Linking Taxonomy, Function, and Metabolism Through Multi-Omics Integration
The increasing use of multiple omics technologies to study diseases and environmental systems has created a growing need to integrate datasets across different layers of biology. In microbiome studies, this integration is especially valuable because taxonomic composition, functional potential, and metabolic output are closely connected.
At BMKGENE, we provide in-depth integration of these datasets, including:
- Metabarcoding/amplicon sequencing: a DNA-based method that profiles microbial communities by sequencing taxonomic marker genes such as 16S rRNA or ITS.
- Shotgun metagenomics: a broader approach that captures all DNA in a sample, allowing researchers to explore both microbial composition and functional potential, including major cellular processes and antibiotic resistance genes.
- Non-targeted metabolomics: an untargeted LC–MS/MS-based approach that profiles metabolites present in a biological sample without prior target selection, offering a snapshot of the system’s biochemical state.
One of the main functional features of interest in microbiome studies is metabolic pathways, as they can have a strong impact on host and ecosystem biology. This makes it especially relevant to compare taxonomic and functional DNA-level data with metabolite concentrations.
How We Integrate Datasets to Identify Meaningful Biological Relationships
Our amplicon–metabolomics integration workflow begins with an overview of each dataset and an assessment of how they relate to one another. We use methods such as Procrustes analysis and two-way orthogonal partial least squares (O2PLS) to evaluate overall similarity and shared variation between datasets.
A key section of the report then focuses on biological associations. Here, we identify highly correlated metabolite–taxa pairs and detect groups of metabolites that follow similar abundance patterns to specific taxa, as shown in Figure 1.
Figure 1. Hierarchical clustering of metabolites and microbial phyla: (A) heatmap; (B) chord diagram.
We also apply multivariate methods to explore shared structures in the data, including coinertia analysis and canonical correspondence analysis. In addition, biomarker discovery is performed using random forest analysis to highlight the most relevant features.
For metagenomics–metabolomics integration, the same general strategy is used, with the additional ability to link metabolites to functional categories identified in the metagenomics dataset. Finally, KEGG pathway analysis of differentially abundant genes and metabolites helps reveal the main metabolic pathways affected in the study (Figure 2).
Figure 2. KEGG pathway analysis of differentially abundant metabolites and functional genes.
Case Study: Multi-Omics Reveals How Sonchus brachyotus DC. Extract Helps Mitigate Oxidative Stress
The value of integrating multiple omics datasets is illustrated by a recent study by Juan et al. (2025). The authors aimed to understand how extracts from Sonchus brachyotus DC. (SBE) may help reduce intestinal oxidative stress and how this effect may be mediated through a microbe–metabolite–gene network.
To investigate this, they used an ethanol-induced acute oxidative stress mouse model and compared a control group with three treatment groups receiving different doses of the plant extract. Gut samples were analyzed at three levels:
- 16S rRNA and ITS sequencing to profile bacterial and fungal composition.
- Untargeted metabolomics to measure small-molecule changes.
- RNA-seq to assess host gene expression.
The main findings from each omics layer were as follows:
Microbiome Profiling
- Ethanol exposure altered the gut microbial community, including changes in both bacterial and fungal taxa.
- Several bacterial genera, such as Alistipes and Ruminiclostridium, were reduced in the ethanol group and partially restored after SBE treatment.
- Fungal communities were also strongly affected, with treatment-associated shifts in genera including Setophoma, Pichia, Rhodotorula, Chaetomium, Fusarium, Filobasidium, and Naganishia.
Metabolomics
- The metabolomics analysis annotated 4,125 metabolites and identified 87 significantly altered metabolites between the control and ethanol groups.
- Ethanol exposure mainly affected metabolite classes such as carboxylic acids and derivatives, organooxygen compounds, fatty acyls, and lactones.
- KEGG enrichment highlighted several relevant pathways, including the serotonergic synapse, lysine biosynthesis, drug metabolism, and arachidonic acid metabolism.
RNA-seq
- Weighted gene co-expression network analysis (WGCNA) linked different gene modules to the treatment groups.
- Differential expression analysis identified eight DEGs between the control and ethanol groups, including Tnfsf10, Rab19, Fas, C2cd4b, Rpl21, Etv4, Zfand2a, and Rasef.
- KEGG analysis highlighted apoptosis, necroptosis, and cytokine signaling, with the apoptotic pathway showing the strongest enrichment.
Integration of the Datasets
By integrating the prescreened genes, metabolites, and microbial taxa, the authors built a correlation network linking eight key DEGs with four pivotal metabolites from the serotonergic synapse pathway and the top 20 enriched bacterial and fungal genera.
This network helped reveal how SBE may influence oxidative stress through coordinated changes in the fungal–metabolite–gene axis. The authors proposed that SBE modulates gut microbial composition, triggers metabolite changes, and ultimately affects Fas/Tnfsf10-mediated apoptosis signaling, contributing to protection against acute oxidative stress.
Why This Study Matters
This study is a strong example of how multi-omics integration can uncover mechanisms that would be difficult to detect using a single dataset alone. In particular, it highlights an important and somewhat unexpected role for the fungal microbiome in oxidative stress regulation, alongside the more commonly studied bacterial community.
By connecting microbial composition, metabolite changes, and host gene expression, multi-omics integration provides a more comprehensive view of biological regulation and helps researchers move from association-based observations toward mechanistic insight.
References
- Juan, Y., Wei, T., Weiwei, Z., et al. (2025). Regulation on microbial composition, serotonergic synapse, and apoptotic signaling pathway by extracts from Sonchus brachyotus DC. (SBE) to improve ethanol-induced acute oxidative stress in mice. Microbiome, 13, 221. DOI: 10.1186/s40168-025-02221-8
Post time: Jun-10-2026
