Orthogonal Projection To Latent Structure Research Articles

Exploring the dynamics of bioactive metabolites changes in barley grains (Hordeum vulgare L.) during roasting: Insights from UPLC-QqQ-MS/MS analysis combined to chemometrics

This investigation delves into the dynamic metabolic shifts within barley grains during the roasting process, employing UPLC-QqQ-MS/MS analysis. The complex spectrum of metabolites before and after roasting is revealed. The resulting data, unveils substantial transformations in chemical composition during roasting. A total of 62 chromatographic peaks spanning phenolic compounds, flavones, Millard Reaction Products, amino acids, lignans, vitamins, folates, and anthocyanins were annotated. Leveraging UPLC-QqQ-MS/MS analysis, we scrutinized the intricate metabolite profile before and after roasting where the roasting process was found to trigger dynamic changes across diverse metabolite classes particularly Millard Reaction Products, produced through the Maillard reaction, with dihydro-5-methyl-5H-cyclopentapyrazine, maltol and hydroxy maltol emerging as discerning markers of roasting progression. Amino acids and sugars showed degradation, while beta-glucan, a signature barley sugar, experienced notable decline. Folate derivatives witnessed pronounced reduction, aligning with the heat sensitivity of folates. Harnessing the power of multivariate data analysis, the consequences of roasting materialize through distinct clusters in PCA and OPLS-DA plots. Noteworthy, roasting duration governs the trajectory of metabolic divergence, culminating in the identification of roasting-specific markers. Epigallocatechin, procyanidin B, 10-HCO-H4 folate, and hordatine A emerge as pivotal discriminators. Orthogonal Projection to Latent Structure (OPLS) analysis linked anti-inflammatory activity with 30-min, 1-hour, and 1.5-hour roasted samples, with hordatine B in addition to some Millard Reaction Products being correlated with pro-inflammatory marker downregulation.. This study encapsulates the intricate metabolic metamorphosis ignited by roasting in barley grains, offering a holistic comprehension of their potential health-enhancing attributes. Key metabolites act as poignant indicators of these transformations, substantiating the complex interplay between roasting and the barley grain metabolome.

Identification of potential quality markers in Indonesia’s Arabica specialty coffee using GC/MS-based metabolomics approach

IntroductionThe cupping test is a widely used method for quality assessment of Arabica coffee. However, the cupping test is limited by the low number of certified panelists and the low throughput. Therefore, an analytical-based quality assessment may be a promising tool to complement the cupping test. A present, there is no report investigating quality marker candidates, focusing only on “specialty” grade Arabica coffee from Indonesia.ObjectiveThis study identified the potential quality marker(s) in Arabica Specialty coffee at different stages (green beans, roasted beans, and brewed coffee.MethodsThe metabolite profiles of ten different Arabica specialty-grade coffees were analyzed with different cup scores using gas chromatography–mass spectrometry (GC/MS). From the ten samples, green coffee beans, roasted coffee beans, and brewed coffee were selected. In addition, an orthogonal projection to latent structure (OPLS) regression analysis was conducted to obtain a potential quality marker based on the variable importance in projection (VIP). The potential quality marker(s) were validated by GC/MS metabolome profiling and OPLS analysis of different sets of samples consisting of 35 Arabica specialty-grade coffee samples.ResultsIn Arabica coffee samples, the OPLS model of the three stages showed galactinol to have a high VIP score. Galactinol showed a consistent positive correlation with cup scores at all stages of coffee production (green beans, roasted beans, and brewed coffee). The correlation suggests galactinol is a potential quality marker after further validation using different samples.ConclusionGC/MS combined with OPLS regression analysis suggested galactinol as a quality marker and provide an early screening method for Arabica coffee quality that complements the cupping test performed by certified panelists.

