Ionic Strength Differentially Affects the Bioavailability of Neutral and Negatively Charged Inorganic Hg Complexes

Mercury (Hg) bioavailability to bacteria in marine systems is the first step toward its bioamplification in food webs. These systems exhibit high salinity and ionic strength that will both alter Hg speciation and properties of the bacteria cell walls. The role of Hg speciation on Hg bioavailability in marine systems has not been teased apart from that of ionic strength on cell wall properties, however. We developed and optimized a whole-cell Hg bioreporter capable of functioning under aerobic and anaerobic conditions and exhibiting no physiological limitations of signal production to changes in ionic strength. We show that ionic strength controls the bioavailability of Hg species, regardless of their charge, possibly by altering properties of the bacterial cell wall. The unexpected anaerobic bioavailability of negatively charged halocomplexes may help explain Hg methylation in marine systems such as the oxygen-deficient zone in the oceanic water column, sea ice or polar snow

Electronic supplementary material from Viral spillover risk increases with climate change in High Arctic lake sediments

Supplementary text, tables, and figures

Divalent Base Cations Hamper Hg^II Uptake

Despite the alarming trends of declining base cation concentrations in boreal lakes, no studies have attempted to predict the consequences of this decline on the geochemical cycle of mercury, a top priority contaminant worldwide. In this study, we used a whole-cell gram-negative bioreporter to evaluate the direction and magnitude of changes in net accumulation of Hg^II by bacteria in response to changing base cation concentrations. We show that regardless of the speciation of Hg^II in solution, increasing divalent base cation concentrations decrease net Hg^II accumulation by the bioreporter, suggesting a protective effect of these cations. Our work suggests that the complexity of the cell wall of gram-negative bacteria must be considered when modeling Hg uptake pathways; we propose that base divalent cations contribute to hamper net Hg^II accumulation by decreasing outer membrane permeability and, therefore, the passive diffusion of Hg^II species to the periplasmic space. This work points to an unsuspected and likely harmful consequence of a delay in recovering from acidification in boreal lakes, in that uptake of Hg^II by bacteria is not only enhanced by the reduced pH but can also be enhanced by a decline in base cation levels

Microbial Community Structure in Lake and Wetland Sediments from a High Arctic Polar Desert Revealed by Targeted Transcriptomics

<div><p>While microbial communities play a key role in the geochemical cycling of nutrients and contaminants in anaerobic freshwater sediments, their structure and activity in polar desert ecosystems are still poorly understood, both across heterogeneous freshwater environments such as lakes and wetlands, and across sediment depths. To address this question, we performed targeted environmental transcriptomics analyses and characterized microbial diversity across three depths from sediment cores collected in a lake and a wetland, located on Cornwallis Island, NU, Canada. Microbial communities were characterized based on 16S rRNA and two functional gene transcripts: <i>mcrA</i>, involved in archaeal methane cycling and <i>glnA</i>, a bacterial housekeeping gene implicated in nitrogen metabolism. We show that methane cycling and overall bacterial metabolic activity are the highest at the surface of lake sediments but deeper within wetland sediments. Bacterial communities are highly diverse and structured as a function of both environment and depth, being more diverse in the wetland and near the surface. Archaea are mostly methanogens, structured by environment and more diverse in the wetland. <i>McrA</i> transcript analyses show that active methane cycling in the lake and wetland corresponds to distinct communities with a higher potential for methane cycling in the wetland. <i>Methanosarcina</i> spp., <i>Methanosaeta</i> spp. and a group of uncultured Archaea are the dominant methanogens in the wetland while <i>Methanoregula</i> spp. predominate in the lake.</p></div

Maximum likelihood tree of bacterial 16S rRNA sequences.

<p>Major phyla, represented by triangles whose area is proportional to the number of sequences, were tested for lineage-specific differences using UniFrac. The lineage-specific analysis tests for each lineage whether the sequences have a different distribution among environments than does the tree overall and therefore highlight which lineage contributed to the differences observed (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089531#pone-0089531-g003" target="_blank">Figure 3</a>). Nodes A-D are significantly unevenly distributed between environments; <i>p</i>-values are: <i>p</i>_A = 1.1×10⁻⁵, <i>p</i>_B = 2.8×10⁻³, <i>p</i>_C = 5.8×10⁻⁴, <i>p</i>_D = 1.3×10⁻³, <i>p</i>_E = 1.0×10⁻², <i>p</i>_F = 1.1×10⁻¹⁹. Pie charts connected to these nodes represent the distribution of environments within each lineage. Numbers adjacent to each node represent aLRT statistics (SH-like supports); only support values >0.50 are shown. Scale bar for branch lengths (expected number of substitutions per site) is shown.</p

Effects of Iron and Dissolved Organic Matter on Bioavailability of Arsenite under Anaerobic Conditions

Understanding the effects of water chemistry on the availability of arsenic (As) to biota is important for predicting the environmental fate of As. The “dissolved” fraction of As (<0.22 μm) is often used as a proxy for bioavailable As. However, As speciation is also influenced by binding to dissolved organic matter (DOM) and colloidal iron (Fe) (oxy)hydroxides, which can impact bioavailability. Here, we use a recently developed Escherichia coli anaerobic biosensor to elucidate the effects of DOM and Fe on arsenite (As(III)) bioavailability under anaerobic conditions, where As can be highly mobile. Microbial As(III) uptake decreased with greater DOM and Fe(III) concentrations, while Fe(II) had no effect. Higher organic sulfur content in DOM was associated with decreased biouptake at low As(III)/C ratios, and X-ray absorption spectroscopy indicated that this was due to binding of As(III) to sulfur ligands like thiols. The 0.1–0.5 kDa size fraction of As was most closely related to the bioavailable As fraction. Because the aquaporin channels mediating As(III) uptake into both microbes and rice plants are structurally similar, our results may also have relevance for understanding of how biogeochemical conditions in rice paddies regulate the plant availability of arsenic

Rarefaction curves for bacterial and archaeal sequences.

<p>Results for two environments are shown: (A) lake; (B) wetland. Dotted lines represent the 95% confidence intervals, as computed by resampling in MOTHUR.</p

UniFrac clustering of environments based on genetic distance.

<p>Three clusters are shown: (A) bacterial 16S; (B) archaeal 16S; (C) <i>mcrA</i> sequences. Samples are letter-coded: L for lake and W for wetland environments; numbers indicate sampling depths of 1, 3 and 10 cm. Scale bars show the distance between clusters in UniFrac units. Numbers adjacent to each node indicate the fraction <i>J</i> of times this node was recovered among 100 replicates.</p

Maximum likelihood tree of <i>mcrA</i> sequences.

<p>Major groups, represented by triangles whose area is proportional to the number of sequences, were tested for lineage-specific differences using UniFrac. The lineage-specific analysis tests for each lineage whether the sequences have a different distribution among environments than does the tree overall and therefore highlight which lineage contributed to the differences observed (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089531#pone-0089531-g003" target="_blank">Figure 3</a>). Nodes A–C are significantly unevenly distributed between environments: <i>p</i>_A = 9.86×10⁻¹³, <i>p</i>_B = 3.50×10⁻² and <i>p</i>_C = 1.97×10⁻¹⁹. Pie charts connected to these nodes represent the distribution of environments within that group of Archaea. Numbers adjacent to each node represent aLRT statistics; only support values >0.90 are shown. Scale bar for branch lengths is shown. *: <i>Methanobacterium</i> sp.</p

Depth profiles showing copy numbers in the pool of cDNA for <i>mcrA</i>, <i>glnA</i> and bacterial 16S rRNA in lake (A) and wetland (B) environments, and gene copy numbers for <i>mcrA</i>, <i>glnA</i> and 16S rDNA in the lake (C) and wetland (D).

<p>Vertical dashed line indicates the limit of quantification for the assay for <i>mcrA</i> gene transcripts. Error bars represent 1 SD.</p