scholarly journals Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data

2021 ◽  
Vol 11 (2) ◽  
pp. 128
Author(s):  
Eunchong Huang ◽  
Sarah Kim ◽  
TaeJin Ahn
Keyword(s):  
Insulin Resistance ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Integrated Analysis ◽  
Omics Data ◽  
Technological Advances ◽  
Interpretation Algorithm ◽  
Next Generation Sequencing Ngs ◽  
Microbiome Data ◽  
The Impact

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.

scholarly journals Metabolome–Microbiome Crosstalk and Human Disease

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 181 ◽  
Author(s):  
Kathleen A. Lee-Sarwar ◽  
Jessica Lasky-Su ◽  
Rachel S. Kelly ◽  
Augusto A. Litonjua ◽  
Scott T. Weiss
Keyword(s):  
Human Body ◽  
Disease Risk ◽  
Human Microbiome ◽  
Treatment Strategies ◽  
Human Microbiome Project ◽  
Combined Use ◽  
Microbiome Data ◽  
The Impact ◽  
Active Investigation ◽  
Human Intestinal Tract

In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these “omics” are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.

scholarly journals Misclassification of a whole genome sequence reference defined by the Human Microbiome Project: a detrimental carryover effect to microbiome studies

2019 ◽  
Author(s):  
DJ Darwin R. Bandoy ◽  
B Carol Huang ◽  
Bart C. Weimer
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Reference Database ◽  
Whole Genome ◽  
Reference Species ◽  
Genome Sequences ◽  
Genome Homology ◽  
Microbiome Data

AbstractTaxonomic classification is an essential step in the analysis of microbiome data that depends on a reference database of whole genome sequences. Taxonomic classifiers are built on established reference species, such as the Human Microbiome Project database, that is growing rapidly. While constructing a population wide pangenome of the bacterium Hungatella, we discovered that the Human Microbiome Project reference species Hungatella hathewayi (WAL 18680) was significantly different to other members of this genus. Specifically, the reference lacked the core genome as compared to the other members. Further analysis, using average nucleotide identity (ANI) and 16s rRNA comparisons, indicated that WAL18680 was misclassified as Hungatella. The error in classification is being amplified in the taxonomic classifiers and will have a compounding effect as microbiome analyses are done, resulting in inaccurate assignment of community members and will lead to fallacious conclusions and possibly treatment. As automated genome homology assessment expands for microbiome analysis, outbreak detection, and public health reliance on whole genomes increases this issue will likely occur at an increasing rate. These observations highlight the need for developing reference free methods for epidemiological investigation using whole genome sequences and the criticality of accurate reference databases.

scholarly journals Computational Modeling of the Human Microbiome

2020 ◽  
Vol 8 (2) ◽  
pp. 197
Author(s):  
Shomeek Chowdhury ◽  
Stephen S. Fong
Keyword(s):  
Computational Modeling ◽  
Human Health ◽  
Large Scale ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Microbial Composition ◽  
Site Specific ◽  
Microbiome Research ◽  
High Level ◽  
The Impact

The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.

scholarly journals Unity of bowel-lung axis and the role of beneficial microbiota in anti-infectious protection

2021 ◽  
Vol 4 (4) ◽  
pp. 355-361
Author(s):  
T.E. Taranushenko ◽  
Keyword(s):  
Immune Responses ◽  
Respiratory Infections ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Medical Community ◽  
Mucosal Inflammation ◽  
Potential Interactions ◽  
The Impact ◽  
And Child Health

NIH Human Microbiome Project determined particular attention of the worldwide medical community to the study of the human microbiome and the assessment of the impact of symbiont microorganisms in the development of various (not only gastrointestinal) disorders. Potential interactions between the bowel and lungs (bowel-lung axis) via microbiota that allow for the possible involvement of microorganisms in the development of respiratory diseases are actively debated. This paper reviews studies on the pattern of interactions between bowel and lungs in infectious diseases associated with mucosal inflammation. The association between gut microbiota and the protective barrier of the respiratory tract based on known mechanisms and novel data derived from recent studies on SARS-CoV-2 is discussed. The relevance of beneficial bacteria (symbionts) in local and systemic immune responses, their disease-modifying and, eventually, therapeutic strategymodifying properties, the ability to be a resource of preventive medicine and an orchestrating tool for infections are addressed. Practitioners’ difficulties with probiotics in preventive and treatment schedules for various conditions are highlighted. Finally, the use of probiotics in children with respiratory infections and COVID-19 is uncovered. KEYWORDS: microbiota, microbiome, probiotics, children, mucosal immunity, Bifidobacterium. FOR CITATION: Taranushenko T.E. Unity of bowel-lung axis and the role of beneficial microbiota in anti-infectious protection. Russian Journal of Woman and Child Health. 2021;4(4):355–361 (in Russ.). DOI: 10.32364/2618-8430-2021-4-4-355-361.

scholarly journals Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 601
Author(s):  
Justin Wagner ◽  
Jayaram Kancherla ◽  
Domenick Braccia ◽  
James Matsumara ◽  
Victor Felix ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Disease Status ◽  
Human Microbiome Project ◽  
Second Phase ◽  
Bioconductor Package ◽  
The Rich ◽  
Rich Data ◽  
Project Data ◽  
Microbiome Data

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual’s health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

scholarly journals ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 103 ◽  
Author(s):  
Subina Mehta ◽  
Marie Crane ◽  
Emma Leith ◽  
Bérénice Batut ◽  
Saskia Hiltemann ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Human Health ◽  
Genetic Material ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Taxonomic Composition ◽  
Sequencing Technologies ◽  
The Galaxy ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

scholarly journals TaxiBGC: a Taxonomy-guided Approach for the Identification of Experimentally Verified Microbial Biosynthetic Gene Clusters in Shotgun Metagenomic Data

2021 ◽  
Author(s):  
Utpal Bakshi ◽  
Vinod K Gupta ◽  
Aileen R Lee ◽  
John M Davis ◽  
Sriram Chandrasekaran ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Microbiome ◽  
Gene Clusters ◽  
Human Microbiome Project ◽  
Metagenomic Data ◽  
Biosynthetic Gene ◽  
Case Control Studies ◽  
Biosynthetic Gene Clusters ◽  
Host Interactions ◽  
Microbiome Data

Biosynthetic gene clusters (BGCs) in microbial genomes encode for the production of bioactive secondary metabolites (SMs). Given the well-recognized importance of SMs in microbe-microbe and microbe-host interactions, the large-scale identification of BGCs from microbial metagenomes could offer novel functional insights into complex chemical ecology. Despite recent progress, currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read-assembly and prediction of a narrow breadth of BGC classes. To overcome these limitations, we developed TaxiBGC (Taxonomy-guided Identification of Biosynthetic Gene Clusters), a computational pipeline for identifying experimentally verified BGCs in shotgun metagenomes by first pinpointing the microbial species likely to produce them. We show that our species-centric approach was able to identify BGCs in simulated metagenomes more accurately than by solely detecting BGC genes. By applying TaxiBGC on 5,423 metagenomes from the Human Microbiome Project and various case-control studies, we identified distinct BGC signatures of major human body sites and candidate stool-borne biomarkers for multiple diseases, including inflammatory bowel disease, colorectal cancer, and psychiatric disorders. In all, TaxiBGC demonstrates a significant advantage over existing techniques for systematically characterizing BGCs and inferring their SMs from microbiome data.

scholarly journals G2S: a new deep learning tool for predicting stool microbiome structure from oral microbiome data

2020 ◽  
Author(s):  
Simone Rampelli ◽  
Marco Candela ◽  
Elena Biagi ◽  
Patrizia Brigidi ◽  
Silvia Turroni
Keyword(s):  
Deep Learning ◽  
Gut Microbiome ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Oral Microbiome ◽  
The Family ◽  
Family Level ◽  
Microbial Metagenomics ◽  
Real Composition ◽  
Microbiome Data

Abstract Background Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. In the frame of meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in microbiology, with a great potential in the field of human microbiome. Results G2S is a bioinformatic tool for the taxonomic prediction of the human stool microbiome directly from oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on data of the Human Microbiome Project, allowing to infer the stool microbiome at the family level more accurately than other approaches. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects. Conclusions G2S infers the family-level taxonomic configuration of the stool microbiome mirroring the real composition with exceptional performance. G2S can be used with modern samples, allowing to predict the eubiotic/dysbiotic state of the gut microbiome when fecal sampling is missing, and especially with ancient samples, as a unique opportunity in the field of paleomicrobiology to recover data related to ancient gut microbiome configurations.

scholarly journals Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 601
Author(s):  
Justin Wagner ◽  
Jayaram Kancherla ◽  
Domenick Braccia ◽  
James Matsumara ◽  
Victor Felix ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Disease Status ◽  
Human Microbiome Project ◽  
Second Phase ◽  
Bioconductor Package ◽  
The Rich ◽  
Rich Data ◽  
Project Data ◽  
Microbiome Data

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual’s health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

Gut microbiota and coronary artery disease

ESC CardioMed ◽  
2018 ◽  
pp. 1085-1090
Author(s):  
Slayman Obeid ◽  
Melroy Miranda ◽  
Thomas F. Lüscher
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Gut Microbiota ◽  
Current Knowledge ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Short Description ◽  
Artery Disease ◽  
The Impact ◽  
Gut Microbiota Composition

Coronary artery disease (CAD) has been historically investigated as a metabolic disease focusing on dyslipidaemia and inflammation. The advent of the Human Microbiome Project has introduced the gut microbiota as a new player in the pathogenesis of CAD. Recent studies have shown that ageing and Western diets not only increase our risk of CAD but also affect gut microbiota composition and its interaction with our metabolic homeostasis. This chapter provides a short description of the historical timeline and the potential sources of the microbiome in humans. It aims to summarize and give a brief overview of the current knowledge on the impact of the gut microbiota on metabolism, with particular emphasis on CAD and the potential of influencing the microbiota to protect against CAD.

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