GC-MS Based Metabolite Profiling and Antibacterial Activity of Torch Ginger (<i>Etlingera elatior</i>) Flowers Extract

Torch ginger (Etlingera elatior) flowers are well known for their antibacterial effects against Staphylococcus aureus, however, the active compounds are still unknown. The purpose of this study was to conduct GC-MS-based metabolite profiling of torch ginger flower and identify compounds correlated with its S. aureus antibacterial activity using Orthogonal Projection to Latent Structure (OPLS). Using the well diffusion method, the antibacterial activity of ethanol extract, hexane, chloroform, and ethyl acetate fractions with a concentration of 80 mg/mL were investigated. The ethyl acetate fraction inhibited S. aureus growth the most (diameter of inhibition zone, DIZ 13.00–13.20 mm), while the hexane (DIZ 9.55–10.05 mm) and chloroform (DIZ 10.00–11.00 mm) fractions had moderate inhibitory activity, but the ethanol extract had no antibacterial effect. Using OPLS analysis, the GC-MS metabolite profile of all extracts and fractions was linked with the profile of antibacterial activity. This analysis revealed that Dodecanoic acid, 5-Tetradecene, and n-Hexadecanoic acid were identified as the compounds that were significantly connected with antibacterial activity.

Metabolomics approach to elucidate the importance of count size in commercial penaeid shrimps: white leg shrimp (Litopenaeus vannamei) and black tiger shrimp (Penaeusmonodon)

Currently, count size is applied globally as a grading standard to sell head-less shell-on farmed shrimp. Although count size does not indicate directly the quality of shrimp, the price of shrimp generally increases proportionally with the increase of shrimp size. The importance of the size of white leg shrimp has been previously reported, where a strong correlation was found between size and metabolome data. In this study, we aimed to improve the predictive power of the orthogonal projection to latent structure (OPLS) model by expanding the metabolite coverage using liquid chromatography mass-spectrometry (LC/MS) and gas chromatography-mass spectrometry (GC/MS) analysis. The training set consisted of 11 different-sized white leg shrimps from Indonesia and was validated in a step-wise manner by introducing an independent dataset consisting of commercial shrimp from the Japanese market. The first validation set consisted of commercial white leg shrimp, resulting in standard deviation error estimation and prediction values of 1.648 and 2.617, respectively. IMP and AMP, which are metabolites responsible for the umami taste in crustaceans, showed the highest variable importance in projection (VIP) scores and positively correlated with the increase in shrimp size. The second validation was carried out to evaluate the applicability of the size-metabolome relationship to other commercial penaeid shrimp species. The commercial black tiger shrimps with count sizes of 31/40, 21/25, 16/20, and 13/15 failed to predict the size of shrimp, suggesting that the importance of size in relation to the metabolome profile was rather species-specific.

Investigation of Yacon Leaves (Smallanthus sonchifolius) for α-Glucosidase Inhibitors Using Metabolomics and In Silico Approach.

Yacon (Smallanthus sonchifolius (Poepp.) H. Robinson) leaves is traditionally consumed as herbal tea in many countries including Indonesia. This plant's antidiabetic properties have been extensively researched, but studies on the responsible active compound identification are scarce. Information on the active compounds is critical for the consistency of Yacon herbal tea quality. The aim of this study was to identify α-glucosidase inhibitors in Indonesian Yacon leaves grown in two different locations using FTIR- and LC-MS/MS-based metabolomics in combination with in silico technique. Yacon leaves ethanol (50 and 95%) and water extracts were tested for α-glucosidase inhibitory activity, with the 95% ethanol extract being the most active. Geographical origins were found to have no major impact on the activity. In parallel, chemical profile of Yacon leaves extract was determined using FTIR and LC-MS/MS. Orthogonal Projection to Latent Structure (OPLS) was used to analyze both sets of data. OPLS analysis of FTIR data showed that compounds associated to α-glucosidase inhibitor activity included those with functional groups -OH, stretched CH, carbonyl, and alkene. It was consistent with the result of OPLS analysis of LC-MS/MS data, which revealed that based on their VIP and Y-related coefficient value, nystose, 1-kestose, luteolin-3'-7-di-O-glucoside, and 1,3-O-dicaffeoilquinic acid isomers, strongly linked to Yacon's α-glucosidase inhibitor activity. In silico study supported these findings, revealing that the four compounds were potent α-glucosidase inhibitors with docking score in the range of -100.216 to -115.657kcal/mol, which are similar to acarbose (-115.774kcal/mol) as a reference drug.

