Integrative Network Modeling of Cognitive Resilience to Alzheimer's Disease

(R01AG057907)

Bin Zhang, PhD

Professor Department of Genetics & Genomic Sciences

Icahn Institute of Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, New York, USA

Email: bin.zhang@mssm.edu

mailto:bin.zhang@mssm.edu

Rationale

No effective method is available for preventing and/or treating this devastating disease.

However, certain individuals of the elder population (≥ 85 years) remain cognitively intact, including some with substantial plaques and neurofibrillary tangle burdens, the two pathological hallmarks for fully symptomatic AD.

The mechanisms of cognitive resilience and protection against AD in these elderly persons remain elusive.

Overall Goals This grant aims to systematically develop and validate molecular network models underlying cognitive resilience to AD risk. Generate and analyze an AD-resilient dataset comprised of matched genetic,

transcriptomic and proteomic data from a large number of very old and AD-resilient human brains

Build network models of cognitive resilience to AD

Extensively validate network drivers using C. elegans and mouse models.

subset samplesAlzh_PFC 310Alzh_CR 263Alzh_VC 190Norm_PFC 153Norm_CR 128Norm_VC 121

Zhang B et al. (2013). Cell 153(3)

Overview of HBBAD Cohort and Analysis Flow

hbtrc_subset_genes_overlap

overlapAlzh_PFCAlzh_CRAlzh_VCNorm_PFCNorm_CRNorm_VCHunt_PFCHunt_CRHunt_VC

Alzh_PFC131931054211022108311029710586113231008211023

Alzh_CR0.8131931045198951133010152103141110010398

Alzh_VC0.840.7913193101071019710693108751011611206

Norm_PFC0.820.750.771319399401049310496951110214

Norm_CR0.780.860.770.751319310253100751064910200

Norm_VC0.80.770.810.80.781319310456968710654

Hunt_PFC0.860.780.820.80.760.79131931018211171

Hunt_CR0.760.840.770.720.810.730.771319310285

Hunt_VC0.840.790.850.770.770.810.850.7813193

subsetsamples

Alzh_PFC310

Alzh_CR263

Alzh_VC190

Norm_PFC153

Norm_CR128

Norm_VC121

Hunt_PFC157

Hunt_CR141

Hunt_VC122

Gene Modules Associated with AD Pathology

Zhang B et al. (2013). Cell 153(3)

Brain regions rank-ordered by the relevance to AD pathology

Region Full NmaeBM10-FP Frontal PoleBM17-OVC Occipital Visual CortexBM20-ITG Inferior Temporal GyrusBM21-MTG Middle Temporal GyrusBM22-STG Superior Temporal GyrusBM23-PCC Posterior Cingulate CortexBM32-AC Anterior CingulateBM36-PHG Parahippocampal GyrusBM38-TP Temporal PoleBM4-PCG Precentral GyrusBM44-IFG Inferior Frontal GyrusBM46-PFC Dorsolateral Prefrontal CortexBM7-SPL Superior Parietal LobuleBM8-FC Prefrontal CortexBMa-AMYG AmygdalaBMb-CD Caudate NucleusBMc-HIPP HippocampusBMd-NAc Nucleus AccumbensBme-PT Putamen

Wang MH, et al. Genome Medicine 8:104

AD-Hary2-Sample-module_infor

RegionFull Nmae# of arrays# of module genesmax module sizemin module size

BM10-FPFrontal Pole6329172519220

BM17-OVCOccipital Visual Cortex5331915480220

BM20-ITGInferior Temporal Gyrus5830033383720

BM21-MTGMiddle Temporal Gyrus5831703836820

BM22-STGSuperior Temporal Gyrus6027981646921

BM23-PCCPosterior Cingulate Cortex5823657403220

BM32-ACAnterior Cingulate5928640463220

BM36-PHGParahippocampal Gyrus6027616471720

BM38-TPTemporal Pole5826245330420

BM4-PCGPrecentral Gyrus4926208362820

BM44-IFGInferior Frontal Gyrus5329125395820

BM46-PFCDorsolateral Prefrontal Cortex5727060517020

BM7-SPLSuperior Parietal Lobule5029658647620

BM8-FCPrefrontal Cortex5629288587822

BMa-AMYGAmygdala5124431581622

BMb-CDCaudate Nucleus5232189438822

BMc-HIPPHippocampus55292681268820

BMd-NAcNucleus Accumbens5125713328520

Bme-PTPutamen5230618566620

Total1053540520102306387

Selective Myelination Vulnerability of 19 Brain Regions to AD

Wang MH, et al. (2016) Genome Medicine 8:104;

* *

*

*

BM44-IFG (Inferior Frontal Gyrus)

BM22-STG (Superior Temporal Gyrus)

BM36-PHG (Parahippocampal Gyrus)

Transcriptomics (RNA-seq) – 4 brain regions Proteomics (BM10, BM36 ongoing) Whole Exome Sequencing Whole Genome Sequencing

Demographic characteristics of MSBB AD samples

The Mount Sinai Cohort of Large-scale Genomic, Transcriptomic and Proteomic Data in Alzheimer's Disease

Wang MH et al. Nature Scientific Data (in press)

Approaches

Subgroups of Interest

OONL (oldest/old normal): Very old (age of death (AoD) ≥ 85) AD-resilient YONL (young/old normal): Young (AoD < 85)

healthy OOAD (oldest/old AD): (AoD ≥85) AD YOAD (young/old AD): Young (AoD < 85) AD.

Data Generation (Prefrontal Cortex)

New

113

137

Total

227

250 192

107

Differential Analysis of the MSBB-AD Data

A) Trend analysis identified HLA-DPA1 with the most significant up-regulation trend.

B) Expression of HLA-DPA1 protein in the temporal cortex of young-old and oldest-old persons with and without dementia.

Differential Analysis of the ROSMAP Data

Differential Analysis of the ROSMAP Data

Summary & Future Work The framework of multiscale network modeling of AD has been

extensively validated. Preliminary analysis of three large transcriptomic datasets identified

novel subnetworks and key regulators of resilience to AD. RNA-sequencing and proteomic profiling of a large number of AD-

resilient and contrast groups are undergoing. Future work Integrate multi-Omics data into high-resolution network models

of resilience to AD Screen a large number of candidate drivers in vivo

Genomics, Informatics, Networks o Bin Zhang, Eric Schadt, Jun Zhu, Minghui

Wang, Zhidong Tu, Joel Dudley, Won-min Song

Tissue collection and profiling o Vahram Haroutunian

Clinical, experimental validation o Sam Gandy, Michelle Ehrlich, Joe Buxbaum,

Stephen Salton, Mary Sano, Charles Mobbs (C. elegans), Kristen Brennand and Aiqun Li (stem cells)

High throughput stem cell production and differentiation o Scott Noggle o Valentina Fossati

Fly models o Koichi Iijima

Data hosting, sharing, competitions o Lara Mangravite o Mette Peters o Ben Logsdon

Team for the AMP-AD U01 Grant

Team for the Resilience R01 Grant Data Integration o Bin Zhang

(contact PI) o Minghui Wang o Ali Sharma o Erming Wang o Chen Ming o Ryan Neff o Shuyu Dan Li o Rong Chen o Zhidong Tu o Li Chen o Lei Guo

Data Generation o Vahram Haroutunian (M-PI) o Pavel Katsel

in vivo Validations o Michelle Ehrlich (M-PI) o Sam Gandy o Charles Mobbs o Mickael Audrain o Tomas Fanytza o Ben Shackleton

Proteomics o Junmin Peng o Kaiwen Yu o Boer Xie

Sample Preparation & Data QC o Chris Gaiteri o David A Bennett o Ryan Johnson o Gregory Klein

Genetics of Resilience o John Kauwe

Acknowledgements

R01 AG046170

R01AG057907

AMP-AD Consortium

M2OVE-AD Consortium

Dr. Suzana Petanceska

Integrative Network Modeling of Cognitive Resilience to Alzheimer's Disease�(R01AG057907)RationaleOverall GoalsSlide Number 4Gene Modules Associated with AD PathologySlide Number 6Slide Number 7Slide Number 8Slide Number 9ApproachesSubgroups of InterestData Generation (Prefrontal Cortex)Slide Number 13Slide Number 14Slide Number 15Slide Number 16Slide Number 17Team for the Resilience R01 GrantAcknowledgements