73 research outputs found
Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
The adaptive immune system recognizes antigens via an immense array of
antigen-binding antibodies and T-cell receptors, the immune repertoire. The
interrogation of immune repertoires is of high relevance for understanding the
adaptive immune response in disease and infection (e.g., autoimmunity, cancer,
HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the
quantitative and molecular-level profiling of immune repertoires thereby
revealing the high-dimensional complexity of the immune receptor sequence
landscape. Several methods for the computational and statistical analysis of
large-scale AIRR-seq data have been developed to resolve immune repertoire
complexity in order to understand the dynamics of adaptive immunity. Here, we
review the current research on (i) diversity, (ii) clustering and network,
(iii) phylogenetic and (iv) machine learning methods applied to dissect,
quantify and compare the architecture, evolution, and specificity of immune
repertoires. We summarize outstanding questions in computational immunology and
propose future directions for systems immunology towards coupling AIRR-seq with
the computational discovery of immunotherapeutics, vaccines, and
immunodiagnostics.Comment: 27 pages, 2 figure
Likelihood-based inference of B-cell clonal families
The human immune system depends on a highly diverse collection of
antibody-making B cells. B cell receptor sequence diversity is generated by a
random recombination process called "rearrangement" forming progenitor B cells,
then a Darwinian process of lineage diversification and selection called
"affinity maturation." The resulting receptors can be sequenced in high
throughput for research and diagnostics. Such a collection of sequences
contains a mixture of various lineages, each of which may be quite numerous, or
may consist of only a single member. As a step to understanding the process and
result of this diversification, one may wish to reconstruct lineage membership,
i.e. to cluster sampled sequences according to which came from the same
rearrangement events. We call this clustering problem "clonal family
inference." In this paper we describe and validate a likelihood-based framework
for clonal family inference based on a multi-hidden Markov Model (multi-HMM)
framework for B cell receptor sequences. We describe an agglomerative algorithm
to find a maximum likelihood clustering, two approximate algorithms with
various trade-offs of speed versus accuracy, and a third, fast algorithm for
finding specific lineages. We show that under simulation these algorithms
greatly improve upon existing clonal family inference methods, and that they
also give significantly different clusters than previous methods when applied
to two real data sets
Quantifying evolutionary constraints on B cell affinity maturation
The antibody repertoire of each individual is continuously updated by the
evolutionary process of B cell receptor mutation and selection. It has recently
become possible to gain detailed information concerning this process through
high-throughput sequencing. Here, we develop modern statistical molecular
evolution methods for the analysis of B cell sequence data, and then apply them
to a very deep short-read data set of B cell receptors. We find that the
substitution process is conserved across individuals but varies significantly
across gene segments. We investigate selection on B cell receptors using a
novel method that side-steps the difficulties encountered by previous work in
differentiating between selection and motif-driven mutation; this is done
through stochastic mapping and empirical Bayes estimators that compare the
evolution of in-frame and out-of-frame rearrangements. We use this new method
to derive a per-residue map of selection, which provides a more nuanced view of
the constraints on framework and variable regions.Comment: Previously entitled "Substitution and site-specific selection driving
B cell affinity maturation is consistent across individuals
Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data
B cells develop high affinity receptors during the course of affinity
maturation, a cyclic process of mutation and selection. At the end of affinity
maturation, a number of cells sharing the same ancestor (i.e. in the same
"clonal family") are released from the germinal center, their amino acid
frequency profile reflects the allowed and disallowed substitutions at each
position. These clonal-family-specific frequency profiles, called "substitution
profiles", are useful for studying the course of affinity maturation as well as
for antibody engineering purposes. However, most often only a single sequence
is recovered from each clonal family in a sequencing experiment, making it
impossible to construct a clonal-family-specific substitution profile. Given
the public release of many high-quality large B cell receptor datasets, one may
ask whether it is possible to use such data in a prediction model for
clonal-family-specific substitution profiles. In this paper, we present the
method "Substitution Profiles Using Related Families" (SPURF), a penalized
tensor regression framework that integrates information from a rich assemblage
of datasets to predict the clonal-family-specific substitution profile for any
single input sequence. Using this framework, we show that substitution profiles
from similar clonal families can be leveraged together with simulated
substitution profiles and germline gene sequence information to improve
prediction. We fit this model on a large public dataset and validate the
robustness of our approach on an external dataset. Furthermore, we provide a
command-line tool in an open-source software package
(https://github.com/krdav/SPURF) implementing these ideas and providing easy
prediction using our pre-fit models.Comment: 23 page
A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis.
The human body generates a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial "V(D)J" rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process. In particular, standard phylogenetic approaches assume the DNA bases of the progenitor (or "naive") sequence arise independently and according to the same distribution, ignoring the complexities of V(D)J rearrangement. In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM). This technique not only integrates a naive rearrangement model with a phylogenetic model for BCR sequence evolution but also naturally accounts for uncertainty in all unobserved variables, including the phylogenetic tree, via posterior distribution sampling
Open Source Tool for VH-replacement Products Discovery and Analysis
In this project an open source tool for discovery and analysis of abnormal immunoglobulin genes rearrangements was developed. The main goal is in finding so-called “footprints” - small parts of incompletely rearranged V-segments in IGH genes. Joint usage of publicly available databases, on-line markup IMGT V-Quest and clonal families analyzer Partis allowed to prepare an informative dataset, which was processed via the developed tool. A few dependencies between patient phenotype and footprints statistics were discovered
The Pipeline Repertoire for Ig-Seq Analysis
With the advent of high-throughput sequencing of immunoglobulin genes (Ig-Seq), the understanding of antibody repertoires and their dynamics among individuals and populations has become an exciting area of research. There is an increasing number of computational tools that aid in every step of the immune repertoire characterization. However, since not all tools function identically, every pipeline has its unique rationale and capabilities, creating a rich blend of useful features that may appear intimidating for newcomer laboratories with the desire to plunge into immune repertoire analysis to expand and improve their research; hence, all pipeline strengths and differences may not seem evident. In this review we provide a practical and organized list of the current set of computational tools, focusing on their most attractive features and differences in order to carry out the characterization of antibody repertoires so that the reader better decides a strategic approach for the experimental design, and computational pathways for the analyses of immune repertoires
Fierce selection and interference in B-cell repertoire response to chronic HIV-1
During chronic infection, HIV-1 engages in a rapid coevolutionary arms race
with the host's adaptive immune system. While it is clear that HIV exerts
strong selection on the adaptive immune system, the characteristics of the
somatic evolution that shape the immune response are still unknown. Traditional
population genetics methods fail to distinguish chronic immune response from
healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell
repertoires and identify complex dynamics with a constant production of better
B-cell receptor mutants that compete, maintaining large clonal diversity and
potentially slowing down adaptation. A substantial fraction of mutations that
rise to high frequencies in pathogen engaging CDRs of B-cell receptors (BCRs)
are beneficial, in contrast to many such changes in structurally relevant
frameworks that are deleterious and circulate by hitchhiking. We identify a
pattern where BCRs in patients who experience larger viral expansions undergo
stronger selection with a rapid turnover of beneficial mutations due to clonal
interference in their CDR3 regions. Using population genetics modeling, we show
that the extinction of these beneficial mutations can be attributed to the rise
of competing beneficial alleles and clonal interference. The picture is of a
dynamic repertoire, where better clones may be outcompeted by new mutants
before they fix
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