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

Systems genomics of salinity stress response in rice

Populations can adapt to stressful environments through changes in gene expression. However, the role of gene regulation in mediating stress response and adaptation remains largely unexplored. Here, we use an integrative field dataset obtained from 780 plants of Oryza sativa ssp. indica (rice) grown in a field experiment under normal or moderate salt stress conditions to examine selection and evolution of gene expression variation under salinity stress conditions. We find that salinity stress induces increased selective pressure on gene expression. Further, we show that trans-eQTLs rather than cis-eQTLs are primarily associated with rice's gene expression under salinity stress, potentially via a few master-regulators. Importantly, and contrary to the expectations, we find that cis-trans reinforcement is more common than cis-trans compensation which may be reflective of rice diversification subsequent to domestication. We further identify genetic fixation as the likely mechanism underlying this compensation/reinforcement. Additionally, we show that cis- and trans-eQTLs are under different selection regimes, giving us insights into the evolutionary dynamics of gene expression variation. By examining genomic, transcriptomic, and phenotypic variation across a rice population, we gain insights into the molecular and genetic landscape underlying adaptive salinity stress responses, which is relevant for other crops and other stresses.

Establishing an optimized ATAC-seq protocol for the maize

The advent of next-generation sequencing in crop improvement offers unprecedented insights into the chromatin landscape closely linked to gene activity governing key traits in plant development and adaptation. Particularly in maize, its dynamic chromatin structure is found to collaborate with massive transcriptional variations across tissues and developmental stages, implying intricate regulatory mechanisms, which highlights the importance of integrating chromatin information into breeding strategies for precise gene controls. The depiction of maize chromatin architecture using Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) provides great opportunities to investigate cis-regulatory elements, which is crucial for crop improvement. In this context, we developed an easy-to-implement ATAC-seq protocol for maize with fewer nuclei and simple equipment. We demonstrate a streamlined ATAC-seq protocol with four key steps for maize in which nuclei purification can be achieved without cell sorting and using only a standard bench-top centrifuge. Our protocol, coupled with the bioinformatic analysis, including validation by read length periodicity, key metrics, and correlation with transcript abundance, provides a precise and efficient assessment of the maize chromatin landscape. Beyond its application to maize, our testing design holds the potential to be applied to other crops or other tissues, especially for those with limited size and amount, establishing a robust foundation for chromatin structure studies in diverse crop species.

Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

MINI-AC: inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.

Candidate regulators of drought stress in tomato revealed by comparative transcriptomic and proteomic analyses

Drought is among the most common abiotic constraints of crop growth, development, and productivity. Integrating different omics approaches offers a possibility for deciphering the metabolic pathways and fundamental mechanisms involved in abiotic stress tolerance. Here, we explored the transcriptional and post-transcriptional changes in drought-stressed tomato plants using transcriptomic and proteomic profiles to determine the molecular dynamics of tomato drought stress responses. We identified 22467 genes and 5507 proteins, among which the expression of 3765 genes and 294 proteins was significantly changed under drought stress. Furthermore, the differentially expressed genes (DEGs) and differentially abundant proteins (DAPs) showed a good correlation (0.743). The results indicated that integrating different omics approaches is promising in exploring the multilayered regulatory mechanisms of plant drought resistance. Gene ontology (GO) and pathway analysis identified several GO terms and pathways related to stress resistance, including response to stress, abiotic stimulus, and oxidative stress. The plant hormone abscisic acid (ABA) plays pivotal roles in response to drought stress, ABA-response element binding factor (AREB) is a key positive regulator of ABA signaling. Moreover, our analysis indicated that drought stress increased the abscisic acid (ABA) content, which activated AREB1 expression to regulate the expression of TAS14, GSH-Px-1, and Hsp, ultimately improving tomato drought resistance. In addition, the yeast one-hybrid assay demonstrated that the AREB1 could bind the Hsp promoter to activate Hsp expression. Thus, this study involved a full-scale analysis of gene and protein expression in drought-stressed tomato, deepening the understanding of the regulatory mechanisms of the essential drought-tolerance genes in tomato.

Integrating ATAC-seq and RNA-seq to identify differentially expressed genes with chromatin-accessible changes during photosynthetic establishment in Populus leaves

Leaves are the primary photosynthetic organs, providing essential substances for tree growth. It is important to obtain an anatomical understanding and regulatory network analysis of leaf development. Here, we studied leaf development in Populus Nanlin895 along a development gradient from the newly emerged leaf from the shoot apex to the sixth leaf (L1 to L6) using anatomical observations and RNA-seq analysis. It indicated that mesophyll cells possess obvious vascular, palisade, and spongy tissue with distinct intercellular spaces after L3. Additionally, vacuoles fuse while epidermal cells expand to form pavement cells. RNA-seq analysis indicated that genes highly expressed in L1 and L2 were related to cell division and differentiation, while those highly expressed in L3 were enriched in photosynthesis. Therefore, we selected L1 and L3 to integrate ATAC-seq and RNA-seq and identified 735 differentially expressed genes (DEGs) with changes in chromatin accessibility regions within their promoters, of which 87 were transcription factors (TFs), such as ABI3VP1, AP-EREBP, MYB, NAC, and GRF. Motif enrichment analysis revealed potential regulatory functions for the DEGs through upstream TFs including TCP, bZIP, HD-ZIP, Dof, BBR-BPC, and MYB. Overall, our research provides a potential molecular foundation for regulatory network exploration in leaf development during photosynthesis establishment.

A multi-level approach reveals key physiological and molecular traits in the response of two rice genotypes subjected to water deficit at the reproductive stage

Rice is more vulnerable to drought than maize, wheat, and sorghum because its water requirements remain high throughout the rice life cycle. The effects of drought vary depending on the timing, intensity, and duration of the events, as well as on the rice genotype and developmental stage. It can affect all levels of organization, from genes to the cells, tissues, and/or organs. In this study, a moderate water deficit was applied to two contrasting rice genotypes, IAC 25 and CIRAD 409, during their reproductive stage. Multi-level transcriptomic, metabolomic, physiological, and morphological analyses were performed to investigate the complex traits involved in their response to drought. Weighted gene network correlation analysis was used to identify the specific molecular mechanisms regulated by each genotype, and the correlations between gene networks and phenotypic traits. A holistic analysis of all the data provided a deeper understanding of the specific mechanisms regulated by each genotype, and enabled the identification of gene markers. Under non-limiting water conditions, CIRAD 409 had a denser shoot, but shoot growth was slower despite better photosynthetic performance. Under water deficit, CIRAD 409 was weakly affected regardless of the plant level analyzed. In contrast, IAC 25 had reduced growth and reproductive development. It regulated transcriptomic and metabolic activities at a high level, and activated a complex gene regulatory network involved in growth-limiting processes. By comparing two contrasting genotypes, the present study identified the regulation of some fundamental processes and gene markers, that drive rice development, and influence its response to water deficit, in particular, the importance of the biosynthetic and regulatory pathways for cell wall metabolism. These key processes determine the biological and mechanical properties of the cell wall and thus influence plant development, organ expansion, and turgor maintenance under water deficit. Our results also question the genericity of the antagonism between morphogenesis and organogenesis observed in the two genotypes.

Spatially resolved gene regulatory networks in Asian rice (Oryza sativa cv. Nipponbare) leaves

Transcriptome profiles in plants are heterogenous at every level of morphological organization. Even within organs, cells of the same type can have different patterns of gene expression depending on where they are positioned within tissues. This heterogeneity is associated with non-uniform distribution of biological processes within organs. The regulatory mechanisms that establish and sustain the spatial heterogeneity are unknown. Here, we identify regulatory modules that support functional specialization of different parts of Oryza sativa cv. Nipponbare leaves by leveraging transcriptome data, transcription factor binding motifs and global gene regulatory network prediction algorithms. We generated a global gene regulatory network in which we identified six regulatory modules that were active in different parts of the leaf. The regulatory modules were enriched for genes involved in spatially relevant biological processes, such as cell wall deposition, environmental sensing and photosynthesis. Strikingly, more than 86.9% of genes in the network were regulated by members of only five transcription factor families. We also generated targeted regulatory networks for the large MYB and bZIP/bHLH families to identify interactions that were masked in the global prediction. This analysis will provide a baseline for future single cell and array-based spatial transcriptome studies and for studying responses to environmental stress and demonstrates the extent to which seven coarse spatial transcriptome analysis can provide insight into the regulatory mechanisms supporting functional specialization within leaves.

Stress responses of plants through transcriptome plasticity by mRNA alternative polyadenylation

The sessile nature of plants confines their responsiveness to changing environmental conditions. Gene expression regulation becomes a paramount mechanism for plants to adjust their physiological and morphological behaviors. Alternative polyadenylation (APA) is known for its capacity to augment transcriptome diversity and plasticity, thereby furnishing an additional set of tools for modulating gene expression. APA has also been demonstrated to exhibit intimate associations with plant stress responses. In this study, we review APA dynamic features and consequences in plants subjected to both biotic and abiotic stresses. These stresses include adverse environmental stresses, and pathogenic attacks, such as cadmium toxicity, high salt, hypoxia, oxidative stress, cold, heat shock, along with bacterial, fungal, and viral infections. We analyzed the overarching research framework employed to elucidate plant APA response and the alignment of polyadenylation site transitions with the modulation of gene expression levels within the ambit of each stress condition. We also proposed a general APA model where transacting factors, including poly(A) factors, epigenetic regulators, RNA m6A modification factors, and phase separation proteins, assume pivotal roles in APA related transcriptome plasticity during stress response in plants.

The SbbHLH041-SbEXPA11 Module Enhances Cadmium Accumulation and Rescues Biomass by Increasing Photosynthetic Efficiency in Sorghum

In plants, expansin genes are responsive to heavy metal exposure. To study the bioremediary potential of this important gene family, we discovered a root-expressed expansin gene in sorghum, SbEXPA11, which is notably upregulated following cadmium (Cd) exposure. However, the mechanism underlying the Cd detoxification and accumulation mediated by SbEXPA11 in sorghum remains unclear. We overexpressed SbEXPA11 in sorghum and compared wild-type (WT) and SbEXPA11-overexpressing transgenic sorghum in terms of Cd accumulation and physiological indices following Cd. Compared with the WT, we found that SbEXPA11 mediates Cd tolerance by exerting reactive oxygen species (ROS)-scavenging effects through upregulating the expression of antioxidant enzymes. Moreover, the overexpression of SbEXPA11 rescued biomass production by increasing the photosynthetic efficiency of transgenic plants. In the pot experiment with a dosage of 10 mg/kg Cd, transgenic sorghum plants demonstrated higher efficacy in reducing the Cd content of the soil (8.62 mg/kg) compared to WT sorghum plants (9.51 mg/kg). Subsequent analysis revealed that the SbbHLH041 transcription factor has the ability to induce SbEXPA11 expression through interacting with the E-box located within the SbEXPA11 promoter. These findings suggest that the SbbHLH041-SbEXPA11 cascade module may be beneficial for the development of phytoremediary sorghum varieties.

Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice

Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.

Characterization of chromatin accessibility and gene expression reveal the key genes involved in cotton fiber elongation

Cotton (Gossypium hirsutum L.) is an important economic crop, and cotton fiber is one of the longest plant cells, which provides an ideal model for the study of cell elongation and secondary cell wall synthesis. Cotton fiber length is regulated by a variety of transcription factors (TF) and their target genes; however, the mechanism of fiber elongation mediated by transcriptional regulatory networks is still unclear to a large extent. Here, we used a comparative assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) assay and RNA-seq analysis to identify fiber elongation transcription factors and genes using the short-fiber mutant ligon linless-2 (Li2 ) and wild type (WT). A total of 499 differential target genes were identified and GO analysis shows that differential genes are mainly involved in plant secondary wall synthesis and microtubule-binding processes. Analysis of the genomic regions preferentially accessible (Peak) has identified a number of overrepresented TF-binding motifs, highlighting sets of TFs that are important for cotton fiber development. Using ATAC-seq and RNA-seq data, we have constructed a functional regulatory network of each TF regulatory target gene and also the network pattern of TF regulating differential target genes. Further, to obtain the genes related to fiber length, the differential target genes were combined with FLGWAS data to identify the genes highly related to fiber length. Our work provides new insights into cotton fiber elongation.

Integrative time-scale and multi-omics analysis of host responses to viroid infection

Viroids are circular RNAs of minimal complexity compelled to subvert plant-regulatory networks to accomplish their infectious process. Studies focused on the response to viroid-infection have mostly addressed specific regulatory levels and considered specifics infection-times. Thus, much remains to be done to understand the temporal evolution and complex nature of viroid-host interactions. Here we present an integrative analysis of the temporal evolution of the genome-wide alterations in cucumber plants infected with hop stunt viroid (HSVd) by integrating differential host transcriptome, sRNAnome and methylome. Our results support that HSVd promotes the redesign of the cucumber regulatory-pathways predominantly affecting specific regulatory layers at different infection-phases. The initial response was characterised by a reconfiguration of the host-transcriptome by differential exon-usage, followed by a progressive transcriptional downregulation modulated by epigenetic changes. Regarding endogenous small RNAs, the alterations were limited and mainly occurred at the late stage. Significant host-alterations were predominantly related to the downregulation of transcripts involved in plant-defence mechanisms, the restriction of pathogen-movement and the systemic spreading of defence signals. We expect that these data constituting the first comprehensive temporal-map of the plant-regulatory alterations associated with HSVd infection could contribute to elucidate the molecular basis of the yet poorly known host-response to viroid-induced pathogenesis.

Optimization of ATAC-seq in wheat seedling roots using INTACT-isolated nuclei

BACKGROUND

The genetic information contained in the genome of an organism is organized in genes and regulatory elements that control gene expression. The genomes of multiple plants species have already been sequenced and the gene repertory have been annotated, however, cis-regulatory elements remain less characterized, limiting our understanding of genome functionality. These elements act as open platforms for recruiting both positive- and negative-acting transcription factors, and as such, chromatin accessibility is an important signature for their identification.

RESULTS

In this work we developed a transgenic INTACT [isolation of nuclei tagged in specific cell types] system in tetraploid wheat for nuclei purifications. Then, we combined the INTACT system together with the assay for transposase-accessible chromatin with sequencing [ATAC-seq] to identify open chromatin regions in wheat root tip samples. Our ATAC-seq results showed a large enrichment of open chromatin regions in intergenic and promoter regions, which is expected for regulatory elements and that is similar to ATAC-seq results obtained in other plant species. In addition, root ATAC-seq peaks showed a significant overlap with a previously published ATAC-seq data from wheat leaf protoplast, indicating a high reproducibility between the two experiments and a large overlap between open chromatin regions in root and leaf tissues. Importantly, we observed overlap between ATAC-seq peaks and cis-regulatory elements that have been functionally validated in wheat, and a good correlation between normalized accessibility and gene expression levels.

CONCLUSIONS

We have developed and validated an INTACT system in tetraploid wheat that allows rapid and high-quality nuclei purification from root tips. Those nuclei were successfully used to performed ATAC-seq experiments that revealed open chromatin regions in the wheat genome that will be useful to identify cis-regulatory elements. The INTACT system presented here will facilitate the development of ATAC-seq datasets in other tissues, growth stages, and under different growing conditions to generate a more complete landscape of the accessible DNA regions in the wheat genome.

PlantCADB: A Comprehensive Plant Chromatin Accessibility Database

Chromatin accessibility landscapes are essential for detecting regulatory elements, illustrating the corresponding regulatory networks, and, ultimately, understanding the molecular basis underlying key biological processes. With the advancement of sequencing technologies, a large volume of chromatin accessibility data has been accumulated and integrated for humans and other mammals. These data have greatly advanced the study of disease pathogenesis, cancer survival prognosis, and tissue development. To advance the understanding of molecular mechanisms regulating plant key traits and biological processes, we developed a comprehensive plant chromatin accessibility database (PlantCADB) from 649 samples of 37 species. These samples are abiotic stress-related (such as heat, cold, drought, and salt; 159 samples), development-related (232 samples), and/or tissue-specific (376 samples). Overall, 18,339,426 accessible chromatin regions (ACRs) were compiled. These ACRs were annotated with genomic information, associated genes, transcription factor footprint, motif, and single-nucleotide polymorphisms (SNPs). Additionally, PlantCADB provides various tools to visualize ACRs and corresponding annotations. It thus forms an integrated, annotated, and analyzed plant-related chromatin accessibility resource, which can aid in better understanding genetic regulatory networks underlying development, important traits, stress adaptations, and evolution.PlantCADB is freely available at https://bioinfor.nefu.edu.cn/PlantCADB/.

Integrated ATAC-Seq and RNA-Seq Data Analysis to Reveal OsbZIP14 Function in Rice in Response to Heat Stress

Transcription factors (TFs) play critical roles in mediating the plant response to various abiotic stresses, particularly heat stress. Plants respond to elevated temperatures by modulating the expression of genes involved in diverse metabolic pathways, a regulatory process primarily governed by multiple TFs in a networked configuration. Many TFs, such as WRKY, MYB, NAC, bZIP, zinc finger protein, AP2/ERF, DREB, ERF, bHLH, and brassinosteroids, are associated with heat shock factor (Hsf) families, and are involved in heat stress tolerance. These TFs hold the potential to control multiple genes, which makes them ideal targets for enhancing the heat stress tolerance of crop plants. Despite their immense importance, only a small number of heat-stress-responsive TFs have been identified in rice. The molecular mechanisms underpinning the role of TFs in rice adaptation to heat stress still need to be researched. This study identified three TF genes, including OsbZIP14, OsMYB2, and OsHSF7, by integrating transcriptomic and epigenetic sequencing data analysis of rice in response to heat stress. Through comprehensive bioinformatics analysis, we demonstrated that OsbZIP14, one of the key heat-responsive TF genes, contained a basic-leucine zipper domain and primarily functioned as a nuclear TF with transcriptional activation capability. By knocking out the OsbZIP14 gene in the rice cultivar Zhonghua 11, we observed that the knockout mutant OsbZIP14 exhibited dwarfism with reduced tiller during the grain-filling stage. Under high-temperature treatment, it was also demonstrated that in the OsbZIP14 mutant, the expression of the OsbZIP58 gene, a key regulator of rice seed storage protein (SSP) accumulation, was upregulated. Furthermore, bimolecular fluorescence complementation (BiFC) experiments uncovered a direct interaction between OsbZIP14 and OsbZIP58. Our results suggested that OsbZIP14 acts as a key TF gene through the concerted action of OsbZIP58 and OsbZIP14 during rice filling under heat stress. These findings provide good candidate genes for genetic improvement of rice but also offer valuable scientific insights into the mechanism of heat tolerance stress in rice.

