The Matrisome of Model Organisms: From In-Silico Prediction to Big-Data Annotation

Abstract

The extracellular matrix (ECM) is the architect of multicellular organisms. The use of various model organisms has helped decipher the mechanisms by which the ECM provides both chemical and mechanical cues that regulate fundamental cellular processes conserved during evolution such as cell migration, invasion, and differentiation.

High-throughput, or –omic, technologies has transfigured biomedical research. It is thus imperative to have a systematic way to identify ECM genes and proteins in large datasets. This requires having a comprehensive catalog of all the components that constitute the ECM. Here, we will describe the key structural features of ECM proteins (signal peptide, presence of protein domains, motifs, or repeats) that can be used to devise computational approaches to predict ECM proteins. We will then present fully automated machine-learning-based algorithms and approaches that have combined protein-sequence analysis and knowledge-based curation to define the matrisome of model organisms. Last, we provide examples of how the definition of the matrisome has facilitated the identification of ECM genes and proteins in –omic datasets and has advanced our understanding of the contribution of the ECM pathophysiological processes such as embryonic development, tissue regeneration, aging, and cancer.