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Citations: 1319
H-Index: 18
i10-index: 28
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2020
Adriaensen, Wim; Cuypers, Bart; Cordero, Carlota F; Mengasha, Bewketu; Blesson, Séverine; Cnops, Lieselotte; Kaye, Paul M; Alves, Fabiana; Diro, Ermias; van Griensven, Johan
Host transcriptomic signature as alternative test-of-cure in visceral leishmaniasis patients co-infected with HIV Journal Article
In: EBioMedicine, vol. 55, 2020, ISSN: 2352-3964.
@article{Adriaensen2020,
title = {Host transcriptomic signature as alternative test-of-cure in visceral leishmaniasis patients co-infected with HIV},
author = {Wim Adriaensen and Bart Cuypers and Carlota F Cordero and Bewketu Mengasha and S\'{e}verine Blesson and Lieselotte Cnops and Paul M Kaye and Fabiana Alves and Ermias Diro and Johan van Griensven},
url = {https://doi.org/10.1016/j.ebiom.2020.102748},
doi = {10.1016/j.ebiom.2020.102748},
issn = {2352-3964},
year = {2020},
date = {2020-05-01},
journal = {EBioMedicine},
volume = {55},
publisher = {Elsevier},
abstract = {BackgroundVisceral leishmaniasis (VL) treatment in HIV patients very often fails and is followed by high relapse and case-fatality rates. Hence, treatment efficacy assessment is imperative but based on invasive organ aspiration for parasite detection. In the search of a less-invasive alternative and because the host immune response is pivotal for treatment outcome in immunocompromised VL patients, we studied changes in the whole blood transcriptional profile of VL-HIV patients during treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
BackgroundVisceral leishmaniasis (VL) treatment in HIV patients very often fails and is followed by high relapse and case-fatality rates. Hence, treatment efficacy assessment is imperative but based on invasive organ aspiration for parasite detection. In the search of a less-invasive alternative and because the host immune response is pivotal for treatment outcome in immunocompromised VL patients, we studied changes in the whole blood transcriptional profile of VL-HIV patients during treatment.
2018
Neuter, Nicolas De; Bittremieux, Wout; Beirnaert, Charlie; Cuypers, Bart; Mrzic, Aida; Moris, Pieter; Suls, Arvid; Tendeloo, Viggo Van; Ogunjimi, Benson; Laukens, Kris; Meysman, Pieter
On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition Journal Article
In: Immunogenetics, vol. 70, no. 3, pp. 159-168, 2018, ISSN: 1432-1211.
@article{DeNeuter2018,
title = {On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition},
author = {Nicolas De Neuter and Wout Bittremieux and Charlie Beirnaert and Bart Cuypers and Aida Mrzic and Pieter Moris and Arvid Suls and Viggo Van Tendeloo and Benson Ogunjimi and Kris Laukens and Pieter Meysman},
url = {https://doi.org/10.1007/s00251-017-1023-5},
doi = {10.1007/s00251-017-1023-5},
issn = {1432-1211},
year = {2018},
date = {2018-03-01},
journal = {Immunogenetics},
volume = {70},
number = {3},
pages = {159-168},
abstract = {Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.