Comprehensive insights into herbicide resistance mechanisms in weeds: a synergistic integration of transcriptomic and metabolomic analyses

Abstract

Omics techniques, including genomics, transcriptomics, proteomics, and metabolomics have smoothed the researcher’s ability to generate hypotheses and discover various agronomically relevant functions and mechanisms, as well as their implications and associations. With a significant increase in the number of cases with resistance to multiple herbicide modes of action, studies on herbicide resistance are currently one of the predominant areas of research within the field of weed science. High-throughput technologies have already started revolutionizing the current molecular weed biology studies. The evolution of herbicide resistance in weeds (particularly via non-target site resistance mechanism) is a perfect example of a complex, multi-pathway integration-induced response. To date, functional genomics, including transcriptomic and metabolomic studies have been used separately in herbicide resistance research, however there is a substantial lack of integrated approach. Hence, despite the ability of omics technologies to provide significant insights into the molecular functioning of weeds, using a single omics can sometimes be misleading. This mini-review will aim to discuss the current progress of transcriptome-based and metabolome-based approaches in herbicide resistance research, along with their systematic integration.

1 Introduction

Herbicide resistance among weeds has become a major reason for food security problems since the early years of synthetic herbicide development. Since then, there has been a continuous increase in the frequency of herbicide resistance cases, a major plant protection challenge encountered by farmers across the planet. Herbicides have been used to control weeds and increase the quality and quantity of major crops since their discovery. However, repeated use of similar herbicidal modes of action (MOAs) and the associated selection pressure have led to the evolution of herbicide resistance in varieties of economically important weedy species such as Alopecurus myosuroides (Lan et al., 2022; Goldberg-Cavalleri et al., 2023), Amaranthus palmeri (Manicardi et al., 2023), Apera spica venti (Košnarová et al., 2021; Papapanagiotou et al., 2022), Bromus sterilis (Sen et al., 2021), Lolium spp (Tehranchian et al., 2019; Ma et al., 2020; Zhu et al., 2023). Recent studies showed that the majority of these weeds were also developing multiple resistances against herbicides with more than one mode of action (Tehranchian et al., 2019; Ma et al., 2020; Košnarová et al., 2021; Lan et al., 2022; Papapanagiotou et al., 2022; Zhu et al., 2023). From a molecular point of view, herbicide resistance mechanisms might be categorized into target-site based resistance (TSR) and non-target-site based resistance (NTSR) (Torra and Alcántara-de la Cruz, 2022). Briefly, TSR comprises single or multiple nucleotide polymorphisms (point mutations), codon deletion (till date there is only one evidence in the case of protoporphyrinogen oxidase in Amaranthus sp.) and target gene overexpression (either via transcriptional regulatory mechanisms or via gene amplification) (Gaines et al., 2020). On the other hand, NTSR constitutes enhanced metabolism [via cytochrome P450s (Cyp450s), glutathione S-transferases (GSTs) and glycosyltransferases (GTs)], reduced uptake and translocation, vacuolar sequestration and increased ability to deal with oxygen radicals (Gaines et al., 2020). More details on these mechanisms can be found in Gaines et al., 2020. Reports show that the NTSR is more complicated and trickier to understand than the TSR (Jugulam and Shyam, 2019). Nevertheless, irrespective of the specific resistance mechanism, herbicide-resistant weeds, if left uncontrolled, will pose a significant threat to the global agricultural sector.

High-throughput molecular technologies (such as genomics, proteomics, metabolomics, transcriptomics etc.) have the potential to offer great analytical opportunities to understand and identify the mechanisms of herbicide resistance, thus leading to the identification of novel herbicidal MOAs (Ravet et al., 2018). Development of herbicide resistance in weeds is similar to that of abiotic stress resistance development in plants and can provide insights into the stress resistance mechanisms in plants in general (Hamouzová et al., 2023). Even though during the last decade, continuous progress has been made in the field of DNA, RNA, and protein-based omics studies in herbicide resistance, substantial research areas are yet to be explored. Additionally, to our knowledge, inadequate initiatives have been taken to integrate multiple omics-based studies to elucidate the mechanisms of herbicide resistance in economically significant weeds. Hence, in this mini-review, we will confine our discussion to transcriptomics, metabolomics and the need for their systematic integration to unravel the molecular mechanisms of herbicide resistance in weeds. The transcriptome and metabolome represent two key layers of biological information that can provide novel insights into how weeds develop resistance against herbicides. While transcriptomics provides a global overview on the expression profile of the genes, metabolomics provides a global overview on metabolic pathways and their regulation (Maroli et al., 2018a).

2 Weeds’ response to herbicide stress: effects of herbicide stress on weeds’ metabolic pathways and relevant genes involved

Among the metabolic pathways, the most important ones are carbohydrate metabolism, amino acid metabolism, polyamine metabolism, and lipid metabolism (Arbona et al., 2013). Carbohydrate metabolism is directly linked to photosynthetic performance and is the primary source of energy during stressful periods. Photosystem inhibitors such as atrazine, chlorotoluron and metribuzin are known to be involved in the disruption of the light-capturing reactions and electron transport chain in chloroplasts. These herbicides inhibit the production of ATP and NADPH (via competitive binding to the plastoquinone binding site (QB) on the D1 protein, which is encoded by the PsbA gene), which eventually affects carbohydrate metabolism in the weeds (Ma et al., 2020; Košnarová et al., 2021; Yang et al., 2022a). As of 2022 (according to https://www.weedscience.org/Home.aspx, accessed on July 23^rd, 2023), 87 cases of resistance against PSII-inhibiting herbicides have been reported globally. Mutations in the PsbA gene and increased target gene and/or protein expression were identified as the mechanisms of resistance (Košnarová et al., 2021; Yang et al., 2022b). Besides interfering with the electron transport chain, herbicides can also have adverse effects on starch accumulation, sucrose transport and plant’s respiration process, which eventually will affect carbohydrate metabolism pathways. In addition to these, accumulation of sugars with no energetic roles (such as oligosaccharides) has also been detected in several plant species, as a response to abiotic stresses. These compounds are well-known for their indirect associations with reactive oxygen species (ROS) scavenging (Arbona et al., 2013). ROS scavenging is a vital mechanism while preventing oxidative stress and associated cellular damage. In the context of herbicide resistance, metabolism of herbicides via metabolic enzymes leads to oxidative stress within the weed. In answer to this, resistant weeds activate enzymes such as superoxide dismutase, catalase, and peroxidases and perform ROS scavenging, which eventually provides them survival advantages over their susceptible counterparts. However, very few such research on herbicide resistant-weeds have been conducted till date. Apart from carbohydrate metabolism, amino acid and lipid metabolism are also significant metabolic pathways. Acetolactate synthase and 5-enolpyruvylshikimate-3-phosphate synthase are among the most important enzymes involved in amino acid metabolic pathways and targeted by commercially important herbicides (Tan et al., 2006). Both of these enzymes are involved in the synthesis of aromatic amino acids (phenylalanine, tyrosine, and tryptophan). These amino acids play important roles during protein synthesis as well as in the production of various plant defense-related secondary metabolites. Among the important enzymes involved in fatty acid metabolism targeted by herbicides is Acetyl-CoA carboxylase (ACCase). ACCase plays a critical role in fatty acid synthesis (Takano et al., 2020). During stress periods, lipids play vital roles, which includes membrane fluidity and integrity, as signaling molecules and oxidative stress management (Liu et al., 2019).

3 Integrated transcriptomics-metabolomics: understanding the adaptation, tolerance, and resistance in plants

In the era of systems biology research, biological processes and gene regulatory networks can be dynamic and hard to interpret. Advanced omics technologies such as metabolomics and transcriptomics, have contributed significantly to explicating the molecular mechanisms of plant responses to different stresses. In the last few decades, information derived from next-generation sequencing technologies paired with advanced statistical and computational approaches has allowed for the characterization of genes involved in plant abiotic stress (Mehta et al., 2019; Sahoo et al., 2020; Aruna Kumara and Thiruchchelvan, 2021). Previously, thorough characterization of genes involved in stress responses was limited to crops and model systems because of the requisite genomic resources, but recent advancements permit the study of non-model species such as weeds. Despite challenges like the development of suitable methods and pipelines, RNA-seq transcriptome sequencing has been extensively used for the identification of gene families involved in herbicide resistance, particularly the NTSR (Zhao et al., 2017; Bai et al., 2020; Chen et al., 2021). Till date, there are many evidence of the use of transcriptomics and metabolomics in herbicide-resistance research. While transcriptomics can identify the differentially expressed genes, the metabolites are the ultimate result of controlled gene transcription and hence the metabolome can be considered the basis of the system’s observed phenotype. In the study conducted by Wrzesińska-Krupa et al., the authors used a de novo transcriptome of Apera spica-venti to perform RNA-sequencing analysis of pinoxaden-resistant and susceptible plants. They identified several important genes responsible for herbicide resistance, such as isoforms of UDP-GTs and GSTs (along with others) as the prime candidate genes (Wrzesińska-Krupa et al., 2023). In another study, the authors used a comparative RNA-seq transcriptomic approach to understand how florpyrauxifen-benzyl treatment can affect phytohormone biosynthesis and signal transduction in resistant and susceptible Echinochloa crus-galli (L.) P. Beauv (Jin et al., 2023). Zhao et al., used transcriptome profiling and identified twenty-four contigs (including isoforms of CytP450s, GSTs, GTs, ABC-transporters, and others) involved in mesosulfuron-methyl resistance in Alopecurus aequalis (Zhao et al., 2017).

In addition to the RNA-seq studies, metabolomics has also been used in herbicide-resistance studies. The metabolome refers to the complete set of metabolites present within a biological sample. Even though the metabolome represents the smallest domain (among genome, transcriptome, proteome, and metabolome), it is more chemically complex and diverse than its counterparts (Kumar et al., 2017). In the context of understanding herbicide resistance in weeds, the metabolome can provide additional information since it represents additional levels of regulation as well as the end products of regulatory processes (Boonchaisri et al., 2020). For example, in the study conducted by Zulet-Gonzalez et al., the authors used non-targeted gas chromatography-mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC–MS) metabolomic profiling and examined the phytotoxic effects of glyphosate on sensitive and resistant populations of Amaranthus palmeri S. Wats. They didn’t find any differences in the metabolic profiles in the absence of herbicide treatment. However, upon treatment with glyphosate, they detected reduced concentrations of quercetin and its derivatives only in the resistant plants (Zulet-Gonzalez et al., 2023). In another study conducted by Tafoya-Razo et al., the authors conducted metabolic fingerprinting of susceptible and resistant common Avena fatua L. populations using Desorption Electrospray Ionization-Mass Spectrometry (DIESI-MS). The authors found that metabolomic fingerprinting can be successfully used to study the diversities of herbicide-resistance mechanisms, which can be used to study the “geographic mosaic of resistance” (Tafoya-Razo et al., 2022). From the perspective of NTSR mechanisms, metabolites can regulate the constitutive and induced expression of Cyp450s, GSTs, and GTs, thus leading to modulation of their activities during herbicide stress. In a different way, metabolites can also regulate the expression of the NTSR genes by altering the chromatin structure of the gene’s regulatory regions. To date, such studies are yet to be conducted. Comparative metabolomics among the treated and untreated resistant and susceptible plants will enable us to discover the detailed metabolites that are involved in herbicide detoxification, along with determining their relative abundance and activities. Table 1 summarizes the list of transcriptomics and metabolomics-based research works related to herbicide sensitivities.

It is a well-known fact that, within a biological system, transcript abundance does not always translate to protein abundance due to several biological and technical factors. These factors might include post-transcriptional and translational regulatory factors, mRNA stability, protein degradation and turnover, etc. Additionally, even if a protein is formed, it may not inevitably be functional (Liu et al., 2016; Manavella et al., 2023). In this context, correlating the transcriptome and the proteome might be misleading. Therefore, we strongly advise systematically integrating the transcriptome and metabolome data instead. However, it is important to note that post-translational modifications (such as phosphorylation and acetylation) cannot be identified through transcriptomics and metabolomics alone. In such cases, mounting up more data (from other omics such as proteomics and genomics) can boost our understanding of biological complexities and minimize bias. To date, even though there is a lack of research on using the transcriptome and metabolome data for integrated analysis in herbicide resistance research, a few such studies have been conducted in other plants. For example, in a study conducted by Xu et al. (2023a), the authors combined transcriptome and metabolome data from two contrasting apple species (cold-resistant vs cold-sensitive cultivars) to identify the key metabolic pathways involved in response to cold stress. They identified differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs), which provided novel insights into the mechanisms of cold resistance in apple trees in response to cold stress during dormancy (Xu et al., 2023a). In another study conducted in 2023, the authors used integrated transcriptomic-metabolomic analysis to investigate the possible roles of flavonoids involved in resistance to powdery mildew in wheat. They identified DEGs and differentially accumulated flavonoids and validated them using qRT-PCR and biochemical analyses (flavonoids and malondialdehyde content measurements, antioxidant enzyme activities), respectively (Xu et al., 2023b).

