Computational detection of antigen-specific B cell receptors following immunization
B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurate...
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| Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2024-08, Vol.121 (35), p.e2401058121 |
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| container_issue | 35 |
| container_start_page | e2401058121 |
| container_title | Proceedings of the National Academy of Sciences - PNAS |
| container_volume | 121 |
| creator | Abbate, Maria Francesca Dupic, Thomas Vigne, Emmanuelle Shahsavarian, Melody A Walczak, Aleksandra M Mora, Thierry |
| description | B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics. |
| doi_str_mv | 10.1073/pnas.2401058121 |
| format | Article |
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High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. 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High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.</description><subject>Antigens</subject><subject>B-Lymphocytes - immunology</subject><subject>Complementarity</subject><subject>Complementarity Determining Regions - genetics</subject><subject>Complementarity Determining Regions - immunology</subject><subject>Complementarity-determining region 3</subject><subject>Computational Biology - methods</subject><subject>Computer applications</subject><subject>COVID-19 - immunology</subject><subject>COVID-19 - prevention & control</subject><subject>COVID-19 - virology</subject><subject>Drug development</subject><subject>High-Throughput Nucleotide Sequencing</subject><subject>Humans</subject><subject>Immunization</subject><subject>Immunization - methods</subject><subject>Influenza Vaccines - immunology</subject><subject>Influenza, Human - immunology</subject><subject>Influenza, Human - prevention & control</subject><subject>Life Sciences</subject><subject>Next-generation sequencing</subject><subject>Precision medicine</subject><subject>Public health</subject><subject>Receptors</subject><subject>Receptors, Antigen, B-Cell - genetics</subject><subject>Receptors, Antigen, B-Cell - immunology</subject><subject>SARS-CoV-2 - immunology</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Statistical analysis</subject><subject>Vaccination</subject><subject>Vaccine development</subject><subject>Vaccines</subject><issn>0027-8424</issn><issn>1091-6490</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkDtPwzAQgC0EoqUws6FILDCknB-J47FUvKRKDMAcOcm5GCVxiBMQ_HpcKAzccqfTp3t8hBxTmFOQ_KJrtZ8zARSSjDK6Q6YUFI1ToWCXTAGYjDPBxIQceP8CACrJYJ9MuKIpDzElD0vXdOOgB-taXUcVDlhu6siZSLeDXWMb-w5La2wZXUYl1nXUY4nd4HofGVfX7t2268g2zdjaz-85h2TP6Nrj0TbPyNP11ePyNl7d39wtF6v4mSbhyMQkxhgpFC-EUgUiKg1oMFGqSpjUvCh1ytIiTTNjKFJglUyxoLoqCiEZ5TNy_jP3Wdd519tG9x-50za_XazyTQ-E5CrYeduwZz9s17vXEf2QN9ZvvtEtutHnPKihUkDGA3r6D31xYx_sBIpCykRgZaBOttRYNFj97f9Vy78Af6N7Nw</recordid><startdate>20240827</startdate><enddate>20240827</enddate><creator>Abbate, Maria Francesca</creator><creator>Dupic, Thomas</creator><creator>Vigne, Emmanuelle</creator><creator>Shahsavarian, Melody A</creator><creator>Walczak, Aleksandra M</creator><creator>Mora, Thierry</creator><general>National Academy of Sciences</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-5456-9361</orcidid><orcidid>https://orcid.org/0000-0002-2686-5702</orcidid><orcidid>https://orcid.org/0009-0006-1718-8769</orcidid><orcidid>https://orcid.org/0009-0007-7805-7081</orcidid></search><sort><creationdate>20240827</creationdate><title>Computational detection of antigen-specific B cell receptors following immunization</title><author>Abbate, Maria Francesca ; 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High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. 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| subjects | Antigens B-Lymphocytes - immunology Complementarity Complementarity Determining Regions - genetics Complementarity Determining Regions - immunology Complementarity-determining region 3 Computational Biology - methods Computer applications COVID-19 - immunology COVID-19 - prevention & control COVID-19 - virology Drug development High-Throughput Nucleotide Sequencing Humans Immunization Immunization - methods Influenza Vaccines - immunology Influenza, Human - immunology Influenza, Human - prevention & control Life Sciences Next-generation sequencing Precision medicine Public health Receptors Receptors, Antigen, B-Cell - genetics Receptors, Antigen, B-Cell - immunology SARS-CoV-2 - immunology Severe acute respiratory syndrome coronavirus 2 Statistical analysis Vaccination Vaccine development Vaccines |
| title | Computational detection of antigen-specific B cell receptors following immunization |
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