Neurodegeneration and Epilepsy in a Zebrafish Model of CLN3 Disease (Batten Disease)

The neuronal ceroid lipofuscinoses are a group of lysosomal storage disorders that comprise the most common, genetically heterogeneous, fatal neurodegenerative disorders of children. They are characterised by childhood onset, visual failure, epileptic seizures, psychomotor retardation and dementia. CLN3 disease, also known as Batten disease, is caused by autosomal recessive mutations in the CLN3 gene, 80–85% of which are a ~1 kb deletion. Currently no treatments exist, and after much suffering, the disease inevitably results in premature death. The aim of this study was to generate a zebrafish model of CLN3 disease using antisense morpholino injection, and characterise the pathological and functional consequences of Cln3 deficiency, thereby providing a tool for future drug discovery. The model was shown to faithfully recapitulate the pathological signs of CLN3 disease, including reduced survival, neuronal loss, retinopathy, axonopathy, loss of motor function, lysosomal storage of subunit c of mitochondrial ATP synthase, and epileptic seizures, albeit with an earlier onset and faster progression than the human disease. Our study provides proof of principle that the advantages of the zebrafish over other model systems can be utilised to further our understanding of the pathogenesis of CLN3 disease and accelerate drug discovery

Inclusive search for same-sign dilepton signatures in pp collisions at root s=7 TeV with the ATLAS detector

An inclusive search is presented for new physics in events with two isolated leptons (e or mu) having the same electric charge. The data are selected from events collected from p p collisions at root s = 7 TeV by the ATLAS detector and correspond to an integrated luminosity of 34 pb(-1). The spectra in dilepton invariant mass, missing transverse momentum and jet multiplicity are presented and compared to Standard Model predictions. In this event sample, no evidence is found for contributions beyond those of the Standard Model. Limits are set on the cross-section in a fiducial region for new sources of same-sign high-mass dilepton events in the ee, e mu and mu mu channels. Four models predicting same-sign dilepton signals are constrained: two descriptions of Majorana neutrinos, a cascade topology similar to supersymmetry or universal extra dimensions, and fourth generation d-type quarks. Assuming a new physics scale of 1 TeV, Majorana neutrinos produced by an effective operator V with masses below 460 GeV are excluded at 95% confidence level. A lower limit of 290 GeV is set at 95% confidence level on the mass of fourth generation d-type quarks

Measurement of the top quark-pair production cross section with ATLAS in pp collisions at \sqrt{s}=7\TeV

A measurement of the production cross-section for top quark pairs(\ttbar) in $pp$ collisions at \sqrt{s}=7 \TeV is presented using data recorded with the ATLAS detector at the Large Hadron Collider. Events are selected in two different topologies: single lepton (electron $e$ or muon $\mu$) with large missing transverse energy and at least four jets, and dilepton ($ee$, $\mu\mu$ or $e\mu$) with large missing transverse energy and at least two jets. In a data sample of 2.9 pb-1, 37 candidate events are observed in the single-lepton topology and 9 events in the dilepton topology. The corresponding expected backgrounds from non-\ttbar Standard Model processes are estimated using data-driven methods and determined to be $12.2 \pm 3.9$ events and $2.5 \pm 0.6$ events, respectively. The kinematic properties of the selected events are consistent with SM \ttbar production. The inclusive top quark pair production cross-section is measured to be \sigmattbar=145 \pm 31 ^{+42}_{-27} pb where the first uncertainty is statistical and the second systematic. The measurement agrees with perturbative QCD calculations.Comment: 30 pages plus author list (50 pages total), 9 figures, 11 tables, CERN-PH number and final journal adde

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011

Measurement of D*+/- meson production in jets from pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

This paper reports a measurement of D*+/- meson production in jets from proton-proton collisions at a center-of-mass energy of sqrt(s) = 7 TeV at the CERN Large Hadron Collider. The measurement is based on a data sample recorded with the ATLAS detector with an integrated luminosity of 0.30 pb^-1 for jets with transverse momentum between 25 and 70 GeV in the pseudorapidity range |eta| < 2.5. D*+/- mesons found in jets are fully reconstructed in the decay chain: D*+ -> D0pi+, D0 -> K-pi+, and its charge conjugate. The production rate is found to be N(D*+/-)/N(jet) = 0.025 +/- 0.001(stat.) +/- 0.004(syst.) for D*+/- mesons that carry a fraction z of the jet momentum in the range 0.3 < z < 1. Monte Carlo predictions fail to describe the data at small values of z, and this is most marked at low jet transverse momentum.Comment: 10 pages plus author list (22 pages total), 5 figures, 1 table, matches published version in Physical Review

