A complementary metaproteomic approach to interrogate microbiome cultivated from clinical colon biopsies.

The human gut microbiome plays a vital role in preserving individual health and is intricately involved in essential functions. Imbalances or dysbiosis within the microbiome can significantly impact human health and are associated with many diseases. Several metaproteomics platforms are currently available to study microbial proteins within complex microbial communities. In this study, we attempted to develop an integrated pipeline to provide deeper insights into both the taxonomic and functional aspects of the cultivated human gut microbiomes derived from clinical colon biopsies. We combined a rapid peptide search by MSFragger against the Unified Human Gastrointestinal Protein database and the taxonomic and functional analyses with Unipept Desktop and MetaLab-MAG. Across seven samples, we identified and matched nearly 36,000 unique peptides to approximately 300 species and 11 phyla. Unipept Desktop provided gene ontology, InterPro entries, and enzyme commission number annotations, facilitating the identification of relevant metabolic pathways. MetaLab-MAG contributed functional annotations through Clusters of Orthologous Genes and Non-supervised Orthologous Groups categories. These results unveiled functional similarities and differences among the samples. This integrated pipeline holds the potential to provide deeper insights into the taxonomy and functions of the human gut microbiome for interrogating the intricate connections between microbiome balance and diseases.

Identification of Simplified Microbial Communities That Inhibit Clostridioides difficile Infection through Dilution/Extinction.

The gastrointestinal microbiome plays an important role in limiting susceptibility to infection with Clostridioides difficile To better understand the ecology of bacteria important for C. difficile colonization resistance, we developed an experimental platform to simplify complex communities of fecal bacteria through dilution and rapidly screen for their ability to resist C. difficile colonization after challenge, as measured by >100-fold reduction in levels of C. difficile in challenged communities. We screened 76 simplified communities diluted from cultures of six fecal donors and identified 24 simplified communities that inhibited C. difficile colonization in vitro Sequencing revealed that simplified communities were composed of 19 to 67 operational taxonomic units (OTUs) and could be partitioned into four distinct community types. One simplified community could be further simplified from 56 to 28 OTUs through dilution and retain the ability to inhibit C. difficile We tested the efficacy of seven simplified communities in a humanized microbiota mouse model. We found that four communities were able to significantly reduce the severity of the initial C. difficile infection and limit susceptibility to disease relapse. Analysis of fecal microbiomes from treated mice demonstrated that simplified communities accelerated recovery of indigenous bacteria and led to stable engraftment of 19 to 22 OTUs from simplified communities. Overall, the insights gained through the identification and characterization of these simplified communities increase our understanding of the microbial dynamics of C. difficile infection and recovery.IMPORTANCEClostridioides difficile is the leading cause of antibiotic-associated diarrhea and a significant health care burden. Fecal microbiota transplantation is highly effective at treating recurrent C. difficile disease; however, uncertainties about the undefined composition of fecal material and potential long-term unintended health consequences remain. These concerns have motivated studies to identify new communities of microbes with a simpler composition that will be effective at treating disease. This work describes a platform for rapidly identifying and screening new simplified communities for efficacy in treating C. difficile infection. Four new simplified communities of microbes with potential for development of new therapies to treat C. difficile disease are identified. While this platform was developed and validated to model infection with C. difficile, the underlying principles described in the paper could be easily modified to develop therapeutics to treat other gastrointestinal diseases.

Emission and dispersal of antibiotic resistance genes through bioaerosols generated during the treatment of municipal sewage.

Wastewater treatment plants act as socio-ecological couplers through the concentration, treatment, and subsequent environmental release of sewage collected from surrounding communities and are often considered hotspots for antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). While studies have identified the release of ARB/ARGs in treated liquid sewage, little is known about potential dispersal through wastewater bioaerosol emissions. The aim of this study was to better define the contribution of WWTP bioaerosols to potential environmental distribution of ARB/ARGs. Bioaerosols were collected immediately upwind and downwind from the aeration tanks of a municipal wastewater treatment plant and liquid sludge samples were obtained from the aeration tanks. From the bioaerosol and liquid samples, qPCR assays identified 44 ARGs that confer resistance to a wide range of antibiotics. Comparison of the ARG profiles across samples showed that the downwind bioaerosol profile was 68% similar to the profile found in liquid sludge samples. Community 16S rRNA gene sequencing also showed that downwind bioaerosols had similar taxonomic profiles as those generated from liquid sludge while the upwind profiles showed a distinct difference. Preliminary ARG dispersion modeling estimated an ARG emission rate of ~10,620 genes per hour from the liquid sludge and indicated that the bioaerosols have the potential to be carried kilometers away from the WWTP source based on wind speed. The overall results from this study suggest that bioaerosols generated during WWTP processes can aid in the emission and dispersal of bacteria and ARGs, resulting in a possible route of human exposure and deposition into surrounding environments.

Microbial Mat Compositional and Functional Sensitivity to Environmental Disturbance.

The ability of ecosystems to adapt to environmental perturbations depends on the duration and intensity of change and the overall biological diversity of the system. While studies have indicated that rare microbial taxa may provide a biological reservoir that supports long-term ecosystem stability, how this dynamic population is influenced by environmental parameters remains unclear. In this study, a microbial mat ecosystem located on San Salvador Island, The Bahamas was used as a model to examine how environmental disturbance affects the protein synthesis potential (PSP) of rare and abundant archaeal and bacterial communities and how these changes impact potential biogeochemical processes. This ecosystem experienced a large shift in salinity (230 to 65 g kg-1) during 2011-2012 following the landfall of Hurricane Irene on San Salvador Island. High throughput sequencing and analysis of 16S rRNA and rRNA genes from samples before and after the pulse disturbance showed significant changes in the diversity and PSP of abundant and rare taxa, suggesting overall compositional and functional sensitivity to environmental change. In both archaeal and bacterial communities, while the majority of taxa showed low PSP across conditions, the overall community PSP increased post-disturbance, with significant shifts occurring among abundant and rare taxa across and within phyla. Broadly, following the post-disturbance reduction in salinity, taxa within Halobacteria decreased while those within Crenarchaeota, Thaumarchaeota, Thermoplasmata, Cyanobacteria, and Proteobacteria, increased in abundance and PSP. Quantitative PCR of genes and transcripts involved in nitrogen and sulfur cycling showed concomitant shifts in biogeochemical cycling potential. Post-disturbance conditions increased the expression of genes involved in N-fixation, nitrification, denitrification, and sulfate reduction. Together, our findings show complex community adaptation to environmental change and help elucidate factors connecting disturbance, biodiversity, and ecosystem function that may enhance ecosystem models.