Orthogonal Projection To Latent Structures Analysis Research Articles

Chemical profiling and unraveling of anti-COVID-19 biomarkers of red sage (Lantana camara L.) cultivars using UPLC-MS/MS coupled to chemometric analysis, in vitro study and molecular docking

Ethnopharmacological relevanceRed sage (Lantana camara L.) (Verbenaceae) is a widely spread plant that was traditionally used in Brazil, India, Kenya, Thailand, Mexico, Nigeria, Australia and Southeast Asia for treating several ailments including rheumatism and leprosy. Despite its historical role in relieving respiratory diseases, limited studies progressed to the plant’s probable inhibition to respiratory viruses especially after the striking spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Aim of the studyThis study aimed to investigate the inhibitory activity of different L. camara cultivars to SARS-CoV-2, that was not previously inspected, and clarify their mechanisms of action in the metabolomics viewpoint, and to determine the biomarkers that are related to such activity using UPLC-MS/MS coupled to in vitro-studies and chemometric analysis. Materials and methodsChemical profiling of different cultivars was accomplished via UPLC-MS/MS. Principle component analysis (PCA) and orthogonal projection to latent structures (OPLS) models were built using SIMCA® (multivariate data analysis software). Cytotoxicity and COVID-19 inhibitory activity testing were done followed by TaqMan Real-time RT-PCR (Reverse transcription polymerase chain reaction) assay that aimed to study extracts' effects on RNA-dependent RNA polymerase (RdRp) and E-genes expression levels. Detected biomarkers from OPLS analysis were docked into potential targets pockets to investigate their possible interaction patterns using Schrodinger® suite. ResultsUPLC-MS/MS analysis of different cultivars yielded 47 metabolites, most of them are triterpenoids and flavonoids. PCA plots revealed that inter-cultivar factor has no pronounced effect on the chemical profiles of extracts except for L. camara, cultivar Drap d'or flowers and leaves extracts as well as for L. camara cv Chelsea gem leaves extract. Among the tested extracts, flowers and leaves extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or showed the highest selectivity indices scoring 12.3, 10.1, 8.6 and 7.8, respectively, indicating their relative high safety and efficacy. Leaves and flowers extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or were the most promising inhibitors to viral plaques exhibiting IC50 values of 3.18, 3.67, 4.18 and 5.01 μg/mL, respectively. This was incremented by OPLS analysis that related their promising COVID-19 inhibitory activities to the presence of twelve biomarkers. Inhibiting the expression of RdRp gene is the major mechanism behind the antiviral activity of most extracts at almost all concentration levels. Molecular docking of the active biomarkers against RdRp revealed that isoverbascoside, luteolin-7,4'-O-diglucoside, camarolic acid and lantoic acid exhibited higher docking scores of −11.378, −10.64, −6.72 and −6.07 kcal/mol, respectively, when compared to remdesivir (−5.75 kcal/mol), thus these four compounds can serve as promising anti-COVID-19 candidates. ConclusionFlowers and leaves extracts of four L. camara cultivars were recognized as rich sources of phytoconstituents possessing anti-COVID-19 activity. Combination of UPLC-MS/MS and chemometrics is a promising approach to detect chemical composition differences among the cultivars and correlate them to COVID-19 inhibitory activities allowing to pinpoint possible biomarkers. Further in-vitro and in-vivo studies are required to verify their activity.

Comparative analysis of Hibiscus sabdariffa (roselle) hot and cold extracts in respect to their potential for α-glucosidase inhibition

Roselle (Hibiscus sabdariffa) is a functional food with potential health benefits, consumed either as hot or cold beverage. To ensure quality control of its various products, accurate measurement of active metabolites is warranted. Herein, we propose a combination of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) and nuclear magnetic resonance (NMR) analytical platforms for the untargeted characterization of metabolites in two roselle cultivars, Aswan and Sudan-1. The analyses revealed 33 metabolites, including sugars, flavonoids, anthocyanins, phenolic and aliphatic organic acids. Their relative contents in cultivars were assessed via principle component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS). Impact of the different extraction methods (decoction, infusion and maceration) was compared by quantitative 1H NMR spectroscopy, revealing cold maceration to be optimal for preserving anthocyanins, whereas infusion was more suited for recovering organic acids. The metabolite pattern revealed by the different extraction methods was found in good correlation for their ability to inhibit α-glucosidase enzyme.

NMR-based Metabolomics Analysis of Liver from C57BL/6 Mouse Exposed to Ionizing Radiation.

The effects of ionizing radiation to human health are of great concern in the field of space exploration and for patients considering radiotherapy. However, to date, the effect of high-dose radiation on metabolism in the liver has not been clearly defined. In this study, 1H nuclear magnetic resonance (NMR)-based metabolomics combined with multivariate data analysis was applied to study the changes of metabolism in the liver of C57BL/6 mouse after whole-body gamma (3.0 and 7.8 Gy) or proton (3.0 Gy) irradiation. Principal component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS) were used for classification and identification of potential biomarkers associated with exposure to gamma and proton radiation. The results show that the radiation exposed groups can be well separated from the control group. Where the same dose was received, the proton exposed group was nevertheless well separated from the gamma-exposed group, indicating that different radiation sources induce different alterations in the metabolic profile. Common among all high-dose gamma and proton exposed groups were the statistically decreased concentrations of choline, O-phosphocholine and trimethylamine N-oxide, while the concentrations of glutamine, glutathione, malate, creatinine, phosphate, betaine and 4-hydroxyphenylacetate were statistically and significantly elevated. Since these altered metabolites are associated with multiple biological pathways, the results suggest that radiation induces abnormality in multiple biological pathways. In particular, metabolites such as 4-hydroxyphenylacetate, betaine, glutamine, choline and trimethylamine N-oxide may be prediagnostic biomarkers candidates for ionizing exposure of the liver.

1H NMR Metabolomics Study of Spleen from C57BL/6 Mice Exposed to Gamma Radiation.

Due to the potential risk of accidental exposure to gamma radiation, it’s critical to identify the biomarkers of radiation exposed creatures. In the present study, NMR based metabolomics combined with multivariate data analysis to evaluate the metabolites changed in the C57BL/6 mouse spleen after 4 days whole body exposure to 3.0 Gy and 7.8 Gy gamma radiations. Principal component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS) are employed for classification and identification potential biomarkers associated with gamma irradiation. Two different strategies for NMR spectral data reduction (i.e., spectral binning and spectral deconvolution) are combined with normalize to constant sum and unit weight before multivariate data analysis, respectively. The combination of spectral deconvolution and normalization to unit weight is the best way for identifying discriminatory metabolites between the irradiation and control groups. Normalized to the constant sum may achieve some pseudo biomarkers. PCA and OPLS results shown that the exposed groups can be well separated from the control group. Leucine, 2-aminobutyrate, valine, lactate, arginine, glutathione, 2-oxoglutarate, creatine, tyrosine, phenylalanine, π-methylhistidine, taurine, myoinositol, glycerol and uracil are significantly elevated while ADP is decreased significantly. These significantly changed metabolites are associated with multiple metabolic pathways and may be potential biomarkers in the spleen exposed to gamma irradiation.

PTH-080 Informing ulcerative colitis pathophysiology and outcomes through metabolic profiling

Introduction Ulcerative colitis (UC) is a chronic gastrointestinal disorder involving a complex interplay between host genetics, mucosal immunology, gut microbiome and the environment. A complete understanding of aetiology and biomarkers predicting outcome are lacking. We used High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR MAS NMR) spectroscopy to generate metabolic profiles from intact colonic biopsies from UC patients and controls, to inform us about disease pathophysiology and severity. Method Colonic tissue biopsies were acquired from 20 individuals (10 UC patients, and 10 controls who had normal mucosa on colonoscopy) at the time of routine colonoscopy. Metabolic signatures were obtained by HR-MAS NMR followed by multivariate pattern-recognition analysis to investigate differences between cohorts. 2 dimensional correlation spectroscopy (COSY) was performed to identify metabolites. Results Principal component analysis (PCA) identified one outlier which was excluded from further analysis. Orthogonal projection to latent structures analysis (OPLS) was applied to remaining cases and distinguished UC and control cohorts with significant predictive accuracy. The OPLS model supported good fit (R2Y 0.8362) and predictability (Q2Y 0.5026) parameters. The data analysis showed that colonic biopsies from UC patients contained relatively higher levels of antioxidants (ascorbate) and ethanolamine compared to those obtained from controls. A novel finding was that the model was capable of stratifying the samples based on severity (quiescent, mild, moderate or severe), which has not been demonstrated in previous metabonomic studies on colonic samples of ulcerative colitis. ![Abstract PTH-080 Figure 1][1] Abstract PTH-080 Figure 1 Conclusion HR-MAS NMR based metabolic profiling has identified distinct differences in UC patients and controls, in particular increased levels of antioxidants and amino acid which may be required in greater quantities during catabolic conditions such as UC. This study shows the utility of HR-MAS NMR in exploring diagnostic possibilities, disease severity stratification and generating a better understanding of UC pathophysiology. Disclosure of interest None Declared. Reference 1. Bjerrum JT, Nielsen OH, Hao F et al . Metabonomics in ulcerative colitis: diagnostics, biomarker identification, and insight into the pathophysiology. Journal of Proteome Research 2010;9(2):954–962 [1]: pending:yes

Metabolomic Analysis in Severe Childhood Pneumonia in The Gambia, West Africa: Findings from a Pilot Study

BackgroundPneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.Methodology/Principal FindingsEleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). ‘Unsupervised’ (blinded) data were analyzed by Principal Component Analysis (PCA), while ‘supervised’ (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5′-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.Conclusions/SignificanceMetabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.