Predicting transcriptional responses to cold stress across plant species.

Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification model to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained by using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species provided at least equivalent performance to models trained and tested in a single species and outperformed single-species models in cross-species prediction. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene-expression patterns in related, less-studied species with sequenced genomes.

Chromatin and regulatory differentiation between bundle sheath and mesophyll cells in maize.

C4 plants partition photosynthesis enzymes between the bundle sheath (BS) and the mesophyll (M) cells for the better delivery of CO2 to RuBisCO and to reduce photorespiration. To better understand how C4 photosynthesis is regulated at the transcriptional level, we performed RNA-seq, ATAC-seq, ChIP-seq and Bisulfite-seq (BS-seq) on BS and M cells isolated from maize leaves. By integrating differentially expressed genes with chromatin features, we found that chromatin accessibility coordinates with epigenetic features, especially H3K27me3 modification and CHH methylation, to regulate cell type-preferentially enriched gene expression. Not only the chromatin-accessible regions (ACRs) proximal to the genes (pACRs) but also the distal ACRs (dACRs) are determinants of cell type-preferentially enriched expression. We further identified cell type-preferentially enriched motifs, e.g. AAAG for BS cells and TGACC/T for M cells, and determined their corresponding transcription factors: DOFs and WRKYs. The complex interaction between cis and trans factors in the preferential expression of C4 genes was also observed. Interestingly, cell type-preferentially enriched gene expression can be fine-tuned by the coordination of multiple chromatin features. Such coordination may be critical in ensuring the cell type-specific function of key C4 genes. Based on the observed cell type-preferentially enriched expression pattern and coordinated chromatin features, we predicted a set of functionally unknown genes, e.g. Zm00001d042050 and Zm00001d040659, to be potential key C4 genes. Our findings provide deep insight into the architectures associated with C4 gene expression and could serve as a valuable resource to further identify the regulatory mechanisms present in C4 species.

PGD: a machine learning-based photosynthetic-related gene detection approach.

BACKGROUND: The primary determinant of crop yield is photosynthetic capacity, which is under the control of photosynthesis-related genes. Therefore, the mining of genes involved in photosynthesis is important for the study of photosynthesis. MapMan Mercator 4 is a powerful annotation tool for assigning genes into proper functional categories; however, in maize, the functions of approximately 22.15% (9520) of genes remain unclear and are labeled "not assigned", which may include photosynthesis-related genes that have not yet been identified. The fast-increasing usage of the machine learning approach in solving biological problems provides us with a new chance to identify novel photosynthetic genes from functional "not assigned" genes in maize. RESULTS: In this study, we proved the ensemble learning model using a voting eliminates the preferences of single machine learning models. Based on this evaluation, we implemented an ensemble based ML(Machine Learning) methods using a majority voting scheme and observed that including RNA-seq data from multiple photosynthetic mutants rather than only a single mutant could increase prediction accuracy. And we call this approach "A Machine Learning-based Photosynthetic-related Gene Detection approach (PGD)". Finally, we predicted 716 photosynthesis-related genes from the "not assigned" category of maize MapMan annotation. The protein localization prediction (TargetP) and expression trends of these genes from maize leaf sections indicated that the prediction was reliable and robust. And we put this approach online base on google colab. CONCLUSIONS: This study reveals a new approach for mining novel genes related to a specific functional category and provides candidate genes for researchers to experimentally define their biological functions.

Parallels between natural selection in the cold-adapted crop-wild relative Tripsacum dactyloides and artificial selection in temperate adapted maize.

Artificial selection has produced varieties of domesticated maize that thrive in temperate climates around the world. However, the direct progenitor of maize, teosinte, is indigenous only to a relatively small range of tropical and subtropical latitudes and grows poorly or not at all outside of this region. Tripsacum, a sister genus to maize and teosinte, is naturally endemic to the majority of areas in the western hemisphere where maize is cultivated. A full-length reference transcriptome for Tripsacum dactyloides generated using long-read Iso-Seq data was used to characterize independent adaptation to temperate climates in this clade. Genes related to phospholipid biosynthesis, a critical component of cold acclimation in other cold-adapted plant lineages, were enriched among those genes experiencing more rapid rates of protein sequence evolution in T. dactyloides. In contrast with previous studies of parallel selection, we find that there is a significant overlap between the genes that were targets of artificial selection during the adaptation of maize to temperate climates and those that were targets of natural selection in temperate-adapted T. dactyloides. Genes related to growth, development, response to stimulus, signaling, and organelles were enriched in the set of genes identified as both targets of natural and artificial selection.

Transcriptomic Analysis of Leaf Sheath Maturation in Maize.

The morphological development of the leaf greatly influences plant architecture and crop yields. The maize leaf is composed of a leaf blade, ligule and sheath. Although extensive transcriptional profiling of the tissues along the longitudinal axis of the developing maize leaf blade has been conducted, little is known about the transcriptional dynamics in sheath tissues, which play important roles in supporting the leaf blade. Using a comprehensive transcriptome dataset, we demonstrated that the leaf sheath transcriptome dynamically changes during maturation, with the construction of basic cellular structures at the earliest stages of sheath maturation with a transition to cell wall biosynthesis and modifications. The transcriptome again changes with photosynthesis and lignin biosynthesis at the last stage of sheath tissue maturation. The different tissues of the maize leaf are highly specialized in their biological functions and we identified 15 genes expressed at significantly higher levels in the leaf sheath compared with their expression in the leaf blade, including the BOP2 homologs GRMZM2G026556 and GRMZM2G022606, DOGT1 (GRMZM2G403740) and transcription factors from the B3 domain, C2H2 zinc finger and homeobox gene families, implicating these genes in sheath maturation and organ specialization.

Transcriptomic Analysis Reveals the Correlation between End-of-Day Far Red Light and Chilling Stress in Setaria viridis.

Low temperature and end-of-day far-red (EOD-FR) light signaling are two key factors limiting plant production and geographical location worldwide. However, the transcriptional dynamics of EOD-FR light conditions during chilling stress remain poorly understood. Here, we performed a comparative RNA-Seq-based approach to identify differentially expressed genes (DEGs) related to EOD-FR and chilling stress in Setaria viridis. A total of 7911, 324, and 13431 DEGs that responded to low temperature, EOD-FR and these two stresses were detected, respectively. Further DEGs analysis revealed that EOD-FR may enhance cold tolerance in plants by regulating the expression of genes related to cold tolerance. The result of weighted gene coexpression network analysis (WGCNA) using 13431 nonredundant DEGs exhibited 15 different gene network modules. Interestingly, a CO-like transcription factor named BBX2 was highly expressed under EOD-FR or chilling conditions. Furthermore, we could detect more expression levels when EOD-FR and chilling stress co-existed. Our dataset provides a valuable resource for the regulatory network involved in EOD-FR signaling and chilling tolerance in C4 plants.

Transcriptomic Profiling Provides Molecular Insights Into Hydrogen Peroxide-Enhanced Arabidopsis Growth and Its Salt Tolerance.

Salt stress is an important environmental factor limiting plant growth and crop production. Plant adaptation to salt stress can be improved by chemical pretreatment. This study aims to identify whether hydrogen peroxide (H2O2) pretreatment of seedlings affects the stress tolerance of Arabidopsis thaliana seedlings. The results show that pretreatment with H2O2 at appropriate concentrations enhances the salt tolerance ability of Arabidopsis seedlings, as revealed by lower Na+ levels, greater K+ levels, and improved K+/Na+ ratios in leaves. Furthermore, H2O2 pretreatment improves the membrane properties by reducing the relative membrane permeability (RMP) and malonaldehyde (MDA) content in addition to improving the activities of antioxidant enzymes, including superoxide dismutase, and glutathione peroxidase. Our transcription data show that exogenous H2O2 pretreatment leads to the induced expression of cell cycle, redox regulation, and cell wall organization-related genes in Arabidopsis, which may accelerate cell proliferation, enhance tolerance to osmotic stress, maintain the redox balance, and remodel the cell walls of plants in subsequent high-salt environments.

Genome-wide identification of R2R3-MYB family genes and gene response to stress in ginger.

Ginger (Zingiber officinale Roscoe) is an important plant used worldwide for medicine and food. The R2R3-MYB transcription factor (TF) family has essential roles in plant growth, development, and stresses resistance, and the number of genes in the family varies greatly among different types of plants. However, genome-wide discovery of ZoMYBs and gene responses to stresses have not been reported in ginger. Therefore, genome-wide analysis of R2R3-MYB genes in ginger was conducted in this study. Protein phylogenetic relations and conserved motifs and chromosome localization and duplication, structure, and cis-regulatory elements were analyzed. In addition, the expression patterns of selected genes were analyzed under two different stresses. A total of 299 candidate ZoMYB genes were discovered in ginger. Based on groupings of R2R3-MYB genes in the model plant Arabidopsis thaliana (L.) Heynh., ZoMYBs were divided into eight groups. Genes were distributed across 22 chromosomes at uneven densities. In gene duplication analysis, 120 segmental duplications were identified in the ginger genome. Gene expression patterns of 10 ZoMYBs in leaves of ginger under abscisic acid (ABA) and low-temperature stress treatments were different. The results will help to determine the exact roles of ZoMYBs in anti-stress responses in ginger.

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays).

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random-forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors.

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.