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Abstract The complexity of environmental factors affecting crops in the field is gradually increasing due to climate change-associated weather events, such as droughts or floods combined with heat waves, coupled with the accumulation of different environmental and agricultural pollutants. The impact of multiple stress conditions on plants was recently termed “multifactorial stress combination” (MFSC) and defined as the occurrence of 3 or more stressors that impact plants simultaneously or sequentially. We recently reported that with the increased number and complexity of different MFSC stressors, the growth and survival of Arabidopsis (Arabidopsis thaliana) seedlings declines, even if the level of each individual stress is low enough to have no significant effect on plants. However, whether MFSC would impact commercial crop cultivars is largely unknown. Here, we reveal that a MFSC of 5 different low-level abiotic stresses (salinity, heat, the herbicide paraquat, phosphorus deficiency, and the heavy metal cadmium), applied in an increasing level of complexity, has a significant negative impact on the growth and biomass of a commercial rice (Oryza sativa) cultivar and a maize (Zea mays) hybrid. Proteomics, element content, and mixOmics analyses of MFSC in rice identified proteins that correlate with the impact of MFSC on rice seedlings, and analysis of 42 different rice genotypes subjected to MFSC revealed substantial genetic variability in responses to this unique state of stress combination. Taken together, our findings reveal that the impacts of MFSC on 2 different crop species are severe and that MFSC may substantially affect agricultural productivity. 

SUMMARY Global warming, climate change, and industrial pollution are altering our environment subjecting plants, microbiomes, and ecosystems to an increasing number and complexity of abiotic stress conditions, concurrently or sequentially. These conditions, termed, “multifactorial stress combination” (MFSC), can cause a significant decline in plant growth and survival. However, the impacts of MFSC on reproductive tissues and yield of major crop plants are largely unknown. We subjected soybean (Glycine max) plants to a MFSC of up to five different stresses (water deficit, salinity, low phosphate, acidity, and cadmium), in an increasing level of complexity, and conducted integrative transcriptomic‐phenotypic analysis of their reproductive and vegetative tissues. We reveal that MFSC has a negative cumulative effect on soybean yield, that each set of MFSC condition elicits a unique transcriptomic response (that is different between flowers and leaves), and that selected genes expressed in leaves or flowers of soybean are linked to the effects of MFSC on different vegetative, physiological, and/or reproductive parameters. Our study identified networks and pathways associated with reactive oxygen species, ascorbic acid and aldarate, and iron/copper signaling/metabolism as promising targets for future biotechnological efforts to augment the resilience of reproductive tissues of major crop plants to MFSC. In addition, we provide unique phenotypic and transcriptomic datasets for dissecting the mechanistic effects of MFSC on the vegetative, physiological, and reproductive processes of a crop plant. 

Abstract The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA‐Seq data generated in the project serve as a valuable resource for discovering tissue‐specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context‐specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context‐specific networks at both transcriptome and interactome levels. BicMix and Cross‐Conditions Cluster Detection algorithms were applied to detect modules based on co‐expression relationships across all the tissues. Soybean context‐specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen‐fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) athttp://soykb.org/SoyCSN. SoyCSN provides a useful web‐based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.