Misfoldome-centered multiOMICS approach to unravel preeclampsia subphenotypes.

以错误折叠组为中心的多组学方法揭示先兆子痫亚表型。

• 批准号: 9269245
• 负责人: IRINA A BUHIMSCHI
• 金额: $ 48.86万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9269245
• 关键词: Affinity; Aging; Algorithms; Alzheimer' s Disease; Amyloid; Antibodies; Bioinformatics; Biological; Biological Markers; Blood; Catalogs; Characteristics; Clinical; Clinical Data; Comorbidity; Complex; Congo Red; Crowding; Cystic Fibrosis; Data; Decidua; Deposition; Diagnostic; Discriminant Analysis; Disease; Dyes; Entropy; Environment; Etiology; Goals; Heterogeneity; Human; Knowledge; Light Chain Deposition Disease; Link; Machine Learning; Malignant Neoplasms; Metabolic Diseases; Methods; Modeling; Molecular; Molecular Conformation; Molecular Profiling; Molecular Target; Neonatal Mortality; Non-Insulin-Dependent Diabetes Mellitus; Organ; Parkinson Disease; Pathway interactions; Patients; Perinatal mortality demographics; Phenotype; Placenta; Pre-Eclampsia; Pregnancy; Pregnancy Outcome; Pregnant Women; Prion Diseases; Protein Conformation; Proteins; Proteome; Proteomics; Research; Risk; Secondary to; Serum; Shotguns; Signs and Symptoms; Source; Specimen; Systems Biology; Techniques; Testing; Therapeutic Intervention; Time; Tissues; Urine; Validation; Villous; Woman; adverse pregnancy outcome; base; biobank; biophysical properties; cohort; curative treatments; cytotoxicity; design; driving force; extracellular; functional outcomes; improved; interest; maternal morbidity; metabolome; metabolomics; misfolded protein; multidisciplinary; neonatal morbidity; neonatal outcome; novel therapeutics; perinatal morbidity; personalized therapeutic; phenome; phenomics; prognostic; protein aminoacid sequence; protein folding; protein misfolding; prototype; public health relevance; transcriptome; transcriptome sequencing; transcriptomics; trophoblast

 DESCRIPTION (provided by applicant): Preeclampsia is a heterogeneous disorder specific to human pregnancy and an important contributor to maternal and neonatal morbidity and mortality worldwide. To date, there is no cure for preeclampsia except delivery. Spearheaded by our prior proteomics research, we discovered that preeclampsia shares characteristics of protein misfolding with established conformational disorders including Alzheimer's. These features involve urine congophilia (affinity for the amyloidophilic dye Congo red), affinity for conformational state-dependent antibodies, and dysregulation in the amyloid proteolytic pathway in the placenta and decidua. Our overarching hypothesis is that the excessive formation of misfolded proteins in preeclampsia is driven by increased macromolecular crowding due to defective clearance and/or underlying metabolic disorders leading to faulty protein folding. As a result, the universe of misfolded proteins (misfoldome) could be a rich source of biomarkers more closely related to disease etiology than the properly folded proteome. We propose to use existing biorepositories to understand the underpinnings of different subtypes of preeclampsia. Specifically, we aim to discover specific markers and druggable targets relevant to each preeclampsia subtype that can be corrected before the onset of manifest disease. To achieve these goals we will investigate 4 "omics" layers: proteomics, transcriptomics, metabolomics and phenomics uniquely integrated through machine learning bioinformatics approaches aimed to solve complex and interconnected systems biology data. These include: Linear Discriminant Analysis (LDA), Conditional Random Fields (CRFs) and "fuzzy" soft clustering algorithms for integration of multi-omics data layers. Specific Aim 1 plans to apply shotgun bottom-up proteomics methods to catalogue the protein components of the misfoldome as reflected in urine congophilic aggregates of women with various clinical subphenotypes of preeclampsia. The proteins and biophysical characteristics of peptide sequences in the misfoldome will be analyzed and compared with those of total urine and serum proteomes. Specific Aim 2 plans to illuminate biological pathways of high interest by triangulating proteomics with transcriptomics (RNAseq on placental villous and decidual tissues) and metabolomics (serum and urine) data. Lastly, Specific Aim 3 will validate urine congophila and the newly discovered molecular signatures in a large biorepository of women followed longitudinally during their first pregnancy (nuMoM2b cohort). Together, the three aims of this proposal offer a unique opportunity toward personalized therapeutic options for preeclampsia before the onset of clinically manifest disease.

 描述（由申请人提供）：先兆子痫是一种人类妊娠特有的异质性疾病，是全球孕产妇和新生儿发病率和死亡率的重要因素。到目前为止，除了分娩外，还没有治愈先兆子痫的方法。由我们先前的蛋白质组学研究带头，我们发现先兆子痫与包括阿尔茨海默氏症在内的已建立的构象障碍共享蛋白质错误折叠的特征。这些特征包括尿嗜酸性粒细胞（对淀粉样蛋白染料刚果红的亲和力）、对构象状态依赖性抗体的亲和力以及胎盘和蜕膜中淀粉样蛋白蛋白水解途径的失调。我们的总体假设是，先兆子痫中错误折叠蛋白质的过度形成是由于清除缺陷和/或潜在代谢紊乱导致的大分子拥挤增加导致错误的蛋白质折叠。因此，错误折叠蛋白质（misfoldome）的宇宙可能是比正确折叠的蛋白质组更密切相关的疾病病因的生物标志物的丰富来源。我们建议使用现有的生物储存库来了解不同亚型先兆子痫的基础。具体来说，我们的目标是发现与每种先兆子痫亚型相关的特定标记物和可药物靶点，这些标记物和靶点可以在明显疾病发作之前得到纠正。为了实现这些目标，我们将研究4个“组学”层：蛋白质组学，转录组学，代谢组学和表型组学，这些组学通过机器学习生物信息学方法独特地整合在一起，旨在解决复杂和相互关联的系统生物学数据。其中包括：线性判别分析（LDA）、条件随机场（CRF）和“模糊”软聚类算法，用于整合多组学数据层。具体目标1计划 应用鸟枪自下而上的蛋白质组学方法对具有各种先兆子痫临床亚表型的妇女的尿嗜酸性聚集体中反映的错误折叠组的蛋白质组分进行编目。将分析错误折叠组中肽序列的蛋白质和生物物理特征，并与总尿液和血清蛋白质组进行比较。具体目标2计划通过将蛋白质组学与转录组学（胎盘绒毛和蜕膜组织的RNAseq）和代谢组学（血清和尿液）数据进行三角测量来阐明高度关注的生物学途径。最后，具体目标3将验证尿液中的嗜酸性粒细胞和新发现的分子特征，在一个大型生物储存库的妇女纵向在他们的第一次怀孕（nuMoM 2b队列）。总之，这一建议的三个目标提供了一个独特的机会，个性化的治疗选择先兆子痫的发病前的临床表现的疾病。