Immune adaptor ADAP in T cells regulates HIV-1 transcription and cell-cell viral spread via different co-receptors

Background: Immune cell adaptor protein ADAP (adhesion and degranulation-promoting adaptor protein) mediates aspects of T-cell adhesion and proliferation. Despite this, a connection between ADAP and infection by the HIV-1 (human immunodeficiency virus-1) has not been explored. Results: In this paper, we show for the first time that ADAP and its binding to SLP-76 (SH2 domain-containing leukocyte protein of 76 kDa) regulate HIV-1 infection via two distinct mechanisms and co-receptors. siRNA down-regulation of ADAP, or expression of a mutant that is defective in associating to its binding partner SLP-76 (termed M12), inhibited the propagation of HIV-1 in T-cell lines and primary human T-cells. In one step, ADAP and its binding to SLP-76 were needed for the activation of NF-κB and its transcription of the HIV-1 long terminal repeat (LTR) in cooperation with ligation of co-receptor CD28, but not LFA-1. In a second step, the ADAP-SLP-76 module cooperated with LFA-1 to regulate conjugate formation between T-cells and dendritic cells or other T-cells as well as the development of the virological synapse (VS) and viral spread between immune cells. Conclusions: These findings indicate that ADAP regulates two steps of HIV-1 infection cooperatively with two distinct receptors, and as such, serves as a new potential target in the blockade of HIV-1 infection

The effectiveness of the 13-valent pneumococcal conjugate vaccine against hypoxic pneumonia in children in Lao People's Democratic Republic: An observational hospital-based test-negative study

Background: Pneumococcal pneumonia is a leading cause of childhood mortality. Pneumococcal conjugate vaccines (PCVs) have been shown to reduce hypoxic pneumonia in children. However, there are no studies from Asia examining the effectiveness of PCVs on hypoxic pneumonia. We describe a novel approach to determine the effectiveness of the 13-valent PCV (PCV13) against hypoxia in children admitted with pneumonia in the Lao People's Democratic Republic. Methods: A prospective hospital-based, test-negative observational study of children aged up to 59 months admitted with pneumonia to a single tertiary hospital in Vientiane was undertaken over 54 months. Pneumonia was defined using the 2013 WHO definition. Hypoxia was defined as oxygen saturation <90% in room air or requiring oxygen supplementation during hospitalisation. Test-negative cases and controls were children with hypoxic and non-hypoxic pneumonia, respectively. PCV13 status was determined by written record. Vaccine effectiveness was calculated using logistic regression. Propensity score and multiple imputation analyses were used to handle confounding and missing data. Findings: There were 826 children admitted with pneumonia, 285 had hypoxic pneumonia and 377 were PCV13-vaccinated. The unadjusted, propensity-score adjusted and multiple-imputation adjusted estimates of vaccine effectiveness against hypoxic pneumonia were 23% (95% confidence interval: -9, 46%; p=0•14); 37% (6, 57%; p=0•02) and 35% (7, 55%; p=0•02) respectively. Interpretation: PCV13 is effective against hypoxic pneumonia in Asia, and should be prioritised for inclusion in national immunisation programs. This single hospital-based, test-negative approach can be used to assess vaccine effectiveness in other similar settings. Funding: Funded by the Bill & Melinda Gates Foundation

Quantitative Understanding of Probabilistic Behavior of Living Cells Operated by Vibrant Intracellular Networks

For quantitative understanding of probabilistic behaviors of living cells, it is essential to construct a correct mathematical description of intracellular networks interacting with complex cell environments, which has been a formidable task. Here, we present a novel model and stochastic kinetics for an intracellular network interacting with hidden cell environments, employing a complete description of cell state dynamics and its coupling to the system network. Our analysis reveals that various environmental effects on the product number fluctuation of intracellular reaction networks can be collectively characterized by Laplace transform of the time-correlation function of the product creation rate fluctuation with the Laplace variable being the product decay rate. On the basis of the latter result, we propose an efficient method for quantitative analysis of the chemical fluctuation produced by intracellular networks coupled to hidden cell environments. By applying the present approach to the gene expression network, we obtain simple analytic results for the gene expression variability and the environment-induced correlations between the expression levels of mutually noninteracting genes. The theoretical results compose a unified framework for quantitative understanding of various gene expression statistics observed across a number of different systems with a small number of adjustable parameters with clear physical meanings.open1143sciescopu

Relationship between Environmental Phthalate Exposure and the Intelligence of School-Age Children

BACKGROUND: Concern over phthalates has emerged because of their potential toxicity to humans. OBJECTIVE: We investigated the relationship between the urinary concentrations of phthalate metabolites and children`s intellectual functioning. METHODS: This study enrolled 667 children at nine elementary schools in five South Korean cities. A cross-sectional examination of urine phthalate concentrations was performed, and scores on neuro-psychological tests were obtained from both the children and their mothers. RESULTS: We measured mono-2-ethylhexyl phthalate (MEHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), both metabolites of di(2-ethylhexyl)phthalate (DEHP), and mono-n-butyl phthalate (MBP), a metabolite of dibutyl phthalate (DBP), in urine samples. The geometric mean (ln) concentrations of MEHP, MEOHP, and MBP were 21.3 mu g/L [geometric SD (GSD) = 2.2 mu g/L; range, 0.5-445.4], 18.0 mu g/L (GSD = 2.4; range, 0.07-291.1), and 48.9 mu g/L (GSD = 2.2; range, 2.1-1645.5), respectively. After adjusting for demographic and developmental covariates, the Full Scale IQ and Verbal IQ scores were negatively associated with DEHP metabolites but not with DBP metabolites. We also found a significant negative relationship between the urine concentrations of the metabolites of DEHP and DBP and children`s vocabulary subscores. After controlling for maternal IQ, a significant inverse relationship between DEHP metabolites and vocabulary subscale score remained. Among boys, we found a negative association between increasing MEHP phthalate concentrations and the sum of DEHP metabolite concentrations and Wechsler Intelligence Scale for Children vocabulary score; however, among girls, we found no significant association between these variables. CONCLUSION: Controlling for maternal IQ and other covariates, the results show an inverse relationship between phthalate metabolites and IQ scores; however, given the limitations in cross-sectional epidemiology, prospective studies are needed to fully explore these associations.This work was funded by the Eco-Technopia 21 project of Korea Institute of Environmental Science and Technology (091-081-059).Cho SC, 2010, J CHILD PSYCHOL PSYC, V51, P1050, DOI 10.1111/j.1469-7610.2010.02250.xKim BN, 2009, BIOL PSYCHIAT, V66, P958, DOI 10.1016/j.biopsych.2009.07.034Tanida T, 2009, TOXICOL LETT, V189, P40, DOI 10.1016/j.toxlet.2009.04.005Ghisari M, 2009, TOXICOL LETT, V189, P67, DOI 10.1016/j.toxlet.2009.05.004Barnett JH, 2009, AM J PSYCHIAT, V166, P909, DOI 10.1176/appi.ajp.2009.08081251Kim Y, 2009, NEUROTOXICOLOGY, V30, P564, DOI 10.1016/j.neuro.2009.03.012Engel SM, 2009, NEUROTOXICOLOGY, V30, P522, DOI 10.1016/j.neuro.2009.04.001Kamrin MA, 2009, J TOXICOL ENV HEAL B, V12, P157, DOI 10.1080/10937400902729226Brown JS, 2009, SCHIZOPHRENIA BULL, V35, P256, DOI 10.1093/schbul/sbm147Bellinger DC, 2008, NEUROTOXICOLOGY, V29, P828, DOI 10.1016/j.neuro.2008.04.005Wolff MS, 2008, ENVIRON HEALTH PERSP, V116, P1092, DOI 10.1289/ehp.11007van Neerven S, 2008, PROG NEUROBIOL, V85, P433, DOI 10.1016/j.pneurobio.2008.04.006Hatch EE, 2008, ENVIRON HEALTH-GLOB, V7, DOI 10.1186/1476-069X-7-27Zevalkink J, 2008, J GENET PSYCHOL, V169, P72Kolarik B, 2008, ENVIRON HEALTH PERSP, V116, P98, DOI 10.1289/ehp.10498SATHYANARAYANA S, 2008, CURR PROBL PEDIAT AD, V38, P34KHO YL, 2008, J ENV HLTH SCI, V34, P271Huang PC, 2007, HUM REPROD, V22, P2715, DOI 10.1093/humrep/dem205Janjua NR, 2007, ENVIRON SCI TECHNOL, V41, P5564, DOI 10.1021/es0628755Meeker JD, 2007, ENVIRON HEALTH PERSP, V115, P1029, DOI 10.1289/ehp.9852Fromme H, 2007, INT J HYG ENVIR HEAL, V210, P21, DOI 10.1016/j.ijheh.2006.09.005Xu Y, 2007, ARCH TOXICOL, V81, P57, DOI 10.1007/s00204-006-0143-8Pereira C, 2007, ACTA HISTOCHEM, V109, P29, DOI 10.1016/j.acthis.2006.09.008Hauser R, 2006, EPIDEMIOLOGY, V17, P682, DOI 10.1097/01.ede.0000235996.89953.d7Zhu DF, 2006, BRAIN, V129, P2923, DOI 10.1093/brain/awl215Andrade AJM, 2006, TOXICOLOGY, V227, P185, DOI 10.1016/j.tox.2006.07.022Lottrup G, 2006, INT J ANDROL, V29, P172, DOI 10.1111/j.1365-2605.2005.00642.xBreous E, 2005, MOL CELL ENDOCRINOL, V244, P75, DOI 10.1016/j.mce.2005.06.009Wenzel A, 2005, MOL CELL ENDOCRINOL, V244, P63, DOI 10.1016/j.mce.2005.02.008Kato K, 2005, ANAL CHEM, V77, P2985, DOI 10.1021/ac0481248Tanaka T, 2005, FOOD CHEM TOXICOL, V43, P581, DOI 10.1016/j.fct.2005.01.001Duty SM, 2005, HUM REPROD, V20, P604, DOI 10.1093/humrep/deh656Kota BP, 2005, PHARMACOL RES, V51, P85, DOI 10.1016/j.phrs.2004.07.012Hays T, 2005, CARCINOGENESIS, V26, P219, DOI 10.1093/carcin/bgh285Hauser R, 2004, ENVIRON HEALTH PERSP, V112, P1734, DOI 10.1289/ehp.7212Bornehag CG, 2004, ENVIRON HEALTH PERSP, V112, P1393, DOI 10.1289/ehp.7187Ishido M, 2004, J NEUROCHEM, V91, P69, DOI 10.1111/j.1471-4159.2004.02696.xMink PJ, 2004, EPIDEMIOLOGY, V15, P385, DOI 10.1097/01.ede.0000128402.86336.7eBellinger DC, 2004, EPIDEMIOLOGY, V15, P383, DOI 10.1097/01.ede.0000129525.15064.a4Shea KM, 2003, PEDIATRICS, V111, P1467Tanaka T, 2002, FOOD CHEM TOXICOL, V40, P1499, DOI 10.1016/S0278-6915(02)00073-XHoppin JA, 2002, ENVIRON HEALTH PERSP, V110, P515SATTLER JM, 2001, ASSESSMENT CHILDRENRice D, 2000, ENVIRON HEALTH PERSP, V108, P511Bellinger DC, 2000, NEUROTOXICOL TERATOL, V22, P133LIM YR, 2000, KOR J CLIN PSYCHOL, V19, P563Braissant O, 1998, ENDOCRINOLOGY, V139, P2748Peters JM, 1997, CARCINOGENESIS, V18, P2029Baldini IM, 1997, PROG NEURO-PSYCHOPH, V21, P925Roberts RA, 1997, FUND APPL TOXICOL, V38, P107PARK KS, 1996, DEV KEDI WISC INDIVIMONZANI F, 1993, CLIN INVESTIGATOR, V71, P367SILVERSTEIN AB, 1990, J CLIN PSYCHOL, V46, P333HINTON RH, 1986, ENVIRON HEALTH PERSP, V70, P195KIM MK, 1986, SEOUL J PSYCHIAT, V11, P194KAUFMAN AS, 1976, CONTEMP EDUC PSYCHOL, V1, P1801

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

IL-1β, IL-6, and RANTES as Biomarkers of Chikungunya Severity

Little is known about the immunopathogenesis of Chikungunya virus. Circulating levels of immune mediators and growth factors were analyzed from patients infected during the first Singaporean Chikungunya fever outbreak in early 2008 to establish biomarkers associated with infection and/or disease severity.Adult patients with laboratory-confirmed Chikungunya fever infection, who were referred to the Communicable Disease Centre/Tan Tock Seng Hospital during the period from January to February 2008, were included in this retrospective study. Plasma fractions were analyzed using a multiplex-microbead immunoassay. Among the patients, the most common clinical features were fever (100%), arthralgia (90%), rash (50%) and conjunctivitis (40%). Profiles of 30 cytokines, chemokines, and growth factors were able to discriminate the clinical forms of Chikungunya from healthy controls, with patients classified as non-severe and severe disease. Levels of 8 plasma cytokines and 4 growth factors were significantly elevated. Statistical analysis showed that an increase in IL-1beta, IL-6 and a decrease in RANTES were associated with disease severity.This is the first comprehensive report on the production of cytokines, chemokines, and growth factors during acute Chikungunya virus infection. Using these biomarkers, we were able to distinguish between mild disease and more severe forms of Chikungunya fever, thus enabling the identification of patients with poor prognosis and monitoring of the disease