Biology subjects

Duvallet, C.

Publications and source records attributed to Duvallet, C..

7 recordsLinked to original sources

bioRxiv2020

Microbial Species Abundance Distributions Guide Human Population Size Estimation from Sewage Metagenomes

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxas relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome. Author summaryWastewater-based epidemiology (WBE) is an emerging field that employs sewage as aggregated samples of human populations. This approach is particularly promising for tracking diseases that can spread asymptomatically in large populations, such as the COVID-19. As a new type of biological data, sewage has its own unique challenges to utilize. While wastewater samples are usually assumed to represent large populations, it is not guaranteed, because of stochasticity in toilet flushes; unlike epidemiological experiments collecting data from individuals, sample size, i.e., the human population size represented by a wastewater sample, is a fundamental yet difficult-to-characterize parameter for sewage samples. Researchers would need to aggregate data from large areas and week-long collection to stabilize data, during which, important spikes in small areas or short time scales may be lost. It also remains challenging to turn viral titers into case prevalences, evaluating representativeness, or comparing measurements across sites/studies. This study provides a framework to estimate human population size from sewage utilizing human gut-associated microorganisms. Through analysis, we demonstrate that variance of taxon abundances and single-nucleotide polymorphism as two variables that change with population size. We provide a new tool MicrobiomeCensus that performs population size estimation from microbial taxon abundances. MicrobiomeCensus outperforms generic algorithms in terms of computational efficiency while at comparable or better accuracy. Using MicrobiomeCensus, we detected population size differences in sewage samples taken in Cambridge, MA, under two sampling approaches, i.e., "grab" or "composite" sampling. This study provides a framework to utilize individual-level microbiomes to learn from sewage, paving the way to prevalence estimation and improved spatio-temporal resolutions in WBE..

bioinformatics↗

bioRxiv2019

24-hour multi-omics analysis of residential sewage reflects human activity and informs public health

High-throughput molecular analysis of sewage is a promising tool for precision public health. Here, we combine sewer network and demographic data to identify a residential catchment for sampling, and explore the potential of applying untargeted genomics and metabolomics to sewage to collect actionable public health data. We find that wastewater sampled upstream in a residential catchment is representative of the human microbiome and metabolome, and we are able to identify glucuronidated compounds indicative of direct human excretion, which are typically degraded too quickly to be detected at treatment plants. We show that diurnal variations during 24-hour sampling can be leveraged to discriminate between biomarkers in sewage that are associated with human activity from those related to the environmental background. Finally, we putatively annotate a suite of human-associated metabolites, including pharmaceuticals, food metabolites, and biomarkers of human health and activity, suggesting that mining untargeted data derived from residential sewage can expand currently-used biomarkers with direct public health or policy relevance.

genomics↗

bioRxiv2019

Multi-site sampling and risk prioritization reveals the public health relevance of antibiotic resistance genes found in wastewater environments

The spread of bacterial antibiotic resistance across human and environmental habitats is a global public health challenge. Wastewater has been implicated as a major source of antibiotic resistance in the environment, as it carries resistant bacteria and resistance genes from humans into natural ecosystems. However, different wastewater environments and antibiotic resistance genes in wastewater do not all present the same level of risk to human health. In this study, we investigate the public health relevance of antibiotic resistance found in wastewater by combining metagenomic sequencing with risk prioritization of resistance genes, analyzing samples across urban sewage system environments in multiple countries. We find that many of the resistance genes commonly found in wastewater are not readily present in humans. Ranking antibiotic resistance genes based on their potential pathogenicity and mobility reveals that most of the resistance genes in wastewater are not clinically relevant. Additionally, we show that residential wastewater resistomes pose greater risk to human health than those in wastewater treatment plant samples, and that residential wastewater can be as risky as hospital effluent. Across countries, differences in antibiotic resistance in residential wastewater can, in some cases, reflect differences in antibiotic drug consumption. Finally, we find that the flow of antibiotic resistance genes is influenced by geographical distance and environmental selection. Taken together, we demonstrate how different analytical approaches can provide greater insights into the public health relevance of antibiotic resistance in wastewater.

