Noam D. Beckmann

Multiscale Analysis of Independent Alzheimer’s Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus

Investigators have long suspected that pathogenic microbes might contribute to the onset and progression of Alzheimers disease (AD) although definitive evidence has not been presented. Whether such findings represent a causal contribution, or reflect opportunistic passengers of neurodegeneration, is also difficult to resolve. We constructed multiscale networks of the late-onset AD-associated virome, integrating genomic, transcriptomic, proteomic, and histopathological data across four brain regions from human post-mortem tissue. We observed increased human herpesvirus 6A (HHV-6A) and human herpesvirus 7 (HHV-7) from subjects with AD compared with controls. These results were replicated in two additional, independent and geographically dispersed cohorts. We observed regulatory relationships linking viral abundance and modulators of APP metabolism, including induction of APBB2, APPBP2, BIN1, BACE1, CLU, PICALM, and PSEN1 by HHV-6A. This study elucidates networks linking molecular, clinical, and neuropathological features with viral activity and is consistent with viral activity constituting a general feature of AD.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes. Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease

BackgroundOligodendrocytes (OLs) and myelin are critical for normal brain function and have been implicated in neurodegeneration. Several lines of evidence including neuroimaging and neuropathological data suggest that Alzheimer’s disease (AD) may be associated with dysmyelination and a breakdown of OL-axon communication.MethodsIn order to understand this phenomenon on a molecular level, we systematically interrogated OL-enriched gene networks constructed from large-scale genomic, transcriptomic and proteomic data obtained from human AD postmortem brain samples. We then validated these networks using gene expression datasets generated from mice with ablation of major gene expression nodes identified in our AD-dysregulated networks.ResultsThe robust OL gene coexpression networks that we identified were highly enriched for genes associated with AD risk variants, such as BIN1 and demonstrated strong dysregulation in AD. We further corroborated the structure of the corresponding gene causal networks using datasets generated from the brain of mice with ablation of key network drivers, such as UGT8, CNP and PLP1, which were identified from human AD brain data. Further, we found that mice with genetic ablations of Cnp mimicked aspects of myelin and mitochondrial gene expression dysregulation seen in brain samples from patients with AD, including decreased protein expression of BIN1 and GOT2.ConclusionsThis study provides a molecular blueprint of the dysregulation of gene expression networks of OL in AD and identifies key OL- and myelination-related genes and networks that are highly associated with AD.

A new molecular signature method for prediction of driver cancer pathways from transcriptional data

Abstract Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.

The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease

Alzheimer’s disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disability. Genetic and genome-wide association studies (GWAS) have identified about 33 risk factor genes for common, late-onset AD (LOAD), but these risk loci fail to account for the majority of affected cases and can neither provide clinically meaningful prediction of development of AD nor offer actionable mechanisms. This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD. Specifically, we generated whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data from multiple regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform.

The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target

By integrating genome, transcriptome and proteome data, Petyuk et al. identify HSPA2 as a driver of pathology in Alzheimer’s disease, replicating the finding in an independent cohort and validating it in two in vitro systems. The results highlight the power of systems approaches for identifying genes involved in disease pathways.

THE HUMAN BRAINOME: HUMAN BRAIN GENOME, TRANSCRIPTOME, AND PROTEOME INTEGRATION

O2-06-01 THE HUMAN BRAINOME: HUMAN BRAIN GENOME, TRANSCRIPTOME, AND PROTEOME INTEGRATION Amanda Myers, Rui Chang, Vladislav A. Petyuk, Manuel RamirezRestrepo, Noam D. Beckmann, Marc Y. R. Henrion, Kuixi Zhu, Sven Wang, Paul D. Piehowski, Jennifer Clarke, Matthew J. Huentelman, Fang Xie, Victor Andreev, Anzhelika Engel, Toumy Guettoche, Loida Navarro, Philip de Jager, Julie A. Schneider, Christopher M. Morris, Ian G. McKeith, Robert H. Perry, Simon Lovestone, Randy L. Woltjer, Thomas G. Beach, Lucia Sue, Andrew P. Lieberman, Roger L. Albin, Isidre Ferrer Abizanda, Deborah C. Mash, ChristineM. Hulette, John F. Ervin, John A. Hardy, Eric M. Reiman, David A. Bennett, Eric Schadt, Richard Smith, University of Miami, Miami, FL, USA; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA; 4 Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA; Food Science and Technology Department, University of Nebraska-Lincoln, Lincoln, NE, USA; 6 Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA; Amgen Inc, One Amgen Center Drive, Thousand Oaks, CA, USA; 8 Arbor Research Collaborative for Health, Ann Arbor, MI, USA; 9 Children’s Hospital of Philadelphia, Philadelphia, PA USA; Oncogenomics Core Facility, Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL, USA; Broad Institute, Cambridge, MA, USA; 12 Brigham and Women’s Hospital, Boston, MA, USA; 13 Harvard Medical School, Boston, MA, USA; 14 Rush Alzheimer’s Disease Center, Chicago, IL, USA; Medical Toxicology Centre, Institute of Neuroscience and the Institute for Ageing and Health, Newcastle upon Tyne, United Kingdom; 16 Institute for Ageing and Health, Newcastle University, Wolfson Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom; Neuropathology and Cellular Pathology, Crawford House, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne, United Kingdom; 18 University of Oxford, Oxford, United Kingdom; Oregon Health & Science University, Portland, OR, USA; Banner Sun Health Research Institute, Sun City, AZ, USA; 21 Department of Pathology, University of Michigan, Ann Arbor, MI, USA; 22 Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Geriatrics Research, Education, and Clinical Center, VAAAHS, Ann Arbor, MI, USA; 24 Catedr atic d’Universitat d’Anatomia Patol ogica, Universitat De Barcelona, Barcelona, Spain; 25 Department of Pathology, Division of Neuropathology, Duke University Medical Center, Durham, NC, USA; Kathleen Price Bryan Brain Bank, Department of Medicine, Division of Neurology, Duke University, Duke University Medical Center, Durham, NC, USA; 27 Department of Molecular Neuroscience and Reta Lila Research Laboratories, University College London Institute of Neurology, London, United Kingdom; Banner Alzheimer’s Institute, Phoenix, AZ, USA; 29 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA; 30 Icahn School of Medicine at Mount Sinai, New York, NY, USA. Contact e-mail: amyers@med.miami.edu