JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from “mixture spectra” to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based¹ proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2–6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS² (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples contain unknown protein modifications, mutations, and contaminants, the related MS/MS spectra also result in false positives, as their corresponding peptides are not in the database. Other false positives may be generated simply by random matches. Therefore, it is of importance to remove these false PSMs to improve dataset quality. One common approach is to filter putative PSMs to achieve a final list with a predefined false discovery rate (FDR) via a target-decoy strategy, in which decoy proteins are merged with target proteins in the same database for estimating false PSMs (23–26). However, the true and false PSMs are not always distinguishable based on matching scores. It is a problem to set up an appropriate score threshold to achieve maximal sensitivity and high specificity (13, 27, 28).De novo methods, including Lutefisk (29), PEAKS (30), NovoHMM (31), PepNovo (32), pNovo (33), Vonovo (34), and UniNovo (35), identify peptide sequences directly from MS/MS spectra. These methods can be used to derive novel peptides and post-translational modifications without a database, which is useful, especially when the related genome is not sequenced. High-resolution MS/MS spectra greatly facilitate the generation of peptide sequences in these de novo methods. However, because MS/MS fragmentation cannot always produce all predicted product ions, only a portion of collected MS/MS spectra have sufficient quality to extract partial or full peptide sequences, leading to lower sensitivity than achieved with the database search methods.To improve the sensitivity of the de novo methods, a hybrid approach has been proposed to integrate peptide sequence tags into PSM scoring during database searches (36). Numerous software packages have been developed, such as GutenTag (37), InsPecT (38), Byonic (39), DirecTag (40), and PEAKS DB (41). These methods use peptide tag sequences to filter a protein database, followed by error-tolerant database searching. One restriction in most of these algorithms is the requirement of a minimum tag length of three amino acids for matching protein sequences in the database. This restriction reduces the sensitivity of the database search, because it filters out some high-quality spectra in which consecutive tags cannot be generated.In this paper, we describe JUMP, a novel tag-based hybrid algorithm for peptide identification. The program is optimized to balance sensitivity and specificity during tag derivation and MS/MS pattern matching. JUMP can use all potential sequence tags, including tags consisting of only one amino acid. When we compared its performance to that of two widely used search algorithms, SEQUEST and Mascot, JUMP identified ∼30% more PSMs at the same FDR threshold. In addition, the program provides two additional features: (i) using tag sequences to improve modification site assignment, and (ii) analyzing co-fragmented peptides from mixture MS/MS spectra.     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.     

N-Terminal-oriented Proteogenomics of the Marine Bacterium Roseobacter Denitrificans Och114 using N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) Labeling and Diagonal Chromatography

Given the ease of whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now purely based on automated prediction. However, errors in gene structure are frequent, the correct determination of start codons being one of the main concerns. Here, we combine protein N termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP Ac-OSu) as a labeling reagent with the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting method to enrich labeled N-terminal peptides for mass spectrometry detection. Protein digestion was performed in parallel with three proteases to obtain a reliable automatic validation of protein N termini. The analysis of these N-terminal enriched fractions by high-resolution tandem mass spectrometry allowed the annotation refinement of 534 proteins of the model marine bacterium Roseobacter denitrificans OCh114. This study is especially efficient regarding mass spectrometry analytical time. From the 534 validated N termini, 480 confirmed existing gene annotations, 41 highlighted erroneous start codon annotations, five revealed totally new mis-annotated genes; the mass spectrometry data also suggested the existence of multiple start sites for eight different genes, a result that challenges the current view of protein translation initiation. Finally, we identified several proteins for which classical genome homology-driven annotation was inconsistent, questioning the validity of automatic annotation pipelines and emphasizing the need for complementary proteomic data. All data have been deposited to the ProteomeXchange with identifier PXD000337.Recent developments in mass spectrometry and bioinformatics have established proteomics as a common and powerful technique for identifying and quantifying proteins at a very broad scale, but also for characterizing their post-translational modifications and interaction networks (1, 2). In addition to the avalanche of proteomic data currently being reported, many genome sequences are established using next-generation sequencing, fostering proteomic investigations of new cellular models. Proteogenomics is a relatively recent field in which high-throughput proteomic data is used to verify coding regions within model genomes to refine the annotation of their sequences (2–8). Because genome annotation is now fully automated, the need for accurate annotation for model organisms with experimental data is crucial. Many projects related to genome re-annotation of microorganisms with the help of proteomics have been recently reported, such as for Mycoplasma pneumoniae (9), Rhodopseudomonas palustris (10), Shewanella oneidensis (11), Thermococcus gammatolerans (12), Deinococcus deserti (13), Salmonella thyphimurium (14), Mycobacterium tuberculosis (15, 16), Shigella flexneri (17), Ruegeria pomeroyi (18), and Candida glabrata (19), as well as for higher organisms such as Anopheles gambiae (20) and Arabidopsis thaliana (4, 5).The most frequently reported problem in automatic annotation systems is the correct identification of the translational start codon (21–23). The error rate depends on the primary annotation system, but also on the organism, as reported for Halobacterium salinarum and Natromonas pharaonis (24), Deinococcus deserti (21), and Ruegeria pomeroyi (18), where the error rate is estimated above 10%. Identification of a correct translational start site is essential for the genetic and biochemical analysis of a protein because errors can seriously impact subsequent biological studies. If the N terminus is not correctly identified, the protein will be considered in either a truncated or extended form, leading to errors in bioinformatic analyses (e.g. during the prediction of its molecular weight, isoelectric point, cellular localization) and major difficulties during its experimental characterization. For example, a truncated protein may be heterologously produced as an unfolded polypeptide recalcitrant to structure determination (25). Moreover, N-terminal modifications, which are poorly documented in annotation databases, may occur (26, 27).Unfortunately, the poor polypeptide sequence coverage obtained for the numerous low abundance proteins in current shotgun MS/MS proteomic studies implies that the overall detection of N-terminal peptides obtained in proteogenomic studies is relatively low. Different methods for establishing the most extensive list of protein N termini, grouped under the so-called “N-terminomics” theme, have been proposed to selectively enrich or improve the detection of these peptides (2, 28, 29). Large N-terminome studies have recently been reported based on resin-assisted enrichment of N-terminal peptides (30) or terminal amine isotopic labeling of substrates (TAILS) coupled to depletion of internal peptides with a water-soluble aldehyde-functionalized polymer (31–35). Among the numerous N-terminal-oriented methods (2), specific labeling of the N terminus of intact proteins with N-tris(2,4,6-trimethoxyphenyl)phosphonium acetyl succinamide (TMPP-Ac-OSu)¹ has proven reliable (21, 36–39). TMPP-derivatized N-terminal peptides have interesting properties for further LC-MS/MS mass spectrometry: (1) an increase in hydrophobicity because of the trimethoxyphenyl moiety added to the peptides, increasing their retention times in reverse phase chromatography, (2) improvement of their ionization because of the introduction of a positively charged group, and (3) a much simpler fragmentation pattern in tandem mass spectrometry. Other reported approaches rely on acetylation, followed by trypsin digestion, and then biotinylation of free amino groups (40); guanidination of lysine lateral chains followed by N-biotinylation of the N termini and trypsin digestion (41); or reductive amination of all free amino groups with formaldehyde preceeding trypsin digestion (42). Recently, we applied the TMPP method to the proteome of the Deinococcus deserti bacterium isolated from upper sand layers of the Sahara desert (13). This method enabled the detection of N-terminal peptides allowing the confirmation of 278 translation initiation codons, the correction of 73 translation starts, and the identification of non-canonical translation initiation codons (21). However, most TMPP-labeled N-terminal peptides are hidden among the more abundant internal peptides generated after proteolysis of a complex proteome, precluding their detection. This results in disproportionately fewer N-terminal validations, that is, 5 and 8% of total polypeptides coded in the theoretical proteomes of Mycobacterium smegmatis (37) and Deinococcus deserti (21) with a total of 342 and 278 validations, respectively.An interesting chromatographic method to fractionate peptide mixtures for gel-free high-throughput proteome analysis has been developed over the last years and applied to various topics (43, 44). This technique, known as COmbined FRActional DIagonal Chromatography (COFRADIC), uses a double chromatographic separation with a chemical reaction in between to change the physico-chemical properties of the extraneous peptides to be resolved from the peptides of interest. Its previous applications include the separation of methionine-containing peptides (43), N-terminal peptide enrichment (45, 46), sulfur amino acid-containing peptides (47), and phosphorylated peptides (48). COFRADIC was identified as the best method for identification of N-terminal peptides of two archaea, resulting in the identification of 240 polypeptides (9% of the theoretical proteome) for Halobacterium salinarum and 220 (8%) for Natronomonas pharaonis (24).Taking advantage of both the specificity of TMPP labeling, the resolving power of COFRADIC for enrichment, and the increase in information through the use of multiple proteases, we performed the proteogenomic analysis of a marine bacterium from the Roseobacter clade, namely Roseobacter denitrificans OCh114. This novel approach allowed us to validate and correct 534 unique proteins (13% of the theoretical proteome) with TMPP-labeled N-terminal signatures obtained using high-resolution tandem mass spectrometry. We corrected 41 annotations and detected five new open reading frames in the R. denitrificans genome. We further identified eight distinct proteins showing direct evidence for multiple start sites.     

Analysis of Proteins That Rapidly Change Upon Mechanistic/Mammalian Target of Rapamycin Complex 1 (mTORC1) Repression Identifies Parkinson Protein 7 (PARK7) as a Novel Protein Aberrantly Expressed in Tuberous Sclerosis Complex (TSC)

Many biological processes involve the mechanistic/mammalian target of rapamycin complex 1 (mTORC1). Thus, the challenge of deciphering mTORC1-mediated functions during normal and pathological states in the central nervous system is challenging. Because mTORC1 is at the core of translation, we have investigated mTORC1 function in global and regional protein expression. Activation of mTORC1 has been generally regarded to promote translation. Few but recent works have shown that suppression of mTORC1 can also promote local protein synthesis. Moreover, excessive mTORC1 activation during diseased states represses basal and activity-induced protein synthesis. To determine the role of mTORC1 activation in protein expression, we have used an unbiased, large-scale proteomic approach. We provide evidence that a brief repression of mTORC1 activity in vivo by rapamycin has little effect globally, yet leads to a significant remodeling of synaptic proteins, in particular those proteins that reside in the postsynaptic density. We have also found that curtailing the activity of mTORC1 bidirectionally alters the expression of proteins associated with epilepsy, Alzheimer''s disease, and autism spectrum disorder—neurological disorders that exhibit elevated mTORC1 activity. Through a protein–protein interaction network analysis, we have identified common proteins shared among these mTORC1-related diseases. One such protein is Parkinson protein 7, which has been implicated in Parkinson''s disease, yet not associated with epilepsy, Alzheimers disease, or autism spectrum disorder. To verify our finding, we provide evidence that the protein expression of Parkinson protein 7, including new protein synthesis, is sensitive to mTORC1 inhibition. Using a mouse model of tuberous sclerosis complex, a disease that displays both epilepsy and autism spectrum disorder phenotypes and has overactive mTORC1 signaling, we show that Parkinson protein 7 protein is elevated in the dendrites and colocalizes with the postsynaptic marker postsynaptic density-95. Our work offers a comprehensive view of mTORC1 and its role in regulating regional protein expression in normal and diseased states.The mechanistic/mammalian target of rapamycin complex 1 (mTORC1)¹ is a serine/threonine protein kinase that is highly expressed in many cell types (1). In the brain, mTORC1 tightly coordinates different synaptic plasticities — long-term potentiation (LTP) and long-term depression (LTD) — the molecular correlates of learning and memory (2–5). Because mTORC1 is at the core of many synaptic signaling pathways downstream of glutamate and neurotrophin receptors, many hypothesize that dysregulated mTORC1 signaling underlies cognitive deficits observed in several neurodegenerative diseases (3, 6–17). For example, mTORC1 and its downstream targets are hyperactive in human brains diagnosed with Alzheimer''s disease (AD) (18–20). Additionally in animal models of autism spectrum disorder (ASD), altered mTORC1 signaling contributes to the observed synaptic dysfunction and aberrant network connectivity (13, 15, 21–27). Furthermore, epilepsy, which is common in AD and ASD, has enhanced mTORC1 activity (28–32).Phosphorylation of mTORC1, considered the active form, is generally regarded to promote protein synthesis (33). Thus, many theorize that diseases with overactive mTORC1 arise from excessive protein synthesis (14). Emerging data, however, show that suppressing mTORC1 activation can trigger local translation in neurons (34, 35). Pharmacological antagonism of N-methyl-d-aspartate (NMDA) receptors, a subtype of glutamate receptors that lies upstream of mTOR activation, promotes the synthesis of the voltage-gated potassium channel, Kv1.1, in dendrites (34, 35). Consistent with these results, in models of temporal lobe epilepsy there is a reduction in the expression of voltage-gated ion channels including Kv1.1 (30, 31, 36). Interestingly in a model of focal neocortical epilepsy, overexpression of Kv1.1 blocked seizure activity (37). Because both active and inactive mTORC1 permit protein synthesis, we sought to determine the proteins whose expression is altered when mTORC1 phosphorylation is reduced in vivo.Rapamycin is an FDA-approved, immunosuppressive drug that inhibits mTORC1 activity (38). We capitalized on the ability of rapamycin to reduce mTORC1 activity in vivo and the unbiased approach of mass spectrometry to identify changes in protein expression. Herein, we provide evidence that mTORC1 activation bidirectionally regulates protein expression, especially in the PSD where roughly an equal distribution of proteins dynamically appear and disappear. Remarkably, using protein–protein interaction networks facilitated the novel discovery that PARK7, a protein thus far only implicated in Parkinson''s disease, (1) is up-regulated by increased mTORC1 activity, (2) resides in the PSD only when mTORC1 is active, and (3) is aberrantly expressed in a rodent model of TSC, an mTORC1-related disease that has symptoms of epilepsy and autism. Collectively, these data provide the first comprehensive list of proteins whose abundance or subcellular distributions are altered with acute changes in mTORC1 activity in vivo.     

Aberrant Splice Variants of HAS1 (Hyaluronan Synthase 1) Multimerize with and Modulate Normally Spliced HAS1 Protein: A POTENTIAL MECHANISM PROMOTING HUMAN CANCER*

Most human genes undergo alternative splicing, but aberrant splice forms are hallmarks of many cancers, usually resulting from mutations initiating abnormal exon skipping, intron retention, or the introduction of a new splice sites. We have identified a family of aberrant splice variants of HAS1 (the hyaluronan synthase 1 gene) in some B lineage cancers, characterized by exon skipping and/or partial intron retention events that occur either together or independently in different variants, apparently due to accumulation of inherited and acquired mutations. Cellular, biochemical, and oncogenic properties of full-length HAS1 (HAS1-FL) and HAS1 splice variants Va, Vb, and Vc (HAS1-Vs) are compared and characterized. When co-expressed, the properties of HAS1-Vs are dominant over those of HAS1-FL. HAS1-FL appears to be diffusely expressed in the cell, but HAS1-Vs are concentrated in the cytoplasm and/or Golgi apparatus. HAS1-Vs synthesize detectable de novo HA intracellularly. Each of the HAS1-Vs is able to relocalize HAS1-FL protein from diffuse cytoskeleton-anchored locations to deeper cytoplasmic spaces. This HAS1-Vs-mediated relocalization occurs through strong molecular interactions, which also serve to protect HAS1-FL from its otherwise high turnover kinetics. In co-transfected cells, HAS1-FL and HAS1-Vs interact with themselves and with each other to form heteromeric multiprotein assemblies. HAS1-Vc was found to be transforming in vitro and tumorigenic in vivo when introduced as a single oncogene to untransformed cells. The altered distribution and half-life of HAS1-FL, coupled with the characteristics of the HAS1-Vs suggest possible mechanisms whereby the aberrant splicing observed in human cancer may contribute to oncogenesis and disease progression.About 70–80% of human genes undergo alternative splicing, contributing to proteomic diversity and regulatory complexities in normal development (1). About 10% of mutations listed so far in the Human Gene Mutation Database (HGMD) of “gene lesions responsible for human inherited disease” were found to be located within splice sites. Furthermore, it is becoming increasingly apparent that aberrant splice variants, generated mostly due to splicing defects, play a key role in cancer. Germ line or acquired genomic changes (mutations) in/around splicing elements (2–4) promote aberrant splicing and aberrant protein isoforms.Hyaluronan (HA)³ is synthesized by three different plasma membrane-bound hyaluronan synthases (1, 2, and 3). HAS1 undergoes alternative and aberrant intronic splicing in multiple myeloma, producing truncated variants termed Va, Vb, and Vc (5, 6), which predicted for poor survival in a cohort of multiple myeloma patients (5). Our work suggests that this aberrant splicing arises due to inherited predispositions and acquired mutations in the HAS1 gene (7). Cancer-related, defective mRNA splicing caused by polymorphisms and/or mutations in splicing elements often results in inactivation of tumor suppressor activity (e.g. HRPT2 (8, 9), PTEN (10), MLHI (11–14), and ATR (15)) or generation of dominant negative inhibitors (e.g. CHEK2 (16) and VWOX (17)). In breast cancer, aberrantly spliced forms of progesterone and estrogen receptors are found (reviewed in Ref. 3). Intronic mutations inactivate p53 through aberrant splicing and intron retention (18). Somatic mutations with the potential to alter splicing are frequent in some cancers (19–25). Single nucleotide polymorphisms in the cyclin D1 proto-oncogene predispose to aberrant splicing and the cyclin D1b intronic splice variant (26–29). Cyclin D1b confers anchorage independence, is tumorogenic in vivo, and is detectable in human tumors (30), but as yet no clinical studies have confirmed an impact on outcome. On the other hand, aberrant splicing of HAS1 shows an association between aberrant splice variants and malignancy, suggesting that such variants may be potential therapeutic targets and diagnostic indicators (19, 31–33). Increased HA expression has been associated with malignant progression of multiple tumor types, including breast, prostate, colon, glioma, mesothelioma, and multiple myeloma (34). The three mammalian HA synthase (HAS) isoenzymes synthesize HA and are integral transmembrane proteins with a probable porelike structural assembly (35–39). Although in humans, the three HAS genes are located on different chromosomes (hCh19, hCh8, and hCh16, respectively) (40), they share a high degree of sequence homology (41, 42). HAS isoenzymes synthesize a different size range of HA molecules, which exhibit different functions (43, 44). HASs contribute to a variety of cancers (45–55). Overexpression of HASs promotes growth and/or metastatic development in fibrosarcoma, prostate, and mammary carcinoma, and the removal of the HA matrix from a migratory cell membrane inhibits cell movement (45, 53). HAS2 confers anchorage independence (56). Our work has shown aberrant HAS1 splicing in multiple myeloma (5) and Waldenstrom''s macroglobulinemia (6). HAS1 is overexpressed in colon (57), ovarian (58), endometrial (59), mesothelioma (60), and bladder cancers (61). A HAS1 splice variant is detected in bladder cancer (61).Here, we characterize molecular and biochemical characteristics of HAS1 variants (HAS1-Vs) (5), generated by aberrant splicing. Using transient transfectants and tagged HAS1 family constructs, we show that HAS1-Vs differ in cellular localization, de novo HA localization, and turnover kinetics, as compared with HAS1-FL, and dominantly influence HAS1-FL when co-expressed. HAS1-Vs proteins form intra- and intermolecular associations among themselves and with HAS1-FL, including covalent interactions and multimer formation. HAS1-Vc supports vigorous cellular transformation of NIH3T3 cells in vitro, and HAS1-Vc-transformed NIH3T3 cells are tumorogenic in vivo.     

