Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms

Despite advances in proteomic technologies, idiosyncratic data issues, for example, incomplete coverage and inconsistency, resulting in large data holes, persist. Moreover, because of naïve reliance on statistical testing and its accompanying <i>p</i> values, differential protein signatures identified from such proteomics data have little diagnostic power. Thus, deploying conventional analytics on proteomics data is insufficient for identifying novel drug targets or precise yet sensitive biomarkers. Complex-based analysis is a new analytical approach that has potential to resolve these issues but requires formalization. We categorize complex-based analysis into five method classes or paradigms and propose an even-handed yet comprehensive evaluation rubric based on both simulated and real data. The first four paradigms are well represented in the literature. The fifth and newest paradigm, the network-paired (NP) paradigm, represented by a method called Extremely Small SubNET (ESSNET), dominates in precision-recall and reproducibility, maintains strong performance in small sample sizes, and sensitively detects low-abundance complexes. In contrast, the commonly used over-representation analysis (ORA) and direct-group (DG) test paradigms maintain good overall precision but have severe reproducibility issues. The other two paradigms considered here are the hit-rate and rank-based network analysis paradigms; both of these have good precision-recall and reproducibility, but they do not consider low-abundance complexes. Therefore, given its strong performance, NP/ESSNET may prove to be a useful approach for improving the analytical resolution of proteomics data. Additionally, given its stability, it may also be a powerful new approach toward functional enrichment tests, much like its ORA and DG counterparts

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization

Integrative Toxicoproteomics Implicates Impaired Mitochondrial Glutathione Import as an Off-Target Effect of Troglitazone

Troglitazone, a first-generation thiazolidinedione of antihyperglycaemic properties, was withdrawn from the market due to unacceptable idiosyncratic hepatotoxicity. Despite intensive research, the underlying mechanism of troglitazone-induced liver toxicity remains unknown. Here we report the use of the <i>Sod2</i>^<i>+/–</i> mouse model of silent mitochondrial oxidative-stress-based and quantitative mass spectrometry-based proteomics to track the mitochondrial proteome changes induced by physiologically relevant troglitazone doses. By quantitative untargeted proteomics, we first globally profiled the <i>Sod2</i>^<i>+/–</i> hepatic mitochondria proteome and found perturbations including GSH metabolism that enhanced the toxicity of the normally nontoxic troglitazone. Short- and long-term troglitazone administration in <i>Sod2</i>^<i>+/–</i> mouse led to a mitochondrial proteome shift from an early compensatory response to an eventual phase of intolerable oxidative stress, due to decreased mitochondrial glutathione (mGSH) import protein, decreased dicarboxylate ion carrier (DIC), and the specific activation of ASK1-JNK and FOXO3a with prolonged troglitazone exposure. Furthermore, mapping of the detected proteins onto mouse specific protein-centered networks revealed lipid-associated proteins as contributors to overt mitochondrial and liver injury when under prolonged exposure to the lipid-normalizing troglitazone. By integrative toxicoproteomics, we demonstrated a powerful systems approach in identifying the collapse of specific fragile nodes and activation of crucial proteome reconfiguration regulators when targeted by an exogenous toxicant

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization

Integrative Toxicoproteomics Implicates Impaired Mitochondrial Glutathione Import as an Off-Target Effect of Troglitazone

Troglitazone, a first-generation thiazolidinedione of antihyperglycaemic properties, was withdrawn from the market due to unacceptable idiosyncratic hepatotoxicity. Despite intensive research, the underlying mechanism of troglitazone-induced liver toxicity remains unknown. Here we report the use of the <i>Sod2</i>^<i>+/–</i> mouse model of silent mitochondrial oxidative-stress-based and quantitative mass spectrometry-based proteomics to track the mitochondrial proteome changes induced by physiologically relevant troglitazone doses. By quantitative untargeted proteomics, we first globally profiled the <i>Sod2</i>^<i>+/–</i> hepatic mitochondria proteome and found perturbations including GSH metabolism that enhanced the toxicity of the normally nontoxic troglitazone. Short- and long-term troglitazone administration in <i>Sod2</i>^<i>+/–</i> mouse led to a mitochondrial proteome shift from an early compensatory response to an eventual phase of intolerable oxidative stress, due to decreased mitochondrial glutathione (mGSH) import protein, decreased dicarboxylate ion carrier (DIC), and the specific activation of ASK1-JNK and FOXO3a with prolonged troglitazone exposure. Furthermore, mapping of the detected proteins onto mouse specific protein-centered networks revealed lipid-associated proteins as contributors to overt mitochondrial and liver injury when under prolonged exposure to the lipid-normalizing troglitazone. By integrative toxicoproteomics, we demonstrated a powerful systems approach in identifying the collapse of specific fragile nodes and activation of crucial proteome reconfiguration regulators when targeted by an exogenous toxicant