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Modulation of multiple protein targets with a single compound is essential for the effective treatment of central nervous system disorders. In our previous G protein-coupled receptor (GPCR) cell-based study, a selective human monoamine oxidase (hMAO)-A inhibitor, eckol, stimulated activity of dopamine D3 and D4 receptors. This result led to our interest in marine phlorotannin-mediated modulation of hMAO enzymes and related GPCRs in neuronal disorders. Here, we evaluate the multi-target effects of phloroglucinol, phlorofucofuroeckol-A (PFF-A), and dieckol by screening their modulatory activity against hMAO-A and -B and various neuronal GPCRs. Among the tested phlorotannins, PFF-A showed the strongest inhibitory activity against both hMAO isoforms, with higher selectivity toward hMAO-B than hMAO-A. Enzyme kinetics and docking data revealed that PFF-A noncompetitively acts on hMAOs into the alternative binding pocket of enzymes with allosteric functions. In a functional assay for GPCR screening, dieckol and PFF-A exhibited a multi-target combination of D3R/D4R agonism and D1/5HT1A/NK1 antagonism. In particular, they effectively stimulated D3R and D4R, compared to other GPCRs. Docking analysis confirmed that dieckol and PFF-A successfully docked into the conserved active sites of D3R and D4R and interacted with aspartyl and serine residues in the orthosteric binding pockets of the respective receptors. Based on our experimental and computational data, we established the structure-activity relationship between tested phlorotannins and target proteins, including hMAOs and GPCRs. Our current findings suggest that hMAO inhibitors dieckol and PFF-A, major phlorotannins of edible brown algae with multi-action on GPCRs, are potential agents for treatment of psychological disorders and Parkinson’s disease.

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ObjectiveThe aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.MethodsWe used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value <0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms.ResultsHierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively.ConclusionThe results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.