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Profiling of Histone Post-translational Modifications in Mouse Brain with High Resolution Top Down Mass Spectrometry Mowei Zhou, Ljiljana Paša-Toli#, and David L Stenoien J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00694 • Publication Date (Web): 06 Dec 2016 Downloaded from http://pubs.acs.org on December 11, 2016

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Journal of Proteome Research

Profiling of Histone Post-translational Modifications in Mouse Brain with High Resolution Top Down Mass Spectrometry

Mowei Zhou1, Ljiljana Paša-Tolić1, and David L. Stenoien1*

1

Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, P.O. Box

999, Richland, WA 99352

KEYWORDS Histones, Post-translational modifications, Top-down mass spectrometry

* To whom correspondence should be addressed. David Stenoien [email protected] Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352.

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ABSTRACT As histones play central roles in most chromosomal functions including regulation of DNA replication, DNA damage repair, and gene transcription, both their basic biology and their roles in disease development have been the subject of intense study. Since multiple PTMs along the entire protein sequence are potential regulators of histones, a top-down approach, where intact proteins are analyzed, is ultimately required for complete characterization of proteoforms. However, significant challenges remain for top-down histone analysis primarily because of deficiencies in separation/resolving power and effective identification algorithms. Here, we used state of the art mass spectrometry and a bioinformatics workflow for targeted data analysis and visualization. The workflow uses ProMex for intact mass deconvolution, MSPathFinder as search engine, and LcMsSpectator as a data visualization tool. When complemented with the open-modification tool TopPIC, this workflow enabled identification of novel histone PTMs including tyrosine bromination on histone H4 and H2A, H3 glutathionylation, and mapping of conventional PTMs along the entire protein for many histone subunits.


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Introduction Histones regulate many nuclear functions including transcription, DNA damage repair, and cell cycle control through dynamic alterations in post translational modifications (PTMs) dispersed on different histone subunits. It has been hypothesized that specific combinations of PTMs form a “histone code” that regulate many of these cellular functions (1). These combinatorial codes can be very complex and the PTMs could be present on multiple histone subunits. In the case of DNA damage repair for example, at least 25 modifications located on all histone subunits (H2A, H2B, H3 and H4) of the core nucleosome are involved in some stage of the repair process (2). Once established, these codes result in the recruitment of non-histone proteins which then carry out specific chromatin based functions such as transcriptional regulation. Although core histones are comprised of only four families, H2A, H2B, H3 and H4, characterization of these histones presents a major analytical challenge, since each histone potentially has a multitude of protein forms generated by different combinations of PTMs. These differentially modified versions of the same gene product are now referred to as proteoforms(3). Most studies to date have focused on single modifications at a time but the available information suggests that multiple PTMs cooperate to regulate complex transcriptional profiles. Traditional antibody-based methods have been used to target some specific proteoforms but typically analyze one PTM at a time making it difficult to measure combinatorial modifications occurring within the same histone molecule. There are also limitations to using antibody based approaches due to problems in specificity of related proteoforms and the potential that adjacent modifications can affect antibody binding (4). Mass spectrometry (MS) is an ideal platform for analyzing the modification of histones because it provides molecular level characterization of proteins. The mass shift introduced by PTMs can be readily detected in MS, and the site of PTM can be localized using gas-phase fragmentation methods.(1, 5, 6) Various strategies, including bottom-up, middle-down, and top-down analyses, have been reported for LC-MS analysis of histones.(5, 7-9) In standard bottom-up experiments, proteins are digested with enzymes such as trypsin to generate small peptides for LC-MS analysis. Because histones are rich in lysine and arginine residues which are the target for trypsin cleavage, it is necessary to derivatize histones to generate long enough peptides with sufficient retention in chromatography to be effectively detected in LC-MS.(7, 10, 11) Precaution must be taken in the sample preparation to minimize artifacts that could potentially obscure the results.(10) Alternatively, enzymes with different site specificity can be used to generate long peptides for middle-down analysis.(8) One major challenge with peptide level analysis is that the information about combinatorial PTMs is lost during the enzymatic digestion. Therefore it is difficult to map the PTMs on the segments back onto the intact protein sequence for defining the synergy of multiple PTMs on remote residues. Additionally, histones H2A and H2B have many gene products that share high sequence homology (sequence variants that only differ in a few residues), which cannot be easily distinguished on the peptide level due to the limited number of unique peptides. Given this, peptide level analysis of histone PTMs from a complex tissue such as mouse brain have shed valuable insight into the presence of combinatorial PTMs and have identified potential histone codes associated with higher order tissue functions. (12) In contrast, intact histones can be directly analyzed in MS without enzymatic digestion using a top-down approach, which can be used to directly profile histone proteoforms carrying all the combinatorial PTMs.(13, 14) However, there remain significant challenges for top-down analyses of histones even with the state-ofthe-art technology. Because of the complexity of histone PTMs, it is common to observe histone 3 ACS Paragon Plus Environment


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proteoforms that cannot be resolved in either MS or chromatography, adding ambiguity to the data interpretation.(15) Furthermore, the combinatorial nature of histone PTMs on multiple potential sites generates extremely large numbers of theoretical proteoforms for consideration by protein identification algorithms despite the very limited number of histone protein sequences.(8) A number of algorithms have been developed to analyze top down data, each of which utilizes different strategy with different strength and weaknesses.(8, 16) To reduce the search space, ProSightPC restricts the number of theoretical proteoforms to only the most anticipated ones based on known modifications in databases.(17) Although this is a quite effective method for proteoform identification with reports of statistical significance of spectral matches,(16) it is possible that unknown modifications will be missed (in particular the truncated forms) or force-matched to other known modifications in this approach where the identification is relied heavily on a well-maintained database with prior knowledge of the types of modifications and their locations. Other top-down data analysis software MSAlign+ and TopPIC(16, 18-20) use spectral alignment and report mass shifts on protein sequences to account for modifications. This open-search strategy allows unexpected modifications to be identified. But there is a limit of two mass shifts per protein sequence (in addition to terminal truncations) to restrict search space to reasonable level. In addition, the confidence of proteoform identification has not been well-established in many top-down analysis workflows due to the lack of robust statistical models for defining false discovery rate, especially for PTM localization which is critical for histones.(15) Therefore, it is often necessary to manually confirm proteoform identifications, yet there are few software solutions to assist in manual validation other than the MASH suite developed by the Ge group.(21, 22) In this work we present a workflow using Informed-Proteomics (https://github.com/PNNL-Comp-Mass-Spec/Informed-Proteomics),(23) for targeted data analysis and visualization from raw data files. This software package uses ProMex for intact mass deconvolution, MSPathFinder as search engine, and LcMsSpectator as a data visualization tool. ProMex sums across retention time to maximize sensitivity and accuracy for low abundance species in MS1 deconvolution. MSPathFinder searches the MS2 data against protein sequence databases with userdefined modifications, similar to the standard bottom-up workflow. LcMsSpectator presents the results from ProMex and MSPathFinder in a format that allows quick manual evaluation of critical attributes for high-confidence proteoform identifications. The application of the Informed-Proteomics for fast profiling of mouse brain histone samples with top-down LC-MS lead to identification of multiple major histone proteoforms (including proteoforms with 3 or 4 modifications) and several previously unreported histone modifications (bromination, glutathionylation).

Experimental Section Histone preparation Brains from C57BL/6J mice were flash frozen in liquid nitrogen and stored at -80˚C until use. Histones were purified using a histone purification kit (Active Motif) according to the manufacturer’s instructions. Basically mouse brains were homogenized in 2 ml of histone extraction buffer using a dounce homogenizer and left at 4 ˚C for 2h on a rotating platform. Tissue debris was removed by centrifugation at 17,000xg for 5 minutes and the supernatant was neutralized by the addition of ¼ volume of 5X neutralization buffer. Crude histones were then loaded onto provided column, washed 3x with wash buffer, and eluted in 2 mls of H3/H4 elution buffer which elutes all histone core subunits. Histones were precipitated by adding perchloric acid to a final concentration of 4% and incubating overnight at 4 ˚C. 4 ACS Paragon Plus Environment

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After centrifugation at 17,000xg for five minutes, pellets were washed 2x with 4% perchloric acid, 2x with acetone containing 0.2% HCl, and 2X with 100% acetone. After precipitation, histones were resuspended in water and stored at -80 ˚C until analysis. LC-MS of intact histones All the histone samples were diluted in water to 0.2 mg/mL for injection of 10 µL onto a Waters NanoAcquity liquid chromatography system. The solvent A used was 1% formic acid in water, and the solvent B was 1% formic acid in acetonitrile. The injected proteins were trapped in a short C18 column (Phenomenex Aeris wide pore 3.6 µm, column inner diameter 150 µm, outer diameter 360 µm, length 5cm) for 20 min at 10% solvent B and a flow rate of 2.5 µL/min for online desalting. Then the flow was reduced to 0.3 µL/min and directed onto the C18 analytical column (Phenomenex 3 µm 300 Å, column inner diameter 75 µm, outer diameter 360 µm, length 70 cm). The gradient started at 10% solvent B and increased to 34% B in 5 min, and it slowly ramped to 43% B at 360 min. Then solvent B was raised to 50% at 400 min with a fast ramp to 90% B for eluting all the remains on the column. The injections were randomized with blank injections of water in between, which did not show significant signal from proteins (data not shown). The MS data were acquired on an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific). The nanoelectrospray source was set to 1.8 kV, with the transfer tube set to 275 °C and RP lens set at 60%. MS1 spectra were acquired with the mass range of 600 – 1200 m/z (with wide quad isolation), at 240k resolution (at m/z of 400), AGC target of 1E6, maximum injection time of 100 ms, and 5 microscans. MS2 spectra were acquired with top speed mode (high charge and most intense, cycle time 20 s) at 120k resolution (at m/z of 400), AGC target 1E6, maximum injection time of 2 s, and 1 microscan. The precursors were selected in the m/z range of 650 – 900 for ions at charge states of 15 -24, with isolation window of 0.6 Da and dynamic exclusion of 90 s. Different charge states from the same precursor were also excluded for MS2 at the same time. Both ETD and HCD spectra were acquired for one precursor selected. The ETD reaction time was 20 ms, with the reagent AGC at 7E5 and maximum injection time of 200 ms. Stepped HCD collision energy was used, which combined the HCD fragments at 20%, 25% and 30% collision energies in one HCD spectrum. Each run was acquired for 420 min. MSPathFinder MSPathFinder (version 1.0.5813) search was completed on an internal server at PNNL (Intel E7-8870 v3 @ 2.10GHz, 3 processors; memory 32 GB; 64-bit Windows Server2012 R2 operation system). Mass error tolerance was set to 10 ppm for precursor and 6 ppm for fragments. Maximum of 4 types of dynamic modifications were allowed on each sequence. The dynamic modifications included are: methionine/cysteine oxidation, serine phosphorylation, tyrosine bromination, glutamine/asparagine deamidation, lysine/N-terminal acetylation, lysine mono-/di-/tri-methylation, cysteine glutathionylation. Previous search with MSAlign found minimal number of proteins other than histones (small numbers of ribosomal proteins and hemoglobin, data not shown) because the samples were highly purified. Therefore, the data were searched against a small protein FASTA database with only mouse histones and hemoglobin to reduce the search time to a reasonable amount. Each search took on average around 800 min on the resource specified. It is noted that searching for less dynamic modifications can greatly reduce the search space and the run time for MSPathFinder.



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TopPIC search and Result filtering Raw data were converted to msalign files with a beta version of MsDeconv,(24) with precursor mass window set to ±0.4 Da. The same small FASTA database was used for TopPIC search.(16, 18, 19) The mass error tolerance was set to 10 ppm. The information from the alternating ETD and HCD spectra of the same precursor were combined for identification. A maximum of 1000 Da and a maximum number of 2 unexpected modifications were allowed. The searches took on average 2-3 h with 8 threads on the same computer used for MSPathFinder search. The results were filtered for identifications with E-value from TopPIC lower than 1E-5, and sequence coverage above 25%. The filtered lists were used as reference for filtering the MSPathFinder results using custom Perl code. For the same scan in the same data file, MSPathFinder results with E-value lower than 1E-40 and identification to the same protein name and length (starting and ending residues) as the results in TopPIC are kept in Table S1. In addition, the identifications in TopPIC were used as reference during manual validation of ambiguous MSPathFinder results. The reported mass shifts in TopPIC were manually fit and evaluated in LcMsSpectator for determining the best matched proteoforms.

