IgE-associated IGHV genes from venom and peanut allergic individuals lack mutational evidence of antigen selection.

Antigen selection of B cells within the germinal center reaction generally leads to the accumulation of replacement mutations in the complementarity-determining regions (CDRs) of immunoglobulin genes. Studies of mutations in IgE-associated VDJ gene sequences have cast doubt on the role of antigen selection in the evolution of the human IgE response, and it may be that selection for high affinity antibodies is a feature of some but not all allergic diseases. The severity of IgE-mediated anaphylaxis is such that it could result from higher affinity IgE antibodies. We therefore investigated IGHV mutations in IgE-associated sequences derived from ten individuals with a history of anaphylactic reactions to bee or wasp venom or peanut allergens. IgG sequences, which more certainly experience antigen selection, served as a control dataset. A total of 6025 unique IgE and 5396 unique IgG sequences were generated using high throughput 454 pyrosequencing. The proportion of replacement mutations seen in the CDRs of the IgG dataset was significantly higher than that of the IgE dataset, and the IgE sequences showed little evidence of antigen selection. To exclude the possibility that 454 errors had compromised analysis, rigorous filtering of the datasets led to datasets of 90 core IgE sequences and 411 IgG sequences. These sequences were present as both forward and reverse reads, and so were most unlikely to include sequencing errors. The filtered datasets confirmed that antigen selection plays a greater role in the evolution of IgG sequences than of IgE sequences derived from the study participants.

Divergent human populations show extensive shared IGK rearrangements in peripheral blood B cells.

We have analysed the transcribed immunoglobulin kappa (IGK) repertoire of peripheral blood B cells from four individuals from two genetically distinct populations, Papua New Guinean and Australian, using high-throughput DNA sequencing. The depth of sequencing data for each individual averaged 5,548 high-quality IGK reads, and permitted genotyping of the inferred IGKV and IGKJ germline gene segments for each individual. All individuals were homozygous at each IGKJ locus and had highly similar inferred IGKV genotypes. Preferential gene usage was seen at both the IGKV and IGKJ loci, but only IGKV segment usage varied significantly between individuals. Despite the differences in IGKV gene utilisation, the rearranged IGK repertoires showed extensive identity at the amino acid level. Public rearrangements (those shared by two or more individuals) made up 60.2% of the total sequenced IGK rearrangements. The total diversity of IGK rearrangements of each individual was estimated to range from just 340 to 549 unique amino acid sequences. Thus, the repertoire of unique expressed IGK rearrangements is dramatically less than previous theoretical estimates of IGK diversity, and the majority of expressed IGK rearrangements are likely to be extensively shared in individual human beings.

The inference of phased haplotypes for the immunoglobulin H chain V region gene loci by analysis of VDJ gene rearrangements.

The existence of many highly similar genes in the lymphocyte receptor gene loci makes them difficult to investigate, and the determination of phased "haplotypes" has been particularly problematic. However, V(D)J gene rearrangements provide an opportunity to infer the association of Ig genes along the chromosomes. The chromosomal distribution of H chain genes in an Ig genotype can be inferred through analysis of VDJ rearrangements in individuals who are heterozygous at points within the IGH locus. We analyzed VDJ rearrangements from 44 individuals for whom sufficient unique rearrangements were available to allow comprehensive genotyping. Nine individuals were identified who were heterozygous at the IGHJ6 locus and for whom sufficient suitable VDJ rearrangements were available to allow comprehensive haplotyping. Each of the 18 resulting IGHV│IGHD│IGHJ haplotypes was unique. Apparent deletion polymorphisms were seen that involved as many as four contiguous, functional IGHV genes. Two deletion polymorphisms involving multiple contiguous IGHD genes were also inferred. Three previously unidentified gene duplications were detected, where two sequences recognized as allelic variants of a single gene were both inferred to be on a single chromosome. Phased genomic data brings clarity to the study of the contribution of each gene to the available repertoire of rearranged VDJ genes. Analysis of rearrangement frequencies suggests that particular genes may have substantially different yet predictable propensities for rearrangement within different haplotypes. Together with data highlighting the extent of haplotypic variation within the population, this suggests that there may be substantial variability in the available Ab repertoires of different individuals.

Benchmarking the performance of human antibody gene alignment utilities using a 454 sequence dataset.

MOTIVATION: Immunoglobulin heavy chain genes are formed by recombination of genes randomly selected from sets of IGHV, IGHD and IGHJ genes. Utilities have been developed to identify genes that contribute to observed VDJ rearrangements, but in the absence of datasets of known rearrangements, the evaluation of these utilities is problematic. We have analyzed thousands of VDJ rearrangements from an individual (S22) whose IGHV, IGHD and IGHJ genotype can be inferred from the dataset. Knowledge of this genotype means that the Stanford_S22 dataset can serve to benchmark the performance of IGH alignment utilities. RESULTS: We evaluated the performance of seven utilities. Failure to partition a sequence into genes present in the S22 genome was considered an error, and error rates for different utilities ranged from 7.1% to 13.7%. AVAILABILITY: Supplementary data includes the S22 genotypes and alignments. The Stanford_S22 dataset and an evaluation tool is available at http://www.emi.unsw.edu.au/~ihmmune/IGHUtilityEval/.

Clustering-based identification of clonally-related immunoglobulin gene sequence sets.

