Mouse anti-Human TNIK monoclonal antibody, clone 5F5

TNIK (TRAF2- and NCK-interacting kinase) was named because of its association with TRAF2 (tumor necrosis factor receptor-associated factor 2). But the relationship between TNIK and TRAF2 is still elusive, in addition to which the involvement of TNIK in JNK activation by TNF alpha hints that there maybe a linkage between TNIK and TRAF2. In this work, we illustrated that TNIK protein levels were dynamic in response to TNF alpha stimulation in an ubiquitin-dependent manner. Further study showed that TRAF2 negatively modulated the levels of TNIK by regulating the ubiquitin conjugation. In conclusion, our data may give evidence that dynamic change of TNIK offers a way to protect cells from outside stimulus.

Background: Previous GWASs have revealed several susceptible variants associated with adolescent idiopathic scoliosis (AIS). Risk prediction based on these variants can potentially improve disease prognosis. We aimed to evaluate the combined effects of genetic factors on the development of AIS and to further develop a genetic predictive model. Methods: A total of 914 AIS patients and 1441 normal controls were included in the discovery stage, which was followed by the replication stage composed of 871 patients and 1239 controls. Genotyping assay was performed to analyze 10 previously reported susceptible variants, including rs678741 of LBX1, rs241215 of, AJAP1, rs13398147 of PAX3, rs16934784 of BNC2, rs2050157 of GPR126, rs2180439 of PAX1, rs4940576 of BCL2, rs7593846 of MEIS1, rs7633294 of MAGI1 and rs9810566 of TNIK. Logistic regression analysis was performed to generate a risk predictive model. The predicted risk score was calculated for each participant in the replication stage. Results: The association of the 10 variants with AIS was successfully validated. The established model could explain approximately 7.9% of the overall variance. In the replication stage, patients were found to have a remarkably higher risk score as compared to the controls (44.2 + 14.4 vs. 33.9 + 12.5, p <0.001). There was a remarkably higher proportion of the risk score i.e. >40 in the patients than in the controls (59% vs. 28.9%, p <0.001). Conclusion: Risk predictive model based on the previously reported genetic variants has a remarkable discriminative power. More clinical and genetic factors need to be studied, to further improve the probability to predict the onset of AIS.