what is comparative case study method

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Comparative case study research.

• Lesley Bartlett Lesley Bartlett University of Wisconsin–Madison
•  and  Frances Vavrus Frances Vavrus University of Minnesota
• https://doi.org/10.1093/acrefore/9780190264093.013.343
• Published online: 26 March 2019

Case studies in the field of education often eschew comparison. However, when scholars forego comparison, they are missing an important opportunity to bolster case studies’ theoretical generalizability. Scholars must examine how disparate epistemologies lead to distinct kinds of qualitative research and different notions of comparison. Expanded notions of comparison include not only the usual logic of contrast or juxtaposition but also a logic of tracing, in order to embrace approaches to comparison that are coherent with critical, constructivist, and interpretive qualitative traditions. Finally, comparative case study researchers consider three axes of comparison : the vertical, which pays attention across levels or scales, from the local through the regional, state, federal, and global; the horizontal, which examines how similar phenomena or policies unfold in distinct locations that are socially produced; and the transversal, which compares over time.

• comparative case studies
• case study research
• comparative case study approach
• epistemology

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Chapter 20 deals with the set of methods used in comparative case study analysis, which focuses on comparing a small or medium number of cases and qualitative data. Structured case study comparisons are a way to leverage theoretical lessons from particular cases and elicit general insights from a population of phenomena that share certain characteristics. The chapter discusses variable-oriented analysis (guided by frameworks), formal concept analysis and qualitative comparative analysis. It goes on to discuss the types of social-ecological systems (SES) problems and research questions commonly addressed by this set of methods, as well as their limitations, resource implications and new emerging research directions. The chapter also includes an in-depth case study showcasing the application of comparative case study analyses, and suggested further readings on these methods.

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Comparative Case Studies: An Innovative Approach

What is a case study and what is it good for? In this article, we argue for a new approach—the comparative case study approach—that attends simultaneously to macro, meso, and micro dimensions of case-based research. The approach engages two logics of comparison: first, the more common compare and contrast; and second, a 'tracing across' sites or scales. As we explicate our approach, we also contrast it to traditional case study research. We contend that new approaches are necessitated by conceptual shifts in the social sciences, specifically in relation to culture, context, space, place, and comparison itself. We propose that comparative case studies should attend to three axes: horizontal, vertical, and transversal comparison. We conclude by arguing that this revision has the potential to strengthen and enhance case study research in Comparative and International Education, clarifying the unique contributions of qualitative research.

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Lesley Bartlett

Higher Education Quarterly

Anna Kosmützky , Terhi Nokkala

Abstract Finding the balance between adequately describing the uniqueness of the context of studied phenomena and maintaining sufficient common ground for comparability and analytical generalization has widely been recognized as a key challenge in international comparative research. Methodological reflections on how to adequately cover context and comparability have extensively been discussed for quantitative survey or secondary data research. In addition, most recently, promising methodological considerations for qualitative comparative research have been suggested in comparative fields related to higher education. The article's aim is to connect this discussion to comparative higher education research. Thus, the article discusses recent advancements in the methodology of qualitative international comparative research, connects them to older analytical methods that have been used within the field in the 1960s and 1970s, and demonstrates their analytical value based on their application to a qualitative small-N case study on research groups in diverse organizational contexts in three country contexts.

John C Weidman

This is the inaugural volume in the PSCIE (Pittsburgh Studies in Comparative and International Education) Series which expands on the life work of University of Pittsburgh professor Rolland G. Paulston (1929-2006). Recognized as a stalwart in the field of comparative and international education, Paulston's most widely recognized contribution is social cartography. He demonstrated that mapping comparative, international, and development education is no easy task and, depending on the perspective of the mapper, there may be multiple cartographies to chart. This collection of nineteen essays and research studies is a festschrift celebrating and developing Robert Paulston's scholarship in comparative, international, and development education (CIDE). Considering key international education issues, national education systems, and social and educational theories, essays in this volume explore and go beyond Paulston's seminal works in social cartography. Organized into three sec...

Ben Hawbaker , Candace Jones , Brooke Boren , Reut Livne-Tarandach

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case or comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

Eleanor Knott

This course focuses on how to design and conduct small-n case study and comparative research. Thinking outside of students' areas of interest and specialisms and topics, students will be encouraged to develop the concepts and comparative frameworks that underpin these phenomena. In other words, students will begin to develop their research topics as cases of something. The course covers questions of design and methods of case study research, from single-n to small-n case studies including discussions of process tracing and Mill's methods. The course addresses both the theoretical and methodological discussions that underpin research design as well as the practical questions of how to conduct case study research, including gathering, assessing and using evidence. Examples from the fields of comparative politics, IR, development studies, sociology and European studies will be used throughout the lectures and seminars.

Reut Livne-Tarandach , Candace Jones

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case and comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

Bedrettin Yazan

Case study methodology has long been a contested terrain in social sciences research which is characterized by varying, sometimes opposing, approaches espoused by many research methodologists. Despite being one of the most frequently used qualitative research methodologies in educational research, the methodologists do not have a full consensus on the design and implementation of case study, which hampers its full evolution. Focusing on the landmark works of three prominent methodologists, namely Robert Yin, Sharan Merriam, Robert Stake, I attempt to scrutinize the areas where their perspectives diverge, converge and complement one another in varying dimensions of case study research. I aim to help the emerging researchers in the field of education familiarize themselves with the diverse views regarding case study that lead to a vast array of techniques and strategies, out of which they can come up with a combined perspective which best serves their research purpose.

The SAGE Handbook of Qualitative Data Analysis

Monika Palmberger

KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie

Markus Siewert

This article presents the case study as a type of qualitative research. Its aim is to give a detailed description of a case study-its definition, some classifications, and several advantages and disadvantages-in order to provide a better understanding of this widely used type of qualitative approac h. In comparison to other types of qualitative research, case studies have been little understood both from a methodological point of view, where disagreements exist about whether case studies should be considered a research method or a research type, and from a content point of view, where there are ambiguities regarding what should be considered a case or research subject. A great emphasis is placed on the disadvantages of case studies, where we try to refute some of the criticisms concerning case studies, particularly in comparison to quantitative research approaches.

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This page deals with the set of methods used in comparative case study analysis, which focuses on comparing a small or medium number of cases and qualitative data. Structured case study comparisons are a way to leverage theoretical lessons from particular cases and elicit general insights from a population of phenomena that share certain characteristics. The content on this page discusses variable-oriented analysis (guided by frameworks), formal concept analysis and qualitative comparative analysis.

The Chapter summary video gives a brief introduction and summary of this group of methods, what SES problems/questions they are useful for, and key resources needed to conduct the methods. The methods video/s introduce specific methods, including their origin and broad purpose, what SES problems/questions the specific method is useful for, examples of the method in use and key resources needed. The Case Studies demonstrate the method in action in more detail, including an introduction to the context and issue, how the method was used, the outcomes of the process and the challenges of implementing the method. The labs/activities give an example of a teaching activity relating to this group of methods, including the objectives of the activity, resources needed, steps to follow and outcomes/evaluation options.

More details can be found in Chapter 20 of the Routledge Handbook of Research Methods for Social-Ecological Systems.

Chapter summary:

Method Summaries

Case studies, comparative case study analysis: comparison of 6 fishing producer organizations.

Dudouet, B. (2023)

Lab teaching/ activity

Tips and tricks.

• Basurto, X., S. Gelcich, and E. Ostrom. 2013. ‘The Social-Ecological System Framework as a Knowledge Classificatory System for Benthic Small-Scale Fisheries.’ Global Environmental Change 23(6):  1366–1380.
• Binder, C., J. Hinkel, P.W.G. Bots, and C. Pahl-Wostl. 2013. ‘Comparison of Frameworks for Analyzing Social-Ecological Systems.’ Ecology and Society 18(4): 26. 
• Ragin, C. 2000. Fuzzy-Set Social Science . Chicago: University of Chicago Press.
• Schneider C.Q., and C. Wagemann. 2012. Set-theoretic Methods for the Social Sciences. A Guide to Qualitative Comparative Analysis . Cambridge: Cambridge University Press.
• Villamayor-Tomas, S., C. Oberlack, G. Epstein, S. Partelow, M. Roggero, E. Kellner, M. Tschopp, and M.  Cox. 2020. ‘Using Case Study Data to Understand SES Interactions: A Model-centered Meta-analysis of SES Framework Applications.’ Current Opinion in Environmental Sustainability .

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Comparative Case Study

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A comparative case study (CCS) is defined as ‘the systematic comparison of two or more data points (“cases”) obtained through use of the case study method’ (Kaarbo and Beasley 1999, p. 372). A case may be a participant, an intervention site, a programme or a policy. Case studies have a long history in the social sciences, yet for a long time, they were treated with scepticism (Harrison et al. 2017). The advent of grounded theory in the 1960s led to a revival in the use of case-based approaches. From the early 1980s, the increase in case study research in the field of political sciences led to the integration of formal, statistical and narrative methods, as well as the use of empirical case selection and causal inference (George and Bennett 2005), which contributed to its methodological advancement. Now, as Harrison and colleagues (2017) note, CCS:

“Has grown in sophistication and is viewed as a valid form of inquiry to explore a broad scope of complex issues, particularly when human behavior and social interactions are central to understanding topics of interest.”

It is claimed that CCS can be applied to detect causal attribution and contribution when the use of a comparison or control group is not feasible (or not preferred). Comparing cases enables evaluators to tackle causal inference through assessing regularity (patterns) and/or by excluding other plausible explanations. In practical terms, CCS involves proposing, analysing and synthesising patterns (similarities and differences) across cases that share common objectives.

What is involved?

Goodrick (2014) outlines the steps to be taken in undertaking CCS.

Key evaluation questions and the purpose of the evaluation: The evaluator should explicitly articulate the adequacy and purpose of using CCS (guided by the evaluation questions) and define the primary interests. Formulating key evaluation questions allows the selection of appropriate cases to be used in the analysis.

Propositions based on the Theory of Change: Theories and hypotheses that are to be explored should be derived from the Theory of Change (or, alternatively, from previous research around the initiative, existing policy or programme documentation).

Case selection: Advocates for CCS approaches claim an important distinction between case-oriented small n studies and (most typically large n) statistical/variable-focused approaches in terms of the process of selecting cases: in case-based methods, selection is iterative and cannot rely on convenience and accessibility. ‘Initial’ cases should be identified in advance, but case selection may continue as evidence is gathered. Various case-selection criteria can be identified depending on the analytic purpose (Vogt et al., 2011). These may include:

• Very similar cases
• Very different cases
• Typical or representative cases
• Extreme or unusual cases
• Deviant or unexpected cases
• Influential or emblematic cases

Identify how evidence will be collected, analysed and synthesised: CCS often applies mixed methods.

Test alternative explanations for outcomes: Following the identification of patterns and relationships, the evaluator may wish to test the established propositions in a follow-up exploratory phase. Approaches applied here may involve triangulation, selecting contradicting cases or using an analytical approach such as Qualitative Comparative Analysis (QCA). Download a Comparative Case Study here Download a longer briefing on Comparative Case Studies here

Useful resources

A webinar shared by Better Evaluation with an overview of using CCS for evaluation.

A short overview describing how to apply CCS for evaluation:

Goodrick, D. (2014). Comparative Case Studies, Methodological Briefs: Impact Evaluation 9 , UNICEF Office of Research, Florence.

An extensively used book that provides a comprehensive critical examination of case-based methods:

Byrne, D. and Ragin, C. C. (2009). The Sage handbook of case-based methods . Sage Publications.

The Comparative Method

POLITICAL SCIENCE: THE STATE OF DISCIPLINE II, Ada W. Finifter, ed., American Political Science Association, 1993

15 Pages Posted: 21 Jun 2011

David Collier

University of California, Berkeley - Department of Political Science

Date Written: 1993

Comparison is a fundamental tool of analysis. It sharpens our powers of description, and plays a central role in concept-formation by bringing into focus suggestive similarities and contrasts among cases. Routinely used in testing hypotheses, it can also contribute to the inductive discovery of new hypotheses and to theory-building. This chapter examines distinct perspectives from the past two decades on the comparative method – understood as the systematic comparison of a relatively small number of cases – focusing specifically on its relationship to experimental, statistical, and case-study approaches. Three main areas of innovation and analytic alternatives have emerged which strengthen the viability of the comparative method: within-case analysis, quantitative techniques employing a relatively small number of cases, and systematic comparison of a small number of cases with the goal of causal analysis, as Lijphart originally advocated. All three of these approaches will persist; substantial exposure to and training in the basic writings on the philosophy of science and logic of inquiry can provide a framework for more informed choices about these methodological alternatives. In this way, the foundation can be laid for an eclectic practice of small-N analysis that takes advantage of opportunities on both sides of what could otherwise be a major intellectual divide.

Keywords: Case Study, Comparative, Experimental

Suggested Citation: Suggested Citation

David Collier (Contact Author)

University of california, berkeley - department of political science ( email ).

210 Barrows Hall Berkeley, CA 94720 United States

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Comparative case studies

Comparative case studies can be useful to check variation in program implementation. 

Comparative case studies are another way of checking if results match the program theory. Each context and environment is different. The comparative case study can help the evaluator check whether the program theory holds for each different context and environment. If implementation differs, the reasons and results can be recorded. The opposite is also true, similar patterns across sites can increase the confidence in results.

Evaluators used a comparative case study method for the National Cancer Institute’s (NCI’s) Community Cancer Centers Program (NCCCP). The aim of this program was to expand cancer research and deliver the latest, most advanced cancer care to a greater number of Americans in the communities in which they live via community hospitals. The evaluation examined each of the program components (listed below) at each program site. The six program components were:

• increasing capacity to collect biospecimens per NCI’s best practices;
• enhancing clinical trials (CT) research;
• reducing disparities across the cancer continuum;
• improving the use of information technology (IT) and electronic medical records (EMRs) to support improvements in research and care delivery;
• improving quality of cancer care and related areas, such as the development of integrated, multidisciplinary care teams; and
• placing greater emphasis on survivorship and palliative care.

The evaluators use of this method assisted in providing recommendations at the program level as well as to each specific program site.

Advice for choosing this method

• Compare cases with the same outcome but differences in an intervention (known as MDD, most different design)
• Compare cases with the same intervention but differences in outcomes (known as MSD, most similar design)

Advice for using this method

• Consider the variables of each case, and which cases can be matched for comparison.
• Provide the evaluator with as much detail and background on each case as possible. Provide advice on possible criteria for matching.

National Cancer Institute, (2007).  NCI Community Cancer Centers Program Evaluation (NCCCP) . Retrieved from website: https://digitalscholarship.unlv.edu/jhdrp/vol8/iss1/4/

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• Sustained and Emerging Impacts Evaluation (SEIE)

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• DOI: 10.56127/ijst.v3i2.1581
• Corpus ID: 271753947

COMPARATIVE STUDY OF MULTI CRITERIA DECISION MAKING METHODS IN A CASE STUDY OF THE BEST EMPLOYEE DECISION SUPPORT SYSTEMS

• Syalis Ibnih , Melati Istini , +3 authors S. I. M. Istini
• Published in International Journal for… 30 July 2024
• Business, Computer Science

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Evaluating wind speed forecasting models: a comparative study of cnn, dan2, random forest and xgboost in diverse south african weather conditions.

1. Introduction

1.1. overview, 1.2. literature review, 1.3. research highlights and contributions.

• Use of gradient ascent with hyperparameter tuning for maximum performance optimisation of the models.
• Performance testing was conducted on the CNN and DAN2 models against a benchmark random forest. The CNN performed better at Napier and Upington stations than the benchmark model; it had lower error metrics and better prediction accuracy.
• Compared to the benchmark model, DAN2 did not perform as well on the wind speed predictions for coastal and inland areas, such as Napier and Noupoort. This may imply that DAN2 is not as good as the CNN model in various geographical contexts.
• In most of the weather conditions, the CNN model was much better at wind speed forecasting compared to DAN2; it had a mean absolute scaled error of less than 1 in all three stations, indicating it performed better than the baseline model.

2.1. Study Area

• Pandas: for data manipulation and analysis.
• NumPy: for numerical computations.
• SciPy: for scientific computing and statistical tests.
• Statsmodels: for time series analysis and statistical modelling.
• Scikit-learn: for machine learning model development and evaluation.
• TensorFlow/Keras: for building and training deep learning models.
• Matplotlib and Seaborn: for data visualization and plotting.

2.2. Models

2.2.1. artificial neural networks, 2.2.2. dynamic architecture for artificial neural networks, 2.2.3. convolutional neural network, 2.2.4. random forest, 2.2.5. xgboost, 2.3. forecast combination using quantile regression averaging, 2.3.1. generalised additive quantile regression model, 2.3.2. quantile regression neural network, 2.4. variable selection, 2.5. metrics for evaluating forecasts, 3. empirical results, 3.1. exploratory data analysis, 3.2. variable selection, 3.3. training loss for dan2 model for all stations, 3.4. forecast accuracy for the models, 3.5. training loss for cnn model for all stations, 3.6. cnn model training and results, napier, noupoort and upington stations, 3.7. forecast accuracy for cnn model, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

Appendix A. Models Configurations

Appendix a.1. dan2, appendix a.2. cnn, appendix b.1. list of covariates used in the study.

