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

Survival analysis of DNA mutation motifs with penalized proportional hazards

Antibodies, an essential part of our immune system, develop through an intricate process to bind a wide array of pathogens. This process involves randomly mutating DNA sequences encoding these antibodies to find variants with improved binding, though mutations are not distributed uniformly across sequence sites. Immunologists observe this nonuniformity to be consistent with "mutation motifs", which are short DNA subsequences that affect how likely a given site is to experience a mutation. Quantifying the effect of motifs on mutation rates is challenging: a large number of possible motifs makes this statistical problem high dimensional, while the unobserved history of the mutation process leads to a nontrivial missing data problem. We introduce an $\ell_1$-penalized proportional hazards model to infer mutation motifs and their effects. In order to estimate model parameters, our method uses a Monte Carlo EM algorithm to marginalize over the unknown ordering of mutations. We show that our method performs better on simulated data compared to current methods and leads to more parsimonious models. The application of proportional hazards to mutation processes is, to our knowledge, novel and formalizes the current methods in a statistical framework that can be easily extended to analyze the effect of other biological features on mutation rates

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

Using genotype abundance to improve phylogenetic inference

Modern biological techniques enable very dense genetic sampling of unfolding evolutionary histories, and thus frequently sample some genotypes multiple times. This motivates strategies to incorporate genotype abundance information in phylogenetic inference. In this paper, we synthesize a stochastic process model with standard sequence-based phylogenetic optimality, and show that tree estimation is substantially improved by doing so. Our method is validated with extensive simulations and an experimental single-cell lineage tracing study of germinal center B cell receptor affinity maturation

A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis

The human body is able to generate 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.Comment: 26 page

Metamaterial superlenses operating at visible wavelength for imaging applications

© 2018 The Authors. Published by Nature. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1038/s41598-018-33572-yIn this paper, a novel design for a metamaterial lens (superlens) based on a Photonic Crystal (PC) operating at visible wavelengths is reported. The proposed superlens consist of a gallium phosphide (GaP) dielectric slab waveguide with a hexagonal array of silver rods embedded within the GaP dielectric. In-house 2DFDTD numerical method is used to design and optimize the proposed superlens. Several superlenses are designed and integrated within a same dielectric platform, promoting the proof-of-concept (POC) of possible construction of an array of superlenses (or sub-lenses to create an M-Lens) for light field imaging applications. It is shown that the concavity of the superlens and positioning of each sub-lens within the array strongly affects the performances of the image in terms of resolution. Defects and various geometrical shapes are introduced to construct and optimize the proposed superlenses and increase the quality of the image resolution. It is shown that the orientation of the active region (ellipse) along x and y axis has tremendous influence on the quality of image resolution. In order to investigate the performance characteristics of the superlenses, transmitted power is calculated using 2D FDTD for image projections at various distances (in x and y plane). It is also shown, how the proposed superlens structures could be fabricated using standard micro fabrication techniques such as electron beam lithography, inductively coupled Reactive ion etching, and glancing angle evaporation methods. To the best of our knowledge, these are the first reported POC of superlenses, integrated in a monolithic platform suitable for high imaging resolution that can be used for light field imaging applications at visible wavelength. The proposed superlenses (integrated in a single platform M-Lens) will have tremendous impact on imaging applications

Focusing behavior of 2-dimensional plasmonic conical zone plate

A conical configuration plasmonic zone plate based on Fresnel zones made up of Au thin film slits is proposed for focusing in the free space with visible illumination. The surface plasmons enable propagation of radiating modes to distances equal to several wavelengths of the illumination field. Through numerical simulations, the conical structure found to yield focal spot beating the diffraction barrier encountered by conventional focusing elements. The focal spot size measured as full-width at half-maximum (FWHM) is observed to be as small as 0.31 times the illumination wavelength at the focal distance of 8 wavelength. Moreover, the simple design rules make it possible to predict and control the focal distances accurately