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

Goh, Wilson Wen Bin; Lee, Yie Hou; Zubaidah, Ramdzan M.; Jin, Jingjing; Dong, Difeng; Lin, Qingsong; Chung, Maxey C. M.; Wong, Limsoon

doi:10.1021/pr1010845

Cited by 51 publications

(56 citation statements)

References 49 publications

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“…2 This correspondence further supports that this group of proteins is likely key drivers for HCC progression.…”

Section: Resultssupporting

confidence: 63%

“…2 Most Mascot hits were also found in Paragon. In addition, Paragon consistently reported more proteins although we found that these were significantly lower ranked.…”

Section: Methodsmentioning

confidence: 99%

“…2 By building clusters around seeds, PEP allows recovery of lower confidence proteins—that is, proteins supported by only a few patients or that the proteins have ratios around 1. It also allows recovery of proteins not immediately evident from the mass spectra or filtered during the data analysis.…”

Section: Introductionmentioning

confidence: 99%

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Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics

et al. 2012

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Traditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method in proteomics that utilizes all detected proteins. We demonstrate its efficacy in a proteomics screen of 5 and 7 liver cancer patients in the moderate and late stage, respectively. Utilizing biological complexes as a cluster vector, and augmenting it with submodules obtained from partitioning an integrated and cleaned protein–protein interaction network, we calculate a Proteomics Signature Profile (PSP) for each patient based on the hit rates of their reported proteins, in the absence of fold change thresholds, against the cluster vector. Using this, we demonstrated that moderate- and late-stage patients segregate with high confidence. We also discovered a moderate-stage patient who displayed a proteomics profile similar to other poor-stage patients. We identified significant clusters using a modified version of the SNet approach. Comparing our results against the Proteomics Expansion Pipeline (PEP) on which the same patient data was analyzed, we found good correlation. Building on this finding, we report significantly more clusters (176 clusters here compared to 70 in PEP), demonstrating the sensitivity of this approach. Gene Ontology (GO) terms analysis also reveals that the significant clusters are functionally congruent with the liver cancer phenotype. PSP is a powerful and sensitive method for analyzing proteomics profiles even when sample sizes are small. It does not rely on the ratio scores but, rather, whether a protein is detected or not. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets.

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“…2 This correspondence further supports that this group of proteins is likely key drivers for HCC progression.…”

Section: Resultssupporting

confidence: 63%

“…2 Most Mascot hits were also found in Paragon. In addition, Paragon consistently reported more proteins although we found that these were significantly lower ranked.…”

Section: Methodsmentioning

confidence: 99%

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Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics

et al. 2012

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“…22 Briefly, the mitoproteome data set identified formed the basis of our seed selection. A seed here is a high-confidence identified protein defined by the criteria as stated above.…”

Section: Methodsmentioning

confidence: 99%

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

Lee

Goh

et al. 2013

J. Proteome Res.

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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 Sod2+/– 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 Sod2+/– 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 Sod2+/– 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.

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“…Contextualization at the level of subnets, or more specifically, protein complexes, can resolve proteomic coverage and consistency issues [15–18]. Use of protein complexes as features for feature-selection instead of predicted clusters from reference networks, is a more powerful approach as protein complexes are enriched for biological signal [19].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics

Goh

2016

BMC Med Genomics

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BackgroundThe hypergeometric enrichment analysis approach typically fares poorly in feature-selection stability due to its upstream reliance on the t-test to generate differential protein lists before testing for enrichment on a protein complex, subnetwork or gene group.MethodsSwapping the t-test in favour of a fuzzy rank-based weight system similar to that used in network-based methods like Quantitative Proteomics Signature Profiling (QPSP), Fuzzy SubNets (FSNET) and paired FSNET (PFSNET) produces dramatic improvements.ResultsThis approach, Fuzzy-FishNET, exhibits high precision-recall over three sets of simulated data (with simulated protein complexes) while excelling in feature-selection reproducibility on real data (based on evaluation with real protein complexes). Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes.ConclusionsBased on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0228-z) contains supplementary material, which is available to authorized users.

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

Cited by 51 publications

References 49 publications

Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics

Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics

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

Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics

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