Anti-Klrk1 Monoclonal antibody, clone DY6 [PE/Cy7®]

Natural killer (NK) cells eliminate abnormal cells through the release of cytolytic granule contents. In this process, NK cells must adhere to target cells through integrin-mediated adhesion, which is highly dependent on the generation of F-actin. EnaNASP-Iike (EVL) is an actin regulatory protein previously shown to regulate integrin-mediated adhesion in other cell types, but its role in NK cell biology is not known. Herein, we show that EVL is recruited to the NK cell cytotoxic synapse and is required for NK cell cytotoxicity. Significantly, EVL is involved in the generation of F-actin at the cytotoxic synapse, antibody-stimulated spreading, and NK cell-target cell adhesion. EVL interacts with WASP (also known as WAS) and VASP and is required for localization of both proteins to the synapse. Recruitment of EVL to points of cellular activation occurs through the receptor NKG2D-DAP10 (also known as KLRK1 and HCST, respectively) via a binding site previously implicated in VAV1 and Grb2 recruitment. Taken together, this study implicates DAP10-mediated Grb2 and VAV1 signaling in the recruitment of an EVL-containing actin regulatory complex to the cytotoxic synapse where it can promote F-actin nucleation leading to NK cell-mediated killing.

Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV(H)) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV(H) status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results: In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgV(H) mutation status from the ZAP70 co-expression network. Conclusions: We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV(H) mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.