A pilot study on the comparative minimum inhibitory and mutant prevention concentration values for moxifloxacin and pradofloxacin against canine and human isolates of Staphylococcus pseudintermedius and S. schleiferi.

BACKGROUND: Moxifloxacin is a fourth-generation fluoroquinolone (FQ) that is approved for use in people to treat a variety of infections. Some veterinary microbiology laboratories report moxifloxacin in culture and sensitivity profiles for Staphylococcus pseudintermedius and S. schleiferi albeit using Clinical & Laboratory Standards Institute (CLSI) breakpoints for S. aureus. Previous studies have shown that S. aureus breakpoints can mischaracterize S. pseudintermedius susceptibility to various drugs. Pradofloxacin is a third generation veterinary FQ with a similar mechanism of action and spectrum of activity to moxifloxacin; however, the dose format (25 mg/mL solution) available in the USA may limit its practical use in large dogs. OBJECTIVE: To determine the minimum inhibitory concentration (MIC), mutant prevention concentration (MPC) and mutant selection window (MSW) of moxifloxacin and pradofloxacin for isolates of S. pseudintermedius and S. schleiferi. METHODS AND MATERIALS: Pulsed-field gel electrophoresis was performed to establish that each bacterial isolate selected for testing represented an unique strain. The MIC, MPC and MSW for moxifloxacin and pradofloxacin were determined from 60 strains of S. pseudintermedius and seven strains of S. schleiferi. RESULTS: The MIC and MPC ranges of moxifloxacin and pradofloxacin for meticillin-susceptible S. pseudintermedius were similar. However, MIC and MPC ranges were much wider and resistance to both drugs was more common for meticillin-resistant strains of S. pseudintermedius and S. schleiferi. CONCLUSIONS AND CLINICAL IMPORTANCE: The narrow MSW of these drugs may reduce the risk of selecting for antibiotic-resistant subpopulations. Pharmacokinetic, pharmacodynamic and safety studies are needed.

The in vitro antibacterial activity of the anthelmintic drug oxyclozanide against common small animal bacterial pathogens.

BACKGROUND: Repurposing existing drugs is one approach to address the growing concerns of multi-drug resistant bacterial pathogens in veterinary medicine. Oxyclozanide is in the anthelmintic drug class salicylanilide, which has been used primarily as a treatment and preventative for Fasciola hepatica in ruminants. The antimicrobial activity of oxyclozanide has been studied in human medicine; its activity against common small animal bacterial pathogens such as Staphylococcus pseudintermedius has yet to be determined. OBJECTIVE: The aim of this study was to measure and establish the minimum inhibitory concentration (MIC) and mutant prevention concentration (MPC) of oxyclozanide against S. pseudintermedius and other common small animal bacterial pathogens. METHODS AND MATERIALS: The MIC and MPC of oxyclozanide were determined from eighteen meticillin sensitive S. pseudintermedius (MSSP) isolates and eleven meticillin-resistant S. pseudintermedius (MRSP), as well as single isolates of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Enterococcus faecalis. RESULTS: The MIC of the eighteen meticillin-sensitive S. pseudintermedius isolates was 0.5-1 µg/mL and the MPC ranged between 16 and 32 µg/mL. The MIC of the eleven meticillin-resistant strains of S. pseudintermedius ranged from 0.5 to 2 µg/mL with a MPC ranging between 16 and 32 µg/mL. A single isolate of meticillin-resistant S. aureus (MRSA) had an MIC of 1 µg/mL and MPC 16 µg/mL. No inhibition of growth was seen at the concentrations tested for bacterial isolate strains E. coli, P. aeruginosa and E. faecalis. CONCLUSION AND CLINICAL IMPORTANCE: Oxyclozanide demonstrated in-vitro antibacterial activity against meticillin-resistant S. pseudintermedius. Further studies are needed to evaluate the potential use of oxyclozanide as a topical bactericidal agent.

A penalized Bayesian approach to predicting sparse protein-DNA binding landscapes.

MOTIVATION: Cellular processes are controlled, directly or indirectly, by the binding of hundreds of different DNA binding factors (DBFs) to the genome. One key to deeper understanding of the cell is discovering where, when and how strongly these DBFs bind to the DNA sequence. Direct measurement of DBF binding sites (BSs; e.g. through ChIP-Chip or ChIP-Seq experiments) is expensive, noisy and not available for every DBF in every cell type. Naive and most existing computational approaches to detecting which DBFs bind in a set of genomic regions of interest often perform poorly, due to the high false discovery rates and restrictive requirements for prior knowledge. RESULTS: We develop SparScape, a penalized Bayesian method for identifying DBFs active in the considered regions and predicting a joint probabilistic binding landscape. Using a sparsity-inducing penalization, SparScape is able to select a small subset of DBFs with enriched BSs in a set of DNA sequences from a much larger candidate set. This substantially reduces the false positives in prediction of BSs. Analysis of ChIP-Seq data in mouse embryonic stem cells and simulated data show that SparScape dramatically outperforms the naive motif scanning method and the comparable computational approaches in terms of DBF identification and BS prediction. AVAILABILITY AND IMPLEMENTATION: SparScape is implemented in C++ with OpenMP (optional at compilation) and is freely available at 'www.stat.ucla.edu/∼zhou/Software.html' for academic use.

