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Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a major threat to human health, as the US mortality rate outweighs those from HIV, tuberculosis, and viral hepatitis combined. In the wake of the COVID-19 pandemic, antibiotic resistant bacterial infections acquired during hospital stays have increased. Instead of designing and deploying new antibiotics which MRSA would quickly develop resistance to, adjuvants are a key strategy to combatting these bacteria. We have evaluated several small molecule antibiotic adjuvants that have strong potentiation with β-lactam antibiotics and are likely inhibiting a master regulatory kinase, Stk1. Here, we investigated how the lead adjuvant exerts its effects in a more comprehensive manner. We hypothesized that the expression levels of key resistance genes would decrease once cotreated with a β-lactam antibiotic (oxacillin) and the adjuvant (compound 8). Furthermore, bioinformatic analyses would reveal biochemical pathways enriched in differentially expressed genes. RNA-seq analysis showed 176 and 233 genes significantly up and downregulated, respectively, in response to cotreatment with compound 8 and oxacillin compared to oxacillin alone. Gene ontology categories that were significantly enriched among downregulated genes involved phosphotransferase systems. Most of the biochemical pathways enriched with significantly downregulated genes involved carbohydrate utilization, such as the citrate cycle and the phosphotransferase system. One of the most populated pathways was S. aureus infection. Results from an interaction network constructed with affected gene products supported the hypothesis that Stk1 is a target of compound 8. This study revealed a dramatic impact of our lead adjuvant on the transcriptome that is consistent with a pleiotropic effect due to Stk1 inhibition. These results point to this antibiotic adjuvant having potential broad therapeutic use in combatting MRSA.
Supplementary materials
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Viering et al Supporting Information
Description
Additional quality control details of RNA samples are found in Supporting Tables S1, RNA-seq read quality control is in Table S2, a mapped reads summary is in Table S3, RT-qPCR primer information is in Table S10, RNA sample Pearson correlation results are in Figure S1, Principal component analysis results are in Figure S2, the number of DEGs among all group comparisons is in Figure S3, KEGG pathway maps enriched with DEGs are in Figures S4-S7, and STRING interaction networks are in Figures S8 and S9 (pdf).
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Viering et al Supporting Information Tables S4-S9
Description
Full differential gene expression data sets for all treatment groups are found in Supporting Tables S4-S9 (.xlsx).
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Viering et al Supporting Information Tables S11-S16
Description
Full GO and KEGG enrichment results are found in Supporting Tables S11-S16 (.xlsx).
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