Host methylation predicts SARS-CoV-2 infection and clinical outcome.

Journal

Communications medicine

ISSN: 2730-664X

Titre abrégé: Commun Med (Lond)

Pays: England

ID NLM: 9918250414506676

Informations de publication

Date de publication:
26 Oct 2021

Historique:

received: 04 03 2021

accepted: 24 09 2021

entrez: 7 2 2023

pubmed: 26 10 2021

medline: 26 10 2021

Statut: epublish

Résumé

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections. Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual’s collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity.

Sections du résumé

BACKGROUND BACKGROUND

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR

METHODS METHODS

We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls.

RESULTS RESULTS

Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively.

CONCLUSIONS CONCLUSIONS

In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual’s collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity.

Autres résumés

Type: plain-language-summary (eng)

Identifiants

DOI: 10.1038/s43856-021-00042-y PMID: 36750622

pubmed: 36750622

doi: 10.1038/s43856-021-00042-y

pii: 10.1038/s43856-021-00042-y

doi:

Types de publication

Journal Article

Langues

eng

Pagination

Informations de copyright

Références

FDA. Coronavirus disease 2019. (COVID-19) emergency use authorizations for medical devices. https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations (2020).

Menachery, V. D. et al. Pathogenic influenza viruses and coronaviruses utilize similar and contrasting approaches to control interferon-stimulated gene responses. mBio 5, e01174–01114 (2014).

doi: 10.1128/mBio.01174-14 pubmed: 24846384 pmcid: 4030454

Marazzi, I. et al. Suppression of the antiviral response by an influenza histone mimic. Nature 483, 428–433 (2012).

doi: 10.1038/nature10892 pubmed: 22419161 pmcid: 3598589

Menachery, V. D. et al. MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape. Proc. Natl Acad. Sci. USA 115, E1012–E1021 (2018).

doi: 10.1073/pnas.1706928115 pubmed: 29339515 pmcid: 5798318

Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395, 565–574 (2020).

doi: 10.1016/S0140-6736(20)30251-8 pubmed: 32007145 pmcid: 7159086

Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).

doi: 10.1016/S0140-6736(20)30183-5 pubmed: 31986264 pmcid: 7159299

Roser, M., Ritchie, H., Ortiz-Ospina, E. & Hasell, J. Coronavirus disease (COVID-19) – statistics and research. Our world in data. https://ourworldindata.org/coronavirus (2020).

Woloshin, S., Patel, N. & Kesselheim, A. S. False negative tests for SARS-CoV-2 infection - challenges and implications. N. Engl. J. Med. 383, e38 (2020).

doi: 10.1056/NEJMp2015897 pubmed: 32502334

Kucirka, L. M., Lauer, S. A., Laeyendecker, O., Boon, D. & Lessler, J. Variation in false-negative rate of reverse transcriptase polymerase chain reaction-based SARS-CoV-2 tests by time since exposure. Ann. Intern. Med. 173, 262–267 (2020).

doi: 10.7326/M20-1495 pubmed: 32422057

Fox-Lewis, S., Muttaiyah, S., Rahnama, F., McAuliffe, G. & Roberts, S. An understanding of discordant SARS-CoV-2 test results: an examination of the data from a central Auckland laboratory. N. Z. Med. J. 133, 81–88 (2020).

pubmed: 32777798

Aslam, A. et al. SARS CoV-2 surveillance and exposure in the perioperative setting with universal testing and personal protective equipment (PPE) policies. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciaa1607 (2020).

doi: 10.1093/cid/ciaa1607 pubmed: 31840746

Lan, L. et al. Positive RT-PCR test results in patients recovered from COVID-19. JAMA 323, 1502–1503 (2020).

doi: 10.1001/jama.2020.2783 pubmed: 32105304 pmcid: 7047852

Vandenberg, O., Martiny, D., Rochas, O., van Belkum, A. & Kozlakidis, Z. Considerations for diagnostic COVID-19 tests. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-00461-z (2020).

doi: 10.1038/s41579-020-00461-z pubmed: 33057203 pmcid: 7556561

Garibaldi, B. T. et al. Patient trajectories among persons hospitalized for COVID-19: a cohort study. Ann. Intern. Med. https://doi.org/10.7326/M20-3905 (2020).

