Poster Presentation CD1-MR1 Workshop 2025

High throughput discovery approaches identify human self-metabolites of several chemical classes as antigens for MAIT cells (#104)

Alejandro Briones-Contreras 1 2 , Lucia Mosquera-Ferreiro 1 2 , Sofia Carreira-Santos 1 2 , Ana Ramirez-Iglesias 1 2 , Martin Kotrulev 1 2 , Beatriz Garcia-Pinel 1 2 , Cristian R. Munteanu 3 , Alejandro Flores-Sepulveda 4 , Hugo Gutierrez-de-Teran 5 , Iria Gomez-Tourino 1 2
  1. Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS). University of Santiago de Compostela, CiMUS research centre, Santiago de Compostela, Spain
  2. Foundation Health Research Institute of Santiago de Compostela (IDIS)., IDIS, Santiago de Compostela, Spain
  3. CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, CITIC, A Coruña, Spain
  4. Department of Chemistry, Biochemistry and Pharmaceutical Sciences. University of Bern, DCBP, Bern, Switzerland
  5. Nanomaterials and Nanotechnology Research Center (CINN), Spanish National Research Council (CSIC), Health Research Institute of Asturias (ISPA), CINN-CSIC-ISPA, Oviedo, Spain

INTRODUCTION

Mucosal invariant T (MAIT) cells and other MR1-restricted T cells recognize metabolites presented by MR1. Although the first identified metabolites were of microbial origin, an increasing body of evidence suggests that MR1 can also present self-metabolites. However, approaches to identifying novel MR1 ligands are usually time-consuming and low-throughput, limiting the advancement of the field.

AIMS

To identify MR1-restricted self-metabolites through high throughput drug discovery-like approaches.

METHODS

We performed virtual docking of all small metabolites from Human Metabolome Database (HMDB, n=23,000) into published MR1 crystal structures, followed by ranking based upon predicted binding energies. We then employed molecular similarity analysis with topological mapping (TMAP) to classify these in silico ligands into chemical classes, and selected representatives from each class. Finally, we evaluated their binding to MR1 through surface plasmon resonance (SPR), followed by activation, blocking and competition assays.

RESULTS

Almost 4,000 HMDB metabolites had similar or better predicted MR1 binding affinities than the original ligands co-crystallized with MR1, and included amino acid derivatives, glucuronides and steroids, among others. TMAP classification grouped them in 200 chemical clusters; 229 in silico ligands, including at least one representative from each cluster, where selected for in vitro analyses. SPR demonstrated that 53% of tested metabolites bind to the MR1 binding groove.  

High-throughput activation assays, based on MAIT TCR-transduced Jurkat and C1R.MR1 cell cocultures, identified human metabolites that activate Jurkat.MAIT cells, and/or increase or decrease MR1 surface expression in C1R.MR1 cells. TCR dependency of these responses was confirmed with blocking (with Anti-MR1) and competition (with 5-OP-RU or Ac-6-FP) assays. Importantly, different metabolites activate different Jurkat.MAIT lines, indicating TCR specificity. Ongoing work includes validation in primary MAIT cells, FluoroSpots and TCR sequencing.

CONCLUSION

We developed a novel, MR1-specific, high-throughput autoantigen discovery platform, and identified novel MAIT autoantigens, having great potential in the autoimmunity and cancer fields.