 ml sterile 0.85% NaCl for 15 s, and plating serial dilutions of the homogenate onto YM agar supplemented with 1 mg/l vancomycin and 50 mg/l rifampicin. Colony forming units (CFU) were counted after incubation of the plates for three days at 28 °C.

The concentration of transported ureides in the leaves was determined according to the protocol of Collier and Tegeder 20 . Briefly, 0.2 g leave tissue was frozen in liquid nitrogen and stored at −80 °C until analysis. The tissue was ground with mortar and pestle and the powder was transferred to 2 ml microtubes with 200 µl of ice-cold sterile deionized water, squeezed with a plastic micro pestle, which was carefully rinsed off with 800 µl sterile H 2 O. The suspension was filtered through a layer of Miracloth (Merck, Darmstadt, Germany) into 1.5 ml microtubes and centrifuged at 20,000 g at 4 °C for 30 minutes. To measure the concentrations of total ureides and allantoic acid, two colorimetric assays were performed using phenylhydrazine and potassium ferricyanide as reaction reagents and 100 µl of the filtrate for each reaction. The absorbance at 520 nm was measured within 30 minutes using a spectrophotometer, and compared to a standard curve of 0 mM, 1 mM, 10 mM, 50 mM, 100 mM, 200 mM, and 300 mM allantoin (Sigma-Aldrich, Munich, Germany). The allantoin concentration was calculated by subtracting the concentration of allantoic acid from the concentration of total ureides.

The nematodes within the roots were stained with 1% acid fuchsin 45 . Sections of 3 cm roots were embedded in glycerol and several slides were prepared to count the nematodes. The microscopic analyses were performed with a stereomicroscope (Olympus Microscope SZX12) and photographed with a digital camera.

Statistical analyses

Graphs were generated, and statistical tests on treatment effects were done using Prism 7 (GraphPad Software, La Jolla, CA, United States). Effects of inoculated nematodes on nodule number, nodule weight, bacteroid CFU, and ureide concentration were tested against the non-inoculated control for each analyzed sampling time using Tukey’s LSD test. To estimate the threshold densities of inoculated P. penetrans above which the shoot weight, root weight, number of viable bacteroids, number or weight of nodules, or concentration of ureides was affected, the data were fit to the Seinhorst equation 30 using the program SeinFit 46 . An example of the output of the program is shown in Supplementary Fig.  S3 .

Data availability

All materials, data and associated protocols are available from the corresponding author on reasonable request.

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Acknowledgements

The study was funded by the German Research Foundation DFG EL 1038/2-1. AE’s participation in conferences was funded by the “Gisela und Hermann Stegemann Foundation”, and “Gemeinschaft der Förderer und Freunde des Julius Kühn-Instituts”. We thank Nicole Viaene for providing the SeinFit program.

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Department of Epidemiology and Pathogen Diagnostics, Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Braunschweig, Germany

Ahmed Elhady, Johannes Hallmann & Holger Heuer

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H.H. and J.H. designed the research plan. A.E. performed the experiments. H.H. and A.E. did the analyses. A.E. wrote the manuscript. H.H. and J.H. revised the manuscript.

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Correspondence to Holger Heuer .

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Elhady, A., Hallmann, J. & Heuer, H. Symbiosis of soybean with nitrogen fixing bacteria affected by root lesion nematodes in a density-dependent manner. Sci Rep 10 , 1619 (2020). https://doi.org/10.1038/s41598-020-58546-x

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DOI : https://doi.org/10.1038/s41598-020-58546-x

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Presence and activity of nitrogen-fixing bacteria in Scots pine needles in a boreal forest: a nitrogen-addition experiment

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Tinkara Bizjak, Anita Sellstedt, Regina Gratz, Annika Nordin, Presence and activity of nitrogen-fixing bacteria in Scots pine needles in a boreal forest: a nitrogen-addition experiment, Tree Physiology , Volume 43, Issue 8, August 2023, Pages 1354–1364, https://doi.org/10.1093/treephys/tpad048

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Endophytic nitrogen-fixing bacteria have been detected and isolated from the needles of conifer trees growing in North American boreal forests. Because boreal forests are nutrient-limited, these bacteria could provide an important source of nitrogen for tree species. This study aimed to determine their presence and activity in a Scandinavian boreal forest, using immunodetection of nitrogenase enzyme subunits and acetylene-reduction assays of native Scots pine ( Pinus sylvestris L.) needles. The presence and rate of nitrogen fixation by endophytic bacteria were compared between control plots and fertilized plots in a nitrogen-addition experiment. In contrast to the expectation that nitrogen-fixation rates would decline in fertilized plots, as seen, for instance, with nitrogen-fixing bacteria associated with bryophytes, there was no difference in the presence or activity of nitrogen-fixing bacteria between the two treatments. The extrapolated calculated rate of nitrogen fixation relevant for the forest stand was 20 g N ha −1  year −1 , which is rather low compared with Scots pine annual nitrogen use but could be important for the nitrogen-poor forest in the long term. In addition, of 13 colonies of potential nitrogen-fixing bacteria isolated from the needles on nitrogen-free media, 10 showed in vitro nitrogen fixation. In summary, 16S rRNA sequencing identified the species as belonging to the genera Bacillus , Variovorax , Novosphingobium , Sphingomonas , Microbacterium and Priestia , which was confirmed by Illumina whole-genome sequencing. Our results confirm the presence of endophytic nitrogen-fixing bacteria in Scots pine needles and suggest that they could be important for the long-term nitrogen budget of the Scandinavian boreal forest.

To date, all plant species studied appear to be inhabited by endophytic bacteria ( Santoyo et al. 2016 ), which are defined as microorganisms that reside in plants but do not cause any symptoms of disease ( Wilson 1995 ). Endophytes can be symbionts, latent pathogens or harmless cohabitants ( Chanway et al. 2014 ). Endophytic bacteria possess a diverse set of plant growth-promoting properties that can positively affect, for example, phytohormone balance ( Bhattacharjee et al. 2008 ), nutrient acquisition ( Santoyo et al. 2016 ), growth and yield ( Ryan et al. 2008 , Khan et al. 2012 ), protection against pathogens ( Wei et al. 2014 ), propagation ( Quambusch et al. 2014 ) and the response to abiotic stress. An important endophytic bacterial community that can help with nutrient acquisition comprises diazotrophic bacteria, which are capable of fixing nitrogen from the atmosphere. The multi-subunit enzyme responsible for the energy-demanding process of nitrogen fixation is nitrogenase ( Doty et al. 2016 ), encoded by the structural genes nifH , nifD and nifK , and regulated by nifA ( Marchal and Vanderleyden 2000 ). Of these genes, nifH is most commonly used as a marker for nitrogen-fixing bacteria ( Zhang et al. 2007 ). Nitrogenase is extremely oxygen-sensitive ( Marchal and Vanderleyden 2000 ) and its activity is upregulated by phosphorus ( Reed et al. 2007 , Matson et al. 2014 ), and probably also by carbohydrates because of the high energy demand of nitrogen fixation ( Welsh 2000 , Zheng et al. 2017 , Benavides et al. 2020 ). It is less clear, however, how a supply of externally added mineral nitrogen fertilizer affects nitrogen fixation. Many studies have shown adverse effects of external nitrogen application or higher natural nitrogen soil availability on nitrogen fixation ( Barron et al. 2011 , Gundale et al. 2011 , Zheng et al. 2017 , Nishida and Suzaki 2018 ), but some have shown no effects ( Moyes et al. 2016 , Zheng et al. 2017 ). Nitrogen fixation could also be downregulated by higher nitrogen concentrations in the environment, as indicated by studies where higher nitrogen-fixation rates have been measured in tissues with a higher carbon-to-nitrogen (C/N) ratio ( Granhall and Lindberg 1978 , Leppänen et al. 2013 , Tormanen and Smolander 2022 ).

In the boreal forest, where plant growth is strongly nitrogen-limited, it has been suggested that in addition to the nitrogen-fixing symbiosis between cyanobacteria and mosses ( DeLuca et al. 2002 ), endophytic nitrogen fixation provides an important additional source of nitrogen ( Moyes et al. 2016 ). Coniferous needles could offer a good habitat for endophytic nitrogen-fixing bacteria, by protecting against adverse environmental conditions and competitors ( Yang et al. 2016 ), and potentially a supply of carbohydrates and energy ( Wurzburger 2016 ). The advantage for conifers would be a potential nitrogen source, either through direct supply from microbe to tree, or through degraded microbial material as a result of a relatively quick microbial turnover ( Wurzburger 2016 ). The possible presence of nitrogen-fixing bacteria inside coniferous needles has been indicated by sequencing data ( Carrell and Frank 2014 , Haas et al. 2018 ), and complementary culturing methods have been used to isolate diverse nitrogen-fixing bacteria from a wide range of trees, including some conifers ( Doty et al. 2009 , Bal et al. 2012 , Puri et al. 2018 ). Usually, their nitrogen-fixing ability is confirmed using either polymerase chain reaction (PCR) amplification to detect the presence of the nifH gene, or an acetylene-reduction assay (ARA) to measure their nitrogen-fixation ability ( Gupta and Roper 2010 , Bal et al. 2012 ). The most commonly occurring bacterial genera represented in nitrogen-fixing bacteria isolated from coniferous needles are Sphingomonas , Bacillus , Rhizobium , Pseudomonas , Caballeronia and Paenibacillus ( Moore et al. 2006 , Izumi et al. 2008 , Puri et al. 2018 ). Using ARA, bacterial nitrogen fixation has been measured in needles from limber pine ( Pinus flexilis ) from North American subalpine forest ( Moyes et al. 2016 ), Scots pine ( Pinus sylvestris ) and Norway spruce ( Picea abies ) from the Scandinavian boreal forest ( Granhall and Lindberg 1978 ), and black pine ( Pinus nigra ) and Douglas fir ( Pseudotsuga menziesii ) from Italy ( Favilli and Messini 1990 ). Furthermore, nitrogen fixation has been measured using labelled 15 N in Douglas fir needles from the temperate forest ( Jones 1970 ). However, the estimated rates of nitrogen fixation between the studies differ significantly, with the amounts of fixed nitrogen ranging from grams to kilograms per hectare and year ( Jones 1970 , Granhall and Lindberg 1978 , Favilli and Messini 1990 , Moyes et al. 2016 ).

In contrast to previously published studies focussing on the presence and activity of nitrogen-fixing bacteria inside needles of native conifer species in the boreal forest, our study is, as far as we know, the first to compare the activity of endophytic nitrogen-fixing bacteria using a nitrogen-addition experiment. The Scots pine was chosen as the study species because, alongside the Norway spruce, it is a dominant tree species in the nitrogen-limited Scandinavian boreal forest ( Berlin et al. 2010 ). The study aimed to confirm the presence of endophytic nitrogen-fixing bacteria inside Scots pine needles, by measuring nitrogen fixation and assessing the effect of nitrogen fertilization on their activity, and isolating and identifying the nitrogen-fixing bacteria. The following hypotheses were addressed: (i) nitrogen-fixing bacteria are present in Scots pine needles; (ii) the bacteria are actively fixing nitrogen and their activity in needles decreases with inorganic nitrogen fertilization; (iii) isolated bacteria, identified by 16S rRNA and whole-genome sequencing, belong to similar genera as found in other conifer species; and (iv) isolated nitrogen-fixing bacteria can fix nitrogen in vitro.

Field site and sample collection

The samples were collected from the Åheden research forest in northern Sweden (N 64° 14′, E 19° 48′). The area in which the field site was located has a mean annual precipitation of 600 mm, a mean annual temperature of 1 °C ( Forsmark et al. 2020 ) and an estimated regional atmospheric nitrogen deposition of less than 2 kg N ha −1  year −1 ( Pihl Karlsson et al. 2012 ). The Åheden research forest is a naturally regenerated boreal forest dominated by approximately 150-year-old Scots pine ( From et al. 2016 ), with some scattered Norway spruce trees in the sub-canopy. The ground vegetation is predominately lingonberry ( Vaccinium vitis-idaea ), heather ( Calluna vulgaris ), red-stemmed feathermoss ( Pleurozium schreberi ), fork mosses ( Dicranum spp.), reindeer lichen ( Cladonia rangiferina ) and scrubby cup lichen ( Cladonia arbuscula ) ( Gundale et al. 2011 ). In 2004, five different nitrogen treatments were established at Åheden in 0.1-ha plots; since then the plots have been fertilized yearly with 0, 3, 6, 12 and 50 kg N ha −1  year −1 , respectively, in the form of solid ammonium nitrate granules (NH 4 NO 3 ). Each nitrogen treatment has six replicates in a randomized block design ( Forsmark et al. 2020 ).

For this study, the plots fertilized with 0 kg N ha −1  year −1 (referred to as control plots) and 50 kg N ha −1  year −1 (referred to as fertilized plots) were selected, as they represented the most contrasting environments at the site. Samples were harvested from two trees from each of the six replicate plots per treatment ( n  = 12 per treatment); tree selection was based on proximity to the centre of the plot and representation of the general appearance, health and size of the trees within the plot. For immunodetection analysis and bacterial isolation, needles from several branches of the same tree were harvested in the summer of 2021, by aseptically collecting 1-year-old ( Meredieu 2002 ) Scots pine needles, which were stored on dry ice for transportation to the laboratory. Several branches from each tree to be used for ARA were aseptically harvested in the summer of 2022 across six sampling days, and stored on ice until analysed in the laboratory. However, at both sampling time points the samples were collected from the same trees and 1-year-old needles were selected as we wanted only healthy green needles.

Protein extraction and immunodetection of NifH protein

One-year-old Scots pine needles were surface sterilized in a vertical laminar flow hood by submersion in 30% hydrogen peroxide for 2 min with continuous shaking, followed by three washes in sterile deionized water for 20 s. Excess water was evaporated by inverting the samples onto sterile filter paper. The surface-sterilized needles were then stored at −80 °C until the start of the experiment. For total protein isolation, the needles were ground to a fine powder using a pestle and mortar, under constant cooling with liquid nitrogen. Total protein was then extracted by adding 2× protein loading buffer (124 mM Tris–HCl, pH 8.6, 5% sodium dodecyl sulfate (SDS), 4% dithiothreitol and 20% glycerol) to the sample, centrifuging at 4 °C for 5 min at maximum speed, and heating the transferred supernatant at 95 °C for 5 min. Samples were separated by SDS-PAGE (12% Mini-PROTEAN ® TGX™ Precast Protein Gels, Bio-Rad, Hercules, CA, USA), with equivalent protein amounts loaded for each sample (measured with a Qubit™ Protein Assay Kit, Invitrogen, Waltham, MA, USA). The separated proteins were transferred onto a nitrocellulose membrane (Amersham Protran, GE Healthcare, Chicago, IL, USA) and stained with Ponceau S as a control for successful transfer. Immunodetection was performed as follows: membranes were blocked for 25 min in 5% (w/v) milk solution dissolved in Tris-buffered saline, 0.1% Tween 20 (TBST) (5% milk powder in 20-mM Tris–HCl, pH 7.4, 180-mM NaCl and 0.1% Tween-20), followed by a 1.5-h incubation at room temperature in a 1:2000 dilution of the primary anti-NifH antibody (Agrisera, Vännäs, Sweden) in TBST containing 2.5% (w/v) milk. After three 15-min wash steps with TBST, the membranes were incubated for 1 h in a dilution of a secondary antibody in TBST containing 2.5% (w/v) milk. The antibody used was horseradish peroxidase-conjugated goat anti-chicken (1:20,000) (Agrisera, Vännäs, Sweden). Again, three 15-min wash steps were performed with TBST. Signal detection was carried out using Pierce™ ECL Western Blotting Substrate (Thermo Fisher Scientific, Waltham, MA, USA) on Azure c600 (Azure Biosystems, Dublin, CA, USA), and quantified using ImageJ ( Schneider et al. 2012 ). To estimate the size of the bands, a PageRuler Prestained Protein Ladder (Thermo Fisher Scientific) was used. To confirm antibody specificity we used positive controls (soybean nodule extract, purified NifH protein with His-tag (Agrisera)) and negative controls (2× protein loading buffer, Bradyrhizobium japonicum liquid culture protein extract), which did not yield in a western blot signal.

Acetylene-reduction assay

The branches harvested for ARA were stored under constant light conditions at room temperature overnight. The next day, for each sample 1-year-old needles were collected from several different branches of the same tree and divided into six subsamples; half of the samples were used to measure acetylene reduction, and half to correct for needle endogenous ethylene production. The needles from each subsample were put into 50 ml glass vials and 8 ml sterile deionized water was added. The ARA was performed as described previously ( Richau et al. 2017 ) with the following modifications: after sealing the glass vials with a rubber septum and replacing 10% of the air with acetylene gas, the samples were incubated at room temperature for 2 h under constant light conditions before 1 ml air was removed with a syringe and analysed for ethylene production on a gas chromatograph (Shimadzu GC-8A, Kyoto, Japan). After the experiment, the needles were dried at 60 °C for 48 h and their dry mass was measured. Because of the high variation in endogenous needle ethylene production observed between individual trees, the ethylene production rates were corrected for sample-specific endogenous ethylene production. Additionally, water samples with injected acetylene were used to correct for any ethylene present due to injected acetylene gas. Specifically, both negative control values were subtracted from the measured acetylene reduction rates from samples, which affected the reported final ethylene production rate.

Carbon and nitrogen content

Dried 1-year-old Scots pine needles from the ARA were ground to a fine powder in a bead mill (Retsch, Haan, Germany), and the carbon and nitrogen content was measured using an Isotope Ratio Mass spectrometer (DeltaV, Thermo Fisher Scientific) coupled with an Elemental analyser (Flash EA 2000, Thermo Fisher Scientific) ( Werner et al. 1999 ).

Isolation of nitrogen-fixing bacteria

Half of the 1-year-old Scots pine needles from each sample were surface sterilized by submersion in 30% hydrogen peroxide for 2 min with continuous shaking, and the other half by submersion in 70% ethanol for 3 min with continuous shaking. Two different sterilization methods were used to increase the total number of bacteria isolated. The needles were then washed three times for 20 s with sterile deionized water, and the excess water was removed by inverting the tube onto sterile filter paper. Five needle pairs were imprinted on tryptic soy agar (TSA) plates (15 g l −1 casein peptone, 5 g l −1 soy peptone, 5 g l −1 NaCl and 15 g l −1 agar), and the plates were incubated at 28 °C for 10 days to confirm the surface sterility of the samples. The same five needle pairs were ground in phosphate-buffered saline (PBS) buffer (137 mM NaCl, 2.7 mM KCl, 10 mM Na 2 HPO 4 , 1.8 mM KH 2 PO 4 , pH 7.4) using FastDNA Spin Kit tubes and beads and a FastPrep instrument (MP Biomedicals, Irvine, CA, USA). The crude extract was filtered using sterile Miracloth with a pore size of 22–25 μm (Merck Millipore, Burlington, MA, USA), and the filtrate was centrifuged at 5000 r.p.m. for 10 min at 8 °C. The pellet was resuspended in PBS buffer and inoculated on nitrogen-free media (semi-solid NFb ( Baldani et al. 2014 ), combined carbon media (CCM) without yeast extract ( Baldani et al. 2014 ) and LGI-P ( Reis et al. 2015 ). The plates (CCM and LGI-P) and tubes (NFb) were incubated at 28 °C for 10 days. Individual colonies were observed and re-cultured on TSA plates.

16S rRNA and whole-genome bacterial sequencing

For 16S rRNA Sanger sequencing of isolated bacteria, the bacteria were grown on TSA plates overnight. Bacterial material was then transferred into an extraction buffer (0.05 M NaOH, 0.25% SDS), which was heated at 97 °C for 15 min before the sample was centrifuged for 4 min at 10,000 r.p.m. The collected supernatant was diluted with Tris-EDTA (TE) buffer (10 mM Tris, 1 mM EDTA, pH 8.0) and the extract was used in a PCR with the 16S ribosomal ribonucleic acid (rRNA) primers 27F/1492R ( Heuer et al. 1997 ). The PCR was carried out using a DreamTaq Hot Start PCR Master Mix (Thermo Fisher Scientific) according to the manufacturer’s instructions, with 1 μl of bacterial extract, 0.5 μM forward and reverse primers, and an annealing temperature of 55 °C. Based on agarose gel separation, all the isolated bacteria had a band of around 1500 bp, so the PCR products were cleaned with ExoSAP-IT PCR Product Cleanup (Applied Biosystems, Waltham, MA, USA) before tube Sanger sequencing (Eurofins, Luxembourg City, Luxembourg). The obtained sequences were analysed using Basic Local Alignment Search Tool (BLAST) ( Altschul et al. 1990 ) and compared with sequences already in the National Center for Biotechnology Information (NCBI) database. A phylogenetic tree showing the similarities between the strains was produced using Phylogeny.fr ( Dereeper et al. 2008 ) according to Gratz et al. (2021 ), using MUSCLE alignment, Gblocks curation, PhyML phylogeny and TreeDyn tree rendering, but with likelihood-ratio test (minimum of SH-like and Chi2-based) instead of bootstrapping.

For whole genome sequencing, liquid Luria broth (LB) media (10 g l −1 tryptone, 5 g l −1 yeast extract and 10 g l −1 NaCl) was inoculated with isolated bacteria overnight before being centrifuged for 5 min at 10,000 r.p.m. The pellet was resuspended in a smaller amount of liquid LB media and the deoxyribonucleic acid (DNA) was isolated using a DNeasy PowerSoil Kit (Qiagen, Venlo, The Netherlands). The amount and quality of the DNA were assessed using Nanodrop before being sent for sequencing and bioinformatic analysis (CD Genomics, New York City, NY, USA through Genohub, Austin, TX, USA). For sequencing, an Illumina NovaSeq6000 (Illumina, San Diego, CA, USA) was used with pair-end 2× 150 base pair sequencing and at least 3 million reads per sample. For the bioinformatic analysis, the two pairs of reads were merged, 1000 sequences were randomly selected, and BLAST ( Altschul et al. 1990 ) was used to compare the obtained sequences with sequences already in the NCBI database (NCBI, Bethesda, MD, USA). The identity was determined based on the sequence hit count. Additionally, Read Assembly and Annotation Pipeline Tool (RAPT) (NCBI, Bethesda, MD, USA) was used for de novo assembly of the bacterial genomes using SKESA and annotation of the genome using Prokaryotic Genome Annotation Pipeline to check for the presence of nif genes within the whole genome sequence.

The bacterial 16S rNRA sequences were deposited in GenBank ( NCBI ), and the unassembled Illumina whole genome sequences were deposited in the Sequence Read Archive (NCBI) ( Sayers et al. 2022 ) ( Table S1 available as Supplementary data at Tree Physiology Online). Bacterial cultures were deposited in the NCCB collection (Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands) ( Table S1 available as Supplementary data at Tree Physiology Online).

In vitro nitrogen fixation of isolated bacteria

The nitrogen-fixation ability of isolated bacteria was measured by ARA. Bacteria were grown in either liquid CCM without yeast extract (bacteria #1, 23, 24, 25, 27, 39) or liquid LGI-P media (bacteria #2, 3, 14, 26, 28, 38-1, 38-2) for 24 h at 28 °C, and then their OD 600 measured. The bacteria were transferred to glass vials sealed with a rubber septum; 10% of the air was replaced by acetylene and, after a 2-h incubation at 30 °C with constant shaking, the ethylene production was measured using a gas chromatograph (Shimadzu GC-8A, Kyoto, Japan). The bacterial OD 600 was measured again after the experiment and this value was used to normalize the ethylene production. Calculated ethylene production rates were corrected for spontaneous acetylene reduction and any endogenous ethylene production by either the media or the bacteria.

All data were analysed using SPSS Statistics 27 (IBM, Armonk, NY, USA). For data from the immunodetection of NifH protein, ARA on needles, in vitro ARA on bacterial cultures, and carbon and nitrogen content, the assumptions of normal distribution and equal variance were checked. For all datasets, both assumptions were met, as well as the assumption of independence. The data from the immunodetection of NifH protein, and carbon and nitrogen content, were then analysed using a two-sample independent t -test. In vitro ARA measurements on isolated bacteria were analysed using a one-way analysis of variance (ANOVA) followed by a Tukey honestly significant difference (HSD) test. For the ARA on needles, a two-way ANOVA was used, using sampling day and nitrogen treatment as variables. A linear regression model was used to analyse the relationship between ARA in 1-year-old Scots pine needles and the C/N ratio. The regression coefficient was tested to see whether there was a statistically significant relationship between the two variables.

The presence of nitrogen-fixing bacteria inside surface-sterilized needles was analysed using immunoblotting of the NifH protein, which is one of the nitrogenase enzyme’s subunits. Our analyses indicated that nitrogen-fixing bacteria were present in the needles from both control and nitrogen-fertilized plots ( Table S2 available as Supplementary data at Tree Physiology Online, Figure 1 ), and the signal intensity of the NifH band was similar between the two treatments (two-sample t -test, P  = 0.84), with values of 5309 and 5481, respectively. This indicated a similar amount of nitrogenase protein in both treatments ( Figure 1 ).

NifH band signal intensity for immunodetection with anti-NifH antibody (mean + SE), indicating the presence of nitrogenase enzyme in 1-year-old Scots pine needles from trees grown in control and nitrogen-fertilized plots (0 and 50 kg N ha −1  year −1 , respectively) ( n  = 12 per treatment, two-sample t -test P  = 0.84, bars with different letters are significantly different).

As the presence of nitrogenase protein does not mean active nitrogen fixation, ARA was used to measure the nitrogenase activity indirectly. As well as reducing dinitrogen to ammonia, nitrogenase enzymes can also reduce acetylene to ethylene, which can be detected with this method. We measured the nitrogenase activity in needles from both the control plots and inorganic nitrogen-fertilized plots ( Table S3 available as Supplementary data at Tree Physiology Online). The average nitrogenase activity was slightly higher in needles from fertilized plots than in control plots ( Figure 2 ), but the difference was not significant (two-way ANOVA, P  = 0.11). The fixation rate for needles from control plots was 0.09 nmol ethylene h −1 and g −1 dry needles, and the rate for fertilized plots was 0.12 nmol ethylene h −1 and g −1 dry needles. There was, however, a significant effect of sampling day, with a difference in nitrogen fixation rates between different sampling dates (two-way ANOVA, P  = 0.02), highlighting the importance of including a sufficient number of negative controls in the measurement protocol for each sampling date.

Ethylene production rate (mean + SE) per hour and gram dry mass of 1-year-old Scots pine needles, indicating nitrogenase enzyme activity in the needles of trees gown in control and nitrogen-fertilized plots (0 and 50 kg N ha −1  year −1 , respectively) ( n  = 12 per treatment, two-sample t -test P  = 0.11, bars with different letters are significantly different).

To determine the possible relevance of the nitrogen-fixation activity for the forest, the nitrogen-fixation rates were extrapolated to provide an estimate for the forest stand. We assumed equal nitrogen-fixation rates across the whole canopy regardless of needle position, an equal rate of nitrogen fixation during the whole growth period, as measured in our study, a 150-day growth period ( Goude et al. 2019 ), a constant 12-h period of daylight ( Moyes et al. 2016 ), and a conversion factor between ethylene production and nitrogen fixation of 3:1 ( Hardy et al. 1968 ). Using an average leaf area index for Scots pine forest across the Swedish boreal forest ( Appiah Mensah et al. 2020 ), the calculated nitrogen-fixation rate for control plots was approximately 11 g N ha −1  year −1 , and for fertilized plots, it was 15 g N ha −1  year −1 .

The carbon and nitrogen content of the needles was investigated to see whether they could affect the nitrogen fixation rates. The average carbon content for needles from the control plots was 51.0 g C g −1 dry mass and from fertilized plots 51.5 g C g −1 dry mass, which was not statistically significant (two-sample t -test, P  = 0.07). The nitrogen content did differ between the two treatments, however, with average nitrogen content in needles from control plots of 0.93 g N g −1 dry mass, and from fertilized plots of 1.37 g N g −1 dry mass. This difference was statistically significant (two-sample t -test, P  < 0.01). There was no significant linear regression relationship between the measured C/N ratios and ethylene production rates (ethylene production rates = −0.0018 × C/N ratio + 0.19, R 2  = 0.097, P  = 0.14) of the needles.

To identify bacteria possibly responsible for the nitrogenase protein content and activity, potential nitrogen-fixing bacteria were isolated from surface-sterilized needles from trees grown in either control or nitrogen-fertilized plots. Three different nitrogen-free media (CCM, LGI-P and NFb) were used, and 13 distinct bacterial colonies were successfully isolated. The bacteria were identified using 16S rRNA Sanger sequencing and Illumina whole-genome sequencing. The 16S rRNA sequences revealed that the isolated bacteria belonged to several different genera: Bacillus , Microbacterium , Variovorax , Priestia , Novosphingobium and Sphingomonas ( Figure 3 ). More specifically, whole-genome sequencing identified the bacteria isolated from control plots as three Bacillus paralicheniformis , two unclassified Novosphingobium , two Variovorax paradoxus , one Microbacterium sp. and one Sphingomonas sp. ( Table 1 ). Of the bacteria isolated from nitrogen-fertilized plots, one was identified as Priestia megaterium , one as B. paralicheniformis , one as V. paradoxus and one as Novosphingobium pokkalii ( Table 1 ). The presence of nif genes was looked at using the assembled and annotated genome and we could detect the presence of sequence for NifU protein in bacteria 1, 2, 3, 14, 23, 24, 27, 38-1 and 39.

A phylogenetic tree (produced using Phylogeny.fr ) of the 13 potentially nitrogen-fixing bacteria, based on 16S rRNA sequencing. The numbers represent the likelihood-ratio test values of the branching points.

Potential nitrogen-fixing bacteria isolated from 1-year-old Scots pine needles. The trees were grown in control plots (0 kg N ha −1  year −1 ) or long-term nitrogen-fertilized plots (50 kg N ha −1  year −1 ), and the bacteria were isolated on different nitrogen-free media (CCM, LGI-P or NFb). The genus of each isolated bacterium was determined by whole-genome sequencing.

To confirm the ability of the 13 isolated bacteria to fix nitrogen, ethylene production was measured during ARA on liquid bacterial cultures. Ten of the 13 isolated bacterial colonies were able to fix nitrogen under specific conditions, producing ethylene to various degrees ( Figure 4 ). Bacterium 25 ( Microbacterium sp.) had the highest nitrogenase activity, whereas bacteria 2 ( B. paralicheniformis ), 14 ( B. paralicheniformis ) and 38-1 ( N. pokkalii ) were not capable of nitrogen fixation under the test conditions.

The nitrogenase activity, measured indirectly through ethylene production, of liquid cultures of 13 isolated bacteria (mean + SE). Bars with different letters are significantly different (one-way ANOVA followed by Tukey HSD test, P  < 0.05).

It has been suggested that endophytic nitrogen fixation in conifer needles could be prevalent across temperate and boreal forests ( Moyes et al. 2016 ), and even a small amount of nitrogen fixed by these bacteria could be ecologically important in nitrogen-limited environments ( Wurzburger 2016 ). The main aim of our study was, therefore, to determine the presence, and measure of the activity, of endophytic nitrogen-fixing bacteria inside 1-year-old needles from Scots pine trees growing in the Scandinavian boreal forest.

By the first hypothesis, bacterial nitrogenase protein was detected in all of the samples analysed, indicating the widespread presence of nitrogen-fixing bacteria in Scots pine needles in this experimental forest area. The needles were surface sterilized before analysis, so the bacterial presence was probably endophytic. There seemed to be a similar abundance of the bacteria in the needles, as the amount of nitrogenase protein did not differ significantly between needles from control and nitrogen-fertilized plots.

The activity of nitrogen-fixing bacteria was measured using ARA. Our study corroborates results reported elsewhere that coniferous needle ethylene production can vary substantially as a result of needle age, position, season and sterilization protocol ( Telewski 1992 , Ievinsh and Tillberg 1995 , Ievinsh and Ozola 1998 , Klintborg et al. 2002 ). Because endogenous ethylene production was highly variable between the trees, we ensured that sample-specific production rates were measured and subtracted from the ARA ethylene production rates. The nitrogen-fixation rates were around 10 times higher than previously reported for the phyllosphere of Scots pine ( Granhall and Lindberg 1978 ), which could be explained by different environmental or experimental factors between the two studies. It was, however, slightly lower, although in the same range, as rates reported for limber pine needles ( Moyes et al. 2016 ), and much lower than rates reported for black pine and Douglas fir needles ( Jones 1970 , Favilli and Messini 1990 ). The nonsignificant difference in nitrogen-fixation rates between needles from trees on control and fertilized plots contradicted our second hypothesis that inorganic nitrogen fertilization would decrease the activity of nitrogen-fixing bacteria. This result is to some extent unexpected, as many studies have reported decreased nitrogen-fixation activity in biotopes enriched with inorganic nitrogen. For instance, from the same site as studied here, the nitrogen fixation of cyanobacteria associated with ground-dwelling moss surfaces was approximately eight times lower on fertilized than on control plots ( Gundale et al. 2011 ). The same trend of decreased nitrogen fixation in response to nitrogen fertilization has also been observed in soil, forest floor and tree canopy leaves of disturbed subtropical forests in China, where the decrease between the two treatments was between 20 and 38% ( Zheng et al. 2017 ). Additionally, it has been reported that nitrogen fixation in root nodules of individual legume trees ( Inga sp.) from tropical forests grown in soils with a higher nitrogen content is almost nonexistent compared with those grown in soils with a lower nitrogen content ( Barron et al. 2011 ). However, there are also studies showing no clear effect of nitrogen availability or fertilization on nitrogen fixation. For example, no correlation was found between endophytic nitrogen fixation and plant-available soil nitrogen for limber pine needles ( Moyes et al. 2016 ). Also, in a rehabilitated subtropical forest, there was no significant effect of nitrogen fertilization on nitrogen fixation in soil and tree canopy leaves ( Zheng et al. 2017 ). Furthermore, using a mathematical model it was suggested that in nitrogen-poor boreal forests obligate nitrogen-fixing bacteria could be prevalent due to the high cost of being facultative ( Menge et al. 2009 ), which could explain the observed no difference in nitrogen fixation rates between control and fertilized plots.

The mechanism behind the influence of external plant nitrogen sources on nitrogen-fixation rates is not fully understood. In the case of needle endophytic nitrogen fixation for trees in a strictly nitrogen-limited environment, one speculation is that fertilization can enhance photosynthesis by increasing the supply of energy available for nitrogen fixation. Alternatively, the opposite could also be argued, as a higher C/N ratio arising from fertilization may mean that the externally supplied nitrogen renders nitrogen fixation redundant. However, in this study, whereas the C/N ratio of the needles significantly decreased with nitrogen fertilization, there was no significant relationship between the needles’ C/N ratio and the nitrogen fixation rates of the needles ( P  = 0.14). A similar result has been found for logging residues of Scots pine, Norway spruce and silver birch ( Betula pendula ), where neither branches nor foliage showed a significant correlation between nitrogen fixation and C/N ratio ( Tormanen and Smolander 2022 ).

To understand better the potential importance of needle nitrogen fixation in a nitrogen-limited forest, we extrapolated our measured nitrogen-fixation rates to a forest scale, calculating it to be less than 20 g N ha −1  year −1 . However, this rate needs to be interpreted with caution, as several assumptions were made, including constant nitrogen fixation during the growth season, which could be incorrect as seasonal variation in conifers has been noticed elsewhere ( Favilli and Messini 1990 ). Our study also indicates that there is significant day-to-day variation in nitrogen-fixation rates, which was not accounted for in our extrapolation. With these caveats in mind, the estimated nitrogen contributed by nitrogen fixation in needles seems very low compared with the estimated nitrogen use of approximately 50 kg N ha −1  year −1 for Scots pine growth in the boreal forest ( Korhonen et al. 2013 ). The contribution of needle nitrogen fixation to tree growth in this forest may, therefore, be rather insignificant in the short term. However, taking into account the approximately 8500-year continuous boreal forest cover in the region ( Barnekow et al. 2008 ), and assuming historically constant nitrogen fixation in needles, the nitrogen fixed by bacteria inside Scots pine needles could be an important nitrogen source in the longer term, contributing to the build-up of the soil nitrogen stock over the long term ( Finér et al. 2003 , Merilä et al. 2014 ).

Largely consistent with our third hypothesis, the endophytic bacteria isolated from 1-year-old Scots pine needles belonged to similar genera as found in other tree species ( Cankar et al. 2005 , Moore et al. 2006 , Izumi et al. 2008 , Puri et al. 2018 ). The 13 isolated bacteria colonies were identified as belonging to the genera Bacillus , Variovorax , Novosphingobium , Sphingomonas , Microbacterium and Priestia . The genera Penibacillus , Rhizobium and Pseudomonas were not present, even though they are commonly found in tree species ( Cankar et al. 2005 , Moore et al. 2006 , Izumi et al. 2008 , Puri et al. 2018 ). Looking more closely at each genus, Bacillus bacteria have been detected previously in various coniferous and deciduous trees ( Izumi et al. 2008 , Puri et al. 2018 ), and have been shown to fix nitrogen and promote plant growth and yield ( Çakmakçı et al. 2001 , Ding et al. 2005 , Yousuf et al. 2017 ). The Gram-positive, rod-shaped ( Dunlap et al. 2015 ) strain of B. paralicheniformis has been reported as a nitrogen-fixing bacterium based on a whole-genome study ( Annapurna et al. 2018 ). Another of the isolated bacteria belonged to the genus Microbacterium , which includes Gram-positive bacteria that have been shown to promote plant growth, chlorophyll content and fruit yield in a few diverse but agriculturally important plant species ( Karlidag et al. 2007 , Schwachtje et al. 2012 , Mutai et al. 2017 , Bal and Adhya 2021 ). This genus includes strains with the nifH gene and the capacity to fix nitrogen ( Ruppel 1989 , Zakhia et al. 2006 , Lin et al. 2012 ), and some Microbacterium strains have been isolated from maple and elm trees ( Shen and Fulthorpe 2015 ). Bacteria from Variovorax have been isolated from poplar trees ( Moore et al. 2006 ), and this genus of Gram-positive bacteria includes strains capable of nitrogen fixation ( Solanki et al. 2016 ) and promoting plant growth ( Maimaiti et al. 2007 ). Specifically, V. paradoxus has been reported as a hydrogen-oxidizing plant growth-promoting bacterium ( Maimaiti et al. 2007 , Han et al. 2011 ). The Gram-positive P. megaterium (previously classified as Bacillus megaterium ) has also been reported as a nitrogen-fixing bacterium ( Ding et al. 2005 , Yousuf et al. 2017 ) and shown to promote plant growth ( Nascimento et al. 2020 , Wang et al. 2021 ). Sphingomonas strains have been detected in willow and elm trees ( Moore et al. 2006 , Doty et al. 2009 , Shen and Fulthorpe 2015 ), and the ability to fix nitrogen has been identified in a few Sphingomonas bacteria ( Castanheira et al. 2014 , Yang et al. 2014 , Lowman et al. 2015 ). Novosphingobium has also been reported as a genus that includes plant growth-promoting nitrogen-fixing bacteria ( Islam et al. 2009 , Rangjaroen et al. 2017 ). Novosphingobium pokkalii has been described as a rhizosphere-associated bacterium with plant growth-promoting properties ( Krishnan et al. 2017 ).

Acetylene-reduction assay (ARA) was used to check in vitro whether the isolated bacteria were endophytic diazotrophic bacteria capable of nitrogen fixation. In support of our fourth hypothesis, 10 out of the 13 bacteria colonies displayed ethylene production, with Microbacterium sp. being the most efficient. The three colonies that did not fix nitrogen in the ARA were identified as two species, B. paralicheniformis and N. pokkalii . It could be that these three bacterial strains did not show nitrogen fixation because of nonoptimal test conditions ( Doty et al. 2009 ), or because their fixation rate was under the detection limit of the ARA. Not all bacteria capable of growing on nitrogen-free media demonstrate nitrogen fixation during an ARA ( Doty et al. 2009 , Padda et al. 2018 , Puri et al. 2018 ). Using assembled and annotated genomes of the bacteria we could detect the sequence for the NifU protein in most of the bacteria, however, we could not detect any other nif genes in the sequences. This could be due to short reads limitation (as we only had 150 bp sequencing length), genome misassemblies or genome and annotation incompleteness ( Chen et al. 2013 , Barbitoff et al. 2020 , Lobb et al. 2020 ).

The fact that most of the isolated endophytic bacteria were capable of nitrogen fixation makes them good candidates for plant growth-promoting bacteria. Nitrogen-fixing bacteria isolated from conifers have been used as plant growth-promoting bacteria in seedlings: the inoculated seedlings were taller and had greater biomass compared with control seedlings grown under both nitrogen-limited conditions ( Puri et al. 2020 ) and in fertilized soil ( Chen et al. 2021 ). However, to analyse their potential as plant growth-promoting bacteria our isolated strains would need to be tested for additional plant growth-promoting properties, such as indole-3-acetic acid production, siderophore production and 1-aminocyclopropane-1-carboxylate deaminase.

Our study has demonstrated that nitrogen-fixing bacteria are present and active in 1-year-old Scots pine needles; their endophytic presence was confirmed by nitrogenase protein immunodetection, and nitrogen fixation was measured using ARA. Strains of the nitrogen-fixing bacteria were isolated from sterile needles by culturing and identified using whole-genome sequencing, and their nitrogen-fixation ability was confirmed by in vitro ARA. Immunodetection of the NifH protein showed no difference between needles from control plots and fertilized plots, and the ARA showed similar fixation rates in needles from both treatments. To scale up estimates of the nitrogen-fixation rates and their impact on the boreal forest more accurately, it is important that the seasonality of nitrogen fixation is assessed, and the effect of variation in light intensity on the nitrogen-fixation capacity of endophytic bacteria inside coniferous needles determined. Even though the amount of nitrogen fixed by these bacteria might not be significant for the trees currently growing in the Scandinavian boreal forest, it could be significant in the longer term. Nitrogen fixation by bacteria within conifer needles may have provided an important source of nitrogen for the forest ecosystem’s structure and function during the millennia that the boreal forest has dominated this landscape.

We are grateful to Knut and Alice Wallenberg for funding T.B.’s PhD position and this study. R.G. and A.N. acknowledge Arevo AB and Stora Enso Oyj, respectively, for allowing the leave of absence to carry out research and PhD supervision.

None declared.

T.B., A.S., R.G. and A.N. conceived and designed the study, T.B. performed the experiments and carried out the statistical analysis, T.B. and A.N. wrote the manuscript, but all authors contributed equally to manuscript revision, and read and approved the submitted version.

Sequencing data and bacterial strains are made available through GenBank, Sequence Read Archive and NCCB collection .

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Nitrogen Fixation and Microbial Communities Associated with Decomposing Seagrass Leaves in Temperate Coastal Waters

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• Published: 14 August 2024
• Volume 87 , article number  106 , ( 2024 )

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• Vasiliki Papazachariou 1 , 2 ,
• Victor Fernández-Juárez 1 ,
• Laura Wegener Parfrey 3 &
• Lasse Riemann 1 , 2  

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Seagrass meadows play pivotal roles in coastal biochemical cycles, with nitrogen fixation being a well-established process associated with living seagrass. Here, we tested the hypothesis that nitrogen fixation is also associated with seagrass debris in Danish coastal waters. We conducted a 52-day in situ experiment to investigate nitrogen fixation (proxied by acetylene reduction) and dynamics of the microbial community (16S rRNA gene amplicon sequencing) and the nitrogen fixing community ( nifH DNA/RNA amplicon sequencing) associated with decomposing Zostera marina leaves. The leaves harboured distinct microbial communities, including distinct nitrogen fixers, relative to the surrounding seawater and sediment throughout the experiment. Nitrogen fixation rates were measurable on most days, but highest on days 3 (dark, 334.8 nmol N g −1 dw h −1 ) and 15 (light, 194.6 nmol N g −1 dw h −1 ). Nitrogen fixation rates were not correlated with the concentration of inorganic nutrients in the surrounding seawater or with carbon:nitrogen ratios in the leaves. The composition of nitrogen fixers shifted from cyanobacterial Sphaerospermopsis to heterotrophic genera like Desulfopila over the decomposition period. On the days with highest fixation, nifH RNA gene transcripts were mainly accounted for by cyanobacteria, in particular by Sphaerospermopsis and an unknown taxon (order Nostocales), alongside Proteobacteria. Our study shows that seagrass debris in temperate coastal waters harbours substantial nitrogen fixation carried out by cyanobacteria and heterotrophic bacteria that are distinct relative to the surrounding seawater and sediments. This suggests that seagrass debris constitutes a selective environment where degradation is affected by the import of nitrogen via nitrogen fixation.

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Introduction

Seagrass meadows are among the most productive marine ecosystems [ 1 , 2 ]. Seagrasses are angiosperms thriving underwater, contributing to primary production through their photosynthesis while offering important ecosystem services such as shore protection, sediment stabilization and biodiversity enhancement [ 3 , 4 ]. Seagrass production is exported to the surroundings as particulate and dissolved organic matter affecting carbon cycling on local and global scales [ 5 , 6 ].

The regulation of nutrient cycling and retention within seagrass meadows occurs through both direct processes involving uptake and assimilation in leaves, roots and rhizomes, as well as indirect mechanisms such as the trapping of organic matter present in suspended particles [ 7 , 8 , 9 , 10 ]. Nitrogen may limit seagrass productivity, especially in oligotrophic environments [ 11 ], and can be supplied through nitrogen fixation associated with aboveground parts such as seagrass leaves, or belowground like roots and rhizosphere. For instance, nitrogen fixation in the rhizosphere can meet nearly all of the plant’s nitrogen requirements, and this assimilated nitrogen can subsequently be transported to the aboveground tissues [ 10 , 12 ]. Studies on nitrogen fixation associated with leaves, especially in temperate waters, are few [ 13 , 14 , 15 , 16 ], but report significant and variable rates associated with epiphytes on Zostera marina leaves [ 17 ].

Seagrass debris represents a substantial biomass in some coastal waters and its degradation by microbes affects local carbon (C), nitrogen (N), phosphorus (P), sulphur and iron cycling [ 18 , 19 , 20 ]. Seagrass debris is characterized by rather high C:N:P ratios and leaves have a higher C:N ratio than rhizomes due to a high cellulose content [ 18 ], making decomposition slow [ 21 ]—slower than other marine litter, such as macroalgal detritus [ 22 ]. During macroalgal decomposition, labile nitrogen is preferentially utilized by microbes compared to carbon, likely leading to N limitation, which in turn might be alleviated by diazotrophic activity [ 23 , 24 ]. Faster utilization of detrital carbon accelerates the macroalgal degradation, while N limitation may hinder microbial processes and slow macroalgal decomposition rates [ 24 ]. Indeed, in detrital macroalgal systems, nitrogen enrichment was connected to microbial proliferation [ 25 ] and nitrogen fixation appeared stimulated by declining C:N ratios during the decomposition process [ 26 ]. Moreover, nitrogen fixation rates were much higher than observed in association with living macroalgae [ 26 ]. Extensive microbial colonization and decomposition of seagrass debris is well-known [ 18 , 27 , 28 ]; however, it is not known whether and to what extent diazotrophs are involved. This is important because nitrogen fixation could influence debris decomposition [ 29 ] and the associated elemental cycling and represent a hitherto overlooked N input to coastal systems [ 17 ].

In this study, we examined nitrogen fixation and the diazotroph community associated with debris of the eelgrass Z. marina . This species thrives in Danish temperate waters [ 30 ], although it has declined during the past century due to eutrophication [ 31 ], and plays a fundamental role in coastal ecosystems throughout the northern hemisphere [ 32 , 33 ]. To our knowledge, it is not known whether nitrogen fixation is associated with debris of Z. marina leaves, but in Danish waters rates of nitrogen fixation were about three times higher in vegetated sediments in comparison with non-vegetated ones [ 34 ]. We, therefore, hypothesized that decomposing eelgrass leaves are foci for nitrogen fixation. Such N import could affect the degradation of eelgrass as well as the cycling of nutrients and carbon in this coastal environment. We expected that the environmental changes occurring during the course of leaf degradation, including C, N and P contents, and nutrient availability in the surrounding seawater, would affect nitrogen fixation and be mirrored in successional changes in the associated communities of heterotrophic and phototrophic bacteria. We specifically addressed the early phases of seagrass decomposition, including the anticipated passive leaching and microbial colonization phases. To address this and determine the relative importance of phototrophic and heterotrophic diazotrophs, we measured nitrogen fixation associated with decomposing seagrass leaves under light and dark conditions over 52 days. In parallel, we explored the associated microbial community composition and dynamics with a specific focus on diazotroph communities.

Materials and Methods

Experimental design and sampling.

Seagrass shoots were collected in July 2022 from a Z. marina meadow in a semi-enclosed bay by Helsingør, Denmark, via freediving (depth < 2 m, 56°2′9.82″N, 12°36′48.96″E). The site was chosen because it harbours a healthy meadow typical for these coastal waters. Ten fresh shoots were added to each of 11 polyester 1.0-mm mesh bags (Hydro-Bios Apparatebau GmbH) (30 × 30 cm) that were then randomly attached to a 2 × 2 m metallic grid at ~ 2-m depth, on bare sediment, roughly 30 m from the meadow (Fig. S1 ). All bags were placed ca. 5 cm above the sediment. Temperature and light were continuously monitored by loggers attached to the grid (HOBO® MX2202 Data Logger, Onset Computer Corp., Bourne, MA, USA). Between 9 and 10 am, on days 0, 1, 3, 4, 7, 10, 15, 22, 36, 43 and 52, one bag was removed and transported to the laboratory in a bucket with ambient seawater. Within 15 min of sampling, leaves were fixed for subsequent RNA extraction or transferred to serum vials for ARA measurements (see below).

At each time point, biomass for microbial community composition analysis was sampled from surrounding seawater and seagrass leaves (~ 8–10 cm sections) in triplicates. Seagrass samples were immediately preserved in 1 ml RNAlater (Thermo Fisher Scientific, MA, USA) in 1.5 ml Eppendorf tubes, and stored at − 20 °C. Surrounding seawater was sampled from within the metallic grid using sterile 5-l plastic bags. Seawater (500 ml) was filtered in triplicates onto Durapore® filters (0.22 µm, 25 mm diameter, Sigma-Aldrich, MA, USA), which were stored at − 20 °C. The filtrate was stored in − 20 °C in duplicates in 15 ml Falcon tubes and further analysed for ammonium (NH 4 + ), nitrate (NO 3 2− ) and phosphate (PO 4 3− ). NH 4 + was quantified fluorometrically [ 35 ]. NO 3 2− and PO 4 3− were quantified using standard colorimetric methods [ 36 , 37 ]. On days 10, 15, 22, 36, 43 and 52, top sediment, to 5-cm depth, was sampled using a 15-ml Falcon tube from within the grid area. On days 0, 15 and 43, seawater was sampled from within the mesh bags using a 500 ml syringe and then filtered and stored for subsequent community analysis (see below).

Measurements of Nitrogen Fixation

Nitrogen fixation was estimated using acetylene reduction assay (ARA) as described earlier [ 16 , 37 ]. Despite the identified pitfalls of ARA as a method, it is the most commonly used method used in studies estimating nitrogen fixation associated with seagrass [ 15 , 17 , 39 ]. Seagrass leaves were randomly collected from the bag at each time point, cut into 10–12-cm-long pieces, and placed in 20 ml serum vials containing 1 ml of 0.2 µm filtered seawater from the sampling site. The vials were then sealed with crimped septa and 2 ml of acetylene (ALPHAGAZ ™ ACETYLEN ≥ 99.6%) was added to the gas phase using a gastight Hamilton syringe to obtain a 10% vol./vol. concentration. For each timepoint, measurements were performed for six seagrass replicates and two controls—light and dark. Due to seagrass fragmentation, only a dark incubation was carried out on day 52. Dark incubations were wrapped in aluminium foil. For the controls, a vial with 1 ml of 0.2 µm filtered seawater and no seagrass leaf controlled for abiotic ethylene production [ 40 ] whereas a vial with leaf but without acetylene added controlled for ethylene produced by the leaf. Any detection of ethylene production measured in the seawater controls (abiotic) was subtracted from the respective set of samples. In the seagrass set of controls (biotic), no ethylene production was measurable. The vials were incubated in situ at 1 m depth between 11 am and 5 pm. Light and temperature were recorded with a Pendant MX Temperature/Light Data Logger. Incubations were terminated when 10 ml of gas was transferred from the incubated vial to a 20 ml vacuumed and crimped serum vial with a gastight Hamilton syringe. Samples were kept at room temperature and measured the next day using a Shimadzu GC-2010 gas chromatograph with a flame ionization detector and a SS Porapak T column (2 mm) with a mesh range 80/100. To extrapolate ARA data (acetylene reduced to ethylene) to fixed N, a conversion factor of 3.9 was used as has previously been used for decomposing litter [ 41 ]. After incubation, the seagrass leaves were dried at 60 °C for 48 h and ground to powder. Each sample was weighed and packed in aluminium pockets for C and N analyses on a EuroVevtor (EuroEA_Elemental analyzer) and P on a FiaStar (FIAstar_5000 Analyzer). C:N ratio was calculated in mole C/g leaf and mole N/g leaf, respectively. The complete dataset consists of %C and %N.

DNA and RNA Extractions

All DNA extractions from seagrass (51 samples), seawater (54 samples) and sediment (18 samples) and RNA extractions from seagrass (6 samples) were carried out in a laboratory where no nifH gene amplification work had been performed. DNA from seagrass (8–10 cm leaf sections) and sediment (1 g per sample) was extracted using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. All samples were eluted in 10 mM Tris buffer (pH 8) and concentrations quantified using the Qubit™ 1 × dsDNA High Sensitivity (ThermoFisher, MA, USA). DNA from seawater was extracted from Durapore® membrane filters using the DNeasy Power Soil Pro Kit, after grinding the filters into powder using a sterilized metallic grinder. Seagrass leaf RNA was extracted using the AllPrep DNA/RNAMini Kit (Qiagen Sciences, MD, USA) according to the manufacturer’s instructions with the modification that the cell lysis step with β-mercaptoethanol, RLT Plus, included zirconium beads from the DNeasy PowerSoil Pro Kit and bead beading (benchtop vortex with bead tube adapter, max speed, 10 min, room temperature). Extracted RNA was quantified (Qubit™ RNA High Sensitivity, ThermoFisher, MA, USA) and stored at − 70 °C.

cDNA Synthesis and Amplicon Generation and Sequencing

cDNA was synthesized using Invitrogen™ SuperScript™ IV Reverse Transcriptase (Thermo Fischers Scientific, Invitrogen™, MA, USA), the nifH3 reverse primer [ 42 ] and 5 μL of RNA extract. Reverse transcriptase-free control reactions were included for all samples to verify complete DNA removal during RNA extraction. Amplicons of nifH were generated in triplicates in a nested PCR [ 43 ]. These primers capture a broad range of both cyanobacterial and non-cyanobacterial diazotrophs [ 44 ]. The PCR reactions (25 μl) contained 12.5 μL MyTaq™ HS Mix 2 × DNA Polymerase (Bioline Reagents) and 0.4 μM forward and reverse primers. Initial denaturation was at 94 °C for 120 s, followed by 30 cycles of 60 s at 94 °C, 60 s at 54 °C, 60 s at 72 °C and a final 420 s at 72 °C. PCR triplicates were pooled, size confirmed by agarose gel electrophoresis and purified (MP Biomedicals™ Geneclean™ Turbo Kit). Amplicon libraries were Illumina indexed (National Genomics Infrastructure, Uppsala, Sweden), purified (Beckman Coulter™ Agencourt AMPure XP, ThermoFisher, MA, USA), quantified (Qubit™ 1X dsDNA High Sensitivity), pooled in equimolar ratios and sequenced using an Illumina MiSeq platform (2 × 300 bp pair-end reads, GeoGenetics Sequencing Core, University of Copenhagen, Denmark). Negative controls of PCR UV-irradiated water were included for each PCR reaction and DNA extraction round; negative controls were checked by gel electrophoresis and never generated visible amplification. These control samples for PCR (a total of eight) and DNA extractions (a total of six) were pooled in two respective pools and sequenced to account for potential background contamination. The read numbers from controls never exceeded 1% of the average read number obtained from samples (54,168 reads per sample) and were judged not to influence the data.

16S rRNA gene amplification

The V3-V4 hypervariable region of the 16S rRNA gene was PCR amplified using the primers 341F/805R (341F, 5′-CCTACGGGNGGCWGCAG-3′; 805R, 5′-GACTACHVGGGTATCTAATCC-3′) [ 45 ]. Initial denaturation was at 94 °C for 180 s, followed by 30 cycles of 45 s at 94 °C, 60 s at 50 °C, 90 s at 72 °C and a final 90 s at 72 °C. All samples were performed in triplicates. The size of the amplicons was confirmed, and they were further purified using MP Biomedicals™ Geneclean™ Turbo Kit (ThermoFisher, MA, USA). An amplicon library was constructed with specific barcodes assigned to each sample and then barcoded amplicons were cleaned (Beckman Coulter™ Agencourt AMPure XP). Finally, amplicons were quantified (Qubit™ 1 × dsDNA High Sensitivity; ThermoFisher, MA, USA), pooled in equimolar ratios and sequenced using an Illumina MiSeq platform with 2 × 300 bp pair-end reads (GeoGenetics Sequencing Core, Copenhagen, Denmark).

Data Analysis and Statistics

All analyses were performed in RStudio (version 4.2.2). Visualization of data was done with ggplot2 [ 46 ] and the Brewer colour palettes. Demultiplexing and trimming of indexes and adaptors were performed by the sequencing facility. Amplicon sequence variants (ASVs) were generated using the DADA2 pipeline [ 47 ] yielding lengths of 325–328 bp ( nifH gene) and 360–368 bp (16S RNA gene). A total of 10,535 nifH ASVs and 4531 16S rRNA ASVs were obtained from the respective 93 and 30 samples. An average number of 500 reads was observed in the controls and no ASV was assigned to any of the controls after trimming and denoising of the sequences. Sequences were processed with the phyloseq package [ 48 ] and after evaluating the rarefaction curves samples with < 1000 reads were removed (Fig. S2 ). For the nifH analysis: one replicate from seagrass days 0 and 4, respectively. For the 16S rRNA gene analysis: one replicate from seagrass day 3. Based on the rarefaction curves for the 16S rRNA gene and nifH analysis, alpha and beta diversity measures data were rarefied to depths of 10,000 and 20,000 reads, respectively. For nifH , six samples were removed since they contained fewer reads than the threshold (day 0 seagrass replicate 1, day 4 seagrass replicates 1 and 2, day 24 seagrass replicate 1 and day 36 seawater replicate 1) and for 16S rRNA genes one sample day 15 seagrass replicate 1. NifH taxonomy was assigned with the ‘assignTaxonomy’ from the DADA2 pipeline, using a nifH reference database (v. June 2017, Zehr lab). 16S rRNA gene ASV taxonomy was assigned according to the SILVA database (silva_nr_v132_train_set). The Shannon diversity index [ 49 ] was calculated based on normalized data (rarefied to even depth) and compared across decomposition days with Wilcoxon Rank sum test followed by Bonferroni correction for multiple comparisons. For non-metric multidimensional scaling (NMDS), Bray–Curtis distance was chosen as the dissimilarity measure ( nifH and 16S rRNA genes). Permutational multivariate analysis of variance (two-factor PERMANOVA) was conducted to assess statistical differences in community composition using the ‘adonis2’ function applying 999 permutations in ‘vegan’ package (v.2.6–4 [ 50 ]). Data on nitrogen fixation were tested for normal distribution between different groups using Levene’s test ( P  < 0.0001) and statistical significance was tested using non-parametric Kruskal–Wallis followed by Dunn’s post hoc test for pairwise comparisons. Statistical significance between days was tested with a non-parametric Kruskal–Wallis ( χ 2  = 56.56, P adj  < 0.001) followed by a post hoc Dunn test using Bonferroni correction. Differences on C:N ratios between different days were tested with analysis of variance (ANOVA) followed by a Tukey’s honest significant difference (HSD) to identify significant pairwise comparisons.

Phylogenetic analysis was used to determine the phylogenetic affiliations of the nine ASVs from the nifH DNA and RNA datasets assigned as UCYN-A ( Candidatus Atelocyanobacterium thalassa) . Nucleotide sequences of these ASVs and 14 reference sequences were aligned using MUSCLE 5 [ 51 ] in Geneious Prime (v.2024.0.2). UCYN-A nucleotide reference sequences were from [ 52 , 53 , 54 ], and the remaining reference sequences were acquired from NCBI [ 55 ]. Then RaxML 8.2.11 was used to construct a maximum-likelihood tree with 1000 bootstraps under the GTR GAMMA model [ 56 ].

Sequences have been deposited in the NCBI GenBank database with accession numbers SAMN41318939-SAMN41319031 classified under the Bio Project number PRJNA1110172.

Dynamics in Nitrogen Fixation and C, N and P Contents During the Decomposition of Seagrass Leaves

During the decomposition period, seawater temperature varied from 18 °C to 21.9 °C (Fig. S3 ). Concentrations of nitrate (NO 3 2− ) and phosphate (PO 4 3− ) in the surrounding seawater varied from 0.35 to 5.88 μM and from 0.16 to 0.45 μM, respectively, and showed peak concentrations on days 7 and 22 (Fig. S4 ). Similarly, ammonia (NH 4 + ) was highest on day 7 (0.12 μM; Fig. S4 ).

Nitrogen fixation rates were measurable on most days. The highest rates were observed on days 3 (dark, 334.8 nmol N g −1 dw h −1 ) and 15 (light, 194.6 nmol N g −1 dw h −1 ; Fig.  1 ) (Kruskal–Wallis, df = 18, P adj  < 0.001). On days 3 and 36, rates under dark conditions were significantly higher than in the light ( P adj  < 0.05) and on day 0 rates were higher in the light ( P adj  = 0.04) (Fig. S5 ). The C:N ratio fluctuated between 25 and 35 and declined to a low of 21.9 on day 52 (C:N ratio on day 52 was significantly lower than on days 3, 7, 10 and 22; ANOVA, P  < 0.001; Fig.  2 ). The P elemental leaf content did not change over the decomposition period (ANOVA, P  < 0.05; Fig. S6 ). Nitrogen fixation under dark and light conditions was not correlated with any of the measured environmental parameters, including nutrient levels in the seawater or seagrass leaves (Fig. S7 ).

Nitrogen fixation rates associated with decomposing Z. marina leaves over time. Leaves ( n  = 6) were incubated under dark (dark grey) and light (white) conditions. The lines in the boxes represent the median. Letters above each bar represent statistical difference ( P adj  < 0.05) between means of the days (light and dark incubations/day) tested with Kruskal–Wallis followed by a posthoc Dunn’s using Bonferroni correction

Carbon (C) to Nitrogen (N) ratios (C:N) of decomposing Z. marina leaves. Letters above each bar represent statistical difference ( P  < 0.05) between means tested with analysis of variance (ANOVA) followed by a post hoc Tukey test (‘TukeyHSD’)

Microbial Community Composition on Decomposing Seagrass Leaves

The microbial community composition on decomposing seagrass leaves, as analysed by 16S rRNA gene amplicon sequencing, was distinct from the surrounding seawater and showed a pronounced temporal succession (Fig.  3 , Fig. S8 ). Sample type (seagrass versus seawater) explained 45% of the variation in microbial communities (PERMANOVA, R 2  = 0.45, P  = 0.001) while time explained 31% of the variation (PERMANOVA, R 2  = 0.31, P  = 0.01). Turnover in bacterial community composition on seagrass was evident in the most abundant genera (Fig.  3 ) and families over time (Fig. S9 ). The genera Lewinella (family Saprospiraceae) and Granulosicoccus (family Thiohalorhabdaceae) were initially abundant (accounting for 10 to 20% of relative abundance) and declined as decomposition progressed (Fig.  3 ). The genus Blastopirellula (family Pirellulaceae) peaked on days 15–36, accounting for up to 10% relative abundance (Fig.  3 ). On day 52, these taxa were replaced by Marinomonas (family Marinomonadaceae) and Reichenbachiella (family Cyclobacteriaceae).

Microbial community composition on seagrass leaves and in the surrounding seawater based on 16S rRNA gene amplicon sequencing. The 20 most dominant Phyla and genera are shown

The composition and succession of diazotrophs was analysed by nifH gene amplicon sequencing. The diazotrophs on seagrass showed a distinct composition relative to diazotrophs in the surrounding seawater and in sediment, with sample type explaining 40% of the variation (PERMANOVA, R 2  = 0.40, P  = 0.001; Fig.  4 ). Communities also changed over time, with decomposition day explaining 21% of the variation (PERMANOVA, R 2  = 0.21, P  < 0.001). Occasional water sampling with a syringe from within the incubation bags, and subsequent nifH gene sequencing, showed that the composition of free-living diazotrophs did not differ from diazotrophs in surrounding seawater (Fig. S10 ); i.e. the incubation in a bag did not select for a distinct community. The α-diversity of diazotrophs increased over the first 10 days and then leveled off (Fig. S11 ). The diazotrophs on seagrass belonged mainly to the classes Gammaproteobacteria, Cyanophyceae, Verrucomicrobiae, Bacteroidia and Deltaproteobacteria (Fig. S12 ). Cyanobacteria were consistently present throughout the incubation but declined gradually from 25% on day 1 to 5% on day 36 (mean of replicates). There was a notable increase on day 43, with cyanobacteria accounting for 39% of relative abundance, before declining to 5% on day 52 (data not shown). A deeper analysis on the genus level showed an extensive succession on seagrass with a change from a prevalence of the filamentous heterocyst forming cyanobacterium Sphaerospermopsis (family Aphanizomenonaceae) during the first 10 days to the emergence of mainly heterotrophic genera, like Desulfopila (days 7–43), and Insolitispirillum (Fig.  5 ). On the first 3–4 days, Sphaerospermopsis accounted for 12–25% (mean of replicates) of the relative abundance, but then gradually disappeared as the community became dominated by heterotrophic taxa. Desulfopila was relatively constant over time accounting for 3–10% (mean of replicates) of the total seagrass community with a peak on day 15. Insolitispirillum was only present from day 10 (0.1%) with an increase on day 36 (8.5%) and on day 52 where it accounted for 22.5% (mean of replicates) of the diazotrophs. Similarly, Shewanella was present on days 15, 36 and 52 only, and accounted for 16% (mean of replicates) on day 52. Diazotrophs in the surrounding seawater were dominated by different symbiotic unicellular cyanobacteria (UCYN-A; Candidatus Atelocyanobacterium Thalassa; class Cyanophyceae) (20–50% relative abundance) and in sediments by Malonomonas (2–4% relative abundance) . Six ASVs from seawater were affiliated with the UCYN-A2 and one with the UCYN-A4 sublineages, respectively (Fig. S13 ). Malonomonas is an anaerobic, microaerotolerant sediment bacterium [ 57 ]. Only one UCYN-A ASV was found in DNA from seagrass samples, and it showed > 95% nucleotide similarity to the six seawater UCYN-A ASVs affiliated with the UCYN-A2 sublineage.

Non-metric multidimensional scaling (NMDS) of Bray–Curtis dissimilarity of nifH gene amplified from DNA samples from seagrass, seawater and sediment over the course of the decomposition experiment. Days are indicated by colour and substrate by shape

Composition of diazotrophs on seagrass, in surrounding seawater and in sediments over time in relative abundance. Based on nifH amplicon sequencing of DNA. Each bar represents one sample replicate. Only the 20 most dominant genera are shown. Sediment sampling started on day 10. One seagrass replicate from each of days 0 and 4 were excluded from the analysis due to low read numbers

The organisms responsible for nitrogen fixation on the seagrass leaves were identified by sequencing nifH RNA on the days of peak nitrogen fixation (3, 15 and 36). The nifH RNA gene transcripts were dominated by Cyanobacteria throughout and the taxa with the highest transcript abundance varied between days and across replicates (Fig.  6 ). The cyanobacterial transcripts were partly accounted for by two ASVs of an unknown genus. They belonged to the order Nostocales and showed 91–92% nucleotide similarity with the Richelia cyanobiont of the diatom Rhizosolenia sp. (HQ586597). On day 3, the nifH gene transcripts were dominated by the cyanobacteria Sphaerospermopsis and Anabaena . On day 3, Sphaerospermopsis was responsible for 52% of the nifH gene transcripts (mean of amplifiable replicates) decreasing to 13% on day 15 and being almost absent on the last day (0.03%). On day 15, more diverse diazotrophs expressed nifH , including cyanobacteria such as Sphaerospermopsis and Candidatus Atelocyanobacterium thalassa (UCYN-A2) representing 13% and 3% of the transcripts and the heterocystous Nunduva (8%), but also with a presence of Insolitispirillum (3%), a purple nonsulphur bacterium from the family Rhodospirillaceae. The non-cyanobacterial nifH transcripts were also partially accounted for by one proteobacterial ASV of unknown genus (17%—mean of replicates) and showed 84.4% nucleotide similarity with Azotobacter salinestris strain CP045302. On day 36, nifH gene transcripts were identifiable from Nunduva (2%), Candidatus Atelocyanobacterium (UCYN-A2) (0.6%), Gloeocapsa (0.05%) and the heterotrophic bacterium Agarivorans (0.1%).

Relative abundance of the top 20 diazotrophs expressing nitrogenase ( nifH RNA gene expression) on the 3 days with highest nitrogen fixation. Each bar represents one replicate

We found that nitrogen fixation is associated with decomposition of the seagrass Z. marina in Danish coastal waters and that the microbes responsible are distinct in composition relative to adjacent water and sediment environments. Our study indicates that seagrass leaves represent a selective environment inhabited by specialized microbes carrying out local nitrogen fixation.

Nitrogen Fixation Associated with Decomposing Seagrass Leaves Is an Overlooked Nitrogen Input to Danish Coastal Waters

We measured nitrogen fixation rates up to 335 nmol N g −1 dw h −1 on decaying seagrass leaves. To our knowledge, these are the first rates reported for Z. marina leaf litter from temperate waters. They are comparable to rates measured on mangrove and macroalgal detritus: up to 380 nmol N g −1 dw h −1 [ 59 ] and 693 nmol N g −1 dw h −1 [ 26 ], respectively, and exceed rates measured on living seagrass leaves [ 16 , 17 , 60 ]. Higher levels of nitrogen fixation on debris relative to live plants seem to be a consistent observation, as it has also been observed for macroalgae [ 26 ] and mangrove leaf litter [ 59 ], possibly due to labile carbon availability during the degradation process [ 6 , 61 ]. Z. marina is widespread in Denmark [ 30 ] and in the northern hemisphere [ 31 , 32 ]. The high rates of nitrogen fixation associated with decaying seagrass leaves reported here suggest that they represent a nitrogen source to Danish coastal environments, which is hitherto unaccounted for.

To gain insight into the metabolism of active diazotrophs during the seagrass decomposition, nitrogen fixation was measured under dark and light conditions. Surprisingly, the dynamics appeared rather inconsistent with no significant overall difference for light and dark incubations, except for days 3 and 36, where rates were highest in the dark and day 0 where rates were highest in the light (Fig.  1 ). While the data from specific days are hard to explain, we suggest that the pattern of mainly fixation in the light in the first part of the degradation and the predominance of dark fixation after day 22 reflects the compositional succession from prevalence of phototrophic cyanobacteria (nitrogen fixation in the light) to heterotrophic bacteria (nitrogen fixation in the dark) when oxygen conditions are conceivably low on the degrading leaves [ 62 ]. The distinction between light and dark fixation is conceivably not clear-cut. Although many diazotrophic cyanobacteria fix nitrogen in the light [ 63 , 64 ], others do also in the dark [ 72 , 74 ] and heterotrophic diazotrophs may also do fixation in the light, and even exploit light [ 66 ]. Still, we note that an initial cyanobacterial fixation in light, supported by cyanobacterial nifH gene expression (Fig.  6 ), is consistent with the idea that this is partially driven by a pre-existing epiphytic community on fresh leaves, as proposed earlier [ 17 , 26 , 67 , 68 ], and partially by acquired diazotrophs due to the decomposition process. Similarly, in early stages of macroalgal decomposition, nitrogen fixation activity under light conditions was associated with cyanobacterial epiphytes [ 26 ]. In general, nitrogen fixation associated with living seagrass leaves in temperate regions vary considerably, ranging from significantly higher rates under light conditions contributing nearly 95% of the total daily rate [ 17 ], to no discernible differences between light and dark nitrogen fixation [ 68 ], and to 99% of the total fixation occurring under dark conditions [ 67 ]. Hence, cyanobacteria likely dominate fixation on seagrass leaves during early degradation, whereas heterotrophic fixation becomes more important in the latter part of the degradation process. The persistent nitrogen fixation throughout the decomposing period under dark conditions, particularly after 22 days when rates were only measurable in the dark, suggests a contribution by heterotrophic diazotrophs. We speculate that this is linked to the establishment of an epiphytic biofilm on seagrass leaves, indicated by a decreased C:N ratio, leading to reduced oxygen availability [ 69 , 70 , 71 ], which could favour nitrogenase activity by heterotrophic bacteria. Still, it is noteworthy that the nifH gene expression is dominated by cyanobacteria, even at day 36 (see below and Fig.  6 ) suggesting they cyanobacteria continue to be active members of the epiphytic biofilm community.

Factors Regulating Nitrogen Fixation During Seagrass Decomposition Are Unclear

We predicted that nitrogen fixation rates on seagrass detritus would be correlated with environmental nutrient concentrations and/or the C:N:P nutrient content of seagrass leaves. These predictions were informed by nutrient dynamics in macroalgal and macrophyte detrital systems [ 26 , 59 , 72 , 73 ], the distinct phases of seagrass decomposition and the associated microbial activity [ 61 ], and the factors known to regulate nitrogen fixation [ 13 , 74 , 75 ]. However, no correlations between nitrogen fixation rates and the measured environmental parameters were found. We expected that the high C:N ratios and high availability of labile C, characteristic for fresh seagrass leaves, would stimulate nitrogen fixation early in decomposition, as observed for some macroalgal detrital systems [ 26 ]. We also predicted that C:N ratios would decrease later in decomposition due to the development of a microbial biofilm and an increased microbial biomass [ 26 , 29 , 76 ]. Our fresh seagrass leaves had a mean C:N ratio of 30 and the C:N ratios trended lower on day 1 (mean 23) and then increased to roughly 35 for days 3–22 before declining to a mean of 22 on day 52. However, there was no correlation between C:N ratios or P content of leaves and nitrogen fixation rates (Fig. S7 ), which were highest on days 3 and 15 (Fig.  1 ). We speculate that the observed nutrient dynamics result from a complex interplay between decomposition of the seagrass leaves and the formation of a microbial biofilm on the decomposing leaves—including both autotrophs and heterotrophs, utilizing nutrients in the seagrass while also fixing C and N [ 26 , 29 , 61 ].

Composition and Temporal Succession of the Microbes Associated with Decomposing Seagrass Leaves

The microbial community associated with decomposing seagrass leaves was distinct from the microbial community found in nearby seawater and sediment. This is consistent with earlier findings on both live seagrass and during decomposition [ 20 , 29 , 61 , 77 , 78 , 79 ] but in contrast with other studies that report overlap in microbial community composition between seagrass and surrounding seawater [ 80 , 81 , 82 ]. Here, we show that the composition of the diazotroph community was also distinct from seawater and sediment throughout decomposition. The microbial community associated with seagrass leaf decomposition consisted mainly of δ-proteobacteria, Cyanophyceae, γ-proteobacteria, α-proteobacteria, Planctomycetes and Bacteroidia. γ-proteobacteria and α-proteobacteria showed a notable increase in both datasets ( nifH and 16S rRNA) over the course of decomposition. Several clades of γ-proteobacteria and α-proteobacteria, recognized as marine copiotrophs [ 83 ], are proposed as indicators of active leaf decomposition [ 20 ]. Moreover, δ-proteobacteria in our study were prevalent throughout the decomposition process, consistent with their suggested importance in the leaching phase of seagrass rhizomes [ 20 ] and on surfaces of decomposing eelgrass leaves [ 29 ]. Overall, communities were dominated by aerobic chemoorganotrophic taxa typically found on seagrass leaves and/or on macroalgae [ 84 , 85 , 86 , 87 ]. For instance, the genus Blastopirellula (family Pirellulaceae) is a common associate of seagrass and macroalgae and has previously been found in the Baltic Sea [ 88 ]. The community composition on decomposing seagrass overlapped with the composition on living seagrass leaves at day 0 here and in previous studies, and includes common surface-attached chemoorganotrophic such as Granulosicoccus , Lewinella and Rhodobacteraceae, as well as Methylotenera that are often in the core microbiome of eelgrass leaves [ 89 ]. On day 52, the taxa that took over, such as the genera Marinomonas and Reichenbachiella , are known for breaking down lignocellulosic material in seagrasses [ 84 ] and complex polysaccharides [ 90 ], respectively.

We observed a significant shift in the nitrogen-fixing community during the degradation process, from early cyanobacterial dominance to a later dominance by heterotrophic bacteria. For instance, the heterocystous cyanobacterium Sphaerospermopsis , which thrives almost exclusively in freshwater habitats [ 91 ], dominated the nitrogen-fixing communities during the first 10 days while anaerobic sulphate-reducing bacteria (particularly members of Desulfopila) , aerobic ( Insolitispirillum ) and facultative anaerobic ( Shewanella ) found in diverse marine habitats, e.g. tidal-flat sediments [ 92 ], dominated later. We speculate that Sphaerospermopsis was part of the existing cyanobacterial epiphytic biofilm on live eelgrass leaves, while heterotrophic bacteria, including sulphate reducing taxa, with specific decomposing metabolic capacities proliferated at later stages of the degradation process. A similar succession has been observed in macroalgal detrital systems with sulphate-reducing bacteria fixing nitrogen particularly in dark incubations [ 26 ]. Interestingly, despite that the diazotrophs associated with decomposing seagrass appeared phylogenetically distinct, we also observed anaerobic diazotrophic taxa, such as Desulfopila and Malonomonas . These have previously been found in sediments [ 93 ] and were also detected in our sediment samples. Specifically, the sulphate-reducers within the genus Desulfopila have been observed in sediments of seagrass ecosystems, contributing to sulphate reduction [ 75 ]. The co-occurrence of such taxa on decomposing seagrass and in sediments may be attributed to either sediment input through resuspension or the active participation of certain taxa typically found in anoxic sediments in the degradation of seagrass.

Based on the analysis of nifH transcripts, nitrogen fixation measured under light conditions was primarily driven by heterocystous cyanobacteria, particularly Sphaerospermopsis and an unknown taxon within the Nostocales order. Although detailed information about the most prevalent Nostoc diazotroph in our nifH transcripts was unavailable, it is unsurprising to find a heterocystous nitrogen-fixing cyanobacterium active in marine coastal environments, particularly in association with seagrass, given their common occurrence in symbiotic associations [ 94 ]. Sphaerospermopsis is known from freshwaters [ 95 ] and has to our knowledge not previously been observed associated with seagrass. It is, however, considered an invasive species, potentially driven by phosphorus availability and eutrophication [ 91 ]. Hence, the finding of prevalent Sphaerospermopsis in seagrass DNA and RNA nifH gene transcripts is likely related to the local low saline (salinity < 20) and relatively rich nutrient conditions. The epilithic Nunduva genus, a filamentous heterocystous cyanobacterium from the Rivulariaceae family, also contributed substantially to the nifH transcripts on seagrass leaf detritus. Nunduva sp. forms mats on rocks in intertidal and supratidal marine coastal waters [ 58 , 96 ]. Interestingly, UCYN-A2 accounted for some of the nifH gene expression associated with the decomposing seagrass, and identical sequences were found in the seagrass-associated DNA, as well as in DNA from the surrounding seawater. This cyanobacterial/haptophyte symbiosis has been recovered from coastal areas worldwide [ 53 , 97 ], including local Danish/Baltic waters [ 54 , 98 , 99 , 100 ]. This is to our knowledge the first observation of UCYN-A2 being associated with eelgrass, but it has locally been detected associated with copepods [ 100 ]. While cyanobacteria dominated the nifH gene transcripts, a few putative heterotrophic taxa were also found (Fig.  6 ).

Concluding Remarks

In this study, we have shown that distinct microbial taxa thrive on decomposing seagrass leaves and that they undergo a pronounced community succession over time as conditions change during the course of decay. Similarly, a succession in diazotrophs was observed, both in composition and in nitrogen fixation activity. Importantly, our work identifies decomposing seagrass as loci for nitrogen fixation in temperate coastal waters, representing a previously overlooked nitrogen source. Seagrass decay and microbial breakdown processes have been extensively studied in relation to C sequestration in marine environments. However, while nitrogen fixation has been documented in live seagrass leaves in diverse marine environments, including the Mediterranean and tropical waters, our study suggests the need for future research to investigate whether eelgrass debris similarly contributes to nitrogen cycling in these environments, as observed in Danish waters.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Cesar O. Pacherres for assistance on installing the underwater experimental setup.

Open access funding provided by Copenhagen University. This work was supported by grant 0217-00089B (Independent Research Fund Denmark) to LR and the Carlsberg Foundation provided support for instrumentation. The Danish National Research Foundation supported activities within the Center for Volatile Interactions (DNRF168).

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Papazachariou, V., Fernández-Juárez, V., Parfrey, L.W. et al. Nitrogen Fixation and Microbial Communities Associated with Decomposing Seagrass Leaves in Temperate Coastal Waters. Microb Ecol 87 , 106 (2024). https://doi.org/10.1007/s00248-024-02424-w

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Characterization of symbiotic and nitrogen fixing bacteria

Fanuel kawaka.

Department of Biological Sciences, Jaramogi Oginga Odinga University of Science and Technology, P.O. Box 210-40601, Bondo, Kenya

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Symbiotic nitrogen fixing bacteria comprise of diverse species associated with the root nodules of leguminous plants. Using an appropriate taxonomic method to confirm the identity of superior and elite strains to fix nitrogen in legume crops can improve sustainable global food and nutrition security. The current review describes taxonomic methods preferred and commonly used to characterize symbiotic bacteria in the rhizosphere. Peer reviewed, published and unpublished articles on techniques used for detection, classification and identification of symbiotic bacteria were evaluated by exploring their advantages and limitations. The findings showed that phenotypic and cultural techniques are still affordable and remain the primary basis of species classification despite their challenges. Development of new, robust and informative taxonomic techniques has really improved characterization and identification of symbiotic bacteria and discovery of novel and new species that are effective in biological nitrogen fixation (BNF) in diverse conditions and environments.

Introduction

The process of biological nitrogen fixation (BNF) is catalyzed by a two-component nitrogenase complex (Yan et al. 2010 ). The enzyme nitrogenase catalyzes the simultaneous reduction of one N 2 and 2 H + to ammonia and a molecule of hydrogen gas. The enzyme consists of two proteins, an iron protein and a molybdenum-iron protein. The entire process uses 16 mol of ATP and a supply of electrons and protons and occurs optimally between legumes and bacteria (de Carvalho et al. 2011 ).

Symbiotic relationship between the roots of legumes and certain soil bacteria accounts for the development of a specific organ, the symbiotic root-nodule, whose primary function is nitrogen fixation (Shvaleva et al. 2010 ). Depending on the type of microorganism, the energy required for the reduction during N fixation is generated by photosynthesis, respiration or fermentation.

High rate of O 2 -respiration is necessary to supply the energy demands of the N reduction process however O 2 also irreversibly inactivates the nitrogenase complex. These conflicting demands are met by control of O 2 flux through a diffusion barrier in the cortex of nodules, which limits permeability to O 2 (Matthay et al. 2019 ). Oxygen is then delivered to the bacteroids by the plant O 2 -carrier, leghemoglobin found in the nodule (Jones et al. 2007 ). To maintain the low-ambient O 2 -concentration within the nodule, N 2 -fixing bacteroids use a high-affinity cytochrome cbb 3 -type oxidase encoded by the fixNOQP operon to produce ATP (Pitcher and Watmough 2004 ).

There are diverse group of symbiotic bacteria found within the roots of legume plants. However, defining the identity of these closely related bacterial species remain a significant and challenging feature among taxonomists. Classification and taxonomy of bacteria nodulating legume plants have significantly changed in the last 30 years. Initially, classification and identification techniques relied mainly on biochemical, nutritional and serological characteristics together with host ranges. Currently, modern molecular tools and techniques have considerably improved the identification of legume nodulating bacteria. Molecular genetic markers are more sensitive and accurate in distinguishing closely related bacterial species and detect higher diversity compared to phenotypic methods. Traits used in phenotypic characterization include colony morphology, physiology or biochemical reactions of bacteria may vary based on the media and laboratory conditions. Nonetheless, all the taxonomic methods and techniques have their own weaknesses in studying the diversity and phylogeny of bacteria (Pontes et al. 2007 ). Studies have demonstrated that accurate identification of bacteria fixing nitrogen in legumes is vital for researchers in applied research and industry particularly those strains with high nitrogen-fixation ability (Franco-Duarte et al. 2019 ). The current review highlights selected common methods of detecting and identifying symbiotic bacteria in legume crops for improvement of nodulation efficiency and present their advantages and limitations.

Phenotypic, cultural and metabolic characteristics

A wide range of morphological, cultural and metabolic characteristics are used to describe and identify nodule bacteria. Phenotypic traits often used include growth rate, mucous production, colony characteristics, and change in pH of the isolates during growth on Yeast Extract Mannitol Agar (YEMA) media (Hungria and Kaschuk 2014 ; Maatallah et al. 2002 ). Growth in YEMA has been used to classify pure bacterial colonies into either slow or fast growers, presence or absence of mucous, low or high pH among other properties such colony colour, margin and diameter (Kawaka and Muoma 2020 ). Microscopy and staining also group isolates into either Gram positive or negative in addition to absorption of Congo red dye (Kawaka et al., 2014 ). In a study conducted in western Kenya, Kawaka et al. ( 2016 ) highlighted phenotypic and cultural characteristics that are commonly used to describe native symbiotic isolates from different soils (Table ​ (Table1). 1 ). As indicated in Table ​ Table1, 1 , the pure bacterial isolates are mostly characterized to give presumptive identity, establish relationships between isolates and to understand their behavior.

Morphological and cultural characteristics of indigenous symbiotic bacterial isolates from legumes

BTB bromothymol blue, EPS exopolysaccharides, ✓ positive reaction, ×  negative

Additional methods such as cell protein banding pattern, multilocus enzyme electrophoresis and tolerance to stress, salinity, heavy metals and high temperatures may be used to characterize nodule bacteria (Dekak et al. 2018 ). These tests were suggested as a way to resolve taxonomic difficulties but were later considered to be impracticable (Graham and Parker 1964 ). Despite the criticism, phenotypic, cultural and metabolic methods are frequently carried out in combination with other techniques and provides the primary basis for species classification (Li et al. 2020 ).

Cross inoculation

The concept of cross inoculation depends on the symbiotic bacteria ability to selectively form nodules with a group of legume hosts (Mendoza-Suárez et al. 2020 ). Nodulated bacterial strains are described as being specific when they are selective in their host range and considered promiscuous when they have a broader range of host (Kawaka 2016 ; Provorov et al. 2013 ). However, several researches have reported that legumes are nodulated with bacteria that are not within their own groups (Pankievicz et al. 2019 ). The cross inoculation method like other earlier methods of identification does not take into consideration the nitrogen fixation abilities of the bacteria. Studies have reported that bacterial strains form nodules on leguminous hosts but only a few of the species can effectively fix nitrogen on those host plants (Bourion et al. 2018 ). Consequently, the use of cross inoculation technique in the classification of nodule bacteria has reduced due to associated setbacks.

Traditionally, classification of symbiotic and nitrogen fixing bacteria was based on cross-inoculation concept that depended largely on the degree of host specificity. It is therefore important that such a classification requires a standardization of nodulation tests and the control of optimal conditions for plant growth. The genes involved in the development of the symbiotic organ in plant roots, stems or nodule are collectively called nodulation ( nod ) genes. These genes are unique to symbiotic bacteria and the phylogenies of nodA , nodB , nodC and nodD resemble each other but vary considerably from the phylogeny of 16S rRNA (Aguilar et al. 2022 ). Studies have indicated that the phylogenies of nod genes may correlate with the host plant (Aguilar et al. 2022 ; Mohammed et al. 2018 ). For example, as a nodulation gene marker, nodC gene is a common nod gene essential for nodulation in all symbiotic bacterial species. Laboratory analyses performed by using a variety of techniques showed various degrees of correlation between symbiotic genes and chromosomal genotypes. Generally, these findings concluded that symbiotic genes appear to have been transferred between strains (Laranjo et al. 2014 ). Symbiotic bacterial genes are usually located on plasmids thus increasing their likelihood of gene transfer. In contrast, nif genes are found in many bacteria besides those fixing nitrogen however it remains unclear whether these genes are evolutionary part of the symbiotic genome or part of the “normal” bacterial genome (Drew et al. 2021 ). Different authors have reported that the phylogeny of nifH closely resembles that of 16S rRNA genes and that these genes probably share a common evolutionary history (Drew et al. 2021 ; Watanabe and Horiike 2021 ). However, there is also evidence of phylogenetic discordance that could be due to lateral transfer of nif genes (Lau et al. 2014 ). Due to convenience and agronomic significance in selecting strains with the potential use as inoculants for particular legumes, many researchers continue to justify the use of this method (Gopalakrishnan et al. 2015 ).

The use of comparative serology provides valuable information about relationships between prokaryotes and has been helpful for rapid identification of various species of bacteria (Fair and Tor 2014 ). The technique differs from other standard procedures only in the preparation of antigens, but it is less time-consuming (Solomon et al. 2012 ). The technique involves the use of antibodies raised against surface antigens of the test strain to detect the presence (or absence) of that strain in a suspension through agglutination, immunodiffusion, immunofluorescence or the enzyme-linked immunosorbent assay (ELISA) (Maurin 2020 ).

Since the antigenic properties of the nodule bacteria are stable characteristics, the method is particularly useful in ecological studies as it does not modify the strain or alter its nodulation competitiveness (Spriggs and Dakora 2009 ). The immunofluorescence technique has also been successfully used to rapidly identify rhizobial strains, though this requires expensive equipment and large quantities of labelled antibody (Spriggs and Dakora 2009 ).

Serological method relies on the reaction of antigen and antibody to assess symbiotic bacterial diversity in the rhizospheric microbiome. Serological studies focusing on indigenous nodule based bacteria demonstrate significant strain variations within and among different geographic regions (Stępkowski et al. 2018 ). The use of serology has made it possible to relate the occurrence of particular serogroups in a particular location to certain soil parameters like pH or total nitrogen content (Pongslip 2012 ; Tesfahunegn and Gebru 2020 ). Apart from using serology to study the diversity of nodulating bacteria, its practical relevance is to identify strains that are vital in managing symbiosis. Despite many studies documenting serological diversity within nodule bacteria populations, relatively few authors have exploited these variations to predict symbiotic performance (Kawaka 2016 ; Kawaka et al. 2018 ; Vitorino and Bessa 2017 ). The use of serology to classify bacteria has weaknesses such as presence of strains that are un-reactive against all antisera tested (Remigi et al. 2016 ), non-reactive strains and cross-reaction of strains with antiserum derived from reference strains (Kawaka et al. 2018 ; Zhang et al. 2014 ).

Antibiotic resistance

Microbial studies in natural habitats require recovery of either the resident population or added cell on selective media that excludes other contaminants in the environment. The absence of suitable media that allows for selective recovery of symbiotic bacteria in soil has hampered studies on the behavior of these bacteria (Ondieki et al. 2017 ). Symbiotic bacteria like other bacteria consist of few naturally occurring mutants that are tolerant to high concentrations of selected antibiotics (Naamala et al. 2016 ). Growing of selected antibiotic resistant mutants in media that contains elevated levels of anti-microbial agents has been used to identify symbiotic bacterial strains and other bacteria (Spriggs and Dakora 2009 ). Culturing these bacteria on YEMA plates containing antibiotic markers target symbiotic strains with resistant strains retaining their biological nitrogen fixation abilities (Kawaka et al. 2018 ; Mora et al. 2014 ). The antibiotic resistant marked strains are identified by the fact that they can grow on media containing the antibiotics while non-marked ones are unable to grow (Knight et al. 2019 ). The technique is preferred when strain identification by serology is not possible as a result of cross reaction of strains or due to lack of antisera. The technique is popular due to the ease of obtaining mutants resistant to streptomycin (Baldani et al. 2014 ; Fair and Tor 2014 ).

Usually the mutant strain will grow on the antibiotic media and other bacteria will be suppressed (Enne et al. 2006 ). It is crucial to ensure that antibiotic-resistant mutants that are selected for inoculation experiments have not lost their ability to form nodules or their ability to fix nitrogen with the host plant. Symbiotic capacity of the mutant is always compared with its parent culture from time to time (Voisin et al. 2015 ). The mutant should also be stable throughout the steps of infection, nodulation, nitrogen-fixation and subsequent re-isolation.

16S rRNA gene sequence

Earlier taxonomic studies described rRNA gene as an ideal marker for bacterial phylogenetic analysis (Gornung 2013 ; Idris et al. 2020 ). Sequencing the 16S rRNA gene is considered as a model genetic marker for classifying and identifying bacterial species including symbionts (Caputo et al. 2019 ). The gene sequence analysis is efficient in classifying poorly described (Clarridge 2004 ), rarely isolated (Fredricks and Relman, 1996 ), phenotypically aberrant strains and identification of novel noncultured bacteria (Clarridge 2004 ; Stöhr et al. 2005 ).

The rRNAs form important parts of ribosomes that that are needed in mRNA translation (Acinas et al. 2004 ). This genetic marker has features making it the preferred technique for phylogenetic analysis. Firstly, 16S rRNA gene is found in all organisms thus allows comparison of genetic relationship among organisms through phylogenetic tree analysis. Secondly, the gene is highly conserved and does not change over time demonstrating that random sequence variations in organisms can provide a precise measure of evolution. The level of conservation in 16S rRNA gene results from its vital role as a key component in the cell compared to other genes like those required for enzyme synthesis. Mutations in enzyme genes are frequently tolerated because they interfere with structures not essential as the rRNA gene. Lastly, 16S rRNA gene sequence is approximately 1500 bp in length including the conserved and variable regions that provide sufficient information required for taxonomy. Usually, conserved regions are important in designing primers and allow alignment of sequences of organisms that are remotely related (Chakravorty et al. 2007 ). Following these advantages, 16S rRNA gene sequence is widely preferred as a technique for phylogenetic classification of symbiotic bacteria (Janda and Abbott 2007 ). Generally, the similarity of 16S rRNA gene sequences is an important threshold for delineation of many species. Consequently, a large number of 16S rRNA gene sequences are available in the nucleotide databases. Based on the sequences availability in the database for comparison, researchers consider 16S rRNA gene as the preferred marker for identification and constructing phylogenies (Fuks et al. 2018 ). Construction of a phylogenetic tree using 16S rRNA has revealed close taxonomic affiliation of symbiotic bacteria species from diverse soils as shown in Fig.  1 (Kawaka et al. 2018 ). The generation of phylogenetic trees uses morphological, biochemical, behavioral or molecular features of species or other group. As indicated in the Fig.  1 , trees depict lines of evolutionary descent of different species, organisms or genes from a common ancestor. Phylogenies are useful for structuring classifications and provide insight into events that occurred during evolution. In addition, trees show descent from a common ancestor and therefore it is crucial to understand phylogenies in order to fully appreciate evidence supporting the theory of evolution.

