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Killinger, Bryan J.

Publications and source records attributed to Killinger, Bryan J..

Activity-based protein profiling identifies alternating activation of enzymes involved in the bifidobacterium shunt pathway or mucin degradation in the gut microbiome response to soluble dietary fiber

While deprivation of dietary fiber has been associated with adverse health outcomes, investigations concerning the effect of dietary fiber on the gut microbiome have been largely limited to compositional sequence-based analyses or utilize a defined microbiota not native to the host. To extend understanding of the microbiome’s functional response to dietary fiber deprivation beyond correlative evidence from sequence-based analyses, approaches capable of measuring functional enzymatic activity are needed. In this study, we use an activity-based protein profiling (ABPP) approach to identify sugar metabolizing and transport proteins in native mouse gut microbiomes that respond with differential activity to the deprivation or supplementation of the soluble dietary fibers inulin and pectin. We found that the microbiome of mice subjected to a high fiber diet high in soluble fiber had increased functional activity of multiple proteins, including glycoside hydrolases, polysaccharide lyases, and sugar transport proteins from diverse taxa. The results point to an increase in activity of the Bifidobacterium shunt metabolic pathway in the microbiome of mice fed high fiber diets. In those subjected to a low fiber diet, we identified a shift from the degradation of dietary fibers to that of gut mucins, in particular by the recently isolated taxon “Musculibacterium intestinale”, which experienced dramatic growth in response to fiber deprivation. When combined with metabolomics and shotgun metagenomics analyses, our findings provide a functional investigation of dietary fiber metabolism in the gut microbiome and demonstrates the power of a combined ABPP-multiomics approach for characterizing the response of the gut microbiome to perturbations.

59 BASIC BIOLOGICAL SCIENCES↗

Activity–Based Protein Profiling of Bile Salt Hydrolysis in the Human Gut Microbiome with Beta–Lactam or Acrylamide–Based Probes

Microbial bile salt hydrolases (BSHs) found in the intestine catalyze the deconjugation of taurine– and glycine–linked bile salts produced in the liver. The resulting bile salts are biological detergents and are critical in aiding lipophilic nutrient digestion. Therefore, the activity of BSHs in the gut microbiome is directly linked to human metabolism and overall health. Bile salt metabolism has also been associated with disease phenotypes such as liver and colorectal cancer. In order to reshape the gut microbiome to optimize bile salt metabolism, tools to characterize and quantify these processes must exist to enable a much–improved understanding of how metabolism goes awry in the face of disease, and how it can be improved through an altered lifestyle and environment. Furthermore, it is necessary to attribute metabolic activity to specific members and BSHs within the microbiome. To this end, we have developed activity–based probes with two different reactive groups to target bile salt hydrolases. Finally, these probes bind similarly to the authentic bile salt substrates, and we demonstrate enzyme labeling of active bile salt hydrolases by using purified protein, cell lysates, and in human stool.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detecting differential protein abundance by combining peptide level P -values

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here in this paper, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