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The human gut microbiome significantly impacts health, prompting a rise in longitudinal studies that capture microbiome samples at multiple time points. Such studies allow researchers to characterize microbiome changes over time, but importantly, also present major analytical challenges due to incomplete or irregular sampling. To address this challenge, longitudinal microbiome studies often employ various interpolation methods, aiming to infer missing microbiome data. However, to date, a comprehensive assessment of such microbiome interpolation techniques, as well as best practice guidelines for interpolating microbiome data, is still lacking. This work aims to fill this gap, rigorously implementing and systematically evaluating a large array of interpolation methods, spanning several different categories, for longitudinal microbiome interpolation. To assess each method and its ability to accurately infer microbiome composition at missing time points, we used three longitudinal microbiome data sets that follow individuals over a long period of time and a leave-one-out approach. Overall, our analysis demonstrated that the K-nearest neighbors algorithm consistently outperforms other methods in interpolation accuracy, yet, accuracy varied widely across data sets, individuals, and time. Factors such as microbiome stability, sample size, and the time gap between interpolated and adjacent samples significantly influenced accuracy, allowing us to develop a model for predicting the expected interpolation accuracy at a missing time point. Our findings, combined, suggest that accurate interpolation in longitudinal microbiome data is feasible, especially in dense cohorts. Furthermore, using our predictive model, future studies can interpolate data only in time points where the expected interpolation accuracy is high. IMPORTANCE: Since missing samples are common in longitudinal microbiome dataset due to inconsistent collection practices, it is important to evaluate and benchmark different interpolation methods for predicting microbiome composition in such samples and facilitate downstream analysis. Our study rigorously evaluated several such methods and identified the K-nearest neighbors approach as particularly effective for this task. The study also notes significant variability in interpolation accuracy among individuals, influenced by factors such as age, sample size, and sampling frequency. Furthermore, we developed a predictive model for estimating interpolation accuracy at a specific time point, enhancing the reliability of such analyses in future studies. Combined, our study, thus, provides critical insights and tools that enhance the accuracy and reliability of data interpolation methods in the growing field of longitudinal microbiome research.
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Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.
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Trasplante de Microbiota Fecal , Heces , Microbioma Gastrointestinal , Humanos , Microbioma Gastrointestinal/fisiología , Heces/microbiología , Tracto Gastrointestinal/microbiología , Bacterias/metabolismo , Bacterias/genética , Bacterias/clasificaciónRESUMEN
Crohn disease (CD) burden has increased with globalization/urbanization, and the rapid rise is attributed to environmental changes rather than genetic drift. The Study Of Urban and Rural CD Evolution (SOURCE, n = 380) has considered diet-omics domains simultaneously to detect complex interactions and identify potential beneficial and pathogenic factors linked with rural-urban transition and CD. We characterize exposures, diet, ileal transcriptomics, metabolomics, and microbiome in newly diagnosed CD patients and controls in rural and urban China and Israel. We show that time spent by rural residents in urban environments is linked with changes in gut microbial composition and metabolomics, which mirror those seen in CD. Ileal transcriptomics highlights personal metabolic and immune gene expression modules, that are directly linked to potential protective dietary exposures (coffee, manganese, vitamin D), fecal metabolites, and the microbiome. Bacteria-associated metabolites are primarily linked with host immune modules, whereas diet-linked metabolites are associated with host epithelial metabolic functions.
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Enfermedad de Crohn , Dieta , Microbioma Gastrointestinal , Población Rural , Población Urbana , Enfermedad de Crohn/microbiología , Enfermedad de Crohn/genética , Humanos , Masculino , Femenino , China/epidemiología , Adulto , Israel/epidemiología , Metabolómica , Estudios de Cohortes , Persona de Mediana Edad , Heces/microbiología , Íleon/microbiología , Íleon/metabolismo , Transcriptoma , Adulto JovenRESUMEN
Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.
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Microbioma Gastrointestinal , Microbiota , Humanos , Multiómica , Microbiota/genética , Bacterias/genética , Microbioma Gastrointestinal/genéticaRESUMEN
The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.
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The vaginal microbiome plays a crucial role in our health. The composition of this community can be classified into five community state types (CSTs), four of which are primarily consisted of Lactobacillus species and considered healthy, while the fifth features non-Lactobacillus populations and signifies a disease state termed Bacterial vaginosis (BV), which is associated with various symptoms and increased susceptibility to diseases. Importantly, however, the exact mechanisms and dynamics underlying BV development are not yet fully understood, including specifically possible routes from a healthy to a BV state. To address this gap, this study set out to characterize the progression from healthy- to BV-associated compositions by analyzing 8026 vaginal samples and using a manifold-detection framework. This approach, inspired by single-cell analysis, aims to identify low-dimensional trajectories in the high-dimensional composition space. It further orders samples along these trajectories and assigns a score (pseudo-time) to each analyzed or new sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV states for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. Several BV indicators can also be successfully predicted based on pseudo-time scores, and key taxa involved in BV development can be identified using this approach. Taken together, these findings demonstrate how manifold detection can be used to successfully characterize the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.
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Microbiota , Vaginosis Bacteriana , Humanos , Femenino , Vagina/microbiología , Vaginosis Bacteriana/microbiología , LactobacillusRESUMEN
The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) - a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsel's test, and several Amsel's criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.
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Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species, and causing adverse effects. To guide personalized pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we develop a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predict drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map a large array of interactions between pharmaceuticals and human gut bacteria and demonstrate that medications' anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.
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Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Microbioma Gastrointestinal , Microbiota , Animales , Humanos , Genómica , DisbiosisRESUMEN
CRISPR-Cas9 has yielded a plethora of effectors, including targeted transcriptional activators, base editors and prime editors. Current approaches for inducibly modulating Cas9 activity lack temporal precision and require extensive screening and optimization. We describe a versatile, chemically controlled and rapidly activated single-component DNA-binding Cas9 switch, ciCas9, which we use to confer temporal control over seven Cas9 effectors, including two cytidine base editors, two adenine base editors, a dual base editor, a prime editor and a transcriptional activator. Using these temporally controlled effectors, we analyze base editing kinetics, showing that editing occurs within hours and that rapid early editing of nucleotides predicts eventual editing magnitude. We also reveal that editing at preferred nucleotides within target sites increases the frequency of bystander edits. Thus, the ciCas9 switch offers a simple, versatile approach to generating chemically controlled Cas9 effectors, informing future effector engineering and enabling precise temporal effector control for kinetic studies.
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Sistemas CRISPR-Cas , Edición Génica , Cinética , Nucleótidos , AdeninaRESUMEN
OBJECTIVE: Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities. DESIGN: We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts. RESULTS: We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy. CONCLUSION: GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.
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Diabetes Gestacional , Microbiota , Embarazo , Femenino , Humanos , Diabetes Gestacional/diagnóstico , Tercer Trimestre del Embarazo , Inflamación , CitocinasRESUMEN
BACKGROUND: The relationship between the gut microbiome and diet has been the focus of numerous recent studies. Such studies aim to characterize the impact of diet on the composition of the microbiome, as well as the microbiome's ability to utilize various compounds in the diet and produce metabolites that may be beneficial for the host. Consumption of dietary fibers (DFs)-polysaccharides that cannot be broken down by the host's endogenous enzymes and are degraded primarily by members of the microbiome-is known to have a profound effect on the microbiome. Yet, a comprehensive characterization of microbiome compositional and functional shifts in response to the consumption of specific DFs is still lacking. RESULTS: Here, we introduce a computational framework, coupling metagenomic sequencing with careful annotation of polysaccharide degrading enzymes and DF structures, for inferring the metabolic ability of a given microbiome sample to utilize a broad catalog of DFs. We demonstrate that the inferred fiber degradation profile (IFDP) generated by our framework accurately reflects the dietary habits of various hosts across four independent datasets. We further demonstrate that IFDPs are more tightly linked to the host diet than commonly used taxonomic and functional microbiome-based profiles. Finally, applying our framework to a set of ~700 metagenomes that represents large human population cohorts from 9 different countries, we highlight intriguing global patterns linking DF consumption habits with microbiome capacities. CONCLUSIONS: Combined, our findings serve as a proof-of-concept for the use of DF-specific analysis for providing important complementary information for better understanding the relationship between dietary habits and the gut microbiome.
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Microbioma Gastrointestinal , Microbiota , Humanos , Fibras de la Dieta , Dieta , MetagenomaRESUMEN
Integrative analysis of microbiome and metabolome data obtained from human fecal samples is a promising avenue for better understanding the interplay between bacteria and metabolites in the human gut, in both health and disease. However, acquiring, processing, and unifying such datasets from multiple sources is a daunting and challenging task. Here we present a publicly available, simple-to-use, curated dataset collection of paired fecal microbiome-metabolome data from multiple cohorts. This data resource allows researchers to easily obtain multiple fully processed and integrated microbiome-metabolome datasets, facilitating the discovery of universal microbe-metabolite links, benchmark various microbiome-metabolome integration tools, and compare newly identified microbe-metabolite findings to other published datasets.
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Microbioma Gastrointestinal , Heces/microbiología , Humanos , Metaboloma , Metabolómica , ARN Ribosómico 16SRESUMEN
MOTIVATION: Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions. RESULTS: The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. AVAILABILITY AND IMPLEMENTATION: These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Microbiota , Filogenia , Microbiota/genética , Metagenómica , Programas InformáticosRESUMEN
A major challenge in working with longitudinal data when studying some temporal process is the fact that differences in pace and dynamics might overshadow similarities between processes. In the case of longitudinal microbiome data, this may hinder efforts to characterize common temporal trends across individuals or to harness temporal information to better understand the link between the microbiome and the host. One possible solution to this challenge lies in the field of "temporal alignment" - an approach for optimally aligning longitudinal samples obtained from processes that may vary in pace. In this work we investigate the use of alignment-based analysis in the microbiome domain, focusing on microbiome data from infants in their first years of life. Our analyses center around two main use-cases: First, using the overall alignment score as a measure of the similarity between microbiome developmental trajectories, and showing that this measure can capture biological differences between individuals. Second, using the specific matching obtained between pairs of samples in the alignment to highlight changes in pace and temporal dynamics, showing that it can be utilized to predict the age of infants based on their microbiome and to uncover developmental delays. Combined, our findings serve as a proof-of-concept for the use of temporal alignment as an important and beneficial tool in future longitudinal microbiome studies.
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MOTIVATION: Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links. RESULTS: We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products. AVAILABILITY AND IMPLEMENTATION: MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Metaboloma , Microbiota , Animales , Humanos , Metabolómica/métodos , Microbiota/genética , Programas Informáticos , Redes y Vías MetabólicasRESUMEN
BACKGROUND: Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. RESULTS: In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. CONCLUSIONS: Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.
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Microbioma Gastrointestinal , Microbiota , Ácidos y Sales Biliares , Disbiosis , Microbioma Gastrointestinal/genética , Humanos , Metaboloma , MetabolómicaRESUMEN
BACKGROUND: Infants with cystic fibrosis (CF) suffer from gastrointestinal (GI) complications, including pancreatic insufficiency and intestinal inflammation, which have been associated with impaired nutrition and growth. Recent evidence identified altered fecal microbiota taxonomic compositions in infants with CF relative to healthy infants that were characterized by differences in the abundances of taxa associated with GI health and nutrition. Furthermore, these taxonomic differences were more pronounced in low length infants with CF, suggesting a potential link to linear growth failure. We hypothesized that these differences would entail shifts in the microbiome's functional capacities that could contribute to inflammation and nutritional failure in infants with CF. RESULTS: To test this hypothesis, we compared fecal microbial metagenomic content between healthy infants and infants with CF, supplemented with an analysis of fecal metabolomes in infants with CF. We identified notable differences in CF fecal microbial functional capacities, including metabolic and environmental response functions, compared to healthy infants that intensified during the first year of life. A machine learning-based longitudinal metagenomic age analysis of healthy and CF fecal metagenomic functional profiles further demonstrated that these differences are characterized by a CF-associated delay in the development of these functional capacities. Moreover, we found metagenomic differences in functions related to metabolism among infants with CF that were associated with diet and antibiotic exposure, and identified several taxa as potential drivers of these functional differences. An integrated metagenomic and metabolomic analysis further revealed that abundances of several fecal GI metabolites important for nutrient absorption, including three bile acids, correlated with specific microbes in infants with CF. CONCLUSIONS: Our results highlight several metagenomic and metabolomic factors, including bile acids and other microbial metabolites, that may impact nutrition, growth, and GI health in infants with CF. These factors could serve as promising avenues for novel microbiome-based therapeutics to improve health outcomes in these infants.
