Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19

Abstract In the post‐large era, various COVID‐19 sequelae are getting more and more attention to health problems. Although the mortality rate of the COVID‐19 infection is now declining, it is often accompanied by new clinical sequelae with different symptoms such as fatigue after infection, loss of...

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Main Authors: Nan Zhang, Xizi Luo, Xiangwen Ji, Tian Tian, Runze Wu, Shishun Zhao, Guoqing Wang
Format: Article
Language:English
Published: Wiley 2025-02-01
Series:Advanced Science
Subjects:
Online Access:https://doi.org/10.1002/advs.202407342
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author Nan Zhang
Xizi Luo
Xiangwen Ji
Tian Tian
Runze Wu
Shishun Zhao
Guoqing Wang
author_facet Nan Zhang
Xizi Luo
Xiangwen Ji
Tian Tian
Runze Wu
Shishun Zhao
Guoqing Wang
author_sort Nan Zhang
collection DOAJ
description Abstract In the post‐large era, various COVID‐19 sequelae are getting more and more attention to health problems. Although the mortality rate of the COVID‐19 infection is now declining, it is often accompanied by new clinical sequelae with different symptoms such as fatigue after infection, loss of smell. The degree of age, gender, virus infection seems to be weakly correlated with clinical symptoms. Human genetic variation plays a significant role in the sequelae of the COVID‐19 infection. This study aims to analyze the genomic differences between individuals with different COVID‐19 sequelae. In this study, the exomes of 97 patients with Omicron with 8 unique clinical manifestations are sequenced, and conducted a systematic analysis. Based on non‐negative matrix factorization algorithms, the trinucleotide mutation spectrum of four long‐term COVID‐19 genomes is summarized and found that individuals with different clinical symptoms have unique DNA mutation patterns and indel patterns. By constructing a Genomic Fingerprinting Framework, the driver genes of variation in each symptomatic population are deciphered and analyzed. This study showed that population‐specific mutational fingerprint differences are the main cause of heterogeneity in long‐term COVID‐19 sequelae. This study provides new ideas and insights into the causes of the long‐term COVID‐19 sequelae.
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spelling doaj-art-24de2dbe2c104bb1bca3856cd79190d72025-08-20T03:09:54ZengWileyAdvanced Science2198-38442025-02-01128n/an/a10.1002/advs.202407342Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19Nan Zhang0Xizi Luo1Xiangwen Ji2Tian Tian3Runze Wu4Shishun Zhao5Guoqing Wang6State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases Key Laboratory of Pathobiology Ministry of Education China‐Japan Union Hospital of Jilin University Changchun 130033 ChinaState Key Laboratory for Zoonotic Diseases Key Laboratory for Zoonosis Research of the Ministry of Education College of Veterinary Medicine Jilin University Changchun 130062 ChinaDepartment of Cardiology and Institute of Vascular Medicine Peking University Third Hospital 49 Huayuanbei Road Beijing 100191 ChinaCollege of Basic Medical Sciences Jilin University Changchun 130021 ChinaCollege of Mathematics Jilin University Changchun 130012 ChinaCollege of Mathematics Jilin University Changchun 130012 ChinaState Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases Key Laboratory of Pathobiology Ministry of Education China‐Japan Union Hospital of Jilin University Changchun 130033 ChinaAbstract In the post‐large era, various COVID‐19 sequelae are getting more and more attention to health problems. Although the mortality rate of the COVID‐19 infection is now declining, it is often accompanied by new clinical sequelae with different symptoms such as fatigue after infection, loss of smell. The degree of age, gender, virus infection seems to be weakly correlated with clinical symptoms. Human genetic variation plays a significant role in the sequelae of the COVID‐19 infection. This study aims to analyze the genomic differences between individuals with different COVID‐19 sequelae. In this study, the exomes of 97 patients with Omicron with 8 unique clinical manifestations are sequenced, and conducted a systematic analysis. Based on non‐negative matrix factorization algorithms, the trinucleotide mutation spectrum of four long‐term COVID‐19 genomes is summarized and found that individuals with different clinical symptoms have unique DNA mutation patterns and indel patterns. By constructing a Genomic Fingerprinting Framework, the driver genes of variation in each symptomatic population are deciphered and analyzed. This study showed that population‐specific mutational fingerprint differences are the main cause of heterogeneity in long‐term COVID‐19 sequelae. This study provides new ideas and insights into the causes of the long‐term COVID‐19 sequelae.https://doi.org/10.1002/advs.202407342exon sequencinggenome mutation patterngenomic fingerprinting frameworklong‐term COVID‐19non‐negative matrix factorization
spellingShingle Nan Zhang
Xizi Luo
Xiangwen Ji
Tian Tian
Runze Wu
Shishun Zhao
Guoqing Wang
Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19
Advanced Science
exon sequencing
genome mutation pattern
genomic fingerprinting framework
long‐term COVID‐19
non‐negative matrix factorization
title Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19
title_full Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19
title_fullStr Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19
title_full_unstemmed Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19
title_short Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19
title_sort genomic patterns are associated with different sequelae of patients with long term covid 19
topic exon sequencing
genome mutation pattern
genomic fingerprinting framework
long‐term COVID‐19
non‐negative matrix factorization
url https://doi.org/10.1002/advs.202407342
work_keys_str_mv AT nanzhang genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19
AT xiziluo genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19
AT xiangwenji genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19
AT tiantian genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19
AT runzewu genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19
AT shishunzhao genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19
AT guoqingwang genomicpatternsareassociatedwithdifferentsequelaeofpatientswithlongtermcovid19