Evidence map›Paper›PMID 37310519›Full record

ArticleVirus genes2023

Intra-host mutation rate of acute SARS-CoV-2 infection during the initial pandemic wave.

Kim El-Haddad, Thamali M Adhikari, Zheng Jin Tu, Yu-Wei Cheng, Xiaoyi Leng, Xiangyi Zhang, Daniel Rhoads, Jennifer S Ko, Sarah Worley, Jing Li and 2 more

Abstract read
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In one paragraph

Article in Virus genes, 2023. 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

12 authors.

Kim El-HaddadCenter for Pediatric Infectious Disease, Cleveland Clinic Children's, R3, 9500 Euclid Avenue, Cleveland, 44195 , OH, USA. elhaddk@ccf.org.
Thamali M AdhikariDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Zheng Jin TuRobert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Yu-Wei ChengRobert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Xiaoyi LengDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Xiangyi ZhangDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Daniel RhoadsRobert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Jennifer S KoRobert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Sarah WorleyDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
Jing LiDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Brian P RubinRobert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Frank P EsperCenter for Pediatric Infectious Disease, Cleveland Clinic Children's, R3, 9500 Euclid Avenue, Cleveland, 44195 , OH, USA. esperf@ccf.org.

Funding

Ellen and Steven Ross Fellowship Research Award IF-110077National Science Foundation CCF-1815139National Science Foundation,United States CCF-2006780National Science Foundation,United States CCF-2200255
6 · The paper itself

Abstract

SARS-CoV-2 mutation is minimized through a proofreading function encoded by NSP-14. Most estimates of the SARS-CoV-2 mutation rate are derived from population based sequence data. Our understanding of SARS-CoV-2 evolution might be enhanced through analysis of intra-host viral mutation rates in specific populations. Viral genome analysis was performed between paired samples and mutations quantified at allele frequencies (AF) ≥ 0.25, ≥ 0.5 and ≥ 0.75. Mutation rate was determined employing F81 and JC69 evolution models and compared between isolates with (ΔNSP-14) and without (wtNSP-14) non-synonymous mutations in NSP-14 and by patient comorbidity. Forty paired samples with median interval of 13 days [IQR 8.5-20] were analyzed. The estimated mutation rate by F81 modeling was 93.6 (95%CI 90.8-96.4], 40.7 (95%CI 38.9-42.6) and 34.7 (95%CI 33.0-36.4) substitutions/genome/year at AF ≥ 0.25, ≥ 0.5, ≥ 0.75 respectively. Mutation rate in ΔNSP-14 were significantly elevated at AF ≥ 0.25 vs wtNSP-14. Patients with immune comorbidities had higher mutation rate at all allele frequencies. Intra-host SARS-CoV-2 mutation rates are substantially higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated mutation rate at low AF. Immunosuppressed patients have elevated mutation rate at all AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling.

Indexed as

COVID-19Genome, ViralHumansMutationMutation RatePandemicsSARS-CoV-2Allele frequencyCOVID-19Mutation rateNSP-14SARS-CoV-2SNV

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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.