Computational deconvolution of the dengue immune response complexity with  identification of novel broadly neutralizing antibodies

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.

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Postdoc Network Meeting

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21.09.2022

Conference end date

23.09.2022

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English

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Yes

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No peer review

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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

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