Biology subjects

Murrell, B.

Publications and source records attributed to Murrell, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Inferring differentiation trajectories from T cell clonotype distributions

Single-cell RNA sequencing (scRNA-seq) enables reconstruction of cellular differentiation trajectories, but most trajectory inference methods rely largely on gene expression and overlook lineage relationships. In T lymphocytes, the T cell receptor (TCR) provides a unique, endogenous barcode that is stably inherited during clonal expansion. Here we introduce PhyloTrajectory, a framework that leverages TCR-based clonotype frequencies together with scRNA-seq to infer T cell differentiation dynamics. By modeling clonotype frequency evolution as a continuous stochastic process, PhyloTrajectory reconstructs differentiation tree topologies from clonotype frequencies across cellular subsets. We investigate the ability to recover the underlying tree topology using simulated data, and apply our approach to CD4+ T cells in murine models of allergic inflammation and viral infection. We further demonstrate that PhyloTrajectory is useful in the context of exogenously introduced barcodes. Overall, this shows that a stochastic model of clonal expansion can be used to infer cell state transitions from clonotype frequencies.

bioinformatics↗

bioRxiv2026

Immune repertoire profiling reveals extensive clonal expansion of T and B cells in CXCL9-rich niches in sinonasal cancer

Sinonasal cancer (SNC) is a rare and aggressive head and neck cancer with limited therapeutic options and an incompletely defined immune microenvironment. Although histopathology remains central to clinical tumor evaluation, immune profiling typically requires tissue dissociation and therefore loses the spatial relationships that shape antitumor immunity. Here, we combined single-cell RNA-sequencing approaches, spatial transcriptomics and Spatial V(D)J, a technique we recently pioneered, to analyse the cellular, spatial, and clonal architecture of SNC. We found that SNC tumors were marked by a robust T cell infiltration, including substantial proportions of classical tissue resident memory (TRM) and exhausted (TExh) CD8+ T cells. This inflamed phenotype was accompanied by an immense infiltration of suppressive regulatory T (Treg) cells, which differentiated towards a Tbet+CXCR3+ phenotype. Spatially, expanded Treg, CD8 T and B cell clones concentrated within specialized peritumoral immune niches enriched for cancer-associated fibroblasts, CXCL9 tumor-associated macrophages and LAMP3IDO1 dendritic cells. T cell clones occupying these niches were clonally related to those infiltrating epithelial tumor regions, linking lymphoid hubs to the broader tumor immune response. Together, these data identify SNC as an inflamed but highly immunoregulatory tumor type and reveal a spatially organized clonal architecture in which suppressive and cytotoxic lymphocyte states coexist within CXCL9 myeloid niches.

immunology↗

bioRxiv2026

Characterizing Physicochemical Selection in Protein Evolution with Property-Informed Models (PRIME)

AbstractStandard probabilistic models of coding sequence evolution effectively identify where and when selection acts but remain agnostic to the mechanistic realization of these forces. We introduce PRIME (PRoperty Informed Models of Evolution), a framework of codon-level maximum likelihood methods--including global (G-PRIME), episodic (E-PRIME), and site-specific (S-PRIME) implementations--that explicitly model amino acid exchange-ability as a function of physicochemical properties. By parameterizing attributes such as molecular volume, hydropathy, and secondary structure propensities, PRIME resolves the biophysical basis of selective constraint across both the sequence and the phylogeny. At the site level, S-PRIME leverages an explicit biophysical taxon-omy to precisely categorize residues as conserved, neutral, or changing for specific properties, resolving selective signals that remain invisible to traditional rate-based metrics. Our analysis of a benchmark of 24 diverse datasets and a genome-wide screen of 18,944 mammalian genes demonstrates that biophysical realism yields substantial improvements in model fit, acting synergistically with rate variation to explain complex evolutionary patterns. We find that power to detect physicochemical constraints at individual sites is fundamentally governed by simple informational redundancy (substitutions per unique amino acid; AUC = 0.91), with sensitivity exceeding 90% in data-rich alignments. E-PRIME reveals a distinct biophysical hierarchy: while core packing and beta-sheet scaffolds are rigidly conserved, alpha-helix propensity and surface electrostatics serve as the primary substrates for adaptive tuning. Furthermore, PRIME importance weights align with aspects of the primary semantic axes of deep learning representations (ESM-2) and capture key features of experimental fitness landscapes. By trans-forming abstract evolutionary rates into interpretable biophysical rules, PRIME provides a useful framework for characterizing the mechanistic drivers of protein diversity.

