The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus'' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.     

Interactions of anthropometric indices,rs9939609 FTO gene polymorphism and breast cancer: A case-control study

Contradictory results were reported on the effect of fat mass- and obesity-associated (FTO) gene and anthropometric measurements on breast cancer (BC). This study aimed to assess the interactions between rs9939609 polymorphism of FTO gene, anthropometric indices and BC risk in Iranian women. This case-control study was performed on 540 women including 180 women with BC and 360 healthy women in Tehran, Iran. Physical activity and dietary intakes were assessed by validated questionnaires. Data on sociodemographic and pathologic factors of the participants as well as their blood samples were collected. The rs9939609 FTO gene polymorphism was genotyped using the tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS-PCR). No significant association was found between BC and risk allele of FTO rs9939609 polymorphism after adjustments for the confounders. However, there was a significant association between rs9939609 polymorphism risk allele and BC risk in females with overweight, even after adjusting for age, family history of BC, abortion, BMI and the number of pregnancies (P < .05). The association was disappeared after further adjustments for lifestyle factors including smoking, alcohol consumption, calorie and macronutrients intake, and physical activity. The FTO gene polymorphism was associated with the risk of BC in overweight individuals. This association was influenced by environmental factors including diet, alcohol consumption and smoking. Future studies are required to confirm the association between the FTO gene and BC in overweight females and to identify the underlying mechanisms.     

Factors affecting spore formation in a Candida albicans strain.

Growth and spore formation of Candida albicans Y-45 was enhanced by low oxygen tension. Mycelium and chlamydospores were abundantly found on rice infusion-Tween 80 agar within 48 to 96 h, and abundant chlamdospore production occurred most rapidly under reduced oxygen tension and incubation at 30 degrees C. Zn, Mg, Mn, anf Fe were tested for their ability to promote filamentation in C. albicans Y-45. Filamentation under conditions of low Mg and high Mn suggested that morphogenesis is possibly correlated with the presence of salts of these heavy metals.     

T cell helper defect in patients with chronic lymphocytic leukemia.

Purified peripheral blood T lymphocytes from normal donors were shown to help allogeneic tonsillar B cells to differentiate and secrete specific anti-SRBC antibody in vitro in a plaque-forming assay. Utilizing this system, a comparison was made between the allogeneic helper activity generated by the T cells of normal individuals and patients with various disease states. Allogeneic helper activity was absent when T lymphocytes from patients with CLL were used. Conversely, relatively normal allogeneic helper function was provided by T cells of patients with a variety of other disorders studied. Thus, a functional deficiency was identified in CLL patients in the subpopulation of regulatory T cells responsible for providing helper activity in allogeneic interactions.     

Health Literacy Measure for Adolescents (HELMA): Development and Psychometric Properties

Background

Health literacy refers to personal competencies for the access to, understanding of, appraisal of and application of health information in order to make sound decisions in everyday life. The aim of this study was to develop and evaluate the psychometric properties of an instrument for the measurement of health literacy among adolescents (the Health Literacy Measure for Adolescents-HELMA).

Methods

This study was made up of two phases, qualitative and quantitative, which were carried out in 2012–2014 in Tehran, Iran. In the qualitative part of the study, in-depth interviews with 67 adolescents aged 15–18 were carried out in 4 high schools to generate the initial item pool for the survey. The content validity of the items was then assessed by an expert panel review (n = 13) and face validity was assessed by interviewing adolescents (n = 16). In the quantitative part of the study, in order to describe the psychometric properties of the scale, validity, reliability (internal consistency and test-retest) and factor analysis were assessed.

Results

An item pool made up of 104 items was generated at the qualitative stage. After content validity was considered, this decreased to 47 items. In the quantitative stage, 582 adolescents aged 15–18 participated in the study with a mean age of 16.2 years. 51.2% of participants were females. In principal component factor analysis, 8 factors were loaded, which accounted for 53.37% of the variance observed. Reliability has been approved by α = 0.93 and the test-retest of the scale at two-week intervals indicated an appropriate stability for the scale (ICC = 0.93). The final questionnaire was approved with 44 items split into eight sections. The sections were titled: gain access to, reading, understanding, appraise, use, communication, self-efficacy and numeracy.

Conclusion

The Health Literacy Measure for Adolescents (HELMA) is a valid and reliable tool for the measurement of the health literacy of adolescents aged 15–18 and can be used to evaluate different levels of functional, interactive, and critical health literacy in adolescents.     

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.     

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.