Genotyping Rickettsia prowazekii Isolates

We developed a typing method that can differentiate 8 strains of Rickettsia prowazekii into 7 genotypes. This method can be used to type and trace the origin of R. prowazekii isolated from samples collected during epidemics after a bioterrorism attack

Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.National Institutes of Health. National Centers for Biomedical Computing (Informatics for Integrating Biology and the Bedside)National Institutes of Health (U.S.) (grant U54LM008748

Variations of Plasmid Content in Rickettsia felis

Background: Since its first detection, characterization of R. felis has been a matter of debate, mostly due to the contamination of an initial R. felis culture by R. typhi. However, the first stable culture of R. felis allowed its precise phenotypic and genotypic characterization, and demonstrated that this species belonged to the spotted fever group rickettsiae. Later, its genome sequence revealed the presence of two forms of the same plasmid, physically confirmed by biological data. In a recent article, Gillespie et al. ( PLoS One. 2007; 2( 3): e266.) used a bioinformatic approach to refute the presence of the second plasmid form, and proposed the creation of a specific phylogenetic group for R. felis. Methodology/ Principal Findings: In the present report, we, and five independent international laboratories confirmed unambiguously by PCR the presence of two plasmid forms in R. felis strain URRWXCal(2)(T), but observed that the plasmid content of this species, from none to 2 plasmid forms, may depend on the culture passage history of the studied strain. We also demonstrated that R. felis does not cultivate in Vero cells at 37 degrees C but generates plaques at 30 degrees C. Finally, using a phylogenetic study based on 667 concatenated core genes, we demonstrated the position of R. felis within the spotted fever group. Significance: We demonstrated that R. felis, which unambiguously belongs to the spotted fever group rickettsiae, may contain up to two plasmid forms but this plasmid content is unstable

Rickettsia Phylogenomics: Unwinding the Intricacies of Obligate Intracellular Life

BACKGROUND: Completed genome sequences are rapidly increasing for Rickettsia, obligate intracellular alpha-proteobacteria responsible for various human diseases, including epidemic typhus and Rocky Mountain spotted fever. In light of phylogeny, the establishment of orthologous groups (OGs) of open reading frames (ORFs) will distinguish the core rickettsial genes and other group specific genes (class 1 OGs or C1OGs) from those distributed indiscriminately throughout the rickettsial tree (class 2 OG or C2OGs). METHODOLOGY/PRINCIPAL FINDINGS: We present 1823 representative (no gene duplications) and 259 non-representative (at least one gene duplication) rickettsial OGs. While the highly reductive (approximately 1.2 MB) Rickettsia genomes range in predicted ORFs from 872 to 1512, a core of 752 OGs was identified, depicting the essential Rickettsia genes. Unsurprisingly, this core lacks many metabolic genes, reflecting the dependence on host resources for growth and survival. Additionally, we bolster our recent reclassification of Rickettsia by identifying OGs that define the AG (ancestral group), TG (typhus group), TRG (transitional group), and SFG (spotted fever group) rickettsiae. OGs for insect-associated species, tick-associated species and species that harbor plasmids were also predicted. Through superimposition of all OGs over robust phylogeny estimation, we discern between C1OGs and C2OGs, the latter depicting genes either decaying from the conserved C1OGs or acquired laterally. Finally, scrutiny of non-representative OGs revealed high levels of split genes versus gene duplications, with both phenomena confounding gene orthology assignment. Interestingly, non-representative OGs, as well as OGs comprised of several gene families typically involved in microbial pathogenicity and/or the acquisition of virulence factors, fall predominantly within C2OG distributions. CONCLUSION/SIGNIFICANCE: Collectively, we determined the relative conservation and distribution of 14354 predicted ORFs from 10 rickettsial genomes across robust phylogeny estimation. The data, available at PATRIC (PathoSystems Resource Integration Center), provide novel information for unwinding the intricacies associated with Rickettsia pathogenesis, expanding the range of potential diagnostic, vaccine and therapeutic targets