Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians

Objectives: To develop a population specific pharmacogenetic acenocoumarol dosing algorithm for north Indian patients and show its efficiency in dosage prediction. Methods: Multiple and linear stepwise regression analyses were used to include age, sex, height, weight, body surface area, smoking status, VKORC1-1639 G.A, CYP4F2 1347 G.A, CYP2C9*2,*3 and GGCX 12970 C.G polymorphisms as variables to generate dosing algorithms. The new dosing models were compared with already reported algorithms and also with the clinical data for various performance measures. Odds ratios for association of genotypes with drug sensitive and resistant groups were calculated. Results: The pharmacogenetic dosing algorithm generated by multiple regression analysis explains 41.4 % (p-value,0.001) of dosage variation. Validation of the new algorithm showed its predictive ability to be better than the already established algorithms based on similar variables. Its validity in our population is reflected by increased sensitivity, specificity, accuracy and decreased rates of over- and under- estimation in comparison to clinical data. The VKORC1-1639 G.A polymorphism was found to be strongly associated with acenocoumarol sensitivity according to recessive model. Conclusions: We have proposed an efficient north India specific pharmacogenetic acenocoumarol dosing algorithm whic

First insights into the phylogenetic diversity of Mycobacterium tuberculosis in Nepal

BACKGROUND: Tuberculosis (TB) is a major public health problem in Nepal. Strain variation in Mycobacterium tuberculosis may influence the outcome of TB infection and disease. To date, the phylogenetic diversity of M. tuberculosis in Nepal is unknown. METHODS AND FINDINGS: We analyzed 261 M. tuberculosis isolates recovered from pulmonary TB patients recruited between August 2009 and August 2010 in Nepal. M. tuberculosis lineages were determined by single nucleotide polymorphisms (SNP) typing and spoligotyping. Drug resistance was determined by sequencing the hot spot regions of the relevant target genes. Overall, 164 (62.8%) TB patients were new, and 97 (37.2%) were previously treated. Any drug resistance was detected in 50 (19.2%) isolates, and 16 (6.1%) were multidrug-resistant. The most frequent M. tuberculosis lineage was Lineage 3 (CAS/Delhi) with 106 isolates (40.6%), followed by Lineage 2 (East-Asian lineage, includes Beijing genotype) with 84 isolates (32.2%), Lineage 4 (Euro-American lineage) with 41 (15.7%) isolates, and Lineage 1 (Indo-Oceanic lineage) with 30 isolates (11.5%). Based on spoligotyping, we found 45 different spoligotyping patterns that were previously described. The Beijing (83 isolates, 31.8%) and CAS spoligotype (52, 19.9%) were the dominant spoligotypes. A total of 36 (13.8%) isolates could not be assigned to any known spoligotyping pattern. Lineage 2 was associated with female sex (adjusted odds ratio [aOR] 2.58, 95% confidence interval [95% CI] 1.42-4.67, p = 0.002), and any drug resistance (aOR 2.79; 95% CI 1.43-5.45; p = 0.002). We found no evidence for an association of Lineage 2 with age or BCG vaccination status. CONCLUSIONS: We found a large genetic diversity of M. tuberculosis in Nepal with representation of all four major lineages. Lineages 3 and 2 were dominating. Lineage 2 was associated with clinical characteristics. This study fills an important gap on the map of the M. tuberculosis genetic diversity in the Asian reg

Ethnic Variation in Inflammatory Profile in Tuberculosis

PMCID: PMC3701709This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Integrated Assessment of Genomic Correlates of Protein Evolutionary Rate

Rates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles (“meta-features”) in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale

The injury epidemiology of cyclists based on a road trauma registry

<p>Abstract</p> <p>Background</p> <p>Bicycle use has increased in some of France's major cities, mainly as a means of transport. Bicycle crashes need to be studied, preferably by type of cycling. Here we conduct a descriptive analysis.</p> <p>Method</p> <p>A road trauma registry has been in use in France since 1996, in a large county around Lyon (the Rhône, population 1.6 million). It covers outpatients, inpatients and fatalities. All injuries are coded using the Abbreviated Injury Scale (AIS). Proxies were used to identify three types of cycling: learning = children (0-10 years old); sports cycling = teenagers and adults injured outside towns; cycling as means of transport = teenagers and adults injured in towns. The study is based on 13,684 cyclist casualties (1996-2008).</p> <p>Results</p> <p>The percentage of cyclists injured in a collision with a motor vehicle was 8% among children, 17% among teenagers and adults injured outside towns, and 31% among those injured in towns. The percentage of serious casualties (MAIS 3+) was 4.5% among children, 10.9% among adults injured outside towns and 7.2% among those injured in towns. Collisions with motor-vehicles lead to more internal injuries than bicycle-only crashes.</p> <p>Conclusion</p> <p>The description indicates that cyclist type is associated with different crash and injury patterns. In particular, cyclists injured in towns (where cycling is increasing) are generally less severely injured than those injured outside towns for both types of crash (bicycle-only crashes and collisions with a motor vehicle). This is probably due to lower speeds in towns, for both cyclists and motor vehicles.</p

Proteinortho: Detection of (Co-)orthologs in large-scale analysis

<p>Abstract</p> <p>Background</p> <p>Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.</p> <p>Results</p> <p>The program <monospace>Proteinortho</monospace> described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply <monospace>Proteinortho</monospace> to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes.</p> <p>Conclusions</p> <p><monospace>Proteinortho</monospace> significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.</p

Modulatory effects of heparin and short-length oligosaccharides of heparin on the metastasis and growth of LMD MDA-MB 231 breast cancer cells in vivo

Expression of the chemokine receptor CXCR4 allows breast cancer cells to migrate towards specific metastatic target sites which constitutively express CXCL12. In this study, we determined whether this interaction could be disrupted using short-chain length heparin oligosaccharides. Radioligand competition binding assays were performed using a range of heparin oligosaccharides to compete with polymeric heparin or heparan sulphate binding to I125 CXCL12. Heparin dodecasaccharides were found to be the minimal chain length required to efficiently bind CXCL12 (71% inhibition; P<0.001). These oligosaccharides also significantly inhibited CXCL12-induced migration of CXCR4-expressing LMD MDA-MB 231 breast cancer cells. In addition, heparin dodecasaccharides were found to have less anticoagulant activity than either a smaller quantity of polymeric heparin or a similar amount of the low molecular weight heparin pharmaceutical product, Tinzaparin. When given subcutaneously in a SCID mouse model of human breast cancer, heparin dodecasaccharides had no effect on the number of lung metastases, but did however inhibit (P<0.05) tumour growth (lesion area) compared to control groups. In contrast, polymeric heparin significantly inhibited both the number (P<0.001) and area of metastases, suggesting a differing mechanism for the action of polymeric and heparin-derived oligosaccharides in the inhibition of tumour growth and metastases

Structure and Age Jointly Influence Rates of Protein Evolution

What factors determine a protein's rate of evolution are actively debated. Especially unclear is the relative role of intrinsic factors of present-day proteins versus historical factors such as protein age. Here we study the interplay of structural properties and evolutionary age, as determinants of protein evolutionary rate. We use a large set of one-to-one orthologs between human and mouse proteins, with mapped PDB structures. We report that previously observed structural correlations also hold within each age group – including relationships between solvent accessibility, designabililty, and evolutionary rates. However, age also plays a crucial role: age modulates the relationship between solvent accessibility and rate. Additionally, younger proteins, despite being less designable, tend to evolve faster than older proteins. We show that previously reported relationships between age and rate cannot be explained by structural biases among age groups. Finally, we introduce a knowledge-based potential function to study the stability of proteins through large-scale computation. We find that older proteins are more stable for their native structure, and more robust to mutations, than younger ones. Our results underscore that several determinants, both intrinsic and historical, can interact to determine rates of protein evolution