Multi-omics analysis reveals the efficacy of two probiotic strains in managing feline chronic kidney disease through gut microbiome and host metabolome

Abstract

Gut dysbiosis has been implicated in the progression of chronic kidney disease (CKD), yet the functional alterations of the microbiome and their links to host metabolism in feline CKD pathophysiology remain unclear. Our previous findings suggested that Lactobacillus mix (Lm) may mitigate CKD progression by modulating gut microbiota composition and restoring microbial balance. In this pilot study, we aimed to evaluate the potential effects of an 8-week Lm intervention in cats with stage 2-3 CKD and to investigate the underlying host-microbiota interactions through integrated multi-omics analysis. We performed full-length 16S rRNA amplicon sequencing and untargeted metabolomics to characterize the intricate interactions between the gut microbiome and host metabolome, and further investigate the modulation of microbial function and its related gut-derived metabolites before and after the intervention. During this period, creatinine and blood urea nitrogen levels were stabilized or reduced in most cats, and gut-derived uremic toxins (GDUTs) showed modest numerical reductions without statistically significant changes. Lm intervention was also associated with increased gut microbial diversity, alterations in specific bacterial taxa, and upregulation of microbial functions involved in GDUTs and short-chain fatty acid (SCFAs) biosynthesis pathways. To further explore individual variations in response, we conducted a post hoc exploratory subgroup analysis based on changes in microbial-derived metabolites. Cats classified as high responders, defined as those with reductions in three GDUTs and increases in SCFAs, exhibited distinct microbiome compositions, microbial functional profiles, and metabolite shifts compared to moderate responders. Among high responders, modulation of microbial pathways involved in GDUTs (tyrosine, tryptophan, and phenylalanine metabolism) and SCFAs (pyruvate, propanoate, and butanoate metabolism) biosynthesis was particularly evident. Notably, the relative abundance of Lm strains was higher in high responders, suggesting a potential association between colonization efficiency and microbial metabolic outcomes. This study demonstrates an Lm-mediated interconnection between the modulation of microbial composition, metabolic functions, and systemic metabolite profiles. Overall, our findings suggest that Lm intervention may influence the gut-kidney axis in cats with CKD. These preliminary, hypothesis-generating results highlight the value of multi-omics approaches for understanding host-microbe interactions and support further investigation into personalized probiotic strategies as potential adjuvant therapies in feline CKD.

Keywords: Lactobacillus mix; chronic kidney disease; colonization efficiency; gut dysbiosis; gut microbiome; host metabolome; host-microbe interactions.

Figure 1

The study design showing kidney functional indicators and gut-derived uremic toxins (GDUTs) of cats with CKD. (A) Flow diagram of the study design and participating cats with CKD. The percentage of (B) serum kidney functional indicators and (C) GDUTs change before and after 8 weeks of Lm intervention. Δ (%) = 100 × (post-treatment value − baseline value)/baseline value.

Figure 2

Microbial composition in cats with CKD before and after Lactobacillus mix (Lm) intervention. (A) Alpha-diversity indices. (B) PCoA plot reflecting beta diversity (Bray–Curtis) of individual cats with CKD. (C) The most abundant bacterial phyla, family, genera, and species. (D) Relative abundance of bacterial species enriched or diminished after Lm intervention. 0W: baseline before Lm intervention; 8W: 8-week Lm intervention. The matched-paired Wilcoxon signed-rank test was used.

Figure 3

Gut microbial functions identified in cats with CKD. (A) The five most abundant KEGG level 2 pathways. (B) Fold change of KEGG level 2 pathways after Lactobacillus mix (Lm) intervention. (C) Fold change of KEGG level 3 pathways in amino acid, carbohydrate, and lipid metabolism after Lm intervention, as determined through the matched-paired Wilcoxon signed-rank test. Fold change = post-treatment (8W)/baseline (0W). 0W: baseline before Lm intervention; 8W: 8-week Lm intervention. (D) Spearman’s correlation analysis of bacterial species and KEGG level 3 pathways. ^*p < 0.05 and ^**p < 0.01.

Figure 4

Metabolome composition in cats with CKD before and after Lactobacillus mix (Lm) intervention. (A) Fold change of serum metabolites showing significant change after Lm intervention (p < 0.1). Fold change = post-treatment (8W)/baseline (0W). (B) Log₁₀ area intensity of serum metabolites diminished after Lm intervention. (C) Concentration of fecal short-chain fatty acids. 0W: baseline before Lm intervention; 8W: 8-week Lm intervention. The matched-paired Wilcoxon signed-rank test was used. (D) Spearman’s correlation analysis of bacterial species and serum metabolites. ^*p < 0.05 and ^**p < 0.01.

Figure 5

The abundance of differential microbial biomarkers, gut microbial functions, and serum metabolites in high responder (HR) and moderate responder (MR) after Lactobacillus mix (Lm) intervention. (A) The grouping criteria for HR and MR cats with CKD. (B) Bacterial species, (C) Gut microbial functions, and (D) Serum metabolites after 8 weeks of Lm intervention, as determined through the Wilcoxon rank-sum test. HR-Lm or MR-Lm: results after 8 weeks of Lm intervention in HR or MR cats with CKD, respectively. ^*p < 0.05.

Figure 6

Differential microbial biomarkers, gut microbial functions, and serum metabolites in high responder (HR) and moderate responder (MR) before and after Lactobacillus mix (Lm) intervention. (A) Relative abundance of differential microbial biomarkers. (B) Log₁₀ area intensity of differential serum metabolites. (C) Fold change of differential KEGG level 3 pathways involved in amino acid, carbohydrate, and lipid metabolism. The Wilcoxon rank-sum and signed-rank tests were utilized to compare unpaired and paired cats with CKD, respectively. HR-Ctrl or MR-Ctrl: the baseline in HR or MR cats before Lm intervention, respectively. HR-Lm or MR-Lm: results after 8 weeks of Lm intervention in HR or MR cats, respectively. ^*p < 0.05 and ^**p < 0.01. (D) Spearman’s correlation analysis of bacterial species and serum metabolites in HR cats.

Figure 7

Regulation of metabolites and microbial functions involved in gut-derived uremic toxin (GDUTs) and short-chain fatty acid (SCFA) biosynthesis in HR cats after Lactobacillus mix (Lm) intervention. Metabolic pathways involved in (A) indoxyl sulfate, (B) p-cresyl sulfate and phenyl sulfate, and (C) SCFA biosynthesis. Fourteen cats-Lm, HR-Lm, or MR-Lm: results after 8 weeks of Lm intervention in 14 cats, HR cats, or MR cats, respectively. (D) Spearman’s correlation analysis of Lm strains and quantitated GDUTs/SCFAs concentration in HR cats.

Figure 8

The proposed mechanism postulates that the Lactobacillus mix is associated with the gut microbiome and host metabolome in managing feline CKD (created with BioRender.com/fn0syc5).