Elucidating the biological characteristics of overweight populations based on urine Raman spectroscopy and bioinformatics analysis.

MotivationDNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes.ResultsHere we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub’s leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

Comparative analysis of targeted metabolomics: Dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

Freezing is employed to preserve the quality of pomegranate juice (PJ) for producing nutritious ready-to-drink beverages. However, the aroma compounds of PJ undergo significant alterations post-freezing. This study aimed to examine the impacts of different freezing temperatures (-20 °C, -40 °C, and -80 °C) on the flavor profiles of PJ, using fresh PJ as a control. The quantitative descriptive sensory analysis (QDA) results showed that grassy, woody, and fruity attributes of PJ were notably diminished following the freezing treatment. In total, 34 volatiles were identified by gas chromatography-mass spectrometry (GC-MS), of which 14 were perceived by GC-olfactometry (GC-O). Together with orthogonal partial least squares-discriminant analysis (OPLS-DA) and OAV tests, five volatiles were determined as key differential markers, and three (hexanol, (Z)-3-hexen-1-ol and β-myrcene) were selected for further odorant addition experiments. The results verified that (Z)-3-hexen-1-ol was the primary odorant for enhancing the grassy and fruity notes of PJ, while hexanol and β-myrcene were crucial for enhancing grassy and woody attributes, respectively.

Pulsed light, as a postharvest technology, is an alternative to traditional fungicides, and can be used on a wide variety of fruit and vegetables for sanitization or pathogen control. In addition to these applications, other effects also are detected in vegetal cells, including changes in metabolism and secondary metabolite production, which directly affect disease control response mechanisms. This study aimed to evaluate pulsed ultraviolet light in controlling postharvest rot, caused by Fusarium pallidoroseum in ‘Spanish’ melon, in natura, and its implications in disease control as a function of metabolomic variation to fungicidal or fungistatic effects. The dose of pulsed light (PL) that inhibited F. pallidoroseum growth in melons (Cucumis melo var. Spanish) was 9 KJ m–2. Ultra-performance liquid chromatography (UPLC) coupled to a quadrupole-time-of-flight (QTOF) mass analyzer identified 12 compounds based on tandem mass spectrometry (MS/MS) fragmentation patterns. Chemometric analysis by Principal Components Analysis (PCA) and Orthogonal Partial Least Squared Discriminant Analysis (OPLS-DA) and corresponding S-Plot were used to evaluate the changes in fruit metabolism. PL technology provided protection against postharvest disease in melons, directly inhibiting the growth of F. pallidoroseum through the upregulation of specific fruit biomarkers such as pipecolic acid (11), saponarin (7), and orientin (3), which acted as major markers for the defense system against pathogens. PL can thus be proposed as a postharvest technology to prevent chemical fungicides and may be applied to reduce the decay of melon quality during its export and storage.

This study investigated the effect of high pressure processing (HPP) on the fatty acids and amino acids content in New Zealand Diamond Shell (Spisula aequilatera), Storm Shell (Mactra murchisoni), and Tua Tua (Paphies donacina) clams. The clam samples were subjected to HPP with varying levels of pressure (100, 200, 300, 400, 500, and 600 MPa) and holding times (5 and 600 s) at 20 °C. Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were deployed to fingerprint the discriminating amino and fatty acids post-HPP processing while considering their inherent biological variation. Aspartic acid (ASP), isoleucine (ILE), leucine (LEU), lysine (LYS), methionine (MET), serine (SER), threonine (THR), and valine (VAL) were identified as discriminating amino acids, while C18:0, C22:1n9, C24:0, and C25:5n3 were identified as discriminating fatty acids. These amino and fatty acids were then subjected to mixed model ANOVA. Mixed model ANOVA was employed to investigate the influence of HPP pressure and holding times on amino acids and fatty acids in New Zealand clams. A significant effect of pressure levels was reported for all three clam species for both amino and fatty acids composition. Additionally, holding time was a significant factor that mainly influenced amino acid content. butnot fatty acids, suggesting that hydrostatic pressure hardly causes hydrolysis of triglycerides. This study demonstrates the applicability of OPLS-DA in identifying the key discriminating chemical components prior to traditional ANOVA analysis. Results from this research indicate that lower pressure and shorter holding time (100 MPa and 5 s) resulted in the least changes in amino and fatty acids content of clams.

