Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information

Predicting gene expression responses to environment in Arabidopsis thaliana using natural variation in DNA sequence

The evolution of gene expression responses are a critical component of adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype to phenotype map is not well resolved for cis regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to environment. We tested if flexible machine learning models could learn some of the underlying cis- regulatory genotype to phenotype map. We tested this approach using cold-responsive transcriptome profiles in 5 diverse Arabidopsis thaliana accessions. We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and down-stream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis- regulatory motifs in DNA sequences to predict expression response to environment. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by biological complexity of regulation and the large potential regulatory code. Overall, DEGs between specific environments can be predicted based on variation in cis- regulatory sequences, although more information needs to be incorporated and better models may be required.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize

Elucidating gene regulatory networks (GRNs) is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. In this study, we integrated publicly available co-expression networks derived from more than 6,000 RNA-seq samples, 283 protein-DNA interaction assays, and 16 million of SNPs used to identify expression quantitative loci (eQTL), to construct TF-target networks. In total, we analyzed ~4.6M interactions to generate four distinct types of TF-target networks: co-expression, protein-DNA interaction (PDI), trans-expression quantitative loci (trans-eQTL), and cis-eQTL combined with PDIs. To improve the functional annotation of TFs based on its target genes, we implemented three different strategies to integrate these four types of networks. We subsequently evaluated the effectiveness of our method through loss-of function mutant and random networks. The multi-network integration allowed us to identify transcriptional regulators of hormone-, metabolic- and development-related processes. Finally, using the topological properties of the fully integrated network, we identified potentially functional redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

Time of day and genotype sensitivity adjust molecular responses to temperature stress in sorghum

Sorghum is one of the four major C4 crops that are considered to be tolerant to environmental extremes. Sorghum shows distinct growth responses to temperature stress depending on the sensitivity of the genetic background. About half of the transcripts in sorghum exhibit diurnal rhythmic expressions emphasizing significant coordination with the environment. However, an understanding of how molecular dynamics contribute to genotype-specific stress responses in the context of the time of day is not known. We examined whether temperature stress and the time of day impact the gene expression dynamics in thermo-sensitive and thermo-tolerant sorghum genotypes. We found that time of day is highly influencing the temperature stress responses, which can be explained by the rhythmic expression of most thermo-responsive genes. This effect is more pronounced in thermo-tolerant genotypes, suggesting a stronger regulation of gene expression by the time of day and/or by the circadian clock. Genotypic differences were mostly observed on average gene expression levels, which may be responsible for contrasting sensitivities to temperature stress in tolerant versus susceptible sorghum varieties. We also identified groups of genes altered by temperature stress in a time-of-day and genotype-specific manner. These include transcriptional regulators and several members of the Ca2+ -binding EF-hand protein family. We hypothesize that expression variation of these genes between genotypes along with time-of-day independent regulation may contribute to genotype-specific fine-tuning of thermo-responsive pathways. These findings offer a new opportunity to selectively target specific genes in efforts to develop climate-resilient crops based on their time-of-day and genotype variation responses to temperature stress.

Genetic and molecular exploration of maize environmental stress resilience: Toward sustainable agriculture

Global climate change exacerbates the effects of environmental stressors, such as drought, flooding, extreme temperatures, salinity, and alkalinity, on crop growth and grain yield, threatening the sustainability of the food supply. Maize (Zea mays) is one of the most widely cultivated crops and the most abundant grain crop in production worldwide. However, the stability of maize yield is highly dependent on environmental conditions. Recently, great progress has been made in understanding the molecular mechanisms underlying maize responses to environmental stresses and in developing stress-resilient varieties due to advances in high-throughput sequencing technologies, multi-omics analysis platforms, and automated phenotyping facilities. In this review, we summarize recent advances in dissecting the genetic factors and networks that contribute to maize abiotic stress tolerance through diverse strategies. We also discuss future challenges and opportunities for the development of climate-resilient maize varieties.

Next-generation bulked segregant analysis for Breeding 4.0

Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.

