Computational identification of signalling pathways in Plasmodium falciparum

Malaria is one of the world’s most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins

Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico

Malaria is one of the world’s most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing drugsmaking it indispensable to discover new drugs. We have established a simple computational tool which analyses the topology of the metabolic network of P. falciparum to identify essential enzymes as possible drug targets.Weinvestigated the essentiality of a reaction in the metabolic network by deleting (knocking-out) such a reaction in silico. The algorithmselected neighbouring compounds of the investigated reaction that had to be produced by alternative biochemical pathways. Using breadth first searches, we tested qualitatively if these products could be generated by reactions that serve as potential deviations of the metabolic flux. With this we identified 70 essential reactions. Our results were compared with a comprehensive list of 38 targets of approved malaria drugs. When combining our approach with an in silico analysis performed recently [Yeh, I., Hanekamp, T., Tsoka, S., Karp, P.D., Altman, R.B., 2004. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res. 14, 917–924] we could improve the precision of the prediction results. Finally we present a refined list of 22 new potential candidate targets for P. falciparum, half of which have reasonable evidence to be valid targets against micro-organisms and cancer

An in silico Approach to Detect Efficient Malaria Drug Targets to Combat the Malaria Resistance Problem

Resistance to malaria drugs is a major challenging problem in most parts of the world especially in the African continent where about ninety per cent of malaria cases occur. As a response to this alarming problem, the World Health Organisation (W.H.O) recommends that all countries experiencing resistance to conventional monotherapies, such as chloroquine, amodiaquine or sulfadoxine–pyrimethamine, should use combination therapies [1]. Therefore there is a need to discover new drug targets that are able to target the malarial parasite at distinct pathways for an efficient malaria drug. In this paper, we presented a machine-learning tool which is able to identify novel drug targets from the metabolic network of Plasmodium falciparum. With our tool we identified among others 19 drug targets confirmed from literature which we analyzed further with a sophisticated gene expression analysis tool. Our data was clustered using common distance similarity measurements and hierarchical clustering to propose a profound combination of drug targets. Our result suggests that two or more enzymatic reactions from the list of our drug targets which span across about ten pathways (Table 2) could be combined to target at distinct time points in the parasite's intraerythrocytic developmental cycle to detect efficient malaria drug target combination

Concurrent detection of autolysosome formation and lysosomal degradation by flow cytometry in a high-content screen for inducers of autophagy

<p>Abstract</p> <p>Background</p> <p>Autophagy mediates lysosomal degradation of cytosolic components. Recent work has associated autophagic dysfunction with pathologies, including cancer and cardiovascular disease. To date, the identification of clinically-applicable drugs that modulate autophagy has been hampered by the lack of standardized assays capable of precisely reporting autophagic activity.</p> <p>Results</p> <p>We developed and implemented a high-content, flow-cytometry-based screening approach for rapid, precise, and quantitative measurements of pharmaceutical control over autophagy. Our assay allowed for time-resolved individual measurements of autolysosome formation and degradation, and endolysosomal activities under both basal and activated autophagy conditions. As proof of concept, we analyzed conventional autophagy regulators, including cardioprotective compounds aminoimidazole carboxamide ribonucleotide (AICAR), rapamycin, and resveratrol, and revealed striking conditional dependencies of rapamycin and autophagy inhibitor 3-methyladenine (3-MA). To identify novel autophagy modulators with translational potential, we screened the Prestwick Chemical Library of 1,120 US Food and Drug Administration (FDA)-approved compounds for impact on autolysosome formation. In all, 38 compounds were identified as potential activators, and 36 as potential inhibitors of autophagy. Notably, amongst the autophagy enhancers were cardiac glycosides, from which we selected digoxin, strophanthidin, and digoxigenin for validation by standard biochemical and imaging techniques. We report the induction of autophagic flux by these cardiac glycosides, and the concentrations allowing for specific enhancement of autophagic activities without impact on endolysosomal activities.</p> <p>Conclusions</p> <p>Our systematic analysis of autophagic and endolysosomal activities outperformed conventional autophagy assays and highlights the complexity of drug influence on autophagy. We demonstrate conditional dependencies of established regulators. Moreover, we identified new autophagy regulators and characterized cardiac glycosides as novel potent inducers of autophagic flux.</p

Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis

Mathematical modeling is required for understanding the complex behavior of large signal transduction networks. Previous attempts to model signal transduction pathways were often limited to small systems or based on qualitative data only. Here, we developed a mathematical modeling framework for understanding the complex signaling behavior of CD95(APO-1/Fas)-mediated apoptosis. Defects in the regulation of apoptosis result in serious diseases such as cancer, autoimmunity, and neurodegeneration. During the last decade many of the molecular mechanisms of apoptosis signaling have been examined and elucidated. A systemic understanding of apoptosis is, however, still missing. To address the complexity of apoptotic signaling we subdivided this system into subsystems of different information qualities. A new approach for sensitivity analysis within the mathematical model was key for the identification of critical system parameters and two essential system properties: modularity and robustness. Our model describes the regulation of apoptosis on a systems level and resolves the important question of a threshold mechanism for the regulation of apoptosis

So rare we need to hunt for them: reframing the ethical debate on incidental findings

Incidental findings are the subject of intense ethical debate in medical genomic research. Every human genome contains a number of potentially disease-causing alterations that may be detected during comprehensive genetic analyses to investigate a specific condition. Yet available evidence shows that the frequency of incidental findings in research is much lower than expected. In this Opinion, we argue that the reason for the low level of incidental findings is that the filtering techniques and methods that are applied during the routine handling of genomic data remove these alterations. As incidental findings are systematically filtered out, it is now time to evaluate whether the ethical debate is focused on the right issues. We conclude that the key question is whether to deliberately target and search for disease-causing variations outside the indication that has originally led to the genetic analysis, for instance by using positive lists and algorithms