LC-MS-based metabonomics analysis. J Chromatogr B Analyt Technol Biomed Life Sci. 2008;866:64-76. [PMID: 17983864 DOI: 10.1016/j.jchromb.2007.10.022]

Untargeted Metabolome Analyses Revealed Potential Metabolic Mechanisms of Leymus chinensis in Response to Simulated Animal Feeding

Leymus chinensis (Trin.) Tzvel., also known as the "Alkali Grass", is a major forage grass in the eastern and northeastern steppe vegetation in the Songnen Prairie. It is of great practical significance for grassland management to understand the influence of animal saliva on L. chinensis during animal feeding. In this study, we used clipping and daubing animal saliva to simulate responses to grazing by L. chinensis, and analyzed the physiological and metabolomic changes in response to simulated animal feeding. Results showed that the effects of animal saliva on physiological and metabolic processes of the treated plants produced a recovery phenomenon. Moreover, the effects of animal saliva produced a large number of differential metabolites related to several known metabolic pathways, among which the flavonoid biosynthesis pathway has undergone significant and persistent changes. We posit that the potential metabolic mechanisms of L. chinensis in response to simulated animal feeding are closely related to flavonoid biosynthesis.

Supplementation of Saussurea costus root alleviates sodium nitrite-induced hepatorenal toxicity by modulating metabolic profile, inflammation, and apoptosis

Sodium nitrite (NaNO2) is a widely used food ingredient, although excessive concentrations can pose potential health risks. In the present study, we evaluated the deterioration effects of NaNO2 additives on hematology, metabolic profile, liver function, and kidney function of male Wistar rats. We further explored the therapeutic potential of supplementation with S. costus root ethanolic extract (SCREE) to improve NaNO2-induced hepatorenal toxicity. In this regard, 65 adult male rats were divided into eight groups; Group 1: control, Groups 2, 3, and 4 received SCREE in 200, 400, and 600 mg/kg body weight, respectively, Group 5: NaNO2 (6.5 mg/kg body weight), Groups 6, 7 and 8 received NaNO2 (6.5 mg/kg body weight) in combination with SCREE (200, 400, and 600 mg/kg body weight), respectively. Our results revealed that the NaNO2-treated group shows a significant change in deterioration in body and organ weights, hematological parameters, lipid profile, and hepatorenal dysfunction, as well as immunohistochemical and histopathological alterations. Furthermore, the NaNO2-treated group demonstrated a considerable increase in the expression of TNF-α cytokine and tumor suppressor gene P53 in the kidney and liver, while a significant reduction was detected in the anti-inflammatory cytokine IL-4 and the apoptosis suppressor gene BCL-2, compared to the control group. Interestingly, SCREE administration demonstrated the ability to significantly alleviate the toxic effects of NaNO2 and improve liver function in a dose-dependent manner, including hematological parameters, lipid profile, and modulation of histopathological architecture. Additionally, SCREE exhibited the ability to modulate the expression levels of inflammatory cytokines and apoptotic genes in the liver and kidney. The phytochemical analysis revealed a wide set of primary metabolites in SCREE, including phenolics, flavonoids, vitamins, alkaloids, saponins and tannins, while the untargeted UPLC/T-TOF-MS/MS analysis identified 183 metabolites in both positive and negative ionization modes. Together, our findings establish the potential of SCREE in mitigating the toxic effects of NaNO2 by modulating metabolic, inflammatory, and apoptosis. Together, this study underscores the promise of SCREE as a potential natural food detoxifying additive to counteract the harmful impacts of sodium nitrite.

Liquid chromatography-electrospray ionization-mass spectrometry/mass spectrometry characterization of depsides and depsidones from the Chilean lichen Parmotrema perlatum

Lichens are recognized by their unique compounds and diverse applications in food, medicines, and cosmetics. Using ultra-high pressure liquid chromatography, coupled with a high-resolution mass spectrometer, metabolomic profiling of the lichen Parmotrema perlatum, from a methanolic extract, was performed. Based on characteristic fragmentation patterns, twenty-five lichenic substances were tentatively identified including 5 depsides, 12 depsidones, 2 diphenyl ethers, 1 aromatic considered as possible artifact, 1 dibenzofuran, 1 carbohydrate, 1 organic acid, and 2 undefined compounds. To the best of our knowledge, this is a more complete report of their phytochemistry from P perlatum. Our findings of the P perlatum profile may contribute and complement the current data of the Parmotrema genus.

