Drug Repositioning for Amyloid Transthyretin Amyloidosis by Interactome Network Corrected by Graph Neural Networks and Transcriptome Analysis.

Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effect for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and Gene Expression Omnibus expression profiles, respectively, then screened drug candidates by a corrected protein-protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data, respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by graph neural networks. An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and DE genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR.

Data-independent acquisition mass spectrometry reveals comprehensive plasma protein profiles in the natural history of patients with hereditary transthyretin amyloidosis (ATTRv).

OBJECTIVES: Hereditary transthyretin amyloidosis (ATTRv) is a rare, fatal, autosomal dominant disease with more than 140 mutations discovered. Three phenotypes of amyloid infiltration are neuropathy (ATTRv-PN), cardiopathy (ATTRv-CM), and neuropathy + cardiopathy (ATTRv-MIX). The lack of ATTR-specific biomarkers, difficulties in biopsy evidence, and limited known pathogenic mechanisms have made diagnosis difficult. Newly emerging noninvasive measures for monitoring progression and disease-modifying therapies have improved early diagnosis and patient management. METHODS: Our research applies the latest technology, Data-Independent Acquisition-Based Quantitative Proteomics (DIA), to reveal comprehensive plasma protein profiles in the natural history of Chinese patients with hereditary transthyretin amyloidosis (ATTRv). We analyzed differentially expressed proteins (DEPs) in three phenotypes (ATTRv-PN, ATTRv-CM, and ATTRv-MIX). RESULTS: Serum samples were collected from a total of 18 patients (6 ATTRv-PN, 5 ATTRv-CM, and 7 ATTRv-MIX patients) and 20 healthy participants as a control group. Combined with the results of the proteomic and bioinformatic analyses, we found 30 DEPs and protein interaction networks clustered in KRT family proteins and DSC3 between ATTRv-PN and the control, which were rich in the estrogen signaling pathway and the cell adhesion molecule (CAM) pathway. CONCLUSION: This study demonstrates a global and significant proteomic profile in different stages of ATTRv.