Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species

Abstract

AbstractIn Populus, drought is a major problem affecting plant growth and development which can be closely reflected by corresponding transcriptomic changes. Nevertheless, how these changes in Populus are not fully understood. Here, we first used meta-analysis and machine learning methods to identify water stress-responsive genes and then performed a systematic approach to discover important gene networks. Our analysis revealed that large transcriptional variations occur during drought stress. These changes were more associated with the response to stress, cellular catabolic process, metabolic pathways, and hormone-related genes. The differential gene coexpression analysis highlighted two acetyltransferase NATA1-like and putative cytochrome P450 genes that have a special contribution in response to drought stress. In particular, the findings showed that MYBs and MAPKs have a prominent role in the drought stress response that could be considered to improve the drought tolerance of Populus. We also suggest ARF2-like and PYL4-like genes as potential markers for use in breeding programs. This study provides a better understanding of how Populus responses to drought that could be useful for improving tolerance to stress in Populus.