Population-level gene expression can repeatedly link genes to functions in maize.

Transcriptome-wide association studies (TWAS) can provide single gene resolution for candidate genes in plants, complementing genome-wide association studies (GWAS) but efforts in plants have been met with, at best, mixed success. We generated expression data from 693 maize genotypes, measured in a common field experiment, sampled over a 2-h period to minimize diurnal and environmental effects, using full-length RNA-seq to maximize the accurate estimation of transcript abundance. TWAS could identify roughly 10 times as many genes likely to play a role in flowering time regulation as GWAS conducted data from the same experiment. TWAS using mature leaf tissue identified known true-positive flowering time genes known to act in the shoot apical meristem, and trait data from a new environment enabled the identification of additional flowering time genes without the need for new expression data. eQTL analysis of TWAS-tagged genes identified at least one additional known maize flowering time gene through trans-eQTL interactions. Collectively these results suggest the gene expression resource described here can link genes to functions across different plant phenotypes expressed in a range of tissues and scored in different experiments.

A common resequencing-based genetic marker data set for global maize diversity.

Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

Quantitative Resistance Loci to Southern Rust Mapped in a Temperate Maize Diversity Panel.

Southern rust is a severe foliar disease of maize (Zea mays) resulting from infection with the obligate biotrophic fungus Puccinia polysora. This disease reduces photosynthetic productivity, which in turn reduces yields, with the greatest yield losses (up to 50%) associated with earlier onset infections. P. polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the U.S. Corn Belt. In summer 2020, a severe outbreak of southern rust was observed in eastern Nebraska, United States. We scored a replicated maize association panel planted in Lincoln, NE for disease severity and found that disease incidence and severity showed significant variation among maize genotypes. Genome-wide association studies identified four loci associated with significant quantitative variation in disease severity. These loci were associated with candidate genes with plausible links to quantitative disease resistance. A transcriptome-wide association study identified additional genes associated with disease severity. Together, these results indicate that substantial diversity in resistance to southern rust exists among current temperate-adapted maize germplasm, including several candidate loci that may explain the observed variation in resistance to southern rust.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

A Subset of Ubiquitin-Conjugating Enzymes Is Essential for Plant Immunity.

Of the three classes of enzymes involved in ubiquitination, ubiquitin-conjugating enzymes (E2) have been often incorrectly considered to play merely an auxiliary role in the process, and few E2 enzymes have been investigated in plants. To reveal the role of E2 in plant innate immunity, we identified and cloned 40 tomato genes encoding ubiquitin E2 proteins. Thioester assays indicated that the majority of the genes encode enzymatically active E2. Phylogenetic analysis classified the 40 tomato E2 enzymes into 13 groups, of which members of group III were found to interact and act specifically with AvrPtoB, a Pseudomonas syringae pv tomato effector that uses its ubiquitin ligase (E3) activity to suppress host immunity. Knocking down the expression of group III E2 genes in Nicotiana benthamiana diminished the AvrPtoB-promoted degradation of the Fen kinase and the AvrPtoB suppression of host immunity-associated programmed cell death. Importantly, silencing group III E2 genes also resulted in reduced pattern-triggered immunity (PTI). By contrast, programmed cell death induced by several effector-triggered immunity elicitors was not affected on group III-silenced plants. Functional characterization suggested redundancy among group III members for their role in the suppression of plant immunity by AvrPtoB and in PTI and identified UBIQUITIN-CONJUGATING11 (UBC11), UBC28, UBC29, UBC39, and UBC40 as playing a more significant role in PTI than other group III members. Our work builds a foundation for the further characterization of E2s in plant immunity and reveals that AvrPtoB has evolved a strategy for suppressing host immunity that is difficult for the plant to thwart.

The tomato Fni3 lysine-63-specific ubiquitin-conjugating enzyme and suv ubiquitin E2 variant positively regulate plant immunity.

The activation of an immune response in tomato (Solanum lycopersicum) against Pseudomonas syringae relies on the recognition of E3 ligase-deficient forms of AvrPtoB by the host protein kinase, Fen. To investigate the mechanisms by which Fen-mediated immunity is regulated, we characterize in this study a Fen-interacting protein, Fni3, and its cofactor, S. lycoperiscum Uev (Suv). Fni3 encodes a homolog of the Ubc13-type ubiquitin-conjugating enzyme that catalyzes exclusively Lys-63-linked ubiquitination, whereas Suv is a ubiquitin-conjugating enzyme variant. The C-terminal region of Fen was necessary for interaction with Fni3, and this interaction was required for cell death triggered by overexpression of Fen in Nicotiana benthamiana leaves. Fni3 was shown to be an active E2 enzyme, but Suv displayed no ubiquitin-conjugating activity; Fni3 and Suv together directed Lys-63-linked ubiquitination. Decreased expression of Fni3, another tomato Ubc13 homolog, Sl-Ubc13-2, or Suv in N. benthamiana leaves diminished cell death associated with Fen-mediated immunity and cell death elicited by several other resistance (R) proteins and their cognate effectors. We also discovered that coexpression of Fen and other R proteins/effectors with a Fni3 mutant that is compromised for ubiquitin-conjugating activity diminished the cell death. These results suggest that Fni3/Sl-Ubc13-2 and Suv regulate the immune response mediated by Fen and other R proteins through Lys-63-linked ubiquitination.

Genomic co-localization of variation affecting agronomic and human gut microbiome traits in a meta-analysis of diverse sorghum.

Substantial functional metabolic diversity exists within species of cultivated grain crops that directly or indirectly provide more than half of all calories consumed by humans around the globe. While such diversity is the molecular currency used for improving agronomic traits, diversity is poorly characterized for its effects on human nutrition and utilization by gut microbes. Moreover, we know little about agronomic traits' potential trade-offs and pleiotropic effects on human nutritional traits. Here we applied a quantitative genetics approach using a meta-analysis and parallel genome-wide association studies of Sorghum bicolor traits describing changes in the composition and function of human gut microbe communities and any of 200 sorghum seed and agronomic traits across a diverse sorghum population to identify associated genetic variants. A total of fifteen multiple-effect loci (MEL) were initially found where different alleles in the sorghum genome produced changes in seed that affected the abundance of multiple bacterial taxa across two human microbiomes in automated in vitro fermentations. Next, parallel genome-wide studies conducted for seed, biochemical, and agronomic traits in the same population identified significant associations within the boundaries of 13/15 MEL for microbiome traits. In several instances, the co-localization of variation affecting gut microbiome and agronomic traits provided hypotheses for causal mechanisms through which variation could affect both agronomic traits and human gut microbes. This work demonstrates that genetic factors affecting agronomic traits in sorghum seed can also drive significant effects on human gut microbes, particularly bacterial taxa considered beneficial. Understanding these pleiotropic relationships will inform future strategies for crop improvement toward yield, sustainability, and human health.

A role for heritable transcriptomic variation in maize adaptation to temperate environments.

BACKGROUND: Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. RESULT: We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. CONCLUSION: Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

SNP discovery in proso millet (Panicum miliaceum L.) using low-pass genome sequencing.

Domesticated ~10,000 years ago in northern China, Proso millet (Panicum miliaceum L.) is a climate-resilient and human health-promoting cereal crop. The genome size of this self-pollinated allotetraploid is 923 Mb. Proso millet seeds are an important part of the human diet in many countries. In the USA, its use is restricted to the birdseed and pet food market. Proso millet is witnessing gradual demand in the global human health and wellness food market owing to its health-promoting properties such as low glycemic index and gluten-free. The breeding efforts for developing improved proso millet cultivars are hindered by the dearth of genomic resources available to researchers. The publication of the reference genome and availability of cost-effective NGS methodologies could lead to the identification of high-quality genetic variants, which can be incorporated into breeding pipelines. Here, we report the identification of single-nucleotide polymorphisms (SNPs) by low-pass (1×) genome sequencing of 85 diverse proso millet accessions from 23 different countries. The 2 × 150 bp Illumina paired-end reads generated after sequencing were aligned to the proso millet reference genome. The resulting sequence alignment information was used to call SNPs. We obtained 972,863 bi-allelic SNPs after quality filtering of the raw SNPs. These SNPs were used to assess the population structure and phylogenetic relationships among the accessions. Most of the accessions were found to be highly inbred with heterozygosity ranging between .05 and .20. Principal component analysis (PCA) showed that PC1 (principal component) and PC2 explained 19% of the variability in the population. PCA also clustered all the genotypes into three groups. A neighbor-joining tree clustered the genotypes into four distinct groups exhibiting diverse representation within the population. The SNPs identified in our study could be used for molecular breeding and genetics research (e.g., genetic and association mapping, and population genetics) in proso millet after proper validation.

Association mapping across a multitude of traits collected in diverse environments in maize.

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum.

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.