Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

Big data and artificial intelligence-aided crop breeding: Progress and prospects

The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding.

Transcriptome profiling of maize transcription factor mutants to probe gene regulatory network predictions

Transcription factors play important roles in regulation of gene expression and phenotype. A variety of approaches have been utilized to develop gene regulatory networks to predict the regulatory targets for each transcription factor, such as yeast-1-hybrid screens and gene co-expression network analysis. Here we identified potential transcription factor targets and used a reverse genetics approach to test the predictions of several gene regulatory networks in maize. Loss-of-function mutant alleles were isolated for 22 maize transcription factors. These mutants did not exhibit obvious morphological phenotypes. However, transcriptomic profiling identified differentially expressed genes in each of the mutant genotypes, and targeted metabolic profiling indicated variable phenolic accumulation in some mutants. An analysis of expression levels for predicted target genes based on yeast-1-hybrid screens identified a small subset of predicted targets that exhibit altered expression levels. The analysis of predicted targets from gene co-expression network-based methods found significant enrichments for prediction sets of some transcription factors, but most predicted targets did not exhibit altered expression. This could result from false-positive gene co-expression network predictions, a transcription factor with a secondary regulatory role resulting in minor effects on gene regulation, or redundant gene regulation by other transcription factors. Collectively, these findings suggest that loss-of-function for single uncharacterized transcription factors might have limited phenotypic impacts but can reveal subsets of gene regulatory network predicted targets with altered expression.

Maize unstable factor for orange1 encodes a nuclear protein that affects redox accumulation during kernel development

The basal endosperm transfer layer (BETL) of the maize (Zea mays L.) kernel is composed of transfer cells for nutrient transport to nourish the developing kernel. To understand the spatiotemporal processes required for BETL development, we characterized 2 unstable factor for orange1 (Zmufo1) mutant alleles. The BETL defects in these mutants were associated with high levels of reactive oxygen species, oxidative DNA damage, and cell death. Interestingly, antioxidant supplementation in in vitro cultured kernels alleviated the cellular defects in mutants. Transcriptome analysis of the loss-of-function Zmufo1 allele showed differential expression of tricarboxylic acid cycle, redox homeostasis, and BETL-related genes. The basal endosperms of the mutant alleles had high levels of acetyl-CoA and elevated histone acetyltransferase activity. The BETL cell nuclei showed reduced electron-dense regions, indicating sparse heterochromatin distribution in the mutants compared with wild-type. Zmufo1 overexpression further reduced histone methylation marks in the enhancer and gene body regions of the pericarp color1 (Zmp1) reporter gene. Zmufo1 encodes an intrinsically disordered nuclear protein with very low sequence similarity to known proteins. Yeast two-hybrid and luciferase complementation assays established that ZmUFO1 interacts with proteins that play a role in chromatin remodeling, nuclear transport, and transcriptional regulation. This study establishes the critical function of Zmufo1 during basal endosperm development in maize kernels.

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration

Elucidating gene regulatory networks 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. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait 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, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally 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.

GRAPHICAL ABSTRACT

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Genome-wide expression quantitative trait locus analysis reveals silk-preferential gene regulatory network in maize

Silk of maize (Zea mays L.) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome-wide identification of silk-preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk-preferential genes that were specifically or preferentially expressed in silk using our population. Silk-preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans-eQTL regulation played a more important role for silk-preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk-preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk-preferential gene Bx10, which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk-preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.

Chemically Mediated Plant-Plant Interactions: Allelopathy and Allelobiosis

Plant-plant interactions are a central driver for plant coexistence and community assembly. Chemically mediated plant-plant interactions are represented by allelopathy and allelobiosis. Both allelopathy and allelobiosis are achieved through specialized metabolites (allelochemicals or signaling chemicals) produced and released from neighboring plants. Allelopathy exerts mostly negative effects on the establishment and growth of neighboring plants by allelochemicals, while allelobiosis provides plant neighbor detection and identity recognition mediated by signaling chemicals. Therefore, plants can chemically affect the performance of neighboring plants through the allelopathy and allelobiosis that frequently occur in plant-plant intra-specific and inter-specific interactions. Allelopathy and allelobiosis are two probably inseparable processes that occur together in plant-plant chemical interactions. Here, we comprehensively review allelopathy and allelobiosis in plant-plant interactions, including allelopathy and allelochemicals and their application for sustainable agriculture and forestry, allelobiosis and plant identity recognition, chemically mediated root-soil interactions and plant-soil feedback, and biosynthesis and the molecular mechanisms of allelochemicals and signaling chemicals. Altogether, these efforts provide the recent advancements in the wide field of allelopathy and allelobiosis, and new insights into the chemically mediated plant-plant interactions.

