Consumer credit information systems: A critical review of the literature. Too little attention paid by lawyers?

This paper reviews the existing literature on consumer credit reporting, the most extensively used instrument to overcome information asymmetry and adverse selection problems in credit markets. Despite the copious literature in economics and some research in regulatory policy, the legal community has paid almost no attention to the legal framework of consumer credit information systems, especially within the context of the European Union. Studies on the topic, however, seem particularly relevant in view of the establishment of a single market for consumer credit. This article ultimately calls for further legal research to address consumer protection concerns and inform future legislation

Fine mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity

Despite progress in defining human leukocyte antigen (HLA) alleles for anti-citrullinated-protein-autoantibody-positive (ACPA(+)) rheumatoid arthritis (RA), identifying HLA alleles for ACPA-negative (ACPA(-)) RA has been challenging because of clinical heterogeneity within clinical cohorts. We imputed 8,961 classical HLA alleles, amino acids, and SNPs from Immunochip data in a discovery set of 2,406 ACPA(-) RA case and 13,930 control individuals. We developed a statistical approach to identify and adjust for clinical heterogeneity within ACPA(-) RA and observed independent associations for serine and leucine at position 11 in HLA-DRbeta1 (p = 1.4 x 10(-13), odds ratio [OR] = 1.30) and for aspartate at position 9 in HLA-B (p = 2.7 x 10(-12), OR = 1.39) within the peptide binding grooves. These amino acid positions induced associations at HLA-DRB1( *)03 (encoding serine at 11) and HLA-B( *)08 (encoding aspartate at 9). We validated these findings in an independent set of 427 ACPA(-) case subjects, carefully phenotyped with a highly sensitive ACPA assay, and 1,691 control subjects (HLA-DRbeta1 Ser11+Leu11: p = 5.8 x 10(-4), OR = 1.28; HLA-B Asp9: p = 2.6 x 10(-3), OR = 1.34). Although both amino acid sites drove risk of ACPA(+) and ACPA(-) disease, the effects of individual residues at HLA-DRbeta1 position 11 were distinct (p \u3c 2.9 x 10(-107)). We also identified an association with ACPA(+) RA at HLA-A position 77 (p = 2.7 x 10(-8), OR = 0.85) in 7,279 ACPA(+) RA case and 15,870 control subjects. These results contribute to mounting evidence that ACPA(+) and ACPA(-) RA are genetically distinct and potentially have separate autoantigens contributing to pathogenesis. We expect that our approach might have broad applications in analyzing clinical conditions with heterogeneity at both major histocompatibility complex (MHC) and non-MHC regions

Allelic based gene-gene interactions in rheumatoid arthritis

The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected

A Novel Metagenomic Short-Chain Dehydrogenase/Reductase Attenuates Pseudomonas aeruginosa Biofilm Formation and Virulence on Caenorhabditis elegans

In Pseudomonas aeruginosa, the expression of a number of virulence factors, as well as biofilm formation, are controlled by quorum sensing (QS). N-Acylhomoserine lactones (AHLs) are an important class of signaling molecules involved in bacterial QS and in many pathogenic bacteria infection and host colonization are AHL-dependent. The AHL signaling molecules are subject to inactivation mainly by hydrolases (Enzyme Commission class number EC 3) (i.e. N-acyl-homoserine lactonases and N-acyl-homoserine-lactone acylases). Only little is known on quorum quenching mechanisms of oxidoreductases (EC 1). Here we report on the identification and structural characterization of the first NADP-dependent short-chain dehydrogenase/reductase (SDR) involved in inactivation of N-(3-oxo-dodecanoyl)-L-homoserine lactone (3-oxo-C12-HSL) and derived from a metagenome library. The corresponding gene was isolated from a soil metagenome and designated bpiB09. Heterologous expression and crystallographic studies established BpiB09 as an NADP-dependent reductase. Although AHLs are probably not the native substrate of this metagenome-derived enzyme, its expression in P. aeruginosa PAO1 resulted in significantly reduced pyocyanin production, decreased motility, poor biofilm formation and absent paralysis of Caenorhabditis elegans. Furthermore, a genome-wide transcriptome study suggested that the level of lasI and rhlI transcription together with 36 well known QS regulated genes was significantly (≥10-fold) affected in P. aeruginosa strains expressing the bpiB09 gene in pBBR1MCS-5. Thus AHL oxidoreductases could be considered as potent tools for the development of quorum quenching strategies

Interaction Analysis between HLA-DRB1 Shared Epitope Alleles and MHC Class II Transactivator CIITA Gene with Regard to Risk of Rheumatoid Arthritis

HLA-DRB1 shared epitope (SE) alleles are the strongest genetic determinants for autoantibody positive rheumatoid arthritis (RA). One of the key regulators in expression of HLA class II receptors is MHC class II transactivator (CIITA). A variant of the CIITA gene has been found to associate with inflammatory diseases

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

Generation and Characterization of Mice Carrying a Conditional Allele of the Wwox Tumor Suppressor Gene

WWOX, the gene that spans the second most common human chromosomal fragile site, FRA16D, is inactivated in multiple human cancers and behaves as a suppressor of tumor growth. Since we are interested in understanding WWOX function in both normal and cancer tissues we generated mice harboring a conditional Wwox allele by flanking Exon 1 of the Wwox gene with LoxP sites. Wwox knockout (KO) mice were developed by breeding with transgenic mice carrying the Cre-recombinase gene under the control of the adenovirus EIIA promoter. We found that Wwox KO mice suffered from severe metabolic defect(s) resulting in growth retardation and all mice died by 3 wk of age. All Wwox KO mice displayed significant hypocapnia suggesting a state of metabolic acidosis. This finding and the known high expression of Wwox in kidney tubules suggest a role for Wwox in acid/base balance. Importantly, Wwox KO mice displayed histopathological and hematological signs of impaired hematopoeisis, leukopenia, and splenic atrophy. Impaired hematopoeisis can also be a contributing factor to metabolic acidosis and death. Hypoglycemia and hypocalcemia was also observed affecting the KO mice. In addition, bone metabolic defects were evident in Wwox KO mice. Bones were smaller and thinner having reduced bone volume as a consequence of a defect in mineralization. No evidence of spontaneous neoplasia was observed in Wwox KO mice. We have generated a new mouse model to inactivate the Wwox tumor suppressor gene conditionally. This will greatly facilitate the functional analysis of Wwox in adult mice and will allow investigating neoplastic transformation in specific target tissues

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models

Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively