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Integration of sensory and molecular inputs from the environment shapes animal behaviour. A major site of exposure to environmental molecules is the gastrointestinal tract, in which dietary components are chemically transformed by the microbiota1 and gut-derived metabolites are disseminated to all organs, including the brain2. In mice, the gut microbiota impacts behaviour3, modulates neurotransmitter production in the gut and brain4,5, and influences brain development and myelination patterns6,7. The mechanisms that mediate the gut-brain interactions remain poorly defined, although they broadly involve humoral or neuronal connections. We previously reported that the levels of the microbial metabolite 4-ethylphenyl sulfate (4EPS) were increased in a mouse model of atypical neurodevelopment8. Here we identified biosynthetic genes from the gut microbiome that mediate the conversion of dietary tyrosine to 4-ethylphenol (4EP), and bioengineered gut bacteria to selectively produce 4EPS in mice. 4EPS entered the brain and was associated with changes in region-specific activity and functional connectivity. Gene expression signatures revealed altered oligodendrocyte function in the brain, and 4EPS impaired oligodendrocyte maturation in mice and decreased oligodendrocyte-neuron interactions in ex vivo brain cultures. Mice colonized with 4EP-producing bacteria exhibited reduced myelination of neuronal axons. Altered myelination dynamics in the brain have been associated with behavioural outcomes7,9-14. Accordingly, we observed that mice exposed to 4EPS displayed anxiety-like behaviours, and pharmacological treatments that promote oligodendrocyte differentiation prevented the behavioural effects of 4EPS. These findings reveal that a gut-derived molecule influences complex behaviours in mice through effects on oligodendrocyte function and myelin patterning in the brain.

Gut dysbiosis, defined as pathogenic alterations in the distribution and abundance of different microbial species, is associated with neuropathic pain in a variety of clinical conditions, but this has not been explored in the context of neuropathy in people with HIV (PWH). We assessed gut microbial diversity and dysbiosis in PWH and people without HIV (PWoH), some of whom reported distal neuropathic pain (DNP). DNP was graded on a standardized, validated severity scale. The gut microbiome was characterized using 16S rRNA sequencing and diversity was assessed using phylogenetic tree construction. Songbird analysis (https://github.com/mortonjt/songbird) was used to produce a multinomial regression model predicting counts of specific microbial taxa through metadata covariate columns. Participants were 226 PWH and 101 PWoH, mean (SD) age 52.0 (13.5), 21.1% female, 54.7% men who have sex with men, 44.7% non-white. Among PWH, median (interquartile range, IQR) nadir and current CD4 were 174 (21, 302) and 618 (448, 822), respectively; 90% were virally suppressed on antiretroviral therapy. PWH and PWoH did not differ with respect to microbiome diversity as indexed by Faith's phylogenetic diversity (PD). More severe DNP was associated with lower alpha diversity as indexed by Faith's phylogenetic diversity in PWH (Spearman's ρ = .224, P = 0.0007), but not in PWoH (Spearman's ρ = .032, P = .748). These relationships were not confounded by demographics or disease factors. In addition, the log-ratio of features identified at the genus level as Blautia to Lachnospira was statistically significantly higher in PWH with DNP than in PWH without DNP (t-test, P = 1.01e-3). Furthermore, the log-ratio of Clostridium features to Lachnospira features also was higher in PWH with DNP than in those without (t-test, P = 6.24e-5). Our results, in combination with previous findings in other neuropathic pain conditions, suggest that gut dysbiosis, particularly reductions in diversity and relative increases in the ratios of Blautia and Clostridium to Lachnospira, may contribute to prevalent DNP in PWH. Two candidate pathways for these associations, involving microbial pro-inflammatory components and microbially-produced anti-inflammatory short chain fatty acids, are discussed. Future studies might test interventions to re-establish a healthy gut microbiota and determine if this prevents or improves DNP. PERSPECTIVE: The association of neuropathic pain in people with HIV with reduced gut microbial diversity and dysbiosis raises the possibility that re-establishing a healthy gut microbiota might ameliorate neuropathic pain in HIV by reducing proinflammatory and increasing anti-inflammatory microbial products.

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

The unexpected roles of the microbiota in cancer challenge explanations of carcinogenesis that focus on tumor-intrinsic properties. Most tumors contain bacteria and viruses, and the host's proximal and distal microbiota influence both cancer incidence and therapeutic responsiveness. Continuing the history of cancer-microbe research, these findings raise a key question: to what extent is the microbiota relevant for clinical oncology? We approach this by critically evaluating three issues: how the microbiota provides a predictive biomarker of cancer growth and therapeutic responsiveness, the microbiota's causal role(s) in cancer development, and how therapeutic manipulations of the microbiota improve patient outcomes in cancer. Clarifying the conceptual and empirical aspects of the cancer-associated microbiota can orient future research and guide its implementation in clinical oncology.

Ulcerative colitis (UC) is driven by disruptions in host-microbiota homoeostasis, but current treatments exclusively target host inflammatory pathways. To understand how host-microbiota interactions become disrupted in UC, we collected and analysed six faecal- or serum-based omic datasets (metaproteomic, metabolomic, metagenomic, metapeptidomic and amplicon sequencing profiles of faecal samples and proteomic profiles of serum samples) from 40 UC patients at a single inflammatory bowel disease centre, as well as various clinical, endoscopic and histologic measures of disease activity. A validation cohort of 210 samples (73 UC, 117 Crohn's disease, 20 healthy controls) was collected and analysed separately and independently. Data integration across both cohorts showed that a subset of the clinically active UC patients had an overabundance of proteases that originated from the bacterium Bacteroides vulgatus. To test whether B. vulgatus proteases contribute to UC disease activity, we first profiled B. vulgatus proteases found in patients and bacterial cultures. Use of a broad-spectrum protease inhibitor improved B. vulgatus-induced barrier dysfunction in vitro, and prevented colitis in B. vulgatus monocolonized, IL10-deficient mice. Furthermore, transplantation of faeces from UC patients with a high abundance of B. vulgatus proteases into germfree mice induced colitis dependent on protease activity. These results, stemming from a multi-omics approach, improve understanding of functional microbiota alterations that drive UC and provide a resource for identifying other pathways that could be inhibited as a strategy to treat this disease.

BackgroundIndividual diet components and specific dietary regimens have been shown to impact the gut microbiome.ObjectivesHere, we explored the contribution of long-term diet by searching for dietary patterns that would best associate with the gut microbiome in a population-based cohort.MethodsUsing a priori and a posteriori approaches, we constructed dietary patterns from an FFQ completed by 1800 adults in the American Gut Project. Dietary patterns were defined as groups of participants or combinations of food variables (factors) driven by criteria ranging from individual nutrients to overall diet. We associated these patterns with 16S ribosomal RNA-based gut microbiome data for a subset of 744 participants.ResultsCompared to individual features (e.g., fiber and protein), or to factors representing a reduced number of dietary features, 5 a posteriori dietary patterns based on food groups were best associated with gut microbiome beta diversity (P ≤ 0.0002). Two patterns followed Prudent-like diets-Plant-Based and Flexitarian-and exhibited the highest Healthy Eating Index 2010 (HEI-2010) scores. Two other patterns presented Western-like diets with a gradient in HEI-2010 scores. A fifth pattern consisted mostly of participants following an Exclusion diet (e.g., low carbohydrate). Notably, gut microbiome alpha diversity was significantly lower in the most Western pattern compared to the Flexitarian pattern (P ≤ 0.009), and the Exclusion diet pattern was associated with low relative abundance of Bifidobacterium (P ≤ 1.2 × 10-7), which was better explained by diet than health status.ConclusionsWe demonstrated that global-diet a posteriori patterns were more associated with gut microbiome variations than individual dietary features among adults in the United States. These results confirm that evaluating diet as a whole is important when studying the gut microbiome. It will also facilitate the design of more personalized dietary strategies in general populations.

Fish are the most diverse and widely distributed vertebrates, yet little is known about the microbial ecology of fishes nor the biological and environmental factors that influence fish microbiota. To identify factors that explain microbial diversity patterns in a geographical subset of marine fish, we analyzed the microbiota (gill tissue, skin mucus, midgut digesta and hindgut digesta) from 101 species of Southern California marine fishes, spanning 22 orders, 55 families and 83 genera, representing ~25% of local marine fish diversity. We compare alpha, beta and gamma diversity while establishing a method to estimate microbial biomass associated with these host surfaces. We show that body site is the strongest driver of microbial diversity while microbial biomass and diversity is lowest in the gill of larger, pelagic fishes. Patterns of phylosymbiosis are observed across the gill, skin and hindgut. In a quantitative synthesis of vertebrate hindguts (569 species), we also show that mammals have the highest gamma diversity when controlling for host species number while fishes have the highest percent of unique microbial taxa. The composite dataset will be useful to vertebrate microbiota researchers and fish biologists interested in microbial ecology, with applications in aquaculture and fisheries management.

The pursuit of highly sensitive and specific cancer diagnostics based on cell-free (cf) nucleic acids isolated from minimally invasive liquid biopsies has been an area of intense research and commercial effort for at least two decades. Most of these tests detect cancer-specific mutations or epigenetic modifications on circulating DNA derived from tumor cells (ctDNA). Although recent FDA approvals of both single and multi-analyte liquid biopsy companion diagnostic assays are proof of the tremendous progress made in this domain, using ctDNA for the diagnosis of early-stage (stage I/II) cancers remains challenging due to several factors, such as low mutational allele frequency in circulation, overlapping profiles in genomic alterations among diverse cancers, and clonal hematopoiesis. This review discusses these analytical challenges, interim solutions, and the opportunity to complement ctDNA diagnostics with microbiome-aware analyses that may mitigate several existing ctDNA assay limitations.

BackgroundDepression and obesity are highly prevalent, often co-occurring conditions marked by inflammation. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how the microbiome mechanistically contributes to pathology remains unclear. Metabolomic investigations into microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we identified key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity.ResultsGram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was more highly predictive of depressive symptomatology-obesity co-occurrences than of obesity or depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among depressive symptomatology, obesity and comorbid obesity-depression. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin.ConclusionsTogether, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.

Periodontitis affects up to 50% of individuals worldwide, and 8.5% are diagnosed with diabetes. The high-comorbidity rate of these diseases may suggest, at least in part, a shared etiology and pathophysiology. Changes in oral microbial communities have been documented in the context of severe periodontitis and diabetes, both independently and together. However, much less is known about the early oral microbial markers of these diseases. We used a subset of the ORIGINS project dataset, which collected detailed periodontal and cardiometabolic information from 787 healthy individuals, to identify early microbial markers of periodontitis and its association with markers of cardiometabolic health. Using state-of-the-art compositional data analysis tools, we identified the log-ratio of Treponema to Corynebacterium bacteria to be a novel Microbial Indicator of Periodontitis (MIP), and found that this MIP correlates with poor periodontal health and cardiometabolic markers early in disease pathogenesis in both subgingival plaque and saliva.

