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The human intestine houses a dense microbial ecosystem in which the struggle for nutrients creates a continual and dynamic selective force. Host-produced mucus glycans provide a ubiquitous source of carbon and energy for microbial species. Not surprisingly, many gut resident bacteria have become highly adapted to efficiently consume numerous distinct structures present in host glycans. We propose that sophistication in mucus consumption is a trait most likely to be found in gut residents that have co-evolved with hosts, microbes that have adapted to the complexity associated with the host glycan landscape.

Motivation: Annotation of operons in a bacterial genome is an important step in determining an organism's transcriptional regulatory program. While extensive studies of operon structure have been carried out in a few species such as Escherichia coli, fewer resources exist to inform operon prediction in newly sequenced genomes. In particular, many extant operon finders require a large body of training examples to learn the properties of operons in the target organism. For newly sequenced genomes, such examples are generally not available; moreover, a model of operons trained on one species may not reflect the properties of other, distantly related organisms. We encountered these issues in the course of predicting operons in the genome of Bacteroides thetaiotaomicron (B.theta), a common anaerobe that is a prominent component of the normal adult human intestinal microbial community. Results: We describe an operon predictor designed to work without extensive training data. We rely on a small set of a priori assumptions about the properties of the genome being annotated that permit estimation of the probability that two adjacent genes lie in a common operon. Predictions integrate several sources of information, including intergenic distance, common functional annotation and a novel formulation of conserved gene order. We validate our predictor both on the known operons of E.coli and on the genome of B.theta, using expression data to evaluate our predictions in the latter. Availability: The software is available online at http://www.cse.wustl.edu/~jbuhler/research/operons Contact: jbuhler@cse.wustl.edu

Background:IBD is characterized by a microbiota dysbiosis that appears to potentiate inflammation. Defining the etiology of IBD and developing new treatments requires understanding the feedback between inflammation and the microbiota. Genetic engineering of commensals provides a new avenue to invest