Gut Microbiota-Produced Succinate Promotes C. difficile Infection after Antibiotic Treatment or Motility Disturbance

Clostridium difficile is a leading cause of antibiotic-associated diarrhea. The mechanisms underlying C. difficile expansion after microbiota disturbance are just emerging. We assessed the gene expression profile of C. difficile within the intestine of gnotobiotic mice to identify genes regulated in response to either dietary or microbiota compositional changes. In the presence of the gut symbiont Bacteroides thetaiotaomicron, C. difficile induces a pathway that metabolizes the microbiota fermentation end-product succinate to butyrate. The low concentration of succinate present in the microbiota of conventional mice is transiently elevated upon antibiotic treatment or chemically induced intestinal motility disturbance, and C. difficile exploits this succinate spike to expand in the perturbed intestine. A C. difficile mutant compromised in succinate utilization is at a competitive disadvantage during these perturbations. Understanding the metabolic mechanisms involved in microbiota-C. difficile interactions may help to identify approaches for the treatment and prevention of C. difficile-associated diseases

Reprograming of gut microbiome energy metabolism by the FUT2 Crohn's disease risk polymorphism.

Fucosyltransferase 2 (FUT2) is an enzyme that is responsible for the synthesis of the H antigen in body fluids and on the intestinal mucosa. The H antigen is an oligosaccharide moiety that acts as both an attachment site and carbon source for intestinal bacteria. Non-secretors, who are homozygous for the loss-of-function alleles of FUT2 gene (sese), have increased susceptibility to Crohn's disease (CD). To characterize the effect of FUT2 polymorphism on the mucosal ecosystem, we profiled the microbiome, meta-proteome and meta-metabolome of 75 endoscopic lavage samples from the cecum and sigmoid of 39 healthy subjects (12 SeSe, 18 Sese and 9 sese). Imputed metagenomic analysis revealed perturbations of energy metabolism in the microbiome of non-secretor and heterozygote individuals, notably the enrichment of carbohydrate and lipid metabolism, cofactor and vitamin metabolism and glycan biosynthesis and metabolism-related pathways, and the depletion of amino-acid biosynthesis and metabolism. Similar changes were observed in mice bearing the FUT2(-/-) genotype. Metabolomic analysis of human specimens revealed concordant as well as novel changes in the levels of several metabolites. Human metaproteomic analysis indicated that these functional changes were accompanied by sub-clinical levels of inflammation in the local intestinal mucosa. Therefore, the colonic microbiota of non-secretors is altered at both the compositional and functional levels, affecting the host mucosal state and potentially explaining the association of FUT2 genotype and CD susceptibility

Evaluation of antigens for the serodiagnosis of kala-azar and oriental sores by means of the indirect immunofluorescence antibody test (IFAT)

Antigens and corresponding sera were collected from travellers with leishmaniasis returning to Germany from different endemic areas of the old world. The antigenicity of these Leishmania strains, which were maintained in Syrian hamsters, was compared by indirect immunofluorescence (IFAT). Antigenicity was demonstrated by antibody titres in 18 sera from 11 patients. The amastigotic stages of nine strains of Leishmania donovani and four strains of Leishmania tropica were compared with each other and with the culture forms of insect flagellates (Strigomonas oncopelti and Leptomonas ctenocephali). Eighteen sera from 11 patients were available for antibody determination with these antigens. The maximal antibody titres in a single serum varied considerably depending on which antigen was used for the test. High antibody levels could only be maintained when Leishmania donovani was employed as the antigen, but considerable differences also occurred between the different strains of this species. The other antigens were weaker. No differences in antigenicity between amastigotes and promastigotes of the same strain were observed. It is important to select suitable antigens. Low titres may be of doubtful specificity and are a poor baseline for the fall in titre which is an essential index of effective treatment.Wir sammelten Parasiten und Seren von Reisenden, die aus verschiedenen endemischen Gebieten der Alten Welt mit einer Leishmaniasis nach Deutschland zurückkehrten. Die Antigenaktivitäten der isolierten und fortlaufend in Goldhamstern gehaltenenLeishmania-Stämme wurden im indirekten Immunofluoreszenztest (IFAT) verglichen. Die Antigenität wurde an Hand von Antikörpertitern in 18 Serumproben von 11 Patienten bewiesen. Neun Stämme desLeishmania donovani-Komplexes und vierLeishmania tropica-Isolate wurden in ihrem amastigoten Stadium miteinander verglichen. Hinzu kamen zwei Insekten-Flagellaten als Kulturformen:Strigomonas oncopelti undLeptomonas ctenocephali. 18 Serumproben von 11 Patienten standen für die Antikörperbestimmung mit diesen Antigenen zur Verfügung. Die maximalen Titerhöhen variierten in ein- und derselben antiserumprobe zum Teil erheblich, je nachdem, welches Antigen für den Test benutzt wurde. Hohe Antikörpertiter konnten nur erhalten werden, wennLeishmania donovani als Antigen vorlag, es ergaben sich aber auch zwischen den einzelnen Stämmen dieser Leishmaniaart erhebliche Unterschiede in der Antigenaktivität. Antigene anderer Art erwiesen sich als wenig wirksam. Zwischen amastigoten und promastigoten Entwicklungsformen einesLeishmania donovani-Stammes konnten keine Unterschiede in der Antigenaktivität erkannt werden. Für den Nachweis möglichst hoher Antikörpertiter im IFAT ist die Auswahl geeigneter Antigene von ausschlaggebender Bedeutung. Niedrige Titer erschweren deren Beurteilung als spezifisch und sind eine schlechte Ausgangsposition für die Beobachtung des obligatorischen Titerabfalles nach erfolgreicher Therapie

Federated Ensemble Regression Using Classification

Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case

Recognition and Degradation of Plant Cell Wall Polysaccharides by Two Human Gut Symbionts

Competition for nutrients contained in diverse types of plant cell wall-associated polysaccharides may explain the evolution of substrate-specific catabolic gene modules in common bacterial members of the human gut microbiota

Support Vector Machines and Kernels for Computational Biology

ISSN:1553-734XISSN:1553-735

Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships

Background: Consistent compositional shifts in the gut microbiota are observed in IBD and other chronic intestinal disorders and may contribute to pathogenesis. The identities of microbial biomolecular mechanisms and metabolic products responsible for disease phenotypes remain to be determined, as do the means by which such microbial functions may be therapeutically modified. Results: The composition of the microbiota and metabolites in gut microbiome samples in 47 subjects were determined. Samples were obtained by endoscopic mucosal lavage from the cecum and sigmoid colon regions, and each sample was sequenced using the 16S rRNA gene V4 region (Illumina-HiSeq 2000 platform) and assessed by UPLC mass spectroscopy. Spearman correlations were used to identify widespread, statistically significant microbial-metabolite relationships. Metagenomes for identified microbial OTUs were imputed using PICRUSt, and KEGG metabolic pathway modules for imputed genes were assigned using HUMAnN. The resulting metabolic pathway abundances were mostly concordant with metabolite data. Analysis of the metabolome-driven distribution of OTU phylogeny and function revealed clusters of clades that were both metabolically and metagenomically similar. Conclusions: The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites. The consistent relationship between inferred metagenomic function and assayed metabolites suggests that metagenomic composition is predictive to a reasonable degree of microbial community metabolite pools. The finding that certain metabolites strongly correlate with microbial community structure raises the possibility of targeting metabolites for monitoring and/or therapeutically manipulating microbial community function in IBD and other chronic diseases

Exploiting physico-chemical properties in string kernels

<p>Abstract</p> <p>Background</p> <p>String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas.</p> <p>Results</p> <p>We propose new string kernels that combine the benefits of physico-chemical descriptors for amino acids with the ones of string kernels. The benefits of the proposed kernels are assessed on two problems: MHC-peptide binding classification using position specific kernels and protein classification based on the substring spectrum of the sequences. Our experiments demonstrate that the incorporation of amino acid properties in string kernels yields improved performances compared to standard string kernels and to previously proposed non-substring kernels.</p> <p>Conclusions</p> <p>In summary, the proposed modifications, in particular the combination with the RBF substring kernel, consistently yield improvements without affecting the computational complexity. The proposed kernels therefore appear to be the kernels of choice for any protein sequence-based inference.</p> <p>Availability</p> <p>Data sets, code and additional information are available from <url>http://www.fml.tuebingen.mpg.de/raetsch/suppl/aask</url>. Implementations of the developed kernels are available as part of the Shogun toolbox.</p

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

<p>Abstract</p> <p>Background</p> <p>The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published <it>q</it>-Norm MKL algorithm.</p> <p>Results</p> <p>We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities<it> ab initio</it> along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.</p> <p>Conclusions</p> <p>We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.</p