scholarly journals specificity : an R package for generalized specificity analysis, with example analyses of Hawaiian foliar endophytic Fungi, human gut microbiome bacteria, and Antarctic glacier bacteria.

2021 ◽  
Author(s):  
John L Darcy ◽  
Anthony S Amend ◽  
Sean O I Swift ◽  
Pacifica S Sommers ◽  
Catherine A Lozupone
Keyword(s):  
Gut Microbiome ◽  
Environmental Variables ◽  
Microbial Community Composition ◽  
Geographic Distance ◽  
Real Life ◽  
R Package ◽  
Environmental Variable ◽  
Human Gut ◽  
Data Set ◽  
Human Gut Microbiome

Understanding the factors that influence microbes' environmental distributions is important for determining drivers of microbial community composition. Species distributions are governed by parameters that influence their dispersal or survival. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogenies. In microbial ecology, "specificity" is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao's (1982, 2010) Quadratic Entropy can be conveniently applied to calculate specificity of a focal species to many different environmental variables, including 1-dimensional variables (vectors), dissimilarity matrices, phylogenies, and ontologies. We present our R package "specificity" for performing the above analyses, and apply it to four real-life microbial datasets to demonstrate its application: fungi living within the leaves of native Hawaiian plants, bacteria from the human gut microbiome, bacteria living within Antarctic glacier ice, and the Earth Microbiome Project data set. Finally, we present an interactive visualization and data exploration tool for these analyses, available as a companion R-package called "specificity.shiny".

scholarly journals LightCUD: a program for diagnosing IBD based on human gut microbiome data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Congmin Xu ◽  
Man Zhou ◽  
Zhongjie Xie ◽  
Mo Li ◽  
Xi Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gut Microbiome ◽  
High Performance ◽  
Machine Learning Algorithms ◽  
Healthy Controls ◽  
Sequencing Data ◽  
Human Gut ◽  
Data Set ◽  
16S Sequencing ◽  
Human Gut Microbiome

Abstract Background The diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome. Results Using a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn’s disease module, which contributed to the optimization of model performance during pre-training. We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage http://cqb.pku.edu.cn/ZhuLab/LightCUD/. The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regarding changes in the gut microbial community. Conclusions As the first released human gut microbiome-based IBD diagnostic tool, LightCUD demonstrates a high-performance for both WGS and 16S sequencing data. The strains that either identify healthy controls from IBD patients or distinguish the specific type of IBD are expected to be clinically important to serve as biomarkers.

Methotrexate-Induced Shifts in the Human Gut Microbiome Decrease Immune Activation

2020 ◽  
Author(s):  
Renuka R. Nayak ◽  
Margaret Alexander ◽  
Ishani Deshpande ◽  
Kye Stapleton-Grey ◽  
Carles Ubeda ◽  
...  
Keyword(s):  
Immune Activation ◽  
Gut Microbiome ◽  
Human Gut ◽  
Human Gut Microbiome

scholarly journals Taxonomic signatures of cause-specific mortality risk in human gut microbiome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aaro Salosensaari ◽  
Ville Laitinen ◽  
Aki S. Havulinna ◽  
Guillaume Meric ◽  
Susan Cheng ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Gut Microbiome ◽  
Human Gut ◽  
Cross Sectional ◽  
Microbiome Composition ◽  
Human Gut Microbiome ◽  
Non Invasive ◽  
Sequencing Technologies

AbstractThe collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.

scholarly journals The seasonal changes of the gut microbiome of the population living in traditional lifestyles are represented by characteristic species-level and functional-level SNP enrichment patterns

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xue Zhu ◽  
Jiyue Qin ◽  
Chongyang Tan ◽  
Kang Ning
Keyword(s):  
Gut Microbiome ◽  
Seasonal Changes ◽  
Dry Season ◽  
Urban Setting ◽  
Snp Analysis ◽  
Wet Season ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Genome Level ◽  
Prevalent Species

Abstract Background Most studies investigating human gut microbiome dynamics are conducted on humans living in an urban setting. However, few studies have researched the gut microbiome of the populations living traditional lifestyles. These understudied populations are arguably better subjects in answering human-gut microbiome evolution because of their lower exposure to antibiotics and higher dependence on natural resources. Hadza hunter-gatherers in Tanzania have exhibited high biodiversity and seasonal patterns in their gut microbiome composition at the family level, where some taxa disappear in one season and reappear later. Such seasonal changes have been profiled, but the nucleotide changes remain unexplored at the genome level. Thus, it is still elusive how microbial communities change with seasonal changes at the genome level. Results In this study, we performed a strain-level single nucleotide polymorphism (SNP) analysis on 40 Hadza fecal metagenome samples spanning three seasons. With more SNP presented in the wet season, eight prevalent species have significant SNP enrichment with the increasing number of SNP calling by VarScan2, among which only three species have relatively high abundances. Eighty-three genes have the most SNP distributions between the wet season and dry season. Many of these genes are derived from Ruminococcus obeum, and mainly participated in metabolic pathways including carbon metabolism, pyruvate metabolism, and glycolysis. Conclusions Eight prevalent species have significant SNP enrichments with the increasing number of SNP, among which only Eubacterium biforme, Eubacterium hallii and Ruminococcus obeum have relatively high species abundances. Many genes in the microbiomes also presented characteristic SNP distributions between the wet season and the dry season. This implies that the seasonal changes might indirectly impact the mutation patterns for specific species and functions for the gut microbiome of the population that lives in traditional lifestyles through changing the diet in wet and dry seasons, indicating the role of these variants in these species’ adaptation to the changing environment and diets.

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