Proteomics: Clinical and research applications in respiratory diseases

Abstract

The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue-specific expression of proteins and the varied post-translational modifications, splice variants and protein-protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.

Keywords: application; clinical; lung disease; proteomics; research.

Figure 1.. The increasing complexity of the proteome.

The flow of information from DNA to mRNA and then protein is associated with ever increasing complexity. This is emphasized at the protein stage where subcellular localization, spatial transiency, multiple isoforms, large numbers of potential post translational modifications and protein-protein interactions lead to changes in expression and function.

Figure 2.. Data-driven analysis aids in proteome visualization.

(A) A volcano plot highlights significantly differences in expressed proteins between Groups A and B. Red indicates proteins that were significantly different (p < 0.05) between the two groups after correcting for multiple comparisons with the Bonferroni test. (B) Hierarchical clustering illustrates groupings of proteins that differ in expression between Group A and B. Color intensity indicates abundance, with increased expression in red, white unchanged, and decreased expression in blue compared to mean values (color bar to left of figure). Pearson’s correlation was used as the distance metric in this cluster. (C) A PLSDA scores plot illustrates distinct clustering between Groups A and B with loadings (D) indicating a distinct signature (determined using LASSO) of 22 proteins that best classified Groups A and B. (E) A protein correlation network based on protein expression in Group A. Each node is a protein, with lines indicating significant correlations (p < 0.05) to other proteins. Line thickness and color indicates Pearson’s correlation coefficient, with node size indicating the number of significant correlations. Significance was determined after correcting for the Type 1 error with the Bonferroni method.

Figure 3.. Proteomic studies in respiratory disease: increasing interest and number of publications.

The number of PubMed citations from the year 2000 to 2017 were recorded for each of the following: Lung Cancer, COPD, Asthma, Pneumonia, and IPF. MESH terms “proteomics” and “specific lung disease” (i.e IPF) were used as input. No filters were applied.

Figure 4.. The peripheral blood proteome of IPF differs from healthy.

Hierarchical clustering of 1129 measured blood proteins in healthy and IPF patients illustrates visually distinct expression in the two groups. Proteomic abundance is displayed with color intensity, with red indicating overabundant proteins and blue indicating underabundant proteins compared to the mean expression level. Clustering was created using unsupervised average linkage with Pearson’s correlation as the distance metric.