Insights Into the Impact of Small RNA SprC on the Metabolism and Virulence of Staphylococcus aureus

Abstract

AimOur previous proteomic analysis showed that small RNA SprC (one of the small pathogenicity island RNAs) of Staphylococcus aureus possesses the ability to regulate the expression of multiple bacterial proteins. In this study, our objective was to further provide insights into the regulatory role of SprC in gene transcription and metabolism of S. aureus.MethodsGene expression profiles were obtained from S. aureus N315 wild-type and its sprC deletion mutant strains by RNA-sequencing (RNA-seq), and differentially expressed genes (DEGs) were screened by R language with a |log2(fold change)| ≥1 and a false discovery rate (FDR) ≤ 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out to understand the significance of the DEGs. The quality of RNA-seq was further verified by quantitative real-time PCR (qRT-PCR), mRNA target prediction, metabolomics analysis and transcript-level expression analysis of genes of sprC complementation strain.ResultsA total of 2497 transcripts were identified, of which 60 transcripts expressions in sprC knockout strain were significantly different (37 up-regulated and 23 down-regulated DEGs). GO analysis showed that the functions of these DEGs were mainly concentrated in the biological process and molecular function related to metabolism and pathogenesis, and a higher number of genes were involved in the oxidation-reduction process, catalytic activity and binding. KEGG pathways enrichment analysis demonstrated that metabolism and pathogenesis were the most affected pathways, such as metabolic pathways, biosynthesis of secondary metabolites, purine metabolism, fructose and mannose metabolism and S. aureus infection. The qRT-PCR results of the DEGs with defined functions in the sprC deletion and complementation strains were in general agreement with those obtained by RNA-seq. Metabolomics analysis revealed 77 specific pathways involving metabolic pathways. Among them, many, such as metabolic pathways, biosynthesis of secondary metabolites and purine metabolism, were consistent with those enriched in the RNA-seq analysis.ConclusionThis study offered valuable and reliable information about the regulatory roles of SprC in S. aureus biology through transcriptomics and metabolomics analysis. These results may provide clues for new potential targets for anti-virulence adjuvant therapy on S. aureus infection.

Integrative omics analysis reveals insights into small colony variants of Staphylococcus aureus induced by sulfamethoxazole-trimethoprim

“…After end-repair, dA-tailing, and adapter ligation, the adapter-modified fragments were amplified to generate the cDNA library using the NEBNext® UltraTM II RNA Library Prep Kit (New England Biolabs, Massachusetts, USA). The cDNA sequencing was performed on the Illumina HiSeq 4000 sequencing platform (Illumina, Inc., California, USA) [ 52 ]. The raw data were aligned to the reference genome sequence of NC_009641.1.…”

“…We utilized the DESeq2 v 1.10.1 package to detect differentially expressed genes (DEGs) between the SCV and its parental strain, with a false discovery rate (FDR) of ≤ 0.05 and |log2(fold change)| of ≥ 1. DEGs were annotated and enriched using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases (R package GOseq v 1.18) [ 52 ]. Data from three biological replicates were used to analyse the difference of gene expression.…”

“…Three biological experiments were performed, and the relative gene expression was calculated using the Eq. 2 −ΔΔCT [ 52 ]. The efficiency of the primers was evaluated through melting curve analysis.…”

“…The collected samples were thawed on ice, and metabolites were extracted using a 50% methanol buffer. The metabolomic compositions were analyzed using the Liquid chromatography tandem mass spectrometry (LC–MS/MS) technique (Thermo Fisher Scientific, Waltham, USA) and the corresponding software, following the previously described methods [ 52 ]. Six biological experiments of each strain were performed.…”

“…The differentially expressed metabolic ions were screened using univariate analysis and variable importance for the projection (VIP) values obtained from partial least squares-discriminant analysis (PLS-DA), a multivariate statistical analysis. The differential ions needed to meet the following conditions: (1) ratio > = 2 or < = 1/2, (2) p -value ≤ 0.05, and (3) VIP ≥ 1[ 52 ].…”