Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease

<p>Abstract</p> <p>Background</p> <p>Systems biology holds promise as a new approach to drug target identification and drug discovery against neglected tropical diseases. Genome-scale metabolic reconstructions, assembled from annotated genomes and a vast array of bioinformatics/biochemical resources, provide a framework for the interrogation of human pathogens and serve as a platform for generation of future experimental hypotheses. In this article, with the application of selection criteria for both <it>Leishmania major </it>targets (e.g. <it>in silico </it>gene lethality) and drugs (e.g. toxicity), a method (MetDP) to rationally focus on a subset of low-toxic Food and Drug Administration (FDA)-approved drugs is introduced.</p> <p>Results</p> <p>This metabolic network-driven approach identified 15 <it>L. major </it>genes as high-priority targets, 8 high-priority synthetic lethal targets, and 254 FDA-approved drugs. Results were compared to previous literature findings and existing high-throughput screens. Halofantrine, an antimalarial agent that was prioritized using MetDP, showed noticeable antileishmanial activity when experimentally evaluated <it>in vitro </it>against <it>L. major </it>promastigotes. Furthermore, synthetic lethality predictions also aided in the prediction of superadditive drug combinations. For proof-of-concept, double-drug combinations were evaluated <it>in vitro </it>against <it>L. major </it>and four combinations involving the drug disulfiram that showed superadditivity are presented.</p> <p>Conclusions</p> <p>A direct metabolic network-driven method that incorporates single gene essentiality and synthetic lethality predictions is proposed that generates a set of high-priority <it>L. major </it>targets, which are in turn associated with a select number of FDA-approved drugs that are candidate antileishmanials. Additionally, selection of high-priority double-drug combinations might provide for an attractive and alternative avenue for drug discovery against leishmaniasis.</p

Integrated metabolic flux and omics analysis of leishmania major metabolism

Leishmaniasis is a virulent parasitic infection that causes a significant threat to human health worldwide. The existing drugs are becoming less effective due to the ability of Leishmania spp. to alter its metabolism to adapt to harsh environments. Understanding how this parasite manipulates its metabolism inside the host (e.g. sandfly and human) might underpin new ways to prevent the disease and develop effective treatment strategies. Despite significant advances in omics technologies, biochemistry of parasites still lacks the understanding of molecular components that determine the metabolic behavior under varying conditions. Metabolic network modeling might be of interest to identify physiologically relevant nodes in a metabolic network. The present work proposes a metabolic model iSK570 (an extension of the iAC560 model) with additional reactions for the metabolism of lipids, long chain fatty acids and carbohydrates to study the metabolic behavior of this parasite. Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm was used to verify the consistency between model flux predictions and gene expression data. Improved flux distributions were obtained, allowing a more accurate understanding of stage-specific metabolism in of promastigotes and amastigotes.This work was supported by the Initial Training Network, GlycoPar, funded by the FP7 Marie Curie Actions of the European Commission (FP7-PEOPLE-2013-ITN-608295). The authors gratefully express appreciation to SilicoLife Lda for providing required infrastructural facilities related to this work. We also thank Bruno Pereira (systems biologist at SilicoLife) and Hugo Giesteira (programmer at SilicoLife) for scientific and technical assistance during various phases of the project.info:eu-repo/semantics/publishedVersio

Systems Approach Reveals Nuclear Factor Erythroid 2-Related Factor 2/Protein Kinase R Crosstalk in Human Cutaneous Leishmaniasis

Leishmania parasites infect macrophages, causing a wide spectrum of human diseases, from cutaneous to visceral forms. In search of novel therapeutic targets, we performed comprehensive in vitro and ex vivo mapping of the signaling pathways upstream and downstream of antioxidant transcription factor [nuclear factor erythroid 2-related factor 2 (Nrf2)] in cutaneous leishmaniasis (CL), by combining functional assays in human and murine macrophages with a systems biology analysis of in situ (skin biopsies) CL patient samples. First, we show the PKR pathway controls the expression and activation of Nrf2 in Leishmania amazonensis infection in vitro. Nrf2 activation also required PI3K/Akt signaling and autophagy mechanisms. Nrf2- or PKR/Akt-deficient macrophages exhibited increased levels of ROS/RNS and reduced expression of Sod1 Nrf2-dependent gene and reduced parasite load. L. amazonensis counteracted the Nrf2 inhibitor Keap1 through the upregulation of p62 via PKR. This Nrf2/Keap1 observation was confirmed in situ in skin biopsies from Leishmania-infected patients. Next, we explored the ex vivo transcriptome in CL patients, as compared to healthy controls. We found the antioxidant response element/Nrf2 signaling pathway was significantly upregulated in CL, including downstream target p62. In silico enrichment analysis confirmed upstream signaling by interferon and PI3K/Akt, and validated our in vitro findings. Our integrated in vitro, ex vivo, and in silico approach establish Nrf2 as a central player in human cutaneous leishmaniasis and reveal Nrf2/PKR crosstalk and PI3K/Akt pathways as potential therapeutic targets

Construcción de una red farmacológica basada en simulaciones farmacodinámicas y farmacocinéticas in silico para la selección racional de medicamentos con actividad leishmanicida

RESUMEN: Según la Organización Mundial de la Salud (OMS), la leishmaniasis está clasificada como una enfermedad tropical desatendida, que afecta principalmente a la población de bajos recursos de países en vía de desarrollo (WHO Leishmaniasis). 98 países alrededor del mundo han declarado la leishmaniasis como enfermedad endémica y anualmente son reportados más de 1.3 millones de casos nuevos, de los cuales entre 20.000 y 30.000 resultan fatales. Sin embargo, es importante tener en cuenta que, debido a que muchos de los casos se presentan en lugares aislados y en ocasiones de difícil acceso, no todos los casos son reportados ni registrados oficialmente. Lo cual haría que estos datos sean mayores en la realidad (Alvar et al., 2012; WHO| Leishmaniasis). La leishmaniasis es una enfermedad producto de la infección por parásitos del género Leishmania, género que a su vez puede dividirse en los subgéneros Leishmania y Viannia. El subgénero Leishmania se encuentra principalmente en el Viejo Mundo (Europa, África y Asia) aunque también se puede encontrar algunas especies en el Nuevo Mundo (América) e incluye especies como: L. major, L. donovani, L. infantum, L. amazonensis, L. mexicana. Por otro lado, el subgénero L. Viannia se presenta en el Nuevo Mundo e incluye especies como: L. braziliensis, L. panamensis, L. guyanensis y L. peruviana. Estos parásitos requieren de insectos flebótomos de los géneros Phlebotomus y Lutzomyia, que actúan como vector para su transmisión (Desjeux, 2004). Por esto, en el ciclo de vida del parásito se pueden observar dos estadios principales: La forma de amastigote en los mamíferos hospederos incluido el humano y la forma de promastigote en el sistema digestivo del insecto vector. La forma de amastigote es intracelular, ligeramente esférica y sin motilidad considerable. Se desarrolla luego de que promastigotes en estado infectivo son transmitidos al mamífero por la picadura de hembras del insecto vector infectadas con el parásito

Genome-scale metabolic reconstruction and analysis of the Trypanosoma brucei metabolism from a Systems biology perspective

