Biology subjects

Beghini, F.

Publications and source records attributed to Beghini, F..

11 recordsLinked to original sources

bioRxiv2026

Social network cycle motifs and gut microbiome strain-sharing

Microbes are commonly transmitted through human social interactions, yet little is known about how higher-order social network structures shape microbial circulation. Here, we analyze network cycles, i.e., closed loops of individuals, in both friendship networks and microbial networks, across 1,787 individuals from 18 isolated Honduran villages. Using strain-level resolution, we construct species-specific microbial networks, study their cyclic structure, and compare them with the social networks in the same population. Cycles were strongly over-represented relative to degree-preserving randomized networks in both social and microbial networks for most species, indicating that microbial transmission frequently occurs within recurrent and clustered groups of hosts. However, the overlap between microbial and social cycles varies substantially across species and individuals, and regression analyses identify a small subset of species whose cyclic sharing patterns are associated with social cycle participation. Notably, several anaerobic species show negative associations, suggesting reliance on repeated local exposures or shared environments only partially aligned with social ties. Consistent with this, many species exhibit niche-like transmission pathways independent of social network structure. Together, these findings show that microbial sharing networks exhibit rich higher-order organization that only partially mirrors human social networks and that reveals that network cycles can provide a useful framework for understanding how repeated exposure might contribute to microbial spread.

microbiology↗

bioRxiv2025

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis

Bacteria use antagonistic interbacterial weapons such as polymorphic toxin secretion systems (TSS) to compete for niches in the human gut microbiome. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including [~]200 effector and immunity genes and applied it to [~]15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states. Decision tree machine learning models integrating bacterial taxonomy with PolyProf had near-perfect accuracy (ROC area 1.00) for all 12 disease states. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, interbacterial antagonism with TSS shapes microbiome development and interpersonal strain sharing. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.

microbiology↗

bioRxiv2025

Characterization of gut microbiomes in rural Honduras reveals novel species and associations with human genetic variation

The gut microbiome is integral to human health, yet research data to date has emphasized industrialized populations. Here, we performed large-scale shotgun metagenomic sequencing on 1,889 individuals from rural Honduras, providing the most comprehensive microbiome dataset from Central America. We identify a distinct microbial composition enriched in Prevotella species, with 861 previously unreported bacterial species. Functional profiling reveals unique carbohydrate metabolism adaptations consistent with high-fiber diets. Longitudinal analysis over two years reveals microbiome instability, with shifts in taxonomic diversity and metabolic potential, including changes associated with SARS-CoV-2 infection. Additionally, we characterize the gut virome and eukaryotic microbiome, identifying novel viral taxa, including Crassvirales phages, and a high prevalence of Blastocystis species in individuals with greater microbial diversity. Finally, by integrating host genomic data obtained from low-pass saliva whole-genome sequencing, we uncover significant host-microbiome associations, highlighting the influence of human genetic variation on microbial composition. People who are more genetically similar also have more similar gut microbiomes. These findings expand our understanding of microbiome diversity in non-industrialized populations, highlighting the uniqueness of those microbiomes and underscoring the need for global microbiome research.

microbiology↗

bioRxiv2023

Detailed Social Network Interactions and Gut Microbiome Strain-Sharing Within Isolated Honduras Villages

When humans assemble into face-to-face social networks, they create an extended environment that permits exposure to the microbiome of other members of a population. Social network interactions may thereby also shape the composition and diversity of the microbiome at individual and population levels. Here, we use comprehensive social network and detailed microbiome sequencing data in 1,098 adults across 9 isolated villages in Honduras to investigate the relationship between social network structure and microbiome composition. Using both species-level and strain-level data, we show that microbial sharing occurs between many relationship types, notably including non-familial and non-household connections. Using strain-sharing data alone, we can confidently predict a wide variety of relationship types (AUC ~0.73). This strain-level sharing extends to second-degree social connections in a network, suggesting the importance of the extended network with respect to microbiome composition. We also observe that socially central individuals are more microbially similar to the overall village than those on the social periphery. Finally, we observe that clusters of microbiome species and strains occur within clusters of people in the village social networks, providing the social niches in which microbiome biology and phenotypic impact are manifested.

