Metab2MeSH: annotating compounds with medical subject headings

Sartor, Maureen A.; Ade, Alexander S.; Wright, Zach; States, David J.; Omenn, Gilbert S.; Athey, Brian D.; Karnovsky, Alla

doi:10.1093/bioinformatics/bts156

Cited by 35 publications

(34 citation statements)

References 14 publications

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“…To generate a Cytoscape network model of the lipid subclasses identified in our lipidomics experiment, we input a curated list of gene or compound identifiers from KEGG (Supplementary Data 5) into the MetScape 3 Cytoscape app as previously described 115,116 . To determine whether any of the network nodes had been associated in the literature with disorders that were phenotypically similar to ZIKV syndrome, we used the MetDisease Cytoscape app 117 to query the Metab2MeSH database 118 for seven Medical Subject Heading (MeSH) terms (‘macular degeneration’, ‘optic nerve diseases’, ‘pregnancy complications’, ‘virus diseases’, ‘brain diseases’, ‘congenital abnormalities’, ‘testicular disease’) arbitrarily selected for this purpose.…”

Section: Methodsmentioning

confidence: 99%

A global lipid map defines a network essential for Zika virus replication

Leier

Weinstein

Kyle

et al. 2020

Preprint

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26Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form 27 replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for 28 disease, is poorly understood. Here, we performed comprehensive lipidomics to create a lipid 29 network map during ZIKV infection. We found that ZIKV significantly alters host lipid 30 composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic 31 expression of ZIKV NS4B protein resulted in similar changes, demonstrating a role for NS4B in 32 modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, 33including human neural progenitor cells, blocked ZIKV infection. Additionally, the sphingolipid 34 ceramide redistributes to ZIKV replication sites and increasing ceramide levels by multiple 35 pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network 36 with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV 37 infection. Results 112 ZIKV alters the lipid landscape of host cells 113To understand how cellular lipid metabolism is altered by ZIKV infection, we carried out 114 a global lipidomic survey of the model Huh7 human hepatic carcinoma cell line 20 infected for 24 115 or 48 hours with Asian lineage ZIKV strain FSS13025 (Fig. 1a). Lipids extracted from 116 populations of mock and infected cells (n = 5 biological replicates per condition) were analyzed 117 with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) (Supplementary Data 118 1 and Supplementary Fig. 1a-f). We identified 340 lipid species spanning the phospholipid, 119 sphingolipid, glycerolipid, and sterol classes for which pairwise comparisons of normalized 120 abundance between mock and infected cells could be made (Supplementary Data 1). Of these, 121 80 species (23.5%) showed significant changes in abundance by 24 hours post-infection (hpi) 122 and 172 species (50.6%) were significantly altered by 48 hpi (P < 0.05, ANOVA or g-test) 123( Supplementary Data 1). Principal component analysis (PCA) of these observations confirmed 124 that infection status (mock or ZIKV) and timepoint (24 or 48 hpi) accounted most of these 125 changes, with changes in lipid composition between mock and infected cells increasing over 126 time (Fig. 1b). 127Next, we examined how ZIKV-induced changes in host lipid composition broke down by 128 subclass and species (Fig. 1c, d). A map of the pairwise correlations of all 340 species at 48 hpi 129 ( Supplementary Fig. 2a) revealed that lipid subclasses largely fell into two groups of species 130 that were either enriched or depleted in abundance ( Supplementary Fig. 2b), suggesting that 131 individual metabolic pathways are up-or down-regulated to create a specific lipid milieu around 132 the events of the viral replication cycle. Supporting this, many of the trends we observed were 133 consistent with earlier reports of functional roles for lipids during flavivirus infection. In ...

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Section: Methodsmentioning

confidence: 99%

A global lipid map defines a network essential for Zika virus replication

Leier

Weinstein

Kyle

et al. 2020

Preprint

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“…Disease proteins were obtained from the Online Mendelian Inheritance in Man (OMIM) database and GWAS studies, and drug targets were obtained from DrugBank. The drug indications were obtained from the Medication Indication – High Precision Subset (MEDI-HPS) [ 23 ], which was further filtered for strong literature evidence by using the Metab2MeSH tool [ 24 ]. Finally, Guney et al manually checked all drug labels to confirm that they were used to treat the disease.…”

Section: Methodsmentioning

confidence: 99%

Using predicate and provenance information from a knowledge graph for drug efficacy screening

Vlietstra¹,

Vos

Sijbers

et al. 2018

J Biomed Semant

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BackgroundBiomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins.ResultsUsing random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best.ConclusionWe conclude that both predicate and provenance information provide added value for drug efficacy screening.Electronic supplementary materialThe online version of this article (10.1186/s13326-018-0189-6) contains supplementary material, which is available to authorized users.

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“…A limitation of pathway mapping is the relatively low number of metabolites included in the analysis, basically due to the lack of reliable metabolite identifiers. Generation of automated annotations [58] and construction and visualization of metabolite networks [59] attempt to overcome these challenges. Based on the observation that metabolites that are functionally related also change in a similar manner over time or over the course of an event that affects this function, data-driven analysis is another effort to include detected but still unidentified metabolites into pathway analysis [60].…”

Section: Data Analysis and Biological Interpretationmentioning

confidence: 99%

Metabolomics in Sepsis and Its Impact on Public Health

Evangelatos

Bauer

Reumann

et al. 2017

Public Health Genomics

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Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of “-omics” technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an “-omics” technology further down in the “-omics” cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

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Metab2MeSH: annotating compounds with medical subject headings

Cited by 35 publications

References 14 publications

A global lipid map defines a network essential for Zika virus replication

A global lipid map defines a network essential for Zika virus replication

Using predicate and provenance information from a knowledge graph for drug efficacy screening

Metabolomics in Sepsis and Its Impact on Public Health

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