Enhanced pan-genomic resources at the maize genetics and genomics database.

Pan-genomes, encompassing the entirety of genetic sequences found in a collection of genomes within a clade, are more useful than single reference genomes for studying species diversity. This is especially true for a species like Zea mays, which has a particularly diverse and complex genome. Presenting pan-genome data, analyses, and visualization is challenging, especially for a diverse species, but more so when pan-genomic data is linked to extensive gene model and gene data, including classical gene information, markers, insertions, expression and proteomic data, and protein structures as is the case at MaizeGDB. Here, we describe MaizeGDB's expansion to include the genic subset of the Zea pan-genome in a pan-gene data center featuring the maize genomes hosted at MaizeGDB, and the outgroup teosinte Zea genomes from the Pan-Andropoganeae project. The new data center offers a variety of browsing and visualization tools, including sequence alignment visualization, gene trees and other tools, to explore pan-genes in Zea that were calculated by the pipeline Pandagma. Combined, these data will help maize researchers study the complexity and diversity of Zea, and to use the comparative functions to validate pan-gene relationships for a selected gene model.

PanEffect: a pan-genome visualization tool for variant effects in maize.

SUMMARY: Understanding the effects of genetic variants is crucial for accurately predicting traits and functional outcomes. Recent approaches have utilized artificial intelligence and protein language models to score all possible missense variant effects at the proteome level for a single genome, but a reliable tool is needed to explore these effects at the pan-genome level. To address this gap, we introduce a new tool called PanEffect. We implemented PanEffect at MaizeGDB to enable a comprehensive examination of the potential effects of coding variants across 50 maize genomes. The tool allows users to visualize over 550 million possible amino acid substitutions in the B73 maize reference genome and to observe the effects of the 2.3 million natural variations in the maize pan-genome. Each variant effect score, calculated from the Evolutionary Scale Modeling (ESM) protein language model, shows the log-likelihood ratio difference between B73 and all variants in the pan-genome. These scores are shown using heatmaps spanning benign outcomes to potential functional consequences. In addition, PanEffect displays secondary structures and functional domains along with the variant effects, offering additional functional and structural context. Using PanEffect, researchers now have a platform to explore protein variants and identify genetic targets for crop enhancement. AVAILABILITY AND IMPLEMENTATION: The PanEffect code is freely available on GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/PanEffect). A maize implementation of PanEffect and underlying datasets are available at MaizeGDB (https://www.maizegdb.org/effect/maize/).

Genome-wide profiling of histone (H3) lysine 4 (K4) tri-methylation (me3) under drought, heat, and combined stresses in switchgrass.

BACKGROUND: Switchgrass (Panicum virgatum L.) is a warm-season perennial (C4) grass identified as an important biofuel crop in the United States. It is well adapted to the marginal environment where heat and moisture stresses predominantly affect crop growth. However, the underlying molecular mechanisms associated with heat and drought stress tolerance still need to be fully understood in switchgrass. The methylation of H3K4 is often associated with transcriptional activation of genes, including stress-responsive. Therefore, this study aimed to analyze genome-wide histone H3K4-tri-methylation in switchgrass under heat, drought, and combined stress. RESULTS: In total, ~ 1.3 million H3K4me3 peaks were identified in this study using SICER. Among them, 7,342; 6,510; and 8,536 peaks responded under drought (DT), drought and heat (DTHT), and heat (HT) stresses, respectively. Most DT and DTHT peaks spanned 0 to + 2000 bases from the transcription start site [TSS]. By comparing differentially marked peaks with RNA-Seq data, we identified peaks associated with genes: 155 DT-responsive peaks with 118 DT-responsive genes, 121 DTHT-responsive peaks with 110 DTHT-responsive genes, and 175 HT-responsive peaks with 136 HT-responsive genes. We have identified various transcription factors involved in DT, DTHT, and HT stresses. Gene Ontology analysis using the AgriGO revealed that most genes belonged to biological processes. Most annotated peaks belonged to metabolite interconversion, RNA metabolism, transporter, protein modifying, defense/immunity, membrane traffic protein, transmembrane signal receptor, and transcriptional regulator protein families. Further, we identified significant peaks associated with TFs, hormones, signaling, fatty acid and carbohydrate metabolism, and secondary metabolites. qRT-PCR analysis revealed the relative expressions of six abiotic stress-responsive genes (transketolase, chromatin remodeling factor-CDH3, fatty-acid desaturase A, transmembrane protein 14C, beta-amylase 1, and integrase-type DNA binding protein genes) that were significantly (P < 0.05) marked during drought, heat, and combined stresses by comparing stress-induced against un-stressed and input controls. CONCLUSION: Our study provides a comprehensive and reproducible epigenomic analysis of drought, heat, and combined stress responses in switchgrass. Significant enrichment of H3K4me3 peaks downstream of the TSS of protein-coding genes was observed. In addition, the cost-effective experimental design, modified ChIP-Seq approach, and analyses presented here can serve as a prototype for other non-model plant species for conducting stress studies.

Maize protein structure resources at the maize genetics and genomics database.

Protein structures play an important role in bioinformatics, such as in predicting gene function or validating gene model annotation. However, determining protein structure was, until now, costly and time-consuming, which resulted in a structural biology bottleneck. With the release of such programs AlphaFold and ESMFold, this bottleneck has been reduced by several orders of magnitude, permitting protein structural comparisons of entire genomes within reasonable timeframes. MaizeGDB has leveraged this technological breakthrough by offering several new tools to accelerate protein structural comparisons between maize and other plants as well as human and yeast outgroups. MaizeGDB also offers bulk downloads of these comparative protein structure data, along with predicted functional annotation information. In this way, MaizeGDB is poised to assist maize researchers in assessing functional homology, gene model annotation quality, and other information unavailable to maize scientists even a few years ago.

Global analysis of switchgrass (Panicum virgatum L.) transcriptomes in response to interactive effects of drought and heat stresses.

BACKGROUND: Sustainable production of high-quality feedstock has been of great interest in bioenergy research. Despite the economic importance, high temperatures and water deficit are limiting factors for the successful cultivation of switchgrass in semi-arid areas. There are limited reports on the molecular basis of combined abiotic stress tolerance in switchgrass, particularly the combination of drought and heat stress. We used transcriptomic approaches to elucidate the changes in the response of switchgrass to drought and high temperature simultaneously. RESULTS: We conducted solely drought treatment in switchgrass plant Alamo AP13 by withholding water after 45 days of growing. For the combination of drought and heat effect, heat treatment (35 °C/25 °C day/night) was imposed after 72 h of the initiation of drought. Samples were collected at 0 h, 72 h, 96 h, 120 h, 144 h, and 168 h after treatment imposition, total RNA was extracted, and RNA-Seq conducted. Out of a total of 32,190 genes, we identified 3912, as drought (DT) responsive genes, 2339 and 4635 as, heat (HT) and drought and heat (DTHT) responsive genes, respectively. There were 209, 106, and 220 transcription factors (TFs) differentially expressed under DT, HT and DTHT respectively. Gene ontology annotation identified the metabolic process as the significant term enriched in DTHT genes. Other biological processes identified in DTHT responsive genes included: response to water, photosynthesis, oxidation-reduction processes, and response to stress. KEGG pathway enrichment analysis on DT and DTHT responsive genes revealed that TFs and genes controlling phenylpropanoid pathways were important for individual as well as combined stress response. For example, hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (HCT) from the phenylpropanoid pathway was induced by single DT and combinations of DTHT stress. CONCLUSION: Through RNA-Seq analysis, we have identified unique and overlapping genes in response to DT and combined DTHT stress in switchgrass. The combination of DT and HT stress may affect the photosynthetic machinery and phenylpropanoid pathway of switchgrass which negatively impacts lignin synthesis and biomass production of switchgrass. The biological function of genes identified particularly in response to DTHT stress could further be confirmed by techniques such as single point mutation or RNAi.

Characterization and Expression Analysis of Common Bean Histone Deacetylase 6 during Development and Cold Stress Response.

Histone deacetylases (HDACs) are important regulators of gene transcription thus controlling multiple cellular processes. Despite its essential role in plants, HDA6 is yet to be validated in common bean. In this study, we show that HDA6 is involved in plant development and stress response. Differential expression of HDA6 was determined in various tissues and the expression was seen to be upregulated with plant age (seedling < flowering < maturity). Higher expression was observed in flowers and pods than in stem, leaf, and root. Upregulation of HDA6 gene during cold stress implies its prominent role in abiotic stress. Furthermore, the HDA6 gene was isolated from three common bean genotypes and sequence analyses revealed homology with functionally characterized homologs in model species. The 53 kDa translated product was detected using an HDA6 specific antibody and recombinant protein overexpressed in Escherichia coli showed HDAC activity in vitro. To our knowledge, this is the first report in the agriculturally important crop common bean describing the functional characterization and biological role of HDA6.

Comparative transcriptome profiling of upland (VS16) and lowland (AP13) ecotypes of switchgrass.

KEY MESSAGE: Transcriptomes of two switchgrass genotypes representing the upland and lowland ecotypes will be key tools in switchgrass genome annotation and biotic and abiotic stress functional genomics. Switchgrass (Panicum virgatum L.) is an important bioenergy feedstock for cellulosic ethanol production. We report genome-wide transcriptome profiling of two contrasting tetraploid switchgrass genotypes, VS16 and AP13, representing the upland and lowland ecotypes, respectively. A total of 268 million Illumina short reads (50 nt) were generated, of which, 133 million were obtained in AP13 and the rest 135 million in VS16. More than 90% of these reads were mapped to the switchgrass reference genome (V1.1). We identified 6619 and 5369 differentially expressed genes in VS16 and AP13, respectively. Gene ontology and KEGG pathway analysis identified key genes that regulate important pathways including C4 photosynthesis, photorespiration and phenylpropanoid metabolism. A series of genes (33) involved in photosynthetic pathway were up-regulated in AP13 but only two genes showed higher expression in VS16. We identified three dicarboxylate transporter homologs that were highly expressed in AP13. Additionally, genes that mediate drought, heat, and salinity tolerance were also identified. Vesicular transport proteins, syntaxin and signal recognition particles were seen to be up-regulated in VS16. Analyses of selected genes involved in biosynthesis of secondary metabolites, plant-pathogen interaction, membrane transporters, heat, drought and salinity stress responses confirmed significant variation in the relative expression reflected in RNA-Seq data between VS16 and AP13 genotypes. The phenylpropanoid pathway genes identified here are potential targets for biofuel conversion.