Olga Troyanskaya and colleagues present genome-wide functional interaction networks for 144 human tissues and cell types. They identify important disease-gene associations by combining data from GWAS and tissue-specific networks. They also developed a webserver, GIANT, that includes multi-gene query capability, network visualization and analysis tools. Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.

Plant drought stress response and resistance are complex biological processes that need to be analyzed at a systems level using genomics and physiological approaches to dissect experimental models that address drought stresses encountered by crops in the field. Toward this goal, a controlled, sublethal, moderate drought (mDr) treatment system was developed in Arabidopsis (Arabidopsis thaliana) as a reproducible assay for the dissection of plant responses to drought. The drought assay was validated using Arabidopsis mutants in abscisic acid (ABA) biosynthesis and signaling displaying drought sensitivity and in jasmonate response mutants showing drought resistance, indicating the crucial role of ABA and jasmonate signaling in drought response and acclimation. A comparative transcriptome analysis of soil water deficit drought stress treatments revealed the similarity of early-stage mDr to progressive drought, identifying common and specific stress-responsive genes and their promoter cis-regulatory elements. The dissection of mDr stress responses using a time-course analysis of biochemical, physiological, and molecular processes revealed early accumulation of ABA and induction of associated signaling genes, coinciding with a decrease in stomatal conductance as an early avoidance response to drought stress. This is accompanied by a peak in the expression of expansin genes involved in cell wall expansion, as a preparatory step toward drought acclimation by the adjustment of the cell wall. The time-course analysis of mDr provides a model with three stages of plant responses: an early priming and preconditioning stage, followed by an intermediate stage preparatory for acclimation, and a late stage of new homeostasis with reduced growth.

Freshwater is a limited and dwindling global resource; therefore, efficient water use is required for food crops that have high water demands, such as rice, or for the production of sustainable energy biomass. We show here that expression of the Arabidopsis HARDY ( HRD ) gene in rice improves water use efficiency, the ratio of biomass produced to the water used, by enhancing photosynthetic assimilation and reducing transpiration. These drought-tolerant, low-water-consuming rice plants exhibit increased shoot biomass under well irrigated conditions and an adaptive increase in root biomass under drought stress. The HRD gene, an AP2/ERF-like transcription factor, identified by a gain-of-function Arabidopsis mutant hrd-D having roots with enhanced strength, branching, and cortical cells, exhibits drought resistance and salt tolerance, accompanied by an enhancement in the expression of abiotic stress associated genes. HRD overexpression in Arabidopsis produces thicker leaves with more chloroplast-bearing mesophyll cells, and in rice, there is an increase in leaf biomass and bundle sheath cells that probably contributes to the enhanced photosynthesis assimilation and efficiency. The results exemplify application of a gene identified from the model plant Arabidopsis for the improvement of water use efficiency coincident with drought resistance in the crop plant rice.

Background: Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression.Results: Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships.Conclusions: Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

Drought stress affects cereals especially during the reproductive stage. The maize (Zea mays) drought transcriptome was studied using RNA-Seq analysis to compare drought-treated and well-watered fertilized ovary and basal leaf meristem tissue. More drought-responsive genes responded in the ovary compared with the leaf meristem. Gene Ontology enrichment analysis revealed a massive decrease in transcript abundance of cell division and cell cycle genes in the drought-stressed ovary only. Among Gene Ontology categories related to carbohydrate metabolism, changes in starch and Suc metabolism-related genes occurred in the ovary, consistent with a decrease in starch levels, and in Suc transporter function, with no comparable changes occurring in the leaf meristem. Abscisic acid (ABA)-related processes responded positively, but only in the ovaries. Related responses suggested the operation of low glucose sensing in drought-stressed ovaries. The data are discussed in the context of the susceptibility of maize kernel to drought stress leading to embryo abortion and the relative robustness of dividing vegetative tissue taken at the same time from the same plant subjected to the same conditions. Our working hypothesis involves signaling events associated with increased ABA levels, decreased glucose levels, disruption of ABA/sugar signaling, activation of programmed cell death/senescence through repression of a phospholipase C-mediated signaling pathway, and arrest of the cell cycle in the stressed ovary at 1 d after pollination. Increased invertase levels in the stressed leaf meristem, on the other hand, resulted in that tissue maintaining hexose levels at an “unstressed” level, and at lower ABA levels, which was correlated with successful resistance to drought stress.