Immunoglobulin variable-region gene mutational lineage tree analysis: application to autoimmune diseases

Immunoglobulin gene analysis as a tool for investigating human immune responses

The human immunoglobulin repertoire is a hugely diverse set of sequences that are formed by processes of gene rearrangement, heavy and light chain gene assortment, class switching and somatic hypermutation. Early B cell development produces diverse IgM and IgD B cell receptors on the B cell surface, resulting in a repertoire that can bind many foreign antigens but which has had self-reactive B cells removed. Later antigen-dependent development processes adjust the antigen affinity of the receptor by somatic hypermutation. The effector mechanism of the antibody is also adjusted, by switching the class of the antibody from IgM to one of seven other classes depending on the required function. There are many instances in human biology where positive and negative selection forces can act to shape the immunoglobulin repertoire and therefore repertoire analysis can provide useful information on infection control, vaccination efficacy, autoimmune diseases, and cancer. It can also be used to identify antigen-specific sequences that may be of use in therapeutics. The juxtaposition of lymphocyte development and numerical evaluation of immune repertoires has resulted in the growth of a new sub-speciality in immunology where immunologists and computer scientists/physicists collaborate to assess immune repertoires and develop models of immune action.

Immunoglobulin gene repertoire diversification and selection in the stomach - from gastritis to gastric lymphomas

Chronic gastritis is characterized by gastric mucosal inflammation due to autoimmune responses or infection, frequently with Helicobacter pylori. Gastritis with H. pylori background can cause gastric mucosa-associated lymphoid tissue lymphoma (MALT-L), which sometimes further transforms into diffuse large B-cell lymphoma (DLBCL). However, gastric DLBCL can also be initiated de novo. The mechanisms underlying transformation into DLBCL are not completely understood. We analyzed immunoglobulin repertoires and clonal trees to investigate whether and how immunoglobulin gene repertoires, clonal diversification, and selection in gastritis, gastric MALT-L, and DLBCL differ from each other and from normal responses. The two gastritis types (positive or negative for H. pylori) had similarly diverse repertoires. MALT-L dominant clones (defined as the largest clones in each sample) presented higher diversification and longer mutational histories compared with all other conditions. DLBCL dominant clones displayed lower clonal diversification, suggesting the transforming events are triggered by similar responses in different patients. These results are surprising, as we expected to find similarities between the dominant clones of gastritis and MALT-L and between those of MALT-L and DLBCL.

Integrating B cell lineage information into statistical tests for detecting selection in Ig sequences

Detecting selection in B cell Ig sequences is critical to understanding affinity maturation and can provide insights into Ag-driven selection in normal and pathologic immune responses. The most common sequence-based methods for detecting selection analyze the ratio of replacement and silent mutations using a binomial statistical analysis. However, these approaches have been criticized for low sensitivity. An alternative method is based on the analysis of lineage trees constructed from sets of clonally related Ig sequences. Several tree shape measures have been proposed as indicators of selection that can be statistically compared across cohorts. However, we show that tree shape analysis is confounded by underlying experimental factors that are difficult to control for in practice, including the sequencing depth and number of generations in each clone. Thus, although lineage tree shapes may reflect selection, their analysis alone is an unreliable measure of in vivo selection. To usefully capture the information provided by lineage trees, we propose a new method that applies the binomial statistical framework to mutations identified based on lineage tree structure. This hybrid method is able to detect selection with increased sensitivity in both simulated and experimental data sets. We anticipate that this approach will be especially useful in the analysis of large-scale Ig sequencing data sets generated by high-throughput sequencing technologies.

Ig gene diversification and selection in follicular lymphoma, diffuse large B cell lymphoma and primary central nervous system lymphoma revealed by lineage tree and mutation analyses

Follicular lymphoma (FL), diffuse large B cell lymphoma (DLBCL) and primary central nervous system lymphoma are B cell malignancies. FL and DLBCL have a germinal center origin. We have applied mutational analyses and a novel algorithm for quantifying shape properties of mutational lineage trees to investigate the nature of the diversification, somatic hypermutation and selection processes that affect B cell clones in these malignancies and reveal whether they differ from normal responses. Lineage tree analysis demonstrated higher diversification and mutations per cell in the lymphoma clones. This was caused solely by the longer diversification times of the malignant clones, as their recent diversification processes were similar to those of normal responses, implying similar mutation frequencies. Since previous analyses of antigen-driven selection were shown to yield false positives, we performed a corrected analysis of replacement and silent mutation patterns, which revealed selection against replacement mutations in the framework regions, responsible for the structural integrity of the B cell receptor, but not for positive selection for replacements in the complementary determining regions. Most replacements, however, were neutral or conservative, suggesting that if at all selection operates in these malignancies it is for structural B cell receptor integrity but not for antigen binding.

