Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.</p> <p>Results</p> <p>We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306–315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs.</p> <p>Conclusion</p> <p>PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.</p

Escherichia coli DNA Helicase II Is Active as a Monomer

Helicases are thought to function as oligomers (generally dimers or hexamers). Here we demonstrate that although Escherichia coli DNA helicase II (UvrD) is capable of dimerization as evidenced by a positive interaction in the yeast two-hybrid system, gel filtration chromatography, and equilibrium sedimentation ultracentrifugation (Kd = 3.4 microM), the protein is active in vivo and in vitro as a monomer. A mutant lacking the C-terminal 40 amino acids (UvrDDelta40C) failed to dimerize and yet was as active as the wild-type protein in ATP hydrolysis and helicase assays. In addition, the uvrDDelta40C allele fully complemented the loss of helicase II in both methyl-directed mismatch repair and excision repair of pyrimidine dimers. Biochemical inhibition experiments using wild-type UvrD and inactive UvrD point mutants provided further evidence for a functional monomer. This investigation provides the first direct demonstration of an active monomeric helicase, and a model for DNA unwinding by a monomer is presented

Escherichia coli MutL Loads DNA Helicase II onto DNA

Previous studies have shown that MutL physically interacts with UvrD (DNA helicase II) (Hall, M. C., Jordan, J. R., and Matson, S. W. (1998) EMBO J. 17, 1535-1541) and dramatically stimulates the unwinding reaction catalyzed by UvrD in the presence and absence of the other protein components of the methyl-directed mismatch repair pathway (Yamaguchi, M., Dao, V., and Modrich, P. (1998) J. Biol. Chem. 273, 9197-9201). The mechanism of this stimulation was investigated using DNA binding assays, single-turnover helicase assays, and unwinding assays involving long duplex DNA substrates. The results indicate that MutL binds DNA and loads UvrD onto the DNA substrate. The interaction between MutL and DNA and that between MutL and UvrD are both important for stimulation of UvrD-catalyzed unwinding. MutL does not clamp UvrD onto the substrate; and therefore, the processivity of unwinding is not increased in the presence of MutL. The implications of these results are discussed, and models are presented for the mechanism of MutL stimulation as well as for the role of MutL as a master coordinator in the methyl-directed mismatch repair pathway

Association of MTHFR gene polymorphisms with breast cancer survival

BACKGROUND: Two functional single nucleotide polymorphisms (SNPs) in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, C677T and A1298C, lead to decreased enzyme activity and affect chemosensitivity of tumor cells. We investigated whether these MTHFR SNPs were associated with breast cancer survival in African-American and Caucasian women. METHODS: African-American (n = 143) and Caucasian (n = 105) women, who had incident breast cancer with surgery, were recruited between 1993 and 2003 from the greater Baltimore area, Maryland, USA. Kaplan-Meier survival and multivariate Cox proportional hazards regression analyses were used to examine the relationship between MTHFR SNPs and disease-specific survival. RESULTS: We observed opposite effects of the MTHFR polymorphisms A1298C and C677T on breast cancer survival. Carriers of the variant allele at codon 1298 (A/C or C/C) had reduced survival when compared to homozygous carriers of the common A allele [Hazard ratio (HR) = 2.05; 95% confidence interval (CI), 1.05–4.00]. In contrast, breast cancer patients with the variant allele at codon 677 (C/T or T/T) had improved survival, albeit not statistically significant, when compared to individuals with the common C/C genotype (HR = 0.65; 95% CI, 0.31–1.35). The effects were stronger in patients with estrogen receptor-negative tumors (HR = 2.70; 95% CI, 1.17–6.23 for A/C or C/C versus A/A at codon 1298; HR = 0.36; 95% CI, 0.12–1.04 for C/T or T/T versus C/C at codon 677). Interactions between the two MTHFR genotypes and race/ethnicity on breast cancer survival were also observed (A1298C, p(interaction )= 0.088; C677T, p(interaction )= 0.026). CONCLUSION: We found that the MTHFR SNPs, C677T and A1298C, were associated with breast cancer survival. The variant alleles had opposite effects on disease outcome in the study population. Race/ethnicity modified the association between the two SNPs and breast cancer survival

Leveraging Biospecimen Resources for Discovery or Validation of Markers for Early Cancer Detection

Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts

A Region Near the C-Terminal End of Escherichia coli DNA Helicase II Is Required for Single-Stranded DNA Binding

The role of the C terminus of Escherichia coli DNA helicase II (UvrD), a region outside the conserved helicase motifs, was investigated by using three mutants: UvrDΔ107C (deletion of the last 107 C-terminal amino acids), UvrDΔ102C, and UvrDΔ40C. This region, which lacks sequence similarity with other helicases, may function to tailor UvrD for its specific in vivo roles. Genetic complementation assays demonstrated that mutant proteins UvrDΔ107C and UvrDΔ102C failed to substitute for the wild-type protein in methyl-directed mismatch repair and nucleotide excision repair. UvrDΔ40C protein fully complemented the loss of helicase II in both repair pathways. UvrDΔ102C and UvrDΔ40C were purified to apparent homogeneity and characterized biochemically. UvrDΔ102C was unable to bind single-stranded DNA and exhibited a greatly reduced single-stranded DNA-stimulated ATPase activity in comparison to the wild-type protein (k(cat) = 0.01% of the wild-type level). UvrDΔ40C was slightly defective for DNA binding and was essentially indistinguishable from wild-type UvrD when single-stranded DNA-stimulated ATP hydrolysis and helicase activities were measured. These results suggest a role for a region near the C terminus of helicase II in binding to single-stranded DNA

Pharmacogenomics in Oncology Care

Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized clinical trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed

Lessons Learned From Past Gene-Environment Interaction Successes.

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned