Cargando…
An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists
Many analogues of the glycolipid alpha-galactosylceramide (α-GalCer) are known to activate iNKT cells through their interaction with CD1d-expressing antigen-presenting cells, inducing the release of Th1 and Th2 cytokines. Because of iNKT cell involvement and associated Th1/Th2 cytokine changes in a...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3909045/ https://www.ncbi.nlm.nih.gov/pubmed/24498010 http://dx.doi.org/10.1371/journal.pone.0087000 |
_version_ | 1782301779735609344 |
---|---|
author | De Spiegeleer, Anton Wynendaele, Evelien Vandekerckhove, Matthias Stalmans, Sofie Boucart, Maxime Van Den Noortgate, Nele Venken, Koen Van Calenbergh, Serge Aspeslagh, Sandrine Elewaut, Dirk |
author_facet | De Spiegeleer, Anton Wynendaele, Evelien Vandekerckhove, Matthias Stalmans, Sofie Boucart, Maxime Van Den Noortgate, Nele Venken, Koen Van Calenbergh, Serge Aspeslagh, Sandrine Elewaut, Dirk |
author_sort | De Spiegeleer, Anton |
collection | PubMed |
description | Many analogues of the glycolipid alpha-galactosylceramide (α-GalCer) are known to activate iNKT cells through their interaction with CD1d-expressing antigen-presenting cells, inducing the release of Th1 and Th2 cytokines. Because of iNKT cell involvement and associated Th1/Th2 cytokine changes in a broad spectrum of human diseases, the design of iNKT cell ligands with selective Th1 and Th2 properties has been the subject of extensive research. This search for novel iNKT cell ligands requires refined structural insights. Here we will visualize the chemical space of 333 currently known iNKT cell activators, including several newly tested analogues, by more than 3000 chemical descriptors which were calculated for each individual analogue. To evaluate the immunological responses we analyzed five different cytokines in five different test-systems. We linked the chemical space to the immunological space using a system biology computational approach resulting in highly sensitive and specific predictive models. Moreover, these models correspond with the current insights of iNKT cell activation by α-GalCer analogues, explaining the Th1 and Th2 biased responses, downstream of iNKT cell activation. We anticipate that such models will be of great value for the future design of iNKT cell agonists. |
format | Online Article Text |
id | pubmed-3909045 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39090452014-02-04 An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists De Spiegeleer, Anton Wynendaele, Evelien Vandekerckhove, Matthias Stalmans, Sofie Boucart, Maxime Van Den Noortgate, Nele Venken, Koen Van Calenbergh, Serge Aspeslagh, Sandrine Elewaut, Dirk PLoS One Research Article Many analogues of the glycolipid alpha-galactosylceramide (α-GalCer) are known to activate iNKT cells through their interaction with CD1d-expressing antigen-presenting cells, inducing the release of Th1 and Th2 cytokines. Because of iNKT cell involvement and associated Th1/Th2 cytokine changes in a broad spectrum of human diseases, the design of iNKT cell ligands with selective Th1 and Th2 properties has been the subject of extensive research. This search for novel iNKT cell ligands requires refined structural insights. Here we will visualize the chemical space of 333 currently known iNKT cell activators, including several newly tested analogues, by more than 3000 chemical descriptors which were calculated for each individual analogue. To evaluate the immunological responses we analyzed five different cytokines in five different test-systems. We linked the chemical space to the immunological space using a system biology computational approach resulting in highly sensitive and specific predictive models. Moreover, these models correspond with the current insights of iNKT cell activation by α-GalCer analogues, explaining the Th1 and Th2 biased responses, downstream of iNKT cell activation. We anticipate that such models will be of great value for the future design of iNKT cell agonists. Public Library of Science 2014-01-31 /pmc/articles/PMC3909045/ /pubmed/24498010 http://dx.doi.org/10.1371/journal.pone.0087000 Text en © 2014 De Spiegeleer et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article De Spiegeleer, Anton Wynendaele, Evelien Vandekerckhove, Matthias Stalmans, Sofie Boucart, Maxime Van Den Noortgate, Nele Venken, Koen Van Calenbergh, Serge Aspeslagh, Sandrine Elewaut, Dirk An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists |
title | An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists |
title_full | An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists |
title_fullStr | An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists |
title_full_unstemmed | An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists |
title_short | An In Silico Approach for Modelling T-Helper Polarizing iNKT Cell Agonists |
title_sort | in silico approach for modelling t-helper polarizing inkt cell agonists |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3909045/ https://www.ncbi.nlm.nih.gov/pubmed/24498010 http://dx.doi.org/10.1371/journal.pone.0087000 |
work_keys_str_mv | AT despiegeleeranton aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT wynendaeleevelien aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT vandekerckhovematthias aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT stalmanssofie aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT boucartmaxime aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT vandennoortgatenele aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT venkenkoen aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT vancalenberghserge aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT aspeslaghsandrine aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT elewautdirk aninsilicoapproachformodellingthelperpolarizinginktcellagonists AT despiegeleeranton insilicoapproachformodellingthelperpolarizinginktcellagonists AT wynendaeleevelien insilicoapproachformodellingthelperpolarizinginktcellagonists AT vandekerckhovematthias insilicoapproachformodellingthelperpolarizinginktcellagonists AT stalmanssofie insilicoapproachformodellingthelperpolarizinginktcellagonists AT boucartmaxime insilicoapproachformodellingthelperpolarizinginktcellagonists AT vandennoortgatenele insilicoapproachformodellingthelperpolarizinginktcellagonists AT venkenkoen insilicoapproachformodellingthelperpolarizinginktcellagonists AT vancalenberghserge insilicoapproachformodellingthelperpolarizinginktcellagonists AT aspeslaghsandrine insilicoapproachformodellingthelperpolarizinginktcellagonists AT elewautdirk insilicoapproachformodellingthelperpolarizinginktcellagonists |