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Fibrosis Quística/complicaciones , Fibrosis Quística/microbiología , Disbiosis/complicaciones , Heces/microbiología , Enfermedades Gastrointestinales/etiología , Metaboloma , Metagenoma , Enfermedades Gastrointestinales/microbiología , Enfermedades Gastrointestinales/fisiopatología , Humanos , Lactante , Estudios Longitudinales , Metabolómica/métodos , Estudios ProspectivosRESUMEN
OBJECTIVES: To identify factors that increase the risk of gastrointestinal-related (GI-related) hospitalization of infants with cystic fibrosis (CF) during the first year of life. METHODS: The Baby Observational and Nutrition Study was a longitudinal, observational cohort of 231 infants diagnosed with CF by newborn screening. We performed a post-hoc assessment of the frequency and indications for GI-related admissions during the first year of life. RESULTS: Sixty-five participants had at least one admission in the first 12âmonths of life. High pancreatic enzyme replacement therapy (PERT) dosing (>2000âlipase units/kg per meal; hazard ratio [HR]â=â14.75, Pâ=â0.0005) and use of acid suppressive medications (HRâ=â4.94, Pâ=â0.01) during the study period were positively associated with subsequent GI-related admissions. High levels of fecal calprotectin (fCP) (>200âµg/g) and higher relative abundance of fecal Klebsiella pneumoniae were also positively associated with subsequent GI-related admissions (HRâ=â2.64, Pâ=â0.033 and HRâ=â4.49, Pâ=â0.002, respectively). During the first 12âmonths of life, participants with any admission had lower weight-for-length z scores (WLZ) (Pâ=â0.01). The impact of admission on WLZ was particularly evident in participants with a GI-related admission (Pâ<â0.0001). CONCLUSIONS: Factors associated with a higher risk for GI-related admission during the first 12âmonths include high PERT dosing, exposure to acid suppressive medications, higher fCP levels, and/or relative abundance of fecal K pneumoniae early in life. Infants with CF requiring GI-related hospitalization had lower WLZ at 12âmonths of age than those not admitted as well as those admitted for non-GI-related indications.
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Fibrosis Quística , Estudios de Cohortes , Fibrosis Quística/complicaciones , Fibrosis Quística/tratamiento farmacológico , Terapia de Reemplazo Enzimático , Hospitalización , Humanos , Lactante , Recién Nacido , Tamizaje NeonatalRESUMEN
BACKGROUND: Microbial communities have become an important subject of research across multiple disciplines in recent years. These communities are often examined via shotgun metagenomic sequencing, a technology which can offer unique insights into the genomic content of a microbial community. Functional annotation of shotgun metagenomic data has become an increasingly popular method for identifying the aggregate functional capacities encoded by the community's constituent microbes. Currently available metagenomic functional annotation pipelines, however, suffer from several shortcomings, including limited pipeline customization options, lack of standard raw sequence data pre-processing, and insufficient capabilities for integration with distributed computing systems. RESULTS: Here we introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic data as input and generate functional profiles. MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with distributed computing clusters, allowing users to take full advantage of available computing resources. Default pipeline settings allow new users to run MetaLAFFA according to common practices while a Python module-based configuration system provides advanced users with a flexible interface for pipeline customization. MetaLAFFA also generates summary statistics for each step in the pipeline so that users can better understand pre-processing and annotation quality. CONCLUSIONS: MetaLAFFA is a new end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and flexible customization options. MetaLAFFA source code is available at https://github.com/borenstein-lab/MetaLAFFA and can be installed via Conda as described in the accompanying documentation.