Transcriptomic, metabolomic, and ATAC-seq analysis reveal the regulatory mechanism of senescence of post-harvest tomato fruit

Several physiological changes occur during fruit storage, which include the regulation of genes, metabolisms and transcription factors. In this study, we compared 'JF308' (a normal tomato cultivar) and 'YS006' (a storable tomato cultivar) to determine the difference in accumulated metabolites, gene expression, and accessible chromatin regions through metabolome, transcriptome, and ATAC-seq analysis. A total of 1006 metabolites were identified in two cultivars. During storage time, sugars, alcohols and flavonoids were found to be more abundant in 'YS006' compared to 'JF308' on day 7, 14, and 21, respectively. Differentially expressed genes, which involved in starch and sucrose biosynthesis were observed higher in 'YS006'. 'YS006' had lower expression levels of CesA (cellulose synthase), PL (pectate lyase), EXPA (expansin) and XTH (xyglucan endoglutransglucosylase/hydrolase) than 'JF308'. The results showed that phenylpropanoid pathway, carbohydrate metabolism and cell wall metabolism play important roles in prolonging the shelf life of tomato (Solanum lycopersicum) fruit. The ATAC-seq analysis revealed that the most significantly up-regulated transcription factors during storage were TCP 2,3,4,5, and 24 in 'YS006' compared to 'JF308' on day 21. This information on the molecular regulatory mechanisms and metabolic pathways of post-harvest quality changes in tomato fruit provides a theoretical foundation for slowing post-harvest decay and loss, and has theoretical importance and application value in breeding for longer shelf life cultivars.

Multi-omics revolution to promote plant breeding efficiency

Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.

Systematic annotation of conservation states provides insights into regulatory regions in rice

Plant genomes contain a large fraction of noncoding sequences. The discovery and annotation of conserved noncoding sequences (CNSs) in plants is an ongoing challenge. Here we report the application of comparative genomics to systematically identify CNSs in 50 well-annotated Gramineae genomes using rice (Oryza sativa) as the reference. We conduct multiple-way whole-genome alignments to the rice genome. The rice genome is annotated as 20 conservation states (CSs) at single-nucleotide resolution using a multivariate hidden Markov model (ConsHMM) based on the multiple-genome alignments. Different states show distinct enrichments for various genomic features, and the conservation scores of CSs are highly correlated with the level of associated chromatin accessibility. We find that at least 33.5% of the rice genome is highly under selection, with more than 70% of the sequence lying outside of coding regions. A catalog of 855,366 regulatory CNSs is generated, and they significantly overlapped with putative active regulatory elements such as promoters, enhancers, and transcription factor binding sites. Collectively, our study provides a resource for elucidating functional noncoding regions of the rice genome and an evolutionary aspect of regulatory sequences in higher plants.

Allele-specific expression and chromatin accessibility contribute to heterosis in tea plants (Camellia sinensis)

Heterosis is extensively used to improve crop productivity, yet its allelic and chromatin regulation remains unclear. Based on our resolved genomes of the maternal TGY and paternal HD, we analyzed the contribution of allele-specific expression (ASE) and chromatin accessibility of JGY and HGY, the artificial hybrids of oolong tea with the largest cultivated area in China. The ASE genes (ASEGs) of tea hybrids with maternal-biased were mainly related to the energy and terpenoid metabolism pathways, whereas the ASEGs with paternal-biased tend to be enriched in glutathione metabolism, and these parental bias of hybrids may coordinate and lead to the acquisition of heterosis in more biological pathways. ATAC-seq results showed that hybrids have significantly higher accessible chromatin regions (ACRs) compared with their parents, which may confer broader and stronger transcriptional activity of genes in hybrids. The number of ACRs with significantly increased accessibility in hybrids was much greater than decreased, and the associated alleles were also affected by differential ACRs across different parents, suggesting enhanced positive chromatin regulation and potential genetic effects in hybrids. Core ASEGs of terpene and purine alkaloid metabolism pathways with significant positive heterosis have greater chromatin accessibility in hybrids, and were potentially regulated by several members of the MYB, DOF and TRB families. The binding motif of CsMYB85 in the promoter ACR of the rate-limiting enzyme CsDXS was verified by DAP-seq. These results suggest that higher numbers and more accessible ACRs in hybrids contribute to the regulation of ASEGs, thereby affecting the formation of heterotic metabolites.

Chromatin dynamics associated with seed desiccation tolerance/sensitivity at early germination in Medicago truncatula

Desiccation tolerance (DT) has contributed greatly to the adaptation of land plants to severe water-deficient conditions. DT is mostly observed in reproductive parts in flowering plants such as seeds. The seed DT is lost at early post germination stage but is temporally re-inducible in 1 mm radicles during the so-called DT window following a PEG treatment before being permanently silenced in 5 mm radicles of germinating seeds. The molecular mechanisms that activate/reactivate/silence DT in developing and germinating seeds have not yet been elucidated. Here, we analyzed chromatin dynamics related to re-inducibility of DT before and after the DT window at early germination in Medicago truncatula radicles to determine if DT-associated genes were transcriptionally regulated at the chromatin levels. Comparative transcriptome analysis of these radicles identified 948 genes as DT re-induction-related genes, positively correlated with DT re-induction. ATAC-Seq analyses revealed that the chromatin state of genomic regions containing these genes was clearly modulated by PEG treatment and affected by growth stages with opened chromatin in 1 mm radicles with PEG (R1P); intermediate openness in 1 mm radicles without PEG (R1); and condensed chromatin in 5 mm radicles without PEG (R5). In contrast, we also showed that the 103 genes negatively correlated with the re-induction of DT did not show any transcriptional regulation at the chromatin level. Additionally, ChIP-Seq analyses for repressive marks H2AK119ub and H3K27me3 detected a prominent signal of H3K27me3 on the DT re-induction-related gene sequences at R5 but not in R1 and R1P. Moreover, no clear H2AK119ub marks was observed on the DT re-induction-related gene sequences at both developmental radicle stages, suggesting that silencing of DT process after germination will be mainly due to H3K27me3 marks by the action of the PRC2 complex, without involvement of PRC1 complex. The dynamic of chromatin changes associated with H3K27me3 were also confirmed on seed-specific genes encoding potential DT-related proteins such as LEAs, oleosins and transcriptional factors. However, several transcriptional factors did not show a clear link between their decrease of chromatin openness and H3K27me3 levels, suggesting that their accessibility may also be regulated by additional factors, such as other histone modifications. Finally, in order to make these comprehensive genome-wide analyses of transcript and chromatin dynamics useful to the scientific community working on early germination and DT, we generated a dedicated genome browser containing all these data and publicly available at https://iris.angers.inrae.fr/mtseedepiatlas/jbrowse/?data=Mtruncatula.

