Exact and efficient phylodynamic simulation from arbitrarily large populations.

Many biological studies involve inferring the genealogical history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling (BDMS) model, there exists an equivalent pure birth process (i.e., no death) with mutation and complete sampling. The final trees generated under these processes have exactly the same distribution. Leveraging this property, we propose a highly efficient algorithm for simulating trees under a general BDMS model. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.

Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph.

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty quantification.

Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs.

Deep mutational scanning (DMS) is a high-throughput experimental technique that measures the effects of thousands of mutations to a protein. These experiments can be performed on multiple homologs of a protein or on the same protein selected under multiple conditions. It is often of biological interest to identify mutations with shifted effects across homologs or conditions. However, it is challenging to determine if observed shifts arise from biological signal or experimental noise. Here, we describe a method for jointly inferring mutational effects across multiple DMS experiments while also identifying mutations that have shifted in their effects among experiments. A key aspect of our method is to regularize the inferred shifts, so that they are nonzero only when strongly supported by the data. We apply this method to DMS experiments that measure how mutations to spike proteins from SARS-CoV-2 variants (Delta, Omicron BA.1, and Omicron BA.2) affect cell entry. Most mutational effects are conserved between these spike homologs, but a fraction have markedly shifted. We experimentally validate a subset of the mutations inferred to have shifted effects, and confirm differences of > 1,000-fold in the impact of the same mutation on spike-mediated viral infection across spikes from different SARS-CoV-2 variants. Overall, our work establishes a general approach for comparing sets of DMS experiments to identify biologically important shifts in mutational effects.

Increased mutation and gene conversion within human segmental duplications.

Single-nucleotide variants (SNVs) in segmental duplications (SDs) have not been systematically assessed because of the limitations of mapping short-read sequencing data1,2. Here we constructed 1:1 unambiguous alignments spanning high-identity SDs across 102 human haplotypes and compared the pattern of SNVs between unique and duplicated regions3,4. We find that human SNVs are elevated 60% in SDs compared to unique regions and estimate that at least 23% of this increase is due to interlocus gene conversion (IGC) with up to 4.3 megabase pairs of SD sequence converted on average per human haplotype. We develop a genome-wide map of IGC donors and acceptors, including 498 acceptor and 454 donor hotspots affecting the exons of about 800 protein-coding genes. These include 171 genes that have 'relocated' on average 1.61 megabase pairs in a subset of human haplotypes. Using a coalescent framework, we show that SD regions are slightly evolutionarily older when compared to unique sequences, probably owing to IGC. SNVs in SDs, however, show a distinct mutational spectrum: a 27.1% increase in transversions that convert cytosine to guanine or the reverse across all triplet contexts and a 7.6% reduction in the frequency of CpG-associated mutations when compared to unique DNA. We reason that these distinct mutational properties help to maintain an overall higher GC content of SD DNA compared to that of unique DNA, probably driven by GC-biased conversion between paralogous sequences5,6.

A biophysical model of viral escape from polyclonal antibodies.

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.

Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19.

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.

Robustness of Phylogenetic Inference to Model Misspecification Caused by Pairwise Epistasis.

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.

Nonparametric coalescent inference of mutation spectrum history and demography.

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.

ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS.

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.

Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity.

COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of SARS-CoV-2 specific monoclonal antibodies have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of bulk and plasma B-cells collected over multiple time points during infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients. Using principled statistical approaches, we determined differential features of BCRs associated with different disease severity. We identified 38 significantly expanded clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2. Using single-cell sequencing, we verified reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in a number of patients. Our results provide important insights for development of rational therapies and vaccines against COVID-19.

Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity.

COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of SARS-CoV-2 specific monoclonal antibodies have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of bulk and plasma B-cells collected over multiple time points during infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients. Using principled statistical approaches, we determined differential features of BCRs associated with different disease severity. We identified 38 significantly expanded clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2. Using single-cell sequencing, we verified reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in a number of patients. Our results provide important insights for development of rational therapies and vaccines against COVID-19.

Inferring evolutionary dynamics of mutation rates through the lens of mutation spectrum variation.

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.

Deep generative models for T cell receptor protein sequences.

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.

A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility.

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.

Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity.

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.

A Diverse Lipid Antigen-Specific TCR Repertoire Is Clonally Expanded during Active Tuberculosis.

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.

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 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.

Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire.

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 TCRß 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 TCRß 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.

A Public Database of Memory and Naive B-Cell Receptor Sequences.

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.

Dynamics of the cytotoxic T cell response to a model of acute viral infection.

UNLABELLED: A detailed characterization of the dynamics and breadth of the immune response to an acute viral infection, as well as the determinants of recruitment to immunological memory, can greatly contribute to our basic understanding of the mechanics of the human immune system and can ultimately guide the design of effective vaccines. In addition to neutralizing antibodies, T cells have been shown to be critical for the effective resolution of acute viral infections. We report the first in-depth analysis of the dynamics of the CD8(+) T cell repertoire at the level of individual T cell clonal lineages upon vaccination of human volunteers with a single dose of YF-17D. This live attenuated yellow fever virus vaccine yields sterile, long-term immunity and has been previously used as a model to understand the immune response to a controlled acute viral infection. We identified and enumerated unique CD8(+) T cell clones specifically induced by this vaccine through a combined experimental and statistical approach that included high-throughput sequencing of the CDR3 variable region of the T cell receptor ß-chain and an algorithm that detected significantly expanded T cell clones. This allowed us to establish that (i) on average, ∼ 2,000 CD8(+) T cell clones were induced by YF-17D, (ii) 5 to 6% of the responding clones were recruited to long-term memory 3 months postvaccination, (iii) the most highly expanded effector clones were preferentially recruited to the memory compartment, and (iv) a fraction of the YF-17D-induced clones could be identified from peripheral blood lymphocytes solely by measuring clonal expansion. IMPORTANCE: The exhaustive investigation of pathogen-induced effector T cells is essential to accurately quantify the dynamics of the human immune response. The yellow fever vaccine (YFV) has been broadly used as a model to understand how a controlled, self-resolving acute viral infection induces an effective and long-term protective immune response. Here, we extend this previous work by reporting the identity of activated effector T cell clones that expand in response to the YFV 2 weeks postvaccination (as defined by their unique T cell receptor gene sequence) and by tracking clones that enter the memory compartment 3 months postvaccination. This is the first study to use high-throughput sequencing of immune cells to characterize the breadth of the antiviral effector cell response and to determine the contribution of unique virus-induced clones to the long-lived memory T cell repertoire. Thus, this study establishes a benchmark against which future vaccines can be compared to predict their efficacy.