The COVID-19 pandemic has brought with it the largest ever cohort of patients requiring mechanical ventilation. Complications associated with such severe viral infections are many-fold, and include increased susceptibility to secondary bacterial infections (Zhou et al., 2020; Langford et al., 2020), as well as post-acute COVID-19, where patients experience symptoms extending beyond 3 weeks from the onset of their first COVID-19 symptoms (Greenhalgh et al., 2020). The first report of secondary infections in COVID-19 patients was from Wuhan in March 2020, where 15% of hospitalized patients developed secondary infections, and of those who did not survive their SARS-CoV-2 infection, 50% had a secondary bacterial infection (Zhou et al., 2020). Since then many COVID-19 studies reporting secondary infections have been published, with a recent meta-analysis of 24 independent studies that included 3338 patients from five countries reporting that 14.3% of hospitalized COVID-19 patients developed a secondary bacterial infection, which is associated with significant morbidity, mortality and the financial costs associated with prolonged hospitilisation (Langford et al., 2020). The incidence of post-acute COVID-19 varies depending on the group of patients considered, with approximately 10% of patients who have tested positive for SARS-CoV-2 virus remaining unwell beyond 3 weeks (Greenhalgh et al., 2020). However, this can be as high as 74% when hospitalised patients are considered, where symptoms include breathlessness and excessive fatigue, with abnormal radiological features reported in 12% of this cohort (Arnold et al., 2020).

The DISCOVER study (DIagnostic and Severity markers of COVID-19 to Enable Rapid triage, REC: 20/YH/0121) was established in March 2020 to collect and analyse longitudinal samples from COVID-19 patients. One study participant, an otherwise healthy male between 45 and 55 years of age presented to hospital with Type-1 respiratory failure (hypoxaemia), 20 days after he tested positive for SARS-CoV-2 by RT-PCR. At the time of testing he was asymptomatic (tested as a house-hold contact of a health-care worker), and this represents Day one on the time-line presented in Figure 1. He became symptomatic for COVID-19 13 days after this, which is within the incubation period for SARS-CoV-2 as defined by the CDC of 14 days (COVID-19 (Coronavirus Disease), 2020), and his health declined over the following week. Upon admission to hospital a chest X-ray was taken, and he was admitted to the ICU where he was mechanically ventilated (Figure 1). An RT-PCR test on an endotracheal sample collected at this time did not detect SARS-CoV-2 suggesting that he had cleared the viral infection.

Figure 1 with 1 supplement see all

Download asset Open asset

Once admitted to our intensive care unit he was assessed, as all our patients are daily, for signs of additional infection through a process led by a consultant medical microbiologist along with senior pharmacist. At these meetings, the clinical status of the patient, radiological features, oxygen requirements, inflammatory indices (WCC, CRP, and procalcitonin) and any culture results are discussed. We had no concerns at the time of admission of this patient to the ICU that he had any infection other than that of SARS-CoV-2. Computerised tomography (CT) scans of the patient were collected through his time in the ICU and the classic features of COVID-19, including ground-glass opacities, were evident throughout (Figure 3—figure supplement 1).

After a week in the ICU he was diagnosed with ventilator-associated pneumonia (VAP) based on the clinical, radiological, biochemical, and microbiological parameters described above. An antibiotic susceptible Pseudomonas aeruginosa strain was cultured from an aspirate collected from his endotracheal tube (ETT-1, Figure 1), (minimum inhibitory concentration (MIC) meropenem 0.5 mg/l; piperacillin-tazobactam (pip/taz) 8 mg/l). He was prescribed a seven-day course of pip/taz (4.5 g every 6 hr), and clinically recovered from this bacterial infection. Eight days after finishing this first course of antibiotics his VAP recurred (diagnosed as described above), and a pip/taz resistant P. aeruginosa was cultured from ETT-2 (minimum inhibitory concentration (MIC) >16 mg/l). He was prescribed a seven-day course of meropenem (1 g every 8 hr) and showed signs of clinical improvement. There were no further concerns about a bacterial infection during his ICU stay (i.e. he did not meet any of the clinical diagnostic criteria described above), although a third P. aeruginosa was cultured from a sample (ETT-3) collected two days after he completed his course of meropenem that was resistant to both pip/taz and meropenem (MICs > 16 mg/l and >8 mg/l respectively). His health continued to improve, and he was discharged to the respiratory ward a week later. The patient attended a follow-up clinic 3 weeks after hospital discharge where he reported on-going symptoms such as breathlessness and myalgia, and there was a significant drop in his oxygen saturations after mild exertion to 84%. Eight months following his positive test he is still reporting breathlessness and fatigue, common symptoms of the newly defined ‘Long COVID’ condition.

