Download or read book STATISTICAL ANALYSIS OF HUMAN written by Youwen Qin and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Statistical Analysis of Human Gastrointestinal Microbiota Using Next Generation Sequencing Data" by Youwen, Qin, 覃友文, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The human gastrointestinal tract is the niche of both commensal and pathogenic microbes which play an important role in human health. This thesis includes two independent studies relevant to analyzing next-generation sequencing data on the human gastrointestinal microbiota. The first study conducted a comparative analysis on 16S rRNA gene sequencing data obtained from gastritis and gastric cancer patients in the Hong Kong (HK) and Korean cohorts. Neisseriaceae and Lachnospiraceae were the important families in segregating gastritis and cancer samples in the HK dataset while it was Streptococcaceae in the Korean dataset. Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria and Fusobacteria were the major phyla in the two cohorts, where they made up >= 99% of the total relative abundance. However, when narrowed down to the family level, the two datasets only shared 5 major families among the 15 and 13 major families in the HK and Korean datasets, respectively. Hierarchical clustering showed that samples were segregated into two major clusters according to the relative abundance of Helicobacteria pylori (H. pylori) in the two datasets. Moreover, the cross-prediction results for gastritis versus cancer between two datasets yielded up to 3 times larger error rates compared to the prediction results within the training set. Taken together, the differences between the HK and Korean cohorts in the gastric microbiota outweighed the similarities. The second study developed a computational workflow to improve the draft genomes assembled from shotgun metagenomic sequencing data. The publicly available sequencing data of 396 human stool samples were downloaded for this purpose. Firstly, 3.9 million genes assembled from 396 samples were clustered into 7,381 co-abundance gene groups (CAGs) according to their pairwise correlations. The CAGs (741 CAGs) with more than 700 genes were defined as metagenomic species (MGSs), while the others (6,640 CAGs) were defined as metagenomic units (MGUs). In order to recover the relevant MGSs of the MGUs, the metagenomic deconvolution framework which decomposes the community-level gene content into taxon-specific gene profile was applied. Overall, 377 MGUs were assigned to 354 relevant MGSs, achieving a 9.57% mean improvement in the gene count of MGSs. Most of these MGSs were annotated to phylum Firmicutes. Specifically, the augmented results of 9 MGSs annotated to genus Faecalibacterium by their relative MGUs achieved average improvement of 21.08% and 17.84% in sensitivity and specificity. Importantly, MGUs included essential genes that were missed in MGSs, such as ribosomal genes, metabolism and transport system genes. Hence, the implementation of metagenomic deconvolution after binning improves the draft genomes of metagenomic species. Subjects: Gastrointestinal system - Microbiology Nucleotide sequence

Download or read book Statistical Analysis of Human Gastrointestinal Microbiota Using Next Generation Sequencing Data written by 覃友文 and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Analysis of Microbiome Data written by Somnath Datta and published by Springer Nature. This book was released on 2021-10-27 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.

Download or read book Statistical Methods for Human Microbiome Data Analysis written by Jun Chen and published by . This book was released on 2012 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Metagenomics for Microbiology written by Jacques Izard and published by Academic Press. This book was released on 2014-11-07 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concisely discussing the application of high throughput analysis to move forward our understanding of microbial principles, Metagenomics for Microbiology provides a solid base for the design and analysis of omics studies for the characterization of microbial consortia. The intended audience includes clinical and environmental microbiologists, molecular biologists, infectious disease experts, statisticians, biostatisticians, and public health scientists. This book focuses on the technological underpinnings of metagenomic approaches and their conceptual and practical applications. With the next-generation genomic sequencing revolution increasingly permitting researchers to decipher the coding information of the microbes living with us, we now have a unique capacity to compare multiple sites within individuals and at higher resolution and greater throughput than hitherto possible. The recent articulation of this paradigm points to unique possibilities for investigation of our dynamic relationship with these cellular communities, and excitingly the probing of their therapeutic potential in disease prevention or treatment of the future. - Expertly describes the latest metagenomic methodologies and best-practices, from sample collection to data analysis for taxonomic, whole shotgun metagenomic, and metatranscriptomic studies - Includes clear-headed pointers and quick starts to direct research efforts and increase study efficacy, eschewing ponderous prose - Presented topics include sample collection and preparation, data generation and quality control, third generation sequencing, advances in computational analyses of shotgun metagenomic sequence data, taxonomic profiling of shotgun data, hypothesis testing, and mathematical and computational analysis of longitudinal data and time series. Past-examples and prospects are provided to contextualize the applications.

