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A multi-tissue gene expression resource representative of the genotypic and phenotypic diversity of modern inbred maize reveals the effect of rare alleles and evolutionary history on the regulation of gene expression. Gene expression and fitness in maize Karl Kremling, Edward Buckler and colleagues report a multi-tissue gene expression resource for maize that includes RNA-sequencing data on seven tissues from across 299 maize lines, which are representative of the genotypic and phenotypic diversity of modern inbred maize. The authors examined how gene expression is regulated, mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. They also show a correlation between cumulative deviation in the expression of the most highly expressed genes and seed-weight fitness. Here we report a multi-tissue gene expression resource that represents the genotypic and phenotypic diversity of modern inbred maize, and includes transcriptomes in an average of 255 lines in seven tissues. We mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. Some of the new mutations that arise in the maize genome can be deleterious; although selection acts to keep deleterious variants rare, their complete removal is impeded by genetic linkage to favourable loci and by finite population size 1 , 2 , 3 , 4 . Modern maize breeders have systematically reduced the effects of this constant mutational pressure through artificial selection and self-fertilization, which have exposed rare recessive variants in elite inbred lines 5 . However, the ongoing effect of these rare alleles on modern inbred maize is unknown. By analysing this gene expression resource and exploiting the extreme diversity and rapid linkage disequilibrium decay of maize 6 , we characterize the effect of rare alleles and evolutionary history on the regulation of expression. Rare alleles are associated with the dysregulation of expression, and we correlate this dysregulation to seed-weight fitness. We find enrichment of ancestral rare variants among expression quantitative trait loci mapped in modern inbred lines, which suggests that historic bottlenecks have shaped regulation. Our results suggest that one path for further genetic improvement in agricultural species lies in purging the rare deleterious variants that have been associated with crop fitness.

Key messageTwo read depth methods were jointly used in next-generation sequencing data to identify deletions in maize population. GWAS by deletions were analyzed for gene expression pattern and classical traits, respectively.Many studies have confirmed that structural variation (SV) is pervasive throughout the maize genome. Deletion is one type of SV that may impact gene expression and cause phenotypic changes in quantitative traits. In this study, two read count approaches were used to analyze the deletions in the whole-genome sequencing data of 270 maize inbred lines. A total of 19,754 deletion windows overlapped 12,751 genes, which were unevenly distributed across the genome. The deletions explained population structure well and correlated with genomic features. The deletion proportion of genes was determined to be negatively correlated with its expression. The detection of gene expression quantitative trait loci (eQTL) indicated that local eQTL were fewer but had larger effects than distant ones. The common associated genes were related to basic metabolic processes, whereas unique associated genes with eQTL played a role in the stress or stimulus responses in multiple tissues. Compared with the eQTL detected by SNPs derived from the same sequencing data, 89.4% of the associated genes could be detected by both markers. The effect of top eQTL detected by SNPs was usually larger than that detected by deletions for the same gene. A genome-wide association study (GWAS) on flowering time and plant height illustrated that only a few loci could be consistently captured by SNPs, suggesting that combining deletion and SNP for GWAS was an excellent strategy to dissect trait architecture. Our findings will provide insights into characteristic and biological function of genome-wide deletions in maize.

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (i) gene-family–guided splitting and (ii) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3′ UTR is more important for fine-tuning mRNA abundance levels while the 5′ UTR is more important for large-scale changes.

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.

Cultivated maize (Zea mays) has retained much of the genetic diversity of its wild ancestors. Here, we performed nontargeted liquid chromatography-mass spectrometry metabolomics to analyze the metabolomes of the 282 maize inbred lines in the Goodman Diversity Panel. This analysis identified a bimodal distribution of foliar metabolites. Although 15% of the detected mass features were present in >90% of the inbred lines, the majority were found in <50% of the samples. Whereas leaf bases and tips were differentiated by flavonoid abundance, maize varieties (stiff-stalk, nonstiff-stalk, tropical, sweet maize, and popcorn) showed differential accumulation of benzoxazinoid metabolites. Genome-wide association studies (GWAS), performed for 3,991 mass features from the leaf tips and leaf bases, showed that 90% have multiple significantly associated loci scattered across the genome. Several quantitative trait locus hotspots in the maize genome regulate the abundance of multiple, often structurally related mass features. The utility of maize metabolite GWAS was demonstrated by confirming known benzoxazinoid biosynthesis genes, as well as by mapping isomeric variation in the accumulation of phenylpropanoid hydroxycitric acid esters to a single linkage block in a citrate synthase-like gene. Similar to gene expression databases, this metabolomic GWAS data set constitutes an important public resource for linking maize metabolites with biosynthetic and regulatory genes.