To understand the cellular and molecular dynamics of this post-acute COVID-19 case from the perspective of both the pathogen and patient’s immune response, longitudinal respiratory and blood samples were collected and analysed with a view to identifying early diagnostic biomarkers of infection onset and potential opportunities for immunotherapeutic intervention.

Metagenomics was used to characterise in depth the composition and genomic features of the bacteria present in this patient’s lower respiratory tract from when he first developed VAP (ETT-1, Figure 1) to when he had recovered from his second VAP (ETT-3, Figure 1). ETT-2 collected during his second bout of VAP was unfortunately not available for sequencing. We extracted the entire genetic material from 500 μl of ETTs 1 and 3 with no bacterial enrichment or human DNA depletion steps, and to ensure data for the bacterial component of the samples was generated, these were sequenced on a PromethION (Oxford Nanopore technology). The bacterial DNA from ETT-1 was entirely that of P. aeruginosa corresponding to multi-locus sequence type ST253 (Jolley et al., 2018), a world-wide clone frequently associated with AMR epidemics (Treepong et al., 2018; Figure 2). There was a greater diversity of bacterial DNA in ETT-3, as the patient recovered from his SARS-CoV-2 and P. aeruginosa infections (Figure 2b), and we were able to construct a whole genome of the P. aeruginosa strain from within this, which again corresponded to multi-locus sequence type ST253. P. aeruginosa is a ubiquitous environmental bacterium, and while it can be found as a commensal in the respiratory tract of some individuals, this is typically only when they have some underlying chronic suppurative lung disease such as bronchiectasis. Prior to his COVID-19 diagnosis this relatively young patient was healthy with no prior medical issues, which suggests the P. aeruginosa infections were hospital or ICU acquired.

Figure 2

Download asset Open asset

Resistance to beta-lactam antibiotics by P. aeruginosa can be multifactorial and includes the acquisition of single nucleotide polymorphisms (SNPs) in efflux and porin genes that affect the passage of the antibiotic into and out of the bacterial cell (Blair et al., 2015). Analysis of the sequence data from ETT-3 generated a whole genome for P. aeruginosa, again corresponding to ST253, that aligned with >99% identify to the genome from ETT-1. There were however two noteworthy SNPs in this later sample that explain the increased AMR of this isolate. The multi-drug efflux pump, MexAB, is transcriptionally regulated by the translated product of the mexR gene, MexR. We found a SNP in mexR that converts a key amino-acid (Arg91-Gln) in the DNA-binding domain of the MexR protein, that would likely result in the de-repression of this efflux system (Saito et al., 2003). A second SNP was found that introduced a premature stop codon (Tyr120-stop) in the gene encoding the outer membrane protein OprD, a protein with a well-established role in the entry of meropenem into the bacterial cell (Quale et al., 2006). The two P. aeruginosa strains could represent a parent strain that acquired resistance upon exposure to antibiotics, that wasn’t fully cleared and so re-emerged; or a new infection or colonisation event with a closely related AMR strain circulating within the ICU. Unfortunately, we are unable to tell which of these scenario is more likely from these data.

Having recovered from both a viral and a recurring bacterial infection, we also sought to analyse the kinetics of the patient’s innate and adaptive immune response across his time in the ICU (at days 23, 28, 38, 45, and 58 from testing positive for SARS-CoV2, Figure 1), as previous work has demonstrated striking immunological features associated with COVID-19 patients (Mathew et al., 2020). Robust activation of a broad range of immune cell subsets was revealed by flow cytometry, when compared to healthy controls. What was particularly striking was the level of activation and proliferation of CD4+, CD8+, and TCR-γδ T-cells, that appeared to wane between days 23–28 but were subsequently boosted after day 28, concomitant with the onset of the secondary bacterial infection (Figure 3a–c and Figure 3e–g, gating strategies in Figure 3—figure supplement 1). Similar levels of activation of T-cells has been reported for subsets of COVID-19 patients in other studies (Mathew et al., 2020). From day 28 a robust and steady increase in the activation and proliferation of conventional and TCR-γδ-T-cells was evident with approximately 20% and 40% of total CD4+ and CD8+ T-cells co-expressing the activation markers HLA-DR and CD38, respectively by day 58. A similar steady increase in activation levels, albeit at lower magnitudes, was observed for Natural Killer (NK) CD56^dim and CD56^bright cells (Figure 3d and h, gating strategies in Figure 3—figure supplement 1). Similar perturbations in the frequency and activation phenotypes of monocytes, blood monocyte-derived macrophages as well as neutrophils could be detected from day 28 onwards (Figure 3—figure supplement 2a–e). A robust IgG response to both SARS-CoV-2 and P. aeruginosa antigen was also detected in this patient (Figure 3—figure supplement 3).

Figure 3 with 3 supplements see all

Download asset Open asset

Given the scale of the cellular response of this patient, we investigated whether the large expansions of CD4+ and CD8+ T-cells were due to a T-cell response targeting SARS-CoV-2, the secondary bacterial infection or to bystander T-cell activation (Sandalova et al., 2010; Rivino et al., 2015). We included the analysis of bystander activation, which is T-cell receptor-independent and cytokine-mediated, as it is known to occur during other types of acute viral infection (Sandalova et al., 2010; Rivino et al., 2015). The role of this type of bystander activated T-cells in the recovery of patients remains largely unclear, as these cells can participate in protective immunity towards the virus but can also contribute to tissue damage (Sandalova et al., 2010; Rivino et al., 2015; Kim and Shin, 2019; Maini et al., 2000). We performed a brief stimulation of the patient’s peripheral blood mononuclear cells (PBMCs), with or without overlapping peptides spanning the sequences of SARS-CoV-2 proteins (spike, membrane (M) and nucleoprotein (N)); of an immunodominant P. aeruginosa antigen (OprF); as well as an immunodominant human Cytomegalovirus protein (HCMV pp65) as an indication of non-T-cell receptor (TCR) driven bystander activation. This was followed by intracellular cytokine staining to detect production of IFN-γ and TNF-α by the specific T cells. Following the encounter with specific peptides we observed a robust CD4+ T-cell response to SARS-CoV-2 which decreased from day 23 to day 58, and a more modest SARS-CoV-2-specific CD8+ T-cell response. This is in line with published work, suggesting higher magnitudes of SARS-CoV-2-specific CD4+ versus CD8+ T-cells in severe COVID-19 patients (Peng et al., 2020), as well as consistent detection of virus-specific CD4+ T cells in recovered patients (Grifoni et al., 2020; Figure 4a–c and Figure 4—figure supplement 1a–b). CD4+ and CD8+ T-cell responses targeting P. aeruginosa peptides could also be detected, and increased over time concomitant with the onset of the recurring bacterial infection (Figure 4b–c).

Figure 4 with 1 supplement see all

Download asset Open asset

The number of T-cells responding specifically to SARS-CoV-2 and P. aeruginosa represent only a fraction of those activated, which suggests that many of those detected in the patient’s blood may be bystander activated. In support of this we detected an increased magnitude of CMV-specific T-cell responses over time, particularly in the CD8+ compartment (Figure 3b–c). We therefore considered whether CMV-specific T-cells were activated and proliferating in the blood of the COVID-19 patient. To test this we performed a brief stimulation of PBMCs with or without HCMV pp65 peptide (CMV) or SARS-CoV-2 spike 1 one peptide pools (the predominant SARS-CoV-2 target in this patient, within those tested) or with a positive control, and assessed the expression of the activation and proliferation markers HLA-DR, CD38 and Ki67 by the cytokine-producing cells that were responding to the peptide pools. Peptide stimulations and negative controls were performed in duplicate wells (Figure 4—figure supplement 1a–b). Both CMV and spike-1-specific CD8+ T-cells showed increased co-expression of HLA-DR and CD38 and of Ki67, indicating that these cells were activated and proliferating ex vivo, while this was less evident for their CD4+ T-cell counterparts (Figure 4d–e and g–h). This observation is in line with previous reports of bystander T-cell activation for CMV-specific CD8+ T-cell populations (Sandalova et al., 2010; Rivino et al., 2015). We furthermore analysed the phenotype of CMV and spike 1-specific T-cells on the basis of expression of CCR7 and CD45RA, which identifies naïve (CCR7+ CD45RA+), central memory (TCM: CCR7+ CD45RA-), effector memory (TEM: CCR7- CD45RA-) and effector memory re-expressing RA T-cells (TEMRA: CCR7-CD45RA+). CMV and spike 1-specific CD4+ T cells were mainly contained respectively within the TEM and the TEM/TCM subsets, while CMV and spike 1-specific CD8+ T cells were mainly contained respectively within the TEMRA and the TEM subsets. The predominant TEMRA phenotype of SARS-CoV-2 specific CD8+ T-cells in convalescent COVID patients is in line with recent work (Neidleman et al., 2020). The presence of a large proportion of CD8+ TEMRA within the CMV and spike 1-specific CD8+ T cell populations also supports the notion that these cells are stimulated through cytokine receptors rather than through the TCR, as TEMRA cells are believed to arise following antigen withdrawal and cytokine-mediated stimulation (for e.g. IL-15), as TCR ligation downregulates CD45RA expression both in vitro and during responses in vivo (Henson et al., 2012).

In summary, the immune profiles show robust innate and adaptive immune activation in a COVID-19 patient experiencing a secondary bacterial infection, and a detectable T-cell and IgG response targeting both SARS-CoV-2 and P. aeruginosa. Furthermore, we show that CD8+ T-cells targeting viral antigens from SARS-CoV-2 and from the persistent virus HCMV display expression of markers of activation and proliferation and are predominantly TEMRA and TEM cells. Although it is a challenging task to demonstrate that these cells have not encountered their cognate viral antigens in vivo, the TEMRA phenotype of these cells makes it unlikely and suggests that these cells may be proliferating in a bystander way through the action of cytokines present in the inflammatory milieu.

In this case report we demonstrate the potential of the application of multidisciplinary technologies to longitudinally-collected patient samples to define the complex dynamics of patient-pathogen-therapy interactions in real-time. Metagenomics directly applied to respiratory samples facilitated the identification of the SNPs responsible for the AMR phenotype of the later P. aeruginosa isolate. However, the most striking feature of this COVID-19 case was the escalating number of circulating activated T-cells more than two months after testing positive for SARS-CoV-2, and 6 weeks after clearing the viral infection. While we could attribute some of this to the recurring bacterial infection, the scale of the activation considered alongside our evidence of increased frequencies of T-cells specific for unrelated antigens, suggests there may be a significant amount of bystander activation.

Studies on large COVID-19 patient cohorts have shown that the period of the peak of T-cell activation in COVID-19 is prolonged compared to other acute viral infections or after vaccination with live attenuated viruses (Mathew et al., 2020), which may be due to the inability of the immune system to downregulate its response. Here, we speculate that due to SARS-CoV2 infection the patient displays a heightened immune system, which was further stimulated by the recurring P. aeruginosa infections, which led to bystander activation of T cells specific for antigens unrelated to either SARS-CoV2 or P. aeruginosa. Bystander T-cell activation could have played a critical role in the severity of illness and the longer-term complications associated with the development of post-acute COVID-19 experienced by this patient (Grifoni et al., 2020). Given the recent appreciation for the role of corticosteroids in reducing the risk of death following SARS-CoV-2 infection by 20% (Sterne et al., 2020), this case suggests that targeting its use to patients with immunological responses as described here, may also improve their long-term recovery.

Human-filtered sequencing data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession PRJEB40239.

1. 1. Gregorova M

   (2020) European Nucleotide Archive

   ID PRJEB40239. Metagenomic analysis of respiratory samples from a COVID-19 ICU patient.

   https://www.ebi.ac.uk/ena/browser/text-search?query=PRJEB40239

1. 1. Alikhan NF
   2. Petty NK
   3. Ben Zakour NL
   4. Beatson SA

   (2011) BLAST ring image generator (BRIG): simple prokaryote genome comparisons

   BMC Genomics 12:402.

   https://doi.org/10.1186/1471-2164-12-402

   • PubMed
   • Google Scholar
2. 1. Amanat F
   2. Stadlbauer D
   3. Strohmeier S
   4. Nguyen THO
   5. Chromikova V
   6. McMahon M
   7. Jiang K
   8. Arunkumar GA
   9. Jurczyszak D
   10. Polanco J
   11. Bermudez-Gonzalez M
   12. Kleiner G
   13. Aydillo T
   14. Miorin L
   15. Fierer DS
   16. Lugo LA
   17. Kojic EM
   18. Stoever J
   19. Liu STH
   20. Cunningham-Rundles C
   21. Felgner PL
   22. Moran T
   23. García-Sastre A
   24. Caplivski D
   25. Cheng AC
   26. Kedzierska K
   27. Vapalahti O
   28. Hepojoki JM
   29. Simon V
   30. Krammer F

   (2020) A serological assay to detect SARS-CoV-2 seroconversion in humans

   Nature Medicine 26:1033–1036.

   https://doi.org/10.1038/s41591-020-0913-5

   • PubMed
   • Google Scholar
3. Preprint

   1. Arnold DT
   2. Hamilton FW
   3. Milne A
   4. Morley AJ
   5. Viner J
   6. Attwood M
   7. Noel A
   8. Gunning S
   9. Hatrick J
   10. Hamilton S
   11. Elvers KT
   12. Hyams C
   13. Bibby A
   14. Moran E
   15. Adamali HI
   16. Dodd JW
   17. Maskell NA
   18. Barratt SL

   (2020) Patient outcomes after hospitalisation with COVID-19 and implications for follow-up: results from a prospective UK cohort

   medRxiv.

   https://doi.org/10.1101/2020.08.12.20173526

   • Google Scholar
4. 1. Blair JM
   2. Webber MA
   3. Baylay AJ
   4. Ogbolu DO
   5. Piddock LJ

   (2015) Molecular mechanisms of antibiotic resistance

   Nature Reviews Microbiology 13:42–51.

   https://doi.org/10.1038/nrmicro3380

   • PubMed
   • Google Scholar
5. Website

   1. COVID-19 (Coronavirus Disease)

   (2020) Interim clinical guidance for management of patients with confirmed coronavirus disease (COVID-19)

   Accessed November 4, 2020.

   https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html
6. 1. Darling AC
   2. Mau B
   3. Blattner FR
   4. Perna NT

   (2004) Mauve: multiple alignment of conserved genomic sequence with rearrangements

   Genome Research 14:1394–1403.

   https://doi.org/10.1101/gr.2289704

   • PubMed
   • Google Scholar
7. 1. DeAngelis KM
   2. Brodie EL
   3. DeSantis TZ
   4. Andersen GL
   5. Lindow SE
   6. Firestone MK

   (2009) Selective progressive response of soil microbial community to wild oat roots

   The ISME Journal 3:168–178.

   https://doi.org/10.1038/ismej.2008.103

   • PubMed
   • Google Scholar
8. Report

   1. EUCAST

   (2019) The European Committee on Antimicrobial Susceptibility Testing

   EUCAST.

   https://eucast.org/ast_of_bacteria/previous_versions_of_documents/?debug=1

   • Google Scholar
9. 1. Greenhalgh T
   2. Knight M
   3. A’Court C
   4. Buxton M
   5. Husain L

   (2020) Management of post-acute covid-19 in primary care

   BMJ 13:m3026.

   https://doi.org/10.1136/bmj.m3026

   • Google Scholar
10. 1. Griffiths RI
    2. Whiteley AS
    3. O'Donnell AG
    4. Bailey MJ

    (2000) Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition

    Applied and Environmental Microbiology 66:5488–5491.

