Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform

Neo Christopher Chung,Howard Choi,Ding Wang,Bilal Mirza,Alexander R Pelletier,Dibakar Sigdel,Wei Wang,Peipei Ping

doi:10.1016/j.yjmcc.2020.05.020

Abstract

ObjectiveDuring cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets. Approach and resultsWe developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy. ConclusionWe have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

Highlights

Pathological progression of chronic diseases often involves dynamic changes of a vast collection of molecular events, including multifactorial alterations across organ functions and biological processes [1,2]
We have developed the Cardiovascular Signature Temporal Clustering Platform (CV.Signature.TCP), a data science package tailored for longitudinal proteomics studies to extract temporal molecular patterns indicative of disease phenotypes
The temporal changes in 3 types of cysteine oxidative stress induced post-translational modifications (O-PTMs) at the proteome level were obtained using a mouse model of cardiac hypertrophy [5]. These proteomic datasets consist of 6 time points (1, 3, 5, 7, 10, 14 days with ISO treatment) and multiple variables, including modification site/occurrence, modification type, and modification occupancy of cysteine O-PTM on cardiac proteins

Summary

Introduction

Pathological progression of chronic diseases often involves dynamic changes of a vast collection of molecular events, including multifactorial alterations across organ functions and biological processes (e.g., genome, proteome, and metabolome) [1,2]. Biomedical innovation and discovery have been supported by two major driving forces: the classical investigative hypothesis-centric approach relies heavily on previously published results; and the recent development of data-driven methods focuses on the understanding of data with the aid of computational intelligence The success of the latter requires a few notable technical considerations. We applied CV.Signature.TCP to analyze this dataset in an unsupervised and nonparametric fashion; our tool identified O-PTM subgroups of temporal importance and enabled further functional delineation. Both the parameter settings and analytical routes of CV.Signature.TCP are generalizable to allow a broader adaptation to other temporal omics data

Methods

Results and discussion

Conclusion