New nucleation sites can lead to different effects.

<p>Upper panel: Black and red lines denote the spatial distribution of histone mark 1 and 2, respectively. The 90 (100) nucleation sites for mark 1 (2) are shown as black (red) points. Lower panels: Zooms on the spatial distribution of mark 1 for 90 nucleation sites (black line) and 100 nucleation sites (green line). The 100 nucleation sites of mark 2 remain unchanged. The arrows tag new inserted nucleation sites of mark 1. Three different nucleation effects can be observed: (a) no change ; (b) narrow spike around nucleation site ; (c) activation of large region. The simulation parameters were , and .</p

Heterochromatic and euchromatic histone modifications form non-overlapping domains on a coarse scale.

<p>We used the measurements obtained in a genome-wide experiment on human CD4+ T cells from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073818#pone.0073818-Barski1" target="_blank">[59]</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073818#pone.0073818-Wang2" target="_blank">[57]</a> and analyzed them using the CCAT (version 3.0) tool <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073818#pone.0073818-Xu1" target="_blank">[73]</a>. We adapt the analysis using slightly less stringent parameters than the default ones allowing for noisy measurements with lower significance in order to obtain a most complete modification landscape. The plot exhibits the significance scores of the histone modifications H3K9me2 and H3K9me3 related to heterochromatin (red) and H3K4me2, H3K4me3, H3K18ac and H3K23ac related to euchromatin (black). We visualized the distribution over entire chromosome 1. Heterochromatin marks were plotted upside down for better visualization. Both sets of modifications form very similar patterns and form regions of higher and lower abundance. We marked some of the regions with high (low) euchromatic and low (high) heterochromatic content green (blue).</p

Illustration of the processes nucleation, propagation, competition and deletion in the computational model.

<p>Only nucleosomes with nucleation sites can directly be modified with the respective modification with rate (probability per histone and time step). Empty nucleosomes with neighboring modified nucleosomes obtain a modification of the same type with rate . This parameter can vary for different modification types (euchromatic or heterochromatic). Multiply modified nucleosomes are not allowed in the model with only competing marks and therefore a new mark will not be set if the histone is already modified. Finally, every modified nucleosome looses its modification with rate .</p

Complete transition of the chromatin landscape for different propagation rates.

<p>The figure shows the spatial distribution of modifications averaged over the last iterations, , for different values of . Despite the purely random distribution of nucleation sites, chromatin domains form around accumulations of nucleation sites in the upper two panels. The other parameters were , , .</p

Euchromatin and heterochromatin marks become anti-correlated on a coarse scale.

<p>Pearson's correlation between modifications within bins of 100 kbp for different chromosomes and two cell lines. Instead of taking into account the individual scores in a bin, we simplify the content by the sum of all scores. It can be clearly observed that euchromatic (H3K9/14ac, H3K18ac, H3K23ac, H3K4me2 and H3K4me3) and heterochromatic histone marks (H3K9me2, and H3K9me3) oppose each other for all considered chromosomes as well as for two different cell lines. We therefore show that these modifications form long domains that are still detectable on a scale of about 1000 histones.</p

Simulation results (blue) and experimental data (red/black) of CD4+ T cells exhibit similar distributions for euchromatin and heterochromatin on chromosome 1.

<p>The red dots show both H3K9me2 and H3K9me3 marks together, i.e. plotting their scores. The black dots exhibit all scores for the marks H3K4me2, H3K4me3, H3K18ac and H3K23ac. For each histone we depict its occupation frequency averaged over the last 100,000(green dots) merely function as initiators of the process whereas propagation acts as the main competitor in the system. The blue dots show the histone mark distribution. The black and red dots correspond to the same experimental data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073818#pone-0073818-g001" target="_blank">Figure 1</a>, this time normalized for better visualization. Model parameters were , , .</p

Switch-like behavior for competing histone marks.

<p>We take the temporal average of the number of with mark modified histones after the simulation reached a stationary-like state, , presenting now the average frequency of a histone mark. A clear transition between two saturated states is observed, where the number of modifications fluctuates maximally for . Inner panels: evolution of the number of modifications for different parameter sets. The other parameters were , , .</p

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

Tissue Specific Phosphorylation of Mitochondrial Proteins Isolated from Rat Liver, Heart Muscle, and Skeletal Muscle

Phosphorylation of mitochondrial proteins in a variety of biological processes is increasingly being recognized and may contribute to the differences in function and energy demands observed in mitochondria from different tissues such as liver, heart, and skeletal muscle. Here, we used a combination of TiO₂ phosphopeptide-enrichment, HILIC fractionation, and LC–MS/MS on isolated mitochondria to investigate the tissue-specific mitochondrial phosphoproteomes of rat liver, heart, and skeletal muscle. In total, we identified 899 phosphorylation sites in 354 different mitochondrial proteins including 479 potential novel sites. Most phosphorylation sites were detected in liver mitochondria (594), followed by heart (448) and skeletal muscle (336), and more phosphorylation sites were exclusively identified in liver mitochondria than in heart and skeletal muscle. Bioinformatics analysis pointed out enrichment for phosphoproteins involved in amino acid and fatty acid metabolism in liver mitochondria, whereas heart and skeletal muscle were enriched for phosphoproteins involved in energy metabolism, in particular, tricarboxylic acid cycle and oxidative phosphorylation. Multiple tissue-specific phosphorylation sites were identified in tissue-specific enzymes such as those encoded by <i>HMGCS2</i>, <i>BDH1</i>, <i>PCK2</i>, <i>CPS1</i>, and <i>OTC</i> in liver mitochondria, and <i>CKMT2</i> and <i>CPT1B</i> in heart and skeletal muscle. Kinase prediction showed an important role for PKA and PKC in all tissues but also for proline-directed kinases in liver mitochondria. In conclusion, we provide a comprehensive map of mitochondrial phosphorylation sites, which covers approximately one-third of the mitochondrial proteome and can be targeted for the investigation of tissue-specific regulation of mitochondrial biological processes