Background & aimsHyperbaric oxygen therapy (HBOT) is a promising treatment for moderate-to-severe ulcerative colitis. However, our current understanding of the host and microbial response to HBOT remains unclear. This study examined the molecular mechanisms underpinning HBOT using a multi-omic strategy.MethodsPre- and post-intervention mucosal biopsies, tissue, and fecal samples were collected from HBOT phase 2 clinical trials. Biopsies and fecal samples were subjected to shotgun metaproteomics, metabolomics, 16s rRNA sequencing, and metagenomics. Tissue was subjected to bulk RNA sequencing and digital spatial profiling (DSP) for single-cell RNA and protein analysis, and immunohistochemistry was performed. Fecal samples were also used for colonization experiments in IL10-/- germ-free UC mouse models.ResultsProteomics identified negative associations between HBOT response and neutrophil azurophilic granule abundance. DSP identified an HBOT-specific reduction of neutrophil STAT3, which was confirmed by immunohistochemistry. HBOT decreased microbial diversity with a proportional increase in Firmicutes and a secondary bile acid lithocholic acid. A major source of the reduction in diversity was the loss of mucus-adherent taxa, resulting in increased MUC2 levels post-HBOT. Targeted database searching revealed strain-level associations between Akkermansia muciniphila and HBOT response status. Colonization of IL10-/- with stool obtained from HBOT responders resulted in lower colitis activity compared with non-responders, with no differences in STAT3 expression, suggesting complementary but independent host and microbial responses.ConclusionsHBOT reduces host neutrophil STAT3 and azurophilic granule activity in UC patients and changes in microbial composition and metabolism in ways that improve colitis activity. Intestinal microbiota, especially strain level variations in A muciniphila, may contribute to HBOT non-response.

Sleep disruption is a challenging and exceedingly common physiological state that contributes to a wide range of biochemical and molecular perturbations and has been linked to numerous adverse health outcomes. Modern society exerts significant pressure on the sleep/wake cycle via myriad factors, including exposure to electric light, psychological stressors, technological interconnection, jet travel, shift work, and widespread use of sleep-affecting compounds. Interestingly, recent research has identified a link between the microbiome and the regulation of sleep, suggesting that interventions targeting the microbiome may offer unique therapeutic approaches to challenges posed by sleep disruption. In this study, we test the hypothesis that administration of a prebiotic diet containing galactooligosaccharides (GOS) and polydextrose (PDX) in adult male rats improves sleep in response to repeated sleep disruption and during recovery sleep. We found that animals fed the GOS/PDX prebiotic diet for 4 weeks exhibit increased non-rapid eye movement (NREM) and rapid eye movement (REM) sleep during 5 days of sleep disruption and increased total sleep time during 24 h of recovery from sleep disruption compared to animals fed a control diet, despite similar baseline sleep characteristics. Further, the GOS/PDX prebiotic diet led to significant changes in the fecal microbiome. Consistent with previous reports, the prebiotic diet increased the relative abundance of the species Parabacteroides distasonis, which positively correlated with sleep parameters during recovery sleep. Taken together, these findings suggest that the GOS/PDX prebiotic diet may offer an approach to improve resilience to the physiologic challenge of sleep disruption, in part through impacts on the microbiome.

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.

Preterm infants are at a greater risk for the development of asthma and atopic disease, which can lead to lifelong negative health consequences. This may be due, in part, to alterations that occur in the gut microbiome and metabolome during their stay in the Neonatal Intensive Care Unit (NICU). To explore the differential roles of family history (i.e., predisposition due to maternal asthma diagnosis) and hospital-related environmental and clinical factors that alter microbial exposures early in life, we considered a unique cohort of preterm infants born ≤ 34 weeks gestational age from two local level III NICUs, as part of the MAP (Microbiome, Atopic disease, and Prematurity) Study. From MAP participants, we chose a sub-cohort of infants whose mothers had a history of asthma and matched gestational age and sex to infants of mothers without a history of asthma diagnosis (control). We performed a prospective, paired metagenomic and metabolomic analysis of stool and milk feed samples collected at birth, 2 weeks, and 6 weeks postnatal age. Although there were clinical factors associated with shifts in the diversity and composition of stool-associated bacterial communities, maternal asthma diagnosis did not play an observable role in shaping the infant gut microbiome during the study period. There were significant differences, however, in the metabolite profile between the maternal asthma and control groups at 6 weeks postnatal age. The most notable changes occurred in the linoleic acid spectral network, which plays a role in inflammatory and immune pathways, suggesting early metabolomic changes in the gut of preterm infants born to mothers with a history of asthma. Our pilot study suggests that a history of maternal asthma alters a preterm infants' metabolomic pathways in the gut, as early as the first 6 weeks of life.

