A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum

Authors: KUMARI, SUNITA - Cold Spring Harbor Laboratory; KUMAR, VIVEK - Cold Spring Harbor Laboratory; BEILSMITH, KATHLEEN - Argonne National Laboratory; SEAVER, SAMUEL - Argonne National Laboratory; CANON, SHANE - Lawrence Berkeley National Laboratory; DEHAL, PARAMVIR - Lawrence Berkeley National Laboratory; GU, TIAN - Argonne National Laboratory; JOACHIMIAK, MARCIN - Lawrence Berkeley National Laboratory; LERMA-ORTIZ, CAUDIA - Argonne National Laboratory; LIU, FILIPE - Argonne National Laboratory; ZHENYUAN, LU - Cold Spring Harbor Laboratory; PEARSON, ERIC - Lawrence Berkeley National Laboratory; RANJAN, PRIYA - Oak Ridge National Laboratory; RIEL, WILLIAM - Lawrence Berkeley National Laboratory; HENRY, CHRISTOPHER - Argonne National Laboratory; ARKIN, ADAM - Lawrence Berkeley National Laboratory; Ware, Doreen

Submitted to: Current Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/2/2021
Publication Date: 11/11/2021
Citation: Kumari, S., Kumar, V., Beilsmith, K., Seaver, S., Canon, S., Dehal, P., Gu, T., Joachimiak, M., Lerma-Ortiz, C., Liu, F., Zhenyuan, L., Pearson, E., Ranjan, P., Riel, W., Henry, C.S., Arkin, A.P., Ware, D. 2021. A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum. Current Plant Biology. https://doi.org/10.1016/j.cpb.2021.100229.
DOI: https://doi.org/10.1016/j.cpb.2021.100229

Interpretive Summary: The U.S. Department of Energy’s (DOE) Genomic Science program provides the KBase platform as a computational infrastructure to address complex biological problems. KBase is a free platform to use for experimental biologists to perform genome-scale analyses easily, using a user-friendly interface and a scalable computing infrastructure to solve the biological problems. Drought is one of the main limiting factors for plant growth particularly in the arid and semi-arid regions. Sorghum is an important food and biofuel crop that has extreme drought tolerance. Under drought conditions, it manipulates the expression of genes, which respond to initial stress and help establish extended stress tolerance via transcription factors that regulate downstream stress-induced gene expression and metabolic pathways.

Technical Abstract: A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by uploading private or public data into the KBase Narrative Interface, analyzing it using a rich, extensible array of computational and data-analytics tools, and securely sharing scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes differentially expressed during drought-stress in Sorghum bicolor, an important sustainable production crop plant. We then show how we can use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data, analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.