Machine learning extracts marks of thiamine's role in cold acclimation in the transcriptome of Vitis vinifera

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Time-course transcriptome analysis reveals gene co-expression networks and transposable element responses to cold stress in cotton

BACKGROUND

Cold stress significantly challenges cotton growth and productivity, yet the genetic and molecular mechanisms underlying cold tolerance remain poorly understood.

RESULTS

We employed RNA-seq and iterative weighted gene co-expression network analysis (WGCNA) to investigate gene and transposable element (TE) expression changes at six cold stress time points (0 h, 2 h, 4 h, 6 h, 12 h, 24 h). Thousands of differentially expressed genes (DEGs) were identified, exhibiting time-specific patterns that highlight a phase-dependent transcriptional response. While the A and D subgenomes contributed comparably to DEG numbers, numerous homeologous gene pairs showed differential expression, indicating regulatory divergence. Iterative WGCNA uncovered 125 gene co-expression modules, with some enriched in specific chromosomes or chromosomal regions, suggesting localized regulatory hotspots for cold stress response. Notably, transcription factors, including MYB73, ERF017, MYB30, and OBP1, emerged as central regulators within these modules. Analysis of 11 plant hormone-related genes revealed dynamic expression, with ethylene (ETH) and cytokinins (CK) playing significant roles in stress-responsive pathways. Furthermore, we documented over 15,000 expressed TEs, with differentially expressed TEs forming five distinct clusters. TE families, such as LTR/Copia, demonstrated significant enrichment in these expression clusters, suggesting their potential role as modulators of gene expression under cold stress.

CONCLUSIONS

These findings provide valuable insights into the complex regulatory networks underlying cold stress response in cotton, highlighting key molecular components involved in cold stress regulation. This study provides potential genetic targets for breeding strategies aimed at enhancing cold tolerance in cotton.

Multi-Omics Analysis Provides Insights into Green Soybean in Response to Cold Stress

Green soybean (Glycine max (L.) Merrill) is a highly nutritious food that is a good source of protein and fiber. However, it is sensitive to low temperatures during the growing season, and enhancing cold tolerance has become a research hotspot for breeding improvement. Background/Objectives: The underlying molecular mechanisms of cold tolerance in green soybean are not well understood. Methods: Here, a comprehensive analysis of transcriptome and metabolome was performed on a cold-tolerant cultivar treated at 10 °C for 24 h. Results: Compared to control groups, we identified 17,011 differentially expressed genes (DEGs) and 129 differentially expressed metabolites (DEMs). The DEGs and DEMs were further subjected to KEGG functional analysis. Finally, 11 metabolites (such as sucrose, lactose, melibiose, and dehydroascorbate) and 17 genes (such as GOLS, GLA, UGDH, and ALDH) were selected as candidates associated with cold tolerance. Notably, the identified metabolites and genes were enriched in two common pathways: 'galactose metabolism' and 'ascorbate and aldarate metabolism'. Conclusions: The findings suggest that green soybean modulates the galactose metabolism and ascorbate and aldarate metabolism pathways to cope with cold stress. This study contributes to a deeper understanding of the complex molecular mechanisms enabling green soybeans to better avoid low-temperature damage.

Unveiling Iso- and Aniso-Hydric Disparities in Grapevine-A Reanalysis by Transcriptome Portrayal Machine Learning

Mechanisms underlying grapevine responses to water(-deficient) stress (WS) are crucial for viticulture amid escalating climate change challenges. Reanalysis of previous transcriptome data uncovered disparities among isohydric and anisohydric grapevine cultivars in managing water scarcity. By using a self-organizing map (SOM) transcriptome portrayal, we elucidate specific gene expression trajectories, shedding light on the dynamic interplay of transcriptional programs as stress duration progresses. Functional annotation reveals key pathways involved in drought response, pinpointing potential targets for enhancing drought resilience in grapevine cultivation. Our results indicate distinct gene expression responses, with the isohydric cultivar favoring plant growth and possibly stilbenoid synthesis, while the anisohydric cultivar engages more in stress response and water management mechanisms. Notably, prolonged WS leads to converging stress responses in both cultivars, particularly through the activation of chaperones for stress mitigation. These findings underscore the importance of understanding cultivar-specific WS responses to develop sustainable viticultural strategies in the face of changing climate.

Special Issue on "Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics 2.0"

The analysis of molecular mechanisms underlying plant adaptation to environmental changes and stress response is crucial for plant biotechnology [...].