Abstract

Abiotic and biotic stress responses are traditionally thought to be regulated by discrete signaling mechanisms. Recent experimental evidence revealed a more complex picture where these mechanisms are highly entangled and can have synergistic and antagonistic effects on each other. In this study, we identified shared stress-responsive genes between abiotic and biotic stresses in rice (Oryza sativa) by performing meta-analyses of microarray studies. About 70% of the 1,377 common differentially expressed genes showed conserved expression status, and the majority of the rest were down-regulated in abiotic stresses and up-regulated in biotic stresses. Using dimension reduction techniques, principal component analysis, and partial least squares discriminant analysis, we were able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of differentially expressed genes. Furthermore, using a random forests decision tree model, eight out of 10 stress conditions were classified with high accuracy. Comparison of genes contributing most to the accurate classification by partial least squares discriminant analysis, recursive-support vector machine, and random forests revealed 196 common genes with a dynamic range of expression levels in multiple stresses. Functional enrichment and coexpression network analysis revealed the different roles of transcription factors and genes responding to phytohormones or modulating hormone levels in the regulation of stress responses. We envisage the top-ranked genes identified in this study, which highly discriminate abiotic and biotic stresses, as key components to further our understanding of the inherently complex nature of multiple stress responses in plants.

Introduction

The need to breed robust and high-productivity crops is more important than ever due to increasingly adverse environmental conditions and scarce natural resources. Food productivity has to be raised by as much as 70% to 100% to meet the nutritional needs of the growing population, which is expected to rise to 9 billion by 2050 (Godfray et al., 2010; Lutz and Samir, 2010). Rice (Oryza sativa) is both a major food crop and a model organism that shares extensive synteny and collinearity with other grasses. Thus, the development of rice that can sustain a wide variety of adverse conditions is vital to meet the imminent global energy demands.

A broad range of stress factors divided into two major categories, namely abiotic stresses encompassing a variety of unfavorable environmental conditions, such as drought, submergence, salinity, heavy metal contamination or nutrient deficiency, and biotic stresses caused by infectious living organisms, such as bacteria, viruses, fungi, or nematodes, negatively affect the productivity and survival of plants. Advancements in whole-genome transcriptome analysis techniques like microarrays and RNA sequencing have revolutionized the identification of changes in gene expression in plants under stress, making it possible now to chart out individual stress-specific biomolecular networks and signaling pathways. However, in field conditions, plants are often subjected to multiple stresses simultaneously, requiring efficient molecular mechanisms to perceive a multitude of signals and to elicit a tailored response (Sharma et al., 2013). Increasing evidence from experimental studies suggests that the cross talk between individual stress response signaling pathways via key regulatory molecules, resulting in the dynamic modulation of downstream effectors, is at the heart of multiple stress tolerance. A number of studies have identified many genes, especially transcription factors (TFs) and hormone response factors, that play a central role in multiple stresses and manifest a signature expression specific to the stress condition. For example, abscisic acid (ABA) response factors are up-regulated in the majority of abiotic stresses, activating an oxidative response to protect cells from reactive oxygen species damage, but were found to be down-regulated in a number of biotic conditions, possibly suppressed by immune response molecules (Cao et al., 2011).

The wide range of abiotic and biotic stress factors and their numerous combinations in natural conditions generate a customized stress response. This suggests that the identification and characterization of key genes and their coexpression partners, which show an expression profile that discriminates abiotic and biotic stress responses, would increase our understanding of plant stress response manyfold and provide targets for genetic manipulation to improve their stress tolerance. The availability of multiple genome-wide transcriptome data sets for the same stress condition provides an opportunity to identify, compare, and contrast the stress-specific gene expression profile of one stress condition with other stresses. Meta-analysis combining similar studies provides a robust statistical framework to reevaluate original findings, improve sensitivity with increased sample size, and test new hypotheses. Meta-analysis of microarray studies is widely used, especially in clinical research, to improve statistical robustness and detect weak signals (Liu et al., 2013; Rung and Brazma, 2013). For instance, thousands of samples belonging to hundreds of cancer types were combined, which provided new insights into the general and specific transcriptional patterns of tumors (Lukk et al., 2010).

Microarray studies are burdened with a high dimensionality of feature space, also called the “curse of dimensionality” (i.e. the availability of very many variables [genes] for very few observations [samples]). Machine learning algorithms (supervised and unsupervised), such as principal component analysis (PCA), decision trees, and support vector machines (SVM), provide a way to efficiently classify two or more classes of data. Further feature selection procedures like recursive-support vector machines (R-SVM) provide means to identify the top features contributing most to the accuracy of classification.

In this study, we performed a meta-analysis of stress response studies in rice using publicly available microarray gene expression data conducted on a single platform (AffymetrixRiceArray). Meta-analysis of abiotic and biotic stresses was performed separately to identify differentially expressed genes (DEGs) involved in multiple stress conditions. The lists of abiotic and biotic DEGs were then compared to identify common genes with conserved and nonconserved gene expression (i.e., whether up-regulated, down-regulated, or oppositely regulated in both the categories), revealing the broad patterns of their involvement in the stress response. In order to test the efficiency of identified common DEGs in the classification of abiotic and biotic stresses as well as individual stresses within abiotic and biotic stresses, we systematically investigated various classification and machine learning techniques, including PCA, partial least squares discriminant analysis (PLS-DA), SVM, and random forest (RF). We characterized the shared DEGs through functional enrichment analysis of gene ontologies, metabolic pathways, TF families, and microRNAs (miRNAs) targeting them. We also analyzed the correlation of coexpression between the common DEGs to find sets of genes showing high coexpression and identify hub genes that show the greatest number of edges over a very high cutoff value.

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