Large-scale network analysis reveals the sequence space architecture of antibody repertoires

By Enkelejda Miho, Rok Roskar, Victor Greiff, Sai T. Reddy. 2019

116 citations recorded by the source corpus.

The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. The major principles that define the architecture of antibody repertoires have remained largely unknown. Here, we establish a high-performance computing platform to construct large-scale networks from comprehensive human and murine antibody repertoire sequencing datasets (>100,000 unique sequences). Leveraging a network-based statistical framework, we identify three fundamental principles of antibody repertoire architecture: reproducibility, robustness and redundancy. Antibody repertoire networks are highly reproducible across individuals despite high antibody sequence dissimilarity. The architecture of antibody repertoires is robust to the removal of up to 50–90% of randomly selected clones, but fragile to the removal of public clones shared among individuals. Finally, repertoire architecture is intrinsically redundant. Our analysis provides guidelines for the large-scale network analysis of immune repertoires and may be used in the future to define disease-associated and synthetic repertoires. Analysis of large-scale immune repertoire architecture remains a challenge. Here, the authors introduce an approach to construct similarity networks from high-throughput antibody repertoire sequencing data, and show that the networks are redundant, robust and reproducible across individuals.

Subfields: Immunology, Molecular Biology

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Citations in the corpus

Protein design and variant prediction using autoregressive generative models (2021, background): “Previous statistical and structural modeling of antibody repertoires 11 – 18 have addressed the characterization of sequences of natural antibodies or predicted higher af ﬁ nity sequences from immunization or selection experiments.”