Data-driven mathematical and visualization approaches for removing rare features for Compositional Data Analysis (CoDA).

Sparse feature tables, in which many features are present in very few samples, are common in big biological data (e.g. metagenomics). Ignoring issues of zero-laden datasets can result in biased statistical estimates and decreased power in downstream analyses. Zeros are also a particular issue for compositional data analysis using log-ratios since the log of zero is undefined. Researchers typically deal with this issue by removing low frequency features, but the thresholds for removal differ markedly between studies with little or no justification. Here, we present CurvCut, an unsupervised data-driven approach with human confirmation for rare-feature removal. CurvCut implements two distinct approaches for determining natural breaks in the feature distributions: a method based on curvature analysis borrowed from thermodynamics and the Fisher-Jenks statistical method. Our results show that CurvCut rapidly identifies data-specific breaks in these distributions that can be used as cutoff points for low-frequency feature removal that maximizes feature retention. We show that CurvCut works across different biological data types and rapidly generates clear visual results that allow researchers to confirm and apply feature removal cutoffs to individual datasets.

AutoPhy: Automated phylogenetic identification of novel protein subfamilies.

Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. However, automation of functional clustering based on phylogenetic trees has been challenging and most of it is done manually. Clustering phylogenetic trees usually requires the delineation of tree-based thresholds (e.g., distances), leading to an ad hoc problem. We propose a new phylogenetic clustering approach that identifies clusters without using ad hoc distances or other pre-defined values. Our workflow combines uniform manifold approximation and projection (UMAP) with Gaussian mixture models as a k-means like procedure to automatically group sequences into clusters. We then apply a "second pass" clade identification algorithm to resolve non-monophyletic groups. We tested our approach with several well-curated protein families (outer membrane porins, acyltransferase, and nuclear receptors) and showed our automated methods recapitulated known subfamilies. We also applied our methods to a broad range of different protein families from multiple databases, including Pfam, PANTHER, and UniProt, and to alignments of RNA viral genomes. Our results showed that AutoPhy rapidly generated monophyletic clusters (subfamilies) within phylogenetic trees evolving at very different rates both within and among phylogenies. The phylogenetic clusters generated by AutoPhy resolved misannotations and identified new protein functional groups and novel viral strains.

Metagenomic analysis of microbial communities and antibiotic resistant genes in the Tijuana river, and potential sources.

The Tijuana River is a transborder river that flows northwest across the border from Baja California in Mexico into Southern California before discharging into the Pacific Ocean. The river is frequently contaminated with raw sewage due to inadequate sanitary infrastructure in Tijuana. To assess the type and degree of microbial contamination, water samples were collected monthly from a near-border and an estuarine site from August 2020 until May 2021. A portion of each sample was used for epifluorescent microscopy and DNA was extracted directly from the rest for shotgun metagenomic sequencing. After sequence quality checking and processing, we used the rapid taxonomic identifier tool Kaiju to characterize the microbial diversity of the metagenomes and matched the sequences against the Comprehensive Antibiotic Resistance Database (CARD) to examine antimicrobial resistance genes (ARGs). Bacterial and viral-like particle (VLP) abundance was consistently higher in the near-border samples than in the estuarine samples, while alpha diversity (within sample biodiversity) was higher in estuarine samples. Beta-diversity analysis found clear compositional separation between samples from the two sites, and the near-border samples were more dissimilar to one another than were the estuarine sites. Near-border samples were dominated by fecal-associated bacteria and bacteria associated with sewage sludge, while estuarine sites were dominated by marine bacteria. ARGs were more abundant at the near-border site, but were also readily detectable in the estuarine samples, and the most abundant ARGs had multi-resistance to beta-lactam antibiotics. SourceTracker analysis identified human feces and sewage sludge to be the largest contributors to the near-border samples, while marine waters dominated estuarine samples except for two sewage overflow dates with high fecal contamination. Overall, our research determined human sewage microbes to be common in the Tijuana River, and the prevalence of ARGs confirms the importance of planned infrastructure treatment upgrades for environmental health.

