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1.
Background
Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Therefore, it is urgent to develop a rational method to infer new indications for approved drugs.Results
In this study, we proposed a novel methodology named HeteSim_DrugDisease (HSDD) for the prediction of drug repositioning. Firstly, we build the drug-drug similarity network and disease-disease similarity network by integrating the information of drugs and diseases. Secondly, a drug-disease heterogeneous network is constructed, which combines the drug similarity network, disease similarity network as well as the known drug-disease association network. Finally, HSDD predicts novel drug-disease associations based on the HeteSim scores of different meta-paths. The experimental results show that HSDD performs significantly better than the existing state-of-the-art approaches. HSDD achieves an AUC score of 0.8994 in the leave-one-out cross validation experiment. Moreover, case studies for selected drugs further illustrate the practical usefulness of HSDD.Conclusions
HSDD can be an effective and feasible way to infer the associations between drugs and diseases using on meta-path-based semantic network analysis.2.
Zhaowei Yan Sheng Ma Yan Zhang La Ma Feng Wang Jian Li Liyan Miao 《Biotechnology letters》2017,39(5):745-750
Objectives
To study the structure of a small GTPaseRhoA in complex with PDZRhoGEF and the inhibitor HL47, and to provide an easier template for R&D of RhoA inhibitor.Results
Our initial attempts to obtain a binary complex of RhoA with the inhibitor HL47 were unsuccessful probably due to the presence of GDP. By targeting a ternary complex involving the RhoA-specific guanine nucleotide exchange factor PDZRhoGEF, we eliminated GDP and obtained a 2.3 Å structure of the RhoA-PDZRhoGEF-inhibitor HL47 ternary complex.Conclusion
This structure provides a new template for target-based pharmaceutical design against RhoA.3.
Background
During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.Methods
We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.Results
We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.Conclusions
We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.4.
Hongchao Ji Zhimin Zhang Hongmei Lu 《Metabolomics : Official journal of the Metabolomic Society》2018,14(5):68
Introduction
Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.Objectives
TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.Methods
TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.Results
TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.Conclusion
TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.5.
Background
Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.Results
Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.Conclusions
We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.6.
Xiangrui Yang Shichao Wu Wanyi Xie Anran Cheng Lichao Yang Zhenqing Hou Xin Jin 《Journal of nanobiotechnology》2017,15(1):91
Background
Since the anticancer drugs have diverse inhibited mechanisms to the cancer cells, the use of two or more kinds of anticancer agents may achieve excellent therapeutic effects, especially to the drug-resistant tumors.Results
In this study, we developed a kind of dual drug [methotrexate (MTX) and 10-hydroxycamptothecine (HCPT)] loaded nanoneedles (DDNDs) with pronounced targeting property, high drug loading and prolonged drug release. The anti-solvent precipitation of the HCPT and MTX modified PEG-b-PLGA (PEG-b-PLGA-MTX, PPMTX) leads to nucleation of nanoneedles with nanocrystalline HCPT as the core wrapped with PPMTX as steric stabilizers. In vitro cell uptake studies showed that the DDNDs revealed an obviously targeting property and entered the HeLa cells easier than the nanoneedles without MTX modification. The cytotoxicity tests illustrated that the DDNDs possessed better killing ability to HeLa cells than the individual drugs or their mixture in the same dose, indicating its good synergistic effect and targeting property. The in vivo studies further confirmed these conclusions.Conclusions
This approach led to a promising sustained drug delivery system for cancer diagnosis and treatment.7.
Background
Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.Methods
In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.Results
We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.Conclusions
The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.8.
Emily G. Armitage Andrew D. Southam 《Metabolomics : Official journal of the Metabolomic Society》2016,12(9):146
Introduction
Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.Objectives
Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.Methods
A literature search of the current relevant primary research was performed.Results
Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.Conclusion
Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.9.
Arunachalam Kalaiarasi Renu Sankar Chidambaram Anusha Kandasamy Saravanan Kalyanasundaram Aarthy Selvaraj Karthic Theodore lemuel Mathuram Vilwanathan Ravikumar 《Biotechnology letters》2018,40(2):249-256
Objectives
Copper oxide nanoparticles (CuO NPs) promoting anticancer activity may be due to the regulation of various classes of histone deacetylases (HDACs).Results
Green-synthesized CuO NPs significantly arrested total HDAC level and also suppressed class I, II and IV HDACs mRNA expression in A549 cells. A549 cells treated with CuO NPs downregulated oncogenes and upregulated tumor suppressor protein expression. CuO NPs positively regulated both mitochondrial and death receptor-mediated apoptosis caspase cascade pathway in A549 cells.Conclusion
Green-synthesized CuO NPs inhibited HDAC and therefore shown apoptosis mediated anticancer activity in A549 lung cancer cell line.10.
Claudio Inostroza-Blancheteau Franklin Magnum de Oliveira Silva Fabiola Durán Jaime Solano Toshihiro Obata Mariana Machado Alisdair R. Fernie Marjorie Reyes-Díaz Adriano Nunes-Nesi 《Metabolomics : Official journal of the Metabolomic Society》2018,14(10):138
Introduction
The native potatoes (Solanum tuberosum ssp. tuberosum L.) cultivated on Chiloé Island in southern Chile have great variability in terms of tuber shape, size, color and flavor. These traits have been preserved throughout generations due to the geographical position of Chiloé, as well as the different uses given by local farmers.Objectives
The present study aimed to investigate the diversity of metabolites in skin and pulp tissues of eleven native accessions of potatoes from Chile, and evaluate the metabolite associations between tuber tissues.Methods
For a deeper characterization of these accessions, we performed a comprehensive metabolic study in skin and pulp tissues of tubers, 3 months after harvesting. Specific targeted quantification of metabolites using 96 well microplates, and high-performance liquid chromatography combined with non-targeted metabolite profiling by gas chromatography time-of-flight mass spectrometry were used in this study.Results
We observed differential levels of antioxidant activity and phenolic compounds between skin and pulp compared to a common commercial cultivar (Desireé). In addition, we uncovered considerable metabolite variability between different tuber tissues and between native potatoes. Network correlation analysis revealed different metabolite associations among tuber tissues that indicate distinct associations between primary metabolite and anthocyanin levels, and antioxidant activity in skin and pulp tissues. Moreover, multivariate analysis lead to the grouping of native and commercial cultivars based on metabolites from both skin and pulp tissues.Conclusions
As well as providing important information to potato producers and breeding programs on the levels of health relevant phytochemicals and other abundant metabolites such as starch, proteins and amino acids, this study highlights the associations of different metabolites in tuber skins and pulp, indicating the need for distinct strategies for metabolic engineering in these tissues. Furthermore, this study shows that native Chilean potato accessions have great potential as a natural source of phytochemicals.11.
Hasun Yu Sungji Choo Junseok Park Jinmyung Jung Yeeok Kang Doheon Lee 《BMC systems biology》2016,10(Z1):S2
Background
Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.Methods
We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.Results
We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.Conclusions
Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.12.
Sung-Hwan Cho Sang-Min Park Ho-Sung Lee Hwang-Yeol Lee Kwang-Hyun Cho 《BMC systems biology》2015,10(1):96
Background
Colorectal cancer arises from the accumulation of genetic mutations that induce dysfunction of intracellular signaling. However, the underlying mechanism of colorectal tumorigenesis driven by genetic mutations remains yet to be elucidated.Results
To investigate colorectal tumorigenesis at a system-level, we have reconstructed a large-scale Boolean network model of the human signaling network by integrating previous experimental results on canonical signaling pathways related to proliferation, metastasis, and apoptosis. Throughout an extensive simulation analysis of the attractor landscape of the signaling network model, we found that the attractor landscape changes its shape by expanding the basin of attractors for abnormal proliferation and metastasis along with the accumulation of driver mutations. A further hypothetical study shows that restoration of a normal phenotype might be possible by reversely controlling the attractor landscape. Interestingly, the targets of approved anti-cancer drugs were highly enriched in the identified molecular targets for the reverse control.Conclusions
Our results show that the dynamical analysis of a signaling network based on attractor landscape is useful in acquiring a system-level understanding of tumorigenesis and developing a new therapeutic strategy.13.
Yili Hu Limin Zhang Hai Wang Shan Xu Ayeesha Mujeeb Guangjun Nie Huiru Tang Yulan Wang 《Metabolomics : Official journal of the Metabolomic Society》2017,13(5):49
Introduction
Amphiphilic copolymer nanoparticle-encapsulated multi-target chemotherapeutic drugs have attracted considerable attention due to their favorable drug efficiency and potential application prospect. Studies have shown that an amphiphilic copolymer, methoxypoly(ethylene glycol)-poly(lactide-co-glycolide) modified with ε-polylysine, and encapsulated with hydrophilic doxorubicin, hydrophobic paclitaxel and survivin siRNA profoundly improved the therapeutic effect both in vitro and in vivo.Objectives
To investigate how MCF-7 cells would response to the exposure of these nanoparticles over with time and assess the biological effects of these nanoparticles and their encapsulated drugs in a holistic manner.Methods
MCF-7 cells were treated with PBS, nanocarrier and three encapsulated drugs, respectively. Metabolic alterations associated with nano-drugs exposure were investigated by performing untargeted NMR metabolomics with combination of targeted fatty acids analysis by GC-MS on cell extracts. Altered metabolic pathways were further validated by qRT-PCR approach.Results
Copolymers showed great biocompatibility with cells as it induced transit metabolic disruptions without affecting cell survival rate. The rapid release of encapsulated doxorubicin resulted in inhibition of glycolysis and DNA synthesis, active proteolysis; these metabolic alternations were recovered after 10 h exposure. However, the combination use of multiple drugs consistently induced cell cycle arrest and apoptosis evidenced by reduction in glycolysis, active proteolysis, stimulated O-GlcNAcylation, reduced the PC:GPC ratio and fatty acids accumulation. Prolonged exposure to encapsulated-multiple-drugs also induced oxidative stress to cells.Conclusion
These findings provide important insight into the biological effects of nanoparticles and their encapsulated drugs while demonstrate that metabolomics is a powerful approach to evaluate the biological effects of nano-drugs.14.
Marco F. Moedas Arno G. van Cruchten Lodewijk IJlst Wim Kulik Isabel Tavares de Almeida Luísa Diogo Ronald J. A. Wanders Margarida F. B. Silva 《Metabolomics : Official journal of the Metabolomic Society》2016,12(8):142
Background
Therapeutic administration of the drug valproate (VPA) results in metabolic changes at the hepatic level that have not been fully characterized. Interference of this branched-chain fatty acid with the oxidative metabolism of amino acids may have consequences for the downstream biosynthesis of essential cofactors.Objectives
We aimed to evaluate the effect of VPA on amino acid and NAD+ metabolism using targeted MS-based metabolite profiling.Methods
Plasma samples from patients under chronic treatment with VPA were analyzed. VPA was administered to Wistar rats mimicking prolonged and acute treatment, the latter with two different doses. Plasma and liver samples were collected for targeted metabolomics studies using UPLC-MS/MS and GC-FID.Results
Analysis of amino acids in rat plasma and liver and in human plasma demonstrated that drug intake is associated with a particularly significant drop in the levels of tryptophan, and increased levels of glycine and lysine. The lowered plasma tryptophan levels prompted us to study the intracellular content of tryptophan and various nicotinamide adenine dinucleotides. A significant decrease of NAD+ and NADP+ was observed in the liver of rats after the single administration of VPA at two different doses, but not after repeated administration.Conclusion
The observed accumulation of kynurenine intermediates in rat liver tissue suggests a drug-induced interference with the de novo pathway of NAD+ biosynthesis. These findings provide novel insights into the mechanisms of VPA associated hepatocellular dysfunction and/or toxicity, but with possible major relevance to the anticancer effects of the drug.15.
N. Cesbron A.-L. Royer Y. Guitton A. Sydor B. Le Bizec G. Dervilly-Pinel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(8):99
Introduction
Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.Objectives
In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.Methods
The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.Results
A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.Conclusion
The workflow generated repeatable and informative fingerprints for robust metabolome characterization.16.
Takuya Mori Hayliang Ngouv Morihiro Hayashida Tatsuya Akutsu Jose C. Nacher 《BMC systems biology》2018,12(1):37
Background
Current technology has demonstrated that mutation and deregulation of non-coding RNAs (ncRNAs) are associated with diverse human diseases and important biological processes. Therefore, developing a novel computational method for predicting potential ncRNA-disease associations could benefit pathologists in understanding the correlation between ncRNAs and disease diagnosis, treatment, and prevention. However, only a few studies have investigated these associations in pathogenesis.Results
This study utilizes a disease-target-ncRNA tripartite network, and computes prediction scores between each disease-ncRNA pair by integrating biological information derived from pairwise similarity based upon sequence expressions with weights obtained from a multi-layer resource allocation technique. Our proposed algorithm was evaluated based on a 5-fold-cross-validation with optimal kernel parameter tuning. In addition, we achieved an average AUC that varies from 0.75 without link cut to 0.57 with link cut methods, which outperforms a previous method using the same evaluation methodology. Furthermore, the algorithm predicted 23 ncRNA-disease associations supported by other independent biological experimental studies.Conclusions
Taken together, these results demonstrate the capability and accuracy of predicting further biological significant associations between ncRNAs and diseases and highlight the importance of adding biological sequence information to enhance predictions.17.
D. Jacob C. Deborde M. Lefebvre M. Maucourt A. Moing 《Metabolomics : Official journal of the Metabolomic Society》2017,13(4):36
Introduction
Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.Objectives
The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.Methods
NMRProcFlow was developed to be a graphical and interactive 1D NMR (1H & 13C) spectra processing tool.Results
NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.Conclusion
Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.18.
Ferran Casbas Pinto Srinivarao Ravipati David A. Barrett T. Charles Hodgman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):81
Introduction
It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives
This work simplifies this process.Methods
A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results
The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion
This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.19.