BackgroundCrohn's disease (CD), an inflammatory bowel disease (IBD) subtype, results from pathologic interactions between host cells and its resident gut microbes. CD manifests in both isolated disease locations (ileum or colon) or a combination of locations (ileocolonic). To date, a comprehensive understanding of how isolated CD subtypes influence molecular profiles remains outstanding. To address this, we sought to define CD location signatures by leveraging a large cross-sectional feature set captured from the stool of over 200 IBD patients and healthy controls using metaproteomics, shotgun metagenomics, 16S rRNA sequencing, metabolomic profiling, and host genetics paired with clinical endoscopic assessments.ResultsNeither metagenomic nor host genetics alone distinguished CD location subtypes. In contrast, ileal and colonic CD were distinguished using mass spectrometry-based methods (metabolomics or metaproteomics) or a combined multi-omic feature set. This multi-omic feature set revealed colonic CD was strongly associated with neutrophil-related proteins. Additionally, colonic CD displayed a disease-severity-related association with Bacteroides vulgatus. Colonic CD and ulcerative colitis profiles harbored strikingly similar feature enrichments compared to ileal CD, including neutrophil-related protein enrichments. Compared to colonic CD, ileal CD profiles displayed increased primary and secondary bile acid levels and concomitant shifts in taxa with noted sensitivities such as Faecalibacterium prausnitzii or affinities for bile acid-rich environments, including Gammaproteobacteria and Blautia sp. Having shown robust molecular and microbial distinctions tied to CD locations, we leveraged these profiles to generate location-specific disease severity biomarkers that surpass the performance of Calprotectin.ConclusionsWhen compared using multi-omics features, colonic- and ileal-isolated CD subtypes display striking differences that suggest separate location-specific pathologies. Colonic CD's strong similarity to ulcerative colitis, including neutrophil and Bacteroides vulgatus involvement, is also evidence of a shared pathology for colonic-isolated IBD subtypes, while ileal CD maintains a unique, bile acid-driven profile. More broadly, this study demonstrates the power of multi-omics approaches for IBD biomarker discovery and elucidating the underlying biology. Video Abstract.

Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in

Liver damage due to chronic alcohol use is among the most prevalent liver diseases. Alcohol consumption frequency is a strong factor of microbiota variance. Here we use isotope labeled [1-13C] ethanol, metagenomics, and metatranscriptomics in ethanol-feeding and intragastric mouse models to investigate the metabolic impacts of alcohol consumption on the gut microbiota. First, we show that although stable isotope labeled [1-13C] ethanol contributes to fatty acid pools in the liver, plasma, and cecum contents of mice, there is no evidence of ethanol metabolism by gut microbiota ex vivo under anaerobic conditions. Next, we observe through metatranscriptomics that the gut microbiota responds to ethanol-feeding by activating acetate dissimilation, not by metabolizing ethanol directly. We demonstrate that blood acetate concentrations are elevated during ethanol consumption. Finally, by increasing systemic acetate levels with glyceryl triacetate supplementation, we do not observe any impact on liver disease, but do induce similar gut microbiota alterations as chronic ethanol-feeding in mice. Our results show that ethanol is not directly metabolized by the gut microbiota, and changes in the gut microbiota linked to ethanol are a side effect of elevated acetate levels. De-trending for these acetate effects may be critical for understanding gut microbiota changes that cause alcohol-related liver disease.

Alterations to the gut microbiome have been reported between children with autism spectrum disorders (ASDs) and typically developing (TD) children. Characterizing these differences has led to the proposal of new treatments for ASD, such as probiotic interventions and fecal matter transplants. However, no study to date has characterized the gut microbiome or metabolome in Pitt Hopkins syndrome (PTHS), a severe ASD with a high incidence of gastrointestinal (GI) disturbances such as constipation. Here, we surveyed the gut microbiome and metabolome in a cohort of PTHS individuals and their unaffected parents. We focused our analysis on Clostridium bolteae, a microbe previously associated with ASD known to chemically modify bile acids in the gut. PTHS individuals carry a higher load of C. bolteae than their parents as well as both ASD and non-ASD individuals from the American Gut Project cohort. Specific metabolites were associated with PTHS, including bile acids and sphingosines. With a metadata reanalysis tool, we found that PTHS-associated metabolites have previously been identified in inflammatory bowel disease and obesity patients. These results suggest microbial involvement in PTHS, but further research must be performed to clarify the exact mechanisms through which microbes may act. Furthermore, new associations between PTHS-specific metabolites and other conditions may lead to additional therapeutic options for PTHS individuals. IMPORTANCE GI disturbances in ASD such as severe constipation can be medically significant and often require medication. This is especially true for individuals with PTHS, suggesting that the gut microbiome may be involved in PTHS's pathology. Revealing associations between specific gut microbes and PTHS may allow the development of new therapeutics or the application of existing therapeutics to ease day-to-day challenges encountered by PTHS individuals. In this study, we characterized an association between C. bolteae and PTHS, in addition to metabolites linked to both PTHS and C. bolteae. We also identified other microbiome-involved medical conditions where PTHS-associated metabolites have been isolated. Utilizing common metabolites to identify conditions with similar phenotypes may suggest new therapeutic options for GI-related symptoms.

Environmental monitoring in public spaces can be used to identify surfaces contaminated by persons with coronavirus disease 2019 (COVID-19) and inform appropriate infection mitigation responses. Research groups have reported detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on surfaces days or weeks after the virus has been deposited, making it difficult to estimate when an infected individual may have shed virus onto a SARS-CoV-2-positive surface, which in turn complicates the process of establishing effective quarantine measures. In this study, we determined that reverse transcription-quantitative PCR (RT-qPCR) detection of viral RNA from heat-inactivated particles experiences minimal decay over 7 days of monitoring on eight out of nine surfaces tested. The properties of the studied surfaces result in RT-qPCR signatures that can be segregated into two material categories, rough and smooth, where smooth surfaces have a lower limit of detection. RT-qPCR signal intensity (average quantification cycle [Cq]) can be correlated with surface viral load using only one linear regression model per material category. The same experiment was performed with untreated viral particles on one surface from each category, with essentially identical results. The stability of RT-qPCR viral signal demonstrates the need to clean monitored surfaces after sampling to establish temporal resolution. Additionally, these findings can be used to minimize the number of materials and time points tested and allow for the use of heat-inactivated viral particles when optimizing environmental monitoring methods. IMPORTANCE Environmental monitoring is an important tool for public health surveillance, particularly in settings with low rates of diagnostic testing. Time between sampling public environments, such as hospitals or schools, and notifying stakeholders of the results should be minimal, allowing decisions to be made toward containing outbreaks of coronavirus disease 2019 (COVID-19). The Safer At School Early Alert program (SASEA) (https://saseasystem.org/), a large-scale environmental monitoring effort in elementary school and child care settings, has processed >13,000 surface samples for SARS-CoV-2, detecting viral signals from 574 samples. However, consecutive detection events necessitated the present study to establish appropriate response practices around persistent viral signals on classroom surfaces. Other research groups and clinical labs developing environmental monitoring methods may need to establish their own correlation between RT-qPCR results and viral load, but this work provides evidence justifying simplified experimental designs, like reduced testing materials and the use of heat-inactivated viral particles.

UNLABELLED: Monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on surfaces is emerging as an important tool for identifying past exposure to individuals shedding viral RNA. Our past work has demonstrated that SARS-CoV-2 reverse transcription-quantitative PCR (RT-qPCR) signals from surfaces can identify when infected individuals have touched surfaces such as Halloween candy, and when they have been present in hospital rooms or schools. However, the sensitivity and specificity of surface sampling as a method for detecting the presence of a SARS-CoV-2 positive individual, as well as guidance about where to sample, has not been established. To address these questions, and to test whether our past observations linking SARS-CoV-2 abundance to Rothia spp. in hospitals also hold in a residential setting, we performed detailed spatial sampling of three isolation housing units, assessing each sample for SARS-CoV-2 abundance by RT-qPCR, linking the results to 16S rRNA gene amplicon sequences to assess the bacterial community at each location and to the Cq value of the contemporaneous clinical test. Our results show that the highest SARS-CoV-2 load in this setting is on touched surfaces such as light switches and faucets, but detectable signal is present in many non-touched surfaces that may be more relevant in settings such as schools where mask wearing is enforced. As in past studies, the bacterial community predicts which samples are positive for SARS-CoV-2, with Rothia sp. showing a positive association. IMPORTANCE: Surface sampling for detecting SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is increasingly being used to locate infected individuals. We tested which indoor surfaces had high versus low viral loads by collecting 381 samples from three residential units where infected individuals resided, and interpreted the results in terms of whether SARS-CoV-2 was likely transmitted directly (e.g. touching a light switch) or indirectly (e.g. by droplets or aerosols settling). We found highest loads where the subject touched the surface directly, although enough virus was detected on indirectly contacted surfaces to make such locations useful for sampling (e.g. in schools, where students do not touch the light switches and also wear masks so they have no opportunity to touch their face and then the object). We also documented links between the bacteria present in a sample and the SARS-CoV-2 virus, consistent with earlier studies.

Lymphocytes within the intestinal epithelial layer (IEL) in mammals have unique composition compared with their counterparts in the lamina propria. Little is known about the role of some of the key colonic IEL subsets, such as TCRαβ+CD8+ T cells, in inflammation. We have recently described liver-enriched innate-like TCRαβ+CD8αα regulatory T cells, partly controlled by the non-classical MHC molecule, Qa-1b, that upon adoptive transfer protect from T cell-induced colitis. In this study, we found that TCRαβ+CD8αα T cells are reduced among the colonic IEL during inflammation, and that their activation with an agonistic peptide leads to significant Qa-1b-dependent protection in an acute model of colitis. Cellular expression of Qa-1b during inflammation and corresponding dependency in peptide-mediated protection suggest that Batf3-dependent CD103+CD11b- type 1 conventional dendritic cells control the protective function of TCRαβ+CD8αα T cells in the colonic epithelium. In the colitis model, expression of the potential barrier-protective gene, Muc2, is enhanced upon administration of a Qa-1b agonistic peptide. Notably, in steady state, the mucin metabolizing Akkermansia muciniphila was found in significantly lower abundance amid a dramatic change in overall microbiome and metabolome, increased IL-6 in explant culture, and enhanced sensitivity to dextran sulfate sodium in Qa-1b deficiency. Finally, in patients with inflammatory bowel disease, we found upregulation of HLA-E, a Qa-1b analog with inflammation and biologic non-response, in silico, suggesting the importance of this regulatory mechanism across species.

BackgroundThe Spacecraft Assembly Facility (SAF) at the NASA's Jet Propulsion Laboratory is the primary cleanroom facility used in the construction of some of the planetary protection (PP)-sensitive missions developed by NASA, including the Mars 2020 Perseverance Rover that launched in July 2020. SAF floor samples (n=98) were collected, over a 6-month period in 2016 prior to the construction of the Mars rover subsystems, to better understand the temporal and spatial distribution of bacterial populations (total, viable, cultivable, and spore) in this unique cleanroom.ResultsCleanroom samples were examined for total (living and dead) and viable (living only) microbial populations using molecular approaches and cultured isolates employing the traditional NASA standard spore assay (NSA), which predominantly isolated spores. The 130 NSA isolates were represented by 16 bacterial genera, of which 97% were identified as spore-formers via Sanger sequencing. The most spatially abundant isolate was Bacillus subtilis, and the most temporally abundant spore-former was Virgibacillus panthothenticus. The 16S rRNA gene-targeted amplicon sequencing detected 51 additional genera not found in the NSA method. The amplicon sequencing of the samples treated with propidium monoazide (PMA), which would differentiate between viable and dead organisms, revealed a total of 54 genera: 46 viable non-spore forming genera and 8 viable spore forming genera in these samples. The microbial diversity generated by the amplicon sequencing corresponded to ~86% non-spore-formers and ~14% spore-formers. The most common spatially distributed genera were Sphinigobium, Geobacillus, and Bacillus whereas temporally distributed common genera were Acinetobacter, Geobacilllus, and Bacillus. Single-cell genomics detected 6 genera in the sample analyzed, with the most prominent being Acinetobacter.ConclusionThis study clearly established that detecting spores via NSA does not provide a complete assessment for the cleanliness of spacecraft-associated environments since it failed to detect several PP-relevant genera that were only recovered via molecular methods. This highlights the importance of a methodological paradigm shift to appropriately monitor bioburden in cleanrooms for not only the aeronautical industry but also for pharmaceutical, medical industries, etc., and the need to employ molecular sequencing to complement traditional culture-based assays. Video abstract.

BackgroundTo initiate fecal and oral collections in prospective cohort studies for microbial analyses, it is essential to understand how field conditions and geographic differences may impact microbial communities. This study aimed to investigate the impact of fecal and oral sample collection methods and room temperature storage on collection samples for studies of the human microbiota.ResultsWe collected fecal and oral samples from participants in two Iranian cohorts located in rural Yazd (n = 46) and urban Gonbad (n = 38) and investigated room temperature stability over 4 days of fecal (RNAlater and fecal occult blood test [FOBT] cards) and comparability of fecal and oral (OMNIgene ORAL kits and Scope mouthwash) collection methods. We calculated interclass correlation coefficients (ICCs) based on 3 alpha and 4 beta diversity metrics and the relative abundance of 3 phyla. After 4 days at room temperature, fecal stability ICCs and ICCs for Scope mouthwash were generally high for all microbial metrics. Similarly, the fecal comparability ICCs for RNAlater and FOBT cards were high, ranging from 0.63 (95% CI: 0.46, 0.75) for the relative abundance of Firmicutes to 0.93 (95% CI: 0.89, 0.96) for unweighted Unifrac. Comparability ICCs for OMNIgene ORAL and Scope mouthwash were lower than fecal ICCs, ranging from 0.55 (95% CI: 0.36, 0.70) for the Shannon index to 0.79 (95% CI: 0.69, 0.86) for Bray-Curtis. Overall, RNAlater, FOBT cards and Scope mouthwash were stable up to 4 days at room temperature. Samples collected using FOBT cards were generally comparable to RNAlater while the OMNIgene ORAL were less similar to Scope mouthwash.ConclusionsAs microbiome measures for feces samples collected using RNAlater, FOBT cards and oral samples collected using Scope mouthwash were stable over four days at room temperature, these would be most appropriate for microbial analyses in these populations. However, one collection method should be consistently since each method may induce some differences.

BackgroundInfectious bacterial diseases exhibiting increasing resistance to antibiotics are a serious global health issue. Bacteriophage therapy is an anti-microbial alternative to treat patients with serious bacterial infections. However, the impacts to the host microbiome in response to clinical use of phage therapy are not well understood.ResultsOur paper demonstrates a largely unchanged microbiota profile during 4 weeks of phage therapy when added to systemic antibiotics in a single patient with Staphylococcus aureus device infection. Metabolomic analyses suggest potential indirect cascading ecological impacts to the host (skin) microbiome. We did not detect genomes of the three phages used to treat the patient in metagenomic samples taken from saliva, stool, and skin; however, phages were detected using endpoint-PCR in patient serum.ConclusionResults from our proof-of-principal study supports the use of bacteriophages as a microbiome-sparing approach to treat bacterial infections. Video abstract.

BackgroundObesity and related comorbidities are major health concerns among many US immigrant populations. Emerging evidence suggests a potential involvement of the gut microbiome. Here, we evaluated gut microbiome features and their associations with immigration, dietary intake, and obesity in 2640 individuals from a population-based study of US Hispanics/Latinos.ResultsThe fecal shotgun metagenomics data indicate that greater US exposure is associated with reduced ɑ-diversity, reduced functions of fiber degradation, and alterations in individual taxa, potentially related to a westernized diet. However, a majority of gut bacterial genera show paradoxical associations, being reduced with US exposure and increased with fiber intake, but increased with obesity. The observed paradoxical associations are not explained by host characteristics or variation in bacterial species but might be related to potential microbial co-occurrence, as seen by positive correlations among Roseburia, Prevotella, Dorea, and Coprococcus. In the conditional analysis with mutual adjustment, including all genera associated with both obesity and US exposure in the same model, the positive associations of Roseburia and Prevotella with obesity did not persist, suggesting that their positive associations with obesity might be due to their co-occurrence and correlations with obesity-related taxa, such as Dorea and Coprococcus.ConclusionsAmong US Hispanics/Latinos, US exposure is associated with unfavorable gut microbiome profiles for obesity risk, potentially related to westernized diet during acculturation. Microbial co-occurrence could be an important factor to consider in future studies relating individual gut microbiome taxa to environmental factors and host health and disease.

