Transcriptomic Approaches to TDP-43 Pathology

TDP-43病理性包涵体与一系列临床综合征相关，包括行为 变异性额颞叶变性（bvFTD）、原发性进行性失语（PPA）或肌萎缩性侧索硬化症 硬化症（ALS）。此外，约10%的bvFTD或PPA患者具有ALS的神经肌肉特征 （ALS-FTD）。除了与TDP-43相关的FTD和ALS谱的临床复杂性外， 这些疾病之间也有许多共同的遗传联系，包括C9 orf 72重复扩增和TARDBP 可导致bvFTD和/或ALS的突变以及仅导致 FTD（例如，GRN）或ALS（例如，SOD1）。该项目的总体假设是分子异质性， 特别是区域可变的基因表达，有助于宏观网络的脆弱性 导致一系列临床异质性综合征。更好地理解 神经解剖学和临床特征的基因表达在当前时代的精准医学，其中有 是反义寡核苷酸（阿索）和腺相关载体（AAV）病毒遗传疗法， 修饰基因表达，最近获得FDA批准用于神经肌肉疾病， C9 orf 72和GRN正在进行中。虽然先前的研究表明，遗传和表观遗传变异有助于 对于FTD和ALS的异质性谱，评价基因表达与 和疾病表型罕见。该项目旨在确定影响生物和遗传因素， 区域解剖疾病负担，这将提供区域特异性生物标志物，以跟踪新出现的遗传 治疗我们提出了三个具体的目标：（1）确定分子基因之间的区域关系 表达和宏观网络，偏向于特定临床综合征的风险。我们会公开审讯- 可用的区域RNA微阵列数据和相关模式的协方差从TDP相关基因， 与不同bvFTD、PPA和ALS相关的结构和功能衍生网络 （2）在基因表达和FTD的体内神经成像网络之间建立收敛性 ALS患者我们将健康个体的区域RNA微阵列数据与体内皮质 厚度和图论网络特征（例如，度，路径长度）定义在核心E中， 来自从核心B招募并在核心中使用3 T MRI扫描的患有遗传性疾病的FTD和/或ALS患者 C.我们假设，典型成年人基因协方差的区域分布将与区域 神经退行性变在基因突变个体中的分布;（3）确定 基因表达修饰剂影响FTD和ALS中的网络结构。使用网络控制原理 理论上，我们将利用基因表达中自然发生的异质性，包括表观遗传（例如，DNA 甲基化）和常见的遗传（例如，等位基因剂量）因素，以确定这些基因增强或 异质性来源的沉默与疾病网络分布的改变有关。

TDP-43 pathological inclusions are associated with a spectrum of clinical syndromes including behavioral variant frontotemporal degeneration (bvFTD), primary progressive aphasia (PPA), or amyotrophic lateral sclerosis (ALS). Moreover, approximately 10% of bvFTD or PPA patients share neuromuscular features of ALS (ALS-FTD). Beyond the clinical complexities of the FTD and ALS spectrum associated with TDP-43, there are also many shared genetic links between these conditions including C9orf72 repeat expansions and TARDBP mutations that can result in bvFTD and/or ALS as well as disparate genetic mutations that exclusively result in FTD (e.g., GRN) or ALS (e.g., SOD1). The overall hypothesis of this project is that molecular heterogeneity, and specifically regionally-variable gene expression, contributes to macroscale network vulnerability contributing to a spectrum of clinically heterogeneous syndromes. It is critical to better understand the neuroanatomic and clinical features of gene expression in the current era of precision medicine in which there are antisense oligonucleotide (ASO) and adeno-associated vector (AAV) viral genetic therapies that aim to modify gene expression which have recently received FDA-approval for neuromuscular disorders and are underway for C9orf72 and GRN. While prior studies suggest that genetic and epigenetic variation contributes to the spectrum of heterogeneity in FTD and ALS, evaluations of the relationships between gene expression and disease phenotype are rare. This project aims to establish biological and genetic factors that influence regional anatomic disease burden which will provide regionally-specific biomarkers to track in emerging genetic therapies. We propose three Specific Aims: (1) Identify regional relationships between molecular gene expression and macroscale networks that bias risk for a specific clinical syndrome. We will interrogate publicly- available regional RNA microarray data and relate patterns of covariance from TDP-associated genes to structurally and functionally-derived networks that are associated with distinct bvFTD, PPA, and ALS syndromes; (2) Establish convergence between gene expression and in vivo neuroimaging networks of FTD and ALS patients. We will relate regional RNA microarray data of healthy individuals to in vivo cortical thickness and graph-theoretic network features (e.g., degree, path length) defined in Core E that are derived from FTD and/or ALS patients with inherited disease recruited from Core B and scanned using 3T MRI in Core C. We hypothesize that the regional distribution of gene covariance in typical adults will relate to the regional distribution of neurodegeneration in individuals with genetic mutations; and (3) Determine the manner in which modifiers of gene expression impact network structure in FTD & ALS. Using principles of network control theory we will leverage naturally-occurring heterogeneity in gene expression, including epigenetic (e.g., DNA methylation) and common genetic (e.g., allele dosage) factors, to determine whether these gene enhancing or silencing sources of heterogeneity are associated with altered network distributions of disease.