The Mycoplasma conjunctivae genome sequencing, annotation and analysis

BACKGROUND: The mollicute Mycoplasma conjunctivae is the etiological agent leading to infectious keratoconjunctivitis (IKC) in domestic sheep and wild caprinae. Although this pathogen is relatively benign for domestic animals treated by antibiotics, it can lead wild animals to blindness and death. This is a major cause of death in the protected species in the Alps (e.g., Capra ibex, Rupicapra rupicapra). METHODS: The genome was sequenced using a combined technique of GS-FLX (454) and Sanger sequencing, and annotated by an automatic pipeline that we designed using several tools interconnected via PERL scripts. The resulting annotations are stored in a MySQL database. RESULTS: The annotated sequence is deposited in the EMBL database (FM864216) and uploaded into the mollicutes database MolliGen http://cbi.labri.fr/outils/molligen/ allowing for comparative genomics. CONCLUSION: We show that our automatic pipeline allows for annotating a complete mycoplasma genome and present several examples of analysis in search for biological targets (e.g., pathogenic proteins).

Genotyping of Francisella tularensis strains by pulsed-field gel electrophoresis, amplified fragment length polymorphism fingerprinting, and 16S rRNA gene sequencing

We evaluated three molecular methods for identification of Francisella strains: pulsed-field gel electrophoresis (PFGE), amplified fragment length polymorphism (AFLP) analysis, and 16S rRNA gene sequencing. The analysis was performed with 54 Francisella tularensis subsp. holarctica, 5 F. tularensis subsp. tularensis, 2 F. tularensis subsp. novicida, and 1 F. philomiragia strains. On the basis of the combination of results obtained by PFGE with the restriction enzymes XhoI and BamHI, PFGE revealed seven pulsotypes, which allowed us to discriminate the strains to the subspecies level and which even allowed us to discriminate among some isolates of F. tularensis subsp. holarctica. The AFLP analysis technique produced some degree of discrimination among F. tularensis subsp. holarctica strains (one primary cluster with three major subclusters and minor variations within subclusters) when EcoRI-C and MseI-A, EcoRI-T and MseI-T, EcoRI-A and MseI-C, and EcoRI-0 and MseI-CA were used as primers. The degree of similarity among the strains was about 94%. The percent similarities of the AFLP profiles of this subspecies compared to those of F. tularensis subsp. tularensis, F. tularensis subsp. novicida, and F. philomiragia were less than 90%, about 72%, and less than 24%, respectively, thus permitting easy differentiation of this subspecies. 16S rRNA gene sequencing revealed 100% similarity for all F. tularensis subsp. holarctica isolates compared in this study. These results suggest that although limited genetic heterogeneity among F. tularensis subsp. holarctica isolates was observed, PFGE and AFLP analysis appear to be promising tools for the diagnosis of infections caused by different subspecies of F. tularensis and suitable techniques for the differentiation of individual strains.

Phylogenetic analysis of Pasteurella multocida subspecies and molecular identification of feline P. multocida subsp. septica by 16S rRNA gene sequencing

Pasteurella multocida is commonly found in the oral cavity of cats and dogs. In humans it is known as an opportunistic pathogen after bites from these animals. Phenotypic identification of P. multocida based on biochemical reactions is often limited and usually only done on a species level, even though 3 subspecies are described. For molecular taxonomy and diagnostic purposes a phylogenetic analysis of the three subspecies of P. multocida based on their 16S rRNA (rrs) gene sequence was therefore carried out. We found P. multocida subsp. septica on a distinguished branch on the phylogenetic tree of Pasteurellaceae, due to a 1.5% divergence of its rrs gene compared to the two other, more closely related subspecies multocida and gallicida. This phylogenetic divergence can be used for the identification of P. multocida subsp. septica by rrs gene determination since they form a phylogenetically well isolated and defined group as shown with a set of feline isolates. Comparison to routine phenotypic identification shows the advantage of the sequence-based identification over conventional methods. It is therefore helpful for future unambiguous identification and molecular taxonomy of P. multocida as well as for epidemiological investigations.

Egalitarianism under Earmark Constraints

Agents with single-peaked preferences share a resource coming from different suppliers; each agent is connected to only a subset of suppliers. Examples include workload balancing, sharing earmarked funds, and rationing utilities after a storm. Unlike in the one supplier model, in a Pareto optimal allocation agents who get more than their peak from underdemanded suppliers, coexist with agents who get less from overdemanded suppliers. Our Egalitarian solution is the Lorenz dominant Pareto optimal allocation. It treats agents with equal demands as equally as the connectivity constraints allow. Together, Strategyproofness, Pareto Optimality, and Equal Treatment of Equals, characterize our solution.

Multiscale methods for shape constraints in deconvolution: Confidence statements for qualitative features

We derive multiscale statistics for deconvolution in order to detect qualitative features of the unknown density. An important example covered within this framework is to test for local monotonicity on all scales simultaneously. We investigate the moderately ill-posed setting, where the Fourier transform of the error density in the deconvolution model is of polynomial decay. For multiscale testing, we consider a calibration, motivated by the modulus of continuity of Brownian motion. We investigate the performance of our results from both the theoretical and simulation based point of view. A major consequence of our work is that the detection of qualitative features of a density in a deconvolution problem is a doable task, although the minimax rates for pointwise estimation are very slow.