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The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. VIDEO ABSTRACT.
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Microbioma Gastrointestinal/genética , Regulación de la Expresión Génica/genética , Síndrome del Colon Irritable/metabolismo , Metaboloma , Purinas/metabolismo , Transcriptoma/genética , Animales , Ácidos y Sales Biliares/metabolismo , Biopsia , Butiratos/metabolismo , Cromatografía Liquida , Estudios Transversales , Epigenómica , Heces/microbiología , Femenino , Microbioma Gastrointestinal/fisiología , Regulación de la Expresión Génica/fisiología , Interacciones Microbiota-Huesped/genética , Humanos , Hipoxantina/metabolismo , Síndrome del Colon Irritable/genética , Síndrome del Colon Irritable/microbiología , Estudios Longitudinales , Masculino , Metaboloma/fisiología , Ratones , Estudios Observacionales como Asunto , Estudios Prospectivos , Programas Informáticos , Espectrometría de Masas en Tándem , Transcriptoma/fisiologíaRESUMEN
Probiotics are widely prescribed for prevention of antibiotics-associated dysbiosis and related adverse effects. However, probiotic impact on post-antibiotic reconstitution of the gut mucosal host-microbiome niche remains elusive. We invasively examined the effects of multi-strain probiotics or autologous fecal microbiome transplantation (aFMT) on post-antibiotic reconstitution of the murine and human mucosal microbiome niche. Contrary to homeostasis, antibiotic perturbation enhanced probiotics colonization in the human mucosa but only mildly improved colonization in mice. Compared to spontaneous post-antibiotic recovery, probiotics induced a markedly delayed and persistently incomplete indigenous stool/mucosal microbiome reconstitution and host transcriptome recovery toward homeostatic configuration, while aFMT induced a rapid and near-complete recovery within days of administration. In vitro, Lactobacillus-secreted soluble factors contributed to probiotics-induced microbiome inhibition. Collectively, potential post-antibiotic probiotic benefits may be offset by a compromised gut mucosal recovery, highlighting a need of developing aFMT or personalized probiotic approaches achieving mucosal protection without compromising microbiome recolonization in the antibiotics-perturbed host.
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Antibacterianos/farmacología , Microbioma Gastrointestinal/efectos de los fármacos , Probióticos/administración & dosificación , Adolescente , Adulto , Anciano , Animales , Trasplante de Microbiota Fecal , Heces/microbiología , Femenino , Humanos , Mucosa Intestinal/efectos de los fármacos , Mucosa Intestinal/microbiología , Lactobacillus/efectos de los fármacos , Lactobacillus/genética , Lactobacillus/aislamiento & purificación , Lactococcus/genética , Lactococcus/aislamiento & purificación , Masculino , Ratones , Ratones Endogámicos C57BL , Persona de Mediana Edad , ARN Ribosómico 16S/análisis , ARN Ribosómico 16S/genética , ARN Ribosómico 16S/metabolismo , Adulto JovenRESUMEN
The intestinal microbiota undergoes diurnal compositional and functional oscillations that affect metabolic homeostasis, but the mechanisms by which the rhythmic microbiota influences host circadian activity remain elusive. Using integrated multi-omics and imaging approaches, we demonstrate that the gut microbiota features oscillating biogeographical localization and metabolome patterns that determine the rhythmic exposure of the intestinal epithelium to different bacterial species and their metabolites over the course of a day. This diurnal microbial behavior drives, in turn, the global programming of the host circadian transcriptional, epigenetic, and metabolite oscillations. Surprisingly, disruption of homeostatic microbiome rhythmicity not only abrogates normal chromatin and transcriptional oscillations of the host, but also incites genome-wide de novo oscillations in both intestine and liver, thereby impacting diurnal fluctuations of host physiology and disease susceptibility. As such, the rhythmic biogeography and metabolome of the intestinal microbiota regulates the temporal organization and functional outcome of host transcriptional and epigenetic programs.
