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A challenge in studying viral immune escape is determining how mutations combine to escape polyclonal antibodies, which can potentially target multiple distinct viral epitopes. Here we introduce a biophysical model of this process that partitions the total polyclonal antibody activity by epitope, and then quantifies how each viral mutation affects the antibody activity against each epitope. We develop software that can use deep mutational scanning data to infer these properties for polyclonal antibody mixtures. We validate this software using a computationally simulated deep mutational scanning experiment, and demonstrate that it enables the prediction of escape by arbitrary combinations of mutations. The software described in this paper is available at https://jbloomlab.github.io/polyclonal.

Likelihood-based phylogenetic inference posits a probabilistic model of character state change along branches of a phylogenetic tree. These models typically assume statistical independence of sites in the sequence alignment. This is a restrictive assumption that facilitates computational tractability, but ignores how epistasis, the effect of genetic background on mutational effects, influences the evolution of functional sequences. We consider the effect of using a misspecified site-independent model on the accuracy of Bayesian phylogenetic inference in the setting of pairwise-site epistasis. Previous work has shown that as alignment length increases, tree reconstruction accuracy also increases. Here, we present a simulation study demonstrating that accuracy increases with alignment size even if the additional sites are epistatically coupled. We introduce an alignment-based test statistic that is a diagnostic for pairwise epistasis and can be used in posterior predictive checks.

Significance Population histories are encoded by genomic variation among modern individuals. Population genetic inference methods, all theoretically rooted in probabilistic population models, can recover complex demographic histories from genomic variation data. However, the mutation process is treated very simply in these models—usually as a single constant. Recent empirical findings show that the mutation process is complex and dynamic over a range of evolutionary timescales and thus, deserving of richer descriptions in population genetic models. Here, we show that complex mutation spectrum histories can be accommodated by extending classical theoretical tools. We develop mathematical optimization methods and software to infer both demographic history and mutation spectrum history, revealing human mutation signatures varying through time and global divergence of mutational processes., As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

CRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms by mutating synthetic genomic barcodes during organismal development. However, these sophisticated biological tools currently use ad-hoc and outmoded computational methods to reconstruct the cell lineage tree from the mutated barcodes. Because these methods are agnostic to the biological mechanism, they are unable to take full advantage of the data’s structure. We propose a statistical model for the mutation process and develop a procedure to estimate the tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. In contrast to existing techniques, our method estimates time along each branch, rather than number of mutation events, thus providing a detailed account of tissue-type differentiation. Via simulations, we demonstrate that our method is substantially more accurate than existing approaches. Our reconstructed trees also better recapitulate known aspects of zebrafish development and reproduce similar results across fish replicates.

Likelihood-based phylogenetic inference posits a probabilistic model of character state change along branches of a phylogenetic tree. These models typically assume statistical independence of sites in the sequence alignment. This is a restrictive assumption that facilitates computational tractability, but ignores how epistasis, the effect of genetic background on mutational effects, influences the evolution of functional sequences. We consider the effect of using a misspecified site-independent model on the accuracy of Bayesian phylogenetic inference in the setting of pairwise-site epistasis. Previous work has shown that as alignment length increases, tree reconstruction accuracy also increases. Here, we present a simulation study demonstrating that accuracy increases with alignment size even if the additional sites are epistatically coupled. We introduce an alignment-based test statistic that is a diagnostic for pair-wise epistasis and can be used in posterior predictive checks.

SummaryCharacterization of germline mutation spectrum variation from population genomics data has shed light on the biological complexity of the mutation process, and its evolution within and between species. This analysis augments available population SNP data with estimates of local ancestral genomic context to assign mutation types and aggregate summary statistics thereof, and is increasingly common. There is a need for standardized computational tools to extract mutation spectrum information from sequencing data. Here I describe mutyper, a command-line utility and Python package that uses an ancestral genome estimate to assign mutation types to SNP data, compute mutation spectra for individuals, and compute sample frequency spectra resolved by mutation type for population genetic inference.Availability and implementationmutyper can be installed using the pip package manager and is compatible with Python 3.6+. Documentation is provided at https://harrispopgen.github.io/mutyper; source code is available at https://github.com/harrispopgen/mutyper.

Booming and busting populations modulate the accumulation of genetic diversity, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs themutation spectrum: the relative mutation rates in different local nucleotide contexts. Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We describemushi: a method to perform fast, nonparametric joint inference of demographic and mutation spectrum histories from allele frequency data. We usemushito reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and produce more accurate time calibration for a previously-reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be productively incorporated in a well-studied theoretical setting, and rigorously inferred from genomic variation data like other features of evolutionary history.

There are many possible failure points in the transmission of genetic information that can produce heritable germline mutations. Once a mutation has been passed from parents to offspring for several generations, it can be difficult or impossible to identify its root cause; however, sometimes the nature of the ancestral and derived DNA sequences can provide mechanistic clues about a genetic change that happened hundreds or thousands of generations ago. Here, we review evidence that the sequence context 'spectrum' of germline mutagenesis has been evolving surprisingly rapidly over the history of humans and other species. We go on to discuss possible causal factors that might underlie rapid mutation spectrum evolution.

Human T cells that recognize lipid Ags presented by highly conserved CD1 proteins often express semi-invariant TCRs, but the true diversity of lipid Ag–specific TCRs remains unknown. We use CD1b tetramers and high-throughput immunosequencing to analyze thousands of TCRs from ex vivo–sorted or in vitro–expanded T cells specific for the mycobacterial lipid Ag, glucose monomycolate. Our results reveal a surprisingly diverse repertoire resulting from editing of germline-encoded gene rearrangements analogous to MHC-restricted TCRs. We used a distance-based metric (TCRDist) to show how this diverse TCR repertoire builds upon previously reported conserved motifs by including subject-specific TCRs. In a South African cohort, we show that TCRDist can identify clonal expansion of diverse glucose monomycolate–specific TCRs and accurately distinguish patients with active tuberculosis from control subjects. These data suggest that similar mechanisms govern the selection and expansion of peptide and lipid Ag–specific T cells despite the nonpolymorphic nature of CD1.

Ryan Emerson and colleagues report immunosequencing of the variable region of the TCRβ chain in 666 individuals with known cytomegalovirus (CMV) status. They show that CMV status and HLA genotype shape the T cell repertoire and demonstrate proof of principle that TCRβ sequencing can be used as a specific diagnostic of pathogen exposure. An individual's T cell repertoire dynamically encodes their pathogen exposure history. To determine whether pathogen exposure signatures can be identified by documenting public T cell receptors (TCRs), we profiled the T cell repertoire of 666 subjects with known cytomegalovirus (CMV) serostatus by immunosequencing. We developed a statistical classification framework that could diagnose CMV status from the resulting catalog of TCRβ sequences with high specificity and sensitivity in both the original cohort and a validation cohort of 120 different subjects. We also confirmed that three of the identified CMV-associated TCRβ molecules bind CMV in vitro, and, moreover, we used this approach to accurately predict the HLA-A and HLA-B alleles of most subjects in the first cohort. As all memory T cell responses are encoded in the common format of somatic TCR recombination, our approach could potentially be generalized to a wide variety of disease states, as well as other immunological phenotypes, as a highly parallelizable diagnostic strategy.

Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.

Individuals with the 2019 coronavirus disease (COVID-19) show varying severity of the disease, ranging from asymptomatic to requiring intensive care. Although monoclonal antibodies specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified, we still lack an understanding of the overall landscape of B cell receptor (BCR) repertoires in individuals with COVID-19. We use high-throughput sequencing of bulk and plasma B cells collected at multiple time points during infection to characterize signatures of the B cell response to SARS-CoV-2 in 19 individuals. Using principled statistical approaches, we associate differential features of BCRs with different disease severity. We identify 38 significantly expanded clonal lineages shared among individuals as candidates for responses specific to SARS-CoV-2. Using single-cell sequencing, we verify the reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identify the natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in some individuals. Our results provide insights important for development of rational therapies and vaccines against COVID-19., Graphical abstract, It is unclear how the dynamics of the humoral immune response to SARS-CoV-2 vary across individuals with different disease severity. Montague et al. develop a principled statistical approach based on time-course, high-throughput B cell repertoire sequences to identify shared, expanding, rare clonal B cell lineages as candidates for responses specific to SARS-CoV-2.

The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity., eLife digest The immune system has two major ways of clearing up an infection. A rapid, first line of defense buys time while the second ‘adaptive’ response disposes of the threat with precision. The adaptive response takes longer to develop but once it has dealt with a disease, it remembers: the next time the body encounters the same threat, the immune system can respond much faster. When cells are infected by a disease-causing microbe, like a bacterium or a virus, they start carrying fragments of that microbe on their surface. Immune cells known as T cells then recognize these fragments using proteins called T cell receptors. Each T cell has a different receptor, which is specific to a precise fragment of a particular microbe. After successfully clearing an infection, some of the T cells that were mobilized remain in the blood. These memory T cells, and their specific receptors, are a lasting trace of the infections a person has encountered in the past. The exact portion of the microbial fragments that the T cells receptors can ‘see’ depends on another set of proteins, called MHC. These hold the fragments at the surface of the infected cells. The genes that code for MHCs are incredibly diverse, to the point that the exact combination of MHCs carried by a cell can be specific to an individual. However, different MHCs present different microbial fragments, and this changes which receptor can recognize the infection. At the level of a population, this mechanism makes it difficult to use T cell receptors to know exactly which diseases people had to face. Here, DeWitt et al. look at the T cell receptor sequences of 666 healthy participants, as well as their MHC variants, to try to reconstruct their disease history. This revealed that many people have clusters of similar T cells receptors sequences that occur together; these could be linked to exposure to common viruses such as parvovirus, influenza, cytomegalovirus and Epstein-Barr virus. Furthermore, examining 3D structures of T cell receptors binding to fragments carried by MHCs helps to identify how changes in the sequence of the MHC can influence which receptor will be able to attach to the complex. These results show that, despite the diversity and complexity of T cell receptors and MHCs, it is possible to spot patterns across people, and to start understanding how those patterns emerge. In addition to fighting body invaders, T cells can also use their receptors to recognize certain protein fragments carried by tumor cells. Improving our knowledge of T cell receptors and MHCs could give new insights to fight cancer.

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 article, 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.

We applied a combinatorial indexing assay, sci-ATAC-seq, to profile genome-wide chromatin accessibility in ∼100,000 single cells from 13 adult mouse tissues. We identify 85 distinct patterns of chromatin accessibility, most of which can be assigned to cell types, and ∼400,000 differentially accessible elements. We use these data to link regulatory elements to their target genes, to define the transcription factor grammar specifying each cell type, and to discover in vivo correlates of heterogeneity in accessibility within cell types. We develop a technique for mapping single cell gene expression data to single-cell chromatin accessibility data, facilitating the comparison of atlases. By intersecting mouse chromatin accessibility with human genome-wide association summary statistics, we identify cell-type-specific enrichments of the heritability signal for hundreds of complex traits. These data define the in vivo landscape of the regulatory genome for common mammalian cell types at single-cell resolution.

Peptide-specific T cells that are restricted by highly polymorphic major histocompatibility complex (MHC) proteins express diverse T-cell receptors (TCRs) that are rarely shared among unrelated individuals. T-cells can also recognize bacterial lipid antigens that bind the relatively non-polymorphic CD1 family of proteins. However, genetic variation in human CD1 genes and TCR diversity expressed by CD1-restricted T-cells have not been quantitatively determined. Here, we show that CD1B is nearly nucleotide-identical across all five continental ancestry groups, providing evidence for purifying selection during human evolution. We used CD1B tetramers loaded with a mycobacterial glycolipid antigen to isolate T-cells from four genetically unrelated South African adults and cataloged thousands of TCRs from in-vitro expanded T-cells using immunosequencing. We identified highly conserved motifs that were co-expressed as a functional heterodimer and significantly enriched among tetramer-positive T-cells sorted directly from peripheral blood. Finally, we show that frequencies of these TCR motifs are increased in the blood of patients with active tuberculosis compared to uninfected controls, a finding that is confirmed by ex-vivo frequencies of tetramer-positive T-cells determined by flow cytometry. These data provide a framework for unbiased definition of TCRs targeting lipid antigens, which can be tested for clinical associations independently of host genetic background.Brief SummaryWe used human genetics and immunosequencing to define a shared T-cell receptor motif that is specific for a mycobacterial lipid antigen and associated with tuberculosis independently of host genetic background.

B and T cells expand clonally in response to pathogenic infection, and their descendants, which share the same receptor sequence, can persist for years, forming the basis of immunological memory. While most T cell receptor (TCR) sequences are seen very rarely, 'public' TCRs are present in many individuals. Using a combination of high throughput immunosequencing, statistical association of particular TCRs with disease status, and machine learning, we identified of a set of public TCRs that discriminates cytomegalovirus (CMV) infection status with high accuracy. This pathogen-specific diagnostic tool uses a very general assay that relies only on a training cohort coupled with immunosequencing and sophisticated data analysis. Since all memory T cell responses are encoded in the common format of somatic TCR rearrangements, a key advantage of reading T cell memory to predict disease status is that this approach should apply to a wide variety of diseases. The underlying dataset is the largest collection of TCRs ever published, including ~300 gigabases of sequencing data and ~85 million unique TCRs across 640 HLA-typed individuals, which constitutes by far the largest such collection ever published. We expect these data to be a valuable public resource for researchers studying the TCR repertoire.

The vast diversity of B-cell receptors (BCR) and secreted antibodies enables the recognition of, and response to, a wide range of epitopes, but this diversity has also limited our understanding of humoral immunity. We present a public database of more than 37 million unique BCR sequences from three healthy adult donors that is many fold deeper than any existing resource, together with a set of online tools designed to facilitate the visualization and analysis of the annotated data. We estimate the clonal diversity of the naive and memory B-cell repertoires of healthy individuals, and provide a set of examples that illustrate the utility of the database, including several views of the basic properties of immunoglobulin heavy chain sequences, such as rearrangement length, subunit usage, and somatic hypermutation positions and dynamics.

Hemeprotein ligand rebinding studies reveal varying IR absorbance and rebinding functions across a cryogenic ensemble. Since IR-active vibrations and rebinding barriers couple to structural coordinates, spectral and functional heterogeneity arise from conformational heterogeneity. Modeling rebinding data as a spectrally resolved superposition of first-order rate processes and employing maximum entropy regularization, protein heterogeneity is imaged as an ensemble occupancy of a spectrum-function phase space. Results from myoglobin rebinding carbon monoxide are discussed.

The nonlinear properties of an encapsulated microbubble of a contrast agent were studied theoretically and experimentally. A modified nonlinear differential equation (Herring equation) was used to describe the radial oscillation of the microbubble and solved numerically. It was found that the nonlinear resonance frequency, at which the peak radial oscillation amplitude occurs, was a decreasing function of the acoustic amplitude of a driving ultrasonic pulse. Optical images of the contrast agent microbubbles under various ultrasonic exposure conditions: 1. sham exposure; 2. 2-MHz spatial peak acoustic pressure=200 kPa, I SATA = 260 mW/cm 2 , duty cycle=7.5%, repetition period=0.0266 ms; 3. 0.5-MHz spatial peak acoustic pressure=200 kPa, I SATA = 130 mW/cm 2 , duty cycle=7.5%, repetition period=0.1067 ms; have also shown that the lower-frequency ultrasound (US) excitation (0.5 MHz) is more effective in disruption of the microbubbles due to acoustic inertial cavitation than the higher frequency US (2 MHz). (E-mail: jwu@zoo.uvm.edu)

Diversity in B lymphocyte antigen specificity, necessary for effective adaptive immunity, arises from structural diversity of the B cell receptor generated by random somatic rearrangement of the immunoglobin heavy (IgH) and light chain loci during lymphocyte development. Antigen-experienced (memory) B cells are stimulated to proliferate upon successful binding of antigen, whereas the extent to which unexperienced (naïve) cells proliferate, if it all, is unclear. During memory clonal expansion, daughter cells undergo somatic hypermutation, which introduces random single nucleotide variants to the rearranged gene segments, selectively optimizing antigen affinity. Among memory B cells that express identical receptor proteins, the extent to which the cells are identical by descent, or rather have convergently evolved from different naïve clones to the same specificity, is also unknown. We performed high-throughput sequencing of rearranged IgH gene segments for B cells sorted into memory (CD19+, CD27+) and naïve (CD19+, CD27-,IgM+, IgD+) samples (∼5 million cells each) from each of three adults. Each sample was split among 188 libraries for multiplex PCR amplification of IgH sequences at ∼10x coverage, allowing us to estimate clonal abundance by measuring the number of libraries occupied by each sequence. Using a maximum entropy inverse model, all three memory samples evince three distinct subpopulations, corresponding to memory cell types of differing abundance. Data from naïve samples suggest extreme diversity with more than 98% of clones occupying one library (i.e., almost surely present in only one cell in the starting material). A measure-theoretic upper bound on mean clone abundance in the naive repertoire was computed based on Monte Carlo simulation, indicating that naïve B cells typically undergo little, if any, expansion. Additionally, somatic hypermutation in the memory samples was investigated to detect convergent evolution of distinct naïve B cell clones toward common amino acid sequence (hence common antigen specificity) in the memory B cell compartment. Disclosures: DeWitt: Adaptive Biotechnologies: Employment, Equity Ownership. Emerson:Adaptive Biotechnologies: Employment, Equity Ownership. Sherwood:Adaptive Biotechnologies: Employment, Equity Ownership. Williamson:Adaptive Biotechnologies: Employment, Equity Ownership. Rieder:Adaptive Biotechnologies: Employment, Equity Ownership. Chung:Adaptive Biotechnologies: Employment, Equity Ownership. Carlson:Adaptive Biotechnologies: Consultancy, Equity Ownership, Patents & Royalties. Robins:Adaptive Biotechnologies: Consultancy, Equity Ownership, Patents & Royalties.

Ultrasound contrast agents (UCA) have been widely used therapeutic applications in medical research and clinical practice. Recently, UCA have been shown to be effective in vitro in ultrasound mediated sonoporation and gene transfection and treating vascular thrombosis. The possible primary reasons proposed for the therapeutic applications of UCA are acoustic cavitation [W. J. Greenleaf et al., Ultrasound Med. Biol. 24, 587–595 (1998)] and acoustic microstreaming surrounding acoustically driven UCA [J. Wu, Ultrasound Med. Biol. 28, 125–129 (2002)]. Our study has shown that low frequency (0.6 MHz) is more effective than high frequency (2 MHz) in effects related to the acoustic cavitation, and the opposite is true for the microstreaming due to the nonlinear oscillation behavior of encapsulated bubbles of UCA.