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.

The Progression in Developing Genomic Resources for Crop Improvement

Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques.

A translatome-transcriptome multi-omics gene regulatory network reveals the complicated functional landscape of maize

BACKGROUND

Maize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome.

RESULTS

We collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31.

CONCLUSIONS

Our findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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.

Charting plant gene functions in the multi-omics and single-cell era

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

Machine learning bridges omics sciences and plant breeding

Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.

Co-expression pan-network reveals genes involved in complex traits within maize pan-genome

BACKGROUND

With the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes.

RESULTS

To address these challenges, we incorporated the gene expression and GWAS-driven traits to extend the knowledge of genotype-phenotype relationships and transcriptional regulatory mechanisms behind the phenotypes. We constructed a large collection of gene co-expression networks and identified more than 2 million co-expressing gene pairs in the GWAS-driven pan-network which contains all the gene-pairs in individual genomes of the nested association mapping (NAM) population. We defined four sub-categories for the pan-network: (1) core-network contains the highest represented ~ 1% of the gene-pairs, (2) near-core network contains the next highest represented 1-5% of the gene-pairs, (3) private-network contains ~ 50% of the gene pairs that are unique to individual genomes, and (4) the dispensable-network contains the remaining 50-95% of the gene-pairs in the maize pan-genome. Strikingly, the private-network contained almost all the genes in the pan-network but lacked half of the interactions. We performed gene ontology (GO) enrichment analysis for the pan-, core-, and private- networks and compared the contributions of variants overlapping with genes and promoters to the GWAS-driven pan-network.

CONCLUSIONS

Gene co-expression networks revealed meaningful information about groups of co-regulated genes that play a central role in regulatory processes. Pan-network approach enabled us to visualize the global view of the gene regulatory network for the studied system that could not be well inferred by the core-network alone.

Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

BACKGROUND

Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus.

RESULTS

We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content.

CONCLUSIONS

We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.

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.

The transcription factor TaMYB31 regulates the benzoxazinoid biosynthetic pathway in wheat

Benzoxazinoids are specialized metabolites that are highly abundant in staple crops, such as maize and wheat. Although their biosynthesis has been studied for several decades, the regulatory mechanisms of the benzoxazinoid pathway remain unknown. Here, we report that the wheat transcription factor MYB31 functions as a regulator of benzoxazinoid biosynthesis genes. A transcriptomic analysis of tetraploid wheat (Triticum turgidum) tissue revealed the up-regulation of two TtMYB31 homoeologous genes upon aphid and caterpillar feeding. TaMYB31 gene silencing in the hexaploid wheat Triticum aestivum significantly reduced benzoxazinoid metabolite levels and led to susceptibility to herbivores. Thus, aphid progeny production, caterpillar body weight gain, and spider mite oviposition significantly increased in TaMYB31-silenced plants. A comprehensive transcriptomic analysis of hexaploid wheat revealed that the TaMYB31 gene is co-expressed with the target benzoxazinoid-encoded Bx genes under several biotic and environmental conditions. Therefore, we analyzed the effect of abiotic stresses on benzoxazinoid levels and discovered a strong accumulation of these compounds in the leaves. The results of a dual fluorescence assay indicated that TaMYB31 binds to the Bx1 and Bx4 gene promoters, thereby activating the transcription of genes involved in the benzoxazinoid pathway. Our finding is the first report of the transcriptional regulation mechanism of the benzoxazinoid pathway in wheat.

Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology

Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the analysis of big data. However, to achieve ideal performance, supervised ML algorithms require large numbers of labeled samples as training data. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, the paradigms of unsupervised learning (UL) and semi-supervised learning (SSL) play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significance and challenges of UL and SSL strategies in plant systems biology.

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.

Assembly of complete diploid-phased chromosomes from draft genome sequences

De novo genome assembly is essential for genomic research. High-quality genomes assembled into phased pseudomolecules are challenging to produce and often contain assembly errors because of repeats, heterozygosity, or the chosen assembly strategy. Although algorithms that produce partially phased assemblies exist, haploid draft assemblies that may lack biological information remain favored because they are easier to generate and use. We developed HaploSync, a suite of tools that produces fully phased, chromosome-scale diploid genome assemblies, and performs extensive quality control to limit assembly artifacts. HaploSync scaffolds sequences from a draft diploid assembly into phased pseudomolecules guided by a genetic map and/or the genome of a closely related species. HaploSync generates a report that visualizes the relationships between current and legacy sequences, for both haplotypes, and displays their gene and marker content. This quality control helps the user identify misassemblies and guides Haplosync’s correction of scaffolding errors. Finally, HaploSync fills assembly gaps with unplaced sequences and resolves collapsed homozygous regions. In a series of plant, fungal, and animal kingdom case studies, we demonstrate that HaploSync efficiently increases the assembly contiguity of phased chromosomes, improves completeness by filling gaps, corrects scaffolding, and correctly phases highly heterozygous, complex regions.

The multiple fates of gene duplications: Deletion, hypofunctionalization, subfunctionalization, neofunctionalization, dosage balance constraints, and neutral variation

Gene duplications have long been recognized as a contributor to the evolution of genes with new functions. Multiple copies of genes can result from tandem duplication, from transposition to new chromosomes, or from whole-genome duplication (polyploidy). The most common fate is that one member of the pair is deleted to return the gene to the singleton state. Other paths involve the reduced expression of both copies (hypofunctionalization) that are held in duplicate to maintain sufficient quantity of function. The two copies can split functions (subfunctionalization) or can diverge to generate a new function (neofunctionalization). Retention of duplicates resulting from doubling of the whole genome occurs for genes involved with multicomponent interactions such as transcription factors and signal transduction components. In contrast, these classes of genes are underrepresented in small segmental duplications. This complementary pattern suggests that the balance of interactors affects the fate of the duplicate pair. We discuss the different mechanisms that maintain duplicated genes, which may change over time and intersect.

Concerted cis and trans effects underpin heightened defense gene expression in multi-herbivore-resistant maize lines

In maize (Zea mays ssp. mays), agriculturally damaging herbivores include lepidopteran insects, such as the European corn borer (Ostrinia nubilalis), and distantly related arthropods, like the two-spotted spider mite (Tetranychus urticae). A small number of maize lines, including B96 and B75, are highly resistant to both herbivores, and B96 is also resistant to thrips. Using T. urticae as a representative pest that causes significant leaf tissue damage, we examined the gene expression responses of these lines to herbivory in comparison with each other and with the susceptible line B73. Upon herbivory, the most resistant line, B96, showed the strongest gene expression response, with a dramatic upregulation of genes associated with jasmonic acid biosynthesis and signaling, as well as the biosynthesis of specialized herbivore deterrent compounds, such as death acids and benzoxazinoids. Extending this work with allele-specific expression analyses in F₁ hybrids, we inferred that the concerted upregulation of many defense genes, including the majority of benzoxazinoid biosynthetic genes in B96, as compared with B73, for the herbivore treatment, resulted from an assemblage of trans control and multiple cis effects acting with similar directionality on gene expression. Further, at the level of individual and potentially rate limiting genes in several major defense pathways, cis and trans effects acted in a reinforcing manner to result in exceptionally high expression in B96. Our study provides a comprehensive resource of cis elements for maize lines important in breeding efforts for herbivore resistance, and reveals potential genetic underpinnings of the origins of multi-herbivore resistance in plant populations.

