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]     

In-depth Characterization of the Cerebrospinal Fluid (CSF) Proteome Displayed Through the CSF Proteome Resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.Cerebrospinal fluid (CSF)¹ surrounds and supports the central nervous system (CNS), including the ventricles and subarachnoid space (1). About 80% of the total protein amount in CSF derives from size-dependent filtration of blood across the blood-brain barrier (BBB), and the rest originate from drainage of interstitial fluid from the CNS (2–4). Because CSF is in direct contact with the CNS, it should be a promising source for finding biomarkers for diseases in the CNS (5).Mapping studies characterizing the human CSF proteome and peptidome has previously been carried out using various experimental designs, including both healthy and disease-affected individuals (5–16). A total of 2630 proteins were detected in normal CSF by immunoaffinity depletion of high abundant proteins followed by strong cation exchange fractionation and LC-MS (5), whereas proteome and peptidome analyses of human CSF (collected for diagnostic purposes and turned out normal) by gel separation and trypsin digestion followed by LC-MS analysis have shown 798 proteins and 563 peptide products (derived from 91 precursor proteins) (6). In another publication, Pan et al. combined several proteomics studies in CSF from both normal subjects and subjects with neurological diseases and created a dataset of 2594 identified proteins (16). But in general, the availability and usefulness of published data from proteome mapping experiments is scarce, and the format of the data often makes searching and comparison across datasets difficult. Thus, organizing the data in online databases would greatly benefit the scientific community by making the data more accessible and easier to query. Current online databases containing MS data for CSF include the Sys-BodyFluid, with a total of 1286 CSF proteins from six studies (17). The proteome identifications database (PRIDE) (18) includes 19 studies on human CSF, but none reporting more than 103 identified proteins.Glycosylation is one of the most common post-translational modifications (PTMs), and many known clinical biomarkers as well as therapeutic targets are glycoproteins (19–25). Furthermore, glycosylation plays important roles in cell communication, signaling, aging, and cell adhesion (26, 27). Nevertheless, there are few studies on glycoprotein identification in CSF. One study identified 216 glycoproteins in CSF using both lectin affinity and hydrazide chemistry (8), and another reported 36 N-linked and 44 O-linked glycosylation sites, from 23 and 22 glycoproteins respectively, by enriching for sialic-acid containing glycopeptides (28).Considering the sparse information about the CSF proteome available in public repositories, we have combined several proteomics approaches to create a map of the global CSF proteome, the CSF glycoproteome, and the respective plasma proteome from a pool of 21 (20 for the plasma pool) neurologically healthy individuals. The large amount of data generated through these four datasets (with linked and complementary information) would not easily be accessible through existing repositories. We therefore developed the open access CSF Proteome Resource (CSF-PR, www.probe.uib.no/csf-pr), an online database including the detailed data from the four different proteomics experiments described in this study. CSF-PR will be particularly useful in guiding the selection of appropriate signature peptides for the development of targeted CSF protein assays.     

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.     

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]     

Enhanced Virus Clearance by Early Inducible Lymphocytic Choriomeningitis Virus-Neutralizing Antibodies in Immunoglobulin-Transgenic Mice

