Date Published: January 30, 2019
Publisher: Public Library of Science
Author(s): Boris L. Zybailov, Galina V. Glazko, Yasir Rahmatallah, Dmitri S. Andreyev, Taylor McElroy, Oleg Karaduta, Stephanie D. Byrum, Lisa Orr, Alan J. Tackett, Samuel G. Mackintosh, Ricky D. Edmondson, Dorothy A. Kieffer, R. J. Martin, Sean H. Adams, Nosratola D. Vaziri, John M. Arthur, Daotai Nie.
Resistant starch is a prebiotic metabolized by the gut bacteria. It has been shown to attenuate chronic kidney disease (CKD) progression in rats. Previous studies employed taxonomic analysis using 16S rRNA sequencing and untargeted metabolomics profiling. Here we expand these studies by metaproteomics, gaining new insight into the host-microbiome interaction.
Differences between cecum contents in CKD rats fed a diet containing resistant starch with those fed a diet containing digestible starch were examined by comparative metaproteomics analysis. Taxonomic information was obtained using unique protein sequences. Our methodology results in quantitative data covering both host and bacterial proteins.
5,834 proteins were quantified, with 947 proteins originating from the host organism. Taxonomic information derived from metaproteomics data surpassed previous 16S RNA analysis, and reached species resolutions for moderately abundant taxonomic groups. In particular, the Ruminococcaceae family becomes well resolved–with butyrate producers and amylolytic species such as R. bromii clearly visible and significantly higher while fibrolytic species such as R. flavefaciens are significantly lower with resistant starch feeding. The observed changes in protein patterns are consistent with fiber-associated improvement in CKD phenotype. Several known host CKD-associated proteins and biomarkers of impaired kidney function were significantly reduced with resistant starch supplementation. Data are available via ProteomeXchange with identifier PXD008845.
Metaproteomics analysis of cecum contents of CKD rats with and without resistant starch supplementation reveals changes within gut microbiota at unprecedented resolution, providing both functional and taxonomic information. Proteins and organisms differentially abundant with RS supplementation point toward a shift from mucin degraders to butyrate producers.
Recent studies point to gut microbiome dysbiosis as one of the key contributors to the progression of chronic kidney disease (CKD) and its complications [1–3]. During the course of CKD, gut dysbiosis increases and compromises the intestinal epithelial barrier, leading to leakage of microbial-derived toxins into the bloodstream and resulting in increased inflammation that may further exacerbate CKD . One suggested contributor to the dysbiosis is increased urea in intestinal fluids. Consequently, the urease-containing species proliferate in the gut, leading to damage of the epithelial barrier. Indeed, the CKD-associated microbiota have been characterized by an increase in bacterial species encoding for urease and uricase, and indole- and p-cresol producing enzymes, and depletion of microbes expressing short-chain fatty acid-forming enzymes .
To characterize differences in metaproteome composition between RS-fed rats with CKD (CKD-RS), and the CKD rats fed with a host-digestible starch (CKD-DS) we employed a combination of different quantitative mass spectrometry-based proteomics techniques—absolute intensity-based quantification (iBAQ), spectral counting and tandem mass tag (TMT) labeling. Fig 1 summarizes the overall experimental and analytical workflow and the methods used.
We used cecal content samples from a previous study in which treatment with dietary resistant starch attenuated the progression of chronic kidney disease in a rat model of adeninine induced renal failure . In the current paper we employed metaproteomics to assess the effect of resistant starch on gut content microbial and host proteins in a chronic kidney disease rat model. For the simultaneous analysis of host and microbial proteins we combined several distinct proteomics pipelines and experimental platforms. This strategy allowed us to overcome specific biases characteristics of different pipelines and to characterize host and microbial proteins to the fullest extent.