Back

WNT6-ACC2-induced accumulation of triacylglycerol rich lipid droplets is exploited by Mycobacterium tuberculosis

Brandenburg, J.; Marwitz, S.; Tazoll, S. C.; Waldow, F.; Kalsdorf, B.; Vierbuchen, T.; Scholzen, T.; Gross, A.; Goldenbaum, S.; Hoelscher, A.; Hein, M.; Linnemann, L.; Reimann, M.; Kispert, A.; Leitges, M.; Rupp, J.; Lange, C.; Niemann, S.; Behrends, J.; Goldmann, T.; Heine, H.; Schaible, U. E.; Hoelscher, C.; Schwudke, D.; Reiling, N.

2020-06-26 immunology
10.1101/2020.06.26.174110 bioRxiv
Show abstract

In view of emerging drug-resistant tuberculosis, host directed therapies are urgently needed to improve treatment outcomes with currently available anti-tuberculosis therapies. One option is to interfere with the formation of lipid-laden “foamy” macrophages in the infected host. Here, we provide evidence that WNT6, a member of the evolutionary conserved WNT signaling pathway, promotes foam cell formation by regulating key lipid metabolic genes including acetyl-CoA carboxylase-2 (ACC2) during pulmonary TB. In addition, we demonstrate that Mycobacterium tuberculosis (Mtb) facilitates its intracellular growth and dissemination in the host by exploiting the WNT6-ACC2 pathway. Using genetic and pharmacological approaches, we show that lack of functional WNT6 or ACC2 significantly reduces intracellular TAG levels, Mtb growth and necrotic cell death of macrophages. In combination with the anti-TB drug isoniazid, pharmacological inhibition of ACC2 improved anti-mycobacterial treatment in vitro and in vivo. Therefore, we propose the WNT6-ACC2 signaling pathway as a promising target for a host-directed therapy to reduce intracellular replication of Mtb by modulating neutral lipid metabolism.Competing Interest StatementDrs. N. Reiling and J. Brandenburg (Research Center Borstel, Leibniz Lung Center, 23845 Borstel, Germany) have filed a patent application entitled ACC inhibitors as means and methods for treating mycobacterial diseases(WO2018007430A1, patent pending).View Full Text

Matching journals

The top 5 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.