microbiology↗

bioRxiv2018

Aerodigestive sampling reveals altered microbial exchange between lung, oropharyngeal, and gastric microbiomes in children with impaired swallow function

11.1 BackgroundChildren with oropharyngeal dysphagia have impaired airway protection mechanisms and are at higher risk for pneumonia and other pulmonary complications. Aspiration of gastric contents is often implicated as a cause for these pulmonary complications, despite being supported by little evidence. The goal of this study is to determine the relative contribution of oropharyngeal and gastric microbial communities to perturbations in the lung microbiome of children with and without oropharyngeal dysphagia and aspiration.\n\n1.2 MethodsWe conducted a prospective cohort study of 222 patients consecutively recruited from a tertiary aerodigestive center undergoing simultaneous esophagogastroduodenoscopy and flexible bronchoscopy. Bronchoalveolar lavage, gastric and oropharyngeal samples were collected and 16S sequencing was performed. A subset of patients also underwent video fluoroscopic swallow studies to assess swallow function and were categorized as aspiration/no aspiration. Microbial communities across the aerodi-gestive tract were compared in patients with and without aspiration by calculating within-patient beta diversities and quantifying microbial exchange across sites.\n\n1.3 ResultsWithin all patients, lung, oropharyngeal and gastric microbiomes overlap. The degree of similarity is the lowest between the oropharynx and lungs (median Jensen-Shannon distance (JSD) = 0.90), and as high between the stomach and lungs as between the oropharynx and stomach (median JSD = 0.55 and 0.56, respectively; p = 0.6). Unlike the oropharyngeal microbiome, lung and gastric communities are highly variable across people and driven primarily by person rather than body site. In patients with aspiration, the lung microbiome more closely resembles oropharyngeal rather than gastric communities and there is greater prevalence of microbial exchange between the lung and oropharynx than between gastric and lung sites (p = 0.04 and 3x10-5, respectively).\n\n1.4 ConclusionsThe gastric and lung microbiomes display significant overlap in patients with intact airway protective mechanisms while the lung and oropharynx remain distinct. In patients with impaired swal-low function and aspiration, the lung microbiome shifts towards oropharyngeal rather than gastric communities. This finding may explain why antireflux surgeries fail to show benefit in pediatric pulmonary outcomes.

microbiology↗

bioRxiv2018

A practical guide to methods controlling false discoveries in computational biology

BackgroundIn high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p-values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as \"informative covariates\" to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigated the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology\n\nResultsMethods that incorporate informative covariates were modestly more powerful than classic approaches, and did not underperform classic approaches, even when the covariate was completely uninformative. The majority of methods were successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we found the improvement of the modern FDR methods over the classic methods increased with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses.\n\nConclusionsModern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

bioinformatics↗

bioRxiv2017

Correcting for batch effects in case-control microbiome studies

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.\n\nAuthor SummaryBatch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fishers method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

bioinformatics↗

bioRxiv2017

Meta Analysis Of Microbiome Studies Identifies Shared And Disease-Specific Patterns

1Hundreds of clinical studies have been published that demonstrate associations between the human microbiome and a variety of diseases. Yet, fundamental questions remain on how we can generalize this knowledge. For example, if diseases are mainly characterized by a small number of pathogenic species, then new targeted antimicrobial therapies may be called for. Alternatively, if diseases are characterized by a lack of healthy commensal bacteria, then new probiotic therapies might be a better option. Results from individual studies, however, can be inconsistent or in conflict, and comparing published data is further complicated by the lack of standard processing and analysis methods.\n\nHere, we introduce the MicrobiomeHD database, which includes 29 published case-control gut microbiome studies spanning ten different diseases. Using standardized data processing and analyses, we perform a comprehensive crossdisease meta-analysis of these studies. We find consistent and specific patterns of disease-associated microbiome changes. A few diseases are associated with many individual bacterial associations, while most show only around 20 genus-level changes. Some diseases are marked by the presence of pathogenic microbes whereas others are characterized by a depletion of health-associated bacteria. Furthermore, over 60% of microbes associated with individual diseases fall into a set of \"core\" health and disease-associated microbes, which are associated with multiple disease states. This suggests a universal microbial response to disease.

microbiology↗