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. IMPORTANCE Clostridioides 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.
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.
Background: The Basic Local Alignment Search Tool (BLAST) from NCBI is the preferred utility for sequence alignment and identification for bioinformatics and genomics research. Among researchers using NCBI's BLAST software, it is well known that analyzing the results of a large BLAST search can be tedious and time-consuming. Furthermore, with the recent discussions over the effects of parameters such as '-max_target_seqs' on the BLAST heuristic search process, the use of these search options are questionable. This leaves using a stand-alone parser as one of the only options of condensing these large datasets, and with few available for download online, the task is left to the researcher to create a specialized piece of software anytime they need to analyze BLAST results. The need for a streamlined and fast script that solves these issues and can be easily implemented into a variety of bioinformatics and genomics workflows was the initial motivation for developing this software. Results: In this study, we demonstrate the effectiveness of BLAST-QC for analysis of BLAST results and its desirability over the other available options. Applying genetic sequence data from our bioinformatic workflows, we establish BLAST_QC's superior runtime when compared to existing parsers developed with commonly used BioPerl and BioPython modules, as well as C and Java implementations of the BLAST_QC program. We discuss the 'max_target_ seqs' parameter, the usage of and controversy around the use of the parameter, and offer a solution by demonstrating the ability of our software to provide the functionality this parameter was assumed to produce, as well as a variety of other parsing options. Executions of the script on example datasets are given, demonstrating the implemented functionality and providing test-cases of the program. BLAST-QC is designed to be integrated into existing software, and we establish its effectiveness as a module of workflows or other processes. Conclusions: BLAST-QC provides the community with a simple, lightweight and portable Python script that allows for easy quality control of BLAST results while avoiding the drawbacks of other options. This includes the uncertain results of applying the-max_target_seqs parameter or relying on the cumbersome dependencies of other options like BioPerl, Java, etc. which add complexity and run time when running large data sets of sequences. BLAST-QC is ideal for use in high-throughput workflows and pipelines common in bioinformatic and genomic research, and the script has been designed for portability and easy integration into whatever type of processes the user may be running.
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