Epistatic Interaction between BANK1 and BLK in Rheumatoid Arthritis: Results from a Large Trans-Ethnic Meta-Analysis

Background

BANK1 and BLK belong to the pleiotropic autoimmune genes; recently, epistasis between BANK1 and BLK was detected in systemic lupus erythematosus. Although BLK has been reproducibly identified as a risk factor in rheumatoid arthritis (RA), reports are conflicting about the contribution of BANK1 to RA susceptibility. To ascertain the real impact of BANK1 on RA genetic susceptibility, we performed a large meta-analysis including our original data and tested for an epistatic interaction between BANK1 and BLK in RA susceptibility.

Patients and Methods

We investigated data for 1,915 RA patients and 1,915 ethnically matched healthy controls genotyped for BANK1 rs10516487 and rs3733197 and BLK rs13277113. The association of each SNP and RA was tested by logistic regression. Multivariate analysis was then used with an interaction term to test for an epistatic interaction between the SNPs in the 2 genes.

Results

None of the SNPs tested individually was significantly associated with RA in the genotyped samples. However, we detected an epistatic interaction between BANK1 rs3733197 and BLK rs13277113 (P_interaction = 0.037). In individuals carrying the BLK rs13277113 GG genotype, presence of the BANK1 rs3733197 G allele increased the risk of RA (odds ratio 1.21 [95% confidence interval 1.04–1.41], P = 0.015. Combining our results with those of all other studies in a large trans-ethnic meta-analysis revealed an association of the BANK1 rs3733197 G allele and RA (1.11 [1.02–1.21], P = 0.012).

Conclusion

This study confirms BANK1 as an RA susceptibility gene and for the first time provides evidence for epistasis between BANK1 and BLK in RA. Our results illustrate the concept of pleiotropic epistatic interaction, suggesting that BANK1 and BLK might play a role in RA pathogenesis.     

Interactions of allelic variance of PNPLA3 with nongenetic factors in predicting nonalcoholic steatohepatitis and nonhepatic complications of severe obesity

Objective : Allelic variation (rs738409C→G) in adiponutrin (patatin‐like phospholipase domain‐containing protein 3, PNPLA3) has been associated with hepatic steatosis and liver fibrosis. The physiologic impact of the PNPLA3 G allele may be exacerbated in patients with severe obesity. In this study, we investigated the interactions of PNPLA3 rs738409 with a broad panel of metabolic and histologic characteristics of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis (NASH) in patients with medically complicated obesity. Design and Methods : Consecutive patients undergoing bariatric surgery were selected for a prospective study. They underwent extensive laboratory and histologic (liver biopsy) assessment, as well as evaluation of rs738409 polymorphism by TaqMan assay. Results : Only 12 (8.3%) of the 144 patients had normal liver histology, with 72 (50%) NASH, of whom 15 (10.4% of total patients) had fibrosis stage 2‐3. PNPLA3 GG genotype correlated positively (P < 0.05) with serum levels of alanine aminotransferase (ALT), asparate aminotransferase (AST), glucose, fibrinogen, and insulin‐dependent diabetes mellitus, homeostasis model assessment—insulin resistance, and presence of NASH. Multivariate analysis indicated that PNPLA3 rs738409 G versus C allele remained an (independent) risk factor for NASH, in addition to CK‐18 >145 IU/l, glucose >100 mg/dl, and C‐reactive protein (CRP) >0.8 mg/dl. The probability of NASH increased from 9% (no risk factor) to 82% if all four risk factors were present. Conclusions : In this cohort of patients with medically complicated obesity, PNPLA3 rs738409 G allelic expression is associated with hepatic (NASH) and nonhepatic complications of obesity, such as insulin resistance. These novel findings may be related to a greater impact of PNPLA3 variant in magnitude and scope in patients with severe obesity than in less obese populations. Further studies are needed to characterize the nature of these associations.     

Blood Microbiota Dysbiosis Is Associated with the Onset of Cardiovascular Events in a Large General Population: The D.E.S.I.R. Study

Aim

We recently described a human blood microbiome and a connection between this microbiome and the onset of diabetes. The aim of the current study was to assess the association between blood microbiota and incident cardiovascular disease.

Methods and Results

D.E.S.I.R. is a longitudinal study with the primary aim of describing the natural history of the metabolic syndrome and its complications. Participants were evaluated at inclusion and at 3-, 6-, and 9-yearly follow-up visits. The 16S ribosomal DNA bacterial gene sequence, that is common to the vast majority of bacteria (Eubac) and a sequence that mostly represents Proteobacteria (Pbac), were measured in blood collected at baseline from 3936 participants. 73 incident cases of acute cardiovascular events, including 30 myocardial infarctions were recorded. Eubac was positively correlated with Pbac (r = 0.59; P<0.0001). In those destined to have cardiovascular complications, Eubac was lower (0.14±0.26 vs 0.12±0.29 ng/µl; P = 0.02) whereas a non significant increase in Pbac was observed. In multivariate Cox analysis, Eubac was inversely correlated with the onset of cardiovascular complications, (hazards ratio 0.50 95% CI 0.35–0.70) whereas Pbac (1.56, 95%CI 1.12–2.15) was directly correlated.

Conclusion

Pbac and Eubac were shown to be independent markers of the risk of cardiovascular disease. This finding is evidence for the new concept of the role played by blood microbiota dysbiosis on atherothrombotic disease. This concept may help to elucidate the relation between bacteria and cardiovascular disease.     

Widespread Occurrence of Chemical Residues in Beehive Matrices from Apiaries Located in Different Landscapes of Western France

Background

The honey bee, Apis mellifera, is frequently used as a sentinel to monitor environmental pollution. In parallel, general weakening and unprecedented colony losses have been reported in Europe and the USA, and many factors are suspected to play a central role in these problems, including infection by pathogens, nutritional stress and pesticide poisoning. Honey bee, honey and pollen samples collected from eighteen apiaries of western France from four different landscape contexts during four different periods in 2008 and in 2009 were analyzed to evaluate the presence of pesticides and veterinary drug residues.

Methodology/Findings

A multi-residue analysis of 80 compounds was performed using a modified QuEChERS method, followed by GC-ToF and LC−MS/MS. The analysis revealed that 95.7%, 72.3% and 58.6% of the honey, honey bee and pollen samples, respectively, were contaminated by at least one compound. The frequency of detection was higher in the honey samples (n = 28) than in the pollen (n = 23) or honey bee (n = 20) samples, but the highest concentrations were found in pollen. Although most compounds were rarely found, some of the contaminants reached high concentrations that might lead to adverse effects on bee health. The three most frequent residues were the widely used fungicide carbendazim and two acaricides, amitraz and coumaphos, that are used by beekeepers to control Varroa destructor. Apiaries in rural-cultivated landscapes were more contaminated than those in other landscape contexts, but the differences were not significant. The contamination of the different matrices was shown to be higher in early spring than in all other periods.

Conclusions/Significance

Honey bees, honeys and pollens are appropriate sentinels for monitoring pesticide and veterinary drug environmental pollution. This study revealed the widespread occurrence of multiple residues in beehive matrices and suggests a potential issue with the effects of these residues alone or in combination on honey bee health.     

Rigorous Training of Dogs Leads to High Accuracy in Human Scent Matching-To-Sample Performance

Human scent identification is based on a matching-to-sample task in which trained dogs are required to compare a scent sample collected from an object found at a crime scene to that of a suspect. Based on dogs’ greater olfactory ability to detect and process odours, this method has been used in forensic investigations to identify the odour of a suspect at a crime scene. The excellent reliability and reproducibility of the method largely depend on rigor in dog training. The present study describes the various steps of training that lead to high sensitivity scores, with dogs matching samples with 90% efficiency when the complexity of the scents presented during the task in the sample is similar to that presented in the in lineups, and specificity reaching a ceiling, with no false alarms in human scent matching-to-sample tasks. This high level of accuracy ensures reliable results in judicial human scent identification tests. Also, our data should convince law enforcement authorities to use these results as official forensic evidence when dogs are trained appropriately.     

Formal Models of the Network Co-occurrence Underlying Mental Operations

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.     

Full validation of therapeutic antibody sequences by middle-up mass measurements and middle-down protein sequencing

The regulatory bodies request full sequence data assessment both for innovator and biosimilar monoclonal antibodies (mAbs). Full sequence coverage is typically used to verify the integrity of the analytical data obtained following the combination of multiple LC-MS/MS datasets from orthogonal protease digests (so called “bottom-up” approaches). Top-down or middle-down mass spectrometric approaches have the potential to minimize artifacts, reduce overall analysis time and provide orthogonality to this traditional approach. In this work we report a new combined approach involving middle-up LC-QTOF and middle-down LC-MALDI in-source decay (ISD) mass spectrometry. This was applied to cetuximab, panitumumab and natalizumab, selected as representative US Food and Drug Administration- and European Medicines Agency-approved mAbs. The goal was to unambiguously confirm their reference sequences and examine the general applicability of this approach. Furthermore, a new measure for assessing the integrity and validity of results from middle-down approaches is introduced – the “Sequence Validation Percentage.” Full sequence data assessment of the 3 antibodies was achieved enabling all 3 sequences to be fully validated by a combination of middle-up molecular weight determination and middle-down protein sequencing. Three errors in the reference amino acid sequence of natalizumab, causing a cumulative mass shift of only ?2 Da in the natalizumab Fd domain, were corrected as a result of this work.     

Dealing with paralogy in RADseq data: in silico detection and single nucleotide polymorphism validation in Robinia pseudoacacia L.

The RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications.