Computational deconvolution of the dengue immune response complexity with identification of novel broadly neutralizing antibodies
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Autor:innen
Natali, Eriberto Noel
Horst, Alexander
Nuvolone, Mario
Babrak, Lmar Marie
Fink, Katja
Traggiai, Elisabetta
Autor:in (Körperschaft)
Publikationsdatum
21.09.2022
Typ der Arbeit
Studiengang
Typ
06 - Präsentation
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Verlagsort / Veranstaltungsort
Gruyères
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Dengue virus poses a serious threat to global health as the causative agent of
the dengue fever. Currently, there is no approved therapeutic, and broadly
neutralizing antibodies recognizing all four serotypes may be an effective
treatment. High-throughput immune repertoire sequencing and bioinformatic
analysis enable in-depth understanding of the immune response in dengue
infection. Here, we use these technologies and apply machine learning to
identify rare and underrepresented broadly neutralizing antibody sequences
through investigation of antibody response in dengue. We observed
challenging the immune system with dengue elicits the following signatures on
the antibody repertoire: (i) an increase of the diversity in the CDR3 regions and
the germline genes; (ii) a change in the architecture by eliciting power-law
network distributions and enrichment in polar amino acids of the CDR3; (iii) an
increase in the expression of transcription factors of the JNK/Fos pathways and
ribosomal proteins. Moreover, our work demonstrates the applicability of
computational methods and machine learning to high-throughput antibody
repertoire sequencing datasets for neutralizing antibody candidate
identification. Further investigation with antibody expression and functional
assays is planned to validate the obtained results.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Postdoc Network Meeting
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
21.09.2022
Enddatum der Konferenz
23.09.2022
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Keine Begutachtung
Open Access-Status
Lizenz
Zitation
NATALI, Eriberto Noel, Alexander HORST, Patrick MEIER, Victor GREIFF, Mario NUVOLONE, Lmar Marie BABRAK, Kristina DJORDJEVIC, Katja FINK, Elisabetta TRAGGIAI und Enkelejda MIHO, 2022. Computational deconvolution of the dengue immune response complexity with identification of novel broadly neutralizing antibodies. Postdoc Network Meeting. Gruyères. 21 September 2022. Verfügbar unter: https://doi.org/10.26041/fhnw-9932