RESUMEN
Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases. IMPLICATIONS: This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.
Asunto(s)
Neoplasias , Proteómica , Humanos , Procesamiento Proteico-Postraduccional , Neoplasias/genética , Ubiquitina/metabolismo , Células Cultivadas , Proteínas Fetales/metabolismo , Proteínas Tirosina Quinasas/metabolismoRESUMEN
Liquid-liquid phase separation (LLPS) is emerging as a mechanism of spatiotemporal regulation that could answer long-standing questions about how order is achieved in biochemical signaling. In this review we discuss how LLPS orchestrates kinase signaling, either by creating condensate structures that are sensed by kinases or by direct LLPS of kinases, cofactors, and substrates - thereby acting as a mechanism to compartmentalize kinase-substrate relationships, and in some cases also sequestering the kinase away from inhibitory factors. We also examine the possibility that selective pressure promotes genomic rearrangements that fuse pro-growth kinases to LLPS-prone protein sequences, which in turn drives aberrant kinase activation through LLPS.
Asunto(s)
Proteínas Intrínsecamente Desordenadas , Humanos , Proteínas Intrínsecamente Desordenadas/química , Proteínas Intrínsecamente Desordenadas/genética , Proteínas Intrínsecamente Desordenadas/metabolismo , Secuencia de AminoácidosRESUMEN
OBJECTIVES: To determine the effects of W100E-Leptin in a streptozotocin-induced diabetic mice model (effects in the body weight, fasting serum glucose and glucose tolerance). METHODS: Intraperitoneal W100E-Leptin application at 1 mg/kg for 13 days. We used 3 experimental groups (n=6). Group 1: Diabetes + W100E-Leptin (intraperitoneal administration), Group 2: Diabetes + buffer (vehicle) and Group 3: Healthy control + buffer (vehicle). RESULTS: We determined the effects of W100E on the behavior of the mice, more active, more hair and a tendency to gain body weight. We did not observe any hypoglycemic effect of W100E-Leptin on serum glucose levels in the tests we performed. CONCLUSIONS: These results show us the need to characterize the effects of this hormone in diabetes. We will continue with the characterization of the change that is generated in the protein regulation caused by W100E-Leptin in the diabetes, to propose this hormone as an adjunct against diabetes
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