Gene-Gene Interactions Lead to Higher Risk for Development of Type 2 Diabetes in an Ashkenazi Jewish Population

Background

Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.

Methods/Principal Findings

Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7–5.3]; P≤0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7–3.4; P≤0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4–2.8; P≤0.0001] and 2.3 [95% CI = 1.2-4.4; P≤0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.

Conclusions

These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P≤0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.     

Metformin attenuates post-epidural fibrosis by inhibiting the TGF-β1/Smad3 and HMGB1/TLR4 signaling pathways

Excessive post-epidural fibrosis is a common cause of recurrent back pain after spinal surgery. Though various treatment methods have been conducted, the safe and effective drug for alleviating post-epidural fibrosis remains largely unknown. Metformin, a medicine used in the treatment of type 2 diabetes, has been noted to relieve fibrosis in various organs. In the present study, we aimed to explore the roles and mechanisms of metformin in scar formation in a mouse model of laminectomy. Post-epidural fibrosis developed in a mouse model of laminectomy by spinous process and the T12-L2 vertebral plate with a rongeur. With the administration of metformin, post-epidural fibrosis was reduced, accompanied with decreased collagen and fibronectin in the scar tissues. Mechanistically, metformin decreased fibronectin and collagen deposition in fibroblast cells, and this effect was dependent on the HMGB1/TLR4 and TGF-β1/Smad3 signalling pathways. In addition, metformin influenced the metabolomics of the fibroblast cells. Taken together, our study suggests that metformin may be a potential option to mitigate epidural fibrosis after laminectomy.     

Protein microarrays guide tolerizing DNA vaccine treatment of autoimmune encephalomyelitis

The diversity of autoimmune responses poses a formidable challenge to the development of antigen-specific tolerizing therapy. We developed 'myelin proteome' microarrays to profile the evolution of autoantibody responses in experimental autoimmune encephalomyelitis (EAE), a model for multiple sclerosis (MS). Increased diversity of autoantibody responses in acute EAE predicted a more severe clinical course. Chronic EAE was associated with previously undescribed extensive intra- and intermolecular epitope spreading of autoreactive B-cell responses. Array analysis of autoantigens targeted in acute EAE was used to guide the choice of autoantigen cDNAs to be incorporated into expression plasmids so as to generate tolerizing vaccines. Tolerizing DNA vaccines encoding a greater number of array-determined myelin targets proved superior in treating established EAE and reduced epitope spreading of autoreactive B-cell responses. Proteomic monitoring of autoantibody responses provides a useful approach to monitor autoimmune disease and to develop and tailor disease- and patient-specific tolerizing DNA vaccines.     

A heterotrimer model of the complete Microprocessor complex revealed by single-molecule subunit counting

During microRNA (miRNA) biogenesis, the Microprocessor complex (MC), composed minimally of Drosha, an RNaseIII enzyme, and DGCR8, a double-stranded RNA-binding protein, cleaves the primary-miRNA (pri-miRNA) to release the pre-miRNA stem–loop structure. Size-exclusion chromatography of the MC, isolated from mammalian cells, suggested multiple copies of one or both proteins in the complex. However, the exact stoichiometry was unknown. Initial experiments suggested that DGCR8 bound pri-miRNA substrates specifically, and given that Drosha could not be bound or cross-linked to RNA, a sequential model for binding was established in which DGCR8 bound first and recruited Drosha. Therefore, many laboratories have studied DGCR8 binding to RNA in the absence of Drosha and have shown that deletion constructs of DGCR8 can multimerize in the presence of RNA. More recently, it was demonstrated that Drosha can bind pri-miRNA substrates in the absence of DGCR8, casting doubt on the sequential model of binding. In the same study, using a single-molecule photobleaching assay, fluorescent protein-tagged deletion constructs of DGCR8 and Drosha assembled into a heterotrimeric complex on RNA, comprising two DGCR8 molecules and one Drosha molecule. To determine the stoichiometry of Drosha and DGCR8 within the MC in the absence of added RNA, we also used a single-molecule photobleaching assay and confirmed the heterotrimeric model of the human MC. We demonstrate that a heterotrimeric complex is likely preformed in the absence of RNA and exists even when full-length proteins are expressed and purified from human cells, and when hAGT-derived tags are used rather than fluorescent proteins.     

Meaning-making in the immune system

Meaning-making is the process by which a system responds to an indeterminate signal. This article focuses on meaning-making in living systems. It proposes several guidelines for studying the process of meaning-making in living systems in general, and in the immune system in particular. Drawing on a general framework for studying meaning-making in living systems, I suggest three basic organizing concepts for studying meaning-making-variability of the signal, context markers, and transgradience. Those concepts present a radical alternative to the information-processing approach that governs biological research and may shed new light on biological processes.     

Development and study of an amperometric biosensor for the in vitro measurement of low concentration of putrescine in blood

An amperometric biosensor was developed for the in vitro determination of putrescine in blood samples because elevated level of putrescine in blood can be a diagnostic indicator of certain kinds of cancer. The electrochemical transducer consisted of a flat form, three electrode amperometric micro-cell fabricated with thin film photolithography on flexible Kapton substrate. An immobilized putrescine oxidase (PUO) layer provided the biocatalytic oxidation of the putrescine, while the generated hydrogen peroxide was detected on the platinum-working electrode. An electropolymerized poly(m-phenylenediamine) (pPDA) size-exclusion layer was used to protect the working electrode from fouling and to prevent signal generation by common electroactive interferents present in blood. The preparation of the biocatalytic enzyme- and outer protective layers was optimized for improved sensitivity and response time. A detection limit of 50 nM was achieved in pH-adjusted whole blood samples, which is below pathological levels.