Large-scale Top-down Proteomics of the Human Proteome: Membrane Proteins,Mitochondria, and Senescence

Top-down proteomics is emerging as a viable method for the routine identification of hundreds to thousands of proteins. In this work we report the largest top-down study to date, with the identification of 1,220 proteins from the transformed human cell line H1299 at a false discovery rate of 1%. Multiple separation strategies were utilized, including the focused isolation of mitochondria, resulting in significantly improved proteome coverage relative to previous work. In all, 347 mitochondrial proteins were identified, including ∼50% of the mitochondrial proteome below 30 kDa and over 75% of the subunits constituting the large complexes of oxidative phosphorylation. Three hundred of the identified proteins were found to be integral membrane proteins containing between 1 and 12 transmembrane helices, requiring no specific enrichment or modified LC-MS parameters. Over 5,000 proteoforms were observed, many harboring post-translational modifications, including over a dozen proteins containing lipid anchors (some previously unknown) and many others with phosphorylation and methylation modifications. Comparison between untreated and senescent H1299 cells revealed several changes to the proteome, including the hyperphosphorylation of HMGA2. This work illustrates the burgeoning ability of top-down proteomics to characterize large numbers of intact proteoforms in a high-throughput fashion.Although traditional bottom-up approaches to mass-spectrometry-based proteomics are capable of identifying thousands of protein groups from a complex mixture, proteolytic digestion can result in the loss of information pertaining to post-translational modifications and sequence variants (1, 2). The recent implementation of top-down proteomics in a high-throughput format using either Fourier transform ion cyclotron resonance (3–5) or Orbitrap instruments (6, 7) has shown an increasing scale of applicability while preserving information on combinatorial modifications and highly related sequence variants. For example, the identification of over 500 bacterial proteins helped researchers find covalent switches on cysteines (7), and over 1,000 proteins were identified from human cells (3). Such advances have driven the detection of whole protein forms, now simply called proteoforms (8), with several laboratories now seeking to tie these to specific functions in cell and disease biology (9–11).The term “proteoform” denotes a specific primary structure of an intact protein molecule that arises from a specific gene and refers to a precise combination of genetic variation, splice variants, and post-translational modifications. Whereas special attention is required in order to accomplish gene- and variant-specific identifications via the bottom-up approach, top-down proteomics routinely links proteins to specific genes without the problem of protein inference. However, the fully automated characterization of whole proteoforms still represents a significant challenge in the field. Another major challenge is to extend the top-down approach to the study of whole integral membrane proteins, whose hydrophobicity can often limit their analysis via LC-MS (5, 12–16). Though integral membrane proteins are often difficult to solubilize, the long stretches of sequence information provided from fragmentation of their transmembrane domains in the gas phase can actually aid in their identification (5, 13).In parallel to the early days of bottom-up proteomics a decade ago (17–21), in this work we brought the latest methods for top-down proteomics into combination with subcellular fractionation and cellular treatments to expand coverage of the human proteome. We utilized multiple dimensions of separation and an Orbitrap Elite mass spectrometer to achieve large-scale interrogation of intact proteins derived from H1299 cells. For this focus issue on post-translational modifications, we report this summary of findings from the largest implementation of top-down proteomics to date, which resulted in the identification of 1,220 proteins and thousands more proteoforms. We also applied the platform to H1299 cells induced into senescence by treatment with the DNA-damaging agent camptothecin.     

Proteomics Analysis of the Cardiac Myofilament Subproteome Reveals Dynamic Alterations in Phosphatase Subunit Distribution

Myofilament proteins are responsible for cardiac contraction. The myofilament subproteome, however, has not been comprehensively analyzed thus far. In the present study, cardiomyocytes were isolated from rodent hearts and stimulated with endothelin-1 and isoproterenol, potent inducers of myofilament protein phosphorylation. Subsequently, cardiomyocytes were “skinned,” and the myofilament subproteome was analyzed using a high mass accuracy ion trap tandem mass spectrometer (LTQ Orbitrap XL) equipped with electron transfer dissociation. As expected, a small number of myofilament proteins constituted the majority of the total protein mass with several known phosphorylation sites confirmed by electron transfer dissociation. More than 600 additional proteins were identified in the cardiac myofilament subproteome, including kinases and phosphatase subunits. The proteomic comparison of myofilaments from control and treated cardiomyocytes suggested that isoproterenol treatment altered the subcellular localization of protein phosphatase 2A regulatory subunit B56α. Immunoblot analysis of myocyte fractions confirmed that β-adrenergic stimulation by isoproterenol decreased the B56α content of the myofilament fraction in the absence of significant changes for the myosin phosphatase target subunit isoforms 1 and 2 (MYPT1 and MYPT2). Furthermore, immunolabeling and confocal microscopy revealed the spatial redistribution of these proteins with a loss of B56α from Z-disc and M-band regions but increased association of MYPT1/2 with A-band regions of the sarcomere following β-adrenergic stimulation. In summary, we present the first comprehensive proteomics data set of skinned cardiomyocytes and demonstrate the potential of proteomics to unravel dynamic changes in protein composition that may contribute to the neurohormonal regulation of myofilament contraction.Myofilament proteins comprise the fundamental contractile apparatus of the heart, the cardiac sarcomere. They are subdivided into thin filament proteins, including actin, tropomyosin, the troponin complex (troponin C, troponin I, and troponin T), and thick filament proteins, including myosin heavy chains, myosin light chains, and myosin-binding protein C. Although calcium is the principal regulator of cardiac contraction through the excitation-contraction coupling process that culminates in calcium binding to troponin C, myofilament function is also significantly modulated by phosphorylation of constituent proteins, such as cardiac troponin I (cTnI),1 cardiac myosin-binding protein C (cMyBP-C), and myosin regulatory light chain (MLC-2). “Skinned” myocyte preparations from rodent hearts, in which the sarcolemmal envelope is disrupted through the use of detergents, have been invaluable in providing mechanistic information on the functional consequences of myofilament protein phosphorylation following exposure to neurohormonal stimuli that activate pertinent kinases prior to skinning or direct exposure to such kinases in active form after skinning (for recent examples, see studies on the phosphorylation of cTnI (1–3), cMyBP-C (4–6), and MLC-2 (7–9)). Nevertheless, to date, only a few myofilament proteins have been studied using proteomics (10–19), and a detailed proteomic characterization of the myofilament subproteome and its associated proteins from skinned myocytes has not been performed. In the present analysis, we used an LTQ Orbitrap XL equipped with ETD (20) to analyze the subproteome of skinned cardiomyocytes with or without prior stimulation. Endothelin-1 and isoproterenol were used to activate the endothelin receptor/protein kinase C and β-adrenoreceptor/protein kinase A pathway, respectively (21, 22). Importantly, the mass accuracy of the Orbitrap mass analyzer helped to distinguish true phosphorylation sites from false assignments, and the sensitivity of the ion trap provided novel insights into the translocation of phosphatase regulatory and targeting subunits following β-adrenergic stimulation.     

Identification of Regulatory and Cargo Proteins of Endosomal and Secretory Pathways in Arabidopsis thaliana by Proteomic Dissection

The cell''s endomembranes comprise an intricate, highly dynamic and well-organized system. In plants, the proteins that regulate function of the various endomembrane compartments and their cargo remain largely unknown. Our aim was to dissect subcellular trafficking routes by enriching for partially overlapping subpopulations of endosomal proteomes associated with endomembrane markers. We selected RABD2a/ARA5, RABF2b/ARA7, RABF1/ARA6, and RABG3f as markers for combinations of the Golgi, trans-Golgi network (TGN), early endosomes (EE), secretory vesicles, late endosomes (LE), multivesicular bodies (MVB), and the tonoplast. As comparisons we used Golgi transport 1 (GOT1), which localizes to the Golgi, clathrin light chain 2 (CLC2) labeling clathrin-coated vesicles and pits and the vesicle-associated membrane protein 711 (VAMP711) present at the tonoplast. We developed an easy-to-use method by refining published protocols based on affinity purification of fluorescent fusion constructs to these seven subcellular marker proteins in Arabidopsis thaliana seedlings. We present a total of 433 proteins, only five of which were shared among all enrichments, while many proteins were common between endomembrane compartments of the same trafficking route. Approximately half, 251 proteins, were assigned to one enrichment only. Our dataset contains known regulators of endosome functions including small GTPases, SNAREs, and tethering complexes. We identify known cargo proteins such as PIN3, PEN3, CESA, and the recently defined TPLATE complex. The subcellular localization of two GTPase regulators predicted from our enrichments was validated using live-cell imaging. This is the first proteomic dataset to discriminate between such highly overlapping endomembrane compartments in plants and can be used as a general proteomic resource to predict the localization of proteins and identify the components of regulatory complexes and provides a useful tool for the identification of new protein markers of the endomembrane system.Membrane compartmentalization is an essential mechanism for eukaryotic life, by which cells separate and control biological processes. Plant growth, development, and adaptation to biotic and abiotic stress all rely on the highly dynamic endomembrane system, yet we know comparatively little about the proteins regulating these dynamic trafficking events. The plasma membrane (PM) provides the main interface between the cell and its environment, mediating the transfer of material to and from the cell and is a primary site for perception of external signals. Transmembrane proteins are synthesized in the endoplasmic reticulum (ER) and trafficked to the PM via the Golgi, although there are other secretory routes for soluble cargo (discussed in (1–4)). Post-Golgi trafficking is the main route by which newly synthesized transmembrane proteins and cell wall glycans are delivered to the PM. In plants, secretory and endocytic traffic converge at the trans-Golgi network (TGN), which also functions as an early endosome (EE). Multivesicular bodies (MVBs) are the other main endosomal compartment in plants and serve as prevacuolar compartments (PVCs) or late endosomes (LE) destined for vacuolar degradation (reviewed (1, 5, 6)).Recycling and sorting of plasma membrane proteins is essential for generating the polar localization of auxin efflux transporters (discussed in (7)), formation of the cell plate during cell division (8–11), and in defense such as localized deposition of papilla reviewed in (12, 13). Furthermore, the subcellular localization of transporters and receptors is dynamically regulated. For example, the boron transporter (BOR1) exhibits polar localization and is internalized and degraded under conditions of high boron to reduce toxicity (14, 15). Similarly the receptor-like kinases (RLKs) flagellin-sensing 2 (FLS2) and brassinosteroid insensitive 1 (BRI1), important transmembrane receptors in antibacterial immunity and plant development, respectively, are constitutively endocytosed and recycled to the PM (16–18). Both receptors and transporters are also cargoes of the LE/MVB trafficking route (16) and are probably sorted to the vacuole for degradation (19, 20). Importantly, FLS2 trafficking via the recycling endocytic or the late endocytic route depends on its activation status; inactive receptors are recycled while ligand-activated receptors are sorted to the late endosomal pathway (16). Similarly, the polar sorting of auxin efflux transporters depends on their phosphorylation status (21). These observations illustrate that membrane compartmentalization underpins important aspects of plant cell biology and has initiated a quest toward a better understanding of the endomembrane compartments and the routes and mechanisms by which cargo is trafficked and sorted within the cell.Membrane trafficking within the cell requires complex machinery consisting of a plethora of coat and adaptor proteins, small GTPases, targeting, tethering, and scission factors (reviewed in (22, 23)). Homologues of some animal and yeast and endomembrane regulators have been identified in plants, but the localization and function of many of these remain to be characterized. For example, members of the RAB GTPase family have been shown to have markedly different roles and localizations in plants compared with their animal and yeast homologs (24). Therefore, acquiring localization data for tethering complexes and other regulators in plant systems is essential. In Arabidopsis thaliana, some of these proteins have been developed as useful probes to visualize the different endomembrane compartments by fusion with fluorescent reporters (9, 25–27). These include regulators of trafficking events such as RAB GTPases that are molecular switches responsible for the assembly of tethering and docking complexes and compartment identity. RAB proteins are widely used markers of endomembrane compartments, for example RABD2a/ARA5 labels the Golgi and TGN/EE as well as post-Golgi vesicles (4, 24, 26, 28), RABF2b/ARA7 localizes to TGN/EE and LE (25), RABF1/ARA6 is a marker of the LE/MVB vesicles (25, 29), and RABG3f localizes to MVBs and the tonoplast (26, 30).Fluorescent-tagged marker lines for the live-cell imaging of plant cells have been invaluable in defining the location of proteins within and between organelles and endomembrane compartments (26). However, microscopic investigation of membrane trafficking is limited by throughput, as only few proteins can be studied simultaneously. A powerful approach to large-scale identification of proteins in endomembrane compartments is through subcellular fractionation based on physical properties to directly isolate or enrich for the subcellular compartment of interest. Subcellular fractionation-based proteomics have been successfully used to decipher the steady state and cargo proteomes of, including but not limited to, the ER, the vacuole, PM, mitochondria and chloroplasts, and smaller vesicle-like compartments such as peroxisomes and Golgi (31–41). However, the smaller, transitory vesicles of the secretory and endocytic pathways have proved challenging to purify for reliable proteomic analysis. To overcome this, affinity purification of vesicles was established in animal cells (42, 43) and recently successfully applied in plants in combination with subcellular fractionation. Affinity purification and mass spectrometry (MS) of syntaxin of plants 61 (SYP61)-positive TGN/EE compartments identified 145 proteins specifically enriched in (44), while affinity isolation of VHA-a1-GFP (vacuolar H⁺ ATPase A1) identified 105 proteins associated with the TGN/EE (45). The VHA-A1 affinity purification data were then further refined using density gradient centrifugation to differentiate cargo and steady-state proteins (45).We have further explored affinity purification of fluorescent-tagged markers localizing to defined compartments to identify proteins associated with trafficking. Our motivation was to dissect the trafficking routes by enriching for partially overlapping subpopulations of endosomal proteomes associated with small GTPases in the RAB family. We selected RABD2a/ARA5, RABF2b/ARA7, RABF1/ARA6, and RABG3f as markers for Golgi/TGN/EE/secretory vesicles, LE/MVB compartments, LE/MVB compartments and LE/MVB/tonoplast, respectively. Additionally, we used Golgi transport 1 (GOT1), which localizes to the Golgi, clathrin light chain 2 (CLC2) labeling clathrin-coated vesicles (CCVs) and pits and the vesicle-associated membrane protein 711 (VAMP711) present at the tonoplast (26, 27, 29, 46, 47) as comparisons. Our objective was to identify transient cargo proteins, tethers, and docking factors associated with dynamic subdomains of the endomembrane system, to supplement better-characterized “steady-state” components, and to identify components of recycling and vacuolar trafficking pathways.     