BACKGROUND: Clonal expansion of B lymphocytes coupled with somatic mutation and antigen selection allow the mammalian humoral immune system to generate highly specific immunoglobulins (IG) or antibodies against invading bacteria, viruses and toxins. The availability of high-throughput DNA sequencing methods is providing new avenues for studying this clonal expansion and identifying the factors guiding the generation of antibodies. The identification of groups of rearranged immunoglobulin gene sequences descended from the same rearrangement (clonally-related sets) in very large sets of sequences is facilitated by the availability of immunoglobulin gene sequence alignment and partitioning software that can accurately predict component germline gene, but has required painstaking visual inspection and analysis of sequences. RESULTS: We have developed and implemented an algorithm for identifying sets of clonally-related sequences in large human immunoglobulin heavy chain gene variable region sequence sets. The program processes sequences that have been partitioned using iHMMune-align, and uses pairwise comparisons of CDR3 sequences and similarity in IGHV and IGHJ germline gene assignments to construct a distance matrix. Agglomerative hierarchical clustering is then used to identify likely groups of clonally-related sequences. The program is available for download from http://www.cse.unsw.edu.au/~ihmmune/ClonalRelate/ClonalRelate.zip. CONCLUSIONS: The method was evaluated on several benchmark datasets and provided a more accurate and considerably faster identification of clonally-related immunoglobulin gene sequences than visual inspection by domain experts.

Individual variation in the germline Ig gene repertoire inferred from variable region gene rearrangements.

Individual variation in the Ig germline gene repertoire leads to individual differences in the combinatorial diversity of the Ab repertoire, but the study of such variation has been problematic. The application of high-throughput DNA sequencing to the study of rearranged Ig genes now makes this possible. The sequencing of thousands of VDJ rearrangements from an individual, either from genomic DNA or expressed mRNA, should allow their germline IGHV, IGHD, and IGHJ repertoires to be inferred. In addition, where previously mere glimpses of diversity could be gained from sequencing studies, new large data sets should allow the rearrangement frequency of different genes and alleles to be seen with clarity. We analyzed the DNA of 108,210 human IgH chain rearrangements from 12 individuals and determined their individual IGH genotypes. The number of reportedly functional IGHV genes and allelic variants ranged from 45 to 60, principally because of variable levels of gene heterozygosity, and included 14 previously unreported IGHV polymorphisms. New polymorphisms of the IGHD3-16 and IGHJ6 genes were also seen. At heterozygous loci, remarkably different rearrangement frequencies were seen for the various IGHV alleles, and these frequencies were consistent between individuals. The specific alleles that make up an individual's Ig genotype may therefore be critical in shaping the combinatorial repertoire. The extent of genotypic variation between individuals is highlighted by an individual with aplastic anemia who appears to lack six contiguous IGHD genes on both chromosomes. These deletions significantly alter the potential expressed IGH repertoire, and possibly immune function, in this individual.

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

BACKGROUND: The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. RESULTS: We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. CONCLUSION: Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Identifying highly mutated IGHD genes in the junctions of rearranged human immunoglobulin heavy chain genes.

The reliable identification of IGHD genes within human immunoglobulin heavy chains is challenging with up to one third of rearrangements having no identifiable IGHD gene. The short, mutated IGHD genes are generally assumed to be indistinguishable from the N-REGIONS of non-template encoded nucleotides that surround them. In this study we have characterised N-REGIONS, demonstrating the importance of nucleotide composition biases in the addition process, including the formation of homopolymer tracts. We then use a simulation approach to determine the likelihood of misidentification of highly mutated IGHD genes among the JUNCTION nucleotides. These likelihoods provide general rules for the identification of mutated D-REGIONs, and suggest that longer D-REGIONs (>25 nucleotides) with as many as ten mutations can be identified with a low risk of error. Shorter D-REGIONs (>16 nucleotides) with as many as four mutations are also identifiable. The reliability of different alignments is dependent upon the junction length (combined N-REGIONs and D-REGION). Data is presented that can guide the alignment of sequences with junction lengths from 5 to 50 nucleotides, including explicit selection between two D-REGION possibilities. The use of such a statistically-based approach to the alignment of IGHD genes will improve the reliability of the partitioning of immunoglobulin sequences, and this in turn will facilitate the study of the many processes that contribute to the diversity of the immunoglobulin repertoire.

iHMMune-align: hidden Markov model-based alignment and identification of germline genes in rearranged immunoglobulin gene sequences.

MOTIVATION: Immunoglobulin heavy chain (IGH) genes in mature B lymphocytes are the result of recombination of IGHV, IGHD and IGHJ germline genes, followed by somatic mutation. The correct identification of the germline genes that make up a variable VH domain is essential to our understanding of the process of antibody diversity generation as well as to clinical investigations of some leukaemias and lymphomas. RESULTS: We have developed iHMMune-align, an alignment program that uses a hidden Markov model (HMM) to model the processes involved in human IGH gene rearrangement and maturation. The performance of iHMMune-align was compared to that of other immunoglobulin gene alignment utilities using both clonally related and randomly selected IGH sequences. This evaluation suggests that iHMMune-align provides a more accurate identification of component germline genes than other currently available IGH gene characterization programs. AVAILABILITY: iHMMune-align cross-platform Java executable and web interface are freely available to academic users and can be accessed at http://www.emi.unsw.edu.au/~ihmmune/.