Quality Assessment and Classification of Codonopsis Radix Based on Fingerprints and Chemometrics.

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS-DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

Characterization of aroma-active compounds in Dongli by quantitative descriptive analysis, gas chromatography-triple quadrupole tandem mass spectrometry, and gas chromatography-olfactometry.

Dongli, or frozen pear, is a traditional Chinese snack with a unique flavor. This study identified the aroma-active volatile compounds (VOCs) in Dongli using quantitative descriptive analysis (QDA), gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS), and gas chromatography-olfactometry (GC-O). QDA indicated that Dongli of all cultivars presented increased sweet and wine aromas. A total of 21 VOCs were identified by GC-MS/MS. Bidirectional orthogonal partial least square (O2PLS) analysis, GC-O analysis, detection frequency analysis (DFA), and relative odor activity values (ROAV) showed that: estragole and anethole contributing "anise, green" aromas were the key aromatic VOCs of fresh pears, while ethyl butanoate, butyl acetate, heptyl acetate, benzaldehyde, and geranyl acetone contributing "sweet, fruity, green" aromas were the key aromatic VOCs of Dongli. The results revealed that the repeated freezing treatment promoted a unique aroma in pears. This study would contribute to developing new pear products. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05463-8.

Statistical integration of two omics datasets using GO2PLS.

BACKGROUND: Nowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace. RESULTS: The simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease. CONCLUSIONS: GO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.

Joint Analysis of Metabolite Markers of Fish Intake and Persistent Organic Pollutants in Relation to Type 2 Diabetes Risk in Swedish Adults.

BACKGROUND: There is conflicting evidence regarding the association between fish intake and type 2 diabetes (T2D) incidence, possibly owing to measurement errors in self-reported intake and coexposure to persistent organic pollutants (POPs) present in fish. OBJECTIVE: The aim of this study was to identify plasma metabolites associated with fish intake and to assess their association with T2D risk, independently of POPs, in Swedish adults. METHODS: In a case-control study nested in the Swedish Västerbotten Intervention Programme, fasting plasma samples from 421 matched T2D case-control pairs of men and women aged 30-60 y at baseline and 10-y follow-up samples from a subset of 149 pairs were analyzed using untargeted metabolomics. Moreover, 16 plasma POPs were analyzed for the 149 pairs who had repeated samples available. Fish-related plasma metabolites were identified using multivariate modelling and partial correlation analysis. Reproducibility of metabolites and metabolite patterns, derived via principal component analysis (PCA), was assessed by intraclass correlation. A unique component of metabolites unrelated to POPs was dissected by integrating metabolites and POPs using 2-way orthogonal partial least squares regression. ORs of T2D were estimated using conditional logistic regression. RESULTS: We identified 31 metabolites associated with fish intake that had poor to good reproducibility. A PCA-derived metabolite pattern strongly correlated with fish intake (ρ = 0.37, P < 0.001) but showed no association with T2D risk. Integrating fish-related metabolites and POPs led to a unique metabolite component independent of POPs, which tended to be inversely associated with T2D risk (OR: 0.75; 95% CI: 0.54, 1.02, P = 0.07). This component mainly consisted of metabolites reflecting fatty fish intake. CONCLUSIONS: Our results suggest that fatty fish intake may be beneficial for T2D prevention, after removing the counteractive effects of coexposure to POPs in Swedish adults. Integrating metabolite markers and POP exposures appears a promising approach to advance the understanding of associations between fish intake and T2D incidence.

Taste Evaluation of Yellowtail (Seriola Quinqueradiata) Ordinary and Dark Muscle by Metabolic Profiling.

We performed metabolic profiling on yellowtail (Seriola quinqueradiata) muscle to develop an objective taste evaluation method for fish meat. Dark (DM) and ordinary (OM) muscle samples before and after storage were subjected to gas chromatography-mass spectrometry (GC-MS) analysis and taste measurements using an electronic tongue. The metabolites identified by the GC-MS analysis were treated as x variables, and the taste values obtained by the electronic tongue were treated as y variables. The relationships between the metabolites and taste attributes were evaluated by two-way orthogonal projections to latent structures (O2PLS) analysis. The O2PLS analyses were normalized in two ways, unit variance (UV) and pareto (Par) scaling. The O2PLS (UV) analysis produced 3+1+0 models in Autofit and this model was statistically significant with R2Y (0.73) and Q2 (0.52) metrics. In particular, significant correlations were found between DM or OM and metabolite intensity and taste attributes, and strong associations were found between "sourness" and lysine, "irritant" and alanine and phenylalanine, "saltiness" and pantothenic acid, and "umami" and creatinine and histidine. The O2PLS (Par) analysis of DM generated significant predictive models for "acidic bitterness," "irritant," "saltiness," "bitterness," "astringency," and "richness." Among these, only "irritant" was affected by storage. This method was thus effective in evaluating the taste of yellowtail muscle.

Integrating omics datasets with the OmicsPLS package.

BACKGROUND: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. RESULTS: We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. CONCLUSIONS: We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages("OmicsPLS").

Separating common from distinctive variation.

BACKGROUND: Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation. RESULTS: The methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods. CONCLUSIONS: All three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted.

A Systems Oncology Approach Identifies NT5E as a Key Metabolic Regulator in Tumor Cells and Modulator of Platinum Sensitivity.

Altered metabolism in tumor cells is required for rapid proliferation but also can influence other phenotypes that affect clinical outcomes such as metastasis and sensitivity to chemotherapy. Here, a genome-wide association study (GWAS)-guided integration of NCI-60 transcriptome and metabolome data identified ecto-5'-nucleotidase (NT5E or CD73) as a major determinant of metabolic phenotypes in cancer cells. NT5E expression and associated metabolome variations were also correlated with sensitivity to several chemotherapeutics including platinum-based treatment. NT5E mRNA levels were observed to be elevated in cells upon in vitro and in vivo acquisition of platinum resistance in ovarian cancer cells, and specific targeting of NT5E increased tumor cell sensitivity to platinum. We observed that tumor NT5E levels were prognostic for outcomes in ovarian cancer and were elevated after treatment with platinum, supporting the translational relevance of our findings. In this work, we integrated and analyzed a plethora of public data, demonstating the merit of such a systems oncology approach for the discovery of novel players in cancer biology and therapy. We experimentally validated the main findings of the NT5E gene being involved in both intrinsic and acquired resistance to platinum-based drugs. We propose that the efficacy of conventional chemotherapy could be improved by NT5E inhibition and that NT5E expression may be a useful prognostic and predictive clinical biomarker.

Two-stage fermentation of corn and soybean meal mixture by Bacillus subtilis and Lactobacillus acidophilus to improve feeding value: optimization, physicochemical property, and microbial community.

Microbial treatment can reduce the antinutritional factors and allergenic proteins in corn-soybean meal mixture (CSMM), but the role of the microbial community in hypoallergenicity and digestibility during the fermentation process remains unclear. Therefore, the fermentation strains of Bacillus and LAB were determined, and the compatibility and fermentation process of two-stage solid fermentation composite bacteria were optimized, and the dynamic changes in physicochemical property and microbial community during two-stage fermentation were investigated. Results showed that Bacillus subtilis NCUBSL003 and Lactobacillus acidophilus NCUA065016 were the best fermentation combinations. The optimal fermentation conditions were inoculum 7.14%, solid-liquid ratio of 1:0.88 and fermentation time of 74.30 h. The contents of TI, ß-conglycinin and glycinin decreased significantly after fermentation. Besides, TCA-SP, small peptides and FAA increased. Bacillus and Lactobacillus were the main genera. Pathogenic bacteria genera were inhibited effectively. This study suggests the feasibility of two-stage fermentation in improving the nutrient values and safety of the CSMM. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-023-01426-7.

Relationship between the dynamics of volatile aroma compounds and microbial succession during the ripening of raw ewe milk-derived Idiazabal cheese.