BLSSpeller to discover novel regulatory motifs in maize

With the decreasing cost of sequencing and availability of larger numbers of sequenced genomes, comparative genomics is becoming increasingly attractive to complement experimental techniques for the task of transcription factor (TF) binding site identification. In this study, we redesigned BLSSpeller, a motif discovery algorithm, to cope with larger sequence datasets. BLSSpeller was used to identify novel motifs in Zea mays in a comparative genomics setting with 16 monocot lineages. We discovered 61 motifs of which 20 matched previously described motif models in Arabidopsis. In addition, novel, yet uncharacterized motifs were detected, several of which are supported by available sequence-based and/or functional data. Instances of the predicted motifs were enriched around transcription start sites and contained signatures of selection. Moreover, the enrichment of the predicted motif instances in open chromatin and TF binding sites indicates their functionality, supported by the fact that genes carrying instances of these motifs were often found to be co-expressed and/or enriched in similar GO functions. Overall, our study unveiled several novel candidate motifs that might help our understanding of the genotype to phenotype association in crops.

Sorghum root epigenetic landscape during limiting phosphorus conditions

Efficient acquisition and use of available phosphorus from the soil is crucial for plant growth, development, and yield. With an ever-increasing acreage of croplands with suboptimal available soil phosphorus, genetic improvement of sorghum germplasm for enhanced phosphorus acquisition from soil is crucial to increasing agricultural output and reducing inputs, while confronted with a growing world population and uncertain climate. Sorghum bicolor is a globally important commodity for food, fodder, and forage. Known for robust tolerance to heat, drought, and other abiotic stresses, its capacity for optimal phosphorus use efficiency (PUE) is still being investigated for optimized root system architectures (RSA). Whilst a few RSA-influencing genes have been identified in sorghum and other grasses, the epigenetic impact on expression and tissue-specific activation of candidate PUE genes remains elusive. Here, we present transcriptomic, epigenetic, and regulatory network profiling of RSA modulation in the BTx623 sorghum background in response to limiting phosphorus (LP) conditions. We show that during LP, sorghum RSA is remodeled to increase root length and surface area, likely enhancing its ability to acquire P. Global DNA 5-methylcytosine and H3K4 and H3K27 trimethylation levels decrease in response to LP, while H3K4me3 peaks and DNA hypomethylated regions contain recognition motifs of numerous developmental and nutrient responsive transcription factors that display disparate expression patterns between different root tissues (primary root apex, elongation zone, and lateral root apex).

Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Multi-Omics Analyses Reveal the Regulatory Network and the Function of ZmUGTs in Maize Defense Response

Maize is one of the major crops in the world; however, diseases caused by various pathogens seriously affect its yield and quality. The maize Rp1-D21 mutant (mt) caused by the intragenic recombination between two nucleotide-binding, leucine-rich repeat (NLR) proteins, exhibits autoactive hypersensitive response (HR). In this study, we integrated transcriptomic and metabolomic analyses to identify differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) in Rp1-D21 mt compared to the wild type (WT). Genes involved in pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) were enriched among the DEGs. The salicylic acid (SA) pathway and the phenylpropanoid biosynthesis pathway were induced at both the transcriptional and metabolic levels. The DAMs identified included lipids, flavones, and phenolic acids, including 2,5-DHBA O-hexoside, the production of which is catalyzed by uridinediphosphate (UDP)-dependent glycosyltransferase (UGT). Four maize UGTs (ZmUGTs) homologous genes were among the DEGs. Functional analysis by transient co-expression in Nicotiana benthamiana showed that ZmUGT9250 and ZmUGT5174, but not ZmUGT9256 and ZmUGT8707, partially suppressed the HR triggered by Rp1-D21 or its N-terminal coiled-coil signaling domain (CCD21). None of the four ZmUGTs interacted physically with CCD21 in yeast two-hybrid or co-immunoprecipitation assays. We discuss the possibility that ZmUGTs might be involved in defense response by regulating SA homeostasis.