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

Growth-limiting drought stress induces time-of-day-dependent transcriptome and physiological responses in hybrid poplar

Drought stress negatively impacts the health of long-lived trees. Understanding the genetic mechanisms that underpin response to drought stress is requisite for selecting or enhancing climate change resilience. We aimed to determine how hybrid poplars respond to prolonged and uniform exposure to drought; how responses to moderate and more severe growth-limiting drought stresses differed; and how drought responses change throughout the day. We established hybrid poplar trees (Populus × 'Okanese') from unrooted stem cutting with abundant soil moisture for 6 weeks. We then withheld water to establish well-watered, moderate and severe growth-limiting drought conditions. These conditions were maintained for 3 weeks during which growth was monitored. We then measured photosynthetic rates and transcriptomes of leaves that had developed during the drought treatments at two times of day. The moderate and severe drought treatments elicited distinct changes in growth and development, photosynthetic rates and global transcriptome profiles. Notably, the time of day of sampling produced the strongest effect in the transcriptome data. The moderate drought treatment elicited global transcriptome changes that were intermediate to the severe and well-watered treatments in the early evening but did not elicit a strong drought response in the morning. Stable drought conditions that are sufficient to limit plant growth elicit distinct transcriptional profiles depending on the degree of water limitation and on the time of day at which they are measured. There appears to be a limited number of genes and functional gene categories that are responsive to all of the tested drought conditions in this study emphasizing the complex nature of drought regulation in long-lived trees.

Understanding of the various aspects of gene regulatory networks related to crop improvement

The hierarchical relationship between transcription factors, associated proteins, and their target genes is defined by a gene regulatory network (GRN). GRNs allow us to understand how the genotype and environment of a plant are incorporated to control the downstream physiological responses. During plant growth or environmental acclimatization, GRNs are diverse and can be differently regulated across tissue types and organs. An overview of recent advances in the development of GRN that speed up basic and applied plant research is given here. Furthermore, the overview of genome and transcriptome involving GRN research along with the exciting advancement and application are discussed. In addition, different approaches to GRN predictions were elucidated. In this review, we also describe the role of GRN in crop improvement, crop plant manipulation, stress responses, speed breeding and identifying genetic variations/locus. Finally, the challenges and prospects of GRN in plant biology are discussed.

Identification of major candidate genes for multiple abiotic stress tolerance at seedling stage by network analysis and their validation by expression profiling in rice (Oryza sativa L.)

A wealth of microarray and RNA-seq data for studying abiotic stress tolerance in rice exists but only limited studies have been carried out on multiple stress-tolerance responses and mechanisms. In this study, we identified 6657 abiotic stress-responsive genes pertaining to drought, salinity and heat stresses from the seedling stage microarray data of 83 samples and used them to perform unweighted network analysis and to identify key hub genes or master regulators for multiple abiotic stress tolerance. Of the total 55 modules identified from the analysis, the top 10 modules with 8-61 nodes comprised 239 genes. From these 10 modules, 10 genes common to all the three stresses were selected. Further, based on the centrality properties and highly dense interactions, we identified 7 intra-modular hub genes leading to a total of 17 potential candidate genes. Out of these 17 genes, 15 were validated by expression analysis using a panel of 4 test genotypes and a pair of standard check genotypes for each abiotic stress response. Interestingly, all the 15 genes showed upregulation under all stresses and in all the genotypes, suggesting that they could be representing some of the core abiotic stress-responsive genes. More pertinently, eight of the genes were found to be co-localized with the stress-tolerance QTL regions. Thus, in conclusion, our study not only provided an effective approach for studying abiotic stress tolerance in rice, but also identified major candidate genes which could be further validated by functional genomics for abiotic stress tolerance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03182-7.

GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

Background

Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures.

Results

GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage.

Conclusions

Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04629-7.

Identification of the accessible chromatin regions in six tissues in the soybean

Accessible chromatin regions (ACRs) are tightly associated with gene expressions in the genome. Conserved non-coding cis-regulatory elements, such as transcription factor binding motifs, are usually found in ACRs, indicating an essential regulatory role of ACRs in the plant genome architecture. However, there have been few studies on soybean ACRs, especially those focusing on specific tissues. Hence, in this study, with the convenient ATAC-seq, we identified the ACRs in six soybean tissues, including root, leaf bud, flower, flower bud, developing seed, and pod. In total, the ACRs occupied about 3.3% of the entire soybean genome. By integrating the results from RNA-seq and transcription factor (TF) ChIP-seq, ACRs were found to be tightly associated with gene expressions and TF binding capacities in soybean. Together, these data provide a comprehensive understanding of the genomic features of ACRs in soybean. As a collection of essential genomic resources, these processed data are made available at datahub.wildsoydb.org.

Transcriptome Meta-Analysis Associated Targeting Hub Genes and Pathways of Drought and Salt Stress Responses in Cotton (Gossypium hirsutum): A Network Biology Approach

Abiotic stress tolerance is an intricate feature controlled through several genes and networks in the plant system. In abiotic stress, salt, and drought are well known to limit cotton productivity. Transcriptomics meta-analysis has arisen as a robust method to unravel the stress-responsive molecular network in crops. In order to understand drought and salt stress tolerance mechanisms, a meta-analysis of transcriptome studies is crucial. To confront these issues, here, we have given details of genes and networks associated with significant differential expression in response to salt and drought stress. The key regulatory hub genes of drought and salt stress conditions have notable associations with functional drought and salt stress-responsive (DSSR) genes. In the network study, nodulation signaling pathways 2 (NSP2), Dehydration-responsive element1 D (DRE1D), ethylene response factor (ERF61), cycling DOF factor 1 (CDF1), and tubby like protein 3 (TLP3) genes in drought and tubby like protein 1 (TLP1), thaumatin-like proteins (TLP), ethylene-responsive transcription factor ERF109 (EF109), ETS-Related transcription Factor (ELF4), and Arabidopsis thaliana homeodomain leucine-zipper gene (ATHB7) genes in salt showed the significant putative functions and pathways related to providing tolerance against drought and salt stress conditions along with the significant expression values. These outcomes provide potential candidate genes for further in-depth functional studies in cotton, which could be useful for the selection of an improved genotype of Gossypium hirsutum against drought and salt stress conditions.