Exploring plant cis-regulatory elements at single-cell resolution: overcoming biological and computational challenges to advance plant research

Cis-regulatory elements (CREs) are important sequences for gene expression and for plant biological processes such as development, evolution, domestication, and stress response. However, studying CREs in plant genomes has been challenging. The totipotent nature of plant cells, coupled with the inability to maintain plant cell types in culture and the inherent technical challenges posed by the cell wall has limited our understanding of how plant cell types acquire and maintain their identities and respond to the environment via CRE usage. Advances in single-cell epigenomics have revolutionized the field of identifying cell-type-specific CREs. These new technologies have the potential to significantly advance our understanding of plant CRE biology, and shed light on how the regulatory genome gives rise to diverse plant phenomena. However, there are significant biological and computational challenges associated with analyzing single-cell epigenomic datasets. In this review, we discuss the historical and foundational underpinnings of plant single-cell research, challenges, and common pitfalls in the analysis of plant single-cell epigenomic data, and highlight biological challenges unique to plants. Additionally, we discuss how the application of single-cell epigenomic data in various contexts stands to transform our understanding of the importance of CREs in plant genomes.

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.

Conserved and variable heat stress responses of the Heat Shock Factor transcription factor family in maize and Setaria viridis

The Heat Shock Factor (HSF) transcription factor family is a central and required component of plant heat stress responses and acquired thermotolerance. The HSF family has dramatically expanded in plant lineages, often including a repertoire of 20 or more genes. Here we assess and compare the composition, heat responsiveness, and chromatin profiles of the HSF families in maize and Setaria viridis (Setaria), two model C4 panicoid grasses. Both species encode a similar number of HSFs, and examples of both conserved and variable expression responses to a heat stress event were observed between the two species. Chromatin accessibility and genome-wide DNA-binding profiles were generated to assess the chromatin of HSF family members with distinct responses to heat stress. We observed significant variability for both chromatin accessibility and promoter occupancy within similarly regulated sets of HSFs between Setaria and maize, as well as between syntenic pairs of maize HSFs retained following its most recent genome duplication event. Additionally, we observed the widespread presence of TF binding at HSF promoters in control conditions, even at HSFs that are only expressed in response to heat stress. TF-binding peaks were typically near putative HSF-binding sites in HSFs upregulated in response to heat stress, but not in stable or not expressed HSFs. These observations collectively support a complex scenario of expansion and subfunctionalization within this transcription factor family and suggest that within-family HSF transcriptional regulation is a conserved, defining feature of the family.

Advances of Apetala2/Ethylene Response Factors in Regulating Development and Stress Response in Maize

Apetala2/ethylene response factor (AP2/ERF) is one of the largest families of transcription factors, regulating growth, development, and stress response in plants. Several studies have been conducted to clarify their roles in Arabidopsis and rice. However, less research has been carried out on maize. In this review, we systematically identified the AP2/ERFs in the maize genome and summarized the research progress related to AP2/ERF genes. The potential roles were predicted from rice homologs based on phylogenetic and collinear analysis. The putative regulatory interactions mediated by maize AP2/ERFs were discovered according to integrated data sources, implying that they involved complex networks in biological activities. This will facilitate the functional assignment of AP2/ERFs and their applications in breeding strategy.

A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species

Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predictor for the transcriptional responses of genes to stress. Machine learning methods can quantitatively estimate expression patterns and gene functions using known annotations and collections of features describing each gene. In this chapter, we describe a supervised machine learning framework to predict stress-responsive genes across species using only features derived from nucleotide sequences, using the example of cold stress-responsive genes in different Panicoid grass species.

Conserved hierarchical gene regulatory networks for drought and cold stress response in Myrica rubra

Stress response in plant is regulated by a large number of genes co-operating in diverse networks that serve multiple adaptive process. To understand how gene regulatory networks (GRNs) modulating abiotic stress responses, we compare the GRNs underlying drought and cold stresses using samples collected at 4 or 6 h intervals within 48 h in Chinese bayberry (Myrica rubra). We detected 7,583 and 8,840 differentially expressed genes (DEGs) under drought and cold stress respectively, which might be responsive to environmental stresses. Drought- and cold-responsive GRNs, which have been built according to the timing of transcription under both abiotic stresses, have a conserved trans-regulator and a common regulatory network. In both GRNs, basic helix-loop-helix family transcription factor (bHLH) serve as central nodes. MrbHLHp10 transcripts exhibited continuous increase in the two abiotic stresses and acts upstream regulator of ASCORBATE PEROXIDASE (APX) gene. To examine the potential biological functions of MrbHLH10, we generated a transgenic Arabidopsis plant that constitutively overexpresses the MrbHLH10 gene. Compared to wild-type (WT) plants, overexpressing transgenic Arabidopsis plants maintained higher APX activity and biomass accumulation under drought and cold stress. Consistently, RNAi plants had elevated susceptibility to both stresses. Taken together, these results suggested that MrbHLH10 mitigates abiotic stresses through the modulation of ROS scavenging.

Mapping responsive genomic elements to heat stress in a maize diversity panel

BACKGROUND

Many plant species exhibit genetic variation for coping with environmental stress. However, there are still limited approaches to effectively uncover the genomic region that regulates distinct responsive patterns of the gene across multiple varieties within the same species under abiotic stress.

RESULTS

By analyzing the transcriptomes of more than 100 maize inbreds, we reveal many cis- and trans-acting eQTLs that influence the expression response to heat stress. The cis-acting eQTLs in response to heat stress are identified in genes with differential responses to heat stress between genotypes as well as genes that are only expressed under heat stress. The cis-acting variants for heat stress-responsive expression likely result from distinct promoter activities, and the differential heat responses of the alleles are confirmed for selected genes using transient expression assays. Global footprinting of transcription factor binding is performed in control and heat stress conditions to document regions with heat-enriched transcription factor binding occupancies.

CONCLUSIONS

Footprints enriched near proximal regions of characterized heat-responsive genes in a large association panel can be utilized for prioritizing functional genomic regions that regulate genotype-specific responses under heat stress.

A comprehensive map of preferentially located motifs reveals distinct proximal cis-regulatory sequences in plants

Identification of cis-regulatory sequences controlling gene expression is an arduous challenge that is being actively explored to discover key genetic factors responsible for traits of agronomic interest. Here, we used a genome-wide de novo approach to investigate preferentially located motifs (PLMs) in the proximal cis-regulatory landscape of Arabidopsis thaliana and Zea mays. We report three groups of PLMs in both the 5'- and 3'-gene-proximal regions and emphasize conserved PLMs in both species, particularly in the 3'-gene-proximal region. Comparison with resources from transcription factor and microRNA binding sites shows that 79% of the identified PLMs are unassigned, although some are supported by MNase-defined cistrome occupancy analysis. Enrichment analyses further reveal that unassigned PLMs provide functional predictions that differ from those derived from transcription factor and microRNA binding sites. Our study provides a comprehensive map of PLMs and demonstrates their potential utility for future characterization of orphan genes in plants.

Temporal regulation of cold transcriptional response in switchgrass

Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal cis-regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using k-mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These k-mers, referred to as putative cis-regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.

The integrated genomics of crop domestication and breeding

As a major event in human civilization, wild plants were successfully domesticated to be crops, largely owing to continuing artificial selection. Here, we summarize new discoveries made during the past decade in crop domestication and breeding. The construction of crop genome maps and the functional characterization of numerous trait genes provide foundational information. Approaches to read, interpret, and write complex genetic information are being leveraged in many plants for highly efficient de novo or re-domestication. Understanding the underlying mechanisms of crop microevolution and applying the knowledge to agricultural productions will give possible solutions for future challenges in food security.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Meta-QTL analysis and candidate genes identification for various abiotic stresses in maize (Zea mays L.) and their implications in breeding programs

Global climate change leads to the concurrence of a number of abiotic stresses including moisture stress (drought, waterlogging), temperature stress (heat, cold), and salinity stress, which are the major factors affecting maize production. To develop abiotic stress tolerance in maize, many quantitative trait loci (QTL) have been identified, but very few of them have been utilized successfully in breeding programs. In this context, the meta-QTL analysis of the reported QTL will enable the identification of stable/real QTL which will pave a reliable way to introgress these QTL into elite cultivars through marker-assisted selection. In this study, a total of 542 QTL were summarized from 33 published studies for tolerance to different abiotic stresses in maize to conduct meta-QTL analysis using BiomercatorV4.2.3. Among those, only 244 major QTL with more than 10% phenotypic variance were preferably utilised to carry out meta-QTL analysis. In total, 32 meta-QTL possessing 1907 candidate genes were detected for different abiotic stresses over diverse genetic and environmental backgrounds. The MQTL2.1, 5.1, 5.2, 5.6, 7.1, 9.1, and 9.2 control different stress-related traits for combined abiotic stress tolerance. The candidate genes for important transcription factor families such as ERF, MYB, bZIP, bHLH, NAC, LRR, ZF, MAPK, HSP, peroxidase, and WRKY have been detected for different stress tolerances. The identified meta-QTL are valuable for future climate-resilient maize breeding programs and functional validation of candidate genes studies, which will help to deepen our understanding of the complexity of these abiotic stresses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01294-9.

