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An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

journal contribution
submitted on 2024-02-22, 05:37 and posted on 2024-02-22, 05:38 authored by Fayaz Ahmad Mir, Raghvendra Mall, Ehsan Ullah, Ahmad Iskandarani, Farhan Cyprian, Tareq A. Samra, Meis Alkasem, Ibrahem Abdalhakam, Faisal Farooq, Shahrad Taheri, Abdul-Badi Abou-Samra

Objectives

To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways.

Methods

We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications.

Results

We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome.

Conclusions

The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.

Other Information

Published in: Journal of Translational Medicine
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1186/s12967-023-04074-x

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Medical Corporation
  • Qatar Metabolic Institute - HMC
  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU
  • Qatar University
  • Qatar University Health - QU
  • College of Medicine - QU HEALTH
  • Weill Cornell Medicine - Qatar

Methodology

We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications.

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