Author Correction. The dengue-specific immune response and antibody identification with machine learning

Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
npj Vaccines
Special issue
DOI of the original publication
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Series
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Volume
9
Issue / Number
16
Pages / Duration
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Publisher / Publishing institution
Nature
Place of publication / Event location
Edition
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Abstract
Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.
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Event
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ISBN
ISSN
2059-0105
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
'https://creativecommons.org/licenses/by/4.0/'
Citation
Natali, E. N., Horst, A., Meier, P., Greiff, V., Nuvolone, M., Babrak, L. M., Fink, K., & Miho, E. (2024). Author Correction. The dengue-specific immune response and antibody identification with machine learning. Npj Vaccines, 9(16). https://doi.org/10.1038/s41541-024-00820-4