Phylogenetic tree of the 16S rRNA gene isolates (in bold) and closely affiliated species

However sequences in certain databases are not regularly updated and accurate leading to lack of consensus on the reliability of 16S rRNA gene sequence data (Clarridge 2004 ; Woo et al. 2008 ). Notwithstanding, the accuracy of 16S rRNA gene analysis is not widely used and only restricted to large and reference laboratories due to technical expertise and high cost (Clarridge 2004 ; Woo et al. 2008 ).

DNA-DNA hybridization

DNA–DNA hybridization (DDH) is a common technique for analyzing genomic similarity to determine bacterial taxonomy (Auch et al. 2010 ; Degefu et al. 2013 ). The technique is used as tool in determining specific variations among closely related microbial species. The method has enjoyed an enormous relevance since 1960s as a regular criterion for describing new bacterial species including symbionts (Krieg, 1988 ). The concept is based on the ability of hybridized DNA of related organisms to withstand thermal variation and entire similarity is calculated from pairwise whole genome comparisons (Markegard et al. 2016 ; Rollinson and Stothard 2017 ).

The DNA molecule is denatured then returns to its original conformation by reducing the temperature, which is referred to as a reassociation temperature and involves three steps (Wang et al. 2014 ). Firstly, shearing of the genomic DNAs of assayed unknown organism and reference strain into small fragments of about 600–800 bp (Fitzgerald et al. 2015 ). Secondly, the mixed DNA fragments of the two strains are heated to dissociate the double strands (Auch et al. 2010 ) and finally cooling the temperature down until the fragments re-anneal (Wang et al. 2014 ). The level of matching base pairings of the two strands depends on the melting temperature of double strands (Auch et al. 2010 ; Wang et al. 2014 ). The genomic relatedness of two strains is estimated from the melting temperature (Gasser et al. 2008 ). Usually, DDH value ≤ 70% is considered as an indication that the unknown bacteria are different from the reference strain (Tindall et al. 2010 ; Wayne et al., 1987 ). Despite estimating similarity between genomes, DDH is a tedious, inaccurate, error prone and gives conflicting results (Rosselló-Mora 2006 ). The method provides non-cumulative relative DNA similarity values and therefore it cannot be used to set up incrementally comparative database (Rosselló-Mora 2006 ). DDH technique requires a large amount of quality DNA, technical expertise, specialized laboratories and applied only on strains that have gene sequences (Tindall et al. 2010 ). Due to the current developments of genome sequencing, DDH method is likely to be replaced by alternative techniques based on genome sequence comparisons (Du et al. 2013 ; Oren 2004 ). Until costs associated with sequencing are reduced, DDH still remains the method of choice to genomically delineate species.

In the recent past, the use of multiple protein-encoding housekeeping genes has gained a wide usage as a tool for investigating taxonomic relationships (Uelze et al. 2020 ). Housekeeping genes was proposed as a portable sequence-based method for identifying clonal relationships among bacteria. The method uses information from multiple genes to give an overall and reliable relationship among organisms. Sequencing of at least five housekeeping genes that are universally distributed as single copies and located at distinct chromosomal loci offers a great promise for bacterial taxonomy. Compared to other taxonomic methods such as 16S rRNA genes, the higher degree of sequence divergence of housekeeping genes is superior for identification purposes. The more conserved 16S rRNA gene sequences do not always allow species discrimination (Ferraz Helene et al. 2022 ; Haque et al. 2017 ). In addition, a small number of carefully selected gene sequences could be equal or even surpass the precision of DNA–DNA hybridization for quantification of genome relatedness (Haque et al. 2017 ). Housekeeping genes yields sequence clusters at a wide range of taxonomic levels ranging from intraspecific through the species level to clusters at higher levels (Leray et al. 2019 ). Together with DNA-DNA hybridization, analysis of housekeeping genes has the potential be considered as a standard practice in bacterial taxonomy.

Whole genome sequencing

Whole Genome Sequencing (WGS) is a technique that analyses the entire chromosomal DNA of an organism and the DNA of mitochondria, chloroplast or plasmids at a single time. WGS is the most informative and comprehensive method of characterizing genomes.

WGS allows the inference of the phylogenetic relationship between a set of bacterial strains. The technique is very appealing and enables the identification of additional classes of mutation that are refractory to detection by exome sequencing. WGS offers the opportunity to interrogate noncoding regions of DNA and identify functionally important sequence variants that influence gene expression.

Currently, researchers have sequenced a large number of bacterial genome and the data is easily accessed from public nucleotide databases such as the Genebank (Land et al. 2015 ). The technology is increasingly being adopted in classifying nitrogen-fixing and related bacteria (Uelze et al. 2020 ). So far complete genome sequences of Rhizobium , Sinorhizobium , Mesorhizobium , Bradyrhizobium and Azorhizobium among others have been sequenced and available for public use (Molina-Sánchez et al. 2015 ; Sugawara et al. 2013 ). The technique provides complete genetic variation and the sequence data can be used for identification and taxonomy of organisms.

Due to the rapid drop in the price of technology, it is projected that many more symbiotic bacteria complete genomes will be sequenced. Sequencing whole genome is still expensive as it requires specialized laboratories and skilled expertise to analyze the sequence data. Researchers still use nucleotide sequences of different genes and genetic fingerprints for phylogenetic and diversity studies despite the markers having limited molecular information. As the cost of sequencing continues to decrease and experience is gained in data analysis and interpretation, it is anticipated that WGS will be the method of choice for future research.

Metagenomics

The technique involves genomic analysis of microorganisms by direct extraction and cloning of DNA from an assemblage of microorganisms. Development of metagenomics stems from the inevitable evidence that uncultured microorganisms represent the vast majority of organisms in most environments. The evidence arise from analyses of 16S rRNA gene sequences amplified directly from the environment, this approach avoided the bias caused by culturing and eventually led to the identification of new microbial lineages (Bowers et al. 2021 ). The microbial world has been revolutionized by analysis of 16S rRNA genes however such studies have yielded only a phylogenetic description with little insight into the genetics, physiology, and biochemistry of the members. The use of metagenomics has provided a second tier of technical innovation that facilitates study of the physiology and ecology of environmental microorganisms (Lear et al. 2021 ). Metagenomics has led to the discovery of novel genes and gene products including the first bacteriorhodopsin of bacterial origin, novel molecules with antimicrobial activity and new proteins, RecA, DNA polymerase, and antibiotic resistance determinants (Kwon et al. 2013 ). The reassembly of multiple genomes provides an insight into energy and nutrient cycling, genome structure, gene function, population genetics and microheterogeneity and lateral gene transfer among members of an uncultured community (Handelsman 2004 ). Utilization of metagenomic sequence information has the potential to facilitate the design of better culturing strategies to link genomic analysis with pure culture studies. Metagenomics has redefined the concept of a genome and accelerated the rate of discovery of new genes. The technique has been widely used in biotechnology to screen functional enzymes, antibiotics and many reagents in libraries from different environments (Popovic et al. 2017 ). However, quite a number of barriers have impeded the discovery of new genes that could be used to solve medical, agricultural, or industrial problems.

Metagenomics is also considered the primary technique for studying phylogeny and taxonomy of complex microbiomes (Berg et al. 2020 ). Microbiome research has evolved rapidly over the past few years however their phylogeny and taxonomy are more complex and less studied (Meisner et al. 2022 ) . The use of metagenomics has significantly enhanced understanding on metabolic, physiological and ecological roles of environmental microorganisms (Strazzulli et al. 2017 ). However, analysis of the microbiome is affected by experimental conditions such as sequencing errors and genomic repeats (Berg et al. 2020 ). Furthermore, the introduction of new sequencing technologies and protocols has led to numerous new methodologies that negatively affect results of the analyses. There are several specific marker/target genes that have been identified for studying microbiome (Meisner et al. 2022 ). These marker genes are functionally conserved across phylogenetic distances thus serving as a molecular clock for studying evolutionary changes. The highly conserved 16S rRNA gene has a crucial cellular role and survival forming the basis of obtaining precise genomic classification of known and unknown microbial taxa.

Comparative proteomics

Proteomics is a high-throughput technology that has been adopted to investigate a wide range of biological aspects including phylogenetic and molecular divergence studies.

In the recent past, considerable attempts have been made to characterize the diversity of proteins expressed in different tissues under a variety of conditions (Faize et al. 2020 ; Kawaka et al. 2018 ). The prospect of identifying bacteria using mass spectrometry (MS) and its role in detection and characterization of microorganisms has elaborately been described (Rahi and Vaishampayan 2020 ). Initially, mass spectrometry was introduced to rapidly identify intact microorganisms (Nomura et al. 2020 ). Following the development of proteomics and bioinformatics, protein databases have successfully supported MS identification of microorganisms. Using 50 subunit ribosomal proteins, many bacterial species have been identified (Tatsukami and Ueda 2016 ). Similarly, matrix-assisted laser desorption/ ionization time-of-flight MS (MALDI TOF MS) correctly identified 408 and 360 g-negative bacilli strains at the genus and species levels at a successful rate of 93% and 82% respectively (Jia et al. 2015 ).

Recently, MS technique for rapid identification and classification of microorganisms has attracted great interests from microbiologists for use in symbiotic bacteria research (Vitorino and Bessa 2017 ). For example, MALDI TOF MS showed a fast and reliable platform for identification and ecological studies of species from the family Rhizobiaceae (Ashfaq et al. 2022 ). MALDI TOF MS has also been applied for in situ identification of plant invasive nodule bacteria in different legumes (Ziegler et al. 2012 ). Nonetheless, the MALDI TOF MS technique requires a well-established reference spectral database for accurate bacterial identification. Sample preparation and period of growth of bacteria such as symbiotic bacteria affects the quality and reproducibility of the protein mass spectra (Mandal et al. 2007 ).

Polyphasic taxonomy

Polyphasic taxonomic approach puts emphasis the use of classical methods in combination with modern genetic/molecular techniques for bacterial delineation (Chan et al. 2012 ). The method takes into account all available phenotypic and genotypic data and integrates them into a consensus type of classification. The classical techniques such as morphological and biochemical descriptions are usually used as well as chemotaxonomic features like cell wall, polar lipid, fatty acid, and respiratory menoquinones (Yadav et al. 2020 ). These crucial diagnostic biomarkers help in the general assignment of isolates to their correct taxa. Chemotaxonomic characteristics are useful in reflecting phylogenies at the genus/family level. Modern molecular techniques focus on variable and conserved regions that are assessed by comparing multiple sequence alignments and viewed as phylogenetic trees (Chowdhury and Garai 2017 ). However these taxonomic classifications do not necessarily define the expected physiological traits since closely related organisms from different locations can have very distinct physiologies and metabolic processes. Therefore, it is essential to conduct laboratory investigations on the isolates from different regions and compare to reference strains of closely related organisms. Despite modern molecular techniques revolutionizing bacterial taxonomy, they still require reinforcement by chemotaxonomic and biochemical considerations. Different studies have shown that a combination of descriptive classical techniques together with modern molecular sequencing methods has resulted in precise identification of new taxa (Berg et al. 2020 ; Hyde et al. 2019 ). In future, to improve the effectiveness of polyphasic taxonomic approach, there needs to be a collaborative effort by specialized laboratories to guarantee a more stable consensus on bacterial classification. Otherwise, the technique will have to cope with challenges such as enormous amounts of data, large numbers of strains and data fusion which will require efficient centralized data storage.

Conclusions

There has been an increase in the number of tools for determining the identity and diversity of microbial samples in the last decades. This review has demonstrated that methods used in taxonomy have their own discriminating power varying from the individual or species levels to the genus, family and higher levels. The techniques further depend on the field of application, particular conditions, the number and the type of strains. The degree of discrimination of a technique may vary and depends on the target bacterial taxon. It is therefore important to adopt the use of a technique with minimal contradictions that emphasizes fast and reliable features for identification.

However, phenotypic and cultural techniques remain the preferred presumptive methods of classifying symbiotic bacteria despite their limitations and challenges. Development of new molecular tools has really improved the identification of new legume bacteria and discovery of elite species that are effective in biological nitrogen fixation. Using an appropriate and informative technique, it is possible to correctly identify novel bacterial species with superior nitrogen fixing abilities. These strains would be vital in developing inoculation programs and boost legume production especially in developing countries facing food and nutrition insecurities under changing climatic conditions.

Acknowledgements

The author is grateful to all the colleagues who offered peer criticism and valuable insights during the writing of this review article. I wish to acknowledge the contribution of Dr Kailash Chand Kumawat from Sam Higginbottom University of Agriculture, Technology and Sciences, India for editing and proof reading this review. Also, authors whose published/unpublished literature was reviewed are further acknowledged.

Author contributions

FK drafted the initial and corrected the final drafts of this manuscript before the final publication. All authors have read and approved the final version of the manuscript.

Availability of data and materials

Declarations.

No applicable.

The author declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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