| Literature DB >> 32586885 |
Catherine Wei Min Ong1,2,3,4, Giovanni Battista Migliori5,3, Mario Raviglione6,7, Gavin MacGregor-Skinner8, Giovanni Sotgiu9, Jan-Willem Alffenaar10,11,12,4, Simon Tiberi13,14,4, Cornelia Adlhoch15,16, Tonino Alonzi17, Sophia Archuleta1, Sergio Brusin15,16, Emmanuelle Cambau18,4, Maria Rosaria Capobianchi19, Concetta Castilletti19, Rosella Centis20, Daniela M Cirillo21,4, Lia D'Ambrosio22, Giovanni Delogu23,24,4, Susanna M R Esposito25, Jose Figueroa26, Jon S Friedland27,4, Benjamin Choon Heng Ho28, Giuseppe Ippolito29, Mateja Jankovic30,4, Hannah Yejin Kim10,11,12, Senia Rosales Klintz15,16, Csaba Ködmön15,16, Eleonora Lalle19, Yee Sin Leo31, Chi-Chiu Leung32, Anne-Grete Märtson33, Mario Giovanni Melazzini34, Saeid Najafi Fard17, Pasi Penttinen15,16, Linda Petrone17, Elisa Petruccioli17, Emanuele Pontali35, Laura Saderi9, Miguel Santin36,37,4, Antonio Spanevello38,39, Reinout van Crevel40,41,4, Marieke J van der Werf15,16, Dina Visca38,39, Miguel Viveiros42,4, Jean-Pierre Zellweger43, Alimuddin Zumla44, Delia Goletti45,46,4.
Abstract
Major epidemics, including some that qualify as pandemics, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), HIV, influenza A (H1N1)pdm/09 and most recently COVID-19, affect the lung. Tuberculosis (TB) remains the top infectious disease killer, but apart from syndemic TB/HIV little is known regarding the interaction of viral epidemics and pandemics with TB. The aim of this consensus-based document is to describe the effects of viral infections resulting in epidemics and pandemics that affect the lung (MERS, SARS, HIV, influenza A (H1N1)pdm/09 and COVID-19) and their interactions with TB. A search of the scientific literature was performed. A writing committee of international experts including the European Centre for Disease Prevention and Control Public Health Emergency (ECDC PHE) team, the World Association for Infectious Diseases and Immunological Disorders (WAidid), the Global Tuberculosis Network (GTN), and members of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Mycobacterial Infections (ESGMYC) was established. Consensus was achieved after multiple rounds of revisions between the writing committee and a larger expert group. A Delphi process involving the core group of authors (excluding the ECDC PHE team) identified the areas requiring review/consensus, followed by a second round to refine the definitive consensus elements. The epidemiology and immunology of these viral infections and their interactions with TB are discussed with implications for diagnosis, treatment and prevention of airborne infections (infection control, viral containment and workplace safety). This consensus document represents a rapid and comprehensive summary on what is known on the topic.Entities:
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Year: 2020 PMID: 32586885 PMCID: PMC7527651 DOI: 10.1183/13993003.01727-2020
Source DB: PubMed Journal: Eur Respir J ISSN: 0903-1936 Impact factor: 16.671
FIGURE 1The lungs and gut are exposed to environmental substances and pathogens. The early protection response to respiratory viruses includes mucus, surfactants and antiviral peptides that can prevent initial attachment and viral entry. Respiratory viruses enter via the respiratory epithelium. Epithelial cells have a key role in initiating the immune response by recognising viral components (pathogen-associated molecular patterns (PAMPs)) via Toll-like receptors (TLRs) and intracellular receptors. These cellular sensors trigger a signalling cascade resulting in the upregulation of type I and III interferon (IFN) and the inflammatory response. This leads to differentiation of dendritic cells that mediate the induction of the adaptive immunity and promote the recruitment of innate immunity cells, in particular neutrophils and natural killer (NK) cells. NK cells have the ability to kill virus-infected cells via perforin–granzyme-dependent mechanisms or by the Fas–Fas ligand pathway. Moreover, alveolar macrophages, recruited monocytes and macrophages as well as dendritic cells pick pathogen components and contribute to the immune response. All of these cells produce cytokines and chemokines that are important for the establishment of the adaptive responses and of the antiviral state. The adaptive response to respiratory viruses is mediated by both T- and B-cell compartments. T-cells contribute to the generation of the B-cell response. B-cells produce antibodies that may neutralise the respiratory viruses directly by binding to viral surface proteins that are essential for entry of the virus into host cells or through the ligation of Fc receptors to trigger the complement cascade and antibody-dependent cell-mediated cytotoxicity. Antibodies are in the form of IgA, mainly in the upper respiratory tract, or IgG, in the lower respiratory tract. Viral clearance is also mediated by CD8+-specific T-cells with cytolytic activity. The protective antiviral T-cell response is mainly mediated by IFN-γ production and is therefore biased toward a T-helper cell (Th) 1 response, whereas other T-cell subsets such as Th2 cells and Th17 cells play a minor role and they may be responsible for lung tissue damage. Moreover, regulatory mechanisms adopted by T-cells such as interleukin (IL)-10 secretion, or upregulation of inhibitory receptors such as programmed cell death protein 1 (PD-1) or expansion of the T-regulatory (Treg) cell subsets, work to balance tissue damage and viral clearance. TNF: tumour necrosis factor; CTL: cytotoxic T-lymphocyte; TFH: T-follicular helper; TGF: transforming growth factor.
Performance characteristics of diagnostic approaches to respiratory infection
| Manual NAAT | + + + | + + + | − | − | − | + + + | + + | + | − | + |
| Automated NAAT | + + + | + + + | + | + | − | + + + | + + + | + + | + + | + + |
| POCT-NAAT | + | + + | + + + | + + + | − | + + + | + + + | + + + | + | + |
| NGS¶ | − | − | − | − | + + | + | + + + | − | − | + + + |
| − | + + | − | − | − | + | + | + + | + | − | |
| − | + | − | − | + | + + + | + + + | − | − | + + + | |
| POCT | − | + | + + + | + + | − | +ƒ | + + | + + + | + + + | NA |
| ELISA | + + | + + | − | − | − | NA | NA | NA | NA | NA |
| Serology | NRU | NRU | − | − | − | Not recommended | Not recommended | NRU | NRU | NA |
| IGRA | ES | ES | − | − | − | + + | + | + + | + | NA |
| TST | NA | NA | NA | NA | NA | + + | + | + + | + + | NA |
Quantitation: −: very poor; +: poor; + +: good; + + +: excellent. NAAT: nucleic acid amplification test; POCT: point-of-care test; NGS: next-generation sequencing; NA: not applicable; NRU: not routinely used; ES: experimental settings only; IGRA: interferon-γ release assay; TST: tuberculin skin test (Mantoux test); TB: tuberculosis. #: considering only the time of the procedure <2 h; ¶: metagenomics and whole-genome sequencing; +: immunofluorescence microscopy on respiratory samples to detect the most common viruses, or Ziehl–Neelsen or auramine/rhodamine staining to detect acid-fast bacilli; §: viral culture established in several eukaryotic cell lines and mycobacterial culture in liquid or solid media; ƒ: the only approved antigen POCT for TB detects lipoarabinomannan in urine samples and has been licensed to diagnose TB in HIV-infected patients and to monitor therapy.
FIGURE 2Proposed mechanism of action of drugs used for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 can enter the cell through angiotensin-converting enzyme 2 (ACE2) and type II transmembrane serine protease (TMPRSS2). Camostat mesylate acts as an inhibitor of TMPRSS2 and umifenovir can inhibit the viral entry to the cell [180, 228, 229]. Chloroquine, hydroxychloroquine and baricitinib mechanisms of action are not fully understood; however, it is proposed that these drugs affect viral entry. Baricitinib also inhibits the AP-2-associated protein kinase [173, 180, 230]. Lopinavir/ritonavir and ASC-09/ritonavir as protease inhibitors inhibit the proteolysis. Lopinavir/ritonavir inhibits specifically the proteinase 3CLpro [231]. Ribavirin and favipiravir both have wide antiviral activity and have the potential to inhibit SARS-CoV-2 RNA replication [232–234]. Azvudine, a nucleoside reverse transcriptase inhibitor, also inhibits RNA replication [235]. A probable mechanism of action for baloxavir marboxil is the inhibition of transcription through inhibiting cap-dependent endonuclease [236]. Favipiravir and remdesivir inhibit the RNA-dependent RNA polymerase (RdRp), which results in reduced RNA synthesis [180, 233, 234, 237]. Adapted from “Coronavirus Replication Cycle” (2020; https://app.biorender.com/biorender-templates).
Drug interactions between tuberculosis (TB) and potential COVID-19 medications
WHO: World Health Organization; INH: isoniazid; RIF: rifampicin; EMB: ethambutol; PZA: pyrazinamide; LFX: levofloxacin; MFX: moxifloxacin; BDQ: bedaquiline; LZD: linezolid; CFZ: clofazimine; Cs: cycloserine; DLM: delamanid; IMI/CIS: imipenem/cilastin; MEM: meropenem; AMI: amikacin; STR: streptomycin; ETO: ethionamide; PTO: prothionamide; PAS: p-aminosalicylic acid; CYP: cytochrome P450; UGT: UDP glucuronosyltransferase. #: recommended based on predicted interaction; ¶: UGT 1A1 is involved in moxifloxacin metabolism and could be involved in umifenovir metabolism (mainly UGT 1A9); +: both drugs are metabolised by CYP3A4; §: CYP3A4 is involved in the metabolism of baloxavir (minor extent) and umifenovir, and clofazimine is a CYP3A4 inhibitor; ƒ: both drugs primarily undergo renal excretion.