A Small Conserved Domain in the Yeast Spa2p Is Necessary and Sufficient for Its Polarized Localization

SPA2 encodes a yeast protein that is one of the first proteins to localize to sites of polarized growth, such as the shmoo tip and the incipient bud. The dynamics and requirements for Spa2p localization in living cells are examined using Spa2p green fluorescent protein fusions. Spa2p localizes to one edge of unbudded cells and subsequently is observable in the bud tip. Finally, during cytokinesis Spa2p is present as a ring at the mother–daughter bud neck. The bud emergence mutants bem1 and bem2 and mutants defective in the septins do not affect Spa2p localization to the bud tip. Strikingly, a small domain of Spa2p comprised of 150 amino acids is necessary and sufficient for localization to sites of polarized growth. This localization domain and the amino terminus of Spa2p are essential for its function in mating. Searching the yeast genome database revealed a previously uncharacterized protein which we name, Sph1p (Spa2p homolog), with significant homology to the localization domain and amino terminus of Spa2p. This protein also localizes to sites of polarized growth in budding and mating cells. SPH1, which is similar to SPA2, is required for bipolar budding and plays a role in shmoo formation. Overexpression of either Spa2p or Sph1p can block the localization of either protein fused to green fluorescent protein, suggesting that both Spa2p and Sph1p bind to and are localized by the same component. The identification of a 150–amino acid domain necessary and sufficient for localization of Spa2p to sites of polarized growth and the existence of this domain in another yeast protein Sph1p suggest that the early localization of these proteins may be mediated by a receptor that recognizes this small domain.Polarized cell growth and division are essential cellular processes that play a crucial role in the development of eukaryotic organisms. Cell fate can be determined by cell asymmetry during cell division (Horvitz and Herskowitz, 1992; Cohen and Hyman, 1994; Rhyu and Knoblich, 1995). Consequently, the molecules involved in the generation and maintenance of cell asymmetry are important in the process of cell fate determination. Polarized growth can occur in response to external signals such as growth towards a nutrient (Rodriguez-Boulan and Nelson, 1989; Eaton and Simons, 1995) or hormone (Jackson and Hartwell, 1990a , b ; Segall, 1993; Keynes and Cook, 1995) and in response to internal signals as in Caenorhabditis elegans (Goldstein et al., 1993; Kimble, 1994; Priess, 1994) and Drosophila melanogaster (St Johnston and Nusslein-Volhard, 1992; Anderson, 1995) early development. Saccharomyces cerevisiae undergo polarized growth towards an external cue during mating and to an internal cue during budding. Polarization towards a mating partner (shmoo formation) and towards a new bud site requires a number of proteins (Chenevert, 1994; Chant, 1996; Drubin and Nelson, 1996). Many of these proteins are necessary for both processes and are localized to sites of polarized growth, identified by the insertion of new cell wall material (Tkacz and Lampen, 1972; Farkas et al., 1974; Lew and Reed, 1993) to the shmoo tip, bud tip, and mother–daughter bud neck. In yeast, proteins localized to growth sites include cytoskeletal proteins (Adams and Pringle, 1984; Kilmartin and Adams, 1984; Ford, S.K., and J.R. Pringle. 1986. Yeast. 2:S114; Drubin et al., 1988; Snyder, 1989; Snyder et al., 1991; Amatruda and Cooper, 1992; Lew and Reed, 1993; Waddle et al., 1996), neck filament components (septins) (Byers and Goetsch, 1976; Kim et al., 1991; Ford and Pringle, 1991; Haarer and Pringle, 1987; Longtine et al., 1996), motor proteins (Lillie and Brown, 1994), G-proteins (Ziman, 1993; Yamochi et al., 1994; Qadota et al., 1996), and two membrane proteins (Halme et al., 1996; Roemer et al., 1996; Qadota et al., 1996). Septins, actin, and actin-associated proteins localize early in the cell cycle, before a bud or shmoo tip is recognizable. How this group of proteins is localized to and maintained at sites of cell growth remains unclear.Spa2p is one of the first proteins involved in bud formation to localize to the incipient bud site before a bud is recognizable (Snyder, 1989; Snyder et al., 1991; Chant, 1996). Spa2p has been localized to where a new bud will form at approximately the same time as actin patches concentrate at this region (Snyder et al., 1991). An understanding of how Spa2p localizes to incipient bud sites will shed light on the very early stages of cell polarization. Later in the cell cycle, Spa2p is also found at the mother–daughter bud neck in cells undergoing cytokinesis. Spa2p, a nonessential protein, has been shown to be involved in bud site selection (Snyder, 1989; Zahner et al., 1996), shmoo formation (Gehrung and Snyder, 1990), and mating (Gehrung and Snyder, 1990; Chenevert et al., 1994; Yorihuzi and Ohsumi, 1994; Dorer et al., 1995). Genetic studies also suggest that Spa2p has a role in cytokinesis (Flescher et al., 1993), yet little is known about how this protein is localized to sites of polarized growth.We have used Spa2p green fluorescent protein (GFP)¹ fusions to investigate the early localization of Spa2p to sites of polarized growth in living cells. Our results demonstrate that a small domain of ∼150 amino acids of this large 1,466-residue protein is sufficient for targeting to sites of polarized growth and is necessary for Spa2p function. Furthermore, we have identified and characterized a novel yeast protein, Sph1p, which has homology to both the Spa2p amino terminus and the Spa2p localization domain. Sph1p localizes to similar regions of polarized growth and sph1 mutants have similar phenotypes as spa2 mutants.     

