Hyperglycaemia and Ischaemia Impair Wound Healing via Toll-like Receptor 4 Pathway Activation in vitro and in an Experimental Murine Model

OBJECTIVE: Diabetes mellitus has reached epidemic proportions. Foot ulceration is a multifactorial complication of diabetes associated with marked morbidity and mortality. Innate immune Toll-like receptor 4 (TLR4) mediated inflammation has been implicated in the systemic pathogenesis of diabetes and may contribute to impairment of wound healing. This study investigates the effect of high glucose and hypoxic conditions on TLR4 activation and signalling in vitro and in vivo. METHODS: Fibroblasts cultured at physiological glucose concentration (5.5 mM) were exposed to glucose concentrations from 0 mM to 25 mM, with duplicates placed in a hypoxic chamber. TLR4 inhibition was assessed in the 25 mM glucose groups. Diabetes was induced in wild type (WT) and TLR4 knockout (KO) C57BL/6 mice by intraperitoneal injection of low dose streptozocin (STZ). Hindlimb ischaemia was induced by femoral artery ligation four weeks post streptozocin, and a full thickness 4 mm skin wound inflicted below the knee. Wound healing was assessed via digital planimetry on days 3, 7, and 14 post surgery. RESULTS: Hypoxic and high glucose (25 mM) conditions led to an increase in TLR4 protein expression, apoptosis, and interleukin (IL)-6 release. Inhibition with a TLR4 neutralising antibody and specific TLR4 antagonist ameliorated the effects of high glucose and ischaemia (p < .05). In vivo, wound healing was significantly impaired in the diabetic ischaemic group at day 14 (p < .05). Diabetic ischaemic wounds in TLR4 KO mice exhibited significantly improved healing rates compared with those in WT mice at all time points. CONCLUSION: Hypoxia stimulates upregulation of TLR4 protein expression and this effect is exaggerated by hyperglycaemia. In TLR4 KO mice, there is a significant improvement in the healing of diabetic ischaemic wounds compared with WT. It is suggested that a synergistic effect between hypoxia and hyperglycaemia impairing wound healing exists, through TLR4 mediated inflammation

The innate immune system, toll-like receptors and dermal wound healing: A review

Wound healing is a complex physiological process comprised of discrete but inter-related and overlapping stages, requiring exact timing and regulation to successfully progress, yet occurs spontaneously in response to injury. It is characterised by four phases, coagulation, inflammation, proliferation and remodelling. Each phase is predominated by particular cell types, cytokines and chemokines. The innate immune system represents the first line of defence against invading microorganisms. It is entirely encoded with the genome, and comprised of a cellular response with specificity provided by pattern recognition receptors (PRRs) such as toll-like receptors (TLRs). TLRs are activated by exogenous microbial pathogen associated molecular patterns (PAMPs), initiating an immune response through the production of pro-inflammatory cytokines and further specialist immune cell recruitment. TLRs are also activated by endogenous molecular patterns termed damage associated molecular patterns (DAMPs). These ligands, usually shielded from the immune system, act as alarm signals alerting the immune system to damage and facilitate the normal wound healing process. TLRs are expressed by cells essential to wound healing such as keratinocytes and fibroblasts, however the specific role of TLRs in this process remains controversial. This article reviews the current knowledge on the potential role of TLRs in dermal wound healing where inflammation arising from pathogenic activation of these receptors appears to play a role in chronic ulceration associated with diabetes, scar hypertrophy and skin fibrosis

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

GROMACS is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world. Here, we describe some of the ways we have been able to realize this through the use of parallelization on all levels, combined with a constant focus on absolute performance. Release 4.6 of GROMACS uses SIMD acceleration on a wide range of architectures, GPU offloading acceleration, and both OpenMP and MPI parallelism within and between nodes, respectively. The recent work on acceleration made it necessary to revisit the fundamental algorithms of molecular simulation, including the concept of neighborsearching, and we discuss the present and future challenges we see for exascale simulation - in particular a very fine-grained task parallelism. We also discuss the software management, code peer review and continuous integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin

Patients' perceptions of the potential of breathing training for asthma: a qualitative study.

Poor symptom control is common in asthma. Breathing training exercises may be an effective adjunct to medication; it is therefore important to understand facilitators and barriers to uptake of breathing training exercises

The Formation of the First Low-Mass Stars From Gas With Low Carbon and Oxygen Abundances

The first stars in the Universe are predicted to have been much more massive than the Sun. Gravitational condensation accompanied by cooling of the primordial gas due to molecular hydrogen, yields a minimum fragmentation scale of a few hundred solar masses. Numerical simulations indicate that once a gas clump acquires this mass, it undergoes a slow, quasi-hydrostatic contraction without further fragmentation. Here we show that as soon as the primordial gas - left over from the Big Bang - is enriched by supernovae to a carbon or oxygen abundance as small as ~0.01-0.1% of that found in the Sun, cooling by singly-ionized carbon or neutral oxygen can lead to the formation of low-mass stars. This mechanism naturally accommodates the discovery of solar mass stars with unusually low (10^{-5.3} of the solar value) iron abundance but with a high (10^{-1.3} solar) carbon abundance. The minimum stellar mass at early epochs is partially regulated by the temperature of the cosmic microwave background. The derived critical abundances can be used to identify those metal-poor stars in our Milky Way galaxy with elemental patterns imprinted by the first supernovae.Comment: 14 pages, 2 figures (appeared today in Nature

Virtual screening for inhibitors of the human TSLP:TSLPR interaction

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) plays a pivotal role in the pathophysiology of various allergy disorders that are mediated by type 2 helper T cell (Th2) responses, such as asthma and atopic dermatitis. TSLP forms a ternary complex with the TSLP receptor (TSLPR) and the interleukin-7-receptor subunit alpha (IL-7Ra), thereby activating a signaling cascade that culminates in the release of pro-inflammatory mediators. In this study, we conducted an in silico characterization of the TSLP: TSLPR complex to investigate the drugability of this complex. Two commercially available fragment libraries were screened computationally for possible inhibitors and a selection of fragments was subsequently tested in vitro. The screening setup consisted of two orthogonal assays measuring TSLP binding to TSLPR: a BLI-based assay and a biochemical assay based on a TSLP: alkaline phosphatase fusion protein. Four fragments pertaining to diverse chemical classes were identified to reduce TSLP: TSLPR complex formation to less than 75% in millimolar concentrations. We have used unbiased molecular dynamics simulations to develop a Markov state model that characterized the binding pathway of the most interesting compound. This work provides a proof-ofprinciple for use of fragments in the inhibition of TSLP: TSLPR complexation

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Feasibility of metabolic imaging of hyperpolarized 13C-pyruvate in human breast cancer

Introduction Imaging of the breast with hyperpolarized 13C yields new challenges compared to imaging the prostate [1]. E.g. large anteroposterior B0 gradients [2] require correction and the anatomy and patient positioning need a new, highly optimized RF coil array for achieving sufficient SNR/spatial resolution. As a first step, we have investigated single-breast imaging in the coronal plane. Methods A BRCA gene carrier with a 38-mm diameter grade 3 triple-negative invasive ductal carcinoma was studied on a 3T MRI (GE Healthcare) using a prototype 8-channel 13C breast coil (Rapid Biomedical), containing 2 transmit/receive coils and 6 receive-only covering both breasts in a prone position. 1H imaging was performed with the body coil. Following injection of 40ml of 250mM 13C-pyruvate, polarized to c. 25%, a 1-minute time series of spirals with IDEAL encoding (3) was collected (flip angle 10°, TR=260ms, 8-step cycle, time resolution 2.08s, 3 x 3-cm thick slices, 3mm gap, 40-pt spiral, 24cm coronal FOV, real pixel size 12 x 12 x 30mm). IDEAL reconstruction of images was optimized separately for each slice to enable independent frequency offsets to be applied. Kinetic modelling was performed in MATLAB, with automated tumour segmentation. Results Tumour pixels were identified by the segmentation algorithm only in the tumour-containing slice 2, and the average estimated flux from pyruvate to lactate kPL within this ROI was 0.022 s-1 (Fig. 1). The frequency shift of pyruvate relative to slice 2 was +6 Hz in slice 3 and -34 Hz in slice 1, confirming a sharp gradient in B0 approaching the nipple, which was corrected by optimizing slices separately (Fig 2). Images of lactate and pyruvate summed over the time course (Fig 3) showed strong signal of both metabolites over the tumour in slice 2, lower pyruvate in the slice toward the chest wall, and no consistent signal in slice 1. Conclusion This first-in-Europe study in breast cancer established the feasibility of obtaining metabolite images with high temporal and moderate spatial resolution in humans in vivo following administration of hyperpolarized 13C-pyruvate. Coronal image orientation allowed application of significant corrections for a known limitation, the anteroposterior B0 gradient, as well as a small FOV to improve spatial resolution. Kinetic rate constants within the tumour were found to be consistent with previous reports in human prostate cancer (1). References 1) Nelson SJ et al. Sci Transl Med 5, 198ra108 (2013). 2) Maril N, et al. Magn. Reson. Med. 2005; 54:1139-1145. 3) Wiesinger F, et al. Magn Reson Med 2012; 68:8-16

Cloud impacts on photochemistry: Building a climatology of photolysis rates from the Atmospheric Tomography mission

Abstract. Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry–climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J-cloudy (standard, sometimes cloudy conditions) to J-clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements. NERC ACSIS LTSM projec