Results & Discussion Rapid Profiling of Histone Proteoforms at the Intact Protein Level Purified intact histones from mouse brain were directly separated by reversed phase LC and analyzed by MS. Figure 1a shows the LC-MS “feature map” of a representative histone sample, where the deconvoluted mass of the detected species are plotted against the LC retention time on the horizontal axis. All major histone families are well-resolved into separation regions in the map. The early eluting species are H4 and H2B, followed by H2A. The H3 eluted last with a broad distribution across the retention time due to the high complexity and limited separation in the LC dimension. A well-resolved region preceding the H3 region at mass of around 15 kDa corresponds to histone H2A.X. There are some other species outside the highlighted regions (RT 100-250 min, less than 15 kDa), most of which can be assigned to truncated forms of histones with sufficient MS2 fragmentation for confirmation. It is noted that the truncated forms are difficult to detect with bottom-up and middle-down because the sites of truncation may not be the expected enzymatic cleavage sites and are thus not searched for in standard workflows. Biologically relevant histone clipping is known to occur and affects gene regulation.(25) A recent study by Tvardovskiy et al. suggests that the clipped histones carry distinct coexisting PTMs from the fulllength counterparts.(26) Therefore, top-down analysis holds great potential for characterizing the modifications on clipped histones. Some residual ribosomal proteins were also detected at the beginning of the separation (data not shown). Several identified truncated forms of histones are included in Table S1. Because the biological implications are unknown and out of the scope of this study, they will not be discussed in further detail. Figure 1b-1d shows the zoom-in views of the regions highlighted in Figure 1a for the major histone families. The repetitive pattern of uniform spacing in the vertical mass axis between features is from the different extent of modification. Most shifts are approximately 14 Da corresponding to one methylation, and some of the wider spaced features differ by 42 Da which is the mass shift of acetylation or trimethylation. The feature map provides a quick assessment of the degree and the complexity of histone modifications.


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Figure 1. LC-MS feature map of purified core histones from mouse brain. (a) Full range showing all major histone families. (b) Zoom-in of the histone H4 region. (c) Zoom-in of the histone H2B and H2A region. (d) Zoom-in of the histone H2A.X and H3 region. The horizontal axis is the LC retention time (RT) in minutes. The vertical axis is the deconvoluted monoisotopic mass of the detected species in kDa. The color of the spots represents the abundance of the detected species in log10 scale as shown by the scheme on the right. Major classes of histones and associated modifications are labeled in the figures.



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The MS2 data were first analyzed with MSPathFinder by specifying the protein sequences with FASTA files and expected modifications. The identified proteoforms are summarized in Table S1. By examining the MS2 identifications, it can be seen that histone proteoforms with specific modifications can be localized into distinct regions in the feature map. The data viewer LcMsSpectator provides a convenient way to locate identified (groups of) proteoforms on the feature map by allowing the MS2 identifications associated with one or more proteins to be highlighted. For H4 (Figure 1b), reversed phase LC provides limited separation because most of the modifications on H4 are acetylation which separates better using hydrophilic-based chromatography.(27) Nonetheless, most of the early eluting H4 proteoforms between 30-40 min identified contain oxidation on methionine residues. Similarly, the oxidized forms of H2B, H2A, and H3 tend to elute ahead of the forms without oxidation. For H2A and H2B, the major classes of different gene products were separated into distinct regions as highlighted in Figure 1c. It can be observed that some of the gene products (such as H2B3A and H2A2A) are primarily only acetylated because the spacing between the features is mostly 42 Da. In contrast, other species (such as H2B1F/1C, and H2A1/3) are modified by methylation to different degrees because the spacing between features is 14 Da. Because H3 is heavily modified, Figure 1d showed significantly less resolved features than Figure 1c. Many of the methylated forms and acetylated forms are overlapping both in the retention time and the mass dimension. Overall, the features corresponding to H3 can be classified into four regions. The major regions are the H3.1/H3.2 eluting around 220 – 300 min, and the H3.3 at 300 - 340 min, both of which have mass between 15.3 – 15.5 kDa. The late eluting H3 (most identifications are H3.1 and H3.2) after 360 min presumably originate from H3 (intact dimers observed in other data acquired with methods optimized for high mass ions, data not shown). In Figure 1d, at 160 – 220 min and around the mass of 15.1 kDa, the majority of the H2A.X species were detected with acetylation and phosphorylation. Some of them were also found to be brominated and will be discussed in detail in the next section. Interestingly, an early eluting H3 group is well resolved from the major H3 species around the retention time 220 - 250 min, with masses higher than 15.5 kDa. The MS2 spectra associated with these species suggest that there are mass shifts of 305 Da on the protein, which can be matched to glutathionylation (Figure S1). Glutathionylated hemoglobin were also observed experimentally (Figure S2 and Figure S3), with the beta globin eluting in the same region as the glutathionylated H3 as shown in Figure 1d. Glutathionylation is a relatively new modification of Histone H3 that is elevated in proliferating cells and cancer cells that regulates chromosomal structure by impacting nucleosome stability. (28, 29) It was suggested that this looser chromatin structure may facilitate chromosome replication in proliferating cells but its presence in a differentiated tissue such as the brain suggests that it may have additional functions perhaps serving as a mechanism by which chromatin can sense and respond to changes in cellular and tissue redox status. (30) Discovery of Unexpected Bromination with High Resolution Data and Visualization Tool The MS2 identifications from MSPathFinder can be manually examined directly by the LcMsSpectator, which provides visualization of MS1 precursor match and MS2 fragment match with automatic annotation. High-confidence identifications typically show minimal deviation from the theoretical isotope distribution of precursor, minimal number of unmatched fragment ions, and uniform distribution of fragment match mass error.(23, 27, 31) Deviation from these criteria often implies false positive match that requires further attention. Figure 2 shows an example where a H4 proteoform was originally matched to 8 ACS Paragon Plus Environment

