Abstract:Incremental selection within a population, defined as limited fitness changes following mutation, is an important aspect of many evolutionary processes. Strongly advantageous or deleterious mutations are detected using the synonymous to non-synonymous mutations ratio. However, there are currently no precise methods to estimate incremental selection. We here provide for the first time such a detailed method and show its precision in multiple cases of micro-evolution. The proposed method is a novel mixed lineage… Show more
“…Analysing the patterns of these lineages can give insights into the diversification and selection processes that lead to clonal evolution [ 78 , 79 ]. Different lineage analyses have led to the calculation of the mutation rate of SHM [ 80 ] and as metrics of the specific selection pressure on a set of mutations in a given branch or at the root of the lineage [ 81 , 82 ]. Figure 4.…”
Section: Evaluation Of Inter- and Intra-clonal Diversity: Where Are Tmentioning
Clones are the fundamental building blocks of immune repertoires. The number of different clones relates to the diversity of the repertoire, whereas their size and sequence diversity are linked to selective pressures. Selective pressures act both between clones and within different sequence variants of a clone. Understanding how clonal selection shapes the immune repertoire is one of the most basic questions in all of immunology. But how are individual clones defined? Here we discuss different approaches for defining clones, starting with how antibodies are diversified during different stages of B cell development. Next, we discuss how clones are defined using different experimental methods. We focus on high-throughput sequencing datasets, and the computational challenges and opportunities that these data have for mining the antibody repertoire landscape. We discuss methods that visualize sequence variants within the same clone and allow us to consider collections of shared mutations to determine which sequences share a common ancestry. Finally, we comment on features of frequently encountered expanded B cell clones that may be of particular interest in the setting of autoimmunity and other chronic conditions.
“…Analysing the patterns of these lineages can give insights into the diversification and selection processes that lead to clonal evolution [ 78 , 79 ]. Different lineage analyses have led to the calculation of the mutation rate of SHM [ 80 ] and as metrics of the specific selection pressure on a set of mutations in a given branch or at the root of the lineage [ 81 , 82 ]. Figure 4.…”
Section: Evaluation Of Inter- and Intra-clonal Diversity: Where Are Tmentioning
Clones are the fundamental building blocks of immune repertoires. The number of different clones relates to the diversity of the repertoire, whereas their size and sequence diversity are linked to selective pressures. Selective pressures act both between clones and within different sequence variants of a clone. Understanding how clonal selection shapes the immune repertoire is one of the most basic questions in all of immunology. But how are individual clones defined? Here we discuss different approaches for defining clones, starting with how antibodies are diversified during different stages of B cell development. Next, we discuss how clones are defined using different experimental methods. We focus on high-throughput sequencing datasets, and the computational challenges and opportunities that these data have for mining the antibody repertoire landscape. We discuss methods that visualize sequence variants within the same clone and allow us to consider collections of shared mutations to determine which sequences share a common ancestry. Finally, we comment on features of frequently encountered expanded B cell clones that may be of particular interest in the setting of autoimmunity and other chronic conditions.
“…Phylogenetic methods are equally important for shorter-time-scale investigations of affinity maturation, such as of the response to vaccination ( 12 ). One may also use trees equipped with ancestral sequences to make statements about the strength of natural selection ( 13 ).…”
B cell receptor sequences evolve during affinity maturation according to a Darwinian process of mutation and selection. Phylogenetic tools are used extensively to reconstruct ancestral sequences and phylogenetic trees from affinity-matured sequences. In addition to using general-purpose phylogenetic methods, researchers have developed new tools to accommodate the special features of B cell sequence evolution. However, the performance of classical phylogenetic techniques in the presence of B cell-specific features is not well understood, nor how much the newer generation of B cell specific tools represent an improvement over classical methods. In this paper we benchmark the performance of classical phylogenetic and new B cell-specific tools when applied to B cell receptor sequences simulated from a forward-time model of B cell receptor affinity maturation toward a mature receptor. We show that the currently used tools vary substantially in terms of tree structure and ancestral sequence inference accuracy. Furthermore, we show that there are still large performance gains to be achieved by modeling the special mutation process of B cell receptors. These conclusions are further strengthened with real data using the rules of isotype switching to count possible violations within each inferred phylogeny.
“…Thus, a clone was defined as a set of sequences similar to one another, up to a distance of four mutations. These methods were extensively validated in previous studies ( 1 – 3 , 25 – 27 ).…”
The naïve immunoglobulin (IG) repertoire in the blood differs from the direct output of the rearrangement process. These differences stem from selection that affects the germline gene usage and the junctional nucleotides. A major complication obscuring the details of the selection mechanism in the heavy chain is the failure to properly identify the D germline and determine the nucleotide addition and deletion in the junction region. The selection affecting junctional diversity can, however, be studied in the light chain that has no D gene. We use probabilistic and deterministic models to infer and disentangle generation and selection of the light chain, using large samples of light chains sequenced from healthy donors and transgenic mice. We have previously used similar models for the beta chain of T-cell receptors and the heavy chain of IGs. Selection is observed mainly in the CDR3. The CDR3 length and mass distributions are narrower after selection than before, indicating stabilizing selection for mid-range values. Within the CDR3, proline and cysteine undergo negative selection, while glycine undergoes positive selection. The results presented here suggest structural selection maintaining the size of the CDR3 within a limited range, and preventing turns in the CDR3 region.
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