Earlier studies have shown that cardiac myosin binding protein-C (cMyBP-C) is easily releasable into the circulation following myocardial infarction (MI) in animal models and patients. However, since its release kinetics has not been clearly demonstrated, no parameters are available to judge its efficacy as a bona fide biomarker of MI in patients with MI. To make this assessment, plasma levels of cMyBP-C and six known biomarkers of MI were determined by sandwich enzyme-linked immunosorbent assay in patients with MI who had before and after Percutaneous Transcoronary Angioplasty (PTCA), as well as healthy controls. Compared to healthy controls (22.3 ± 2.4 ng/mL (n=54)), plasma levels of cMyBP-C were significantly increased in patients with MI (105.1 ± 8.8 ng/mL (n=65), P<0.001). Out of 65 patients, 24 had very high levels of plasma cMyBP-C (116.5 ± 13.3 ng/mL), indicating high probability of MI. Importantly, cMyBP-C levels were significantly decreased in patients (n=40) at 12 hours post-PTCA (41.2 ± 9.3 ng/mL, P<0.001), compared to the patients with MI. Receiver operating characteristic analysis revealed that a plasma cMyBP-C reading of 68.1 ng/mL provided a sensitivity of 66.2% and a specificity of 100%. Also, myoglobin, carbonic anhydrase and creatine kinase-MB levels were significantly increased in MI patients who also had higher cMyBP-C levels. In contrast, levels of cardiac troponin I, glycogen phosphorylase and heart-type fatty acid binding protein were not significantly changed in the samples, indicating the importance of evaluating the differences in release kinetics of these biomarkers in the context of accurate diagnosis. Our findings suggest that circulating cMyBP-C is a sensitive and cardiac-specific biomarker with potential utility for the accurate diagnosis of MI.
Acute coronary syndrome; cardiac biomarker; cardiac myosin binding protein-C; contractile protein; cMyBP-C; myocardial infarction
Safe, accurate methods to reliably measure circulating red blood cell (RBC) kinetics are critical tools to investigate pathophysiology and therapy of anemia, including hemolytic anemias. This study documents the ability of a method using biotin-labeled RBCs (BioRBCs) to measure RBC survival (RCS) shortened by coating with a highly purified monomeric immunoglobulin G antibody to D antigen.
STUDY DESIGN AND METHODS
Autologous RBCs from 10 healthy D+ subjects were labeled with either biotin or 51Cr (reference method), coated (opsonized) either lightly (n = 4) or heavily (n = 6) with anti-D, and transfused. RCS was determined for BioRBCs and for 51Cr independently as assessed by three variables: 1) posttransfusion recovery at 24 hours (PTR24) for short-term RCS; 2) time to 50% decrease of the label (T50), and 3) mean potential life span (MPL) for long-term RCS.
BioRBCs tracked both normal and shortened RCS accurately relative to 51Cr. For lightly coated RBCs, mean PTR24, T50, and MPL results were not different between BioRBCs and 51Cr. For heavily coated RBCs, both short-term and long-term RCS were shortened by approximately 17 and 50%, respectively. Mean PTR24 by BioRBCs (84 ± 18%) was not different from 51Cr (81 ± 10%); mean T50 by BioRBCs (23 ± 17 days) was not different from 51Cr (22 ± 18 days).
RCS shortened by coating with anti-D can be accurately measured by BioRBCs. We speculate that BioRBCs will be useful for studying RCS in conditions involving accelerated removal of RBCs including allo- and autoimmune hemolytic anemias.
InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.
Mammalian non-coding micro RNAs (miRNAs) are a class of gene regulators that have been linked to immune system function. Here, we have investigated the role of miR-155 during an autoimmune inflammatory disease. Consistent with a positive role for miR-155 in mediating inflammatory responses, Mir155−/− mice were highly resistant to experimental autoimmune encephalomyelitis (EAE). miR-155 functions in the hematopoietic compartment to promote the development of inflammatory T cells including the T helper 17 (Th17) cell and Th1 cell subsets. Furthermore, the major contribution of miR-155 to EAE was CD4+ T cell intrinsic, whereas miR-155 was also required for optimum dendritic cell production of cytokines that promoted Th17 cell formation. Our study shows that one aspect of miR-155 function is the promotion of T cell-dependent tissue inflammation, suggesting that miR-155 might be a promising therapeutic target for the treatment of autoimmune disorders.
Motivation: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance.
Results: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues.
Contact: firstname.lastname@example.org; email@example.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment.
The regulation of gene expression is tightly interwoven with metabolism and signal transduction. A realistic view of gene regulatory networks should therefore not only include direct interactions resulting from transcription regulation, but also indirect regulatory interactions mediated by metabolic effectors and signaling molecules. Ignoring these indirect interactions during the analysis of the network dynamics may lead crucial feedback loops to be missed. We present a method for systematically deriving indirect interactions from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. This approach leads to novel insights as exemplified here on the carbon assimilation network of E. coli. We show that the derived gene regulatory network is densely connected, that the signs of the indirect interactions are largely fixed by the direction of metabolic fluxes, and that a change in flux direction may invert the sign of indirect interactions. Therefore the feedback structure of the network is much more complex than usually assumed; it appears robust to changes in the kinetic properties of its components and it can be flexibly rewired when the environment changes.
The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.
The analysis of gene evolution is a powerful approach to recognize the genetic features that contribute to the fitness of organisms. It was shown previously that selective constraints on protein sequences increase with expression level. This observation was surprising because there is a priori no reason why lowly expressed genes should be less important than highly expressed genes for the proper function of an organism. Here we show that selective pressure on the evolution of gene dosage, which is another important aspect of gene evolution, is also directly dependent on gene expression level. To explain these observations, we propose a model based on the fact that gene expression is a costly process (notably protein synthesis), so that there is an optimal expression level for each gene corresponding to a trade-off between the benefit and the cost of its expression. This model predicts that selective pressure on gene expression level or on the encoded protein should on average be stronger in highly expressed genes, providing a simple and common explanation for the general relationship observed between gene expression and the different facets of gene evolution.
The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total ∼58 000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein–protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).
Clinical and 3D dosimetric parameters are associated with symptomatic radiation pneumonitis rates in retrospective studies. Such parameters include: mean lung dose (MLD), radiation (RT) dose to perfused lung (via SPECT), and pre-RT lung function. Based on prior publications, we defined pre-RT criteria hypothesized to be predictive for later development of pneumonitis. We herein prospectively test the predictive abilities of these dosimetric/functional parameters on two cohorts of patients from Duke and the Netherlands Cancer Institute (NKI).
Methods and Materials
For the Duke cohort, 55 eligible patients treated between 1999-2005 on a prospective IRB-approved study to monitor RT-induced lung injury were analyzed. A similar group of patients treated at the NKI between 1996-2002 were identified. Patients believed to be at high and low risk for pneumonitis were defined based on: a) MLD; b) OpRP (sum of predicted perfusion reduction based on regional dose response curve); and c) pre-RT DLCO. All doses reflected tissue density heterogeneity. The rates of grade ≥2 pneumonitis in the “presumed” high and low risk groups were compared using Fisher’s exact test.
In the Duke group, pneumonitis rates in patients prospectively deemed to be at “high” vs. “low” risk are 7/20 and 9/35, respectively; p=0.33 one tailed Fisher’s. Similarly, comparable rates for the NKI group are 4/21 and 6/44, respectively, p=0.41 one-tailed Fisher’s.
The prospective model is unable to accurately segregate patients into high vs. low risk groups. However, considered retrospectively, these data are consistent with prior studies suggesting that dosimetric (e.g. MLD) and functional (e.g. PFTs or SPECT) parameters are predictive for RT-induced pneumonitis. Additional work is needed to better identify, and prospectively assess, predictors of RT-induced lung injury.
Radiation pneumonitis; Predictive models; Dose-volume histogram; Function; Lung cancer
The purpose of the study was to reassess the utility of transforming growth factor-beta-1 (TGF-β1) together with dosimetric and tumor parameters as a predictor for radiation pneumonitis (RP). Of the 121 patients studied, 32 (26.4%) developed grade ≥ 1 RP, and 27 (22.3%) developed grade ≥ 2 RP. For the endpoint of grade ≥ 1 RP, those with V30 > 30% and an end-RT/baseline TGF-β1 ratio ≥ 1 had a significantly higher incidence of RP than did those with V30 > 30% and an end-RT/baseline TGF-β1 ratio < 1. For most other patient groups, there were no clear associations between TGF-β1 values and rates of RP. These findings suggest that TGF-β1 is generally not predictive for RP except for the group of patients with a high V30.
TGF-β1; radiation pneumonitis
InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (), and for download by anonymous FTP (). The InterProScan search tool is now also available via a web service at .
The advent of fully sequenced genomes opens the ground for the reconstruction of metabolic pathways on the basis of the identification of enzyme-coding genes. Here we describe PRIAM, a method for automated enzyme detection in a fully sequenced genome, based on the classification of enzymes in the ENZYME database. PRIAM relies on sets of position-specific scoring matrices (‘profiles’) automatically tailored for each ENZYME entry. Automatically generated logical rules define which of these profiles is required in order to infer the presence of the corresponding enzyme in an organism. As an example, PRIAM was applied to identify potential metabolic pathways from the complete genome of the nitrogen-fixing bacterium Sinorhizobium meliloti. The results of this automated method were compared with the original genome annotation and visualised on KEGG graphs in order to facilitate the interpretation of metabolic pathways and to highlight potentially missing enzymes.
In order to prepare for whole-genome expression analysis in Sinorhizobium meliloti, pilot DNA macroarrays were designed for 34 genes of known regulation. The experimental parameters assessed were the length of the PCR products, the influence of a tag at the 5′ end of the primers, and the method of RNA labeling. Variance and principal-component analysis showed that the most important nonbiological parameter was the labeling method. The sizes of PCR products were also found to be important, whereas the influence of 5′ tags was minimal. The variability between replicated spots on a membrane was found to be low. These experimental procedures were validated by analyzing the effects of microaerobic conditions on gene expression.
InterPro, an integrated documentation resource of protein families, domains and functional sites, was created in 1999 as a means of amalgamating the major protein signature databases into one comprehensive resource. PROSITE, Pfam, PRINTS, ProDom, SMART and TIGRFAMs have been manually integrated and curated and are available in InterPro for text- and sequence-based searching. The results are provided in a single format that rationalises the results that would be obtained by searching the member databases individually. The latest release of InterPro contains 5629 entries describing 4280 families, 1239 domains, 95 repeats and 15 post-translational modifications. Currently, the combined signatures in InterPro cover more than 74% of all proteins in SWISS-PROT and TrEMBL, an increase of nearly 15% since the inception of InterPro. New features of the database include improved searching capabilities and enhanced graphical user interfaces for visualisation of the data. The database is available via a webserver (http://www.ebi.ac.uk/interpro) and anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro).
We report on a large-scale expressed sequence tag (EST) sequencing and analysis program aimed at characterizing the sets of genes expressed in roots of the model legume Medicago truncatula during interactions with either of two microsymbionts, the nitrogen-fixing bacterium Sinorhizobium meliloti or the arbuscular mycorrhizal fungus Glomus intraradices. We have designed specific tools for in silico analysis of EST data, in relation to chimeric cDNA detection, EST clustering, encoded protein prediction, and detection of differential expression. Our 21 473 5′- and 3′-ESTs could be grouped into 6359 EST clusters, corresponding to distinct virtual genes, along with 52 498 other M.truncatula ESTs available in the dbEST (NCBI) database that were recruited in the process. These clusters were manually annotated, using a specifically developed annotation interface. Analysis of EST cluster distribution in various M.truncatula cDNA libraries, supported by a refined R test to evaluate statistical significance and by ‘electronic northern’ representation, enabled us to identify a large number of novel genes predicted to be up- or down-regulated during either symbiotic root interaction. These in silico analyses provide a first global view of the genetic programs for root symbioses in M.truncatula. A searchable database has been built and can be accessed through a public interface.
Rodents immunized with complete Freund's adjuvant (CFA) are resistant to subsequent attempts to induce autoimmune disease, while animals immunized with incomplete Freund's adjuvant (IFA) remain susceptible. Mycobacterial extracts can stimulate inducible nitric oxide synthase (NOS2) gene transcription. Robust expression of NOS2 has been linked to suppression of T cell proliferation and alterations in immune responses. Our studies investigated the hypothesis that the immunoprotective effect of CFA before immunization requires functional NOS2. NOS2 gene expression is chronically elevated in lymph nodes and spleens of CFA-immunized mice. Maximal expression of NOS2 after CFA immunization requires the presence of functional type I tumor necrosis factor α receptor (TNFR1) and interferon γ. Groups of nontreated and CFA-preimmunized male C57BL/6J or C57BL/6NOS2−/− mice were immunized with myelin oligodendrocyte glycoprotein (MOG) peptide 35–55 in CFA to induce experimental allergic encephalomyelitis (EAE). Wild-type C57BL/6J mice were protected from the development of symptoms of EAE, while the NOS2−/− mice failed to be protected. NOS2-dependent effects of CFA included an augmentation of the MOG-specific IgG1 response, a decrease in interleukin 6 production by MOG-reactive lymphocytes, and a marked decrease in mononuclear cell infiltrates in the central nervous system. These studies support the hypothesis that CFA immunization modulates immune responses through a nitric oxide–dependent mechanism.
experimental allergic encephalomyelitis; Freund's adjuvant; immunosuppression; interleukin 6; tumor necrosis factor α
To evaluate the role of uridylyl-transferase, the Sinorhizobium meliloti glnD gene was isolated by heterologous complementation in Azotobacter vinelandii. The glnD gene is cotranscribed with a gene homologous to Salmonella mviN. glnD1::Ω or mviN1::Ω mutants could not be isolated by a powerful sucrose counterselection procedure unless a complementing cosmid was provided, indicating that glnD and mviN are members of an indispensable operon in S. meliloti.
ProDom contains all protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases (http://www.toulouse. inra.fr/prodom.html ). ProDom-CG results from a similar domain analysis as applied to completed genomes (http://www.toulouse.inra.fr/prodomCG.html ). Recent improvements to the ProDom database and its server include: scaling up to include sequences from TrEMBL, addition of Pfam-A entries to the set of expert validated families, assignment of stable accession numbers, consistency indicators for domain families, domain arrangements of sub-families and links to Pfam-A.