Les progrès récents dans la modélisation informatique des réseaux biologiques permettent maintenant aux chercheurs d'étudier le métabolisme cellulaire des organismes. Dans ce projet, ces approches ont été utilisées pour analyser le métabolisme de Trypanosoma brucei. Ce parasite protozoaire est responsable de la trypanosomiase africaine, une maladie mortelle chez l'homme et qui entraine des dégâts importants dans les élevages. Ce parasite est principalement retrouvé dans les régions d'Afrique sub-sahariennes. Durant cette thèse, des informations sur le métabolisme de T. brucei ont été recueillies à partir d'études publiées, bases de données et de communication personnelle avec des experts qui étudient les différents aspects du métabolisme des trypanosomatides. Cette information a été mise à disposition de la communauté à travers la base de données TrypanoCyc. La base de données a été publiée en Novembre 2014 et a eu plus de 4200 visiteurs provenant de plus de cent pays depuis Novembre 2015. Un modèle métabolique à l'échelle du génome de T. brucei a également été reconstruit sur la base des informations recueillies. Ce modèle a permis de faciliter l'étude du métabolisme de T. brucei en utilisant une approche de biologie des systèmes. Des algorithmes basés sur l'analyse de balance des flux ont été conçus pour optimiser la visualisation et l'étude des propriétés métaboliques du parasite. En utilisant l'algorithme iMat, des modèles spécifiques de la forme sanguine de T. brucei ont été générés à partir des informations fournies par les études publiées et les annotations présentent dans. Enfin, un algorithme a été conçu pour optimiser encore ces modèles spécifiques afin d'améliorer la cohérence de leurs prédictions avec les résultats publiés. Les modèles ainsi créés, spécifiques à la forme sanguine, ont montré une meilleure puissance prédictive que le modèle initial à l'échelle du génome, en particulier pour prédire le comportement métabolique spécifique de différents mutants de T. brucei. ABSTRACT : Recent advances in computational modelling of biological networks have helped researchers study the cellular metabolism of organisms. In this project, these approaches were used to analyze Trypanosoma brucei metabolism. This protozoan parasite is the causative agent of African trypanosomiasis, a lethal disease which has been responsible for huge loss of lives and livestock in Sub- Saharan Africa since ancient times. Information on T. brucei metabolism was gathered from published studies, databases and from personal communication with experts studying different areas of Trypanosomatid research. This information has been presented to the public through the TrypanoCyc Database, a community annotated T. brucei database. The database was published in November 2014 and has had over 4200 visitors from more than 100 countries as of November 2015. A manually curated genome-scale metabolic model for T. brucei was also built based on the gathered information to facilitate the study of T. brucei metabolism using systems biology approaches. Flux balance analysis based algorithms were designed to optimize visualization and study interesting metabolic properties. Blood-stream form specific metabolic models were generated using information available from published studies and the TrypanoCyc annotations with the help of the iMAT algorithm. Finally, an algorithm was designed to further optimize these stage specific models to improve the consistency of their predictions with results published in previous studies. These stage-specific models were observed to have a clear advantage over the genome-scale model when predicting stage-specific behaviour of T. brucei, particularly when predicting mutant behaviour

Caracterização celular e imunológica da interação células dendríticas- Leishmania braziliensis

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Medicina, Programa de Pós-Graduação em Patologia Molecular, 2014.A infecção por Leishmania (Viannia) braziliensis tem um amplo espectro de manifestações clínicas e imunológicas. Uma variedade de células estão envolvidas na defesa do hospedeiro contra o parasito. Entre elas, as células dendríticas (DCs) são elementos chaves para o sistema imune, pois atuam como sentinelas na periferia e alertam os linfócitos T do tipo do antígeno invasor, direcionando sua polarização e inicializando uma resposta imune. Portanto, o objetivo deste trabalho foi estudar a interação de células dendríticas humanas com L. braziliensis, já que, esse contato desencadeia estímulos para a produção de citocinas e moléculas co-estimulatórias envolvidas na resposta inflamatória, contribuindo para o desenvolvimento de uma resposta imune adaptativa protetora. Assim, monócitos humanos foram isolados a partir de células mononucleares de sangue periférico, purificados por separação magnética e colocados em cultura com IL-4 e GM-CSF para gerar DCs imaturas, as quais foram infectadas com promastigotas de L. braziliensis durante 12 e 24 h. As células foram caracterizadas por citometria de fluxo, usando os marcadores de superfície CD1a, HLA-DR, CD86 (B7-2)e DC-SIGN (CD209). Foi observada uma taxa de infecção de 46±3.5% com 7,2±0.6 amastigotas/DCs e 52.5± 2.4% com 6,1±1.6 amastigotas/DCs após 12 e 24 h de cultura, respectivamente. Os marcadores de superfície revelaram uma porcentagem de expressão de 70,5% (CD1a), 87,9% (HLA-DR), 94% (CD86) e 97,5% (DC-SIGN) nas células não infectadas; e 71% (CD1a), 89,9% (HLA-DR), 98,3% (CD86) e 97% (DC-SIGN) para células infectadas após 24 h. Após 12 h de infecção não detectamos alterações no padrão de expressão de moléculas de superfície. A inibição da apoptose pode tanto favorecer a apresentação dos antígenos quanto a disseminação do parasito. Além disso, foram encontradas principalmente as citocinas IL-12p40 e TNF-α nos sobrenadantes das culturas, enquanto não detectamos as citocinas IL-10, IL-6, IL-1β e TGF-β. __________________________________________________________________________ ABSTRACTLeishmania (Viannia) braziliensis infection leads to a broad spectrum of clinical and immunological manifestations. A great variety of cells is involved in host defense against the parasite. Among those, dendritic cells (DCs) are key elements of the immune system, which acts as sentinels in the periphery and alert T lymphocytes about the type of invading antigen, addressing their polarization and initiating an early immune response. The aim of this work was study the interaction of human dendritic cells with L. braziliensis, this contact triggers stimuli for the production of cytokine and co-stimulatory molecules involved in the inflammatory response, contributing to the development of protective adaptive immune response. Thereby, monocytes were isolated from peripheral blood mononuclear cells, subsequently purified by magnetic separation, and cultured with IL-4 and GM-CSF to generate immature DCs, which were infected with L. braziliensis promastigotes during 12 e 24 h. The cells where characterized by flow cytometry using surface markers CD1a, HLA-DR, CD86 and DC-SIGN. It was observed an infection rate of 46±3.5% with 7,140±0.6 amastigotes/DC and 52.50± 2.4% with 6,1±1.6 amastigotes/DC 12 and 24 h post infection, respectively. The surface markers showed a percentage of expression of 70.5% (CD1a), 87.9% (HLA-DR), 94% (CD86) and 97.5% (DC-SIGN) in non-infected cells; and 71% (CD1a), 89.9% (HLA-DR), 98.3% (CD86) and 97% (DC-SIGN) in infected cells after 24 h infection. After 12 h of infection, we not detected differences of surface molecules expression. Moreover, cell death delay can either stimulate antigen- presentation or favor parasite dissemination. Additionally, the cytokines IL-12p40 and TNF- α were detected in the culture supernatants, whereas IL-10, IL-6, IL-1β and TGF-β were not detected

Improved computational model of the malaria metabolic network and flux analysis for drug target prediction

In recent years, genome scale metabolic models have become an important tool for identifying potential drug targets against pathogens. These are particularly important where cultivation and genetic manipulation (conditional knockouts) are difficult. Malaria is a globally important disease infecting 212 million cases and causing more than 400,000 deaths in 2015. The resistance of the parasite to all antimalarial drugs on the market emphasises the urgent need to identify new drug targets. There are a few malaria metabolic models that have already been developed; however, these models are limited in terms of network size or input of accurate experimentally derived metabolomics and biomass data. With extensive curation and utilisation of parasite-specific constraints in the improvement of existing metabolic network models, a highly curated metabolic network model of Plasmodium falciparum, iFT342, was developed here. The model has updated gene and reaction annotations as well as additional species identifiers that will facilitate ease in comparison with other models. The model has no dead-end metabolites (compared to 5 to 39% for other highly curated models) and has the highest percentage of live reactions. With the addition of experimentally measured biomass composition and metabolite fluxes for glucose and 18 amino acids, iFT342 was able to model in vitro parasite growth in restricted glucose environment with remarkable fidelity. In addition, through single gene knockout analysis, the model was able to significantly enrich the number of experimentally validated essential genes (true positives) in the predicted essential gene set, and had the highest percentage of true positive predictions compared with other malaria models. Finally, as proof of concept, inhibition of parasite growth was demonstrated using gemcitabine, which targets UMP-CMP kinase, a novel target predicted by the model. Gemcitabine inhibited parasite growth in a dose-dependent fashion exhibiting an IC50 in the low micromolar range and blocked the development of the parasite from the trophozoite to the schizont stage