Measurement of inclusive two-particle angular correlations in pp collisions with the ATLAS detector at the LHC

We present a measurement of two-particle angular correlations in proton- proton collisions at s√=900 GeV and 7 TeV. The collision events were collected during 2009 and 2010 with the ATLAS detector at the Large Hadron Collider using a single-arm minimum bias trigger. Correlations are measured for charged particles produced in the kinematic range of transverse momentum p T  > 100 MeV and pseudorapidity |η| < 2.5. A complex structure in pseudorapidity and azimuth is observed at both collision energies. Results are compared to pythia 8 and herwig++ as well as to the AMBT2B, DW and Perugia 2011 tunes of pythia 6. The data are not satisfactorily described by any of these models

The main rhinovirus respiratory tract adhesion site (ICAM-1) is upregulated in smokers and patients with chronic airflow limitation (CAL).

BACKGROUND: ICAM-1 is a major receptor for ~60% of human rhinoviruses, and non-typeable Haemophilus influenzae, two major pathogens in COPD. Increased cell-surface expression of ICAM-1 in response to tobacco smoke exposure has been suggested. We have investigated epithelial ICAM-1 expression in both the large and small airways, and lung parenchyma in smoking-related chronic airflow limitation (CAL) patients. METHODS: We evaluated epithelial ICAM-1 expression in resected lung tissue: 8 smokers with normal spirometry (NLFS); 29 CAL patients (10 small-airway disease; 9 COPD-smokers; 10 COPD ex-smokers); Controls (NC): 15 normal airway/lung tissues. Immunostaining with anti-ICAM-1 monoclonal antibody was quantified with computerized image analysis. The percent and type of cells expressing ICAM-1 in large and small airway epithelium and parenchyma were enumerated, plus percentage of epithelial goblet and submucosal glands positive for ICAM- 1. RESULTS: A major increase in ICAM-1 expression in epithelial cells was found in both large (p < 0.006) and small airways (p < 0.004) of CAL subjects compared to NC, with NLFS being intermediate. In the CAL group, both basal and luminal areas stained heavily for ICAM-1, so did goblet cells and sub-mucosal glands, however in either NC or NLFS subjects, only epithelial cell luminal surfaces stained. ICAM-1 expression on alveolar pneumocytes (mainly type II) was slightly increased in CAL and NLFS (p < 0.01). Pack-years of smoking correlated with ICAM-1 expression (r = 0.49; p < 0.03). CONCLUSION: Airway ICAM-1 expression is markedly upregulated in CAL group, which could be crucial in rhinoviral and NTHi infections. The parenchymal ICAM-1 is affected by smoking, with no further enhancement in CAL subjects

β-catenin, Twist and Snail: Transcriptional regulation of EMT in smokers and COPD, and relation to airflow obstruction.

COPD is characterised by poorly reversible airflow obstruction usually due to cigarette smoking. The transcription factor clusters of β-catenin/Snail1/Twist has been implicated in the process of epithelial mesenchymal transition (EMT), an intermediate between smoking and airway fibrosis, and indeed lung cancer. We have investigated expression of these transcription factors and their "cellular localization" in bronchoscopic airway biopsies from patients with COPD, and in smoking and non-smoking controls. An immune-histochemical study compared cellular protein expression of β-catenin, Snail1 and Twist, in these subject groups in 3 large airways compartment: epithelium (basal region), reticular basement membrane (Rbm) and underlying lamina propria (LP). β-catenin and Snail1 expression was generally high in all subjects throughout the airway wall with marked cytoplasmic to nuclear shift in COPD (P < 0.01). Twist expression was generalised in the epithelium in normal but become more basal and nuclear with smoking (P < 0.05). In addition, β-catenin and Snail1 expression, and to lesser extent of Twist, was related to airflow obstruction and to expression of a canonical EMT biomarker (S100A4). The β-catenin-Snail1-Twist transcription factor cluster is up-regulated and nuclear translocated in smokers and COPD, and their expression is closely related to both EMT activity and airway obstruction

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease

Biomarkers are becoming increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered 'synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: they target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that these agents can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and substantially improve early detection of cancer compared with current clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and point-of-care diagnostics.National Institutes of Health (U.S.) (Bioengineering Research Partnership R01 CA124427)Kathy and Curt Marble Cancer Research FundNational Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (F32CA159496-01