BackgroundImproving probiotic engraftment in the human gut requires a thorough understanding of the in vivo adaptive strategies of probiotics in diverse contexts. However, for most probiotic strains, these in vivo genetic processes are still poorly characterized. Here, we investigated the effects of gut selection pressures from human, mice, and zebrafish on the genetic stability of a candidate probiotic Lactiplantibacillus plantarum HNU082 (Lp082) as well as its ecological and evolutionary impacts on the indigenous gut microbiota using shotgun metagenomic sequencing in combination with isolate resequencing methods.ResultsWe combined both metagenomics and isolate whole genome sequencing approaches to systematically study the gut-adaptive evolution of probiotic L. plantarum and the ecological and evolutionary changes of resident gut microbiomes in response to probiotic ingestion in multiple host species. Independent of host model, Lp082 colonized and adapted to the gut by acquiring highly consistent single-nucleotide mutations, which primarily modulated carbohydrate utilization and acid tolerance. We cultivated the probiotic mutants and validated that these gut-adapted mutations were genetically stable for at least 3 months and improved their fitness in vitro. In turn, resident gut microbial strains, especially competing strains with Lp082 (e.g., Bacteroides spp. and Bifidobacterium spp.), actively responded to Lp082 engraftment by accumulating 10-70 times more evolutionary changes than usual. Human gut microbiota exhibited a higher ecological and genetic stability than that of mice.ConclusionsCollectively, our results suggest a highly convergent adaptation strategy of Lp082 across three different host environments. In contrast, the evolutionary changes within the resident gut microbes in response to Lp082 were more divergent and host-specific; however, these changes were not associated with any adverse outcomes. This work lays a theoretical foundation for leveraging animal models for ex vivo engineering of probiotics to improve engraftment outcomes in humans. Video abstract.

BackgroundDetermining the role of fomites in the transmission of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 public health restrictions and attempt to safely reopen economies. Expanding COVID-19 testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical-grade swab for inhibition to RNA. For three of these swabs, we evaluated performance to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients including COVID-19+ patients. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent.ResultsCompared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4× higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic (SYN) swab. The limit of detection (LoD) of SARS-CoV-2 from floor samples collected using the consumer-grade plastic (CGp) or research-grade plastic The Microsetta Initiative (TMI) swabs was similar or better than the SYN swab, further suggesting that swab type does not impact RNA recovery as measured by the abundance of SARS-CoV-2. The LoD for TMI was between 0 and 362.5 viral particles, while SYN and CGp were both between 725 and 1450 particles. Lastly microbiome analyses (16S rRNA gene sequencing) of paired samples (nasal and floor from same patient room) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type, but instead driven by the patient and sample type.ConclusionsCompared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer-grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity and lack of antibiotics in these samples makes it possible to perform concomitant microbiome analyses. Video abstract.

BackgroundSARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.MethodsWe collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.ResultsSixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.ConclusionsThese results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

The development of an extensive toolkit for potential point-of-care diagnostics that is expeditiously adaptable to new emerging pathogens is of critical public health importance. Recently, a number of novel CRISPR-based diagnostics have been developed to detect SARS-CoV-2. Herein, we outline the development of an alternative CRISPR nucleic acid diagnostic utilizing a Cas13d ribonuclease derived from Ruminococcus flavefaciens XPD3002 (CasRx) to detect SARS-CoV-2, an approach we term SENSR (sensitive enzymatic nucleic acid sequence reporter) that can detect attomolar concentrations of SARS-CoV-2. We demonstrate 100% sensitivity in patient-derived samples by lateral flow and fluorescence readout with a detection limit of 45 copy/μL. This technology expands the available nucleic acid diagnostic toolkit, which can be adapted to combat future pandemics.

Background Trimethylamine-N-oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases following consumption of red meat. This metabolic change is thought to be partly because of the expansion of gut microbes able to use nutrients abundant in red meat. Methods and Results We used data from a randomized crossover study to estimate the degree to which TMAO can be estimated from fecal microbial composition. Healthy participants received a series of 3 diets that differed in protein source (red meat, white meat, and non-meat), and fecal, plasma, and urine samples were collected following 4 weeks of exposure to each diet. TMAO was quantitated in plasma and urine, while shotgun metagenomic sequencing was performed on fecal DNA. While the cai gene cluster was weakly correlated with plasma TMAO (rho=0.17, P=0.0007), elastic net models of TMAO were not improved by abundances of bacterial genes known to contribute to TMAO synthesis. A global analysis of all taxonomic groups, genes, and gene families found no meaningful predictors of TMAO. We postulated that abundances of known genes related to TMAO production do not predict bacterial metabolism, and we measured choline- and carnitine-trimethylamine lyase activity during fecal culture. Trimethylamine lyase genes were only weakly correlated with the activity of the enzymes they encode. Conclusions Fecal microbiome composition does not predict systemic TMAO because, in this case, gene copy number does not predict bacterial metabolic activity. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01427855.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Inflammatory disorders of the skin are frequently associated with inflammatory bowel diseases (IBDs). To explore mechanisms by which these organs communicate, we performed single-cell RNA-Seq analysis on fibroblasts from humans and mice with IBD. This analysis revealed that intestinal inflammation promoted differentiation of a subset of intestinal stromal fibroblasts into preadipocytes with innate antimicrobial host defense activity. Furthermore, this process of reactive adipogenesis was exacerbated if mouse skin was inflamed as a result of skin wounding or infection. Since hyaluronan (HA) catabolism is activated during skin injury and fibroblast-to-adipocyte differentiation is dependent on HA, we tested the hypothesis that HA fragments could alter colon fibroblast function by targeted expression of human hyaluronidase-1 in basal keratinocytes from mouse skin. Hyaluronidase expression in the skin activated intestinal stromal fibroblasts, altered the fecal microbiome, and promoted excessive reactive adipogenesis and increased inflammation in the colon after challenge with dextran sodium sulfate. The response to digested HA was dependent on expression of TLR4 by preadipocytes. Collectively, these results suggest that the association between skin inflammation and IBD may be due to recognition by mesenchymal fibroblasts in the colon of HA released during inflammation of the skin.

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Graves' Disease is the most common organ-specific autoimmune disease and has been linked in small pilot studies to taxonomic markers within the gut microbiome. Important limitations of this work include small sample sizes and low-resolution taxonomic markers. Accordingly, we studied 162 gut microbiomes of mild and severe Graves' disease (GD) patients and healthy controls. Taxonomic and functional analyses based on metagenome-assembled genomes (MAGs) and MAG-annotated genes, together with predicted metabolic functions and metabolite profiles, revealed a well-defined network of MAGs, genes and clinical indexes separating healthy from GD subjects. A supervised classification model identified a combination of biomarkers including microbial species, MAGs, genes and SNPs, with predictive power superior to models from any single biomarker type (AUC = 0.98). Global, cross-disease multi-cohort analysis of gut microbiomes revealed high specificity of these GD biomarkers, notably discriminating against Parkinson's Disease, and suggesting that non-invasive stool-based diagnostics will be useful for these diseases.

Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial differences (beta diversity), followed by principal-coordinate analysis (PCoA). Uniform Manifold Approximation and Projection (UMAP) is an alternative method that can reduce the dimensionality of beta diversity distance matrices. Here, we demonstrate the benefits and limitations of using UMAP for dimensionality reduction on microbiome data. Using real data, we demonstrate that UMAP can improve the representation of clusters, especially when the clusters are composed of multiple subgroups. Additionally, we show that UMAP provides improved correlation of biological variation along a gradient with a reduced number of coordinates of the resulting embedding. Finally, we provide parameter recommendations that emphasize the preservation of global geometry. We therefore conclude that UMAP should be routinely used as a complementary visualization method for microbiome beta diversity studies. IMPORTANCE UMAP provides an additional method to visualize microbiome data. The method is extensible to any beta diversity metric used with PCoA, and our results demonstrate that UMAP can indeed improve visualization quality and correspondence with biological and technical variables of interest. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/knightlab-analyses/umap-microbiome-benchmarking; additionally, we have provided a QIIME 2 plugin for UMAP at https://github.com/biocore/q2-umap.

Carbon fixation by chemoautotrophic microbes such as homoacetogens had a major impact on the transition from the inorganic to the organic world. Recent reports have shown the presence of genes for key enzymes associated with the Wood-Ljungdahl pathway (WLP) in the phylum Actinobacteria, which adds to the diversity of potential autotrophs. Here, we compiled 42 actinobacterial metagenome-assembled genomes (MAGs) from new and existing metagenomic datasets and propose three novel classes, Ca. Aquicultoria, Ca. Geothermincolia and Ca. Humimicrobiia. Most members of these classes contain genes coding for acetogenesis through the WLP, as well as a variety of hydrogenases (NiFe groups 1a and 3b-3d; FeFe group C; NiFe group 4-related hydrogenases). We show that the three classes acquired the hydrogenases independently, yet the carbon monoxide dehydrogenase/acetyl-CoA synthase complex (CODH/ACS) was apparently present in their last common ancestor and was inherited vertically. Furthermore, the Actinobacteria likely donated genes for CODH/ACS to multiple lineages within Nitrospirae, Deltaproteobacteria (Desulfobacterota), and Thermodesulfobacteria through multiple horizontal gene transfer events. Finally, we show the apparent growth of Ca. Geothermincolia and H2-dependent acetate production in hot spring enrichment cultures with or without the methanogenesis inhibitor 2-bromoethanesulfonate, which is consistent with the proposed homoacetogenic metabolism.

The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network. By traversing a composition-similarity-based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiomes at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes. IMPORTANCE It remains unclear whether and how compositional changes at the "community to community" level among microbiomes are linked to the origin and evolution of global microbiome diversity. Here we propose a microbiome transition model and a network-based analysis framework to describe and simulate the variation and dispersal of the global microbial beta-diversity across multiple habitats. The traversal of a transition network with 177,022 samples shows the inherent homology of microbiome at the global scale. Then a global roadmap of microbiome dispersal derived from the network tracks the potential paths of formulating and propagating microbiome diversity. Such search-based microbiome network provides a readily expanded reference for tracing the origin and evolution of existing or new microbiomes at the global scale.

Wastewater-based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 (coronavirus disease 2019) infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance, as universities are characterized by largely asymptomatic populations and are potential hot spots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (geographic information systems)-enabled building-level wastewater monitoring system associated with the on-campus residences of 7,614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed on the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10- to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the "Return to Learn" program at the University of California (UC) San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. COVID-19 testing rates increased by 1.9 to 13× following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen. IMPORTANCE Wastewater-based epidemiology can be particularly valuable at university campuses where high-resolution spatial sampling in a well-controlled context could not only provide insight into what affects campus community as well as how those inferences can be extended to a broader city/county context. In the present study, a large-scale wastewater surveillance was successfully implemented on a large university campus enabling early detection of 85% of COVID-19 cases thereby averting potential outbreaks. The highly automated sample processing to reporting system enabled dramatic reduction in the turnaround time to 5 h (sample to result time) for 96 samples. Furthermore, miniaturization of the sample processing pipeline brought down the processing cost significantly ($13/sample). Taken together, these results show that such a system could greatly ameliorate long-term surveillance on such communities as they look to reopen.