    https://doi.org/10.1128/AEM.66.12.5488-5491.2000

    • PubMed
    • Google Scholar
11. 1. Grifoni A
    2. Weiskopf D
    3. Ramirez SI
    4. Mateus J
    5. Dan JM
    6. Moderbacher CR
    7. Rawlings SA
    8. Sutherland A
    9. Premkumar L
    10. Jadi RS
    11. Marrama D
    12. de Silva AM
    13. Frazier A
    14. Carlin AF
    15. Greenbaum JA
    16. Peters B
    17. Krammer F
    18. Smith DM
    19. Crotty S
    20. Sette A

    (2020) Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals

    Cell 181:1489–1501.

    https://doi.org/10.1016/j.cell.2020.05.015

    • PubMed
    • Google Scholar
12. 1. Henson SM
    2. Riddell NE
    3. Akbar AN

    (2012) Properties of end-stage human T cells defined by CD45RA re-expression

    Current Opinion in Immunology 24:476–481.

    https://doi.org/10.1016/j.coi.2012.04.001

    • PubMed
    • Google Scholar
13. 1. Jolley KA
    2. Bray JE
    3. Maiden MCJ

    (2018) Open-access bacterial population genomics: bigsdb software, the PubMLST.org website and their applications

    Wellcome Open Research 3:124.

    https://doi.org/10.12688/wellcomeopenres.14826.1

    • PubMed
    • Google Scholar
14. 1. Kim T-S
    2. Shin E-C

    (2019) The activation of bystander CD8⁺ T cells and their roles in viral infection

    Experimental & Molecular Medicine 51:1–9.

    https://doi.org/10.1038/s12276-019-0316-1

    • Google Scholar
15. 1. Kolmogorov M
    2. Yuan J
    3. Lin Y
    4. Pevzner PA

    (2019) Assembly of long, error-prone reads using repeat graphs

    Nature Biotechnology 37:540–546.

    https://doi.org/10.1038/s41587-019-0072-8

    • Google Scholar
16. 1. Köster J
    2. Rahmann S

    (2012) Snakemake--a scalable bioinformatics workflow engine

    Bioinformatics 28:2520–2522.

    https://doi.org/10.1093/bioinformatics/bts480

    • PubMed
    • Google Scholar
17. 1. Langford BJ
    2. So M
    3. Raybardhan S
    4. Leung V
    5. Westwood D
    6. MacFadden DR
    7. Soucy J-PR
    8. Daneman N

    (2020) Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis

    Clinical Microbiology and Infection 26:1622–1629.

    https://doi.org/10.1016/j.cmi.2020.07.016

    • Google Scholar
18. 1. Maini MK
    2. Boni C
    3. Lee CK
    4. Larrubia JR
    5. Reignat S
    6. Ogg GS
    7. King AS
    8. Herberg J
    9. Gilson R
    10. Alisa A
    11. Williams R
    12. Vergani D
    13. Naoumov NV
    14. Ferrari C
    15. Bertoletti A

    (2000) The role of Virus-Specific Cd8+ cells in liver damage and viral control during persistent hepatitis B virus infection

    Journal of Experimental Medicine 191:1269–1280.

    https://doi.org/10.1084/jem.191.8.1269

    • Google Scholar
19. 1. Mathew D
    2. Giles JR
    3. Baxter AE

    (2020) Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

    Science 6508:eabc8511.

    https://doi.org/10.1126/science.abc8511

    • Google Scholar
20. 1. Neidleman J
    2. Luo X
    3. Frouard J
    4. Xie G
    5. Gill G
    6. Stein ES
    7. McGregor M
    8. Ma T
    9. George AF
    10. Kosters A
    11. Greene WC
    12. Vasquez J
    13. Ghosn E
    14. Lee S
    15. Roan NR

    (2020) SARS-CoV-2-Specific T cells exhibit phenotypic features of helper function, lack of terminal differentiation, and high proliferation potential

    Cell Reports Medicine 1:100081.

    https://doi.org/10.1016/j.xcrm.2020.100081

    • PubMed
    • Google Scholar
21. Preprint

    1. Peng Y
    2. Mentzer AJ
    3. Liu G

    (2020) Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent COVID-19 patients

    bioRxiv.

