Human origin ascertained for SARS-CoV-2 Omicron-like spike sequences detected in wastewater: a targeted surveillance study of a cryptic lineage in an urban sewershed

Shafer, Martin M.; Gregory, Devon A.; Bobholz, Max; Roguet, Adélaïde; Haddock, Luis A; Rushford, Clayton; Vuyk, William; Janssen, Kayley H.; Ries, Hunter; Pilch, Hannah E.; Mullen, Paige A.; Fahney, Rebecca B.; Wei, Wanting; Lambert, Matthew C.; Wenzel, Jeff; Halfmann, Peter; Kawaoka, Yoshihiro; Wilson, Nancy A.; Friedrich, Thomas C.; Pray, Ian W.; Westergaard, Ryan P.; O’Connor, David H.; Johnson, Marc C.

doi:10.1101/2022.10.28.22281553

Cited by 10 publications

(8 citation statements)

References 46 publications

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“…We estimate that more than nearly one in a thousand of all infections, and potentially as many as one in 200, may become persistent for at least two months. The harbouring of these persistent infections in the general community may also help explain the early detection of cryptic lineages circulating in wastewaters 34,35 long before they spread in the population at large.…”

Section: Discussionmentioning

confidence: 99%

High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study

Ghafari

Hall

Golubchik

et al. 2023

Preprint

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Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may act as viral reservoirs that could seed future outbreaks1–5, give rise to highly divergent lineages6–8, and contribute to cases with post-acute Coronavirus disease 2019 (COVID-19) sequelae (Long Covid)9,10. However, the population prevalence of persistent infections, their viral load kinetics, and evolutionary dynamics over the course of infections remain largely unknown. We identified 381 infections lasting at least 30 days, of which 54 lasted at least 60 days. These persistently infected individuals had more than 50% higher odds of self-reporting Long Covid compared to the infected controls, and we estimate that 0.09-0.5% of SARS-CoV-2 infections can become persistent and last for at least 60 days. In nearly 70% of the persistent infections we identified, there were long periods during which there were no consensus changes in virus sequences, consistent with prolonged presence of non-replicating virus. Our findings also suggest reinfections with the same major lineage are rare and that many persistent infections are characterised by relapsing viral load dynamics. Furthermore, we found a strong signal for positive selection during persistent infections, with multiple amino acid substitutions in the Spike and ORF1ab genes emerging independently in different individuals, including mutations that are lineage-defining for SARS-CoV-2 variants, at target sites for several monoclonal antibodies, and commonly found in immunocompromised patients11–14. This work has significant implications for understanding and characterising SARS-CoV-2 infection, epidemiology, and evolution.

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Section: Discussionmentioning

confidence: 99%

High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study

Ghafari

Hall

Golubchik

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, at time periods where a high level of virus diversity is expected, there are highly mutated sequences present in the wastewater (Figure 7). This suggest cryptic sequences in wastewater may be used to observe potential upcoming variants, similar to how known sequences have been back-traced to particular buildings using wastewater [33].…”

Section: Signature Exposures and Highly Mutated Sequences In Wastewat...mentioning

confidence: 84%

SARS-CoV-2’s evolutionary capacity is mostly driven by host antiviral molecules

Lamb

Luka

Saathoff

et al. 2023

Preprint

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The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to human infection since its emergence in 2019. Here we use machine learning models to identify shared signatures, i.e., common underlying mutational processes, and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and not virus properties alone are associated with the rise and fall of regional SARS-CoV-2 reported infection numbers. This impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity, and the effectiveness of government stringency. In terms of underlying evolutionary change, we used non-negative matrix factorisation to observe three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signatures 0, 1 and 3 were biased to C->T, T->C/A->G and G->T point mutations as would be expected of host antiviral molecules APOBEC, ADAR and ROS effects, respectively. We also observe a shift amidst the pandemic in relative mutational signature activity from predominantly APOBEC-like changes to an increasingly high proportion of changes consistent with ADAR editing. This could represent changes in how the virus and the host immune response interact, and indicates how SARS-CoV-2 may continue to accumulate mutations in the future. Linkage of the detected mutational signatures to the VOC defining amino acids substitutions indicates the majority of SARS-CoV-2's evolutionary capacity is likely to be associated with the action of host antiviral molecules rather than virus replication errors.

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“…Additionally, at time periods where a high level of virus diversity is expected, there are highly mutated sequences present in the wastewater ( Fig 7C ). This suggests cryptic sequences in wastewater may be used to observe potential upcoming variants, similar to how known sequences have been back-traced to particular buildings using wastewater [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

Mutational signature dynamics indicate SARS-CoV-2’s evolutionary capacity is driven by host antiviral molecules

Lamb,

Luka,

Saathoff

et al. 2024

PLoS Comput Biol

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The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to more efficient human infection since the emergence of this new human coronavirus in late 2019. Here, we use machine learning models to identify shared signatures, i.e., common underlying mutational processes and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and virus properties were associated with the waves of regional SARS-CoV-2 reported infection numbers and this impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity and the effectiveness of government stringency. To assess underlying evolutionary change, we used non-negative matrix factorisation and observed three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signatures 1, 2 and 3 were biased to C→T, T→C/A→G and G→T point mutations. We hypothesise assignments of these mutational signatures to the host antiviral molecules APOBEC, ADAR and ROS respectively. We observe a shift amidst the pandemic in relative mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. This could represent changes in how the virus and the host immune response interact and indicates how SARS-CoV-2 may continue to generate variation in the future. Linkage of the detected mutational signatures to the VOC-defining amino acids substitutions indicates the majority of SARS-CoV-2’s evolutionary capacity is likely to be associated with the action of host antiviral molecules rather than virus replication errors.

show abstract

Human origin ascertained for SARS-CoV-2 Omicron-like spike sequences detected in wastewater: a targeted surveillance study of a cryptic lineage in an urban sewershed

Cited by 10 publications

References 46 publications

High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study

High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study

SARS-CoV-2’s evolutionary capacity is mostly driven by host antiviral molecules

Mutational signature dynamics indicate SARS-CoV-2’s evolutionary capacity is driven by host antiviral molecules

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