Similarly, among the herbicide resistance mechanisms, NTSRs are always believed to be a cumulative effect of several gene families (Ghanizadeh and Harrington, 2017). Hence, the transcriptome can only identify the expression of the important genes, which is often inadequate when studying the weeds as a system. Additionally, the relationship between metabolite and transcript levels might go beyond merely the fact that gene expression affects the global metabolite levels. Alternatively, gene expression might also be regulated by the metabolites, indicating an interconnection between the two (Anjali et al., 2023). Apart from the functional prediction of genes and coexpression analyses, integrated transcriptome-metabolome can also be used to analyze the organizational principle of the whole system (such as systems characterization of pathways) (Xue et al., 2021). In the current scenario, correlating the transcriptome and the metabolome data might allow us to discover the key genes and the metabolic pathways associated with the herbicide resistance in the system. Parallel analysis of transcript and metabolite profiling can expose novel gene-to-metabolite networks and hence can realistically screen candidate genes involve in herbicide resistance (for example, exactly which isoforms of Cyp450s or GSTs or GTs) (Fukushima et al., 2009; Nakabayashi and Saito, 2015). Currently, there are quite a few comprehensible gene-coexpression tools that are specifically designed for plant genomics research. Some of them include Arabidopsis Coexpression Tool (ACT), Arabidopsis thaliana transcription factor and protein interaction database (ATTED-II), Genevestigator, Co-expression Platform (CoP), PlantPAN 3.0 and GENEVESTIGATOR V4 (Nakabayashi and Saito, 2015; Zogopoulos et al., 2021). Apart from the mentioned comprehensible gene-coexpression tools, there are also several publicly available tools and software platforms that could assist in the parallel analysis of transcriptome and metabolome data. Such tools include MetaboAnalyst (current version: MetaboAnalyst 5.0), GEMINI (Gene Expression and Metabolism Integrated for Network Inference), Plant MetGenMAP, mixOmics, etc. MetaboAnalyst (https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml) is a user-friendly web-based platform designed exclusively for metabolomics data analysis that offers a wide range of capabilities, from raw MS spectra processing to comprehensive data normalization and integration with other omics data (Pang et al., 2022). Plant MetGenMAP (http://bioinfo.bti.cornell.edu/cgi-bin/MetGenMAP/home.cgi) is a web-based system that allows the user to identify the changed pathways from gene expression and/or metabolite profile data. This system enables data visualization in a biochemical pathway context (Joung et al., 2009). GEMINI is a software tool designed to infer regulatory networks that connect genes and metabolites, and hence can be considered as an important tool for the integrated analysis of transcriptomics and metabolomics data (Chandrasekaran, 2019). mixOmics is a collaborative R package-based project (between Australia, France, and Canada) that focuses on multi-omics data integration. This tool can be used for integrative analysis of transcriptomics and metabolomics data, with a special emphasis on dimension reduction (Rohart et al., 2017). Figure 1 describes a schematic framework of the experimental design based on integrated transcriptomics and metabolomics in weeds. Following the analysis of individual transcriptome and metabolome data, statistical and bioinformatic techniques, such as correlation analysis, can be used to integrate them. The coexpression of genes and the production of metabolites can be measured by different types of correlation coefficients such as Pearson correlation coefficient (r), Spearman rank correlation (ρ), Kendall’s Tau (τ) and weighted gene coexpression network analysis (WGCNA). For example, in a study conducted by Yang et al., the authors had used Pearson correlation coefficients and their corresponding p-values to discover metabolites and related genes from a combined metabolomic and transcriptomic analysis (Yang et al., 2022b). In another study conducted by Zhang et al., the authors had successfully identified key structural genes responsible for anthocyanin biosynthesis mechanisms in Vaccinium corymbosum L. using integrated transcriptome and metabolome analysis. They had also used Pearson correlation coefficient values for screening out the genes of interest (Zhang et al., 2022b). In the context of herbicide resistance mechanism discovery, these tools can be used to assess how changes in gene expression levels correspond to variations in metabolite abundances, and vice versa. Based on the integration results, the genes showing significant correlations with specific metabolites can be identified. Prior knowledge of metabolic pathways in combination with the metabolome result can be used to assess the possible biological relevance of these identified genes to herbicide resistance.