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Ritmo Circadiano , Colon/microbiología , Microbioma Gastrointestinal , Transcriptoma , Animales , Cromatina/metabolismo , Colon/metabolismo , Vida Libre de Gérmenes , Hígado/metabolismo , Ratones , Microscopía Electrónica de RastreoRESUMEN
Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.
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Algoritmos , Glucemia/análisis , Diabetes Mellitus Tipo 2/sangre , Periodo Posprandial , Diabetes Mellitus Tipo 2/dietoterapia , Diabetes Mellitus Tipo 2/microbiología , Dieta para Diabéticos , Microbioma Gastrointestinal , Humanos , Teléfono InteligenteRESUMEN
Host-microbiome co-evolution drives homeostasis and disease susceptibility, yet regulatory principles governing the integrated intestinal host-commensal microenvironment remain obscure. While inflammasome signaling participates in these interactions, its activators and microbiome-modulating mechanisms are unknown. Here, we demonstrate that the microbiota-associated metabolites taurine, histamine, and spermine shape the host-microbiome interface by co-modulating NLRP6 inflammasome signaling, epithelial IL-18 secretion, and downstream anti-microbial peptide (AMP) profiles. Distortion of this balanced AMP landscape by inflammasome deficiency drives dysbiosis development. Upon fecal transfer, colitis-inducing microbiota hijacks this microenvironment-orchestrating machinery through metabolite-mediated inflammasome suppression, leading to distorted AMP balance favoring its preferential colonization. Restoration of the metabolite-inflammasome-AMP axis reinstates a normal microbiota and ameliorates colitis. Together, we identify microbial modulators of the NLRP6 inflammasome and highlight mechanisms by which microbiome-host interactions cooperatively drive microbial community stability through metabolite-mediated innate immune modulation. Therefore, targeted "postbiotic" metabolomic intervention may restore a normal microenvironment as treatment or prevention of dysbiosis-driven diseases.
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Colon/inmunología , Colon/microbiología , Inflamasomas/inmunología , Microbiota , Receptores de Superficie Celular/metabolismo , Transducción de Señal , Animales , Péptidos Catiónicos Antimicrobianos , Colitis/inducido químicamente , Colitis/tratamiento farmacológico , Colon/metabolismo , Disbiosis/metabolismo , Vida Libre de Gérmenes , Enfermedades Inflamatorias del Intestino/inducido químicamente , Enfermedades Inflamatorias del Intestino/tratamiento farmacológico , Interleucina-18/inmunología , Ratones , Ratones Endogámicos C57BL , Receptores de Superficie Celular/genética , Taurina/administración & dosificaciónRESUMEN
All domains of life feature diverse molecular clock machineries that synchronize physiological processes to diurnal environmental fluctuations. However, no mechanisms are known to cross-regulate prokaryotic and eukaryotic circadian rhythms in multikingdom ecosystems. Here, we show that the intestinal microbiota, in both mice and humans, exhibits diurnal oscillations that are influenced by feeding rhythms, leading to time-specific compositional and functional profiles over the course of a day. Ablation of host molecular clock components or induction of jet lag leads to aberrant microbiota diurnal fluctuations and dysbiosis, driven by impaired feeding rhythmicity. Consequently, jet-lag-induced dysbiosis in both mice and humans promotes glucose intolerance and obesity that are transferrable to germ-free mice upon fecal transplantation. Together, these findings provide evidence of coordinated metaorganism diurnal rhythmicity and offer a microbiome-dependent mechanism for common metabolic disturbances in humans with aberrant circadian rhythms, such as those documented in shift workers and frequent flyers.
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Relojes Circadianos , Ritmo Circadiano , Intolerancia a la Glucosa , Microbiota , Animales , Disbiosis/microbiología , Disbiosis/fisiopatología , Conducta Alimentaria , Homeostasis , Humanos , Síndrome Jet Lag/fisiopatología , Enfermedades Metabólicas/microbiología , Enfermedades Metabólicas/fisiopatología , Ratones , Obesidad/metabolismo , SueñoRESUMEN
The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.
