Back

Real-time measurements of ATP dynamics via ATeams in Plasmodium falciparum reveal drug-class-specific response patterns

Springer, E.; Heimsch, K. C.; Rahlfs, S.; Becker, K.; Przyborski, J. M.

2023-12-21 microbiology
10.1101/2023.12.20.572598 bioRxiv
Show abstract

Malaria tropica, caused by the parasite Plasmodium falciparum (P. falciparum) remains one of the greatest public health burdens for humankind. Due to its pivotal role in parasite survival, the energy metabolism of P. falciparum is an interesting target for drug design. To this end, analysis of the central metabolite ATP is of great interest. So far, only cell disruptive or intensiometric ATP assays have been available in this system, with various drawbacks for mechanistic interpretation, and partly inconsistent results. To address this, we have established fluorescent probes, based on FRET and known as ATeam, for use in blood stage parasites. ATeams are capable of measuring MgATP2- levels in a ratiometric manner, thereby facilitating in cellulo measurements of ATP dynamics in real-time using fluorescence microscopy and plate reader detection, and overcoming many of the obstacles of established ATP analysis methods. Additionally, we established a superfolder variant of the ratiometric pH sensor pHluorin (sfpHluorin) in P. falciparum to monitor pH homeostasis and control for pH fluctuations, which may affect ATeam measurements. We characterized recombinant ATeam and sfpHluorin protein in vitro and stably integrated the sensors into the genome of the P. falciparum NF54attB cell line. Using these new tools, we found distinct sensor response patterns caused by several different drug classes. Arylamino alcohols increased and redox-cyclers decreased ATP, doxycycline caused first-cycle cytosol alkalization, and 4-aminoquinolines caused aberrant proteolysis. Our results open up a completely new perspective on drugs mode of action with possible implications for target identification and drug development.

Matching journals

The top 8 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.