Metabolic dysregulation and gut dysbiosis linked to hyperandrogenism in female mice.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a common endocrine pathology in women. In addition to infertility, women with PCOS have metabolic dysregulation which predisposes them to Type 2 diabetes, cardiovascular disease and non-alcoholic fatty liver disease. Moreover, women with PCOS have changes in their gut microbial community that may be indicative of dysbiosis. While hyperandrogenism is associated with both the development of metabolic dysfunction and gut dysbiosis in females, the mechanisms involved are not well understood. METHODS: We used dihydrotestosterone (DHT) and ovariectomy (OVX) mouse models coupled with metabolic assessments and 16S rRNA gene sequencing to explore the contributions of hyperandrogenism and oestrogen deficiency to the development of insulin resistance and gut microbial dysbiosis in pubertal female mice. RESULTS: We demonstrated that, while DHT treatment or OVX alone were insufficient to induce insulin resistance during the pubertal-to-adult transition, combining OVX with DHT resulted in insulin resistance similar to that observed in letrozole-treated mice with elevated testosterone and decreased oestrogen levels. In addition, our results showed that OVX and DHT in combination resulted in a distinct shift in the gut microbiome compared to DHT or OVX alone, suggesting that the substantial metabolic dysregulation occurring in the OVX + DHT model was accompanied by unique changes in the abundances of gut bacteria including S24-7, Rikenellaceae and Mucispirillum schaedleri. CONCLUSIONS: While hyperandrogenism plays an important role in the development of metabolic dysregulation in female mice, our results indicate that investigation into additional factors influencing insulin resistance and the gut microbiome during the pubertal-to-adult transition could provide additional insight into the pathophysiology of PCOS.

Genetic hypogonadal mouse model reveals niche-specific influence of reproductive axis and sex on intestinal microbial communities.

BACKGROUND: The gut microbiome has been linked to many diseases with sex bias including autoimmune, metabolic, neurological, and reproductive disorders. While numerous studies report sex differences in fecal microbial communities, the role of the reproductive axis in this differentiation is unclear and it is unknown how sex differentiation affects microbial diversity in specific regions of the small and large intestine. METHODS: We used a genetic hypogonadal mouse model that does not produce sex steroids or go through puberty to investigate how sex and the reproductive axis impact bacterial diversity within the intestine. Using 16S rRNA gene sequencing, we analyzed alpha and beta diversity and taxonomic composition of fecal and intestinal communities from the lumen and mucosa of the duodenum, ileum, and cecum from adult female (n = 20) and male (n = 20) wild-type mice and female (n = 17) and male (n = 20) hypogonadal mice. RESULTS: Both sex and reproductive axis inactivation altered bacterial composition in an intestinal section and niche-specific manner. Hypogonadism was significantly associated with bacteria from the Bacteroidaceae, Eggerthellaceae, Muribaculaceae, and Rikenellaceae families, which have genes for bile acid metabolism and mucin degradation. Microbial balances between males and females and between hypogonadal and wild-type mice were also intestinal section-specific. In addition, we identified 3 bacterial genera (Escherichia Shigella, Lachnoclostridium, and Eggerthellaceae genus) with higher abundance in wild-type female mice throughout the intestinal tract compared to both wild-type male and hypogonadal female mice, indicating that activation of the reproductive axis leads to female-specific differentiation of the gut microbiome. Our results also implicated factors independent of the reproductive axis (i.e., sex chromosomes) in shaping sex differences in intestinal communities. Additionally, our detailed profile of intestinal communities showed that fecal samples do not reflect bacterial diversity in the small intestine. CONCLUSIONS: Our results indicate that sex differences in the gut microbiome are intestinal niche-specific and that sampling feces or the large intestine may miss significant sex effects in the small intestine. These results strongly support the need to consider both sex and reproductive status when studying the gut microbiome and while developing microbial-based therapies.

