Identification of Sequences in the Polysialyltransferases ST8Sia II and ST8Sia IV That Are Required for the Protein-specific Polysialylation of the Neural Cell Adhesion Molecule, NCAM

The polysialyltransferases ST8Sia II and ST8Sia IV polysialylate the glycans of a small subset of mammalian proteins. Their most abundant substrate is the neural cell adhesion molecule (NCAM). An acidic surface patch and a novel α-helix in the first fibronectin type III repeat of NCAM are required for the polysialylation of N-glycans on the adjacent immunoglobulin domain. Inspection of ST8Sia IV sequences revealed two conserved polybasic regions that might interact with the NCAM acidic patch or the growing polysialic acid chain. One is the previously identified polysialyltransferase domain (Nakata, D., Zhang, L., and Troy, F. A. (2006) Glycoconj. J. 23, 423–436). The second is a 35-amino acid polybasic region that contains seven basic residues and is equidistant from the large sialyl motif in both polysialyltransferases. We replaced these basic residues to evaluate their role in enzyme autopolysialylation and NCAM-specific polysialylation. We found that replacement of Arg²⁷⁶/Arg²⁷⁷ or Arg²⁶⁵ in the polysialyltransferase domain of ST8Sia IV decreased both NCAM polysialylation and autopolysialylation in parallel, suggesting that these residues are important for catalytic activity. In contrast, replacing Arg⁸²/Arg⁹³ in ST8Sia IV with alanine substantially decreased NCAM-specific polysialylation while only partially impacting autopolysialylation, suggesting that these residues may be particularly important for NCAM polysialylation. Two conserved negatively charged residues, Glu⁹² and Asp⁹⁴, surround Arg⁹³. Replacement of these residues with alanine largely inactivated ST8Sia IV, whereas reversing these residues enhanced enzyme autopolysialylation but significantly reduced NCAM polysialylation. In sum, we have identified selected amino acids in this conserved polysialyltransferase polybasic region that are critical for the protein-specific polysialylation of NCAM.Polysialic acid is a linear homopolymer of α2,8-linked sialic acid that is added to a small subset of mammalian glycoproteins by the polysialyltransferases (polySTs)3 ST8Sia II (STX) and ST8Sia IV (PST) (1–4). Substrates for the polySTs include the neural cell adhesion molecule (NCAM) (5, 6), the α-subunit of the voltage-dependent sodium channel (7, 8), CD36, a scavenger receptor found in milk (9), neuropilin-2 expressed by dendritic cells (10), and the polySTs themselves, which can polysialylate their own N-glycans in a process called autopolysialylation (11, 12). This small number of polysialylated proteins and other evidence from our laboratory (13–15) suggest that polysialylation is a protein-specific modification that requires an initial protein-protein interaction between the polySTs and their glycoprotein substrates.The most abundant polysialylated protein is NCAM. The three major NCAM isoforms consist of five Ig domains, two fibronectin type III repeats, and a transmembrane domain and cytoplasmic tail (NCAM140 and NCAM180) or a glycosylphosphatidylinositol anchor (NCAM120) (16). Polysialylation takes place primarily on two N-linked glycans in the Ig5 domain (17). We have previously shown that a truncated NCAM140 protein consisting of Ig5, the first fibronectin type III repeat (FN1), the transmembrane region, and cytoplasmic tail is fully polysialylated (13). However, a protein consisting of Ig5, the transmembrane region, and cytoplasmic tail is not polysialylated (13). This suggests that the polySTs recognize and bind the FN1 domain to polysialylate N-glycans on the adjacent Ig5 domain. We subsequently identified an acidic patch unique to NCAM FN1, consisting of Asp⁴⁹⁷, Asp⁵¹¹, Glu⁵¹², and Glu⁵¹⁴ (15).⁴ When three of these residues (Asp⁵¹¹, Glu⁵¹², and Glu⁵¹⁴) are mutated to alanine or arginine, NCAM polysialylation is reduced or abolished, suggesting that the acidic patch is part of a larger recognition region. We anticipate that within this putative recognition region there will be amino acids required for mediating polyST-NCAM binding, and those that do not mediate binding per se but instead are required for correct positioning of the enzyme-substrate complex for polysialylation. For example, we have identified a novel α-helix in the FN1 domain that when replaced leads to polysialylation of O-glycans found on the FN1 domain rather than N-glycans on the Ig5 domain (14). This helix may mediate an interdomain interaction that positions the Ig5 N-glycans for polysialylation by an enzyme bound to the FN1 domain (14). Alternatively, the helix could act as a secondary interaction site that positions the polyST properly on the substrate.The expression of the polySTs is developmentally regulated with high levels of STX and moderate levels of PST expressed throughout the developing embryo (2, 18, 19). STX levels decline