Goh, J. C. | bioRxiv Science

bioRxiv2024

Parallel comparison reveals conservation and re-wiring of abiotic stress response networks across three plant species

Abiotic stresses trigger significant alterations in plant morphology, yield, and nutrient composition. To counter these stresses, plants have developed strategies involving gene expression adjustments, impacting their growth and development to ensure survival and reproduction. Although transcriptomic studies have extensively examined stress responses, our understanding of their conservation remains limited. We conducted a comprehensive study on the effects of 24 different environmental and nutrient conditions on the growth yield of three hydroponically grown leafy crops (cai xin, lettuce, and spinach). This research identified optimal conditions for maximizing yield and vitamin content and pinpointed conditions that induce abiotic stress. Our findings highlight the substantial impact of stress on various plant functions, with photosynthesis playing a pivotal role in vitamin content regulation. A detailed comparative analysis of gene expression across these crops revealed conserved gene sets potentially crucial for abiotic stress response. Furthermore, we introduced a novel method that merges regression and genomics to construct reliable gene regulatory networks for stress response. Despite significant conservation of these networks among the studied crops, their key transcription factors showed limited similarity to those in Arabidopsis thaliana, suggesting notable functional divergence. To give easy access to the data and comparative transcriptomic tools, we established StressPlantTools, an public online database (https://stress.sbs.ntu.edu.sg/), facilitating further research into stress-responsive genes.

plant biology↗

bioRxiv2021

Bacteria.guru: comparative transcriptomics and co-expression database for bacterial pathogens

SummaryThe bacterial kingdom comprises unicellular prokaryotes able to establish symbioses from mutualism to parasitism. To combat bacterial pathogenicity, we need an enhanced understanding of gene function and regulation, which will mediate the development of novel antimicrobials. Gene expression can predict gene function, but there lacks a database enabling expansive inter- and intraspecific exploration of gene expression profiles and co-expression networks for bacteria. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and studied bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, co-expression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid the discovery of multi-faceted antibiotic targets. Hence, we believe bacteria.guru will facilitate future bacterial research. AvailabilityThe database and co-expression networks are freely available from https://bacteria.guru/. The sample annotations are found in the supplemental data.

bioinformatics↗