Proteomics for systems toxicology

Abstract

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

Keywords: Computational analysis; Quantitative proteomics; Systems toxicology.

Fig. 1

Experimental methods for analysis of proteomic alterations. (A) Gel-based and gel-free proteomics workflows. (B) Methods for targeted mass spectrometry analysis. Selected reaction monitoring (SRM) commonly relies on a triple-quadruple mass spectrometry-instrument. Specific peptide/fragment mass pairs (transitions) are selected and generated with quadrupole mass filters (Q1–Q3). During a targeted experiment the mass-spectrometer can cycle though several transitions to allow for multiplexing. Parallel reaction monitoring (PRM) is a related technology, which relies on a high resolution fragment mass-analyzer such as an Orbitrap rather than a quadruple. With this, all fragment ions of the selected peptides can be identified and quantified in parallel. (C) Mass spectrometry-based phospho-profiling workflow.

Fig. 2

Workflow for computational analysis of proteomics data. Most crucial is the generation of a high-quality quantitative proteomics dataset (left panel). The generated quantitative proteomics data include the expression matrix and lists of differentially expressed proteins. To derive biological insights from this data, a multitude of analysis approaches can be employed (right panel).

Fig. 3

Impact of cigarette smoke exposure on the rat lung proteome. (A) Summary of rat exposure study. (B) Tobacco smoke exposure showed strong overall impact on the lung proteome. Heatmap shows significantly altered proteins (FDR-adjusted p-value < 0.05) in at least one cigarette smoke exposure condition. Each row represents a protein, each column a sample (six biological replicates), and the log2 fold-change expression values compared with sham (fresh air) exposure is color-coded. (C) Gene set enrichment analysis (GSEA) shows a concentration-dependent gene set perturbation by cigarette smoke and a partial recovery after 42 days of fresh air exposure. The heatmap shows the significance of association (− log10 adjusted p-value) of up- (red) and down- (blue) regulated proteins with gene sets. Select gene sets enriched for up-regulated proteins by cigarette smoke exposure are highlighted for three different clusters. (D) Functional interaction network of significantly up-regulated proteins upon cigarette smoke exposure shows affected functional clusters including xenobiotic metabolism, response to oxidative stress, and inflammatory response. (E) Overall, the identified functional clusters show corresponding mRNA upregulation. mRNA expression changes were measured for the same lung tissue samples and compared with the protein expression changes. The heatmap compares differential protein (left) and mRNA (right) regulation (signed − log10 q-value) for the identified protein clusters and exposure conditions. The bar plot indicates the percent of the genes that show consistent, statistically significant up-regulation of the mRNA transcript upon 90-day smoke exposure (q-value < 0.05). Note that—while overall consistent—the “translation” and “unfolded protein response” clusters show less mRNA up-regulation.