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Potent modulation of the CepR quorum sensing receptor and virulence in a Burkholderia cepacia complex member using non-native lactone ligands

This work is licensed under a Creative Commons Attribution 4.0 International License.The Burkholderia cepacia complex (Bcc) is a family of closely related bacterial pathogens that are the causative agent of deadly human infections. Virulence in Bcc species has been shown to be controlled by the CepI/CepR quorum sensing (QS) system, which is mediated by an N-acyl L-homoserine lactone (AHL) signal (C8-AHL) and its cognate LuxR-type receptor (CepR). Chemical strategies to block QS in Bcc members would represent an approach to intercept this bacterial communication process and further delineate its role in infection. In the current study, we sought to identify non-native AHLs capable of agonizing or antagonizing CepR, and thereby QS, in a Bcc member. We screened a library of AHL analogs in cell-based reporters for CepR, and identified numerous highly potent CepR agonists and antagonists. These compounds remain active in a Bcc member, B. multivorans, with one agonist 250-fold more potent than the native ligand C8-AHL, and can affect QS-controlled motility. Further, the CepR antagonists prolong C. elegans survival in an infection model. These AHL analogs are the first reported non-native molecules that both directly modulate CepR and impact QS-controlled phenotypes in a Bcc member, and represent valuable chemical tools to assess the role of QS in Bcc infections

Reactivity of penicillin-binding proteins with peptidoglycan-mimetic beta-lactams: what's wrong with these enzymes?

Beta-lactams exert their antibiotic action through their inhibition of bacterial DD-peptidases (penicillin-binding proteins). Bacteria, in general, carry several such enzymes localized on the outside of their cell membrane to catalyze the final step in cell wall (peptidoglycan) synthesis. They have been classified into two major groups, one of high molecular weight, the other of low. Members of the former group act as transpeptidases in vivo, and their inhibition by beta-lactams leads to cessation of bacterial growth. The latter group consists of DD-carboxypeptidases, and their inhibition by beta-lactams is generally not fatal to bacteria. We have previously shown that representatives of the former group are ineffective at catalyzing the hydrolysis/aminolysis of peptidoglycan-mimetic peptides in vitro [Anderson et al. (2003) Biochem. J. 373, 949-955]. The theme of these experiments is expanded in the present paper where we describe the synthesis of a series of beta-lactams (penicillins and cephalosporins) containing peptidoglycan-mimetic side chains and the kinetics of their inhibition of a panel of penicillin-binding proteins spanning the major classes (Escherichia coli PBP 2 and PBP 5, Streptococcus pneumoniae PBP 1b, PBP 2x and PBP 3, the Actinomadura R39 DD-peptidase, and the Streptomyces R61 DD-peptidase). The results of these experiments mirror and expand the previous results with peptides. Neither peptides nor beta-lactams with appropriate peptidoglycan-mimetic side chains react with the solubilized constructs of membrane-bound penicillin binding proteins (the first five enzymes above) at rates exceeding those of generic analogues. Such peptides and beta-lactams do react at greatly enhanced rates with certain soluble low molecular weight enzymes (R61 and R39 DD-peptidases). The former result is unexpected and interesting. Why do the majority of penicillin-binding proteins not recognize elements of local peptidoglycan structure? Possible answers are discussed. That this question needs to be asked casts fascinating shadows on current studies of penicillin-binding proteins for new drug design

A duck with four legs: Investigating the structure of conceptual knowledge using picture drawing in semantic dementia

In Study 1, six patients with semantic dementia were asked to produce drawings of concrete concepts from dictation of their names. The drawings were characterised by a loss of distinctive features. In the artefact domain, this feature loss resulted in representations that were increasingly box-like. In the living domain, as well as distinctive features being lost, there was a tendency for patients to include incorrect features that resulted in more familiar and "prototypical" representations. A second study included two further conditions in the drawing assessment: immediate and delayed copying of line drawings of concrete concepts. Analysis of the drawings produced by three patients with semantic dementia confirmed that overall performance was significantly influenced by the task condition (immediate delayed) and severity of disease. The rate of intruding features, but not of omitted ones, was influenced by the domain of the item, with a greater proportion of intrusions in the living than in the nonliving domain. There was also a significant effect of feature distinctiveness on the proportions of these error types: Intruded features were most likely to come from the pool of properties that are shared across domain

Temporal trends and forecasting of COVID-19 hospitalisations and deaths in Scotland using a national real-time patient-level data platform: a statistical modelling study

This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director General Health and Social Care. The original EAVE project was funded by the NIHR Health Technology Assessment programme (11/46/23).Background   As the COVID-19 pandemic continues, national-level surveillance platforms with real-time individual person-level data are required to monitor and predict the epidemiological and clinical profile of COVID-19 and inform public health policy. We aimed to create a national dataset of patient-level data in Scotland to identify temporal trends and COVID-19 risk factors, and to develop a novel statistical prediction model to forecast COVID-19-related deaths and hospitalisations during the second wave.  Methods   We established a surveillance platform to monitor COVID-19 temporal trends using person-level primary care data (including age, sex, socioeconomic status, urban or rural residence, care home residence, and clinical risk factors) linked to data on SARS-CoV-2 RT-PCR tests, hospitalisations, and deaths for all individuals resident in Scotland who were registered with a general practice on Feb 23, 2020. A Cox proportional hazards model was used to estimate the association between clinical risk groups and time to hospitalisation and death. A survival prediction model derived from data from March 1 to June 23, 2020, was created to forecast hospital admissions and deaths from October to December, 2020. We fitted a generalised additive spline model to daily SARS-CoV-2 cases over the previous 10 weeks and used this to create a 28-day forecast of the number of daily cases. The age and risk group pattern of cases in the previous 3 weeks was then used to select a stratified sample of individuals from our cohort who had not previously tested positive, with future cases in each group sampled from a multinomial distribution. We then used their patient characteristics (including age, sex, comorbidities, and socioeconomic status) to predict their probability of hospitalisation or death.  Findings   Our cohort included 5 384 819 people, representing 98·6% of the entire estimated population residing in Scotland during 2020. Hospitalisation and death among those testing positive for SARS-CoV-2 between March 1 and June 23, 2020, were associated with several patient characteristics, including male sex (hospitalisation hazard ratio [HR] 1·47, 95% CI 1·38–1·57; death HR 1·62, 1·49–1·76) and various comorbidities, with the highest hospitalisation HR found for transplantation (4·53, 1·87–10·98) and the highest death HR for myoneural disease (2·33, 1·46–3·71). For those testing positive, there were decreasing temporal trends in hospitalisation and death rates. The proportion of positive tests among older age groups (>40 years) and those with at-risk comorbidities increased during October, 2020. On Nov 10, 2020, the projected number of hospitalisations for Dec 8, 2020 (28 days later) was 90 per day (95% prediction interval 55–125) and the projected number of deaths was 21 per day (12–29). Interpretation The estimated incidence of SARS-CoV-2 infection based on positive tests recorded in this unique data resource has provided forecasts of hospitalisation and death rates for the whole of Scotland. These findings were used by the Scottish Government to inform their response to reduce COVID-19-related morbidity and mortality.Publisher PDFPeer reviewe

Cohort profile : early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database

Funding: The original EAVE project was funded by the National Institute for Health Research Health Technology Assessment Programme (project number 13/34/14). EAVE II is funded by the Medical Research Council [MR/R008345/1] and supported by the Scottish Government. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK.PostprintPeer reviewe

Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality

Funding: HRS and MF are supported by the Medical Research Council [MR/R008345/1]. CJ’s salary came through MRC core funding MC_UU_12023/26. SJS is funded by the Wellcome Trust [WT 209560/Z/17/Z]. CRS has received funding from the Medical Research Council [MR/R008345/1], the National Institute for Health Research [11/46/23] and the New Zealand Health Research Council [20/1018] and Ministry for Business, Innovation and Employment. EV is funded by the Medical Research Council [MR/R008345/1] through the EAVE II grant and supported by the Scottish Government. We also acknowledge the support of HDR UK. The views and opinions expressed here are those of the authors and do not necessarily reflect those of the Health Technology Assessment programme, NIHR, NHS, or the UK Department of Health.Background Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. Methods Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. Conclusions The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected.Publisher PDFPeer reviewe

Impact of COVID-19 on accident and emergency attendances and emergency and planned hospital admissions in Scotland:an interrupted timeseries analysis

Funding: This analysis is part of the Early Assessment of COVID-19 epidemiology and Vaccine/anti-viral Effectiveness (EAVE II) study. EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE -The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through the Scottish Government DGHealth and Social Care. HRS is supported by the Medical Research Council [MR/R008345/1].Objectives: Following the outbreak of SARS-CoV-2, health systems and the populations who use them have faced unprecedented challenges. We aimed to measure the impact of COVID-19 on the uptake of hospital-based care at a national level. Design: The study period (weeks ending 05 January to 28 June 2020) encompassed the pandemic announcement by the World Health Organization (WHO) and the initiation of the UK lockdown. We undertook an interrupted time-series analysis to evaluate the impact of these events on hospital services at a national level and across demographics, clinical specialties and NHS Health Boards. Setting: Scotland, UK. Participants: Patients receiving hospital care from NHS Scotland.Main outcome measures: A&E attendances, and emergency and planned hospital admissions measured using the relative change of weekly counts in 2020 to the averaged counts for equivalent weeks in 2018 and 2019. Results: Before the pandemic announcement, the uptake of hospital care was largely consistent with historical levels. This was followed by sharp drops in all outcomes until UK lockdown, where activity began to steadily increase. This time-period saw an average reduction of -40.7% (95% CI: -47.7 to -33.7) in A&E attendances, -25.8% (95% CI: -31.1 to -20.4) in emergency hospital admissions and -60.9% (95% CI: -66.1 to -55.7) in planned hospital admissions, in comparison to the 2018-2019 averages. All subgroup trends were broadly consistent within outcomes, but with notable variations across age groups, specialties and geography. Conclusions: COVID-19 has had a profoundly disruptive impact on hospital-based care across NHS Scotland. This has likely led to an adverse effect on non-COVID-19 related illnesses, increasing the possibility of potentially avoidable morbidity and mortality. Further research is required to elucidate these impacts.PostprintPeer reviewe

Anti-schistosomal activities of quinoxaline-containing compounds:From hit identification to lead optimisation

Schistosomiasis is a neglected disease of poverty that is caused by infection with blood fluke species contained within the genus Schistosoma. For the last 40 years, control of schistosomiasis in endemic regions has predominantly been facilitated by administration of a single drug, praziquantel. Due to limitations in this mono-chemotherapeutic approach for sustaining schistosomiasis control into the future, alternative anti-schistosomal compounds are increasingly being sought by the drug discovery community. Herein, we describe a multi-pronged, integrated strategy that led to the identification and further exploration of the quinoxaline core as a promising anti-schistosomal scaffold