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Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Lithium Chloride (LiCl) toxicity, mode of action and cellular responses have been the subject of active investigations over the past decades. In yeast, LiCl treatment is reported to reduce the activity and alters the expression of PGM2, a gene that encodes a phosphoglucomutase involved in sugar metabolism. Reduced activity of phosphoglucomutase in the presence of galactose causes an accumulation of intermediate metabolites of galactose metabolism leading to a number of phenotypes including growth defect. In the current study, we identify two understudied yeast genes, YTA6 and YPR096C that when deleted, cell sensitivity to LiCl is increased when galactose is used as a carbon source. The 5'-UTR of PGM2 mRNA is structured. Using this region, we show that YTA6 and YPR096C influence the translation of PGM2 mRNA.

Heavy metal and metalloid contaminations are among the most concerning types of pollutant in the environment. Consequently, it is important to investigate the molecular mechanisms of cellular responses and detoxification pathways for these compounds in living organisms. To date, a number of genes have been linked to the detoxification process. The expression of these genes can be controlled at both transcriptional and translational levels. In baker's yeast, Saccharomyces cerevisiae, resistance to a wide range of toxic metals is regulated by glutathione S-transferases. Yeast URE2 encodes for a protein that has glutathione peroxidase activity and is homologous to mammalian glutathione S-transferases. The URE2 expression is critical to cell survival under heavy metal stress. Here, we report on the finding of two genes, ITT1, an inhibitor of translation termination, and RPS1A, a small ribosomal protein, that when deleted yeast cells exhibit similar metal sensitivity phenotypes to gene deletion strain for URE2. Neither of these genes were previously linked to metal toxicity. Our gene expression analysis illustrates that these two genes affect URE2 mRNA expression at the level of translation.