Serum S100B levels after meningioma surgery: A comparison of two laboratory assays

<p>Abstract</p> <p>Background</p> <p>S100B protein is a potential biomarker of central nervous system insult. This study quantitatively compared two methods for assessing serum concentration of S100B.</p> <p>Methods</p> <p>A prospective, observational study performed in a single tertiary medical center. Included were fifty two consecutive adult patients undergoing surgery for meningioma that provided blood samples for determination of S100B concentrations. Eighty samples (40 pre-operative and 40 postoperative) were randomly selected for batch testing. Each sample was divided into two aliquots. These were analyzed by ELISA (Sangtec) and a commercial kit (Roche Elecsys^®) for S100B concentrations. Statistical analysis included regression modelling and Bland-Altman analysis.</p> <p>Results</p> <p>A parsimonious linear model best described the prediction of commercial kit values by those determined by ELISA (y = 0.045 + 0.277*x, x = ELISA value, R²= 0.732). ELISA measurements tended to be higher than commercial kit measurements. This discrepancy increased linearly with increasing S100B concentrations. At concentrations above 0.7 μg/L the paired measurements were consistently outside the limits of agreement in the Bland-Altman display. Similar to other studies that used alternative measurement methods, sex and age related differences in serum S100B levels were not detected using the Elecsys^®(p = 0.643 and 0.728 respectively).</p> <p>Conclusion</p> <p>Although a generally linear relationship exists between serum S100B concentrations measured by ELISA and a commercially available kit, ELISA values tended to be higher than commercial kit measurements particularly at concentrations over 0.7 μg/L, which are suggestive of brain injury. International standardization of commercial kits is required before the predictive validity of S100B for brain damage can be effectively assessed in clinical practice.</p

Molecular Determinants of S100B Oligomer Formation

Background: S100B is a dimeric protein that can form tetramers, hexamers and higher order oligomers. These forms have been suggested to play a role in RAGE activation. Methodology/Principal Findings: Oligomerization was found to require a low molecular weight trigger/cofactor and could not be detected for highly pure dimer, irrespective of handling. Imidazol was identified as a substance that can serve this role. Oligomerization is dependent on both the imidazol concentration and pH, with optima around 90 mM imidazol and pH 7, respectively. No oligomerization was observed above pH 8, thus the protonated form of imidazol is the active species in promoting assembly of dimers to higher species. However, disulfide bonds are not involved and the process is independent of redox potential. The process was also found to be independent of whether Ca 2+ is bound to the protein or not. Tetramers that are purified from dimers and imidazol by gel filtration are kinetically stable, but dissociate into dimers upon heating. Dimers do not revert to tetramer and higher oligomer unless imidazol is again added. Both tetramers and hexamers bind the target peptide from p53 with retained stoichiometry of one peptide per S100B monomer, and with high affinity (lgK = 7.360.2 and 7.260.2, respectively in 10 mM BisTris, 5 mM CaCl 2, pH 7.0), which is less than one order of magnitude reduced compared to dimer under the same buffer conditions. Conclusion/Significance: S100B oligomerization requires protonated imidazol as a trigger/cofactor. Oligomers ar

The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study

Background: Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern. Methods: The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses. Results: Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status. Conclusions: Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups

Epigenetic regulation of S100 protein expression

S100 proteins are small, calcium-binding proteins whose genes are localized in a cluster on human chromosome 1. Through their ability to interact with various protein partners in a calcium-dependent manner, the S100 proteins exert their influence on many vital cellular processes such as cell cycle, cytoskeleton activity and cell motility, differentiation, etc. The characteristic feature of S100 proteins is their cell-specific expression, which is frequently up- or downregulated in various pathological states, including cancer. Changes in S100 protein expression are usually characteristic for a given type of cancer and are therefore often considered as markers of a malignant state. Recent results indicate that changes in S100 protein expression may depend on the extent of DNA methylation in the S100 gene regulatory regions. The range of epigenetic changes occurring within the S100 gene cluster has not been defined. This article reviews published data on the involvement of epigenetic factors in the control of S100 protein expression in development and cancer

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization

The German MultiCare-study: Patterns of multimorbidity in primary health care – protocol of a prospective cohort study

