EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments

Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience

Global warming poses major challenges for plant survival and agricultural productivity. Thus, efforts to enhance stress resilience in plants are key strategies for protecting food security. Gene regulatory networks (GRNs) are a critical mechanism conferring stress resilience. Until recently, predicting GRNs of the individual cells that make up plants and other multicellular organisms was impeded by aggregate population scale measurements of transcriptome and other genome-scale features. With the advancement of high-throughput single cell RNA-seq and other single cell assays, learning GRNs for individual cells is now possible, in principle. In this article, we report on recent advances in experimental and analytical methodologies for single cell sequencing assays especially as they have been applied to the study of plants. We highlight recent advances and ongoing challenges for scGRN prediction, and finally, we highlight the opportunity to use scGRN discovery for studying and ultimately enhancing abiotic stress resilience in plants.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

Warm nights disrupt transcriptome rhythms in field-grown rice panicles

In rice, a small increase in nighttime temperature reduces grain yield and quality. How warm nighttime temperatures (WNT) produce these detrimental effects is not well understood, especially in field conditions where the typical day-to-night temperature fluctuation exceeds the mild increase in nighttime temperature. We observed genome-wide disruption of gene expression timing during the reproductive phase in field-grown rice panicles acclimated to 2 to 3 °C WNT. Transcripts previously identified as rhythmically expressed with a 24-h period and circadian-regulated transcripts were more sensitive to WNT than were nonrhythmic transcripts. The system-wide perturbations in transcript levels suggest that WNT disrupt the tight temporal coordination between internal molecular events and the environment, resulting in reduced productivity. We identified transcriptional regulators whose predicted targets are enriched for sensitivity to WNT. The affected transcripts and candidate regulators identified through our network analysis explain molecular mechanisms driving sensitivity to WNT and identify candidates that can be targeted to enhance tolerance to WNT.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

Back to the future: Implications of genetic complexity for the structure of hybrid breeding programs

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate contribution of inbred lines reduce effective population size and constrain the accessible genetic space. Under these conditions, long-term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study, we therefore aim to propose a rationale for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this, we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space through program decentralization and disproportionate contribution of parental inbred lines, is required to expose additive genetic variation and thus facilitate heritable genetic gains under high levels of genetic complexity. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

Chromatin accessibility and translational landscapes of tea plants under chilling stress

Plants have evolved regulatory mechanisms at multiple levels to regulate gene expression in order to improve their cold adaptability. However, limited information is available regarding the stress response at the chromatin and translational levels. Here, we characterize the chromatin accessibility, transcriptional, and translational landscapes of tea plants in vivo under chilling stress for the first time. Chilling stress significantly affected both the transcription and translation levels as well as the translation efficiency of tea plants. A total of 3010 genes that underwent rapid and independent translation under chilling stress were observed, and they were significantly enriched in the photosynthesis-antenna protein and phenylpropanoid biosynthesis pathways. A set of genes that were significantly responsive to cold at the transcription and translation levels, including four (+)-neomenthol dehydrogenases (MNDs) and two (E)-nerolidol synthases (NESs) arranged in tandem on the chromosomes, were also found. We detected potential upstream open reading frames (uORFs) on 3082 genes and found that tea plants may inhibit the overall expression of genes by enhancing the translation of uORFs under chilling stress. In addition, we identified distal transposase hypersensitive sites (THSs) and proximal THSs and constructed a transcriptional regulatory network for tea plants under chilling stress. We also identified 13 high-confidence transcription factors (TFs) that may play a crucial role in cold regulation. These results provide valuable information regarding the potential transcriptional regulatory network in plants and help to clarify how plants exhibit flexible responses to chilling stress.

A comprehensive transcriptome analysis of contrasting rice cultivars highlights the role of auxin and ABA responsive genes in heat stress response

Sensing a change in ambient temperature is key to survival among all living organisms. Temperature fluctuations due to climate change are a matter of grave concern since it adversely affects growth and eventually the yield of crop plants, including two of the major cereals, i.e., rice and wheat. Thus, to understand the response of rice seedlings to elevated temperatures, we performed microarray-based transcriptome analysis of two contrasting rice cultivars, Annapurna (heat tolerant) and IR64 (heat susceptible), by subjecting their seedlings to 37 °C and 42 °C, sequentially. The transcriptome analyses revealed a set of uniquely regulated genes and related pathways in red rice cultivar Annapurna, particularly associated with auxin and ABA as a part of heat stress response in rice. The changes in expression of few auxin and ABA associated genes, such as OsIAA13, OsIAA20, ILL8, OsbZIP12, OsPP2C51, OsDi19-1 and OsHOX24, among others, were validated under high-temperature conditions using RT-qPCR. In particular, the expression of auxin-inducible SAUR genes was enhanced considerably at both elevated temperatures. Further, using genes that expressed inversely under heat vs. cold temperature conditions, we built a regulatory network between transcription factors (TF) such as HSFs, NAC, WRKYs, bHLHs or bZIPs and their target gene pairs and determined regulatory coordination in their expression under varying temperature conditions. Our work thus provides useful insights into temperature-responsive genes, particularly under elevated temperature conditions, and could serve as a resource of candidate genes associated with thermotolerance or downstream components of temperature sensors in rice.