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phosphorylation on Y88 and then manually corrected to be bromination (theoretical mass difference 0.06 Da for the 2nd isotope of bromine). In Figure 2a, the majority of the fragment ions in the ETD spectrum are annotated and matched to the expected H4 sequence. Figure 2b and 2c show the “error map” of all the fragment matches along the sequence when the ETD fragments are matched to bromination and phosphorylation on Y88, respectively. It is obvious that when the fragments are matched to phosphorylation (Figure 2c), all the z ions containing Y88 (highlighted with the gray vertical bar) show abrupt change of mass error from positive (orange color) to negative values (green color). In contrast, the error map for matching to bromination (Figure 2b) show negligible color change across all fragments. This indicates that the mass errors for all the matches are uniform, consistent with high precision mass measurement expected on high resolution mass spectrometers, where the most of the mass error originates from the fluctuation of external parameters such as voltage, temperature, pressure over extended period of time.(32-34) Although all the listed fragments in Figure 2 are within an absolute mass error of 10 ppm, the minimal variation in mass error in Figure 2b strongly favors the identification of bromination over phosphorylation on Y88. In the HCD spectrum of the same precursor, the y15 and y16 fragments exhibit unique isotope pattern from bromine (Figure S4), further supporting the identification of bromine in this H4 proteoform. The tryptic peptide KTVTAMDVVYALKR analyzed with standard bottom-up workflow also supports this unexpected modification on H4. More specifically, the unique bromine isotope pattern (79Br and 81Br has near equal relative natural abundance, ~50% each) reshapes the isotopic distribution of peptides (primarily from 13C isotope), causing the first and third isotopic peaks standing out from the rest of the distribution (Figure 3a, insert, theoretical distribution of bromine containing H4 peptide shown in blue). In contrast, the non-Br containing peptide fragments (such as c4, c5, c9 from m/z 440-520 in Figure 3c) show regular isotopic distribution, distinct from the ones containing Br (such as c10, c11 from m/z 600650 in Figure 3c). Similar results can be observed for HCD fragments, where the y fragments larger than y4 started to carry the unique Br isotope pattern (likewise, c fragments larger than c9 carry Br isotope). The sample principle applies to top-down data (Figure 2 and Figure S4), but the high mass and large contribution of 13C isotope in intact proteins masks the unique Br isotope pattern and makes it difficult to discern, expect on smaller fragments from the intact proteins (such as inserts shown in Figure S4). The bromination site is thus determined to be the tyrosine (Y88 in full length H4) based on the high confidence matches in both bottom-up and top-down data. After including bromination as a variable modification and restricting the mass error tolerance to 6 ppm for MS2 fragments, MSPathFinder identified additional brominated histone proteoforms in the sample which were previously mistaken as phosphorylation (previous data not shown). Among those, the brominated H2A.X proteoforms can also be easily distinguished in the feature map as shown in Figure 1d because of minimal overlapping with other species in the retention time and mass space. The sample shown in the figure exhibit significant higher bromination level on H2A.X than other samples analyzed in this study, which even showed multiple bromine addition onto H2A.X. These brominated forms can be readily visualized in Figure 1d as a “ladder” of features 80 Da apart. Although not all of them can be identified with high quality ETD spectra, the precursor isotope distribution clearly suggest that the H2A.X proteoforms in this region do not carry phosphorylation (Figure S5). It is unclear what the function and origin of histone bromination is in our mouse brain samples but it may be related to protein oxidation. Peroxidases are known to generate hypobromous acid in vivo during inflammation and this acid then brominates aromatic compounds including tyrosine. (35) This is an important process in the pathogenesis of allergic reactions such as asthma where the brominated proteins 9 ACS Paragon Plus Environment


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are proteolyzed and bromotyrosine is secreted in urine where it may serve as a biomarker. To our knowledge this is the first report of histone bromination adding to the growing list of histone modifications. It is also possible that some of the previously identified phosphorylations may have been brominations due to the nearly identical masses of these two modifications on the intact protein level (2nd isotope from bromination is 79.910 and phosphorylation is 79.966, the difference between the two is ~ 5ppm for intact H4, and even less for other histone families which have higher mass). For phosphorylation identified with bottom-up workflow on the peptide level, the mass difference between bromination and phosphorylation translates into a mass error on the order of 20 ppm, which is expected to be easily differentiable on modern mass spectrometers unless a low resolution platform was used. There are also multiple tyrosines present that are not observed to be brominated indicating this modification is site specific.


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Figure 2. (a) Annotated ETD spectrum for a histone H4 proteoform containing bromine on tyrosine 88. All major peaks are matched to the protein sequence with the modifications. (b) Fragment mass error map for the identified H4 proteoform assuming bromination on Y88: S1Ac K20me2 M84Oxidation Y88Br. (c) Fragment mass error map for matching the identified proteoform to phosphorylated Y88 instead of brominated Y88. The horizontal axis of the error map is aligned with the protein sequence from Nterminus on the left to C-terminus on the right. Fragments with different charge states are stacked in the vertical axis. The color of the spots represents the mass error of the fragment match, with the color scale shown in the top left corner of (b). The c ion series starts from the N-terminus of the protein on the left side and the error map is shown below the listed protein sequence, while the z ion series starts from the right side and is above the protein sequence. Note that the mass error across all the matched fragment ions is more consistent with bromination than with phosphorylation on Y88 (location highlighted in the map). If the proteoform is matched to Y88Phos, most z ions covering Y88 showed significantly different mass error from the rest, as indicated by the abrupt change of color in the error map left to Y88 in z ions and right to Y88 in c ions in (c).



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Figure 3. (a) ETD spectrum of the tryptic H4 peptide containing bromine, which is consistent with the bromination detected in the top-down experiment. Insert shows the near complete overlay of the experimental isotope distribution in red and the theoretical distribution in blue for the tryptic peptide possessing the bromine isotope. (b) ETD fragment mass error map, with the mass matched to the second isotope of bromine. (c) Zoom-in of the ETD spectrum showing the unique isotope distribution of bromine on the major fragment ions. (d) Zoom-in of the HCD spectrum showing the major fragments for localizing the bromination site. Fragments containing the unique bromine isotope pattern are labeled with orange arrows, while the adjacent fragment without bromine is highlighted with gray arrows. The c9/c10 pair in ETD and y4/y5 pair in HCD allow confident localization of the modification site to be the 10th residue of tyrosine.