• The production and regulation of defensive specialized metabolites play a central role in pathogen resistance in maize (Zea mays) and other plants. Therefore, identification of genes involved in plant specialized metabolism can contribute to improved disease resistance. • We used comparative metabolomics to identify previously unknown antifungal metabolites in maize seedling roots, and investigated the genetic and physiological mechanisms underlying their natural variation using quantitative trait locus mapping and comparative transcriptomics approaches. • Two maize metabolites, smilaside A (3,6-diferuloyl-3′,6′-diacetylsucrose) and smiglaside C (3,6-diferuloyl-2′,3′,6′-triacetylsucrose), were identified that could contribute to maize resistance against Fusarium graminearum and other fungal pathogens. Elevated expression of an ethylene signaling gene, ETHYLENE INSENSITIVE 2 (ZmEIN2), co-segregated with a decreased smilaside A : smiglaside C ratio. Pharmacological and genetic manipulation of ethylene availability and sensitivity in vivo indicated that, whereas ethylene was required for the production of both metabolites, the smilaside A : smiglaside C ratio was negatively regulated by ethylene sensitivity. This ratio, rather than the absolute abundance of these two metabolites, was important for maize seedling root defense against F. graminearum. • Ethylene signaling regulates the relative abundance of the two F. graminearum-resistancerelated metabolites and affects resistance against F. graminearum in maize seedling roots.

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.

Specialized metabolites constitute key layers of immunity that underlie disease resistance in crops; however, challenges in resolving pathways limit our understanding of the functions and applications of these metabolites. In maize ( Zea mays ), the inducible accumulation of acidic terpenoids is increasingly considered to be a defence mechanism that contributes to disease resistance. Here, to understand maize antibiotic biosynthesis, we integrated association mapping, pan-genome multi-omic correlations, enzyme structure–function studies and targeted mutagenesis. We define ten genes in three zealexin ( Zx ) gene clusters that encode four sesquiterpene synthases and six cytochrome P450 proteins that collectively drive the production of diverse antibiotic cocktails. Quadruple mutants in which the ability to produce zealexins (ZXs) is blocked exhibit a broad-spectrum loss of disease resistance. Genetic redundancies ensuring pathway resiliency to single null mutations are combined with enzyme substrate promiscuity, creating a biosynthetic hourglass pathway that uses diverse substrates and in vivo combinatorial chemistry to yield complex antibiotic blends. The elucidated genetic basis of biochemical phenotypes that underlie disease resistance demonstrates a predominant maize defence pathway and informs innovative strategies for transferring chemical immunity between crops. In maize, a comprehensive set of approaches enabled the authors to analyse the biosynthetic pathway of the zealexin group of terpenoids and characterize the role of these antibiotic compounds in disease resistance.

Carbon (C) and nitrogen (N) metabolismare critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea maysssp.mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophylla, chlorophyllb, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.

Background MiRNAs play essential roles in plant development and response to biotic and abiotic stresses through interaction with their target genes. The expression level of miRNAs shows great variations among different plant accessions, developmental stages, and tissues. Little is known about the content within the plant genome contributing to the variations in plants. This study aims to identify miRNA expression-related quantitative trait loci (miR-QTLs) in the maize genome. Results The miRNA expression level from next generation sequencing (NGS) small RNA libraries derived from mature leaf samples of the maize panel (200 maize lines) was estimated as phenotypes, and maize Hapmap v3.2.1 was chosen as the genotype for the genome-wide association study (GWAS). A total of four significant miR-eQTLs were identified contributing to miR156k-5p, miR159a-3p, miR390a-5p and miR396e-5p, and all of them are trans-eQTLs. In addition, a strong positive coexpression of miRNA was found among five miRNA families. Investigation of the effects of these miRNAs on the expression levels and target genes provided evidence that miRNAs control the expression of their targets by suppression and enhancement. Conclusions These identified significant miR-eQTLs contribute to the diversity of miRNA expression in the maize penal at the developmental stages of mature leaves in maize, and the positive and negative regulation between miRNA and its target genes has also been uncovered.