ReviewmAbs
Understanding the human antibody repertoire.
Review in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 113 citations in OpenAlex.
- Immunoglobulins with Non-Canonical Functions in Inflammatory and Autoimmune Disease States.International journal of molecular sciences · 2020Pooled it
- What Antibody Repertoires See: Structural and Immunogenetic Insights Into Influenza A Virus Hemagglutinin Recognition.Immunological reviews · 2026Review
- DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.Communications biology · 2026Article
- Development and Preliminary Validation of a MALDI-TOF MS Assay Using KTI as an Internal Standard for Serum M-Protein Light Chain Analysis in Multiple Myeloma: A Pilot Study.Medicina (Kaunas, Lithuania) · 2026Article
- Scaling antibody language models improves structure aware representation for antibody engineering.Communications biology · 2026Article
- Structural promiscuity in the human circulatory IgA1 clonal repertoire.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Prototype timsOmni Platform Enables Confident Annotation of the Key Hypervariable CDR3 Regions of IgG Immunoglobulins Using Low- and High-Energy Electron-Based Fragmentation.Analytical chemistry · 2026Article
- Phage Display Technology: Design, Library Construction, and Panning Strategies for Antibody Development.Biomolecules & therapeutics · 2026Review
- Large-scale paired chain BCR analysis reveals antibody clonal family inference bias and enhances resolution with machine learning.PLoS computational biology · 2026Article
- Tumor-specific antibody cocktail treatment suppresses colorectal tumor growth in mice.Cancer immunology, immunotherapy : CII · 2026Article
- Delineation of the antibody light chain biases and kinetics following primary and secondary influenza virus infections of ferrets.ImmunoHorizons · 2026Article
- Modeling antibody recognition across SARS-CoV-2 and SARS-CoV-1 using epitope-informed transfer learning.Frontiers in immunology · 2026Article
- Article
- How and when organisms edit their own genomes.Nature genetics · 2025Review
- New insights into antibody structure with implications for specificity, variable region restriction and isotype choice.Nature reviews. Immunology · 2025Review
- Conserved heavy/light contacts and germline preferences revealed by a large-scale analysis of natively paired human antibody sequences and structural data.Communications biology · 2025Article
- Open-source antibodies as a path to enhanced research reproducibility and transparency.New biotechnology · 2025Article
- Epitope-Specific Antibodies in Allergic Disease and Clinical Tolerance.Immunological reviews · 2025Review
- Generating combinatorial diversity via engineered V(D)J-like recombination in Saccharomyces cerevisiae.Nature communications · 2025Article
- Building a neural network model to define DNA sequence specificity in V(D)J recombination.Nucleic acids research · 2025Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The origins of the various elements in the human antibody repertoire have been and still are subject to considerable uncertainty. Uncertainty in respect of whether the various elements have always served a specific defense function or whether they were co-opted from other organismal roles to form a crude naïve repertoire that then became more complex as combinatorial mechanisms were added. Estimates of the current size of the human antibody naïve repertoire are also widely debated with numbers anywhere from 10 million members, based on experimentally derived numbers, to in excess of one thousand trillion members or more, based on the different sequences derived from theoretical combinatorial calculations. There are questions that are relevant at both ends of this number spectrum. At the lower bound it could be questioned whether this is an insufficient repertoire size to counter all the potential antigen-bearing pathogens. At the upper bound the question is rather simpler: How can any individual interrogate such an astronomical number of antibody-bearing B cells in a timeframe that is meaningful? This review evaluates the evolutionary aspects of the adaptive immune system, the calculations that lead to the large repertoire estimates, some of the experimental evidence pointing to a more restricted repertoire whose variation appears to derive from convergent 'structure and specificity features', and includes a theoretical model that seems to support it. Finally, a solution that may reconcile the size difference anomaly, which is still a hot subject of debate, is suggested.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.