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

Abstract

Sulfate-reducing bacteria have been extensively studied for their potential in heavy-metal bioremediation. However, the occurrence of elevated nitrate in contaminated environments has been shown to inhibit sulfate reduction activity. Although the inhibition has been suggested to result from the competition with nitrate-reducing bacteria, the possibility of direct inhibition of sulfate reducers by elevated nitrate needs to be explored. Using Desulfovibrio vulgaris as a model sulfate-reducing bacterium, functional genomics analysis reveals that osmotic stress contributed to growth inhibition by nitrate as shown by the upregulation of the glycine/betaine transporter genes and the relief of nitrate inhibition by osmoprotectants. The observation that significant growth inhibition was effected by 70 mM NaNO 3 but not by 70 mM NaCl suggests the presence of inhibitory mechanisms in addition to osmotic stress. The differential expression of genes characteristic of nitrite stress responses, such as the hybrid cluster protein gene, under nitrate stress condition further indicates that nitrate stress response by D. vulgaris was linked to components of both osmotic and nitrite stress responses. The involvement of the oxidative stress response pathway, however, might be the result of a more general stress response. Given the low similarities between the response profiles to nitrate and other stresses, less-defined stress response pathways could also be important in nitrate stress, which might involve the shift in energy metabolism. The involvement of nitrite stress response upon exposure to nitrate may provide detoxification mechanisms for nitrite, which is inhibitory to sulfate-reducing bacteria, produced by microbial nitrate reduction as a metabolic intermediate and may enhance the survival of sulfate-reducing bacteria in environments with elevated nitrate level.

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

“…By comparison, the regulation of the oxidativestress response does rely on PerR in B. subtilis, but the constituents of the PerR regulon differ significantly between B. subtilis and D. vulgaris H. 66 . Interestingly, although genes in the PerR regulon are considered to be specifically involved in resistance to oxidative stress in D. vulgaris H. and other bacteria 46,67 , they are repeatedly upregulated under many other stress conditions tested on D. vulgaris H. [38][39][40]48,51,53 (FIG. 3c).…”

“…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 .…”

“…Thus, although the core metabolic machinery for sulphate reduction is conserved in all studied SRMs, there is a substantial variation in the mechanisms of redox cycling and electron flow, and novel mechanisms of energy conservation may remain to be discovered. Indeed, the plethora of redox proteins that are involved in energy metabolism in D. vulgaris H. exhibit complex gene expression patterns that are specific to distinct stress conditions, demonstrating the importance of adjustments in energy metabolism pathways as a central strategy in the stress response [37][38][39][40][41] . Even less is known about sulphate reduction and electron transport in sulphate-reducing Nitrospira spp.…”

“…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).…”

GeoChip-based analysis of the microbial community functional structures in simultaneous desulfurization and denitrification process

“…3). It is possible that the SRB were inhibited by the high concentration of nitrate as previously reported (Greene et al, 2003;He et al, 2010;Hubert and Voordouw, 2007).…”