Download or read book Statistical Analysis of Microbiome Data with R written by Yinglin Xia and published by Springer. This book was released on 2018-10-06 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.

Download or read book Computational and Statistical Methods for Extracting Biological Signal from High Dimensional Microbiome Data written by Gibraan Rahman and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next-generation sequencing (NGS) has effected an explosion of research into the relationship between genetic information and a variety of biological conditions. One of the most exciting areas of study is how the trillions of microbial species that we share this Earth with affect our health. However, the process of extracting useful biological insights from this breadth of data is far from trivial. There are numerous statistical and computational considerations in addition to the already complex and messy biological problems. In this thesis, I describe my work on developing and implementing software to tackle the complex world of statistical microbiome analysis. In the first part of this thesis, we review the applications and challenges of performing dimensionality reduction on microbiome data comprising thousands of microbial taxa. When dealing with this high dimensionality, it is imperative to be able to get an overview of the community structure in a lower dimensional space that can be both visualized and interpreted. We review the statistical considerations for dimensionality reduction and the existing tools and algorithms that can and cannot address them. This includes discussions about sparsity, compositionality, and phylogenetic signal. We also make recommendations about tools and algorithms to consider for different use-cases. In the second part of this thesis, we present a new software, Evident, designed to assist researchers with statistical analysis of microbiome effect sizes and power analysis. Effect sizes of statistical tests are not widely reported in microbiome datasets, limiting the interpretability of community differences such as alpha and beta diversity. As more large microbiome studies are produced, researchers have the opportunity to mine existing datasets to get a sense of the effect size for different biological conditions. These, in turn, can be used to perform power analysis prior to designing an experiment, allowing researchers to better allocate resources. We show how Evident is scalable to dozens of datasets and provides easy calculation and exploration of effect sizes and power analysis from existing data. In the third part of this thesis, we describe a novel investigation into the joint microbiome and metabolome axis in colorectal cancer. In most cases of sporadic colorectal cancers (CRC), tumorigenesis is a multistep process driven by genomic alterations in concert with dietary influences. In addition, mounting evidence has implicated the gut microbiome as an effector in the development and progression of CRC. While large meta-analyses have provided mechanistic insight into disease progression in CRC patients, study heterogeneity has limited causal associations. To address this limitation, multi-omics studies on genetically controlled cohorts of mice were performed to distinguish genetic and dietary influences. Diet was identified as the major driver of microbial and metabolomic differences, with reductions in alpha diversity and widespread changes in cecal metabolites seen in HFD-fed mice. Similarly, the levels of non-classic amino acid conjugated forms of the bile acid cholic acid (AA-CAs) increased with HFD. We show that these AA-CAs signal through the nuclear receptor FXR and membrane receptor TGR5 to functionally impact intestinal stem cell growth. In addition, the poor intestinal permeability of these AA-CAs supports their localization in the gut. Moreover, two cryptic microbial strains, Ileibacterium valens and Ruminococcus gnavus, were shown to have the capacity to synthesize these AA-CAs. This multi-omics dataset from CRC mouse models supports diet-induced shifts in the microbiome and metabolome in disease progression with potential utility in directing future diagnostic and therapeutic developments. In the fourth chapter, we demonstrate a new framework for performing differential abundance analysis using customized statistical modeling. As we learn more and more about the relationship between the microbiome and biological conditions, experimental protocols are becoming more and more complex. For example, meta-analyses, interventions, longitudinal studies, etc. are being used to better understand the dynamic nature of the microbiome. However, statistical methods to analyze these relationships are lacking--especially in the field of differential abundance. Finding biomarkers associated with conditions of interest must be performed with statistical care when dealing with these kinds of experimental designs. We present BIRDMAn, a software package integrating probabilistic programming with Stan to build custom models for analyzing microbiome data. We show that, on both simulated and real datasets, BIRDMAn is able to extract novel biological signals that are missed by existing methods. These chapters, taken together, advance our knowledge of statistical analysis of microbiome data and provide tools and references for researchers looking to perform analysis on their own data.

Download or read book An Integrated Analysis of Microbiomes and Metabolomics written by Yinglin Xia and published by American Chemical Society. This book was released on 2022-03-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because the microbial community is dynamic, an individual’s microbiota at a given time is varied, and many factors, including age, host genetics, diet, and the local environment, significantly change the microbiota. Thus, microbiome researchers have naturally expanded their research to look for insights into the interaction of the microbiome with other “omics”. Metabolites (small molecules) are the intermediate or end products of metabolism. Metabolites have various functions. The microbial-derived metabolites play an important role in the function of the microbiome. Thus, the advancement in microbiome studies is becoming particularly critical for the integration of microbial DNA sequencing data with other omics data, especially microbiome-metabolomics integration.

Download or read book Molecular Methods for Evaluating the Human Microbiome written by Katherine Margaret Kennedy and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Investigating Human Gut Microbiome in Obesity with Machine Learning Methods written by Yuqing Zhong and published by . This book was released on 2017 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obesity is a common disease among all ages that has threatened human health and has become a global concern. Gut microbiota can affect human metabolism and thus may modulate obesity. Certain mixes of gut microbiota can protect the host to be healthy or predispose the host to obesity. Modern next-generation sequencing technique allows accessing huge amount of genetic information underlying microbiota and thus provides new insights into the functionality of these micro-organisms and their interactions with the host. Multiple previous studies have demonstrated that the microbiome might contribute to obesity by increasing dietary energy harvest, promoting fat deposition and triggering systemic inflammation. However, these researches are either based on lab cultivation studies or basic statistical analysis. In order to further explore how gut microbiota affect obesity, this thesis utilize a series of machine learning methods to analyze large amount of metagenomics data from human gut microbiome. The publicly available HMP (Human Microbiome Project) metagenomic sequencing data, contain microbiome data for healthy adults, including overweight and obese individuals, were used for this study. HMP gut data were organized based on two different feature definitions: taxonomic information and metabolic reconstruction information. Several widely used classification algorithms: namely Naive Bayes, Random Forest, SVM and elastic net logistic regression were applied to predict healthy or obese status of the subjects based on the cross-validation accuracy. Furthermore, the corresponding feature selection algorithms were used to identify signature features in each dataset that lead to the differences between healthy and obese samples. The results showed that these algorithms perform poorly on taxonomic data than metabolic pathway data though lots of selected taxa are still supported by literature. Among all the combinations between different algorithms and data, elastic net logistic regression has the best cross-validation performance and thus becomes the best model. In this model, several important features are found and some of these are consistent with the previous studies. Rerunning classifiers by using features selected by elastic net logistic regression again further improved the performance of the classifiers. On the other hand, this study uncovered some new features that haven't been supported by previous studies. The new features could also be the potential target to distinguish obese and healthy subjects. The present thesis work compares the strengths and weaknesses of different machine learning techniques with different types of features originating from the same metagenomics data. The features selected by these models could provide a deep understanding of the metabolic mechanisms of micro-organisms. It is therefore worth to comprehensively understand the differences of gut microbiota between healthy and obese subjects, and particularly how gut microbiome affects obesity.

Download or read book Some Topics on Statistical Analysis of Genetic Imprinting Data and Microbiome Compositional Data written by Fan Xia and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Some Topics on Statistical Analysis of Genetic Imprinting Data and Microbiome Compositional Data" by Fan, Xia, 夏凡, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Genetic association study is a useful tool to identify the genetic component that is responsible for a disease. The phenomenon that a certain gene expresses in a parent-of-origin manner is referred to as genomic imprinting. When a gene is imprinted, the performance of the disease-association study will be affected. This thesis presents statistical testing methods developed specially for nuclear family data centering around the genetic association studies incorporating imprinting effects. For qualitative diseases with binary outcomes, a class of TDTI* type tests was proposed in a general two-stage framework, where the imprinting effects were examined prior to association testing. On quantitative trait loci, a class of Q-TDTI(c) type tests and another class of Q-MAX(c) type tests were proposed. The proposed testing methods flexibly accommodate families with missing parental genotype and with multiple siblings. The performance of all the methods was verified by simulation studies. It was found that the proposed methods improve the testing power for detecting association in the presence of imprinting. The class of TDTI* tests was applied to a rheumatoid arthritis study data. Also, the class of Q-TDTI(c) tests was applied to analyze the Framingham Heart Study data. The human microbiome is the collection of the microbiota, together with their genomes and their habitats throughout the human body. The human microbiome comprises an inalienable part of our genetic landscape and contributes to our metabolic features. Also, current studies have suggested the variety of human microbiome in human diseases. With the high-throughput DNA sequencing, the human microbiome composition can be characterized based on bacterial taxa relative abundance and the phylogenetic constraint. Such taxa data are often high-dimensional overdispersed and contain excessive number of zeros. Taking into account of these characteristics in taxa data, this thesis presents statistical methods to identify associations between covariate/outcome and the human microbiome composition. To assess environmental/biological covariate effect to microbiome composition, an additive logistic normal multinomial regression model was proposed and a group l1 penalized likelihood estimation method was further developed to facilitate selection of covariates and estimation of parameters. To identify microbiome components associated with biological/clinical outcomes, a Bayesian hierarchical regression model with spike and slab prior for variable selection was proposed and a Markov chain Monte Carlo algorithm that combines stochastic variable selection procedure and random walk metropolis-hasting steps was developed for model estimation. Both of the methods were illustrated using simulations as well as a real human gut microbiome dataset from The Penn Gut Microbiome Project. DOI: 10.5353/th_b5223971 Subjects: Genomic imprinting - Statistical methods Body, Human - Microbiology - Statistical methods