Cellular Protein Trafficking: A New Player in Low-Temperature Response Pathway

Unlike animals, plants are unable to escape unfavorable conditions, such as extremities of temperature. Among abiotic variables, the temperature is notableas it affects plants from the molecular to the organismal level. Because of global warming, understanding temperature effects on plants is salient today and should be focused not only on rising temperature but also greater variability in temperature that is now besetting the world's natural and agricultural ecosystems. Among the temperature stresses, low-temperature stress is one of the major stresses that limits crop productivity worldwide. Over the years, although substantial progress has been made in understanding low-temperature response mechanisms in plants, the research is more focused on aerial parts of the plants rather than on the root or whole plant, and more efforts have been made in identifying and testing the major regulators of this pathway preferably in the model organism rather than in crop plants. For the low-temperature stress response mechanism, ICE-CBF regulatory pathway turned out to be the solely established pathway, and historically most of the low-temperature research is focused on this single pathway instead of exploring other alternative regulators. In this review, we tried to take an in-depth look at our current understanding of low temperature-mediated plant growth response mechanism and present the recent advancement in cell biological studies that have opened a new horizon for finding promising and potential alternative regulators of the cold stress response pathway.

Metabolic Insight into Cold Stress Response in Two Contrasting Maize Lines

Maize (Zea mays L.) is sensitive to a minor decrease in temperature at early growth stages, resulting in deteriorated growth at later stages. Although there are significant variations in maize germplasm in response to cold stress, the metabolic responses as stress tolerance mechanisms are largely unknown. Therefore, this study aimed at providing insight into the metabolic responses under cold stress at the early growth stages of maize. Two inbred lines, tolerant (B144) and susceptible (Q319), were subjected to cold stress at the seedling stage, and their corresponding metabolic profiles were explored. The study identified differentially accumulated metabolites in both cultivars in response to induced cold stress with nine core conserved cold-responsive metabolites. Guanosine 3′,5′-cyclic monophosphate was detected as a potential biomarker metabolite to differentiate cold tolerant and sensitive maize genotypes. Furthermore, Quercetin-3-O-(2″′-p-coumaroyl)sophoroside-7-O-glucoside, Phloretin, Phloretin-2′-O-glucoside, Naringenin-7-O-Rutinoside, L-Lysine, L-phenylalanine, L-Glutamine, Sinapyl alcohol, and Feruloyltartaric acid were regulated explicitly in B144 and could be important cold-tolerance metabolites. These results increase our understanding of cold-mediated metabolic responses in maize that can be further utilized to enhance cold tolerance in this significant crop.

Cis-regulatory sequences in plants: Their importance, discovery, and future challenges

The identification and characterization of cis-regulatory DNA sequences and how they function to coordinate responses to developmental and environmental cues is of paramount importance to plant biology. Key to these regulatory processes are cis-regulatory modules (CRMs), which include enhancers and silencers. Despite the extraordinary advances in high-quality sequence assemblies and genome annotations, the identification and understanding of CRMs, and how they regulate gene expression, lag significantly behind. This is especially true for their distinguishing characteristics and activity states. Here, we review the current knowledge on CRMs and breakthrough technologies enabling identification, characterization, and validation of CRMs; we compare the genomic distributions of CRMs with respect to their target genes between different plant species, and discuss the role of transposable elements harboring CRMs in the evolution of gene expression. This is an exciting time to study cis-regulomes in plants; however, significant existing challenges need to be overcome to fully understand and appreciate the role of CRMs in plant biology and in crop improvement.

Recent Advances in the Analysis of Cold Tolerance in Maize

Maize (Zea mays L.) is an annual grass that originated in tropical and subtropical regions of the New World. Maize is highly sensitive to cold stress during seed gemination and the seedling phase, which can lead to reductions in plant vigor and grain production. There are large differences in the morphological and physiological changes caused by cold stress among maize varieties. In general, cold tolerant varieties have a stronger ability to maintain such changes in traits related to seed germination, root phenotypes, and shoot photosynthesis. These morphological and physiological characteristics have been widely used to evaluate the cold tolerance of maize varieties in genetic analyses. In recent years, considerable progress has been made in elucidating the mechanisms of maize in response to cold tolerance. Several QTL, GWAS, and transcriptomic analyses have been conducted on various maize genotypes and populations that show large variations in cold tolerance, resulting in the discovery of hundreds of candidate cold regulation genes. Nevertheless, only a few candidate genes have been functionally characterized. In the present review, we summarize recent progress in molecular, physiological, genetic, and genomic analyses of cold tolerance in maize. We address the advantages of joint analyses that combine multiple genetic and genomic approaches to improve the accuracy of identifying cold regulated genes that can be further used in molecular breeding. We also discuss the involvement of long-distance signaling in plant cold tolerance. These novel insights will provide a better mechanistic understanding of cold tolerance in maize.