Biology subjects

Rogers, D.

Publications and source records attributed to Rogers, D..

At least 19 recordsLinked to original sources

bioRxiv2026

The Human Gut Cell Atlas v1.0: A Unified, Community-BuiltReference of Cellular Diversity Across the Gut

Single-cell RNA-sequencing (scRNA-seq) studies have transformed our understanding of cellular diversity in the human intestine, but differences in anatomical sampling, experimental processing and cell-type nomenclature limit comparison across datasets. Here, we present the Human Gut Cell Atlas (HGCA) V1, a community-curated reference of 944,390 healthy and disease-adjacent intestinal cells from 502 samples, 265 donors and 24 landmark studies, linked to detailed anatomical, clinical, and technical metadata. As this metadata is of critical importance to human biology, we developed MetaManager, an open source data management framework for standardized metadata collection and tracking. A five-level consensus taxonomy resolves 94 homeostatic cell identities and enables transferrable, high-resolution annotation of new gut datasets. By assembling cell identity, sampling metadata, and independent spatial validation, intestinal segment and radial tissue depth were revealed to systematically shape cellular composition and transcriptional state, enabling inference of the presence of sampling-dependent tissue niches, including lymphoid follicles. Reference mapping accurately and rapidly recovered cohort-level Crohn's disease and treatment signatures without de novo annotation, while mapping human intestinal organoids quantified cell-type-specific identity, regional specification, maturation, and transcriptional fidelity to native tissue. The HGCA V1 provides a standardized cellular, anatomical coordinate, and metadata system for reproducible, high-quality analysis of intestinal biology across healthy tissue, disease-associated remodelling, and experimental models, providing a continuously evolving community reference for cumulative discovery in human intestinal biology.

systems biology↗

bioRxiv2026

Evaluating Brain Flow Index from a temple-worn wearable against depth-resolved time-domain NIRS during head-down tilt and postural transitions in healthy young men

Significance: Wearable optical sensors could make continuous cerebral hemodynamic monitoring practical in daily life. Because any surface head sensor also samples extracerebral tissue, demonstrating cerebral relevance requires a reference that separates deep from superficial hemodynamics. Aim: We evaluated whether the Temple Brain Flow Index (Temple-BF), derived from a temple-worn photoplethysmographic wearable, covaried more strongly with the deeper (brain-assigned) than the superficial (scalp-assigned) hemodynamics recovered from time-domain near-infrared spectroscopy (TD-NIRS) during controlled postural challenges. Approach: Sixty sessions from 44 healthy young adult men were analyzed across three physiological challenges: 30{degrees} head-down tilt, stand-to-squat, and stand-to-supine transitions. Optical density and mean time of flight from a three-module TD-NIRS system were inverted with a Monte Carlo-derived two-layer model to obtain scalp- and brain-layer hemoglobin time series. Temple-BF was compared with these model-derived signals using lag-adjusted and zero-lag correlations, paired brain-versus-scalp comparisons, heart rate and short-separation-HbO adjusted partial correlations. Results: Median lag-adjusted correlations between Temple-BF and brain-layer {Delta}HbO traces were 0.912, 0.843, and 0.839 for head-down tilt, stand-to-squat, and stand-to-supine transitions, respectively; corresponding scalp-layer medians were 0.494, 0.700, and 0.767. Paired brain-minus-scalp differences were significant for head-down tilt (median 0.333, p < 0.001) and stand-to-squat (0.110, p < 0.001) but not for stand-to-supine (0.036, p = 0.082) transitions. Zero-lag brain-layer medians were 0.826, 0.701, and 0.796, and the association persisted after adjustment for heart rate (partial r = 0.832, 0.850, 0.654) and for short-separation superficial HbO (0.649, 0.570, 0.438). Transition-window correlations had medians of 0.878, 0.861, and 0.876, with directionally concordant transition responses in 59 of 60 sessions. Conclusions: Temple-BF tracked model-derived brain-layer TD-NIRS {Delta}HbO across all three postural challenges, with larger median correlations against the brain-layer than the scalp compartment in all three protocols, and transition responses in the same direction for almost all sessions. These results support Temple-BF as a relative marker of cerebral hemodynamic change during postural perturbations. Establishing cerebral specificity at the temple, and excluding residual systemic and superficial contributions, will require flow-sensitive references and fuller systemic monitoring.

neuroscience↗

bioRxiv2026

Parallel evolution under constraint shapes echinocandin resistance in Candida auris

Drug resistance emerges repeatedly in outbreaks of Candida fungal pathogens, but little is known about its origins or persistence. Here, we investigated the evolutionary processes shaping echinocandin resistance in Candida auris, a globally emerging and predominantly clonal fungal pathogen. Genome-wide association across over 600 isolates identified mutations in the {beta}-1,3-glucan synthase gene FKS1 as the most significant driver of resistance to an echinocandin drug. Ancestral reconstruction of this population traced shared resistance mutations among small groups typically consisting of 2-3 closely related isolates, but clusters could include up to 16 isolates. Nearly all resistant clusters consisted of isolates collected in the same year and region, consistent with local transmission. To further examine population-level selection, we measured adaptive signatures in FKS1 and the highly diverged paralog FKS2 across 22,000 genomes. This revealed excess nonsynonymous polymorphisms in FKS1, primarily due to independent, recurrent mutations at resistance hotspots, consistent with parallel evolution and incomplete fixation of adaptive alleles. In FKS2, there is no evidence of hotspots and little support for diversifying selection. Together, these results indicate that resistance mutations emerge under strong genetic constraint, with adaptation restricted to only one FKS homolog and predominantly at mutational hotspots.

genetics↗

bioRxiv2026

BET BD2 inhibition facilitates SPOP-mediated degradation of chromatin-associated BRD4/BRD4-NUT, a therapeutic vulnerability in NUT carcinoma

BET bromodomain inhibitors block binding of BET family bromodomains 1 and 2 (BD1, BD2) to chromatin and have demonstrated clinical activity in NUT carcinoma (NC), a BRD-NUT fusion-driven cancer, but toxicity from BD1 inhibition has limited their effectiveness. We investigated whether selective inhibition of BRD4 bromodomain 2 (BD2) could retain antitumor activity while reducing toxicity. NC cells were uniquely sensitive to the novel BRD4-BD2 inhibitor DC-9476 and other BD2-selective inhibitors, which induced differentiation and growth arrest. A CRISPR knockout screen identified the BRD4-targeting E3 ligase SPOP as the top resistance hit. BD2 inhibition, but not BD1-selective or pan-BET inhibition, triggered SPOP-dependent proteasomal degradation of BRD4 and BRD4-NUT; SPOP loss prevented degradation and largely rescued BD2 inhibitor-induced differentiation and growth arrest. Unexpectedly, BRD4 and BRD4-NUT remained chromatin-associated during BD2 inhibition, whereas BD1 or pan-BET inhibition displaced them. Together with evidence that ectopic BRD4-NUT expression sensitizes BRD4 to degradation, these findings support a model in which BRD4-NUT megadomains create a high-density, degradation-competent SPOP substrate pool of BRD4 and BRD4-NUT upon BD2 inhibition, whereas pan-BET inhibition disperses this substrate and limits efficient degradation. In preclinical NC models, BD2-selective inhibition achieved greater tumor growth inhibition and survival benefit than pan-BET inhibition, revealing a therapeutic vulnerability.

cancer biology↗

bioRxiv2026

Physics-aware measurement-supervised deep learning enables point spread function inversion in soft X-ray tomography

Soft X-ray tomography (SXT) is an emerging modality for whole-cell 3D imaging in near-native states. However, the effective spatial resolution is limited by optical artifacts characterized by the point spread function (PSF). Standard reconstruction methods force a compromise between structural sharpness and noise, failing to fully resolve these depth-dependent artifacts. By embedding experimentally measured, depth-variant PSFs into a differentiable forward model, we demonstrate a physics-aware computational optimization that bypasses these limitations to recover high-frequency cellular ultrastructure. The structural fidelity was validated using split-tilt Fourier ring correlation (FRC), alongside an experimental bead phantom tomogram, providing supporting evidence that the recovered high-frequency features reflect genuine specimen structure rather than fabricated artifacts. Our method effectively increases FRC spatial resolution and recovers cellular ultrastructure. Furthermore, under sparse-angular subsampling, the framework maintained spatial resolution using half the projection angles, a computational proxy pointing toward the potential for reduced radiation exposure in future acquisitions. This hardware-free, computational approach offers a route toward mitigating the optical and dosimetric constraints that currently limit nanoscale soft X-ray tomography.

Cell Biology↗

bioRxiv2026

Decoding Spatial Attention in the Cocktail Party Problem Using Wearable Whole-head High-Density fNIRS

Spatial attention is critical for solving the cocktail party problem, a longstanding problem in neuroscience and artificial speech recognition. The ability to decode where humans are attending in a cocktail party like scene would empower applications in brain computer interfaces and assistive devices such as hearing aids. Here we demonstrate that, in an overt attention task, the attended spatial location can be decoded robustly from single trial hemodynamic responses, using a wearable whole head high density fNIRS system. We also identify critical brain regions that make the highest contribution to decoding accuracy. Specifically, we find that decoding based on a small fraction of channels within the left and right inferior parietal lobule (IPL), achieve maximal decoding accuracy comparable to all channels. These results open the way for the design of novel BCIs and assistive devices integrated with fNIRS, that can be steered by spatial attention.

neuroscience↗

bioRxiv2025

Standardizing RNA-seq Analysis of Fungal Pathogens Using BRC-Analytics and Agentic AI: A Candidozyma auris Case Study

Candidozyma auris has emerged as a critical global health threat due to multidrug resistance and healthcare-associated transmission. While RNA-seq has become the primary tool for studying C. auris pathogenesis, inconsistent use of reference genomes and bioinformatics tools complicate cross-study comparisons. Here we demonstrate how BRC-Analytics, a platform for pathogen genomics, combined with an agentic AI assistant, enables reproducible RNA-seq analysis. By re-analyzing data from two publications we achieved near-perfect correlation with published results despite annotation version differences. We addressed provenance challenges associated with using AI agents with Galaxy by forcing them to invoke Galaxys native tools rather than manipulating data directly. For custom analyses outside Galaxys toolset, we provide standalone JupyterLite notebooks that reproduce our analysis without AI involvement. This framework--combining AI-assisted automation with rigorous provenance tracking--establishes a template for standardized, reproducible fungal pathogen genomics. To the best of our knowledge, this is the first example of integration between public data repositories, reproducible analysis workflows, and agentic AI tools. Our subsequent efforts will focus on improving the seamlessness of this integration.

genomics↗

bioRxiv2025

Self-Supervised Missing Wedge Correction in Soft X-Ray Tomography: Towards Accurate Cellular Morphology and Volume Quantification

Soft X-Ray tomography (SXT) is a non-invasive bio-imaging technique that enables 3D imaging of cellular structures in large volume, with a unique resolution range that bridges the gap between fluorescence and transmission electron microscopy. However, a fundamental limitation, the missing wedge artefact caused by incomplete tilt-series acquisition, introduces systematic structural elongation in the reconstructed tomograms. This artifact compromises accurate quantitative biological analysis by overestimating cellular and organelle volumes. To overcome this persistent issue, we introduce a novel, self-supervised missing wedge correction model that learns key sine-wave patterns from existing SXT sinograms of the tilt series stacks. This model can be applied to recover the missing-angle region of the sinogram, reducing distortions and elongations in the reconstructed tomograms. We demonstrate a significant quantitative improvement in artifact removal, achieving faithful recovery of the spherical morphology of lipid droplets. We further applied this method to Plasmodium falciparum hemozoin crystals, a biomarker in antimalarial drug efficacy studies. Our model successfully reduced volume overestimation, achieving up to a 16% decrease in distorted volume. This level of precision is paramount for correctly interpreting the mode of action of antimalarial drugs.

cell biology↗

bioRxiv2025

From data to publication in a browser with BRC-Analytics: Evolutionary dynamics of coding overlaps in measles virus

The analytical landscape of pathogen research is often fragmented, hindering transparency and reproducibility due to diverse genomic data sources, numerous software tools, and suboptimal integration methods. Here we introduce BRC-analytics, a novel browser-based environment that unifies authoritative sources of genomic data with community-curated best analysis practices on a freely accessible public computational infrastructure. We demonstrate its capabilities by analyzing the evolutionary dynamics within the P/V/C locus of the measles virus, a complex system involving overlapping coding regions and RNA editing. Our analysis, conducted entirely within BRC-analytics, reveals asymmetric evolution of the locuss reading frames under distinct selective pressures. BRC-analytics streamlines the entire research process--from data collection and primary analysis (e.g., variant calling) to interpretation (e.g., using integrated JupyterLite notebooks and LLMs) and publication--into a single web browser session. This eliminates the need for local installations and manual data transfers, implicitly tracking provenance and ensuring reproducibility. The platforms goal is to provide true data-to-publication functionality, making advanced pathogen genomics accessible to a broader research community regardless of their computational expertise or infrastructure access.

genomics↗

bioRxiv2025

Relative contributions of the ERG11VF125AL and MRR1AN647T mutations to fluconazole resistance in Clade III Candidozyma (Candida) auris clinical isolates

ObjectivesCandidozyma (Candida) auris is an emerging fungal pathogen of global concern that often exhibits multi-drug resistance. Over 90% of isolates are resistant to fluconazole. Of the six described clades of C. auris, Clade III has been found to be nearly universally fluconazole resistant and almost every Clade III isolate described carries a mutation in the gene encoding the fluconazole target sterol demethylase (ERG11) leading to a VF125AL substitution and a mutation leading to a N647T substitution in the gene encoding Mrr1a, a transcriptional regulator of the Mdr1 transporter. Both mutations have been shown to contribute to fluconazole resistance in C. auris. MethodsIn the present study we introduced the Clade III MRR1A mutation into a Clade I background using CRISPR-Cas9 gene editing. In two Clade III clinical isolates we corrected the native MRR1A and ERG11 mutations to their wild-type sequences as well as disrupted MDR1. Triazole susceptibilities and MDR1 gene expression were measured in all strains. ResultsIntroduction of the N647T substitution in a Clade I background confers a modest reduction in fluconazole and voriconazole susceptibility. Similarly, correction of MRR1A or disruption of MDR1 in each Clade III background resulted in a one-dilution decrease in fluconazole and voriconazole MIC while the ERG11 correction resulted in a three-dilution decrease in fluconazole and voriconazole MIC. ConclusionsOur findings show that while the MRR1A mutation makes a modest contribution, the ERG11 mutation is responsible for most of the fluconazole resistance observed in Clade III isolates. We also show that while these mutations likewise affect voriconazole susceptibility, they have no effect on susceptibility to itraconazole, isavuconazole, or posaconazole suggesting the potential therapeutic utility of these antifungals for infections due to Clade III isolates of C. auris.

microbiology↗

bioRxiv2025

High-Density Multi-Distance fNIRS Enhances Detection of Brain Activity during a Word-Color Stroop Task

STRUCTURED ABSTRACTO_ST_ABSSignificanceC_ST_ABSFunctional Near-Infrared Spectroscopy (fNIRS) enables neuroimaging in scenarios where other modalities are less suitable, such as during motion tasks or in low-resource environments. Sparse fNIRS arrays with 30mm channel spacing are widely used but have limited spatial resolution. High-density (HD) arrays with overlapping, multi-distance channels improve sensitivity and localization but increase costs and setup times. A statistical comparison of HD and sparse arrays is needed for evaluating the benefits and trade-offs of HD arrays. AimThis study provides a statistical comparison of HD and sparse fNIRS performance to inform array selection in future research. ApproachWe measured prefrontal cortex (PFC) activation during congruent and incongruent Word-Color Stroop (WCS) tasks using both Sparse and HD arrays for 17 healthy adult participants, comparing dorsolateral PFC channel and image results at the group level. ResultsWhile both arrays detected activation in channel space during incongruent WCS, channel and image space results demonstrated superior localization and sensitivity with the HD array for all WCS. ConclusionsSparse channel data may suitably detect activation from cognitively demanding tasks, like incongruent WCS. However, the HD array outperformed Sparse in detecting and localizing brain activity in image space, particularly during lower cognitive load tasks, making them more suitable for neuroimaging applications.

bioengineering↗

bioRxiv2024

Demonstrating Soft X-Ray Tomography in the lab for correlative cryogenic biological imaging using X-rays and light microscopy

Soft X-ray tomography (SXT) enables native-contrast three-dimensional (3D) imaging of fully hydrated, cryogenically preserved biological samples, revealing ultrastructural details without the need for staining, embedding, or sectioning. Traditionally available only at synchrotron facilities, recent advances in laser-driven plasma sources have led to the development of compact soft X-ray microscopes, such as the SXT-100. The SXT-100 achieves imaging resolutions down to 54 nm full-pitch, with tomograms acquired in 30 minutes to two hours. Integrated with an epifluorescence microscope, the SXT-100 facilitates correlative workflows by bridging fluorescence and electron microscopy while preserving the structural integrity of vitrified samples. We demonstrate the capabilities of the SXT-100 through various use cases, including imaging Euglena gracilis, Saccharomyces cerevisiae yeast cells, and nanoparticles in mammalian cells. The relatively short tomogram acquisition times, the virtually non-destructive nature of soft X-ray tomography, and its quantitative imaging capabilities underscore its potential as a powerful tool for advanced biological imaging. Future developments promise enhanced throughput and deeper integration with emerging correlative imaging modalities, and a wider variety of sample types including tissue.

biophysics↗

bioRxiv2024

Inclusivity in fNIRS Studies: Quantifying the Impact of Hair and Skin Characteristics on Signal Quality with Practical Recommendations for Improvement

Functional Near-Infrared Spectroscopy (fNIRS) holds transformative potential for research and clinical applications in neuroscience due to its non-invasive nature and adaptability to real-world settings. However, despite its promise, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can significantly impact the absorption and scattering of near-infrared light. If not properly addressed, these factors risk biasing fNIRS research by disproportionately affecting signal quality across diverse populations. Our results quantify the impact of various hair properties, skin pigmentation as well as head size, sex and age on signal quality, providing quantitative guidance for future hardware advances and methodological standards to help overcome these critical barriers to inclusivity in fNIRS studies. We provide actionable guidelines for fNIRS researchers, including a suggested metadata table and recommendations for cap and optode configurations, hair management techniques, and strategies to optimize data collection across varied participants. This research paves the way for the development of more inclusive fNIRS technologies, fostering broader applicability and improved interpretability of neuroimaging data in diverse populations.

neuroscience↗

bioRxiv2024

ninjaCap: A fully customizable and 3D printable headgear for fNIRS and EEG brain imaging

SignificanceAccurate sensor placement is vital for non-invasive brain imaging, particularly for functional near infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts like EEG. Custom, manually prepared probe layouts on textile caps are often imprecise and labor-intensive. AimWe introduce a method for creating personalized, 3D-printed headgear, enabling accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts, reducing costs and manual labor. ApproachOur approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. ResultsOur ninjaCap method offers 2.2{+/-}1.5 mm probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. ConclusionsThe ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.

neuroscience↗

bioRxiv2023

CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

cell biology↗

bioRxiv2023

The sterol C-24 methyltransferase encoding gene, erg6, is essential for viability of Aspergillus species.

Ergosterol is a critical component of fungal plasma membranes. Although many currently available antifungal compounds target the ergosterol biosynthesis pathway for antifungal effect, current knowledge regarding ergosterol synthesis remains incomplete for filamentous fungal pathogens like Aspergillus fumigatus. Here, we show for the first time that the lipid droplet-associated sterol C-24 methyltransferase, Erg6, is essential for A. fumigatus viability. We further show that this essentiality extends to additional Aspergillus species, including A. lentulus, A. terreus, and A. nidulans. Neither the overexpression of a putative erg6 paralog, smt1, nor the exogenous addition of ergosterol could rescue erg6 deficiency. Importantly, Erg6 downregulation results in a dramatic decrease in ergosterol and accumulation in lanosterol and is further characterized by diminished sterol-rich plasma membrane domains (SRDs) at hyphal tips. Unexpectedly, erg6 repressed strains demonstrate wild-type susceptibility against the ergosterol-active triazole and polyene antifungals. Finally, repressing erg6 expression reduced fungal burden accumulation in a murine model of invasive aspergillosis. Taken together, our studies suggest that Erg6, which shows little homology to mammalian proteins, is potentially an attractive antifungal drug target for therapy of Aspergillus infections. IMPORTANCEA. fumigatus is the most common pathogen that causes invasive aspergillosis, a life-threatening fungal infection with more than 300,000 cases reported annually. Available antifungals to treat Aspergillus-related infection are limited to three drug classes targeting the plasma membrane (ergosterol) or the cell wall, each of which suffer from either host toxicity or rising resistance levels. As ergosta-type sterols are absent in mammalian cells but are essential for fungal viability, the ergosterol biosynthesis pathway remains an enticing target for the development of new antifungals. Although ergosterol biosynthesis has been well studied in model yeast, only a few genes have been genetically characterized in A. fumigatus. Here, we characterize Erg6, one of the fungus-specific sterol biosynthesis genes, as an essential gene in Aspergillus species. We further provide in vivo evidence of the importance of Erg6 for establishment of invasive aspergillosis. Given the importance of Erg6 in other fungal systems for growth, stress resistance, and virulence, our study suggests that development of Erg6 inhibitors may be a promising strategy for developing novel broad-spectrum antifungals.

microbiology↗

bioRxiv2022

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

The T cell receptor (TCR) determines the specificity and affinity for both foreign and self-peptides presented by MHC. It is established that self-pMHC reactivity impacts T cell function, but it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-pMHC reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that predicts self-reactivity directly from TCR{beta} sequences. This approach revealed that n-nucleotide additions and acidic amino acids weaken self-reactivity. We tested our ML predictions of TCR{beta} sequence self-reactivity using retrogenic mice. Extrapolating our analyses to independent datasets, we found high predicted self-reactivity for regulatory CD4+ T cells and low predicted self-reactivity for T cells responding to chronic infection. Our analyses suggest a potential trade-off between repertoire diversity and self-reactivity intrinsic to the architecture of a TCR repertoire.

immunology↗

bioRxiv2022

Chronic infection control relies on T cells with lower foreign antigen binding strength generated by N-nucleotide diversity

The pathogens to which T cells respond is determined by the T cell receptors (TCRs) present in an individuals repertoire. Although more than 90% of the TCR repertoire is generated by terminal deoxynucleotidyl transferase (TdT)-mediated N-nucleotide addition during V(D)J recombination, the benefit of TdT-modified TCRs remains unclear. Here, we computationally and experimentally investigated whether TdT systematically modifies the affinity distribution of a TCR repertoire in ways that impacts acute or chronic infection. Our computational model predicts a shift toward low-affinity T cells over time during chronic, but not acute, infections. Elimination of low-affinity T cells in silico substantially delayed chronic infection clearance. Corroborating an affinity-centric benefit for TCR diversity, we showed that infection of TdT-deficient mice delayed the clearance of a chronic viral pathogen, while acute viral control was unaffected. Our data thus suggest that TdT-mediated TCR diversity is of particular benefit in the control of prolonged pathogen replication.

immunology↗