Novel functions of CCM1 delimit the relationship of PTB/PH domains.

BACKGROUND: Three NPXY motifs and one FERM domain in CCM1 makes it a versatile scaffold protein for tethering the signaling components together within the CCM signaling complex (CSC). The cellular role of CCM1 protein remains inadequately expounded. Both phosphotyrosine binding (PTB) and pleckstrin homology (PH) domains were recognized as structurally related but functionally distinct domains. METHODS: By utilizing molecular cloning, protein binding assays and RT-qPCR to identify novel cellular partners of CCM1 and its cellular expression patterns; by screening candidate PTB/PH proteins and subsequently structurally simulation in combining with current X-ray crystallography and NMR data to defined the essential structure of PTB/PH domain for NPXY-binding and the relationship among PTB, PH and FERM domain(s). RESULTS: We identified a group of 28 novel cellular partners of CCM1, all of which contain either PTB or PH domain(s), and developed a novel classification system for these PTB/PH proteins based on their relationship with different NPXY motifs of CCM1. Our results demonstrated that CCM1 has a wide spectrum of binding to different PTB/PH proteins and perpetuates their specificity to interact with certain PTB/PH domains through selective combination of three NPXY motifs. We also demonstrated that CCM1 can be assembled into oligomers through intermolecular interaction between its F3 lobe in FERM domain and one of the three NPXY motifs. Despite being embedded in FERM domain as F3 lobe, F3 module acts as a fully functional PH domain to interact with NPXY motif. The most salient feature of the study was that both PTB and PH domains are structurally and functionally comparable, suggesting that PTB domain is likely evolved from PH domain with polymorphic structural additions at its N-terminus. CONCLUSIONS: A new ß1A-strand of the PTB domain was discovered and new minimum structural requirement of PTB/PH domain for NPXY motif-binding was determined. Based on our data, a novel theory of structure, function and relationship of PTB, PH and FERM domains has been proposed, which extends the importance of the NPXY-PTB/PH interaction on the CSC signaling and/or other cell receptors with great potential pointing to new therapeutic strategies. GENERAL SIGNIFICANCE: The study provides new insight into the structural characteristics of PTB/PH domains, essential structural elements of PTB/PH domain required for NPXY motif-binding, and function and relationship among PTB, PH and FERM domains.

Effects of Starting a Gastroenterology Fellowship Training Program on Quality Measures of Colonoscopy.

OBJECTIVES: Adenoma detection rate (ADR) is the most established indicator of the quality of screening colonoscopy. The effect of gastroenterology (GI) fellows on the quality of screening colonoscopies has been evaluated previously; however, the effect of starting a new GI fellowship program on the quality of screening colonoscopies has not been studied. The aim of our study was to assess the effects of starting a GI fellowship program and the participation of fellows in screening colonoscopies on ADR and other measures of quality. METHODS: This was a retrospective, cross-sectional study of all screening colonoscopies performed 20 months before and 20 months after starting the GI fellowship at our medical center (November 2010-February 2014). Colonoscopy procedure notes and pathology records were reviewed for each patient. Data from the two periods were compared using either the Fisher exact test or the two-sample t test. RESULTS: A total of 2127 complete colonoscopies were included in the analysis. The mean age of patients was 58.8 ± 6.6 years. Of the 2127 colonoscopies, GI fellows were involved in 385 (18%), whereas 1742 (82%) were performed solely by GI attendings (attending physicians). Multivariate analysis using relative risk (RR) of regression was done. The after starting the GI fellowship period was significantly associated with an increase in ADR (RR 1.19, 95% confidence interval 1.10-1.30, P < 0.001) and advanced adenoma detection rate (RR 1.17, 95% confidence interval 1.00-1.38, P < 0.001) compared with the before starting the GI fellowship period. In the after starting the GI fellowship period, the polyp detection rate and ADR for colonoscopies performed by the attending physicians with the fellows were significantly higher than colonoscopies performed solely by the same attendings (58.4% vs 44.5%, P = 0.001, 42.0% vs 32.9%, P = 0.017, respectively). CONCLUSIONS: Starting a GI fellowship program significantly increased the polyp detection rate, ADR, and advanced ADR.

Improving LASSO performance for Grey Leaf Spot disease resistance prediction based on genotypic data by considering all possible two-way SNP interactions.

Disease resistance prediction using genotypic data has been widely pursued in animal as well as plant research, mostly in cases where genotypic data can be readily available for a large number of subjects. With the evolution of SNP marker genotyping technology and the consequent cost reduction for genotyping thousands of SNP markers, significant research effort is being undertaken in the statistics and machine learning community to perform efficient analysis of these multidimensional datasets. For large plant breeding programs, besides identifying biomarkers associated with disease resistance, developing accurate predictive models of the phenotype based on the genotype alone is one of the most relevant scientific goals, as it allows for efficient selection without having to grow and phenotype every individual. While the importance of interactions for understanding diseases has been shown in many studies, the majority of the existing methods are limited by considering each biomarker as an independent variable, completely ignoring complex interactions among biomarkers. In this study, logistic regression p-value, Pearson correlation and mutual information were calculated for all two-way SNP interactions with respect to the Grey Leaf Spot (GLS) disease resistance phenotype. These interactions were subsequently ranked based on these measures and the performance of the LASSO algorithm for GLS disease resistance prediction was then shown to be maximized by adding the top 10 000 two-way interactions from the logistic regression p-value based rank. The logistic regression p-value based rank also led to an error rate of more than 3 percentual points lower than not adding any interaction and more than 3.5 percentual points lower than adding interactions chosen at random.