• diff1—This variable represents the first difference of the wind speed (diff1 = W t − W t − 1 ), derived from historical wind speed data. It serves as one of the predictors or explanatory variables in the analysis, potentially indicating the effect of past wind speed on the current wind speed.
• diff2—Similar to diff1, this variable represents the second wind speed difference (diff2 = W t − W t − 2 ), derived from historical data. It is another predictor variable used to examine the influence of wind speed in the previous period on the current wind speed.
• noltrend—The noltrend variable is derived from a cubic regression spline model. In this context, it likely captures the trend component of the data after removing any nonlinear patterns through regression splines.
• WS_62_min—represents the minimum wind speed recorded at the stations. Wind speed measures how fast the air is moving at a particular location. In this case, it specifically refers to the wind speed measured at a height of 62 m above the ground.
• WS_62_max—represents the maximum wind speed recorded at the stations.
• WS_62_stdv—refers to the standard deviation of wind speeds measured 62 m above the ground at the stations.
• Tair_mean represents the stations’ mean (average) air temperature. Air temperature refers to the measure of the warmth or coldness of the air in a particular location.
• Tair_min—represents the minimum air temperature at the stations.
• Tair_max—represents the highest air temperature ever recorded at the stations.
• Tair_stdv—represents the standard deviation of air temperature at the stations. The standard deviation is a statistical measure that quantifies the amount of variability or dispersion in a set of values.
• Tgrad_mean—this represents the average temperature gradient at the stations. Temperature gradient reflects the speed of temperature alteration relative to distance or height.
• Tgrad_min—represents the minimum temperature gradient at the stations.
• Tgrad_max—represents the highest temperature gradient recorded at the stations.
• Tgrad_stdv—represents the standard deviation of the temperature gradient at the stations. The variable helps to understand how much the temperature gradients vary from the average value.
• Pbaro_mean—represents the average barometric pressure at the Napier station. Barometric pressure, also called atmospheric pressure, is the force exerted by the weight of the air above a specific area.
• Pbaro_min—represents the lowest barometric pressure recorded at the stations during the day.
• Pbaro_max—represents the highest barometric pressure recorded at the station during the day.
• Pbaro_stdv—represents the variation or dispersion in the barometric pressure values at the station.
• RH_mean represents the stations’ mean (average) relative humidity. Relative humidity measures the amount of moisture in the air relative to the maximum amount of moisture the air can hold at a given temperature.
• RH_min—represents the minimum relative humidity at the stations. Relative humidity is typically expressed as a percentage (%), with 100% indicating that the air is saturated with moisture and lower percentages indicating drier air.
• RH_max—represents the highest relative humidity recorded at the stations during the day.
• RH_stdv—represents the variation or dispersion in the relative humidity values at the stations.

Appendix C. Time Series Decomposition at the Three Stations

Appendix c.1. multiplicative time series decomposition of wind speed at 62 m at the three stations.

Click here to enlarge figure

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Mugware, F.W.; Sigauke, C.; Ravele, T. Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions. Forecasting 2024 , 6 , 672-699. https://doi.org/10.3390/forecast6030035

Mugware FW, Sigauke C, Ravele T. Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions. Forecasting . 2024; 6(3):672-699. https://doi.org/10.3390/forecast6030035

Mugware, Fhulufhelo Walter, Caston Sigauke, and Thakhani Ravele. 2024. "Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions" Forecasting 6, no. 3: 672-699. https://doi.org/10.3390/forecast6030035

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• Published: 16 August 2024

Comparative effectiveness analysis of survival with first-line palbociclib or ribociclib plus AI in HR + /HER2- advanced breast cancer (CEPRA study): preliminary analysis of real-world data from Thailand

• Thanate Dajsakdipon 1 ,
• Thiti Susiriwatananont 2 ,
• Concord Wongkraisri 3 ,
• Suthinee Ithimakin 3 ,
• Napa Parinyanitikul 2 ,
• Archara Supavavej 4 ,
• Arunee Dechaphunkul 5 ,
• Patrapim Sunpaweravong 5 ,
• Sunee Neesanun 6 ,
• Charuwan Akewanlop 3 ,
• Thitiya Dejthevaporn 1 on behalf of

TSCO Breast Oncology Group

BMC Cancer volume  24 , Article number:  1018 ( 2024 ) Cite this article

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The current standard first-line treatment for hormone receptor-positive/human epidermal growth factor receptor 2 negative (HR + /HER2 −) advanced breast cancer (ABC) is a combination of aromatase inhibitor (AI) plus CDK4/6 inhibitors (CDK4/6i). Direct comparison trials of different CDK4/6i are scarce. This real-world study compared the effectiveness of first-line AI plus ribociclib versus palbociclib.

This multicenter retrospective cohort study, conducted in six cancer centers in Thailand, enrolled patients with HR + /HER2 − ABC treated with first-line AI, and either ribociclib or palbociclib. Propensity score matching (PSM) was performed. The primary endpoint was overall survival (OS). Secondary endpoints included progression-free survival (PFS), overall response rate (ORR), time to chemotherapy (TTC), and adverse events.

Of the 250 patients enrolled, 134 patients with ribociclib and 49 patients with palbociclib were captured after PSM. Baseline characteristics were well-balanced between groups. Median PFS in patients receiving ribociclib and palbociclib were 27.9 and 31.8 months, respectively (hazard ratio: 0.87; 0.55–1.37). The median OS in the AI + ribociclib arm was 48.7 months compared to 59.1 months in the AI + palbociclib arm (hazard ratio: 0.55; 0.29–1.05). The median TTC in the AI + palbociclib group was 56 months, but not reached in the AI + ribociclib group ( p  = 0.42). The ORR of AI + ribociclib and AI + palbociclib were comparable (40.5% vs. 53.6%, p  = 0.29). Patients receiving palbociclib demonstrated a higher proportion of neutropenia compared to those receiving ribociclib, despite a similar dose reduction rate ( p  = 0.28). Hepatitis rate was similar between the ribociclib (21%) and palbociclib groups (22%). Additionally, a low incidence of QT prolongation was observed in both the ribociclib (5%) and palbociclib groups (4%).

This preliminary analysis of a real-world study demonstrated the comparable effectiveness of ribociclib and palbociclib with AI as an initial therapy for HR + /HER2 − ABC. No statistically significant difference in PFS, OS, and TTC was found in patients treated with AI combined with palbociclib or ribociclib. Longer follow-up and further prospective randomized head-to-head studies are warranted.

Peer Review reports

Breast cancer is the most prevalent cancer among females globally, including Thailand [ 1 ]. Its diverse subtypes hinge on staining for hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2), with HR-positive and HER2-negative (HR + /HER2 −) breast cancer being the most prevalent [ 2 ]. Controlling disease, bolstering overall survival (OS), and improving the quality of life (QoL) are the primary aims of advanced breast cancer (ABC) treatment. Developing the treatment for locally advanced/metastatic breast cancer (LA/MBC) relies on factors, including tumor subtype, disease burden, Eastern Cooperative Oncology Group performance status(ECOG-PS), comorbidities, and economic considerations [ 3 ].

The initial treatment typically is upfront hormonal therapy for patients with HR + /HER2 − LA/MBC experiencing non-visceral crises. Selective estrogen receptor modulators, non-steroidal aromatase inhibitors (NSAIs), steroidal aromatase inhibitors (SAIs), and selective estrogen receptor downregulators are among the variety of options available [ 2 ]. Recent studies indicate that initiating treatment with upfront hormonal therapy improves progression-free survival (PFS) and enhances patients’ QoL. First-line tamoxifen provided a PFS of approximately 8 months [ 4 ], whereas PFS from NSAIs and fulvestrant were approximately 12 months [ 4 , 5 ] and 14 months, respectively [ 4 ].

Cyclin-dependent kinase (CDK) is a crucial molecule for cancer cell division. The interaction of cyclin D1 with CDK4 and CDK6 in the cell cycle causes hyperphosphorylation of the retinoblastoma gene ( Rb ), thereby activating the cell to pass from the G-phase checkpoint to the S phase (replication phase) of the cell cycle. Cyclin D-CDK4/6-Rb pathway alterations, such as cyclin D amplification, Rb gene loss or mutation, or P16 loss, cause uncontrolled cell cycle progression. Consequently, cancer cells divide rapidly and metastasize [ 6 ].

Currently, drugs that inhibit the CDK4/6-Rb pathway (CDK4/6 inhibitors [CDK4/6i]), such as palbociclib, ribociclib, and abemaciclib, have been approved as an effective therapy for ER + /HER2 − ABC. Studies have revealed the benefit of CDK4/6i when combined with NSAI as first-line treatment which prolongs PFS and increases overall response rates (ORR) compared to AI monotherapy. Data from current studies indicate the median PFS for palbociclib, ribociclib, and abemaciclib of 28, 25, and 28 months, respectively [ 7 , 8 , 9 ]. The ORR from the combination CDK4/6i and NSAI stands at 53%–59% compared to AI which typically yields an ORR of approximately 30%–40%. Regarding OS, palbociclib, ribociclib, and abemaciclib have reported median OS of 53.9, 63.9, and 66.8 months [ 7 , 8 , 10 , 11 , 12 , 13 ], respectively. Notably, ribociclib is the only CDK4/6i that exhibited a significant OS improvement when compared to AI monotherapy in the first-line setting. Additionally, these agents in combination with fulvestrant provide gains in PFS and ORR in the second-line setting [ 14 , 15 , 16 ].

The side effects of CDK4/6i vary according to the specific drug. The main side effects of palbociclib include neutropenia, whereas ribociclib includes neutropenia, QT prolongation, and hepatotoxicity. Abemaciclib is more associated with diarrhea with less frequent myelosuppression [ 7 , 8 , 9 ].

Presently, prospective studies reported no evidence, and real-world evidence (RWE) that directly compares the efficacy and toxicities between CDK4/6i types when combined with AI for treating patients with HR + /HER2 − LA/MBC remains limited.

Palbociclib, ribociclib, and abemaciclib obtained Thai FDA approval in 2017, 2018, and 2020, respectively. However, reported outcomes concerning their efficacy in the first-line setting, as well as comparative efficacy among the different CDK4/6i, remain lacking. Furthermore, we aim to investigate the toxicity profile of these CDK4/6i in Thai patients which may diverge from that observed in reports from Western countries.

This multicenter study aimed to investigate the efficacy differences between ribociclib and palbociclib when combined with NSAI for first-line therapy of HR + /HER2 − LA/MBC in real-life clinical practice.

Patients and methods

Inclusion criteria were age of ≥ 18 years and histologically or cytologically confirmed HR + /HER2 − ABC diagnosis, defined as tumors expressing estrogen and/or progesterone receptors of > 1% and HER2 negativity determined by immunohistochemistry scores of 0, 1 + , or 2 + with negative results by in situ hybridization. Additionally, patients must have received first-line treatment with AI combined with ribociclib or palbociclib. This study included menopausal or premenopausal patients receiving ovarian function suppression. All patients included in the study were diagnosed with LA/MBC from January 1, 2017, to October 31, 2022, with the last follow-up cut-off date on September 30, 2023. Exclusion criteria were insufficient or missing data and previous chemotherapy for treatment in a metastatic setting.

Study design

This multicenter retrospective cohort study was conducted across six medical institutions/centers in Thailand. The study aimed to compare the efficacy of ribociclib and palbociclib when combined with AI as a first-line therapy for HR + /HER2 − ABC. The primary endpoint was OS by propensity score-match (PSM) analysis, whereas secondary endpoints were PFS, subgroup analysis of OS, time to chemotherapy (TTC), response rate, CDK4/6i dose modification rate, and toxicity. The Institutional Review Board of all participating institutions approved the study.

Statistical analysis

The chi-square test or Fisher’s test was used to compare qualitative variables, whereas the Student’s t -test was used to compare quantitative variables. The Mann–Whitney U test was utilized to compare medians. PSM was conducted to balance baseline characteristics. The Kaplan–Meier method was used to estimate PFS, OS, and TTC with group comparisons conducted using the log-rank test. A Cox proportional hazard model was used to estimate hazard ratios.

PSM analysis was conducted to minimize potential selection bias due to the lack of randomization. Propensity scores for AI with palbociclib vs. AI with ribociclib were estimated using a logistic regression model based on clinically selected covariates, including age, ECOG-PS, de novo metastasis, visceral metastasis, and level of estrogen receptor (ER) expression (< 50% or ≥ 50%). Propensity score-adjusted analyses were conducted in the sensitivity analysis. The results are presented as hazard ratios and 95% confidence intervals (CI). The p -value of < 0.05 was considered statistically significant. All analyses were conducted using STATA version 14.

Patients’ clinical characteristics

Figure  1 illustrates the patient consort diagram, indicating that an initial 539 patients with HR + /HER2 − ABC receiving first-line therapy were collected from six tertiary care centers. Of these patients, 250 received first-line treatment with a combination of AI and CDK4/6i. Out of these 250 patients, PSM analysis revealed 183 matched patients for analysis, including 134 patients receiving AI combined with ribociclib and 49 patients treated with AI combined with palbociclib.

Patient consort diagram. Abbreviations: CRA: Chulabhorn Royal Academy, CU: Chulalongkorn Memorial Hospital, PSU: Prince of Songkhla University, RA: Ramathibodi Hospital, SPR: Sawanpracharak Hospital, SI: Siriraj Hospital, AI: aromatase inhibitor, HR: hormone receptor, HER-2: Human Epidermal Growth Factor Receptor 2, MBC: metastatic breast cancer, CDK4/6i: cyclin-dependent kinases 4 and 6 inhibitor

Baseline characteristics were well-balanced between the two groups (Table  1 ). The median age in the ribociclib and palbociclib groups was 58 and 56 years, respectively. The majority of patients demonstrated an ECOG-PS score of 0–1 (84% in the ribociclib group vs. 87% in the palbociclib group). The proportion of patients with high ER expression (≥ 50%) was comparable in both groups (93% vs. 86%). Visceral metastases were reported in 55% and 53% of patients receiving ribociclib and palbociclib, respectively. Additionally, the majority of patients exhibited fewer than three metastatic sites (78% vs. 75%) (Table  1 ).

Comparative effectiveness analysis of palbociclib and ribociclib

Overall survival

The data cut-off date for OS analysis was September 30, 2023. The median follow-up time was 29 months (95% CI: 26.15–31.85). Death events occurred in 45 (33%) of 134 and 15 (30%) of 49 patients in the ribociclib and palbociclib groups, respectively.

The unadjusted analysis of the entire cohort revealed the comparable median survival between the ribociclib and palbociclib groups at 51.2 months and 57.6 months, respectively (hazard ratio: 0.72, 95% CI: 0.44–1.17, p  = 0.18) (Fig.  2 A). Following PSM analysis, the adjusted median OS demonstrated a trend toward shorter OS with ribociclib + AI (48.7 months among patients receiving ribociclib and 59.1 months in the palbociclib group (hazard ratio of death: 0.55, 95% CI: 0.29–1.05, p -value: 0.07) (Fig.  2 B).

Kaplan–Meier curve of overall survival. A Overall Survival (unadjusted). B Overall Survival (adjusted). Abbreviations: AI: aromatase inhibitor, mos: months, HR: hazard ratio, NE: not estimate

A subgroup analysis of OS revealed no preferential benefit of either palbociclib or ribociclib (Fig.  3 ) across baseline characteristics of the patients. OS benefit was observed with ribociclib among patients with ≥ 3 metastasis sites and patients with coronary disease. However, the 95% CI was very wide and should be interpreted with caution.

Exploratory analysis of overall survival in subgroup. Abbreviations: AI: aromatase inhibitor, yrs: years, ECOG PS: Eastern Cooperative Oncology Group performance status, CAD: coronary artery disease, ER: estrogen receptor, PR: progesterone receptor, DFI: disease-free interval, ET endocrine therapy, HR: hazard ratio, CI: confidence interval

Progression free survival

Of 250 patients with HR + /HER2 − ABC receiving first-line AI + CDK4/6i, the median PFS was 26.9 (95% CI: 23.5–32.6) and 29.6 (95%CI 18.2–50.8) months (hazard ratio: 0.97, 95% CI: 0.7–1.4) in the ribociclib and palbociclib groups, respectively. A consistent indifference in the median PFS was observed from 180 patients available for analysis after PSM, including 27.9 months (95% CI: 21.8–38.3) and 31.8 months (95% CI: 19.7–57.4) in the ribociclib and palbociclib groups, respectively (hazard ratio: 0.87, 95%CI 0.55–1.37) (Fig.  4 ).

Kaplan–Meier curve of progression free survival. A Progression free survival (unadjusted). B Progression free survival (adjusted). Abbreviations: AI: aromatase inhibitor, mos: months, HR: hazard ratio, NE: not estimate

Univariate and multivariate analysis of overall survival

A univariate analysis identified age at diagnosis, ECOG-PS, menopausal status, comorbidities, de novo metastasis, and the number of metastatic sites to be associated with OS. Only four factors remained independently associated with OS after multivariable analysis (Table  2 ). The favorable prognostic factors include having one metastatic site and postmenopausal status, whereas adverse prognostic factors include worsening of ECOG-PS and the presence of coronary artery disease. Notably, different CDK4/6i types (ribociclib vs. palbociclib) were not associated with OS outcome in both univariate and multivariate analyses.

Time to chemotherapy and total lines of treatment

Time to first chemotherapy was comparable between both groups (Fig.  5 ). The median TTC was 56 months (95% CI: 39.37–72.63) in the palbociclib group while in the ribociclib group it was not reached ( p  = 0.42). Patients who received palbociclib had a trend to receive more subsequent therapy with a median of three lines (95% CI: 2.05–4.08) compared to two (95% CI: 1.8–2.8) lines in the ribociclib group ( p  = 0.3).

Time to chemotherapy.  Abbreviations: AI: aromatase inhibitor, mos: months, HR: hazard ratio, NE: not estimate

Response rate

The ORRs in the AI + ribociclib and AI + palbociclib groups were 40.5% and 53.6%, respectively ( p  = 0.29). Similarly, the disease control rate was excellent in both groups (89.6% in the ribociclib group and 92.7% in the palbociclib group, p  = 0.29). The median time to response was 15.5 (95% CI: 13.57–17.71) and 11.7 (95% CI: 11–37.14) weeks among patients receiving AI + ribociclib and AI + palbociclib ( p  = 0.12), respectively.

Neutropenia and anemia are common toxicities observed in both groups. Grades 3–4 neutropenia was significantly less frequent among patients with ribociclib therapy (48% vs. 69%, p  = 0.02). Additionally, grades 3–4 thrombocytopenia occurred in only 2% of patients in the ribociclib group compared to 8% in the palbociclib group ( p  = 0.001). Grades 3–4 anemia was numerically more frequently observed in patients receiving palbociclib (6% vs. 3%, p  = 0.44). Abnormal aspartate transaminase/alanine aminotransferase elevation, mostly in grades 1–2, was seen in 19% and 22% of patients in the ribociclib and palbociclib groups, respectively ( p  = 0.59). QTc prolongation was reported in 5.1% and 4% of patients in the ribociclib and palbociclib groups, respectively ( p  = 0.91) (Table  3 ).

