Production of individualized V gene databases reveals high levels of immunoglobulin genetic diversity

Abstract

Comprehensive knowledge of immunoglobulin genetics is required to advance our understanding of B cell biology. Validated immunoglobulin variable (V) gene databases are close to completion only for human and mouse. We present a novel computational approach, IgDiscover, that identifies germline V genes from expressed repertoires to a specificity of 100%. IgDiscover uses a cluster identification process to produce candidate sequences that, once filtered, results in individualized germline V gene databases. IgDiscover was tested in multiple species, validated by genomic cloning and cross library comparisons and produces comprehensive gene databases even where limited genomic sequence is available. IgDiscover analysis of the allelic content of the Indian and Chinese-origin rhesus macaques reveals high levels of immunoglobulin gene diversity in this species. Further, we describe a novel human IGHV3-21 allele and confirm significant gene differences between Balb/c and C57BL6 mouse strains, demonstrating the power of IgDiscover as a germline V gene discovery tool.

Particulate Array of Well-Ordered HIV Clade C Env Trimers Elicits Neutralizing Antibodies that Display a Unique V2 Cap Approach

“…Most of the vaccine-induced Env-specific mAbs were clonally unrelated, consistent with previous reports (Phad et al, 2015). We applied the recently developed IgDiscover tool (Corcoran et al, 2016) to generate individualized VH, VK and VL databases of animal D11 for accurate gene assignment and SHM calculations. The individualized database resulted in a total of 93, 73 and 44 sequences for VH, VK and VL, respectively, of which 47 VH, 52 VK and 28 VL were not previously described (submitted to Genbank under accession numbers KY196572 – KY196664).…”

“…A panel of NHP mAbs was isolated, including mAbs that recapitulated the plasma 16055 neutralizing activity. We examined the genetic properties of these mAbs using an individualized V gene database generated from the donor animal using Next Generation Sequencing (NGS) and analysis by the computational tool IgDiscover (Corcoran et al, 2016). Knowledge of the specific allelic content of V genes in a given donor animal allows analysis of clonal relationships and precise calculations of SHM levels of mAbs isolated from that animal.…”

Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data

“…Along with technical hurdles, major issues involve legal constraints such as donor consent and privacy, as well as intellectual property concerns. The Tools & Resources Working Group has several ongoing initiatives, including (1) the establishment of a common file format that builds on previous efforts 13,14 to allow interoperability of AIRR-seq analysis tools; (2) improvements to existing germ-line gene/allele databases and nomenclature, including the development of a framework for reporting novel variable (V), diversity (D) and joining (J) alleles computationally inferred from AIRR-seq data 15,16 (such alleles do not meet current criteria for inclusion in the widely used International Immunogenetics Information system IMGT database); (3) the construction of a foundation for software tool validation, based on benchmarking of repertoire simulation tools with a variety of summary statistics to ensure that they accurately reflect the characteristics of biological data sets; and (4) the development of biological reference samples that can be used as controls for amplification and sequencing protocols in collaboration with industry and the US National Institute of Standards and Technology. Finally, the Minimal Standards Working Group is seeking to improve the reproducibility of AIRR-seq experiments and promote data sharing and reuse through the establishment of standards for depositing AIRR-seq data in the public domain.…”

Immunoglobulin gene analysis as a tool for investigating human immune responses

“…We also need to recognize that allelic variants may exist in the population that may not be represented in germline gene databases and therefore some “mutations” from germline may be miscalled. These could potentially skew hypermutation data from different patients and there are now methods for predicting germline genes by inference from high throughput data which can help overcome this issue 117, 118, 119, 120…”