Eya2 is critical for the E2A‑HLF‑mediated immortalization of mouse hematopoietic stem/progenitor cells

Maharjan, Bishnu Devi; Ono, Ryoichi; Nosaka, Tetsuya

doi:10.3892/ijo.2019.4673

Cited by 1 publication

(1 citation statement)

References 51 publications

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“…The complex network contained five genes, which were CYP4B1, GALNT3, DAO, NDST4, EYA2, and 13 metabolites, which were Sphingomyelin, Dihydroceramide, Sphingosine, Thiamin diphosphate, 1-Acyl-sn-glycero-3-phosphocholine, Phosphatidylcholine, Choline, Phosphatidate, Phosphatidylserine, Phosphatidylethanolamine, L-Cysteine, beta-D-Galactosyl-1,4-beta-D-Glucosylceramide and Sulfatide. Related genes encode enzymes belonging to different superfamilies, catalyzing many reactions involved in: metabolism of certain xenobiotics ( Lim et al, 2020 ; Baer and Rettie, 2006 ), posttranslational modification of protein ( Takashi and Fukumoto, 2020 ), N-methyl-d-aspartate receptor regulation, glutamate metabolism ( Yang et al, 2013 ), modification in the heparan sulfate biosynthetic pathway ( Li et al, 2018 ) and transcriptional activation ( Devi Maharjan et al, 2019 ). The results of the presented integrated metabolomics and transcriptomics analysis prove that the pathway is concentrated on Sphingolipid metabolism and Glycerophospholipid metabolism, which is consistent with the enrichment results.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Metabolomics and Transcriptomics Reveals Novel Biomarkers and Mechanistic Insights on Lorlatinib Crosses the Blood-Brain Barrier

Chen

Shi

et al. 2021

Front. Pharmacol.

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Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.

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