Global identification and integrated analysis of heat-responsive long non-coding RNAs in contrasting rice cultivars

Identified 2743 rice lncRNAs LncRNAs in response to heat stress Function prediction of HRLs Network among HRLs, genes and miRNAs co-localization of HRLs with QTLs Significant motifs in HRL sequences Long non-coding RNAs (lncRNAs) play vital roles in plant responses to environmental challenges. A better understanding of the gene regulation mediated by lncRNAs and their systematic identification would provide great benefits for modern agriculture. In this study, we performed strand-specific RNA sequencing for two rice varieties, heat-tolerant ZS97B and heat-susceptible SYD2 under heat stress. In total, 2743 putative lncRNAs were identified, and their expression profiles in response to heat treatments were established. We identified 231 differentially expressed lncRNAs (DELs) under heat stress, including 31 DELs common to both varieties and 103 and 97 specific to ZS97B and SYD2, respectively, all defined as heat-responsive lncRNAs (HRLs). The target-coding genes of HRLs were predicted, and GO and KEGG annotations of HRL targets revealed functions in which HRLs might be involved. The interaction network between HRLs, target genes and relevant miRNAs was constructed. The HRLs and their targets were compared with publicly available QTLs for rice seedling growth under heat stimulus. Ten HRLs and twelve target genes were linked with five heat stress-relevant QTLs. Sequence analysis revealed several motifs significantly enriched within the 231 HRL sequences. Our findings provide a valuable resource for further characterization of lncRNAs in terms of heat response and plant heat tolerance improvement.

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

Motivation

Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

Results

In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.

Availability and implementation

The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolutionary systems biology reveals patterns of rice adaptation to drought-prone agro-ecosystems

Rice (Oryza sativa) was domesticated around 10,000 years ago and has developed into a staple for half of humanity. The crop evolved and is currently grown in stably wet and intermittently dry agro-ecosystems, but patterns of adaptation to differences in water availability remain poorly understood. While previous field studies have evaluated plant developmental adaptations to water deficit, adaptive variation in functional and hydraulic components, particularly in relation to gene expression, has received less attention. Here, we take an evolutionary systems biology approach to characterize adaptive drought resistance traits across roots and shoots. We find that rice harbors heritable variation in molecular, physiological, and morphological traits that is linked to higher fitness under drought. We identify modules of co-expressed genes that are associated with adaptive drought avoidance and tolerance mechanisms. These expression modules showed evidence of polygenic adaptation in rice subgroups harboring accessions that evolved in drought-prone agro-ecosystems. Fitness-linked expression patterns allowed us to identify the drought-adaptive nature of optimizing photosynthesis and interactions with arbuscular mycorrhizal fungi. Taken together, our study provides an unprecedented, integrative view of rice adaptation to water-limited field conditions.

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.

Gene regulatory circuitry of plant-environment interactions: scaling from cells to the field

Plant growth and development is the product of layers of sensing and regulation that are modulated by multifactorial environmental cues. Innovations in genomics currently allow gene regulatory control to be quantified at multiple scales and high resolution in defined cell populations and even in individual cells or nuclei in plants. The application of these 'omic technologies in highly controlled, as well as field environments is revolutionizing the recognition of factors critical to spatial and temporal responses to single or multiple environmental cues. Within and pan-species comparisons illuminate deeply conserved circuitry and targets of selection. This knowledge can benefit the breeding and engineering of crops with greater resilience to climate variability and the ability to augment nutrition through plant-microbial interactions.

Future Challenges in Plant Systems Biology

Plant systems biology is currently facing several important challenges, whose nature depend on the considered frame of reference and associated scale. This review covers some of the issues associated respectively with the molecular, tissue, and whole-plant scales, as well as discusses the potential for latest advances in synthetic biology and machine-learning methods to be of use in the future of plant systems biology.

Rice functional genomics: decades' efforts and roads ahead

Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.

Epigenetic features improve TALE target prediction

Background

The yield of many crop plants can be substantially reduced by plant-pathogenic Xanthomonas bacteria. The infection strategy of many Xanthomonas strains is based on transcription activator-like effectors (TALEs), which are secreted into the host cells and act as transcriptional activators of plant genes that are beneficial for the bacteria.The modular DNA binding domain of TALEs contains tandem repeats, each comprising two hyper-variable amino acids. These repeat-variable diresidues (RVDs) bind to their target box and determine the specificity of a TALE.All available tools for the prediction of TALE targets within the host plant suffer from many false positives. In this paper we propose a strategy to improve prediction accuracy by considering the epigenetic state of the host plant genome in the region of the target box.

Results

To this end, we extend our previously published tool PrediTALE by considering two epigenetic features: (i) chromatin accessibility of potentially bound regions and (ii) DNA methylation of cytosines within target boxes. Here, we determine the epigenetic features from publicly available DNase-seq, ATAC-seq, and WGBS data in rice.We benchmark the utility of both epigenetic features separately and in combination, deriving ground-truth from RNA-seq data of infections studies in rice. We find an improvement for each individual epigenetic feature, but especially the combination of both.Having established an advantage in TALE target predicting considering epigenetic features, we use these data for promoterome and genome-wide scans by our new tool EpiTALE, leading to several novel putative virulence targets.

Conclusions

Our results suggest that it would be worthwhile to collect condition-specific chromatin accessibility data and methylation information when studying putative virulence targets of Xanthomonas TALEs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-08210-z).

