Dataset Information

Antibodies Set Boundaries Limiting Microbial Metabolite Penetration and the Resultant Mammalian Host Response.

ABSTRACT: Although the mammalian microbiota is well contained within the intestine, it profoundly shapes development and metabolism of almost every host organ. We questioned the range and depth of microbial metabolite penetration into the host, and how this is modulated by intestinal immunity. Chemically identical microbial and host metabolites were distinguished by stable isotope tracing from ¹³C-labeled live non-replicating Escherichia coli, differentiating ¹²C host isotopes with high-resolution mass spectrometry. Hundreds of endogenous microbial compounds penetrated 23 host tissues and fluids after intestinal exposure: subsequent ¹²C host metabolome signatures included lipidemia, reduced glycolysis, and inflammation. Penetrant bacterial metabolites from the small intestine were rapidly cleared into the urine, whereas induced antibodies curtailed microbial metabolite exposure by accelerating intestinal bacterial transit into the colon where metabolite transport mechanisms are limiting. Pervasive penetration of microbial molecules can cause extensive host tissue responses: these are limited by immune and non-immune intestinal mucosal adaptations to the microbiota.

SUBMITTER: Uchimura Y

PROVIDER: S-EPMC6162337 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Antibodies Set Boundaries Limiting Microbial Metabolite Penetration and the Resultant Mammalian Host Response.

Uchimura Yasuhiro Y Fuhrer Tobias T Li Hai H Lawson Melissa A MA Zimmermann Michael M Yilmaz Bahtiyar B Zindel Joel J Ronchi Francesca F Sorribas Marcel M Hapfelmeier Siegfried S Ganal-Vonarburg Stephanie C SC Gomez de Agüero Mercedes M McCoy Kathy D KD Sauer Uwe U Macpherson Andrew J AJ

Immunity 20180904 3

Although the mammalian microbiota is well contained within the intestine, it profoundly shapes development and metabolism of almost every host organ. We questioned the range and depth of microbial metabolite penetration into the host, and how this is modulated by intestinal immunity. Chemically identical microbial and host metabolites were distinguished by stable isotope tracing from <sup>13</sup>C-labeled live non-replicating Escherichia coli, differentiating <sup>12</sup>C host isotopes with h ...[more]

PMID: 30193848

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Project description:BackgroundAntimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.ResultsBy generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.ConclusionsWe developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.

Dataset Information

Antibodies Set Boundaries Limiting Microbial Metabolite Penetration and the Resultant Mammalian Host Response.

Publications

Antibodies Set Boundaries Limiting Microbial Metabolite Penetration and the Resultant Mammalian Host Response.

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