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Immunogenetics2026; 78(1); 8; doi: 10.1007/s00251-026-01400-7

Identification of gene conversion events in horse IGHV suggests preferential hotspots for diversification.

Abstract: The humoral immune response relies on a diverse antibody repertoire, which is expanded through processes such as somatic hypermutation, class-switch recombination and gene conversion. These processes are primarily mediated by activation-induced cytidine deaminase (AID). Gene conversion generates diversity in immunoglobulin heavy and light chains (IGHVs) in species such as chickens and rabbits, though it has not been widely studied. Since 80% of the equine IGHV repertoire originates from only three functional gene segments, we examined gene conversion events in horses to assess their role in antibody diversification. Using a modified version of BrepConvert, which optimized analysis time, we identified gene conversion events in 6.9% of immunoglobulin sequences. The results showed a local preference, with most events occurring at the beginning of framework region 1 (FR1) and within complementarity-determining region 2 (CDR2). Pseudogenes IGHV4-35, IGHV4-53, and IGHV4-38 were utilized most frequently, while functional genes IGHV4-21, IGHV4-22, and IGHV4-29 exhibited the highest event frequencies. Interestingly, while most mismatched regions were only three nucleotides long, 91% of these events are flanked by specific sequences (six nucleotides at the 5' end and one nucleotide at the 3' end). Furthermore, functional pseudogene pairs often share identical leader regions of 5-26 nucleotides, suggesting expanded events. We also identified a potential association between these events and local non-B DNA conformations, as well as with the zinc finger protein ZNF691, which supports the involvement of DNA-binding factors. Together, these findings demonstrate that gene conversion significantly contributes to equine antibody diversity by targeting specific IGHV regions.
© 2026. The Author(s).
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Publication Date: 2026-05-19 PubMed ID: 42151624PubMed Central: PMC13183705DOI: 10.1007/s00251-026-01400-7Google Scholar: Lookup
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  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigates how gene conversion contributes to creating antibody diversity in horses by identifying specific hotspots within immunoglobulin heavy chain variable (IGHV) genes where these events preferentially occur.
  • The research reveals that gene conversion plays a significant role in equine immune diversity, focusing on particular gene regions and pseudogenes, and highlights potential molecular mechanisms driving this process.

Background

  • The humoral immune response requires a wide variety of antibodies to recognize diverse pathogens.
  • Antibody diversity is generated through multiple mechanisms, including:
    • Somatic hypermutation (introduces point mutations)
    • Class-switch recombination (changes antibody isotype)
    • Gene conversion (replaces short DNA segments using related sequences as templates)
  • Activation-induced cytidine deaminase (AID) is a key enzyme mediating these processes.
  • Gene conversion has been well-studied in species like chickens and rabbits but less so in horses.
  • In horses, 80% of antibody diversity in IGHV genes derives from just three functional gene segments, raising questions about additional mechanisms like gene conversion to explain diversity.

Study Goals

  • To detect and characterize gene conversion events within the horse IGHV repertoire.
  • To identify specific regions in IGHV genes where gene conversion preferentially occurs (“hotspots”).
  • To analyze which pseudogenes contribute most frequently to these gene conversions.
  • To explore molecular features associated with gene conversion, such as sequence motifs and DNA conformations.

Methods

  • Genomic analysis of horse IGHV sequences was performed using a modified tool called BrepConvert optimized for speed and accuracy.
  • Immunoglobulin sequences were examined to detect gene conversion events, defined as short DNA segments replaced by sequences derived from pseudogenes.
  • Location of conversion events was mapped to IGHV gene regions including framework regions (FR1) and complementarity-determining regions (CDRs).
  • Frequency and distribution of events involving different pseudogenes and functional genes were analyzed.
  • Sequence motifs flanking gene conversion tracts were identified to understand targeting preferences.
  • Associations were sought between gene conversion sites and structural DNA features (non-B DNA conformations) or DNA-binding proteins like ZNF691.

Key Findings

  • Gene conversion events were detected in approximately 6.9% of immunoglobulin sequences, indicating it is a significant diversification mechanism in horses.
  • These events showed a strong local preference:
    • Predominantly concentrated at the start of the Framework Region 1 (FR1).
    • Also frequent within Complementarity-Determining Region 2 (CDR2), an important antigen binding site.
  • Pseudogenes IGHV4-35, IGHV4-53, and IGHV4-38 were most commonly used as templates in gene conversion.
  • Functional IGHV genes IGHV4-21, IGHV4-22, and IGHV4-29 showed the highest frequencies of conversion events, suggesting active diversification in these segments.
  • Most mismatched gene conversion tracts were short, typically three nucleotides long.
  • Approximately 91% of these conversion events were flanked by specific nucleotide sequences:
    • Six nucleotides at the 5′ junction.
    • One nucleotide at the 3′ junction.
  • Functional pseudogene pairs often shared identical leader sequences ranging from 5 to 26 nucleotides, suggesting coordinated or expanded conversion events across genes.
  • Potential linkages were found between gene conversion hotspots and:
    • Non-B DNA structures, which are local DNA conformations that differ from the typical right-handed double helix.
    • The zinc finger protein ZNF691, implicating DNA-binding factors in targeting or regulating gene conversion.

Significance and Implications

  • This research demonstrates that gene conversion is a key mechanism contributing to antibody diversity in horses.
  • It identifies specific IGHV sub-regions and pseudogenes as hotspots, which informs how the immune system maintains diversity despite limited initial gene segments.
  • Findings about conserved sequence motifs and DNA structures provide insight into possible molecular control mechanisms of gene conversion.
  • The association with the ZNF691 protein suggests gene conversion might be influenced or directed by specific DNA-binding proteins, opening avenues for further mechanistic studies.
  • The improved computational tool used (modified BrepConvert) provides a methodology that can be applied to study gene conversion in other species as well.

Conclusion

  • Gene conversion plays a substantial role in diversifying the horse antibody repertoire by favoring certain IGHV regions and using specific pseudogenes as donors.
  • The study enhances our understanding of how equine immune systems generate variability, which can be crucial for disease resistance and vaccine design.
  • These discoveries pave the way for more detailed functional and mechanistic investigations into antibody gene diversification in horses and potentially other species.

Cite This Article

APA
Pinto JEL, Gervásio JHB, Ng JC, Gomes-Silva A, de Matos Guedes HL, Cunha L, Castilho LR, da Silva JL, Franco GR, Navas C, Felicori LF. (2026). Identification of gene conversion events in horse IGHV suggests preferential hotspots for diversification. Immunogenetics, 78(1), 8. https://doi.org/10.1007/s00251-026-01400-7

Publication

Immunogenetics
ISSN: 1432-1211
NlmUniqueID: 0420404
Country: United States
Language: English
Volume: 78
Issue: 1
PII: 8

Researcher Affiliations

Pinto, Juliana Edelvacy Lima
  • Laboratory of Synthetic Biology and Biomimetics, Department of Biochemistry and Immunology, Institute of Biological Sciences (ICB), Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
Gervásio, João Henrique Brandão
  • Laboratory of Synthetic Biology and Biomimetics, Department of Biochemistry and Immunology, Institute of Biological Sciences (ICB), Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
  • Model-Based Evolutionary Genomic Unit, Okinawa Institute of Science and Technology (OIST), Tancha, 904-0412, Okinawa, Japan.
Ng, Joseph Chi-Fung
  • Research Department of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
Gomes-Silva, Adriano
  • Interdisciplinary Laboratory of Medical Research, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
  • Mycobacteriosis Clinical Research Laboratory, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
de Matos Guedes, Herbert L
  • Laboratory of Immunobiotechnology, Department of Immunology, Paulo de Góes Institute of Microbiology, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
  • Laboratory of Clinical Immunology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
Cunha, Luiz
  • Vital Brazil Institute, Niterói, RJ, Brazil.
Castilho, Leda R
  • Cell Culture Engineering Laboratory, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
da Silva, Jerson Lima
  • Institute of Medical Biochemistry Leopoldo de Meis, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Franco, Glória Regina
  • Laboratory of Biochemical Genetics, Department of Biochemistry and Immunology, Institute of Biological Sciences (ICB), Federal University of Minas Gerais, Belo Horizonte, MG, 30161-970, Brazil.
Navas, Carlena
  • Laboratory of Synthetic Biology and Biomimetics, Department of Biochemistry and Immunology, Institute of Biological Sciences (ICB), Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
Felicori, Liza Figueiredo
  • Laboratory of Synthetic Biology and Biomimetics, Department of Biochemistry and Immunology, Institute of Biological Sciences (ICB), Federal University of Minas Gerais, Belo Horizonte, MG, Brazil. lizaffelicori@gmail.com.

