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Convergent selection in antibody repertoires is revealed by deep learning

Simon Friedensohn, Daniel Neumeier, Tarik A Khan, Lucia Csepregi, View ORCID ProfileCristina Parola, View ORCID ProfileArthur R Gorter de Vries, Lena Erlach, View ORCID ProfileDerek M Mason, View ORCID ProfileSai T Reddy
doi: https://doi.org/10.1101/2020.02.25.965673
Simon Friedensohn
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Daniel Neumeier
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Tarik A Khan
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Lucia Csepregi
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Cristina Parola
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Arthur R Gorter de Vries
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Lena Erlach
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Derek M Mason
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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Sai T Reddy
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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  • ORCID record for Sai T Reddy
  • For correspondence: sai.reddy@ethz.ch
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SUMMARY

Adaptive immunity is driven by the ability of lymphocytes to undergo V(D)J recombination and generate a highly diverse set of immune receptors (B cell receptors/secreted antibodies and T cell receptors) and their subsequent clonal selection and expansion upon molecular recognition of foreign antigens. These principles lead to remarkable, unique and dynamic immune receptor repertoires1. Deep sequencing provides increasing evidence for the presence of commonly shared (convergent) receptors across individual organisms within one species2-4. Convergent selection of specific receptors towards various antigens offers one explanation for these findings. For example, single cases of convergence have been reported in antibody repertoires of viral infection or allergy5-8. Recent studies demonstrate that convergent selection of sequence motifs within T cell receptor (TCR) repertoires can be identified on an even wider scale9,10. Here we report that there is extensive convergent selection in antibody repertoires of mice for a range of protein antigens and immunization conditions. We employed a deep learning approach utilizing variational autoencoders (VAEs) to model the underlying process of B cell receptor (BCR) recombination and assume that the data generation follows a Gaussian mixture model (GMM) in latent space. This provides both a latent embedding and cluster labels that group similar sequences, thus enabling the discovery of a multitude of convergent, antigen-associated sequence patterns. Using a linear, one-versus-all support vector machine (SVM), we confirm that the identified sequence patterns are predictive of antigenic exposure and outperform predictions based on the occurrence of public clones. Recombinant expression of both natural and in silico-generated antibodies possessing convergent patterns confirms their binding specificity to target antigens. Our work highlights to which extent convergence in antibody repertoires can occur and shows how deep learning can be applied for immunodiagnostics and antibody discovery and engineering.

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Posted February 26, 2020.
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Convergent selection in antibody repertoires is revealed by deep learning
Simon Friedensohn, Daniel Neumeier, Tarik A Khan, Lucia Csepregi, Cristina Parola, Arthur R Gorter de Vries, Lena Erlach, Derek M Mason, Sai T Reddy
bioRxiv 2020.02.25.965673; doi: https://doi.org/10.1101/2020.02.25.965673
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Convergent selection in antibody repertoires is revealed by deep learning
Simon Friedensohn, Daniel Neumeier, Tarik A Khan, Lucia Csepregi, Cristina Parola, Arthur R Gorter de Vries, Lena Erlach, Derek M Mason, Sai T Reddy
bioRxiv 2020.02.25.965673; doi: https://doi.org/10.1101/2020.02.25.965673

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