Real-world practice of CDK4/6i dosing and dose modification

Nearly all patients (98.6%) in the palbociclib group received the full starting dose of 125 mg daily whereas 86.6% in the ribociclib group received the starting dose of 600 mg daily ( p  = 0.004). A higher proportion of patients receiving palbociclib experienced myelosuppression, but the rate of CDK4/6i dose reduction was similar (65% and 60% among ribociclib and palbociclib, respectively). All dose reductions occurred in patients who were started on full doses of both agents except for one patient receiving ribociclib at a starting dose of 400 mg who still required a dose adjustment. A similar proportion of patients in both groups required a one-dose level reduction (49% vs. 48%). Additionally, 16% and 11% of patients in the ribociclib and palbociclib groups, respectively, underwent a two-dose level reduction. The median time to dose reduction was 42 days (95% CI: 27.35–56.65) and 49 days (95% CI: 14.31–83.69) in the ribociclib and palbociclib groups, respectively ( p  = 0.01).

CDK4/6i in combination with AI has revolutionized the treatment of HR + /HER2 − ABC and become a standard of care globally. An unsurpassed PFS gain over that of ET alone has been consistently reported in pivotal randomized controlled trials (RCTs) of palbociclib [ 7 ], ribociclib [ 8 , 17 ], and abemaciclib [ 9 ] whereas the OS improvement was statistically and/or clinically significant with ribociclib [ 8 , 10 ] and abemaciclib [ 13 ], respectively. However, no RCT and only a few RWEs directly compared the OS of ribociclib and palbociclib [ 18 , 19 ].

Our study investigated the outcome of first-line treatment with palbociclib or ribociclib in combination with AI for HR + /HER2 − ABC. We collected real-world practice data from six centers across Thailand. A PSM was used to balance the patients’ clinicopathological characteristics and minimize the bias of the retrospective nature of real-world data. We also included ER expression levels using the cut-off level of 50% in both the baseline characteristics and efficacy analysis. The 50% cutoff was chosen as a surrogate for endocrine responsiveness which has been demonstrated in various settings [ 20 , 21 , 22 , 23 ]. We revealed no statistically significant differences in both OS and PFS between palbociclib and ribociclib as first-line treatment in our cohort. A multivariate Cox regression analysis, showing a hazard ratio for the survival of 0.51 ( p  = 0.06, 95% CI: 0.8–1.04) confirmed this result.

The decision to select one agent over the other remained upon physicians’ judgment/experiences together with the side effect profiles and the patients themselves due to the lack of direct RCT comparing the effectiveness of the currently available CDK4/6i. Thus, a comparative analysis of the RWE was used to decipher the dilemma and contribute complementary information to that of RCT. As abemaciclib was the last agent in this class to receive approval in Thailand, we only compared the efficacy of palbociclib and ribociclib in a real-world situation with the main interest in OS. Both the adjusted OS and PFS of our palbociclib cohort were not statistically different from that of ribociclib. These results were congruent with other RWE reports that compared ribociclib and palbociclib outcomes [ 18 , 19 ]. Numerically, the OS and PFS of both groups were consistent with those of large pivotal trials (PALOMA-2, MONALEESA-2, and MONALEESA-7), except for a somewhat lower OS in our ribociclib cohort (48.7 months vs. 63.9 and 58.7 months in MONALEESA-2 and MONALEESA-7, respectively) [ 8 , 10 ]. Notably, compared to other RWE of individual CDK4/6i, our study demonstrated longer PFS in both treatment arms [ 7 , 17 , 24 , 25 , 26 , 27 , 28 ]. Several factors may have contributed to these differences. Patients in our cohort exhibited a lower tumor burden, with > 80% having fewer than three metastatic sites and > 90% having high ER expression (ER ≥ 50%) compared to other trials, thereby representing an endocrine-sensitive population. These results demonstrate that palbociclib and ribociclib are highly effective in endocrine-sensitive patients with low tumor burden and high ER expression. Several factors may have contributed to the numerically shorter OS in ribociclib arm in our study. We revealed a higher number of subsequent therapies in the palbociclib group which could affect OS. In addition, as palbociclib was the first CDK4/6i approved in Thailand, which resulted in longer follow-up periods and potentially more accurate OS assessment in this group.

The results of our study were in contrast to an analysis by Jhaveri et al. [ 29 ], which uses a matching adjusted indirect comparison to individual data from MONALEESA-2 and PALOMA-2, demonstrating a greater OS in favor of ribociclib as a first-line regimen. However, its generalizability to a broader patient population in practice is limited, considering the strict and narrow inclusion/exclusion in RCT.

The ORR of the treatment was comparable between the two groups (48% with ribociclib vs. 53.6% with palbociclib). Our real-world finding aligned with MONALEESA and PALOMA-2 trials [ 7 , 12 , 17 ] and previous RWE trials [ 30 , 31 , 32 , 33 , 34 ].

Our study emphasized neutropenia as the primary adverse event as well as a higher myelosuppression incidence with palbociclib compared to ribociclib in terms of tolerability. This indicated a greater bone marrow toxicity in palbociclib compared to ribociclib, possibly due to pharmacokinetic variances [ 35 , 36 ]. Nearly all patients in the palbociclib group received a full starting dose compared to only 86.6% in the ribociclib group. A similar rate of dose reduction was demonstrated, mostly due to myelosuppression, and this rate was congruent with other reports from Asian [ 25 , 37 ] and Western populations alike [ 28 , 38 ].

This study represents the first and largest multicenter cohort data in the real-world practice of first-line ribociclib + AI versus palbociclib + AI in metastatic HR + , HER- breast cancer in Southeast Asia, thereby providing valuable insights into the context of a non-Western population. Importantly, the absence of randomized phase 3 trials comparing the efficacy between palbociclib + AI and ribociclib + AI emphasizes the significance of this study in addressing this literature gap. Despite its strengths, this study contained limitations. Firstly, being retrospective in nature, inherent bias may exist in patient selection at the outset. However, we mitigated this bias by using a PSM. Secondly, our study lacked data on patient-reported outcomes, QoL, and economic outcomes. Thirdly, information on subsequent treatment regimens was incomplete. In addition, this study involves a relatively small number of patients and also with imbalance of numbers of patients in palbociclib arm as only ribociclib was reimbursed in Thailand. And lastly, the median follow-up time of the cohort is relatively short compared to others as the drugs have just received approval in Thailand in 2017. Thus, longer follow-up and update of the current result would be vital to confirm the real effectiveness of both agents.

In summary, our RWE from Thai population indicated no differences in overall outcomes, TTC, and response rates between ribociclib and palbociclib as a first-line therapy despite the inconsistent OS gain of first-line ribociclib and palbociclib in RCT.

Availability of data and materials

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

Abbreviations

Advanced breast cancer

Cyclin-dependent kinase

Eastern Cooperative Oncology Group

Estrogen receptor

Endocrine therapy

Hormone receptor

Human epidermal growth factor receptor 2

Locally advanced/metastatic breast cancer

Non-steroidal aromatase inhibitors

Overall response rate

Progesterone receptor

Performance status

• Propensity score matching

Quality of life

Retinoblastoma gene

Randomized controlled trial

Real-world evidence

Steroidal aromatase inhibitors

Time to chemotherapy

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Acknowledgements

We gratefully acknowledge the TSCO Breast Oncology Group for their pivotal role in initiating the idea for this study and Miss Pawina Wamalun from the Department of Medicine Siriraj Hospital for her assistance in data gathering.

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Division of Medical Oncology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

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Contributions

All authors contributed to the study conception and design. Material preparation was performed by Thanate Dajsakdipon(T.D.), Thiti Susiriwatananont(T.S.) and Concord Wongkraisri(C.W.). Data collection was performed by Thanate Dajsakdipon(T.D), Thiti Susiriwatananont(T.S.), Concord Wongkraisri(C.W.), Suthinee Ithimakin(S.I.), Napa Parinyanitikul(N.P.), Archara Supavavej(A.S.), Arunee Dechaphunkul(A.D.), Patrapim Sunpaweravong(P.S.), Sunee Neesanun(S.N.), Charuwan Akewanlop(C.A.) and Thitiya Dejthevaporn(T.D.) Data analysis was performed by Thanate Dajsakdipon(T.D.) and Thitiya Dejthevaporn(T.D.). The first draft of the manuscript was written by Thanate Dajsakdipon(T.D.). All authors commented on previous version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Thitiya Dejthevaporn .

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This study protocol was approved by

1. Human Research Ethics Committee, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand, Reference number: COA.MURA 2023/216

2. Institutional Review Board, Faculty of medicine, Chulalongkorn university, Bangkok, Thailand, Reference number: COA.No. 1193/2023

3. Institutional Review Board, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, Reference number: COA no. SI 142/2023

4. Human Research Ethics Committee, Chulabhorn Royal Academy, Bangkok, Thailand, Reference number: EC 061/2566(2023)

5. Institutional Review Board, Prince of Songkla University, Songkhla, Thailand, Reference number: REC 66–218-14–1

6. Institutional Review Board, Sawanpracharak Hospital, Nakhonsawan, Thailand, Reference number: COA. 30/2566(2023)

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Dajsakdipon, T., Susiriwatananont, T., Wongkraisri, C. et al. Comparative effectiveness analysis of survival with first-line palbociclib or ribociclib plus AI in HR + /HER2- advanced breast cancer (CEPRA study): preliminary analysis of real-world data from Thailand. BMC Cancer 24 , 1018 (2024). https://doi.org/10.1186/s12885-024-12765-x

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Comparative analyses suggest a link between mRNA splicing, stability, and RNA covalent modifications in flowering plants

• Kyle Palos 1 ,
• Anna C. Nelson Dittrich 1 ,
• Eric H. Lyons 2 ,
• Brian D. Gregory 3 &
• Andrew D. L. Nelson 1  

BMC Plant Biology volume  24 , Article number:  768 ( 2024 ) Cite this article

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In recent years, covalent modifications on RNA nucleotides have emerged as pivotal moieties influencing the structure, function, and regulatory processes of RNA Polymerase II transcripts such as mRNAs and lncRNAs. However, our understanding of their biological roles and whether these roles are conserved across eukaryotes remains limited.

In this study, we leveraged standard polyadenylation-enriched RNA-sequencing data to identify and characterize RNA modifications that introduce base-pairing errors into cDNA reads. Our investigation incorporated data from three Poaceae ( Zea mays , Sorghum bicolor , and Setaria italica ), as well as publicly available data from a range of stress and genetic contexts in Sorghum and Arabidopsis thaliana . We uncovered a strong enrichment of RNA covalent modifications (RCMs) deposited on a conserved core set of nuclear mRNAs involved in photosynthesis and translation across these species. However, the cohort of modified transcripts changed based on environmental context and developmental program, a pattern that was also conserved across flowering plants. We determined that RCMs can partly explain accession-level differences in drought tolerance in Sorghum, with stress-associated genes receiving a higher level of RCMs in a drought tolerant accession. To address function, we determined that RCMs are significantly enriched near exon junctions within coding regions, suggesting an association with splicing. Intriguingly, we found that these base-pair disrupting RCMs are associated with stable mRNAs, are highly correlated with protein abundance, and thus likely associated with facilitating translation.

Conclusions

Our data point to a conserved role for RCMs in mRNA stability and translation across the flowering plant lineage.

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There are over 100 RNA covalent modifications (RCMs) that are deposited on all classes of RNAs at various stages of their lifecycle [ 1 ]. RCMs, collectively referred to as the “epitranscriptome” [ 2 , 3 ] are known to influence RNA stability, splicing, structure, intra- and intermolecular interactions, and translation [ 4 , 5 , 6 , 7 , 8 , 9 ]. While most RCMs have been observed on ribosomal and transfer RNAs, they have also been observed on messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs), albeit at lower levels [ 4 , 8 , 10 ]. Although pseudouridine is the most abundant modification among all classes of RNA [ 10 ], N 6 -methyladenosine (m 6 A) is the most abundant internal mRNA RCM targeting approximately 30% of all mRNAs and is present at ∼  0.4% of all adenosine nucleotides in cellular RNAs [ 11 , 12 ]. Additional internal RCMs, such as pseudouridine, N 1 -methyladenosine (m 1 A), and 5-methylcytidine (m 5 C), have been observed on mRNAs in a number of eukaryotic systems, although their functional significance on these RNA molecules is not always clear [ 13 , 14 , 15 ].

Much of what we do know about the epitranscriptome is drawn from studies on m 6 A in mammalian and plant systems, but can likely be applied to many of the other modification classes. With the notable exception of pseudouridine, which results from the isomerization of uridine [ 16 , 17 , 18 , 19 ], RCMs are deposited through the enzymatic activity of highly conserved proteins called writers (e.g., methyltransferases), “interpreted”, or bound, by RNA binding proteins called readers, and removed by eraser enzymes (e.g., demethylases) [ 5 , 20 , 21 , 22 ]. Arabidopsis and mammals have a similar m 6 A frequency across the transcriptome ( ∼  1 m 6 A per 2000 nucleotides), which is targeted to a similar consensus motif RRACH (R = G or A; H = U, A, or C), and has a similar 3’ mRNA bias around the stop codon [ 11 , 23 ]. In plants and mammals, pseudouridine has been found to localize primarily to the CDS and 5’ UTR of mRNAs, is deposited on a wide array of mRNAs, and prefers the first U of a triplet codon (e.g., UUC/UCU/UUU) [ 17 ]. Motif preferences for m5C can vary depending on the methyltransferase involved, but in humans are generally found on C/G rich regions of mRNAs [ 24 ]. While less is known about binding preferences of the other predominant RCMs, such as m 1 A and m 3 C, aberrant addition (or removal) has been observed as a hallmark for tumorigenesis [ 25 , 26 ]. Importantly, loss of many of the writers, readers, and erasers for these RCMs is lethal or causes severe developmental defects in all tested systems [ 14 , 27 , 28 , 29 , 30 ].

RCMs function through a variety of molecular mechanisms to regulate mRNA stability, structure, and splicing. RCMs induce changes in base-pairing properties and higher order structure, ostensibly allowing for increased flexibility in interactors, translational capacity, and structure-associated stabilization [ 31 ]. There is a close association between splicing and m6A deposition [ 23 , 32 ], with recent studies revealing that m 6 A is specifically excluded from splice junctions due to physical occlusion by the exon junction complex, a process that ultimately impacts cytoplasmic mRNA decay [ 33 , 34 ]. Pseudouridine has also been implicated in mRNA splicing, stability, and translational efficiency [ 17 , 35 , 36 ], as well as the stability and maturation of lncRNAs such as the telomerase RNA [ 37 , 38 ]. Other modifications, such as m 1 A, m 3 C, and m 5 C are also believed to be important for mRNA stability, mostly due to phenotypes associated with loss of function mutants in their associated writer proteins [ 25 , 35 , 39 , 40 ]. Thus, RCMs are both a ubiquitous and critical aspect of an RNA’s lifecycle, but specific mechanistic details for many of them are still lacking.

Given the high frequency with which essential RNAs such as rRNAs, tRNAs and snoRNAs are modified, it has historically been difficult to use genetic approaches to monitor the RCM status of mRNAs and lncRNAs. As a result, most studies have relied either on transcriptome-wide antibody-based (m 6 A), or modification-specific chemical-genomic approaches (e.g., bisulfite sequencing for m 5 C; [ 7 , 10 , 41 , 42 ]). An alternative sequencing-based approach relies on the propensity for RCMs that occur at the interface between the canonical base-pairing edge (i.e., the Watson-Crick-Franklin, or WCF base-pairing edge) to be misidentified by reverse transcriptases during the cDNA synthesis step [ 43 , 44 , 45 , 46 , 47 ]. This misidentification results in non-random misincorporations or deletions at modified residues. Multiple algorithms, including HAMR and ModTect [ 15 , 44 ], have been developed to infer modification status, and class, based on these “sequencing errors”. While this approach cannot detect RCMs outside of the WCF base-pairing edge, over 30 modification types can be identified, including m 1 A, pseudouridine, m 3 C, and m 5 C [ 15 , 44 , 48 , 49 ]. Importantly, where there is overlap (such as for m 5 C, m 3 C, and m 1 A), the antibody, chemical-genomic, and sequencing error-based approaches are largely in agreement [ 14 , 15 ]. Inferring modification status via HAMR or ModTect allows both for the repurposing of existing RNA-seq data, of which there are petabases available in NCBI’s SRA [ 50 ], and the side-by-side calculation of modification and transcript abundance.

Here we utilized both HAMR and ModTect to analyze new and publicly available RNA-seq in Arabidopsis and three agronomically important and closely related grasses: Sorghum bicolor , Zea mays , and Setaria italica . We used these diverse stress, tissue-atlas, and developmental datasets in an attempt to better understand what some of these less characterized RCMs might be doing in plants and how they might be conserved, both functionally and in terms of their target genes. We present an in-depth comparative assessment of RCM dynamics across these plant species and uncover new insights into RNA splicing, RNA stability, and plant responses to stress. We demonstrate a level of conservation of target genes previously unseen for RCMs, and reveal that RCMs may participate in population-level variation in stress responses. Mechanistically, we identify a link between splicing and RCMs that is present in all examined systems. Finally, through the analysis of RNA decay pathways in Arabidopsis, we establish that RCMs are associated with unusually stable mRNAs, an aspect that may facilitate their continued translation, as we observe a link between modification state and protein abundance.

