Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection

  1. Flora Mikaeloff  Is a corresponding author
  2. Marco Gelpi
  3. Rui Benfeitas
  4. Andreas D Knudsen
  5.  Beate Vestad
  6.  Julie Høgh
  7. Johannes R Hov
  8. Thomas Benfield
  9. Daniel Murray
  10. Christian G Giske
  11. Adil Mardinoglu
  12. Marius Trøseid
  13. Susanne D Nielsen
  14. Ujjwal Neogi  Is a corresponding author
  1. Karolinska Institute, Sweden
  2. Rigshospitalet, Denmark
  3. Oslo University Hospital, Norway
  4. Copenhagen University Hospital, Denmark
  5. King's College London, United Kingdom

Abstract

Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PLWH). Still, a systematic and in-depth characterization of metabolic risk profile during successful long-term treatment is lacking. Here, we used multi-omics (plasma lipidomic, metabolomic, and fecal 16S microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PLWH. Through network analysis and similarity network fusion (SNF), we identified three groups of PLWH (SNF-1 to 3): healthy (HC)-like (SNF-1), mild at-risk (SNF-3), and severe at-risk (SNF-2). The PLWH in the SNF-2 (45%) had a severe at-risk metabolic profile with increased visceral adipose tissue, BMI, higher incidence of metabolic syndrome (MetS), and increased di- and triglycerides despite having higher CD4+ T-cell counts than the other two clusters. However, the HC-like and the severe at-risk group had a similar metabolic profile differing from HIV-negative controls (HNC), with dysregulation of amino acid metabolism. At the microbiome profile, the HC-like group had a lower α-diversity, a lower proportion of men having sex with men (MSM) and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of MSM, which could potentially lead to higher systemic inflammation and increased cardiometabolic risk profile. The multi-omics integrative analysis also revealed a complex microbial interplay of the microbiome-derived metabolites in PLWH. Those severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their dysregulated metabolic traits, aiming to achieve healthier aging.

Data availability

All of the data generated or analyzed during this study are included in this published article and/or the supplementary materials. Created datasets and code are publicly available. The metabolomics and lipidomics data are available from https://doi.org/10.6084/m9.figshare.14356754.v1 and https://doi.org/10.6084/m9.figshare.14509452.v1. All the codes are available at github: https://github.com/neogilab/HIV_multiomics

Article and author information

Author details

  1. Flora Mikaeloff

    Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden
    For correspondence
    flora.mikaeloff@ki.se
    Competing interests
    The authors declare that no competing interests exist.
  2. Marco Gelpi

    Rigshospitalet, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  3. Rui Benfeitas

    Department of Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
    Competing interests
    The authors declare that no competing interests exist.
  4. Andreas D Knudsen

    Rigshospitalet, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  5.  Beate Vestad

    Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
    Competing interests
    The authors declare that no competing interests exist.
  6.  Julie Høgh

    Rigshospitalet, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  7. Johannes R Hov

    Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
    Competing interests
    The authors declare that no competing interests exist.
  8. Thomas Benfield

    Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre, Denmark
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0698-9385
  9. Daniel Murray

    Rigshospitalet, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  10. Christian G Giske

    Department of Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
    Competing interests
    The authors declare that no competing interests exist.
  11. Adil Mardinoglu

    Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Marius Trøseid

    Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
    Competing interests
    The authors declare that no competing interests exist.
  13. Susanne D Nielsen

    Rigshospitalet, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  14. Ujjwal Neogi

    Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden
    For correspondence
    ujjwal.neogi@ki.se
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0844-3338

Funding

Vetenskapsrådet (2017-01330,2018-06156,2021-01756)

  • Ujjwal Neogi

Novo Nordisk

  • Susanne D Nielsen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Ethical approval was obtained by the Regional Ethics Committee of Copenhagen (COCOMO: H-15017350) and Etikprövningsmyndigheten, Sweden (Dnr: 2022-01353-01). Informed consent was obtained from all participants and delinked before analysis.

Copyright

© 2023, Mikaeloff et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,651
    views
  • 252
    downloads
  • 12
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Flora Mikaeloff
  2. Marco Gelpi
  3. Rui Benfeitas
  4. Andreas D Knudsen
  5.  Beate Vestad
  6.  Julie Høgh
  7. Johannes R Hov
  8. Thomas Benfield
  9. Daniel Murray
  10. Christian G Giske
  11. Adil Mardinoglu
  12. Marius Trøseid
  13. Susanne D Nielsen
  14. Ujjwal Neogi
(2023)
Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection
eLife 12:e82785.
https://doi.org/10.7554/eLife.82785

Share this article

https://doi.org/10.7554/eLife.82785

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.