evolutionary biology↗

bioRxiv2025

Spontaneous Emergence of Symmetry in a Generative Model of Protein Structure

Generative models are becoming powerful tools for protein design, enabling the creation of novel protein structures and sequences. Recent approaches have shown success using diffusion models and flow matching to sample realistic protein folds. Many methods explicitly incorporate structural biases or constraints--for example, enforcing symmetry during generation--to steer the design process. Here we report the spontaneous emergence of structural symmetry in a transformer-based generative model for proteins, without any symmetry-specific conditioning during training or constraints during generation. In our flow-matching model, a single attention head in an SE(3)-equivariant transformer layer was found to be primarily responsible for the models ability to generate symmetric arrangements of backbone residues across chains, or repeating motifs within a chain. Our results show that protein generative models can learn high-level structural patterns implicitly from training data. This opens new questions about interpretability and control in generative design: understanding how and why a single attention head can govern a complex global property like symmetry may inform future model architectures and help exploit emergent behaviors for better protein engineering.

bioinformatics↗

bioRxiv2025

VRC01 Selects Rare HIV Escape Mutations After Acquisition in Antibody-Mediated Prevention Trials

Broadly neutralizing antibodies (bnAbs) show promise in HIV prevention, yet viral escape remains a challenge. In the Antibody Mediated Prevention (AMP) trials, the CD4 binding site (CD4bs) bNAb VRC01 blocked acquisition by VRC01-sensitive strains. However, its influence on viral evolution post-acquisition is not fully understood. Here we analyzed >12,000 HIV env sequences from 47 participants from the AMP trials, identifying VRC01-mediated de novo escape mutations in 8 of 26 VRC01-treated participants but none in 21 placebo participants. These mutations were found at very low frequency (<1%) in global viruses. Escape mutations, primarily located in the Loop-D and {beta}23/V5 regions of Env, conferred cross-resistance to several CD4bs bnAbs, while more potent CD4bs bnAbs like N6 and 1-18 largely retained their activity. Our findings demonstrate that prophylactic VRC01 can select for viral escape after infection, underscoring the need for next-generation bnAbs with improved breadth and potency to enhance durability and efficacy of antibody-based HIV prevention.

microbiology↗

bioRxiv2025

Long-Read Deep Sequencing Reveals High Rates of Multilineage Transmission and Rapid Viral Population Changes in Acute HIV Infection

Understanding the selective forces acting upon HIV early in infection is crucial to design prevention strategies. By leveraging deep sequencing and the short diagnostic intervals of the FRESH and RV217 cohorts (median 4 days) between the last-negative and first-positive RNA tests, we captured a precise and early snapshot of acute HIV infection. The frequency of multiple transmitted viruses of 38% in these as well as placebo recipients from the AMP trials was higher than previously published, with the true frequency likely to be higher. The relative abundance of lineages fluctuated substantially over time in two-thirds of the multilineage infections, generating uncertainty in identifying the specific viruses that were transmitted and founding the infection. Viral populations exhibited diversity and selection on the Gag and Env proteins at the earliest times examined, with sites inferred to be undergoing negative selection most evident. These data may help explain vaccination failures and provide new targets for prevention.

microbiology↗

bioRxiv2025

Influence of the broadly neutralizing antibody VRC01 on HIV breakthrough virus populations in antibody-mediated prevention trials

In the HIV antibody mediated prevention (AMP) trials, the broadly neutralizing antibody VRC01 demonstrated protective efficacy against new diagnoses with susceptible HIV strains. To understand how VRC01 shaped breakthrough infections, we performed deep sequencing on 172 participants in the placebo and treatment arms, generating 63,444 gag-{Delta}pol (2.5 kb) and 53,088 rev-env-{Delta}nef (3 kb) sequences. Sequences were classified into transmitted founder lineages (TFLs), and infections with multiple distinct lineages were determined. Multilineage infections were detected in [~]38% of participants in both the African (HVTN 703/HPTN 081) and Americas/Europe (HVTN 704/HPTN 085) cohorts, regardless of placebo or treatment group, or cohort. The high levels of multilineage infections could be attributed to minor lineages (<5% abundance) identified in 20% of participants. Infection with VRC01 discordant viruses (IC80s >3-fold different) was observed in 40% of multilineage infections, with a trend toward greater intra-host neutralization differences with increasing VRC01 dose (Jonckheere-Terpstra test, p=0.072). In six VRC01 treated participants who acquired both sensitive (IC80<1{micro}g/ml) and resistant viruses (IC80>3{micro}g/ml), the sensitive lineages declined over time. Recombination was pervasive, observed in 63% of multilineage infections at the time of HIV diagnosis. In one treated participant infected with VRC01 discordant lineages, recombinant viruses preferentially inherited the resistance mutation (binomial p=0.004). In conclusion, our in-depth analysis of breakthrough viruses in the AMP trials revealed a high frequency of multilineage infections, including infections with viruses with different VRC01 sensitivities. This analysis also highlights the role of recombination in shaping intra-host viral evolution and facilitating escape from VRC01. SignificanceThis work advances our understanding of the diversity of initial viral infection and evolution and in vivo activity of broadly neutralizing antibodies (bNAbs). Deep sequencing revealed [~]38% of HIV acquisitions with multiple transmitted founder lineages, higher than previous studies. These occurrences were similar in the placebo and VRC01 groups. Viral recombination among post-acquisition variants was common under antibody selection and appeared to favor resistant sequences in the treatment group. These data suggest as with single antiviral therapy, passive and active immunization of bNAbs should be directed at multiple antigen targets of HIV-1.

molecular biology↗

bioRxiv2025

difFUBAR: Scalable Bayesian comparison of adaptive evolution

While many phylogenetic methods exist to characterize evolutionary pressure at individual codon sites, relatively few allow direct comparison between different a priori selected sets of branches. Such comparisons may be useful for pinpointing precisely the codon sites that are under differing selective pressures due to differing environmental contexts, or differing genomic contexts via epistatic interactions. Indeed, this was only recently addressed by an approach, developed in the frequentist framework, that proposes a site-wise likelihood ratio hypothesis test. Previously, we have demonstrated that approximate grid-based Bayesian approaches to characterizing site-wise variation in selection parameters can outperform individual site-wise likelihood ratio tests. Such grid-based approaches can exhibit poor computational scaling when the number of site-wise parameters expands, but here we show that this is still tractable up to four parameters, and that a simple subtree-likelihood caching strategy can provide efficiency improvements in some cases. We propose difFUBAR, which allows the demarcation of two branch sets of interest and, optionally, a background set, and estimates joint site-specific posterior distributions over , {omega}1,{omega} 2, and{omega} BG using a Gibbs sampler. Evidence for hypotheses of interest can then be quantified directly from the posterior distribution, and we standardly report P ({omega}1 > {omega}2), P ({omega}2 > {omega}1), P ({omega}1 > 1), and P ({omega}2 > 1). We characterize the computational and statistical performance of this approach on previous simulations, comparing it to the site-wise likelihood ratio test approach, where it shows moderate statistical benefits, and substantial computational gains, typically being more than two orders of magnitude faster on the same datasets. We also demonstrate that it can scale to datasets of over ten thousand taxa, on a laptop in under ten minutes. difFUBAR is implemented in MolecularEvolution.jl - a Julia framework for phylogenetic model development - and can be run locally, or online via a Colab notebook.

bioinformatics↗

bioRxiv2025

Detection of PCR chimeras in adaptive immune receptor repertoire sequencing using hidden Markov models

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) has emerged as a central approach for studying T cell and B cell receptor populations, and is now an important component of studies of autoimmunity, immune responses to pathogens, vaccines, allergens, and cancers, and for antibody discovery. When amplifying the rearranged V(D)J genes encoding antigen receptors, each cycle of the Polymerase Chain Reaction (PCR) can produce spurious "chimeric" hybrids of two or more different template sequences. While the generation of chimeras is well understood in bacterial and viral sequencing, and there are dedicated tools to detect such sequences in bacterial and viral datasets, this is not the case for AIRR-seq. Further, the process that results in immune receptor sequences has domain-specific challenges, such as somatic hypermutation (SHM), and domain-specific opportunities, such as relatively well-known germline gene "reference" sequences. Here we describe CHMMAIRRa, a hidden Markov model for detecting chimeric sequences in AIRR-seq data, that specifically models SHM and incorporates germline reference sequences. We use simulations to characterize the performance of CHMMAIRRa and compare it to existing methods from other domains, we test the effect of PCR conditions on chimerism using IgM libraries generated in this study, and we apply CHMMAIRRa to four published AIRR-seq datasets to show the extent and impact of artifactual chimerism.

bioinformatics↗

bioRxiv2024

The Continuous Language of Protein Structure

Just as language is composed of sublexical tokens that combine to form words, sentences, and paragraphs, protein backbones are composed of sub-structural elements that combine to form helices, sheets, folds, domains, and chains. Autoregressive language models operate on discrete tokens, whereas protein structure is inherently continuous, and generative approaches to protein design have borrowed more from image generation than language modeling. But autoregressive models do not inherently require their inputs and outputs to be discrete. Here we describe a generative autoregressive language model over the continuous space of protein backbones, where the distribution over the placement of each successive amino acid is conditioned on all preceding residues, and can be sampled from one residue after another. We show that this approach can learn to sample diverse and realistic protein chains, opening a new potential avenue for in silico protein design.

bioinformatics↗

bioRxiv2024

Antigen affinity and site of immunization dictate B cell recall responses

Protective antibodies against HIV-1 require unusually high levels of somatic hypermutations (SHMs) introduced in germinal centers (GCs). To achieve this, a sequential vaccination approach was proposed. Using HIV-1 antibody knock-in mice with fate-mapping genes, we examined if antigen affinity affects the outcome of the B cell recall response. Compared to high affinity boost, low affinity boost resulted in decreased numbers of memory-derived B cells in secondary GCs, but with higher average SHM, indicating an affinity threshold for memory B cells to enter secondary GCs. Upon boosting local LNs, numbers of residual GC B cells increased independent on antigen affinity, while average SHM decreased. Our results demonstrate that antigen affinity and location of the boost affect the outcome of the B cell recall response. These results can help guide the design of vaccine immunogens aiming to selectively engage specific B cell clones for further SHM diversification.

immunology↗

bioRxiv2024

Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2

The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike proteins receptor binding domain (RBD), crucial for host cell invasion, while the virus evolves to evade antibody recognition. Here, we establish a synthetic coevolution system combining high-throughput screening of antibody and RBD variant libraries with protein mutagenesis, surface display, and deep sequencing. Additionally, we train a protein language machine learning model that predicts antibody escape to RBD variants. Synthetic coevolution reveals antagonistic and compensatory mutational trajectories of neutralizing antibodies and SARS-CoV-2 variants, enhancing the understanding of this evolutionary conflict.

synthetic biology↗

bioRxiv2024

Neutralisation sensitivity of the SARS-CoV-2 BA.2.87.1 variant

Against the backdrop of the rapid global takeover and dominance of BA.1/BA.2 and subsequently BA.2.86 lineages, the emergence of a highly divergent SARS-CoV-2 variant warrants characterization and close monitoring. Recently, another such BA.2 descendent, designated BA.2.87.1, was detected in South Africa. Here, we show using spike-pseudotyped viruses that BA.2.87.1 is less resistant to neutralisation by prevailing antibody responses in Sweden than other currently circulating variants such as JN.1. Further we show that a monovalent XBB.1.5-adapted booster enhanced neutralising antibody titers to BA.2.87.1 by almost 4-fold. While BA.2.87.1 may not outcompete other currently-circulating lineages, the repeated emergence and transmission of highly diverged variants suggests that another large antigenic shift, similar to the replacement by Omicron, may be likely in the future.

immunology↗

bioRxiv2023

Full-spike deep mutational scanning helps predict the evolutionary success of SARS-CoV-2 clades

SARS-CoV-2 variants acquire mutations in spike that promote immune evasion and impact other properties that contribute to viral fitness such as ACE2 receptor binding and cell entry. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning to measure how >9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry, or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully impacted ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456, and 473--however, the antigenic impacts of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.

microbiology↗

bioRxiv2023

Sensitivity of BA.2.86 to prevailing neutralising antibody responses

A new SARS-CoV-2 variant, designated BA.2.86, has recently emerged with over 30 spike mutations relative to its parental BA.2, raising questions about its degree of resistance to neutralising antibodies. Using a spike-pseudotyped virus model we characterise neutralisation of BA.2.86 by clinically relevant monoclonal antibodies and by two cohorts of serum sampled from Stockholm, including both a recent cohort, and one sampled prior to the arrival of XBB in Sweden.

immunology↗

bioRxiv2023

Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations - application to HIV-1 quasispecies

Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies. Author SummaryThere is a great need to understand the genetic diversity of pathogens in an accurate and timely manner, but many errors can be introduced during the sample handling and sequencing steps which may prevent accurate analyses. In some cases, the errors introduced during these steps can be indistinguishable from real genetic variation and prevent analyses from identifying true sequence variation present in the pathogen population. There are established methods which can help to prevent these types of errors, but can involve many different steps and variables, all of which must be optimized and tested together to ensure the desired effect. Here we show results from testing different methods on a set of HIV+ blood plasma samples and arrive at a streamlined laboratory protocol and bioinformatic pipeline which prevents or corrects for different types of errors that can arise in sequence datasets. These methods should be an accessible starting point for anyone wanting accurate sequencing without extensive optimizations.

molecular biology↗

bioRxiv2022

MIGH-T: A Multi-Parametric and High-Throughput Platform for Host-Virus Binding Screens

Speed is key during infectious disease outbreaks. It is essential, for example, to identify critical host binding factors to the pathogens as fast as possible. The complexity of host plasma membrane is often a limiting factor hindering fast and accurate determination of host binding factors as well as high-throughput screening for neutralizing antimicrobial drug targets. Here we describe a multi-parametric and high-throughput platform tackling this bottleneck and enabling fast screens for host binding factors as well as new antiviral drug targets. The sensitivity and robustness of our platform was validated by blocking SARS-CoV-2 spike particles with nanobodies and IgGs from human serum samples. TeaserA fast screening platform tackling host-pathogen interactions.

synthetic biology↗

bioRxiv2022

Omicron sublineage BA.2.75.2 exhibits extensive escape from neutralising antibodies

Several sublineages of omicron have emerged with additional mutations that may afford further antibody evasion. Here, we characterise the sensitivity of emerging omicron sublineages BA.2.75.2, BA.4.6, and BA.2.10.4 to antibody-mediated neutralisation, and identify extensive escape by BA.2.75.2. BA.2.75.2 was resistant to neutralisation by Evusheld (tixagevimab + cilgavimab), but remained sensitive to bebtelovimab. In recent serum samples from blood donors in Stockholm, Sweden, BA.2.75.2 was neutralised, on average, at titers approximately 6.5-times lower than BA.5, making BA.2.75.2 the most neutralisation resistant variant evaluated to date. These data raise concerns that BA.2.75.2 may effectively evade humoral immunity in the population.

immunology↗