MeSH Terms

  • Animals
  • Gene Conversion
  • Horses / genetics
  • Horses / immunology
  • Immunoglobulin Heavy Chains / genetics
  • Immunoglobulin Variable Region / genetics
  • Complementarity Determining Regions / genetics
  • Somatic Hypermutation, Immunoglobulin / genetics
  • Antibody Diversity / genetics
  • Cytidine Deaminase / genetics

Conflict of Interest Statement

Declarations. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that the research documented in this manuscript was conducted in the absence of any commercial or financial relationships that could be construed as conflicts of interest. Ethics approval: The experimental project on the Use of Animals was approved by the Ethics Committee of the Federal University of Minas Gerais (CEUA-UFMG) under the protocol number 190/2018.

References

This article includes 70 references
  1. Alamyar E, Giudicelli V, Li S, Duroux P, Lefranc MP. IMGT/HighV-QUEST: the IMGT web portal for immunoglobulin (IG) or antibody and T cell receptor (TR) analysis from NGS high throughput and deep sequencing. Immunome Res 8:26.
    doi: 10.4172/1745-7580.1000054google scholar: lookup
  2. Arakawa H, Hauschild J, Buerstedde JM. Requirement of the activationinduced deaminase (AID) gene for immunoglobulin gene conversion. Science 295:1301–1306.
    doi: 10.1126/science.1067761pubmed: 11847344google scholar: lookup
  3. Bacolla A, Cooper DN, Vasquez KM, Tainer JA. Non-B DNA structure and mutations causing human genetic disease. eLS .
    doi: 10.1002/9780470015902.a0026930google scholar: lookup
  4. Bailey TL. STREME: accurate and versatile sequence motif discovery. Bioinformatics 37:2834–2840.
    doi: 10.1093/bioinformatics/btab203pmc: PMC8479671pubmed: 33760053google scholar: lookup
  5. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME Suite. Nucleic Acids Res 43:W39–W49.
    doi: 10.1093/nar/gkv416pmc: PMC4489269pubmed: 25953851google scholar: lookup
  6. Baker MD, Read LR. High-frequency gene conversion between repeated Cµ sequences integrated at the chromosomal immunoglobulin µ locus in mouse hybridoma cells. Mol Cell Biol 15:766–771.
    doi: 10.1128/MCB.15.2.766pmc: PMC231946pubmed: 7823944google scholar: lookup
  7. Becker RS, Knight KL. Somatic diversification of immunoglobulin heavy chain VDJ genes: evidence for somatic gene conversion in rabbits. Cell 63:987–997.
    doi: 10.1016/0092-8674(90)90502-Hpubmed: 2124176google scholar: lookup
  8. Behring EV. Ueber das zustandekommen der diphtherie-immunität und der tetanus-immunität bei thieren. Privatbibliothek Emil-von-Behring .
    pubmed: 1749380
  9. Bischof JM, Chiang AP, Scheetz TE. Genome-wide identification of pseudogenes capable of disease-causing gene conversion. Hum Mutat 27:545–552.
    doi: 10.1002/humu.20335pubmed: 16671097google scholar: lookup
  10. Bothwell AL, Paskind M, Reth M. Somatic variants of murine immunoglobulin lambda light chains. Nature 298:380–382.
    doi: 10.1038/298380a0pubmed: 6283385google scholar: lookup
  11. Bransteitter R, Pham P, Calabrese P, Goodman MF. Biochemical analysis of hypermutational targeting by wild type and mutant activation-induced cytidine deaminase. J Biol Chem 279:51612–51621.
    doi: 10.1074/jbc.M408135200pubmed: 15371439google scholar: lookup
  12. Castro-Mondragon JA, Riudavets-Puig R, Rauluseviciute I. JASPAR 2022: the 9th release of the openaccess database of transcription factor binding profiles. Nucleic Acids Res 50:D165–D173.
    doi: 10.1093/nar/gkab1113pmc: PMC8728201pubmed: 34850907google scholar: lookup
  13. Cer R, Bruce K, Donohue D. Searching for non-B DNA-forming motifs using nBMST (non-B DNA motif search tool). Curr Protoc Hum Genet 73 18.7.1–18.7.13.
    pmc: PMC3350812pubmed: 22470144doi: 10.1002/0471142905.hg1807s73google scholar: lookup
  14. Cer RZ, Donohue DE, Mudunuri US. Non-B DB v2.0: a database of predicted non-B DNA-forming motifs and its associated tools. Nucleic Acids Res 41:D94–D100.
    doi: 10.1093/nar/gks1219pmc: PMC3531222pubmed: 23125372google scholar: lookup
  15. Chakraborty A, Tapryal N, Islam A. Human DNA polymerase η promotes RNA-templated error-free repair of DNA double-strand breaks. J Biol Chem 299:102991.
    doi: 10.1016/j.jbc.2023.102991pmc: PMC10011834pubmed: 36758800google scholar: lookup
  16. Charrier C, Joshi K, Coutinho-Budd J. Inhibition of SRGAP2 function by its human-specific paralogs induces neoteny during spine maturation.. Cell 149:923–935.
    doi: 10.1016/j.cell.2012.03.034pmc: PMC3357949pubmed: 22559944google scholar: lookup
  17. Cheetham SW, Faulkner GJ, Dinger ME. Overcoming challenges and dogmas to understand the functions of pseudogenes.. Nat Rev Genet 21:191–201.
    doi: 10.1038/s41576-019-0196-1pubmed: 31848477google scholar: lookup
  18. Chiang JJ, Sparrer KM, van Gent M. Viral unmasking of cellular 5S rRNA pseudogene transcripts induces RIG-I-mediated immunity.. Nat Immunol 19:53–62.
    doi: 10.1038/s41590-017-0005-ypmc: PMC5815369pubmed: 29180807google scholar: lookup
  19. Chuzhanova N, Chen JM, Bacolla A. Gene conversion causing human inherited disease: evidence for involvement of non-B-DNA-forming sequences and recombination-promoting motifs in DNA breakage and repair.. Hum Mutat 30:1189–1198.
    doi: 10.1002/humu.21021pmc: PMC2779867pubmed: 19431182google scholar: lookup
  20. Clegg J. Gene conversions in the horse alpha-globin gene complex.. Mol Biol Evol 4:492–503.
    doi: 10.1093/oxfordjournals.molbev.a040459pubmed: 2835578google scholar: lookup
  21. Di Noia JM, Neuberger MS. Molecular mechanisms of antibody somatic hypermutation.. Annu Rev Biochem 76:1–22.
    doi: 10.1146/annurev.biochem.76.061705.090740pubmed: 17328676google scholar: lookup
  22. Duvvuri B, Wu GE. Gene conversion-like events in the diversification of human rearranged IGHV3-23* 01 gene sequences.. Front Immunol 3:158.
    doi: 10.3389/fimmu.2012.00158pmc: PMC3375636pubmed: 22715339google scholar: lookup
  23. Gabernet G, Marquez S, Bjornson R. nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework.. PLOS Comput Biol 20:e1012265.
    doi: 10.1371/journal.pcbi.1012265pmc: PMC11305553pubmed: 39058741google scholar: lookup
  24. Grundbacher FJ. Behring’s discovery of diphtheria and tetanus antitoxins.. Immunol Today 13:188–190.
    doi: 10.1016/0167-5699(92)90125-Cpubmed: 1642758google scholar: lookup
  25. Gupta S, Stamatoyannopoulos JA, Bailey T, Noble WS. Quantifying similarity between motifs.. Genome Biol 8:R24.
    doi: 10.1186/gb-2007-8-2-r24pmc: PMC1852410pubmed: 17324271google scholar: lookup
  26. Gu Z, Gu L, Eils R. circlize implements and enhances circular visualization in R.. Bioinformatics 30:2811–2812.
    doi: 10.1093/bioinformatics/btu393pubmed: 24930139google scholar: lookup
  27. Harris CR, Millman KJ, van der Walt SJ. Array programming with NumPy.. Nature 585:357–362.
    doi: 10.1038/s41586-020-2649-2pmc: PMC7759461pubmed: 32939066google scholar: lookup
  28. Harris RS, Sale JE, Petersen-Mahrt SK, Neuberger MS. AID is essential for immunoglobulin V gene conversion in a cultured B cell line.. Curr Biol 12:435–438.
    doi: 10.1016/S0960-9822(02)00700-7pubmed: 11882297google scholar: lookup
  29. Hilliker AJ, Harauz G, Reaume AG. Meiotic gene conversion tract length distribution within the rosy locus of Drosophila melanogaster.. Genetics 137:1019–1026.
    doi: 10.1093/genetics/137.4.1019pmc: PMC1206049pubmed: 7982556google scholar: lookup
  30. Hwang SY, Kang MA, Baik CJ. CTCF cooperates with CtIP to drive homologous recombination repair of double-strand breaks.. Nucleic Acids Res 47:9160–9179.
    doi: 10.1093/nar/gkz646pmc: PMC6753481pubmed: 31340001google scholar: lookup
  31. Jeffreys AJ, May CA. Intense and highly localized gene conversion activity in human meiotic crossover hot spots. Nat Genet 36:151–156.
    doi: 10.1038/ng1287pubmed: 14704667google scholar: lookup
  32. Johnston SE, Bérénos C, Slate J, Pemberton JM. Conserved Genetic Architecture Underlying Individual Recombination Rate Variation in a Wild Population of Soay Sheep (Ovis aries). Genetics 203:583–598.
    doi: 10.1534/genetics.115.185553pmc: PMC4858801pubmed: 27029733google scholar: lookup
  33. Kamaliyan Z, Clarke TL. Zinc finger proteins: Guardians of genome stability. Front Cell Dev Biol 12:1448789.
    doi: 10.3389/fcell.2024.1448789pmc: PMC11306022pubmed: 39119040google scholar: lookup
  34. Kawamoto T, Araki K, Sonoda E. Dual roles for DNA polymerase η in homologous DNA recombination and translesion DNA synthesis. Mol Cell 20:793–799.
    doi: 10.1016/j.molcel.2005.10.023pubmed: 16337602google scholar: lookup
  35. Khan T, Rahman M, Ahmed I. Human leukocyte antigen class II gene diversity tunes antibody repertoires to common pathogens. Front Immunol 13:856497.
    doi: 10.3389/fimmu.2022.856497pmc: PMC9393332pubmed: 36003377google scholar: lookup
  36. Kong A, Gudbjartsson DF, Sainz J. A high-resolution recombination map of the human genome. Nat Genet 31:241–247.
    doi: 10.1038/ng917pubmed: 12053178google scholar: lookup
  37. Lefranc MP, Giudicelli V, Duroux P. IMGT, the international ImMunoGeneTics information system 25 years on. Nucleic Acids Res 43:D413–D422.
    doi: 10.1093/nar/gku1056pmc: PMC4383898pubmed: 25378316google scholar: lookup
  38. Mallaby J, Mwangi W, Ng J. Diversification of immunoglobulin genes by gene conversion in the domestic chicken (Gallus gallus domesticus). Discov Immunol 2:kyad002.
    doi: 10.1093/discovimm/kyad002pmc: PMC10917233pubmed: 38567069google scholar: lookup
  39. Mansai SP, Innan H. The power of the methods for detecting interlocus gene conversion. Genetics 184:517–527.
    doi: 10.1534/genetics.109.111161pmc: PMC2828729pubmed: 19948889google scholar: lookup
  40. Manso TC, Groenner-Penna M, Minozzo JC. Next-generation sequencing reveals new insights about gene usage and CDR-H3 composition in the horse antibody repertoire. Mol Immunol 105:251–259.
    doi: 10.1016/j.molimm.2018.12.002pubmed: 30562645google scholar: lookup
