Assessing factors for reliable quantitative proteomics based on two‐dimensional gel electrophoresis

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Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of posttranslational modifications in the diversity of metabolic and life-history traits. Molecular & Cellular Proteomics 12: 10.1074/mcp.M112.024349, 720 -735, 2013.The key problem in understanding genotype-phenotype relationships is the complexity arising from multiple levels of cellular functioning. Among them, metabolic networks involve series of interconnected chemical reactions catalyzed by enzymes, allowing the transformation of input substrates into intermediate or final metabolites. These networks play an essential role in an organism's growth, reproduction, and ability to maintain cell integrity and to respond to environmental changes (1, 2). The metabolic fluxes, as well as the metabolite concentrations, are governed by the activity of the enzymes, which depends on three types of factors: kinetic parameters, enzyme abundance, and activation state of the enzyme. The kinetic parameters are determined by the sequence and the three-dimensional structure of the protein (3). The abundance of the enzymes is the result of numerous molecular processes taking place from the transcriptional to the translational level, including epigenetic modifications of the DNA and chromatin (4), transcriptional regulation by transcription factors (5), mRNA capping and splicing and small RNA regulation (6), protein turnover (7), etc. The enzyme activation state is primarily because of post-translational modifications of the native protein, themselves highly regulated (8, 9). Other mechanisms involved in enzyme activity are proteinprotein interactions and allosteric regulation, such mechanisms being sometimes mediated through post-translational modifications (10, 11). The resulting isoforms can display differences in activity, affinity for partners (protein or effectors), and stability (12). The most studied modification is the reversibl...

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Linking Post-Translational Modifications and Variation of Phenotypic Traits

Albertin

Marullo

Bely

et al. 2013

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“…There is significant interest in stable isotope labeling strategies of proteins or peptides as with every measurement there is the potential to use an internal reference allowing relative quantitation comparison, which significantly increases sensitivity of detection of change in abundance. Isobaric labeling techniques such as tandem mass tags (11,12) or isobaric tags for relative or absolute quantitation (iTRAQ) 1 (13,14) allow multiplexing of four, six and eight separately labeled samples within one experiment. In contrast to most other quantitative proteomics methods where precursor ion intensities are measured, here the measurement and ensuing quantitation of iTRAQ reporter ions occurs after fragmentation of the precursor ion.…”

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confidence: 99%

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Karp

Huber

Sadowski

et al. 2010

466

459

iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variancestabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variancestabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry. Molecular & Cellular Proteomics 9:1885-1897, 2010.Different techniques are being used and developed in the field of proteomics to allow quantitative comparison of samples between one state and another. These can be divided into gel-(1-4) or mass spectrometry-based (5-8) techniques. Comparative studies have found that each technique has strengths and weaknesses and plays a complementary role in proteomics (9, 10). There is significant interest in stable isotope labeling strategies of proteins or peptides as with every measurement there is the potential to use an internal reference allowing relative quantitation comparison, which significantly increases sensitivity of detection of change in abundance. Isobaric labeling techniques such as tandem mass tags (11, 12) or isobaric tags for relative or absolute quantitation (iTRAQ) 1 (13, 14) allow multiplexing of four, six and eight separately labeled samples within one experiment. In contrast to mos...

“…Quantitative proteomics, the study of global changes in protein expression, is a rapidly growing field where two-dimensional (2D) 1 gel electrophoresis (1,2), differential labeling of protein and peptides with stable isotopes (3), and label-free mass spectrometric peak intensity measurements (4) are pivotal approaches to measuring changes in the expression level of proteins. The output of large scale genomic sequencing and gene expression studies have driven the need to globally assess protein behavior, which is central to our understanding of cellular function and disease processes.…”

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confidence: 99%

“…To build on established approaches, this study used the Student's t test because the majority of published DIGE studies utilize this methodology to identify significant changes in expression. 2 The Student's t test is a simple test that assumes the data are randomly sampled from normal distributions and shows homogeneity of variance. Regardless of the statistical approach used, all tests have underlying assumptions that need to be considered, and all univariate tests suffer from the issue of multiple testing.…”

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confidence: 99%

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Karp

McCormick²,

Russell

et al. 2007

152

137

In quantitative proteomics, the false discovery rate (FDR) can be defined as the number of false positives within statistically significant changes in expression. False positives accumulate during the simultaneous testing of expression changes across hundreds or thousands of protein or peptide species when univariate tests such as the Student's t test are used. Currently most researchers rely solely on the estimation of p values and a significance threshold, but this approach may result in false positives because it does not account for the multiple testing effect. For each species, a measure of significance in terms of the FDR can be calculated, producing individual q values. The q value maintains power by allowing the investigator to achieve an acceptable level of true or false positives within the calls of significance. The q value approach relies on the use of the correct statistical test for the experimental design. In this situation, a uniform p value frequency distribution when there are no differences in expression between two samples should be obtained. Here we report a bias in p value distribution in the case of a three-dye DIGE experiment where no changes in expression are occurring. The bias was shown to arise from correlation in the data from the use of a common internal standard. With a two-dye schema, where each sample has its own internal standard, such bias was removed, enabling the application of the q value to two different proteomics studies. In the case of the first study, we demonstrate that 80% of calls of significance by the more traditional method are false positives. In the second, we show that calculating the q value gives the user control over the FDR. These studies demonstrate the power and ease of use of the q value in correcting for multiple testing. This work also highlights the need for robust experimental design that includes the appropriate application of statistical procedures. Molecular & Cellular Proteomics 6: 1354 -1364, 2007.Quantitative proteomics, the study of global changes in protein expression, is a rapidly growing field where two-dimensional (2D) 1 gel electrophoresis (1, 2), differential labeling of protein and peptides with stable isotopes (3), and label-free mass spectrometric peak intensity measurements (4) are pivotal approaches to measuring changes in the expression level of proteins. The output of large scale genomic sequencing and gene expression studies have driven the need to globally assess protein behavior, which is central to our understanding of cellular function and disease processes. In quantitative studies, the quantitation of the protein or peptide signal allows the comparison of protein levels from one state with another. Regardless of the technique used, the data generated can be analyzed with a variety of statistical approaches. In a simple comparison of two samples, changes in protein expression are considered to be significant when above a specified threshold (5). More rigorous studies incorporating replicates use univariate (1, 6) or multivari...