Chemical and Pharmacological Studies of Ardisia Elliptica: Antiplatelet, Anticoagulant Activities and Multivariate Data Analysis for Drug Discovery

Ph.DDOCTOR OF PHILOSOPH

The Utility of Amino Acid Metabolites in the Diagnosis of Major Depressive Disorder and Correlations with Depression Severity

Major depressive disorder (MDD) is a highly prevalent and disabling condition with a high disease burden. There are currently no validated biomarkers for the diagnosis and treatment of MDD. This study assessed serum amino acid metabolite changes between MDD patients and healthy controls (HCs) and their association with disease severity and diagnostic utility. In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. For amino acid profiling, serum samples were analysed and quantified by liquid chromatography-mass spectrometry (LC-MS). Receiver-operating characteristic (ROC) curves were used to classify putative candidate biomarkers. MDD patients had significantly higher serum levels of glutamic acid, aspartic acid and glycine but lower levels of 3-Hydroxykynurenine; glutamic acid and phenylalanine levels also correlated with depression severity. Combining these four metabolites allowed for accurate discrimination of MDD patients and HCs, with 65.7% of depressed patients and 62.9% of HCs correctly classified. Glutamic acid, aspartic acid, glycine and 3-Hydroxykynurenine may serve as potential diagnostic biomarkers, whereas glutamic acid and phenylalanine may be markers for depression severity. To elucidate the association between these indicators and clinical features, it is necessary to conduct additional studies with larger sample sizes that involve a spectrum of depressive symptomatology

Dried Blood Spots as Matrix for Evaluation of Valproate Levels and the Immediate and Delayed Metabolomic Changes Induced by Single Valproate Dose Treatment

The immediate and delayed metabolic changes in rats treated with valproate (VPA), a drug used for the treatment of epilepsy, were profiled. An established approach using dried blood spots (DBS) as sample matrices for gas chromatography/mass spectrometry-based metabolomics profiling was modified using double solvents in the extraction of analytes. With the modified method, some of the previously undetectable metabolites were recovered and subtle differences in the metabolic changes upon exposure to a single dose of VPA between males and female rats were identified. In male rats, changes in 2-hydroxybutyric acid, pipecolic acid, tetratriacontane and stearic acid were found between the control and treatment groups at various time points from 2.5 h up to 24 h. In contrast, such differences were not observed in female rats, which could be caused by the vast inter-individual variations in metabolite levels within the female group. Based on the measured DBS drug concentrations, clearance and apparent volume of distribution of VPA were estimated and the values were found to be comparable to those estimated previously from full blood drug concentrations. The current study indicated that DBS is a powerful tool to monitor drug levels and metabolic changes in response to drug treatment

Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes

Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes

<img src='/image/spc_char/beta.gif' border=0 >-Amyrin from <i>Ardisia elliptica</i> is more potent than aspirin in inhibiting collagen-induced platelet aggregation

275-279Ardisia elliptica Thunberg (Myrsinaceae) is a medicinal plant traditionally used for alleviating chest pains, treatment of fever, diarrhoea, liver poisoning and parturition complications. The objectives of the study were to investigate the effect of A. elliptica on collagen induced platelet aggregation and to isolate and purify potential antiplatelet components. Fresh A. elliptica leaves were extracted using methanol (70% v/v) by Soxhlet extraction and the extract was analysed for its inhibition of collagen-induced platelet aggregation. Inhibition of platelet aggregation was assessed by incubating the extracts with rabbit blood and collagen in a whole blood aggregometer and measuring the impedance. The leaf extract was found to inhibit platelet aggregation with an IC50 value of 167 g/ml. Using bioassay guided fractionation, -amyrin was isolated and purified. The IC50 value of -amyrin was found to be 4.5 g/ml (10.5 M) while that of aspirin was found to be 11 g/ml (62.7 M), indicating that -amyrin was six times as active as aspirin in inhibiting platelet aggregation. This paper is the first report that -amyrin isolated from A. elliptica is more potent than aspirin in inhibiting collagen-induced platelet aggregation. In conclusion, A. elliptica leaves were found to inhibit collagen-induced platelet aggregation and one of the bioactive components responsible for the observed effect was determined to be -amyrin

Characteristics of pulmonary artery strain assessed by cardiovascular magnetic resonance imaging and associations with metabolomic pathways in human ageing

BackgroundPulmonary artery (PA) strain is associated with structural and functional alterations of the vessel and is an independent predictor of cardiovascular events. The relationship of PA strain to metabolomics in participants without cardiovascular disease is unknown.MethodsIn the current study, community-based older adults, without known cardiovascular disease, underwent simultaneous cine cardiovascular magnetic resonance (CMR) imaging, clinical examination, and serum sampling. PA global longitudinal strain (GLS) analysis was performed by tracking the change in distance from the PA bifurcation to the pulmonary annular centroid, using standard cine CMR images. Circulating metabolites were measured by cross-sectional targeted metabolomics analysis.ResultsAmong n = 170 adults (mean age 71 ± 6.3 years old; 79 women), mean values of PA GLS were 16.2 ± 4.4%. PA GLS was significantly associated with age (β = −0.13, P = 0.017), heart rate (β = −0.08, P = 0.001), dyslipidemia (β = −2.37, P = 0.005), and cardiovascular risk factors (β = −2.49, P = 0.001). Alanine (β = −0.007, P = 0.01) and proline (β = −0.0009, P = 0.042) were significantly associated with PA GLS after adjustment for clinical risk factors. Medium and long-chain acylcarnitines were significantly associated with PA GLS (C12, P = 0.027; C12-OH/C10-DC, P = 0.018; C14:2, P = 0.036; C14:1, P = 0.006; C14, P = 0.006; C14-OH/C12-DC, P = 0.027; C16:3, P = 0.019; C16:2, P = 0.006; C16:1, P = 0.001; C16:2-OH, P = 0.016; C16:1-OH/C14:1-DC, P = 0.028; C18:1-OH/C16:1-DC, P = 0.032).ConclusionBy conventional CMR, PA GLS was associated with aging and vascular risk factors among a contemporary cohort of older adults. Metabolic pathways involved in PA stiffness may include gluconeogenesis, collagen synthesis, and fatty acid oxidation

Integration of the cortical haemodynamic response measured by functional near-infrared spectroscopy and amino acid analysis to aid in the diagnosis of major depressive disorder

10.3390/diagnostics11111978Diagnostics1111197