Transcriptomic and Physiological Response of Durum Wheat Grain to Short-Term Heat Stress during Early Grain Filling

In a changing climate, extreme weather events such as heatwaves will be more frequent and could affect grain weight and the quality of crops such as wheat, one of the most significant crops in terms of global food security. In this work, we characterized the response of Triticum turgidum L. spp. durum wheat to short-term heat stress (HS) treatment at transcriptomic and physiological levels during early grain filling in glasshouse experiments. We found a significant reduction in grain weight (23.9%) and grain dimensions from HS treatment. Grain quality was also affected, showing a decrease in starch content (20.8%), in addition to increments in grain protein levels (14.6%), with respect to the control condition. Moreover, RNA-seq analysis of durum wheat grains allowed us to identify 1590 differentially expressed genes related to photosynthesis, response to heat, and carbohydrate metabolic process. A gene regulatory network analysis of HS-responsive genes uncovered novel transcription factors (TFs) controlling the expression of genes involved in abiotic stress response and grain quality, such as a member of the DOF family predicted to regulate glycogen and starch biosynthetic processes in response to HS in grains. In summary, our results provide new insights into the extensive transcriptome reprogramming that occurs during short-term HS in durum wheat grains.

A comprehensive transcription factor and DNA-binding motif resource for the construction of gene regulatory networks in Botrytis cinerea and Trichoderma atroviride

Botrytis cinerea and Trichoderma atroviride are two relevant fungi in agricultural systems. To gain insights into these organisms' transcriptional gene regulatory networks (GRNs), we generated a manually curated transcription factor (TF) dataset for each of them, followed by a GRN inference utilizing available sequence motifs describing DNA-binding specificity and global gene expression data. As a proof of concept of the usefulness of this resource to pinpoint key transcriptional regulators, we employed publicly available transcriptomics data and a newly generated dual RNA-seq dataset to build context-specific Botrytis and Trichoderma GRNs under two different biological paradigms: exposure to continuous light and Botrytis-Trichoderma confrontation assays. Network analysis of fungal responses to constant light revealed striking differences in the transcriptional landscape of both fungi. On the other hand, we found that the confrontation of both microorganisms elicited a distinct set of differentially expressed genes with changes in T. atroviride exceeding those in B. cinerea. Using our regulatory network data, we were able to determine, in both fungi, central TFs involved in this interaction response, including TFs controlling a large set of extracellular peptidases in the biocontrol agent T. atroviride. In summary, our work provides a comprehensive catalog of transcription factors and regulatory interactions for both organisms. This catalog can now serve as a basis for generating novel hypotheses on transcriptional regulatory circuits in different experimental contexts.

Rice functional genomics: decades' efforts and roads ahead

Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.

Physiological and Proteomic Responses of Contrasting Alfalfa (Medicago sativa L.) Varieties to High Temperature Stress

High temperature (HT) is an important factor for limiting global plant distribution and agricultural production. As the global temperature continues to rise, it is essential to clarify the physiological and molecular mechanisms of alfalfa responding the high temperature, which will contribute to the improvement of heat resistance in leguminous crops. In this study, the physiological and proteomic responses of two alfalfa (Medicago sativa L.) varieties contrasting in heat tolerance, MS30 (heat-tolerant) and MS37 (heat-sensitive), were comparatively analyzed under the treatments of continuously rising temperatures for 42 days. The results showed that under the HT stress, the chlorophyll content and the chlorophyll fluorescence parameter (Fv/Fm) of alfalfa were significant reduced and some key photosynthesis-related proteins showed a down-regulated trend. Moreover, the content of Malondialdehyde (MDA) and the electrolyte leakage (EL) of alfalfa showed an upward trend, which indicates both alfalfa varieties were damaged under HT stress. However, because the antioxidation-reduction and osmotic adjustment ability of MS30 were significantly stronger than MS37, the damage degree of the photosynthetic system and membrane system of MS30 is significantly lower than that of MS37. On this basis, the global proteomics analysis was undertaken by tandem mass tags (TMT) technique, a total of 6,704 proteins were identified and quantified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that a series of key pathways including photosynthesis, metabolism, adjustment and repair were affected by HT stress. Through analyzing Venn diagrams of two alfalfa varieties, 160 and 213 differentially expressed proteins (DEPs) that had dynamic changes under HT stress were identified from MS30 and MS37, respectively. Among these DEPs, we screened out some key DEPs, such as ATP-dependent zinc metalloprotease FTSH protein, vitamin K epoxide reductase family protein, ClpB3, etc., which plays important functions in response to HT stress. In conclusion, the stronger heat-tolerance of MS30 was attributed to its higher adjustment and repair ability, which could cause the metabolic process of MS30 is more conducive to maintaining its survival and growth than MS37, especially at the later period of HT stress. This study provides a useful catalog of the Medicago sativa L. proteomes with the insight into its future genetic improvement of heat-resistance.

Characterization of Chromatin Accessibility and Gene Expression upon Cold Stress Reveals that the RAV1 Transcription Factor Functions in Cold Response in Vitis Amurensis

Cold tolerance is regulated by a variety of transcription factors (TFs) and their target genes. Except for the well-characterized C-repeat binding factors (CBFs)-dependent transcriptional cascade, the mechanisms of cold tolerance mediated by other transcriptional regulatory networks are still largely unknown. Here, we used the assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA-seq to identify cold responsive TFs in Vitis amurensis, a grape species with high cold hardiness. Nine TFs, including CBF4, RAV1 and ERF104, were identified after cold treatment. Weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) analysis revealed that these TFs may regulate cold response through different pathways. As a prime candidate TF, overexpression of VaRAV1 in grape cells improved its cold tolerance. The transgenic cells exhibited low electrolyte leakage and malondialdehyde content and high peroxidase activity. Moreover, the TF gene TCP8 and a gene involving in homogalacturonan biosynthesis were found to be regulated by VaRAV1, suggesting that the contribution of VaRAV1 to cold tolerance may be achieved by enhancing the stability of cell membrane and regulating the expression of target genes involved in plant cell wall composition. Our work provides novel insights into plant response to cold stress and demonstrates the utility of ATAC-seq and RNA-seq for the rapid identification of TFs in response to cold stress in grapevine. VaRAV1 may play an important role in adaption to cold stress.

Addressing noise in co-expression network construction

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.

Prospects and challenges of epigenomics in crop improvement

BACKGROUND

The advent of high-throughput epigenome mapping techniques has ushered in a new era of multiomics with powerful tools now available to map and record genomic output at different levels. Integrating the different components of the epigenome from these multiomics measures allows investigations of cis-regulatory elements on a genome-scale. Mapping of chromatin state, chromatin accessibility dynamics, and higher-order chromatin structure enables a new level of understanding of cell fate determination, identity and function in normal growth and development, disease resistance, and yield.

