Dataset Information

Alterations in B cell development, CDR-H3 repertoire and dsDNA-binding antibody production among C57BL/6 ?D-iD mice congenic for the lupus susceptibility loci sle1, sle2 or sle3.

ABSTRACT: Systemic lupus erythematosus (SLE) is an autoimmune disease that reflects a failure to block the production of self-reactive antibodies, especially those that bind double-stranded DNA (dsDNA). Backcrossing the lupus-prone NZM2410 genome onto C57BL/6 led to the identification of three genomic intervals, termed sle1, sle2 and sle3, which are associated with lupus susceptibility. We previously generated a C57BL/6 strain congenic for an immunoglobulin DH locus (?D-iD) that enriches for arginine at dsDNA-binding positions. We individually introduced the ?D-iD allele into the three sle strains to test whether one or more of these susceptibility loci could affect the developmental fate of B cells bearing arginine-enriched CDR-H3s, the CDR-H3 repertoire created by the DH and the prevalence of dsDNA-binding antibodies. We found that the combination of the ?D-iD allele and the sle1 locus led to a decrease in mature, recirculating B cell numbers and an increase in marginal zone cell numbers while maintaining a highly charged CDR-H3 repertoire. ?D-iD and sle2 had no effect on peripheral B cell numbers, but the CDR-H3 repertoire was partially normalized. ?D-iD and sle3 led to an increase in marginal zone B cell numbers, with some normalization of hydrophobicity. Mice with ?D-iD combined with either sle1 or sle3 had increased production of dsDNA-binding IgM and IgG by 12 months of age. These findings indicate that the peripheral CDR-H3 repertoire can be categorically manipulated by the effects of nonimmunoglobulin genes.

SUBMITTER: Khass M

PROVIDER: S-EPMC5551388 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

Alterations in B cell development, CDR-H3 repertoire and dsDNA-binding antibody production among C57BL/6 ΔD-iD mice congenic for the lupus susceptibility loci sle1, sle2 or sle3.

Khass Mohamed M Schelonka Robert L RL Liu Cun Ren CR Elgavish Ada A Morel Laurence L Burrows Peter D PD Schroeder Harry W HW

Autoimmunity 20170201 1

Systemic lupus erythematosus (SLE) is an autoimmune disease that reflects a failure to block the production of self-reactive antibodies, especially those that bind double-stranded DNA (dsDNA). Backcrossing the lupus-prone NZM2410 genome onto C57BL/6 led to the identification of three genomic intervals, termed sle1, sle2 and sle3, which are associated with lupus susceptibility. We previously generated a C57BL/6 strain congenic for an immunoglobulin D<sub>H</sub> locus (ΔD-iD) that enriches for ar ...[more]

PMID: 28166678

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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

Alterations in B cell development, CDR-H3 repertoire and dsDNA-binding antibody production among C57BL/6 ?D-iD mice congenic for the lupus susceptibility loci sle1, sle2 or sle3.

Publications

Alterations in B cell development, CDR-H3 repertoire and dsDNA-binding antibody production among C57BL/6 ΔD-iD mice congenic for the lupus susceptibility loci sle1, sle2 or sle3.

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