Identification of post-transcriptional RNA modifications in three poaceae

To determine the degree to which the epitranscriptome is conserved at a gene level, we performed poly-A enriched RNA-sequencing on soil grown seedlings of Zea mays , Sorghum bicolor , and Setaria italica (Maize, Sorghum, and Setaria, respectively, going forward) two weeks after germination. Paired-end, 150 base-pair RNA-sequencing was performed on aboveground tissue for two biological replicates ( ∼  20 million reads per replicate; Fig. 1 A). Following read mapping, modified sites were inferred using HAMR and ModTect (see Methods ). Both of these algorithms take advantage of the propensity for certain reverse transcriptases to misinterpret ribonucleotides that are modified along the canonical base-pairing edge, and as a result, arrest, skip over the RCM, or incorporate non-reference nucleotides (i.e., SNPs) in the synthesized cDNA [ 51 ]. This misincorporation is non-random, and both HAMR and ModTect use the resulting population of SNPs to infer modification class. Importantly, both HAMR and ModTect disregard biallelic SNPs that could arise from natural variation, and instead focus on high coverage sites with all three non-reference nucleotides observed (HAMR) or incorporate information about both mismatches and deletions at the modified residue (ModTect). RNA-seq data were fed into both algorithms and sites that were predicted by both algorithms and both replicates were retained for subsequent analyses as high confidence RCMs. From this approach, 5,434-7,020 unique RCMs were identified in 1,944-2,542 transcripts in the three species (Fig.  1 B, C, Additional File 1 ). These modifications represent the seven major classes detected by HAMR, with the m1A|m1I|ms2i6A class being the most common in all three species (approximately 33% of all identified RCMs; Fig. S1 A ). An examination of sequencing data across modified transcripts reveals a drop in coverage at the modification site, coinciding with a previously observed RT-arrest (Fig. S1 B; [ 52 ]). Additionally, we find that mRNAs marked by RCMs are present at a substantially higher abundance relative to non-modified mRNAs (modified median TPM = 78.5, n  = 2521 mRNAs, not modified median TPM = 10.2, n  = 16,537, p  < 2.2e-16, Fig. S1 E). Enriched gene ontology terms of modified RNAs in each Poaceae demonstrates significant over-representation in photosynthesis and cytoplasmic translation pathways (Fig.  1 D). These data suggest that RCMs are targeted to conserved cohorts of photosynthesis and translation-associated RNAs in these grass seedlings.

RCM identification in three model Grasses. ( A ) Schematic of experimental design. Left and middle: Two biological replicates of grass seedlings from each species at a similar developmental timepoint were used to generate paired-end RNA-seq libraries. Right: Depiction of RCMs causing errors in reverse transcriptase nucleotide incorporation. Different modifications will result in a non-random pattern of SNPs or deletions at the modified site in the resulting cDNA. Machine Learning algorithms such as HAMR and ModTect can infer the modification class based on these errors. ( B ) Venn diagram showing the overlap of modified sites in the Sorghum transcriptome between biological replicates and two RCM detection algorithms. The union of all four categories ( n  = 7020) was kept for downstream analyses. ( C ) Summarizing the number of modified sites and number of modified transcripts in all three sampled Grasses. ( D ) Enriched GO terms of the modified RNAs in ( C )

To determine if the conservation of RCM deposition goes beyond functional pathway and extends to the genes themselves, we next examined if RCMs are found on transcripts derived from orthologous genes using both sequence homology and synteny. Of the 1,912 possible modified orthologs (the smallest observed number of modified genes in Setaria), we observed 1,074 modified orthologs in one or both of the other species, and 474 orthologs that were modified in all three. This is significantly more orthologs than expected by chance, accounting for the number of annotated and expressed orthologs ( p  < 2.2e-16 multi-set hypergeometric test, Fig. S1 C). We then assessed whether orthologous mRNAs are marked by a similar number of RCMs across species, allowing us to understand whether the RCM density of the targeted RNA is a conserved feature. Indeed, for all three combinations of comparisons between two sampled species, there is a significant positive relationship between the number of RCMs deposited on orthologous mRNAs (Sorghum: Maize r  = .39, Sorghum: Setaria r  = .43, Setaria: Maize: r  = .32, p  < 2.2e-16, Fig. S1 D). These findings further demonstrate that the targeting and density of RCM deposition are conserved features in the sampled Grasses.

RCMs are dynamically deposited on mRNAs based on tissue and abiotic stress

Our previous analyses were limited to a single developmental time point across three species. Previous reports [ 14 , 30 , 53 ] suggest that RCMs may play important roles in post-transcriptional RNA regulation and thus would be dynamically deposited across development or environmental changes. Therefore, we chose to examine RCMs across diverse tissues and environmental contexts using RNA-seq from publicly available datasets [ 54 , 55 , 56 ]. We focused our efforts on Sorghum, first identifying RCMs in a large-scale tissue expression atlas by McCormick and co-authors [ 55 ] containing 137 sequencing samples across 48 tissues/stages/conditions (see Methods ). From these data we identified 266,710 modifications on 6,805 unique transcripts, representing 19.3% of the expressed (TPM > = 1) Sorghum transcriptome. To determine whether the same repertoire of transcripts are being targeted with RCMs as our seedling data from Fig.  1 , or if our data were biased towards identifying a distinct subset of RNAs, we compared the modified RNAs in each dataset. We found 1,813 of the 2,542 modified Sorghum seedling mRNAs are also modified in the McCormick et al., tissue expression atlas. This overlap is substantially more than expected by chance ( p  < 2.2e-16, Fig. S2 A) and likely reflects similarity in molecular processes within tissues examined.

We next aimed to characterize the tissue specificity of RCMs in the McCormick Sorghum tissue expression atlas. Using a modified calculation for Tau, a value typically used to calculate tissue specificity based on RNA abundance [ 57 ], we observed that most transcripts were modified in a very context-specific manner (Fig. S2 B). The transcripts with the lowest Tau value, and therefore modified under the broadest context, were mostly associated with core cellular processes such as translation, whereas those transcripts modified in the most specific context were associated with mRNA maturation and abiotic stress responses (Fig. S2 C, D). We also clustered transcripts based on the tissue type in which they were modified, the number of modifications identified, and the average number of modifications per transcript (Fig.  2 A). In our analysis we observed a strong clustering based on tissue similarity, with seed and roots being notable exceptions. We find a strong bias for RCMs in this dataset towards root samples where both the number of RCMs and number of mRNA targets is substantially higher than other tissues (Fig.  2 A and Fig. S2 E). In contrast, seeds displayed the fewest number of modified transcripts, but the average number of modifications per transcript was very similar to that seen in leaf and root tissues where the number of modified sites and transcripts were much higher (Fig.  2 A). We observed a large number of transcripts that are modified in all tissues ( n  = 851; Fig.  2 B and Fig. S3 A), highlighting the existence of a core repertoire of RCM-targeted mRNAs. As expected, based on their presence across a broad tissue and developmental context, these mRNAs are enriched for terms associated with cytoplasmic mRNA translation (Fig. S3 B). However, the majority of transcripts were more restricted in terms of the tissue or developmental context under which they were modified and were enriched for more specialized GO terms. For instance, root-specific modified mRNAs were enriched for rhizosphere-associated terms [ 58 ] such as oxidation management, generic methylation, and aromatic compound biosynthesis (Fig. S3 C). Leaf-specific modified mRNAs were enriched for photosynthetic terms, whereas seed-specific mRNAs were enriched for lipid storage, ABA response, and cold acclimation terms (Fig. S3 D, E). Importantly, the observed increase in context-specific modifications was not simply due to differences in the most abundant transcripts in each tissue. Indeed, we observed a low (although positive, r  = .34) correlation between RNA abundance and modification levels (Fig.  2 C; top ) in the Sorghum tissue atlas. Thus, although there is a core set of modified Sorghum transcripts, most are targeted for RCMs in a context- or developmentally-specific manner.

RCM dynamics across development and during stress. ( A ) Summary of RCM findings from re-analysis of Sorghum tissue expression atlas [ 55 ]. Hierarchical clustering using the number of RCMs per transcripts as input. For ease of viewing, branches were collapsed based on tissue and modification similarity. See Fig. S2 E for un-collapsed tree. Tree tip labels denote the broad tissue category with the number after the tissue representing the number of SRAs used to summarize that broad tissue. Modified sites : Boxplots showing the total number of RCMs per SRA in each broad tissue category. Modified RNAs : The total number of modified RNAs (genes) per SRA in each tissue category. Modifications per RNA : The RCM density per RNA per SRA in each broad tissue category. ( B ) Upset plot quantifying the shared modified RNAs across the broad tissue categories in ( A ). ( C ) RCM-RNA abundance correlation distribution of all modified RNAs across the McCormick et al. tissue expression atlas [ 55 ] and the Varoquaux et al. [ 66 ] Sorghum drought experiment. The dashed red line shows the mean Pearson Correlation Coefficient of each experiment ( r  = .34 Tissue, r  = .18 Drought). (D) Heatmap of RNA abundance and RCM changes in Varoquaux et al. re-analysis. Each row is a modified RNA that has been filtered for RNA abundance variability (see methods). Rows were clustered based on their abundance and RCM density values. The heatmap was grouped into four distinct clusters (C1-C4, n – values for each cluster shown) using k-means. SC = drought susceptible control, SD = drought susceptible treatment, TC = drought tolerant control, TD = drought tolerant treatment. At the time point analyzed, the drought susceptible genotype is RTx430 and the tolerant genotype is BTx642. ( E ) Enriched GO terms of the modified RNAs in each of the four clusters in ( D )

The conservation of RCMs on orthologous genes in our grass seedling data suggest that the developmental and tissue contexts under which these marks are deposited might also be conserved. To address this contextual conservation, we examined RCMs in two publicly available Arabidopsis thaliana tissue atlases [ 54 , 56 ]. Both atlases examined similar developmental stages, but did so under slightly different conditions (e.g., constant light and sterile MS media for Mergner et al. [ 54 ] vs. cycling light and soil for Klepikova et al. [ 56 ]). Importantly, while the Klepikova tissue atlas is primarily used by the community to examine transcript abundance in the Arabidopsis EFP browser [ 59 ], the work by Mergner et al. performed a paired assessment of transcript and protein abundance, which we used to examine the relationship between modifications and translation (see below). Due to differences in experimental design, we analyzed each of these atlases separately. A full breakdown of examined tissues, as well as total number of modifications identified, can be found in Additional File 2 . Like Sorghum, clustering of Arabidopsis tissues by RCM density placed similar tissues together (Mergner et al.: Fig. S4 A,B, Klepikova et al.: Fig. S5 A, B). While Arabidopsis root tissues did not display a significantly elevated level of RCMs as in Sorghum, Arabidopsis seed transcripts had a reduced pool of very highly modified transcripts in both atlases. Similar to our observations in Sorghum, the seed-specific modified transcripts were enriched for lipid, nutrient, and ABA-response terms (Fig. S6 A, B, C, D). Thus, as in Sorghum, the Arabidopsis epitranscriptome is diverse, highly context-specific, and appears to be associated with transcripts critical for cellular function.

Given the similar patterns of RCM abundance in Sorghum and Arabidopsis, we next examined whether orthologous mRNAs between Sorghum and Arabidopsis, which last shared a common ancestor  ∼  150–250 million years ago [ 60 ], are targeted by RCMs. While we identified fewer mRNA targets of RCMs in both Arabidopsis expression atlases relative to the Sorghum expression atlas (Mergner: 1,324, Klepikova: 2,495 vs. 6,845 in McCormick Sorghum), the number of modified orthologous mRNAs (658 Mergner vs. McCormick; 1,180 Klepikova vs. McCormick) was significantly more than expected by chance for all possible mRNA combinations (Fig. S6 E, F, p  = .001 Hypergeometric test). We conclude that this class of RCMs target an ancient conserved repertoire of translation and photosynthetic related mRNAs.

Given the developmental differences in RCM deposition in both Sorghum and Arabidopsis, as well as reports associating RNA modifications with plant stress responses [ 9 , 14 , 61 , 62 , 63 , 64 , 65 ], we next determined if RCMs are associated with drought stress in Sorghum. We utilized a publicly available field-grown Sorghum transcriptome dataset that sampled well-watered and water-limited (i.e., drought treatment) Sorghum leaves and roots from drought tolerant and susceptible genotypes across weekly timepoints [ 66 ]. We focused on a post-flowering time point (week 10) where one genotype (BTx642) is considered drought tolerant and the other genotype (RTx430) is drought susceptible. Counterintuitively, we observed a shift towards a more negative correlation between RNA and RCM abundance during drought stress relative to the Sorghum tissue atlas (Fig.  2 C, bottom). A heatmap comparing transcript and RCM abundance of the top 50% most variably expressed and modified root transcripts ( n  = 878) between the two genotypes and treatments clustered into four groups (Fig.  2 D). One of these clusters (Cluster 3) showed similar increases in transcript abundance levels between the two genotypes under water limiting conditions but showed an increase in RCMs specifically in the drought tolerant genotype (Fig.  2 D). An examination of enriched GO terms revealed that Cluster 3 contained both heat shock proteins as well as water transport proteins (Fig.  2 E), suggesting that RCMs may be associated with the drought response in the tolerant genotype.

RCMs accumulate near exon-exon junctions and are associated with splicing events

Thus far we have observed an association between RCMs and plant developmental and environmental transcriptional responses. To gain functional insight into RCMs, we analyzed their accumulation and distribution across mRNA topologies using our seedling RNA-seq data for all three species. RCMs were enriched across the CDS, with a bias towards the 3’ CDS, and in the 3’ UTR of mRNAs (Fig.  3 A). Like m 6 A [ 23 ], we also observed that RCMs are biased towards being deposited on abnormally long exons (median length of modified exons = 361 nts, median unmodified = 147 nts, median all exons = 148 nts, Fig.  3 B, p  < 2.2e-16 ). Additionally, we observed a significantly higher proportion of expressed transcripts that are multi-exonic being targeted by RCMs, relative to mono-exonic transcripts ( ∼  11–14% vs  ∼  5–8%, Fig. 3 C, p  < 7.1e-15). This was also the case for long non-coding RNAs (lncRNAs) where 4.8% of mono-exonic lncRNAs are marked by RCMs (17/355) and 8.4% of multi-exonic lncRNAs are targeted by RCMs (59/704, p  = .044; Fig. S7 A). A closer examination of distinguishing CDS features uncovered a dramatic buildup of RCMs on both 5’ and 3’ edges of exon-exon junctions (EJs) relative to start and stop codons (Fig.  3 D). Thus, these data suggest that RCMs likely play a role in the regulation of RNA splicing on diverse transcript types.

RCMs accumulate at exon-exon junctions and are associated with splicing. ( A ) Metagene plot showing the genic distribution of grass RCMs identified in Fig.  1 . Ten nucleotide windows are plotted where the signal is the sum of RCMs in that window and plotted as density. ( B ) Density distribution of exon lengths for RCM marked exons vs exons not marked by RCMs. (Student’s t-test, p < 2.2e-16). ( C ) Proportion of expressed transcripts that receive RCMs in grass seedlings between mono-exonic and multi-exonic transcripts. Multi-exonic transcripts are significantly more likely to be modified than mono-exonic (Chi-squared test, all p-values < 7.1e-15). ( D ) Density of Sorghum seedling RCMs at start codons, stop codons, and 5’/3 exon-exon junctions. Density curves are plotted over a density histogram, both using a two-nucleotide window. The dotted line represents the first nucleotide of the start and stop codon, and the first nucleotide of the intron for 5’/3’ exon junction panels. ( E ) Histogram quantifying the number of Sorghum seedling RCMs accumulating at terminal exon-exon junctions vs the first exon-exon junction. ( F ) Box Plot and dot plot overlaid quantifying the proportion of RNAs that are marked by RCMs or not in each SRAs in the McCormick et al Sorghum tissue expression atlas. The x-axis splits the data by the number of isoforms a gene is expressing. RCM proportion increases as the number of isoforms expressed increases ( p  < 2.2e-16 one-way ANOVA)

This observation of RCMs preferentially occurring at EJs was initially made by Vandivier et al [ 14 ] on degrading mRNAs, whereas degrading mRNAs likely make up a small proportion of our dataset. Thus, these findings suggest a steady-state RCM enrichment at mRNA EJs. A transcriptome-wide 3’ bias was observed for these EJ-enriched RCMs (Fig.  3 E ) . This terminal EJ enrichment was not due to 3’ sequencing bias that is often observed in poly-A enriched transcriptome datasets (Fig. S7 B, C, D). Interestingly, genes that express multiple isoforms are more likely to be modified than single isoform transcripts with similar numbers of exons (Fig.  3 F, p  < 2.2e-16). However, we observe no significant difference in the buildup of modified sites at alternatively spliced junctions vs canonical splice sites (Fig. S8 , see Methods ). These data suggest that the increase in modification frequency at genes with more isoforms is likely due to the presence of more exon junctions. Thus, RCMs appear to be positively associated with splicing in plants.

RCMs are positively associated with stable and translating mRNAs

Because Mergner et al. [ 54 ] measured RNA and protein abundances from matched Arabidopsis samples they were able to correlate RNA: protein abundances across their samples. Therefore, we examined whether mRNAs marked with RCMs displayed a higher or lower RNA: protein correlation. A difference in RNA: protein correlation could suggest a RCM function in RNA stability and/or translation efficiency. mRNAs that are not marked with RCMs across the Mergner et al. atlas showed a lower median RNA: protein correlation ( n  = 3,361, r  = .68) compared to RCM marked mRNAs which showed a significantly higher RNA: protein correlation ( n  = 332, r > = 0.758, p  < .05, Fig. S9 A). Due to this positive correlation between transcripts harboring base-pair disrupting modifications and their translation products, it is possible that these RCMs are positively influencing translation, either by reducing structural complexity or by stabilizing these transcripts.

To test for a relationship between RCMs and mRNA decay, we first examined publicly available transcriptomic data from Arabidopsis lines deficient in cytoplasmic mRNA decay pathways [ 67 ] by Sorenson and co-authors. Cytoplasmic mRNAs usually decay through three pathways: decapping (5’ -> 3’), the RNA exosome (3’ -> 5’), or an exosome independent 3’ -> 5’ decay pathway. Decapping occurs through a multi-subunit complex that is scaffolded by VARICOSE in plants and metazoans (VCS, [ 68 ]). Meanwhile, the exosome independent 3’ -> 5’ decay pathways occurs through SUPPRESSOR OF VARICOSE (SOV), which contributes to the decay of decapped RNAs and is also known as DIS3L2 in other Eukaryotes [ 69 , 70 , 71 ]. Sorenson et al took an RNA-seq approach to examine mRNA decay dynamics following transcriptional arrest (via cordycepin) in four Arabidopsis genotypes that vary in their cytoplasmic mRNA decay factors (wild-type, sov knockout, vcs knockout, and sov/vcs double knockout). If RCMs are primarily marking mRNAs for degradation (as initially suggested by Vandivier et al [ 14 ]), then a buildup should be observed after transcriptional arrest in genetic backgrounds deficient for mRNA decay.

The majority of modified transcripts at the beginning and end of the time series were mRNAs (Fig.  4 A). As expected from arresting transcription, each genotype, with the exception of the sov / vcs double mutant, displayed a  ∼  25–50% decrease in the total number of observable protein-coding transcripts eight hours post-treatment (see Methods ; Fig. S9 B). The number of observed RCMs increased in all genotypes after arresting transcription, as did the proportion of modified mRNAs (Fig.  4 B and Fig. S9 C), suggesting two possibilities: 1 ) that modification abundance increases with transcript age, or 2 ) that non-modified transcripts are degraded more quickly leading to a higher proportion of transcripts detectable over time containing RCMs.

RCMs mark mRNAs that degrade slowly. ( A ) Reanalysis of Sorenson et al. 2018 Arabidopsis dataset [ 67 ]. Distribution of Arabidopsis gene types that receive RCMs at time point 0 and 8 h after arresting transcription. tRNA = transfer RNA, sn/snoRNA = small nuclear/small nucleolar RNA, rRNA = ribosomal RNA, mRNA = messenger RNA, lncRNA = long noncoding RNA. ( B ) Number of Arabidopsis RCMs predicted at each time point after arresting transcription and in each RNA degradation genotype. ( C ) Change in number of detectable RNAs over time after arresting transcription. Solid lines represent mRNAs that have a RCM at time point 0 while dashed lines represent mRNAs that have no RCMs at any time point. Expressed genes are those that have a normalized expression value > = 1 (see methods). ( D ) Comparing RNA abundance change after transcription arrest at each time point. Red boxplots (left grouping) are mRNAs with an RCM at time point 0, blue boxplots (right group) are mRNAs that have no RCMs at any time point. Comparisons at time points 7, 15, and 30, p  > .05, all time points starting at 60 min and afterwards, p  < .001 by one-way ANOVA. ( E ) Boxplot showing the distribution of ɑ-decay rates between RCM marked mRNAs at time point 0 and mRNAs that have no RCMs at any time point. (F) Modified figure from Sorenson et al. 2018 Fig. 1C showing the number of mRNAs that belong to different decay categories. Categories 1–14 are inconsequential for our conclusions. Group 15 represents genotype independent decay, (decay rates are not affected by the genotypes in the Sorenson et al. study). This category may represent targets of the exosome. * P-value between modified/never modified for Group 15 calculated by Chi-squared test

To more directly test the association of RCMs with RNA degradation, we analyzed whether the pool of transcripts that are modified at time point 0 were still detectable and modified at subsequent time points. Surprisingly, mRNAs modified at time point 0 were nearly all detectable 8 h after transcription arrest while mRNAs that were not modified at time point 0 declined by more than 40% (Fig.  4 C). These results indicate that RCMs mark mRNAs which degrade slower than the entire mRNA pool. Indeed, mRNAs that are not targeted by RCMs show a significantly larger magnitude of TPM decrease relative to mRNAs marked with RCMs at time point 0 (Fig.  4 D). These results strongly suggest that RCM marked mRNAs degrade at a slower rate relative to mRNAs that are not marked by RCMs.

Sorenson et al. also modeled the initial decay rate of all mRNAs (alpha decay rate) based on read abundance after transcription arrest. Based on these values, mRNAs that are modified at time point 0 in wild-type show a significantly lower (slower) alpha decay rate relative to all other mRNAs (Fig.  4 E). Sorenson et al. used the decay rates across genotypes to assign mRNAs to genotype-dependent RNA degradation pathways (Fig.  4 F x-axis groups 1–14, see Fig. 1C from Sorenson et al.). For mRNAs that could not be assigned to the VARICOSE or SOV degradation pathways, Sorenson et al. hypothesized that they were likely targets of the RNA exosome (x-axis group 15; Fig.  4 F). Interestingly, modified mRNAs are significantly biased towards being assigned to this group and thus are likely targets of the RNA exosome (Fig.  4 F p  < 2.2e-16, Pearson’s Chi-squared test). While the importance of RCMs on rRNAs and tRNA stability and function has been known for decades, based on these data, we would argue that the HAMR/ModTect class of RCMs appear to be marks of Pol-II transcript stability, rather than marks of degradation.

RCMs are not associated with nonsense-mediated mRNA decay

To further investigate whether RCM marked mRNAs have an association with RNA degradation pathways, we next turned our focus to the Nonsense Mediated mRNA Decay (NMD) pathway. NMD is responsible for degrading aberrant mRNAs that have a premature termination codon. This is often recognized as a termination codon upstream of the exon junction complex (EJC, [ 72 ]). Given the accumulation of RCMs at exon-exon junctions and the recent report of RNA degradation intermediates accumulating near exon-exon junctions [ 73 ], we tested whether modified mRNAs were likely targets of NMD. SUPPRESSOR OF MORPHOLOGICAL DEFECTS ON GENITALIA7 (SMG7) is a critical component of early NMD signaling in most Eukaryotes [ 74 ]. Gloggnitzer and co-authors [ 75 ] performed RNA-seq in Arabidopsis with a loss-of-function smg7 mutant in a genetic background avoiding the strong autoimmune response of knocking out NMD ( pad4; [ 75 ]). We re-processed the RNA-seq data generated by Gloggnitzer et al. and identified RCMs from their data. The up-regulated mRNAs in the smg7 genotype represent both direct and indirect targets of NMD silencing. We observed strong statistical evidence that NMD targets are actually under-represented (that is, depleted) from mRNAs containing RCMs (5,275 mRNAs containing RCMs, 656 up-regulated mRNAs in smg7 , overlap = 103, p  < 2.2e-16, Fig. S10 ). Furthermore, we identify no significant differences in RCM distribution at exon-exon junctions between pad4 and smg7-pad4 RCMs (Fig. S10 B, p  = .986), or between smg7 RCMs in mRNAs up-regulated in smg7 vs. those not differentially abundant in smg7 (Fig. S10 C, p  = .144). In agreement with the smg7 results which examined a single tissue, there is no significant overlap between predicted NMD targets and the RCMs identified in the Mergner et al. tissue atlas (Fig. S10 D, p  = .304). Collectively, these results suggest RCMs are not associated with the NMD pathway.

In this study we used a bioinformatic workflow consisting of HAMR and ModTect [ 14 , 15 , 44 ] to predict RCMs in diverse species across the flowering plant lineage in order to clarify RCM distribution and putative function on mRNAs. Of note, because these algorithms rely on a certain minimum depth of expression in order to statistically call a transcript as being modified, and we tended towards a conservative definition of when to call an RCM (present at the same site in bio-replicates based on both algorithms), making conclusions about low abundance transcripts can be problematic. Despite these caveats, we observed a substantial number of transcripts whose modification status changes along developmental or environmental gradients. In perhaps the most extensive comparative analysis to date, we demonstrated that the RCMs detected by HAMR and ModTect (e.g., those that fall along the WCF base-pairing edge) are found on a large, yet contextually discrete set of transcripts. From these diverse analyses we believe that a number of conclusions can be made that provide lessons as to the function of RCMs in eukaryotes.

RCMs are not found on all expressed transcripts, nor are they always on the most abundant transcripts, suggesting a contextual specificity. Indeed, there appear to be two classes of transcripts that receive RCMs in plants. There are a core set of transcripts associated with photosynthesis and cellular metabolism that are modified in each tested tissue or developmental context. These transcripts tend to be abundantly modified and are the targets of all of the major base-pair disrupting modifications. Due to their critical functions, these genes are conserved, as is their modification status, across the plant lineage. The other class of modified transcripts receive RCMs in a more context-specific manner. These transcripts may be expressed in multiple tissues but are generally targeted by modifications in a more limited subset of conditions. Functional enrichment suggests that these transcripts are modified in response to environmental or developmental cues.

We observed similar patterns of RCM deposition on all transcripts, with a bias towards longer exons and transcripts containing multiple exons. In contrast to m 6 A, these RCMs are enriched at exon splice junctions. Interestingly, while we see an increase in RCM abundance on transcripts with multiple isoforms, we see no association between RCM status and alternative splicing or intron retention, suggesting that modifications are more of a general marker of splicing rather than a driver of isoform selection. We would propose that these modifications are deposited on the base-pairing edge of the newly transcribed mRNA to reduce structure at exon junctions in order to facilitate splicing, whereas m6A is occluded from these sites due to its interference with binding of the splicing machinery [ 33 , 34 ]. This model would further suggest that, like m 6 A, these other RCMs are deposited co-transcriptionally and thus their writers should appear in complexes with transcriptional and epigenetic machinery. In support of this hypothesis, the m 5 C methyltransferases NSUN3 and DNMT2 are known to interact with hnRNPK for Pol-II recruitment and modulating chromatin state in response to stress [ 76 ].

Previous work in Arabidopsis comparing RCM abundance in degrading and total mRNA populations found evidence for RCMs as a marker for mRNA degradation [ 14 ] whereas others have suggested that loss of RCMs leads to unstable mRNAs [ 10 ]. In an attempt to resolve these conflicting models, we turned to an elegant time-series RNA-seq experiment in Arabidopsis examining the impacts of mutants in the mRNA decay pathway on RNA turnover [ 67 ]. We used these data to assess the relationship between degradation and RCM status. To our surprise, we found that RCMs are more likely to mark extremely stable mRNAs, rather than those that are rapidly degrading. Our finding that most of the RCMs fall on transcripts associated with core cellular processes fits with the notion that these transcripts would also be more stable, a finding reported by Sorenson et al. and others [ 77 ]. Interestingly, we also observe a positive relationship between RCM abundance and protein abundance in the Arabidopsis tissue atlas, further suggesting that RCMs, as a whole, have a positive influence on mRNA stability. Importantly, we cannot definitively say that degrading RNAs are not marked, just that the RCMs we can monitor are enriched on mRNAs with long half-lives. A key difference between our work and that of Vandivier et al. [ 14 ] is in the depth of sequencing, both for the grass seedling experiments that we performed and for the additional datasets we reprocessed, and so further examination of the connection between these base-pair disrupting RCMs and RNA degradation is warranted.

In this work, we used a comparative transcriptomic approach to better understand the conservation and function of a poorly studied class of RNA modifications. Grass seedlings contained similar cohorts of modified transcripts, a finding that spurred us to look in other datasets that incorporated additional stress or developmental timepoints in both Sorghum and Arabidopsis. Again, we observed a conserved, core set of modified transcripts, but these expanded datapoints allowed us to uncover context specificity to the epitranscriptome, suggesting that some, but not all, transcripts are modified at key steps in their functional lifecycle. Attempts to better understand function of these base pair disrupting modifications revealed that they are likely important for splicing, translation, and intriguingly, for RNA stability. Similarities across the Angiosperm lineage, along with supporting literature in metazoans, suggests that these modifications, like m 6 A, are a deeply conserved aspect of RNA biology for which we still know very little.

Plant growth

Seed from Zea mays (acc. B73), Sorghum bicolor (acc. BTx642), and Setaria italica (acc. Yugu1) were sown onto damp soil approximately 2 cm below the surface, and stratified for one week in the dark at 4 °C. Seeds were then transferred to a growth cabinet with lights configured to a long-day cycle (16 h light, 8 h dark). All species’ seeds germinated within a day of each other and were allowed to grow for two weeks once germinated. Seedling shoot tissue was collected and instantly frozen with liquid nitrogen.

RNA-sequencing library preparation

RNA from two biological replicates in each species was isolated using TriZol per the manufacturer’s instructions (ThermoFisher #15596026). Paired-end and strand specific sequencing libraries were generated for each species using the YourSeq FT Strand-Specific mRNA library prep kit (Active Motif #23001). Libraries were sequenced on a Novogene Novaseq platform.

Read processing and modification identification

Raw sequencing reads were trimmed for adapters and low-quality bases using fastp (v 0.23.4, [ 78 ]) with default settings for paired end reads. Trimmed reads were then used to call HAMR modifications using the PAMLINC workflow ( https://github.com/chosenobih/pamlinc ). Briefly, this workflow maps reads to each species’ reference genome (obtained from Ensembl Plants in January 2022) using BowTie2 (v 2.2.5, [ 79 ]) and retains unique alignments that don’t overlap exon-junctions or deletions. The HAMR statistical model is then run on these filtered alignment files (BAM files) to predict RCMs.

In addition, a similar algorithm, ModTect (v 1.7.5, [ 15 ]), was used to call RCMs. For ModTect prediction, trimmed reads were mapped to the same Ensembl Plants genomes and associated annotation files as above using STAR (v 2.7.10b, [ 80 ]). The following non-default STAR arguments were used for mapping: --outFilterIntronMotifs RemoveNoncanonical, --alignIntronMax 10000, --outSAMstrandField intronMotif. Raw BAM outputs were then used as input for ModTect with the following arguments: –minBaseQual 30, --readlength 150, and --regionFile genes.bed which is a four-column file of all gene regions in the annotation file.

For sequencing data generated in this study, RCM sites were retained that overlapped both ModTect and HAMR predictions among both sequencing replicates (Fig.  1 B). For public sequencing data, RCMs were identified using only ModTect and RCMs were retained that were common among three or more experiments (i.e., three or more distinct SRAs).

RNA abundance measurement

Trimmed fastq files were used to measure RNA abundance using Salmon (v 1.10.0, [ 81 ]) in “quant” mode against a decoy aware transcriptome index. The tximport (v 1.28, [ 82 ]) R package was then used to import Salmon quant files and generate gene or transcript level transcript per million values, specifying the following tximport option: countsFromAbundance = “lengthScaledTPM”. RNAs used for downstream RCM and expression analyses were those with at least one transcript per million in one experiment. For the reanalysis of the Varoquaux et al., dataset in Fig.  2 D, root tissue was chosen for comparison. Transcripts whose RNA abundance did not vary in the root control vs. drought treatments were removed by filtering out the bottom 50% of transcripts based on their abundance variability measured by median absolute deviation. The frequency of RCM density for every transcript (number of RCMs in a condition / total number of RCMs across all root samples) was computed and then transcripts without RCMs in any dataset was removed. For the reanalysis of Sorenson et al. 2018, we used RPKM (reads per kilobase per million mapped) values that the authors provide in their supplemental data which accounts for stable transcripts containing an increasing proportion of sequencing reads.

Alternative splicing

Two approaches were used to infer alternative splicing. First, genes that express multiple isoforms can be inferred from Salmon [ 81 ] using the tximport [ 82 ] R package as described above. Specifying the tximport “txOut” argument gives isoform level quantifications. Isoforms expressed above one TPM were retained. Genes expressing more than one isoform were inferred to be undergoing alternative splicing. Second, we examined statistically significant changes in splicing patterns between experimental conditions using EventPointer [ 83 ].

GO term enrichment

GO terms were downloaded for each species from Ensembl Plants. GO term enrichment was performed using the clusterProfiler (v 4.8.2, [ 84 ]) R package, specifically the “enricher” function was used, specifying: (1) the input gene set, (2) a p-value cutoff of 0.05, (3) Benjamini and Hochberg multiple testing correction, (4) a background gene set (usually, all expressed genes (TPM > = 1)), (5) a minimum and maximum gene-set size of 10 and 500, respectively, and (6) a q-value cutoff of 0.05. Fold enrichment was calculated using the following functions: parse_ratio(GeneRatio) / parse_ratio(BgRatio).

Data analyses

All analyses and statistical testing was performed using the R programming language (v 4.2.0, [ 85 ]). Tests for over or underrepresentation were performed using the phyper function (i.e., a hypergeometric test) in R. Correlations between RNA abundance and RCMs were performed by collecting the RNA abundance values and RCM density values (number of RCMs per transcript) from each sequencing experiment of interest. Pearson Correlation Coefficients were then calculated from the RNA abundance and RCM density using the “cor” function in R. To measure statistical significance of mean-separation between groups, the Student’s t-test was used for two group comparisons, while one-way Anova was used for more-than-two group comparisons.

All visualizations were generated in R from various packages including: ggplot2 (v 3.4.4, [ 86 ]), ggvenn (v 0.1.10, [ 87 ]), enrichPlot (v 1.20.0, [ 88 ]), complexUpset (v 1.3.3, [ 89 ]), complexHeatmap (v 2.16., [ 90 ]), and the ggtree package (v 3.8.2, [ 91 ])

Data availability

The accession numbers of RNA-sequencing data generated in this study can be found at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1116564 . The scripts and intermediate files for analyses are available at: https://github.com/kylepalos/comparative-PRM-paper .

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Acknowledgements

We would like to acknowledge support from NSF IOS PGRP 2023310 (to ADLN, EHL, and BDG), NSF MCB 2120131 (to ADLN), and NSF-DBI-1743442 to EHL. We would like to thank Drs Duke Pauli (University of Arizona) and Susan Schroeder (Oklahoma University) for insightful comments during the development of the project. We would like to thank Drs. Rebecca Murphy (Centenary College of Louisiana) and Daryl Morishige for providing useful insight into the Sorghum tissue atlas. Finally, we would like to thank members of the Gregory and Nelson labs for constructive feedback.

We would like to acknowledge support from NSF IOS PGRP 2023310 (to ADLN, EHL, and BDG), NSF MCB 2120131 (to ADLN), and NSF-DBI-1743442 to EHL.

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Kyle Palos, Anna C. Nelson Dittrich & Andrew D. L. Nelson

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KP, ACND, EHL, BDG, and ADLN developed the project. ACDN grew plants, isolated RNA, and performed RNA-seq. KP analyzed data, EHL, BDG, and ADLN provided feedback on data analysis. KP and ADLN wrote the manuscript. All authors edited and approved the manuscript.

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Supplementary Material 1: Additional File 1: Overview of RCMs identified in all three grass seedlings.

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Supplementary Material 3: Additional File 2: Summary of RNA-seq datasets used and the number of RCMs identified across the tissue expression atlases.

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Palos, K., Nelson Dittrich, A.C., Lyons, E.H. et al. Comparative analyses suggest a link between mRNA splicing, stability, and RNA covalent modifications in flowering plants. BMC Plant Biol 24 , 768 (2024). https://doi.org/10.1186/s12870-024-05486-7

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Comparative analysis of bacterial populations in sulfonylurea-sensitive and -resistant weeds: insights into community composition and catabolic gene dynamics

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• Jan Homa   ORCID: orcid.org/0000-0002-8846-033X 1 ,
• Wiktoria Wilms 1 ,
• Katarzyna Marcinkowska 2 ,
• Paweł Cyplik 3 ,
• Łukasz Ławniczak 1 ,
• Marta Woźniak-Karczewska 1 ,
• Michał Niemczak 1 &
• Łukasz Chrzanowski 1  

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This study aimed to compare the impact of iodosulfuron-methyl-sodium and an iodosulfuron-based herbicidal ionic liquid (HIL) on the microbiomes constituting the epiphytes and endophytes of cornflower ( Centaurea cyanus L.). The experiment involved biotypes of cornflower susceptible and resistant to acetolactate synthase inhibition, examining potential bacterial involvement in sulfonylurea herbicide detoxification. We focused on microbial communities present on the surface and in the plant tissues of roots and shoots. The research included the synthesis and physicochemical analysis of a novel HIL, evaluation of shifts in bacterial community composition, analysis of the presence of catabolic genes associated with sulfonylurea herbicide degradation and determination of their abundance in all experimental variants. Overall, for the susceptible biotype, the biodiversity of the root microbiome was higher compared to shoot microbiome; however, both decreased notably after herbicide or HIL applications. The herbicide-resistant biotype showed lower degree of biodiversity changes, but shifts in community composition occurred, particularly in case of HIL treatment.

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Introduction

Sulfonylurea-based herbicides are widely used in agriculture to limit the growth of monocotyledonous and dicotyledonous weeds (Barros et al. 2016 ). Such herbicides act via blocking the operation of acetolactate synthase (ALS), an enzyme responsible for the synthesis of branched amino acids (BCAA), such as leucine, valine and isoleucine (Forouzesh et al. 2015 ; Heap, n.d.; Rosario et al. 2011 ). Furthermore, this enzyme is only present in both plant and bacterial cells, which limits the toxicity of sulfonylurea-based compounds to animals and people (Chipman et al. 1998 ; Whitcomb 1999 ). The sulfonylurea herbicides effectively control weeds using low doses of active ingredient of several grams per hectare (Sarmah and Sabadie 2002 ). Consequently, sulfonylurea herbicides are very popular among farmers and constitute one of the most popular herbicide classes in the world (United Nations Food and Agriculture Organisation, n.d.). The persistence of this group of compounds in the environment strongly depends on soil pH values, with half-life values spanning from weeks in acidic soil up to years under alkaline conditions (Lei et al. 2023b ). For example, the half-lives of nicosulfuron shift from 15 to 20 days in case of acidic pH to 190–250 days in case of neutral/alkaline pH (Zhong et al. 2023 ). In the latter case, the residual herbicides may migrate to groundwater and surface water, posing a threat to aquatic plants, non-target crops, aquatic animals as well as humans (Zhong et al. 2023 ). It has been confirmed that exposure to nicosulfuron can induce morphological and behavioural changes in spined toad tadpoles (Cheron et al. 2023 ) as well as induce hyperglycemia, increase the risks of cardiovascular diseases and lead to denaturation of serum albumin in humans (Zhong et al. 2023 ). Moreover, the presence of sulfonylurea herbicides may alter the enzymatic activity soil microorganisms as decreased activity of dehydrogenases and phosphatases upon exposure has been reported (Medo et al. 2020 ). In addition to these issues, one of the most challenging problems is the rapid rate of global development of weed resistance to this group of herbicides (Heap, n.d.). The spread of weed resistance to applied herbicides is a major issue in worldwide crop protection (Lei et al. 2023b ). Currently, 272 species of weeds have evolved herbicide-resistant biotypes in 100 crop plants across 72 countries (Heap, n.d.; Marcinkowska et al. 2023 ; Zhong et al. 2023 ). To date, cornflower biotypes resistant to herbicides have only been confirmed in Poland (Heap, n.d.).

Adjuvants are widely used in all herbicidal mixtures to improve the properties of the final formulation (Aparecida et al. 2013 ; Mesnage et al. 2014 , 2013 ; Wilms et al. 2020b ). Recently, concerns regarding high toxicity of some additives have been raised, e.g., in the case of ethoxylated etheralkylamine or solvent naphtha (Defarge et al. 2018 ; Mesnage et al. 2013 ; Mesnage and Antoniou 2018 ). As a result, herbicidal ionic liquids (HILs) have been proposed in order to limit the use of adjuvants (Pernak et al. 2011 ; Wilms et al. 2020b ). This class of compounds combines the herbicidal activity of typical formulations with improved surface active properties in a single compound, thus allowing for simplification of herbicidal formulations for field use (Niemczak et al. 2019 , 2017 ; Pernak et al. 2016 , 2011 ; Tang et al. 2020 , 2018 ; Wang et al. 2019 ; Wilms et al. 2020b ). Another potential benefit of transforming herbicides into an ionic liquid form is the great potential of adjusting physicochemical properties of such compounds via selection of cations and ease of their modifications, which also allows to fine tune biological properties (Wilms et al. 2020b ). Nevertheless, most of the currently available research regarding HILs is focused on synthesis, physicochemical properties and determination of herbicidal activity towards weeds and crop plants (Wilms et al. 2020b ). Available studies regarding the microbiome rarely traverse beyond toxicity tests of HILs toward model microorganisms or biodegradation studies (Wilms et al. 2020b ); however, recently this sentiment is slowly changing and more insight into HILs interactions with non-target organisms is being provided (Parus et al. 2023 , 2021 ; Stachowiak et al. 2021 ; Wilms et al. 2023a , b ; Woźniak-Karczewska et al. 2022 ). This is especially important in the case of HILs based on sulfonylurea herbicides, as negative impacts of sole sulfonylurea herbicides on the environment as well as metabolic pathways utilised by the bacteria in order to dissipate such xentobiotics have been well evidenced (Lei et al. 2023a ; Zhong et al. 2023 ).

It has already been reported that the cation and anion in HILs are degraded at different rates, which may indicate that ionic liquids are rather an application form of the herbicide than an entity that is stable in the environment (Wilms et al. 2023b , a ). As evidenced by Wilms et al. ( 2023a , b ), sorption is a factor significantly decreasing the bioavailability of cations in soil, which may result in their bioaccumulation and limited degradation, and potentially negatively affect the biotransformation of herbicidal anions (Wilms et al. 2023a , b ). These findings, which put the integrity of HILs under question, are supported by the research of Woźniak-Karczewska et al. ( 2022 ), which demonstrated that the cation and anion adsorption parameters of 2,4-D HILs were completely independent and the cations’ adsorption K f values were correlated with its hydrophobicity (Woźniak-Karczewska et al. 2022 ). Furthermore, studies investigating soil microcosm exposed to HILs reveal that the cation selection is the determining factor in microbiome community composition changes (Wilms et al. 2023b ; Woźniak-Karczewska et al. 2022 ). As was demonstrated by the abovementioned studies, the form of the HIL and structure of the cation in particular have significant implications for environmental behaviour of such xenobiotics.

To summarize, although the overall awareness regarding the environmental impact of sulfonylurea compounds has been steadily increasing during recent years, the number of studies which evaluate the in-depth impact of such herbicides on the diversity of the soil microbiome is limited; among such reports, experiment are mainly focused on nicosulfuron, while data regarding the impact of other sulfonylureas is scarce; comparison of classic herbicides and HILs usually revolves around their efficacy in fighting weeds, with limited insight into environmental impacts, which notably restricts the possibility to conduct a proper risk assessment; finally, some studies have hinted that HILs may be an effective tools to control herbicide-resistant weed species (Marcinkowska et al. 2023 ; Pernak et al. 2022 ); however, no insightful rationale which would elucidate this phenomenon has been presented to date.

The importance of this study is associated with an attempt to tackle the abovementioned knowledge gaps. The aim was to evaluate the effect of iodosulfuron-methyl-sodium (selected as a model herbicide for effective weed control) and HILs based on the same active ingredient on the microbiome associated with cornflower, a commonly occurring weed in field crops. The novelty of the described findings results from the fact that, for the first time, the impact of herbicides and corresponding HILs on herbicide-resistant weeds was evaluated at the genetic level. We assumed that the microbiome present on the surface of the plant and in its tissues may aid the weeds in deactivation of active ingredient in the spray solution. In order to verify this hypothesis, we investigated microbiome diversity of susceptible ( S ) and resistant ( R ) biotypes of cornflower and assessed the presence and abundance of genes responsible for degrading sulfonylurea-based herbicides in the soil, rhizosphere and among the plants’ epiphytes and endophytes. All of the currently known genes that participate in the degradation of sulfonylurea herbicides were selected for this purpose. The obtained data may be valuable for defining the scope of risk assessment necessary for registering HILs as commercial agrochemicals, which contributes to its high significance.

Materials and methods

Chemical reagents.

The following reagents were used during the experiments: iodosulfuron-methyl sodium salt (96.6%, PESTINOVA, Jaworzno, Poland), 2-dimethylaminoethanol (≥ 99.5%, Sigma-Aldrich, Saint Louis, MO, USA) and 1-bromotetradecane (97.0%, Sigma-Aldrich, Saint Louis, MO, USA), methanol, acetonitrile, chloroform (Avantor, Gliwice, Poland) and deionized water (conductivity < 0.1 μS·cm − 1, obtained using a HLP Smart 1000 demineralizer, Poznań, Poland).

Synthesis and analysis of HILs

Synthesis of n -tetradecylcholine bromide.

A quaternization reaction was used to synthesize N -tetradecylcholine bromide based on the following steps: (i) 30 mL of acetonitrile were added to a 100-mL round-bottom flask, followed by 2-dimethylaminoethanol (0.05 mol) and 1-bromotetradecane (0.0505 mol); (ii) the flask was equipped with a Teflon-coated magnetic stirring bar; (iii) all the reactants were subjected to stirring for 48 h at 60 °C; (iv) evaporation of the solvent was carried out using a vacuum evaporator; (v) the residues were treated with ethyl acetate (100 mL) which allowed to precipitate the product as a white solid; (vi) after cooling the solution to 5 °C, separation of the product was carefully conducted using vacuum filtration with a glass filter funnel; (vii) finally, the isolated filtrate was washed using cooled ethyl acetate and subjected to drying under reduced pressure at 50 °C for 24 h.

Synthesis of the herbicidal ionic liquid

In order to obtain the actual herbicidal ionic liquid, an ion exchange reaction was conducted with the use of an Easy-Max reactor based on a procedure described in a previous study (Niemczak et al. 2020 ). The general outline of the reaction included the following steps: (i) methanol (15 mL) was introduced into a 100-mL reaction vessel and used to dissolve N -tetradecylcholine bromide (0.01 mol); (ii) sodium salt of iodosulfuron-methyl (0.0102 mol) dissolved in methanol (15 mL) was added to the system in order to conduct the ion exchange; (iii) the reaction vessel was equipped with a mechanical stirrer, the system was stirred at 50 °C for 15 min and subsequently cooled to 0 °C; (iv) the inorganic by-product (NaBr) was removed via filtration and then the solvent was removed via evaporation; (v) the residue of the crude product was dissolved in chloroform (15 mL) in order to remove any impurities; (vi) the purified product was separated via filtration and residual chloroform was evaporated; (vii) in the last step, the product was dried under reduced pressure at 50 °C for 24 h and stored over P 4 O 10 .

Spectral analysis

In order to confirm the structure of the final product and analyse its purity, 1 H and 13 C NMR spectra was well as IR spectra were obtained and analysed. Varian VNMR-S 400 MHz spectrometer was used to obtain the NMR spectra, with operating frequency at 400 MHz for 1 H NMR (with the tetramethylsilane as the internal standard) and 100 MHz for 13 C NMR, respectively. In case of IR spectra, the semi-automated system EasyMax 102 (Mettler Toledo, Switzerland) with a ReactIR iC15 probe was used to obtain spectral data, which was processed using the iCIR 4.3 software.

Water content

Karl Fischer titration method, which is a standard way to determine water content in synthesised HILs (Niemczak et al. 2019 ; Niu et al. 2018 ; Stachowiak et al. 2022 ; Tang et al. 2018 ; Wilms et al. 2020a ), was employed in order to evaluate the water content in all the samples, with the use of a TitroLine 7500 KF trace apparatus (SI Analytics, Germany). The procedure is based on the following steps: (i) samples were dissolved in dehydrated methanol; (ii) the concentration of water in pure methanol as well as the sample solutions was determined based on titration; (iii) the water content in pure products was established based on the difference between results obtained in step (ii).

Melting point

The MP 90 melting point system (Mettler Toledo, Switzerland) calibrated based on certified reference substances was used to measure the melting points of all the obtained compounds.

Plant material and method of weed control

Evaluation of herbicidal efficacy was carried out with the use of susceptible and herbicide-resistant cultivars of cornflower ( Centaurea cyanus L.) at the Institute of Plant Protection – National Research Institute. Previous tests carried out with the resistant cultivar confirmed that it exhibits resistance to herbicides from the group of ALS inhibitors. The respective resistance index (RI) classified using Beckie and Tardif’s scale modified for ALS inhibitors (Beckie and Tardif 2012 ) exceeded the value of 71.4, which corresponds to very high resistance (Burgos 2015 ).

In order to conduct the experiment, pots with a commercial, acidic medium (Kronen, Cerekwica, Poland) were used to sow cornflower seeds (depth of 1 cm), which were subsequently kept in a greenhouse under controlled conditions (temperature at 20 ± 2 °C, air humidity at 60%, photoperiod of 16/8 h day/night). Appropriate soil moisture in the pots was ensured by watering. After 16 days, the number of seedlings per pot was thinned to four. Treatment with the studied herbicidal ionic liquid was carried out at the 4-leaf stage (BBCH 14), along with a commercial herbicide formulation Autumn 10 WG (10% iodosulfuron-methyl-sodium; Bayer, Germany) which served as a reference. The doses of both HIL and the reference herbicide were equal to 10 g of iodosulfuron-methyl sodium salt per ha (calculated with regard to the active substance). A moving nozzle sprayer equipped with flat-fan TeeJet 110/02 VP nozzle (TeeJet Technologies, Wheaton, IL, USA) which delivers 200 L/ha of spray solution at an operating pressure of 0.2 MPa and constant speed of 3.1 m/s was used to carry out the treatment. The spraying system was placed at a distance of 40 cm above the plants. After treatment, the respective sample series were placed in the greenhouse again to ensure controlled environmental conditions. The experiment was terminated after 3 weeks. After this period, both-treated samples and non-treated controls were disassembled into three respective sub-samples which included the above-ground part of the plants, their roots and soil from the vicinity of plants and their rhizosphere, which were collected for subsequent analyses.

Isolation of microbial communities from greenhouse samples

Isolation of microorganisms from the soil and plants’ rhizosphere.

In order to conduct the isolation of soil microorganisms, soil samples (2 g) collected as described in the previous step were homogenised, introduced into sterile Erlenmeyer flasks (150 mL) and subjected to an isolation procedure described by Hallmann et al . (2017) (Hallmann et al. 2007 ). In general, the soil was inoculated with a 50% TSB medium (25 mL) and incubated at 30 °C for 48 h on a rotary shaker (120 rpm). Subsequently, after decantation of the medium from soil, the microbial cells were separated via centrifugation (4500 rpm, 4 °C for 15 min) and stored using glycerol stocks (20% v/v, − 80 °C). All samples were prepared with three repetitions.

Isolation of epiphytes from plant tissues

For isolation of epiphytes, the plant material was washed under tap water to remove soil residues for approx. 10–15 min (Anjum and Chandra 2015 ). After washing, 1 g of the respective plant material (either leaves and shoots or roots) was separated and introduced into a sterile beaker under a laminar flow cabinet. Afterwards, sterile deionized water was added and the content was stirred for 1 min. Subsequently, 1 mL of the leachate after washing was transferred into a sterile Erlenmeyer flask (150 mL) using a pipette, followed by inoculation with a 50% TSB medium (25 ml) and incubation at 30 °C for 48 h on a rotary shaker (120 rpm). Finally, the microbial cells were centrifuged (15 min, 4500 rpm, 4 °C) and stored in glycerol stocks (20% v/v, − 80 °C), in accordance with section ‘ Isolation of microorganisms from the soil and plants’ rhizosphere ’. All samples were prepared with three repetitions.

Isolation of endophytes from plant tissues

In case of isolation of endophytes, the plant material was washed, then drained from excess water and cut into three equal parts (approx. 0.3 g each). The parts were introduced into a sterile beaker under a laminar flow cabinet. To extract the endophytes, the plant material was subjected to treatment with 70% ethanol for 30 s, followed by sodium hypochlorite solution (2%, v/v, with 2 mL/L of Triton X-100 surfactant) for 2 min, and 70% ethanol again for 30 s. The samples were then rinsed with deionized water five times, then ground under the laminar flow cabinet using a sterile mortar. Subsequently, 1 mL of the ground plant material was transferred into a sterile Erlenmeyer flask (150 mL), followed by inoculation with a 50% TSB medium (25 mL) and incubation at 30 °C for 72 h on a rotary shaker (120 rpm). Finally, the microbial cells were centrifuged (15 min, 4500 rpm, 4 °C) and stored in glycerol stocks (20% v/v, − 80 °C), in accordance with section ‘ Isolation of microorganisms from the soil and plants’ rhizosphere ’. All samples were prepared with triple repetitions.

Identification of microbial community composition via 16 s RNA sequencing

Isolation of dna for 16s rna sequencing.

