Patient-derived mutations within the N-terminal domains of p85α impact PTEN or Rab5 binding and regulation

The p85α protein regulates flux through the PI3K/PTEN signaling pathway, and also controls receptor trafficking via regulation of Rab-family GTPases. In this report, we determined the impact of several cancer patient-derived p85α mutations located within the N-terminal domains of p85α previously shown to bind PTEN and Rab5, and regulate their respective functions. One p85α mutation, L30F, significantly reduced the steady state binding to PTEN, yet enhanced the stimulation of PTEN lipid phosphatase activity. Three other p85α mutations (E137K, K288Q, E297K) also altered the regulation of PTEN catalytic activity. In contrast, many p85α mutations reduced the binding to Rab5 (L30F, I69L, I82F, I177N, E217K), and several impacted the GAP activity of p85α towards Rab5 (E137K, I177N, E217K, E297K). We determined the crystal structure of several of these p85α BH domain mutants (E137K, E217K, R262T E297K) for bovine p85α BH and found that the mutations did not alter the overall domain structure. Thus, several p85α mutations found in human cancers may deregulate PTEN and/or Rab5 regulated pathways to contribute to oncogenesis. We also engineered several experimental mutations within the p85α BH domain and identified L191 and V263 as important for both binding and regulation of Rab5 activit

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine