Publication
IgG Antibody responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their effective potential for disease diagnosis and pathological antigenic mimicry
| dc.contributor.author | Fonseca, André | |
| dc.contributor.author | Szysz, Mateusz | |
| dc.contributor.author | Ly, Hoang Thien | |
| dc.contributor.author | Cordeiro, Clara | |
| dc.contributor.author | Sepúlveda, Nuno | |
| dc.date.accessioned | 2024-01-31T10:41:20Z | |
| dc.date.available | 2024-01-31T10:41:20Z | |
| dc.date.issued | 2024-01-15 | |
| dc.date.updated | 2024-01-26T14:10:47Z | |
| dc.description.abstract | The diagnosis and pathology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections. Materials and Methods: In this work, we analyzed a large public dataset on the IgG antibodies to 3054 EBV peptides to understand whether these immune responses could help diagnose patients and trigger pathological autoimmunity; we used healthy controls (HCs) as a comparator cohort. Subsequently, we aimed at predicting the disease status of the study participants using a super learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets. Results: When we compared the data of all ME/CFS patients or the data of a subgroup of those patients with non-infectious or unknown disease triggers to the data of the HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. However, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from the HCs with 100% and 90% accuracies in the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies. Conclusions: In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis in a subset of patients. However, the peptides associated with these antibodies are less likely to induce autoimmune B-cell responses that could explain the pathogenesis of ME/CFS. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Medicina 60 (1): 161 (2024) | pt_PT |
| dc.identifier.doi | 10.3390/medicina60010161 | pt_PT |
| dc.identifier.eissn | 1648-9144 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/20326 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | Analysis of high-throughput antibody data for better understanding of immunogenetics and epidemiology of malaria | |
| dc.relation | Centre of Statistics and its Applications | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Biomarker discovery | pt_PT |
| dc.subject | Disease pathogenesis | pt_PT |
| dc.subject | Autoimmunity | pt_PT |
| dc.subject | Antigenic mimicry | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.title | IgG Antibody responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their effective potential for disease diagnosis and pathological antigenic mimicry | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Analysis of high-throughput antibody data for better understanding of immunogenetics and epidemiology of malaria | |
| oaire.awardTitle | Centre of Statistics and its Applications | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F147629%2F2019/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00006%2F2020/PT | |
| oaire.citation.issue | 1 | pt_PT |
| oaire.citation.startPage | 161 | pt_PT |
| oaire.citation.title | Medicina | pt_PT |
| oaire.citation.volume | 60 | pt_PT |
| oaire.fundingStream | OE | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Fonseca | |
| person.familyName | Henrique Cordeiro | |
| person.givenName | André | |
| person.givenName | Clara Maria | |
| person.identifier.ciencia-id | C71E-21A1-E882 | |
| person.identifier.orcid | 0000-0001-8249-0354 | |
| person.identifier.orcid | 0000-0002-1026-6078 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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