Discovering Molecular and Neural Biomarkers of Social and Language Development in ASD Toddlers

严重程度不同的社交情绪、社交过程和社交沟通症状出现在 自闭症谱系障碍(ASD)的头几年，给患者带来了无处不在的终身挑战， 父母和心理医生。缺乏验证的早期生物学和临床ASD亚型阻碍了准确的 临床进展和治疗结果的预测。ASD社交情感症状是ASD特有的体征 并且可以通过母亲的眼神跟踪(ET)来索引；社交差异无疑是由神经引起的 功能障碍，如我们的资助发现的，但神经和ET社会基础的分子病理生物学 异质性还没有被很好地理解。对社交情感语言的fMRI神经反应亚型是已知的 从我们的赠款工作中，还有一组自闭症儿童的非典型皮质模式，这些儿童的语言和语言能力都很差 社会结果。我们最近的工作确定了与这些亚群相关的基因表达特征，并且 失调的信号通路与社会症状的严重程度有关。缺少的是这些不同的成像， 社会和基因发现在ASD亚型中是相关的，以及社会神经背后的分子驱动因素 活动。目的1-发现ASD社会性神经和社会性ET亚型的潜在分子驱动因素。vbl.使用 一种无监督的数据驱动的患者精准医学方法(相似网络融合) 分型，我们将整合多模式社交临床，社交眼球跟踪，社交语音激活功能磁共振， 基于MRI的皮质分离，以及来自一大批新的幼儿队列的转录数据 ASD社会神经功能障碍亚型潜在的分子失调。复制分析将使用 我们现有的NIDCD队列作为一个独立的样本。将测试子类型的健壮性和 再现性。每种模式中区分ASD亚型的特征将通过对每种类型的测试来确定 每个数据通道上的一对子类型。目标2-与以下相关的产前分子过程和细胞类型 ASD神经-社会亚型。使用标准化的BrainSpan表达数据绘制产前时空图 ASD亚型中被干扰的通路的活动，我们将了解ASD中通路何时何地被干扰 亚型通常在产前发育过程中上调。顶端差异表达(DE)基因 每条被破坏的途径都将在围棋生物过程中进行分析，以获得丰富的营养。使用标准单元类型- ，我们将测试ASD神经-社会亚型中的DE基因是否丰富产前和 出生后细胞类型特异性标志物，并确定哪些神经社会亚型强烈表达最近 确定了在ASD神经前体和神经元中过度活跃的多路径DE-ASD网络。目标3- 根据ASD亚型预测预后。对于每个ASD亚型的幼儿，我们使用贝叶斯多模式 稀疏线性回归学习确定哪些转录、脑成像、眼球跟踪和治疗 每个亚型的特征预测了每个儿童在临床摄入后1.5年的结果、社交和语言。这 为个体患者的临床可操作预后亚型标记物开辟道路。

Social affect, social processing, and social communication symptoms differing in degrees of severity appear in the first years of life in autism spectrum disorder (ASD), giving rise to pervasive, lifelong challenges to patients, parents, and therapists. Lack of verified early-age biological and clinical ASD subtypes prevents accurate prediction of clinical progression and treatment outcome. ASD social affective symptoms are ASD specific signs and can be indexed by motherese eye tracking (ET); social differences are undoubtedly caused by neural dysfunction such as those found by our grant, but the molecular pathobiology underlying neural and ET social heterogeneity are not well understood. Subtypes of fMRI neural responses to social affective language are known from our grant work, as are atypical cortical patterning for a subgroup of ASD toddlers with poor language and social outcomes. Our recent work identifies gene expression features associated with such subgroups, and dysregulated signaling pathways are related to social symptom severity. Missing is how these various imaging, social and gene findings are connected in ASD subtypes, and what molecular drivers underlie social neural activity. AIM 1 - discover underlying molecular drivers of ASD social neural and social ET subtypes. Using an unsupervised data-driven precision medicine method (Similarity Network Fusion (SNF)) for patient subtyping, we will integrate multimodality social clinical, social eye tracking, social speech activation fMRI, MRI-based cortical parcellation, and transcriptomic data from a large new cohort of toddlers to discover molecular dysregulations underlying subtypes of ASD social neural dysfunction. Replication analyses will use our existent NIDCD cohort as an independent sample. Subtypes will be tested for robustness and reproducibility. Features in each modality that differentiate ASD subtypes will be identified via tests of each pair of subtypes over each data modality. AIM 2 - prenatal molecular processes and cell types associated with ASD neural-social subtypes. Using normative BrainSpan expression data to map prenatal temporal-spatial activity of pathways disrupted in ASD subtypes, we will learn when and where disrupted pathways in ASD subtypes are normally upregulated during prenatal development. Top differentially expressing (DE) genes in each disrupted pathway will be analyzed for enrichment in GO biological processes. Using normative cell type- specific gene markers, we will test whether DE genes in ASD neural-social subtypes enrich prenatal and postnatal cell type-specific markers, and determine which neural-social subtypes strongly express a recently identified multi-pathway DE-ASD network that is over-active in ASD neural progenitors and neurons. AIM 3 – prognosis prediction from ASD subtypes. For toddlers in each ASD subtype, we use Bayesian multimodality sparse linear regression learning to determine which transcriptomic, brain imaging, eye tracking and treatment features in each subtype predict each child's outcome social and language 1.5 years after clinical intake. This opens avenues for clinical actionable prognostic outcome subtype markers for individual patients.