Resistant starch alters gut microbiome and metabolomic profiles concurrent with amelioration of chronic kidney disease in rats - PubMed (original) (raw)

Resistant starch alters gut microbiome and metabolomic profiles concurrent with amelioration of chronic kidney disease in rats

Dorothy A Kieffer et al. Am J Physiol Renal Physiol. 2016.

Abstract

Patients and animals with chronic kidney disease (CKD) exhibit profound alterations in the gut environment including shifts in microbial composition, increased fecal pH, and increased blood levels of gut microbe-derived metabolites (xenometabolites). The fermentable dietary fiber high amylose maize-resistant starch type 2 (HAMRS2) has been shown to alter the gut milieu and in CKD rat models leads to markedly improved kidney function. The aim of the present study was to identify specific cecal bacteria and cecal, blood, and urinary metabolites that associate with changes in kidney function to identify potential mechanisms involved with CKD amelioration in response to dietary resistant starch. Male Sprague-Dawley rats with adenine-induced CKD were fed a semipurified low-fiber diet or a high-fiber diet [59% (wt/wt) HAMRS2] for 3 wk (n = 9 rats/group). The cecal microbiome was characterized, and cecal contents, serum, and urine metabolites were analyzed. HAMRS2-fed rats displayed decreased cecal pH, decreased microbial diversity, and an increased Bacteroidetes-to-Firmicutes ratio. Several uremic retention solutes were altered in the cecal contents, serum, and urine, many of which had strong correlations with specific gut bacteria abundances, i.e., serum and urine indoxyl sulfate were reduced by 36% and 66%, respectively, in HAMRS2-fed rats and urine p-cresol was reduced by 47% in HAMRS2-fed rats. Outcomes from this study were coincident with improvements in kidney function indexes and amelioration of CKD outcomes previously reported for these rats, suggesting an important role for microbial-derived factors and gut microbe metabolism in regulating host kidney function.

Keywords: chronic kidney disease; dietary fiber; gut microbiota; resistant starch; uremic retention solutes.

Copyright © 2016 the American Physiological Society.

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Figures

Fig. 1.

Fig. 1.

Principal coordinate analysis score plots of cecal contents, serum, and urine metabolites from male rats with chronic kidney disease (CKD) in the model validation group fed a low fiber diet or high amylose maize-resistant starch type 2 (HAMRS2). Ellipses represent 95% confidence intervals based on Hotelling's _T_2 statistic, and each symbol represents a rat. Metabolites that contributed to these plots are shown in Tables 2–4. Metabolomic analysis was performed on 9 rats/group, the model was developed using 6 rats/group, and model validation was performed using 3 rats/group.

Fig. 2.

Fig. 2.

A: unweighted UniFrac beta-diversity principal coordinate analysis plot displays separation between treatment groups based on the cecal microbiota of male CKD rats fed a low-fiber diet or HAMRS2. Axes represent percentage of the variance that can be accounted for based on the cecal microbiota profile. Ellipses represent 95% confidence intervals based on Hotelling's _T_2 statistic, and each symbol represents a rat. n = 9 rats/group. B: percent change of select cecal bacteria in CKD HAMRS2-fed rats relative to CKD low fiber-fed rats. Bacteria included had a minimum of 0.05% mean abundance in each group and P values of ≤0.05. Bacteria are listed to the lowest level of classification (i.e., if the last taxon assignment is f_, family is the lowest level of classification; p_ is phylum, o_ is order, and g_ is genus). n = 9 rats/group

Fig. 3.

Fig. 3.

Partial least squares-discriminant analysis scores plots based on serum, urine, and cecal metabolites of male CKD rats fed a low fiber diet or HAMRS2. Ellipses represent 95% confidence intervals based on Hotelling's _T_2 statistic, and each symbol represents a rat. Metabolites that contributed to these plots are shown in Tables 2–4. Metabolomic analysis was performed on 9 rats/group, the model was developed using 6 rats/group, and model validation was performed using 3 rats/group. QIPH, quantifier ion peak height.

Fig. 4.

Fig. 4.

Spearman's correlation matrix of cecal bacteria versus metadata and uremic retention solutes in the cecal contents, serum, and urine of male CKD rats fed a low fiber diet or HAMRS2 (n = 18 total rats combined). Bacteria included had a minimum of 0.05% mean abundance in each group and an adjusted Mann-Whitney _U_-test P value of ≤0.05. Bacteria are listed to the lowest level of classification. Metabolites were selected based on being identified as uremic retention solutes. *Metabolites that had a mean bootstrapped variable importance in projection of ≥1. Colonic tight junction data were imputed for 2 rats/group using _k_-nearest neighbors, as described in

materials and methods

. The direction of ellipses represent positive or negative correlation, and the width of ellipse represents strength of correlation (narrow ellipse = stronger correlation).

Fig. 5.

Fig. 5.

Working model of how HAMRS2 may improve CKD. SCFAs, short-chain fatty acids.

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