Global Subcellular Characterization of Protein Degradation Using Quantitative Proteomics

Protein degradation provides an important regulatory mechanism used to control cell cycle progression and many other cellular pathways. To comprehensively analyze the spatial control of protein degradation in U2OS osteosarcoma cells, we have combined drug treatment and SILAC-based quantitative mass spectrometry with subcellular and protein fractionation. The resulting data set analyzed more than 74,000 peptides, corresponding to ∼5000 proteins, from nuclear, cytosolic, membrane, and cytoskeletal compartments. These data identified rapidly degraded proteasome targets, such as PRR11 and highlighted a feedback mechanism resulting in translation inhibition, induced by blocking the proteasome. We show this is mediated by activation of the unfolded protein response. We observed compartment-specific differences in protein degradation, including proteins that would not have been characterized as rapidly degraded through analysis of whole cell lysates. Bioinformatic analysis of the entire data set is presented in the Encyclopedia of Proteome Dynamics, a web-based resource, with proteins annotated for stability and subcellular distribution.Targeted protein degradation is an important regulatory mechanism that allows co-ordination of cellular pathways in response to environmental and temporal stimuli (1). The control of diverse biochemical pathways, including cell cycle progression and the response to DNA damage, is mediated, at least in part, by dynamic alterations in protein degradation (2). Previous large scale proteomics studies in mammalian cells have shown that the rate of protein degradation can vary from the timescale of minutes, to essentially infinite stability for metastable proteins (3–8).Most intracellular proteins have similar degradation rates, with a half-life approximating the cell doubling rate. Under 5% of proteins display degradation rates more than threefold faster than the proteome average (3–5, 7). However, degradation rates for individual proteins can change, for example depending on either the cell cycle stage, or signaling events, and can also vary depending on subcellular localization. Disruption of such regulated protein stability underlies the disease mechanisms responsible for forms of cancer, e.g. p53 (9, 10) and the proto-oncogene c-Myc (11).Detection of rapidly degraded proteins can be difficult because of their low abundance. However, advances in mass spectrometry based proteomics have enabled in-depth quantitative analysis of cellular proteomes (12–14). Stable isotope labeling by amino acids in cell culture (SILAC)¹ (15), has been widely used to measure protein properties such as abundance, interactions, modifications, turnover, and subcellular localization under different conditions (16). Subcellular fractionation and protein size separation are also powerful techniques that enhance in-depth analysis of cellular proteomes. Not only do these fractionation techniques increase total proteome coverage, they also provide biological insight regarding how protein behavior differs between subcellular compartments. For example, subcellular fractionation has highlighted differences in the rate of ribosomal protein degradation between the nucleus and cytoplasm, (7, 17). Other studies have also demonstrated the benefit of in-depth subcellular fractionation and created methods for the characterization of how proteomes are localized in organelles (18–20).In this study we have used SILAC-based quantitative mass spectrometry combined with extensive subcellular and protein-level fractionation to identify rapidly degraded proteins in human U2OS cells. We provide a proteome level characterization of a major feedback mechanism involving inhibition of protein translation when the proteasome is inhibited. We also present the Encyclopedia of Proteome Dynamics, a user-friendly online resource providing access to the entire data set.     

MASH Suite Pro: A Comprehensive Software Tool for Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics is arguably a disruptive technology for the comprehensive analysis of all proteoforms arising from genetic variation, alternative splicing, and posttranslational modifications (PTMs). However, the complexity of top-down high-resolution mass spectra presents a significant challenge for data analysis. In contrast to the well-developed software packages available for data analysis in bottom-up proteomics, the data analysis tools in top-down proteomics remain underdeveloped. Moreover, despite recent efforts to develop algorithms and tools for the deconvolution of top-down high-resolution mass spectra and the identification of proteins from complex mixtures, a multifunctional software platform, which allows for the identification, quantitation, and characterization of proteoforms with visual validation, is still lacking. Herein, we have developed MASH Suite Pro, a comprehensive software tool for top-down proteomics with multifaceted functionality. MASH Suite Pro is capable of processing high-resolution MS and tandem MS (MS/MS) data using two deconvolution algorithms to optimize protein identification results. In addition, MASH Suite Pro allows for the characterization of PTMs and sequence variations, as well as the relative quantitation of multiple proteoforms in different experimental conditions. The program also provides visualization components for validation and correction of the computational outputs. Furthermore, MASH Suite Pro facilitates data reporting and presentation via direct output of the graphics. Thus, MASH Suite Pro significantly simplifies and speeds up the interpretation of high-resolution top-down proteomics data by integrating tools for protein identification, quantitation, characterization, and visual validation into a customizable and user-friendly interface. We envision that MASH Suite Pro will play an integral role in advancing the burgeoning field of top-down proteomics.With well-developed algorithms and computational tools for mass spectrometry (MS)¹ data analysis, peptide-based bottom-up proteomics has gained considerable popularity in the field of systems biology (1–9). Nevertheless, the bottom-up approach is suboptimal for the analysis of protein posttranslational modifications (PTMs) and sequence variants as a result of protein digestion (10). Alternatively, the protein-based top-down proteomics approach analyzes intact proteins, which provides a “bird''s eye” view of all proteoforms (11), including those arising from sequence variations, alternative splicing, and diverse PTMs, making it a disruptive technology for the comprehensive analysis of proteoforms (12–24). However, the complexity of top-down high-resolution mass spectra presents a significant challenge for data analysis. In contrast to the well-developed software packages available for processing data from bottom-up proteomics experiments, the data analysis tools in top-down proteomics remain underdeveloped.The initial step in the analysis of top-down proteomics data is deconvolution of high-resolution mass and tandem mass spectra. Thorough high-resolution analysis of spectra by horn (THRASH), which was the first algorithm developed for the deconvolution of high-resolution mass spectra (25), is still widely used. THRASH automatically detects and evaluates individual isotopomer envelopes by comparing the experimental isotopomer envelope with a theoretical envelope and reporting those that score higher than a user-defined threshold. Another commonly used algorithm, MS-Deconv, utilizes a combinatorial approach to address the difficulty of grouping MS peaks from overlapping isotopomer envelopes (26). Recently, UniDec, which employs a Bayesian approach to separate mass and charge dimensions (27), can also be applied to the deconvolution of high-resolution spectra. Although these algorithms assist in data processing, unfortunately, the deconvolution results often contain a considerable amount of misassigned peaks as a consequence of the complexity of the high-resolution MS and MS/MS data generated in top-down proteomics experiments. Errors such as these can undermine the accuracy of protein identification and PTM localization and, thus, necessitate the implementation of visual components that allow for the validation and manual correction of the computational outputs.Following spectral deconvolution, a typical top-down proteomics workflow incorporates identification, quantitation, and characterization of proteoforms; however, most of the recently developed data analysis tools for top-down proteomics, including ProSightPC (28, 29), Mascot Top Down (also known as Big-Mascot) (30), MS-TopDown (31), and MS-Align+ (32), focus almost exclusively on protein identification. ProSightPC was the first software tool specifically developed for top-down protein identification. This software utilizes “shotgun annotated” databases (33) that include all possible proteoforms containing user-defined modifications. Consequently, ProSightPC is not optimized for identifying PTMs that are not defined by the user(s). Additionally, the inclusion of all possible modified forms within the database dramatically increases the size of the database and, thus, limits the search speed (32). Mascot Top Down (30) is based on standard Mascot but enables database searching using a higher mass limit for the precursor ions (up to 110 kDa), which allows for the identification of intact proteins. Protein identification using Mascot Top Down is fundamentally similar to that used in bottom-up proteomics (34), and, therefore, it is somewhat limited in terms of identifying unexpected PTMs. MS-TopDown (31) employs the spectral alignment algorithm (35), which matches the top-down tandem mass spectra to proteins in the database without prior knowledge of the PTMs. Nevertheless, MS-TopDown lacks statistical evaluation of the search results and performs slowly when searching against large databases. MS-Align+ also utilizes spectral alignment for top-down protein identification (32). It is capable of identifying unexpected PTMs and allows for efficient filtering of candidate proteins when the top-down spectra are searched against a large protein database. MS-Align+ also provides statistical evaluation for the selection of proteoform spectrum match (PrSM) with high confidence. More recently, Top-Down Mass Spectrometry Based Proteoform Identification and Characterization (TopPIC) was developed (http://proteomics.informatics.iupui.edu/software/toppic/index.html). TopPIC is an updated version of MS-Align+ with increased spectral alignment speed and reduced computing requirements. In addition, MSPathFinder, developed by Kim et al., also allows for the rapid identification of proteins from top-down tandem mass spectra (http://omics.pnl.gov/software/mspathfinder) using spectral alignment. Although software tools employing spectral alignment, such as MS-Align+ and MSPathFinder, are particularly useful for top-down protein identification, these programs operate using command line, making them difficult to use for those with limited knowledge of command syntax.Recently, new software tools have been developed for proteoform characterization (36, 37). Our group previously developed MASH Suite, a user-friendly interface for the processing, visualization, and validation of high-resolution MS and MS/MS data (36). Another software tool, ProSight Lite, developed recently by the Kelleher group (37), also allows characterization of protein PTMs. However, both of these software tools require prior knowledge of the protein sequence for the effective localization of PTMs. In addition, both software tools cannot process data from liquid chromatography (LC)-MS and LC-MS/MS experiments, which limits their usefulness in large-scale top-down proteomics. Thus, despite these recent efforts, a multifunctional software platform enabling identification, quantitation, and characterization of proteins from top-down spectra, as well as visual validation and data correction, is still lacking.Herein, we report the development of MASH Suite Pro, an integrated software platform, designed to incorporate tools for protein identification, quantitation, and characterization into a single comprehensive package for the analysis of top-down proteomics data. This program contains a user-friendly customizable interface similar to the previously developed MASH Suite (36) but also has a number of new capabilities, including the ability to handle complex proteomics datasets from LC-MS and LC-MS/MS experiments, as well as the ability to identify unknown proteins and PTMs using MS-Align+ (32). Importantly, MASH Suite Pro also provides visualization components for the validation and correction of the computational outputs, which ensures accurate and reliable deconvolution of the spectra and localization of PTMs and sequence variations.     

Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

Microtubule-Dependent mRNA Transport in Fungi

The localization and local translation of mRNAs constitute an important mechanism to promote the correct subcellular targeting of proteins. mRNA localization is mediated by the active transport of mRNPs, large assemblies consisting of mRNAs and associated factors such as RNA-binding proteins. Molecular motors move mRNPs along the actin or microtubule cytoskeleton for short-distance or long-distance trafficking, respectively. In filamentous fungi, microtubule-based long-distance transport of vesicles, which are involved in membrane and cell wall expansion, supports efficient hyphal growth. Recently, we discovered that the microtubule-mediated transport of mRNAs is essential for the fast polar growth of infectious filaments in the corn pathogen Ustilago maydis. Combining in vivo UV cross-linking and RNA live imaging revealed that the RNA-binding protein Rrm4, which constitutes an integral part of the mRNP transport machinery, mediates the transport of distinct mRNAs encoding polarity factors, protein synthesis factors, and mitochondrial proteins. Moreover, our results indicate that microtubule-dependent mRNA transport is evolutionarily conserved from fungi to higher eukaryotes. This raises the exciting possibility of U. maydis as a model system to uncover basic concepts of long-distance mRNA transport.In order to compartmentalize functions, eukaryotic cells need to sort their proteins to distinct subcellular sites. A widespread mechanism for the spatiotemporal regulation of protein expression is localized translation, i.e., the concerted action of mRNA localization and confined translation. Thereby, the correct subcellular localization of translation products is promoted, and the deleterious mislocalization of proteins is prevented (5, 37).Most commonly, mRNA localization is mediated by active transport along the actin or microtubule cytoskeleton for short-distance or long-distance mRNA transport, respectively. Transported mRNAs contain specific cis-acting sequences that function as zipcodes to determine the correct subcellular destination. These RNA elements are recognized by RNA-binding proteins that combine with accessory factors to form higher-order ribonucleoprotein complexes, designated mRNPs (40, 82). Adaptor proteins are thought to connect mRNPs to molecular motors that actively transport them along the cytoskeleton to their final destination (88, 92). Commonly, premature translation is inhibited during mRNP transport by specific inhibitors. Upon arrival mRNAs are offloaded and kept in place by anchoring factors. The local phosphorylation of RNA-binding proteins then triggers unloading and the release of translational inhibitor (39, 68). When the formation of transport-competent mRNPs fails, mRNAs are translated at wrong locations, leading to the mislocalization of the encoded proteins. An example of the importance of mRNA localization is the local synthesis of morphogens during oogenesis and embryogenesis in Drosophila melanogaster, which determines the two main body axes of developing embryos (55, 59).In fungi, actin-dependent transport was quite extensively studied for Saccharomyces cerevisiae and was recently discovered in filaments of Candida albicans (25, 65, 68). Examples of long-distance mRNA transport along microtubules have so far been reported only for the corn pathogen Ustilago maydis. Study of the role of RNA-binding proteins during filamentous growth and pathogenic development revealed that microtubule-dependent mRNP transport is essential for the fast polar growth of infectious hyphae (6, 7, 26, 45). In this review we will introduce the basic aspects of short- and long-distance mRNA transports in fungal and animal models. In addition, we will shortly address polar growth and microtubule-dependent transport in filamentous fungi. This will be the foundation to present recent advances in the microtubule-dependent transport of mRNAs in U. maydis.     

Coomassie Blue as a Near-infrared Fluorescent Stain: A Systematic Comparison With Sypro Ruby for In-gel Protein Detection

Quantitative proteome analyses suggest that the well-established stain colloidal Coomassie Blue, when used as an infrared dye, may provide sensitive, post-electrophoretic in-gel protein detection that can rival even Sypro Ruby. Considering the central role of two-dimensional gel electrophoresis in top-down proteomic analyses, a more cost effective alternative such as Coomassie Blue could prove an important tool in ongoing refinements of this important analytical technique. To date, no systematic characterization of Coomassie Blue infrared fluorescence detection relative to detection with SR has been reported. Here, seven commercial Coomassie stain reagents and seven stain formulations described in the literature were systematically compared. The selectivity, threshold sensitivity, inter-protein variability, and linear-dynamic range of Coomassie Blue infrared fluorescence detection were assessed in parallel with Sypro Ruby. Notably, several of the Coomassie stain formulations provided infrared fluorescence detection sensitivity to <1 ng of protein in-gel, slightly exceeding the performance of Sypro Ruby. The linear dynamic range of Coomassie Blue infrared fluorescence detection was found to significantly exceed that of Sypro Ruby. However, in two-dimensional gel analyses, because of a blunted fluorescence response, Sypro Ruby was able to detect a few additional protein spots, amounting to 0.6% of the detected proteome. Thus, although both detection methods have their advantages and disadvantages, differences between the two appear to be small. Coomassie Blue infrared fluorescence detection is thus a viable alternative for gel-based proteomics, offering detection comparable to Sypro Ruby, and more reliable quantitative assessments, but at a fraction of the cost.Gel electrophoresis is an accessible, widely applicable and mature protein resolving technology. As the original top-down approach to proteomic analyses, among its many attributes the high resolution achievable by two dimensional gel-electrophoresis (2DE)¹ ensures that it remains an effective analytical technology despite the appearance of alternatives. However, in-gel detection remains a limiting factor for gel-based analyses; available technology generally permits the detection and quantification of only relatively abundant proteins (35). Many critical components in normal physiology and also disease may be several orders of magnitude less abundant and thus below the detection threshold of in-gel stains, or indeed most techniques. Pre- and post-fractionation technologies have been developed to address this central issue in proteomics but these are not without limitations (1–5). Thus improved detection methods for gel-based proteomics continue to be a high priority, and the literature is rich with different in-gel detection methods and innovative improvements (6–34). This history of iterative refinement presents a wealth of choices when selecting a detection strategy for a gel-based proteomic analysis (35).Perhaps the best known in-gel detection method is the ubiquitous Coomassie Blue (CB) stain; CB has served as a gel stain and protein quantification reagent for over 40 years. Though affordable, robust, easy to use, and compatible with mass spectrometry (MS), CB staining is relatively insensitive. In traditional organic solvent formulations, CB detects ∼ 10 ng of protein in-gel, and some reports suggest poorer sensitivity (27, 29, 36, 37). Sensitivity is hampered by relatively high background staining because of nonspecific retention of dye within the gel matrix (32, 36, 38, 39). The development of colloidal CB (CCB) formulations largely addressed these limitations (12); the concentration of soluble CB was carefully controlled by sequestering the majority of the dye into colloidal particles, mediated by pH, solvent, and the ionic strength of the solution. Minimizing soluble dye concentration and penetration of the gel matrix mitigated background staining, and the introduction of phosphoric acid into the staining reagent enhanced dye-protein interactions (8, 12, 40), contributing to an in-gel staining sensitivity of 5–10 ng protein, with some formulations reportedly yielding sensitivities of 0.1–1 ng (8, 12, 22, 39, 41, 42). Thus CCB achieved higher sensitivity than traditional CB staining, yet maintained all the advantages of the latter, including low cost and compatibility with existing densitometric detection instruments and MS. Although surpassed by newer methods, the practical advantages of CCB ensure that it remains one of the most common gel stains in use.Fluorescent stains have become the routine and sensitive alternative to visible dyes. Among these, the ruthenium-organometallic family of dyes have been widely applied and the most commercially well-known is Sypro Ruby (SR), which is purported to interact noncovalently with primary amines in proteins (15, 18, 19, 43). Chief among the attributes of these dyes is their high sensitivity. In-gel detection limits of < 1 ng for some proteins have been reported for SR (6, 9, 14, 44, 45). Moreover, SR staining has been reported to yield a greater linear dynamic range (LDR), and reduced interprotein variability (IPV) compared with CCB and silver stains (15, 19, 46–49). SR is easy to use, fully MS compatible, and relatively forgiving of variations in initial conditions (6, 15). The chief consequence of these advances remains high cost; SR and related stains are notoriously expensive, and beyond the budget of many laboratories. Furthermore, despite some small cost advantage relative to SR, none of the available alternatives has been consistently and quantitatively demonstrated to substantially improve on the performance of SR under practical conditions (9, 50).Notably, there is evidence to suggest that CCB staining is not fundamentally insensitive, but rather that its sensitivity has been limited by traditional densitometric detection (50, 51). When excited in the near IR at ∼650 nm, protein-bound CB in-gel emits light in the range of 700–800 nm. Until recently, the lack of low-cost, widely available and sufficiently sensitive infrared (IR)-capable imaging instruments prevented mainstream adoption of in-gel CB infrared fluorescence detection (IRFD); advances in imaging technology are now making such instruments far more accessible. Initial reports suggested that IRFD of CB-stained gels provided greater sensitivity than traditional densitometric detection (50, 51). Using CB R250, in-gel IRFD was reported to detect as little as 2 ng of protein in-gel, with a LDR of about an order of magnitude (2 to 20 ng, or 10 to 100 ng in separate gels), beyond which the fluorescent response saturated into the μg range (51). Using the G250 dye variant, it was determined that CB-IRFD of 2D gels detected ∼3 times as many proteins as densitometric imaging, and a comparable number of proteins as seen by SR (50). This study also concluded that CB-IRFD yielded a significantly higher signal to background ratio (S/BG) than SR, providing initial evidence that CB-IRFD may be superior to SR in some aspects of stain performance (50).Despite this initial evidence of the viability of CB-IRF as an in-gel protein detection method, a detailed characterization of this technology has not yet been reported. Here a more thorough, quantitative characterization of CB-IRFD is described, establishing its lowest limit of detection (LLD), IPV, and LDR in comparison to SR. Finally a wealth of modifications and enhancements of CCB formulations have been reported (8, 12, 21, 24, 26, 29, 40, 41, 52–54), and likewise there are many commercially available CCB stain formulations. To date, none of these formulations have been compared quantitatively in terms of their relative performance when detected using IRF. As a general detection method for gel-based proteomics, CB-IRFD was found to provide comparable or even slightly superior performance to SR according to most criteria, including sensitivity and selectivity (50). Furthermore, in terms of LDR, CB-IRFD showed distinct advantages over SR. However, assessing proteomes resolved by 2DE revealed critical distinctions between CB-IRFD and SR in terms of protein quantification versus threshold detection: neither stain could be considered unequivocally superior to the other by all criteria. Nonetheless, IRFD proved the most sensitive method of detecting CB-stained protein in-gel, enabling high sensitivity detection without the need for expensive reagents or even commercial formulations. Overall, CB-IRFD is a viable alternative to SR and other mainstream fluorescent stains, mitigating the high cost of large-scale gel-based proteomic analyses, making high sensitivity gel-based proteomics accessible to all labs. With improvements to CB formulations and/or image acquisition instruments, the performance of this detection technology may be further enhanced.     

Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize preanalytical and analytical variation in comparative proteomics experiments.Access to proteomics performance standards is essential for several reasons. First, to generate the highest quality data possible, proteomics laboratories routinely benchmark and perform quality control (QC)1 monitoring of the performance of their instrumentation using standards. Second, appropriate standards greatly facilitate the development of improvements in technologies by providing a timeless standard with which to evaluate new protocols or instruments that claim to improve performance. For example, it is common practice for an individual laboratory considering purchase of a new instrument to require the vendor to run “demo” samples so that data from the new instrument can be compared head to head with existing instruments in the laboratory. Third, large scale proteomics studies designed to aggregate data across laboratories can be facilitated by the use of a performance standard to measure reproducibility across sites or to compare the performance of different LC-MS configurations or sample processing protocols used between laboratories to facilitate development of optimized standard operating procedures (SOPs).Most individual laboratories have adopted their own QC standards, which range from mixtures of known synthetic peptides to digests of bovine serum albumin or more complex mixtures of several recombinant proteins (1). However, because each laboratory performs QC monitoring in isolation, it is difficult to compare the performance of LC-MS platforms throughout the community.Several standards for proteomics are available for request or purchase (2, 3). RM8327 is a mixture of three peptides developed as a reference material in collaboration between the National Institute of Standards and Technology (NIST) and the Association of Biomolecular Resource Facilities. Mixtures of 15–48 purified human proteins are also available, such as the HUPO (Human Proteome Organisation) Gold MS Protein Standard (Invitrogen), the Universal Proteomics Standard (UPS1; Sigma), and CRM470 from the European Union Institute for Reference Materials and Measurements. Although defined mixtures of peptides or proteins can address some benchmarking and QC needs, there is an additional need for more complex reference materials to fully represent the challenges of LC-MS data acquisition in complex matrices encountered in biological samples (2, 3).Although it has not been widely distributed as a reference material, the yeast Saccharomyces cerevisiae proteome has been extensively used by the proteomics community to characterize the capabilities of a variety of LC-MS-based approaches (4–15). Yeast provides a uniquely attractive complex performance standard for several reasons. Yeast encodes a complex proteome consisting of ∼4,500 proteins expressed during normal growth conditions (7, 16–18). The concentration range of yeast proteins is sufficient to challenge the dynamic range of conventional mass spectrometers; the abundance of proteins ranges from fewer than 50 to more than 10⁶ molecules per cell (4, 15, 16). Additionally, it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins (5, 9, 16, 17, 19, 20) as well as LC-MS/MS data sets showing good correlation between LC-MS/MS detection efficiency and the protein abundance estimates (4, 11, 12, 15). Finally, it is inexpensive and easy to produce large quantities of yeast protein extract for distribution.In this study, we describe large scale production of a yeast S. cerevisiae performance standard, which we offer to the community through NIST. Through a series of interlaboratory studies, we created a reference data set characterizing the yeast performance standard and defining reasonable performance of ion trap-based LC-MS platforms in expert laboratories using a series of performance metrics. This publicly available data set provides a basis for additional laboratories using the yeast standard to benchmark their own performance as well as to improve upon the current status by evolving protocols, improving instrumentation, or developing new technologies. Finally, we demonstrate how the yeast performance standard, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix.     

Identification and Validation of Novel Spinophilin-associated Proteins in Rodent Striatum Using an Enhanced ex Vivo Shotgun Proteomics Approach

Spinophilin regulates excitatory postsynaptic function and morphology during development by virtue of its interactions with filamentous actin, protein phosphatase 1, and a plethora of additional signaling proteins. To provide insight into the roles of spinophilin in mature brain, we characterized the spinophilin interactome in subcellular fractions solubilized from adult rodent striatum by using a shotgun proteomics approach to identify proteins in spinophilin immune complexes. Initial analyses of samples generated using a mouse spinophilin antibody detected 23 proteins that were not present in an IgG control sample; however, 12 of these proteins were detected in complexes isolated from spinophilin knock-out tissue. A second screen using two different spinophilin antibodies and either knock-out or IgG controls identified a total of 125 proteins. The probability of each protein being specifically associated with spinophilin in each sample was calculated, and proteins were ranked according to a χ² analysis of the probabilities from analyses of multiple samples. Spinophilin and the known associated proteins neurabin and multiple isoforms of protein phosphatase 1 were specifically detected. Multiple, novel, spinophilin-associated proteins (myosin Va, calcium/calmodulin-dependent protein kinase II, neurofilament light polypeptide, postsynaptic density 95, α-actinin, and densin) were then shown to interact with GST fusion proteins containing fragments of spinophilin. Additional biochemical and transfected cell imaging studies showed that α-actinin and densin directly interact with residues 151–300 and 446–817, respectively, of spinophilin. Taken together, we have developed a multi-antibody, shotgun proteomics approach to characterize protein interactomes in native tissues, delineating the importance of knock-out tissue controls and providing novel insights into the nature and function of the spinophilin interactome in mature striatum.Genomic sequencing has revealed the full repertoire of ∼20,000 proteins that can be expressed in most mammals. Innate biochemical or enzymatic activities of many proteins are critical to their function, but these activities are often modified by interactions with other proteins. Moreover, many proteins have no known catalytic activity and are thought to serve structural roles in assembling protein complexes, greatly increasing the efficiency and fidelity of intracellular processes. Thus, systematic definition of protein interactomes promises tremendous insight into biochemical mechanisms underlying the functions of many proteins.A prime example of the importance of protein-protein interactions for modifying biological function is the postsynaptic density (PSD),¹ an actin-rich organelle localized to neuronal dendritic spines that contains receptors, kinases, phosphatases, and scaffolding proteins (1, 2). Dynamic changes in enzymatic activities and protein-protein interactions underlie changes in the size and shape of both PSDs and dendritic spines as well as the modulation of PSD-targeted neurotransmitter receptors that are critical for synaptic plasticity, learning, and memory. Furthermore, dendritic spine morphology and number are altered in many neurological disorders, including Parkinson disease (PD), Angelman syndrome, and fragile X syndrome (3–7).Spinophilin (neurabin II) is an F-actin- and protein phosphatase 1 (PP1)-binding protein with no known catalytic function (8–10). It is highly expressed in brain and is localized to dendritic spines and PSDs where it plays a key role targeting PP1 to regulate synaptic plasticity, learning, and memory (11–14). Spinophilin associates with its homolog neurabin, which is also a PP1- and F-actin-binding protein that regulates synaptic plasticity and dendrite morphology (14–16). The interaction between spinophilin and the γ1 isoform of PP1 is enhanced in an animal model of PD (17), perhaps contributing to the altered phosphorylation of synaptic proteins, such as CaMKII and glutamate receptor subunits observed following dopamine (DA) depletion (18–20). DA depletion also decreases the number of dendritic spines on striatal medium spiny neurons (4, 5). Spine density is regulated by dynamic changes in the F-actin cytoskeleton, and spinophilin regulates dendritic spine density during development (21). Indeed, candidate protein or generic protein-protein interaction screens have identified many additional spinophilin-associated proteins (SpAPs) that modulate F-actin dynamics and/or cell morphology (22–27; for a review, see Ref. 28), consistent with the idea that spinophilin is an archetypical scaffolding protein. However, these interactions have mostly been characterized in vitro and/or following protein overexpression in cultured cells, and the inter-relationship of these interactions in vivo is largely unknown. Although the spinophilin interactome appears to dictate the biological roles of spinophilin, the composition of these complexes in the mature brain is poorly understood.Co-immunoprecipitation is commonly used to confirm the biological relevance of specific bivalent protein-protein interactions in native tissues that were initially identified using generic molecular approaches, such as yeast two-hybrid screening. Prior studies combined this approach with mass spectrometry-based proteomics methods to more broadly characterize the composition of mammalian signaling complexes and the PSD interactome, such as the signalosome associated with synaptic N-methyl-d-aspartate receptors (29) and complexes associated with other PSD-enriched proteins (30). In addition, proteomics methodologies were used to identify over 1100 protein components of the PSD (30). Indeed, the potential for shotgun proteomics studies to provide novel insights into protein function in the brain is increasingly recognized (31). Moreover, computational approaches are being developed to identify potential protein-protein interactions (32). However, validation of specific interactions among the very large data sets of candidates typically identified using these approaches can be daunting. In addition, most proteomics analyses have relied on a single antibody to the target protein of interest with, at best, an unrelated non-immune IgG as a negative control, necessitating the use of very high quality antibodies.We developed a systematic shotgun proteomics approach to define protein interactomes in a native tissue context. We used this approach to characterize the composition of spinophilin complexes isolated from rodent striatum and confirmed the association of multiple, novel SpAPs. Furthermore, we extensively characterized the interaction of two additional SpAPs, α-actinin and densin, using biochemical and imaging techniques. Our studies directly illustrate the importance of appropriate subcellular fractionation conditions, using multiple antibodies to the protein of interest, and the underappreciated, critical role of analyzing parallel samples prepared from knock-out (KO) animals. Thus, our findings demonstrate a methodological framework with key controls that can be broadly applied to characterizing protein interactomes, in addition to providing novel insights into the role of spinophilin in controlling synaptic signaling.     

