Rabbit Anti-Human STK10 Polyclonal antibody

The crystal structure of the kinase domain from human checkpoint kinase 2 (Chk2) has shown, for the first time, the reciprocal exchange of activation segments between two adjacent molecules and provides the molecular basis for understanding the observed mode of Chk2 kinase activation via trans-autophosphorylation. With further examples of activation segment exchanged kinase domains now publicly available (i.e. Ste20-like kinase, Ser/Thr kinase 10 and Death-associated protein kinase 3), we suggest that this phenomenon represents a common mechanism of activation amongst a particular subset of protein kinases, that is, those that are dimeric (either transiently or constitutively), that undergo activation by autophosphorylation and that have activation segment amino acid sequences that do not resemble those of their substrate consensus sequence.

Background: We have recently identified a number of Quantitative Trait Loci (QTL) contributing to the 2-fold muscle weight difference between the LG/J and SM/J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior (TA) muscle of each strain by RNA-Seq. Results: 13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG/J and SM/J Regulatory Network (LSRN). The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN (Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10) residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes (QTGs) underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM/J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM/J compared to the LG/J strain (p<0.03). Conclusion: Thus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.