Response measures undertaken in European Union (EU)/European Economic Area (EEA) Member States and the UK at the national level as of April 3, 2020
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The data on response measures are based on information available from official public sources as of Friday April 3, 2020 at 18:00 and may not capture measures being taken by countries that are not reported on publicly available websites. The situation is evolving rapidly and this represents a snapshot of the measures that countries in the EU/EEA and the UK reported to date. The response measures displayed are national measures, reported on official public websites. Response measures collected include: mass gathering cancellations (for specific events or a ban on gatherings of a particular size); closure of public spaces (including restaurants, entertainment venues, nonessential shops, etc.); closure of educational institutions (including day care or nursery, primary schools, and secondary schools and higher education); stay-at-home recommendations for risk groups or vulnerable populations (e.g. the elderly, people with underlying health conditions, physically disabled people, etc.); stay-at-home recommendations for the general population (which are voluntary or not enforced); and stay-at-home orders for the general population (these are enforced and also referred to as “lockdown”). The data on response measures has several limitations. First, there is substantial heterogeneity in physical distancing policies and their implementation between countries. For instance, the level of enforcement of measures may vary between countries and there may be specific rules and exceptions to the measures, making interpretation of the data challenging. The measures displayed in these figures are measures reported at a national level and it should be noted that due to the evolution of the outbreak in certain regions, regional or local measures often preceded national ones. The exact dates of introduction were often available from official sources but delays in their implementation may have occurred. Additionally, availability of public data from official government sources varies among countries. For some countries, data are no longer available on official websites concerning measures that are no longer in force, which may result in the data for more recent measures being more complete.
Consensus statements derived from the Delphi process and the level of agreement achieved
| 0 (0.0) | 6 (16.2) | 6 (16.2) | 10 (27.0) | 15 (40.5) | 3.9±1.1 | |
| 1 (2.7) | 5 (13.5) | 10 (27.0) | 12 (32.4) | 9 (24.3) | 3.6±1.1 | |
| 0 (0.0) | 0 (0.0) | 4 (10.8) | 10 (27.0) | 23 (62.2) | 4.5±0.7 | |
| 0 (0.0) | 4 (10.8) | 7 (18.9) | 17 (46.0) | 9 (24.3) | 3.8±0.9 | |
| 4 (10.8) | 7 (18.9) | 10 (27.0) | 10 (27.0) | 6 (16.2) | 3.2±1.2 | |
| 0 (0.0) | 0 (0.0) | 1 (2.7) | 7 (18.9) | 29 (78.4) | 4.8±0.5 | |
| 0 (0.0) | 0 (0.0) | 3 (8.1) | 7 (18.9) | 27 (73.0) | 4.7±0.6 | |
| 0 (0.0) | 1 (2.7) | 0 (0.0) | 10 (27.0) | 26 (70.3) | 4.6±0.6 | |
| 0 (0.0) | 3 (8.1) | 7 (18.9) | 18 (48.7) | 9 (24.3) | 3.9±0.9 | |
| 2 (5.4) | 6 (16.2) | 8 (21.6) | 18 (32.5) | 9 (24.3) | 3.5±1.2 | |
| 1 (2.7) | 5 (13.5) | 8 (21.6) | 15 (40.5) | 8 (21.6) | 3.7±1.1 | |
| 4 (10.8) | 8 (21.6) | 10 (27.0) | 9 (24.3) | 6 (16.2) | 3.1±1.3 | |
| 0 (0.0) | 0 (0.0) | 3 (8.1) | 6 (16.2) | 28 (75.7) | 4.7±0.6 | |
| 0 (0.0) | 0 (0.0) | 1 (2.7) | 9 (24.3) | 27 (73.0) | 4.7±0.5 | |
| 0 (0.0) | 5 (13.5) | 7 (18.9) | 5 (13.5) | 20 (54.1) | 4.1±1.1 | |
| 0 (0.0) | 0 (0.0) | 4 (10.8) | 14 (37.8) | 19 (51.4) | 4.4±0.7 | |
| 0 (0.0) | 5 (13.5) | 7 (18.9) | 12 (32.4) | 13 (35.1) | 3.9±1.1 | |
| 3 (8.1) | 3 (8.1) | 6 (16.2) | 8 (21.6) | 17 (46.0) | 3.9±1.3 |
Data are presented as n (%), unless otherwise stated. BCG: bacille Calmette–Guérin; RCT: randomised controlled trial; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; CT: computed tomography; SpO: arterial oxygen saturation measured by pulse oximetry; PaO: arterial oxygen tension; FIO: inspiratory oxygen fraction; IL: interleukin; ICU: intensive care unit; TB: tuberculosis; HCW: healthcare worker.