Conference overview: Molecular mechanisms of metal toxicity and carcinogenesis

Chronic exposure to many heavy metals and metal-derivatives is associated with an increased risk of cancer, although the mechanisms of tumorigenesis are largely unknown. Approximately 125 scientists attended the 3rd Conference on Molecular Mechanisms of Metal Toxicity and Carcinogenesis and presented the latest research concerning these mechanisms. Major areas of focus included exposure assessment and biomarker identification, roles of ROS and antioxidants in carcinogenesis, mechanisms of metal-induced DNA damage, metal signalling, and the development of animal models for use in metal toxicology studies. Here we highlight some of the research presented, and summarize the conference proceedings.     

Junctional adhesion molecule a serves as a receptor for prototype and field-isolate strains of mammalian reovirus

Reovirus infections are initiated by the binding of viral attachment protein sigma1 to receptors on the surface of host cells. The sigma1 protein is an elongated fiber comprised of an N-terminal tail that inserts into the virion and a C-terminal head that extends from the virion surface. The prototype reovirus strains type 1 Lang/53 (T1L/53) and type 3 Dearing/55 (T3D/55) use junctional adhesion molecule A (JAM-A) as a receptor. The C-terminal half of the T3D/55 sigma1 protein interacts directly with JAM-A, but the determinants of receptor-binding specificity have not been identified. In this study, we investigated whether JAM-A also mediates the attachment of the prototype reovirus strain type 2 Jones/55 (T2J/55) and a panel of field-isolate strains representing each of the three serotypes. Antibodies specific for JAM-A were capable of inhibiting infections of HeLa cells by T1L/53, T2J/55, and T3D/55, demonstrating that strains of all three serotypes use JAM-A as a receptor. To corroborate these findings, we introduced JAM-A or the structurally related JAM family members JAM-B and JAM-C into Chinese hamster ovary cells, which are poorly permissive for reovirus infection. Both prototype and field-isolate reovirus strains were capable of infecting cells transfected with JAM-A but not those transfected with JAM-B or JAM-C. A sequence analysis of the sigma1-encoding S1 gene segment of the strains chosen for study revealed little conservation in the deduced sigma1 amino acid sequences among the three serotypes. This contrasts markedly with the observed sequence variability within each serotype, which is confined to a small number of amino acids. Mapping of these residues onto the crystal structure of sigma1 identified regions of conservation and variability, suggesting a likely mode of JAM-A binding via a conserved surface at the base of the sigma1 head domain.     

Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.     

Pathway analysis for genome-wide genetic variation data: Analytic principles,latest developments,and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for indepth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years,uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold:(1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data;(2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and(3)discuss remaining challenges and future directions for further method developments.     

Prevalence and impact of minority variant drug resistance mutations in primary HIV-1 infection

Objective

To evaluate minority variant drug resistance mutations detected by the oligonucleotide ligation assay (OLA) but not consensus sequencing among subjects with primary HIV-1 infection.

Design/Methods

Observational, longitudinal cohort study. Consensus sequencing and OLA were performed on the first available specimens from 99 subjects enrolled after 1996. Survival analyses, adjusted for HIV-1 RNA levels at the start of antiretroviral (ARV) therapy, evaluated the time to virologic suppression (HIV-1 RNA<50 copies/mL) among subjects with minority variants conferring intermediate or high-level resistance.

Results

Consensus sequencing and OLA detected resistance mutations in 5% and 27% of subjects, respectively, in specimens obtained a median of 30 days after infection. Median time to virologic suppression was 110 (IQR 62–147) days for 63 treated subjects without detectable mutations, 84 (IQR 56–109) days for ten subjects with minority variant mutations treated with ≥3 active ARVs, and 104 (IQR 60–162) days for nine subjects with minority variant mutations treated with <3 active ARVs (p = .9). Compared to subjects without mutations, time to virologic suppression was similar for subjects with minority variant mutations treated with ≥3 active ARVs (aHR 1.2, 95% CI 0.6–2.4, p = .6) and subjects with minority variant mutations treated with <3 active ARVs (aHR 1.0, 95% CI 0.4–2.4, p = .9). Two subjects with drug resistance and two subjects without detectable resistance experienced virologic failure.

Conclusions

Consensus sequencing significantly underestimated the prevalence of drug resistance mutations in ARV-naïve subjects with primary HIV-1 infection. Minority variants were not associated with impaired ARV response, possibly due to the small sample size. It is also possible that, with highly-potent ARVs, minority variant mutations may be relevant only at certain critical codons.     

Predictors of Intelligence at the Age of 5: Family,Pregnancy and Birth Characteristics,Postnatal Influences,and Postnatal Growth

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003–2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence – Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child’s sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child’s head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude.