key: cord-0684521-s529slm0
authors: Rockett, R. J.; Draper, J.; Gall, M.; Sim, E. M.; Arnott, A.; Agius, J. E.; Johnson-Mackinnon, J.; Martinez, E.; Drew, A. P.; Lee, C.; Ngo, C.; Ramsperger, M.; Ginn, A. N.; Wang, Q.; Fennell, M.; Ko, D.; Huston, L.; Kairaitis, L.; Holmes, E. C.; O'Sullivan, M. N.; Chen, S. C.-A.; Kok, J.; Dwyer, D. E.; Sintchenko, V.
title: Co-infection with SARS-COV-2 Omicron and Delta Variants Revealed by Genomic Surveillance
date: 2022-02-15
journal: nan
DOI: 10.1101/2022.02.13.22270755
sha: 05ae257ca5ea82e1e7a1035ba3f59b9498c11a2c
doc_id: 684521
cord_uid: s529slm0

We identified the co-infection of the SARS-CoV-2 Omicron and Delta variants in two epidemiologically unrelated patients with chronic kidney disease requiring haemodialysis. Both SARS-CoV-2 variants were co-circulating locally at the time of detection. Amplicon- and probe-based sequencing using short- and long-read technologies identified and quantified Omicron and Delta subpopulations in respiratory samples from the two patients. These findings highlight the importance of genomic surveillance in vulnerable populations.

Australia. 8 Only genomes confidently assigned to SARS-CoV-2 lineages (Supplementary Figure S1 ) are reported to the health authorities and shared globally via GISAID (https://www.gisaid.org). In contrast to the majority of community samples sequenced, those obtained from Cases A and B had unexpectedly high numbers of "heterozygous" (i.e., mixed nucleotides at a single site) calls (Supplementary Figure S2 ) and could not be unambiguously assigned to a SARS-CoV-2 lineage by the Pangolin software. This observation triggered a case review which revealed that both patients had chronic kidney disease due to type 2 diabetes, obesity and ischaemic heart disease. In addition, both were receiving haemodialysis treatment for 4-5 hours thrice weekly at the same community dialysis centre and therefore potentially exposed to multiple COVID-19 cases during treatment session. Given the high community PCR did not detect human influenza viruses A or B, respiratory syncytial virus, parainfluenza viruses 1, 2, and 3, human metapneumovirus or rhinovirus in samples from both cases. A sample from an epidemiologically linked household contact of Case A, Case C, who was diagnosed several days after Case A was also sequenced.

Following the observation of high numbers of heterozygous calls in samples collected from Cases A and B, (both exposed to COVID-19 patients potentially infected with different co-circulating lineages), additional viral sequencing and viral culture was performed. Due to the low viral load in the Day 0 samples from both cases, they were only able to be sequenced using Midnight primers and Illumina sequencing, while two longitudinal samples for each case, with increased viral loads were subjected to further analyses (Supplementary Table S1 ). Two respiratory swabs collected from Case A on Days 2 and 3, as well as two respiratory swabs from Case B collected on Days 3 and 11, were subjected to nucleic acid extraction, quantitative SARS-CoV-2 PCR and genome sequencing using short-read (NextSeq 500 (Illumina)) and is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 15, 2022. ; https://doi.org/10.1101/2022.02.13.22270755 doi: medRxiv preprint long-read (GridION (Oxford Nanopore Technologies; ONT)) protocols using Midnight primers. We also employed the probe-based Illumina Respiratory Viral Oligo panel (RVOP) 9 to collect reads unbiased by SARS-CoV-2 PCR amplification (see Online Methods for details). Viral yield in samples was variable but still significant and suggesting the presence of viable virus (Table 1 ). Careful review of the relative frequency of 17 Omicron lineage-defining markers and 10 VOC Delta lineage-defining markers 10 clearly demonstrated co-infection with both lineages (Figures 1 & 2A) . The overall proportion of Delta and Omicron was highly concordant between all three sequencing methods ( Figure S3 ), however four lineage markers showed evidence of amplification bias when SARS-CoV-2 was amplified using Midnight primers ( Figure S5 ). Population analysis of genomic data generated using RVOP methods estimated that the VOC proportions in samples from Case A were 21% Omicron and 77% Delta on Day 2, compared to 45% Omicron and 53% Delta on Table 1 ). These conclusions were supported by matching the Omicron sequence from Case A to the Omicron genome recovered from their household contact (Case C, Figure 2 ). Viral culture of the Case A, Day 3 sample yielded Delta four days post-infection, the consensus genome recovered from this culture and matched the genome reconstructed from the mixed sample. It is likely that Delta had overgrew Omicron as TMPRSS2 enhanced VeroE6 cells are less permissible to Omicron, but highly adapted to Delta infection. 11 Viral culture was retrospectively and unsuccessfully attempted for the specimen collected from Case B, Day 2.

A previously described immunofluorescence assay (IFA) 12 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 15, 2022. Genomic epidemiology has rapidly become a high-resolution tool for local and international public health surveillance and disease control. However, the international coverage of SARS-CoV-2 genomic surveillance remains heavily biased towards countries with specialised genomic facilities and research programs. 3, 13 Furthermore, genomic surveillance relies on data sharing by multiple and geographically distributed providers which employ different sequencing and bioinformatic techniques. The reliance on consensus genome data and strict data quality criteria used by genomic laboratories and data sharing environments were designed to minimise the noise from laboratory contamination events and sequencing imperfections. However, such quality metrics can, by design, filter out potentially significant cases associated with high heterozygosity due to mixed viral populations as presented here.

In conclusion, these findings demonstrated the capacity of clinically and epidemiologically informed genomic surveillance to diagnose co-infections with SARS-CoV-2 variants and highlight the needed for deeper analysis of genomic surveillance data in clinical and public health contexts. SARS-CoV-2 co-infections, particularly when they occur in vulnerable hosts may drive saltational evolution, thus emphasising the important role COVID-19 genomic surveillance will play in diagnostic virology, in the era of mass vaccination. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 15, 2022. ; https://doi.org/10.1101/2022.02.13.22270755 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 15, 2022. ; https://doi.org/10.1101/2022.02.13.22270755 doi: medRxiv preprint

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The authors acknowledge the Sydney Informatics Hub and the use of the University of Sydney's high-performance computing cluster, Artemis. The authors are indebted to all