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Clostridioides difficile (formerly Clostridium difficile) infection (CDI) can result from the disruption of the resident gut microbiota. Western diets and popular weight-loss diets drive large changes in the gut microbiome; however, the literature is conflicted with regard to the effect of diet on CDI. Using the hypervirulent strain C. difficile R20291 (RT027) in a mouse model of antibiotic-induced CDI, we assessed disease outcome and microbial community dynamics in mice fed two high-fat diets in comparison with a high-carbohydrate diet and a standard rodent diet. The two high-fat diets exacerbated CDI, with a high-fat/high-protein, Atkins-like diet leading to severe CDI and 100% mortality and a high-fat/low-protein, medium-chain-triglyceride (MCT)-like diet inducing highly variable CDI outcomes. In contrast, mice fed a high-carbohydrate diet were protected from CDI, despite the high levels of refined carbohydrate and low levels of fiber in the diet. A total of 28 members of the Lachnospiraceae and Ruminococc high-fat/high-protein weight-loss diets may enhance CDI risk during antibiotic treatment, possibly due to the synergistic effects of a loss of the microorganisms that normally inhibit C. difficile overgrowth and an abundance of amino acids that promote C. difficile overgrowth. In contrast, a high-carbohydrate diet might be protective, despite reports on the recent evolution of enhanced carbohydrate metabolism in C. difficile. Copyright © 2020 Mefferd et al.Flagellin, the agent of prokaryotic flagellar motion, is very widely distributed and is the H antigen of serology. Flagellin molecules have a variable region that confers serotype specificity, encoded by the middle of the gene, and also conserved regions encoded by the two ends of the gene. We collected all available prokaryotic flagellin protein sequences and found the variable region diversity to be at two levels. In each species investigated, there are hypervariable region (HVR) forms without detectable homology in protein sequences between them. There is also considerable variation within HVR forms, indicating that some have been diverging for thousands of years and that interphylum horizontal gene transfers make a major contribution to the evolution of such atypical diversity.IMPORTANCE Bacterial and archaeal flagellins are remarkable in having a shared region with variation in housekeeping proteins and a region with extreme diversity, perhaps greater than for any other protein. Analysis of the 113,285 available full-gene sequences of flagellin genes from published bacterial and archaeal sequences revealed the nature and enormous extent of flagellin diversity. There were 35,898 unique amino acid sequences that were resolved into 187 clusters. Analysis of the Escherichia coli and Salmonella enterica flagellins revealed that the variation occurs at two levels. The first is the division of the variable regions into sequence forms that are so divergent that there is no meaningful alignment even within species, and these corresponded to the E. coli or S. enterica H-antigen groups. The second level is variation within these groups, which is extensive in both species. Shared sequence would allow PCR of the variable regions and thus strain-level analysis of microbiome DNA. Copyright © 2020 Hu and Reeves.Understanding the basic biology that underpins soil microbiome interactions is required to predict the metaphenomic response to environmental shifts. A significant knowledge gap remains in how such changes affect microbial community dynamics and their metabolic landscape at microbially relevant spatial scales. Using a custom-built SoilBox system, here we demonstrated changes in microbial community growth and composition in different soil environments (14%, 24%, and 34% soil moisture), contingent upon access to reservoirs of nutrient sources. The SoilBox emulates the probing depth of a common soil core and enables determination of both the spatial organization of the microbial communities and their metabolites, as shown by confocal microscopy in combination with mass spectrometry imaging (MSI). Using chitin as a nutrient source, we used the SoilBox system to observe increased adhesion of microbial biomass on chitin islands resulting in degradation of chitin into N-acetylglucosamine (NAG) and chitobiose. With mrphological diversity of microbial communities remains a challenge due to the dynamic and complex nature of soil microenvironments. The SoilBox system, demonstrated in this work, simulates an ∼12-cm soil depth, similar to a typical soil core, and provides a platform that facilitates imaging the molecular and topographical landscape of soil microbial communities as a function of environmental gradients. Moreover, the nondestructive harvesting of soil microbial communities for the imaging experiments can enable simultaneous multiomics analysis throughout the depth of the SoilBox. Our results show that by correlating molecular and optical imaging data obtained using the SoilBox platform, deeper insights into the nature of specific soil microbial interactions can be achieved. Copyright © 2020 Bhattacharjee et al.Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, Unitee microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases. Copyright © 2020 Huang et al.Persister cells are genetically identical variants in a bacterial population that have phenotypically modified their physiology to survive environmental stress. In bacterial pathogens, persisters are able to survive antibiotic treatment and reinfect patients in a frustrating cycle of chronic infection. NVP-DKY709 To better define core persistence mechanisms for therapeutics development, we performed transcriptomics analyses of Burkholderia thailandensis populations enriched for persisters via three methods flow sorting for low proton motive force, meropenem treatment, and culture aging. Although the three persister-enriched populations generally displayed divergent gene expression profiles that reflect the multimechanistic nature of stress adaptations, there were several common gene pathways activated in two or all three populations. These include polyketide and nonribosomal peptide synthesis, Clp proteases, mobile elements, enzymes involved in lipid metabolism, and ATP-binding cassette (ABC) transporter systems. In parrobial therapy as the modern standard for medical treatment. Yet, even in those early days of discovery, it was noted that a small subset of cells (∼1 in 105) survived antibiotic treatment and continued to persist, leading to recurrence of chronic infection. These persisters are phenotypic variants that have modified their physiology to survive environmental stress. In this study, we have performed three transcriptomic screens to identify persistence genes that are common between three different stressor conditions. In particular, we identified genes that function in the synthesis of secondary metabolites, small molecules, and complex lipids, which are likely required to maintain the persistence state. Targeting universal persistence genes can lead to the development of clinically relevant antipersistence therapeutics for infectious disease management.High-throughput sequencing has allowed unprecedented insight into the composition and function of complex microbial communities. With metatranscriptomics, it is possible to interrogate the transcriptomes of multiple organisms simultaneously to get an overview of the gene expression of the entire community. Studies have successfully used metatranscriptomics to identify and describe relationships between gene expression levels and community characteristics. However, metatranscriptomic data sets contain a rich suite of additional information that is just beginning to be explored. Here, we focus on antisense expression in metatranscriptomics, discuss the different computational strategies for handling it, and highlight the strengths but also potentially detrimental effects on downstream analysis and interpretation. We also analyzed the antisense transcriptomes of multiple genomes and metagenome-assembled genomes (MAGs) from five different data sets and found high variability in the levels of antisense transcripti We explored some potential drivers of antisense transcription, but more importantly, this study serves as a starting point, highlighting topics for future research and providing guidelines to include antisense expression in generic bioinformatic pipelines for metatranscriptomic data. Copyright © 2020 Michaelsen et al.When studying the microbiome using next-generation sequencing, the DNA extraction method, sequencing procedures, and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing and eight different types of high- or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity, and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with a maximum 3-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for observed richness, but diversity and compositional profcontrols and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiota studies. Our methods were applied not only to commonly studied samples for microbiota analysis, e.g., feces, but also to more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g., urine and tumor biopsy specimens) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiota studies, especially when low bacterial biomass is expected. Copyright © 2020 Ducarmon et al.