Mohamed Uduman

Quantifying selection in high-throughput Immunoglobulin sequencing data sets

High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data

UNLABELLED Advances in high-throughput sequencing technologies now allow for large-scale characterization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized computational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for determining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pressure, and calculating sequence chemical properties. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. AVAILABILITY AND IMPLEMENTATION Change-O is freely available for non-commercial use and may be downloaded from http://clip.med.yale.edu/changeo. CONTACT steven.kleinstein@yale.edu.

High-resolution antibody dynamics of vaccine-induced immune responses

Significance The immune system must constantly adapt to combat infections and other challenges. This is accomplished by continuously evolving the antibody repertoire, and by maintaining memory of prior challenges. By using next-generation DNA sequencing technology, we have examined the shear amount of antibody made by individuals during a flu vaccination trial. We demonstrate one of the first characterizations of the fast antibody dynamics through time in multiple individuals responding to an immune challenge. The adaptive immune system confers protection by generating a diverse repertoire of antibody receptors that are rapidly expanded and contracted in response to specific targets. Next-generation DNA sequencing now provides the opportunity to survey this complex and vast repertoire. In the present work, we describe a set of tools for the analysis of antibody repertoires and their application to elucidating the dynamics of the response to viral vaccination in human volunteers. By analyzing data from 38 separate blood samples across 2 y, we found that the use of the germ-line library of V and J segments is conserved between individuals over time. Surprisingly, there appeared to be no correlation between the use level of a particular VJ combination and degree of expansion. We found the antibody RNA repertoire in each volunteer to be highly dynamic, with each individual displaying qualitatively different response dynamics. By using combinatorial phage display, we screened selected VH genes paired with their corresponding VL library for affinity against the vaccine antigens. Altogether, this work presents an additional set of tools for profiling the human antibody repertoire and demonstrates characterization of the fast repertoire dynamics through time in multiple individuals responding to an immune challenge.

pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires

UNLABELLED Driven by dramatic technological improvements, large-scale characterization of lymphocyte receptor repertoires via high-throughput sequencing is now feasible. Although promising, the high germline and somatic diversity, especially of B-cell immunoglobulin repertoires, presents challenges for analysis requiring the development of specialized computational pipelines. We developed the REpertoire Sequencing TOolkit (pRESTO) for processing reads from high-throughput lymphocyte receptor studies. pRESTO processes raw sequences to produce error-corrected, sorted and annotated sequence sets, along with a wealth of metrics at each step. The toolkit supports multiplexed primer pools, single- or paired-end reads and emerging technologies that use single-molecule identifiers. pRESTO has been tested on data generated from Roche and Illumina platforms. It has a built-in capacity to parallelize the work between available processors and is able to efficiently process millions of sequences generated by typical high-throughput projects. AVAILABILITY AND IMPLEMENTATION pRESTO is freely available for academic use. The software package and detailed tutorials may be downloaded from http://clip.med.yale.edu/presto.

Improved methods for detecting selection by mutation analysis of Ig V region sequences

Statistical methods based on the relative frequency of replacement mutations in B lymphocyte Ig V region sequences have been widely used to detect the forces of selection that shape the B cell repertoire. However, current methods produce an unexpectedly high frequency of false positives and are sensitive to intrinsic biases of somatic hypermutation that can give the appearance of selection. The new statistical test proposed here provides a better trade-off between sensitivity and specificity compared with previous approaches. The low specificity of existing methods was shown in silico to result from an interaction between the effects of positive and negative selection. False detection of positive selection was confirmed in vivo through a re-analysis of published sequence data from diffuse large B cell lymphomas, highlighting the need for re-analysis of some existing studies. The sensitivity of the proposed method to detect selection was validated using new Ig transgenic mouse models in which positive selection was expected to be a significant force, as well as with a simulation-based approach. Previous concerns that intrinsic biases of somatic hypermutation could give the appearance of selection were addressed by extending the current mutation models to more fully account for the impact of microsequence on relative mutability and to include transition bias. High specificity was confirmed using a large set of non-productively rearranged Ig sequences. These results show that selection can be detected in vivo with high specificity using the new method proposed here, allowing greater insight into the existence and direction of antigen-driven selection.

Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data

Analyses of somatic hypermutation (SHM) patterns in B cell immunoglobulin (Ig) sequences contribute to our basic understanding of adaptive immunity, and have broad applications not only for understanding the immune response to pathogens, but also to determining the role of SHM in autoimmunity and B cell cancers. Although stochastic, SHM displays intrinsic biases that can confound statistical analysis, especially when combined with the particular codon usage and base composition in Ig sequences. Analysis of B cell clonal expansion, diversification, and selection processes thus critically depends on an accurate background model for SHM micro-sequence targeting (i.e., hot/cold-spots) and nucleotide substitution. Existing models are based on small numbers of sequences/mutations, in part because they depend on data from non-coding regions or non-functional sequences to remove the confounding influences of selection. Here, we combine high-throughput Ig sequencing with new computational analysis methods to produce improved models of SHM targeting and substitution that are based only on synonymous mutations, and are thus independent of selection. The resulting “S5F” models are based on 806,860 Synonymous mutations in 5-mer motifs from 1,145,182 Functional sequences and account for dependencies on the adjacent four nucleotides (two bases upstream and downstream of the mutation). The estimated profiles can explain almost half of the variance in observed mutation patterns, and clearly show that both mutation targeting and substitution are significantly influenced by neighboring bases. While mutability and substitution profiles were highly conserved across individuals, the variability across motifs was found to be much larger than previously estimated. The model and method source code are made available at http://clip.med.yale.edu/SHM

Detecting selection in immunoglobulin sequences

The ability to detect selection by analyzing mutation patterns in experimentally derived immunoglobulin (Ig) sequences is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers and the diversification of pre-immune repertoires in certain species. Despite its importance, quantifying selection in experimentally derived sequences is fraught with difficulties. The necessary parameters for statistical tests (such as the expected frequency of replacement mutations in the absence of selection) are non-trivial to calculate, and results are not easily interpretable when analyzing more than a handful of sequences. We have developed a web server that implements our previously proposed Focused binomial test for detecting selection. Several features are integrated into the web site in order to facilitate analysis, including V(D)J germline segment identification with IMGT alignment, batch submission of sequences and integration of additional test statistics proposed by other groups. We also implement a Z-score-based statistic that increases the power of detecting selection while maintaining specificity, and further allows for the combined analysis of sequences from different germlines. The tool is freely available at http://clip.med.yale.edu/selection.

Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles

Significance High-throughput sequencing of B-cell immunoglobulin receptors is providing unprecedented insight into adaptive immunity. A key step in analyzing these data involves assignment of the germline variable (V), diversity (D), and joining (J) gene-segment alleles that comprise each immunoglobulin sequence by matching them against a database of known V(D)J alleles. However, this process will fail for sequences that use previously undetected alleles, whose frequency in the population is unclear. Here we describe TIgGER, a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by a subject, including novel alleles. The application of TIgGER identifies a surprisingly high frequency of novel alleles, highlighting the critical need for this approach. Individual variation in germline and expressed B-cell immunoglobulin (Ig) repertoires has been associated with aging, disease susceptibility, and differential response to infection and vaccination. Repertoire properties can now be studied at large-scale through next-generation sequencing of rearranged Ig genes. Accurate analysis of these repertoire-sequencing (Rep-Seq) data requires identifying the germline variable (V), diversity (D), and joining (J) gene segments used by each Ig sequence. Current V(D)J assignment methods work by aligning sequences to a database of known germline V(D)J segment alleles. However, existing databases are likely to be incomplete and novel polymorphisms are hard to differentiate from the frequent occurrence of somatic hypermutations in Ig sequences. Here we develop a Tool for Ig Genotype Elucidation via Rep-Seq (TIgGER). TIgGER analyzes mutation patterns in Rep-Seq data to identify novel V segment alleles, and also constructs a personalized germline database containing the specific set of alleles carried by a subject. This information is then used to improve the initial V segment assignments from existing tools, like IMGT/HighV-QUEST. The application of TIgGER to Rep-Seq data from seven subjects identified 11 novel V segment alleles, including at least one in every subject examined. These novel alleles constituted 13% of the total number of unique alleles in these subjects, and impacted 3% of V(D)J segment assignments. These results reinforce the highly polymorphic nature of human Ig V genes, and suggest that many novel alleles remain to be discovered. The integration of TIgGER into Rep-Seq processing pipelines will increase the accuracy of V segment assignments, thus improving B-cell repertoire analyses.

Taking Advantage: High-Affinity B Cells in the Germinal Center Have Lower Death Rates, but Similar Rates of Division, Compared to Low-Affinity Cells

B lymphocytes producing high-affinity Abs are critical for protection from extracellular pathogens, such as bacteria and parasites. The process by which high-affinity B cells are selected during the immune response has never been elucidated. Although it has been shown that high-affinity cells directly outcompete low-affinity cells in the germinal center (GC), whether there are also intrinsic differences between these cells has not been addressed. It could be that higher affinity cells proliferate more rapidly or are more likely to enter cell cycle, thereby outgrowing lower affinity cells. Alternatively, higher affinity cells could be relatively more resistant to cell death in the GC. By comparing high- and low-affinity B cells for the same Ag, we show here that low-affinity cells have an intrinsically higher death rate than do cells of higher affinity, even in the absence of competition. This suggests that selection in the GC reaction is due at least in part to the control of survival of higher affinity B cells and not by a proliferative advantage conferred upon these cells compared with lower affinity B cells. Control over survival rather than proliferation of low- and high-affinity B cells in the GC allows greater diversity not only in the primary response but also in the memory response.

Cell subset prediction for blood genomic studies

BackgroundGenome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative.ResultsWe have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations with subset-specific genes across a set of samples. Validation using multiple experimental datasets demonstrates that SPEC can accurately identify the source of a gene signature as myeloid or lymphoid, as well as differentiate between B cells, T cells, NK cells and monocytes. Using SPEC, we predict that myeloid cells are the source of the interferon-therapy response gene signature associated with HCV patients who are non-responsive to standard therapy.ConclusionsSPEC is a powerful technique for blood genomic studies. It can help identify specific cell subsets that are important for understanding disease and therapy response. SPEC is widely applicable since only gene expression profiles from total PBMCs are required, and thus it can easily be used to mine the massive amount of existing microarray or RNA-seq data.