Abiotic Stress-Responsive miRNA and Transcription Factor-Mediated Gene Regulatory Network in Oryza sativa: Construction and Structural Measure Study

Climate changes and environmental stresses have a consequential association with crop plant growth and yield, meaning it is necessary to cultivate crops that have tolerance toward the changing climate and environmental disturbances such as water stress, temperature fluctuation, and salt toxicity. Recent studies have shown that trans-acting regulatory elements, including microRNAs (miRNAs) and transcription factors (TFs), are emerging as promising tools for engineering naive improved crop varieties with tolerance for multiple environmental stresses and enhanced quality as well as yield. However, the interwoven complex regulatory function of TFs and miRNAs at transcriptional and post-transcriptional levels is unexplored in Oryza sativa. To this end, we have constructed a multiple abiotic stress responsive TF-miRNA-gene regulatory network for O. sativa using a transcriptome and degradome sequencing data meta-analysis approach. The theoretical network approach has shown the networks to be dense, scale-free, and small-world, which makes the network stable. They are also invariant to scale change where an efficient, quick transmission of biological signals occurs within the network on extrinsic hindrance. The analysis also deciphered the existence of communities (cluster of TF, miRNA, and genes) working together to help plants in acclimatizing to multiple stresses. It highlighted that genes, TFs, and miRNAs shared by multiple stress conditions that work as hubs or bottlenecks for signal propagation, for example, during the interaction between stress-responsive genes (TFs/miRNAs/other genes) and genes involved in floral development pathways under multiple environmental stresses. This study further highlights how the fine-tuning feedback mechanism works for balancing stress tolerance and how timely flowering enable crops to survive in adverse conditions. This study developed the abiotic stress-responsive regulatory network, APRegNet database (http://lms.snu.edu.in/APRegNet), which may help researchers studying the roles of miRNAs and TFs. Furthermore, it advances current understanding of multiple abiotic stress tolerance mechanisms.

A quantitative genomics map of rice provides genetic insights and guides breeding

Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects based on eight genome-wide association study cohorts. Population genetic analyses revealed that domestication, local adaptation and heterosis are all associated with QTN allele frequency changes. A genome navigation system, RiceNavi, was developed for QTN pyramiding and breeding route optimization, and implemented in the improvement of a widely cultivated indica variety. This work presents an efficient platform that bridges ever-increasing genomic knowledge and diverse improvement needs in rice.

Machine learning in plant science and plant breeding

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Accessible chromatin regions and their functional interrelations with gene transcription and epigenetic modifications in sorghum genome

Accessible chromatin regions (ACRs) provide physical scaffolds to recruit transcriptional co-regulators and displace their nearby nucleosomes in multiple plant species. Characterization of ACRs and investigation of their biological effects in Sorghum bicolor has lagged behind. Regulation of gene expression relies on the transcriptional co-regulators that are recruited to ACRs to affect epigenomic modifications of surrounding nucleosomes. In this study, we employed transposase-accessible chromatin sequencing to identify ACRs and decipher how the presence of ACRs affects gene expression and epigenetic signatures in the Sorghum genome. As a result, 21 077 ACRs, which are mapped to 22.9% of genes and 2.7% of repeats, were identified. The profiling of ACRs on gene structures reveals a narrow and sharp peak around the transcription start site, with relatively weak and broad signals covering the entire gene body and an explicit but wide peak from the transcription termination site to its downstream regions. We discovered that the correlations between gene expression levels and profiled ACR densities are dependent on the positions of ACRs. The occurrence of genic ACRs cumulatively enhances the transcriptional activity of intergenic ACR-associated genes. In addition, an intricate crosstalk among ACRs, gene expression, and epigenetic marks has been unveiled by integrating multiple-omics analyses of whole-genome bisulfite sequencing, 6mA immunoprecipitation followed by sequencing, RNA sequencing, chromatin immunoprecipitation sequencing, and DNase I hypersensitive sites sequencing datasets. Our study provides a genome-wide landscape of ACRs in sorghum, decrypts their interrelations with various epigenetic marks, and sheds new light on their roles in transcriptional regulation.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Reproductive tissues-specific meta-QTLs and candidate genes for development of heat-tolerant rice cultivars

By integrating genetics and genomics data, reproductive tissues-specific and heat stress responsive 35 meta-QTLs and 45 candidate genes were identified, which could be exploited through marker-assisted breeding for fast-track development of heat-tolerant rice cultivars. Rice holds the key to future food security. In rice-growing areas, temperature has already reached an optimum level for growth, hence, any further increase due to global climate change could significantly reduce rice yield. Several mapping studies have identified a plethora of reproductive tissue-specific and heat stress associated inconsistent quantitative trait loci (QTL), which could be exploited for improvement of heat tolerance. In this study, we performed a meta-analysis on previously reported QTLs and identified 35 most consistent meta-QTLs (MQTLs) across diverse genetic backgrounds and environments. Genetic and physical intervals of nearly 66% MQTLs were narrower than 5 cM and 2 Mb respectively, indicating hotspot genomic regions for heat tolerance. Comparative analyses of MQTLs underlying genes with microarray and RNA-seq based transcriptomic data sets revealed a core set of 45 heat-responsive genes, among which 24 were reproductive tissue-specific and have not been studied in detail before. Remarkably, all these genes corresponded to various stress associated functions, ranging from abiotic stress sensing to regulating plant stress responses, and included heat-shock genes (OsBiP2, OsMed37_1), transcription factors (OsNAS3, OsTEF1, OsWRKY10, OsWRKY21), transmembrane transporters (OsAAP7A, OsAMT2;1), sugar metabolizing (OsSUS4, α-Gal III) and abiotic stress (OsRCI2-7, SRWD1) genes. Functional data evidences from Arabidopsis heat-shock genes also suggest that OsBIP2 may be associated with thermotolerance of pollen tubes under heat stress conditions. Furthermore, promoters of identified genes were enriched with heat, dehydration, pollen and sugar responsive cis-acting regulatory elements, proposing a common regulatory mechanism might exist in rice for mitigating reproductive stage heat stress. These findings strongly support our results and provide new candidate genes for fast-track development of heat-tolerant rice cultivars.

Genes of the RAV Family Control Heading Date and Carpel Development in Rice

In plants, correct formation of reproductive organs is critical for successful seedset and perpetuation of the species. Plants have evolved different molecular mechanisms to coordinate flower and seed development at the proper time of the year. Among the plant-specific RELATED TO ABI3 AND VP1 (RAV) family of transcription factors, only TEMPRANILLO1 (TEM1) and TEM2 have been shown to affect reproductive development in Arabidopsis (Arabidopsis thaliana). They negatively regulate the floral transition through direct repression of FLOWERING LOCUS T and GIBBERELLIN 3-OXIDASE1/2, encoding major components of the florigen. Here we identify RAV genes from rice (Oryza sativa), and unravel their regulatory roles in key steps of reproductive development. Our data strongly suggest that, like TEMs, OsRAV9/OsTEM1 has a conserved function as a repressor of photoperiodic flowering upstream of the floral activators OsMADS14 and Hd3a, through a mechanism reminiscent of that one underlying floral transition in temperate cereals. Furthermore, OsRAV11 and OsRAV12 may have acquired a new function in the differentiation of the carpel and the control of seed size, acting downstream of floral homeotic factors. Alternatively, this function may have been lost in Arabidopsis. Our data reveal conservation of RAV gene function in the regulation of flowering time in monocotyledonous and dicotyledonous plants, but also unveil roles in the development of rice gynoecium.

The transcription factor GATA10 regulates fertility conversion of a two-line hybrid tms5 mutant rice via the modulation of UbL40 expression

The thermosensitive genic male sterile 5 (tms5) mutation causes thermosensitive genic male sterility in rice (Oryza sativa) through loss of RNase Z^S1 function, which influences ubiquitin fusion ribosomal protein L40 (Ub_L40 ) messenger RNA levels during male development. Here, we used ATAC-seq, combined with analysis of H3K9ac and H3K4me2, to identify changes in accessible chromatin during fertility conversion of the two-line hybrid rice Wuxiang S (WXS) derived from a mutant tms5 allele. Furthermore, RNA-seq and bioinformatic analyses identified specific transcription factors (TFs) in differentially accessible chromatin regions. Among these TFs, only GATA10 targeted Ub_L40 . Osgata10 knockout mutations, which resulted in low expression of Ub_L40 and a tendency toward male fertility, confirmed that GATA10 regulated fertility conversion via the modulation of Ub_L40 . Meanwhile, GATA10 acted as a mediator for interactions with ERF65, which revealed that transcriptional regulation is a complex process involving multiple complexes of TFs, namely TF modules. It appears that the ERF141/MADS7/MADS50/MYB modules affect metabolic processes that control anther and pollen development, especially cell wall formation. Our analysis revealed that these modules directly or indirectly affect metabolic pathway-related genes to coordinate plant growth with proper anther development, and furthermore, that GATA10 regulates fertility conversion via the modulation of Ub_L40 expression.

Antisense-mediated S-adenosyl-L-methionine decarboxylase silencing affects heat stress responses of tobacco plants

Understanding the molecular mode(s) of plant tolerance to heat stress (HS) is crucial since HS is a potential threat to sustainable agriculture and global crop production. Polyamines (PAs) seem to exert multifaceted effects in plant growth and development and responses to abiotic and biotic stresses, presumably via their homeostasis, chemical interactions and contribution to hydrogen peroxide (H2O2) cellular 'signatures'. Downregulation of the apoplastic POLYAMINE OXIDASE (PAO) gene improved thermotolerance in tobacco (Nicotiana tabacum L.) transgenics. However, in the present work we show that transgenic tobacco plants with antisense-mediated S-ADENOSYL-L-METHIONINE DECARBOXYLASE silencing (AS-NtSAMDC) exhibited enhanced sensitivity and delayed responses to HS which was accompanied by profound injury upon HS removal (recovery), as assessed by phenological, physiological and biochemical characteristics. In particular, the AS-NtSAMDC transgenics exhibited significantly reduced rate of photosynthesis, as well as enzymatic and non-enzymatic antioxidants. These transgenics suffered irreversible damage, which significantly reduced their growth potential upon return to normal conditions. These data reinforce the contribution of increased PA homeostasis to tolerance, and can move forward our understanding on the PA-mediated mechanism(s) conferring tolerance to HS that might be targeted via traditional or biotechnological breeding for developing HS tolerant plants.

