Assessment and Improvement of Statistical Tools for Comparative Proteomics Analysis of Sparse Data Sets with Few Experimental Replicates

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard <i>t</i> test, moderated <i>t</i> test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available

Assessment and Improvement of Statistical Tools for Comparative Proteomics Analysis of Sparse Data Sets with Few Experimental Replicates

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard <i>t</i> test, moderated <i>t</i> test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available

Spatial and Temporal Effects in Protein Post-translational Modification Distributions in the Developing Mouse Brain

Protein post-translational modification (PTM) is a powerful way to modify the behavior of cellular proteins and thereby cellular behavior. Multiple recent studies of evolutionary trends have shown that certain pairs of protein post-translational modifications tend to occur closer to each other than expected at random. This type of observation may form the basis of a proposed “PTM code”, whereby protein function is controlled by complex patterns of multiple PTMs. This code could provide an additional, powerful level of regulatory control for protein function and is a plausible explanation for observations of increasingly frequent and diverse protein modification in cell biology. In this study, we use mass spectrometry and proteomic strategies to present biological data showing spatiotemporal PTM co-localization across multiple PTM categories, which display changes over development of the brain. This may be an indication of the existence of a PTM-based functional coding mechanism, which would significantly expand our view of the ways in which cells use protein PTMs in complex signaling networks

Spatial and Temporal Effects in Protein Post-translational Modification Distributions in the Developing Mouse Brain

Protein post-translational modification (PTM) is a powerful way to modify the behavior of cellular proteins and thereby cellular behavior. Multiple recent studies of evolutionary trends have shown that certain pairs of protein post-translational modifications tend to occur closer to each other than expected at random. This type of observation may form the basis of a proposed “PTM code”, whereby protein function is controlled by complex patterns of multiple PTMs. This code could provide an additional, powerful level of regulatory control for protein function and is a plausible explanation for observations of increasingly frequent and diverse protein modification in cell biology. In this study, we use mass spectrometry and proteomic strategies to present biological data showing spatiotemporal PTM co-localization across multiple PTM categories, which display changes over development of the brain. This may be an indication of the existence of a PTM-based functional coding mechanism, which would significantly expand our view of the ways in which cells use protein PTMs in complex signaling networks

Performance of Isobaric and Isotopic Labeling in Quantitative Plant Proteomics

Mass spectrometry has become indispensable for peptide and protein quantification in proteomics studies. When proteomics technologies are applied to understand the biology of plants, two-dimensional gel electrophoresis is still the prevalent method for protein fractionation, identification, and quantitation. In the present work, we have used LC–MS to compare an isotopic (ICPL) and isobaric (iTRAQ) chemical labeling technique to quantify proteins in the endosperm of <i>Ricinus communis</i> seeds at three developmental stages (IV, VI, and X). Endosperm proteins of each stage were trypsin-digested in-solution, and the same amount of peptides was labeled with ICPL and iTRAQ tags in two orders (forward and reverse). Each sample was submitted to nanoLC coupled to an LTQ-Orbitrap high-resolution mass spectrometer. Comparing labeling performance, iTRAQ was able to label 99.8% of all identified unique peptides, while 94.1% were labeled by ICPL. After statistical analysis, it was possible to quantify 309 (ICPL) and 321 (iTRAQ) proteins, from which 95 are specific to ICPL, 107 to iTRAQ, and 214 common to both labeling strategies. We noted that the iTRAQ quantification could be influenced by the tag. Even though the efficiency of the iTRAQ and ICPL in protein quantification depends on several parameters, both labeling methods were able to successfully quantify proteins present in the endosperm of castor bean during seed development and, when combined, increase the number of quantified proteins

Performance of Isobaric and Isotopic Labeling in Quantitative Plant Proteomics

Mass spectrometry has become indispensable for peptide and protein quantification in proteomics studies. When proteomics technologies are applied to understand the biology of plants, two-dimensional gel electrophoresis is still the prevalent method for protein fractionation, identification, and quantitation. In the present work, we have used LC–MS to compare an isotopic (ICPL) and isobaric (iTRAQ) chemical labeling technique to quantify proteins in the endosperm of <i>Ricinus communis</i> seeds at three developmental stages (IV, VI, and X). Endosperm proteins of each stage were trypsin-digested in-solution, and the same amount of peptides was labeled with ICPL and iTRAQ tags in two orders (forward and reverse). Each sample was submitted to nanoLC coupled to an LTQ-Orbitrap high-resolution mass spectrometer. Comparing labeling performance, iTRAQ was able to label 99.8% of all identified unique peptides, while 94.1% were labeled by ICPL. After statistical analysis, it was possible to quantify 309 (ICPL) and 321 (iTRAQ) proteins, from which 95 are specific to ICPL, 107 to iTRAQ, and 214 common to both labeling strategies. We noted that the iTRAQ quantification could be influenced by the tag. Even though the efficiency of the iTRAQ and ICPL in protein quantification depends on several parameters, both labeling methods were able to successfully quantify proteins present in the endosperm of castor bean during seed development and, when combined, increase the number of quantified proteins