Download or read book Evaluating Molecular Methods for Human Microbiome Analysis written by Katherine Margaret Kennedy and published by . This book was released on 2014 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: In human microbiome analysis, sequencing of bacterial 16S rRNA genes has revealed a role for the gut microbiota in maintaining health and contributing to various pathologies. Novel community analysis techniques must be evaluated in terms of bias, sensitivity, and reproducibility and compared to existing techniques to be effectively implemented. Next-generation sequencing technologies offer many advantages over traditional fingerprinting methods, but this extensive evaluation required for the most efficacious use of data has not been performed previously. Illumina libraries were generated from the V3 region of the 16S rRNA gene of samples taken from 12 unique sites within the gastrointestinal tract for each of 4 individuals. Fingerprint data were generated from these samples and prominent bands were sequenced. Sequenced bands were matched with OTUs within their respective libraries. The results demonstrate that denaturing gradient gel electrophoresis (DGGE) represents relatively abundant bacterial taxa (>0.1%). The [beta]-diversity of all samples was compared using Principal Coordinates Analysis (PCoA) of UniFrac distances and Multi-Response Permutation Procedure (MRPP) was applied to measure sample cluster strength and significance; indicator species analysis of fingerprint bands and Illumina OTUs were also compared. The results demonstrate overall similarities between community profiling methods but also indicate that sequence data were not subject to the same limitations observed with the DGGE method (i.e., only abundant taxa bands are resolved, unable to distinguish disparate samples). In addition, the effect of stochastic fluctuations in PCR efficiency ("PCR drift") has not been rigorously tested and may differ for DGGE and next-generation sequencing. I compared pooled and individual reactions for samples of high and low template concentration for both Illumina and DGGE using the combined V3-V4 region of the 16S rRNA gene, and demonstrated that template concentration has a greater impact on reproducibility than pooling. This research shows congruity between two disparate molecular methods, identifies sources of bias, and establishes new guidelines for minimizing bias in microbial community analyses.

Download or read book Microbiome Analysis written by Robert G. Beiko and published by . This book was released on 2018 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Visual and Functional Analysis of Microbiome Data written by Achal Dhariwal and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The advancements in next-generation sequencing technologies have revolutionized microbiome research by allowing culture-independent high-throughput profiling of the genetic contents of microbial communities. Nowadays, 16S rRNA based marker gene sequencing is widely used to characterize the taxonomic composition and phylogenetic diversity of complex microbial communities. However, statistical, visual and functional analysis of such data possess great challenges. In addition, many aspects of the current approaches can be improved to get a better understanding of communities. The proper analysis of the resulting large and complicated datasets remains a key bottleneck in current microbiome studies. Over the last decade, powerful computational pipelines and standard protocols have been developed to support efficient raw data processing and annotation of microbiome data. The focus has now shifted towards downstream statistical analysis and functional interpretation. To address this bottleneck, we have developed MicrobiomeAnalyst, a user-friendly web-based tool that incorporates recent progresses in statistics and interactive visualization techniques, coupled with novel knowledge bases, to facilitate comprehensive analysis of common data sets generated from microbiome studies. MicrobiomeAnalyst contains four major components, including i) a module for community diversity profiling, comparative analysis and functional prediction of 16S rRNA marker gene data; ii) a module for exploratory data analysis, functional profiling and metabolic network visualization for shotgun metagenomics or metatranscriptomics data; iii) a module to help users to interpret their taxa of interest via enrichment analysis against ~300 taxon sets manually collected from recent literature and public databases; and iv) a module to allow users to visually explore their data sets within the context of compatible public data (meta-analysis) for pattern discovery and biological insights. The tool is freely accessible at http://www.microbiomeanalyst.ca. " --

Download or read book The Human Microbiome Diet and Health written by Food Forum and published by National Academies Press. This book was released on 2013-02-27 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Food Forum convened a public workshop on February 22-23, 2012, to explore current and emerging knowledge of the human microbiome, its role in human health, its interaction with the diet, and the translation of new research findings into tools and products that improve the nutritional quality of the food supply. The Human Microbiome, Diet, and Health: Workshop Summary summarizes the presentations and discussions that took place during the workshop. Over the two day workshop, several themes covered included: The microbiome is integral to human physiology, health, and disease. The microbiome is arguably the most intimate connection that humans have with their external environment, mostly through diet. Given the emerging nature of research on the microbiome, some important methodology issues might still have to be resolved with respect to undersampling and a lack of causal and mechanistic studies. Dietary interventions intended to have an impact on host biology via their impact on the microbiome are being developed, and the market for these products is seeing tremendous success. However, the current regulatory framework poses challenges to industry interest and investment.

Download or read book GUT Microbiome written by David A. Johnson and published by Nova Science Publishers. This book was released on 2015-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The gastrointestinal (GI) tract is home to trillions of microorganisms and contains more genetic information than that which exists in the human genome. It is, in fact, the largest immune system in the body. Study of the GI tract microbiome and its influence on both health and disease states have demonstrated the importance in maintaining health. The microbiome has a significant role in the assembly of micronutrients and vitamins and immune system processing. Recently, there has been a focus on the cross-talk between gut immunity and the host microbiome and the subsequent effect of this interaction on a broad range of diseases. The application of next-generation sequencing technologies to the study of human-associated microbial communities has markedly advanced our understanding of these effects. Changes in human-associated microbial communities have been implicated in the etiology and increased incidence of ever growing chronic conditions including obesity, diabetes, and inflammatory bowel disease. Although recognizably understanding the full spectrum of the role of the "gut microbiome" in health and disease is still in a relative infant states, it is clear that our bacterial flora play a much larger role in systemic diseases than previously appreciated. Healthcare for disease management has typically focused on specific therapy with pharmacologic, device or surgical intervention. As we further expand our understanding of the importance of gut microbiome, it is certain that we will see major changes to disease management strategies. Presently, we can see "footprints" of specific bacterial shifts in healthy ones versus those with a disease. Whether shifting the bacteria colonization away from the perceived imbalance in disease, will modify the disease expression remains to be seen. Clearly in the next decade, we will see profound changes in the way we approach current disease intervention/prevention. The intent of the authors of this book is to provide the most current assessment and analysis of what will likely in the coming decade to be the most exciting expansion in a new understanding of complex relationships of disease pathophysiology as well as therapeutic options for therapy. Additionally, it is the intent not to provide specific answers, but rather hopefully push clinicians to "think outside of the box" and raise great questions to direct research and/or translational therapies for redefining and optimizing "best practice" treatment strategies for our patients!

Download or read book The Human Gut Microbiome written by Josué Pérez Santiago and published by . This book was released on 2012 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human gut microbiome plays an essential role in the maturation and maintenance of the gut associated lymphoid tissue (GALT), yet how the composition of this microbiome assists in this function is not completely understood. Most previous gut flora studies have been limited since they required the isolation and cultivation of microbes, and many of the gut microbes cannot be cultured. The advent of next generation sequencing has improved the identification of the composition of the human gut microbiome, but these techniques require better methods for analyzing these high dimensional data. To overcome these bioinformatics challenges, a pipeline was developed and applied for the study of the gut microbiome during Human Immunodeficiency Virus (HIV) infection. The hallmark of HIV infection is the depletion of CD4 T cells, particularly in the gastrointestinal (GI) tract. This depletion compromises gut integrity and results in the translocation of microbial products into the bloodstream. Using next generation sequencing, we found that during untreated HIV infection, gut bacterial profiles segregated 11 participants according to their systemic lymphocyte percentages (Group 1 : median CD4% = 9.5% vs. Group 2 : median CD4% = 33%). Additionally, regression analyses showed that Lactobacillales in the distal gut were associated with higher CD4 counts and CD4%, less immune activation, less viral replication, less gut T cell proliferation and less microbial translocation in untreated and treated HIV infection. Changes of gut bacterial populations may reflect gut immune health and therefore interventions targeted to gut microbiota might help rebuild gut integrity during HIV infection.