Disparate Dynamics of Gene Body and cis-Regulatory Element Evolution Illustrated for the Senescence-Associated Cysteine Protease Gene SAG12 of Plants

Gene regulation networks precisely orchestrate the expression of genes that are closely associated with defined physiological and developmental processes such as leaf senescence in plants. The Arabidopsis thaliana senescence-associated gene 12 (AtSAG12) encodes a cysteine protease that is (i) involved in the degradation of chloroplast proteins and (ii) almost exclusively expressed during senescence. Transcription factors, such as WRKY53 and WRKY45, bind to W-boxes in the promoter region of AtSAG12 and play key roles in its activation. Other transcription factors, such as bZIPs, might have accessory functions in their gene regulation, as several A-boxes have been identified and appear to be highly overrepresented in the promoter region compared to the whole genome distribution but are not localized within the regulatory regions driving senescence-associated expression. To address whether these two regulatory elements exhibiting these different properties are conserved in other closely related species, we constructed phylogenetic trees of the coding sequences of orthologs of AtSAG12 and screened their respective 2000 bp promoter regions for the presence of conserved cis-regulatory elements, such as bZIP and WRKY binding sites. Interestingly, the functional relevant upstream located W-boxes were absent in plant species as closely related as Arabidopsis lyrata, whereas an A-box cluster appeared to be conserved in the Arabidopsis species but disappeared in Brassica napus. Several orthologs were present in other species, possibly because of local or whole genome duplication events, but with distinct cis-regulatory sites in different locations. However, at least one gene copy in each family analyzed carried one W-box and one A-box in its promoter. These gene differences in SAG12 orthologs are discussed in the framework of cis- and trans-regulatory factors, of promoter and gene evolution, of genetic variation, and of the enhancement of the adaptability of plants to changing environmental conditions.

Local adaptation contributes to gene expression divergence in maize

Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.

Maize Inbred Line B96 Is the Source of Large-Effect Loci for Resistance to Generalist but Not Specialist Spider Mites

Maize (Zea mays subsp. mays) yield loss from arthropod herbivory is substantial. While the basis of resistance to major insect herbivores has been comparatively well-studied in maize, less is known about resistance to spider mite herbivores, which are distantly related to insects and feed by a different mechanism. Two spider mites, the generalist Tetranychus urticae, and the grass-specialist Oligonychus pratensis, are notable pests of maize, especially during drought conditions. We assessed resistance (antibiosis) to both mites of 38 highly diverse maize lines, including several previously reported to be resistant to one or the other mite species. We found that line B96, as well as its derivatives B49 and B75, were highly resistant to T. urticae. In contrast, neither these three lines, nor any others included in our study, were notably resistant to the specialist O. pratensis. Quantitative trait locus (QTL) mapping with replicate populations from crosses of B49, B75, and B96 to susceptible B73 identified a QTL in the same genomic interval on chromosome 6 for T. urticae resistance in each of the three resistant lines, and an additional resistance QTL on chromosome 1 was unique to B96. Single-locus genotyping with a marker coincident with the chromosome 6 QTL in crosses of both B49 and B75 to B73 revealed that the respective QTL was large-effect; it explained ∼70% of the variance in resistance, and resistance alleles from B49 and B75 acted recessively as compared to B73. Finally, a genome-wide haplotype analysis using genome sequence data generated for B49, B75, and B96 identified an identical haplotype, likely of initial origin from B96, as the source of T. urticae resistance on chromosome 6 in each of the B49, B75, and B96 lines. Our findings uncover the relationship between intraspecific variation in maize defenses and resistance to its major generalist and specialist spider mite herbivores, and we identified loci for use in breeding programs and for genetic studies of resistance to T. urticae, the most widespread spider mite pest of maize.

Getting back to the grass roots: harnessing specialized metabolites for improved crop stress resilience

Roots remain an understudied site of complex and important biological interactions mediating plant productivity. In grain and bioenergy crops, grass root specialized metabolites (GRSM) are central to key interactions, yet our basic knowledge of the chemical language remains fragmentary. Continued improvements in plant genome assembly and metabolomics are enabling large-scale advances in the discovery of specialized metabolic pathways as a means of regulating root-biotic interactions. Metabolomics, transcript coexpression analyses, forward genetic studies, gene synthesis and heterologous expression assays drive efficient pathway discoveries. Functional genetic variants identified through genome wide analyses, targeted CRISPR/Cas9 approaches, and both native and non-native overexpression studies critically inform novel strategies for bioengineering metabolic pathways to improve plant traits.