Following infection of mice with lymphocytic choriomeningitis virus (LCMV), virus-neutralizing antibodies appear late, after 30 to 60 days. Such neutralizing antibodies play an important role in protection against reinfection. To analyze whether a neutralizing antibody response which developed earlier could contribute to LCMV clearance during the acute phase of infection, we generated transgenic mice expressing LCMV-neutralizing antibodies. Transgenic mice expressing the immunoglobulin μ heavy chain of the LCMV-neutralizing monoclonal antibody KL25 (H25 transgenic mice) mounted LCMV-neutralizing immunoglobulin M (IgM) serum titers within 8 days after infection. This early inducible LCMV-neutralizing antibody response significantly improved the host’s capacity to clear the infection and did not cause an enhancement of disease after intracerebral (i.c.) LCMV infection. In contrast, mice which had been passively administered LCMV-neutralizing antibodies and transgenic mice exhibiting spontaneous LCMV-neutralizing IgM serum titers (HL25 transgenic mice expressing the immunoglobulin μ heavy and the κ light chain) showed an enhancement of disease after i.c. LCMV infection. Thus, early-inducible LCMV-neutralizing antibodies can contribute to viral clearance in the acute phase of the infection and do not cause antibody-dependent enhancement of disease.Against many cytopathic viruses such as poliovirus, influenza virus, rabies virus, and vesicular stomatitis virus, protective virus-neutralizing antibodies are generated early, within 1 week after infection (3, 31, 36, 44, 49). In contrast, several noncytopathic viruses (e.g., human immunodeficiency virus and hepatitis viruses B and C in humans or lymphocytic choriomeningitis virus [LCMV] in mice) elicit poor and delayed virus-neutralizing antibody responses (1, 7, 20, 24, 27, 35, 45, 48).In the mouse, the natural host of LCMV, the acute LCMV infection is predominantly controlled by cytotoxic T lymphocytes (CTLs) in an obligatory perforin-dependent manner (13, 18, 28, 50). In addition to the CTL response, LCMV-specific antibodies are generated. Early after infection (by day 8), a strong antibody response specific for the internal viral nucleoprotein (NP) is mounted (7, 19, 23, 28). These early LCMV NP-specific antibodies exhibit no virus-neutralizing capacity (7, 10). Results from studies of B-cell-depleted mice and B-cell-deficient mice implied that the early LCMV NP-specific antibodies are not involved in the clearance of LCMV (8, 11, 12, 40). Late after infection (between days 30 and day 60), LCMV-neutralizing antibodies develop (7, 19, 22, 28, 33); these antibodies are directed against the surface glycoprotein (GP) of LCMV (9, 10). LCMV-neutralizing antibodies have an important function in protection against reinfection (4, 6, 38, 41, 47).In some viral infections, subprotective virus-neutralizing antibody titers can enhance disease rather than promote host recovery (i.e., exhibit antibody-dependent enhancement of disease [ADE] [14, 15, 21, 46]). For example, neutralizing antibodies are involved in the resolution of a primary dengue virus infection and in the protection against reinfection. However, if subprotective neutralizing antibody titers are present at the time of reinfection, a severe form of the disease (dengue hemorrhagic fever/dengue shock syndrome [15, 21]), which might be caused by Fc receptor-mediated uptake of virus-antibody complexes leading to an enhanced infection of monocytes (15, 16, 25, 39), can develop. Similarly, an enhancement of disease after intracerebral (i.c.) LCMV infection was observed in mice which had been treated with virus-neutralizing antibodies before the virus challenge (6). ADE in LCMV-infected mice was either due to an enhanced infection of monocytes by Fc receptor-mediated uptake of antibody-virus complexes or due to CTL-mediated immunopathology caused by an imbalanced virus spread and CTL response.To analyze whether LCMV-neutralizing antibodies generated early after infection improve the host’s capacity to clear the virus or enhance immunopathological disease, immunoglobulin (Ig)-transgenic mice expressing LCMV-neutralizing IgM antibodies were generated. After LCMV infection of transgenic mice expressing the Ig heavy chain (H25 transgenic mice), LCMV-neutralizing serum antibodies were mounted within 8 days, which significantly improved the host’s capacity to eliminate LCMV. H25 transgenic mice did not show any signs of ADE after i.c. LCMV infection.Transgenic mice expressing the Ig heavy and light chains (HL25 transgenic mice) exhibited spontaneous LCMV-neutralizing serum antibodies and confirmed the protective role of preexisting LCMV-neutralizing antibodies, even though the neutralizing serum antibodies were of the IgM isotype. Similar to mice which had been treated with LCMV-neutralizing antibodies, HL25 transgenic mice developed an enhanced disease after i.c. LCMV infection, which indicated that ADE was due to an imbalance between virus spread and CTL response. Thus, the early-inducible LCMV-neutralizing antibody response significantly enhanced clearance of the acute infection without any risk of causing ADE.     

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]     

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]     

Printor, a Novel TorsinA-interacting Protein Implicated in Dystonia Pathogenesis

Early onset generalized dystonia (DYT1) is an autosomal dominant neurological disorder caused by deletion of a single glutamate residue (torsinA ΔE) in the C-terminal region of the AAA⁺ (ATPases associated with a variety of cellular activities) protein torsinA. The pathogenic mechanism by which torsinA ΔE mutation leads to dystonia remains unknown. Here we report the identification and characterization of a 628-amino acid novel protein, printor, that interacts with torsinA. Printor co-distributes with torsinA in multiple brain regions and co-localizes with torsinA in the endoplasmic reticulum. Interestingly, printor selectively binds to the ATP-free form but not to the ATP-bound form of torsinA, supporting a role for printor as a cofactor rather than a substrate of torsinA. The interaction of printor with torsinA is completely abolished by the dystonia-associated torsinA ΔE mutation. Our findings suggest that printor is a new component of the DYT1 pathogenic pathway and provide a potential molecular target for therapeutic intervention in dystonia.Early onset generalized torsion dystonia (DYT1) is the most common and severe form of hereditary dystonia, a movement disorder characterized by involuntary movements and sustained muscle spasms (1). This autosomal dominant disease has childhood onset and its dystonic symptoms are thought to result from neuronal dysfunction rather than neurodegeneration (2, 3). Most DYT1 cases are caused by deletion of a single glutamate residue at positions 302 or 303 (torsinA ΔE) of the 332-amino acid protein torsinA (4). In addition, a different torsinA mutation that deletes amino acids Phe³²³–Tyr³²⁸ (torsinA Δ323–328) was identified in a single family with dystonia (5), although the pathogenic significance of this torsinA mutation is unclear because these patients contain a concomitant mutation in another dystonia-related protein, ϵ-sarcoglycan (6). Recently, genetic association studies have implicated polymorphisms in the torsinA gene as a genetic risk factor in the development of adult-onset idiopathic dystonia (7, 8).TorsinA contains an N-terminal endoplasmic reticulum (ER)³ signal sequence and a 20-amino acid hydrophobic region followed by a conserved AAA⁺ (ATPases associated with a variety of cellular activities) domain (9, 10). Because members of the AAA⁺ family are known to facilitate conformational changes in target proteins (11, 12), it has been proposed that torsinA may function as a molecular chaperone (13, 14). TorsinA is widely expressed in brain and multiple other tissues (15) and is primarily associated with the ER and nuclear envelope (NE) compartments in cells (16–20). TorsinA is believed to mainly reside in the lumen of the ER and NE (17–19) and has been shown to bind lamina-associated polypeptide 1 (LAP1) (21), lumenal domain-like LAP1 (LULL1) (21), and nesprins (22). In addition, recent evidence indicates that a significant pool of torsinA exhibits a topology in which the AAA⁺ domain faces the cytoplasm (20). In support of this topology, torsinA is found in the cytoplasm, neuronal processes, and synaptic terminals (2, 3, 15, 23–26) and has been shown to bind cytosolic proteins snapin (27) and kinesin light chain 1 (20). TorsinA has been proposed to play a role in several cellular processes, including dopaminergic neurotransmission (28–31), NE organization and dynamics (17, 22, 32), and protein trafficking (27, 33). However, the precise biological function of torsinA and its regulation remain unknown.To gain insights into torsinA function, we performed yeast two-hybrid screens to search for torsinA-interacting proteins in the brain. We report here the isolation and characterization of a novel protein named printor (protein interactor of torsinA) that interacts selectively with wild-type (WT) torsinA but not the dystonia-associated torsinA ΔE mutant. Our data suggest that printor may serve as a cofactor of torsinA and provide a new molecular target for understanding and treating dystonia.     

Cerebrospinal Fluid Peptides as Potential Parkinson Disease Biomarkers: A Staged Pipeline for Discovery and Validation

Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson''s Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression.Parkinson disease (PD)¹, the second most common neurodegenerative disease after Alzheimer disease (AD), afflicts roughly 2% of persons over the age of 65 years (1, 2). Currently, PD diagnosis is mainly based on observation of the cardinal motor indicators of the disease, patient response to drug treatment, and medical history (3, 4). There is an appreciable misdiagnosis rate (4), particularly at early disease stages. Additionally, no objective measure of disease progression or treatment effects has been established. Thus, objective, reliable, and reproducible biomarkers are clearly needed to aid in the diagnosis of PD and tracking or predicting the disease progression.The most sensitive tests developed to date are based on imaging modalities, which can detect functional and structural abnormalities even prior to the onset of motor dysfunction (5, 6). However, the usefulness of neuroimaging techniques is limited by high cost, limited accessibility, difficulty in reliable differentiation of PD from other atypical parkinsonian disorders and subjection to confounding factors such as medication and compensatory responses (4–7). Biochemical and molecular markers in cerebrospinal fluid (CSF) and other body fluids have also been actively investigated (5, 8–12). The most extensively studied candidate in CSF is probably α-synuclein, the major protein component of Lewy bodies and Lewy neurites, the pathological hallmarks of PD (2). The current consensus is that CSF α-synuclein concentrations are generally lower in patients with PD compared with controls (5, 8–10); the sensitivity and specificity, however, appear to be only moderate, and no correlation with PD severity or progression has been observed (8, 9). Notably, all these CSF protein markers are measured using antibody-based assays, which are often associated with relatively high variability, particularly when different detection techniques (different antibodies, sample preparation, calibrators, etc.) are used, leading to discrepant results across laboratories (5). It should also be stressed that this high variability in immunoassays is not unique to PD, because similar difficulty is encountered in AD and other related disorders (13, 14).One strategy to avoid the inherent technical limitations associated with antibodies is to use alternative techniques in which unique peptides are selected and precisely quantified with mass spectrometry (MS) techniques, for example, accurate inclusion mass screening (AIMS) (15) and selected reaction monitoring (SRM) (16–18). To this end, in the last few years, we and others have utilized proteomic technologies to identify novel proteins and peptides associated with different disease states and stages (5, 6, 19–25). Using brain tissue or CSF, these unbiased proteomic profiling studies have revealed disease-related alterations in hundreds of peptides derived from many proteins (19–25). However, there are no quantitative assays for the majority of these candidate proteins/peptides, and development of such assays is limited by the lack of antibodies available for many of them. Thus, although a large library of potential peptide biomarkers has been developed, the vast majority never reach the stage of validation and clinical testing, hampered by the difficulty of de novo development of immunoassays, a process that is time consuming, prohibitively expensive to develop and very difficult to multiplex.In this study, we aim to establish a PD biomarker identification and verification pipeline, with the goal of prioritizing candidates and swiftly developing reliable quantitative assays. We focused on identifying peptides by SRM and AIMS, because these targeted proteomic technologies have been proposed as the basis of a viable biomarker pipeline (16) and have become a powerful tool in biomarker discovery because of their high sensitivity, accuracy and specificity. SRM, in particular, has emerged as an alternative to immunoaffinity-based measurements of defined protein sets with excellent reproducibility across different laboratories and instrument platforms (17, 18). The staged pipeline in the current investigation (Fig. 1) includes: (1) data-dependent and bioinformatic prioritization of thousands of candidate biomarkers identified in our previous profiling studies, (2) de novo development of antibody-free multiplex SRM assays to reliably measure tens to hundreds of peptides simultaneously, and (3) multiplex biomarker verification studies allowing identification and validation of models or panels of candidates in independent sample sets, two of which were used in this study.Open in a separate windowFig. 1.Overview of the workflow used for CSF peptide biomarker discovery and validation. AD, Alzheimer disease; AIMS, accurate inclusion mass screening; CO, healthy controls; DDA, data-dependent acquisition; PD, Parkinson disease; SRM, selected reaction monitoring.