"Pseudo-Beijing": evidence for convergent evolution in the direct repeat region of Mycobacterium tuberculosis

Background Mycobacterium tuberculosis has a global population structure consisting of six main phylogenetic lineages associated with specific geographic regions and human populations. One particular M. tuberculosis genotype known as “Beijing” has repeatedly been associated with drug resistance and has been emerging in some parts of the world. “Beijing” strains are traditionally defined based on a characteristic spoligotyping pattern. We used three alternative genotyping techniques to revisit the phylogenetic classification of M. tuberculosis complex (MTBC) strains exhibiting the typical “Beijing” spoligotyping pattern. Methods and Findings MTBC strains were obtained from an ongoing molecular epidemiological study in Switzerland and Nepal. MTBC genotyping was performed based on SNPs, genomic deletions, and 24-loci MIRU-VNTR. We identified three MTBC strains from patients originating from Tibet, Portugal and Nepal which exhibited a spoligotyping patterns identical to the classical Beijing signature. However, based on three alternative molecular markers, these strains were assigned to Lineage 3 (also known as Delhi/CAS) rather than to Lineage 2 (also known as East-Asian lineage). Sequencing of the RD207 in one of these strains showed that the deletion responsible for this “Pseudo-Beijing” spoligotype was about 1,000 base pairs smaller than the usual deletion of RD207 in classical “Beijing” strains, which is consistent with an evolutionarily independent deletion event in the direct repeat (DR) region of MTBC. Conclusions We provide an example of convergent evolution in the DR locus of MTBC, and highlight the limitation of using spoligotypes for strain classification. Our results indicate that a proportion of “Beijing” strains may have been misclassified in the past. Markers that are more phylogenetically robust should be used when exploring strain-specific differences in experimental or clinical phenotypes.

Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study)

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents.

Estimating the number of motor units using random sums with independently thinned terms

The problem of estimating the numbers of motor units N in a muscle is embedded in a general stochastic model using the notion of thinning from point process theory. In the paper a new moment type estimator for the numbers of motor units in a muscle is denned, which is derived using random sums with independently thinned terms. Asymptotic normality of the estimator is shown and its practical value is demonstrated with bootstrap and approximative confidence intervals for a data set from a 31-year-old healthy right-handed, female volunteer. Moreover simulation results are presented and Monte-Carlo based quantiles, means, and variances are calculated for N in{300,600,1000}.