Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.The work was partially supported by the Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174 and Sara Borrell fellowship to ELM) and Ministry of Science and Innovation (MTM2008-06747-C02-02 and FPU fellowship award to VU), Spain; AGAUR-Generalitat de Catalunya (Grant 2009SGR-581); Fundaciola Maratode TV3; Red Tematica de Investigacion Cooperativa en Cancer (RTICC); Asociacion Espanola Contra el Cancer (AECC); EU-FP7-201663; and RO1-CA089715 and CA34627; the Spanish National Institute for Bioinformatics (www.inab.org); and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA. MD Anderson support for this project included U01 CA 127615 (XW); R01 CA 74880 (XW); P50 CA 91846 (XW, CPD); Betty B. Marcus Chair fund in Cancer Prevention (XW); UT Research Trust fund (XW) and R01 CA 131335 (JG)

Automated Force Volume Image Processing for Biological Samples

Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image

Immunohistochemical Analysis of the Effects of Cross-innervation of Murine Thyroarytenoid and Sternohyoid Muscles

This work uses cross-innervation of respiratory muscles of different developmental origins to probe myogenic and neurogenic mechanisms regulating their fiber types. The thyroarytenoid (TA) originates from the sixth branchial arch, whereas the sternohyoid (SH) is derived from somitic mesoderm. Immunohistochemical analysis using highly specific monoclonal antibodies to myosin heavy chain (MyHC) isoforms reveals that normal rat SH comprises slow, 2a, 2x, and 2b fibers, as in limb fast muscles, whereas the external division of the TA has only 2b/eo fibers coexpressing 2B and extraocular (EO) MyHCs. Twelve weeks after cross-innervation with the recurrent laryngeal nerve, the SH retained slow and 2a fibers, greatly increased the proportion of 2x fibers, and their 2b fibers failed to express EO MyHC. In the cross-innervated TA, the SH nerve failed to induce slow and 2A MyHC expression and failed to suppress EO MyHC expression in 2b/eo fibers. However, 2x fibers amounting to 4.2% appeared de novo in the external division of the TA. We conclude that although MyHC gene expression in these muscles can be modulated by neural activity, the patterns of response to altered innervation are largely myogenically determined, thus supporting the idea that SH and TA differ in muscle allotype. (J Histochem Cytochem 58:1057–1065, 2010

Mitochondrial DNA variants of respiratory complex I that uniquely characterize haplogroup T2 are associated with increased risk of age-related macular degeneration.

BACKGROUND: Age-related macular degeneration (AMD), a chronic neurodegenerative and neovascular retinal disease, is the leading cause of blindness in elderly people of western European origin. While structural and functional alterations in mitochondria (mt) and their metabolites have been implicated in the pathogenesis of chronic neurodegenerative and vascular diseases, the relationship of inherited variants in the mitochondrial genome and mt haplogroup subtypes with advanced AMD has not been reported in large prospective cohorts. METHODOLOGY/PRINICIPAL FINDINGS: We examined the relationship of inherited mtDNA variants with advanced AMD in 1168 people using a three-stage design on samples from 12-year and 10-year prospective studies on the natural history of age-related eye disease. In Stage I we resequenced the entire genome in 99 elderly AMD-free controls and 215 people with advanced AMD from the 12-year study. A consistent association with AMD in 14 of 17 SNPs characterizing the mtDNA T haplogroup emerged. Further analysis revealed these associations were driven entirely by the T2 haplogroup, and characterized by two variants in Complex I genes (A11812G of MT-ND4 and A14233G of MT-ND6). We genotyped T haplogroups in an independent sample of 490 cases and 61 controls from the same study (Stage II) and in 56 cases and 246 controls from the 10-year study (Stage III). People in the T2 haplogroup were approximately 2.5 times more likely to have advanced AMD than their peers (odds ratio [OR] = 2.54, 95%CI 1.36-4.80, P<or=0.004) after considering the totality of evidence. Findings persisted after considering the impact of AMD-associated variants A69S and Y402H (OR = 5.19, 95%CI 1.19-22.69, P<or=0.029). CONCLUSION: Loci defining the mtDNA T2 haplogroup and Complex I are reasonable targets for novel functional analyses and therapeutic research in AMD