Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Journal stats
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Physician-Scientist Development
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • In-Press Preview
    • Resource and Technical Advances
    • Clinical Research and Public Health
    • Research Letters
    • Editorials
    • Perspectives
    • Physician-Scientist Development
    • Reviews
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Resource and Technical Advances
  • Clinical Research and Public Health
  • Research Letters
  • Editorials
  • Perspectives
  • Physician-Scientist Development
  • Reviews
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Journal stats
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
Microbiome-derived metabolites shape CD4+ T cell differentiation and immune aging in HIV-1 infection
Amanda Cabral Da Silva, Luke Flantzer, Jaclyn Weinberg, Shuya Kyu, Lisa P. Daley-Bauer, Anyce Godoy, Ana Carolina Santana, Aarthi Talla, Amber Lynn Rittgers, Sarah Welbourn, David Ezra Gordon, Jeffery Alan Tomalka, Vincent C. Marconi, Dean P. Jones, Souheil-Antoine Younes
Amanda Cabral Da Silva, Luke Flantzer, Jaclyn Weinberg, Shuya Kyu, Lisa P. Daley-Bauer, Anyce Godoy, Ana Carolina Santana, Aarthi Talla, Amber Lynn Rittgers, Sarah Welbourn, David Ezra Gordon, Jeffery Alan Tomalka, Vincent C. Marconi, Dean P. Jones, Souheil-Antoine Younes
View: Text | PDF
Research Article AIDS/HIV Aging Immunology

Microbiome-derived metabolites shape CD4+ T cell differentiation and immune aging in HIV-1 infection

  • Text
  • PDF
Abstract

The role of aromatic gut-derived bacterial metabolites (GDBMs) in shaping immune cell metabolism and function remains poorly explored. Using ex vivo metabolomic profiling of paired plasma and CD4+ T cells from people living with HIV-1 (PLWH), we identified a network of aromatic GDBMs whose cell-associated abundance, rather than systemic levels, was linked to broad alterations in CD4+ T cell metabolic and functional states. Among these, p-cresol sulfate (PCS) emerged as a mechanistic prototype. Ex vivo flow cytometry and scRNA-seq of CD4+ T cells stratified by cell-associated PCS levels revealed dose-dependent enrichment of transcriptional programs associated with impaired differentiation, regulatory-like identity, and cellular senescence. In vitro transcriptomic and proteomic analyses of PCS-exposed CD4+ T cells demonstrated induction of cell-cycle arrest, mitochondrial dysfunction, and senescence-associated programs, including upregulation of p16 and p21. Integration of these immunometabolic findings with HIV-1 reservoir measurements revealed that CD4+ T cell states defined by cell-associated GDBMs track with intact proviral DNA levels in vivo. These findings define a microbiome-derived axis that reshapes CD4+ T cell metabolism and fate, promotes immune aging in PLWH, and may foster immunometabolic states linked to long-term HIV-1 reservoir persistence.

Authors

Amanda Cabral Da Silva, Luke Flantzer, Jaclyn Weinberg, Shuya Kyu, Lisa P. Daley-Bauer, Anyce Godoy, Ana Carolina Santana, Aarthi Talla, Amber Lynn Rittgers, Sarah Welbourn, David Ezra Gordon, Jeffery Alan Tomalka, Vincent C. Marconi, Dean P. Jones, Souheil-Antoine Younes

×

Figure 6

PCS induces transcriptomic reprogramming in proliferating CD4+ T cells.

Options: View larger image (or click on image) Download as PowerPoint
PCS induces transcriptomic reprogramming in proliferating CD4+ T cells.
...
Bulk RNA-seq was performed on sorted CTVloCD4+ T cells after 6 days of in vitro stimulation with anti-CD3/CD28 in the presence of 0–100 μM PCS. Heatmaps display normalized gene expression (z scores) across treatment conditions for selected pathways and gene sets. (A) Genes from the AhR signaling pathway. (B) TGF-β-Treg signaling pathway. (C) Glycolysis pathway. (D) mTOR signaling pathway. (E) Notch signaling pathway. (F) Wnt/B-catenin signaling pathway. (G) Stemness-associated genes. (H) T cell exhaustion markers. (I) Immune senescence markers. Each column represents one sample, and each row corresponds to an individual gene. Expression levels are color-coded from low (blue) to high (red). [&]Indicate that this heatmap was generated from nonproliferating CTVhi cells. Differential gene expression was determined using an FDR-adjusted P < 0.05 with a minimum |log2 fold change| ≥ 1. n = 5 independent donors.

Copyright © 2026 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts