Back

TCAIM controls effector T cell generation by preventing Mitochondria-Endoplasmic Reticulum Contact Site-initiated Cholesterol Biosynthesis

Iwert, C.; Stein, J.; Appelt, C.; Vogt, K.; Rainer, R. J.; Tummler, K.; Muehle, K.; Stanko, K.; Schumann, J.; Uebe, D.; Juerchott, K.; Lisec, J.; Janek, K.; Gille, C.; Textoris-Taube, K.; Sai, S.; Petersen, A.; Kuehl, A. A.; Klipp, E.; Meisel, C.; Sawitzki, B.

2021-04-21 immunology
10.1101/2021.04.20.440500 bioRxiv
Show abstract

T cells need to adapt their cellular metabolism for effector cell differentiation. This relies on alterations in mitochondrial physiology. Which signals and molecules regulate those alterations remains unclear. We recently reported, that the mitochondrial protein TCAIM inhibits activation-induced changes in mitochondrial morphology and function and thus, CD4+ effector T cell formation. Using conditional TCAIM knock-in (KI) and knockout (KO) mice, we now show that it also applies to CD8+ T cells and more importantly, delineate the molecular processes in mitochondria by which TCAIM controls effector cell differentiation. TCAIM KI resulted in reduced activation-induced HIF1 protein expression. Metabolomics and transcriptional data in combination with mathematical flux modeling revealed an impaired induction of anabolic pathways, especially of the mevalonate pathway and cholesterol biosynthesis in TCAIM KI CD8+ T cells. Addition of cholesterol completely rescued HIF1 protein expression, activation and proliferation of TCAIM KI CD8+ T cells. At the molecular level, TCAIM delayed activation-induced mitochondria-ER contact (MERC) formation by binding to MERC promoting proteins such as RMD3 and VDAC2. In summary, we demonstrate that TCAIM suppresses effector cell differentiation by inhibiting MERC formation, which induce HIF1-mediated increase in cellular metabolism and cholesterol biosynthesis.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.