Biology subjects

Barton, J.

Publications and source records attributed to Barton, J..

9 recordsLinked to original sources

bioRxiv2026

Dopamine Agonists in Parkinson's Disease Decouple Risk-Taking from Reward-Paired Cues by Enhancing Risky Choice in Their Absence

Background: Common side effects of dopamine replacement therapy (DRT) for Parkinson's Disease (PD) are addiction-like impulse control disorders, including compulsive gambling. Gambling products such as slot machines prominently feature reward-paired audiovisual stimuli, which can promote riskier choices on laboratory gambling tasks in both humans and rodents. In rats, risky decision making in the presence of reward-paired cues is dependent on dopamine D3 receptor activity and is increased by ropinirole, a D2/3 receptor agonist with a higher affinity for D3 receptors. In humans, it remains uncertain whether cue-induced risky choice is amplified by dopamine agonism. Methods: We tested effects of DRT on cue-induced risky choice in 62 patients with PD (34 on levodopa monotherapy, 27 on levodopa and dopamine agonists). Patients performed two versions of a risky decision-making task: one with and one without reward-paired sensory cues, both ON and OFF DRT. A group of 36 age-matched controls completed the same task versions without DRT. Results: Across all groups, participants made riskier choices when the task included reward-paired sensory cues. Levodopa monotherapy did not affect risky decision-making. However, a combination of levodopa and dopamine agonists increased risky choices specifically in the uncued version of the task, making performance indistinguishable from that on the cued task. Conclusions: Dopamine agonists combined with levodopa promote risk-taking even in the absence of reward-paired cues. This could translate into risky reward-seeking behaviors that are decoupled from contextual factors.

neuroscience↗

bioRxiv2026

Germline-encoded V(D)J gene usage does not impose strict constraints on the epitope-specificity of T cell receptors

The theoretical diversity of T cell receptors (TCRs), generated through V(D)J recombination, is enormous, yet the diversity of TCRs capable of recognizing the same epitope remains unknown. Defining this TCR solution space is essential for uncovering basic principles that govern TCR specificity. Using single-cell RNA and TCR sequencing, we generated ultra-deep (more than 4000 unique TCRs per epitope) epitope-specific TCR libraries derived from 560 immunized C57BL/6 mice, identifying over 27,000 unique epitope-reactive TCRs across three distinct CD8+ T cell epitopes presented by two major histocompatibility complex (MHC) class I alleles. Saturation analyses indicated that the solution space for all studied epitopes comprises many tens of thousands of unique TCRs. Despite highly skewed and peptide-dependent VJ-usage patterns, nearly the entire set of functional germline V/ and J/ segments was detected at least once within each epitope-specific repertoire. Therefore, diversity of epitope-specific TCRs is not limited by distinct germline combinations but rather can emerge from a near-to-complete combinatorial space of - and -chain, V and J segments paired with compatible CDR3 sequences.

immunology↗

bioRxiv2026

IMMREP25: Unseen Peptides

T cell receptors (TCRs) can bind to peptides presented by MHC molecules (pMHC) as a first step to trigger a T cell response. Reliable approaches to predict TCR:pMHC binding would have broad applications in clinical diagnostics, therapeutics, and the fundamental understanding of molecular interactions. IMMREP is a community organized series of prediction contests that asks participants to predict TCR:pMHC binding on unpublished datasets. Previous iterations in 2022 and 2023 showed multiple approaches can predict TCR-pMHC binding with significant accuracy (median AUC_0.1[≥]0.7) for peptides where experimental data is available ("seen" peptides). In contrast, models did not outperform random guessing for peptides that have no such data available ("unseen" peptides). Here we report on the results of IMMREP25, which focused solely on unseen peptides in order to evaluate the cutting edge of the field. We received 126 named submissions predicting the specificity of 1,000 TCRs against twenty unseen peptides restricted by one of two MHC molecules (HLA-A*02:01 and HLA-B*40:01). The best performing methods showed a macro-AUC_0.1 of 0.60, significantly better than random, demonstrating significant advances in the field. The top performing methods incorporated structural modeling into their approach, indicating that especially for unseen peptides, a structural understanding aids in the prediction of TCR:pMHC interactions. The results from this benchmark highlight the significant challenges remaining for TCR:pMHC predictions and will inform future method development.

bioinformatics↗

bioRxiv2025

Dual Role of Glycosylation in Resistance to CD4-binding Site Broadly Neutralizing Antibodies

Broadly neutralizing antibodies (bNAbs) provide a useful tool for HIV cure strategies because of their ability to target conserved regions on the envelope (Env) protein in the context of both virions and infected cells. One of the most well studied bNAbs is the CD4 binding site (CD4bs) antibody, VRC01 and others in its class. A major obstacle to effective cure strategies with bNAbs is viral immune escape. A deeper understanding of escape pathways from VRC01-class antibodies in genetically diverse samples is needed. Using an in vitro viral escape assay where infected CD4+ T cells were cultured in the presence of increasing VRC01 concentrations, complete resistance to VRC01 was detected in Env 246.F3 by day 42. We determined that resistance was due to a mutation at position N276 that resulted in elimination of the glycan-attachment site. As the loss of a glycan at this site is known to increase virus sensitivity to CD4bs antibodies, we explored this finding further. Specifically, we introduced N276D mutation into the 12-virus global Env panel and measured neutralization susceptibility to a panel of CD4bs bNAbs. This N276D mutation increased resistance or sensitivity depending on the Env and bNAb in question, emphasizing a dual role of glycan N276 in VRC01-class bNAb neutralization. The role of this glycan in escape was demonstrated to be dependent on the context of the Env, the bNAb and the glycosylation complexity of the virus due to producer cells. These findings underscore the complexity of glycosylation in genetically diverse HIV escape from antibodies. ImportanceDespite decades of research, an HIV cure remains elusive, largely due to the viruss immense genetic variability and ability to evade immune clearance. While antiretroviral therapy (ART) suppresses viral replication, it does not eradicate the virus and presents long-term challenges related to toxicity, access, and adherence. Broadly neutralizing antibodies (bNAbs), particularly those targeting the conserved CD4 binding site such as VRC01 and others in its class, offer promise for durable control or cure by targeting both circulating virus and infected cells. However, viral escape from bNAbs remains a critical hurdle. In this study, we demonstrate that glycan-mediated escape from VRC01-class bNAbs is highly context-dependent--shaped by Env, bNAb, and the glycosylation patterns introduced by the producer cell. These findings emphasize the dual role of glycans in affecting antibody sensitivity and underscore the importance of viral and host factors in shaping effective bNAb-based cure strategies across diverse HIV-1 strains.

immunology↗

bioRxiv2024

A generative foundation model for antibody sequence understanding

Here we introduce FAbCon, a generative antibody-specific language model comprising 2.4 billion parameters. A commonly accepted wisdom in developing large language models is that increasing model scale will translate to higher performance on downstream tasks. Starting from a 144-million parameter setup, we show that progressively larger models achieve greater accuracy in predicting antigen binding and can also be used to design new antibodies with good predicted developability potential. FAbCon is available on huggingface.co/alchemab.

bioinformatics↗

bioRxiv2024

Enhancing Antibody Language Models with Structural Information

The central tenet of molecular biology is that a proteins amino acid sequence determines its three-dimensional structure, and thus its function. However, proteins with similar sequences do not always fold into the same shape, and vice-versa, dissimilar sequences can adopt similar folds. In this work, we explore antibodies, a class of proteins in the immune system, whose local shapes are highly unpredictable, even with small variations in their sequence. Inspired by the CLIP method [1], we propose a multimodal contrastive learning approach, contrastive sequence-structure pre-training (CSSP), which amalgamates the representations of antibody sequences and structures in a mutual latent space. Integrating structural information leads both antibody and protein language models to show better correspondence with structural similarity and improves accuracy and data efficiency in downstream binding prediction tasks. We provide an optimised CSSP-trained model, AntiBERTa2-CSSP, for non-commercial use at https://huggingface.co/alchemab.

bioinformatics↗

bioRxiv2023

Antimicrobial Activity of NK Cells to Trypanosoma cruzi Infected Human Primary Keratinocytes

Infection with the protozoan parasite Trypanosoma cruzi is causative for Chagas disease, which is a highly neglected tropical disease prevalent in Latin America. Humans are primary infected through vectorial transmission by blood-sucking triatomine bugs. The parasite enters the human host through mucous membranes or small skin lesions. Since keratinocytes are the predominant cell type in the epidermis, they play a critical role in detecting disruptions in homeostasis and aiding in pathogen elimination by the immune system in the human skin as alternative antigen-presenting cells. Interestingly, keratinocytes also act as a reservoir for T. cruzi, as the skin has been identified as a major site of persistent infection in mice with chronic Chagas disease. Moreover, there are reports of the emergence of T. cruzi amastigote nests in the skin of immunocompromised individuals who are experiencing reactivation of Chagas disease. This observation implies that the skin may serve as a site for persistent parasite presence during chronic human infection too and underscores the significance of investigating the interactions between T. cruzi and skin cells. Consequently, the primary objective of this study was to establish and characterize the infection kinetics in human primary epidermal keratinocytes (hPEK). Our investigation focused on surface molecules that either facilitated or hindered the activation of natural killer (NK) cells, which play a crucial role in controlling the infection. To simulate the in vivo situation in humans, an autologous co-culture model was developed to examine the interactions between T. cruzi infected keratinocytes and NK cells. We evaluated the degranulation, cytokine production, and cytotoxicity of NK cells in response to the infected keratinocytes. We observed a strong activation of NK cells by infected keratinocytes, despite minimal alterations in the expression of activating or inhibitory ligands on NK cell receptors. However, stimulation with recombinant interferon-gamma (IFN-{gamma}), a cytokine known to be present in significant quantities during chronic T. cruzi infections in the host, resulted in a substantial upregulation of these ligands on primary keratinocytes. Overall, our findings suggest the crucial role of NK cells in controlling acute T. cruzi infection in the upper layer of the skin and shed light on keratinocytes as potential initial targets of infection.

immunology↗

bioRxiv2022

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

bioinformatics↗

bioRxiv2021

Deciphering the language of antibodies using self-supervised learning

An individuals B cell receptor (BCR) repertoire encodes information about past immune responses, and potential for future disease protection. Deciphering the information stored in BCR sequence datasets will transform our fundamental understanding of disease and enable discovery of novel diagnostics and antibody therapeutics. One of the grand challenges of BCR sequence analysis is the prediction of BCR properties from their amino acid sequence alone. Here we present an antibody-specific language model, AntiBERTa, which provides a contextualised representation of BCR sequences. Following pre-training, we show that AntiBERTa embeddings learn biologically relevant information, generalizable to a range of applications. As a case study, we demonstrate how AntiBERTa can be fine-tuned to predict paratope positions from an antibody sequence, outperforming public tools across multiple metrics. To our knowledge, AntiBERTa is the deepest protein family-specific language model, providing a rich representation of BCRs. AntiBERTa embeddings are primed for multiple downstream tasks and can improve our understanding of the language of antibodies.

bioinformatics↗