  41. Manteca Vilanova X, De Briyne N, Beaver B, Turner PV. Horse welfare during equine chorionic gonadotropin (eCG) production. Animals 9:1053.
    doi: 10.3390/ani9121053pmc: PMC6940776pubmed: 31805698google scholar: lookup
  42. McCarrey JR. Nucleotide sequence of the promoter region of a tissue-specific human retroposon: comparison with its housekeeping progenitor. Gene 61:291–298.
    doi: 10.1016/0378-1119(87)90466-4pubmed: 3446575google scholar: lookup
  43. McCormack WT, Thompson CB. Chicken IgL variable region gene conversions display pseudogene donor preference and 5’to 3’polarity. Genes Dev 4:548–558.
    doi: 10.1101/gad.4.4.548pubmed: 2113879google scholar: lookup
  44. McKinney W. pandas: a foundational Python library for data analysis and statistics. Python high Perform Sci Comput 14(9):1–9.
  45. Meyer A, Parng CL, Hansal SA, Osborne BA, Goldsby RA. Immunoglobulin gene diversification in cattle. Int Rev Immunol 15(3–4):165–183.
    doi: 10.3109/08830189709068175pubmed: 9222818google scholar: lookup
  46. Miller HC, Lambert DM. Gene duplication and gene conversion in class II MHC genes of New Zealand robins (Petroicidae). Immunogenetics 56:178–191.
    doi: 10.1007/s00251-004-0678-xpubmed: 15138734google scholar: lookup
  47. Muramatsu M, Kinoshita K, Fagarasan S. Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme. Cell 102:553–563.
    doi: 10.1016/S0092-8674(00)00078-7pubmed: 11007474google scholar: lookup
  48. Navas C, Manso T, Martins F. The major role of junctional diversity in the horse antibody repertoire. Mol Immunol 151:231–241.
    doi: 10.1016/j.molimm.2022.09.004pubmed: 36179605google scholar: lookup
  49. Pieraccioli M, Nicolai S, Antonov A. ZNF281 contributes to the DNA damage response by controlling the expression of XRCC2 and XRCC4. Oncogene 35:2592–2601.
    doi: 10.1038/onc.2015.320pubmed: 26300006google scholar: lookup
  50. Pilzecker B, Jacobs H. Mutating for Good: DNA Damage Responses During Somatic Hypermutation. Front Immunol 10:438.
    doi: 10.3389/fimmu.2019.00438pmc: PMC6423074pubmed: 30915081google scholar: lookup
  51. Revy P, Muto T, Levy Y. Activation-induced cytidine deaminase (AID) deficiency causes the autosomal recessive form of the Hyper-IgM syndrome (HIGM2). Cell 102:565–575.
    doi: 10.1016/S0092-8674(00)00079-9pubmed: 11007475google scholar: lookup
  52. Reynaud CA, Anquez V, Grimal H, Weill JC. A hyperconversion mechanism generates the chicken light chain preimmune repertoire. Cell 48:379–388.
    doi: 10.1016/0092-8674(87)90190-2pubmed: 3100050google scholar: lookup
  53. Reynaud CA, Dahan A, Anquez V, Weill JC. Somatic hyperconversion diversifies the single Vh gene of the chicken with a high incidence in the D region. Cell 59:171–183.
    doi: 10.1016/0092-8674(89)90879-9pubmed: 2507167google scholar: lookup
  54. Rio DC, Ares M, Hannon GJ, Nilsen TW. Purification of RNA using TRIzol (TRI reagent). Cold Spring Harb Protoc 2010:pdb.prot5439.
    pubmed: 20516177doi: 10.1101/pdb.prot5439google scholar: lookup
  55. Seo H, Hirota K, Ohta K. Molecular mechanisms of avian immunoglobulin gene diversification and prospect for industrial applications. Front Immunol 15:1453833.
    doi: 10.3389/fimmu.2024.1453833pmc: PMC11427246pubmed: 39346918google scholar: lookup
  56. Sepúlveda-Yáñez JH, Alvarez Saravia D, Pilzecker B. Tandem Substitutions in Somatic Hypermutation. Front Immunol 12:807015.
    doi: 10.3389/fimmu.2021.807015pmc: PMC8781386pubmed: 35069591google scholar: lookup
  57. Silva GM, Navas C, Carvalho MB. Characterization of the lambda light chain repertoire and non-coding regions of equine immunoglobulins using the EquCab3 genome. Mol Immunol 188:1–11.
    doi: 10.1016/j.molimm.2024.12.001pubmed: 41166796google scholar: lookup
  58. Singh JK, van Attikum H. DNA double-strand break repair: Putting zinc fingers on the sore spot. Semin Cell Dev Biol 113:65–74.
    doi: 10.1016/j.semcdb.2020.10.011pubmed: 32962915google scholar: lookup
  59. Takuno S, Nishio T, Satta Y, Innan H. Preservation of a pseudogene by gene conversion and diversifying selection. Genetics 180:517–531.
    doi: 10.1534/genetics.108.089011pmc: PMC2535702pubmed: 18757945google scholar: lookup
  60. Tallmadge RL, Tseng CT, King RA, Felippe MJ. Developmental progression of equine immunoglobulin heavy chain variable region diversity. Dev Comp Immunol 41(1):33–43.
    doi: 10.1016/j.dci.2013.03.020pmc: PMC3672396pubmed: 23567345google scholar: lookup
  61. Tang ES, Martin A. Immunoglobulin gene conversion: synthesizing antibody diversification and DNA repair. DNA Repair 6:1557–1571.
    doi: 10.1016/j.dnarep.2007.05.002pubmed: 17600774google scholar: lookup
  62. Thompson CB, Neiman PE. Somatic diversification of the chicken immunoglobulin light chain gene is limited to the rearranged variable gene segment. Cell 48:369–378.
    doi: 10.1016/0092-8674(87)90189-6pubmed: 3100049google scholar: lookup
  63. Walther S, Rusitzka TV, Diesterbeck US, Czerny CP. Equine immunoglobulins and organization of immunoglobulin genes. Dev Comp Immunol 53:303–319.
    doi: 10.1016/j.dci.2015.07.005pubmed: 26219564google scholar: lookup
  64. Walther S, Tietze M, Czerny CP. Development of a bioinformatics framework for the detection of gene conversion and the analysis of combinatorial diversity in immunoglobulin heavy chains in four cattle breeds. PLoS ONE 11:e0164567.
    doi: 10.1371/journal.pone.0164567pmc: PMC5102495pubmed: 27828971google scholar: lookup
  65. Wang M, Kong L. pblat: a multithread blat algorithm speeding up aligning sequences to genomes. BMC Bioinformatics 20:1–4.
    doi: 10.1186/s12859-019-3054-0pmc: PMC6334396pubmed: 30646844google scholar: lookup
  66. Wang Y, Zhang S, Yang X. Mesoscale DNA feature in antibody-coding sequence facilitates somatic hypermutation. Cell 186:2193–2207.
    doi: 10.1016/j.cell.2023.03.030pubmed: 37098343google scholar: lookup
  67. Wibmer CK, Mashilo P. Exploiting V-Gene Bias for Rapid, High-Throughput Monoclonal Antibody Isolation from Horses. Viruses 14:2172.
    doi: 10.3390/v14102172pmc: PMC9609571pubmed: 36298728google scholar: lookup
  68. Wickham H, François R, Henry L. dplyr: A Grammar of Data Manipulation. R package version 1.1.4.
  69. Widera G, Flavell R. The nucleotide sequence of the murine I-E beta b immune response gene: evidence for gene conversion events in class II genes of the major histocompatibility complex. EMBO J 3:1221–1225.
    doi: 10.1002/j.1460-2075.1984.tb01955.xpmc: PMC557502pubmed: 6086309google scholar: lookup
  70. Zylberman V, Sanguineti S, Pontoriero AV. Development of a hyperimmune equine serum therapy for COVID-19 in Argentina. Med (B Aires) 80:1–6.
    pubmed: 32658841

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