doi: 10.7326/M20-3905 pubmed: 32960645 pmcid: 7530643

Bilinski, A. & Emanuel, E. J. COVID-19 and excess all-cause mortality in the US and 18 comparison countries. JAMA 324, 2100–2102 (2020).

doi: 10.1001/jama.2020.20717 pubmed: 33044514 pmcid: 7551217

McElvaney, O. J. et al. A linear prognostic score based on the ratio of interleukin-6 to interleukin-10 predicts outcomes in COVID-19. EBioMedicine 61, 103026 (2020).

doi: 10.1016/j.ebiom.2020.103026 pubmed: 33039714 pmcid: 7543971

Aref-Eshghi, E. et al. Evaluation of DNA methylation episignatures for diagnosis and phenotype correlations in 42 Mendelian neurodevelopmental disorders. Am. J. Hum. Genet. 106, 356–370 (2020).

doi: 10.1016/j.ajhg.2020.01.019 pubmed: 32109418 pmcid: 7058829

Bollepalli, S., Korhonen, T., Kaprio, J., Anders, S. & Ollikainen, M. EpiSmokEr: a robust classifier to determine smoking status from DNA methylation data. Epigenomics 11, 1469–1486 (2019).

doi: 10.2217/epi-2019-0206 pubmed: 31466478

Agha, G. et al. Blood leukocyte DNA methylation predicts risk of future myocardial infarction and coronary heart disease. Circulation 140, 645–657 (2019).

doi: 10.1161/CIRCULATIONAHA.118.039357 pubmed: 31424985 pmcid: 6812683

Jurmeister, P. et al. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Sci. Transl. Med. 11, eaaw8513 (2019).

Benhamida, J. K. et al. Reliable clinical MLH1 promoter hypermethylation assessment using a high-throughput genome-wide methylation array platform. J. Mol. Diagn. 22, 368–375 (2020).

doi: 10.1016/j.jmoldx.2019.11.005 pubmed: 31881335 pmcid: 7103764

Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

doi: 10.1038/nature26000 pubmed: 29539639 pmcid: 6093218

Karimi, S. et al. The central nervous system tumor methylation classifier changes neuro-oncology practice for challenging brain tumor diagnoses and directly impacts patient care. Clin. Epigenetics 11, 185 (2019).

doi: 10.1186/s13148-019-0766-2 pubmed: 31806041 pmcid: 6896594

Korshunov, A. et al. DNA-methylation profiling discloses significant advantages over NanoString method for molecular classification of medulloblastoma. Acta Neuropathol. 134, 965–967 (2017).

doi: 10.1007/s00401-017-1776-9 pubmed: 29027579

Saben, J. L. et al. The emergency medicine specimen bank: an innovative approach to biobanking in acute care. Acad. Emerg. Med. 26, 639–647 (2019).

pubmed: 30239069

Pidsley, R. et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 17, 208 (2016).

doi: 10.1186/s13059-016-1066-1 pubmed: 27717381 pmcid: 5055731

Robinson, J. et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 43, D423–D431 (2015).

doi: 10.1093/nar/gku1161 pubmed: 25414341

Boorgula, M. P. et al. Replicated methylation changes associated with eczema herpeticum and allergic response. Clin. Epigenetics 11, 122 (2019).

doi: 10.1186/s13148-019-0714-1 pubmed: 31443688 pmcid: 6706929

Gunderson, K. L., Steemers, F. J., Lee, G., Mendoza, L. G. & Chee, M. S. A genome-wide scalable SNP genotyping assay using microarray technology. Nat. Genet. 37, 549–554 (2005).

doi: 10.1038/ng1547 pubmed: 15838508

Zhou, W., Triche, T. J. Jr., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).

pubmed: 30085201 pmcid: 6237738

Triche, T. J. Jr., Weisenberger, D. J., Van Den Berg, D., Laird, P. W. & Siegmund, K. D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 41, e90 (2013).

doi: 10.1093/nar/gkt090 pubmed: 23476028 pmcid: 3627582

Zhou, W., Laird, P. W. & Shen, H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 45, e22 (2017).

pubmed: 27924034

Rahmani, E. et al. GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data. Bioinformatics 33, 1870–1872 (2017).

doi: 10.1093/bioinformatics/btx059 pubmed: 28177067 pmcid: 5870777

Rahmani, E. et al. Genome-wide methylation data mirror ancestry information. Epigenetics Chromatin 10, 1 (2017).