Figure 1

4 Summary and outlook: current challenges and way forward

Individually, both metabolomics and transcriptomics are powerful tools for understanding the molecular basis of herbicide resistance. However, determining whether expression of a particular gene or increased concentration of a particular metabolite is the cause or consequence of herbicide adaptation is a difficult task. Such questions can be answered by integrated transcriptome-metabolome studies. Despite significant advances in omics-based studies of herbicide resistance, to the best of our knowledge, to date there have been inadequate efforts made to investigate the power and potential of transcriptome-metabolome correlation analysis in herbicide resistance. This can likely be attributed to the limited availability of genomic and metabolome resources in weeds. Even though RNA-seq studies have been successfully used to study evolutionary processes in weeds, they have their own challenges. The main issue to address is the proportion of mismatches, especially when genetic differences between species are high. In such cases, the portion of the reads mapping ambiguously to more than one contig increases, which might lead to erroneous result interpretation and further downstream processes (Fukushima et al., 2009). Hence, high-resolution genomic analyses of weedy plants will be needed in the future. Alongside transcriptome studies, metabolome studies on weeds also have numerous challenges, which might range from constrained genomic resources and reference databases to the complexity of the metabolite profiles. In weeds, the lack of reference databases can make it difficult to identify and annotate all the metabolites. Furthermore, there might be many anonymous metabolites, which can also impede precise result interpretations and hence make biomarker discovery difficult in weeds.

Besides individual omics, the application of integrated omics approaches to weeds can also be challenging and requires careful consideration. One of the most important data integration challenges is that the transcriptome and metabolite information do not correlate for some of the genes and metabolites. This might be due to complicated regulatory networks ranging from feedback loops to co-regulation and compensatory mechanisms (Stelling et al., 2002; Vemuri and Aristidou, 2005). Additionally, metabolites within cells can have varying turnover rates. Hence, even if gene expression remains constant, that does not imply that metabolite levels will also remain constant (Li et al., 2022). Hence, in these cases, gene-metabolite correlations may not be evident from short time scale experiments. Also, there can be some technical factors and threshold effects involved, which can initiate weak correlations. For example, minor changes in gene expression might not lead to measurable changes in metabolite levels until a certain threshold is reached, or there might be some abnormalities regarding the normalization methods and data preprocessing choices that might obscure the true correlations. Addressing these challenges might require sophisticated and refined statistical analysis methods, and weeds might have unknown statistical challenges that need to be addressed (Kumar et al., 2017). Another challenge might be the huge diversity among the weed species, resulting in substantial physiological and genetic heterogeneity even within a single species. Due to species diversity, each might have its own unique genetic makeup and biological characteristics, and hence the resistance mechanisms might also vary significantly (Owen, 2016). High species diversity might also limit the broader applicability of research findings from multi-omics data since the perceptions gained from one environment may not apply directly to another. This scenario might demand more advanced bioinformatics pipelines to accommodate the unique genomic features of each species. This will require specialized expertise. However, the most crucial challenge in integrated transcriptomic-metabolomic analyses is based on the fact that there is no direct connection between metabolite and transcript. The differentially expressed transcripts cannot be mapped to a single metabolome database. Hence, the data analysis becomes more complicated; however, this impasse can be overcome by reducing the number of metabolites. Comparative metabolomic analyses, such as comparison between herbicide-resistant and susceptible weeds and identification of the specific features, can be implied to reduce the undesirable metabolites. Nevertheless, despite the challenges, transcriptomic-metabolomic integration is a powerful combination and a fascinating field of research that undeniably requires additional exploration and the involvement of more geneticists and weed scientists.

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