Time-course analysis ofStreptococcus sanguinisafter manganese depletion reveals changes in glycolytic, nucleotide, and redox metabolites

Abstract

IntroductionManganese is important for the endocarditis pathogen, Streptococcus sanguinis. Little is known about why manganese is required for virulence or how it impacts the metabolome of streptococci.ObjectivesWe applied untargeted metabolomics to cells and media to understand temporal changes resulting from manganese depletion.MethodsEDTA was added to a S. sanguinis manganese-transporter mutant in aerobic fermentor conditions. Cell and media samples were collected pre- and post-EDTA treatment. Metabolomics data were generated using positive and negative modes of data acquisition on an LC-MS/MS system. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis using Short Time series Expression Miner (STEM).ResultsWe observed quantitative changes in 534 and 422 metabolites in cells and media, respectively, after EDTA addition. The 173 cellular metabolites identified as significantly different indicated enrichment of purine and pyrimidine metabolism. Further multivariate analysis revealed that the top 15 cellular metabolites belonged primarily to lipids and redox metabolites. The STEM analysis revealed global changes in cells and media in comparable metabolic pathways. Products of glycolysis such as pyruvate and fructose-1,6-bisphosphate increased, suggesting that enzymes that act on them may require manganese for activity or expression. Nucleosides accumulated, possibly due to a blockage in conversion to nucleobases. Simultaneous accumulation of ortho-tyrosine and reduced glutathione suggests that cells were unable to utilize glutathione as a reductant.ConclusionDifferential analysis of metabolites revealed the activation of a number of metabolic pathways in response to manganese depletion, many of which may be connected to carbon catabolite repression.

Manganese transport by Streptococcus sanguinis in acidic conditions and its impact on growth in vitro and in vivo

“…), which were also significantly downregulated in our recent study of manganese depletion (Puccio et al , 2020). Many of the pathways highlighted by this analysis, such as carbon metabolism and amino acid metabolism, were also affected by manganese reduction in our recent transcriptomic and metabolomic studies (Puccio et al , 2020, Puccio et al , 2021b). We observed significant decreases in the expression of sugar transport genes ( Error!…”

“…We recently reported that manganese depletion led to induction of carbon catabolite repression in S. sanguinis (Puccio et al , 2020, Puccio et al , 2021b). Given that both addition of acid and EDTA (Puccio et al , 2021b) led to reduced manganese levels in the fermentor (Figure 4), we hypothesize that the impact on glycolysis is similar to what we observed in our previous study. Additionally, expression of genes encoding glycolytic enzymes responded similarly in both studies ( Error!…”

“…), indicating that the regulatory mechanisms controlling the expression of these genes may have a manganese-related aspect. Thus we hypothesize that there is likely an accumulation of FBP occurring in cells growing at reduced pH due to reduced activity of Fba, Fbp, or both enzymes (Puccio et al , 2021b). This may be the cause behind the changes in expression we observed in the CcpA regulon ( Error!…”

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies

“…The exchange reactions were constrained at corresponding uptake rates provided in VMH database 104 or values calculated based on a previous study 121 for simulating the LAB growth phenotype under various diet conditions or BHI media, respectively. All pairwise interactions were simulated using microbiome modeling toolbox 122 .…”

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet-induced and strain-specific probiotic idiosyncrasies

“…The exchange reactions were constrained at corresponding uptake rates provided in VMH database 104 or values calculated based on a previous study 121 for simulating the LAB growth phenotype under various diet conditions or BHI media, respectively. All pairwise interactions were simulated using microbiome modeling toolbox 122 .…”