Evidence map›Paper›PMID 42321162›Full record

ArticleNature communications2026

Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.

Mark P Khurana, Alexandros Katsiferis, Neil Scheidwasser, Mathilde Marie Brünnich Sloth, Jacob Curran-Sebastian, Christian Morgenstern, Man-Hung Eric Tang, Jannik Fonager, Morten Rasmussen, Marc Stegger and 10 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Mark P KhuranaSection for Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark. mark.khurana@sund.ku.dk.ORCID 0000-0002-1123-7674
Alexandros KatsiferisSection for Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Neil ScheidwasserSection for Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Mathilde Marie Brünnich SlothSection for Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0003-2261-8779
Jacob Curran-SebastianData Science Section, IT University of Copenhagen, Copenhagen, Denmark.
Christian MorgensternDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.ORCID 0000-0003-3735-1130
Man-Hung Eric TangDepartment of Sequencing and Bioinformatics, Statens Serum Institut, Copenhagen, Denmark.ORCID 0000-0002-3483-0219
Jannik FonagerDepartment of Virology and Microbiological Preparedness, Statens Serum Institut, Copenhagen, Denmark.ORCID 0000-0003-1688-1482
Morten RasmussenDepartment of Virology and Microbiological Preparedness, Statens Serum Institut, Copenhagen, Denmark.ORCID 0000-0001-7491-4137
Marc SteggerDepartment of Sequencing and Bioinformatics, Statens Serum Institut, Copenhagen, Denmark.ORCID 0000-0003-0321-1180
Sune LehmannDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.ORCID 0000-0001-6099-2345
Charles WhittakerDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.ORCID 0000-0002-5003-2575
Laust H MortensenStatistics Denmark, Copenhagen, Denmark.
Pikka JokelainenInfectious Disease Preparedness and One Health, Statens Serum Institut, Copenhagen, Denmark.ORCID 0000-0002-3035-5094
Moritz U G KraemerDepartment of Biology, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-8838-7147
Neil M FergusonDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.ORCID 0000-0002-1154-8093
Mahan GhafariDepartment of Biology, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-4123-3287
Tyra G KrauseSection of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
David A Duchêne *Section for Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-5479-1974
Samir Bhatt *Section for Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-0891-4611

Funding

Danmarks Grundforskningsfond (Danish National Research Foundation) DNRF160
6 · The paper itself

Abstract

Persistent SARS-CoV-2 infections have been hypothesized to play a key role in the emergence of variants of concern. However, the factors determining which individuals are at risk and their viral molecular signatures during infection remain poorly understood. Using Denmark's extensive COVID-19 surveillance, comprising over 700,000 genomes, we identify 303 persistent infections and, critically, link them to health and sociodemographic data. Our analysis confirms the hypothesis that immunocompromised individuals are at the highest risk of experiencing persistent infections. Other disease groups associated with mortality, such as diabetes, show no such associations. Among these persistent infections, the viral sequences exhibit signs of positive selection, with recurrent mutations linked to treatment resistance. Our findings suggest that immunosuppression plays a key role in the emergence of novelty in persistent infections.

Indexed as

COVID-19Genome, ViralMutationSARS-CoV-2DenmarkHumansImmunocompromised HostImmunosuppression Therapy

Identifiers

PMID42321162
PMCPMC13433919

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.