Genetic screening of the human kinome identifies a cellular signalling network predictive of brain atrophy in Alzheimer’s disease Becky Inkster, DPhil 1,2,3, Maria Vounou, MPhil 3, Giovanni Montana, PhD 3,4, Alzheimer’s Disease Neuroimaging Initiative 5 1Department of Psychiatry, University of Cambridge, UK, 2Department of Medicine, Centre for Neuroscience, Hammersmith Hospital, Imperial College London, UK, 3Department of Mathematics, Statistics Section, Imperial College London, UK, 4Department of Biomedical Engineering, King's College London, UK, 5Alzheimer’s Disease Neuroimaging Initiative (ADNI), USA Background & Objectives Searching for gene variants that influence brain atrophy in Alzheimer’s disease (AD) is highly warranted. Conventional genome-wide approaches (i) have limited statistical power and (ii) fail to incorporate information about intrinsic biological relationships. Here we address these key issues by using a biological model that (i) alleviates the volume of genetic information and (ii) is supported by prior evidence of aberrant cellular signalling associated with AD pathology. Our model is the human kinome. Genetically, it represents less than 2% of the human genome; functionally, however, it regulates signalling cascades that control the majority of cellular functions in the brain (e.g., transcription, translation, DNA repair, cell adhesion, migration, proliferation etc.). The schematic to the right represents all genes that are part of the human kinome (Manning et al., Science, 2002). Methods In the ADNI sample (www.adni-info.org), we applied a penalized multivariate method for kinome-wide detection of markers associated with voxel-wise longitudinal changes (in AD patients and controls) (using similar methods as previously published on genome-wide data by Vounou et al., Neuroimage, 2012). We initially used penalized linear discriminant analysis to identify voxels that provided an imaging signature of brain atrophy with high classification accuracy. We subsequently used this multivariate phenotype for kinome-wide association testing using sparse reduced rank regression. Genetic markers were ranked in order of importance of association to the phenotype using data re-sampling methods. We included Apoe in our analysis as a positive control genetic marker as substantial a priori evidence has shown associations between Apoe epsilon 4 and brain atrophy in AD patients. By adding this marker we could establish relative statistical contributions of the human kinome gene variants. the Results APP Aβ PS RAB1A ADCK1 ERBB4 ROCK1 ROCK1 activation promotes "bad cleavage" of APP = boosting A42 production (bad!) Amyloid Beta Processing cofilin LIMK2 Rho-induced reorganization of the actin cytoskeleton actin Binds to -prevents Actin function Aβ pep treatment Rho CDC42 CIT ADF CDC42BPA Cytoskeletal Pathologies Diabetes, bp, obesity, glucose cholesterol GRK4 ULK4 YES1 SRC HIPK2 CAMK4 CAMK2 CAMK1 KSR2 FER PRKCQ ALK BRD2 APLK3 CREB memory ERBB4 DYRK3 EPHA6 Memory & CREB Regulation Diabetes, blood pressure etc. The figures on the right illustrates how our identified kinases (red) relate to established AD genetic factors (blue and white). We searched across ~518 genes (~6000 available SNPs). Other schematics are not shown on the right, but do exist for different categories (e.g., myelin/white matter pathology; MAK, STK24). Discussion We identified a specific genetic network of kinases that putatively represent a set of functionally diverse genetic factors that underlie AD neurodegeneration (susceptibility or progression). Many of the kinases we identified play hierarchical (i.e., upstream) regulatory roles (shown in red above right) directly linked to established biological evidence related to AD pathology (e.g., amyloid beta processing, memory regulation and CREB activation, cytoskeletal pathologies, apoptosis, white matter pathology, ERK, p53, diabetes, blood pressure and hypertension). One particularly intriguing kinase that we identified through our analysis is ADCK1. Only 3 articles have ever been published (PubMed) on ADCK1 gene function, none of which have been investigated in the field of neurology. Interestingly, 1 of these 3 articles demonstrates that ADCK1 interacts with RAB1A (Stelzl et al., 2005); RAB1A regulates AD-related PS-mediated gamma cleavage (Komamo et al., 2002). It is also interesting that we identified kinases related to diabetes given that diabetes is one of the most robust reported risk factors of AD (Williams et al., 2010). We also identified several CAMKs, which mediate hippocampal-dependent learning and memory. Also identified, ErbB4 protects against Abeta-induced hippocampal impairment. Taken together, if validated, our findings could have targeted therapeutic implications for AD and possibly other neurodegenerative disorders.