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Analyzing Thiol-Dependent Redox Networks in the Presence of Methylene Blue and Other Antimalarial Agents with RT-PCR-Supported in silico Modeling

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-123751
  • BACKGROUND: In the face of growing resistance in malaria parasites to drugs, pharmacological combination therapies are important. There is accumulating evidence that methylene blue (MB) is an effective drug against malaria. Here we explore the biological effects of both MB alone and in combination therapy using modeling and experimental data. RESULTS: We built a model of the central metabolic pathways in P. falciparum. Metabolic flux modes and their changes under MB were calculated by integrating experimental data (RT-PCR data on mRNAs forBACKGROUND: In the face of growing resistance in malaria parasites to drugs, pharmacological combination therapies are important. There is accumulating evidence that methylene blue (MB) is an effective drug against malaria. Here we explore the biological effects of both MB alone and in combination therapy using modeling and experimental data. RESULTS: We built a model of the central metabolic pathways in P. falciparum. Metabolic flux modes and their changes under MB were calculated by integrating experimental data (RT-PCR data on mRNAs for redox enzymes) as constraints and results from the YANA software package for metabolic pathway calculations. Several different lines of MB attack on Plasmodium redox defense were identified by analysis of the network effects. Next, chloroquine resistance based on pfmdr/and pfcrt transporters, as well as pyrimethamine/sulfadoxine resistance (by mutations in DHF/DHPS), were modeled in silico. Further modeling shows that MB has a favorable synergism on antimalarial network effects with these commonly used antimalarial drugs. CONCLUSIONS: Theoretical and experimental results support that methylene blue should, because of its resistance-breaking potential, be further tested as a key component in drug combination therapy efforts in holoendemic areas.zeige mehrzeige weniger

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Metadaten
Autor(en): J. Zirkel, A. Cecil, F. Schäfer, S. Rahlfs, A. Ouedraogo, K. Xiao, S. Sawadogo, B. Coulibaly, K. Becker, T. Dandekar
URN:urn:nbn:de:bvb:20-opus-123751
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):Bioinformatics and Biology Insights
Erscheinungsjahr:2012
Band / Jahrgang:6
Seitenangabe:287-302
Originalveröffentlichung / Quelle:Bioinformatics and Biology Insights 2012:6 287–302. doi: 10.4137/BBI.S10193
DOI:https://doi.org/10.4137/BBI.S10193
PubMed-ID:https://pubmed.ncbi.nlm.nih.gov/PMC3516044
Allgemeine fachliche Zuordnung (DDC-Klassifikation):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Freie Schlagwort(e):combination therapy; drug; elementary mode analysis; malaria; metabolic flux; methylene blue; pathway; resistance
Datum der Freischaltung:23.12.2015
Anmerkungen:
This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.