Increase of secondary metabolites in sweet basil (Ocimum basilicum L.) leaves by exposure to N2O5 with plasma technology.

Exposure to N2O5 generated by plasma technology activates immunity in Arabidopsis through tryptophan metabolites. However, little is known about the effects of N2O5 exposure on other plant species. Sweet basil synthesizes many valuable secondary metabolites in its leaves. Therefore, metabolomic analyses were performed at three different exposure levels [9.7 (Ex1), 19.4 (Ex2) and 29.1 (Ex3) µmol] to assess the effects of N2O5 on basil leaves. As a result, cinnamaldehyde and phenolic acids increased with increasing doses. Certain flavonoids, columbianetin, and caryophyllene oxide increased with lower Ex1 exposure, cineole and methyl eugenol increased with moderate Ex2 exposure and L-glutathione GSH also increased with higher Ex3 exposure. Furthermore, gene expression analysis by quantitative RT-PCR showed that certain genes involved in the syntheses of secondary metabolites and jasmonic acid were significantly up-regulated early after N2O5 exposure. These results suggest that N2O5 exposure increases several valuable secondary metabolites in sweet basil leaves via plant defense responses in a controllable system.

Metabolomic analysis reveals Ligilactobacillus salivarius CCFM 1266 fermentation improves dairy product quality.

Previous studies have demonstrated that Ligilactobacillus salivarius CCFM 1266 exhibits anti-inflammatory properties and the capability to synthesize niacin. This study aimed to investigate the fermentative abilities of L. salivarius CCFM 1266 in fermented milk. Metabonomic analysis revealed that fermentation by L. salivarius CCFM 1266 altered volatile flavor compounds and metabolite profiles, including heptanal, nonanal, and increased niacin production. Genomic investigations confirmed that L. salivarius CCFM 1266 possess essential genes for the metabolism of fructose and mannose, affirming its proficiency in utilizing fructooligosaccharides and mannan oligosaccharides. The addition of fructooligosaccharides and mannan oligosaccharides during the fermentation process significantly facilitated the proliferation of L. salivarius CCFM 1266 in fermented milk, with growth exceeding 107 colony-forming units (CFU)/mL. This intervention not only augmented the microbial density but also modified the metabolite composition of fermented milk, resulting in an elevated presence of advantageous flavor compounds such as nonanal, 2,3-pentanedione, and 3-methyl-2-butanone. However, its influence on improving the texture of fermented milk was observed to be minimal. Co-fermentation of L. salivarius CCFM 1266 with commercial fermentation starters indicated that L. salivarius CCFM 1266 was compatible, similarly altering metabolite composition and increasing niacin content in fermented milk. In summary, the findings suggest that L. salivarius CCFM 1266 holds substantial promise as an adjunctive fermentation starter, capable of enhancing the nutritional diversity of fermented milk products.

[Differential analysis of aqueous humor metabolomics between presenile cataract and senile cataract].

Objective: To explore the differences in metabolites and metabolic pathways in the aqueous humor between patients with presenile cataracts and senile cataracts. Methods: This metabolomic study was conducted at Tianjin Medical University Eye Hospital from August 2020 to September 2022. Eight patients with presenile cataracts (8 eyes) and 8 patients with senile cataracts (9 eyes) were included. Data were collected, including age, gender, preoperative uncorrected visual acuity, intraocular pressure, lens dysfunction index, and axial length. Aqueous humor and anterior capsule tissue samples were obtained during cataract surgery. Metabolites in the aqueous humor were detected using Liquid Chromatography-Mass Spectrometry in a non-targeted approach. The principal component analysis, differential analysis, clustering analysis, and correlation analysis were performed to identify differentially expressed metabolites. These metabolites were ranked based on the fold change (FC). The receiver operating characteristic (ROC) curve analysis and metabolic enrichment analysis were used to identify differential pathways and potential biomarkers for presenile cataracts. Immunohistochemistry was conducted on anterior capsule tissues, and pyruvate levels were measured by colorimetry to validate metabolomic results. Results: Patients with presenile cataracts included 7 males and 1 female, with a mean age of (37.50±4.90) years. Patients with senile cataracts were 7 males and 1 female, with a mean age of (73.44±5.22) years. Except for age, there were no significant differences in baseline data (P>0.05). A total of 347 differential metabolites were identified, 10 of which were potential biomarkers for presenile cataract according to the ROC curve analysis (all P<0.05), including propoxycaine (log2FC=7.26), 2-methyl-2, 3, 4, 5-tetrahydro-1, 5-benzodiazepine-4-ketone (log2FC=6.35), l-pyroglutamic acid (log2FC=-1.72), leanly-proline (log2FC=-0.77), and choline (log2FC=-0.56) in the positive ion mode, and N-phenylacetyl glutamine (log2FC=-1.84), pyruvate (log2FC=1.07), ascorbic acid (log2FC=0.92), pseudouracil nucleoside (log2FC=-0.68), and palmitic acid (log2FC=-0.51) in the negative ion mode. The metabolic enrichment analysis identified 72 differential pathways (32 cationic and 40 anionic), with significant differences in glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle (P<0.05). The experimental validation showed reduced lactate dehydrogenase and increased pyruvate levels in patients with presenile cataracts (P<0.05). Conclusions: Pyruvate and nine other metabolites may serve as potential biomarkers for presenile cataracts. Pathways involving glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle are notably dysregulated in patients with presenile cataracts.

Metabolomics-based study on the significance of differential metabolite binding IgG isoforms in Hemolytic disease of newborn.

BACKGROUND: Hemolytic disease of the newborn (HDN) is a common condition that can have a severe impact on the health of newborns due to the hemolytic reactions it triggers. Although numerous studies have focused on understanding the pathogenesis of HDN, there are still many unanswered questions. METHODS: In this retrospective study, serum samples were collected from 15 healthy newborns and 8 infants diagnosed with hemolytic disease. The relationship between different metabolites and various IgG subtypes in Healthy, HDN and BLI groups was studied by biochemical technique and enzyme-linked immunosorbent assay (ELISA). Metabolomics analysis was conducted to identify the differential metabolites associated with HDN. Subsequently, Pearson's correlation analysis was used to determine the relation of these differential metabolites with IgG isoforms. The relationship between the metabolites and IgG subtypes was observed after treatment. RESULTS: The study results revealed that infants with hemolytic disease exhibited abnormal elevations in TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4 levels when compared to healthy newborns. Additionally, differences in metabolite contents were also observed. N, N-DIMETHYLARGININE showed negative correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4, while 2-HYDROXYBUTYRATE, AMINOISOBUTANOATE, Inosine, and ALLYL ISOTHIOCYANATE exhibited positive correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4. Through metabolomics-based research, we have discovered associations between differential metabolites and different IgG isoforms during the onset of HDN. CONCLUSION: These findings suggest that changes in metabolite and IgG isoform levels are linked to HDN. Understanding the involvement of IgG isoforms and metabolites can provide valuable guidance for the diagnosis and treatment of HDN.

Unveiling the metabolic and coagulation disruptions in SARS-CoV-2-associated acute macular neuroretinopathy: A case-control study.

SARS-CoV-2 infection has been associated with the increased incidence of acute macular neuroretinopathy (AMN), an infrequent ocular disorder. However, the precise mechanisms underpinning AMN in the context of SARS-CoV-2 infection (AMN-SARS-CoV-2) remain elusive. In this case-control study, 14 patients diagnosed with AMN-SARS-CoV-2 between 2022/12 and 2023/3 were enrolled and compared with 14 SARS-CoV-2-infected individuals without AMN, who served as controls (SARS-CoV-2-no AMN). Metabolomic profiling using ultrahigh-performance liquid chromatography-online electrospray mass spectrometry revealed significant alterations in serum metabolites in AMN-SARS-CoV-2 patients. Coagulation abnormalities were observed in AMN-SARS-CoV-2 patients, and their relationship with metabolic disorders was studied. Finally, a predictive model for AMN-SARS-CoV-2 was established. Seventy-six upregulated and 42 downregulated metabolites were identified in AMN-SARS-CoV-2 cases. Notably, arginine metabolism within the urea cycle was significantly altered, evidenced by variations in ornithine, citrulline,  l-proline, and ADAM levels, correlating with abnormal coagulation markers like platelet crit, fibrinogen degradation product, and fibrinogen. Additionally, increased arginase 1 (AGR1) activity within the urea cycle and reduced nitric oxide synthase activity were observed in AMN-SARS-CoV-2. The integration of urea cycle metabolite levels with coagulation parameters yielded a robust discriminatory model for AMN-SARS-CoV-2, as evidenced by an area under the curve of 0.96. The findings of the present study enhance our comprehension of the underlying metabolic mechanisms associated with AMN-SARS-CoV-2 and offer potential diagnostic markers for this uncommon ocular disorder within the context of SARS-CoV-2 infection.

Integrative metagenomics, volatilomics and chemometrics for deciphering the microbial structure and core metabolic network during Chinese rice wine (Huangjiu) fermentation in different regions.

Huangjiu is a spontaneously fermented alcoholic beverage, that undergoes intricate microbial compositional changes. This study aimed to unravel the flavor and quality formation mechanisms based on the microbial metabolism of Huangjiu. Here, metagenome techniques, chemometrics analysis, and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) metabolomics combined with microbial metabolic network were employed to investigate the distinctions and relationship between the microbial profiles and the quality characteristics, flavor metabolites, functional metabolic patterns of Huangjiu across three regions. Significant variations (P < 0.05) were observed in metabolic rate of physicochemical parameters and biogenic amine concentration among three regions. 8 aroma compounds (phenethyl acetate, phenylethyl alcohol, isobutyl alcohol, ethyl octanoate, ethyl acetate, ethyl hexanoate, isoamyl alcohol, and diethyl succinate) out of 448 volatile compounds were identified as the regional chemical markers. 25 dominant microbial genera were observed through metagenomic analysis, and 13 species were confirmed as microbial markers in three regions. A metabolic network analysis revealed that Saccharomycetales (Saccharomyces), Lactobacillales (Lactobacillus, Weissella, and Leuconostoc), and Eurotiales (Aspergillus) were the predominant populations responsible for substrate, flavor (mainly esters and phenylethyl alcohol) metabolism, Lactobacillales and Enterobacterales were closely linked with biogenic amine. These findings provide scientific evidence for regional microbial contributions to geographical characteristics of Huangjiu, and perspectives for optimizing microbial function to promote Huangjiu quality.

Exploring sample treatment strategies for untargeted metabolomics: A comparative study of solid phase microextraction (SPME) and homogenization with solid-liquid extraction (SLE) in renal tissue.

BACKGROUND: The selection of the sample treatment strategy is a crucial step in the metabolomics workflow. Solid phase microextraction (SPME) is a sample processing methodology with great potential for use in untargeted metabolomics of tissue samples. However, its utilization is not as widespread as other standard protocols involving steps of tissue collection, metabolism quenching, homogenization, and extraction of metabolites by solvents. Since SPME allows us to perform all these steps in one action in tissue samples, in addition to other advantages, it is necessary to know whether this methodology produces similar or comparable metabolome and lipidome coverage and performance to classical methods. RESULTS: SPME and homogenization with solid-liquid extraction (Homo-SLE) sample treatment methods were applied to healthy murine kidney tissue, followed by comprehensive metabolomics and lipidomics analyses. In addition, it has been tested whether freezing and storage of the tissue causes alterations in the renal metabolome and lipidome, so the analyses were performed on fresh and frozen tissue samples Lipidomics analysis revealed the exclusive presence of different structural membrane and intracellular lipids in the Homo-SLE group. Conversely, all annotated metabolites were detected in both groups. Notably, the freezing of the sample mainly causes a decrease in the levels of most lipid species and an increase in metabolites such as amino acids, purines, and pyrimidines. These alterations are principally detected in a statistically significant way by SPME methodology. Finally, the samples of both methodologies show a positive correlation in all the analyses. SIGNIFICANCE: These results demonstrate that in SPME processing, as long as the fundamentals of non-exhaustive extraction in a pre-equilibrium kinetic regime, extraction in a tissue localized area, the chemistry of the fiber coating and non-homogenization of the tissue are taken into account, is an excellent method to use in kidney tissue metabolomics; since this methodology presents an easy-to-use, efficient, and less invasive approach that simplifies the different sample processing steps.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Integrated transcriptomics and metabolomics study of embryonic breast muscle of Jiaji ducks.

Because number of matured muscle fibers in poultry does not increase after birth, the meat yield is mainly determined during embryogenesis. We previously indicated breast muscle grew rapidly from 18th day after hatching (E18) to E27, and almost stopped from E27 to E34 of Jiaji ducks, while the mechanism is unclear. This study utilized RNA-seq to explore the related genes of muscle development and their relationship with small molecule metabolites at E18, E27 and E34 of Jiaji ducks. Several thousand differentially expressed genes (DEGs) were detected among E18, E27 and E34. DEGs expression profiles included 8 trend maps, among which trend 1 was opposite to and trend 6 was consistent with breast muscle development trend of Jiaji ducks. Through joint analysis between trend 1 of DEGs and trend 1 of differential metabolites (DEMs), protein digestion and absorption pathway stood out. The decrease of COL8A2 gene expression will lead to the decrease of arginine content, which will inhibit the development of breast muscle in embryonic Jiaji duck. Similarly, joint analysis between trend 6 of DEGs and trend 6 of DEMs indicated the increase of GAMT gene expression will cause the increase of proline content, and then promote the development of breast muscle of Jiaji duck in embryonic period. These results will be helpful for further understanding the mechanism of muscle yields of Jiaji ducks.

Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways.

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

Exploring disease-specific metabolite signatures in hereditary angioedema patients.

Introduction: Hereditary angioedema (HAE) is a rare, life-threatening autosomal dominant genetic disorder caused by a deficient and/or dysfunctional C1 esterase inhibitor (C1-INH) (type 1 and type 2) leading to recurrent episodes of edema. This study aims to explore HAE patients' metabolomic profiles and identify novel potential diagnostic biomarkers for HAE. The study also examined distinguishing HAE from idiopathic angioedema (AE). Methods: Blood plasma samples from 10 HAE (types 1/2) patients, 15 patients with idiopathic AE, and 20 healthy controls were collected in Latvia and analyzed using LC-MS based targeted metabolomics workflow. T-test and fold change calculation were used to identify metabolites with significant differences between diseases and control groups. ROC analysis was performed to evaluate metabolite based classification model. Results: A total of 33 metabolites were detected and quantified. The results showed that isovalerylcarnitine, cystine, and hydroxyproline were the most significantly altered metabolites between the disease and control groups. Aspartic acid was identified as a significant metabolite that could differentiate between HAE and idiopathic AE. The mathematical combination of metabolites (hydroxyproline * cystine)/(creatinine * isovalerylcarnitine) was identified as the diagnosis signature for HAE. Furthermore, glycine/asparagine ratio could differentiate between HAE and idiopathic AE. Conclusion: Our study identified isovalerylcarnitine, cystine, and hydroxyproline as potential biomarkers for HAE diagnosis. Identifying new biomarkers may offer enhanced prospects for accurate, timely, and economical diagnosis of HAE, as well as tailored treatment selection for optimal patient care.

GC/MS-based untargeted metabolomics reveals the differential metabolites for discriminating vintage of Chenxiang-type baijiu.

The "outstanding and unique aged aroma" of Chinese Chenxiang-type baijiu (CXB)-Daoguang 25 (DG25) mainly originates from a "extraordinary storage technology" of Mujiuhai (a wooden container), so it is mysterious and interesting. In this study, an untargeted GC/MS-based metabolomics was used to reveals the volatile differential metabolites for discriminating six different vintages of DG25 combing with chemometrics. A total of 100 volatile metabolites (including unknowns) were extracted and identified, including esters (41%), alcohols (10%) and acids (7%) so on. Finally, 33 differential metabolites were identified as aging-markers. Among them, 25 aging-markers showed a downtrend, including 17 esters such as ethyl acetate, ethyl hexanoate and ethyl palmitate so on. Moreover, it was interesting and to further study that furans showed a significant downtrend. Statistically speaking, ethyl benzoate played an important role in discriminating vintage of 1Y and 3Y, and the other 24 differential metabolites with downtrend discriminating the unstored (0Y-aged) DG25. Eight differential metabolites, such as ethyl octanoate, benzaldehyde, 3-methylbutanol and 1,1-diethoxyaccetal so on increased during aging of DG25, and they played a statistical role in discriminating the 5Y-, 10Y- and 20Y-aged DG25. This study provides a theoretical basis way for the formation mechanism of aging aroma for CXB.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Intercropping with maize and sorghum-induced saikosaponin accumulation in Bupleurum chinense DC. by liquid chromatography-mass spectrometry-based metabolomics.

Bupleuri Radix is an important medicinal plant, which has been used in China and other Asian countries for thousands of years. Cultivated Bupleurum chinense DC. (B. chinense) is the main commodity of Bupleuri Radix. The benefits of intercropping with various crops for B. chinense have been recognized; however, the influence of intercropping on the chemical composition of B. chinense is still unclear yet. In this study, intercropping with sorghum and maize exhibited little effect on the root length, root diameter, and single root mass of B. chinense. Only the intercropping with sorghum increased the root length of B. chinense slightly compared to the monocropping. In addition, 200 compounds were identified by UHPLC-Q-TOF-MS, and metabolomic combined with the Venn diagram and heatmap analysis showed apparent separation between the intercropped and monocropped B. chinense samples. Intercropping with sorghum and maize could both increase the saikosaponins, fatty acyls, and organic acids in B. chinense while decreasing the phospholipids. The influence of intercropping on the saikosaponin biosynthesis was probably related with the light intensity and hormone levels in B. chinense. Moreover, we found intercropping increased the anti-inflammatory activity of B. chinense. This study provides a scientific reference for the beneficial effect of intercropping mode of B. chinense.

Metabolomic pathways in food allergy.

Food allergy (FA) is a widespread issue, affecting as many as 10% of the population. Over the past two to three decades, the prevalence of FA has been on the rise, particularly in industrialized and westernized countries. FA is a complex, multifactorial disease mediated by type 2 immune responses and involving environmental and genetic factors. However, the precise mechanisms remain inadequately understood. Metabolomics has the potential to identify disease endotypes, which could beneficially promote personalized prevention and treatment. A metabolome approach would facilitate the identification of surrogate metabolite markers reflecting the disease activity and prognosis. Here, we present a literature overview of recent metabolomic studies conducted on children with FA.

Characterizing human postprandial metabolic response using multiway data analysis.

INTRODUCTION: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.