doi: 10.1186/s13072-016-0108-y pubmed: 28149326 pmcid: 5267476

Rahmani, E. et al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat. Methods 13, 443–445 (2016).

doi: 10.1038/nmeth.3809 pubmed: 27018579 pmcid: 5548182

Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011).

doi: 10.1038/nmeth.1681 pubmed: 21892150

Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

doi: 10.1093/nar/gkw377 pubmed: 27141961 pmcid: 4987924

Harris, M. A. et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32, D258–D261 (2004).

doi: 10.1093/nar/gkh036 pubmed: 14681407

Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

doi: 10.1093/nar/gkw1092 pubmed: 27899662

Huang, R. et al. The NCATS BioPlanet - an integrated platform for exploring the universe of cellular signaling pathways for toxicology, systems biology, and chemical genomics. Front. Pharmacol. 10, 445 (2019).

doi: 10.3389/fphar.2019.00445 pubmed: 31133849 pmcid: 6524730

Slenter, D. N. et al. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. 46, D661–D667 (2018).

doi: 10.1093/nar/gkx1064 pubmed: 29136241

Cavalcante, R. G. & Sartor, M. A. annotatr: genomic regions in context. Bioinformatics 33, 2381–2383 (2017).

doi: 10.1093/bioinformatics/btx183 pubmed: 28369316 pmcid: 5860117

Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Methodol. 58, 267–288 (1996).

Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).

doi: 10.1111/j.1467-9868.2005.00503.x

Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

doi: 10.18637/jss.v033.i01 pubmed: 20808728 pmcid: 2929880

van Iterson, M., van Zwet, E. W., Consortium, B. & Heijmans, B. T. Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biol. 18, 19 (2017).

doi: 10.1186/s13059-016-1131-9 pubmed: 28129774 pmcid: 5273857

Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

doi: 10.1038/nature12787 pubmed: 24670763 pmcid: 5215096

Iwata, H. et al. PARP9 and PARP14 cross-regulate macrophage activation via STAT1 ADP-ribosylation. Nat. Commun. 7, 12849 (2016).

doi: 10.1038/ncomms12849 pubmed: 27796300 pmcid: 5095532

Lee, J. S. & Shin, E. C. The type I interferon response in COVID-19: implications for treatment. Nat. Rev. Immunol. 20, 585–586 (2020).

doi: 10.1038/s41577-020-00429-3 pubmed: 32788708 pmcid: 8824445

Jiang, H. et al. DNA methylation markers in the diagnosis and prognosis of common leukemias. Signal. Transduct. Target. Ther. 5, 3 (2020).

doi: 10.1038/s41392-019-0090-5 pubmed: 32296024 pmcid: 6959291

Ciolfi, A. et al. Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature. Clin. Epigenetics 12, 7 (2020).

doi: 10.1186/s13148-019-0804-0 pubmed: 31910894 pmcid: 6947958

Wang, L., Ni, S., Du, Z. & Li, X. A six-CpG-based methylation markers for the diagnosis of ovarian cancer in blood. J. Cell. Biochem. 121, 1409–1419 (2020).

doi: 10.1002/jcb.29376 pubmed: 31502352

Imgenberg-Kreuz, J. et al. Shared and unique patterns of DNA methylation in systemic lupus erythematosus and primary Sjogren’s syndrome. Front. Immunol. 10, 1686 (2019).

doi: 10.3389/fimmu.2019.01686 pubmed: 31428085 pmcid: 6688520

Bend, E. G. et al. Gene domain-specific DNA methylation episignatures highlight distinct molecular entities of ADNP syndrome. Clin. Epigenetics 11, 64 (2019).

doi: 10.1186/s13148-019-0658-5 pubmed: 31029150 pmcid: 6487024

Wang, C., Chen, L., Yang, Y., Zhang, M. & Wong, G. Identification of potential blood biomarkers for Parkinson’s disease by gene expression and DNA methylation data integration analysis. Clin. Epigenetics 11, 24 (2019).

doi: 10.1186/s13148-019-0621-5 pubmed: 30744671 pmcid: 6371578

Panagopoulou, M. et al. Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers. Oncogene 38, 3387–3401 (2019).

doi: 10.1038/s41388-018-0660-y pubmed: 30643192

Breton, C. V. et al. Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group. Environ. Health Perspect. 125, 511–526 (2017).