after birth, although PST expression persists in specific regions of the adult brain where polysialylated NCAM is involved in neuronal regeneration and synaptic plasticity (18–23). The large size and negative charge of polysialic acid disrupt NCAM-dependent and NCAM-independent interactions, thereby negatively modulating cell adhesion (24–26). Simultaneous disruption of both PST and STX in mice results in severe neuronal defects and death usually within 4 weeks after birth (27). Interestingly, when NCAM expression is also eliminated in these mice, they have a nearly normal phenotype, suggesting the main function of polysialic acid is to modulate NCAM-mediated cell adhesion during development (27). In addition, re-expression of highly polysialylated NCAM has been associated with several cancers, including neuroblastomas, gliomas, small cell lung carcinomas, and Wilms tumor. The presence of polysialic acid is thought to promote cancer cell growth and invasiveness (28–35).Sialyltransferases, including the polySTs, have three motifs required for catalytic activity (36–38) (see Fig. 1A). Sialyl motif Large (SML) is thought to bind the donor substrate CMP-sialic acid (39), whereas sialyl motif Small (SMS) is believed to bind both donor and carbohydrate acceptor substrates (40). The sialyl motif Very Small (SMVS) has a conserved His residue that is required for catalytic activity (38, 41). However, the precise function of this motif is unknown. An additional 4-amino acid motif, motif III, is conserved in the sialyltransferases (42–44). It was suggested that this motif, and particularly His and Tyr residues within its sequence, may be required for optimal activity and acceptor recognition (42).Open in a separate windowFIGURE 1.PST and STX polybasic regions and mutants generated for this study. A, representation of the polySTs and their polybasic regions and sialyl motifs. The PBR is a 35-amino acid region present in both PST and STX, equidistant from the SML of each enzyme and rich in conserved positively charged amino acids. The PSTD is a region identified by Nakata et al. (47) that is 32 amino acids in length, rich in basic residues, and contiguous with the SMS of the enzymes. The sialyl motifs (SML, SMS, SMVS, and motif III) are regions of homology found in all sialyltransferases that are believed to be involved in substrate and donor interactions. B, PSTD of PST and the mutants made in this region that are used in this study. C, PBR of PST and STX and the mutants made in this region that are used in this study.Angata et al. (45) used chimeric enzymes to identify regions within the polySTs required for catalytic activity and NCAM polysialylation. Sequences from PST, STX, and ST8Sia III were used to construct the chimeric proteins. ST8Sia III is an α2,8-sialyltransferase that typically adds one or two sialic acid residues to glycoprotein or glycolipid substrates, can autopolysialylate its own glycans, but cannot polysialylate NCAM (46). Deletion analysis showed that amino acids 62–356 are required for PST catalytic activity. Replacement of segments of this region with corresponding STX or ST8Sia III sequences led to the suggestion that amino acids 62–127 and possibly 194–267 of PST may be required for NCAM recognition (45).Recently, Troy and co-workers (47, 48) identified a stretch of basic residues, termed the polysialyltransferase domain (PSTD), which is only observed in the two polySTs and not in other sialyltransferases. The PSTD is contiguous with SMS and extends from amino acids 246–277 in PST and 261–292 in STX. Mutation analysis demonstrated that the overall positive charge of this motif is important for activity and identified specific residues required for NCAM polysialylation (Arg²⁵², Ile²⁷⁵, Lys²⁷⁶, and Arg²⁷⁷) (47).In this study, we have scanned the critical polyST regions identified by the work of Angata et al. (45) for sequences that may be involved in protein-protein recognition and NCAM polysialylation. We identified a second polybasic motif that we named the polybasic region (PBR). The PBR is conserved in PST and STX and is located equidistant from the SML of each enzyme. It consists of 35 amino acids of which 7 are the basic amino acids Arg and Lys. We found that the replacement of two specific residues within the PBR (Arg⁸² and Arg⁹³ of PST and Arg⁹⁷ and Lys¹⁰⁸ of STX) have a greater negative effect on NCAM polysialylation than on autopolysialylation. Replacement of acidic residues surrounding PST Arg⁹³ led to a similar disparate effect on these processes. Comparison of the critical residues in both the PSTD and PBR demonstrated that the replacement of PSTD residues had an equally negative impact on both NCAM polysialylation and enzyme autopolysialylation, whereas replacement of selected PBR residues more severely impacted NCAM polysialylation, suggesting that the PBR residues may play important roles in NCAM-specific polysialylation.     