microbiology↗

bioRxiv2022

Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4

Metagenomic assembly enables novel organism discovery from microbial communities, but from most metagenomes it can only capture few abundant organisms. Here, we present a method - MetaPhlAn 4 - to integrate information from both metagenome assemblies and microbial isolate genomes for improved and more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01M prokaryotic reference and metagenome-assembled genomes, we defined unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains [~]20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome, and proved more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlighted previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mice microbiomes, and showed that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains. MetaPhlAn 4 thus integrates the novelty of metagenomic assemblies with the sensitivity and fidelity of reference-based analyses, providing efficient metagenomic profiling of uncharacterized species and enabling deeper and more comprehensive microbiome biomarker detection.

bioinformatics↗

bioRxiv2020

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have advanced dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1,262 metagenomes) and IBD (1,635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4,077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

microbiology↗

bioRxiv2020

A step-by-step sequence-based analysis of virome enrichment protocol for freshwater and sediment samples

Cultivation-free metagenomic analysis afforded unprecedented details on the diversity, structure and potential functions of microbial communities in different environments. When employed to study the viral fraction of the community that is recalcitrant to cultivation, metagenomics can shed light into the diversity of viruses and their role in natural ecosystems. However, despite the increasing interest in virome metagenomics, methodological issues still hinder the proper interpretation and comparison of results across studies. Virome enrichment experimental protocols are key multi-step processes needed for separating and concentrating the viral fraction from the whole microbial community prior to sequencing. However, there is little information on their efficiency and their potential biases. To fill this gap, we used metagenomic and amplicon sequencing to examine the microbial community composition through the serial filtration and concentration steps commonly used to produce viral-enriched metagenomes. The analyses were performed on water and sediment samples from an Alpine lake. We found that, although the diversity of the retained microbial communities declined progressively during the serial filtration, the final viral fraction contained a large proportion (from 10% to 40%) of non-viral taxa, and that the efficacy of filtration showed biases based on taxonomy. Our results quantified the amount of bacterial genetic material in viromes and highlighted the influence of sample type on the enrichment efficacy. Moreover, since viral-enriched samples contained a significant portion of microbial taxa, computational sequence analysis should account for such biases in the downstream interpretation pipeline. ImportanceFiltration is a commonly used method to enrich viral particles in environmental samples. However, there is little information on its efficiency and potential biases on the final result. Using a sequence-based analysis on water and sediment samples, we found that filtration efficacy is dependent on sample type and that the final virome contained a large proportion of non-viral taxa. Our finding stressed the importance of downstream analysis to avoid biased interpretation of data.

microbiology↗

bioRxiv2020

Strengthening The Organizing and Reporting of Microbiome Studies (STORMS)

BackgroundHuman microbiome research is a growing field with the potential for improving our understanding and treatment of diseases and other conditions. The field is interdisciplinary, making concise organization and reporting of results across different styles of 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. MethodsA multidisciplinary group of microbiome epidemiology researchers reviewed elements of available reporting guidelines for observational and genetic studies and adapted these for application to culture-independent human microbiome studies. New reporting elements were developed for laboratory, bioinformatic, and statistical analyses tailored to microbiome studies, and other parts of these checklists were streamlined to keep reporting manageable. ResultsSTORMS is a 17-item checklist for reporting on human microbiome studies, organized into six sections covering typical sections of a scientific publication, presented as a table with space for author-provided details and intended for inclusion in supplementary materials. ConclusionsSTORMS provides guidance for authors and standardization for interdisciplinary microbiome studies, facilitating complete and concise reporting and augments information extraction for downstream applications. AvailabilityThe STORMS checklist is available as a versioned spreadsheet from https://www.stormsmicrobiome.org/.

genomics↗

bioRxiv2018

Tobacco exposure associated with oral microbiota oxygen utilization in the New York City Health and Nutrition Examination Study

