第1279回生物科学セミナー

High throughput drug screening and the dissection of the Rhodoquinone synthesis pathway in C.elegans

Professor Andrew Fraser(THE DONNELLY CENTRE, UNIVERSITY OF TORONTO, CANADA)

2019年04月16日(火)    17:00-18:30  理学部3号館 326号室   

Drugs are powerful research tools — they let us switch on or off specific pathways and examine the effects. In the first part of this talk, I’ll focus on new drug screening technologies we have developed for examining the rapid responses of C.elegans to a wide range of compounds. These allow to measure the immediate effects of drugs on worms — this has given us really unexpected insights. We find that worms have complex responses to many different drugs and that the pathways involved are largely unexplored.

One of the key insights we’ve gained from this way to look at rapid drug responses is seeing how worms respond to drugs that affect aerobic metabolism — this has real medical importance. Parasitic Helminths infect around a quarter of all humans — they are one of the major human pathogens. Most parasitic helminths (PHs) undergo major shifts in their metabolism following host infection. While PHs use standard aerobic metabolism during their free-living stages, many PHs live in hypoxic conditions in their host — to survive they use unusual metabolic pathways to make ATP. In particular they rely on Rhodoquinone (RQ) as a key electron carrier. While RQ is very similar to Ubiquinone, humans do not make RQ — RQ-utilizing anaerobic pathways are thus a perfect drug target but (a) no commercial drugs exist that target RQ synthesis (b) the key enzymes required for RQ synthesis are unknown. One key difficulty in studying RQ synthesis is the lack of a tractable genetic system — one cannot study this in yeast/mammals since they lack this pathway. C.elegans however does make RQ. I will present the work we have done to establish C.elegans as a powerful model for studying RQ synthesis and for identifying new drugs that target this pathway. We will also present a new pathway for RQ synthesis along with both genetic and biochemical data to support our model.