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Dieta , Microbioma Gastrointestinal/fisiología , Metaboloma/genética , Suero/metabolismo , Adulto , Pan , Estudios de Cohortes , Femenino , Voluntarios Sanos , Humanos , Estilo de Vida , Aprendizaje Automático , Masculino , Metabolómica , Persona de Mediana Edad , Enfermedad del Hígado Graso no Alcohólico/genética , Oxigenasas/genética , Estándares de Referencia , Reproducibilidad de los Resultados , Estaciones del AñoRESUMEN
Patterns of sequencing coverage along a bacterial genome-summarized by a peak-to-trough ratio (PTR)-have been shown to accurately reflect microbial growth rates, revealing a new facet of microbial dynamics and host-microbe interactions. Here, we introduce Compute PTR (CoPTR): a tool for computing PTRs from complete reference genomes and assemblies. Using simulations and data from growth experiments in simple and complex communities, we show that CoPTR is more accurate than the current state of the art while also providing more PTR estimates overall. We further develop a theory formalizing a biological interpretation for PTRs. Using a reference database of 2935 species, we applied CoPTR to a case-control study of 1304 metagenomic samples from 106 individuals with inflammatory bowel disease. We show that growth rates are personalized, are only loosely correlated with relative abundances, and are associated with disease status. We conclude by showing how PTRs can be combined with relative abundances and metabolomics to investigate their effect on the microbiome.
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Metagenómica , Microbiota , Estudios de Casos y Controles , Genoma Bacteriano , Humanos , Metagenoma , Microbiota/genéticaRESUMEN
Differences in the presence of even a few genes between otherwise identical bacterial strains may result in critical phenotypic differences. Here we systematically identify microbial genomic structural variants (SVs) and find them to be prevalent in the human gut microbiome across phyla and to replicate in different cohorts. SVs are enriched for CRISPR-associated and antibiotic-producing functions and depleted from housekeeping genes, suggesting that they have a role in microbial adaptation. We find multiple associations between SVs and host disease risk factors, many of which replicate in an independent cohort. Exploring genes that are clustered in the same SV, we uncover several possible mechanistic links between the microbiome and its host, including a region in Anaerostipes hadrus that encodes a composite inositol catabolism-butyrate biosynthesis pathway, the presence of which is associated with lower host metabolic disease risk. Overall, our results uncover a nascent layer of variability in the microbiome that is associated with microbial adaptation and host health.
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Bacterias/genética , Susceptibilidad a Enfermedades/microbiología , Microbioma Gastrointestinal/genética , Genes Bacterianos/genética , Variación Genética , Salud , Interacciones Microbiota-Huesped/genética , Adaptación Fisiológica/genética , Bacterias/clasificación , Bacterias/crecimiento & desarrollo , Bacterias/metabolismo , Butiratos/metabolismo , Estudios de Cohortes , Ecosistema , Eubacterium/genética , Eubacterium/metabolismo , Heces/microbiología , Microbioma Gastrointestinal/fisiología , Interacciones Microbiota-Huesped/fisiología , Humanos , Inositol/metabolismo , Metagenómica , Viabilidad Microbiana/genética , Factores de RiesgoRESUMEN
As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research. We highlight key approaches to alleviate these potential pitfalls, combining experimental planning strategies and data analysis methods.
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Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.