Integrative Proteomic Profiling of Protein Activity and Interactions Using Protein Arrays

Proteomic studies based on abundance, activity, or interactions have been used to investigate protein functions in normal and pathological processes, but their combinatory approach has not been attempted. We present an integrative proteomic profiling method to measure protein activity and interaction using fluorescence-based protein arrays. We used an on-chip assay to simultaneously monitor the transamidating activity and binding affinity of transglutaminase 2 (TG2) for 16 TG2-related proteins. The results of this assay were compared with confidential scores provided by the STRING database to analyze the functional interactions of TG2 with these proteins. We further created a quantitative activity-interaction map of TG2 with these 16 proteins, categorizing them into seven groups based upon TG2 activity and interaction. This integrative proteomic profiling method can be applied to quantitative validation of previously known protein interactions, and in understanding the functions and regulation of target proteins in biological processes of interest.Proteomics is the large-scale analysis of whole proteins and their role in biological systems. Abundance-based proteomics assigns protein functions in normal and pathological processes by quantification of global differences in protein expression levels (1). This classic approach identifies functional biomarkers by comparing samples from healthy individuals and patients. However, this abundance-based approach provides only indirect information about protein function (2). The abundance of a protein is not necessarily correlated with its activity because protein activities are predominantly regulated by a series of post-translational modifications (1, 2). Activity-based proteomics (activity-based protein profiling) is therefore considered an alternative approach to assigning protein functions in biological processes of interest (3). In this approach, specific activity-based probes using fluorescent, radioactive, and affinity tags are usually designed for detection of protein activity (2, 4–6). Activity-based proteomics identifies markers by comparative analyses of activity profiles between healthy and diseased cells and tissues (3, 7, 8). This approach is also used for profiling enzyme inhibitors, for developing therapeutic reagents, and for diagnosis (2, 5). Another functional proteomic approach using large-scale analysis is interactomics or interaction proteomics, which is a useful method for understanding the regulation of proteins in biological systems (9). To elucidate bioactive protein interactions with proteins or ligands, a number of technologies are currently used including the yeast two-hybrid system, affinity purification and mass spectrometry, the protein fragment complementation assay, the luminescence-based mammalian interactome, and protein arrays (9–13). Global differences in the dynamics of the interactome between healthy and diseased individuals provide new insights into causes of disease and can be used for biomarker identification and drug discovery (14–16). Thus, combinatory analyses of abundance, activity, and interaction have great potential in revealing regulation mechanisms and functions of proteins, although such an integrated proteomic approach has not been widely used.These proteomic methods have been coupled with various detection methods including one- or two-dimensional gel electrophoresis, one- or two-dimensional liquid chromatography and tandem mass spectrometry, surface plasmon resonance, and fluorometric assays for analyses of the proteome (9, 11). In combination with specific probes, colorimetric and fluorometric assays using multiwell plates have been extensively used for the determination of the abundance and activity of various proteins. Although often limited by the amount of sample, these methods nonetheless facilitate real-time measurement of changes in protein activity and high-throughput analyses of protein abundances and activities (17). Surface plasmon resonance, a method that does not necessitate labeling of proteins, has also been used for analysis of protein abundance, activity, and binding affinity (18–20). Using only very small amounts of sample, the microarray combined with fluorometric probes is a promising technology for the rapid analysis of a wide variety of biomolecular interactions, protein abundances, and activities. This approach has been used for serodiagnosis and identification of biomarkers by abundance-based protein profiling in human sera (21–25). It has also been used for kinetic studies of carbohydrate-protein (17) and peptide-protein interactions (26, 27). In addition, this technology has been used for the rapid determination of enzyme activities and for the identification of enzyme substrates and inhibitors (24, 28–33). However, combinatory profiling of protein activities and interactions based on array technology has yet to be reported.Using protein arrays, we propose as a model system an integrative proteomic approach for simultaneous profiling of the transamidating activity and interactions of transglutaminase 2 (TG2) with TG2-related proteins. TG2, known as tissue transglutaminase, is a member of the calcium-dependent transglutaminase family. Its activity and interactions are associated with a wide variety of diseases and cellular events (34). TG2 is implicated in the pathogenesis of a wide variety of diseases including inflammatory diseases such as celiac sprue, neurodegenerative disorders such as Huntington''s, Alzheimer''s, and Parkinson''s disease, as well as cancers, cardiovascular diseases, and diabetes (34–36). TG2 is also involved in various cellular events including cell growth, cell differentiation, cell adhesion, extracellular matrix crosslinking, and apoptosis (34, 37, 38). In the present study, the transamidating activity and binding affinity of TG2 for 16 proteins were simultaneously monitored using Cy5-conjugated TG2 and protein arrays (Fig. 1). Using this large-scale analysis, we constructed a quantitative activity-interaction (AI)¹ map to describe the quantitative interaction of TG2 with its related proteins. Thus, this integrative proteomic approach can be used to characterize functions and regulation mechanisms of a target protein in many biological processes of interest.Open in a separate windowFig. 1.Schematic diagram for the simultaneous analysis of transamidating activity and interaction of TG2 with TG2-related proteins. BAPA, 5-(biotinamido)pentylamine; Pr, protein; SA, streptavidin; TG2, transglutaminase 2.