PurposeThe effect of tobacco exposure on the oral microbiome has not been established. MethodsWe performed amplicon sequencing of the 16S ribosomal RNA gene V4 variable region to estimate bacterial community characteristics in 259 oral rinse samples, selected based on self-reported smoking and serum cotinine levels, from the 2013-14 New York City Health and Nutrition Examination Study. We identified differentially abundant operational taxonomic units (OTUs) by primary and secondhand tobacco exposure, and employed "microbe set enrichment analysis" to assess shifts in microbial oxygen utilization. ResultsCigarette smoking was associated with depletion of aerobic OTUs (Enrichment Score test statistic ES = -0.75, p = 0.002) with a minority (29%) of aerobic OTUs enriched in current smokers compared to never smokers. Consistent shifts in the microbiota were observed for current cigarette smokers as for non-smokers with secondhand exposure as measured by serum cotinine levels. Differential abundance findings were similar in crude and adjusted analyses. ConclusionResults support a plausible link between tobacco exposure and shifts in the oral microbiome at the population level through three lines of evidence: 1) a shift in microbiota oxygen utilization associated with primary tobacco smoke exposure, 2) consistency of abundance fold-changes associated with current smoking and shifts along the gradient of secondhand smoke exposure among non-smokers, and 3) consistency after adjusting for a priori hypothesized confounders. HighlightsO_LICigarette smoke was associated with microbial anaerobiosis in oral rinse specimens C_LIO_LIThe microbiome shifts associated with smoking and secondhand exposure were correlated C_LIO_LIShifts in oral bacterial oxygen utilization may mediate smoking and health outcomes C_LIO_LIWe propose "microbe set enrichment analysis" for interpreting shifts in the microbiome C_LI

epidemiology↗

bioRxiv2017

Sociodemographic patterning in the oral microbiome of a diverse sample of New Yorkers

11.1 PurposeVariations in the oral microbiome are potentially implicated in social inequalities in oral disease, cancers, and metabolic disease. We describe sociodemographic variation of oral microbiomes in a diverse sample.\n\n1.2 MethodsWe performed 16S rRNA sequencing on mouthwash specimens in a subsample (n=282) of the 2013-14 population-based New York City Health and Nutrition Examination Study (NYC-HANES). We examined differential abundance of 216 operational taxonomic units (OTUs), and alpha and beta diversity by age, sex, income, education, nativity, and race/ethnicity. For comparison, we also examined differential abundance by diet, smoking status, and oral health behaviors.\n\n1.3 Results69 OTUs were differentially abundant by any sociodemographic variable (false discovery rate < 0.01), including 27 by race/ethnicity, 21 by family income, 19 by education, three by sex. We also found 49 differentially abundant by smoking status, 23 by diet, 12 by oral health behaviors. Genera differing for multiple sociodemographic characteristics included Lactobacillus, Prevotella, Porphyromonas, Fusobacterium.\n\n1.4 ConclusionsWe identified oral microbiome variation consistent with health inequalities, with more taxa differing by race/ethnicity than diet, and more by SES variables than oral health behaviors. Investigation is warranted into possible mediating effects of the oral microbiome in social disparities in oral, metabolic and cancers.\n\nHighlightsO_LIMost microbiome studies to date have had minimal sociodemographic variability, limiting what is known about associations of social factors and the microbiome.\nC_LIO_LIWe examined the oral microbiome in a population-based sample of New Yorkers with wide sociodemographic variation.\nC_LIO_LINumerous taxa were differentially abundant by race/ethnicity, income, education, marital status, and nativity.\nC_LIO_LIFrequently differentially abundant taxa include Porphyromonas, Fusobacterium, Streptococcus, and Prevotella, which are associated with oral and systemic disease.\nC_LIO_LIMediation of health disparities by microbial factors may represent an important intervention site to reduce health disparities, and should be explored in prospective studies.\nC_LI

epidemiology↗

bioRxiv2017

Accessible, curated metagenomic data through ExperimentHub

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut \"enterotypes\", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

bioinformatics↗