Dissection of the Candida albicans class I chitin synthase promoters

We acknowledge financial support from the Biotechnology and Biological Sciences Research Council (10161), Medical Research Council (New Investigator Award to C.A.M.), the European Community FUNGALWALL and SIGNALPATH initiatives and the Wellcome Trust.Peer reviewedPublisher PD

Cell wall stress induces alternative fungal cytokinesis and septation strategies

Peer reviewedPublisher PD

Real-Time Control, Acquisition and Data Treatment for Beam Current Transformers in a Transfer Line

Particle beams are transferred from the 1 GeV Booster to the 26 GeV Proton Synchrotron and to an experimental area, ISOLDE. The characteristics of the beams and their destination change on a 1.2 s cycle basis. There are six beam current transformers to measure the beam intensities, i.e. the number of particles passing through the transfer lines. On each pulse of the Booster, a real-time system, called BTTR (Beam Transfer TRansformers), acquires the transformer values, selects the range, executes a calibration, and treats the data. Part of the treatment is the subtraction of the base-value, which includes systematic perturbations, acquired in the absence of beam. The system also handles asynchronous tasks, such as acquisition of base-value, readout of calibration factors and other diagnostic actions. The concept of the BTTR and its design are presented, as well as some practical results

Fungal Chitin Dampens Inflammation through IL-10 Induction Mediated by NOD2 and TLR9 Activation

Funding: JW and NARG thank the Wellcome Trust (080088, 086827, 075470), The Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (097377) and the European Union ALLFUN (FP7/2007 2013, HEALTH-2010-260338) for funding. MGN was supported by a Vici grant of the Netherlands Organisation for Scientific Research. AJPB and DMM were funded by STRIFE, ERC-2009-AdG-249793 and AJPB additionally by FINSysB, PITN-GA-2008-214004 and the BBSRC [BB/F00513X/1]. MDL was supported by the MRC (MR/J008230/1). GDB and SV were funded by the Wellcome Trust (086558) and TB and MK were funded by the Deutsche Forschungsgemeinschaft (Bi 696/3-1; Bi 696/5-2; Bi 696/10-1). MS was supported by the Deutsche Forschungsgemeinschaft (Sch 897/1-3) and the National Institute of Dental and Craniofacial Research (R01 DE017514-01). TDK and RKSM were funded by the National Institute of Health (AR056296, AI101935) and the American Lebanese Syrian Associated Charities (ALSAC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

The colon microcosm: a novel in vitro model to study Candida albicans colonisation of the human colon

Candida albicans colonises the gastro-intestinal (GI) tract of over 60% of the population. In severely ill or immune compromised patients, this fungus can escape the gut, disseminate through the body and cause systemic disease. Most research in the field has focused on defining traits that contribute directly to virulence; there are comparatively few studies which have addressed how C. albicans colonises and persists in the gut. Furthermore, such studies have typically been performed mouse models devoid of resident GI bacteria, completely neglecting the major impact of the local microbiota on GI colonisation. How, then, does C. albicans persist in the GI tract in the presence of the normal gut microbiota? To address this question, a novel in vitro two-phase anaerobic fermentation system that simulates the human colon microenvironment has been developed. This “colon microcosm” supports the growth of human faecal microbiota in liquid anaerobic colon medium (phase 1) and C. albicans growth on agar plugs which are added to the medium to mimic the epithelial surface (phase 2). The impact of C. albicans upon the faecal microbiota is monitored by examining the planktonic phase (phase 1), whilst the effect of the microbiota on the growth of C. albicans is monitored after extracting C. albicans cells from the agar plugs (phase 2). The results of assays carried out to validate the model will be presented, as will data from pilot studies which illustrate the potentially exploitable impact of the human GI microbiota from healthy individuals on C. albicans growth

The PKC, HOG and Ca2+ signalling pathways co-ordinately regulate chitin synthesis in Candida albicans

Open Access via PMC2649417Peer reviewedPublisher PD

The Fungal Cell Wall : Structure, Biosynthesis, and Function

N.G. is funded by the Wellcome Trust via a senior investigator award and a strategic award and by the MRC Centre for Medical Mycology. C.M. acknowledges the support of the Wellcome Trust and the MRC. N.G. and C.M. are part of the MRC Centre for Medical Mycology. J.P.L. acknowledges support from ANR, Aviesan, and FRM.Peer reviewedPublisher PD

The Mnn2 mannosyltransferase family modulates mannoprotein fibril length, immune recognition and virulence of Candida albicans

Copyright: © 2013 Hall et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The fungal cell wall is the first point of interaction between an invading fungal pathogen and the host immune system. The outer layer of the cell wall is comprised of GPI anchored proteins, which are post-translationally modified by both N- and O-linked glycans. These glycans are important pathogen associated molecular patterns (PAMPs) recognised by the innate immune system. Glycan synthesis is mediated by a series of glycosyl transferases, located in the endoplasmic reticulum and Golgi apparatus. Mnn2 is responsible for the addition of the initial α1,2-mannose residue onto the α1,6-mannose backbone, forming the N-mannan outer chain branches. In Candida albicans, the MNN2 gene family is comprised of six members (MNN2, MNN21, MNN22, MNN23, MNN24 and MNN26). Using a series of single, double, triple, quintuple and sextuple mutants, we show, for the first time, that addition of α1,2-mannose is required for stabilisation of the α1,6-mannose backbone and hence regulates mannan fibril length. Sequential deletion of members of the MNN2 gene family resulted in the synthesis of lower molecular weight, less complex and more uniform N-glycans, with the sextuple mutant displaying only un-substituted α1,6-mannose. TEM images confirmed that the sextuple mutant was completely devoid of the outer mannan fibril layer, while deletion of two MNN2 orthologues resulted in short mannan fibrils. These changes in cell wall architecture correlated with decreased proinflammatory cytokine induction from monocytes and a decrease in fungal virulence in two animal models. Therefore, α1,2-mannose of N-mannan is important for both immune recognition and virulence of C. albicans.Wellcome TrustMRC New Investigator Awar

Architecture of the dynamic fungal cell wall

The fungal cell wall is essential for growth and survival, and is a key target for antifungal drugs and the immune system. The cell wall must be robust but flexible, protective and shielding yet porous to nutrients and membrane vesicles and receptive to exogenous signals. Most fungi have a common inner wall skeleton of chitin and β-glucans that functions as a flexible viscoelastic frame to which a more diverse set of outer cell wall polymers and glycosylated proteins are attached. Whereas the inner wall largely determines shape and strength, the outer wall confers properties of hydrophobicity, adhesiveness, and chemical and immunological heterogeneity. The spatial organization and dynamic regulation of the wall in response to prevailing growth conditions enable fungi to thrive within changing, diverse and often hostile environments. Understanding this architecture provides opportunities to develop diagnostics and drugs to combat life-threatening fungal infections

Interaction between stock indices via changepoint analysis

Stock market indices from several countries are modelled as discretely sampled diffusions whose parameters change at certain times. To estimate these times of parameter changes we employ both a sequential likelihood-ratio test and a non-parametric, spectral algorithm designed specifically for time series with multiple changepoints. Finally, we use point-process techniques to model relationships between changepoints of different financial time series. Copyright © 2006 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55814/1/653_ftp.pd