Performance of Isobaric and Isotopic Labeling in Quantitative Plant Proteomics

Mass spectrometry has become indispensable for peptide and protein quantification in proteomics studies. When proteomics technologies are applied to understand the biology of plants, two-dimensional gel electrophoresis is still the prevalent method for protein fractionation, identification, and quantitation. In the present work, we have used LC–MS to compare an isotopic (ICPL) and isobaric (iTRAQ) chemical labeling technique to quantify proteins in the endosperm of <i>Ricinus communis</i> seeds at three developmental stages (IV, VI, and X). Endosperm proteins of each stage were trypsin-digested in-solution, and the same amount of peptides was labeled with ICPL and iTRAQ tags in two orders (forward and reverse). Each sample was submitted to nanoLC coupled to an LTQ-Orbitrap high-resolution mass spectrometer. Comparing labeling performance, iTRAQ was able to label 99.8% of all identified unique peptides, while 94.1% were labeled by ICPL. After statistical analysis, it was possible to quantify 309 (ICPL) and 321 (iTRAQ) proteins, from which 95 are specific to ICPL, 107 to iTRAQ, and 214 common to both labeling strategies. We noted that the iTRAQ quantification could be influenced by the tag. Even though the efficiency of the iTRAQ and ICPL in protein quantification depends on several parameters, both labeling methods were able to successfully quantify proteins present in the endosperm of castor bean during seed development and, when combined, increase the number of quantified proteins

Performance of Isobaric and Isotopic Labeling in Quantitative Plant Proteomics

Mass spectrometry has become indispensable for peptide and protein quantification in proteomics studies. When proteomics technologies are applied to understand the biology of plants, two-dimensional gel electrophoresis is still the prevalent method for protein fractionation, identification, and quantitation. In the present work, we have used LC–MS to compare an isotopic (ICPL) and isobaric (iTRAQ) chemical labeling technique to quantify proteins in the endosperm of <i>Ricinus communis</i> seeds at three developmental stages (IV, VI, and X). Endosperm proteins of each stage were trypsin-digested in-solution, and the same amount of peptides was labeled with ICPL and iTRAQ tags in two orders (forward and reverse). Each sample was submitted to nanoLC coupled to an LTQ-Orbitrap high-resolution mass spectrometer. Comparing labeling performance, iTRAQ was able to label 99.8% of all identified unique peptides, while 94.1% were labeled by ICPL. After statistical analysis, it was possible to quantify 309 (ICPL) and 321 (iTRAQ) proteins, from which 95 are specific to ICPL, 107 to iTRAQ, and 214 common to both labeling strategies. We noted that the iTRAQ quantification could be influenced by the tag. Even though the efficiency of the iTRAQ and ICPL in protein quantification depends on several parameters, both labeling methods were able to successfully quantify proteins present in the endosperm of castor bean during seed development and, when combined, increase the number of quantified proteins

Precision Mapping of Coexisting Modifications in Histone H3 Tails from Embryonic Stem Cells by ETD-MS/MS

Post-translational modifications (PTMs) of histones play a major role in regulating chromatin dynamics and influence processes such as transcription and DNA replication. Here, we report 114 distinct combinations of coexisting PTMs of histone H3 obtained from mouse embryonic stem (ES) cells. Histone H3 N-terminal tail peptides (amino acids 1–50, 5–6 kDa) were separated by optimized weak cation exchange/hydrophilic interaction liquid chromatography (WCX/HILIC) and sequenced online by electron transfer dissociation (ETD) tandem mass spectrometry (MS/MS). High mass accuracy and near complete sequence coverage allowed unambiguous mapping of the major histone marks and discrimination between isobaric and nearly isobaric PTMs such as trimethylation and acetylation. Hierarchical data analysis identified H3K27me2-H3K36me2 as the most frequently observed PTMs in H3. Modifications at H3 residues K27 and K36 often coexist with the abundant mark K23ac, and we identified two frequently occurring quadruplet marks ‘K9me1K23acK27me2K36me2’ and ‘K9me3K23acK27me2K36me’, which might indicate a role in crosstalk. Co-occurrence frequency analysis revealed also an interplay between methylations of K9, K27, and K36, suggesting interdependence between histone methylation marks. We hypothesize that the most abundant coexisting PTMs may provide a signature for the permissive state of mouse ES cells

Взаємозв'язок між якістю корпоративного управління та вартості банку в концентрованій системі власності: теоретичні основи

This article presents the results of evidence of corporate governance variable endogeneity and proposes different instruments for estimation by instrumental variables approach. Moreover, results by simultaneous equation approach indicate a relation of reverse causality between corporate governance quality and bank valuation. The empirical methodology developed in this article may be further used in the research; the study of relationship between corporate governance quality and bank value under the optimal ratio of ownership concentration is another promising area of study.У даній статті представлені результати докази корпоративного управління змінної ендогенного і пропонує різні інструменти для оцінки за допомогою інструментальних змінних підходу. Крім того, результати шляхом одночасного рівняння підходу вказують на ставлення зворотного причинно-наслідкового зв'язку між якістю корпоративного управління та оцінки банку. Емпірична методологія, розроблена в даній статті може бути додатково використаний в дослідженнях; вивчення взаємозв'язку між якістю корпоративного управління і вартістю банку при оптимальному співвідношенні концентрації власності є ще однією перспективною галуззю дослідження