Tracking DOT1L methyltransferase activity by stable isotope labelling using a selective synthetic co-factor.

Epigenetic processes influence health and disease through mechanisms which alter gene expression. In contrast to genetic changes which affect DNA sequences, epigenetic marks include DNA base modifications or post-translational modification (PTM) of proteins. Histone methylation is a prominent and versatile example of an epigenetic marker: gene expression or silencing is dependent on the location and extent of the methylation. Protein methyltransferases exhibit functional redundancy and broad preferences for multiple histone residues, which presents a challenge for the study of their individual activities. We developed an isotopically labelled analogue of co-factor S-adenosyl-L-methionine (13CD3-BrSAM), with selectivity for the histone lysine methyltransferase DOT1L, permitting tracking of methylation activity by mass spectrometry (MS). This concept could be applied to other methyltransferases, linking PTM discovery to enzymatic mediators.

An Integrated Chemical Proteomics Approach for Quantitative Profiling of Intracellular ADP-Ribosylation.

ADP-ribosylation is integral to a diverse range of cellular processes such as DNA repair, chromatin regulation and RNA processing. However, proteome-wide investigation of its cellular functions has been limited due to numerous technical challenges including the complexity of the poly(ADP-ribose) (PAR) chains, low abundance of the modification and lack of sensitive enrichment methods. We herein show that an adenosine analogue with a terminal alkyne functionality at position 2 of the adenine (2-alkyne adenosine or 2YnAd) is suitable for selective enrichment, fluorescence detection and mass spectrometry proteomics analysis of the candidate ADP-ribosylome in mammalian cells. Although similar labelling profiles were observed via fluorescence imaging for 2YnAd and 6YnAd, a previously reported clickable NAD+ precursor, quantitative mass spectrometry analysis of the two probes in MDA-MB-231 breast cancer cells revealed a significant increase in protein coverage of the 2YnAd probe. To facilitate global enrichment of ADP-ribosylated proteins, we developed a dual metabolic labelling approach that involves simultaneous treatment of live cells with both 2YnAd and 6YnAd. By combining this dual metabolic labelling strategy with highly sensitive tandem mass tag (TMT) isobaric mass spectrometry and hierarchical Bayesian analysis, we have quantified the responses of thousands of endogenous proteins to clinical PARP inhibitors Olaparib and Rucaparib.

A quantitative atlas of histone modification signatures from human cancer cells.

BACKGROUND: An integral component of cancer biology is the understanding of molecular properties uniquely distinguishing one cancer type from another. One class of such properties is histone post-translational modifications (PTMs). Many histone PTMs are linked to the same diverse nuclear functions implicated in cancer development, including transcriptional activation and epigenetic regulation, which are often indirectly assayed with standard genomic technologies. Thus, there is a need for a comprehensive and quantitative profiling of cancer lines focused on their chromatin modification states. RESULTS: To complement genomic expression profiles of cancer lines, we report the proteomic classification of 24 different lines, the majority of which are cancer cells, by quantifying the abundances of a large panel of single and combinatorial histone H3 and H4 PTMs, and histone variants. Concurrent to the proteomic analysis, we performed transcriptomic analysis on histone modifying enzyme abundances as a proxy for quantifying their activity levels. While the transcriptomic and proteomic results were generally consistent in terms of predicting histone PTM abundance from enzyme abundances, several PTMs were regulated independently of the modifying enzyme expression. In addition, combinatorial PTMs containing H3K27 methylation were especially enriched in breast cell lines. Knockdown of the predominant H3K27 methyltransferase, enhancer of zeste 2 (EZH2), in a mouse mammary xenograft model significantly reduced tumor burden in these animals and demonstrated the predictive utility of proteomic techniques. CONCLUSIONS: Our proteomic and genomic characterizations of the histone modification states provide a resource for future investigations of the epigenetic and non-epigenetic determinants for classifying and analyzing cancer cells.

Proteomics in epigenetics: new perspectives for cancer research.

The involvement of epigenetic processes in the origin and progression of cancer is now widely appreciated. Consequently, targeting the enzymatic machinery that controls the epigenetic regulation of the genome has emerged as an attractive new strategy for therapeutic intervention. The development of epigenetic drugs requires a detailed knowledge of the processes that govern chromatin regulation. Over the recent years, mass spectrometry (MS) has become an indispensable tool in epigenetics research. In this review, we will give an overview of the applications of MS-based proteomics in studying various aspects of chromatin biology. We will focus on the use of MS in the discovery and mapping of histone modifications and how novel proteomic approaches are being utilized to identify and study chromatin-associated proteins and multi-subunit complexes. Finally, we will discuss the application of proteomic methods in the diagnosis and prognosis of cancer based on epigenetic biomarkers and comment on their future impact on cancer epigenetics.

A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression.

In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2008). Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9, 458-474.; DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2010b). A network flow model for biclustering via optimal re-ordering of data matrices. J. Global. Optim. 47, 343-354.) to identify biclusters corresponding to subsets of chemicals that have similar responses over distinct subsets of the in vitro assays. The biclustering of the in vitro assays is shown to result in significant clustering based on assay target (e.g., cytochrome P450 [CYP] and nuclear receptors) and type (e.g., downregulated BioMAP and biochemical high-throughput screening protein kinase activity assays). An optimal method based on mixed-integer linear optimization for reordering sparse data matrices (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Li, G. Y., Rabinowitz, J. D., and Rabitz, H. A. (2010a). Enhancing molecular discovery using descriptor-free rearrangement clustering techniques for sparse data sets. AIChE J. 56, 405-418.; McAllister, S. R., DiMaggio, P. A., and Floudas, C. A. (2009). Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem. J. Global. Optim. 45, 111-129) is then applied to the in vivo data set (21.7% sparse) in order to cluster end points that have similar lowest effect level (LEL) values, where it is observed that the end points are effectively clustered according to (1) animal species (i.e., the chronic mouse and chronic rat end points were clearly separated) and (2) similar physiological attributes (i.e., liver- and reproductive-related end points were found to separately cluster together). As the liver and reproductive end points exhibited the largest degree of correlation, we further analyzed them using regularized logistic regression in a rank-and-drop framework to identify which subset of in vitro features could be utilized for in vivo toxicity prediction. It was observed that the in vivo end points that had similar LEL responses over the 309 chemicals (as determined by the sparse clustering results) also shared a significant subset of selected in vitro descriptors. Comparing the significant descriptors between the two different categories of end points revealed a specificity of the CYP assays for the liver end points and preferential selection of the estrogen/androgen nuclear receptors by the reproductive end points.

Collective mass spectrometry approaches reveal broad and combinatorial modification of high mobility group protein A1a.

Transcriptional states are formed and maintained by the interaction and post-translational modification (PTM) of several chromatin proteins, such as histones and high mobility group (HMG) proteins. Among these, HMGA1a, a small heterochromatin-associated nuclear protein has been shown to be post-translationally modified, and some of these PTMs have been linked to apoptosis and cancer. In cancerous cells, HMGA1a PTMs differ between metastatic and nonmetastatic cells, suggesting the existence of an HMGA1a PTM code analogous to the "histone code." In this study, we expand on current knowledge by comprehensively characterizing PTMs on HMGA1a purified from human cells using both nanoflow liquid chromatography collision activated dissociation mediated Bottom Up and electron-transfer dissociation facilitated middle and Top Down mass spectrometry (MS). We find HMGA1a to be pervasively modified with many types of modifications such as methylation, acetylation, and phosphorylation, including finding novel sites. While Bottom Up MS identified lower level modification sites, Top and Middle Down MS were utilized to identify the most commonly occurring combinatorially modified forms. Remarkably, although we identify several individual modification sites through our Bottom Up and Middle Down MS analyses, we find relatively few combinatorially modified forms dominate the population through Top Down proteomics. The main combinatorial PTMs we find through the Top Down approach are N-terminal acetylation, Arg25 methylation along with phosphorylation of the three most C-terminal serine residues in primarily a diphosphorylated form. This report presents one of the most detailed analyses of HMGA1a to date and illustrates the strength of using a combined MS effort.

A novel approach for untargeted post-translational modification identification using integer linear optimization and tandem mass spectrometry.

A novel algorithm, PILOT_PTM, has been developed for the untargeted identification of post-translational modifications (PTMs) on a template sequence. The algorithm consists of an analysis of an MS/MS spectrum via an integer linear optimization model to output a rank-ordered list of PTMs that best match the experimental data. Each MS/MS spectrum is analyzed by a preprocessing algorithm to reduce spectral noise and label potential complimentary, offset, isotope, and multiply charged peaks. Postprocessing of the rank-ordered list from the integer linear optimization model will resolve fragment mass errors and will reorder the list of PTMs based on the cross-correlation between the experimental and theoretical MS/MS spectrum. PILOT_PTM is instrument-independent, capable of handling multiple fragmentation technologies, and can address the universe of PTMs for every amino acid on the template sequence. The various features of PILOT_PTM are presented, and it is tested on several modified and unmodified data sets including chemically synthesized phosphopeptides, histone H3-(1-50) polypeptides, histone H3-(1-50) tryptic fragments, and peptides generated from proteins extracted from chromatin-enriched fractions. The data sets consist of spectra derived from fragmentation via collision-induced dissociation, electron transfer dissociation, and electron capture dissociation. The capability of PILOT_PTM is then benchmarked using five state-of-the-art methods, InsPecT, Virtual Expert Mass Spectrometrist (VEMS), Mod(i), Mascot, and X!Tandem. PILOT_PTM demonstrates superior accuracy on both the small and large scale proteome experiments. A protocol is finally developed for the analysis of a complete LC-MS/MS scan using template sequences generated from SEQUEST and is demonstrated on over 270,000 MS/MS spectra collected from a total chromatin digest.

High throughput characterization of combinatorial histone codes.

