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

ACCESS DATA

Publications

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

Similar Datasets

Project description:Microbial ecological processes are frequently studied in the presence of perturbations rather than in undisturbed environments, despite the relatively stable conditions dominating many microbial habitats. To examine processes influencing microbial community structuring in the absence of strong external perturbations, three unperturbed aquifers in Ohio (Greene, Licking, and Athens) were sampled over 2 years and analyzed using geochemical measurements, 16S rRNA gene sequencing, and ecological modeling. Redox conditions ranging from highly reducing to more oxidizing distinguished aquifer geochemistry across the three locations. Distinct microbial communities were present in each aquifer, and overall community structure was related to geochemistry, although community composition was more similar between the Athens and Licking locations. The ecological processes acting upon microbial assemblages within aquifers were varied; geochemical changes affected the Athens location, while time or some unknown factor affected Greene County. Stochastic processes, however, dominated the Licking aquifer, suggesting a decoupling between environmental fluctuations and community development. Although physicochemical differences might be expected to drive variable selection, dispersal limitation (inability to mix) explained differences between Athens and Licking. Finally, community complexity as measured by "cohesion" indicated that less-interconnected communities experienced higher turnover and were more likely to be affected by stochastic processes. Conversely, more-interconnected communities experienced lower turnover and susceptibility to homogenizing selection. Based upon these data, we support the hypothesis that unperturbed environments house dynamic microbial communities due to external and internal forces. IMPORTANCE Many microbial ecology studies have examined community structuring processes in dynamic or perturbed situations, while stable environments have been investigated to a lesser extent. Researchers have predicted that environmental communities never truly reach a steady state but rather exist in states of constant flux due to internal, rather than external, dynamics. The research presented here utilized a combined null model approach to examine the deterministic and stochastic processes responsible for observed community differences in unperturbed, groundwater ecosystems. Additionally, internal dynamics were investigated by relating a recently published measure of community complexity (cohesion) to ecological structuring processes. The data presented here suggest that communities that are more cohesive, and therefore more complex, are more likely affected by homogenizing selection, while less-complex communities are more susceptible to dispersal. By understanding the relationship between internal dynamics and community structuring processes, insight about microbial population development in natural systems can be obtained.

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.

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.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets