Research

Principal Investigator: Zhang, B - contact PI
Project Dates: 05/15/2014 - 08/31/2023
Sponsor: National Institutes of Health

Goal: This proposal aims to develop a multiscale-network approach to elucidating the complexity of AD  based on existing AD-related large scale molecular data and the high-impact, high-resolution complementary datasets generated through this application.

Summary: Pathological and molecular heterogeneity in Alzheimer's disease (AD) has complicated the task of discovering disease-modifying treatments and developing accurate in vivo indices for diagnosis and clinical prognosis. In this project, we will systematically identify and characterize molecular subtypes of AD by developing and employing cutting-edge network biology approaches to multiple existing large-scale genetic, gene expression, proteomic and functional MRI datasets. We will investigate the functional roles of key drivers underlying predicted AD subtypes in hiPSC-derived neural co-culture systems and AD mouse models.

Principal Investigator: Salton S (contact), Ehrlich M, Zhang B – MPIs
Project Dates: 09/30/2018 - 04/30/2023
Sponsor: National Institutes of Health / National Institute of Mental Health 

Goal: The goal is to utilize high-throughput genomics and proteomics to identify shared and unique molecular pathways that regulate the pathogenesis of comorbid Alzheimer’s disease (AD) and Major Depressive Disorder (MDD).

Summary: Comorbidity of Alzheimer's disease (AD) and Major Depressive Disorder (MDD) is frequent but unexplained by common genetic variants. In this project, we will generate transcriptomic and proteomic data from the dorsolateral prefrontal cortex (DLPFC) from a new cohort of AD patients with and without comorbid MDD, MDD patients without AD, and control subjects, and then perform multiscale network analysis to identify shared and distinct molecular mechanisms that regulate these two diseases. We will determine the roles of the top key drivers and subnetworks in comorbid MDD plus AD in cognitive dysfunction, depression-like behavior, and the development of neuropathology using mouse models.

Principal Investigator: Zhang, B (contact); Ehlich,M; Haroutunian,V- M-PIs
Project Dates: 09/15/2017 - 05/31/2022
Sponsor: National Institutes of Health /National Institute on Aging 

Goal: We will perform systems genetics and integrative network biology analyses on the large-scale high-dimensional molecular profiling data to identify genetic variants, genes, proteins, and molecular networks underlying cognitive resilience to AD risk. 

Summary: 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 Alzheimer's disease (AD). The mechanisms of cognitive resilience and protection against AD in these elderly persons remain elusive. This project aims to systematically identify and validate genetic variants, genes, proteins, and molecular networks underlying cognitive resilience to AD risk and to build a comprehensive unbiased signaling pathway map underlying cognitive resilience to AD. Our study will not only present a global landscape of the interplays among genetic variants, mRNAs and proteins responsible for cognitive resilience to AD but also pinpoint critical network structures and key drivers that can potentially lead to development of novel prevention strategies in combating AD.

Principal Investigator: Zhang, Bin
Project Dates: 09/15/2016 - 08/31/2021
Sponsor: National Institutes of Health /National Institute on Aging

Goal: This proposal will address the genes and pathways that differentiate AD progression between females and males by analysis of human postmortem brain gene expression data at the network level

Summary: The burden of Alzheimer's disease (AD) at the patient level falls disproportionately on females, as many studies find that age-matched females have a higher proportion of AD cases. Indeed, evidence suggests that the APOE ε4 allele (ApoE4), which is the strongest risk factor for AD, may have an especially large effect in females compared to males. In this project, we will first systematically identify genes and pathways that differentiate AD progression between females and males, as well as ApoE4 carriers and non-carriers, and then validate the differences in gene and protein expression levels using additional postmortem human brain samples. A number of top gender specific drivers of AD will be tested in animal models, such as female and male ApoE4 KI mice without and with 3xTg AD background. This project will enhance our understanding of AD biology and pave a path towards distinct targeted drug discovery efforts for AD in females and males, which will be crucial to help decrease the burden of this devastating disease.

Principal Investigator: Zhang, B; Goate, A (contact) - M-PIs
Project Dates: 07/15/2016 - 05/31/2021
Sponsor: National Institutes of Health /National Institute on Aging

Goal: This proposal will integrate data from genetic studies and gene expression/regulation studies to identify risk and resilience genes to pinpoint key networks that functionally drive AD development and progression. 

Summary: Alzheimer's disease (AD) is the most common form of dementia but has no effective prevention or treatment. Developing a comprehensive picture of the genetic architecture of AD including a network level assessment of risk/resilience genes is essential to develop novel therapeutic targets. This study aims to define molecular networks enriched for AD risk/resilience genes and to identify known drugs that influence these networks. We will experimentally validate the top in silico predictions of implicated networks, genetic variation and candidate drugs.

Principal Investigator: Cai, D (contact), La Du, Zhang, B, MPIs
Project Dates: 09/01/2019 - 08/31/2020
Sponsor: National Institutes of Health 

Goal: This application aims to systematically construct, characterize, and validate APOE2-specific molecular networks (MNs) in the context of interacting with other APOE alleles and sex that potentially impact longevity, learning and memory, as well as AD development and progression using pre-existing human brain data sets integrated with new data sets generated from additional human brain samples, iPSC-derived brain cells, and humanized APOE mouse models.

Summary: APOE2 is associated with a lower risk and delayed onset for AD. However, very limited studies have addressed the protective effects of APOE2. In this study, we will leverage pre-existing human brain data sets and integrate them with new data sets to be generated from additional human brain samples, ε2/ε3 and ε2/ε4 iPSC-derived brain cells and EFAD animal models. We will perform integrative high-resolution network modeling analysis to identify molecular networks and key regulators mediating APOE2-specific protective effects against aging and AD. The top key drivers of the most informative subnetworks of APOE2 will be validated using gene perturbation techniques in iPSC-derived brain cells and EFAD mice.

Principal Investigator: Zhang, B (contact); Haroutunian, V; Roussos, P: Noggle, S-M- PIs
Project Dates: 09/01/17 - 08/31/20
Sponsor: National Institutes of Health /National Institute on Aging

Goal: This application proposes to generate matched large-scale proteomic and epigenetic data as well as cell type specific transcriptomic and epigenomic data from the parahippocampal gyrus and develop novel network inference and analysis approaches to integrate all these multi-Omics data as well as cognitive, pathological and physiological data to construct high-resolution, multiscale molecular networks in the parahippocampal gyrus in AD.

Summary: The previously established Mount Sinai Brain Bank AD (MSBB-AD) cohort includes whole genome sequencing data from 350 donors and RNA sequencing data from four brain regions of over 200 donors. To enhance the MSBB-AD cohort, we will generate additional, matched epigenetic and proteomic data from the the parahippocampal gyrus (PHG) in the same tissues and donors. We will also generate cell type specific transcriptomic and epigenomic data from the PHG. Molecular network models will be established by integrating the matched multi-Omic data and subsequently validated in AD transgenic mouse primary brain cells and human iPSC derived brain cell cultures to determine the mechanisms underlying vulnerability of the PHG.

Principal Investigator: Zhang, B. (contact); Jabs, E.; M-PIs
Project Dates: 08/01/2018 - 07/31/2020
Sponsor: National Institutes of Health 

Goal: This project will apply a multiscale network biology approach to these and related datasets to elucidate the molecular processes controlling skull development & the pathogenesis of CS and other craniofacial disorders.

Summary: Mutations in ~70 genes have been found that cause craniosynostosis (CS) in humans, particularly for syndromic forms. However, the genetic etiology is unknown for most CS cases, especially sporadic and non-syndromic forms. In this application for secondary bioinformatics analyses, the goal is to use an integrative multiscale network biology approach to elucidate the molecular processes and their key drivers in craniofacial suture formation and CS. In this project, we will 1) assemble and perform quality control on large scale datasets generated through the above grant and relevant datasets in the literature, 2) perform differential gene, non-coding RNA (ncRNA) and splicing analyses and identify molecular changes in suture and CS development, and 3) integrate suture-related molecular data and build multiscale gene, ncRNA and splicing network models of suture and CS development.  Testable hypotheses through mechanistic network models and predicted novel key regulators will be developed for future functional validations in cellular and ultimately, animal model systems by the scientific community.

Principal Investigator: Huang, K
Project Dates: Ongoing
Sponsor: Mount Sinai Seed fund 

Goal: This project will identify cancer genetic predisposition in diverse ancestries. The findings will significantly improve the understanding of how cancer-predisposing DDR genes give rise to cancer in minorities, advancing personalized genetic screening cancer across different ancestry populations.

Summary:  Cancer risk differs across ancestries. While some of these differences may be attributed to non-genetic factors such as access to health care or diet, much can likely be explained by cross-ancestry differences in the genomic architectures and frequencies of inherited genetic predisposition. Large-scale sequencing studies by our group and others discovered that high-penetrance cancer-predisposing germline variation of DNA damage repair (DDR) genes affected 5~10% of adult cancers. However, most studies were performed on individuals of European ancestry. Little is known of high-penetrance variations of these DDR genes in other populations, limiting the clinical utility of genetic testing for minorities. To meet this urgent need, the objective of this project is to identify DDR genetic predisposition and its somatic consequences. Specifically, we will identify DDR genes in multiple cohorts and determine how germline DDR genes shape somatic alterations.

Principal Investigator: Huang, K
Project Dates: Ongoing
Sponsor: Mount Sinai Seed Fund

Goal: This project will identify druggable kinase and phosphosignaling aberrations using rapidly growing proteomic and phosphoproteomic datasets of patient cohorts. The findings will improve kinase target prioritization, rational drug design, and precise prediction of treatment response in cancer.

Summary: Protein kinases directly regulate signaling pathways, affecting cellular functions and phenotypes. Identifying aberrant kinase signaling at systematic scales is required to advance our understanding of disease mechanisms, identification of drug targets, and design of personalized treatments. In this project, we will systematically discover dysregulated kinase proteins and phosphosignaling in cancer by developing novel computational tools to analyze high-dimensional proteomic and phosphoproteomic datasets, and further validate the observations with high throughput experimental approaches.