Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis.

Background A major cause of disability in secondary progressive multiple sclerosis (SPMS) is progressive brain atrophy, whose pathogenesis is not fully understood. The objective of this study was to identify protein biomarkers of brain atrophy in SPMS. Methods We used surface-enhanced laser desorption-ionization time-of-flight mass spectrometry to carry out an unbiased search for serum proteins whose concentration correlated with the rate of brain atrophy, measured by serial MRI scans over a 2-year period in a well-characterized cohort of 140 patients with SPMS. Protein species were identified by liquid chromatography-electrospray ionization tandem mass spectrometry. Results There was a significant (p<0.004) correlation between the rate of brain atrophy and a rise in the concentration of proteins at 15.1 kDa and 15.9 kDa in the serum. Tandem mass spectrometry identified these proteins as alpha-haemoglobin and beta-haemoglobin, respectively.  The abnormal concentration of free serum haemoglobin was confirmed by ELISA (p<0.001). The serum lactate dehydrogenase activity was also highly significantly raised (p<10-12) in patients with secondary progressive multiple sclerosis. Conclusions An underlying low-grade chronic intravascular haemolysis is a potential source of the iron whose deposition along blood vessels in multiple sclerosis plaques contributes to the neurodegeneration and consequent brain atrophy seen in progressive disease. Chelators of free serum iron will be ineffective in preventing this neurodegeneration, because the iron (Fe2+) is chelated by haemoglobin

Toward a descriptive model of solar particles in the heliosphere

During a workshop on the interplanetary charged particle environment held in 1987, a descriptive model of solar particles in the heliosphere was assembled. This model includes the fluence, composition, energy spectra, and spatial and temporal variations of solar particles both within and beyong 1 AU. The ability to predict solar particle fluences was also discussed. Suggestions for specific studies designed to improve the basic model were also made

The transcriptional programme of Salmonella enterica serovar Typhimurium reveals a key role for tryptophan metabolism in biofilms

<p>Abstract</p> <p>Background</p> <p>Biofilm formation enhances the capacity of pathogenic <it>Salmonella </it>bacteria to survive stresses that are commonly encountered within food processing and during host infection. The persistence of <it>Salmonella </it>within the food chain has become a major health concern, as biofilms can serve as a reservoir for the contamination of food products. While the molecular mechanisms required for the survival of bacteria on surfaces are not fully understood, transcriptional studies of other bacteria have demonstrated that biofilm growth triggers the expression of specific sets of genes, compared with planktonic cells. Until now, most gene expression studies of <it>Salmonella </it>have focused on the effect of infection-relevant stressors on virulence or the comparison of mutant and wild-type bacteria. However little is known about the physiological responses taking place inside a <it>Salmonella </it>biofilm.</p> <p>Results</p> <p>We have determined the transcriptomic and proteomic profiles of biofilms of <it>Salmonella enterica </it>serovar Typhimurium. We discovered that 124 detectable proteins were differentially expressed in the biofilm compared with planktonic cells, and that 10% of the <it>S</it>. Typhimurium genome (433 genes) showed a 2-fold or more change in the biofilm compared with planktonic cells. The genes that were significantly up-regulated implicated certain cellular processes in biofilm development including amino acid metabolism, cell motility, global regulation and tolerance to stress. We found that the most highly down-regulated genes in the biofilm were located on <it>Salmonella </it>Pathogenicity Island 2 (SPI2), and that a functional SPI2 secretion system regulator (<it>ssrA</it>) was required for <it>S</it>. Typhimurium biofilm formation. We identified STM0341 as a gene of unknown function that was needed for biofilm growth. Genes involved in tryptophan (<it>trp</it>) biosynthesis and transport were up-regulated in the biofilm. Deletion of <it>trpE </it>led to decreased bacterial attachment and this biofilm defect was restored by exogenous tryptophan or indole.</p> <p>Conclusions</p> <p>Biofilm growth of <it>S</it>. Typhimurium causes distinct changes in gene and protein expression. Our results show that aromatic amino acids make an important contribution to biofilm formation and reveal a link between SPI2 expression and surface-associated growth in <it>S</it>. Typhimurium.</p

New technologies for diagnosing active TB: the VANTDET diagnostic accuracy study

BackgroundTuberculosis (TB) is a devastating disease for which new diagnostic tests are desperately needed.ObjectiveTo validate promising new technologies [namely whole-blood transcriptomics, proteomics, flow cytometry and quantitative reverse transcription-polymerase chain reaction (qRT-PCR)] and existing signatures for the detection of active TB in samples obtained from individuals with suspected active TB.DesignFour substudies, each of which used samples from the biobank collected as part of the interferon gamma release assay (IGRA) in the Diagnostic Evaluation of Active TB study, which was a prospective cohort of patients recruited with suspected TB.SettingSecondary care.ParticipantsAdults aged ≥ 16 years presenting as inpatients or outpatients at 12 NHS hospital trusts in London, Slough, Oxford, Leicester and Birmingham, with suspected active TB.InterventionsNew tests using genome-wide gene expression microarray (transcriptomics), surface-enhanced laser desorption ionisation time-of-flight mass spectrometry/liquid chromatography–mass spectrometry (proteomics), flow cytometry or qRT-PCR.Main outcome measuresArea under the curve (AUC), sensitivity and specificity were calculated to determine diagnostic accuracy. Positive and negative predictive values were calculated in some cases. A decision tree model was developed to calculate the incremental costs and quality-adjusted life-years of changing from current practice to using the novels tests.ResultsThe project, and four substudies that assessed the previously published signatures, measured each of the new technologies and performed a health economic analysis in which the best-performing tests were evaluated for cost-effectiveness. The diagnostic accuracy of the transcriptomic tests ranged from an AUC of 0.81 to 0.84 for detecting all TB in our cohort. The performance for detecting culture-confirmed TB or pulmonary TB was better than for highly probable TB or extrapulmonary tuberculosis (EPTB), but was not high enough to be clinically useful. None of the previously described serum proteomic signatures for active TB provided good diagnostic accuracy, nor did the candidate rule-out tests. Four out of six previously described cellular immune signatures provided a reasonable level of diagnostic accuracy (AUC = 0.78–0.92) for discriminating all TB from those with other disease and latent TB infection in human immunodeficiency virus-negative TB suspects. Two of these assays may be useful in the IGRA-positive population and can provide high positive predictive value. None of the new tests for TB can be considered cost-effective.LimitationsThe diagnostic performance of new tests among the HIV-positive population was either underpowered or not sufficiently achieved in each substudy.ConclusionsOverall, the diagnostic performance of all previously identified ‘signatures’ of TB was lower than previously reported. This probably reflects the nature of the cohort we used, which includes the harder to diagnose groups, such as culture-unconfirmed TB or EPTB, which were under-represented in previous cohorts.Future workWe are yet to achieve our secondary objective of deriving novel signatures of TB using our data sets. This was beyond the scope of this report. We recommend that future studies using these technologies target specific subtypes of TB, specifically those groups for which new diagnostic tests are required.FundingThis project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and NIHR partnership

Kawasaki Disease Patient Stratification and Pathway Analysis Based on Host Transcriptomic and Proteomic Profiles

The aetiology of Kawasaki disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host ‘omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple ‘omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering, and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infections at the transcriptomic and proteomic levels through comparison of ‘omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both ‘omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both ‘omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition

Financial considerations in the conduct of multi-centre randomised controlled trials: evidence from a qualitative study.

National Coordinating Centre for Research Methodology; Medical Research Council, UK Department of Health; Chief Scientist OfficeNot peer reviewedPublisher PD

Structure of the germline genome of Tetrahymena thermophila and relationship to the massively rearranged somatic genome

The germline genome of the binucleated ciliate Tetrahymena thermophila undergoes programmed chromosome breakage and massive DNA elimination to generate the somatic genome. Here, we present a complete sequence assembly of the germline genome and analyze multiple features of its structure and its relationship to the somatic genome, shedding light on the mechanisms of genome rearrangement as well as the evolutionary history of this remarkable germline/soma differentiation. Our results strengthen the notion that a complex, dynamic, and ongoing interplay between mobile DNA elements and the host genome have shaped Tetrahymena chromosome structure, locally and globally. Non-standard outcomes of rearrangement events, including the generation of short-lived somatic chromosomes and excision of DNA interrupting protein-coding regions, may represent novel forms of developmental gene regulation. We also compare Tetrahymenas germline/soma differentiation to that of other characterized ciliates, illustrating the wide diversity of adaptations that have occurred within this phylum.</p

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

Background: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. Methods: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. Findings: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. Interpretation: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics

Transverse energy production and charged-particle multiplicity at midrapidity in various systems from sNN=7.7\sqrt{s_{NN}}=7.7 to 200 GeV

Measurements of midrapidity charged particle multiplicity distributions, $dN_{\rm ch}/d\eta$, and midrapidity transverse-energy distributions, $dE_T/d\eta$, are presented for a variety of collision systems and energies. Included are distributions for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$, 130, 62.4, 39, 27, 19.6, 14.5, and 7.7 GeV, Cu$+$Cu collisions at $\sqrt{s_{_{NN}}}=200$ and 62.4 GeV, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV, U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV, $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV, $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV, and $p$$+$$p$ collisions at $\sqrt{s_{_{NN}}}=200$ GeV. Centrality-dependent distributions at midrapidity are presented in terms of the number of nucleon participants, $N_{\rm part}$, and the number of constituent quark participants, $N_{q{\rm p}}$. For all $A$$+$$A$ collisions down to $\sqrt{s_{_{NN}}}=7.7$ GeV, it is observed that the midrapidity data are better described by scaling with $N_{q{\rm p}}$ than scaling with $N_{\rm part}$. Also presented are estimates of the Bjorken energy density, $\varepsilon_{\rm BJ}$, and the ratio of $dE_T/d\eta$ to $dN_{\rm ch}/d\eta$, the latter of which is seen to be constant as a function of centrality for all systems.Comment: 706 authors, 32 pages, 20 figures, 34 tables, 2004, 2005, 2008, 2010, 2011, and 2012 data. v2 is version accepted for publication in Phys. Rev.

Estrogen Regulates the Satellite Cell Compartment in Females

Skeletal muscle mass, strength, and regenerative capacity decline with age, with many measures showing a greater deterioration in females around the time estrogen levels decrease at menopause. Here, we show that estrogen deficiency severely compromises the maintenance of muscle stem cells (i.e., satellite cells) as well as impairs self-renewal and differentiation into muscle fibers. Mechanistically, by hormone replacement, use of a selective estrogen-receptor modulator (bazedoxifene), and conditional estrogen receptor knockout, we implicate 17β-estradiol and satellite cell expression of estrogen receptor α and show that estrogen signaling through this receptor is necessary to prevent apoptosis of satellite cells. Early data from a biopsy study of women who transitioned from peri- to post-menopause are consistent with the loss of satellite cells coincident with the decline in estradiol in humans. Together, these results demonstrate an important role for estrogen in satellite cell maintenance and muscle regeneration in females