Back

Diethylcarbamazine elicits Ca2+ signals through TRP-2 channels that are potentiated by emodepside in Brugia malayi muscles

Williams, P. D.; Kashyap, S. S.; Robertson, A.; Martin, R. J.

2023-04-11 pharmacology and toxicology
10.1101/2023.04.10.536248 bioRxiv
Show abstract

Filarial nematode infections are a major health concern in several countries. Lymphatic filariasis is caused by Wucheria bancrofti and Brugia spp. affecting over 120 million people. Heavy infections can lead to elephantiasis having serious effects on individuals lives. Although current anthelmintics are effective at killing the microfilariae in the bloodstream, they have little to no effect against adult parasites found in the lymphatic system. The anthelmintic diethylcarbamazine is one of the central pillars of lymphatic filariasis control. Recent studies have reported that diethylcarbamazine can open Transient Receptor Potential (TRP) channels on the muscles of adult female Brugia malayi leading to contraction and paralysis. Diethylcarbamazine has synergistic effects in combination with emodepside on Brugia inhibiting motility: emodepside is an anthelmintic that has effects on filarial nematodes and is under trials for treatment of river blindness. Here we have studied the effects of diethylcarbamazine on single Brugia muscle cells by measuring the change in Ca2+ fluorescence in the muscle using Ca2+-imaging techniques. Diethylcarbamazine interacts with the TRPC orthologue receptor TRP-2 to promote Ca2+ entry into the Brugia muscle cells which can activate SLO-1 Ca2+ activated K+ channels, the putative target of emodepside. A combination of diethylcarbamazine and emodepside leads to a bigger Ca2+ signal than when either compound is applied alone. Our study shows that diethylcarbamazine targets TRP channels to promote Ca2+ entry that is increased by emodepside activation of SLO-1 channels.

Matching journals

The top 2 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.