Abundant and persistent sulfur-oxidizing microbial populations are responsive to hypoxia in the Chesapeake Bay.

The number, size and severity of aquatic low-oxygen dead zones are increasing worldwide. Microbial processes in low-oxygen environments have important ecosystem-level consequences, such as denitrification, greenhouse gas production and acidification. To identify key microbial processes occurring in low-oxygen bottom waters of the Chesapeake Bay, we sequenced both 16S rRNA genes and shotgun metagenomic libraries to determine the identity, functional potential and spatiotemporal distribution of microbial populations in the water column. Unsupervised clustering algorithms grouped samples into three clusters using water chemistry or microbial communities, with extensive overlap of cluster composition between methods. Clusters were strongly differentiated by temperature, salinity and oxygen. Sulfur-oxidizing microorganisms were found to be enriched in the low-oxygen bottom water and predictive of hypoxic conditions. Metagenome-assembled genomes demonstrate that some of these sulfur-oxidizing populations are capable of partial denitrification and transcriptionally active in a prior study. These results suggest that microorganisms capable of oxidizing reduced sulfur compounds are a previously unidentified microbial indicator of low oxygen in the Chesapeake Bay and reveal ties between the sulfur, nitrogen and oxygen cycles that could be important to capture when predicting the ecosystem response to remediation efforts or climate change.

Interaction dynamics and virus-host range for estuarine actinophages captured by epicPCR.

Viruses impact microbial diversity, gene flow and function through virus-host interactions. Although metagenomics surveys are rapidly cataloguing viral diversity, methods are needed to capture specific virus-host interactions in situ. Here, we leveraged metagenomics and repurposed emulsion paired isolation-concatenation PCR (epicPCR) to investigate viral diversity and virus-host interactions in situ over time in an estuarine environment. The method fuses a phage marker, the ribonucleotide reductase gene, with the host 16S rRNA gene of infected bacterial cells within emulsion droplets providing single-cell resolution for dozens of samples. EpicPCR captured in situ virus-host interactions for viral clades with no closely related database representatives. Abundant freshwater Actinobacteria lineages, in particular Rhodoluna sp., were the most common hosts for these poorly characterized viruses, with interactions correlated with environmental factors. Multiple methods used to identify virus-host interactions, including epicPCR, identified different and largely non-overlapping interactions within the vast virus-host interaction space. Tracking virus-host interaction dynamics also revealed that multi-host viruses had significantly longer periods with observed virus-host interactions, whereas single-host viruses were observed interacting with hosts at lower minimum abundances, suggesting more efficient interactions. Capturing in situ interactions with epicPCR revealed environmental and ecological factors shaping virus-host interactions, highlighting epicPCR as a valuable technique in viral ecology.

Contribution of Time, Taxonomy, and Selective Antimicrobials to Antibiotic and Multidrug Resistance in Wastewater Bacteria.

The use of nontherapeutic broad-spectrum antimicrobial agents triclosan (TCS) and benzalkonium chloride (BC) can contribute to bacterial resistance to clinically relevant antibiotics. Antimicrobial-resistant bacteria within wastewater may reflect the resistance burden within the human microbiome, as antibiotics and pathogens in wastewater can track with clinically relevant parameters during perturbations to the community. In this study, we monitored culturable and resistant wastewater bacteria and cross-resistance to clinically relevant antibiotics to gauge the impact of each antimicrobial and identify factors influencing cross-resistance profiles. Bacteria resistant to TCS and BC were isolated from wastewater influent over 21 months, and cross-resistance, taxonomy, and monthly changes were characterized under both antimicrobial selection regimes. Cross-resistance profiles from each antimicrobial differed within and between taxa. BC-isolated bacteria had a significantly higher prevalence of resistance to "last-resort antibiotic" colistin, while isolates resistant to TCS exhibited higher rates of multidrug resistance. Prevalence of culturable TCS-resistant bacteria decreased over time following Food and Drug Administration (FDA) TCS bans. Cross-resistance patterns varied according to sampling date, including among the most clinically important antibiotics. Correlations between strain-specific resistance profiles were largely influenced by taxonomy, with some variations associated with sampling date. The results reveal that time, taxonomy, and selection by TCS and BC impact features of cross-resistance patterns among diverse wastewater microorganisms, which could reflect the variety of factors influencing resistance patterns relevant to a community microbiome.

Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.

BACKGROUND: Microbial processes are intricately linked to the depletion of oxygen in in-land and coastal water bodies, with devastating economic and ecological consequences. Microorganisms deplete oxygen during biomass decomposition, degrading the habitat of many economically important aquatic animals. Microbes then turn to alternative electron acceptors, which alter nutrient cycling and generate potent greenhouse gases. As oxygen depletion is expected to worsen with altered land use and climate change, understanding how chemical and microbial dynamics impact dead zones will aid modeling efforts to guide remediation strategies. More work is needed to understand the complex interplay between microbial genes, populations, and biogeochemistry during oxygen depletion. RESULTS: Here, we used 16S rRNA gene surveys, shotgun metagenomic sequencing, and a previously developed biogeochemical model to identify genes and microbial populations implicated in major biogeochemical transformations in a model lake ecosystem. Shotgun metagenomic sequencing was done for one time point in Aug., 2013, and 16S rRNA gene sequencing was done for a 5-month time series (Mar.-Aug., 2013) to capture the spatiotemporal dynamics of genes and microorganisms mediating the modeled processes. Metagenomic binning analysis resulted in many metagenome-assembled genomes (MAGs) that are implicated in the modeled processes through gene content similarity to cultured organism and the presence of key genes involved in these pathways. The MAGs suggested some populations are capable of methane and sulfide oxidation coupled to nitrate reduction. Using the model, we observe that modulating these processes has a substantial impact on overall lake biogeochemistry. Additionally, 16S rRNA gene sequences from the metagenomic and amplicon libraries were linked to processes through the MAGs. We compared the dynamics of microbial populations in the water column to the model predictions. Many microbial populations involved in primary carbon oxidation had dynamics similar to the model, while those associated with secondary oxidation processes deviated substantially. CONCLUSIONS: This work demonstrates that the unique capabilities of resident microbial populations will substantially impact the concentration and speciation of chemicals in the water column, unless other microbial processes adjust to compensate for these differences. It further highlights the importance of the biological aspects of biogeochemical processes, such as fluctuations in microbial population dynamics. Integrating gene and population dynamics into biogeochemical models has the potential to improve predictions of the community response under altered scenarios to guide remediation efforts.