    https://doi.org/10.1101/2020.06.05.134551

    • Google Scholar
22. 1. Quale J
    2. Bratu S
    3. Gupta J
    4. Landman D

    (2006) Interplay of efflux system, ampC, and oprD expression in carbapenem resistance of Pseudomonas aeruginosa clinical isolates

    Antimicrobial Agents and Chemotherapy 50:1633–1641.

    https://doi.org/10.1128/AAC.50.5.1633-1641.2006

    • PubMed
    • Google Scholar
23. 1. Quigley KJ
    2. Reynolds CJ
    3. Goudet A
    4. Raynsford EJ
    5. Sergeant R
    6. Quigley A
    7. Worgall S
    8. Bilton D
    9. Wilson R
    10. Loebinger MR
    11. Maillere B
    12. Altmann DM
    13. Boyton RJ

    (2015) Chronic infection by Mucoid Pseudomonas aeruginosa Associated with Dysregulation in T-Cell Immunity to Outer Membrane Porin F

    American Journal of Respiratory and Critical Care Medicine 191:1250–1264.

    https://doi.org/10.1164/rccm.201411-1995OC

    • PubMed
    • Google Scholar
24. 1. Rivino L
    2. Kumaran EA
    3. Thein TL
    4. Too CT
    5. Gan VC
    6. Hanson BJ
    7. Wilder-Smith A
    8. Bertoletti A
    9. Gascoigne NR
    10. Lye DC
    11. Leo YS
    12. Akbar AN
    13. Kemeny DM
    14. MacAry PA

    (2015) Virus-specific T lymphocytes home to the skin during natural dengue infection

    Science Translational Medicine 7:278ra35.

    https://doi.org/10.1126/scitranslmed.aaa0526

    • PubMed
    • Google Scholar
25. 1. Saito K
    2. Akama H
    3. Yoshihara E
    4. Nakae T

    (2003) Mutations affecting DNA-binding activity of the MexR repressor of mexR-mexA-mexB-oprM operon expression

    Journal of Bacteriology 185:6195–6198.

    https://doi.org/10.1128/JB.185.20.6195-6198.2003

    • PubMed
    • Google Scholar
26. 1. Sandalova E
    2. Laccabue D
    3. Boni C
    4. Tan AT
    5. Fink K
    6. Ooi EE
    7. Chua R
    8. Shafaeddin Schreve B
    9. Ferrari C
    10. Bertoletti A

    (2010) Contribution of herpesvirus specific CD8 T cells to anti-viral T cell response in humans

    PLOS Pathogens 6:e1001051.

    https://doi.org/10.1371/journal.ppat.1001051

    • PubMed
    • Google Scholar
27. 1. Stadlbauer D
    2. Amanat F
    3. Chromikova V

    (2020) Sars‐cov‐2 seroconversion in humans: a detailed protocol for a serological assay, antigen production, and test setup

    Current Protocols 57:e100.

    https://doi.org/10.1002/cpmc.100

    • Google Scholar
28. 1. Sterne JAC
    2. Murthy S
    3. Diaz JV
    4. Slutsky AS
    5. Villar J
    6. Angus DC
    7. Annane D
    8. Azevedo LCP
    9. Berwanger O
    10. Cavalcanti AB
    11. Dequin PF
    12. Du B
    13. Emberson J
    14. Fisher D
    15. Giraudeau B
    16. Gordon AC
    17. Granholm A
    18. Green C
    19. Haynes R
    20. Heming N
    21. Higgins JPT
    22. Horby P
    23. Jüni P
    24. Landray MJ
    25. Le Gouge A
    26. Leclerc M
    27. Lim WS
    28. Machado FR
    29. McArthur C
    30. Meziani F
    31. Møller MH
    32. Perner A
    33. Petersen MW
    34. Savovic J
    35. Tomazini B
    36. Veiga VC
    37. Webb S
    38. Marshall JC
    39. WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group

    (2020) Association between administration of systemic corticosteroids and mortality among critically ill patients with COVID-19: a Meta-analysis

    Jama 324:1330–1341.

    https://doi.org/10.1001/jama.2020.17023

    • PubMed
    • Google Scholar
29. 1. Treepong P
    2. Kos VN
    3. Guyeux C
    4. Blanc DS
    5. Bertrand X
    6. Valot B
    7. Hocquet D

    (2018) Global emergence of the widespread Pseudomonas aeruginosa ST235 clone

    Clinical Microbiology and Infection 24:258–266.

    https://doi.org/10.1016/j.cmi.2017.06.018

    • PubMed
    • Google Scholar
30. 1. Vaser R
    2. Sović I
    3. Nagarajan N
    4. Šikić M

    (2017) Fast and accurate de novo genome assembly from long uncorrected reads

    Genome Research 27:737–746.

    https://doi.org/10.1101/gr.214270.116

    • PubMed
    • Google Scholar
31. 1. Wood DE
    2. Lu J
    3. Langmead B

    (2019) Improved metagenomic analysis with Kraken 2

    Genome Biology 20:257.

    https://doi.org/10.1186/s13059-019-1891-0

    • PubMed
    • Google Scholar
32. 1. Zhou F
    2. Yu T
    3. Du R
    4. Fan G
    5. Liu Y
    6. Liu Z
    7. Xiang J
    8. Wang Y
    9. Song B
    10. Gu X
    11. Guan L
    12. Wei Y
    13. Li H
    14. Wu X
    15. Xu J
    16. Tu S
    17. Zhang Y
    18. Chen H
    19. Cao B

    (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

    The Lancet 395:1054–1062.

    https://doi.org/10.1016/S0140-6736(20)30566-3

    • Google Scholar

Author details

1. Michaela Gregorova

   School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Daniel Morse and Tarcisio Brignoli

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1605-0558

2. Daniel Morse

   School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation

   Contributed equally with

   Michaela Gregorova and Tarcisio Brignoli

   Competing interests

   No competing interests declared

3. Tarcisio Brignoli

   School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation

   Contributed equally with

   Michaela Gregorova and Daniel Morse

   Competing interests

   No competing interests declared

4. Joseph Steventon

   School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Fergus Hamilton

   North Bristol NHS Trust, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Mahableshwar Albur

   North Bristol NHS Trust, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9792-7280

7. David Arnold

   North Bristol NHS Trust, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3158-7740

8. Matthew Thomas

   North Bristol NHS Trust, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

9. Alice Halliday

   School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

10. Holly Baum

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

11. Christopher Rice

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

12. Matthew B Avison

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Formal analysis, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

13. Andrew D Davidson

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Formal analysis, Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1136-4008

14. Marianna Santopaolo

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Formal analysis, Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

15. Elizabeth Oliver

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

16. Anu Goenka

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Resources, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

17. Adam Finn

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

18. Linda Wooldridge

    Bristol Veterinary School in the Faculty of Health Sciences, Bristol, United Kingdom

    Contribution

    Resources, Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

19. Borko Amulic

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

20. Rosemary J Boyton

    1. Department of Infectious Disease, Imperial College London, London, United Kingdom
    2. Lung Division, Royal Brompton & Harefield NHS Foundation Trust, London, United Kingdom

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

21. Daniel M Altmann

    Department of Infectious Disease, Imperial College London, London, United Kingdom

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

22. David K Butler

    Department of Infectious Disease, Imperial College London, London, United Kingdom

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

23. Claire McMurray

    Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

24. Joanna Stockton

    Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

25. Sam Nicholls

    Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom

    Contribution

    Data curation, Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

26. Charles Cooper

    Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom

    Contribution

    Data curation, Formal analysis, Visualization

    Competing interests

    No competing interests declared

27. Nicholas Loman

    Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Visualization

    Competing interests

    No competing interests declared

28. Michael J Cox

    Lung Division, Royal Brompton & Harefield NHS Foundation Trust, London, United Kingdom

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

29. Laura Rivino

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Ruth C Massey

    For correspondence

    laura.rivino@bristol.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6213-9794

30. Ruth C Massey

    School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Laura Rivino

    For correspondence

    ruth.massey@bristol.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8154-4039

• 3,085

  views

• 346

  downloads

• 25

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.