Proteomic and Isotopic Response of Desulfovibrio vulgaris to DsrC Perturbation

Abstract

Dissimilatory sulfate reduction is a microbial energy metabolism that can produce sulfur isotopic fractionations over a large range in magnitude. Calibrating sulfur isotopic fractionation in laboratory experiments allows for better interpretations of sulfur isotopes in modern sediments and ancient sedimentary rocks. The proteins involved in sulfate reduction are expressed in response to environmental conditions, and are collectively responsible for the net isotopic fractionation between sulfate and sulfide. We examined the role of DsrC, a key component of the sulfate reduction pathway, by comparing wildtype Desulfovibrio vulgaris DSM 644 T to strain IPFG07, a mutant deficient in DsrC production. Both strains were cultivated in parallel chemostat reactors at identical turnover times and cell specific sulfate reduction rates. Under these conditions, sulfur isotopic fractionations between sulfate and sulfide of 17.3 ± 0.5‰ or 12.6 ± 0.5‰ were recorded for the wildtype or mutant, respectively. The enzymatic machinery that produced these different fractionations was revealed by quantitative proteomics. Results are consistent with a cellular-level response that throttled the supply of electrons and sulfur supply through the sulfate reduction pathway more in the mutant relative to the wildtype, independent of rate. We conclude that the smaller fractionation observed in the mutant strain is a consequence of sulfate reduction that proceeded at a rate that consumed a greater proportion of the strains overall capacity for sulfate reduction. These observations have consequences for models of sulfate reducer metabolism and how it yields different isotopic fractionations, notably, the role of DsrC in central energy metabolism.

DsrCis involved in fermentative growth and interacts directly with theFlxABCD–HdrABCcomplex inDesulfovibrio vulgarisHildenborough

“…To further evaluate the effects of modifying DsrC, the proteomes of DvH WT and DvH IPFG09 were measured under respiratory and fermentative conditions. Cells were collected at the late exponential phase, and the whole cell proteome analysed using the dimethylation-deuteration and oxygen-exchange in isobaric peptide terminal labelling (diDO-IPTL) methodology (Waldbauer et al, 2017) The genome of DvH contains 3545 predicted protein-coding genes, and the proteomics analyses covered between 33% and 39% of the genome, similar to previous reports (Leavitt et al, 2019). Two protein classes are poorly detected by MS analysis due to technical reasons, namely cytochromes with hemes c and integral membrane proteins.…”

“…Strain Dv H IPFG07 was previously shown to have a slower growth rate and achieve lower cell density than Dv H WT in sulfate respiration, a phenotype that was even more pronounced in Dv H IPFG09 (Leavitt et al, 2016; Santos et al, 2015). In this work, IPFG07 and IPFG09 strains were compared with Dv H WT in sulfate respiration in conditions that differed from previous studies (Leavitt et al, 2016, 2019; Santos et al, 2015) by the absence of yeast extract in the media, by measuring metabolic products, and studying also pyruvate fermentation.…”

Text-Mining to Identify Gene Sets Involved in Biocorrosion by Sulfate-Reducing Bacteria: A Semi-Automated Workflow

“…Next, sulfite is partially reduced to hydrogen sulfide (H 2 S) by DsrAB (Dissimilatory sulfite reductase). Finally, DsrC carries out the final sulfite reduction to H 2 S ( Figure 6 ) [ 60 ].…”

Archaeal lipid hydrogen isotopes in a marine thaumarchaeon

“…N. maritimus expresses simple patterns of 2 eL/W values, where the dominant feature is a dependence on the number of rings, with the most 2 H-depleted values in BP-0 and the least depleted in BP-3 (Figure 1). Mean total assemblage values of 2 eL/W show little sensitivity to changes in growth rate as promoted by different fluxes of electron donor, especially when compared to the fatty acids of bacteria grown with similar strategies (Kopf, 2015;Kopf et al, 2015;Leavitt et al, 2019) The model developed above (Section 4) explored how these patterns can help distinguish the H sources for BP biosynthesis and enabled estimation of the KIEs for the various enzymatic steps.…”