Inferring B cell specificity for vaccines using a Bayesian mixture model

Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. Th...
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Gespeichert in:

Autor*in:	Fowler, Anna [verfasserIn] Galson, Jacob D. Trück, Johannes Kelly, Dominic F. Lunter, Gerton

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	B cell receptor Vaccination Immune repertoire High-throughput sequencing

Anmerkung:	© The Author(s) 2020

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 21(2020), 1 vom: 22. Feb.
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; day:22 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s12864-020-6571-7

Katalog-ID:	SPR02716313X

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520			\|a Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells.
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allfields	10.1186/s12864-020-6571-7 doi (DE-627)SPR02716313X (SPR)s12864-020-6571-7-e DE-627 ger DE-627 rakwb eng Fowler, Anna verfasserin (orcid)0000-0003-1793-0047 aut Inferring B cell specificity for vaccines using a Bayesian mixture model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. B cell receptor (dpeaa)DE-He213 Vaccination (dpeaa)DE-He213 Immune repertoire (dpeaa)DE-He213 High-throughput sequencing (dpeaa)DE-He213 Galson, Jacob D. aut Trück, Johannes aut Kelly, Dominic F. aut Lunter, Gerton aut Enthalten in BMC genomics London : BioMed Central, 2000 21(2020), 1 vom: 22. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:21 year:2020 number:1 day:22 month:02 https://dx.doi.org/10.1186/s12864-020-6571-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 22 02
spelling	10.1186/s12864-020-6571-7 doi (DE-627)SPR02716313X (SPR)s12864-020-6571-7-e DE-627 ger DE-627 rakwb eng Fowler, Anna verfasserin (orcid)0000-0003-1793-0047 aut Inferring B cell specificity for vaccines using a Bayesian mixture model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. B cell receptor (dpeaa)DE-He213 Vaccination (dpeaa)DE-He213 Immune repertoire (dpeaa)DE-He213 High-throughput sequencing (dpeaa)DE-He213 Galson, Jacob D. aut Trück, Johannes aut Kelly, Dominic F. aut Lunter, Gerton aut Enthalten in BMC genomics London : BioMed Central, 2000 21(2020), 1 vom: 22. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:21 year:2020 number:1 day:22 month:02 https://dx.doi.org/10.1186/s12864-020-6571-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 22 02
allfields_unstemmed	10.1186/s12864-020-6571-7 doi (DE-627)SPR02716313X (SPR)s12864-020-6571-7-e DE-627 ger DE-627 rakwb eng Fowler, Anna verfasserin (orcid)0000-0003-1793-0047 aut Inferring B cell specificity for vaccines using a Bayesian mixture model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. B cell receptor (dpeaa)DE-He213 Vaccination (dpeaa)DE-He213 Immune repertoire (dpeaa)DE-He213 High-throughput sequencing (dpeaa)DE-He213 Galson, Jacob D. aut Trück, Johannes aut Kelly, Dominic F. aut Lunter, Gerton aut Enthalten in BMC genomics London : BioMed Central, 2000 21(2020), 1 vom: 22. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:21 year:2020 number:1 day:22 month:02 https://dx.doi.org/10.1186/s12864-020-6571-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 22 02
allfieldsGer	10.1186/s12864-020-6571-7 doi (DE-627)SPR02716313X (SPR)s12864-020-6571-7-e DE-627 ger DE-627 rakwb eng Fowler, Anna verfasserin (orcid)0000-0003-1793-0047 aut Inferring B cell specificity for vaccines using a Bayesian mixture model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. B cell receptor (dpeaa)DE-He213 Vaccination (dpeaa)DE-He213 Immune repertoire (dpeaa)DE-He213 High-throughput sequencing (dpeaa)DE-He213 Galson, Jacob D. aut Trück, Johannes aut Kelly, Dominic F. aut Lunter, Gerton aut Enthalten in BMC genomics London : BioMed Central, 2000 21(2020), 1 vom: 22. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:21 year:2020 number:1 day:22 month:02 https://dx.doi.org/10.1186/s12864-020-6571-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 22 02
allfieldsSound	10.1186/s12864-020-6571-7 doi (DE-627)SPR02716313X (SPR)s12864-020-6571-7-e DE-627 ger DE-627 rakwb eng Fowler, Anna verfasserin (orcid)0000-0003-1793-0047 aut Inferring B cell specificity for vaccines using a Bayesian mixture model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. B cell receptor (dpeaa)DE-He213 Vaccination (dpeaa)DE-He213 Immune repertoire (dpeaa)DE-He213 High-throughput sequencing (dpeaa)DE-He213 Galson, Jacob D. aut Trück, Johannes aut Kelly, Dominic F. aut Lunter, Gerton aut Enthalten in BMC genomics London : BioMed Central, 2000 21(2020), 1 vom: 22. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:21 year:2020 number:1 day:22 month:02 https://dx.doi.org/10.1186/s12864-020-6571-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 22 02
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Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. 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spellingShingle	Fowler, Anna misc B cell receptor misc Vaccination misc Immune repertoire misc High-throughput sequencing Inferring B cell specificity for vaccines using a Bayesian mixture model
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topic_title	Inferring B cell specificity for vaccines using a Bayesian mixture model B cell receptor (dpeaa)DE-He213 Vaccination (dpeaa)DE-He213 Immune repertoire (dpeaa)DE-He213 High-throughput sequencing (dpeaa)DE-He213
topic	misc B cell receptor misc Vaccination misc Immune repertoire misc High-throughput sequencing
topic_unstemmed	misc B cell receptor misc Vaccination misc Immune repertoire misc High-throughput sequencing
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title	Inferring B cell specificity for vaccines using a Bayesian mixture model
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title_full	Inferring B cell specificity for vaccines using a Bayesian mixture model
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author_browse	Fowler, Anna Galson, Jacob D. Trück, Johannes Kelly, Dominic F. Lunter, Gerton
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title_sort	inferring b cell specificity for vaccines using a bayesian mixture model
title_auth	Inferring B cell specificity for vaccines using a Bayesian mixture model
abstract	Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. © The Author(s) 2020
abstractGer	Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. © The Author(s) 2020
abstract_unstemmed	Background Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells. © The Author(s) 2020
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title_short	Inferring B cell specificity for vaccines using a Bayesian mixture model
url	https://dx.doi.org/10.1186/s12864-020-6571-7
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author2	Galson, Jacob D. Trück, Johannes Kelly, Dominic F. Lunter, Gerton
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up_date	2024-07-04T00:40:38.792Z
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Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Results Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. Conclusion Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">B cell receptor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vaccination</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Immune repertoire</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-throughput sequencing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Galson, Jacob D.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trück, Johannes</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kelly, Dominic F.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lunter, Gerton</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC genomics</subfield><subfield code="d">London : BioMed Central, 2000</subfield><subfield code="g">21(2020), 1 vom: 22. 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Inferring B cell specificity for vaccines using a Bayesian mixture model

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