Genomic imbalance determines positive and negative modulation of gene expression in diploid maize

Genomic imbalance caused by changing the dosage of individual chromosomes (aneuploidy) has a more detrimental effect than varying the dosage of complete sets of chromosomes (ploidy). We examined the impact of both increased and decreased dosage of 15 distal and 1 interstitial chromosomal regions via RNA-seq of maize (Zea mays) mature leaf tissue to reveal new aspects of genomic imbalance. The results indicate that significant changes in gene expression in aneuploids occur both on the varied chromosome (cis) and the remainder of the genome (trans), with a wider spread of modulation compared with the whole-ploidy series of haploid to tetraploid. In general, cis genes in aneuploids range from a gene-dosage effect to dosage compensation, whereas for trans genes the most common effect is an inverse correlation in that expression is modulated toward the opposite direction of the varied chromosomal dosage, although positive modulations also occur. Furthermore, this analysis revealed the existence of increased and decreased effects in which the expression of many genes under genome imbalance are modulated toward the same direction regardless of increased or decreased chromosomal dosage, which is predicted from kinetic considerations of multicomponent molecular interactions. The findings provide novel insights into understanding mechanistic aspects of gene regulation.

Analysis of the transcriptomic, metabolomic, and gene regulatory responses to Puccinia sorghi in maize

Common rust, caused by Puccinia sorghi, is a widespread and destructive disease of maize. The Rp1-D gene confers resistance to the P. sorghi IN2 isolate, mediating a hypersensitive cell death response (HR). To identify differentially expressed genes (DEGs) and metabolites associated with the compatible (susceptible) interaction and with Rp1-D-mediated resistance in maize, we performed transcriptomics and targeted metabolome analyses of P. sorghi IN2-infected leaves from the near-isogenic lines H95 and H95:Rp1-D, which differed for the presence of Rp1-D. We observed up-regulation of genes involved in the defence response and secondary metabolism, including the phenylpropanoid, flavonoid, and terpenoid pathways. Metabolome analyses confirmed that intermediates from several transcriptionally up-regulated pathways accumulated during the defence response. We identified a common response in H95:Rp1-D and H95 with an additional H95:Rp1-D-specific resistance response observed at early time points at both transcriptional and metabolic levels. To better understand the mechanisms underlying Rp1-D-mediated resistance, we inferred gene regulatory networks occurring in response to P. sorghi infection. A number of transcription factors including WRKY53, BHLH124, NKD1, BZIP84, and MYB100 were identified as potentially important signalling hubs in the resistance-specific response. Overall, this study provides a novel and multifaceted understanding of the maize susceptible and resistance-specific responses to P. sorghi.

Machine learning in plant science and plant breeding

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

Identification of Key Tissue-Specific, Biological Processes by Integrating Enhancer Information in Maize Gene Regulatory Networks

Enhancers are key players in the spatio-temporal coordination of gene expression during numerous crucial processes, including tissue differentiation across development. Characterizing the transcription factors (TFs) and genes they connect, and the molecular functions underpinned is important to better characterize developmental processes. In plants, the recent molecular characterization of enhancers revealed their capacity to activate the expression of several target genes. Nevertheless, identifying these target genes at a genome-wide level is challenging, particularly for large-genome species, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to plant regulatory networks remains poorly understood. Here, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage (V2-IST) and husks (bracts) at flowering. Using systems biology, we integrate genomic, epigenomic, and transcriptomic data to model the regulatory relationships between TFs and their potential target genes, and identify regulatory modules specific to husk and V2-IST. We show that leaves at the V2-IST stage are characterized by the response to hormones and macromolecules biogenesis and assembly, which are regulated by the BBR/BPC and AP2/ERF TF families, respectively. In contrast, husks are characterized by cell wall modification and response to abiotic stresses, which are, respectively, orchestrated by the C2C2/DOF and AP2/EREB families. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped part of the regulatory network in each tissue, and that MITEs have provided potential new TF binding sites involved in husk tissue-specificity.

