Guthrie, L. | Search bioRxiv

bioRxiv2026

Host, diet, microbiome, and xenobiotic plasma metabolites associate with kidney cell-type transcriptional programs in CKD

BackgroundChronic kidney disease (CKD) is associated with widespread alterations in the circulating metabolome, including metabolites influenced by diet and the gut microbiome that may represent modifiable disease pathways. However, the extent to which metabolites from distinct origins are associated with kidney cell-type-specific injury programs remains incompletely characterized. MethodsWe analyzed plasma metabolomics data from 240 Kidney Precision Medicine Project (KPMP) participants (207 CKD, 33 healthy reference) and validated findings in an independent cohort (n=883). We applied unsupervised clustering to identify metabotypes and elastic net classification to generate a continuous metabotype score. We classified metabolites by origin (host-derived, diet-derived, microbiome-dependent, xenobiotic) and trained random forest models to predict filtration markers and structural injury. In 66 participants with matched kidney single-cell RNA sequencing, we regressed podocyte and proximal tubule pseudobulk expression against plasma metabolites and computed hub scores quantifying transcriptional connectivity. ResultsTwo reproducible metabotypes separated CKD from healthy states (elastic net CV AUC=0.984 discovery, 0.997 validation), with the metabotype score strongly correlating with eGFR ({rho}=-0.79). Although they represented a minor subset of the circulating metabolome, diet-and microbiome-dependent metabolites such as myo-inositol, guaiacol sulfate, and indole derivatives were among the strongest metabotype-defining features. Metabolites improved prediction of filtration markers beyond clinical covariates, most notably for BUN ({Delta}R{superscript 2}=+0.29), driven by urea, guanidinosuccinate, and the microbiome-derived arabitol/xylitol. Plasma metabolites were associated with interstitial fibrosis (415 metabolites) and tubular atrophy (505 metabolites) and distinguished opposing proximal tubule transcriptional programs. Across kidney cell types, environmental xenobiotics (PFHxS, halogenated benzoic acids) activated aryl hydrocarbon receptor, inflammatory, and fibrotic programs in podocytes, while endogenous metabolites engaged inflammatory and oxidative stress programs in proximal tubule. ConclusionPlasma metabotypes capture metabolic heterogeneity beyond eGFR, with metabolites of distinct origins associated with cell-type-specific kidney injury programs, supporting plasma metabolomics as a noninvasive approach for molecular patient stratification in CKD. Key PointsO_LIUnsupervised clustering defined reproducible plasma metabotypes spanning host-, diet-, microbiome-, and xenobiotic-derived metabolites that separated CKD from healthy reference states. C_LIO_LIDiet- and microbiome-dependent metabolites were among the strongest metabotype-defining features and showed the strongest associations with podocyte and proximal tubule gene expression. C_LIO_LIMetabolites of distinct origins associated with different cell-type transcriptional programs in podocytes and proximal tubule. C_LI

Systems Biology↗

bioRxiv2021

Impact of a 7-day homogeneous diet on interpersonal variation in human gut microbiomes and metabolomes

Metabolism of dietary compounds by the gut microbiota generates a vast array of microbiome-dependent metabolites (MDMs), which are highly variable between individuals. The uremic MDMs (uMDMs) phenylacetylglutamine (PAG), p-cresol sulfate (PCS) and indoxyl sulfate (IS) accumulate during renal failure and are associated with poor outcomes. Targeted dietary interventions may reduce toxic MDMs generation; however, it is unclear if interindividual differences in diet or gut microbiome dominantly contribute to MDM variance. Here we use a 7-day homogeneous average American diet to standardize dietary precursor availability in 21 healthy individuals. Notably, the coefficient of variation in three uMDMs of interest, PAG, PCS, and IS (primary outcome), did not significantly decrease. The majority of circulating MDMs maintained variation despite identical diets. These results highlight the highly personalized profile of MDMs and limited contribution of short-term dietary heterogeneity, suggesting that dietary modification may need to be paired with microbial therapies to control MDM profiles.

microbiology↗

bioRxiv2021

A metabolomics pipeline enables mechanistic interrogation of the gut microbiome

Gut microbes modulate host phenotypes and are associated with numerous health effects in humans, ranging from cancer immunotherapy response to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microbes has hindered defining mechanistic connections between individual microbial strains and host phenotypes. One key way the gut microbiome influences host physiology is through the production of small molecules1-3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites (MDMs) in diverse sample types. We report the metabolic profiles of 178 gut microbe strains using our library of 833 metabolites. Leveraging this metabolomics resource we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover novel metabolism in Bacteroides, and employ comparative genomics-based discovery of candidate biochemical pathways. MDMs can be detected in diverse biofluids in gnotobiotic and conventional mice and traced back to corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microbe and microbe-host interactions.

microbiology↗