doi: 10.1289/EHP595 pubmed: 28362264 pmcid: 5382002

Rakyan, V. K., Down, T. A., Balding, D. J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).

doi: 10.1038/nrg3000 pubmed: 21747404 pmcid: 3508712

Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).

doi: 10.1186/1471-2105-13-86 pubmed: 22568884 pmcid: 3532182

Michels, K. B. et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods 10, 949–955 (2013).

doi: 10.1038/nmeth.2632 pubmed: 24076989

Hamano, E. et al. Polymorphisms of interferon-inducible genes OAS-1 and MxA associated with SARS in the Vietnamese population. Biochem. Biophys. Res. Commun. 329, 1234–1239 (2005).

doi: 10.1016/j.bbrc.2005.02.101 pubmed: 15766558 pmcid: 7092916

Zhang, Q. et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science 370, eabd4570 (2020).

Pairo-Castineira, E. et al. Genetic mechanisms of critical illness in COVID-19. Nature 591, 92–98 (2021).

doi: 10.1038/s41586-020-03065-y pubmed: 33307546

He, J. et al. Association of SARS susceptibility with single nucleic acid polymorphisms of OAS1 and MxA genes: a case-control study. BMC Infect. Dis. 6, 106 (2006).

doi: 10.1186/1471-2334-6-106 pubmed: 16824203 pmcid: 1550407

Zhou, Y. et al. Coagulation factors and the incidence of COVID-19 severity: Mendelian randomization analyses and supporting evidence. Signal. Transduct. Target. Ther. 6, 222 (2021).

Horton, R. et al. Gene map of the extended human MHC. Nat. Rev. Genet. 5, 889–899 (2004).

doi: 10.1038/nrg1489 pubmed: 15573121

Tsai, P. C. & Bell, J. T. Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int. J. Epidemiol. 44, 1429–1441 (2015).

doi: 10.1093/ije/dyv041 pubmed: 25972603 pmcid: 4588864

Sadikovic, B., Aref-Eshghi, E., Levy, M. A. & Rodenhiser, D. DNA methylation signatures in mendelian developmental disorders as a diagnostic bridge between genotype and phenotype. Epigenomics 11, 563–575 (2019).

doi: 10.2217/epi-2018-0192 pubmed: 30875234

Hovestadt, V. et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 125, 913–916 (2013).

doi: 10.1007/s00401-013-1126-5 pubmed: 23670100 pmcid: 3661908

Vasudevan, H. N. et al. DNA methylation profiling demonstrates superior diagnostic classification to RNA-sequencing in a case of metastatic meningioma. Acta Neuropathol. Commun. 8, 82 (2020).

doi: 10.1186/s40478-020-00952-3 pubmed: 32517746 pmcid: 7285578

Voet, D. & Voet, J. Biochemistry 4th edn (Wiley & Sons, 2011).

Oran, D. P. & Topol, E. J. Prevalence of asymptomatic SARS-CoV-2 infection: a narrative review. Ann. Intern. Med. 173, 362–367 (2020).

doi: 10.7326/M20-3012 pubmed: 32491919

Tan, F. et al. Viral transmission and clinical features in asymptomatic carriers of SARS-CoV-2 in Wuhan, China. Front. Med. 7, 547 (2020).

doi: 10.3389/fmed.2020.00547

Buitrago-Garcia, D. et al. Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: a living systematic review and meta-analysis. PLoS Med. 17, e1003346 (2020).

doi: 10.1371/journal.pmed.1003346 pubmed: 32960881 pmcid: 7508369

Zou, L. et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N. Engl. J. Med. 382, 1177–1179 (2020).

doi: 10.1056/NEJMc2001737 pubmed: 32074444 pmcid: 7121626

Hu, Z. et al. Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China. Sci. China Life Sci. 63, 706–711 (2020).

doi: 10.1007/s11427-020-1661-4 pubmed: 32146694

Liu, Y. et al. Viral dynamics in mild and severe cases of COVID-19. Lancet Infect. Dis. 20, 656–657 (2020).

doi: 10.1016/S1473-3099(20)30232-2 pubmed: 32199493 pmcid: 7158902

RC Group et al. Dexamethasone in hospitalized patients with Covid-19 - preliminary report. N. Engl. J. Med. 384, 693–704 (2021).