Background Multimorbidity is a highly frequent condition in older people, but well designed longitudinal studies on the impact of multimorbidity on patients and the health care system have been remarkably scarce in numbers until today. Little is known about the long term impact of multimorbidity on the patients' life expectancy, functional status and quality of life as well as health care utilization over time. As a consequence, there is little help for GPs in adjusting care for these patients, even though studies suggest that adhering to present clinical practice guidelines in the care of patients with multimorbidity may have adverse effects. Methods The study is designed as a multicentre prospective, observational cohort study of 3.050 patients aged 65 to 85 at baseline with at least three different diagnoses out of a list of 29 illnesses and syndromes. The patients will be recruited in approx. 120 to 150 GP surgeries in 8 study centres distributed across Germany. Information about the patients' morbidity will be collected mainly in GP interviews and from chart reviews. Functional status, resources/risk factors, health care utilization and additional morbidity data will be assessed in patient interviews, in which a multitude of well established standardized questionnaires and tests will be performed. Discussion The main aim of the cohort study is to monitor the course of the illness process and to analyse for which reasons medical conditions are stable, deteriorating or only temporarily present. First, clusters of combinations of diseases/disorders (multimorbidity patterns) with a comparable impact (e.g. on quality of life and/or functional status) will be identified. Then the development of these clusters over time will be analysed, especially with regard to prognostic variables and the somatic, psychological and social consequences as well as the utilization of health care resources. The results will allow the development of an instrument for prediction of the deterioration of the illness process and point at possibilities of prevention. The practical consequences of the study results for primary care will be analysed in expert focus groups in order to develop strategies for the inclusion of the aspects of multimorbidity in primary care guidelines

Identification of Y-Box Binding Protein 1 As a Core Regulator of MEK/ERK Pathway-Dependent Gene Signatures in Colorectal Cancer Cells

Transcriptional signatures are an indispensible source of correlative information on disease-related molecular alterations on a genome-wide level. Numerous candidate genes involved in disease and in factors of predictive, as well as of prognostic, value have been deduced from such molecular portraits, e.g. in cancer. However, mechanistic insights into the regulatory principles governing global transcriptional changes are lagging behind extensive compilations of deregulated genes. To identify regulators of transcriptome alterations, we used an integrated approach combining transcriptional profiling of colorectal cancer cell lines treated with inhibitors targeting the receptor tyrosine kinase (RTK)/RAS/mitogen-activated protein kinase pathway, computational prediction of regulatory elements in promoters of co-regulated genes, chromatin-based and functional cellular assays. We identified commonly co-regulated, proliferation-associated target genes that respond to the MAPK pathway. We recognized E2F and NFY transcription factor binding sites as prevalent motifs in those pathway-responsive genes and confirmed the predicted regulatory role of Y-box binding protein 1 (YBX1) by reporter gene, gel shift, and chromatin immunoprecipitation assays. We also validated the MAPK-dependent gene signature in colorectal cancers and provided evidence for the association of YBX1 with poor prognosis in colorectal cancer patients. This suggests that MEK/ERK-dependent, YBX1-regulated target genes are involved in executing malignant properties

Inhibition of Lassa Virus Glycoprotein Cleavage and Multicycle Replication by Site 1 Protease-Adapted α1-Antitrypsin Variants

The virus family Arenaviridae includes several hemorrhagic fever causing agents such as Lassa, Guanarito, Junin, Machupo, and Sabia virus that pose a major public health concern to the human population in West African and South American countries. Current treatment options to control fatal outcome of disease are limited to the ribonucleoside analogue ribavirin, although its use has some significant limitations. The lack of effective treatment alternatives emphasizes the need for novel antiviral therapeutics to counteract these life-threatening infections. Maturation cleavage of the viral envelope glycoprotein by the host cell proprotein convertase site 1 protease (S1P) is critical for infectious virion production of several pathogenic arenaviruses. This finding makes this protease an attractive target for the development of novel anti-arenaviral therapeutics. We demonstrate here that highly selective S1P-adapted α1-antitrypsins have the potential to efficiently inhibit glycoprotein processing, which resulted in reduced Lassa virus replication. Our findings suggest that S1P should be considered as an antiviral target and that further optimization of modified α1-antitrypsins could lead to potent and specific S1P inhibitors with the potential for treatment of certain viral hemorrhagic fevers