DNA was extracted using the Genomic Mini Spin kit (060-100S, A&A Biotechnology, Gdańsk, Poland) in accordance with the protocol provided by the manufacturer. After elution of the purified DNA, the isolates were stored at − 80 °C after neutralization to ensure minimal degradation of the matrix.

Evaluation of isolation efficiency was based on a fluorometric analysis with the use of Qbit 3.0 device and the Qubit™ dsDNA HS Assay Kit (Q32851, ThermoFisher Scientific, Waltham, MA, USA). In case of each sample, three independent DNA extractions were carried out and the material was combined following positive quantification.

PCR amplification and NGS sequencing

PCR and NGS sequencing are commonly employed methods when it comes to analysis of xenobiotics impact on soil microbiome, that is able to accurately demonstrate shifts in composition of bacterial community (Huang et al. 2023 ; Pang et al. 2023 ; Wilms et al. 2023b , a ). Therefore, we have decided to utilize similar approach in this study. Briefly, PCR was conducted using the Ion 16S™ Metagenomics Kit (A26216, Life Technologies, Carlsbad, CA, USA) in accordance with the protocol provided by the manufacturer. The kit is designed to amplify the V2–V9 regions of the bacterial 16S rRNA gene. The reaction consisted of 15 µL of 2 × Environmental Master Mix, 3 µL of the appropriate primer and 12 µL of the previously isolated DNA sample and was carried out using the VeritiPro thermal cycler (Life Technologies, Carlsbad, CA, USA). The following temperature program was applied: initial denaturation for 10 min at 95 °C; 25 cycles of denaturation for 30 s at 95 °C; annealing for 30 s at 58 °C; extension for 20 s at 72 °C; and a final extension for 7 min at 72 °C.

The products of the PCR reaction were purified with the use of the Agencourt AMPure XP Reagent (A63880, Beckman Coulter, Pasadena, CA, USA) in accordance with the protocol provided by the manufacturer. The DNA was bound to magnetic beads and contaminants were purified using ethanol. Afterwards, the DNA was eluted with the use of nuclease-free water or low-TE buffer. Subsequently, a library was prepared and purified using Agencourt AMPure XP Reagent (A63880, Beckman Coulter, Pasadena, CA, USA) in accordance with the instructions of the Ion Plus Fragment Library Kit (4,471,252, Life Technologies, Carlsbad, CA, USA). Upon isolation, the DNA was subjected to storage. Determination of library concentration was conducted with the use of Ion Universal Library Quantitation Kit and Quant Studio 5 real-time PCR instrument (A26217, Life Technologies, Carlsbad, CA, USA). In the next step, the library concentration was diluted to 10 pM and onto beads in emulsion PCR was carried out using the Ion PGM™ Hi-Q™ View OT2 Kit reagent kit and an Ion One Touch 2 Instrument (A29900, Life Technologies, Carlsbad, CA, USA). The beads were then purified with the use of an Ion One Touch ES Instrument (Life Technologies, Carlsbad, CA, USA) and subjected to sequencing using the Ion PGM™ Hi-Q™ View Sequencing Kit (A29900) on an Ion 316™ Chip Kit v2 BC (Life Technologies, Carlsbad, CA, USA).

Bioinformatic analysis

Reads of sequences originating from Ion Torrent (Thermo Fisher Scientific, Waltham, MA, USA) were obtained in the BAM format and imported into the CLC Genomics Workbench 20.0 software (Qiagen, Hilden, Germany). The data was then processed with the use of CLC Microbial Genomics Module 20.1.1 (Qiagen, Hilden, Germany). All chimeric and low-quality reads were removed (based on a quality limit of 0.05 and an ambiguous limit of ‘N’), while the remaining sequences were clustered against the SILVA v119 database. A 97% similarity threshold was used for operational taxonomic units (OTU), and statistically significant differences were evaluated using one-way ANOVA.

DNA isolation and storage

Isolation of DNA was conducted using the Genomic Mini kit (A&A Biotechnology, Gdańsk, Poland) in accordance with the protocol provided by the manufacturer. In order to confirm the purity and quantity of the isolated DNA, a Qubit 4 Fluorometer and Qubit™ dsDNA HS Assay Kit (Q32851, Thermofisher Scientific, Waltham, MA, USA) was used. Upon isolation, the obtained genetic material was stored in a 0.1 M Tris buffer at − 20 °C for no longer than 2 weeks.

Determination of genes responsible for degradation of sulfonylurea herbicides

Genes and primer sequences for pcr analysis.

All genes and corresponding primer sequences used for the analysis were presented in Table S1 .

Microbial material

Hanshlegiella zhihuiae S113 (DSM 18984), Streptomyces griseolus 14,576–4 ( Streptomyces halstedii DSM 40854) and Bacillus subtilis 168 (DSM 23778) were used as positive control to ensure proper optimization of PCR reaction parameters. All the strains were obtained for Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures and cultivated in accordance with the provided protocols.

PCR reaction

After performing a temperature optimisation for all the primers and ensuring that each primer produced only the main product, appropriate annealing temperatures were established, which were presented in Table S2 . Furthermore, the genes were tested using negative templates and no visible products were observed on electrophoretic gel under the temperatures listed in Table S2 . In each case, DNA originating from a respective biological sample (25 ng) was used as a template under the following reaction conditions: initial denaturation (1 × ; 4 min at 95 °C), 40 steps of denaturation (30 s at 95 °C), primer annealing (45 s at temperature listed in Table S2 ) and extension (1 min 45 s at 72 °C), followed by final extension step (5 min at 72 °C) and storage step (∞ min at 4 °C). Control samples with sterile deionized water used as template were also tested.

Electrophoresis parameters

In order to separate the PCR reaction products, a 1.5% agarose gel (Prona Agarose; Basica Le, Burgos, Spain) stained with Midori Green Advance DNA Stain (Nippon Genetics Europe, Düren, Germany) for 70 min at 120 V with the use of MultiSub Midi (CleaverScientific, Rugby, UK). Subsequent visualization carried out using a microDOC (CleaverScientific, Rugby, UK) apparatus.

Genes and primer sequences for qPCR analysis

Based on previous studies regarding genes abundance in HILs treated soil, the methodology was adapted and utilised in this study (Wilms et al. 2023a , b ). Briefly, analysis of gene expression was conducted using the Power SYBR Green PCR Master Mix (Life Technologies, Carlsbad, CA, USA) and ABI 7500 SDS (Applied Bio-systems, Thermo Fischer Scientific, Waltham, MA, USA) in accordance with the protocol provided by the manufacturer. Real-time PCR was carried out with the use of primers listed in Table S3 . Amplification of a bacterial 16 S ribosomal RNA fragment with the use of primers and a TaqMan MGB probe was carried out in order to quantify the total bacterial RNA with the use of a TaqMan Universal Master Mix II (Life Technologies, Carlsbad, CA, USA) and ABI 7500 SDS (Applied Biosystems, Thermo Fischer Scientific, Waltham, MA, USA). Each analysis was carried out with three repetitions. The mean expression index was used to evaluate gene expression in each sample, which was calculated based on three analyses using the following formula: C T target/ C T 16 S. The index represents the level of the specific gene relative to the universal gene (16 S RNA) within the entire metabiome.

Statistical analysis

The presented results were calculated as average values based on at least four replicates prepared for each sample in a randomized setup, with standard errors of the mean (SEM) for each respective set of samples obtained using the following equation:

where SEM is the standard error of the mean, s is the sample standard deviation and n is the number of samples.

The datasets were subjected to one-way ANOVA analysis with p  < 0.05 in order to determine the statistical significance of evaluated differences and pairwise Kruskal–Wallis tests were conducted for metagenomic data.

Results and discussion

Previous research regarding the environmental effects of ionic liquids with herbicidal activity clearly demonstrated that cation is solely responsible for the toxicity towards the microbiome and the driving force behind structural shifts in the soil microcosms (Stachowiak et al. 2021 ; Wilms et al. 2023a , b ; Woźniak-Karczewska et al. 2022 ). However, no evidence has been presented so far that similar phenomena occur in the tissues of plants. In this study, we aimed to investigate whether epiphytes and endophytes react in a similar manner compared to soil and rhizosphere microbiome (Fig.  1 ). Additionally, we selected genes which participate in the biotransformation of sulfonylureas to biologically inactive forms, and we investigated their natural occurrence as well as abundance in isolated environmental samples. This was conducted in order to determine whether HILs had a stronger detrimental effect than the classic form of herbicide on bacteria capable of handling sulfonylurea herbicides, or rather enhanced their proliferation in the microbiome.

Scheme of molecular analyses performed on susceptible (light green) and resistant (dark green) cultivars of Centaurea cyanus

Two of the mentioned genes are well established genes from S. griseolus 14,576–4 (class: Actinomycetia ) (Harder et al. 1991 ; Hussain and Ward 2003 ; Omer et al. 1990 ), while additional four novel genes have been discovered recently. Three of them can be found in B. subtilis YB 1 (class: Bacilli ), all involved in the production of extracellular enzymes that possess a secondary function of degrading active form of various sulfonylureas (Kang et al. 2012 , 2014 ; Lu et al. 2012 ; Z. Zhang et al. 2018a , b ; Zhang et al. 2020 ). The last gene encodes an enzyme from H. zhihuiae S113 (class: Alphaproteobacteria ) with the primal function of degrading sulfonylureas (Hang et al. 2012 ; Liu et al. 2019 ); moreover, this gene seems to be expressed on a constant basis (Hang et al. 2012 ).

Due to the fact that we selected genes that encode extracellular enzymes, we have decided to perform our studies on both susceptible and resistant biotypes of cornflower in order to determine whether the biotype’s resistance is correlated with the composition of its microbiome.

Synthesis and analysis of HIL

The initial stage of experiments was focused on the synthesis of HIL which was later used for comparative purposes with the commercial herbicide during subsequent studies. The structure of the obtained HIL, its basic physico-chemical properties and synthesis yield are shown in Table  1 . The isolated and purified product, obtained with a 92% yield, was a solid at room temperature. Analysis of the melting point revealed that this salt melts at approx. 90 °C, which allows to classify it as an ionic liquid (IL). It was also noted that the product is not hygroscopic, but contains 2.42% of water, clearly indicating that this IL formed a monohydrate (molar ratio equal to 1.09). The obtained NMR spectra are presented in the ESI (Fig. S1 - S3 ).

Herbicidal efficacy evaluation

In the second stage of studies, the commercial herbicide and HIL (both based on N -tetradecylcholine iodosulfuron-methyl as the active compound) were employed to control cornflower biotypes both susceptible and resistant to ALS inhibitors. The tested HIL and commercial product showed herbicidal activity against the susceptible ( S ) cornflower, while they did not effectively control the biotype resistant ( R ) to herbicides from the group of sulfonylureas (Fig. S4 ). The fresh weight reduction for the susceptible population was 87% for HIL and 94% for the reference product, respectively. In case of the resistant population, both compounds induced mild symptoms of plant damage, with their effectiveness below 15%. Similar to the susceptible samples, a slightly higher efficacy of the herbicide over HIL was observed, although these differences were not statistically significant.

In order to prevent weed resistance to ALS inhibitors (and herbicides in general), it is crucial to use herbicides with different modes of action (Heap, n.d.). Over the past 2 years, two reports regarding ionic liquids comprising two or three active ingredients with distinct modes of action have been released (Marcinkowska et al. 2023 ; Pernak et al. 2022 ). In the study by Pernak et al. ( 2022 ), HILs containing tribenuron methyl and herbicides from the group of synthetic auxins were tested (Pernak et al. 2022 ). The obtained results indicate that ionic liquids containing an anion from the phenoxy acid group may limit the development of cornflower resistance to ALS. Additionally, in the work of Marcinkowska et al. ( 2023 ), the influence of HILs containing double or triple anions (sulfonylurea and auxin-like herbicides) on weed control of herbicide-resistant cornflower was investigated (Marcinkowska et al. 2023 ), revealing that the tested compounds efficiently reduced the resistant biotype.

Impact of HIL on the microbiome

The goal of the third stage of studies was to evaluate the influence of the herbicide and HIL on susceptible and resistant biotypes of cornflower. The description was divided into five subsections, which respectively focus on shifts in the microbiome of the root surface and inner tissues as well as shoot surface and inner tissues, with the final section dedicated to the assessment of biodiversity indices.

Root surfaces

The analysis of the root surface microbiome of susceptible or herbicide-resistant cornflower revealed that Proteobacteria was the dominant type in all analysed plants, with a relative abundance exceeding 90%, which is consistent with the fact that it is one of the most abundant and diverse types of bacteria present in rhizosphere soil, particularly in the rhizosphere of agricultural crops as well as weeds (Parus et al. 2023 ). Moreover, many members of this group, such as Pseudomonas putida , are well known for their ability to degrade herbicides and other organic xenobiotics (Kivisaar 2020 ; Poblete-Castro et al. 2012 ). The presence of specific soil contaminants usually results in proliferation of key players associated with their degradation; thus, a relatively high abundance of herbicide-degrading representatives of Proteobacteria can be expected in treated soil. The second most abundant type, Firmicutes , ranged from 0.2 to 9.0%, with its lowest relative abundance observed in the root epiphytes of the susceptible cornflower biotype treated with sterile water. Similar abundance levels were noted in samples from both resistant and susceptible biotypes treated with herbicides and the HIL which were comparable to resistant biotype control samples. This finding is notable, given the recognized value of Firmicutes in agroecology (Hashmi et al. 2020 ). Notably, their ability to modulate plant hormonal production or produce analogues of plant hormones could assist plants in coping with herbicidal stress, potentially enhancing their resistance (Hashmi et al. 2020 ). The contribution of other bacterial types did not exceed 0.05% (Fig. S5 ). The dominant bacterial classes in this niche were Gammaproteobacteria , Alphaproteobacteria , Betaproteobacteria and Bacilli .

Root inner tissues

The analysis of cornflower endophytes, in untreated plants (controls) and plants after herbicide treatment belonging both to resistant and susceptible cultivars, revealed no significant differences in microbiome composition present in the inner tissues of their roots (Fig.  2 ). In all studied plant tissues, Proteobacteria type predominated in controls, contributing from 92 to 99%. Similarly, high levels of Proteobacteria were observed in the internal root tissue of herbicide-treated susceptible weeds (99.6%). However, in herbicide-resistant cornflower treated with HIL, the contribution of Proteobacteria in endophytes decreased to 45%, with the Firmicutes becoming the dominant type at 54%. This phenomenon can be attributed to the fact that the mechanism of action of HILs is dictated by the activity of the cation, and the susceptibility of bacteria is affected by the structure of their cell wall (Parus et al. 2021 ; Stachowiak et al. 2021 ; Wilms et al. 2023a , b ). Hence, it is probable that endophyte population was more affected by the cation present in the HILs molecule than by the sole herbicide. Untreated plants also exhibited the presence of Actinobacteria , ranging from 3.8 to 5.4%, which could not be observed in any of the treated plants. The presence of Actinobacteria is considered beneficial in terms of plant growth stimulation as well as plant survival and soil management (Aamir et al. 2019 ; Singh and Dubey 2018 ).

Analysis of the inner root tissue microbiome composition of susceptible and herbicide-resistant cornflower. C, untreated control; H, herbicide treatment; HILs, herbicidal ionic liquid treatment

At the class level, the dominant ones included Alphaproteobacteria , Gammaproteobacteria , Actinobacteria and Bacilli . The increase in the contribution of the Bacilli class was particularly prominent in the internal root tissue of plants resistant to herbicides treated with HILs, in case of which they were recorded at 55% of the general population. This contradicts with the previous research conducted by Stachowiak et al. ( 2021 ) which demonstrated that Bacillus cereus (member of the Bacilli class) is generally more sensitive to ionic forms of iodosulphuron-methyl than P. putida (member of the Gammaproteobacteria class) (Stachowiak et al. 2021 ).

Shoot surfaces

The microbiomes of the shoot surface in both untreated sensitive and resistant biotypes did not differ significantly (Fig.  3 ). In both cases, the dominant type was Firmicutes (90–92.5%). However, subjecting the plants to both herbicide and HIL treatment led to significant changes in the shoot surface microbiome of both plant types. These changes are different in S and R biotypes, suggesting that the microbiome may contribute to the plant’s resistance to the herbicide in the studied plants. In the case of the resistant populations treated with the sole herbicide, the contribution of Firmicutes decreased to 61%, with Proteobacteria accounting for the remaining 39%. The decreased abundance of Firmicutes contradicts with the previously presented increase for root inner tissues, and indicates a plausible migration of bacteria belonging to this phylum from shoot surface into the roots. On the contrary, Proteobacteria dominated the microbiome of the shoot surface of susceptible cornflower treated with the herbicide, constituting 99.7% of the microbiome. This dominance (exceeding 98%) is also evident in both S and R populations after the application of HILs. However, it must be noted that the phyllosphere microbiome exhibits greater dynamism compared to rhizosphere and endosphere environments (Dastogeer et al. 2020 ). Namely, microbial inhabitants experience variations in heat, moisture and radiation throughout the day and seasons, and the phyllosphere is mainly exposed to herbicidal spraying. Furthermore, these environmental factors impact plant functions such as photosynthesis, respiration and water uptake, thereby indirectly shaping the composition of the microbiome. Overall, these results indicate that in case of resistant biotypes HILs contribute to a significant shift in the structure of the microbiome compared to the reference herbicide, similar to the inner root tissue experiment.

