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Host Plasma Microenvironment in Immunometabolically Impaired HIV Infection Leads to Dysregulated Monocyte Function and Synaptic Transmission Ex Vivo

  • Flora Mikaeloff
  • , Marco Gelpi
  • , Alejandra Escós
  • , Tianqi Wang
  • , Soham Gupta
  • , Anna Olofsson
  • , Sara Svensson Akusjärvi
  • , Sabrina Schuster
  • , Prajakta Naval
  • , Vikas Sood
  • , Negin Nikouyan
  • , Andreas D. Knudsen
  • , Beate Vestad
  • , Julie Høgh
  • , Johannes R. Hov
  • , Thomas Benfield
  • , Marius Trøseid
  • , Vinay Pawar
  • , Marijana Rucevic
  • , Rui Benfeitas
  • Ákos Végvári, Liam O'Mahony, Rajkumar Savai, Niklas K. Björkström, Magda Lourda, João Pedro de Magalhães, Siegfried Weiss, Adil Mardinoglu, Mukesh Kumar Varshney, Annika C. Karlsson, Yasir Ahmed Syed, Susanne D. Nielsen, Ujjwal Neogi
  • Karolinska Institutet
  • University of Copenhagen
  • Cardiff University
  • University of Oslo
  • Institute of Clinical Medicine
  • Olink Bioscience
  • Science for Life Laboratory
  • Justus Liebig University Giessen
  • Max Planck Institute for Heart and Lung Research
  • University of Birmingham
  • Helmholtz Centre for Infection Research
  • KTH Royal Institute of Technology
  • King's College London

Research output: Contribution to journalArticlepeer-review

Abstract

Risk stratification using multi-omics data deepens understanding of immunometabolism in successfully treated people with HIV (PWH) is inadequately explained. A personalized medicine approach integrating blood cell transcriptomics, plasma proteomics, and metabolomics is employed to identify the mechanisms of immunometabolic complications in prolonged treated PWH from the COCOMO cohort. Among the PWHs, 44% of PWH are at risk of experiencing immunometabolic complications identified using the network-based patient stratification method. Utilizing advanced machine learning techniques and a Bayesian classifier, five plasma protein biomarkers; Tubulin Folding Cofactor B (TBCB), Gamma-Glutamylcyclotransferase (GGCT), Taxilin Alpha (TXLNA), Pyridoxal Phosphate Binding Protein (PLPBP) and Large Tumor Suppressor Kinase 1 (LATS1) are identified as highly differentially abundant between healthy control (HC)-like and immunometabolically at-risk PWHs (all FDR<10−10). The personalized metabolic models predict metabolic perturbations, revealing disruptions in central carbon metabolic fluxes and host tryptophan metabolism in at-risk phenotype. Functional assays in primary cells and cortical forebrain organoids (FBOs) further validate this. Metabolic perturbations lead to persistent monocyte activation, thereby impairing their functions ex vivo. Furthermore, the chronic inflammatory plasma microenvironment contributes to synaptic dysregulation in FBOs. The endogenous plasma inflammatory microenvironment is responsible for chronic inflammation in treated immunometabolically complicated at-risk PWH who have a higher risk of cardiovascular and neuropsychiatric disorders.

Original languageEnglish
Article number2416453
JournalAdvanced Science
Volume12
Issue number16
DOIs
Publication statusPublished - 24 Apr 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 5 - Gender Equality
    SDG 5 Gender Equality

Keywords

  • HIV/AIDS
  • Integrative omics
  • patient stratification
  • personalized metabolic models

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