Long-term transcriptional memory in rice plants submitted to salt shock

A first salt shock event alters transcriptional and physiological responses to a second event, being possible to identify 26 genes associated with long-term memory. Soil salinity significantly affects rice cultivation, resulting in large losses in growth and productivity. Studies report that a disturbing event can prepare the plant for a subsequent event through memory acquisition, involving physiological and molecular processes. Therefore, genes that provide altered responses in subsequent events define a category known as "memory genes". In this work, the RNA-sequencing (RNA-Seq) technique was used to analyse the transcriptional profile of rice plants subjected to different salt shock events and to characterise genes associated with long-term memory. Plants subjected to recurrent salt shock showed differences in stomatal conductance, chlorophyll index, electrolyte leakage, and the number of differentially expressed genes (DEGs), and they had lower Na⁺/K⁺ ratios than plants that experienced only one stress event. Additionally, the mammalian target of rapamycin (mTOR) pathways, and carbohydrate and amino acid-associated pathways were altered under all conditions. Memory genes can be classified according to their responses during the first event (+ or -) and the second shock event (+ or -), being possible to observe a larger number of transcripts for groups [+ /-] and [-/ +], genes characterised as "revised response." This is the first long-term transcriptional memory study in rice plants under salt shock, providing new insights into the process of plant memory acquisition.

Considerations in the analysis of plant chromatin accessibility data

Transcriptional control is exerted primarily through the binding of transcription factor proteins to regulatory elements in DNA. By virtue of eukaryotic DNA being complexed with histones, transcription factor binding to DNA alters or eliminates histone-DNA contacts, leading to increased accessibility of the DNA region to nuclease enzymes. This hypersensitivity to nuclease digestion has been used to define DNA binding events and regulatory elements across genomes, and to compare these attributes between cell types or conditions. These approaches make it possible to define the regulatory elements in a genome as well as to predict the regulatory networks of transcription factors and their target genes in a given cell state. As these chromatin accessibility assays are increasingly used, it is important to consider how to analyze the resulting data to avoid artifactual results or misinterpretation. In this review, we focus on some of the key technical and computational caveats associated with plant chromatin accessibility data, including strategies for sample preparation, sequencing, read mapping, and downstream analyses.

The strength and pattern of natural selection on gene expression in rice

Levels of gene expression underpin organismal phenotypes^1,2, but the nature of selection that acts on gene expression and its role in adaptive evolution remain unknown^1,2. Here we assayed gene expression in rice (Oryza sativa)³, and used phenotypic selection analysis to estimate the type and strength of selection on the levels of more than 15,000 transcripts^4,5. Variation in most transcripts appears (nearly) neutral or under very weak stabilizing selection in wet paddy conditions (with median standardized selection differentials near zero), but selection is stronger under drought conditions. Overall, more transcripts are conditionally neutral (2.83%) than are antagonistically pleiotropic⁶ (0.04%), and transcripts that display lower levels of expression and stochastic noise^7-9 and higher levels of plasticity⁹ are under stronger selection. Selection strength was further weakly negatively associated with levels of cis-regulation and network connectivity⁹. Our multivariate analysis suggests that selection acts on the expression of photosynthesis genes^4,5, but that the efficacy of selection is genetically constrained under drought conditions¹⁰. Drought selected for earlier flowering^11,12 and a higher expression of OsMADS18 (Os07g0605200), which encodes a MADS-box transcription factor and is a known regulator of early flowering¹³-marking this gene as a drought-escape gene^11,12. The ability to estimate selection strengths provides insights into how selection can shape molecular traits at the core of gene action.

An inferred fitness consequence map of the rice genome

The extent to which sequence variation impacts plant fitness is poorly understood. High-resolution maps detailing the constraint acting on the genome, especially in regulatory sites, would be beneficial as functional annotation of noncoding sequences remains sparse. Here, we present a fitness consequence (fitCons) map for rice (Oryza sativa). We inferred fitCons scores (ρ) for 246 inferred genome classes derived from nine functional genomic and epigenomic datasets, including chromatin accessibility, messenger RNA/small RNA transcription, DNA methylation, histone modifications and engaged RNA polymerase activity. These were integrated with genome-wide polymorphism and divergence data from 1,477 rice accessions and 11 reference genome sequences in the Oryzeae. We found ρ to be multimodal, with ~9% of the rice genome falling into classes where more than half of the bases would probably have a fitness consequence if mutated. Around 2% of the rice genome showed evidence of weak negative selection, frequently at candidate regulatory sites, including a novel set of 1,000 potentially active enhancer elements. This fitCons map provides perspective on the evolutionary forces associated with genome diversity, aids in genome annotation and can guide crop breeding programs.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Transcriptomic data-driven discovery of global regulatory features of rice seeds developing under heat stress

Plants respond to abiotic stressors through a suite of strategies including differential regulation of stress-responsive genes. Hence, characterizing the influences of the relevant global regulators or on stress-related transcription factors is critical to understand plant stress response. Rice seed development is highly sensitive to elevated temperatures. To elucidate the extent and directional hierarchy of gene regulation in rice seeds under heat stress, we developed and implemented a robust multi-level optimization-based algorithm called Minimal Regulatory Network identifier (MiReN). MiReN could predict the minimal regulatory relationship between a gene and its potential regulators from our temporal transcriptomic dataset. MiReN predictions for global regulators including stress-responsive gene Slender Rice 1 (SLR1) and disease resistance gene XA21 were validated with published literature. It also predicted novel regulatory influences of other major regulators such as Kinesin-like proteins KIN12C and STD1, and WD repeat-containing protein WD40. Out of the 228 stress-responsive transcription factors identified, we predicted de novo regulatory influences on three major groups (MADS-box M-type, MYB, and bZIP) and investigated their physiological impacts during stress. Overall, MiReN results can facilitate new experimental studies to enhance our understanding of global regulatory mechanisms triggered during heat stress, which can potentially accelerate the development of stress-tolerant cultivars.

