Constructing endophenotypes of complex disease using non-negative matrix factorization and adjusted rand index

[[abstract]]Complex diseases are typically caused by combinations of molecular disturbances that vary widely among different patients. Endophenotypes, a combination of genetic factors associated with a disease, offer a simplified approach to dissect complex trait by reducing genetic heterogeneity. Because molecular dissimilarities often exist between patients with indistinguishable disease symptoms, these unique molecular features may reflect pathogenic heterogeneity. To detect molecular dissimilarities among patients and reduce the complexity of high-dimension data, we have explored an endophenotype-identification analytical procedure that combines non-negative matrix factorization (NMF) and adjusted rand index (ARI), a measure of the similarity of two clusterings of a data set. To evaluate this procedure, we compared it with a commonly used method, principal component analysis with k-means clustering (PCA-K). A simulation study with gene expression dataset and genotype information was conducted to examine the performance of our procedure and PCA-K. The results showed that NMF mostly outperformed PCA-K. Additionally, we applied our endophenotype-identification analytical procedure to a publicly available dataset containing data derived from patients with late-onset Alzheimer’s disease (LOAD). NMF distilled information associated with 1,116 transcripts into three metagenes and three molecular subtypes (MS) for patients in the LOAD dataset: MS1 (), MS2 (), and MS3 (). ARI was then used to determine the most representative transcripts for each metagene; 123, 89, and 71 metagene-specific transcripts were identified for MS1, MS2, and MS3, respectively. These metagene-specific transcripts were identified as the endophenotypes. Our results showed that 14, 38, 0, and 28 candidate susceptibility genes listed in AlzGene database were found by all patients, MS1, MS2, and MS3, respectively. Moreover, we found that MS2 might be a normal-like subtype. Our proposed procedure provides an alternative approach to investigate the pathogenic mechanism of disease and better understand the relationship between phenotype and genotype.[[notice]]補正完

Differential localization of A-Raf regulates MST2-mediated Apoptosis during Epithelial Differentiation

A-Raf belongs to the family of oncogenic Raf kinases that are involved in mitogenic signaling by activating the MEK-ERK pathway. Low kinase activity of A-Raf toward MEK suggested that A-Raf might have alternative functions. We recently identified A-Raf as a potent inhibitor of the proapoptotic mammalian sterile 20-like kinase (MST2) tumor suppressor pathway in several cancer entities including head and neck, colon, and breast. Independent of kinase activity, A-Raf binds to MST2 thereby efficiently inhibiting apoptosis. Here, we show that the interaction of A-Raf with the MST2 pathway is regulated by subcellular compartmentalization. Although in proliferating normal cells and tumor cells A-Raf localizes to the mitochondria, differentiated non-carcinogenic cells of head and neck epithelia, which express A-Raf at the plasma membrane. The constitutive or induced re-localization of A-Raf to the plasma membrane compromises its ability to efficiently sequester and inactivate MST2, thus rendering cells susceptible to apoptosis. Physiologically, A-Raf re-localizes to the plasma membrane upon epithelial differentiation in vivo. This re-distribution is regulated by the scaffold protein kinase suppressor of Ras 2 (KSR2). Downregulation of KSR2 during mammary epithelial cell differentiation or siRNA-mediated knockdown re-localizes A-Raf to the plasma membrane causing the release of MST2. By using the MCF7 cell differentiation system, we could demonstrate that overexpression of A-Raf in MCF7 cells, which induces differentiation. Our findings offer a new paradigm to understand how differential localization of Raf complexes affects diverse signaling functions in normal cells and carcinomas.Science Foundation Irelan