We present a novel method utilizing "saltless" pH gradient weak cation exchange-hydrophilic interaction liquid chromatography directly coupled to electron transfer dissociation (ETD) mass spectrometry for the automated on-line high throughput characterization of hypermodified combinatorial histone codes. This technique, performed on a low resolution mass spectrometer, displays an improvement over existing methods with an approximately 100-fold reduction in sample requirements and analysis time. The scheme presented is capable of identifying all of the major combinatorial histone codes present in a sample in a 2-h analysis. The large N-terminal histone peptides are eluted by the pH and organic solvent weak cation exchange-hydrophilic interaction liquid chromatography gradient and directly introduced via nanoelectrospray ionization into a benchtop linear quadrupole ion trap mass spectrometer equipped with ETD. Each polypeptide is sequenced, and the modification sites are identified by ETD fragmentation. The isobaric trimethyl and acetyl modifications are resolved chromatographically and confidently distinguished by the synthesis of mass spectrometric and chromatographic information. We demonstrate the utility of the method by complete characterization of human histone H3.2 and histone H4 from butyrate-treated cells, but it is generally applicable to the analysis of highly modified peptides. We find this methodology very useful for chromatographic separation of isomeric species that cannot be separated well by any other chromatographic means, leading to less complicated tandem mass spectra. The improved separation and increased sensitivity generated novel information about much less abundant forms. In this method demonstration we report over 200 H3.2 forms and 70 H4 forms, including forms not yet detected in human cells, such as the remarkably highly modified histone H3.2 K4me3K9acK14acK18acK23acK27acK36me3. Such detail provided by our proteomics platform will be essential for determining how histone modifications occur and act in combination to propagate the histone code during transcriptional events and could greatly enable sequencing of the histone component of human epigenomes.

A mixed integer linear optimization framework for the identification and quantification of targeted post-translational modifications of highly modified proteins using multiplexed electron transfer dissociation tandem mass spectrometry.

Here we present a novel methodology for the identification of the targeted post-translational modifications present in highly modified proteins using mixed integer linear optimization and electron transfer dissociation (ETD) tandem mass spectrometry. For a given ETD tandem mass spectrum, the rigorous set of modified forms that satisfy the mass of the precursor ion, within some tolerance error, are enumerated by solving a feasibility problem via mixed integer linear optimization. The enumeration of the entire superset of modified forms enables the method to normalize the relative contributions of the individual modification sites. Given the entire set of modified forms, a superposition problem is then formulated using mixed integer linear optimization to determine the relative fractions of the modified forms that are present in the multiplexed ETD tandem mass spectrum. Chromatographic information in the mass and time dimension is utilized to assess the likelihood of the assigned modification states, to average several tandem mass spectra for confident identification of lower level forms, and to infer modification states of partially assigned spectra. The utility of the proposed computational framework is demonstrated on an entire LC-MS/MS ETD experiment corresponding to a mixture of highly modified histone peptides. This new computational method will facilitate the unprecedented LC-MS/MS ETD analysis of many hypermodified proteins and offer novel biological insight into these previously understudied systems.

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies.

BACKGROUND: The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters. RESULTS: In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods. CONCLUSION: We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.

A hybrid method for peptide identification using integer linear optimization, local database search, and quadrupole time-of-flight or OrbiTrap tandem mass spectrometry.

A novel hybrid methodology for the automated identification of peptides via de novo integer linear optimization, local database search, and tandem mass spectrometry is presented in this article. A modified version of the de novo identification algorithm PILOT, is utilized to construct accurate de novo peptide sequences. A modified version of the local database search tool FASTA is used to query these de novo predictions against the nonredundant protein database to resolve any low-confidence amino acids in the candidate sequences. The computational burden associated with performing several alignments is alleviated with the use of distributive computing. Extensive computational studies are presented for this new hybrid methodology, as well as comparisons with MASCOT for a set of 38 quadrupole time-of-flight (QTOF) and 380 OrbiTrap tandem mass spectra. The results for our proposed hybrid method for the OrbiTrap spectra are also compared with a modified version of PepNovo, which was trained for use on high-precision tandem mass spectra, and the tag-based method InsPecT. The de novo sequences of PILOT and PepNovo are also searched against the nonredundant protein database using CIDentify to compare with the alignments achieved by our modifications of FASTA. The comparative studies demonstrate the excellent peptide identification accuracy gained from combining the strengths of our de novo method, which is based on integer linear optimization, and database driven search methods.

De novo peptide identification via tandem mass spectrometry and integer linear optimization.

A novel methodology for the automated de novo identification of peptides via integer linear optimization (also referred to as integer linear programming or ILP) and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. A variety of challenging peptide identification problems, accompanied by a comparative study with five state-of-the-art methods, are examined to illustrate the proposed method's ability to address (a) residue-dependent fragmentation properties that result in missing ion peaks and (b) the variability of resolution in different mass analyzers. A preprocessing algorithm is utilized to identify important m/z values in the tandem mass spectrum. Missing peaks, due to residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to select the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the ILP sequencing stages with the experimental tandem mass spectrum. The novel, proposed de novo method, denoted as PILOT, is compared to existing popular methods such as Lutefisk, PEAKS, PepNovo, EigenMS, and NovoHMM for a set of spectra resulting from QTOF and ion trap instruments.