Immune Cells Harbor Their Own Microbiome-Derived Metabolome: A New Layer of Immunometabolic Regulation
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (PREreview)
Abstract
Current models of microbiome–immune crosstalk center on extracellular receptor-mediated signaling, yet a critical observation challenges this paradigm: intracellular concentrations of gut-derived bacterial metabolites (GDBMs) in CD4⁺ T cells do not correlate with paired plasma levels, and it is intracellular — not circulating — GDBM burden that associates with metabolic pathway disruption and immune senescence. Here we propose the concept of an intracellular microbiome metabolome: a pool of aromatic GDBMs actively accumulated through carrier-mediated transport, retained through transcriptional suppression of efflux transporters, and integrated into host metabolic networks where metabolites directly engage intracellular senescence pathways. Using p-cresol sulfate (PCS) as a mechanistic prototype, we review transcriptomic, proteomic, and metabolomic evidence implicating SLCO4A1/OATP4A1 as the primary entry transporter, whose suppression following PCS exposure creates a feed-forward intracellular retention loop. Once accumulated, PCS functions as a direct agonist of the aryl hydrocarbon receptor (AhR), engaging five downstream effector programs — TGF-β/SMAD signaling, Wnt/β-catenin reprogramming, Foxp3-dependent Treg induction, Notch dysregulation, and PTGS2/COX-2 induction with coordinate HPGD suppression driving PGE₂ excess via EP2/EP4/cAMP/CREM — that converge on mTOR suppression, glycolytic collapse, and mitochondrial dysfunction. This metabolic collapse in turn activates the GCN2/integrated stress response as a downstream consequence, driving p16/CDKN2A and p21/CDKN1A induction and the full immunometabolic signature of accelerated CD4⁺ T cell aging. The plasma–intracellular dissociation explains why circulating GDBM levels have failed to predict immune outcomes in HIV-1 infection, chronic kidney disease, and aging, and positions intracellular GDBM quantification as the biologically relevant exposure metric. We discuss three therapeutic intervention layers: reduction of microbial metabolite production, blockade of SLCO4A1-mediated entry and efflux suppression, and targeting the AhR signaling axis with downstream metabolic and GCN2/ISR consequences.
Article activity feed
-
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19696538.
Major issues
Bias toward pathogenic AhR ligands The study focuses predominantly on uremic toxins (e.g., PCS, IS, PAG) as AhR ligands, without addressing the well-established functional diversity of AhR activation by different ligand classes (e.g., indole derivatives such as I3C). This limits the generalizability of the proposed model and overlooks potential context-dependent or protective effects of microbiome-derived metabolites.
Restricted cellular scope The conclusions are heavily based on CD4⁺ T cells, yet are discussed in a broader immunological context. The absence of validation in other immune cell types (e.g., monocytes, macrophages) limits the general applicability of the framework.
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19696538.
Major issues
Bias toward pathogenic AhR ligands The study focuses predominantly on uremic toxins (e.g., PCS, IS, PAG) as AhR ligands, without addressing the well-established functional diversity of AhR activation by different ligand classes (e.g., indole derivatives such as I3C). This limits the generalizability of the proposed model and overlooks potential context-dependent or protective effects of microbiome-derived metabolites.
Restricted cellular scope The conclusions are heavily based on CD4⁺ T cells, yet are discussed in a broader immunological context. The absence of validation in other immune cell types (e.g., monocytes, macrophages) limits the general applicability of the framework.
Insufficient resolution of CD4⁺ T cell heterogeneity The analysis does not adequately distinguish between different CD4⁺ T cell subsets. Observed transcriptional and metabolic changes may reflect shifts in subset composition (e.g., Treg enrichment) rather than true cell-intrinsic reprogramming
Overextension of conclusions from associative human dataWhile human cohort data are included, the associations between intracellular metabolites and immune dysfunction remain correlative. The manuscript occasionally implies stronger causal inference than is supported by the data.
Minor issues
Clarity and conciseness The manuscript is highly dense and mechanistically detailed, which at times obscures the central conceptual advances. Streamlining certain sections could improve readability.
Distinction between hypothesis and evidence Some proposed mechanisms (e.g., transporter-specific roles or hierarchical pathway structure) are presented with strong language despite being inferential. Clearer separation between established findings and hypotheses would improve clarity
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they used generative AI to come up with new ideas for their review.
-