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]     

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]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

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]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

Identification of a Novel Proteoform of Prostate Specific Antigen (SNP-L132I) in Clinical Samples by Multiple Reaction Monitoring

Prostate specific antigen (PSA) is a well-established tumor marker that is frequently employed as model biomarker in the development and evaluation of emerging quantitative proteomics techniques, partially as a result of wide access to commercialized immunoassays serving as “gold standards.” We designed a multiple reaction monitoring (MRM) assay to detect PSA proteoforms in clinical samples (n = 72), utilizing the specificity and sensitivity of the method. We report, for the first time, a PSA proteoform coded by SNP-L132I (rs2003783) that was observed in nine samples in both heterozygous (n = 7) and homozygous (n = 2) expression profiles. Other isoforms of PSA, derived from protein databases, were not identified by four unique proteotypic tryptic peptides. We have also utilized our MRM assay for precise quantitative analysis of PSA concentrations in both seminal and blood plasma samples. The analytical performance was evaluated, and close agreement was noted between quantitations based on three selected peptides (LSEPAELTDAVK, IVGGWECEK, and SVILLGR) and a routinely used commercialized immunoassay. Additionally, we disclose that the peptide IVGGWECEK is shared with kallikrein-related peptidase 2 and therefore is not unique for PSA. Thus, we propose the use of another tryptic sequence (SVILLGR) for accurate MRM quantification of PSA in clinical samples.With the move toward biomarker verification and the clinical implementation of novel assays, mass-spectrometry-based quantitative analysis of biomarkers is increasingly becoming an important route for current proteomics studies. Although MS instrumentation offers various powerful strategies for biomarker discovery (1), the validation phase for these putative protein candidates still relies primarily on immunoreaction-based assays such as ELISA (2). These immunoassays are considered to be effective diagnostic tools and are routinely used in clinical practice, but they are often associated with the lengthy and expensive development of high-quality antibodies, and sometimes significant differences exist between tests from different vendors. Furthermore, immunoassays depend on indirect readouts (colorimetric, fluorescent, or radioactive) and may produce false positive results as a result of nonspecific binding. Nevertheless, MS nowadays is able to measure analytes with high quantitative accuracy, and established MS methods originally developed for the quantitation of small molecules, such as multiple reaction monitoring (MRM)¹ (3), have been successfully introduced for proteins (4–6). As compared with traditional ELISA techniques, MRM assays can be cost-efficient, utilize quickly developed methods, and offer exceptional multiplexing capability (7).Interestingly, prostate specific antigen (PSA), a successful biomarker of prostate cancer, has been frequently chosen as a model protein in MRM method development studies (8–21). PSA is a prostatic kallikrein-related serine peptidase (KLK3) with restricted chymotrypsin-like specificity that is mainly responsible for the liquefaction of seminal coagulum via degradation of the major gel-forming proteins SEMG1 and SEMG2 (22–24). Catalytically active PSA is a 237-amino-acid single-chain glycoprotein with a molecular weight close to 28 kDa (25, 26). Abundant prostate-restricted expression of the epithelial cells and the release of mainly catalytic PSA into seminal fluids in concentrations of approximately 5 to 50 μmol/l are regulated by the nuclear androgen receptor, with levels in blood normally being a million-fold lower (20 pmol/l). PSA is non-catalytic and predominantly lined in a covalent complex with α-1-antichymotrypsin (SERPINA3) (27–29). PSA levels in blood may become elevated because of benign conditions including prostatitis or benign prostate hyperplasia, but modestly elevated PSA in the blood of a middle-aged patient is also strongly associated with metastasis or death from prostate cancer decades later (30, 31). PSA screening can reduce cancer-related deaths, but it may also lead to overdiagnosis and overtreatment (32, 33). Thus controversy remains regarding the merits of the PSA test (34, 35), although it persists as a mainstay in the monitoring of therapeutic intervention and in the detection of disease recurrence or progression (36).PSA was chosen as a model protein in the first isotope-dilution MS study that measured protein concentrations directly in serum without using immunoaffinity chromatographic enrichment (8). The heavy-isotope-labeled tryptic peptide of PSA, IVGGWECEK (¹³C₂ and ¹⁵N₁ on each Gly residue), was utilized as an internal standard (IS) and known amounts of purified PSA were spiked into female serum, and a selected reaction monitoring (SRM) transition channel (y-7) was monitored with excellent reproducibility, achieving a limit of detection of 4.5 μg/ml. PSA and five other proteins were selected in a multiplexing study that systematically selected the most useful signature peptides and monitored three transitions per peptide (9). The most abundant transitions (IVGGWECEK: 539.3 → 865.3 and LSEPAELTDAVK: 636.7 → 943.4) were used for quantification on nano-flow LC combined with a hybrid QTrap mass spectrometer. This work was further explored in an encouraging interlaboratory study that compared MRM analytical performance on seven proteins and three different MS platforms (11) while using differently labeled LSEPAELTDAVK (+8 Da), eliminating the interference in the y-9 transition channel previously reported. Excellent sensitivity was obtained using a combination of immunoextraction and product ion monitoring on a linear ion trap instrument (Thermo LTQ) (10). Also in that study, LSEPAELTDAVK was selected for the quantification of recombinant PSA spiked into female plasma, because three additional PSA peptides (HSQPWQVLVASR, HSLFHPEDTGQVFQVSHSFPHPLYDMSLLK, and FLRPGDDSSHDLMLLR) were noticed to ionize less efficiently. Notably, this methodological study reported for the first time the quantification of PSA in two prostate cancer patient samples (300 and 5000 ng/ml) using MRM-MS. Prostate cancer cell lines were also investigated in an SRM-MS assay in order to correlate PSA levels with clinical tests selecting two signature peptides, LSEPEALTAVK and HSQPWQVLVASR (21).Although the progress of methodological developments has accelerated, promising successful clinical implementation in the near future, the number of real samples from patients remains low (n = 9 with prostate cancer (13) and n = 3 with benign prostate hyperplasia (12)) with LSEPAELTDAVK used for quantification. The same group has utilized IVGGWECEK for the specific detection of cysteine-containing peptides in plasma using laser-induced photo dissociation (photo-SRM) for protein quantification (17). These important studies offered PSA quantification in patient samples at levels of 4 to 30 ng/ml following albumin depletion, tryptic digestion, solid-phase extraction, and conventional HPLC separation of 100 μl serum. For further validation, PSA concentrations determined via MS methods were correlated to a clinical ELISA test with high concordance (13). A novel enrichment strategy employing mass spectrometric immunoassay SRM was applied to access PSA in serum samples measuring SVILLGR as well as the isoform specific tryptic peptide DTIVANP (19). N-linked glycopeptides of PSA were targeted in a study by the same group selectively capturing and quantifying NKSVILLGR in female serum spiked with known amounts of PSA (18).PSA was also included in a protein panel developed for monitoring primary urothelial cell carcinomas of bladder (14). A larger number of patient samples (n = 14 control and n = 17 cancer patients) were systematically screened by the nano-LC-MRM assay intended to detect and quantify a few endogenous proteins in urine. Advanced technology integrating isoelectric focusing on a digital ProteomeChip (Cell Biosciences, Santa Clara, CA) used for the selective enrichment of proteotypic peptides with nano-LC-SRM-MS was demonstrated in the quantification of PSA spiked into female serum and in prostate cancer patients using both LSEPAELTDAVK and IVGGWECEK (20). Recently, a study has been published reporting on an MRM assay developed for the differential quantification of free and total PSA (fPSA and tPSA, respectively) in clinical serum samples (n = 9) with concentrations of 0.3 to 18.9 ng/ml, determined by an immunoassay (15). Good sensitivity was achieved, with limits of quantification of 2.03 and 0.86 ng/ml for fPSA and tPSA, respectively. The same research group has further improved the sensitivity of the assay, reaching PSA quantification in spiked female serum at sub-ng/ml levels, and also in a low number of clinical samples, utilizing advanced high-pressure, high-resolution liquid chromatographic separations without the involvement of antibodies (16).All of these previous reports presented two peptides selected for the quantification of PSA in spiked serum/plasma and in a limited number of clinical samples. However, none of the publications mentioned the fact that IVGGWECEK is not unique for PSA and is also in present in human kallikrein-related peptidase 2 (KLK2 or hK2), or that LSEPAELTDAVK is coded on the exon of KLK3 with a single nucleotide polymorphism (SNP), resulting in the amino acid exchange of L132I (rs2003783).Because of its inherent high selectivity and sensitivity, we have chosen MRM to identify and monitor proteoforms (37) of PSA in clinical samples. For this purpose we developed an MRM assay based on theoretically derived tryptic peptides of 10 PSA isoforms. Because MRM assay outcomes rely on the detection of a specific peptide of the given protein and tryptic digestion might not always be complete, we screened multiple proteotypic peptides with multiple transitions.Our study is the first to report on the detection of a proteoform of PSA as the translated gene product of an SNP variant of the KLK3 gene (L132I; rs2003783). It is our conclusion that based on its frequency (ca. 10% worldwide), this allele should also be monitored in order to quantify PSA appropriately, using the signature peptide LSEPA(L/I)TDAVK, in samples with homogeneous and heterogeneous allele expressions. Additionally, we used three different signature peptides to present data about the analytical performance of our nano-flow LC-MS/MS approach for quantifying PSA in seminal fluid and blood relative to commercialized immunoassays in the largest clinical sample set reported so far (n = 72).     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[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]