Pet-Human Gut Microbiome Host Classifier Using Data from Different Studies

Nadia Bykova; Nikita Litovka; Anna Popenko; Sergey Musienko

doi:10.3390/microorganisms8101591

Pet-Human Gut Microbiome Host Classifier Using Data from Different Studies

Microorganisms ◽

10.3390/microorganisms8101591 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1591

Author(s):

Nadia Bykova ◽

Nikita Litovka ◽

Anna Popenko ◽

Sergey Musienko

Keyword(s):

Gut Microbiome ◽

Environmental Data ◽

Domestic Cat ◽

Sequencing Data ◽

Human Gut ◽

Human Gut Microbiome ◽

Feature Importance ◽

Importance Analysis ◽

Using Data ◽

Sources Of Contamination

(1) Background: microbiome host classification can be used to identify sources of contamination in environmental data. However, there is no ready-to-use host classifier. Here, we aimed to build a model that would be able to discriminate between pet and human microbiomes samples. The challenge of the study was to build a classifier using data solely from publicly available studies that normally contain sequencing data for only one type of host. (2) Results: we have developed a random forest model that distinguishes human microbiota from domestic pet microbiota (cats and dogs) with 97% accuracy. In order to prevent overfitting, samples from several (at least four) different projects were necessary. Feature importance analysis revealed that the model relied on several taxa known to be key components in domestic cat and dog microbiomes (such as Fusobacteriaceae and Peptostreptococcaeae), as well as on some taxa exclusively found in humans (as Akkermansiaceae). (3) Conclusion: we have shown that it is possible to make a reliable pet/human gut microbiome classifier on the basis of the data collected from different studies.

Download Full-text

Analysis of Human Gut Microbiome: Taxonomy and Metabolic Functions in Thai Adults

Genes ◽

10.3390/genes12030331 ◽

2021 ◽

Vol 12 (3) ◽

pp. 331

Author(s):

Nachon Raethong ◽

Massalin Nakphaichit ◽

Narissara Suratannon ◽

Witida Sathitkowitchai ◽

Wanlapa Weerapakorn ◽

...

Keyword(s):

Gut Microbiome ◽

Asian Country ◽

Rrna Gene ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Human Gut ◽

Thai Population ◽

Shotgun Metagenomics ◽

Human Gut Microbiome ◽

Metabolic Functions

The gut microbiome plays a major role in the maintenance of human health. Characterizing the taxonomy and metabolic functions of the human gut microbiome is necessary for enhancing health. Here, we analyzed the metagenomic sequencing, assembly and construction of a meta-gene catalogue of the human gut microbiome with the overall aim of investigating the taxonomy and metabolic functions of the gut microbiome in Thai adults. As a result, the integrative analysis of 16S rRNA gene and whole metagenome shotgun (WMGS) sequencing data revealed that the dominant gut bacterial families were Lachnospiraceae and Ruminococcaceae of the Firmicutes phylum. Consistently, across 3.8 million (M) genes annotated from 163.5 gigabases (Gb) of WMGS sequencing data, a significant number of genes associated with carbohydrate metabolism of the dominant bacterial families were identified. Further identification of bacterial community-wide metabolic functions promisingly highlighted the importance of Roseburia and Faecalibacterium involvement in central carbon metabolism, sugar utilization and metabolism towards butyrate biosynthesis. This work presents an initial study of shotgun metagenomics in a Thai population-based cohort in a developing Southeast Asian country.

Download Full-text

Metabolic Modeling of CommonEscherichia coliStrains in Human Gut Microbiome

BioMed Research International ◽

10.1155/2014/694967 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Yue-Dong Gao ◽

Yuqi Zhao ◽

Jingfei Huang

Keyword(s):

Gut Microbiome ◽

Metabolic Networks ◽

High Throughput Sequencing ◽

Carbon Sources ◽

Microbial Interactions ◽

Dynamic Responses ◽

Sequencing Data ◽

Human Gut ◽

E Coli ◽

Human Gut Microbiome

The recent high-throughput sequencing has enabled the composition ofEscherichia colistrains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1) exploring the genetic differences betweenE. colistrains in human gut and (2) dynamic responses ofE. colito diverse stress conditions. As a result, we investigated theE. colistrains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the three most commonE. colistrains, includingE. coliHS, UTI89, and CFT073. The metabolic models show obvious strain-specific characteristics, both in network contents and in behaviors. We predicted optimal biomass production for three models on four different carbon sources (acetate, ethanol, glucose, and succinate) and found that these stress-associated genes were involved in host-microbial interactions and increased in human obesity. Besides, it shows that the growth rates are similar among the models, but the flux distributions are different, even inE. colicore reactions. The correlations between human diabetes-associated metabolic reactions in theE. colimodels were also predicted. The study provides a systems perspective onE. colistrains in human gut microbiome and will be helpful in integrating diverse data sources in the following study.

Download Full-text

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

BioData Mining ◽

10.1186/s13040-021-00241-2 ◽

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.

Download Full-text