Large-scale network analysis reveals the sequence space architecture of antibody repertoires
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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.
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Current bioinformatic analyses of NGS repertoires focus on large-scale decoding of immune responses, with several potential applications for therapeutic design.
“Current bioinformatic analyses of NGS repertoires focus on large-scale decoding of the immune responses, with several potential applications for therapeutic design [22, 25, 214].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [214]
Human antibody repertoires can maintain their fundamental sequence diversity despite the removal of as many as 50-90% of sequences.
“Human antibody repertoires can maintain their fundamental sequence diversity despite the removal of as many as 50–90% of sequences [214].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [214]
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- Authors
- Miho, E., Roškar, R., Greiff, V., & Reddy, S. T.
- Affiliations
- FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH)
- ETH Zurich (CH)
- University of Oslo (NO)
- Corresponding author
- Greiff, Victor
- Reddy, Sai T.
- Venue
- Nature Communications
- Published
- 2019-03-21
- Volume
- 10
- Issue
- 1
- Pages
- 1321-1321
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6428871
- OpenAlex
- W2924247059
- Cited by
- 138
- References
- 75
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 22 · 2023: 15 · 2022: 29 · 2021: 27 · 2020: 14 · 2019: 9 · 2018: 5 · 2017: 3
- FWCI
- 13.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (170110, 143869)
- Eidgenössische Technische Hochschule Zürich
- SystemsX.ch
- Swiss Vaccine Research Institute
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- antibody repertoire, sequence similarity networks, network architecture, network robustness, reproducibility, immune repertoire sequencing, high-performance computing
- MeSH terms
- Animals, Antigens, B-Lymphocytes, Clone Cells, Hepatitis B Surface Antigens, Humans, Immunization, Muramidase, Ovalbumin, Neural Networks, Computer, Peptide Library, Amino Acid Motifs, Mice, Single-Chain Antibodies, Datasets as Topic
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