Abstract
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.
Highlights
The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them
In order to extract the construction principles of antibody repertoires from such a high-dimensional similarity space, we developed a largescale network analysis approach, which was based on representing complementarity determining region 3 (CDR3) a.a. clones as sequence-nodes connected by similarity-edges
Leveraging a custom-developed analysis platform for generating large-scale networks from datasets of millions of unique CDR3 a. a. sequences, we have discovered fundamental principles of antibody repertoire architecture such as: (i) reproducibility (ii) robustness and (iii) redundancy
Summary
The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. As of yet, only networks expressing clonal similarity relations of one nucleotide (nt) or one amino acid (a.a.) between sequences have been investigated[7,8,9,10,11,12], which, considering recently discovered biases in VDJ recombination and SHM targeting[15,16,17,18,19,20,21], may not be sufficient for a comprehensive immunological appreciation of repertoire architecture. To reveal the antibody repertoire architecture by quantitative statistical analysis, we implement a high-performance computing platform for network analysis and coupled it with large-scale antibody repertoire sequencing data from murine and human Bcell subsets. This leads us to address the following key questions: (i) Is the antibody repertoire architecture reproducible across individuals?
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