Identification of resistant pharmaceuticals in ozonation using QSAR modeling and their fate in electro-peroxone process

The abatements of 89 pharmaceuticals in secondary effluent by ozonation and the electro-peroxone (E-peroxone) process were investigated. Based on the results, a quantitative structure-activity relationship (QSAR) model was developed to explore relationship between chemical structure of pharmaceuticals and their oxidation rates by ozone. The orthogonal projection to latent structure (OPLS) method was used to identify relevant chemical descriptors of the pharmaceuticals, from large number of descriptors, for model development. The resulting QSAR model, based on 44 molecular descriptors related to the ozone reactivity of the pharmaceuticals, showed high goodness of fit(R2 = 0.963) and predictive power (Q2 = 0.84). After validation, the model was used to predict second-order rate constants of 491 pharmaceuticals of special concern ( $${k_{{{\rm{O}}_3}}}$$ ) including the 89 studied experimentally. The predicted $${k_{{{\rm{O}}_3}}}$$ values and experimentally determined pseudo-first order rate constants of the pharmaceuticals’ abatement during ozonation (kOZ) and the E-peroxone process (kEP) were then used to assess effects of switching from ozonation to the E-peroxone process on removal of these pharmaceuticals. The results indicate that the E-peroxone process could accelerate the abatement of pharmaceuticals with relatively low ozone reactivity ( $${k_{{{\rm{O}}_3}}} < \; \sim {10^2}{{\rm{M}}^{- 1}} \cdot {{\rm{s}}^{ - 1}}$$ ) than ozonation (3–10 min versus 5–20 min). The validated QSAR model predicted 66 pharmaceuticals to be highly O3-resistant. The developed QSAR model may be used to estimate the ozone reactivity of pharmaceuticals of diverse chemistry and thus predict their fate in ozone-based processes.

Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods.

Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant-pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography-mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy.

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

NMR-Based Metabolic Profiling of Field-Grown Leaves from Sugar Beet Plants Harbouring Different Levels of Resistance to Cercospora Leaf Spot Disease.

Cercospora leaf spot (CLS) is one of the most serious leaf diseases for sugar beet (Beta vulgaris L.) worldwide. The breeding of sugar beet cultivars with both high CLS resistance and high yield is a major challenge for breeders. In this study, we report the nuclear magnetic resonance (NMR)-based metabolic profiling of field-grown leaves for a subset of sugar beet genotypes harbouring different levels of CLS resistance. Leaves were collected from 12 sugar beet genotypes at four time points: seedling, early growth, root enlargement, and disease development stages. 1H-NMR spectra of foliar metabolites soluble in a deuterium-oxide (D2O)-based buffer were acquired and subjected to multivariate analyses. A principal component analysis (PCA) of the NMR data from the sugar beet leaves shows clear differences among the growth stages. At the later time points, the sugar and glycine betaine contents were increased, whereas the choline content was decreased. The relationship between the foliar metabolite profiles and resistance level to CLS was examined by combining partial least squares projection to latent structure (PLS) or orthogonal PLS (OPLS) analysis and univariate analyses. It was difficult to build a robust model for predicting precisely the disease severity indices (DSIs) of each genotype; however, GABA and Gln differentiated susceptible genotypes (genotypes with weak resistance) from resistant genotypes (genotypes with resistance greater than a moderate level) before inoculation tests. The results suggested that breeders might exclude susceptible genotypes from breeding programs based on foliar metabolites profiled without inoculation tests, which require an enormous amount of time and effort.

Application of a new method, orthogonal projection to latent structure (OPLS) combined with principal component analysis (PCA), to screening of prostaglandin E2 production inhibitory flavonoids in Scutellaria Root.

A new method consisting of orthogonal projection to latent structure (OPLS) and modified principal component analysis (PCA) was applied to a screening of bioactive compounds from natural products. In this report, extracts of 52 Scutellaria Root (the root ofScutellaria baicalensis Georgi) samples were analyzed by high-performance liquid chromatography (HPLC), and their inhibitory activities towards prostaglandin E2 (PGE2) production in a murine macrophage-like cell line J774.1 were examined. Wogonin and oroxylin A were predicted to be strong inhibitors of PGE2 production by OPLS analysis of the data. However, 6-methoxywogonin, which has been reported to have inhibitory activity, was omitted. Modified PCA was then applied to these data as a filter to exclude compounds less relevant to the activity, and OPLS analysis was applied to the modified data. As a result, this method predicted wogonin, oroxylin A and 6-methoxywogonin to be strong inhibitors of PGE2 production without any prior knowledge. The predictions by the OPLS combined with PCA method of PGE2 production inhibitory activities of 52 samples showed good agreement with the actual data. This method is simple and effective and can be used in screening of bioactive natural compounds without any prior knowledge.

1H NMR Metabolomics Study of Metastatic Melanoma in C57BL/6J Mouse Spleen.

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.

Metastatic Melanoma Induced Metabolic Changes in C57BL/6J Mouse Stomach Measured by 1H NMR Spectroscopy

Melanoma is a malignant tumor of melanocytes with high capability of invasion and rapid metastasis to other organs. Malignant melanoma is the most common metastatic malignancy found in Gastrointestinal Tract (GI). In this work, the 1H NMR-based metabolomics approach is used to investigate the metabolite profile differences of stomach tissue extracts of metastatic B16-F10 melanoma and control groups in C57BL/6J mouse and to search for specific metabolite biomarker candidates. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to evaluate important metabolites responsible for discriminating the control and the melanoma groups. Both PCA and OPLS results reveal that the melanoma group can be well separated from its control group. Among the 50 identified metabolites, it is found that the concentrations of 19 metabolites are significantly changed with the levels of O-phosphocholine and hypoxanthine down-regulated while the levels of isoleucine, leucine, valine, isobutyrate, threonine, cadaverine, alanine, glutamate, glutamine, methionine, citrate, asparagine, tryptophan, glycine, serine, uracil, and formate up-regulated in the melanoma group. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in stomach.

Identification of Serum Proton NMR Metabolomic Fingerprints Associated with Hepatocellular Carcinoma in Patients with Alcoholic Cirrhosis

Metabolomics depicts metabolic changes in biologic systems using a multiparametric analysis technique. This study assessed the metabolomic profiles of serum, obtained by proton nuclear magnetic resonance (NMR) spectroscopy, from cirrhotic patients with and without hepatocellular carcinoma (HCC). The study included 154 consecutive patients with compensated biopsy-proven alcoholic cirrhosis. Among these, 93 had cirrhosis without HCC, 28 had biopsy-proven HCC within the Milan criteria and were eligible for curative treatment (small HCC), and 33 had HCC outside the Milan criteria (large HCC). Proton spectra were acquired at 500 MHz. An orthogonal partial latent structure [orthogonal projection to latent structure (OPLS)] analysis model was built to discriminate large HCC spectra from cirrhotic spectra. Small HCC spectra were secondarily projected using previously built OPLS discriminant components. The OPLS model showed discrimination between cirrhotic and large HCC spectra. Metabolites that significantly increased with large HCC were glutamate, acetate, and N-acetyl glycoproteins, whereas metabolites that correlated with cirrhosis were lipids and glutamine. Projection of small HCC samples into the OPLS model showed a heterogeneous distribution between large HCC and cirrhotic samples. Small HCC patients with metabolomic profile similar to those of large HCC group had higher incidences of recurrence or death during follow-up. Serum NMR-based metabolomics identified metabolic fingerprints that could be specific to large HCC in cirrhotic livers. From a metabolomic standpoint, some patients with small HCC, who are eligible for curative treatments, seem to behave as patients with advanced cancerous disease. It would be useful to further prospectively investigate these patients to define a subgroup with a worse prognosis.

Statistically Integrated Metabonomic−Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.