Simultaneous Polar Metabolite and N‑Glycan Extraction Workflow for Joint-Omics Analysis: A Synergistic Approach for Novel Insights into Diseases

Bioinformatics and machine learning
tools have made it possible
to integrate data across different -omics platforms for novel multiomic
insights into diseases. To synergistically process -omics data in
an integrative manner, analyte extractions for each -omics type need
to be done on the same set of clinical samples. Therefore, we introduce
a simultaneous dual extraction method for generating both metabolomic
(polar metabolites only) and glycomic (protein-derived N-glycans only)
profiles from one sample with good extraction efficiency and reproducibility.
As proof of the usefulness of the extraction and joint-omics workflow,
we applied it on platelet samples obtained from a cohort study comprising
66 coronary heart disease (CHD) patients and 34 matched healthy community-dwelling
controls. The metabolomics and N-glycomics data sets were subjected
to block partial least-squares–discriminant analysis (block-PLS-DA)
based on sparse generalized canonical correlation analysis (CCA) for
identifying relevant mechanistic interactions between metabolites
and glycans. This joint-omics investigation revealed intermodulative
roles that protein-bound carbohydrates or glycoproteins and amino
acids have in metabolic pathways and through intermediate protein
dysregulations. It also suggested a protective role of the glyco-redox
network in CHD, demonstrating proof-of-principle for a joint-omics
analysis in providing new insights into disease mechanisms, as enabled
by a simultaneous polar metabolite and protein-derived N-glycan extraction
workflow.