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.

For decades, lithium chloride (LiCl) has been used as a treatment option for those living with bipolar disorder (BD). As a result, many studies have been conducted to examine its mode of action, toxicity, and downstream cellular responses. We know that LiCl is able to affect cell signaling and signaling transduction pathways through protein kinase C and glycogen synthase kinase-3, which are considered to be important in regulating gene expression at the translational level. However, additional downstream effects require further investigation, especially in translation pathway. In yeast, LiCl treatment affects the expression, and thus the activity, of PGM2, a phosphoglucomutase involved in sugar metabolism. Inhibition of PGM2 leads to the accumulation of intermediate metabolites of galactose metabolism causing cell toxicity. However, it is not fully understood how LiCl affects gene expression in this matter. In this study, we identified three genes, NAM7, PUS2, and RPL27B, which increase yeast LiCl sensitivity when deleted. We further demonstrate that NAM7, PUS2, and RPL27B influence translation and exert their activity through the 5′-Untranslated region (5′-UTR) of PGM2 mRNA in yeast.

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.

The coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted the development of diagnostic and therapeutic frameworks for timely containment of this pandemic. Here, we utilized our non-conventional computational algorithm, InSiPS, to rapidly design and experimentally validate peptides that bind to SARS-CoV-2 spike (S) surface protein. We previously showed that this method can be used to develop peptides against yeast proteins, however, the applicability of this method to design peptides against other proteins has not been investigated. In the current study, we demonstrate that two sets of peptides developed using InSiPS method can detect purified SARS-CoV-2 S protein via ELISA and Surface Plasmon Resonance (SPR) approaches, suggesting the utility of our strategy in real time COVID-19 diagnostics. Mass spectrometry-based salivary peptidomics shortlist top SARS-CoV-2 peptides detected in COVID-19 patients’ saliva, rendering them attractive SARS-CoV-2 diagnostic targets that, when subjected to our computational platform, can streamline the development of potent peptide diagnostics of SARS-CoV-2 variants of concern. Our approach can be rapidly implicated in diagnosing other communicable diseases of immediate threat.