Molecular mechanisms underlying gene regulatory variation of maize metabolic traits.

Variation in gene expression levels is pervasive among individuals and races or varieties, and has substantial agronomic consequences, for example, by contributing to hybrid vigor. Gene expression level variation results from mutations in regulatory sequences (cis) and/or transcription factor (TF) activity (trans), but the mechanisms underlying cis and/or trans-regulatory variation of complex phenotypes remain largely unknown. Here, we investigated gene expression variation mechanisms underlying the differential accumulation of the insecticidal compounds maysin and chlorogenic acid in silks of two widely used maize (Zea mays) inbreds, B73 and A632. By combining transcriptomics and cistromics, we identified 1,338 silk direct targets of the maize R2R3-MYB TF Pericarp color1 (P1), consistent with it being a regulator of maysin and chlorogenic acid biosynthesis. Among these P1 targets, 464 showed allele-specific expression (ASE) between B73 and A632 silks. Allelic DNA-affinity purification sequencing identified 34 examples in which P1 allelic specific binding (ASB) correlated with cis-expression variation. From previous yeast one-hybrid studies, we identified nine TFs potentially implicated in the control of P1 targets, with ASB to 83 out of 464 ASE genes (cis) and differential expression of 4 out of 9 TFs between B73 and A632 silks (trans). These results provide a molecular framework for understanding universal mechanisms underlying natural variation of gene expression levels, and how the regulation of metabolic diversity is established.

Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize.

Elucidating gene regulatory networks (GRNs) 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. In this study, we integrated publicly available co-expression networks derived from more than 6,000 RNA-seq samples, 283 protein-DNA interaction assays, and 16 million of SNPs used to identify expression quantitative 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, protein-DNA interaction (PDI), trans-expression quantitative loci (trans-eQTL), and cis-eQTL combined with PDIs. To improve the functional annotation of TFs based on its target genes, we implemented three different strategies to integrate these four types of networks. We subsequently evaluated the effectiveness of our method through loss-of function mutant and random networks. The multi-network integration allowed us to identify transcriptional regulators of hormone-, metabolic- and development-related processes. Finally, using the topological properties of the fully integrated network, we identified potentially functional 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.

Multi-scale physiological responses to nitrogen supplementation of maize hybrids.

Maize (Zea mays) production systems are heavily reliant on the provision of managed inputs such as fertilizers to maximize growth and yield. Hence, the effective use of N fertilizer is crucial to minimize the associated financial and environmental costs, as well as maximize yield. However, how to effectively utilize N inputs for increased grain yields remains a substantial challenge for maize growers that requires a deeper understanding of the underlying physiological responses to N fertilizer application. We report a multi-scale investigation of five field-grown maize hybrids under low or high N supplementation regimes that includes the quantification of phenolic and prenyl-lipid compounds, cellular ultrastructural features, and gene expression traits at three developmental stages of growth. Our results reveal that maize perceives the lack of supplemented N as a stress and, when provided with additional N, will prolong vegetative growth. However, the manifestation of the stress and responses to N supplementation are highly hybrid-specific. Eight genes were differentially expressed in leaves in response to N supplementation in all tested hybrids and at all developmental stages. These genes represent potential biomarkers of N status and include two isoforms of Thiamine Thiazole Synthase involved in vitamin B1 biosynthesis. Our results uncover a detailed view of the physiological responses of maize hybrids to N supplementation in field conditions that provides insight into the interactions between management practices and the genetic diversity within maize.

Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift.

In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC-MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants.

Rhamnose in plants - from biosynthesis to diverse functions.

In plants, the deoxy sugar l-rhamnose is widely present as rhamnose-containing polymers in cell walls and as part of the decoration of various specialized metabolites. Here, we review the current knowledge on the distribution of rhamnose, highlighting the differences between what is known in dicotyledoneuos compared to commelinid monocotyledoneous (grasses) plants. We discuss the biosynthesis and transport of UDP-rhamnose, as well as the transfer of rhamnose from UDP-rhamnose to various primary and specialized metabolites. This is carried out by rhamnosyltransferases, enzymes that can use a large variety of substrates. Some unique characteristics of rhamnose synthases, the multifunctional enzymes responsible for the conversion of UDP-glucose into UDP-rhamnose, are considered, particularly from the perspective of their ability to convert glucose present in flavonoids. Finally, we discuss how little is still known with regards to how plants rescue rhamnose from the many compounds to which it is linked, or how rhamnose is catabolized.

Discovery of modules involved in the biosynthesis and regulation of maize phenolic compounds.

Phenolic compounds are among the most diverse and widespread of specialized plant compounds and underly many important agronomic traits. Our comprehensive analysis of the maize genome unraveled new aspects of the genes involved in phenylpropanoid, monolignol, and flavonoid production in this important crop. Remarkably, just 19 genes accounted for 70 % of the overall mRNA accumulation of these genes across 95 tissues, indicating that these are the main contributors to the flux of phenolic metabolites. Eighty genes with intermediate to low expression play minor and more specialized roles. Remaining genes are likely undergoing loss of function or are expressed in limited cell types. Phylogenetic and expression analyses revealed which members of gene families governing metabolic entry and branch points exhibit duplication, subfunctionalization, or loss of function. Co-expression analysis applied to genes in sequential biosynthetic steps revealed that certain isoforms are highly co-expressed and are candidates for metabolic complexes that ensure metabolite delivery to correct cellular compartments. Co-expression of biosynthesis genes with transcription factors discovered connections that provided candidate components for regulatory modules governing this pathway. Our study provides a comprehensive analysis of maize phenylpropanoid related genes, identifies major pathway contributors, and novel candidate enzymatic and regulatory modules of the metabolic network.