Nathan Daniel Price - US grants

DESCRIPTION (provided by applicant): An invariant feature of the pathological cascade in Alzheimer's diseases (AD) is a reactive gliosis, reflecting an underlying alteration in the innate immune activation state within the brain. Innate immune signaling is altered early in AD, but is also skewed towards an activated state as a consequence of brain aging. There is strong genetic evidence that innate immunity has a significant role in AD. Variants in two genetic loci that play roles in the complement cascade, CR1 and CLU, show significant genetic associations with AD, and rare coding variants in TREM2 also confer substantial risk for AD. Numerous experimental studies in AD mouse models show that manipulating innate immune pathways can have positive or negative effects on proteostasis, cognition and neurodegeneration. At least when assessing A? pathology as an endpoint, the beneficial effects of some innate immune system manipulations are robust. We propose to identify therapeutic targets within the innate immune signaling cascade in AD that could be safely manipulated to provide disease modification in AD. However, because of the complexity of, and the gaps in our knowledge regarding, innate immune signaling within the CNS, a systems level approach that integrates multiple types of data will be required to achieve this goal. Indeed, development of any innate immune therapy will need to be finely tuned and extensively validated in order to be further developed as a potential AD therapy. We will use a multifaceted systems level approach to identify targets within innate immune signaling pathways that can safely provide disease modifying effects in AD. Comprehensive, transcriptomic, genetic and pathological data from both humans and mouse models will be generated, integrated and analyzed in novel ways. This integrated data will then be used to guide multiple preclinical target validation studies of key innate immune targets in both APP and tau mouse models as well as non-transgenic mice. These studies will dramatically accelerate the identification and validation of disease modifying innate immune modulatory strategies in AD and will provide important insights into how these various manipulations of innate immune activation states alter normal behaviors with an emphasis on cognition.

METABOLIC NETWORK ANALYSIS OF BIOCHEMCIAL TRAJECTORIES IN ALZHEIMER'S DISEASE ABSTRACT Despite advances, clinical trials have not yielded therapies to prevent or slow progression of Alzheimer's Disease (AD) with recent failures highlighting our incomplete knowledge of disease mechanisms. Accumulating evidence suggests the synaptic failure in AD is associated with dysregulation in multiple networks and that AD is not a singular condition but may be a combination of altered networks calling for a systems approach. AD susceptibility is likely influenced by many different common and rare genomic variants spread across hundreds of genes. Such genetic heterogeneity poses enormous challenges in defining disease mechanisms and approaches for drug development. Increasing evidence supports that AD is a metabolic disease with diabetes co-morbidity and a range of metabolic perturbations occurring early in disease. APOE4 is the strongest genetic risk factor for AD. APOE functions in lipid metabolism and presence of the APOE4 variant is correlated with higher cholesterol levels in the blood, suggesting again an important role for metabolism in AD. In addition, most of the genes that have recently been implicated in AD suggest a role for lipid processing, immune function regulation and phagocytosis that are all related to metabolic functions. Yet, a detailed mapping of interconnected metabolic networks that fail in AD are not defined. Metabolomics allows simultaneous measurement of 100's to 1000's of metabolites for mapping perturbations into metabolic networks, enabling a systems approach to the study of AD. The metabolome captures net influences of the genome, gut microbiome and environment in AD. Metabolomics data provides a functional readout of effects of genetic variants on metabolism, reducing the complexity of genetics to common effects on metabolic pathways that are implicated in disease. As part of NIA's large initiatives AMP-AD and MOVE-AD, we have established the AD Metabolomics Consortium (ADMC) aiming at building a comprehensive metabolomics database for AD for interrogation of global metabolic failures in disease and to define metabolic pathways that are implicated in cognitive decline. By leveraging large NIH investments in the AD Neuroimaging Initiative (ADNI), ADMC, AMP- AD and MOVE-AD and by adding experts in metabolic reconstruction and modeling we will frame the metabolic basis for AD progression. In Aim 1, we propose to profile longitudinal samples from ADNI cohorts where deep phenotype data exists that enables us to track early biochemical changes related to both CSF amyloid-beta and tau pathology and across the trajectory of disease. Through imaging data we will link peripheral metabolic changes to changes in the brain. In Aim 2, we propose to create an integrated molecular Atlas for AD. This resource for the AD community will connect genotypes and metabotypes, providing a functional readout of genetic variants implicated in AD. In Aim 3, we propose to build the first genome-scale model of metabolic changes underlying AD progression. Together, this effort will provide a biochemical roadmap towards effective early interventions for AD.

