Barton, J. | Search bioRxiv

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↗