scholarly journals Terpenoid Accumulation Links Plant Health and Flammability in the Cypress-Bark Canker Pathosystem

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 651
Author(s):  
Gianni Della Rocca ◽  
Roberto Danti ◽  
Carmen Hernando ◽  
Mercedes Guijarro ◽  
Marco Michelozzi ◽  
...  
Keyword(s):  
Fire Risk ◽  
Disease Status ◽  
Fold Increase ◽  
Epidemiological Data ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Resistant Clone ◽  
Susceptible Clone ◽  
Flammability Parameters

To explore the possible relationship between diseased trees and wildfires, we assessed the flammability of canker-resistant and susceptible common cypress clones that were artificially infected with Seiridium cardinale compared to healthy trees. This study explored the effect of terpenoids produced by the host plant in response to infection and the presence of dead plant portions on flammability. Terpenoids were extracted and quantified in foliage and bark samples by gas chromatography–mass spectrometry (GC–MS). A Mass Loss Calorimeter was used to determine the main flammability descriptors. The concentration of terpenoids in bark and leaf samples and the flammability parameters were compared using a generalized linear mixed models (GLMM) model. A partial least square (PLS) model was generated to predict flammability based on the content of terpenoid, clone response to bark canker and the disease status of the plants. The total terpenoid content drastically increased in the bark of both cypress clones after infection, with a greater (7-fold) increase observed in the resistant clone. On the contrary, levels of terpenoids in leaves did not alter after infection. The GLMM model showed that after infection, plants of the susceptible clone appeared to be much more flammable in comparison to those of resistant clones, showing higher ignitability, combustibility, sustainability and consumability. This was mainly due to the presence of dried crown parts in the susceptible clone. The resistant clone showed a slightly higher ignitability after infection, while the other flammability parameters did not change. The PLS model (R2Y = 56%) supported these findings, indicating that dead crown parts and fuel moisture content accounted for most of the variation in flammability parameters and greatly prevailed on terpenoid accumulation after infection. The results of this study suggest that a disease can increase the flammability of trees. The deployment of canker-resistant cypress clones can reduce the flammability of cypress plantations in Mediterranean areas affected by bark canker. Epidemiological data of diseased tree distribution can be an important factor in the prediction of fire risk.

scholarly journals Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 36
Author(s):  
Khushman Taunk ◽  
Priscilla Porto-Figueira ◽  
Jorge A. M. Pereira ◽  
Ravindra Taware ◽  
Nattane Luíza da Costa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fatty Acid Biosynthesis ◽  
Solid Phase ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Control Group ◽  
Potential Candidate ◽  
Metabolic Pathway Analysis

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups—including naphthalene derivatives, phenols, and organosulphurs—augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

scholarly journals Characterization of α-Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by Gas Chromatography-Mass Spectrometry-Based Metabolomics and Molecular Docking Simulation

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2402 ◽  
Author(s):  
Suganya Murugesu ◽  
Zalikha Ibrahim ◽  
Qamar-Uddin Ahmed ◽  
Nik-Idris Nik Yusoff ◽  
Bisha-Fathamah Uzir ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Molecular Docking ◽  
Acid Methyl Ester ◽  
Docking Simulation ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Glucosidase Inhibitors ◽  
Clinacanthus Nutans

Background: Clinacanthus nutans (C. nutans) is an Acanthaceae herbal shrub traditionally consumed to treat various diseases including diabetes in Malaysia. This study was designed to evaluate the α-glucosidase inhibitory activity of C. nutans leaves extracts, and to identify the metabolites responsible for the bioactivity. Methods: Crude extract obtained from the dried leaves using 80% methanolic solution was further partitioned using different polarity solvents. The resultant extracts were investigated for their α-glucosidase inhibitory potential followed by metabolites profiling using the gas chromatography tandem with mass spectrometry (GC-MS). Results: Multivariate data analysis was developed by correlating the bioactivity, and GC-MS data generated a suitable partial least square (PLS) model resulting in 11 bioactive compounds, namely, palmitic acid, phytol, hexadecanoic acid (methyl ester), 1-monopalmitin, stigmast-5-ene, pentadecanoic acid, heptadecanoic acid, 1-linolenoylglycerol, glycerol monostearate, alpha-tocospiro B, and stigmasterol. In-silico study via molecular docking was carried out using the crystal structure Saccharomyces cerevisiae isomaltase (PDB code: 3A4A). Interactions between the inhibitors and the protein were predicted involving residues, namely LYS156, THR310, PRO312, LEU313, GLU411, and ASN415 with hydrogen bond, while PHE314 and ARG315 with hydrophobic bonding. Conclusion: The study provides informative data on the potential α-glucosidase inhibitors identified in C. nutans leaves, indicating the plant’s therapeutic effect to manage hyperglycemia.

