Integrated Metagenomic and Metabolomic Analysis

Metagenomic and metabolomics integrated analysis, which can explain the correlation between specific flora, gene function and specific metabolites from multiple perspectives, is used to screen key functional strains and metabolites for changes in the metabolic state of flora or functional metabolic small molecules. At present, more and more life science researchers are applying integrated metagenome and metabolome analysis to better explain life science problems from multiple levels and perspectives by analyzing biological problems from both microbial and metabolic perspectives, and by correlating microbes with metabolites.

Creative Proteomics is an industry-leading multi-omics research expert. We play an important role in helping researchers explore the intrinsic relationship between microorganisms, metabolites and phenotypes by integrating and analyzing metagenomic and metabolomic data, as well as performing quantitative association analysis and functional association analysis on different microorganisms and different metabolites, respectively.

Proposed model of the microbiome–exercise interaction. (Scheiman J, et al. 2019)

Advantages of integrated metagenomic and metabolomic analysis

• Comprehensiveness in biological analysis. Integrated metagenomic and metabolomic analysis is its ability to provide a more comprehensive view of biological systems. By simultaneously analyzing both genetic and metabolic components, researchers can gain a deeper understanding of the dynamic interactions between these molecules.
• Unraveling gene-environment interactions. The integration of metagenomic and metabolomic data enables researchers to unravel the intricate gene-environment interactions that underlie various biological processes.
• Systems biology and network analysis. Integrated metagenomic and metabolomic analysis enable researchers to construct complex networks of gene-metabolite interactions. These networks offer a systems-level perspective, illustrating the intricate regulatory relationships and feedback loops within cellular processes.
• Biomarker discovery and disease diagnostics.
• The integration of metagenomic and metabolomic data holds great promise in the discovery of biomarkers for disease diagnosis and prognosis. These biomarkers can potentially aid in early disease detection, allowing for timely intervention and improved patient outcomes.
• Advancing agricultural biotechnology.
• Integrated metagenomic and metabolomic analysis plays a pivotal role in crop improvement and yield optimization. By analyzing the genetic makeup of crops alongside their metabolic profiles, researchers can uncover critical genes and metabolic pathways responsible for desirable agronomic traits.

Applications of integrated metagenomic and metabolomic analysis

• Disease research. Disease marker research, disease mechanism research, disease treatment research, gut microbes and disease research.
• Drug research. Drug metabolism research.
• Biological research. Phenotype-related research, brain-gut-axis research, development of microbial agents such as probiotics.
• Food science research. Exploration of fermentation processes for fermented foods such as tea and curd.
• Environmental Research. Inter-root microbial research, environmental research such as water and soil.

Our service workflow

Creative Proteomics is a professional multi-omics integrated analysis research team, we provide high-quality data processing and bioinformatics analysis, and provide customized metagenomic and metabolomic research strategies to meet client needs. If you are interested in us, please feel free to contact us.

Reference

1. Scheiman J,Luber JM,Chavkin TA, et al. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat Med. 2019;25 (7):1104-1109.

An integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington's disease

Journal: Neurobiol Dis

Published: 2021

Abstract

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder in which the onset and severity of symptoms are influenced by various environmental factors. Recent findings emphasize the importance of the gastrointestinal microbiome in mediating bidirectional gut-brain axis communication through circulating factors. The authors integrated metagenomic and metabolomic analyses, and utilizing sequencing using the birdshot method, we investigated the gut microbial composition of the R6/1 transgenic HD mouse model from 4 to 12 weeks of age (early puberty to adulthood) and also performed targeted metabolomics on the plasma of these 12-week-old mice (9 mice in each group) in order to study the potential impact of gut dysbiosis on the metabolomic profile of the plasma.

Results

At the phylum level, the gut microbiomes of both groups were dominated by members of the Bacteroidetes and Firmicutes phyla, followed by members of the Proteobacteria, Actinobacteria, and Verrucomicrobia phyla in much lower abundances. The phylum composition was examined at 5 different time points based on Birdshot metagenomic sequencing data ( Figure 1). No significant differences were observed when HD mice were compared with WT mice at any time point.

Figure 1

KO pathways identified by sPLS-DA at week 12 included galactose metabolism and benzoate degradation, both of which showed reduced levels in the HD gut microbiome compared to WT littermates. In addition, sulfur metabolism, lysine degradation, glutathione metabolism, and butyric acid metabolism were identified by sPLS-DA and showed increased levels of the HD gut microbiome at 12 weeks of age compared with WT littermates (Figure 2).

Figure 2

Multi-omics integration reveals previously unknown relationships between the gut microbiome and plasma metabolites. The authors used DIABLO to integrate CLR-transformed birdshot metagenomic data and median-normalized plasma metabolome data to characterize 30 bacterial species, 20 genes, and metabolites that were highly correlated.Further visualization in Cytoscape showed that many microbes were found to be highly correlated with butyrate, homocitrulline, ATP, L-asparagine, 3 -methylhistidine, orotic acid, and isobutyrylglycine were highly correlated (Figure 3).

Figure 3

Conclusion

Instability of the HD gut microbiota during the premotor symptomatic phase of the disease, which may have dire consequences for host health. Combining metagenomic data and metabolomic data, the authors demonstrate through the present study that HD gut microbiome function is disturbed prior to the onset of significant cognitive and motor dysfunction, suggesting a potential role for the gut in regulating the pathogenesis of HD, possibly through plasma metabolites that mediate specific alterations in gut-brain signaling.

Reference

1. Kong G,Ellul S,Narayana VK, et al. An integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington's disease. Neurobiol Dis. 2021;148:105199.