Why to Integrated Analyze Metagenomics and Metabolomics

Metabolic products, commonly formed under the influence of bodily enzymes, represent the ultimate expression of genes and bear closest resemblance to biological phenotypic characteristics among the four primary "omics" disciplines: Genomics, Proteomics, Transcriptomics, and Metabolomics. The study of Metabolomics can illuminate the generation of aberrant metabolites in complex diseases and elucidate disease-related metabolic regulation pathways. Theoretically, it is better positioned to unravel the relationships between genes and phenotypes.

Metagenomics, which forms one of the critical methods in microbial genomic studies, offers the capability to research all microbial genomes present in a given sample directly. Theoretically, it enables the acquisition of DNA information from all microorganisms, inclusive of bacteria, archaea, viruses, and fungi, with a level of specificity to distinct subtypes of species. In contrast, sequencing methods like bacterial 16S rDNA and fungal ITS regions typically amplify only specific conservative regions and are more suitable for interpreting the diversity, richness, and community structure of microbial populations. These methods are often deployed to study taxonomic levels up to the order and genus level, and lack precision when examining the specific species - the terminal level - or exploring their functional genes, in contrast to metagenomics.

The analysis content in metagenomics primarily comprises two parts. First is community composition - metagenomic sequencing boasts a higher taxonomic resolution, facilitating discussions at the species or strain levels. Secondly, it pertains to the functional gene composition. Unlike amplicons targeting specific genes, metagenomic sequencing can retrieve all genes from a microbial community. By comparing these retrieved genes with different functional gene databases, it's possible to annotate as many functions as possible. As such, metagenomics allows for research into the overall/specific microbial groups' gene function and metabolic pathways.

Metagenomic sequencing of the microbiota in conjunction with host metabolomics, or an integrated analysis involving bacterial 16S rDNA sequencing, metagenomics, and host metabolomics, can offer a multi-omics perspective to explore and elucidate the relationship between gut microbes and human, as well as between endophytic fungi and plant traits. These areas form the focal point of contemporary research.

How to Integrated Analysis of Metagenomics and Metabolomics

Integrated analysis techniques of metagenomics and metabolomics illuminate the intricate association between gut microbiota and the host at an advanced level. Similar to the association analysis approach of the metabolome and 16S rDNA sequencing, the combined analysis of metagenomics and metabolomics first identifies significantly disparate species-level microbiota and secondary metabolites. Subsequently, a Spearman correlation analysis is conducted between metabolites exhibiting significant differences derived from metabolomics and distinct species (at the species level) obtained from metagenomic sequencing. This unveils the relationships between significantly differential metabolites and differing species. Based on the computation results, appropriate selection criteria are applied to establish the relationship and network graphics of differential species and metabolites among others. Furthermore, metagenomics can be associated with metabolites through its metabolic pathways and gene functions. Based on the results of metagenomic analysis, we can obtain species annotation information and KEGG functional annotation information for the assembled Unigene sequences. By analyzing the relationship between species and Unigene, we can acquire the corresponding KEGG annotation information for the species. According to the KEGG metabolic pathways, we can combine metabolomics and metagenomics data to identify species and metabolites that have significantly changed in the same biological process (KEGG Pathway), thereby quickly identifying key species and understanding the potential KEGG functions possessed by the species.

Given the complementary benefits of 16S rDNA, ITS region amplicon sequencing, and metagenomic sequencing, as well as the cost-effectiveness of amplicon sequencing, researchers typically first employ a majority of microbial samples for 16S rDNA, ITS region, and other amplicon sequencing. This is done to perform a preliminary analysis of microbial community structure and explore genus-level differences. Subsequently, a selective subset of representative microbial samples is chosen for metagenomic sequencing. This further delves into species-level and gene function aspects of microbial populations, with a coupled analysis along with metabolomics to unfold the interactive mechnisms between the microbiota and the host.

Research Approach for Integrative Analysis of Metageomics and Metabolomics

Case of Integrated Metagenomic and Metabolomic Analysis

Integrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer

Journal: Gut (IF=31.793)
Published: August 2022
Sample Type: Human Fecal samples
Methods: Metagenomic Sequencing, Metabolomics

The incidence of early-onset colorectal cancer (EO-CRC) is on a steady rise. This study uses metagenomic and metabolomic analysis to investigate the interrelationships among the gut microbiota, metabolites, and microbial enzymes in EO-CRC patients, and evaluates their potential as non-invasive biomarkers. A total of 130 late-onset colorectal cancer (LO-CRC) patients, 114 EO-CRC patients, and age-matched healthy controls (97 LO-Control and 100 EO-Control) formed the testing cohort and were utilized to construct the colorectal cancer classifier. Further validity of study findings were confirmed using another independent cohort composed of 38 LO-CRC patients along with 24 EO-CRC, 22 LO-Control, and 24 EO-Control subjects, which served as a validation cohort.

Results revealed that both LO-CRC and EO-CRC patients exhibited a significant reduction in gut bacterial alpha diversity as compared to controls. Despite shared deviations, integrated analysis revealed distinct microbiota-metabolite associations in LO-CRC and EO-CRC. Characteristics of LO-CRC include enrichment of Fusobacterium nucleatum and depletion of short-chain fatty acids, diminished microbial GABA biosynthesis, and alteration in acetate/ethanol metabolism directing towards acetyl-CoA. In contrast, multi-omic features in EO-CRC frequently correlate with enrichment of Flavonifractor plautii, escalating tryptophan, bile acid, and choline metabolism. Notably, bacteria associated with red meat consumption, choline metabolites, and the upregulated KEGG (KO) genes axis, pldB and cbh, may constitute potential tumorigenic stimuli in EO-CRC.

This study's predictive model based on metagenome, metabolome, and KO gene markers performs impressively in distinguishing EO-CRC from controls which providing insights into the pathogenesis of EO-CRC and LO-CRT. Furthermore, the potential of microbiota-derived biomarkers as non-invasive indicators can be utilized to accurately detect and differentiate individuals with EO-CRC.

A freshwater fish-based diet alleviates liver steatosis by modulating gut microbiota and metabolites: a clinical randomized controlled trial in Chinese patients with nonalcoholic fatty liver disease

Journal: American Journal of Gastroenterology (IF=12.045)
Published: October 2022
Sample Type: Human feces Research
Methods: Metagenomic Sequencing; Metabolomics

The direction of this research was to evaluate the impact of two isoenergetic diets (a diet predominantly based on freshwater fish (Group F) and another combining freshwater fish with red meat interchangeably (Group F/M)) on the hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD), and to investigate its association with gut microbiota. A total of 34 NAFLD patients with hepatic steatosis ≥ 10% participated in a randomized controlled trial over 84 days, with participants split evenly between Group F and Group F/M. The research indicators included hepatic lipid content, gut microbiota, and their metabolites in NAFLD patients.

Trial Results: At the conclusion of the intervention, Group F exhibited a significantly larger reduction in hepatic steatosis compared to Group F/M. Among the 16 secondary clinical endpoints, Group F improved noticeably more on seven markers, including alanine aminotransferase and γ-glutamyltransferase, when compared with Group F/M. Moreover, the study found that alternating consumption of freshwater fish and red meat did not exacerbate NAFLD.

Comparatively, Group F registered a significant increase in fecal rods, short-chain fatty acids (SCFAs), nonconjugated bile acids enrichment, and had higher consumption rates of Coprococcus 9 and conjugated bile acids compared with Group F/M. Based on these findings, the authors suggested that higher consumption of freshwater fish could benefit NAFLD by modulating the gut microbiota and its metabolites. In terms of dietary management for NAFLD, alternating intake of freshwater fish and red meat to achieve similar total animal protein and fat content may pose no harm to NAFLD patients.

Xylitol Augments Propionate Synthesis in the Colon via Gut Microbiota Cross-Feeding

Journal: Microbiome (Impact Factor: 16.837)
Published: March 2021
Sample Type: Mouse feces and colon tissue
Research Techniques: 16S rDNA sequencing, ITS sequencing, Metagenomic Sequencing, Metabolomics

Xylitol, a white or transparent polyol or sugar alcohol, undergoes digestion by colonic microbiota, fostering the proliferation of beneficial bacteria and the production of short-chain fatty acids (SCFAs). However, the mechanisms underlying these effects remain unclear. In this study, mice were fed diets supplemented with 0%, 2% (2.17 g/kg/day), or 5% (5.42 g/kg/day) xylitol for three months. Simultaneously, a colonic simulation system (CDMN) received a 3% (weight/volume) (equivalent to 0.27 g/kg/day in adults) xylitol supplementation for seven days. Through 16S rDNA sequencing, quantification of beneficial metabolic biomarkers, metabolomic, and metatranscriptomic analyses, researchers explored the prebiotic mechanisms of xylitol.

The study revealed that, both in vivo and in vitro simulations, xylitol had no significant impact on the community structure of the intestinal microbiota. However, xylitol increased all SCFAs, particularly propionate in the lumen and butyrate in the mucosa, with associated changes in bacterial taxa in the in vitro simulation. During the utilization of xylitol, a phenomenon of cross-feeding emerged among Lactobacillus reuteri, Bacteroides fragilis, and Escherichia coli, wherein one organism consumed metabolites excreted by another.

At the molecular level, researchers identified xylitol dehydrogenase (EC1.1.1.14), xylulokinase (EC2.7.1.17), and xylulose-phosphate isomerase (EC5.1.3.1) as key enzymes in xylitol metabolism, present in the genera Bacteroides and family Lachnospiraceae. Hence, researchers inferred that Bacteroides and Lachnospiraceae are pivotal bacteria in xylitol digestion. Additionally, xylitol influenced the propionate metabolic pathway, significantly enhancing the transcription of phosphotransacetylase (EC2.3.1.8) in Bifidobacterium, thereby increasing propionate production.

The findings suggest that key enzymes involved in xylitol digestion from different bacteria can collectively support the growth of the microbiota. Based on cross-feeding and competition among bacteria, xylitol dynamically balances the proportions of the gut microbiota, thereby promoting the relevant enzymatic metabolism of xylitol and short-chain fatty acids.

References

1. Kong C, Liang L, Liu G, Du L, Yang Y, Liu J, Shi D, Li X, Ma Y. Integrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer. Gut. 2022 Aug 11:gutjnl-2022-327156.
2. He K, Guo LL, Tang H, Peng X, Li J, Feng S, Bie C, Chen W, Li Y, Wang M, Tang S. A Freshwater Fish-Based Diet Alleviates Liver Steatosis by Modulating Gut Microbiota and Metabolites: A Clinical Randomized Controlled Trial in Chinese Participants With Nonalcoholic Fatty Liver Disease. Am J Gastroenterol. 2022 Oct 1;117(10):1621-1631.
3. Xiang S, Ye K, Li M, Ying J, Wang H, Han J, Shi L, Xiao J, Shen Y, Feng X, Bao X, Zheng Y, Ge Y, Zhang Y, Liu C, Chen J, Chen Y, Tian S, Zhu X. Xylitol enhances synthesis of propionate in the colon via cross-feeding of gut microbiota. Microbiome. 2021 Mar 18;9(1):62.