Somatic hypermutation and antigen-driven selection of B cells are altered in autoimmune diseases

B cells have been found to play a critical role in the pathogenesis of several autoimmune (AI) diseases. A common feature amongst many AI diseases is the formation of ectopic germinal centers (GC) within the afflicted tissue or organ, in which activated B cells expand and undergo somatic hypermutation (SHM) and antigen-driven selection on their immunoglobulin variable region (IgV) genes. However, it is not yet clear whether these processes occurring in ectopic GCs are identical to those in normal GCs. The analysis of IgV mutations has aided in revealing many aspects concerning B cell expansion, mutation and selection in GC reactions. We have applied several mutation analysis methods, based on lineage tree construction, to a large set of data, containing IgV productive and non-productive heavy and light chain sequences from several different tissues, to examine three of the most profoundly studied AI diseases - Rheumatoid Arthritis (RA), Multiple Sclerosis (MS) and Sjögren's Syndrome (SS). We have found that RA and MS sequences exhibited normal mutation spectra and targeting motifs, but a stricter selection compared to normal controls, which was more apparent in RA. SS sequence analysis results deviated from normal controls in both mutation spectra and indications of selection, also showing differences between light and heavy chain IgV and between different tissues. The differences revealed between AI diseases and normal control mutation patterns may result from the different microenvironmental influences to which ectopic GCs are exposed, relative to those in normal secondary lymphoid tissues.

IgTree: creating Immunoglobulin variable region gene lineage trees

Lineage trees describe the microevolution of cells within an organism. They have been useful in the study of B cell affinity maturation, which is based on somatic hypermutation of immunoglobulin genes in germinal centers and selection of the resulting mutants. Our aim was to create and implement an algorithm that can generate lineage trees from immunoglobulin variable region gene sequences. The IgTree program implements the algorithm we developed, and generates lineage trees. Original sequences found in experiments are assigned to either leaves or internal nodes of the tree. Each tree node represents a single mutation separating the sequences. The mutations that separate the sequences from each other can be point mutations, deletions or insertions. The program can deal with gaps and find potential reversion mutations. The program also enumerates mutation frequencies and sequence motifs around each mutation, on a per-tree basis. The algorithm has proven useful in several studies of immunoglobulin variable region gene mutations.

Antigen-driven selection in germinal centers as reflected by the shape characteristics of immunoglobulin gene lineage trees: a large-scale simulation study

During the immune response, the generation of memory B lymphocytes in germinal centers involves affinity maturation of the cells' antigen receptors, based on somatic hypermutation of receptor genes and antigen-driven selection of the resulting mutants. Affinity maturation is vital for immune protection, and is the basis of humoral immune learning and memory. Lineage trees of somatically hypermutated immunoglobulin genes often serve to qualitatively illustrate claims concerning the dynamics of affinity maturation in germinal centers. Here, we derive the quantitative relationships between parameters characterizing affinity maturation dynamics (proliferation, differentiation and mutation rates, initial affinity of the Ig to the antigen, and selection thresholds) and the mathematical properties of lineage trees, using a computer simulation which combines mathematical models for all mature B cell populations, stochastic models of hypermutation and selection, lineage tree generation and measurement of graphical tree characteristics. We identified seven key lineage tree properties, and found correlations of these with initial clone affinity and with the selection threshold. These two parameters were found to be the main factors affecting lineage tree shapes in both primary and secondary response trees. The results also confirm that recycling from centrocytes back to centroblasts is highly likely.

The HLA gene complex in thyroid autoimmunity: from epidemiology to etiology

The autoimmune thyroid diseases (AITD) comprise a cadre of complex diseases whose underlying pathoetiology stems from a genetic-environmental interaction, between susceptibility genes (e.g. CTLA-4, HLA-DR, thyroglobulin) and environmental triggers (e.g. dietary iodine), that orchestrates the initiation of an autoimmune response to thyroid antigens, leading to the onset of disease. Abundant epidemiological data, including family and twin studies, point to a strong genetic influence on the development of AITD. Several AITD susceptibility genes have been identified, with HLA genes, in particular, appearing to be of major importance. Early studies showed association of HLA-DR3 with Graves' disease (GD) in Caucasians. More recently, the importance of an amino acid substitution at position 74 of the DR beta 1 chain of HLA-DR3 (DRb1-Arg74), in susceptibility to Graves' disease, has been shown. Furthermore, there is increasing evidence for a genetic interaction between thyroglobulin variants and DRb1-Arg74 in conferring risk for GD. Mechanistically, the presence of an arginine at position 74 elicits a significant structural change in the peptide binding pocket of HLA-DR, potentially affecting the binding of pathogenic thyroidal peptides. Future therapeutic interventions may attempt to exploit this new bolus of knowledge by endeavoring to block or modulate pathogenic peptide presentation by HLA-DR.

Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: chronic activation, normal selection

Autoimmune diseases show high diversity in the affected organs, clinical manifestations and disease dynamics. Yet they all share common features, such as the ectopic germinal centers found in many affected tissues. Lineage trees depict the diversification, via somatic hypermutation (SHM), of immunoglobulin variable-region (IGV) genes. We previously developed an algorithm for quantifying the graphical properties of IGV gene lineage trees, allowing evaluation of the dynamical interplay between SHM and antigen-driven selection in different lymphoid tissues, species, and disease situations. Here, we apply this method to ectopic GC B cell clones from patients with Myasthenia Gravis, Rheumatoid Arthritis, and Sjögren's Syndrome, using data scaling to minimize the effects of the large variability due to methodological differences between groups. Autoimmune trees were found to be significantly larger relative to normal controls. In contrast, comparison of the measurements for tree branching indicated that similar selection pressure operates on autoimmune and normal control clones.

Models for antigen receptor gene rearrangement: CDR3 length

Despite the various processing steps involved in V(D)J recombination, which could potentially introduce many biases in the length distribution of complementarity determining region 3 (CDR3) segments, the observed CDR3 length distributions for complete repertoires are very close to a normal-like distribution. This raises the question of whether this distribution is simply a result of the random steps included in the process of gene rearrangement, or has been optimized during evolution. We have addressed this issue by constructing a simulation of gene rearrangement, which takes into account the DNA modification steps included in the process, namely hairpin opening, nucleotide additions, and nucleotide deletions. We found that the near-Gaussian- shape of CDR3 length distribution can only be obtained under a relatively narrow set of parameter values, and thus our model suggests that specific biases govern the rearrangement process. In both B-cell receptor (BCR) heavy chain and T-cell receptor beta chain, we obtained a Gaussian distribution using identical parameters, despite the difference in the number and the lengths of the D segments. Hence our results suggest that these parameters most likely reflect the optimal conditions under which the rearrangement process occurs. We have subsequently used the insights gained in this study to estimate the probability of occurrence of two exactly identical BCRs over the course of a human lifetime. Whereas identical rearrangements of the heavy chain are highly unlikely to occur within one human lifetime, for the light chain we found that this probability is not negligible, and hence the light chain CDR3 alone cannot serve as an indicator of B-cell clonality.

Designing an A* algorithm for calculating edit distance between rooted-unordered trees

Tree structures are useful for describing and analyzing biological objects and processes. Consequently, there is a need to design metrics and algorithms to compare trees. A natural comparison metric is the "Tree Edit Distance," the number of simple edit (insert/delete) operations needed to transform one tree into the other. Rooted-ordered trees, where the order between the siblings is significant, can be compared in polynomial time. Rooted-unordered trees are used to describe processes or objects where the topology, rather than the order or the identity of each node, is important. For example, in immunology, rooted-unordered trees describe the process of immunoglobulin (antibody) gene diversification in the germinal center over time. Comparing such trees has been proven to be a difficult computational problem that belongs to the set of NP-Complete problems. Comparing two trees can be viewed as a search problem in graphs. A* is a search algorithm that explores the search space in an efficient order. Using a good lower bound estimation of the degree of difference between the two trees, A* can reduce search time dramatically. We have designed and implemented a variant of the A* search algorithm suitable for calculating tree edit distance. We show here that A* is able to perform an edit distance measurement in reasonable time for trees with dozens of nodes.

Feedback Loops, Reversals and Nonlinearities in Lymphocyte Development

Systems of differentiating cells are often regarded by experimental biologists as unidirectional processes, in which cells spend a fixed time at each successive developmental stage. However, mathematical modeling has in several cases revealed that differentiating cell systems are more complex than previously believed. For example, non-linear transitions, feedback effects, and even apparent reversals have been suggested by our studies on models for the development of lymphocytes and their receptor repertoires, and are reviewed in this paper. These studies have shown that cell population growth in developing lymphocyte subsets is usually nonlinear, as it depends on the density of cells in each compartment. Additionally, T cell development has been shown to be subject to feedback regulation by mature T cell subsets, and B cell development has been shown to include a phenotypic reflux from an advanced to an earlier developmental stage. The challenges we face in our efforts to understand how the repertoires of these cells are generated and regulated are also discussed here.