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This example highlights the power of high resolution mass spectrometry for identifying unexpected modifications in top-down mass spectrometry, especially for those with similar mass that could be easily mistaken as other modifications. Likewise, the difference of trimethylation and acetylation on histones (theoretical mass difference 0.06 Da) can be distinguished at the fragment level in top-down analysis based on small variation in the error map near the modified residues.(23, 27) Similar concept of using high mass accuracy to correct fragment assignment has also been described previously by Moradian et al,(8) and LcMsSpectator offers a software platform for easily accessing the necessary information. Yet in high throughput analysis, a generous mass error tolerance is typically given and such modifications with similar mass cannot be differentiated, leading to ambiguous identifications in both bottom-up and topdown analyses. Scoring functions that consider mass accuracy of fragment match and favor the uniform mass error in fragment matches could be incorporated into top-down informatics in order to utilize the high resolution data to the maximum extent and minimize false identifications. Previous studies have also shown that correcting the mass drift can greatly reduce the experimental mass error, allowing the use of narrow mass tolerance windows for database search to improve the success rate of protein identification in bottom-up workflow.(34) Thus a similar approach can be designed for improving top-down data analysis. Identification of Novel Histone Modification Sites The benefit of performing data analysis without an annotated database is that it is possible to identify novel proteoforms. Figure 4 shows the fragment match mass error map for ETD and HCD of a H2A2A proteoform where the two termini are modified with acetylation. While ETD tends to provide more uniform fragmentation across the backbone, HCD favors cleavage at specific regions, one of which is not well covered by ETD. More specifically in this example, ETD has limited coverage in the mid-region of the sequence, but HCD provides good coverage around residue 40 -50 with very strong b ions (Figure S6). Overall the two techniques provide complementary information for high-confidence identification of proteoforms. It is noted that the acetylation could be matched to either the C-terminal lysine or the 3rd last lysine from the C-terminus based on y1 – y3 ions in HCD. However, these signature fragments are at very low abundance and amongst other fragments, thus the two possible acetylation sites cannot be completely distinguished directly from the data in this study. Acetylation near C-terminus has also been found in H2A1 and H2A2C, which coexists with other modifications near the N-terminus (Table S1). This acetylation site has not been represented in the UniProt database but it has been reported in a recent publication.(36)



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Figure 4. Fragment error map for (a) ETD and (b) HCD of the identified H2A2A proteoform containing acetylation at the two termini (S1Ac+K129Ac). The ticks on the horizontal axis of the error maps show the amino acid of the protein. The white arrows in the error maps indicate the direction that the ion series start with.


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Another novel H2A2 proteoform containing phosphorylation at S16 (annotated ETD and HCD spectra in Figure S7), which has not been previously reported to our knowledge, was observed. It is known that ETD is better than HCD at preserving labile modifications such as phosphorylation, therefore the modification sites can be determined by examining the ETD fragments which bear the mass shifts of the expected modifications. Manual inspection of the fragment match error map as shown in Figure 5 suggests that the ETD spectrum can fit proteoforms containing N-terminal acetylation, M51 methionine oxidation, and one phosphorylation at multiple sites, all of which give reasonably good sequence coverage. Many of the signature fragments for localization of the PTMs at different residues show comparable intensities, implying they coexist in comparable abundances in the precursor. For example, the phosphorylated c16 has similar intensity to c16 without phosphorylation (60% and 40%, respectively, spectra in Figure S8), which is the only differentiating fragment for localizing the phosphate group on S18 instead of S16. However, phosphorylation on S18, S19 and Y39 all share the same c16 that does not contain the modification. S19Phos is less favored than S18Phos because it missed c18 compared to S18Phos (data not shown), thus is not included in the discussion. The Y39Phos proteoform is supported by a few c ions between residues 19 – 38 that do not contain phosphorylation. The data suggest that the precursor is a mixture of multiple phosphorylated proteoforms, including an unreported phosphorylation site at S16, and a potential site at Y39 as well. Most computer algorithms do not consider multiplexed spectra(8) and this could become problematic for proteoform identifications in top-down analysis of histones because of the limited separation and the complexity of the sample. In this case, using prior knowledge based searches will return the known proteoform hit with S1Phos, leaving the other possible phosphorylation forms undiscovered. Informatics workflows targeting multiplexed spectra based on the intensities of signature fragments have been reported for histone analysis.(5, 37) Yet most of them still involved close manual intervention and are not suitable for high-throughput exploratory analysis.



