Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units

Themoula Charalampous, Adela Alcolea-Medina, Luke B Snell, Tom G S Williams, Rahul Batra, Christopher Alder, Andrea Telatin, Luigi Camporota, Christopher I S Meadows, Duncan Wyncoll, Nicholas A Barrett, Carolyn J Hemsley, Lisa Bryan, William Newsholme, Sara E Boyd, Anna Green, Ula Mahadeva, Amita Patel, Penelope R Cliff, Andrew J Page, Justin O'Grady, Jonathan D Edgeworth, Themoula Charalampous, Adela Alcolea-Medina, Luke B Snell, Tom G S Williams, Rahul Batra, Christopher Alder, Andrea Telatin, Luigi Camporota, Christopher I S Meadows, Duncan Wyncoll, Nicholas A Barrett, Carolyn J Hemsley, Lisa Bryan, William Newsholme, Sara E Boyd, Anna Green, Ula Mahadeva, Amita Patel, Penelope R Cliff, Andrew J Page, Justin O'Grady, Jonathan D Edgeworth

Abstract

Background: Clinical metagenomics (CMg) has the potential to be translated from a research tool into routine service to improve antimicrobial treatment and infection control decisions. The SARS-CoV-2 pandemic provides added impetus to realise these benefits, given the increased risk of secondary infection and nosocomial transmission of multi-drug-resistant (MDR) pathogens linked with the expansion of critical care capacity.

Methods: CMg using nanopore sequencing was evaluated in a proof-of-concept study on 43 respiratory samples from 34 intubated patients across seven intensive care units (ICUs) over a 9-week period during the first COVID-19 pandemic wave.

Results: An 8-h CMg workflow was 92% sensitive (95% CI, 75-99%) and 82% specific (95% CI, 57-96%) for bacterial identification based on culture-positive and culture-negative samples, respectively. CMg sequencing reported the presence or absence of β-lactam-resistant genes carried by Enterobacterales that would modify the initial guideline-recommended antibiotics in every case. CMg was also 100% concordant with quantitative PCR for detecting Aspergillus fumigatus from 4 positive and 39 negative samples. Molecular typing using 24-h sequencing data identified an MDR-K. pneumoniae ST307 outbreak involving 4 patients and an MDR-C. striatum outbreak involving 14 patients across three ICUs.

Conclusion: CMg testing provides accurate pathogen detection and antibiotic resistance prediction in a same-day laboratory workflow, with assembled genomes available the next day for genomic surveillance. The provision of this technology in a service setting could fundamentally change the multi-disciplinary team approach to managing ICU infections. The potential to improve the initial targeted treatment and rapidly detect unsuspected outbreaks of MDR-pathogens justifies further expedited clinical assessment of CMg.

Conflict of interest statement

JOG has received speaking honoraria, consultancy fees, in-kind contributions or research funding from Oxford Nanopore, Simcere, Becton-Dickinson and Heraeus Medical. The remaining authors declare that they have no competing interests.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Schematic workflow representing the main steps of the CMg workflow. A. Sample processing of respiratory samples during which sample is treated with saponin to lyse human cells followed by nuclease treatment of human DNA and microbial cells are bead-beaten and automated microbial NA extraction is carried out. B. DNA is prepared for nanopore sequencing using the Rapid PCR Barcoding kit (SQK-RPB004), then the library is sequenced either with a Flonge (1 sample only) or with a GridION (6 samples). C. Real-time data acquisition is carried out by MinKNOW during which squiggle plots are converted into raw sequencing data and low-quality reads are removed (qscore = > 6). Real-time basecalling and demultiplexing (multiplex runs only) of raw data are done simultaneously by Guppy. Human reads (if any) are then firstly subtracted via read-based alignment offline; pathogen identification (ID) and AMR gene detection are then followed in real-time after 2 h of sequencing. K-mer-based classification is used for microbial ID (WIMP within EPI2ME), and offline read-based alignment for AMR gene detection based on pathogen/s (if any) identified by the previous step (above pre-defined thresholds) is followed by Scagaire. After 24 h of sequencing, downstream offline analysis can be carried out (if needed) for molecular typing to characterised identified pathogens for public health purposes
Fig. 2
Fig. 2
Identification of MDR K. pneumoniae and C. striatum outbreaks across the ICU network based on combined epidemiological and CMg analysis. Overlapping ward stays for patients involved in putative outbreaks of A) MDR K. pneumoniae and B)C. striatum. Each row represents a unique patient. Patients are ordered by ward of first positive (ascending) and then by patient ID (ascending). The horizontal axis shows the ward stays from April 1 to June 20. Non-ITU wards are coloured in grey. ITU wards are labelled 1–10 represented by a unique colour. Periods outside the hospital are represented in white. MDR-K. pneumoniae or C. striatum positive and negative respiratory samples by culture are marked as (+) or (-), respectively. Additionally, positive respiratory samples by CMg and culture are marked as “”. Positive blood cultures are marked as “B” and sequenced blood cultures are marked as “”. Patients with a CMg-aligned sequence have an S number (respiratory sample) or KP number (blood culture) adjacent to their identification number on the left of each bar. The number of SNPs for each CMg sample is also shown on the vertical axis. A) CMg was performed on MDR-K. pneumoniae in respiratory samples from patients 1054 and 301 and bloodstream infection isolates on patients 301 and 968 retrieved from the routine diagnostic laboratory (time point marked as “B”). Possible chain of transmission is from top to bottom. No sequenced patient could link 968 to 1517 or 618 to 740 and so were assumed to be due to cryptic transmission via other non-sequenced patients. B) CMg was performed on C. striatum in respiratory samples from patients 618, 677, 740 and 749. All other patients were linked by epidemiology only

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