A genetic linkage map of the vervet monkey (Chlorocebus aethiops sabaeus).

The spectacular progress in genomics increasingly highlights the importance of comparative biology in biomedical research. In particular, nonhuman primates, as model systems, provide a crucial intermediate between humans and mice. The close similarities between humans and other primates are stimulating primate studies in virtually every area of biomedical research, including development, anatomy, physiology, immunology, and behavior. The vervet monkey (Chlorocebus aethiops sabaeus) is an important model for studying human diseases and complex traits, especially behavior. We have developed a vervet genetic linkage map to enable mapping complex traits in this model organism and facilitate comparative genomic analysis between vervet and other primates. Here we report construction of an initial genetic map built with about 360 human orthologous short tandem repeats (STRs) that were genotyped in 434 members of an extended vervet pedigree. The map includes 226 markers mapped in a unique order with a resolution of 9.8 Kosambi centimorgans (cM) in the vervet monkey genome, and with a total length (including all 360 markers) of 2726 cM. At least one complex and 11 simple rearrangements in marker order distinguish vervet chromosomes from human homologs. While inversions and insertions can explain a similar number of changes in marker order between vervet and rhesus homologs, mostly inversions are observed when vervet chromosome organization is compared to that in human and chimpanzee. Our results support the notion that large inversions played a less prominent role in the evolution within the group of the Old World monkeys compared to the human and chimpanzee lineages.

Convergent linkage evidence from two Latin-American population isolates supports the presence of a susceptibility locus for bipolar disorder in 5q31-34.

We performed a whole genome microsatellite marker scan in six multiplex families with bipolar (BP) mood disorder ascertained in Antioquia, a historically isolated population from North West Colombia. These families were characterized clinically using the approach employed in independent ongoing studies of BP in the closely related population of the Central Valley of Costa Rica. The most consistent linkage results from parametric and non-parametric analyses of the Colombian scan involved markers on 5q31-33, a region implicated by the previous studies of BP in Costa Rica. Because of these concordant results, a follow-up study with additional markers was undertaken in an expanded set of Colombian and Costa Rican families; this provided a genome-wide significant evidence of linkage of BPI to a candidate region of approximately 10 cM in 5q31-33 (maximum non-parametric linkage score=4.395, P<0.00004). Interestingly, this region has been implicated in several previous genetic studies of schizophrenia and psychosis, including disease association with variants of the enthoprotin and gamma-aminobutyric acid receptor genes.

Genome scan meta-analysis of schizophrenia and bipolar disorder, part I: Methods and power analysis.

This is the first of three articles on a meta-analysis of genome scans of schizophrenia (SCZ) and bipolar disorder (BPD) that uses the rank-based genome scan meta-analysis (GSMA) method. Here we used simulation to determine the power of GSMA to detect linkage and to identify thresholds of significance. We simulated replicates resembling the SCZ data set (20 scans; 1,208 pedigrees) and two BPD data sets using very narrow (9 scans; 347 pedigrees) and narrow (14 scans; 512 pedigrees) diagnoses. Samples were approximated by sets of affected sibling pairs with incomplete parental data. Genotypes were simulated and nonparametric linkage (NPL) scores computed for 20 180-cM chromosomes, each containing six 30-cM bins, with three markers/bin (or two, for some scans). Genomes contained 0, 1, 5, or 10 linked loci, and we assumed relative risk to siblings (lambda(sibs)) values of 1.15, 1.2, 1.3, or 1.4. For each replicate, bins were ranked within-study by maximum NPL scores, and the ranks were averaged (R(avg)) across scans. Analyses were repeated with weighted ranks ((sqrt)N[genotyped cases] for each scan). Two P values were determined for each R(avg): P(AvgRnk) (the pointwise probability) and P(ord) (the probability, given the bin's place in the order of average ranks). GSMA detected linkage with power comparable to or greater than the underlying NPL scores. Weighting for sample size increased power. When no genomewide significant P values were observed, the presence of linkage could be inferred from the number of bins with nominally significant P(AvgRnk), P(ord), or (most powerfully) both. The results suggest that GSMA can detect linkage across multiple genome scans.