Dataset Information

Microbial Community Structure and Arsenic Biogeochemistry in Two Arsenic-Impacted Aquifers in Bangladesh.

ABSTRACT: Long-term exposure to trace levels of arsenic (As) in shallow groundwater used for drinking and irrigation puts millions of people at risk of chronic disease. Although microbial processes are implicated in mobilizing arsenic from aquifer sediments into groundwater, the precise mechanism remains ambiguous. The goal of this work was to target, for the first time, a comprehensive suite of state-of-the-art molecular techniques in order to better constrain the relationship between indigenous microbial communities and the iron and arsenic mineral phases present in sediments at two well-characterized arsenic-impacted aquifers in Bangladesh. At both sites, arsenate [As(V)] was the major species of As present in sediments at depths with low aqueous As concentrations, while most sediment As was arsenite [As(III)] at depths with elevated aqueous As concentrations. This is consistent with a role for the microbial As(V) reduction in mobilizing arsenic. 16S rRNA gene analysis indicates that the arsenic-rich sediments were colonized by diverse bacterial communities implicated in both dissimilatory Fe(III) and As(V) reduction, while the correlation analyses involved phylogenetic groups not normally associated with As mobilization. Findings suggest that direct As redox transformations are central to arsenic fate and transport and that there is a residual reactive pool of both As(V) and Fe(III) in deeper sediments that could be released by microbial respiration in response to hydrologic perturbation, such as increased groundwater pumping that introduces reactive organic carbon to depth.IMPORTANCE The consumption of arsenic in waters collected from tube wells threatens the lives of millions worldwide and is particularly acute in the floodplains and deltas of southern Asia. The cause of arsenic mobilization from natural sediments within these aquifers to groundwater is complex, with recent studies suggesting that sediment-dwelling microorganisms may be the cause. In the absence of oxygen at depth, specialist bacteria are thought able to use metals within the sediments to support their metabolism. Via these processes, arsenic-contaminated iron minerals are transformed, resulting in the release of arsenic into the aquifer waters. Focusing on a field site in Bangladesh, a comprehensive, multidisciplinary study using state-of-the-art geological and microbiological techniques has helped better understand the microbes that are present naturally in a high-arsenic aquifer and how they may transform the chemistry of the sediment to potentially lethal effect.

SUBMITTER: Gnanaprakasam ET

PROVIDER: S-EPMC5705915 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Microbial Community Structure and Arsenic Biogeochemistry in Two Arsenic-Impacted Aquifers in Bangladesh.

Gnanaprakasam Edwin T ET Lloyd Jonathan R JR Boothman Christopher C Ahmed Kazi Matin KM Choudhury Imtiaz I Bostick Benjamin C BC van Geen Alexander A Mailloux Brian J BJ

mBio 20171128 6

Long-term exposure to trace levels of arsenic (As) in shallow groundwater used for drinking and irrigation puts millions of people at risk of chronic disease. Although microbial processes are implicated in mobilizing arsenic from aquifer sediments into groundwater, the precise mechanism remains ambiguous. The goal of this work was to target, for the first time, a comprehensive suite of state-of-the-art molecular techniques in order to better constrain the relationship between indigenous microbia ...[more]

PMID: 29184025

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Project description:Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H2S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H2S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H2S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H2S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H2S concentration with remarkably high fidelity (R2 > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H2S concentration (R2 = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments. IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H2S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

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Dataset Information

Microbial Community Structure and Arsenic Biogeochemistry in Two Arsenic-Impacted Aquifers in Bangladesh.

Publications

Microbial Community Structure and Arsenic Biogeochemistry in Two Arsenic-Impacted Aquifers in Bangladesh.

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