Cheese microbiota contributes to various biochemical processes that lead to the formation of volatile compounds and the development of flavour during ripening. Nonetheless, the role of these microorganisms in volatile aroma compounds production is little understood. This work reports for the first time the dynamics and odour impact of volatile compounds, and their relationship to microbial shifts during the ripening of a raw ewe milk-derived cheese (Idiazabal). By means of SPME-GC-MS, 81 volatile compounds were identified, among which acids predominated, followed by esters, ketones and alcohols. The ripening time influenced the abundance of most volatile compounds, thus the moments of greatest abundance were determined (such as 30-60 days for acids). Through Odour Impact Ratio (OIR) values, esters and acids were reported as the predominant odour-active chemical families, while individually, ethyl hexanoate, ethyl 3-methyl butanoate, ethyl butanoate, butanoic acid or 3-methyl butanal were notable odorants, which would provide fruity, rancid, cheesy or malt odour notes. Using a bidirectional orthogonal partial least squares (O2PLS) approach with Spearman's correlations, 12 bacterial genera were reported as key bacteria for the volatile and aromatic composition of Idiazabal cheese, namely Psychrobacter, Enterococcus, Brevibacterium, Streptococcus, Leuconostoc, Chromohalobacter, Chryseobacterium, Carnobacterium, Lactococcus, Obesumbacterium, Stenotrophomonas and Flavobacterium. Non-starter lactic acid bacteria (NSLAB) were highly related to the formation of certain acids, esters and alcohols, such as 3-hexenoic acid, ethyl butanoate or 1-butanol. On the other hand, the starter LAB (SLAB) was related to particular ketones production, specifically 3-hydroxy-2-butanone; and environmental and/or non-desirable bacteria to certain ketones, hydrocarbons and sulphur compounds formation, such as 2-propanone, t-3-octene and dimethyl sulphone. Additionally, the SLAB Lactococcus and Psychrobacter, Brevibacterium and Chromohalobacter were described as having a negative effect on aroma development caused by NSLAB and vice versa. These results provide novel knowledge to help understand the aroma formation in a raw ewe milk-derived cheese.

Region, vintage, and grape maturity co-shaped the ionomic signatures of the Cabernet Sauvignon wines.

The ionic elements in wine and in vineyards are gaining attention due to characterization of the wine traits, wine origin tracing, and vine nutrient judging. In this experiment, 19 elements were detected by inductively coupled plasma mass spectrometry (ICP-MS) in 69 wine samples from 4 regions, 3 vintages, and 3 grape maturity levels. Furthermore, the elements related to vine development, such as N, P, K, Ca, Mg, Cu, Fe, Zn and Cu in the vineyard soil and petioles were determined. Two orthogonal partial least squares discriminant analysis (O2PLS-DA) showed that K, Mn, Co, Sr, B, Si, Pb, Ni, Cu, and Zn were important elements in distinguishing the regions. High-temperature vintages can bring wines with high levels of Sr in wine. Na, Ca, K, Mg, Rb, Al, Rb, Pb and Fe can be used as signature elements to distinguish wines made from 2 grape maturities. And Cu, Zn, and Mn were the key elements used to differentiate the petioles in the 4 regions. Partial square regression (PLSR) analysis showed that soil pH was positively correlated with Al, B, Ba, K, Pb, Mn, Sr and Rb in wine, and K in wine was significantly positively correlated with element K in the soil. In conclusion, the elemental contents in wine are shaped by the combination of origin, vintage and grape maturity, while some key elements can be used as indicators of origin traceability.

Exploring the core functional microbiota related with flavor compounds in fermented soy sauce from different sources.

Flavor, the most important quality index of soy sauce, is mostly influenced by the microbiota in fermented food ecosystem, however, the association between microorganisms and soy sauce flavor is still poorly understood. Therefore, the bacterial and fungal profiles, physicochemical parameters, and flavor compounds (9 organic acids, 17 free amino acids and 97 volatile flavor compounds) of 5 different source soy sauce were investigated using high-throughput sequencing, HPLC, amino acid analyzer and SPME/LLE-GC-MS, and their correlations were explored. A total of 3 fungal genera and 12 bacterial genera were identified as potential flavor-producing microorganisms by multivariate data and correlation analysis. Notably, Lactobacillus and Tetragenococcus were strongly positively correlated with succinic acid and lactic acid, respectively. Moreover, not only fungi, but also bacteria were found to be closely correlated with volatiles. Finally, 5 screened potential flavor-producing microorganisms were validated using a rapid fermentation model, with multiple strains showing the potential to improve the soy sauce flavor, with Lactobacillus fermentum being the most significant. Our research will provide a theoretical basis for the regulation and enhancement of soy sauce flavor.

Relationship between the Dynamics of Gross Composition, Free Fatty Acids and Biogenic Amines, and Microbial Shifts during the Ripening of Raw Ewe Milk-Derived Idiazabal Cheese.

This study reports for the first time the relationship between bacterial succession, characterized by high-throughput sequencing (sequencing of V3-V4 16S rRNA regions), and the evolution of gross composition, free fatty acids (FFAs) and biogenic amines (BAs) during cheese ripening. Specifically, Idiazabal PDO cheese, a raw ewe milk-derived semi-hard o hard cheese, was analysed. Altogether, 8 gross parameters were monitored (pH, dry matter, protein, fat, Ca, Mg, P and NaCl) and 21 FFAs and 8 BAs were detected. The ripening time influenced the concentration of most physico-chemical parameters, whereas the producer mainly affected the gross composition and FFAs. Through an O2PLS approach, the non-starter lactic acid bacteria Lactobacillus, Enterococcus and Streptococcus were reported as positively related to the evolution of gross composition and FFAs release, while only Lactobacillus was positively related to BAs production. Several environmental or non-desirable bacteria showed negative correlations, which could indicate the negative impact of gross composition on their growth, the antimicrobial effect of FFAs and/or the metabolic use of FFAs by these genera, and their ability to degrade BAs. Nonetheless, Obesumbacterium and Chromohalobacter were positively associated with the synthesis of FFAs and BAs, respectively. This research work provides novel information that may contribute to the understanding of possible functional relationships between bacterial communities and the evolution of several cheese quality and safety parameters.

Investigating the impact of Down syndrome on methylation and glycomics with two-stage PO2PLS.

Down syndrome (DS) is a condition that leads to precocious and accelerated aging in affected subjects. Several alterations in DS cases have been reported at a molecular level, particularly in methylation and glycosylation. Investigating the relation between methylation, glycomics and DS can lead to new insights underlying the atypical aging. We consider a data integration approach, where we investigate how DS affects the parts of glycomics and methylation which are correlated, and which CpG sites and glycans are relevant. Our motivating datasets consist of methylation and glycomics data, measured on 29 DS patients and their unaffected siblings and mothers. The family-based case-control design needs to be taken into account when studying the relationship between methylation, glycomics and DS. We propose a two-stage approach to first integrate methylation and glycomics data, and then link the joint information to Down syndrome. For the data integration step, we consider probabilistic two-way orthogonal partial least squares (PO2PLS). PO2PLS models two omics datasets in terms of low-dimensional joint and omic-specific latent components, and takes into account heterogeneity across the omics data. The relationship between the omics data can be statistically tested. The joint components represent the joint information in methylation and glycomics. In the second stage, we apply a linear mixed model to the relationship between DS and the joint methylation and glycomics components. For the components that are significantly as sociated with DS, we identify the most important CpG sites and glycans. A simulation study is conducted to evaluate the performance of our approach. The results showed that the effects of DS on the omics data can be detected in a large sample size, and the accuracy of the feature selection was high in both small and large sample sizes. Our approach is applied to the DS datasets, a significant effect of DS on the joint components is found. The identified CpG sites and glycans appeared to be related to DS. Our proposed method that jointly analyzes multiple omics data with an outcome variable may provide new insight into the molecular implications of DS at different omics levels.

Estimation of the effect of surrogate multi-omic biomarkers.

Multiple technologies which measure the same omics data set but are based on different aspects of the molecules exist. In practice, studies use different technologies and have therefore different biomarkers. An example is the glycan age index, which is constructed by three different ultra-performance liquid chromatography (UPLC) IgG glycans, and is a biomarker for biological age. A second technology is liquid chromatography- mass spectrometry (LCMS). To estimate the effect of a biomarker on an outcome variable, two issues need to be addressed. Firstly, a measurement error is needed to map one technology to the other one using a calibration study. Here, we consider two approaches, namely one based on the chemical properties of the two technologies and one based on the estimation of this relationship using O2PLS. Secondly, the use of an approximation of the biomarker in the main study needs to be taken into account by use of a regression calibration method. The performance of the two approaches is studied via simulations. The methods are used to estimate the relationship between glycan age and menopause. We have data from two cohorts, namely Korcula and Vis. In conclusion, (1) both measurement error models give similar results and suggest that there is an association between the glycan age index and the menopause status, (2) the chemical mapping approach outperforms O2PLS in the low measurement error variance, while on the larger measurement error variance, O2PLS works better, (3) statistical efficiency is lost due to increased noise level by adding irrelevant information.

1H NMR-based metabolomics study of breast meat from Pekin and Linwu duck of different ages and relation to meat quality.

This study investigated the effects of breed and age on meat quality, and metabolite profiles of duck breast meat, and the relationship between changes in metabolite profiles and the meat quality. The meat quality and 1H nuclear magnetic resonance (NMR)-based metabolomics of breast meat from Pekin and Linwu ducks at 2 different ages (42 and 72d) was analyzed. The results showed that age exerted a greater effect on the observed meat quality traits of breast meat than breed, and its interaction (breed × age) effect on pH values and yellowness (b*) of duck breast meat was significant. Total of 32 metabolites were detected in breast meat of Pekin and Linwu duck. The difference of metabolite profiles in breast meat between Pekin and Linwu duck at 72 d was greater than that at 42 d, while the effects of age on metabolites of duck meat from both breeds were similar. Anserine, aspartate, and carnosine were the most relevant metabolites of duck breast meat quality, and nicotinamide in duck breast meat was negatively correlated with cooking loss. These results provide an overall perspective for bridging the gap between the breed and age on duck meat quality and metabolome, and improve the understanding of the relationship between metabolites and duck meat quality.

The Wild Rice Locus CTS-12 Mediates ABA-Dependent Stomatal Opening Modulation to Limit Water Loss Under Severe Chilling Stress.

A near-isogenic line (NIL) DC90 which was generated by introgressing a wild rice (Oryza rufipogon Griff.) locus CTS-12 into the 9311(Oryza sativa L. ssp. indica) background confers chilling tolerance phenotype. Here, our pilot trials showed that chilling tolerance was positively correlated with abscisic acid (ABA) biosynthesis. To understand how CTS-12 mediated the ABA-dependent multi-levels of regulation, the integration of transcriptomic and metabolomic profiling using the two-way orthogonal projections to latent structures (O2PLS) and discriminant analysis (OPLS-DA) modeling was performed to investigate the mechanisms underlying chilling tolerance. Our results revealed that metabolic shifts, including the activation of stachyose biosynthesis, amino acid metabolism pathways, phenylpropanoid/flavonoid biosynthesis, ABA biosynthesis, and perturbation of glycolysis, occurred under chilling treatment; in the recovery period, glutamate-related pathways, ß-alanine biosynthesis and degradation, and serotonin biosynthesis pathways were differentiated between 9311 and DC90. Particularly, the differentially accumulated metabolites (DAMs) and differentially expressed genes (DEGs), including galactinol, ß-alanine, glutamate, naringenin, serotonin, ABA, and LOC_Os03g44380 (9-cis-epoxycarotenoid dioxygenase 3, OsNCED3), might be involved in the chilling tolerance variation of 9311 and DC90. CRISPR/Cas9-edited OsNCED3 resulted in chilling sensitive of japonica rice ZH11, demonstrating the involvement of ABA pathway in chilling stress response. In addition, chilling tolerance of rice was associated with the balance of water uptake and loss that was modulated by stomatal movement under chilling stress. Therefore, we speculated that the CTS-12-mediated ABA signaling pathway leads to transcriptional regulation of chilling-responsive genes and, in turn, triggers metabolic shifts to coordinately regulate the stomatal movement of guard cells. The results of this study improve our understanding of the multilevel regulation of wild rice in response to chilling stress.

Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography-Mass Spectrometry Data.

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.

Metabolomics study of Populus type propolis.

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and UV spectroscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400m, originating from P. nigra and P. x euramericana buds. Samples collected at 400-500m were of mixed origin, with variable amounts of all detected metabolites.

A Metabolomic Approach to Target Compounds from the Asteraceae Family for Dual COX and LOX Inhibition.

The application of metabolomics in phytochemical analysis is an innovative strategy for targeting active compounds from a complex plant extract. Species of the Asteraceae family are well-known to exhibit potent anti-inflammatory (AI) activity. Dual inhibition of the enzymes COX-1 and 5-LOX is essential for the treatment of several inflammatory diseases, but there is not much investigation reported in the literature for natural products. In this study, 57 leaf extracts (EtOH-H2O 7:3, v/v) from different genera and species of the Asteraceae family were tested against COX-1 and 5-LOX while HPLC-ESI-HRMS analysis of the extracts indicated high diversity in their chemical compositions. Using O2PLS-DA (R2 > 0.92; VIP > 1 and positive Y-correlation values), dual inhibition potential of low-abundance metabolites was determined. The O2PLS-DA results exhibited good validation values (cross-validation = Q2 > 0.7 and external validation = P2 > 0.6) with 0% of false positive predictions. The metabolomic approach determined biomarkers for the required biological activity and detected active compounds in the extracts displaying unique mechanisms of action. In addition, the PCA data also gave insights on the chemotaxonomy of the family Asteraceae across its diverse range of genera and tribes.