Identification of a small molecule inhibitor of Ebolavirus genome replication and transcription using in silico screening.

Ebola virus (EBOV) causes a severe haemorrhagic fever in humans and has a mortality rate over 50%. With no licensed drug treatments available, EBOV poses a significant threat. Investigations into possible therapeutics have been severely hampered by the classification of EBOV as a BSL4 pathogen. Here, we describe a drug discovery pathway combining in silico screening of compounds predicted to bind to a hydrophobic pocket on the nucleoprotein (NP); with a robust and rapid EBOV minigenome assay for inhibitor validation at BSL2. One compound (MCCB4) was efficacious (EC50 4.8 μM), exhibited low cytotoxicity (CC50 > 100 μM) and was specific, with no effect on either a T7 RNA polymerase driven firefly luciferase or a Bunyamwera virus minigenome. Further investigations revealed that this small molecule inhibitor was able to outcompete established replication complexes, an essential aspect for a potential EBOV treatment

A comparison of host gene expression signatures associated with infection in vitro by the Makona and Ecran (Mayinga) variants of Ebola virus

The Ebola virus (EBOV) variant Makona (which emerged in 2013) was the causative agent of the largest outbreak of Ebola Virus Disease recorded. Differences in virus-host interactions between viral variants have potential consequences for transmission, disease severity and mortality. A detailed profile of the cellular changes induced by the Makona variant compared with other Ebola virus variants was lacking. In this study, A549 cells, a human cell line with a robust innate response, were infected with the Makona variant or with the Ecran variant originating from the 1976 outbreak in Central Africa. The abundance of viral and cellular mRNA transcripts was profiled using RNASeq and differential gene expression analysis performed. Differences in effects of each virus on the expression of interferon-stimulated genes were also investigated in A549 NPro cells where the type 1 interferon response had been attenuated. Cellular transcriptomic changes were compared with those induced by human respiratory syncytial virus (HRSV), a virus with a similar genome organisation and replication strategy to EBOV. Pathway and gene ontology analysis revealed differential expression of functionally important genes; including genes involved in the inflammatory response, cell proliferation, leukocyte extravasation and cholesterol biosynthesis. Whilst there was overlap with HRSV, there was unique commonality to the EBOV variants

Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus

Background In 2014, Western Africa experienced an unanticipated explosion of Ebola virus infections. What distinguishes fatal from non-fatal outcomes remains largely unknown, yet is key to optimising personalised treatment strategies. We used transcriptome data for peripheral blood taken from infected and convalescent recovering patients to identify early stage host factors that are associated with acute illness and those that differentiate patient survival from fatality. Results The data demonstrate that individuals who succumbed to the disease show stronger upregulation of interferon signalling and acute phase responses compared to survivors during the acute phase of infection. Particularly notable is the strong upregulation of albumin and fibrinogen genes, which suggest significant liver pathology. Cell subtype prediction using messenger RNA expression patterns indicated that NK-cell populations increase in patients who survive infection. By selecting genes whose expression properties discriminated between fatal cases and survivors, we identify a small panel of responding genes that act as strong predictors of patient outcome, independent of viral load. Conclusions Transcriptomic analysis of the host response to pathogen infection using blood samples taken during an outbreak situation can provide multiple levels of information on both disease state and mechanisms of pathogenesis. Host biomarkers were identified that provide high predictive value under conditions where other predictors, such as viral load, are poor prognostic indicators. The data suggested that rapid analysis of the host response to infection in an outbreak situation can provide valuable information to guide an understanding of disease outcome and mechanisms of disease

Elucidating variations in the nucleotide sequence of Ebola virus associated with increasing pathogenicity

Background Ebolaviruses cause a severe and often fatal haemorrhagic fever in humans, with some species such as Ebola virus having case fatality rates approaching 90%. Currently, the worst Ebola virus outbreak since the disease was discovered is occurring in West Africa. Although thought to be a zoonotic infection, a concern is that with increasing numbers of humans being infected, Ebola virus variants could be selected which are better adapted for human-to-human transmission. Results To investigate whether genetic changes in Ebola virus become established in response to adaptation in a different host, a guinea pig model of infection was used. In this experimental system, guinea pigs were infected with Ebola virus (EBOV), which initially did not cause disease. To simulate transmission to uninfected individuals, the virus was serially passaged five times in naïve animals. As the virus was passaged, virulence increased and clinical effects were observed in the guinea pig. An RNAseq and consensus mapping approach was then used to evaluate potential nucleotide changes in the Ebola virus genome at each passage. Conclusions Upon passage in the guinea pig model, EBOV become more virulent, RNA editing and also coding changes in key proteins become established. The data suggest that the initial evolutionary trajectory of EBOV in a new host can lead to a gain in virulence. Given the circumstances of the sustained transmission of EBOV in the current outbreak in West Africa, increases in virulence may be associated with prolonged and uncontrolled epidemics of EBOV

HIV interactions with monocytes and dendritic cells: viral latency and reservoirs

HIV is a devastating human pathogen that causes serious immunological diseases in humans around the world. The virus is able to remain latent in an infected host for many years, allowing for the long-term survival of the virus and inevitably prolonging the infection process. The location and mechanisms of HIV latency are under investigation and remain important topics in the study of viral pathogenesis. Given that HIV is a blood-borne pathogen, a number of cell types have been proposed to be the sites of latency, including resting memory CD4+ T cells, peripheral blood monocytes, dendritic cells and macrophages in the lymph nodes, and haematopoietic stem cells in the bone marrow. This review updates the latest advances in the study of HIV interactions with monocytes and dendritic cells, and highlights the potential role of these cells as viral reservoirs and the effects of the HIV-host-cell interactions on viral pathogenesis

Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study.

BACKGROUND: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS: We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London

Risk of adverse outcomes in patients with underlying respiratory conditions admitted to hospital with COVID-19:a national, multicentre prospective cohort study using the ISARIC WHO Clinical Characterisation Protocol UK

Background Studies of patients admitted to hospital with COVID-19 have found varying mortality outcomes associated with underlying respiratory conditions and inhaled corticosteroid use. Using data from a national, multicentre, prospective cohort, we aimed to characterise people with COVID-19 admitted to hospital with underlying respiratory disease, assess the level of care received, measure in-hospital mortality, and examine the effect of inhaled corticosteroid use. Methods We analysed data from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study. All patients admitted to hospital with COVID-19 across England, Scotland, and Wales between Jan 17 and Aug 3, 2020, were eligible for inclusion in this analysis. Patients with asthma, chronic pulmonary disease, or both, were identified and stratified by age (<16 years, 16–49 years, and ≥50 years). In-hospital mortality was measured by use of multilevel Cox proportional hazards, adjusting for demographics, comorbidities, and medications (inhaled corticosteroids, short-acting β-agonists [SABAs], and long-acting β-agonists [LABAs]). Patients with asthma who were taking an inhaled corticosteroid plus LABA plus another maintenance asthma medication were considered to have severe asthma. Findings 75 463 patients from 258 participating health-care facilities were included in this analysis: 860 patients younger than 16 years (74 [8·6%] with asthma), 8950 patients aged 16–49 years (1867 [20·9%] with asthma), and 65 653 patients aged 50 years and older (5918 [9·0%] with asthma, 10 266 [15·6%] with chronic pulmonary disease, and 2071 [3·2%] with both asthma and chronic pulmonary disease). Patients with asthma were significantly more likely than those without asthma to receive critical care (patients aged 16–49 years: adjusted odds ratio [OR] 1·20 [95% CI 1·05–1·37]; p=0·0080; patients aged ≥50 years: adjusted OR 1·17 [1·08–1·27]; p<0·0001), and patients aged 50 years and older with chronic pulmonary disease (with or without asthma) were significantly less likely than those without a respiratory condition to receive critical care (adjusted OR 0·66 [0·60–0·72] for those without asthma and 0·74 [0·62–0·87] for those with asthma; p<0·0001 for both). In patients aged 16–49 years, only those with severe asthma had a significant increase in mortality compared to those with no asthma (adjusted hazard ratio [HR] 1·17 [95% CI 0·73–1·86] for those on no asthma therapy, 0·99 [0·61–1·58] for those on SABAs only, 0·94 [0·62–1·43] for those on inhaled corticosteroids only, 1·02 [0·67–1·54] for those on inhaled corticosteroids plus LABAs, and 1·96 [1·25–3·08] for those with severe asthma). Among patients aged 50 years and older, those with chronic pulmonary disease had a significantly increased mortality risk, regardless of inhaled corticosteroid use, compared to patients without an underlying respiratory condition (adjusted HR 1·16 [95% CI 1·12–1·22] for those not on inhaled corticosteroids, and 1·10 [1·04–1·16] for those on inhaled corticosteroids; p<0·0001). Patients aged 50 years and older with severe asthma also had an increased mortality risk compared to those not on asthma therapy (adjusted HR 1·24 [95% CI 1·04–1·49]). In patients aged 50 years and older, inhaled corticosteroid use within 2 weeks of hospital admission was associated with decreased mortality in those with asthma, compared to those without an underlying respiratory condition (adjusted HR 0·86 [95% CI 0·80−0·92]). Interpretation Underlying respiratory conditions are common in patients admitted to hospital with COVID-19. Regardless of the severity of symptoms at admission and comorbidities, patients with asthma were more likely, and those with chronic pulmonary disease less likely, to receive critical care than patients without an underlying respiratory condition. In patients aged 16 years and older, severe asthma was associated with increased mortality compared to non-severe asthma. In patients aged 50 years and older, inhaled corticosteroid use in those with asthma was associated with lower mortality than in patients without an underlying respiratory condition; patients with chronic pulmonary disease had significantly increased mortality compared to those with no underlying respiratory condition, regardless of inhaled corticosteroid use. Our results suggest that the use of inhaled corticosteroids, within 2 weeks of admission, improves survival for patients aged 50 years and older with asthma, but not for those with chronic pulmonary disease

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

Background: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. Methods: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. Results: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. Conclusions: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. Trial registration: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.</p