Comparative genomes of Chlamydia pneumoniae and C. trachomatis

Chlamydia are obligate intracellular eubacteria that are phylogenetically separated from other bacterial divisions. C. trachomatis and C. pneumoniae are both pathogens of humans but differ in their tissue tropism and spectrum of diseases. C. pneumoniae is a newly recognized species of Chlamydia that is a natural pathogen of humans, and causes pneumonia and bronchitis. In the United States, approximately 10% of pneumonia cases and 5% of bronchitis cases are attributed to C. pneumoniae infection. Chronic disease may result following respiratory-acquired infection, such as reactive airway disease, adult-onset asthma and potentially lung cancer. In addition, C. pneumoniae infection has been associated with atherosclerosis. C. trachomatis infection causes trachoma, an ocular infection that leads to blindness, and sexually transmitted diseases such as pelvic inflammatory disease, chronic pelvic pain, ectopic pregnancy and epididymitis. Although relatively little is known about C. trachomatis biology, even less is known concerning C. pneumoniae. Comparison of the C. pneumoniae genome with the C. trachomatis genome will provide an understanding of the common biological processes required for infection and survival in mammalian cells. Genomic differences are implicated in the unique properties that differentiate the two species in disease spectrum. Analysis of the 1,230,230-nt C. pneumoniae genome revealed 214 protein-coding sequences not found in C. trachomatis, most without homologues to other known sequences. Prominent comparative findings include expansion of a novel family of 21 sequence-variant outer-membrane proteins, conservation of a type-III secretion virulence system, three serine/threonine protein kinases and a pair of parologous phospholipase-D-like proteins, additional purine and biotin biosynthetic capability, a homologue for aromatic amino acid (tryptophan) hydroxylase and the loss of tryptophan biosynthesis genes.     

Modelling disease outbreaks in realistic urban social networks

Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.