Genome-wide alternative polyadenylation dynamics in response to biotic and abiotic stresses in rice

Alternative polyadenylation (APA) is an important way to regulate gene expression at the post-transcriptional level, and is extensively involved in plant stress responses. However, the systematic roles of APA regulation in response to abiotic and biotic stresses in rice at the genome scale remain unknown. To take advantage of available RNA-seq datasets, using a novel tool APAtrap, we identified thousands of genes with significantly differential usage of polyadenylation [poly(A)] sites in response to the abiotic stress (drought, heat shock, and cadmium) and biotic stress [bacterial blight (BB), rice blast, and rice stripe virus (RSV)]. Genes with stress-responsive APA dynamics commonly exhibited higher expression levels when their isoforms with short 3' untranslated region (3' UTR) were more abundant. The stress-responsive APA events were widely involved in crucial stress-responsive genes and pathways: e.g. APA acted as a negative regulator in heat stress tolerance; APA events were involved in DNA repair and cell wall formation under Cd stress; APA regulated chlorophyll metabolism, being associated with the pathogenesis of leaf diseases under RSV and BB challenges. Furthermore, APA events were found to be involved in glutathione metabolism and MAPK signaling pathways, mediating a crosstalk among the abiotic and biotic stress-responsive regulatory networks in rice. Analysis of large-scale datasets revealed that APA may regulate abiotic and biotic stress-responsive processes in rice. Such post-transcriptome diversities contribute to rice adaption to various environmental challenges. Our study would supply useful resource for further molecular assisted breeding of multiple stress-tolerant cultivars for rice.

On the problem of confounders in modeling gene expression

Motivation

Modeling of Transcription Factor (TF) binding from both ChIP-seq and chromatin accessibility data has become prevalent in computational biology. Several models have been proposed to generate new hypotheses on transcriptional regulation. However, there is no distinct approach to derive TF binding scores from ChIP-seq and open chromatin experiments. Here, we review biases of various scoring approaches and their effects on the interpretation and reliability of predictive gene expression models.

Results

We generated predictive models for gene expression using ChIP-seq and DNase1-seq data from DEEP and ENCODE. Via randomization experiments, we identified confounders in TF gene scores derived from both ChIP-seq and DNase1-seq data. We reviewed correction approaches for both data types, which reduced the influence of identified confounders without harm to model performance. Also, our analyses highlighted further quality control measures, in addition to model performance, that may help to assure model reliability and to avoid misinterpretation in future studies.

Availability and implementation

The software used in this study is available online at https://github.com/SchulzLab/TEPIC.

Supplementary information

Supplementary data are available at Bioinformatics online.

Inference of plant gene regulatory networks using data-driven methods: A practical overview

Transcriptional regulation is a complex and dynamic process that plays a vital role in plant growth and development. A key component in the regulation of genes is transcription factors (TFs), which coordinate the transcriptional control of gene activity. A gene regulatory network (GRN) is a collection of regulatory interactions between TFs and their target genes. The accurate delineation of GRNs offers a significant contribution to our understanding about how plant cells are organized and function, and how individual genes are regulated in various conditions, organs or cell types. During the past decade, important progress has been made in the identification of GRNs using experimental and computational approaches. However, a detailed overview of available platforms supporting the analysis of GRNs in plants is missing. Here, we review current databases, platforms and tools that perform data-driven analyses of gene regulation in Arabidopsis. The platforms are categorized into two sections, 1) promoter motif analysis tools that use motif mapping approaches to find TF motifs in the regulatory sequences of genes of interest and 2) network analysis tools that identify potential regulators for a set of input genes using a range of data types in order to generate GRNs. We discuss the diverse datasets integrated and highlight the strengths and caveats of different platforms. Finally, we shed light on the limitations of the above approaches and discuss future perspectives, including the need for integrative approaches to unravel complex GRNs in plants.

Ethylene-mediated signaling confers thermotolerance and regulates transcript levels of heat shock factors in rice seedlings under heat stress

BACKGROUND

Agriculture is highly dependent on climate. Increases in temperature caused by global warming pose challenges for crop production. Heat stress induces oxidative damage to cell membranes and then causes cell death. Plants have developed various responses to elevated temperatures, including hormone signaling pathways and heat shock factors that elevate their thermotolerance. In response to heat stress, the gaseous hormone ethylene is produced through regulation of the expression of signaling-related genes to modulate resource allocation dynamics. For comprehensive understanding of the role of ethylene, this study used an ethylene precursor to analyze the ethylene signaling pathway involved in adjustment of the homeostasis of the antioxidant system and to evaluate heat shock factor expression in rice seedlings under heat stress.