scholarly journals Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 286
Author(s):  
Thijs T. Wingelaar ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Pieter-Jan A.M. van Ooij ◽  
Rigo Hoencamp ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Oxygen Toxicity ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Exhaled Breath ◽  
Chromatography Mass Spectrometry

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

scholarly journals Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

2019 ◽  
Vol 6 (3) ◽  
pp. 190002
Author(s):  
Qi Zhou ◽  
Shaomin Liu ◽  
Ye Liu ◽  
Huanlu Song
Keyword(s):  
Multivariate Analysis ◽  
Electronic Nose ◽  
Corn Oil ◽  
Solid Phase ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Coefficient Method

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction–gas chromatography–mass spectrometry corresponding to 4-methyl-2-pentanol, ( E )-2-hexenal, 1-tridecene, hexyl acetate, ( Z )-3-hexenyl acetate, ( E )-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

scholarly journals Correlation: Between Autochthonous Microbial Diversity and Volatile Metabolites During the Fermentation of Nongxiang Daqu

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuke Deng ◽  
Dan Huang ◽  
Baolin Han ◽  
Xinqian Ning ◽  
Dong Yu ◽  
...  
Keyword(s):  
Microbial Diversity ◽  
High Throughput Sequencing ◽  
Vital Role ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Flavor Compounds ◽  
Volatile Metabolites ◽  
Partial Least Square Analysis ◽  
Volatile Flavor

Daqu is an important saccharifying and fermenting agent. It provides various microorganisms and enzymes for the fermentation of Baijiu and plays a vital role in the formation of Baijiu flavor. However, it is difficult to obtain information on microbial growth and metabolism in time for Daqu production. Therefore, the “Qu Xiang” obtained by smelling is an important index in the traditional production process to evaluate the microbial fermentation in the process of Daqu-making, “Qu Xiang” mainly represents the volatile flavor compounds in Daqu. The microbial diversity and volatile metabolites on 0, 6, 16, and 29 days of the fermentation process were measured using high-throughput sequencing and gas chromatography–mass spectrometry. Significant differences were found in the composition of the microbial community. Pseudomonas, Weissella, Bacillus, and Pelomonas were the main bacterial genera. Alternaria, Rhizopus, and Pichia are the main fungal genera. A total of 32 differential volatile metabolites were detected in samples at four time points using differential metabolic analysis. The correspondence of prevailing microorganisms with differential metabolites distinguished by Spearman correlation and two-way orthogonal partial least square analysis show that Saccharopolyspora exhibited a significant connection for the 12 differential metabolites. A significant positive correlation was observed between Rhizomucor and 13 different metabolites. These findings further understanding of the metabolism of microorganisms in Daqu fermentation and also help to control the microorganisms in the Daqu-making process, to obtain more stable Baijiu products.

scholarly journals The Metabolomic Profile of Lymphoma Subtypes: A Pilot Study

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2367 ◽  
Author(s):  
Barberini ◽  
Noto ◽  
Fattuoni ◽  
Satta ◽  
Zucca ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Small Sample Size ◽  
B Cell Lymphoma ◽  
Small Sample ◽  
Partial Least Square ◽  
Lymphocytic Leukemia ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Metabolomic Analysis ◽  
Population Controls

Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20–74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed; for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma.

scholarly journals Metabolomic Analysis of Plasma from GABAB(1) Knock-Out Mice Reveals Decreased Levels of Elaidic Trans-Fatty Acid

Metabolites ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 484
Author(s):  
Claudia Fattuoni ◽  
Luigi Barberini ◽  
Antonio Noto ◽  
Paolo Follesa
Keyword(s):  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Gamma Aminobutyric Acid ◽  
Metabolomic Analysis ◽  
Wild Type ◽  
Metabolic Pattern ◽  
Knock Out ◽  
Spontaneous Seizures ◽  
Knock Out Mice

Mice lacking the GABAB(1) subunit of gamma-aminobutyric acid (GABA) type B receptors exhibit spontaneous seizures, hyperalgesia, hyperlocomotor activity, and memory impairment. Although mice lacking the GABAB(1) subunit are viable, they are sterile, and to generate knockout (KO) mice, it is necessary to cross heterozygous (HZ) mice. The aim of our study was to detect the metabolic differences between the three genotypes of GABAB(1) KO mice in order to further characterize this experimental animal model. Plasma samples were collected from wild-type (WT), HZ, and KO mice. Samples were analyzed by means of a gas chromatography-mass spectrometry (GC-MS) platform. Univariate t-test, and partial least square discriminant analysis (PLS-DA) were performed to compare the metabolic pattern of different genotypes. The metabolomic analysis highlighted differences between the three genotypes and identified some metabolites less abundant in KO mice, namely elaidic acid and other fatty acids, and chiro-inositol.

scholarly journals Effects of pH and Cultivation Time on the Formation of Styrene and Volatile Compounds by Penicillium expansum

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1333
Author(s):  
Hye Kim ◽  
Sang Lee ◽  
Jeong-Ah Seo ◽  
Young-Suk Kim
Keyword(s):  
Volatile Compounds ◽  
Critical Factor ◽  
Partial Least Square ◽  
Least Square ◽  
Stir Bar Sorptive Extraction ◽  
Penicillium Expansum ◽  
Gas Chromatography Mass Spectrometry ◽  
Postharvest Diseases ◽  
Potato Dextrose Broth ◽  
Cultivation Time

Styrene can be formed by the microbial metabolism of bacteria and fungi. In our previous study, styrene was determined as a spoilage marker of Fuji apples decayed by Penicillium expansum, which is responsible for postharvest diseases. In the present study, P. expansum was cultivated in potato dextrose broth added with phenylalanine—which is a precursor of styrene—using different initial pH values and cultivation times. Volatile compounds were extracted and analyzed using gas chromatography-mass spectrometry (GC-MS) combined with stir-bar sorptive extraction. The 76 detected volatile compounds included 3-methylbutan-1-ol, 3-methyl butanal, oct-1-en-3-ol, geosmin, nonanal, hexanal, and γ-decalactone. In particular, the formation of 10 volatile compounds derived from phenylalanine (including styrene and 2-phenylethanol) showed different patterns according to pH and the cultivation time. Partial least square-discriminant analysis (PLS-DA) plots indicated that the volatile compounds were affected more by pH than by the cultivation time. These results indicated that an acidic pH enhances the formation of styrene and that pH could be a critical factor in the production of styrene by P. expansum. This is the first study to analyze volatile compounds produced by P. expansum according to pH and cultivation time and to determine their effects on the formation of styrene.

scholarly journals Chemical Markers to Distinguish the Homo- and Heterozygous Bitter Genotype in Sweet Almond Kernels

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 747
Author(s):  
Stefania Vichi ◽  
Morgana N. Mayer ◽  
Maria G. León-Cárdenas ◽  
Beatriz Quintanilla-Casas ◽  
Alba Tres ◽  
...  
Keyword(s):  
Solid Phase ◽  
Single Gene ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Gas Chromatography Mass Spectrometry ◽  
Chemical Markers ◽  
Volatile Metabolites ◽  
First Time ◽  
Sweet Almond

Bitterness in almonds is controlled by a single gene (Sk dominant for sweet kernel, sk recessive for bitter kernel) and the proportions of the offspring genotypes (SkSk, Sksk, sksk) depend on the progenitors’ genotype. Currently, the latter is deduced after crossing by recording the phenotype of their descendants through kernel tasting. Chemical markers to early identify parental genotypes related to bitter traits can significantly enhance the efficiency of almond breeding programs. On this basis, volatile metabolites related to almond bitterness were investigated by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry coupled to univariate and multivariate statistics on 244 homo- and heterozygous samples from 42 different cultivars. This study evidenced the association between sweet almonds’ genotype and some volatile metabolites, in particular benzaldehyde, and provided for the first time chemical markers to discriminate between homo- and heterozygous sweet almond genotypes. Furthermore, a multivariate approach based on independent variables was developed to increase the reliability of almond classification. The Partial Least Square-Discriminant Analysis classification model built with selected volatile metabolites that showed discrimination capacity allowed a 98.0% correct classification. The metabolites identified, in particular benzaldehyde, become suitable markers for the early genotype identification in almonds, while a DNA molecular marker is not yet available.

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