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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.