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

Abstract

In 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.

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

“…Recently, diverse machine learning models have proven effective in accurately forecasting and refining plant tissue culture procedures. These models have been applied in various investigations, including in vitro mutagenesis, micropropagation, regeneration studies, plant system biology, in vitro organogenesis, stress physiology, and salt stress [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Only a few studies have used machine learning models to examine drought stress responses.…”

“…Das Choudhury et al [34] introduced HyperStressPropagateNet, a deep neural network for analyzing drought stress propagation in plants through hyperspectral imagery, showing a strong correlation with soil water content. Tahmasebi et al [35] applied a meta-analysis and machine learning to identify drought-responsive genes in Populus, revealing significant transcriptional variations and potential markers for breeding programs. These contributions highlight the role of advanced computational methods in enhancing our understanding of plant responses to drought stress.…”

Genome-wide profiling of histone (H3) lysine 4 (K4) tri-methylation (me3) under drought, heat, and combined stresses in switchgrass

“…Similarly, H3K4me3 marked over ten ABC transporter C family member genes in our study in response to drought, heat, and combined stresses, suggesting a functional role in RSA remodeling by modulating ion homeostasis. In poplar, the possibility of a change in phosphorylation is one of the most critical regulatory ways to modulate stress responses has been reported [ 115 ]. Also, they identified amino acid transporter (AVT6A-like) as the most interconnected gene with protein phosphatase 4 mediated phosphorylation when subjected to drought stress [ 115 ].…”

“…In poplar, the possibility of a change in phosphorylation is one of the most critical regulatory ways to modulate stress responses has been reported [ 115 ]. Also, they identified amino acid transporter (AVT6A-like) as the most interconnected gene with protein phosphatase 4 mediated phosphorylation when subjected to drought stress [ 115 ]. Similarly, we found enriched AVT6A-like transporter levels during drought, heat, and combined stresses in switchgrass.…”

The Euphrates Poplar Responses to Abiotic Stress and Its Unique Traits in Dry Regions of China (Xinjiang and Inner Mongolia): What Should We Know?

“…Under low-temperature stress, genes and their functions were analyzed [45]. With the increasing growth of omics data, bioinformatics methods have advanced [46], including those of genome, transcriptome [47], and proteome, alongside the quick development of computer technology [48]. Bioinformatics is also built on the fundamental idea that any biological mechanism comprises many molecular events, and that knowing the interplay within and between distinct levels of genomic architecture is the only way to comprehend phenotypic features [49,50].…”