A Universal Chemical Enrichment Method for Mapping the Yeast N-glycoproteome by Mass Spectrometry (MS)

Glycosylation is one of the most common and important protein modifications in biological systems. Many glycoproteins naturally occur at low abundances, which makes comprehensive analysis extremely difficult. Additionally, glycans are highly heterogeneous, which further complicates analysis in complex samples. Lectin enrichment has been commonly used, but each lectin is inherently specific to one or several carbohydrates, and thus no single or collection of lectin(s) can bind to all glycans. Here we have employed a boronic acid-based chemical method to universally enrich glycopeptides. The reaction between boronic acids and sugars has been extensively investigated, and it is well known that the interaction between boronic acid and diols is one of the strongest reversible covalent bond interactions in an aqueous environment. This strong covalent interaction provides a great opportunity to catch glycopeptides and glycoproteins by boronic acid, whereas the reversible property allows their release without side effects. More importantly, the boronic acid-diol recognition is universal, which provides great capability and potential for comprehensively mapping glycosylation sites in complex biological samples. By combining boronic acid enrichment with PNGase F treatment in heavy-oxygen water and MS, we have identified 816 N-glycosylation sites in 332 yeast proteins, among which 675 sites were well-localized with greater than 99% confidence. The results demonstrated that the boronic acid-based chemical method can effectively enrich glycopeptides for comprehensive analysis of protein glycosylation. A general trend seen within the large data set was that there were fewer glycosylation sites toward the C termini of proteins. Of the 332 glycoproteins identified in yeast, 194 were membrane proteins. Many proteins get glycosylated in the high-mannose N-glycan biosynthetic and GPI anchor biosynthetic pathways. Compared with lectin enrichment, the current method is more cost-efficient, generic, and effective. This method can be extensively applied to different complex samples for the comprehensive analysis of protein glycosylation.Glycosylation is an extremely important protein modification that frequently regulates protein folding, trafficking, and stability. It is also involved in a wide range of cellular events (1) such as immune response (2, 3), cell proliferation (4), cell-cell interactions (5), and signal transduction (6). Aberrant protein glycosylation is believed to have a direct correlation with the development of several diseases, including diabetes, infectious diseases, and cancer (7–11). Secretory proteins frequently get glycosylated, including those in body fluids such as blood, saliva, and urine (12, 13). Samples containing these proteins can be easily obtained and used for diagnostic and therapeutic purposes. Several glycoproteins have previously been identified as biomarkers, including Her2/Neu in breast cancer (14), prostate-specific antigen (PSA) in prostate cancer (15), and CA125 in ovarian cancer (16, 17), which highlights the clinical importance of identifying glycoproteins as indicators or biomarkers of diseases. Therefore, effective methods for systematic analysis of protein glycosylation are essential to understand the mechanisms of glycobiology, identify drug targets and discover biomarkers.Approximately half of mammalian cell proteins are estimated to be glycosylated at any given time (18). There have been many reports regarding identification of protein glycosylation sites and elucidation of glycan structures (19–30). Glycan structure analysis can lead to potential therapeutic and diagnostic applications (31, 32), but it is also critical to identify which proteins are glycosylated as well as the sites at which the modification occurs. Despite progress in recent years, the large-scale analysis of protein glycosylation sites using MS-based proteomics methods is still a challenge. Without an effective enrichment method, the low abundance of glycoproteins prohibits the identification of the majority of sites using the popular intensity-dependent MS sequence method.About a decade ago, a very beautiful and elegant method based on hydrazide chemistry was developed to enrich glycopeptides. Hydrazide conjugated beads reacted with aldehydes formed from the oxidation of cis-diols in glycans (33). This method has been extensively applied to many different types of biological samples (34–41). Besides the hydrazide-based enrichment method, lectins have also been frequently used to enrich glycopeptides or glycoproteins before MS analysis (28, 29, 42–46). However, there are many different types of lectins, and each is specific to certain glycans (47, 48). Therefore, no combination of lectins can bind to all glycosylated peptides or proteins, which prevents comprehensive analysis of protein glycosylation. Because of the complexity of biological samples, effective enrichment methods are critical for the comprehensive analysis of protein glycosylation before MS analysis.One common feature of all glycoproteins and glycopeptides is that they contain multiple hydroxyl groups in their glycans. From a chemistry point of view, this can be exploited to effectively enrich them. Ideally, chemical enrichment probes must have both strong and specific interactions with multiple hydroxyl groups. The reaction between boronic acids and 1,2- or 1,3-cis-diols in sugars has been extensively studied (49–52) and applied for the small-scale analysis of glycoproteins (53–55). Furthermore, boronate affinity chromatography has been employed for the analysis of nonenzymatically glycated peptides (56, 57). Boronic acid-based chemical enrichment methods are expected to have great potential for global analysis of glycopeptides when combined with modern MS-based proteomics techniques. However, the method has not yet been used for the comprehensive analysis of protein N-glycosylation in complex biological samples (58).Yeast is an excellent model biological system that has been extensively used in a wide range of experiments. Last year, two papers reported the large-scale analysis of protein N-glycosylation in yeast (59, 60). In one study, a new MS-based method was developed based on N-glycopeptide mass envelopes with a pattern via metabolic incorporation of a defined mixture of N-acetylglucosamine isotopologs into N-glycans. Peptides with the recoded envelopes were specifically targeted for fragmentation, facilitating high confidence site mapping (59). Using this method, 133 N-glycosylation sites were confidently identified in 58 yeast proteins. When combined with an effective enrichment method, this MS-based analysis will provide a more complete coverage of the N-glycoproteome. The other work combined lectin enrichment with digestion by two enzymes (Glu_c and trypsin) to increase the peptide coverage, and 516 well-localized N-glycosylation sites were identified in 214 yeast proteins by MS (60).Here we have comprehensively identified protein N-glycosylation sites in yeast by combining a boronic acid-based chemical enrichment method with MS-based proteomics techniques. Magnetic beads conjugated with boronic acid were systematically optimized to selectively enrich glycosylated peptides from yeast whole cell lysates. The enriched peptides were subsequently treated with Peptide-N4-(N-acetyl-beta-glucosaminyl)asparagine amidase (PNGase F)¹ in heavy-oxygen water. Finally, peptides were analyzed by an on-line LC-MS system. Over 800 protein N-glycosylation sites were identified in the yeast proteome, which clearly demonstrates that the boronic acid-based chemical method is an effective enrichment method for large-scale analysis of protein glycosylation by MS.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[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]     

Phosphorylated Tau Interacts with c-Jun N-terminal Kinase-interacting Protein 1 (JIP1) in Alzheimer Disease

In Alzheimer disease (AD) and frontotemporal dementia the microtubule-associated protein Tau becomes progressively hyperphosphorylated, eventually forming aggregates. However, how Tau dysfunction is associated with functional impairment is only partly understood, especially at early stages when Tau is mislocalized but has not yet formed aggregates. Impaired axonal transport has been proposed as a potential pathomechanism, based on cellular Tau models and Tau transgenic mice. We recently reported K369I mutant Tau transgenic K3 mice with axonal transport defects that suggested a cargo-selective impairment of kinesin-driven anterograde transport by Tau. Here, we show that kinesin motor complex formation is disturbed in the K3 mice. We show that under pathological conditions hyperphosphorylated Tau interacts with c-Jun N-terminal kinase- interacting protein 1 (JIP1), which is associated with the kinesin motor protein complex. As a result, transport of JIP1 into the axon is impaired, causing JIP1 to accumulate in the cell body. Because we found trapping of JIP1 and a pathological Tau/JIP1 interaction also in AD brain, this may have pathomechanistic implications in diseases with a Tau pathology. This is supported by JIP1 sequestration in the cell body of Tau-transfected primary neuronal cultures. The pathological Tau/JIP1 interaction requires phosphorylation of Tau, and Tau competes with the physiological binding of JIP1 to kinesin light chain. Because JIP1 is involved in regulating cargo binding to kinesin motors, our findings may, at least in part, explain how hyperphosphorylated Tau mediates impaired axonal transport in AD and frontotemporal dementia.The microtubule-associated protein Tau is predominantly found in the axonal compartment of neurons, where it binds to microtubules (1). In human brain, six isoforms of Tau are expressed, due to alternative splicing of exons 2, 3 and 10 (2). Tau consists of an amino-terminal projection domain followed by 3 or 4 microtubule binding repeats (3R or 4R), due to splicing of exon 10, and a carboxyl-terminal tail region. In the AD³ and FTD brain, Tau forms filamentous inclusions (3). They are found in nerve cell bodies and apical dendrites as neurofibrillary tangles (NFTs), in distal dendrites as neuropil threads, and in the abnormal neurites that are associated with some amyloid plaques (neuritic plaques) (3). Hyperphosphorylation of Tau is thought to be an initiating step (4), as it detaches Tau from microtubules and makes it prone to form aggregates (1, 5). Whereas in AD no mutations have been identified in the MAPT gene encoding Tau, so far 42 intronic and exonic mutations have been found in familial forms of FTD (6). Their identification assisted in the generation of transgenic mouse models that reproduce NFT formation and memory impairment (7).The models were also instrumental in testing hypotheses that had been brought forward to link Tau pathology to functional impairment (8–10). In particular, defects in axonal transport have been implicated in neurodegenerative disorders (11, 12). Tau binding to microtubules affects axonal transport (13), and in cell culture overexpression of Tau was shown to lead to impaired transport of mitochondria and vesicles (14, 15). Axonal transport defects have also been reproduced in wild-type Tau transgenic mice (16) and in K369I mutant Tau K3 mice (17), whereas Tau expression failed to inhibit axonal transport in other systems (18, 19). This apparent discrepancy may depend on the type of cargos analyzed and, specifically, the experimental paradigm, e.g. using phosphorylated (16, 17, 20) versus non-phosphorylated Tau (18).To dissect Tau-mediated axonal transport defects at a molecular level, we used K3 mice that overexpress human Tau carrying the pathogenic FTD K369I mutation (17). We observed a pronounced hyperphosphorylation of transgenic Tau in many brain areas. Clinically, the mice present with an early onset motor phenotype that is, at least in part, caused by impairment of axonal transport in neurons of the substantia nigra. Interestingly, only selected aspects of anterograde axonal transport were impaired, in particular those of kinesin-I motor complex-driven vesicles and mitochondria. Our data suggest a selective impairment of axonal transport rather than a generalized, non-selective blockage of microtubules that has been established in cell culture systems, which fail to phosphorylate Tau at the high levels that are found in vivo even under physiological conditions. More importantly, in AD and FTD Tau is even more phosphorylated, i.e. hyperphosphorylated at physiological sites and de novo at pathological sites, preventing it from binding to microtubules (1).Based on our findings of an impaired kinesin-I-driven axonal transport in the K3 mice, we speculated that hyperphosphorylated Tau may impair anterograde transport by interfering directly with components of the kinesin-I motor complex rather than disrupting the binding of the kinesin heavy chain (see below) to microtubules. Axonal transport along microtubules is mediated by members of the kinesin superfamily (KIF) of motor proteins (21–23). The KIFs typically consist of an ATPase domain that interacts with microtubules and drives movement and a domain that links to cargos, either directly or indirectly, as in the case of KIF5, by assembling with the kinesin light chain (KLC) to form the kinesin-I (KIF5/KLC) motor complex (24). In addition, increasing evidence suggests that scaffolding proteins mediate and regulate the binding of cargos to KIFs (21, 25–27). These include the scaffold protein JNK-interacting protein (JIP) that is involved in the linkage of cargos to the kinesin-I motor complex via KLC (25, 28–33).Here, by using the K3 mouse model, we identified a novel interaction of Tau and JIP in neurons that causes a trapping of JNK interacting protein 1 (JIP1) in the cell body of K3 mice, cell culture systems, and human AD brain. We found that the pathological interaction of hyperphosphorylated Tau and JIP1 competes with the physiological binding of JIP1 to KLC.