An overview of major metagenomic studies on human microbiomes in health and disease

Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions

Various fields have been identified in the "omics" era, such as genomics, proteomics, transcriptomics, metabolomics, phenomics, and metagenomics. Among these, metagenomics has enabled a significant increase in discoveries related to the microbial world. Newly discovered microbiomes in different ecologies provide meaningful information on the diversity and functions of microorganisms on the Earth. Therefore, the results of metagenomic studies have enabled new microbe-based applications in human health, agriculture, and the food industry, among others. This review summarizes the fundamental procedures on recent advances in bioinformatic tools. It also explores up-to-date applications of metagenomics in human health, food study, plant research, environmental sciences, and other fields. Finally, metagenomics is a powerful tool for studying the microbial world, and it still has numerous applications that are currently hidden and awaiting discovery. Therefore, this review also discusses the future perspectives of metagenomics.

Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data

The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic methods in recent years. In this paper, machine learning methods are used to analyze metagenomic sequencing data of acne, i.e., all kinds of lipids in the face skin. Firstly, lipids data of the diseased skin (DS) samples and the healthy skin (HS) samples of acne patients and the normal control (NC) samples of healthy person are, respectively, analyzed by using principal component analysis (PCA) and kernel principal component analysis (KPCA). Then, the lipids which have main influence on each kind of sample are obtained. In addition, a multiset canonical correlation analysis (MCCA) is utilized to get lipids which can differentiate the face skins of the above three samples. The experimental results show the machine learning methods can effectively analyze metagenomic sequencing data of acne. According to the results, lipids which only influence one of the three samples or the lipids which simultaneously have different degree of influence on these three samples can be used as indicators to judge skin statuses.

Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons

Background

Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions.

Results

In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101).

Conclusion

With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

Estimating the total genome length of a metagenomic sample using k-mers

Background

Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.

Results

As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.

Conclusions

We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5467-x) contains supplementary material, which is available to authorized users.