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

  • 1,688
    views
  • 254
    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. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.