Microbial communities in the intestinal tract, known as the gut microbiome, regulate many critical aspects of host physiology. Previous studies have shown that the diversity of the gut microbiome differs between the sexes. There are also many diseases with a sex bias linked to the gut microbiome, including autoimmune, metabolic, neurological, and reproductive disorders. The gut microbiome differentiates during puberty, but it is unknown if the reproductive axis, the system responsible for sexual maturation and production of gonadal sex hormones, is critical for this process. Furthermore, since most studies use feces to examine the gut microbiome, it is unknown how sex influences the microbial communities within different segments of the small and large intestine. To address this gap in knowledge, we used DNA-based molecular methods to compare the intestinal-specific microbiomes of a mouse model with a genetically inactivated reproductive axis to that of wild-type mice. We found that both sex and the reproductive axis impacted gut microbial diversity in an intestinal section-specific manner. We also detected significant differences in intestinal microbial diversity between male and female mutant mice, suggesting that sex chromosome factors also affect the gut microbiome. We also showed that fecal samples were dissimilar to small intestine microbial communities, indicating that studies only sampling feces likely miss sex differences specific to the small intestine. Our results strongly support the need to consider both sex and reproductive status when studying the gut microbiome and while developing microbial-based therapies.

Taxonomic and functional restoration of tallgrass prairie soil microbial communities in comparison to remnant and agricultural soils.

Restoring ecosystems requires the re-establishment of diverse soil microbial communities that drive critical ecosystem functions. In grasslands, restoration and management require the application of disturbances like fire and grazing. Disturbances can shape microbial taxonomic composition and potentially functional composition as well. We characterized taxonomic and functional gene composition of soil communities using whole genome shotgun metagenomic sequencing to determine how restored soil communities differed from pre-restoration agricultural soils and original remnant soils, how management affects soil microbes, and whether restoration and management affect the number of microbial genes associated with carbohydrate degradation. We found distinct differences in both taxonomic and functional diversity and composition among restored, remnant, and agricultural soils. Remnant soils had low taxonomic and functional richness and diversity, as well as distinct composition, indicating that restoration of agricultural soils does not re-create soil microbial communities that match remnants. Prescribed fire management increased functional diversity, which also was higher in more recently planted restorations. Finally, restored and post-fire soils included high abundances of genes encoding cellulose-degrading enzymes, so restorations and their ongoing management can potentially support functions important in carbon cycling.

Metagenome-Assembled Genomes from Murine Fecal Microbiomes Dominated by Uncharacterized Bacteria.

The laboratory mouse gut microbiome has been extensively studied, but our understanding of its diversity remains incomplete. We report the assembly of 51 draft metagenome-assembled genomes (MAGs) from murine fecal samples dominated by uncharacterized bacteria. These MAGs add to our understanding of gut microbial diversity in this critical model organism.

Sex, puberty, and the gut microbiome.

In brief: Sex differences in the gut microbiome may impact multiple aspects of human health and disease. In this study, we review the evidence for microbial sex differences in puberty and adulthood and discuss potential mechanisms driving differentiation of the sex-specific gut microbiome. Abstract: In humans, the gut microbiome is strongly implicated in numerous sex-specific physiological processes and diseases. Given this, it is important to understand how sex differentiation of the gut microbiome occurs and how these differences contribute to host health and disease. While it is commonly believed that the gut microbiome stabilizes after 3 years of age, our review of the literature found considerable evidence that the gut microbiome continues to mature during and after puberty in a sex-dependent manner. We also review the intriguing, though sparse, literature on potential mechanisms by which host sex may influence the gut microbiome, and vice versa, via sex steroids, bile acids, and the immune system. We conclude that the evidence for the existence of a sex-specific gut microbiome is strong but that there is a dearth of research on how host-microbe interactions lead to this differentiation. Finally, we discuss the types of future studies needed to understand the processes driving the maturation of sex-specific microbial communities and the interplay between gut microbiota, host sex, and human health.

Metagenomic Analysis of Microbial Contamination in the U.S. Portion of the Tijuana River Watershed.

The Tijuana River watershed is binational, flowing from Tijuana, Mexico into San Diego and Imperial Beach, USA. Aging sewage and stormwater infrastructure in Tijuana has not kept pace with population growth, causing overflows into this watershed during major rainfall or equipment failures. The public health consequences of this impaired watershed on the surrounding communities remain unknown. Here, we performed untargeted metagenomic sequencing to better characterize the sewage contamination in the Tijuana River, identifying potential pathogens and molecular indicators of antibiotic resistance in surface waters. In 2019-2020, water samples were collected within 48 h of major rainfall events at five transborder flow sites and at the mouth of the river in the US portion of the Tijuana River and estuary. After filtration, DNA was extracted and sequenced, and sequences were run through the Kaiju taxonomic classification program. A pathogen profile of the most abundant disease-causing microbes and viruses present in each of the samples was constructed, and specific markers of fecal contamination were identified and linked to each site. Results from diversity analysis between the sites showed clear distinction as well as similarities between sites and dates, and antibiotic-resistant genes were found at each site. This serves as a baseline characterization of microbial exposures to these local communities.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis.

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.

Gut Metabolites Are More Predictive of Disease and Cohoused States than Gut Bacterial Features in a Polycystic Ovary Syndrome-Like Mouse Model.

Polycystic ovary syndrome (PCOS) impacts ∼10% of reproductive-aged women worldwide. In addition to infertility, women with PCOS suffer from metabolic dysregulation which increases their risk of developing type 2 diabetes, cardiovascular disease, and nonalcoholic fatty liver disease. Studies have shown differences in the gut microbiome of women with PCOS compared to controls, a pattern replicated in PCOS-like mouse models. Recently, using a letrozole (LET)-induced mouse model of PCOS, we demonstrated that cohousing was protective against development of metabolic and reproductive phenotypes and showed via 16S amplicon sequencing that this protection correlated with time-dependent shifts in gut bacteria. Here, we applied untargeted metabolomics and shotgun metagenomics approaches to further analyze the longitudinal samples from the cohousing experiment. Analysis of beta diversity found that untargeted metabolites had the strongest correlation to both disease and cohoused states and that shifts in metabolite diversity were detected prior to shifts in bacterial diversity. In addition, log2 fold analyses found numerous metabolite features, particularly bile acids (BAs), to be highly differentiated between placebo and LET, as well as LET cohoused with placebo versus LET. Our results indicate that changes in gut metabolites, particularly BAs, are associated with a PCOS-like phenotype as well as with the protective effect of cohousing. Our results also suggest that transfer of metabolites via coprophagy occurs rapidly and may precipitate changes in bacterial diversity. This study joins a growing body of research linking changes in primary and secondary BAs to host metabolism and gut microbes relevant to the pathology of PCOS. IMPORTANCE Using a combination of untargeted metabolomics and metagenomics, we performed a comparative longitudinal analysis of the feces collected in a cohousing study with a PCOS-like mouse model. Our results showed that gut metabolite composition experienced earlier and more pronounced differentiation in both the disease model and cohoused mice compared with the microbial composition. Notably, statistical and machine learning approaches identified shifts in the relative abundance of primary and secondary BAs, which have been implicated as modifiers of gut microbial growth and diversity. Network correlation analysis showed strong associations between particular BAs and bacterial species, particularly members of Lactobacillus, and that these correlations were time and treatment dependent. Our results provide novel insights into host-microbe relationships related to hyperandrogenism in females and indicate that focused research into small-molecule control of gut microbial diversity and host physiology may provide new therapeutic options for the treatment of PCOS.

Compositional Data Analysis of Periodontal Disease Microbial Communities.

Periodontal disease (PD) is a chronic, progressive polymicrobial disease that induces a strong host immune response. Culture-independent methods, such as next-generation sequencing (NGS) of bacteria 16S amplicon and shotgun metagenomic libraries, have greatly expanded our understanding of PD biodiversity, identified novel PD microbial associations, and shown that PD biodiversity increases with pocket depth. NGS studies have also found PD communities to be highly host-specific in terms of both biodiversity and the response of microbial communities to periodontal treatment. As with most microbiome work, the majority of PD microbiome studies use standard data normalization procedures that do not account for the compositional nature of NGS microbiome data. Here, we apply recently developed compositional data analysis (CoDA) approaches and software tools to reanalyze multiomics (16S, metagenomics, and metabolomics) data generated from previously published periodontal disease studies. CoDA methods, such as centered log-ratio (clr) transformation, compensate for the compositional nature of these data, which can not only remove spurious correlations but also allows for the identification of novel associations between microbial features and disease conditions. We validated many of the studies' original findings, but also identified new features associated with periodontal disease, including the genera Schwartzia and Aerococcus and the cytokine C-reactive protein (CRP). Furthermore, our network analysis revealed a lower connectivity among taxa in deeper periodontal pockets, potentially indicative of a more "random" microbiome. Our findings illustrate the utility of CoDA techniques in multiomics compositional data analysis of the oral microbiome.

Altered microbiomes in thirdhand smoke-exposed children and their home environments.

INTRODUCTION: Tobacco smoke contains numerous toxic chemicals that accumulate in indoor environments creating thirdhand smoke (THS). We investigated if THS-polluted homes differed in children's human and built-environment microbiomes as compared to THS-free homes. METHODS: Participants were n = 19 THS-exposed children and n = 10 unexposed children (≤5 years) and their caregivers. Environmental and biological samples were analyzed for THS pollutants and exposure. Swab samples were collected from the built-environment (floor, table, armrest, bed frame) and child (finger, nose, mouth, and ear canal), and 16S ribosomal RNA genes were analyzed for bacterial taxa using high-throughput DNA sequencing. RESULTS: Phylogenetic α-diversity was significantly higher for the built-environment microbiomes in THS-polluted homes compared to THS-free homes (p < 0.014). Log2-fold comparison found differences between THS-polluted and THS-free homes for specific genera in samples from the built-environment (e.g., Acinetobacter, Bradyrhizobium, Corynebacterium, Gemella, Neisseria, Staphylococcus, Streptococcus, and Veillonella) and in samples from children (esp. Corynebacterium, Gemella, Lautropia, Neisseria, Rothia, Staphylococcus, and Veillonella). CONCLUSION: When exposed to THS, indoor and children microbiomes are altered in an environment-specific manner. Changes are similar to those reported in previous studies for smokers and secondhand smoke-exposed persons. THS-induced changes in child and built-environmental microbiomes may play a role in clinical outcomes in children. IMPACT: Despite smoking bans, children can be exposed to tobacco smoke residue (i.e., thirdhand smoke) that lingers on surfaces and in settled house dust. Thirdhand smoke exposure is associated with changes in the microbiomes of the home environment and of the children living in these homes. Thirdhand smoke is associated with increased phylogenetic diversity of the home environment and changes in the abundances of several genera of the child microbiome known to be affected by active smoking and secondhand smoke (e.g., Corynebacterium, Staphylococcus, Streptococcus). Thirdhand smoke exposure by itself may induce alterations in the microbiome that play a role in childhood pathologies.

Building a Diverse Workforce and Thinkforce to Reduce Health Disparities.

The Research Centers in Minority Institutions (RCMI) Program was congressionally mandated in 1985 to build research capacity at institutions that currently and historically recruit, train, and award doctorate degrees in the health professions and health-related sciences, primarily to individuals from underrepresented and minority populations. RCMI grantees share similar infrastructure needs and institutional goals. Of particular importance is the professional development of multidisciplinary teams of academic and community scholars (the "workforce") and the harnessing of the heterogeneity of thought (the "thinkforce") to reduce health disparities. The purpose of this report is to summarize the presentations and discussion at the RCMI Investigator Development Core (IDC) Workshop, held in conjunction with the RCMI Program National Conference in Bethesda, Maryland, in December 2019. The RCMI IDC Directors provided information about their professional development activities and Pilot Projects Programs and discussed barriers identified by new and early-stage investigators that limit effective career development, as well as potential solutions to overcome such obstacles. This report also proposes potential alignments of professional development activities, targeted goals and common metrics to track productivity and success.

Alterations in Gut Microbiota Do Not Play a Causal Role in Diet-independent Weight Gain Caused by Ovariectomy.

Acute estrogen deficiency in women can occur due to many conditions including hyperprolactinemia, chemotherapy, GnRH agonist treatment, and removal of hormone replacement therapy. Ovariectomized (OVX) rodent models, often combined with a high-fat diet (HFD), have been used to investigate the effects of decreased estrogen production on metabolism. Since evidence suggests that gut microbes may facilitate the protective effect of estrogen on metabolic dysregulation in an OVX + HFD model, we investigated whether the gut microbiome plays a role in the diet-independent weight gain that occurs after OVX in adult female mice. 16S rRNA gene sequence analysis demonstrated that OVX was not associated with changes in overall gut bacterial biodiversity but was correlated with a shift in beta diversity. Using differential abundance analysis, we observed a difference in the relative abundance of a few bacterial taxa, such as Turicibacter, 3 to 5 weeks after OVX, which was subsequent to the weight gain that occurred 2 weeks postsurgery. A cohousing study was performed to determine whether exposure to a healthy gut microbiome was protective against the development of the metabolic phenotype associated with OVX. Unlike mouse models of obesity, HFD maternal-induced metabolic dysregulation, or polycystic ovary syndrome, cohousing OVX mice with healthy mice did not improve the metabolic phenotype of OVX mice. Altogether, these results indicate that changes in the gut microbiome are unlikely to play a causal role in diet-independent, OVX-induced weight gain (since they occurred after the weight gain) and cohousing with healthy mice did not have a protective effect.

Quantitative profiling of built environment bacterial and fungal communities reveals dynamic material dependent growth patterns and microbial interactions.

Indoor microbial communities vary in composition and diversity depending on material type, moisture levels, and occupancy. In this study, we integrated bacterial cell counting, fungal biomass estimation, and fluorescence-assisted cell sorting (FACS) with amplicon sequencing of bacterial (16S rRNA) and fungal (ITS) communities to investigate the influence of wetting on medium density fiberboard (MDF) and gypsum wallboard. Surface samples were collected longitudinally from wetted materials maintained at high relative humidity (~95%). Bacterial and fungal growth patterns were strongly time-dependent and material-specific. Fungal growth phenotypes differed between materials: spores dominated MDF surfaces while fungi transitioned from spores to hyphae on gypsum. FACS confirmed that most of the bacterial cells were intact (viable) on both materials over the course of the study. Integrated cell count and biomass data (quantitative profiling) revealed that small changes in relative abundance often resulted from large changes in absolute abundance, while negative correlations in relative abundances were explained by rapid growth of only one group of bacteria or fungi. Comparisons of bacterial-bacterial and fungal-bacterial networks suggested a top-down control of fungi on bacterial growth, possibly via antibiotic production. In conclusion, quantitative profiling provides novel insights into microbial growth dynamics on building materials with potential implications for human health.

Chemical composition of material extractives influences microbial growth and dynamics on wetted wood materials.

The impact of material chemical composition on microbial growth on building materials remains relatively poorly understood. We investigate the influence of the chemical composition of material extractives on microbial growth and community dynamics on 30 different wood species that were naturally inoculated, wetted, and held at high humidity for several weeks. Microbial growth was assessed by visual assessment and molecular sequencing. Unwetted material powders and microbial swab samples were analyzed using reverse phase liquid chromatography with tandem mass spectrometry. Different wood species demonstrated varying susceptibility to microbial growth after 3 weeks and visible coverage and fungal qPCR concentrations were correlated (R2 = 0.55). Aspergillaceae was most abundant across all samples; Meruliaceae was more prevalent on 8 materials with the highest visible microbial growth. A larger and more diverse set of compounds was detected from the wood shavings compared to the microbial swabs, indicating a complex and heterogeneous chemical composition within wood types. Several individual compounds putatively identified in wood samples showed statistically significant, near-monotonic associations with microbial growth, including C11H16O4, C18H34O4, and C6H15NO. A pilot experiment confirmed the inhibitory effects of dosing a sample of wood materials with varying concentrations of liquid C6H15NO (assuming it presented as Diethylethanolamine).

