Biology subjects

Parola, C.

Publications and source records attributed to Parola, C..

3 recordsLinked to original sources

bioRxiv2020

Convergent selection in antibody repertoires is revealed by deep learning

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.

immunology↗

bioRxiv2019

Molecular design, optimization and genomic integration of chimeric B cell receptors in murine B cells

Immune cell therapies based on the integration of synthetic antigen receptors provide a powerful strategy for the treatment of diverse diseases, most notably retargeting T cells engineered to express chimeric antigen receptors (CAR) for cancer therapy. In addition to T lymphocytes, B lymphocytes may also represent valuable immune cells that can be engineered for therapeutic purposes such as protein replacement therapy or recombinant antibody production. In this article, we report a promising concept for the molecular design, optimization and genomic integration of a novel class of synthetic antigen receptors, chimeric B cell receptors (CBCR). We initially optimized CBCR expression and detection by modifying the extracellular surface tag, the transmembrane regions and intracellular signaling domains. For this purpose, we stably integrated a series of CBCR variants into immortalized B cell hybridomas using CRISPR-Cas9. Subsequently, we developed a reliable and consistent pipeline to precisely introduce cassettes of several kilobases size into the genome of primary murine B cells, again via CRISPR-Cas9 induced HDR. Finally, we were able to show the robust surface expression and antigen recognition of a synthetic CBCR in primary B cells. We anticipate that CBCRs and our approach for engineering primary B cells will be a valuable tool for the advancement of future B cell-based immune therapies.

immunology↗

bioRxiv2018

High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis

Antibody engineering is performed to improve therapeutic properties by directed evolution, usually by high-throughput screening of phage or yeast display libraries. Engineering antibodies in mammalian cells offers advantages associated with expression in their final therapeutic format (full-length glycosylated IgG), however, the inability to express large and diverse libraries severely limits their potential throughput. To address this limitation, we have developed homology-directed mutagenesis (HDM), a novel method which extends the concept of CRISPR/Cas9-mediated homology-directed repair (HDR). HDM leverages oligonucleotides with degenerate codons to generate site-directed mutagenesis libraries in mammalian cells. By improving HDM efficiency (>35-fold) and combining mammalian display screening with next-generation sequencing (NGS), we validated this approach can be used for key applications in antibody engineering at high-throughput: rational library construction, novel variant discovery, affinity maturation, and deep mutational scanning (DMS). We anticipate that HDM will be a valuable tool for engineering and optimizing antibodies in mammalian cells, and eventually enable directed evolution of other complex proteins and cellular therapeutics.

bioengineering↗