MutRank: an R shiny web-application for exploratory targeted mutual rank-based coexpression analyses integrated with user-provided supporting information

The rapid assignment of genotypes to phenotypes has been a historically challenging process. The discovery of genes encoding biosynthetic pathway enzymes for defined plant specialized metabolites has been informed and accelerated by the detection of gene clusters. Unfortunately, biosynthetic pathway genes are commonly dispersed across chromosomes or reside in genes clusters that provide little predictive value. More reliably, transcript abundance of genes underlying biochemical pathways for plant specialized metabolites display significant coregulation. By rapidly identifying highly coexpressed transcripts, it is possible to efficiently narrow candidate genes encoding pathway enzymes and more easily predict both functions and functional associations. Mutual Rank (MR)-based coexpression analyses in plants accurately demonstrate functional associations for many specialized metabolic pathways; however, despite the clear predictive value of MR analyses, the application is uncommonly used to drive new pathway discoveries. Moreover, many coexpression databases aid in the prediction of both functional associations and gene functions, but lack customizability for refined hypothesis testing. To facilitate and speed flexible MR-based hypothesis testing, we developed MutRank, an R Shiny web-application for coexpression analyses. MutRank provides an intuitive graphical user interface with multiple customizable features that integrates user-provided data and supporting information suitable for personal computers. Tabular and graphical outputs facilitate the rapid analyses of both unbiased and user-defined coexpression results that accelerate gene function predictions. We highlight the recent utility of MR analyses for functional predictions and discoveries in defining two maize terpenoid antibiotic pathways. Beyond applications in biosynthetic pathway discovery, MutRank provides a simple, customizable and user-friendly interface to enable coexpression analyses relating to a breadth of plant biology inquiries. Data and code are available at GitHub: https://github.com/eporetsky/MutRank.

Stable unmethylated DNA demarcates expressed genes and their cis-regulatory space in plant genomes

The genomic sequences of crops continue to be produced at a frenetic pace. It remains challenging to develop complete annotations of functional genes and regulatory elements in these genomes. Chromatin accessibility assays enable discovery of functional elements; however, to uncover the full portfolio of cis-elements would require profiling of many combinations of cell types, tissues, developmental stages, and environments. Here, we explore the potential to use DNA methylation profiles to develop more complete annotations. Using leaf tissue in maize, we define ∼100,000 unmethylated regions (UMRs) that account for 5.8% of the genome; 33,375 UMRs are found greater than 2 kb from genes. UMRs are highly stable in multiple vegetative tissues, and they capture the vast majority of accessible chromatin regions from leaf tissue. However, many UMRs are not accessible in leaf, and these represent regions with potential to become accessible in specific cell types or developmental stages. These UMRs often occur near genes that are expressed in other tissues and are enriched for binding sites of transcription factors. The leaf-inaccessible UMRs exhibit unique chromatin modification patterns and are enriched for chromatin interactions with nearby genes. The total UMR space in four additional monocots ranges from 80 to 120 megabases, which is remarkably similar considering the range in genome size of 271 megabases to 4.8 gigabases. In summary, based on the profile from a single tissue, DNA methylation signatures provide powerful filters to distill large genomes down to the small fraction of putative functional genes and regulatory elements.

Insights Into the Regulation of the Expression Pattern of Calvin-Benson-Bassham Cycle Enzymes in C3 and C4 Grasses

C₄ photosynthesis is characterized by the compartmentalization of the processes of atmospheric uptake of CO₂ and its conversion into carbohydrate between mesophyll and bundle-sheath cells. As a result, most of the enzymes participating in the Calvin-Benson-Bassham (CBB) cycle, including RubisCO, are highly expressed in bundle-sheath cells. There is evidence that changes in the regulatory sequences of RubisCO contribute to its bundle-sheath-specific expression, however, little is known about how the spatial-expression pattern of other CBB cycle enzymes is regulated. In this study, we use a computational approach to scan for transcription factor binding sites in the regulatory regions of the genes encoding CBB cycle enzymes, SBPase, FBPase, PRK, and GAPDH-B, of C₃ and C₄ grasses. We identified potential cis-regulatory elements present in each of the genes studied here, regardless of the photosynthetic path used by the plant. The trans-acting factors that bind these elements have been validated in A. thaliana and might regulate the expression of the genes encoding CBB cycle enzymes. In addition, we also found C₄-specific transcription factor binding sites in the genes encoding CBB cycle enzymes that could potentially contribute to the pathway-specific regulation of gene expression. These results provide a foundation for the functional analysis of the differences in regulation of genes encoding CBB cycle enzymes between C₃ and C₄ grasses.