Background: A set of logical criteria that can accurately identify and verify the cultivation region of raw materials is a critical tool for the scientific management of traditional herbal medicine. Methods and Results: Volatile compounds were obtained from 19 and 32 samples of Angelica gigas Nakai cultivated in Korea and China, respectively, by using steam distillation extraction. The metabolites were identified using GC/MS by querying against the NIST reference library. Data binning was performed to normalize the number of variables used in statistical analysis. Multivariate statistical analyses, such as Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were performed using the SIMCA-P software. Significant variables with a Variable Importance in the Projection (VIP) score higher than 1.0 as obtained through OPLS-DA and those that resulted in p-values less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Among the 19 variables extracted, styrene, α-pinene, and β-terpinene were selected as markers to indicate the origin of A. gigas. Conclusions: The statistical model developed was suitable for determination of the geographical origin of A. gigas. The cultivation regions of six Korean and eight Chinese A. gigas. samples were predicted using the established OPLS-DA model and it was confirmed that 13 of the 14 samples were accurately classified.

Herba Patriniae (HP) is widely used as a medicinal and edible material in China. Besides food value, HP attracts more attention due to its medicinal potential. Patrinia villosa Juss. (PV) and Patrinia scabiosaefolia Fisch. (PS) are the two species origins of HP. These two of HP show different effects on cell proliferation, migration, angiogenesis and anti-diabetic. As we have previously reported, PV and PS show significant differences on their anti-inflammatory ability in the same experimental model. Comparing the ingredient profiles of two different sources will not only facilitate the understanding of their medicinal effects, but also help the development and research of new activities. However, still now, there is no systematic and detailed study to compare the components of PV and PS. In present study, ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was employed to achieve a high-throughput qualitative and thorough analysis of the chemical composition spectrum of HP. A total of 164 compounds were identified, among these compounds, 127 compounds were identified from PV, and 107 compounds were identified from PS. Most of the chemical components was discovered for the first time. Flavonoids, saponins, terpenoids and organic acids, as the main ingredients in PV and PS were 45.45 %vs 28.46 %, 12.61 % vs. 32.09 %, 14.33 % vs. 22.38 % and 14.58 % vs. 6.79 %, respectively. Flavonoids are the main components of PV, while PS is rich in saponins. PV and PS were classified into two groups by principal component analysis (PCA) and screened out the main molecular differences responsible by orthogonal partial least squares discriminant analysis (OPLS-DA). All the results will be a guide for the quality control, functional activity research, or better clinic use based on the ingredients profile between these two species. Besides, this first study on ingredients profile of two species origins will be beneficial for potential and best resources utilization of both PV and PS.

Background: The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.Methodology: Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients’ samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLSDA) models.Results: In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.Conclusion: Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and multivariate statistical analysis were used to differentiate mezcales elaborated with four agave species. The FT-MIR data matrix was subjected to spectral transformations using first and second derivatives. The Partial Least Squares (PLS)-Discriminant Analysis (DA) with the matrix transformed by the first and second derivative allowed the differentiation of mezcales. While Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was more robust when it was analyzed with second-derivative data. Pairwise comparisons by OPLS-DA allowed mezcales to be correctly discriminated, mainly between Agave karwinskii and Agave potatorum (Q2 = 0.654 and p – value < 0.01; R2Y = 0.985 and p-value < 0.01) and between Agave angustifolia and Agave karwinskii (Q2 = 0.563 and p-value = 0.01; R2Y = 0.989 and p-value = 0.01). FT-MIR spectrophotometry and the PLS-Regression (PLS-R) were applied to predict the ethanol percentage (% v/v) of mezcales collected in 2022 based on the PLS-R model previously run on samples evaluated in 2021.