Summary: Alzheimer?s disease (AD) is twice as prevalent in AA and 1.5 times in LA populations compared to Caucasians. Despite this, non-Caucasians are vastly under-represented in AD research including clinical trials and genetic studies. Further, there are no multi-omics studies to date in diverse populations. Based on the rationale that multi-omics studies can identify disease-relevant pathways and therapeutic targets, AMP-AD Target Discovery and Preclinical Validation Project was launched. This effort led to the generation and analysis of RNA- sequencing (RNAseq) based transcriptome, whole genome sequence (WGS), proteome, metabolome and epigenome data on >2,500 brain samples from Caucasian patients with AD and non-AD neuropathologies, as well as unaffected controls. This vast amount of data has been made available to the research community. These data have also been utilized to identify or validate potential risk mechanisms in AD and other neurodegenerative diseases and led to the data-driven identification and nomination of over 500 key driver genes/candidate targets for AD. Despite these advances for Caucasians, the multi-omic landscape of diverse AD, non-AD and control brains are unknown. Multi-omics profiling of diverse cohorts is essential for the discovery of the full spectrum of disease-relevant therapeutic targets that will be of utility to all populations affected with AD. In this supplement, we propose to generate and analyze RNAseq data from 3 brain regions of 331 AA or LA patients that represent the spectrum of AD and related disorders. The objective of this proposal is to identify pathways, molecules and potential therapeutic targets of AD in these diverse cohorts utilizing brain transcriptome and other existing data. Comparative studies will be conducted between findings from this proposal and those from the Caucasian cohorts within AMP-AD. These comparisons are expected to identify both shared and population-specific AD pathways and molecules and can ultimately inform the extent of utility for AD therapeutic targets across populations. The proposal is ?shovel-ready? with available samples and data. We will leverage existing infrastructure, protocols, analytic pipelines that we and others already established for large-scale multi-omics studies in AMP-AD. Thus, the proposed study is poised to successfully fill the knowledge gap for disease pathways and therapeutic targets of AD for minority populations. The specific aims are: 1) To generate a brain transcriptome map in diverse populations across the AD spectrum: We will perform brain RNAseq in 3 brain regions (993 brain samples) to generate a detailed brain transcriptome map in these diverse populations. 2) To identify molecular targets for AD in diverse populations: Using data from Aim 1, we will discover differentially expressed genes and networks in AD, their behavior across multiple brain regions and multiple pathologies and nominate a list of therapeutic targets. If funded, this study will enhance the target discovery potential of these diverse cohorts and establish the groundwork for these pioneering multi-omics studies in minority populations.

PROJECT SUMMARY  Our  goal  in  this  proposal  is  to  identify  biological  networks  involved  in  synchronizing  placental  growth  and  maturity. To accomplish this goal, we have established a collaborative effort between the Center for Prevention  of  Preterm  Birth  at  Cincinnati  Children?s  Hospital  Medical  Center  (CCHMC)  and  the  Institute  for  Systems  Biology (ISB) in Seattle to conduct a systems level analysis of ?omics? data. Perturbed growth and maturity can  lead  to  placental  insufficiency,  which  underlies  a  significant  proportion  of  adverse  pregnancy  outcomes,  such  as preterm birth.  A paucity of knowledge regarding normal placental development and maturity greatly hinders  any  study  of  placental  insufficiency.  Placental  growth  and  development  occurs  throughout  gestation  and  reaches maturity at term. Therefore, it is critical to identify the networks involved and to assess them over the  length of gestation. Our central hypothesis is that key biological networks vital to placental growth and  maturity  can  be  identified  through  the  intersection  of  transcriptomic,  proteomic,  and  metabolomics  data  from  term  and  preterm  placentae.  Furthermore,  utilizing  longitudinal  proteomics  and  metabolomics  data,  we  can  determine  how  those  pathways  change  over  gestation  and  differ  between  normal  and  preterm  placentae. We will test this hypothesis through the following aims:   Aim  1:  Identification  of  key  gene  and  metabolite  signatures  involved  in  placental  development  by  analyzing  longitudinal  ?omics?  data.  Using  publically  available  transcriptomic  data,  we  will  generate  a  molecular profile of expressed genes in placental development throughout gestation.  We will also determine  the  placental  secretome  and  identify  biomarker  signatures  that  appear  in  maternal  urine  that  reflect  placental  maturation.   Aim  2:  Identification  of  molecular  pathways  associated  with  placental  maturity.  We  will  utilize  network  topology algorithms to identify changes in molecular pathways in preterm and term placentae. These data will  be  combined  with  publically  available  data  to  identify  molecular  pathways  and  genes  within  those  pathways  that differ between term and preterm placentae to provide insight into placental maturity.   Aim 3: Generation of a placenta-­specific transcriptional network for identifying regulatory mechanisms  involved  in  placental  maturity.  We  will  construct  genome-­scale,  tissue  specific  models  of  placental  transcriptional  regulatory  networks  using  our  newly-­developed  Transcriptional  Regulatory  Network  Analysis  (TRENA)  approach,  which  leverages  a  wealth  of  information  from  the  NIH?s  ENCODE  project.  We  will  characterize  which  transcriptional  regulators  are  most  likely  responsible  for  perturbed  gene  expression,  their  signaling pathways and downstream targets. Previously unknown or understudied networks or genes identified  targeted for further analyses in placental growth and maturity and future prospective clinical studies.