Analysis of the shoot surface microbiome composition of susceptible and herbicide-resistant cornflower. C, untreated control; H, herbicide treatment; HILs, herbicidal ionic liquid treatment

Shoot inner tissues

The analysis of the microbiome in both untreated sensitive and resistant biotypes subjected to treatment with sterile water revealed significant changes in their microbiomes (Fig.  4 ). Bacteria from the Firmicutes type dominated the tissue of the herbicide-resistant plants, specifically the class Bacilli (99%), while Proteobacteria were dominant (96.5%) in the tissue of herbicide-susceptible plants, with the class Gammaproteobacteria accounting for 91% of the microbial composition. The application of the herbicide did not cause changes in the microbiome of the sensitive populations, but in the case of the resistant populations, the bacterial community structure shifted significantly and resembled that of herbicide susceptible biotypes, albeit Cyanobacteria were only detected in R biotypes. This finding further suggest that, in the examined plants, the microbiome may be partially responsible for the weeds’ resistance to herbicides, as Cyanobacteria are known to improve plants’ stress tolerance (Álvarez et al. 2023 ). Proteobacteria was the dominant type in both cases, making up 91.5% of bacteria present in these tissues, of which Gammaproteobacteria dominated. However, the addition of herbicide in the form of HIL resulted in a significant differentiation of the endophytes of both populations. Actinobacteria dominated (56% and 73.5%, respectively) in both the S and R weeds. Additionally, in the sensitive population, the second dominant type was Proteobacteria (43.5%), while in the resistant population, it was Firmicutes (25%). In terms of class contributions in these microbiomes, Actinobacteria (56%) and Alphaproteobacteria (43%) dominated in the sensitive plant, while in the resistant biotype, Actinobacteria accounted for 73%, while Bacilli constituted 25%. It can be clearly seen that the HIL compound had a significant impact on the composition of the microbiome of the examined plants; namely, the core microbiome remained the same as in untreated plants, but ionic liquid treatment resulted in an unexpected spike in the population of Actinobacteria.

Analysis of the inner shoot tissue composition of microbiome of susceptible and herbicide-resistant cornflower. C, untreated control; H, herbicide treatment; HILs, herbicidal ionic liquid treatment

Evaluation of biodiversity in soil microbiomes

The analysis of diversity parameters of bacterial communities revealed several key patterns in the plant microbiomes (Table  2 ). Firstly, it was observed that the root microbiome exhibited higher biodiversity compared to the shoot microbiome; furthermore, the biodiversity of microorganisms on the surfaces of shoot and roots surpassed that within their respective tissues. In the case of the phyllosphere, this phenomenon is caused by the fact that leaf surfaces are relatively lacking in nutrients in comparison to the rhizosphere and endosphere. Additionally, variations in heat, moisture and radiation throughout the day and seasons naturally result in lower biodiversity (Dastogeer et al. 2020 ). Additionally, Wagner et al. (2015) demonstrated that host plant genetic control of the microbiome is evident in leaves but not in roots (Wagner et al. 2016 ). Consequently, in this study, root endophyte biodiversity was more evident than shoot endophyte diversity.

The microbiome of untreated plants exhibited greater biodiversity than that of plants subjected to herbicidal treatment. Such an outcome is to be expected; awareness of the impact of herbicides on the soil microbiome is growing, and alterations in the soil microbiome are assumed to impact crucial nutrient cycling and processes between plants and soil (Ruuskanen et al. 2023 ). However, no significant differences were observed in the root epiphyte microbiome of both susceptible and resistant weeds. Distinct variations were noticed in the shoot epiphyte microbiome in both plant types, which is probably caused by the specific properties of the phyllosphere mentioned above (Dastogeer et al. 2020 ).

Gene presence in biological samples

Following the assessment of the impact of the herbicide and HIL on the structure of the microbiome of sensitive and resistant biotypes of cornflower, we have attempted to determine the presence of genes that encode enzymes involved in the catabolism of sulfonylurea herbicides in plant surface and tissues as well as rhizosphere. The selected genes originate from soil-borne bacteria and have been proven to participate in the transformation of sulfonylurea herbicides into biologically inactive forms (Hang et al. 2012 ; Harder et al. 1991 ; Hussain and Ward 2003 ; Kang et al. 2014 , 2012 ; Lu et al. 2012 ; Omer et al. 1990 ; H. Zhang et al. 2018a , b ; Z. Zhang et al. 2018a , b ). Moreover, evidence suggests that bacteria with the ability to produce enzymes that detoxify iodosulfuron may enable other bacteria susceptible to sulfonylurea herbicides to grow in a sulfonylurea-contaminated environment (Arabet et al. 2014 ). Thus, they either promote the spread of genes encoding resistance to herbicides or induce the evolution of resistance in susceptible bacteria when the minimal lethal concentration is not reached.

B. subtilis YB1, initially discovered in Chinese farmlands polluted with sulfonylureas, demonstrated the capability to degrade up to 80% of nicosulfuron in liquid medium (Lu et al. 2012 ), and was proven to utilise nicosulfuron as a sole carbon source under aerobic conditions (Kang et al. 2012 ). Further analyses in a chamber study confirmed its efficacy in degrading a wide array of herbicides from this group, including rimsulfuron, bensulfuron methyl, pyrazosulfuron-ethyl, cinosulfuron and tribenuron-methyl (Zhang et al. 2020 ). Crucially, the enzymes involved in the degradation, namely manganese ABC transporter, vegetative catalase 1 and acetoin dehydrogenase E1 (Kang et al. 2014 ; Z. Zhang et al. 2018a , b ), are extracellular proteins that have been shown to be expressed after induction (Kang et al. 2014 , 2012 ), and act via pyrimidine ring and sulfonylurea bridge cleavage (Z. Zhang et al. 2018a , b ). Unfortunately, we were unable to obtain strain YB1; hence, the model strain B. subtilis 168 was used instead of B. subtilis YB1. However, the amino acid sequence of B. subtilis YB1 matches that of B. subtilis 168 very closely; the manganese ABC transporter sequence is 99% identical, vegetative catalase 1 is a 100% match and the acetoin dehydrogenase amino acid sequence matches by 99% (Kang et al. 2014 ; Z. Zhang et al. 2018a , b ).

Another bacterium with sulfonylurea-degrading ability utilised in this study is S. griseolus 14,576–4 (also known as S. halstedii ) (Harder et al. 1991 ; Omer et al. 1990 ). S. griseolus can metabolize various sulfonylurea herbicides into often less phytotoxic compounds, which is facilitated by two sulfonylurea-inducible cytochrome P-450 monooxygenases: cytochrome P-450 SU-1 and cytochrome P-450 SU-2 (Omer et al. 1990 ). Partial characterization and reconstitution studies suggest that the two inducible P-450 monooxygenase systems in S. griseolus share similarities with the three-component cytochrome P-450 CAM camphor oxidation system found in P. putida (Harder et al. 1991 ; Omer et al. 1990 ).

Another bacterium utilized in this study, H. zhihuiae S133, was isolated from heavily sulfonylurea herbicide-contaminated farmland soil in Jiangsu province, China (Wen et al. 2011 ). It is capable of producing a constitutively expressed deestrification estrase, SulE , which can transform thifensulfuron-methyl, metsulfuron-methyl, bensulfuron-methyl, ethametsulfuron-methyl and chlorimuron-ethyl into herbicidally inactive corresponding acids forms (Hang et al. 2012 ; Liu et al. 2019 ). Furthermore, it has been shown that this bacterium can survive in the rhizosphere of cucumber, colonize its roots and efficiently degrade chlorimuron-ethyl in the plants’ rhizosphere (H. Zhang et al. 2018a , b ). In addition, H. zhihuiae CHL1, with 98% similarity of 16S rRNA gene sequence to H. zhihuiae S133, was isolated from soil by (Yang et al. 2014 ). This strain efficiently transforms chlorimuron-ethyl, metsulfuron-methyl and tribenuron-methyl (Yang et al. 2014 ). Therefore, the dissemination of SulE enzyme may be more widespread in the soil environment than initially assumed.

Soil and rhizosphere

All investigated genes encoding the aforementioned enzymes, except for vegetative catalase from B. subtilis , were found in the samples isolated from soil (Table  3 ). In case of acetoin dehydrogenase-encoding genes, no discernible trend was immediately visible, as both primers targeting this enzyme’s gene were present at similar rates in all samples isolated from both susceptible and resistant cultivars’ soil. Regarding the manganese ABC transporter, the cytochrome P450 enzyme genes appeared to be more prevalent in the soil with the resistant biotype. As for the sulfonylurea deestrification estrase SulE encoding gene, it was primarily found in both cultivars in samples exposed to HIL rather than the herbicide, although it could also be detected in the soil of untreated resistant weeds.

A single general trend was observed in the case of rhizosphere, namely, a decrease in the presence of genes derived from B. subtilis in control samples and samples exposed to herbicidal treatment, while in the susceptible population, the previously found genes encoding B. subtilis enzymes and vegetative catalase were present. Moreover, an increase of the presence of both P450 SU-1 and SulE encoding genes could be observed. The latter result is particularly notable, as it has been proven H. zhihuaiae is capable of penetrating plant tissues and persisting as an endophyte (H. Zhang et al. 2018a , b ). Additionally, it has been evidenced that root secretions promote the growth of this bacterium.

Root epiphytes and endophytes

Generally, the number of gene copies in both cornflower populations in root samples appears lower than in the case of rhizosphere (Table  4 ). Notably, more gene copies can be observed in the susceptible cultivar compared to the resistant one. However, of the greatest importance is the fact that the SulE gene can be observed in both epi- and endophytes of susceptible cultivar treated with HILs, and in epiphytes in the case of commercial herbicide treatment. In contrast, in the resistant cultivar, SulE genes were present only in control sample’s epiphytes. According to Zhang et. al. (2018), H. zhihuaiae can biotransform and thus detoxify sulfonylurea herbicides in the plant rhizosphere (H. Zhang et al. 2018a , b ). Additionally, primers targeting the SulE gene have been used as a tool-marker to isolate new strains of Hanschlegiella bacteria (Yang et al. 2014 ).

The acetoin dehydrogenase-encoding gene and the gene encoding manganese ABC transporter were present in samples isolated both from epiphytes and endophytes in root samples of susceptible and resistant cornflower. However, vegetative catalase was more commonly found in S biotype than in the R biotype. Notably, the cytochrome P450 enzyme encoding-gene was found only in the susceptible population treated with HILs.

Shoot epiphytes and endophytes

Samples isolated from shoots and foliage were analysed (Table  5 ). It appears that genes encoding enzymes participating in sulfonylurea degradation were less commonly found in the S biotype compared to the R biotypes. Vegetative catalase, acetoin dehydrogenase, manganese ABC transporter and sulfonylurea esterase-encoding genes were found in epiphytic samples of both susceptible and resistant populations treated with the herbicide. However, after treatment with HILs, these genes were only found in resistant plants. On the other hand, all genes encoding enzymes conferring resistance to sulfonylureas were found in endophytic samples isolated from the resistant biotype, while only SulE and P450 were found in the susceptible biotype. The acetoin dehydrogenase gene was also detected in resistant cornflower treated with HILs compared to the same population treated with the sole herbicide.

The analysis of gene presence in environmental samples of soil and plant tissues revealed an important aspect associated with the genes encoding enzymes that facilitate bacterial degradation of sulfonylureas, namely that genes originating from B. subtilis , S. griseolus and especially H. zhihuaiae were more widespread in samples treated with herbicide and HILs than initially suspected. They were the most common in soil and rhizosphere, with slightly higher prevalence in samples in which the resistant biotype of cornflower was growing. Higher diversity of genes was generally observed in the resistant biotype samples derived from the shoots and foliage; in contrast, this trend was reversed in case of samples derived from plant roots as the susceptible biotype was characterized by a broader spectrum of determined genes. Moreover, no conclusive observations regarding the impact of the HILs and herbicide on the gene frequency could be made.

Gene abundance in biological samples

In order to provide further insight regarding genes associated with degradation of sulfonylurea herbicides, the final stage of studies was focused on the assessment of their abundance using RT-qPCR technique in the microbiomes originating from the surface and inner tissues of roots and shoot obtained from both susceptible and resistant biotypes, using 16S RNA gene as a reference.

The Cytochrome P-450 SU-1 genes, which were determined as present in shoot samples of both susceptible and resistant biotypes as well as in root samples of resistant biotypes, could not be efficiently quantified via RT-qPCR (Fig.  5 and Table S1 ). This finding indicates that, although the genes are present, their abundance in samples is low.

Log2-fold change values determined by real-time PCR of ABC, Vegecat, ACTH and SulE genes from samples isolated from resistant and susceptible cultivars of cornflower. Green indicates control group, blue indicates group of plants sprayed with herbicide and yellow indicates group of plants sprayed with HILs

The root surface of the susceptible biotype has exhibited a higher enrichment in the examined genes compared to the root surface of the resistant biotype. This phenomenon might be attributed to the fact that resistant plants are inherently better adapted to biotransform sulfonylureas utilising molecular mechanisms present in their tissues, rather than relying on external factors, such as microbes. In contrast, susceptible plants, lacking these molecular mechanisms, are more likely to cope with the presence of xenobiotics with the assistance of specialised bacteria possessing genes participating in herbicide degradation (Tétard-Jones and Edwards 2016 ). The acetoin dehydrogenase gene was not observed in either weed biotype, and the vegetative catalase gene was absent in resistant cornflower. However, the vegetative catalase gene was present in the susceptible population, with slightly higher number of genes found in HIL-treated plants compared to herbicide-treated plants, while the gene could not be quantified in control plants. These findings correspond well with the results described in section ‘ Root surfaces ’, as Firmicutes were characterized by the second highest abundance in the microbiome of HIL- and herbicide-treated susceptible plants, whereas the lowest Firmicutes ratio was observed in the control sample (which was lower by one order of magnitude compared to treated samples). Moreover, genes encoding manganese ABC transporter were found in all treatments of sensitive cornflower, with up to a twofold higher abundance in HIL-treated resistant biotype. For the SulE de-esterification esterase genes, they were present in all treatments of the sensitive biotype, with the highest abundance in herbicide-treated samples. Based on these results, it is challenging to determine the impact of herbicide form (ionic liquid or pure herbicide) on the abundance of genes encoding enzymes degrading sulfonylureas, but a clear trend shows these genes are more frequent in the microbiome of plants subjected to either treatment than in the control.

In the inner parts of roots, only manganese ABC transporter could be quantified in the resistant population, suggesting that the abundance of other genes was too low to be detected despite being identified in previous analyses. Intriguingly, vegetative catalase was detected at similar levels in susceptible plants’ roots as in the rhizosphere, despite a significant shift in microbial community composition. This phenomenon is even more evident for manganese ABC transporter, as it was the least abundant in HILs treatment, despite Firmicutes making up 54% of the community, whereas in case of herbicide treatment it was twofold higher, despite Firmicutes constituting less than 5% of the isolated microbiome.

The shoot surface and inner tissues of the S biotype were similar to the root tissue of R biotype, with quantifiable genes only in herbicidal treatment, despite some being detected in previous analyses, suggesting their scarcity in the microcosm. On the other hand, a significantly higher number of gene copies encoding vegetative catalase and acetoin dehydrogenase were detected in resistant weeds after herbicide application than in the untreated population, suggesting bacteria may aid the plant in detoxifying this herbicide. The absence of these genes in samples subjected to HIL treatment implies that cation toxicity impacts the inner tissue microbiome, aligning well with recent trends in utilizing HILs as potential tools for controlling herbicide-resistant weeds (Marcinkowska et al. 2023 ; Pernak et al. 2022 ).

Conclusions

Initial evaluation of herbicidal efficacy against cornflower revealed that the herbicide and HIL exhibit comparable activity, which was satisfactory (> 85%) in case of susceptible biotypes and low (< 15%) for resistant biotypes. On the basis of these results, it can be established that HIL based on a single herbicide are not effective for control of herbicide resistant weeds; thus, the previously mentioned necessity to employ several active compounds seems justified. However, subsequent genetic analyses elucidate additional aspects: (i) treatment with HIL results in significant shifts in bacterial microbiomes, as evidenced by a higher ratio of Actinobacteria in shoot inner tissue for both biotypes as well as increased abundance of Firmicutes in root and shoot endophytes of the resistant biotype; (ii) the presence of the herbicide or HIL leads to decreased microbial alpha-diversity (reflected by decreased OUTs, Chao1 and Shannon indices) relative to control, and the effect of HIL was particularly visible in case of shoot endophytes; (iii) application of HIL decreased the occurrence and abundance of genes associated with the degradation of sulfonylurea herbicides in root and shoot inner tissues of resistant biotypes compared to both herbicide-treated samples and untreated control.

The abovementioned findings contribute to the novelty and significance of the study, as they highlight the potential of HILs to hinder microbiome conferred herbicide resistance in weeds. On the other hand, the obtained results also indicate that the impact of HILs on the soil microbiome is higher compared to classic herbicides, which should be included as an additional factor in risk assessment during the registration of such compounds.

Data availability

Data will be made available on reasonable request.

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This work was supported by funds from the National Science Centre, Poland conferred on the basis of the decision 2018/29/B/NZ9/01136.

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Department of Chemical Technology, Poznan University of Technology, 60-965, Poznan, Poland

Jan Homa, Wiktoria Wilms, Łukasz Ławniczak, Marta Woźniak-Karczewska, Michał Niemczak & Łukasz Chrzanowski

Department of Weed Science, Institute of Plant Protection – National Research Institute, 60-318, Poznan, Poland

Katarzyna Marcinkowska

Department of Food Technology of Plant Origin, Poznan University of Life Sciences, 60-624, Poznan, Poland

Paweł Cyplik

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Jan Homa conceptualized the study, conducted formal analyses, carried out investigations, drafted the original manuscript, and participated in the revision and editing process, as well as contributed to visualization tasks. Wiktoria Wilms contributed significantly to the investigation process, reviewed and edited the manuscript, and aided in visualization efforts. Katarzyna Marcinkowska and Paweł Cyplik both contributed to formal analysis, investigation, resourcing, and visualization tasks. Łukasz Ławniczak reviewed and edited the manuscript. Marta Woźniak-Karczewska was primarily involved in the investigation phase. Michał Niemczak contributed to formal analysis, investigation, resourcing, and visualization. Łukasz Chrzanowski provided contributions to conceptualization, resource management, supervision, project administration, and securing funding for the research. All authors read and approved the final manuscript.

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Homa, J., Wilms, W., Marcinkowska, K. et al. Comparative analysis of bacterial populations in sulfonylurea-sensitive and -resistant weeds: insights into community composition and catabolic gene dynamics. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-34593-z

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Received : 19 April 2024

Accepted : 29 July 2024

Published : 16 August 2024

DOI : https://doi.org/10.1007/s11356-024-34593-z

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