UHMK1 is a novel splicing regulatory kinase.

The U2AF Homology Motif Kinase 1 (UHMK1) is the only kinase that contains the U2AF homology motif, a common protein interaction domain among splicing factors. Through this motif, UHMK1 interacts with the splicing factors SF1 and SF3B1, known to participate in the 3' splice site recognition during the early steps of spliceosome assembly. Although UHMK1 phosphorylates these splicing factors in vitro, the involvement of UHMK1 in RNA processing has not previously been demonstrated. Here, we identify novel putative substrates of this kinase and evaluate UHMK1 contribution to overall gene expression and splicing, by integrating global phosphoproteomics, RNA-seq, and bioinformatics approaches. Upon UHMK1 modulation, 163 unique phosphosites were differentially phosphorylated in 117 proteins, of which 106 are novel potential substrates of this kinase. Gene Ontology analysis showed enrichment of terms previously associated with UHMK1 function, such as mRNA splicing, cell cycle, cell division, and microtubule organization. The majority of the annotated RNA-related proteins are components of the spliceosome but are also involved in several steps of gene expression. Comprehensive analysis of splicing showed that UHMK1 affected over 270 alternative splicing events. Moreover, splicing reporter assay further supported UHMK1 function on splicing. Overall, RNA-seq data demonstrated that UHMK1 knockdown had a minor impact on transcript expression and pointed to UHMK1 function in epithelial-mesenchymal transition. Functional assays demonstrated that UHMK1 modulation affects proliferation, colony formation, and migration. Taken together, our data implicate UHMK1 as a splicing regulatory kinase, connecting protein regulation through phosphorylation and gene expression in key cellular processes.

Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry.

BACKGROUND: Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk. METHODS: We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD). RESULTS: During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs. CONCLUSION: Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance.

The CATS (FAM64A) protein is a substrate of the Kinase Interacting Stathmin (KIS).

The CATS protein (also known as FAM64A and RCS1) was first identified as a novel CALM (PICALM) interactor that influences the subcellular localization of the leukemogenic fusion protein CALM/AF10. CATS is highly expressed in cancer cell lines in a cell cycle dependent manner and is induced by mitogens. CATS is considered a marker for proliferation, known to control the metaphase-to-anaphase transition during the cell division. Using CATS as a bait in a yeast two-hybrid screen we identified the Kinase Interacting Stathmin (KIS or UHMK1) protein as a CATS interacting partner. The interaction between CATS and KIS was confirmed by GST pull-down, co-immunoprecipitation and co-localization experiments. Using kinase assay we showed that CATS is a substrate of KIS and mapped the phosphorylation site to CATS serine 131 (S131). Protein expression analysis revealed that KIS levels changed in a cell cycle-dependent manner and in the opposite direction to CATS levels. In a reporter gene assay KIS was able to enhance the transcriptional repressor activity of CATS, independent of CATS phophorylation at S131. Moreover, we showed that CATS and KIS antagonize the transactivation capacity of CALM/AF10.In summary, our results show that CATS interacts with and is a substrate for KIS, suggesting that KIS regulates CATS function.