The dengue-specific immune response and antibody identification with machine learning
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 ena...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | npj Vaccines |
| Online Access: | https://doi.org/10.1038/s41541-023-00788-7 |
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| _version_ | 1850110036244692992 |
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| author | Eriberto Noel Natali Alexander Horst Patrick Meier Victor Greiff Mario Nuvolone Lmar Marie Babrak Katja Fink Enkelejda Miho |
| author_facet | Eriberto Noel Natali Alexander Horst Patrick Meier Victor Greiff Mario Nuvolone Lmar Marie Babrak Katja Fink Enkelejda Miho |
| author_sort | Eriberto Noel Natali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e8f1dd8a1f454833a2e7771ef700925c |
| institution | OA Journals |
| issn | 2059-0105 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Vaccines |
| spelling | doaj-art-e8f1dd8a1f454833a2e7771ef700925c2025-08-20T02:37:55ZengNature Portfolionpj Vaccines2059-01052024-01-019111510.1038/s41541-023-00788-7The dengue-specific immune response and antibody identification with machine learningEriberto Noel Natali0Alexander Horst1Patrick Meier2Victor Greiff3Mario Nuvolone4Lmar Marie Babrak5Katja Fink6Enkelejda Miho7FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life SciencesFHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life SciencesFHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life SciencesDepartment of Immunology, Oslo University Hospital Rikshospitalet and University of OsloDepartment of Molecular Medicine, University of PaviaFHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life SciencesImmunoScapeFHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life SciencesAbstract 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.https://doi.org/10.1038/s41541-023-00788-7 |
| spellingShingle | Eriberto Noel Natali Alexander Horst Patrick Meier Victor Greiff Mario Nuvolone Lmar Marie Babrak Katja Fink Enkelejda Miho The dengue-specific immune response and antibody identification with machine learning npj Vaccines |
| title | The dengue-specific immune response and antibody identification with machine learning |
| title_full | The dengue-specific immune response and antibody identification with machine learning |
| title_fullStr | The dengue-specific immune response and antibody identification with machine learning |
| title_full_unstemmed | The dengue-specific immune response and antibody identification with machine learning |
| title_short | The dengue-specific immune response and antibody identification with machine learning |
| title_sort | dengue specific immune response and antibody identification with machine learning |
| url | https://doi.org/10.1038/s41541-023-00788-7 |
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