RESULTS

Levels of cell membrane oxidation and ion leakage were reduced in rice seedlings under heat treatment combined with ethylene precursor treatment, conferring enhanced thermotolerance. Reduction of the fresh weight and chlorophyll a/b ratio in rice seedlings was lower in rice seedlings under heat stress with ethylene precursor treatment than in those under heat stress only. Moreover, reduction of antioxidant response caused by heat stress was ameliorated by treatment with ethylene precursors such as catalase and total peroxidase. Quantitative reverse transcriptase-polymerase chain reaction showed higher expression levels of heat shock factors such as HSFA1a and HSFA2a, c, d, e, and f and ethylene-signaling-related genes such as ethylene insensitive 2, ethylene insensitive-like 1, and ethylene insensitive-like 2 in rice seedlings under heat stress with ethylene precursor treatment than in rice seedlings under heat stress only.

CONCLUSION

Ethylene-mediated signaling was involved in the reduction of oxidative damage, maintenance of chlorophyll content, and enhancement of thermotolerance in rice seedlings under heat stress. Furthermore, this study revealed heat shock factors and ethylene-signaling-related genes involved in complex network regulation that confers thermotolerance to rice seedlings.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

PCSD: a plant chromatin state database

Genome-wide maps of chromatin states have become a powerful representation of genome annotation and regulatory activity. We collected public and in-house plant epigenomic data sets and applied a Hidden Markov Model to define chromatin states, which included 290 553 (36 chromatin states), 831 235 (38 chromatin states) and 3 936 844 (26 chromatin states) segments across the whole genome of Arabidopsis thaliana, Oryza sativa and Zea mays, respectively. We constructed a Plant Chromatin State Database (PCSD, http://systemsbiology.cau.edu.cn/chromstates) to integrate detailed information about chromatin states, including the features and distribution of states, segments in states and related genes with segments. The self-organization mapping (SOM) results for these different chromatin signatures and UCSC Genome Browser for visualization were also integrated into the PCSD database. We further provided differential SOM maps between two epigenetic marks for chromatin state comparison and custom tools for new data analysis. The segments and related genes in SOM maps can be searched and used for motif and GO analysis, respectively. In addition, multi-species integration can be used to discover conserved features at the epigenomic level. In summary, our PCSD database integrated the identified chromatin states with epigenetic features and may be beneficial for communities to discover causal functions hidden in plant chromatin.

Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships

Long non-coding RNAs (lncRNAs) represent a diverse class of regulatory loci with roles in development and stress responses throughout all kingdoms of life. LncRNAs, however, remain under-studied in plants compared to animal systems. To address this deficiency, we applied a machine learning prediction tool, Classifying RNA by Ensemble Machine learning Algorithm (CREMA), to analyze RNAseq data from 11 plant species chosen to represent a wide range of evolutionary histories. Transcript sequences of all expressed and/or annotated loci from plants grown in unstressed (control) conditions were assembled and input into CREMA for comparative analyses. On average, 6.4% of the plant transcripts were identified by CREMA as encoding lncRNAs. Gene annotation associated with the transcripts showed that up to 99% of all predicted lncRNAs for Solanum tuberosum and Amborella trichopoda were missing from their reference annotations whereas the reference annotation for the genetic model plant Arabidopsis thaliana contains 96% of all predicted lncRNAs for this species. Thus a reliance on reference annotations for use in lncRNA research in less well-studied plants can be impeded by the near absence of annotations associated with these regulatory transcripts. Moreover, our work using phylogenetic signal analyses suggests that molecular traits of plant lncRNAs display different evolutionary patterns than all other transcripts in plants and have molecular traits that do not follow a classic evolutionary pattern. Specifically, GC content was the only tested trait of lncRNAs with consistently significant and high phylogenetic signal, contrary to high signal in all tested molecular traits for the other transcripts in our tested plant species.

Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis

Abiotic stress tolerance is a complex trait regulated by multiple genes and gene networks in plants. A range of abiotic stresses are known to limit rice productivity. Meta-transcriptomics has emerged as a powerful approach to decipher stress-associated molecular network in model crops. However, retaining specificity of gene expression in tolerant and susceptible genotypes during meta-transcriptome analysis is important for understanding genotype-dependent stress tolerance mechanisms. Addressing this aspect, we describe here "abiotic stress tolerant" (ASTR) genes and networks specifically and differentially expressing in tolerant rice genotypes in response to different abiotic stress conditions. We identified 6,956 ASTR genes, key hub regulatory genes, transcription factors, and functional modules having significant association with abiotic stress-related ontologies and cis-motifs. Out of the 6956 ASTR genes, 73 were co-located within the boundary of previously identified abiotic stress trait-related quantitative trait loci. Functional annotation of 14 uncharacterized ASTR genes is proposed using multiple computational methods. Around 65% of the top ASTR genes were found to be differentially expressed in at least one of the tolerant genotypes under different stress conditions (cold, salt, drought, or heat) from publicly available RNAseq data comparison. The candidate ASTR genes specifically associated with tolerance could be utilized for engineering rice and possibly other crops for broad-spectrum tolerance to abiotic stresses.

