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Identifying Antimalarial Drug Targets by Cellular Network Analysis
[chapter]
2016
Current Topics in Malaria
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Malaria is one of the most deadly parasitic infectious diseases and identifying novel drug targets is mandatory for the development of new drugs. To find drug targets, metabolic and signaling networks have been constructed. These networks have been investigated by graph theoretical methods. Furthermore, mechanistic models have been set up based on stoichiometric equations. At equilibrium, production and consumption of internal metabolites need to be balanced leading to a large set of flux
doi:10.5772/65432
fatcat:ykwxzydv4rfgdfvyuvz52zfc7i
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... ons, and this can be used for metabolic flux simulations to identify drug targets. Analysis of flux variability and knockout simulations were applied to detect potential drug targets whose absence reduces the predicted biomass production and hence viability of the parasite in the host cell. Furthermore, not only the parasite was studied, but also the interaction between the host and the parasite, and, based on experimental expression data, stage-specific metabolic models of the parasite were developed, particularly during the red-blood cell stage. In this chapter, these various network-based approaches for drug target prediction will be explained and summarized.
Meta-Path Based Gene Ontology Profiles for Predicting Drug-Disease Associations
2021
IEEE Access
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KITIPORN PLAIMAS received the Dr. rer. nat. degree in applied mathematics from Heidelberg University, in 2011. ...
doi:10.1109/access.2021.3065280
fatcat:apaktwmq7vd4hcxngtuaaqvitm
Identifying essential genes in bacterial metabolic networks with machine learning methods
2010
BMC Systems Biology
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Plaimas et al. ...
BMC Systems Biology 2010, 4:56 http://www.biomedcentral.com/1752-0509/4/56 Page 6 of 16
This article is available from: http://www.biomedcentral.com/1752-0509/4/56 © 2010 Plaimas et al; licensee BioMed ...
doi:10.1186/1752-0509-4-56
pmid:20438628
pmcid:PMC2874528
fatcat:uevlv6ablnbhtgqzogt4si377q
DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations
2015
Bioinformatics and Biology Insights
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Suratanee and Plaimas used a network search algorithm for finding novel proteins associated with inflammatory bowel disease in a protein-protein interaction network. 13 They took the disease-gene association ...
doi:10.4137/bbi.s35237
pmid:26673408
pmcid:PMC4674013
fatcat:5ole4oofnbhmlkogcbxwxfv26q
Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations
2021
Applied Sciences
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Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was
doi:10.3390/app11072914
fatcat:g7vg4kvsabhz3d4epaqdhhq77u
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... ted for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease.
Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions
2018
PLoS ONE
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Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases.
doi:10.1371/journal.pone.0199435
pmid:29949603
pmcid:PMC6021074
fatcat:m3heqtlxynebbn6tafmrc3kunq
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... wever, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a largescale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible diseasegene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an Fmeasure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease-gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science.
Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing
2022
Biomolecules
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Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network
doi:10.3390/biom12050690
pmid:35625619
fatcat:w26kd23w7rfbpixgmn4zsjuaoe
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... ion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.
Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins
2020
International Journal of Molecular Sciences
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Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The
doi:10.3390/ijms21041310
pmid:32075230
pmcid:PMC7072978
fatcat:7xi6ptfj65b4ppw4pdfbns47ge
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... on scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science.
Molecular Karyotyping and Exome Analysis of Salt-Tolerant Rice Mutant from Somaclonal Variation
2014
The Plant Genome
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LPT123-TC171 is a salt-tolerant (ST) and drought-tolerant (DT) rice line that was selected from somaclonal variation of the original Leuang Pratew 123 (LPT123) rice cultivar. The objective of this study was to identify the changes in the rice genome that possibly lead to ST and/or DT characteristics. The genomes of LPT123 and LPT123-TC171 were comparatively studied at the four levels of whole chromosomes (chromosome structure including telomeres, transposable elements, and DNA sequence changes)
doi:10.3835/plantgenome2014.04.0016
fatcat:puis6yxyufci3arknavbnqtl3a
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... by using next-generation sequencing analysis. Compared with LPT123, the LPT123-TC171 line displayed no changes in the ploidy level, but had a significant deficiency of chromosome ends (telomeres). The functional genome analysis revealed new aspects of the genome response to the in vitro cultivation condition, where exome sequencing revealed the molecular spectrum and pattern of changes in the somaclonal variant compared with the parental LPT123 cultivar. Mutation detection was performed, and the degree of mutations was evaluated to estimate the impact of mutagenesis on the protein functions. Mutations within the known genes responding to both drought and salt stress were detected in 493 positions, while mutations within the genes responding to only salt stress were found in 100 positions. The possible functions of the mutated genes contributing to salt or drought tolerance were discussed. It was concluded that the ST and DT characteristics in the somaclonal variegated line resulted from the base changes in the salt-and drought-responsive genes rather than the changes in chromosome structure or the large duplication or deletion in the specific region of the genome.
Two-State Co-Expression Network Analysis to Identify Genes Related to Salt Tolerance in Thai rice
2018
Genes
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Khao Dawk Mali 105 (KDML105) rice is one of the most important crops of Thailand. It is a challenging task to identify the genes responding to salinity in KDML105 rice. The analysis of the gene co-expression network has been widely performed to prioritize significant genes, in order to select the key genes in a specific condition. In this work, we analyzed the two-state co-expression networks of KDML105 rice under salt-stress and normal grown conditions. The clustering coefficient was applied
doi:10.3390/genes9120594
fatcat:gv4znxkz2rgungrvxzranv6hwa
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... both networks and exhibited significantly different structures between the salt-stress state network and the original (normal-grown) network. With higher clustering coefficients, the genes that responded to the salt stress formed a dense cluster. To prioritize and select the genes responding to the salinity, we investigated genes with small partners under normal conditions that were highly expressed and were co-working with many more partners under salt-stress conditions. The results showed that the genes responding to the abiotic stimulus and relating to the generation of the precursor metabolites and energy were the great candidates, as salt tolerant marker genes. In conclusion, in the case of the complexity of the environmental conditions, gaining more information in order to deal with the co-expression network provides better candidates for further analysis.
Machine learning based analyses on metabolic networks supports high-throughput knockout screens
2008
BMC Systems Biology
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Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics. Results: This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental
doi:10.1186/1752-0509-2-67
pmid:18652654
pmcid:PMC2526078
fatcat:qjnttyoz6jdhngd5535iezh5he
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... ataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium. Conclusion: Our analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets.
Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes
2021
International Journal of Molecular Sciences
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Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring
doi:10.3390/ijms221810019
pmid:34576183
fatcat:dbm4rvzwjne3jpo2oq3cxbfgai
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... ractions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of Plasmodium falciparum genes based on connection profiles in a heterogeneous network between human and Plasmodium falciparum proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model's predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, PF3D7_1248700 and PF3D7_0401800 were found to be involved with muscle contraction and striated muscle tissue development, while PF3D7_1303800 and PF3D7_1201000 were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science.
In Vitro Effects of Cannabidiol on Activated Immune–Inflammatory Pathways in Major Depressive Patients and Healthy Controls
2022
Pharmaceuticals
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Major depressive disorder and major depressive episodes (MDD/MDE) are characterized by the activation of the immune–inflammatory response system (IRS) and the compensatory immune–regulatory system (CIRS). Cannabidiol (CBD) is a phytocannabinoid isolated from the cannabis plant, which is reported to have antidepressant-like and anti-inflammatory effects. The aim of the present study is to examine the effects of CBD on IRS, CIRS, M1, T helper (Th)-1, Th-2, Th-17, T regulatory (Treg) profiles, and
doi:10.3390/ph15040405
pmid:35455402
pmcid:PMC9032852
fatcat:mcwllh2gangcbmywgih77sfiou
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... growth factors in depression and healthy controls. Culture supernatant of stimulated (5 μg/mL of PHA and 25 μg/mL of LPS) whole blood of 30 depressed patients and 20 controls was assayed for cytokines using the LUMINEX assay. The effects of three CBD concentrations (0.1 µg/mL, 1 µg/mL, and 10 µg/mL) were examined. Depression was characterized by significantly increased PHA + LPS-stimulated Th-1, Th-2, Th-17, Treg, IRS, CIRS, and neurotoxicity profiles. CBD 0.1 µg/mL did not have any immune effects. CBD 1.0 µg/mL decreased CIRS activities but increased growth factor production, while CBD 10.0 µg/mL suppressed Th-1, Th-17, IRS, CIRS, and a neurotoxicity profile and enhanced T cell growth and growth factor production. CBD 1.0 to 10.0 µg/mL dose-dependently decreased sIL-1RA, IL-8, IL-9, IL-10, IL-13, CCL11, G-CSF, IFN-γ, CCL2, CCL4, and CCL5, and increased IL-1β, IL-4, IL-15, IL-17, GM-CSF, TNF-α, FGF, and VEGF. In summary, in this experiment, there was no beneficial effect of CBD on the activated immune profile of depression and higher CBD concentrations can worsen inflammatory processes.
Comparison between the Transcriptomes of 'KDML105' Rice and a Salt-Tolerant Chromosome Segment Substitution Line
2019
Genes
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'KDML105' rice, known as jasmine rice, is grown in northeast Thailand. The soil there has high salinity, which leads to low productivity. Chromosome substitution lines (CSSLs) with the 'KDML105' rice genetic background were evaluated for salt tolerance. CSSL18 showed the highest salt tolerance among the four lines tested. Based on a comparison between the CSSL18 and 'KDML105' transcriptomes, more than 27,000 genes were mapped onto the rice genome. Gene ontology enrichment of the significantly
doi:10.3390/genes10100742
pmid:31554292
pmcid:PMC6827086
fatcat:6u2k5s6dyrcdffdcufujxpnyla
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... fferentially expressed genes (DEGs) revealed that different mechanisms were involved in the salt stress responses between these lines. Biological process and molecular function enrichment analysis of the DEGs from both lines revealed differences in the two-component signal transduction system, involving LOC_Os04g23890, which encodes phototropin 2 (PHOT2), and LOC_Os07g44330, which encodes pyruvate dehydrogenase kinase (PDK), the enzyme that inhibits pyruvate dehydrogenase in respiration. OsPHOT2 expression was maintained in CSSL18 under salt stress, whereas it was significantly decreased in 'KDML105', suggesting OsPHOT2 signaling may be involved in salt tolerance in CSSL18. PDK expression was induced only in 'KDML105'. These results suggested respiration was more inhibited in 'KDML105' than in CSSL18, and this may contribute to the higher salt susceptibility of 'KDML105' rice. Moreover, the DEGs between 'KDML105' and CSSL18 revealed the enrichment in transcription factors and signaling proteins located on salt-tolerant quantitative trait loci (QTLs) on chromosome 1. Two of them, OsIRO2 and OsMSR2, showed the potential to be involved in salt stress response, especially, OsMSR2, whose orthologous genes in Arabidopsis had the potential role in photosynthesis adaptation under salt stress.
Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells
2020
Molecules
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Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any healing agent. An antiviral drug is urgently required for dengue treatment. Some potential antiviral agents are still in the process of drug discovery, but the development of more effective active molecules is in critical demand. Herein, we aimed
doi:10.3390/molecules25081883
pmid:32325755
pmcid:PMC7221756
fatcat:z5fyjptbxngkplxvt6e42bhg2q
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... provide an efficient technique for target prediction using homopharma and network-based methods, which is reliable and expeditious to hunt for the possible human targets of three phenolic lipids (anarcardic acid, cardol, and cardanol) related to dengue viral (DENV) infection as a case study. Using several databases, the similarity search and network-based analyses were applied on the three phenolic lipids resulting in the identification of seven possible targets as follows. Based on protein annotation, three phenolic lipids may interrupt or disturb the human proteins, namely KAT5, GAPDH, ACTB, and HSP90AA1, whose biological functions have been previously reported to be involved with viruses in the family Flaviviridae. In addition, these phenolic lipids might inhibit the mechanism of the viral proteins: NS3, NS5, and E proteins. The DENV and human proteins obtained from this study could be potential targets for further molecular optimization on compounds with a phenolic lipid core structure in anti-dengue drug discovery. As such, this pipeline could be a valuable tool to identify possible targets of active compounds.
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