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Figure 5. Zoom-in of the fragment mass error maps of c ions in the N-terminal region of H2A2 when matching to phosphorylation at (a) S1, (b) S16, (c) S18, and (d) Y39. The modified residues are shown in red in the sequence on the top, which is aligned with the corresponding c ion series in the horizontal axis in the error maps. The brighter regions in (b-d) indicate c ions which do not contain phosphate group, and the transition points into darker regions represent the first matched c ions containing phosphorylation that define the site of phosphorylation on the protein. N-terminal acetylation and methionine oxidation at M51 are unambiguously identified. However, the spectrum can be matched with good sequence coverage with the phosphate group located at several residues, implying that the precursor contains multiple phosphorylated proteoforms.


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Conclusion Overall, it has been shown that Informed-Proteomics is a robust toolset for exploratory profiling of histone proteoforms in biological samples. It is noted that there are many other software tools being actively developed for top-down analysis (http://www.topdownproteomics.org/software) and this work is not aimed as an exhaustive comparison of all the tools available. Instead, we believe it is beneficial to combine the strength of different software solutions to tackle complex biological problems, especially for uncharacterized modifications which may have been overlooked. As discussed in the data presented here, the LC-MS feature map provides quick assessment of the intact protein species and can be potentially used as a fingerprint for comparing samples with biological differences, such as control vs. disease. Informed-Proteomics, combined with the open-modification search tool TopPIC, offers an alternative informatics solution for exploratory top-down analysis that complements the knowledge-based data analysis workflow. The automatic annotation and interactive views in LcMsSpectator greatly simplifies manual inspection and verification of proteoform identifications. The visualization of fragment mass error in the error map allows manual interrogation of ambiguous spectra and discovery of novel PTMs. This simplified process would potentially encourage more researchers to manually check their data and generate more knowledge that can be incorporated in searching algorithms and scoring functions to improve the confidence level for automated high-throughput top-down analysis. For example, the tyrosine bromination identified here would have been assigned as a phosphorylation due to the close masses of these two modifications. However, manual inspection of the data and the follow up peptide level experiments enabled unambiguous identification of tyrosine bromination as a novel histone PTM. Ideally, known histone PTMs in databases can be referenced, similar in the approach used in ProSight PC, to make educated “guesses” for proteoform identifications with limited sequence coverage or ambiguous modification site assignments. It is anticipated that a combination of multiple algorithms can be used at different steps of the data analysis for maximum spectral match rates of the experimental data at high confidence, as a future direction of development for top-down data analysis algorithms. Supporting Information: The following files are available free of charge at ACS website http://pubs.acs.org: Figure S1. Experimentally observed histone H3 with glutathione modification on the cysteine. Figure S2. Experimentally observed hemoglobin alpha chain with glutathione modification on cysteine. Figure S3. Experimentally observed hemoglobin beta chain with glutathione modification on cysteine. Figure S4. Annotated HCD spectrum for the bromine containing H4. Figure S5. MS1 spectra for the major H2A.X species containing one acetylation. Figure S6. Annotated ETD and HCD spectra for the novel H2A2A proteoform containing acetylation at the two termini. Figure S7. Annotated (a) ETD and (b) HCD spectra for the H2A2A proteoform containing phosphorylation at S16. Figure S8. Zoom-in of the annotated ETD spectra for the phosphorylated H2A proteoform. Table S1. Summary for the identified proteoforms with high scores.

Corresponding Author David L. Stenoien. [email protected]. Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352.



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Author Contributions The manuscript was written through contributions of all authors. The experiments were designed by all authors and carried out by MZ and DLS. All authors have given approval to the final version of the manuscript. Funding Sources This research was funded by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research. PNNL is a multiprogram national laboratory operated by Battelle for the U.S. DOE under Contract DE- AC05-76RL01830. Acknowledgements The authors thank Christopher Wilkins, Jung Kap Park, and Sangtae Kim at the Pacific Northwest National Laboratory (PNNL) for developing the Informed-Proteomics; Xiaowen Liu and Qiang Kou at Indiana University for customizing the TopPIC software used in this work. We appreciate the help from other PNNL colleagues: Matthew Monroe and Nikola Tolić for data analysis; Carrie Nicora for preparing the bottom-up sample; Anil K. Shukla, Rosalie K. Chu, and Ron Moore for the LCMS experiments: and Charles Timchalk for providing mouse brain samples. The research was performed in the Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) national user facility at the Pacific Northwest National Laboratory (PNNL) in Richland, WA.


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