Dataset Information

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach.

ABSTRACT: New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

SUBMITTER: Kaltdorf M

PROVIDER: S-EPMC4911368 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach.

Kaltdorf Martin M Srivastava Mugdha M Gupta Shishir K SK Liang Chunguang C Binder Jasmin J Dietl Anna-Maria AM Meir Zohar Z Haas Hubertus H Osherov Nir N Krappmann Sven S Dandekar Thomas T

Frontiers in molecular biosciences 20160617

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly express ...[more]

PMID: 27379244

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Project description:BACKGROUND: The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. RESULTS: In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for ?-1,2-mannosyltransferase and erg6 that encodes for ?(24)-sterol C-methyltransferase. CONCLUSIONS: Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug targets. The preferred profile for fungal targets includes proteins conserved among fungi, but absent in the human genome. These characteristics potentially minimize toxic side effects exerted by pharmacological inhibition of the cellular targets. From this first step of post-genomic analysis, we obtained information relevant to future new drug development.

Project description:Toxoplasma gondii is an obligate intracellular apicomplexan parasite that can infect a wide range of warm-blooded animals including humans. In humans and other intermediate hosts, toxoplasma develops into chronic infection that cannot be eliminated by host's immune response or by currently used drugs. In most cases, chronic infections are largely asymptomatic unless the host becomes immune compromised. Thus, toxoplasma is a global health problem and the situation has become more precarious due to the advent of HIV infections and poor toleration of drugs used to treat toxoplasma infection, having severe side effects and also resistance have been developed to the current generation of drugs. The emergence of these drug resistant varieties of T. gondii has led to a search for novel drug targets. We have performed a comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen T. gondii. The enzymes in the unique pathways of T. gondii, which do not show similarity to any protein from the host, represent attractive potential drug targets. We have listed out 11 such potential drug targets which are playing some important work in more than one pathway. Out of these, one important target is Glutamate dehydrogenase enzyme; it plays crucial part in oxidation reduction, metabolic process and amino acid metabolic process. As this is also present in the targets of tropical diseases of TDR (Tropical disease related Drug) target database and no PDB and MODBASE 3D structural model is available, homology models for Glutamate dehydrogenase enzyme were generated using MODELLER9v6. The model was further explored for the molecular dynamics simulation study with GROMACS, virtual screening and docking studies with suitable inhibitors against the NCI diversity subset molecules from ZINC database, by using AutoDock-Vina. The best ten docking solutions were selected (ZINC01690699, ZINC17465979, ZINC17465983, ZINC18141294_03, ZINC05462670, ZINC01572309, ZINC18055497_01, ZINC18141294, ZINC05462674 and ZINC13152284_01). Further the Complexes were analyzed through LIGPLOT. On the basis of Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds, specifically ZINC01690699 (as it has minimum energy score and one of the highest number of interactions with the active site residue), could be promising inhibitors for T. gondii using Glutamate dehydrogenase as Drug target.

Project description:High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

Project description:Introduction: Published preclinical and clinical studies support the anti-inflammatory activity of CBD, but the molecular targets (e.g., genes and proteins) that are involved in its mechanisms of action remain unclear. Herein, a network-based pharmacology analysis was performed to aid in the identification of potential molecular targets for CBD's anti-inflammatory activity. Materials and Methods: Target genes and proteins were obtained from several online databases, including Swiss target prediction, Online Mendelian Inheritance in Man, and the DrugBank database. A compound-target-disease network was constructed with Cytoscape tool, and a network of protein-protein interactions was established with the Search Tool for the Retrieval of Interacting Genes/Proteins database. Lead proteins identified from the compound-target-disease network were further studied for their interactions with CBD by computational docking. In addition, biological pathways involved in CBD's anti-inflammatory activity were identified with the Gene Ontology enrichment and the Kyoto Encyclopedia of Genes and Genomes analysis. Results: A panel of proteins, including cellular tumor antigen p53, NF-kappa-B essential modulator, tumor necrosis factor (TNF) receptor, transcription factor p65, NF-kappa-B p105, NF-kappa-B inhibitor alpha, inhibitor of nuclear factor kappa-B kinase subunit alpha, and epidermal growth factor receptor, were identified as lead targets involved in CBD's anti-inflammatory activity. This finding was further supported by molecular docking, which showed interactions between the lead proteins and CBD. In addition, several signaling pathways, including TNF, toll-like receptor, mitogen-activated protein kinases, nuclear factor kappa-light-chain-enhancer of activated B cells, and nucleotide-binding oligomerization domain-like receptors, were identified as key regulators in the mediation of CBD's anti-inflammatory activity. Conclusion: A network-based pharmacology analysis identified potential molecular targets and signaling pathways for CBD's anti-inflammatory activity. Findings from this study add to the growing body of data supporting the utilization of CBD as a promising anti-inflammatory natural product.

Dataset Information

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach.

Publications

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach.

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