Melatonin and IP(3)-induced Ca(2+) Release from Intracellular Stores in the Malaria Parasite Plasmodium falciparum within Infected Red Blood Cells

IP(3)-dependent Ca(2+) signaling controls a myriad of cellular processes in higher eukaryotes and similar signaling pathways are evolutionarily conserved in Plasmodium, the intracellular parasite that causes malaria. We have reported that isolated, permeabilized Plasmodium chabaudi, releases Ca(2+) upon addition of exogenous IP(3). In the present study, we investigated whether the IP(3) signaling pathway operates in intact Plasmodium falciparum, the major disease-causing human malaria parasite. P. falciparum-infected red blood cells (RBCs) in the trophozoite stage were simultaneously loaded with the Ca(2+) indicator Fluo-4/AM and caged-IP(3). Photolytic release of IP(3) elicited a transient Ca(2+) increase in the cytosol of the intact parasite within the RBC. The intracellular Ca(2+) pools of the parasite were selectively discharged, using thapsigargin to deplete endoplasmic reticulum (ER) Ca(2+) and the antimalarial chloroquine to deplete Ca(2+) from acidocalcisomes. These data show that the ER is the major IP(3)-sensitive Ca(2+) store. Previous work has shown that the human host hormone melatonin regulates P. falciparum cell cycle via a Ca(2+)-dependent pathway. In the present study, we demonstrate that melatonin increases inositol-polyphosphate production in intact intraerythrocytic parasite. Moreover, the Ca(2+) responses to melatonin and uncaging of IP(3) were mutually exclusive in infected RBCs. Taken together these data provide evidence that melatonin activates PLC to generate IP(3) and open ER-localized IP(3)-sensitive Ca(2+) channels in P. falciparum. This receptor signaling pathway is likely to be involved in the regulation and synchronization of parasite cell cycle progression.

Efficient solutions to factored MDPs with imprecise transition probabilities

When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MOP transition models from an expert or estimation from data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while various solution algorithms exist for MDP-IPs, they often require external calls to optimization routines and thus can be extremely time-consuming in practice. To address this deficiency, we introduce the factored MDP-IP and propose efficient dynamic programming methods to exploit its structure. Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional ""flat"" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs while producing the lowest error of any approximation algorithm evaluated. (C) 2011 Elsevier B.V. All rights reserved.