Response of Desulfovibrio vulgaris to Alkaline Stress

Abstract

The response of exponentially growing Desulfovibrio vulgaris Hildenborough to pH 10 stress was studied using oligonucleotide microarrays and a study set of mutants with genes suggested by microarray data to be involved in the alkaline stress response deleted. The data showed that the response of D. vulgaris to increased pH is generally similar to that of Escherichia coli but is apparently controlled by unique regulatory circuits since the alternative sigma factors (sigma S and sigma E) contributing to this stress response in E. coli appear to be absent in D. vulgaris. Genes previously reported to be up-regulated in E. coli were up-regulated in D. vulgaris; these genes included three ATPase genes and a tryptophan synthase gene. Transcription of chaperone and protease genes (encoding ATP-dependent Clp and La proteases and DnaK) was also elevated in D. vulgaris. As in E. coli, genes involved in flagellum synthesis were down-regulated. The transcriptional data also identified regulators, distinct from sigma S and sigma E, that are likely part of a D. vulgaris Hildenboroughspecific stress response system. Characterization of a study set of mutants with genes implicated in alkaline stress response deleted confirmed that there was protective involvement of the sodium/proton antiporter NhaC-2, tryptophanase A, and two putative regulators/histidine kinases (DVU0331 and DVU2580).Sulfate-reducing bacteria (SRB) are ubiquitous in nature and play an important role in global carbon and sulfur cycling. Their habitat range includes freshwater, marine, and hypersaline aquatic systems, cold oceanic sediments, the deep subsurface, hydrothermal vents, and hot springs (11,26,35). Although long thought to have a relatively restricted catabolic range, this functionally defined assemblage is now recognized to be remarkably versatile. SRB mediate the degradation of aromatic compounds once thought to be refractory to anaerobic degradation, including benzene (2,3,8,22,23), and reduce a variety of metals, including radionuclides (19,21,24,35). For these reasons they have also been studied for possible use in the bioremediation of environments contaminated with organic and metal pollutants.Desulfovibrio vulgaris Hildenborough is one of the bettercharacterized SRB. This gram-negative deltaproteobacterium, isolated 60 years ago from clay soil in Hildenborough, Kent (United Kingdom), has served as one of the principal models for resolving the physiological and genetic basis of sulfate respiration. The recent completion of its genome sequence (14) has enabled genome-wide expression studies (6,13,27,36,37) that are now beginning to resolve its adaptive response to changing environmental parameters. Although this information is essential for predicting its behavior in possible applications for bioremediation, information about the range of conditions that support D. vulgaris growth or survival remains scarce.Alkaline environments are common in nature (e.g., alkaline groundwater, lakes, and intestinal segments of some higher organisms) and in site...

Impact of elevated nitrate on sulfate-reducing bacteria: a comparative Study of Desulfovibrio vulgaris

“…(Mukhopadhyay et al, 2007), high oxygen (air) (Mukhopadhyay et al, 2007) and alkaline stress (Stolyar et al, 2007). The highest values for gene overlap proportions (Figure 3) and gene expression correlations (Supplementary Figure S4) were observed between time points of the same stress response, as expected.…”

How sulphate-reducing microorganisms cope with stress: lessons from systems biology

“…A variety of 'omics' tools, targeting biological systems at various scales, are used in combination with conventional genetic and biochemical approaches to obtain system-level measurements for subsequent modelling and simulation of the system under study (see the figure). For instance, microbial populations can be phenotypically characterized in terms of their biochemistry, physiology and ecology and then analysed using high-throughput 'omics' tools, such as those provided by transcriptomics (for example, microarrays, RNA-sequencing (RNA-seq) or whole-transcriptome shotgun sequencing), proteomics (for example, mass spectrometry to identify proteins, protein complexes and post-translational modifications) 37,48,51,147,148 and metabolomics (for example, metabolite profiling and analysis of metabolite fluxes) 37,38,55,149 . At the community scale, high-throughput metagenomic technologies, such as large-scale genome sequencing 97 , GeoChip 15,109 and PhyloChip…”

“…This knowledge is essential if we are to generate predictive models of the stress responses of SRMs to environmental factors, and to develop effective SRM-based biotechnologies. 40,52 , heat shock 50,53 , KCl 37 , nitrate salts 39 , nitrite salts 40,52 , heat shock 50,53 , starvation 54 and alkaline pH 55 . starvation 54 and alkaline pH 55 .…”

“…40,52 , heat shock 50,53 , KCl 37 , nitrate salts 39 , nitrite salts 40,52 , heat shock 50,53 , starvation 54 and alkaline pH 55 . starvation 54 and alkaline pH 55 . 40,52 , the reduction in energy production is reflected in the downregulation of the ATP synthase, membrane hydrogenases and the DsrMKJOP transmembrane complex (FIG.…”

“…The data obtained through the above approaches can then be integrated by system modelling and simulation (for example, by pathway inference and the discovery of new pathways 149 ; by the development of models for cellular stress responses [37][38][39][40]48,51,[53][54][55] , for the evolutionary trajectories of genes 85 , pathways, cells, populations and communities 97 , and for cellular and community networks 51,150 ; and possibly by mathematical modelling, simulation and prediction of stress responses across different organizational levels such as cells, populations and communities).…”