Dataset Information

Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.

ABSTRACT:

Background and objective

Stable isotope labeling by amino acids in cell culture (SILAC) is a practical and powerful approach for quantitative proteomic analysis. A key advantage of SILAC is the ability to simultaneously detect the isotopically labeled peptides in a single instrument run and so guarantee relative quantitation for a large number of peptides without introducing any variation caused by separate experiment. However, there are a few approaches available to assessing protein ratios and none of the existing algorithms pays considerable attention to the proteins having only one peptide hit.

Methods

We introduce new quantitative approaches to dealing with SILAC protein-level summary using classification-based methodologies, such as Gaussian mixture models with EM algorithms and its Bayesian approach as well as K-means clustering. In addition, a new approach is developed using Gaussian mixture model and a stochastic, metaheuristic global optimization algorithm, particle swarm optimization (PSO), to avoid either a premature convergence or being stuck in a local optimum.

Results

Our simulation studies show that the newly developed PSO-based method performs the best among others in terms of F1 score and the proposed methods further demonstrate the ability of detecting potential markers through real SILAC experimental data.

Conclusions

No matter how many peptide hits the protein has, the developed approach can be applicable, rescuing many proteins doomed to removal. Furthermore, no additional correction for multiple comparisons is necessary for the developed methods, enabling direct interpretation of the analysis outcomes.

SUBMITTER: Kim S

PROVIDER: S-EPMC5260509 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Publications

Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.

Kim Seongho S Carruthers Nicholas N Lee Joohyoung J Chinni Sreenivasa S Stemmer Paul P

Computer methods and programs in biomedicine 20160922

<h4>Background and objective</h4>Stable isotope labeling by amino acids in cell culture (SILAC) is a practical and powerful approach for quantitative proteomic analysis. A key advantage of SILAC is the ability to simultaneously detect the isotopically labeled peptides in a single instrument run and so guarantee relative quantitation for a large number of peptides without introducing any variation caused by separate experiment. However, there are a few approaches available to assessing protein ra ...[more]

PMID: 28110720

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Project description:Many of the enzymes involved in xenobiotic metabolism are maintained at a low basal level and are only synthesized in response to activation of upstream sensor/effector proteins. This induction can have implications in a variety of contexts, particularly during the study of the pharmacokinetics, pharmacodynamics, and drug-drug interaction profile of a candidate therapeutic compound. Previously, we combined in vivo SILAC material with a targeted high resolution single ion monitoring (tHR/SIM) LC-MS/MS approach for quantification of 197 peptide pairs, representing 51 drug metabolism enzymes (DME), in mouse liver. However, as important enzymes (for example, cytochromes P450 (Cyp) of the 1a and 2b subfamilies) are maintained at low or undetectable levels in the liver of unstimulated metabolically labeled mice, quantification of these proteins was unreliable. In the present study, we induced DME expression in labeled mice through synchronous ligand-mediated activation of multiple upstream nuclear receptors, thereby enhancing signals for proteins including Cyps 1a, 2a, 2b, 2c, and 3a. With this enhancement, 115 unique, lysine-containing, Cyp-derived peptides were detected in the liver of a single animal, as opposed to 56 in a pooled sample from three uninduced animals. A total of 386 peptide pairs were quantified by tHR/SIM, representing 68 Phase I, 30 Phase II, and eight control proteins. This method was employed to quantify changes in DME expression in the hepatic cytochrome P450 reductase null (HRN) mouse. We observed compensatory induction of several enzymes, including Cyps 2b10, 2c29, 2c37, 2c54, 2c55, 2e1, 3a11, and 3a13, carboxylesterase (Ces) 2a, and glutathione S-transferases (Gst) m2 and m3, along with down-regulation of hydroxysteroid dehydrogenases (Hsd) 11b1 and 17b6. Using DME-enhanced in vivo SILAC material with tHR/SIM, therefore, permits the robust analysis of multiple DME of importance to xenobiotic metabolism, with improved utility for the study of drug pharmacokinetics, pharmacodynamics, and of chemically treated and genetically modified mouse models.

Dataset Information

Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.

Background and objective

Methods

Results

Conclusions

Publications

Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.

Similar Datasets

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