Biology subjects

Walczak, A. M.

Publications and source records attributed to Walczak, A. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Unbiased identification of responding T cell clones from longitudinal repertoire sequencing with CloneSearch

T cells activate and expand upon interaction with cognate antigen, derived from pathogens or mutated proteins. T cell clones can be identified by their T cell receptor (TCR) which can act as a unique barcode to track their expansion. Longitudinal TCR sequencing can be used to track T cell responses to a large array of stimuli. However, experimental identification of T cell clones of interest is challenging, especially when information about the driving antigen is lacking. Computational identification based on clonal dynamics is an antigen-agnostic alternative. However, it is subject to sequencing noise and biological variability, and relies on the choice of particular time points that are compared to find expanding and contracting clones. We present CloneSearch, a method to identify expanding and contracting T cell clones from longitudinal TCR sequencing which is agnostic to the time of stimulus and can account for the noise these clones are subject to. We show that CloneSeach can recapitulate previously identified responses from published data, and expand the analysis to show identification of previously undetected responses from these same datasets. We make CloneSearch available at https://github.com/mm523/CloneSearch.

immunology↗

bioRxiv2025

Coarse-graining reveals collective predictive information in a sensory population

Biological systems perform complex computations using hundreds of individual actors, but they do so efficiently and in a way that can be read out and interpreted by other biological networks. Coarsegraining may allow for key collective features to be effectively and efficiently communicated. In the brain, early sensory systems perform prediction, which can compensate for lags in neural processing. This computation is collective, meaning it relies upon interactions between many neurons, and operates in complex, dynamic natural environments. Taking these two facets of biological complexity together, we search for maximally-predictive collective variables in large groups of retinal ganglion cells responding to dynamic natural visual scenes. To find collective variables that best capture predictive computations in the neurons, we apply a tractable, approximate implementation of the information bottleneck method to neural data. We infer a lower-dimensional representation that is maximally informative about the future neural activity. We observe scaling relationships between this mutual information estimate, neural subset size, and information decay timescale. Further, the structure of collective modes changes for predicting at short versus longer timescales.

neuroscience↗

bioRxiv2025

Paraplume: A fast and accurate paratope prediction method provides insights into repertoire-scale binding dynamics

The specific region of an antibody responsible for binding to an antigen, known as the paratope, is essential for immune recognition. Accurate identification of this small yet critical region can accelerate the development of therapeutic antibodies. Determining paratope locations typically relies on modeling the antibody structure, which is computationally intensive and difficult to scale across large antibody repertoires. We introduce Paraplume, a sequence-based paratope prediction method that leverages embeddings from protein language models (PLMs), without the need for structural input and achieves superior performance across multiple benchmarks compared to current methods. In addition, reweighting PLM embeddings using Paraplume predictions yields more informative sequence representations, improving downstream tasks such as affinity prediction, binder classification, and epitope binning. Applied to large antibody repertoires, Paraplume reveals that antigen-specific somatic hypermutations are associated with larger paratopes, suggesting a potential mechanism for affinity enhancement. Our findings position PLM-based paratope prediction as a powerful, scalable alternative to structure-dependent approaches, opening new avenues for understanding antibody evolution.

bioinformatics↗

bioRxiv2025

Profiling a large HIV-1 elite neutralizer cohort reveals remarkable CD4bs bNAb for HIV-1 prevention and therapy

Administration of HIV-1 neutralizing antibodies can suppress viremia and prevent infection in vivo. However, clinical use is challenged by broad envelope sequence diversity and rapid emergence of viral escape1-9. Here, we performed single B cell profiling of 32 top HIV-1 elite neutralizers to identify broadly neutralizing antibodies (bNAbs) with highest potency and breadth for clinical application. From 831 expressed monoclonal antibodies, we identified 04_A06, a new VH1-2-encoded CD4 binding site bNAb with remarkable breadth and potency against extended multiclade pseudovirus panels (GeoMean IC50 = 0.059 {micro}g/ml, breadth = 98.5%, 332 virus strains). Moreover, 04_A06 was not susceptible to classic viral CD4bs escape variants and maintained full viral suppression in HIV-1-infected humanized mice. Structural analyses revealed that antiviral activity is mediated by an unusually long 11-amino acid heavy chain insertion. This insertion facilitates inter-protomer contacts and interactions with highly conserved residues on the adjacent gp120 protomer. Finally, 04_A06 demonstrated high activity against contemporaneously circulating viruses from the Antibody Mediated Prevention (AMP) trials (GeoMean IC50 = 0.082 {micro}g/ml, breadth = 98.4%, 191 virus strains) and in silico modeling for 04_A06LS predicted HIV-1 prevention efficacy of >93%. Thus, 04_A06 will provide unique opportunities for effective treatment and prevention strategies of HIV-1 infection.

immunology↗

bioRxiv2025

Learning evolutionary parameters from genealogies using allelic trees

Cellular diversification in processes from development to cancer progression and affinity maturation is often linked to the appearance of new mutations, generating genetic heterogeneity. Describing the underlying coupled genetic and growth processes that result in the observed diversity in cell populations is informative about the timing, drivers and outcomes of cell fates. Current approaches based on phylogenetic methods do not cover the entire range of evolutionary rates, often making artificial assumptions about the timing of events. We introduce CBA, a probabilistic method that infers the division, degradation and mutation rates from the observed genetic diversity in a population of cells. It uses a summarized backbone tree, intermediary between the true cell tree and the allelic tree representing the ancestral relationships between types, called a monogram, which allows for efficient sampling of possible phylogenies consistent with the observed mutational signatures. We demonstrate the accuracy of our method on simulated data and compare its performance to standard phylogenetic approaches.

genetics↗

bioRxiv2025

Inferring resource competition in microbial communities from time series

The competition for resources is a defining feature of microbial communities. In many contexts, from soils to host-associated communities, highly diverse microbes are organized into metabolic groups or guilds with similar resource preferences. The resource preferences of individual taxa that give rise to these guilds are critical for understanding fluxes of resources through the community and the structure of diversity in the system. However, inferring the metabolic capabilities of individual taxa, and their competition with other taxa, within a community is challenging and unresolved. Here we address this gap in knowledge by leveraging dynamic measurements of abundances in communities. We show that simple correlations are often misleading in predicting resource competition. We show that spectral methods such as the cross-power spectral density (CPSD) and coherence that account for time-delayed effects are superior metrics for inferring the structure of resource competition in communities. We first demonstrate this fact on synthetic data generated from consumer-resource models with time-dependent resource availability, where taxa are organized into groups or guilds with similar resource preferences. By applying spectral methods to oceanic plankton time-series data, we demonstrate that these methods detect interaction structures among species with similar genomic sequences. Our results indicate that analyzing temporal data across multiple timescales can reveal the underlying structure of resource competition within communities.

ecology↗

bioRxiv2024

Single cells can resolve graded stimuli

Cells use signalling pathways as windows into the environment to gather information, transduce it into their interior, and use it to drive behaviours. MAPK (ERK) is a highly conserved signalling pathway in eukaryotes, directing multiple fundamental cellular behaviours such as proliferation, migration, and differentiation, making it of few central hubs in the signalling circuitry of cells. Despite this versatility of behaviors, population-level measurements have reported low information content (<1 bit) relayed through the ERK pathway, rendering the population barely able to distinguish the presence or absence of stimuli. Here, we contrast the information transmitted by a single cell and a population of cells. Using a combination of optogenetic experiments, data analysis based on information theory framework, and numerical simulations we quantify the amount of information transduced from the receptor to ERK, from responses to singular, brief and sparse input pulses. We show that single cells are indeed able to resolve between graded stimuli, yielding over 2 bit of information, however showing a large population heterogeneity.

biophysics↗

bioRxiv2024

Optimizing information transmission in neural induction constrains cell surface contacts of ascidian embryos

The onset of neural induction in the anterior ectoderm of ascidian embryos is regulated at the extracellular level by FGF signaling molecules, which control the acquisition of neural fate through the activation of the ERK pathway. Among the anterior ectoderm cells exposed to FGF, only a fraction will acquire neural fate. The selection of neural precursors depends on the quasi-invariant geometry of the embryo, which imposes upon each ectoderm cell a precise area of cell surface contact with underlying FGF-expressing (mesendoderm) cells. Here, we investigate information transmission between FGF and activated ERK and how this depends on the geometry of the system. Optimizing information transmission with the constraint that the total FGF-emitting surface area is restricted, as in the embryo, we find that the surface contacts with FGF that maximize information transmission are close to those observed experimentally. This information optimal solution is compatible with the anterior ectoderm cells having different areas of cell surface exposure to FGF, allowing the embryo to use cell surface areas as a regulatory mechanism for differentiating the outcome of cells that sense a constant FGF concentration.

biophysics↗

bioRxiv2023

Computational detection of antigen specific B cell receptors following immunization

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and Sars-CoV-2 infections. We show that different lineages converge to the same responding CDR3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

immunology↗

bioRxiv2023

Modelling collective behavior in groups of mice housed under semi-naturalistic conditions

In social behavior research, the focus often remains on animal dyads, limiting the understanding of complex interactions. Recent trends favor naturalistic setups, offering unique insights into intricate social behaviors. Social behavior stems from chance, individual preferences, and group dynamics, necessitating high-resolution quantitative measurements and statistical modeling. This study leverages the Eco-HAB system, an automated experimental setup which employs RFID tracking to observe naturally formed mouse cohorts in a controlled yet naturalistic setting, and uses statistical inference models to decipher rules governing the collective dynamics of groups of 10-15 individuals. Applying maximum entropy models on the coarse-grained co-localization patterns of mice unveils social rules in mouse hordes, quantifying sociability through pairwise interactions within groups, the impact of individual versus social preferences, and the effects of considering interaction structures among three animals instead of two. Reproducing co-localization patterns of individual mice reveals stability over time, with the statistics of the inferred interaction strength capturing social structure. By separating interactions from individual preferences, the study demonstrates that altering neuronal plasticity in the prelimbic cortex - the brain structure crucial for sociability - does not eliminate signatures of social interactions, but makes the transmission of social information between mice more challenging. The study demonstrates how the joint probability distribution of the mice positions can be used to quantify sociability.

animal behavior and cognition↗

bioRxiv2023

TULIP - a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes

The accurate prediction of binding between T-cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a novel method, TULIP, that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

bioinformatics↗

bioRxiv2022

Combining mutation and recombination statistics to infer clonal families in antibody repertoires

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from high-throughput sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures scanning two orders of magnitude.

immunology↗

bioRxiv2022

Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino acid composition that, one the one hand, underlie the antigens probability of triggering a response, and on the other hand the T-cell receptors ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity. More broadly, diffRBM provides a general framework to detect, interpret and leverage selected features in biological data.

bioinformatics↗

bioRxiv2022

Inspecting the interaction between HIV and the immune system through genetic turnover

Chronic infections of the human immunodeficiency virus (HIV) create a very complex co-evolutionary process, where the virus tries to escape the continuously adapting host immune system. Quantitative details of this process are largely unknown and could help in disease treatment and vaccine development. Here we study a longitudinal dataset of ten HIV-infected people, where both the B-cell receptors and the virus are deeply sequenced. We focus on simple measures of turnover, which quantify how much the composition of the viral strains and the immune repertoire change between time points. At the single-patient level, the viral-host turnover rates do not show any statistically significant correlation, however they correlate if the information is aggregated across patients. In particular, we identify an anti-correlation: large changes in the viral pool composition come with small changes in the B-cell receptor repertoire. This result seems to contradict the naive expectation that when the virus mutates quickly, the immune repertoire needs to change to keep up. However, we show that the observed anti-correlation naturally emerges and can be understood in terms of simple population-genetics models.

evolutionary biology↗

bioRxiv2022

Granger causality analysis for calcium transients in neuronal networks: challenges and improvements

One challenge in neuroscience is to understand how information flows between neurons in vivo to trigger specific behaviors. Granger causality (GC) has been proposed as a simple and effective measure for identifying dynamical interactions. At single-cell resolution however, GC analysis is rarely used compared to directionless correlation analysis. Here, we study the applicability of GC analysis for calcium imaging data in diverse contexts. We first show that despite underlying linearity assumptions, GC analysis successfully retrieves non-linear interactions in a synthetic network simulating intracellular calcium fluctuations of spiking neurons. We highlight the potential pitfalls of applying GC analysis on real in vivo calcium signals, and offer solutions regarding the choice of GC analysis parameters. We took advantage of calcium imaging datasets from motoneurons in embryonic zebrafish to show how the improved GC can retrieve true underlying information flow. Applied to the network of brainstem neurons of larval zebrafish, our pipeline reveals strong driver neurons in the locus of the mesencephalic locomotor region (MLR), driving target neurons matching expectations from anatomical and physiological studies. Altogether, this practical toolbox can be applied on in vivo population calcium signals to increase the selectivity of GC to infer flow of information across neurons.

neuroscience↗

bioRxiv2022

Inferring the T-cells repertoire dynamics of healthy individuals

The adaptive immune system is a diverse ecosystem that responds to pathogens by selecting cells with specific receptors. While clonal expansion in response to particular immune challenges has been extensively studied, we do not know the neutral dynamics that drive the immune system in absence of strong stimuli. Here we learn the parameters that underlie the clonal dynamics of the T-cell repertoire in healthy individuals of different ages, by applying Bayesian inference to longitudinal immune repertoire sequencing (RepSeq) data. Quantifying the experimental noise accurately for a given RepSeq technique allows us to disentangle real changes in clonal frequencies from noise. We find that the data are consistent with clone sizes following a geometric Brownian motion, and show that its predicted steady state is in quantitative agreement with the observed power-law behaviour of the clone-size distribution. The inferred turnover time scale of the repertoire increases substantially with patient age, and depends on the clone size in some individuals.

immunology↗

bioRxiv2022

Population based selection shapes the T cell receptor repertoire during thymic development

One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to charactarize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4+ and CD8+ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results are consistent with a collective or statistical model for T-cell specificity, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level.

immunology↗

bioRxiv2021

Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunitys success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population wide vaccines and therapeutics. Yet many of these public receptors are shared by chance. We present a statistical approach, defined in terms of a probabilistic V(D)J recombination model enhanced by a selection factor, that describes repertoire diversity and predicts with high accuracy the spectrum of repertoire overlap in healthy individuals. The model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnosis tool for predicting SARS-CoV-2 status from repertoire data.

immunology↗