Hybrid sequencing reveals insight into heat sensing and signaling of bread wheat

Wheat (Triticum aestivum L.), a globally important crop, is challenged by increasing temperatures (heat stress, HS). However its polyploid nature, the incompleteness of its genome sequences and annotation, the lack of comprehensive HS-responsive transcriptomes and the unexplored heat sensing and signaling of wheat hinder our full understanding of its adaptations to HS. The recently released genome sequences of wheat, as well as emerging single-molecular sequencing technologies, provide an opportunity to thoroughly investigate the molecular mechanisms of the wheat response to HS. We generated a high-resolution spatio-temporal transcriptome map of wheat flag leaves and filling grain under HS at 0 min, 5 min, 10 min, 30 min, 1 h and 4 h by combining full-length single-molecular sequencing and Illumina short reads sequencing. This hybrid sequencing newly discovered 4947 loci and 70 285 transcripts, generating the comprehensive and dynamic list of HS-responsive full-length transcripts and complementing the recently released wheat reference genome. Large-scale analysis revealed a global landscape of heat adaptations, uncovering unexpected rapid heat sensing and signaling, significant changes of more than half of HS-responsive genes within 30 min, heat shock factor-dependent and -independent heat signaling, and metabolic alterations in early HS-responses. Integrated analysis also demonstrated the differential responses and partitioned functions between organs and subgenomes, and suggested a differential pattern of transcriptional and alternative splicing regulation in the HS response. This study provided comprehensive data for dissecting molecular mechanisms of early HS responses in wheat and highlighted the genomic plasticity and evolutionary divergence of polyploidy wheat.

Recent advances in gene function prediction using context-specific coexpression networks in plants

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)–seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF–TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

Multi-study inference of regulatory networks for more accurate models of gene regulation

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.

RiceMetaSysB: a database of blast and bacterial blight responsive genes in rice and its utilization in identifying key blast-resistant WRKY genes

Nearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like 'common genes among varieties' and 'strains' have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.

Evolutionary and ecological functional genomics, from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.

A workflow for simplified analysis of ATAC-cap-seq data in R

Background

Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis.

Results

We developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F-score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed.

Conclusions

Our package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor.

Elucidation and analyses of the regulatory networks of upland and lowland ecotypes of switchgrass in response to drought and salt stresses

Switchgrass is an important bioenergy crop typically grown in marginal lands, where the plants must often deal with abiotic stresses such as drought and salt. Alamo is known to be more tolerant to both stress types than Dacotah, two ecotypes of switchgrass. Understanding of their stress response and adaptation programs can have important implications to engineering more stress tolerant plants. We present here a computational study by analyzing time-course transcriptomic data of the two ecotypes to elucidate and compare their regulatory systems in response to drought and salt stresses. A total of 1,693 genes (target genes or TGs) are found to be differentially expressed and possibly regulated by 143 transcription factors (TFs) in response to drought stress together in the two ecotypes. Similarly, 1,535 TGs regulated by 110 TFs are identified to be involved in response to salt stress. Two regulatory networks are constructed to predict their regulatory relationships. In addition, a time-dependent hidden Markov model is derived for each ecotype responding to each stress type, to provide a dynamic view of how each regulatory network changes its behavior over time. A few new insights about the response mechanisms are predicted from the regulatory networks and the time-dependent models. Comparative analyses between the network models of the two ecotypes reveal key commonalities and main differences between the two regulatory systems. Overall, our results provide new information about the complex regulatory mechanisms of switchgrass responding to drought and salt stresses.

A Framework for Engineering Stress Resilient Plants Using Genetic Feedback Control and Regulatory Network Rewiring

Crop disease leads to significant waste worldwide, both pre- and postharvest, with subsequent economic and sustainability consequences. Disease outcome is determined both by the plants' response to the pathogen and by the ability of the pathogen to suppress defense responses and manipulate the plant to enhance colonization. The defense response of a plant is characterized by significant transcriptional reprogramming mediated by underlying gene regulatory networks, and components of these networks are often targeted by attacking pathogens. Here, using gene expression data from Botrytis cinerea-infected Arabidopsis plants, we develop a systematic approach for mitigating the effects of pathogen-induced network perturbations, using the tools of synthetic biology. We employ network inference and system identification techniques to build an accurate model of an Arabidopsis defense subnetwork that contains key genes determining susceptibility of the plant to the pathogen attack. Once validated against time-series data, we use this model to design and test perturbation mitigation strategies based on the use of genetic feedback control. We show how a synthetic feedback controller can be designed to attenuate the effect of external perturbations on the transcription factor CHE in our subnetwork. We investigate and compare two approaches for implementing such a controller biologically-direct implementation of the genetic feedback controller, and rewiring the regulatory regions of multiple genes-to achieve the network motif required to implement the controller. Our results highlight the potential of combining feedback control theory with synthetic biology for engineering plants with enhanced resilience to environmental stress.

Auxin decreases chromatin accessibility through the TIR1/AFBs auxin signaling pathway in proliferative cells

Chromatin accessibility is closely associated with chromatin functions such as gene expression, DNA replication, and maintenance of DNA integrity. However, the relationship between chromatin accessibility and plant hormone signaling has remained elusive. Here, based on the correlation between chromatin accessibility and DNA damage, we used the sensitivity to DNA double strand breaks (DSBs) as an indicator of chromatin accessibility and demonstrated that auxin regulates chromatin accessibility through the TIR1/AFBs signaling pathway in proliferative cells. Treatment of proliferating plant cells with an inhibitor of the TIR1/AFBs auxin signaling pathway, PEO-IAA, caused chromatin loosening, indicating that auxin signaling functions to decrease chromatin accessibility. In addition, a transcriptome analysis revealed that several histone H4 genes and a histone chaperone gene, FAS1, are positively regulated through the TIR1/AFBs signaling pathway, suggesting that auxin plays a role in promoting nucleosome assembly. Analysis of the fas1 mutant of Arabidopsis thaliana confirmed that FAS1 is required for the auxin-dependent decrease in chromatin accessibility. These results suggest that the positive regulation of chromatin-related genes mediated by the TIR1/AFBs auxin signaling pathway enhances nucleosome assembly, resulting in decreased chromatin accessibility in proliferative cells.

Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

Our study exploits time—the relatively unexplored fourth dimension of gene regulatory networks (GRNs)—to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. We introduce several conceptual innovations to the analysis of time-series data in the area of predictive GRNs. Our resulting network now provides the “transcriptional logic” for transcription factor perturbations aimed at improving N-use efficiency, an important issue for global food production in marginal soils and for sustainable agriculture. More broadly, the combination of the time-based approaches we develop and deploy can be applied to uncover the temporal “transcriptional logic” for any response system in biology, agriculture, or medicine.

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and ¹⁵NO₃⁻ uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.

Plant Life in Extreme Environments: How Do You Improve Drought Tolerance?

Systems studies of drought stress in resurrection plants and other xerophytes are rapidly identifying a large number of genes, proteins and metabolites that respond to severe drought stress or desiccation. This has provided insight into drought resistance mechanisms, which allow xerophytes to persist under such extreme environmental conditions. Some of the mechanisms that ensure cellular protection during severe dehydration appear to be unique to desert species, while many other stress signaling pathways are in common with well-studied model and crop species. However, despite the identification of many desiccation inducible genes, there are few "gene-to-field" examples that have led to improved drought tolerance and yield stability derived from resurrection plants, and only few examples have emerged from model species. This has led to many critical reviews on the merit of the experimental approaches and the type of plants used to study drought resistance mechanisms. This article discusses the long-standing arguments between the ecophysiology and molecular biology communities, on how to "drought-proof" future crop varieties. It concludes that a more positive and inclusive dialogue between the different disciplines is needed, to allow us to move forward in a much more constructive way.

Q&A: How do gene regulatory networks control environmental responses in plants?

A gene regulatory network (GRN) describes the hierarchical relationship between transcription factors, associated proteins, and their target genes. Studying GRNs allows us to understand how a plant's genotype and environment are integrated to regulate downstream physiological responses. Current efforts in plants have focused on defining the GRNs that regulate functions such as development and stress response and have been performed primarily in genetically tractable model plant species such as Arabidopsis thaliana. Future studies will likely focus on how GRNs function in non-model plants and change over evolutionary time to allow for adaptation to extreme environments. This broader understanding will inform efforts to engineer GRNs to create tailored crop traits.

Profiling of Accessible Chromatin Regions across Multiple Plant Species and Cell Types Reveals Common Gene Regulatory Principles and New Control Modules

The transcriptional regulatory structure of plant genomes remains poorly defined relative to animals. It is unclear how many cis-regulatory elements exist, where these elements lie relative to promoters, and how these features are conserved across plant species. We employed the assay for transposase-accessible chromatin (ATAC-seq) in four plant species (Arabidopsis thaliana, Medicago truncatula, Solanum lycopersicum, and Oryza sativa) to delineate open chromatin regions and transcription factor (TF) binding sites across each genome. Despite 10-fold variation in intergenic space among species, the majority of open chromatin regions lie within 3 kb upstream of a transcription start site in all species. We find a common set of four TFs that appear to regulate conserved gene sets in the root tips of all four species, suggesting that TF-gene networks are generally conserved. Comparative ATAC-seq profiling of Arabidopsis root hair and non-hair cell types revealed extensive similarity as well as many cell-type-specific differences. Analyzing TF binding sites in differentially accessible regions identified a MYB-driven regulatory module unique to the hair cell, which appears to control both cell fate regulators and abiotic stress responses. Our analyses revealed common regulatory principles among species and shed light on the mechanisms producing cell-type-specific transcriptomes during development.

Profiling of Accessible Chromatin Regions across Multiple Plant Species and Cell Types Reveals Common Gene Regulatory Principles and New Control Modules

The transcriptional regulatory structure of plant genomes remains poorly defined relative to animals. It is unclear how many cis-regulatory elements exist, where these elements lie relative to promoters, and how these features are conserved across plant species. We employed the assay for transposase-accessible chromatin (ATAC-seq) in four plant species (Arabidopsis thaliana, Medicago truncatula, Solanum lycopersicum, and Oryza sativa) to delineate open chromatin regions and transcription factor (TF) binding sites across each genome. Despite 10-fold variation in intergenic space among species, the majority of open chromatin regions lie within 3 kb upstream of a transcription start site in all species. We find a common set of four TFs that appear to regulate conserved gene sets in the root tips of all four species, suggesting that TF-gene networks are generally conserved. Comparative ATAC-seq profiling of Arabidopsis root hair and non-hair cell types revealed extensive similarity as well as many cell-type-specific differences. Analyzing TF binding sites in differentially accessible regions identified a MYB-driven regulatory module unique to the hair cell, which appears to control both cell fate regulators and abiotic stress responses. Our analyses revealed common regulatory principles among species and shed light on the mechanisms producing cell-type-specific transcriptomes during development.