Steve M. M. Sweet

Mapping intact protein isoforms in discovery mode using top-down proteomics

A full description of the human proteome relies on the challenging task of detecting mature and changing forms of protein molecules in the body. Large-scale proteome analysis has routinely involved digesting intact proteins followed by inferred protein identification using mass spectrometry. This ‘bottom-up’ process affords a high number of identifications (not always unique to a single gene). However, complications arise from incomplete or ambiguous characterization of alternative splice forms, diverse modifications (for example, acetylation and methylation) and endogenous protein cleavages, especially when combinations of these create complex patterns of intact protein isoforms and species. ‘Top-down’ interrogation of whole proteins can overcome these problems for individual proteins, but has not been achieved on a proteome scale owing to the lack of intact protein fractionation methods that are well integrated with tandem mass spectrometry. Here we show, using a new four-dimensional separation system, identification of 1,043 gene products from human cells that are dispersed into more than 3,000 protein species created by post-translational modification (PTM), RNA splicing and proteolysis. The overall system produced greater than 20-fold increases in both separation power and proteome coverage, enabling the identification of proteins up to 105 kDa and those with up to 11 transmembrane helices. Many previously undetected isoforms of endogenous human proteins were mapped, including changes in multiply modified species in response to accelerated cellular ageing (senescence) induced by DNA damage. Integrated with the latest version of the Swiss-Prot database, the data provide precise correlations to individual genes and proof-of-concept for large-scale interrogation of whole protein molecules. The technology promises to improve the link between proteomics data and complex phenotypes in basic biology and disease research.

The MMSET histone methyl transferase switches global histone methylation and alters gene expression in t(4;14) multiple myeloma cells

The multiple myeloma SET domain (MMSET) protein is overexpressed in multiple myeloma (MM) patients with the translocation t(4;14). Although studies have shown the involvement of MMSET/Wolf-Hirschhorn syndrome candidate 1 in development, its mode of action in the pathogenesis of MM is largely unknown. We found that MMSET is a major regulator of chromatin structure and transcription in t(4;14) MM cells. High levels of MMSET correlate with an increase in lysine 36 methylation of histone H3 and a decrease in lysine 27 methylation across the genome, leading to a more open structural state of the chromatin. Loss of MMSET expression alters adhesion properties, suppresses growth, and induces apoptosis in MM cells. Consequently, genes affected by high levels of MMSET are implicated in the p53 pathway, cell cycle regulation, and integrin signaling. Regulation of many of these genes required functional histone methyl-transferase activity of MMSET. These results implicate MMSET as a major epigenetic regulator in t(4;14)+ MM.

Size-Sorting Combined with Improved Nanocapillary Liquid Chromatography-Mass Spectrometry for Identification of Intact Proteins up to 80 kDa

Despite the availability of ultra-high-resolution mass spectrometers, methods for separation and detection of intact proteins for proteome-scale analyses are still in a developmental phase. Here we report robust protocols for online LC-MS to drive high-throughput top-down proteomics in a fashion similar to that of bottom-up proteomics. Comparative work on protein standards showed that a polymeric stationary phase led to superior sensitivity over a silica-based medium in reversed-phase nanocapillary LC, with detection of proteins >50 kDa routinely accomplished in the linear ion trap of a hybrid Fourier transform mass spectrometer. Protein identification was enabled by nozzle-skimmer dissociation and detection of fragment ions with <10 ppm mass accuracy for highly specific database searching using tailored software. This overall approach led to identification of proteins up to 80 kDa, with 10-60 proteins identified in single LC-MS runs of samples from yeast and human cell lines prefractionated by their molecular mass using a gel-based sieving system.

SLoMo: Automated Site Localization of Modifications from ETD/ECD Mass Spectra

Recently, software has become available to automate localization of phosphorylation sites from CID data and to assign associated confidence scores. We present an algorithm, SLoMo (Site Localization of Modifications), which extends this capability to ETD/ECD mass spectra. Furthermore, SLoMo caters for both high and low resolution data and allows for site-localization of any UniMod post-translational modification. SLoMo accepts input data from a variety of formats (e.g., Sequest, OMSSA). We validate SLoMo with high and low resolution ETD, ECD, and CID data.

Large Scale Localization of Protein Phosphorylation by Use of Electron Capture Dissociation Mass Spectrometry

We used on-line electron capture dissociation (ECD) for the large scale identification and localization of sites of phosphorylation. Each FT-ICR ECD event was paired with a linear ion trap collision-induced dissociation (CID) event, allowing a direct comparison of the relative merits of ECD and CID for phosphopeptide identification and site localization. Linear ion trap CID was shown to be most efficient for phosphopeptide identification, whereas FT-ICR ECD was superior for localization of sites of phosphorylation. The combination of confident CID and ECD identification and confident CID and ECD localization is particularly valuable in cases where a phosphopeptide is identified just once within a phosphoproteomics experiment.

Kinetics of Re-establishing H3K79 Methylation Marks in Global Human Chromatin

We employ a stable isotope strategy wherein both histones and their methylations are labeled in synchronized human cells. This allows us to differentiate between old and new methylations on pre-existing versus newly synthesized histones. The strategy is implemented on K79 methylation in an isoform-specific manner for histones H3.1, H3.2, and H3.3. Although levels of H3.3K79 monomethylation are higher than that of H3.2/H3.1, the rate of establishing the K79 methylation is the same for all three isoforms. Surprisingly, we find that pre-existing “old” histones continue to be K79-monomethylated and -dimethylated at a rate equal to the newly synthesized histones. These observations imply that some degree of positional “scrambling” of K79 methylation occurs through the cell cycle.

Total kinetic analysis reveals how combinatorial methylation patterns are established on lysines 27 and 36 of histone H3

We have developed a targeted method to quantify all combinations of methylation on an H3 peptide containing lysines 27 and 36 (H3K27-K36). By using stable isotopes that separately label the histone backbone and its methylations, we tracked the rates of methylation and demethylation in myeloma cells expressing high vs. low levels of the methyltransferase MMSET/WHSC1/NSD2. Following quantification of 99 labeled H3K27-K36 methylation states across time, a kinetic model converged to yield 44 effective rate constants qualifying each methylation and demethylation step as a function of the methylation state on the neighboring lysine. We call this approach MS-based measurement and modeling of histone methylation kinetics (M4K). M4K revealed that, when dimethylation states are reached on H3K27 or H3K36, rates of further methylation on the other site are reduced as much as 100-fold. Overall, cells with high MMSET have as much as 33-fold increases in the effective rate constants for formation of H3K36 mono- and dimethylation. At H3K27, cells with high MMSET have elevated formation of K27me1, but even higher increases in the effective rate constants for its reversal by demethylation. These quantitative studies lay bare a bidirectional antagonism between H3K27 and H3K36 that controls the writing and erasing of these methylation marks. Additionally, the integrated kinetic model was used to correctly predict observed abundances of H3K27-K36 methylation states within 5% of that actually established in perturbed cells. Such predictive power for how histone methylations are established should have major value as this family of methyltransferases matures as drug targets.

Signal Transducers and Activators of Transcription-3 Binding to the Fibroblast Growth Factor Receptor Is Activated by Receptor Amplification

Fibroblast growth factor receptors (FGFR) are cell surface tyrosine kinases that function in cell proliferation and differentiation. Aberrant FGFR signaling occurs in diverse cancers due to gene amplification, but the associated oncogenic mechanisms are poorly understood. Using a proteomics approach, we identified signal transducers and activators of transcription-3 (STAT3) as a receptor-binding partner that is mediated by Tyr(677) phosphorylation on FGFR. Binding to activated FGFR was essential for subsequent tyrosine phosphorylation and nuclear translocation of STAT3, along with activation of its downstream target genes. Tyrosine phosphorylation of STAT3 was also dependent on concomitant FGFR-dependent activity of SRC and JAK kinases. Lastly, tyrosine (but not serine) phosphorylation of STAT3 required amplified FGFR protein expression, generated either by enforced overexpression or as associated with gene amplification in cancer cells. Our findings show that amplified FGFR expression engages the STAT3 pathway, and they suggest therapeutic strategies to attack FGFR-overexpressing cancers.

Intact mass detection, interpretation, and visualization to automate Top‐Down proteomics on a large scale

Applying high‐throughput Top‐Down MS to an entire proteome requires a yet‐to‐be‐established model for data processing. Since Top‐Down is becoming possible on a large scale, we report our latest software pipeline dedicated to capturing the full value of intact protein data in automated fashion. For intact mass detection, we combine algorithms for processing MS1 data from both isotopically resolved (FT) and charge‐state resolved (ion trap) LC‐MS data, which are then linked to their fragment ions for database searching using ProSight. Automated determination of human keratin and tubulin isoforms is one result. Optimized for the intricacies of whole proteins, new software modules visualize proteome‐scale data based on the LC retention time and intensity of intact masses and enable selective detection of PTMs to automatically screen for acetylation, phosphorylation, and methylation. Software functionality was demonstrated using comparative LC‐MS data from yeast strains in addition to human cells undergoing chemical stress. We further these advances as a key aspect of realizing Top‐Down MS on a proteomic scale.

Differential phosphoproteomics of fibroblast growth factor signaling: identification of Src family kinase-mediated phosphorylation events.

Activation of signal transduction by the receptor tyrosine kinase, fibroblast growth factor receptor (FGFR), results in a cascade of protein−protein interactions that rely on the occurrence of specific tyrosine phosphorylation events. One such protein recruited to the activated receptor complex is the nonreceptor tyrosine kinase, Src, which is involved in both initiation and termination of further signaling events. To gain a further understanding of the tyrosine phosphorylation events that occur during FGF signaling, with a specific focus on those that are dependent on Src family kinase (SFK) activity, we have applied SILAC combined with chemical inhibition of SFK activity to search for phosphorylation events that are dependent on SFK activity in FGF stimulated cells. In addition, we used a more targeted approach to carry out high coverage phosphopeptide mapping of one Src substrate protein, the multifunctional adaptor Dok1, and to identify SFK-dependent Dok1 binding partners. From these analyses we identify 80 SFK-dependent phosphorylation events on 40 proteins. We further identify 18 SFK-dependent Dok1 interactions and 9 SFK-dependent Dok1 phosphorylation sites, 6 of which had not previously been known to be SFK-dependent.