Activin A Suppresses Osteoblast Mineralization Capacity by Altering Extracellular Matrix (ECM) Composition and Impairing Matrix Vesicle (MV) Production

During bone formation, osteoblasts deposit an extracellular matrix (ECM) that is mineralized via a process involving production and secretion of highly specialized matrix vesicles (MVs). Activin A, a transforming growth factor-β (TGF-β) superfamily member, was previously shown to have inhibitory effects in human bone formation models through unclear mechanisms. We investigated these mechanisms elicited by activin A during in vitro osteogenic differentiation of human mesenchymal stem cells (hMSC). Activin A inhibition of ECM mineralization coincided with a strong decline in alkaline phosphatase (ALP¹) activity in extracellular compartments, ECM and matrix vesicles. SILAC-based quantitative proteomics disclosed intricate protein composition alterations in the activin A ECM, including changed expression of collagen XII, osteonectin and several cytoskeleton-binding proteins. Moreover, in activin A osteoblasts matrix vesicle production was deficient containing very low expression of annexin proteins. ECM enhanced human mesenchymal stem cell osteogenic development and mineralization. This osteogenic enhancement was significantly decreased when human mesenchymal stem cells were cultured on ECM produced under activin A treatment. These findings demonstrate that activin A targets the ECM maturation phase of osteoblast differentiation resulting ultimately in the inhibition of mineralization. ECM proteins modulated by activin A are not only determinant for bone mineralization but also possess osteoinductive properties that are relevant for bone tissue regeneration.The quality of bone tissue is determined by the balanced action of the anabolic bone cells, the osteoblasts, and their catabolic counterparts, the osteoclasts. This process of bone remodeling occurs throughout life and can be influenced by a wide variety of molecules, having ultimately an impact on the quality of bone (1, 2). Activins and inhibins are members of the TGF-β superfamily with predominant antagonistic effects in their classically known target tissues, such as in gonadotropin producing cells in the pituitary and their role in reproduction (3, 4). Like other TGF-β member, activins elicit biological responses by binding to type I and II serine/threonine kinase receptors at the cell surface. Upon ligand binding, signaling is further transduced in the cytoplasm by phosphorylated Smad protein complexes that once in the nucleus regulate gene expression. This signaling pathway is highly complex because of crosstalk between different ligands (Activins, BMPs, TGF-β) binding to multiple serine/threonine kinase receptors that activate different Smad proteins signaling to the nucleus. Activin is known to signal using type II receptors ACVR2A or ACVR2B and the type I receptor ACVRIB (shared with BMPs) activating Smad2 and 3 proteins (shared with TGF-β). Inhibins exert their inhibitory effects on activin by competitive binding to the activin receptors in the presence of betaglycan. This signaling regulates a wide array of biological activities from cell proliferation, differentiation to tumor development and endocrine signaling (5, 6) in many cell lineages like hematopoietic (7, 8) and monocyte/macrophage (9, 10). Several consequences of these reproductive hormones, especially those of activin A, are also described in relation to bone metabolism. Activin A is present in bone tissue (11, 12) affecting both osteoclasts and osteoblasts. While having a consistent pro-osteoclastogenic effect (9, 13), the activin A impact on osteoblast differentiation is more controversial (see (14) for review) Several reports support a stimulatory effect of activin A on osteoblast differentiation and mineralization in vitro and in vivo (9, 15, 16). On the other hand, two different studies, using rat and human bone formation models, have demonstrated that activin A treatment has a coherent inhibitory influence on osteogenesis leading to significant reduction of the mineralization capacity (11, 17). These opposing effects of activin A on osteoblastogenesis may simply reflect species differences, however, it may be also driven by heterogeneity of the used cell model or the stage of osteoblast differentiation (14). Nevertheless, a negative role of activin A in bone formation is also supported by other in vivo studies in mice and primates in which blockage of activin signaling resulted in increased bone mass (18, 19). Moreover, transgenic mice overexpressing human inhibin A showed increased bone formation (20).The extracellular compartment is crucial for bone because it determines most of the bone quality properties (21, 22), including its strength, stability, and integrity. Interestingly, a mature extracellular matrix (ECM) is characterized by the capacity to mineralize even in the absence of further osteoblast activity (11, 23). This biomineralization process is complex and not fully elucidated but it is thought to be started within MVs (24). Osteoblasts in bone and other cells in mineralization competent tissues, such as cartilage (25), tendon (26), teeth (27), and calcifying vasculature (28) produce and release from their plasma membrane these vesicles with diameters ranging between 50 and 200 nm. It is inside these membrane-enclosed particles that first crystals of mineral are formed and grow, before the vesicle membrane is permeated and the mineral crystallization advances into the ECM (29, 30). In this context, proteins that can mobilize calcium and inorganic phosphate (Pi), the backbone of the hydroxyapatite crystals present in bone, are of utmost importance. P_i donor proteins found in MVs include alkaline phosphatase (ALP) and inorganic pyrophosphatases (31) whereas the annexin family of proteins is postulated to be crucial for calcium influx into the vesicles (32–34).In this study we investigated the inhibitory effect of activin A on human mesenchymal stem cells (hMSC) derived osteoblast differentiation and mineralization. We have previously shown that in human osteoblast cultures activin A influences the expression of many ECM genes altering ECM maturity (11). Thus, we focused our analysis on extracellular environment changes, namely the ECM and matrix vesicles (MVs). The characterization of these compartments was done using the state-of-the-art quantitative proteomics tools including SILAC metabolic labeling and mass spectrometry. Furthermore, the importance of ECM composition for osteoblast differentiation was also determined.     

Unbiased Quantitation of Escherichia coli Membrane Proteome Using Phase Transfer Surfactants

We developed a sample preparation protocol for rapid and unbiased analysis of the membrane proteome using an alimentary canal-mimicking system in which proteases are activated in the presence of bile salts. In this rapid and unbiased protocol, immobilized trypsin is used in the presence of deoxycholate and lauroylsarcosine to increase digestion efficiency as well as to increase the solubility of the membrane proteins. Using 22.5 μg of Escherichia coli whole cell lysate, we quantitatively demonstrated that membrane proteins were extracted and digested at the same level as soluble proteins without any solubility-related bias. The recovery of membrane proteins was independent of the number of transmembrane domains per protein. In the analysis of the membrane-enriched fraction from 22.5 μg of E. coli cell lysate, the abundance distribution of the membrane proteins was in agreement with that of the membrane protein-coding genes when this protocol, coupled with strong cation exchange prefractionation prior to nano-LC-MS/MS analysis, was used. Because this protocol allows unbiased sample preparation, protein abundance estimation based on the number of observed peptides per protein was applied to both soluble and membrane proteins simultaneously, and the copy numbers per cell for 1,453 E. coli proteins, including 545 membrane proteins, were successfully obtained. Finally, this protocol was applied to quantitative analysis of guanosine tetra- and pentaphosphate-dependent signaling in E. coli wild-type and relA knock-out strains.Despite the importance of cell surface biology, the conventional shotgun proteomics strategy generally underrepresents the membrane proteome because of inadequate solubilization and protease digestion (1, 2). The ageless gel strategy, consisting of SDS-PAGE followed by in-gel digestion, can partially solve this problem (3–5), but the recovery from in-gel digestion is generally lower than that from in-solution digestion, and this approach is far from suitable for a rapid, simple, and high throughput automated system. Numerous approaches have been reported to overcome the difficulties in membrane proteome analysis, such as the use of surfactants (2, 6–11), organic solvents (6, 7, 12–15), or chaotropic reagents (2, 6, 16). Acid-labile surfactants, such as RapiGest SF, are among the most promising additives to enhance protein solubilization without interfering with LC-MS performance (6, 10, 17–19). However, the cleavage step at acidic pH causes loss of hydrophobic peptides because of coprecipitation with the hydrophobic part of RapiGest SF (20). Recently, we developed a new protocol to dissolve and digest membrane proteins with the aid of a removable phase transfer surfactant (PTS),1 such as sodium deoxycholate (SDC) (20). The solubility of membrane proteins with SDC was comparable to that with sodium dodecyl sulfate. In addition, the activity of trypsin was enhanced ∼5-fold in the presence of 1% SDC because this rapid PTS method mimics conditions in the alimentary canal in which bile salts such as cholate and deoxycholate are secreted together with trypsin. After tryptic digestion, SDC is removed prior to LC-MS/MS analysis by adding an organic solvent followed by pH-induced transfer of the surfactant to the organic phase, whereas tryptic peptides remain in the aqueous phase. This protocol offers a significant improvement in identifying membrane proteins by increasing the recovery of hydrophobic tryptic peptides compared with the protocols using urea and RapiGest SF.The goal of this study is to establish a membrane proteomics method that is unbiased with respect to protein solubility, hydrophobicity, and protein abundance; i.e. membrane proteins can be as efficiently extracted and digested as soluble proteins. So far, to our knowledge, little information about the recovery of the membrane proteome has been reported. Instead, the number of identified membrane proteins or the content of membrane proteins identified in the membrane-enriched fraction has been used as an indicator of the efficiency of procedures for membrane proteome analysis (4, 5, 21–23). However, these parameters usually depend on the experimental conditions, including the sample preparation procedure and LC-MS instrument used. Therefore, it is difficult to compare data obtained with these protocols except in the case of direct comparison. Furthermore, there has been no report quantitatively comparing the recovery of membrane proteome with that of soluble proteins.In this study, we used a modified version of our PTS protocol with immobilized trypsin columns to reduce the digestion time and evaluated its suitability for unbiased quantitation of the membrane proteome. In addition, we applied this protocol to estimate the copy numbers per cell of 1,453 proteins, including 545 membrane proteins, using the exponentially modified protein abundance index (emPAI). Finally, this rapid and unbiased PTS protocol was applied to the quantitative analysis of Escherichia coli BW25113 wild-type and relA knock-out (KO) strains.     

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.     

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.     

Proteomic Analyses Reveal Divergent Ubiquitylation Site Patterns in Murine Tissues

Posttranslational modifications of proteins increase the complexity of the cellular proteome and enable rapid regulation of protein functions in response to environmental changes. Protein ubiquitylation is a central regulatory posttranslational modification that controls numerous biological processes including proteasomal degradation of proteins, DNA damage repair and innate immune responses. Here we combine high-resolution mass spectrometry with single-step immunoenrichment of di-glycine modified peptides for mapping of endogenous putative ubiquitylation sites in murine tissues. We identify more than 20,000 unique ubiquitylation sites on proteins involved in diverse biological processes. Our data reveals that ubiquitylation regulates core signaling pathways common for each of the studied tissues. In addition, we discover that ubiquitylation regulates tissue-specific signaling networks. Many tissue-specific ubiquitylation sites were obtained from brain highlighting the complexity and unique physiology of this organ. We further demonstrate that different di-glycine-lysine-specific monoclonal antibodies exhibit sequence preferences, and that their complementary use increases the depth of ubiquitylation site analysis, thereby providing a more unbiased view of protein ubiquitylation.Ubiquitin is a small 76-amino-acid protein that is conjugated to the ε-amino group of lysines in a highly orchestrated enzymatic cascade involving ubiquitin activating (E1), ubiquitin conjugating (E2), and ubiquitin ligase (E3) enzymes (1). Ubiquitylation is involved in the regulation of diverse cellular processes including protein degradation (2, 3, 4), DNA damage repair (5, 6), DNA replication (7), cell surface receptor endocytosis, and innate immune signaling (8, 9). Deregulation of protein ubiquitylation is implicated in the development of cancer and neurodegenerative diseases (10, 11). Inhibitors targeting the ubiquitin proteasome system are used in the treatment of hematologic malignancies such as multiple myeloma (12, 13).Recent developments in the mass spectrometry (MS)-based proteomics have greatly expedited proteome-wide analysis of posttranslational modifications (PTMs) (14–17). Large-scale mapping of ubiquitylation sites by mass spectrometry is based on the identification of the di-glycine remnant that results from trypsin digestion of ubiquitylated proteins and remains attached to ubiquitylated lysines (18). Recently, two monoclonal antibodies were developed that specifically recognize di-glycine remnant modified peptides enabling their efficient enrichment from complex peptide mixtures (19, 20). These antibodies have been used to identify thousands of endogenous ubiquitylation sites in human cells, and to quantify site-specific changes in ubiquitylation in response to different cellular perturbations (20–22). It should be noted that the di-glycine remnant is not specific for proteins modified by ubiquitin but also proteins modified by NEDD8 or ISG15 generate an identical di-glycine remnant on modified lysines making it impossible to distinguish between these modifications by mass spectrometry. However, expression of NEDD8 in mouse tissues was shown to be developmentally down-regulated (23), and ISG15 expression in bovine tissues is low in the absence of interferon stimulation (24). In cell culture experiments it was shown that a great majority of sites identified using di-glycine-lysine-specific antibodies stems from ubiquitylated peptides (20).The rates of cell proliferation and protein turnover in mammals vary dramatically between different tissues. Immortalized cell lines, often derived from cancer, are selected for high proliferation rates and fail to represent the complex conditions in tissues. Tissue proteomics can help to gain a more comprehensive understanding of physiological processes in multicellular organisms. Analysis of tissue proteome and PTMs can provide important insights into tissue-specific processes and signaling networks that regulate these processes (25–32). In addition, development of mass spectrometric methods for analysis of PTMs in diseased tissues might lead to the identification of biomarkers.In this study, we combined high-resolution mass spectrometry with immunoenrichment of di-glycine modified peptides to investigate endogenous ubiquitylation sites in murine tissues. We identified more than 20,000 ubiquitylation sites from five different murine tissues and report the largest ubiquitylation dataset obtained from mammalian tissues to date. Furthermore, we compared the performance of the two monoclonal di-glycine-lysine-specific antibodies available for enrichment of ubiquitylated peptides, and reveal their relative preferences for different amino acids flanking ubiquitylation sites.     

Quantification of Extracellular Matrix Proteins from a Rat Lung Scaffold to Provide a Molecular Readout for Tissue Engineering

The use of extracellular matrix (ECM)¹ scaffolds, derived from decellularized tissues for engineered organ generation, holds enormous potential in the field of regenerative medicine. To support organ engineering efforts, we developed a targeted proteomics method to extract and quantify extracellular matrix components from tissues. Our method provides more complete and accurate protein characterization than traditional approaches. This is accomplished through the analysis of both the chaotrope-soluble and -insoluble protein fractions and using recombinantly generated stable isotope labeled peptides for endogenous protein quantification. Using this approach, we have generated 74 peptides, representing 56 proteins to quantify protein in native (nondecellularized) and decellularized lung matrices. We have focused on proteins of the ECM and additional intracellular proteins that are challenging to remove during the decellularization procedure. Results indicate that the acellular lung scaffold is predominantly composed of structural collagens, with the majority of these proteins found in the insoluble ECM, a fraction that is often discarded using widely accepted proteomic methods. The decellularization procedure removes over 98% of intracellular proteins evaluated and retains, to varying degrees, proteoglycans and glycoproteins of the ECM. Accurate characterization of ECM proteins from tissue samples will help advance organ engineering efforts by generating a molecular readout that can be correlated with functional outcome to drive the next generation of engineered organs.Organ transplantation is an established, lifesaving therapy for patients with chronic end-stage diseases. However, transplantation as a therapeutic option is limited by availability of suitable donor organs (1). Although advancements in surgical techniques, such as successful implementation of bilateral lung transplants and improved immunosuppressant treatments, have led to more successful outcomes in recent years, the percentage of people that die while on the transplant wait list has increased (2, 3). One attractive approach to meet this demand is the in vitro generation of organs using decellularized tissues as scaffolds for recellularization. For complex organs such as the lung, these tissue scaffolds can be derived from a donor organ that would have otherwise been unfit for transplantation. This whole organ scaffold can be recellularized using a patient''s own primary or stem-derived cells, thus eliminating many issues related to graft/host incompatibility. This approach was recently used to generate lungs that, when implanted in rat recipients, allowed for gas exchange (4, 5). However, examination of the lung indicated leakage of erythrocytes into the alveolar space, indicating a compromised capillary-endothelial barrier. These exciting results highlighted the potential of the method for organ transplantation but also the need for improved molecular readouts to guide engineering efforts.Efficient reseeding of decellularized scaffolds has been shown to be dependent on retaining native ECM structural integrity and elasticity (6). Local variations in expression of abundant proteins in the ECM scaffolding (collagens, laminins, fibronectins) have been correlated to variance in cell repopulation and subsequent proliferation (7). It is thought that retaining specific ECM components and architectures may allow cells to be directed back to a tissue-specific niche during reseeding and that small changes in abundance of these molecular cues can drastically affect the recellularization process (8). Current methods used to characterize the protein composition of native and acellular tissues involve antibody- or dye-based staining, hydroxyproline assays assessing collagen content, or relative quantification of proteins by liquid chromatography tandem mass spectrometry (LC-MS/MS) (9, 10). All of these methods either fall short in specificity, accurate quantification, or both. A more complete and accurate method for protein characterization would provide a valuable tool for tissue engineering efforts, while shedding light on the possible molecular mechanisms resulting in cell seeding variability and alterations in mechanical properties of engineered lung tissues.Current relative quantification strategies (iTRAQ, Spectral Counting, dimethyl labeling, others) (11–15) perform well when the majority of protein in samples does not change, there are approximately equal increases and decreases in protein levels, or in cases where proteins that are known not to change in abundance can be used for normalization. However, normalization steps often employed have the potential to introduce experimental bias (16). The decellularization process differentially removes and enriches proteins in the ECM scaffolding, depleting some proteins with high efficiency while leaving others mostly intact. This makes relative comparisons between native and decellularized lung challenging. Although strategies can be employed in an attempt to normalize data (17), there is a distinct advantage to quantification methods using stable isotope labeled (SIL) peptides in this application. Here, we developed ECM targeted, isotopically labeled peptides using the QconCAT approach first described by Beynon et al. (18). SIL quantification allows for intra- and intersample comparison of heterogeneous tissues, such as native organs and decellularized scaffolds, with high accuracy and precision.The ECM is largely responsible for defining the biomechanical properties of organs. Maintaining structural rigidity and native microarchitecture through the decellularization process makes an acellular organ a good candidate to serve as a tissue scaffold (19, 20). These same characteristics are a central reason why the ECM is challenging to characterize using common bottom-up proteomics approaches (21). Currently accepted and widely used digestion methods require proteins to be solubilized for bottom-up proteomic analysis (22). Recent papers have reported characterization of the ECM fraction from tissues through the use of strong chaotropes (11, 21, 23–27) or cellular fractionation followed by strong detergent (10, 28, 29). However, in our experience, these protocols invariably yield various sizes of an insoluble protein-containing pellet when applied to a variety of tissue samples (heart, lung, and mammary gland). On one end of the spectrum, methods utilizing deglycosylation and enzymatic digestions for clarification of partial solubilized protein slurries yields good ECM coverage with a high number of spectral matches for collagen alpha-1(I), a highly abundant ECM protein in lung (28). On the other end of the spectrum, methods using only detergents or chaotropes for solubilization result in protein pellets that are generally removed prior to LC-MS/MS analysis. These pellets often contained a majority of fibrillar proteins, resulting in quantitative errors. Consistent with this finding, several of these studies characterizing tissue engineered lungs do not report the identification of collagen alpha-1(I) (8, 10, 30). We believe these observations result from a failure to solubilize and enzymatically digest insoluble ECM proteins. To this end, we explored the use of chemical digestion of the insoluble pellet to improve coverage of the ECM proteome from tissue. This method has been used to quantify protein levels from native and decellularized lungs to determine decellularization specificity and efficiency. The accurate characterization of ECM proteins from lung samples should advance tissue engineering efforts by yielding a readout that can be correlated with functional outcome to drive further development.     

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.     

Differential Regulation of Cell Type-specific Apoptosis by Stromelysin-3: A POTENTIAL MECHANISM VIA THE CLEAVAGE OF THE LAMININ RECEPTOR DURING TAIL RESORPTION IN XENOPUS LAEVIS*

Matrix metalloproteinases (MMPs) have been extensively studied because of their functional attributes in development and diseases. However, relatively few in vivo functional studies have been reported on the roles of MMPs in postembryonic organ development. Amphibian metamorphosis is a unique model for studying MMP function during vertebrate development because of its dependence on thyroid hormone (T3) and the ability to easily manipulate this process with exogenous T3. The MMP stromelysin-3 (ST3) is induced by T3, and its expression correlates with cell death during metamorphosis. We have previously shown that ST3 is both necessary and sufficient for larval epithelial cell death in the remodeling intestine. To investigate the roles of ST3 in other organs and especially on different cell types, we have analyzed the effect of transgenic overexpression of ST3 in the tail of premetamorphic tadpoles. We report for the first time that ST3 expression, in the absence of T3, caused significant muscle cell death in the tail of premetamorphic transgenic tadpoles. On the other hand, only relatively low levels of epidermal cell death were induced by precocious ST3 expression in the tail, contrasting what takes place during natural and T3-induced metamorphosis when ST3 expression is high. This cell type-specific apoptotic response to ST3 in the tail suggests distinct mechanisms regulating cell death in different tissues. Furthermore, our analyses of laminin receptor, an in vivo substrate of ST3 in the intestine, suggest that laminin receptor cleavage may be an underlying mechanism for the cell type-specific effects of ST3.The extracellular matrix (ECM),³ the dynamic milieu of the cell microenvironment, plays a critical role in dictating the fate of the cell. The cross-talk between the cell and ECM and the timely catabolism of the ECM are crucial for tissue remodeling during development (1). Matrix metalloproteinases (MMPs), extrinsic proteolytic regulators of the ECM, mediate this process to a large extent. MMPs are a large family of Zn²⁺-dependent endopeptidases potentially capable of cleaving the extracellular as well as nonextracellular proteins (2–9). The MMP superfamily includes collagenases, gelatinases, stromelysins, and membrane-type MMPs based on substrate specificity and domain organization (2–4). MMPs have been implicated to influence a wide range of physiological and pathological processes (10–13). The roles of MMPs appear to be very complex. For example, MMPs have been suggested to play roles in both tumor promotion and suppression (13–19). Unfortunately, relatively few functional studies have been carried out in vivo, especially in relation to the mechanisms involved during vertebrate development.Amphibian metamorphosis presents a fascinating experimental model to study MMP function during postembryonic development. A unique and salient feature of the metamorphic process is the absolute dependence on the signaling of thyroid hormone (20–23). This makes it possible to prevent metamorphosis by simply inhibiting the synthesis of endogenous T3 or to induce precocious metamorphosis by merely adding physiological levels of T3 in the rearing water of premetamorphic tadpoles. Gene expression screens have identified the MMP stromelysin-3 (ST3) as a direct T3 response gene (24–27). Expression studies have revealed a distinct spatial and temporal ST3 expression profile in correlation with metamorphic event, especially cell death (25, 28–31). Organ culture studies on intestinal remodeling have directly substantiated an essential role of ST3 in larval epithelial cell death and ECM remodeling (32). Furthermore, precocious expression of ST3 alone in premetamorphic tadpoles through transgenesis is sufficient to induce ECM remodeling and larval epithelial apoptosis in the tadpole intestine (33). Thus, ST3 appears to be necessary and sufficient for intestinal epithelial cell death during metamorphosis.ST3 was first isolated as a breast cancer-associated gene (34), and unlike most other MMPs, ST3 is secreted as an active protease through a furin-dependent intracellular activation mechanism (35). Like many other MMPs, ST3 is expressed in a number of pathological processes, including most human carcinomas (11, 36–40), as well as in many developmental processes in mammals (10, 34, 41–43), although the physiological and pathological roles of ST3 in vivo are largely unknown in mammals. Interestingly, compared with other MMPs, ST3 has only weak activities toward ECM proteins in vitro but stronger activities against non-ECM proteins like α1 proteinase inhibitor and IGFBP-1 (44–46). Although ST3 may cleave ECM proteins strongly in the in vivo environment, these findings suggest that the cleavage of non-ECM proteins is likely important for its biological roles. Consistently, we have recently identified a cell surface receptor, laminin receptor (LR) as an in vivo substrate of ST3 in the tadpole intestine during metamorphosis (47–49). Analyses of LR expression and cleavage suggest that LR cleavage by ST3 is likely an important mechanism by which ST3 regulates the interaction between the larval epithelial cells and the ECM to induce cell death during intestinal remodeling (47, 48).Here, to investigate the role of ST3 in the apoptosis in other tissues during metamorphosis and whether LR cleavage serves as a mechanism for ST3 to regulate the fate of different cell types, we have analyzed the effects of precocious expression of ST3 in premetamorphic tadpole tail. The tail offers an opportunity to examine the effects of ST3 on different cell types. The epidermis, the fast and slow muscles, and the connective tissue underlying the epidermis in the myotendinous junctions and surrounding the notochord constitute the major tissue types in tail (50). Even though death is the destiny of all these cell types, it is not clear whether they all die through similar or different mechanisms. Microscopic and histochemical analyses have shown that at least the muscle and epidermal cells undergo T3-dependent apoptosis during metamorphosis (23, 29, 51, 52). To study whether ST3 regulates apoptosis of these two cell types, we have made use of the transgenic animals that express a transgenic ST3 under the control of a heat shock-inducible promoter (33). We show that whereas extensive apoptosis is present in both the epidermis and muscles during natural as well as T3-induced metamorphosis, transgenic expression of ST3 induces cell death predominantly in the muscles. Furthermore, we show that LR is expressed in the epidermis and connective tissue but not in muscles of the tadpole tail. More importantly, LR cleavage products are present in the tail during natural metamorphosis but not in transgenic tadpoles overexpressing ST3. These results suggest that ST3 has distinct effects on the epidermis and muscles in the tail, possibly because of the tissue-specific expression and function of LR.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Deep Proteomic Evaluation of Primary and Cell Line Motoneuron Disease Models Delineates Major Differences in Neuronal Characteristics

The fatal neurodegenerative disorders amyotrophic lateral sclerosis and spinal muscular atrophy are, respectively, the most common motoneuron disease and genetic cause of infant death. Various in vitro model systems have been established to investigate motoneuron disease mechanisms, in particular immortalized cell lines and primary neurons. Using quantitative mass-spectrometry-based proteomics, we compared the proteomes of primary motoneurons to motoneuron-like cell lines NSC-34 and N2a, as well as to non-neuronal control cells, at a depth of 10,000 proteins. We used this resource to evaluate the suitability of murine in vitro model systems for cell biological and biochemical analysis of motoneuron disease mechanisms. Individual protein and pathway analysis indicated substantial differences between motoneuron-like cell lines and primary motoneurons, especially for proteins involved in differentiation, cytoskeleton, and receptor signaling, whereas common metabolic pathways were more similar. The proteins associated with amyotrophic lateral sclerosis also showed distinct differences between cell lines and primary motoneurons, providing a molecular basis for understanding fundamental alterations between cell lines and neurons with respect to neuronal pathways with relevance for disease mechanisms. Our study provides a proteomics resource for motoneuron research and presents a paradigm of how mass-spectrometry-based proteomics can be used to evaluate disease model systems.Motoneurons are extremely extended neurons that mediate the control of all muscle types by the central nervous system. Therefore, diseases involving progressive motoneuron degeneration such as amyotrophic lateral sclerosis (ALS)¹ (OMIM: 105400) or spinal muscle atrophy (OMIM: 253300) are particularly devastating and generally fatal disorders. Today, ALS is believed to form a phenotypic continuum with the disease entity frontotemporal lobe degeneration (OMIM: 600274) (1, 2). About 10% of ALS cases are known to be inherited, but the vast majority are considered sporadic. The number of inherited cases might be underestimated because of incomplete family histories, non-paternity, early death of family members, or incomplete penetrance (3).Mutations in several genes have been reported for the familial form, including in Sod1 (4), Als2 (5), Setx (6), Vapb (7), Tardbp (8, 9), Fus/Tls (10, 11), Vcp (12), Pfn1 (13), and several others (reviewed in Ref. 14). The most frequent genetic cause of inherited ALS was recently shown to be a hexanucleotide repeat expansion in an intron of a gene of unknown function called C9orf72 (15–17). Based on the spectrum of known mutations, several disease mechanisms for ALS have been proposed, including dysfunction of protein folding, axonal transport, RNA splicing, and metabolism (reviewed in Refs. 14, 18, and 19). Despite intensive research, it is still unclear whether a main common molecular pathway or mechanism underlies motoneuron degeneration in ALS and frontotemporal lobe degeneration. Spinal muscle atrophy is caused by homozygous mutations or deletions in the survival of motor neuron gene (Smn1) that presumably impair the RNA metabolism through diminished functionality of the Smn1 gene product (20). Over recent decades several model systems have been established to investigate ALS (21). These include transgenic animal models such as mouse (22), drosophila (23), and zebrafish (24). In cell-based studies, primary motoneurons cultured from rodent embryos (25) or motoneuron-like cell lines are employed. Primary cells are considered to more closely mimic the in vivo situation, but they are more challenging to establish and maintain. In contrast, the degree of functional relevance of cell lines can be difficult to establish, but they can be propagated without limitation and are well suited for high-throughput analysis. In particular, the spinal cord neuron–neuroblastoma hybrid cell line NSC-34 (26) and the mouse neuroblastoma cell line N2a (27) are widely used not only to assess motoneuron function, but also to study disease mechanisms in motoneurons (28, 29).As proteins are the functional actors in cells, proteomics should be able to make important contributions to the characterization and evaluation of cellular models. In particular, by identifying and quantifying the expressed proteins and bioinformatically interpreting the results, one can obtain enough information to infer functional differences. Our laboratory has previously shown proof of concept of such an approach by comparing the expression levels of about 4,000 proteins between primary hepatocytes and a hepatoma cell line (30). Very recently, mass-spectrometry-based proteomics has achieved sufficient depth and accuracy to quantify almost the entire proteome of mammalian cell lines (31–33). Furthermore, new instrumentation and algorithms now make it possible to perform label-free quantification between multiple cellular systems and with an accuracy previously associated only with stable isotope labeling techniques (34, 35).To evaluate the suitability of motoneuron-like cell lines as cellular model systems for research on ALS and related disorders, we characterized the proteomes of two widely used cell lines, NSC-34 and N2a, and compared them with the proteomes of mouse primary motoneurons and non-neuronal control cell lines. To generate primary motoneurons, we employed a recently described culturing system that makes it possible to isolate highly enriched motoneuron populations in less than 8 h (25). We identified more than 10,000 proteins and investigated differences in quantitative levels of individual neuron-associated proteins and pathways related to motoneuron function and disease mechanisms.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

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.     

Conserved Peptide Fragmentation as a Benchmarking Tool for Mass Spectrometers and a Discriminating Feature for Targeted Proteomics

Quantifying the similarity of spectra is an important task in various areas of spectroscopy, for example, to identify a compound by comparing sample spectra to those of reference standards. In mass spectrometry based discovery proteomics, spectral comparisons are used to infer the amino acid sequence of peptides. In targeted proteomics by selected reaction monitoring (SRM) or SWATH MS, predetermined sets of fragment ion signals integrated over chromatographic time are used to identify target peptides in complex samples. In both cases, confidence in peptide identification is directly related to the quality of spectral matches. In this study, we used sets of simulated spectra of well-controlled dissimilarity to benchmark different spectral comparison measures and to develop a robust scoring scheme that quantifies the similarity of fragment ion spectra. We applied the normalized spectral contrast angle score to quantify the similarity of spectra to objectively assess fragment ion variability of tandem mass spectrometric datasets, to evaluate portability of peptide fragment ion spectra for targeted mass spectrometry across different types of mass spectrometers and to discriminate target assays from decoys in targeted proteomics. Altogether, this study validates the use of the normalized spectral contrast angle as a sensitive spectral similarity measure for targeted proteomics, and more generally provides a methodology to assess the performance of spectral comparisons and to support the rational selection of the most appropriate similarity measure. The algorithms used in this study are made publicly available as an open source toolset with a graphical user interface.In “bottom-up” proteomics, peptide sequences are identified by the information contained in their fragment ion spectra (1). Various methods have been developed to generate peptide fragment ion spectra and to match them to their corresponding peptide sequences. They can be broadly grouped into discovery and targeted methods. In the widely used discovery (also referred to as shotgun) proteomic approach, peptides are identified by establishing peptide to spectrum matches via a method referred to as database searching. Each acquired fragment ion spectrum is searched against theoretical peptide fragment ion spectra computed from the entries of a specified sequence database, whereby the database search space is constrained to a user defined precursor mass tolerance (2, 3). The quality of the match between experimental and theoretical spectra is typically expressed with multiple scores. These include the number of matching or nonmatching fragments, the number of consecutive fragment ion matches among others. With few exceptions (4–7) commonly used search engines do not use the relative intensities of the acquired fragment ion signals even though this information could be expected to strengthen the confidence of peptide identification because the relative fragment ion intensity pattern acquired under controlled fragmentation conditions can be considered as a unique “fingerprint” for a given precursor. Thanks to community efforts in acquiring and sharing large number of datasets, the proteomes of some species are now essentially mapped out and experimental fragment ion spectra covering entire proteomes are increasingly becoming accessible through spectral databases (8–16). This has catalyzed the emergence of new proteomics strategies that differ from classical database searching in that they use prior spectral information to identify peptides. Those comprise inclusion list sequencing (directed sequencing), spectral library matching, and targeted proteomics (17). These methods explicitly use the information contained in empirical fragment ion spectra, including the fragment ion signal intensity to identify the target peptide. For these methods, it is therefore of highest importance to accurately control and quantify the degree of reproducibility of the fragment ion spectra across experiments, instruments, labs, methods, and to quantitatively assess the similarity of spectra. To date, dot product (18–24), its corresponding arccosine spectral contrast angle (25–27) and (Pearson-like) spectral correlation (28–31), and other geometrical distance measures (18, 32), have been used in the literature for assessing spectral similarity. These measures have been used in different contexts including shotgun spectra clustering (19, 26), spectral library searching (18, 20, 21, 24, 25, 27–29), cross-instrument fragmentation comparisons (22, 30) and for scoring transitions in targeted proteomics analyses such as selected reaction monitoring (SRM)¹ (23, 31). However, to our knowledge, those scores have never been objectively benchmarked for their performance in discriminating well-defined levels of dissimilarities between spectra. In particular, similarity scores obtained by different methods have not yet been compared for targeted proteomics applications, where the sensitive discrimination of highly similar spectra is critical for the confident identification of targeted peptides.In this study, we have developed a method to objectively assess the similarity of fragment ion spectra. We provide an open-source toolset that supports these analyses. Using a computationally generated benchmark spectral library with increasing levels of well-controlled spectral dissimilarity, we performed a comprehensive and unbiased comparison of the performance of the main scores used to assess spectral similarity in mass spectrometry.We then exemplify how this method, in conjunction with its corresponding benchmarked perturbation spectra set, can be applied to answer several relevant questions for MS-based proteomics. As a first application, we show that it can efficiently assess the absolute levels of peptide fragmentation variability inherent to any given mass spectrometer. By comparing the instrument''s intrinsic fragmentation conservation distribution to that of the benchmarked perturbation spectra set, nominal values of spectral similarity scores can indeed be translated into a more directly understandable percentage of variability inherent to the instrument fragmentation. As a second application, we show that the method can be used to derive an absolute measure to estimate the conservation of peptide fragmentation between instruments or across proteomics methods. This allowed us to quantitatively evaluate, for example, the transferability of fragment ion spectra acquired by data dependent analysis in a first instrument into a fragment/transition assay list used for targeted proteomics applications (e.g. SRM or targeted extraction of data independent acquisition SWATH MS (33)) on another instrument. Third, we used the method to probe the fragmentation patterns of peptides carrying a post-translation modification (e.g. phosphorylation) by comparing the spectra of modified peptide with those of their unmodified counterparts. Finally, we used the method to determine the overall level of fragmentation conservation that is required to support target-decoy discrimination and peptide identification in targeted proteomics approaches such as SRM and SWATH MS.     

Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues

The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics.We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics.Utilizing HRM, we profiled acetaminophen (APAP)¹-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD).Our findings imply that DIA should be the preferred method for quantitative protein profiling.Quantitative mass spectrometry is a powerful and widely used approach to identify differentially abundant proteins, e.g. for proteome profiling and biomarker discovery (1). Several tens of thousands of peptides and thousands of proteins can be routinely identified from a single sample injection in shotgun proteomics (2). Shotgun proteomics, however, is limited by low analytical reproducibility. This is due to the complexity of the samples that results in under sampling (supplemental Fig. 1) and to the fact that the acquisition of MS2 spectra is often triggered outside of the elution peak apex. As a result, only 17% of the detectable peptides are typically fragmented, and less than 60% of those are identified. This translates in reliable identification of only 10% of the detectable peptides (3). The overlap of peptide identification across technical replicates is typically 35–60% (4), which results in inconsistent peptide quantification. Alternatively to shotgun proteomics, selected reaction monitoring (SRM) enables quantification of up to 200–300 peptides at very high reproducibility, accuracy, and precision (5–8).Data-independent acquisition (DIA), a novel acquisition type, overcomes the semistochastic nature of shotgun proteomics (9–18). Spectra are acquired according to a predefined schema instead of dependent on the data. Targeted analysis of DIA data was introduced with SWATH-MS (19). For the originally published SWATH-MS, the mass spectrometer cycles through 32 predefined, contiguous, 25 Thomson wide precursor windows, and records high-resolution fragment ion spectra (19). This results in a comprehensive measurement of all detectable precursors of the selected mass range. The main novelty of SWATH-MS was in the analysis of the collected DIA data. Predefined fragment ions are extracted using precompiled spectrum libraries, which results in SRM-like data. Such targeted analyses are now enabled by several publicly available computational tools, in particular Spectronaut², Skyline (20), and OpenSWATH (21). The accuracy of peptide identification is evaluated based on the mProphet method (22).We introduce a novel SWATH-MS-type DIA workflow termed hyper reaction monitoring (HRM) (reviewed in (23)) implemented on a Thermo Scientific Q Exactive platform. It consists of comprehensive DIA acquisition and targeted data analysis with retention-time-normalized spectral libraries (24). Its high accuracy of peptide identification and quantification is due to three aspects. First, we developed a novel, improved DIA method. Second, we reimplemented the mProphet (22) approach in the software Spectronaut (www.spectronaut.org). Third, we developed large, optimized, and retention-time-normalized (iRT) spectral libraries.We compared HRM and state-of-the-art shotgun proteomics in terms of ability to discover differentially abundant proteins. For this purpose, we used a “profiling standard sample set” with 12 non-human proteins spiked at known absolute concentrations into a stable human cell line protein extract. This resulted in quasi complete data sets for HRM and the detection of a larger number of differentially abundant proteins as compared with shotgun proteomics. We utilized HRM to identify changes in the proteome in primary three-dimensional human liver microtissues after APAP exposure (25–27). These primary hepatocytes exhibit active drug metabolism. With a starting material of only 12,000 cells per sample, the abundance of 2,830 proteins was quantified over an APAP concentration range. Six novel NAPQI-cysteine proteins adducts that might be relevant for the toxicity of APAP were found and quantified mainly on mitochondrion-related proteins.     

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.