Temporal variations in bacterial community diversity and composition throughout intensive care unit renovations.

BACKGROUND: Inanimate surfaces within a hospital serve as a reservoir of microbial life that may colonize patients and ultimately result in healthcare associated infections (HAIs). Critically ill patients in intensive care units (ICUs) are particularly vulnerable to HAIs. Little is known about how the microbiome of the ICU is established or what factors influence its evolution over time. A unique opportunity to bridge the knowledge gap into how the ICU microbiome evolves emerged in our health system, where we were able to characterize microbial communities in an established hospital ICU prior to closing for renovations, during renovations, and then after re-opening. RESULTS: We collected swab specimens from ICU bedrails, computer keyboards, and sinks longitudinally at each renovation stage, and analyzed the bacterial compositions on these surfaces by 16S rRNA gene sequencing. Specimens collected before ICU closure had the greatest alpha diversity, while specimens collected after the ICU had been closed for over 300 days had the least. We sampled the ICU during the 45 days after re-opening; however, within that time frame, the alpha diversity never reached pre-closure levels. There were clear and significant differences in microbiota compositions at each renovation stage, which was driven by environmental bacteria after closure and human-associated bacteria after re-opening and before closure. CONCLUSIONS: Overall, we identified significant differences in microbiota diversity and community composition at each renovation stage. These data help to decipher the evolution of the microbiome in the most critical part of the hospital and demonstrate the significant impacts that microbiota from patients and staff have on the evolution of ICU surfaces. Video Abstract.

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics.

BACKGROUND: Microbial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to human behavior. METHODS: Here, we demonstrate a novel application of SourceTracker to work with metagenomic datasets and tested this approach using sink samples from a study of coastal marine environments. Source environment metagenomes were obtained from metagenomics studies of gut, freshwater, marine, sand and soil environments. As part of this effort, we implemented features for determining the stability of source proportion estimates, including precision visualizations for performance optimization, and performed domain-specific source-tracking analyses (i.e., Bacteria, Archaea, Eukaryota and viruses). We also applied SourceTracker to metagenomic libraries generated from samples collected from the International Space Station (ISS). RESULTS: SourceTracker proved highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of environmental source origins for both the coastal marine and ISS samples. These findings indicated that applying SourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.

Viruses in the Built Environment (VIBE) meeting report.

BACKGROUND: During a period of rapid growth in our understanding of the microbiology of the built environment in recent years, the majority of research has focused on bacteria and fungi. Viruses, while probably as numerous, have received less attention. In response, the Alfred P. Sloan Foundation supported a workshop entitled "Viruses in the Built Environment (VIBE)," at which experts in environmental engineering, environmental microbiology, epidemiology, infection prevention, fluid dynamics, occupational health, metagenomics, and virology convened to synthesize recent advances and identify key research questions and knowledge gaps regarding viruses in the built environment. RESULTS: Four primary research areas and funding priorities were identified. First, a better understanding of viral communities in the built environment is needed, specifically which viruses are present and their sources, spatial and temporal dynamics, and interactions with bacteria. Second, more information is needed about viruses and health, including viral transmission in the built environment, the relationship between virus detection and exposure, and the definition of a healthy virome. The third research priority is to identify and evaluate interventions for controlling viruses and the virome in the built environment. This encompasses interactions among viruses, buildings, and occupants. Finally, to overcome the challenge of working with viruses, workshop participants emphasized that improved sampling methods, laboratory techniques, and bioinformatics approaches are needed to advance understanding of viruses in the built environment. CONCLUSIONS: We hope that identifying these key questions and knowledge gaps will engage other investigators and funding agencies to spur future research on the highly interdisciplinary topic of viruses in the built environment. There are numerous opportunities to advance knowledge, as many topics remain underexplored compared to our understanding of bacteria and fungi. Video abstract.