Black poplar is a woody species rich in bioactive phenolic compounds with promising pharmaceutical and cosmetic applications. In wild populations, genetic diversity and environmental variability affect phenolic content and bioactivities. Plant tissue culture, conducted under controlled conditions, offers a suitable alternative for industrial bioproduction. The aim of this study was the development of in vitro shoot cultures of black poplar in Magenta™ and RITA® systems for efficient and constant phenolic productivity. Following the initiation of in vitro lines, significant growth enhancement was achieved through selection of fast-growing lines. UPLC-QTOF-MS-based untargeted metabolomic analyses were carried out, and allowed the identification of flavan-3-ols, proanthocyanidins, flavonols and salicinoids. As a result, 32 compounds were described for the first time in P. nigra, including 15 metabolites previously identified in other Populus species and 17 additional compounds not yet identified in the Populus genus. The productivity of the major phenolic compounds was substantially higher in the RITA® system, showing a 2.6-fold increase compared to Magenta™. Targeted metabolomics followed by Principal Component Analysis were performed to study the metabolic changes during 8 weeks of culture in RITA® system, revealing that most metabolites accumulated during the three first weeks of growth. Optimal shoot growth and phenolic content were achieved in RITA® system after 3 weeks of culture. Finally, the effects of both SA and MeJa treatments were characterized by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) which identified metabolites with Variable Importance in Projection (VIP >1), notably flavan-3-ols and proanthocyanidins as biomarkers common to both elicitation treatments. In conclusion, in vitro shoot culture in RITA® system and metabolomics studies allowed to design a specific process for efficient bioproduction of black poplar bioactive phenolics.

Combined effects of salinity, temperature and hypoxia on Daphnia magna metabolism

Objective: This study is designed to explore the impact of Ultraviolet-A (UVA) radiation on the urinary metabolome. The key novelty of this research lies in its focus on recording and analysing UVA-induced modifications, a less explored aspect in human metabolomic studies. Thus, study’s aim is identification specific metabolomic changes associated with UVA exposure, emphasizing its potential implications in health and disease biomarker discovery. Methods: Twenty-Eight urine samples have been collected from fourteen adult healthy individual sat pre-UV exposure (UVS1) and post-UV exposure (UVS2) conditions. Urinary metabolomic profiling was detected by using Liquid Chromatography – High Resolution Mass Spectrometry (LC-HRMS) Data modelling by Principal Components Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were applied after data extraction and normalizing. Results: Multivariate data analysis by PCA and OPLS-DA models confirm clear separation of the observations as a marked difference based on R2Xcum 0.795, Q2cum 0.590 and R2Xcum: 0.69, R2Ycum: 0.98 and Q2cum: 0.83, respectively, in metabolite profile among study groups, non-exposure and UV-exposure. According for these effects, our results identified significant metabolic changes for important seventeen urinary metabolites. The pathway of significantly metabolites were marked in amino acids (Histidine, Tyrosine, Lysine, Arginine and proline metabolism), carbohydrate and lipids and their derivatives. Conclusion: Current study concluded that these metabolites may be a potential biomarkers for predictive the impacts of UV sourced by sunlight exposure on some metabolic pathways, including: amino acid, carbohydrates, lipids, peptides, xenobiotics, and Co-factors and vitamins metabolism, which may be influenced by UV sunlight exposure.

Machine learning-assisted HS-GC-IMS for discrimination and traceability of baby bottles based on volatile fingerprints.

Lard imparts unique organoleptic properties that underpin its essential role in Chinese gastronomy; however, the specific lipid precursors contributing to its aroma remain unclear. This study explores the flavor formation mechanism of lard by comparing its texture and aroma at two preparation temperatures, 130 °C and 100 °C. We identified a total of 256 and 253 lipids at these temperatures, respectively, with triacylglycerols (TGs) and diacylglycerols (DGs) being the predominant lipid species. An HS-GC-IMS analysis detected 67 volatile compounds, predominantly aldehydes, acids, and alcohols. A subsequent Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) identified 49 discriminatory lipids and 20 differential volatiles. A correlation analysis showed a positive relationship between aldehydes and unsaturated triglycerides in lard, with TG (16:1-16:1-18:0), TG (17:2-18:1-18:1), TG (16:1-17:1-18:1), and TG (18:1-18:1-20:1) identified as characteristic markers at both temperatures. Furthermore, there was a positive correlation between ketones and alcohols and phospholipids and sphingolipids containing unsaturated fatty acid chains. TGs and glycerophospholipids (GPs), rich in polyunsaturated fatty acids, are likely key precursors driving the formation of distinct flavors during lard processing. This study elucidates the mechanistic interactions between lipids and volatile organic compounds, providing a framework for optimizing lard processing protocols and flavor modulation.

Differentiation between house and cave edible bird's nests by chemometric analysis of amino acid composition data