Dataset Information

In the Absence of Central pre-B Cell Receptor Selection, Peripheral Selection Attempts to Optimize the Antibody Repertoire by Enriching for CDR-H3 Y101.

ABSTRACT: Sequential developmental checkpoints are used to "optimize" the B cell antigen receptor repertoire by minimizing production of autoreactive or useless immunoglobulins and enriching for potentially protective antibodies. The first and apparently most impactful checkpoint requires ?HC to form a functional pre-B cell receptor (preBCR) by associating with surrogate light chain, which is composed of VpreB and ?5. Absence of any of the preBCR components causes a block in B cell development that is characterized by severe immature B cell lymphopenia. Previously, we showed that preBCR controls the amino acid content of the third complementary determining region of the H chain (CDR-H3) by using a VpreB amino acid motif (RDR) to select for tyrosine at CDR-H3 position 101 (Y101). In antibodies bound to antigen, Y101 is commonly in direct contact with the antigen, thus preBCR selection impacts the antigen binding characteristics of the repertoire. In this work, we sought to determine the forces that shape the peripheral B cell repertoire when it is denied preBCR selection. Using bromodeoxyuridine incorporation and evaluation of apoptosis, we found that in the absence of preBCR there is increased turnover of B cells due to increased apoptosis. CDR-H3 sequencing revealed that this is accompanied by adjustments to D_H identity, D_H reading frame, J_H, and CDR-H3 amino acid content. These adjustments in the periphery led to wild-type levels of CDR-H3 Y101 content among transitional (T1), mature recirculating, and marginal zone B cells. However, peripheral selection proved incomplete, with failure to restore Y101 levels in follicular B cells and increased production of dsDNA-binding IgM antibodies.

SUBMITTER: Khass M

PROVIDER: S-EPMC5810287 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

In the Absence of Central pre-B Cell Receptor Selection, Peripheral Selection Attempts to Optimize the Antibody Repertoire by Enriching for CDR-H3 Y101.

Khass Mohamed M Blackburn Tessa T Elgavish Ada A Burrows Peter D PD Schroeder Harry W HW

Frontiers in immunology 20180207

Sequential developmental checkpoints are used to "optimize" the B cell antigen receptor repertoire by minimizing production of autoreactive or useless immunoglobulins and enriching for potentially protective antibodies. The first and apparently most impactful checkpoint requires μHC to form a functional pre-B cell receptor (preBCR) by associating with surrogate light chain, which is composed of VpreB and λ5. Absence of any of the preBCR components causes a block in B cell development that is cha ...[more]

PMID: 29472919

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Project description:Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The homology modeling of non-H3 CDRs is more accurate because non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. The GBM method simplifies feature selection and can easily integrate new data, compared to manual sequence rule curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation (CV) scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy of the concerned loops from 84.5% ± 0.24% to 88.16% ± 0.056%. The GBM models reduce the errors in specific cluster membership misclassifications when the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data to further improve prediction accuracy in future studies.

Dataset Information

In the Absence of Central pre-B Cell Receptor Selection, Peripheral Selection Attempts to Optimize the Antibody Repertoire by Enriching for CDR-H3 Y101.

Publications

In the Absence of Central pre-B Cell Receptor Selection, Peripheral Selection Attempts to Optimize the Antibody Repertoire by Enriching for CDR-H3 Y101.

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