OBJECTIVE

In this paper, the recent advances in epigenomics research of rice, maize, and wheat are reviewed, and the development trends of epigenomics of major crops in the coming years are projected.

METHODS

We highlight the role of epigenomics in regulating growth and development and identifying potential distal cis-regulatory elements in three major crops, and discuss the prospects and challenges for new epigenetics-mediated breeding technologies in crop improvement.

CONCLUSION

In this review, we summarize and analyze recent epigenomic advances in three major crops epigenomics and discuss possibilities and challenges for future research in the field.

A High-Throughput 3'-Tag RNA Sequencing for Large-Scale Time-Series Transcriptome Studies

RNA sequencing (RNA-seq) has proven invaluable for exploring gene expression variation under complex environmental cues. However, the cost of standard RNA-seq (e.g., Illumina TruSeq or NEBNext) remains a barrier for high-throughput applications. 3'-Tag RNA-seq (3'-TagSeq) is a cost-effective solution that permits large-scale experiments. Unlike standard RNA-seq, which generates sequencing libraries for full-length mRNAs, 3'-TagSeq only generates a single fragment from the 3' end of each transcript (a tag read) and quantifies gene expression by tag abundance. Consequently, 3'-TagSeq requires lower sequencing depth (~5 million reads per sample) than standard RNA-seq (~30 million reads per sample), which reduces costs and allows increased technical and biological replication in experiments. Because 3'-TagSeq is considerably cheaper than standard RNA-seq while exhibiting comparable accuracy and reproducibility, researchers focusing on gene expression levels in large or extensive time-series experiments might find 3'-TagSeq to be superior to standard RNA-seq. In this chapter, we describe 3'-TagSeq sequencing library preparation and provide example bioinformatics and statistical analyses of gene expression data.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Reading between the Lines: RNA-seq Data Mining Reveals the Alternative Message of the Rice Leaf Transcriptome in Response to Heat Stress

Rice (Oryza sativa L.) is a major food crop but heat stress affects its yield and grain quality. To identify mechanistic solutions to improve rice yield under rising temperatures, molecular responses of thermotolerance must be understood. Transcriptional and post-transcriptional controls are involved in a wide range of plant environmental responses. Alternative splicing (AS), in particular, is a widespread mechanism impacting the stress defence in plants but it has been completely overlooked in rice genome-wide heat stress studies. In this context, we carried out a robust data mining of publicly available RNA-seq datasets to investigate the extension of heat-induced AS in rice leaves. For this, datasets of interest were subjected to filtering and quality control, followed by accurate transcript-specific quantifications. Powerful differential gene expression (DE) and differential AS (DAS) identified 17,143 and 2162 heat response genes, respectively, many of which are novel. Detailed analysis of DAS genes coding for key regulators of gene expression suggests that AS helps shape transcriptome and proteome diversity in response to heat. The knowledge resulting from this study confirmed a widespread transcriptional and post-transcriptional response to heat stress in plants, and it provided novel candidates for rapidly advancing rice breeding in response to climate change.

A Genome Doubling Event Reshapes Rice Morphology and Products by Modulating Chromatin Signatures and Gene Expression Profiling

Evolutionarily, polyploidy represents a smart method for adjusting agronomically important in crops through impacts on genomic abundance and chromatin condensation. Autopolyploids have a relatively concise genetic background with great diversity and provide an ideal system to understand genetic and epigenetic mechanisms attributed to the genome-dosage effect. However, whether and how genome duplication events during autopolyploidization impact chromatin signatures are less understood in crops. To address it, we generated an autotetraploid rice line from a diploid progenitor, Oryza sativa ssp. indica 93-11. Using transposase-accessible chromatin sequencing, we found that autopolyploids lead to a higher number of accessible chromatin regions (ACRs) in euchromatin, most of which encode protein-coding genes. As expected, the profiling of ACR densities supported that the effect of ACRs on transcriptional gene activities relies on their positions in the rice genome, regardless of genome doubling. However, we noticed that genome duplication favors genic ACRs as the main drivers of transcriptional changes. In addition, we probed intricate crosstalk among various kinds of epigenetic marks and expression patterns of ACR-associated gene expression in both diploid and autotetraploid rice plants by integrating multiple-omics analyses, including chromatin immunoprecipitation sequencing and RNA-seq. Our data suggested that the combination of H3K36me2 and H3K36me3 may be associated with dynamic perturbation of ACRs introduced by autopolyploidization. As a consequence, we found that numerous metabolites were stimulated by genome doubling. Collectively, our findings suggest that autotetraploids reshape rice morphology and products by modulating chromatin signatures and transcriptional profiling, resulting in a pragmatic means of crop genetic improvement.

Transcriptome profiling of two rice genotypes under mild field drought stress during grain-filling stage

Drought is one of the most critical abiotic stresses that threaten crop production worldwide. This stress affects the rice crop in all stages of rice development; however, the occurrence during reproductive and grain-filling stages has the most impact on grain yield. Although many global transcriptomic studies have been performed during the reproductive stage in rice, very limited information is available for the grain-filling stage. Hence, we intend to investigate how the rice plant responds to drought stress during the grain-filling stage and how the responses change over time under field conditions. Two rice genotypes were selected for RNA-seq analysis: '4610', previously reported as a moderately tolerant breeding line, and Rondo, an elite indica rice cultivar susceptible to drought conditions. Additionally, 10 agronomic traits were evaluated under normal irrigated and drought conditions. Leaf tissues were collected during grain-filling stages at two time points, 14 and 21 days after the drought treatment, from both the drought field and normal irrigated field conditions. Based on agronomic performances, '4610' was less negatively affected than Rondo under mild drought conditions, and expression profiling largely aligned with the phenotypic data. The transcriptomic data indicated that, in general, '4610' had much earlier responses than its counterpart in mitigating the impact of drought stress. Several key genes and gene families related to drought stress or stress-related conditions were found differentially expressed in this study, including transcription factors, drought tolerance genes and reactive oxygen species scavengers. Furthermore, this study identified novel differentially expressed genes (DEGs) without function annotations that may play roles in drought tolerance-related functions. Some of the important DEGs detected in this study can be targeted for future research.