Dataset Information

VAV1 and BAFF, via NF?B pathway, are genetic risk factors for myasthenia gravis.

ABSTRACT: OBJECTIVE: To identify novel genetic loci that predispose to early-onset myasthenia gravis (EOMG) applying a two-stage association study, exploration, and replication strategy. METHODS: Thirty-four loci and one confirmation loci, human leukocyte antigen (HLA)-DRA, were selected as candidate genes by team members of groups involved in different research aspects of MG. In the exploration step, these candidate genes were genotyped in 384 EOMG and 384 matched controls and significant difference in allele frequency were found in eight genes. In the replication step, eight candidate genes and one confirmation loci were genotyped in 1177 EOMG patients and 814 controls, from nine European centres. RESULTS: ALLELE FREQUENCY DIFFERENCES WERE FOUND IN FOUR NOVEL LOCI: CD86, AKAP12, VAV1, B-cell activating factor (BAFF), and tumor necrosis factor-alpha (TNF-?), and these differences were consistent in all nine cohorts. Haplotype trend test supported the differences in allele frequencies between cases and controls. In addition, allele frequency difference in female versus male patients at HLA-DRA and TNF-? loci were observed. INTERPRETATION: The genetic associations to EOMG outside the HLA complex are novel and of interest as VAV1 is a key signal transducer essential for T- and B-cell activation, and BAFF is a cytokine that plays important roles in the proliferation and differentiation of B-cells. Moreover, we noted striking epistasis between the predisposing VAV1 and BAFF haplotypes; they conferred a greater risk in combination than alone. These, and CD86, share the same signaling pathway, namely nuclear factor-kappaB (NF?B), thus implicating dysregulation of proinflammatory signaling in predisposition to EOMG.

SUBMITTER: Avidan N

PROVIDER: S-EPMC4184684 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

VAV1 and BAFF, via NFκB pathway, are genetic risk factors for myasthenia gravis.

Avidan Nili N Le Panse Rozen R Harbo Hanne F HF Bernasconi Pia P Poulas Konstantinos K Ginzburg Elizabeta E Cavalcante Paola P Colleoni Lara L Baggi Fulvio F Antozzi Carlo C Truffault Frédérique F Horn-Saban Shirley S Pöschel Simone S Zagoriti Zoi Z Maniaol Angelina A Lie Benedicte A BA Bernard Isabelle I Saoudi Abdelhadi A Illes Zsolt Z Casasnovas Pons Carlos C Casasnovas Pons Carlos C Melms Arthur A Tzartos Socrates S Willcox Nicholas N Kostera-Pruszczyk Anna A Tallaksen Chantal C Mantegazza Renato R Berrih-Aknin Sonia S Miller Ariel A

Annals of clinical and translational neurology 20140411 5

<h4>Objective</h4>To identify novel genetic loci that predispose to early-onset myasthenia gravis (EOMG) applying a two-stage association study, exploration, and replication strategy.<h4>Methods</h4>Thirty-four loci and one confirmation loci, human leukocyte antigen (HLA)-DRA, were selected as candidate genes by team members of groups involved in different research aspects of MG. In the exploration step, these candidate genes were genotyped in 384 EOMG and 384 matched controls and significant di ...[more]

PMID: 25356403

Similar Datasets

Project description:BackgroundApproximately 10% to 20% of myasthenia gravis (MG) patients have experienced a myasthenic crisis (MC), which contributes to morbidity and mortality. MC triggered by infection is associated with poor outcomes. However, there is a lack of prognostic factors that clinicians can utilize to target interventions for preventing recurrent infection-triggered MC. This study aimed to characterize clinical manifestations, comorbidities, and biochemical profiles associated with recurrent infection-triggered MC in MG patients.MethodsThis retrospective study included 272 MG patients hospitalized with an infection requiring at least 3 days of antibiotics from January 2001 to December 2019. Patients were further stratified into non-recurrent or recurrent infection groups. Clinical features such as gender, age, concomitant diseases, acetylcholine receptor antibodies and biochemical data (including electrolytes and coagulants), muscle strength of pelvic and shoulder girdle, bulbar and respiratory function, management with an endotracheal tube, Foley catheter, or plasmapheresis, duration of hospitalization, and culture pathogens were recorded.ResultsThe recurrent infection group was significantly older than the non-recurrent group (median age, 58.5 versus 52.0 years). Pneumonia was the most common infection and Klebsiella pneumoniae was the most common pathogen. The presence of concomitant diabetes mellitus, activated partial thromboplastin time prolongation, the duration of hospitalization, and hypomagnesaemia were independently associated with recurrent infection. The presence of deep vein thrombosis, thymic cancer, and electrolyte imbalances i.e., hypokalemia, and hypoalbuminemia were significantly associated with a risk for infection. The influence of endotracheal intubation, anemia, and plasmapheresis during hospitalization were inconsistent.ConclusionsThe independent risk factors for recurrent infections in MG patients identified in this study include the presence of concomitant diabetes mellitus, hypomagnesaemia, activated partial thromboplastin time prolongation, and longer duration of hospitalization, highlighting the need for targeted interventions to prevent recurrent infections in this population. Further research and prospective studies are warranted to validate these findings and refine interventions for optimizing patient care.

Project description:Myasthenia gravis (MG) is an autoimmune disease affecting the neuromuscular junction, whose clinical hallmark is muscle weakness and early fatigability. Azathioprine (AZA) is commonly used in Myasthenia Gravis therapy. AZA is a purine antagonist, which inhibits the cell cycle in the resting and DNA synthesis phases. It is usually used as an immunosuppressant to block T- and B-cell proliferation. AZA, bioconverted to 6-mercaptopurine by glutathione S-transferase (GST), can be metabolized either through the hypoxanthine phosphoribosyl transferase pathway to active 6-thioguanine nucleotides (6-TGN) or through the thiopurine S-methyltransferase (TPMT) pathway to inactive methyl-thiopurine metabolites. The incorporation of active 6-TGNs into DNA, causing breaks in DNA strands resulting in interference with RNA production and thereby protein synthesis, is responsible for the drug effect. The response to AZA is determined by which metabolic pathway is being favored. While balanced use of both HPRT and TPMT pathways results in responsiveness to AZA, hyperactivity of TPMT skews the balance towards the TPMT pathway and results in unresponsiveness to AZA with no pharmacological effect. In contrast, low TPMT activity skews the balance towards the HPRT pathway, resulting in increasing side-effects due to the accumulation of 6-TGNs. Current treatments for MG therapy are often inadequate because less than 40% of patients achieve complete remission with available drugs. This reflects the lack of drugs acting on target sites for which there is strong evidence of pathogenicity and the inability to identify responder patients. No criteria for responsiveness are available, exposing patients to unpredictable failures and unpredictable side effects. These individuals are at particular risk for adverse drug reaction (ADRs) or therapeutic failure. Genetic profiling with the Affymetrix drug metabolizing enzymes and transports genotyping array offers the ability to determine 1,931 variants and 225 genes involved in drug metabolism and disposition. Accordingly, the study of well-known drug-metabolizing genes can be involved in the specific metabolic pathway of a drug, which is more likely to define genotype-phenotype association and thereby genotype profiles relevant to drug response that can be applied as predictive biomarkers for pharmacological treatment.

Dataset Information

VAV1 and BAFF, via NF?B pathway, are genetic risk factors for myasthenia gravis.

Publications

VAV1 and BAFF, via NFκB pathway, are genetic risk factors for myasthenia gravis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets