Salmonella enterica serovar Typhimurium can differentiate into hyperflagellated swarmer cells on agar of an appropriate consistency (0.5 to 0.8%), allowing efficient colonization of the growth surface. Flagella are essential for this form of motility. In order to identify genes involved in swarming, we carried out extensive transposon mutagenesis of serovar Typhimurium, screening for those that had functional flagella yet were unable to swarm. A majority of these mutants were defective in lipopolysaccharide (LPS) synthesis, a large number were defective in chemotaxis, and some had defects in putative two-component signaling components. While the latter two classes were defective in swarmer cell differentiation, representative LPS mutants were not and could be rescued for swarming by external addition of a biosurfactant. A mutation in waaG (LPS core modification) secreted copious amounts of slime and showed a precocious swarming phenotype. We suggest that the O antigen improves surface "wettability" required for swarm colony expansion, that the LPS core could play a role in slime generation, and that multiple two-component systems cooperate to promote swarmer cell differentiation. The failure to identify specific swarming signals such as amino acids, pH changes, oxygen, iron starvation, increased viscosity, flagellar rotation, or autoinducers leads us to consider a model in which the external slime is itself both the signal and the milieu for swarming motility. The model explains the cell density dependence of the swarming phenomenon.

The chemotaxis system plays an essential role in swarm cell differentiation and motility. We show in this study that two (Tsr and Tar) of the four chemoreceptors in Escherichia coli can support swarming individually, but sensing their most powerful chemoattractants is not necessary. Conditions that abolish chemotaxis toward serine (presence of serine concentrations that saturate Tsr, or mutations in Tsr that destroy serine binding) have no effect on swarming. Similar results were obtained for the aspartate and maltose chemoreceptor Tar. We also show that although a mutation in the signaling domain of Tsr that inhibits CheA kinase abolishes swarming, nonchemotactic flagellar switch mutants can swarm. Our results suggest that during swarming, the chemoreceptors signal through the chemotaxis pathway and induce swarmer cell differentiation in response to signals other than their known chemoeffectors.

Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.

Escherichia coli DNA polymerase III subunits and ␥ are produced from one gene, dnaX, by a programmed ribosomal frameshift which generates the C terminal of ␥ within the reading frame. To help evaluate the role of the dispensable ␥, the distribution of and ␥ homologs in several other species and the sequence of the Salmonella typhimurium dnaX were determined. All four enterobacteria tested produce and ␥ homologs. S. typhimurium dnaX is 83% identical to E. coli dnaX, but all four components of the frameshift signal are 100% conserved.

We have developed a novel method that uses implicit literature relationships (concepts related via shared, intermediate concepts) to cluster related genes. Genes are evaluated for implicit connections within a network of biomedical objects (other genes, ontological concepts and diseases) that are connected via their co-occurrences in Medline titles and/or abstracts. On the basis of these implicit relationships, individual gene pairs are scored using a probability-based algorithm. Scores are generated for all pairwise combinations of genes, which are then clustered based on the scores. We applied this method to a test set composed of nine functional groups with known relationships. The method scored highly for all nine groups and significantly better than a benchmark co-occurrence-based method for six groups. We then applied this method to gene sets specific to two previously defined breast tumor subtypes. Analysis of the results recapitulated known biological relationships and identified novel pathway relationships unique to each tumor subtype. We demonstrate that this method provides a valuable new means of identifying and visualizing significantly related genes within gene lists via their implicit relationships in the literature.