Multi-Omic and Functional Genomics in Neurodegenerative Diseases

Most of neurodegenerative cases present a complex genetic architecture, in which many genes and pathways are associated the etiology of diseases, and  downstream effects of genetic variants can affect a myriad of factors in biological networks, including transcriptomic, proteomic, epigenomic, among others, that could converge into disease.  The analysis of data sets with several omics assays can reveal novel mechanistic insights, otherwise missed in omic-specific analyses. Different correlation and network-based approaches can be used to integrate data across multiple omics layers, to generate data-driven hypothesis, that investigating the quantitative effect that a specific altered feature in one layer (e.g. genetic variant, over-expressed or silenced gene in a specific cell type) alters an entire subnetwork.

Currently, we are actively mining into genetic, transcriptomic and proteomic datasets from human dataset as well as IPSC-derived neurons, microglia and/or astrocytes. We are investigating how post-transcriptional effects modulate protein synthesis, and the effect these have in AD. To do so, we are proposing an analytical framework that will later be applied to interpretation of large number of brains and IPS-derived cells and isogenic controls. In parallel, we are employing network analysis and Bayesian causal modeling methodologies, to model and integrate transcript and protein co-expreesion levels, with genomic data. We are studying networks that are altered in affected brains, compared to control neuropathologically free brains, and also the networks altered by specific pathological mutations in APP, PSEN1 and PSEN2 as well coding variants with high effect on risk, in genes like APOE, TREM2, ABCA7, SORL1 and PLD3.

Manhattan plot and correlation curves for differentially expressed genes in Neurodegeneration

Publications

  1. Tavana JP, Rosene M, Jensen NO, Ridge PG, Kauwe JS, Karch CM. RAB10: an Alzheimer’s disease resilience locus and potential drug target. Clin Interv Aging. 2018 Dec 28;14:73-79. doi: 10.2147/CIA.S159148. eCollection 2019. Review. PMID: 3064339
  2. Jiang S, Wen N, Li Z, Dube U, Del Aguila J, Budde J, Martinez R, Hsu S, Fernandez MV, Cairns NJ; Dominantly Inherited Alzheimer Network (DIAN); International FTD-Genomics Consortium (IFGC), Harari O, Cruchaga C, Karch CM.  Integrative system biology analyses of CRISPR-edited iPSC-derived neurons and human brains reveal deficiencies of presynaptic signaling in FTLD and PSP.  Transl Psychiatry. 2018; 8(1):265.
  3. Karch CM, Hernández D, Wang JC, Marsh J, Hewitt AW, Hsu S, Norton J, Levitch D, Donahue T, Sigurdson W, Ghetti B, Farlow M, Chhatwal J, Berman S, Cruchaga C, Morris JC, Bateman RJ; Dominantly Inherited Alzheimer Network (DIAN), Pébay A, Goate AM.  Human fibroblast and stem cell resource from the Dominantly Inherited Alzheimer Network. Alzheimers Res Ther. 2018; 10(1):69.  PMCID: PMC6060509
  4. Hsu S, Gordon BA, Hornbeck R, Norton JB, Levitch D, Louden A, Ziegemeier E, Laforce R Jr, Chhatwal J, Day GS, McDade E, Morris JC, Fagan AM, Benzinger TLS, Goate AM, Cruchaga C, Bateman RJ; Dominantly Inherited Alzheimer Network (DIAN), Karch CM. Discovery and validation of autosomal dominant Alzheimer’s disease mutations.  Alzheimers Res Ther. 2018; 10(1):67.  PMCID: PMC6052673
  5. Ibanez L, Dube U, Davis AA, Fernandez MV, Budde J, Cooper B, Diez-Fairen M, Ortega-Cubero S, Pastor P, Perlmutter JS, Cruchaga C, Benitez BA. Pleiotropic Effects of Variants in Dementia Genes in Parkinson Disease. Front Neuroscience 2018; 12:230. doi: 10.3389/fnins.2018.00230   PMCID: PMC59027
  6. Cruchaga C, Del-Aguila JL, Saef B, Black K, Fernandez MV, Budde J, Ibanez L, Kapoor M, Tosto G, Mayeux RP, Holtzman DM, Fagan AM, Morris JC, Bateman RJ, Goate AM; Dominantly Inherited Alzheimer Network (DIAN); Disease Neuroimaging Initiative (ADNI); NIA-LOAD family study, Harari O. Polygenic risk score of sporadic late-onset Alzheimer’s disease reveals a shared architecture with the familial and early-onset forms. Alzheimers Dement 2018; (2):205-214. PMCID:PMC5803427
  7. Huang KL, Marcora E, Pimenova AA, Di Narzo AF, Kapoor M, Jin SC, Harari O, Bertelsen S, Fairfax BP, Czajkowski J, Chouraki V, .., , Del-Aguila JL, Fernandez MV, Ibañez L; International Genomics of Alzheimer’s Project; Alzheimer’s Disease Neuroimaging Initiative, Sims R, Escott-Price V, Mayeux R, Haines JL, Farrer LA, Pericak-Vance MA, Lambert JC, van Duijn C, Launer L, Seshadri S, Williams J, Amouyel P, Schellenberg GD, Zhang B, Borecki I, Kauwe JSK, Cruchaga C, Hao K, Goate AM.  A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat Neurosci. 2017; 20(8):1052-1061.  PMCID: PMC5759334
  8. Harari O, Cruchaga C.  Paving the road for the study of epigenetics in neurodegenerative  diseases. Acta Neuropathol 2016; 132(4):483-5. 
  9. Lord, J and Cruchaga C. The Epigenetic Landscape of Alzheimer’s Disease. Nature Neuroscience 2014; 17(9):1138-40.