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.
Preeclampsia is a heterogeneous multi-systemic disorder of human pregnancy with unknown etiology and an important contributor to maternal and perinatal morbidity and mortality. We determined preeclampsia shares features of protein misfolding with established protein conformational disorders including as Alzheimer's disease. Using machine learning algorithms uniquely available to our team, we will integrate four 'omics' layers centered on the misfoldome: proteomics, transcriptomics, metabolomics and phenomics with ultimate goal toward discovery of personalized molecular signatures and druggable targets that can be corrected before onset of manifest disease.
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