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Dieta/estadística & datos numéricos , Ambiente , Composición Familiar , Microbioma Gastrointestinal/genética , Estilo de Vida , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Estudios de Cohortes , Femenino , Interacción Gen-Ambiente , Glucosa/metabolismo , Voluntarios Sanos , Herencia/genética , Humanos , Israel , Masculino , Persona de Mediana Edad , Obesidad/metabolismo , Fenotipo , Polimorfismo de Nucleótido Simple/genética , ARN Bacteriano/análisis , ARN Bacteriano/genética , ARN Ribosómico 16S/análisis , Reproducibilidad de los Resultados , Estudios en Gemelos como Asunto , Gemelos/genética , Adulto JovenRESUMEN
The COVID-19 pandemic has the potential to affect the human microbiome in infected and uninfected individuals, having a substantial impact on human health over the long term. This pandemic intersects with a decades-long decline in microbial diversity and ancestral microbes due to hygiene, antibiotics, and urban living (the hygiene hypothesis). High-risk groups succumbing to COVID-19 include those with preexisting conditions, such as diabetes and obesity, which are also associated with microbiome abnormalities. Current pandemic control measures and practices will have broad, uneven, and potentially long-term effects for the human microbiome across the planet, given the implementation of physical separation, extensive hygiene, travel barriers, and other measures that influence overall microbial loss and inability for reinoculation. Although much remains uncertain or unknown about the virus and its consequences, implementing pandemic control practices could significantly affect the microbiome. In this Perspective, we explore many facets of COVID-19-induced societal changes and their possible effects on the microbiome, and discuss current and future challenges regarding the interplay between this pandemic and the microbiome. Recent recognition of the microbiome's influence on human health makes it critical to consider both how the microbiome, shaped by biosocial processes, affects susceptibility to the coronavirus and, conversely, how COVID-19 disease and prevention measures may affect the microbiome. This knowledge may prove key in prevention and treatment, and long-term biological and social outcomes of this pandemic.
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COVID-19/microbiología , Hipótesis de la Higiene , Microbiota , Anciano , Antiinfecciosos/uso terapéutico , COVID-19/mortalidad , Ingestión de Alimentos , Femenino , Humanos , Lactante , Control de Infecciones/métodos , Masculino , Microbiota/efectos de los fármacos , Distanciamiento Físico , EmbarazoRESUMEN
Non-caloric artificial sweeteners (NAS) are among the most widely used food additives worldwide, regularly consumed by lean and obese individuals alike. NAS consumption is considered safe and beneficial owing to their low caloric content, yet supporting scientific data remain sparse and controversial. Here we demonstrate that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota. These NAS-mediated deleterious metabolic effects are abrogated by antibiotic treatment, and are fully transferrable to germ-free mice upon faecal transplantation of microbiota configurations from NAS-consuming mice, or of microbiota anaerobically incubated in the presence of NAS. We identify NAS-altered microbial metabolic pathways that are linked to host susceptibility to metabolic disease, and demonstrate similar NAS-induced dysbiosis and glucose intolerance in healthy human subjects. Collectively, our results link NAS consumption, dysbiosis and metabolic abnormalities, thereby calling for a reassessment of massive NAS usage.
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Tracto Gastrointestinal/efectos de los fármacos , Tracto Gastrointestinal/microbiología , Intolerancia a la Glucosa/inducido químicamente , Intolerancia a la Glucosa/microbiología , Microbiota/efectos de los fármacos , Edulcorantes/efectos adversos , Animales , Antibacterianos/farmacología , Aspartame/efectos adversos , Peso Corporal/efectos de los fármacos , Dieta Alta en Grasa , Grasas de la Dieta/farmacología , Heces/microbiología , Femenino , Vida Libre de Gérmenes , Glucosa/metabolismo , Intolerancia a la Glucosa/metabolismo , Humanos , Masculino , Síndrome Metabólico/inducido químicamente , Síndrome Metabólico/metabolismo , Síndrome Metabólico/microbiología , Ratones , Ratones Endogámicos C57BL , Sacarina/administración & dosificación , Sacarina/efectos adversos , Sacarosa/efectos adversos , Sacarosa/análogos & derivados , Relación Cintura-CaderaRESUMEN
Age-related macular degeneration (AMD) is the major cause of blindness in developed nations. AMD is characterized by retinal pigmented epithelial (RPE) cell dysfunction and loss of photoreceptor cells. Epidemiologic studies indicate important contributions of dietary patterns to the risk for AMD, but the mechanisms relating diet to disease remain unclear. Here we investigate the effect on AMD of isocaloric diets that differ only in the type of dietary carbohydrate in a wild-type aged-mouse model. The consumption of a high-glycemia (HG) diet resulted in many AMD features (AMDf), including RPE hypopigmentation and atrophy, lipofuscin accumulation, and photoreceptor degeneration, whereas consumption of the lower-glycemia (LG) diet did not. Critically, switching from the HG to the LG diet late in life arrested or reversed AMDf. LG diets limited the accumulation of advanced glycation end products, long-chain polyunsaturated lipids, and their peroxidation end-products and increased C3-carnitine in retina, plasma, or urine. Untargeted metabolomics revealed microbial cometabolites, particularly serotonin, as protective against AMDf. Gut microbiota were responsive to diet, and we identified microbiota in the Clostridiales order as being associated with AMDf and the HG diet, whereas protection from AMDf was associated with the Bacteroidales order and the LG diet. Network analysis revealed a nexus of metabolites and microbiota that appear to act within a gut-retina axis to protect against diet- and age-induced AMDf. The findings indicate a functional interaction between dietary carbohydrates, the metabolome, including microbial cometabolites, and AMDf. Our studies suggest a simple dietary intervention that may be useful in patients to arrest AMD.
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Glucemia/metabolismo , Microbioma Gastrointestinal/fisiología , Índice Glucémico/fisiología , Degeneración Macular/metabolismo , Retina/metabolismo , Animales , Productos Finales de Glicación Avanzada/metabolismo , Metaboloma/fisiología , Metabolómica , RatonesRESUMEN
Genomic diversity within species can be driven by both point mutations and larger structural variations, but so far, strain-tracking approaches have focused mostly on the former. In a recent issue of Nature Biotechnology, Ley and colleagues1 introduce SynTracker, a tool designed for scalable strain tracking with microsynteny in low-coverage metagenomic settings.
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Metagenómica , Metagenómica/métodos , Bacterias/genética , Bacterias/clasificación , Programas InformáticosRESUMEN
Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number of instances used for training the model, an approach called 'leave-one-out cross-validation' is often used. In this design, a separate model is built for predicting each data instance after training on all other instances. Since this results in a single test data point available per model trained, predictions are aggregated across the entire dataset to calculate common rank-based performance metrics such as the area under the receiver operating characteristic or precision-recall curves. In this work, we demonstrate that this approach creates a negative correlation between the average label of each training fold and the label of its corresponding test instance, a phenomenon that we term distributional bias. As machine learning models tend to regress to the mean of their training data, this distributional bias tends to negatively impact performance evaluation and hyperparameter optimization. We show that this effect generalizes to leave-P-out cross-validation and persists across a wide range of modeling and evaluation approaches, and that it can lead to a bias against stronger regularization. To address this, we propose a generalizable rebalanced cross-validation approach that corrects for distributional bias. We demonstrate that our approach improves cross-validation performance evaluation in synthetic simulations and in several published leave-one-out analyses.
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Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.
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BACKGROUND: Esophageal adenocarcinoma (EAC) is rising in incidence, and established risk factors do not explain this trend. Esophageal microbiome alterations have been associated with Barrett's esophagus (BE) and dysplasia and EAC. The oral microbiome is tightly linked to the esophageal microbiome; this study aimed to identify salivary microbiome-related factors associated with BE, dysplasia, and EAC. METHODS: Clinical data and oral health history were collected from patients with and without BE. The salivary microbiome was characterized, assessing differential relative abundance of taxa by 16S rRNA gene sequencing and associations between microbiome composition and clinical features. Microbiome metabolic modeling was used to predict metabolite production. RESULTS: A total of 244 patients (125 non-BE and 119 BE) were analyzed. Patients with high-grade dysplasia (HGD)/EAC had a significantly higher prevalence of tooth loss (P = 0.001). There were significant shifts with increased dysbiosis associated with HGD/EAC, independent of tooth loss, with the largest shifts within the genus Streptococcus. Modeling predicted significant shifts in the microbiome metabolic capacities, including increases in L-lactic acid and decreases in butyric acid and L-tryptophan production in HGD/EAC. CONCLUSIONS: Marked dysbiosis in the salivary microbiome is associated with HGD and EAC, with notable increases within the genus Streptococcus and accompanying changes in predicted metabolite production. Further work is warranted to identify the biological significance of these alterations and to validate metabolic shifts. IMPACT: There is an association between oral dysbiosis and HGD/EAC. Further work is needed to establish the diagnostic, predictive, and causal potential of this relationship.