Application of Weighted Gene Co-Expression Network Analysis to Metabolomic Data from an ApoA-I Knockout Mouse Model

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography-Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets.

Hydroxy fatty acids in the surface Earth system

Lipids are ubiquitous and highly abundant in a wide range of organisms and have been found in various types of environmental media. These molecules play a crucial role as organic tracers by providing a chemical perspective on viewing the material world, as well as offering a wealth of information on metabolic activities. Among the diverse lipid compounds, hydroxy fatty acids (HFAs) with one to multiple hydroxyl groups attached to the carbon chain stand out as important biomarkers for different sources of organic matter. HFAs are widespread in nature and are involved in biotransformation and oxidation processes in living organisms. The unique chemical and physical properties attributed to the hydroxyl group make HFAs ideal biomarkers in biomedicine and environmental toxicology, as well as organic geochemistry. The molecular distribution patterns of HFAs can be unique and diagnostic for a given class of organisms, including animals, plants, and microorganisms. Thus, HFAs can act as a valuable proxy for understanding the ecological relationships between different organisms and their environment. Furthermore, HFAs have numerous industrial applications due to their higher reactivity, viscosity, and solvent miscibility. This review paper integrates the latest research on the sources and chemical analyses of HFAs, as well as their applications in industrial/medicinal production and as biomarkers in environmental studies. This review article also provides insights into the biogeochemical cycles of HFAs in the surface Earth system, highlighting the importance of these compounds in understanding the complex interactions between living organisms and the environment.

Biodegradation of furfuryl alcohol by indigenous Bacillus species of industrial effluent-contaminated sites: estimation, biokinetics and toxicity assessment of bio-transformed metabolites

Furfuryl alcohol (FA) and other furanic compounds have garnered considerable attention in the quest for sustainable alternatives. FA-based resins have been used in various sectors that entail the release of FA into the environment. Hence, to ensure sustainability in this scenario, devising a dependable approach to its degradation is imperative. Given the crucial role of bacterial strains in the biodegradation of various organic pollutants, this study investigates the microbial degradation of FA, using bacterial strains isolated from sites that are constantly exposed to industrial waste. Three potential isolates were identified as B. paramycoides, B. cereus, and B. tequilensis by 16S rRNA gene sequencing. At a concentration of 300 µg/ml, these isolates demonstrated efficient FA degradation; 60-70% (at 300 µg/ml FA) and 50-60%, (at 500 µg/ml FA). Fourier-transform infrared (FTIR) spectroscopy and High-Performance Liquid Chromatography (HPLC) analysis further supported the result that the bacterial isolates consumed FA as the carbon source. Liquid chromatography-mass spectrometry (LC-MS) facilitates the detection of the major metabolic intermediate product in which FA gets transformed. The prominent peaks at 113 and 119 m/z obtained in the MS spectra of the degraded FA samples indicated the possibility of the conversion of FA into furoic acid or levulinic acid. The phytotoxicity bioassay findings revealed the non-toxic nature of the bio-transformed products as compared to pure FA. This investigation presents the initial documentation of the FA degradative potential of Bacillus strains, thereby augmenting the understanding of the prospective implementation of Bacillus species in industrial waste treatment projects.

Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms

Significance

Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information.

Aim

We aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images.

Approach

TPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images.

Results

Optimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics.

Conclusions

Denoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.

PlantMetSuite: A User-Friendly Web-Based Tool for Metabolomics Analysis and Visualisation

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored to plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Recently, deep learning methods have been developed to facilitate the extraction of quantitative information from such images. Here, we employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-SNR, two-photon images. Two-photon excited fluorescence (TPEF) images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used. We assess the impact of the specific denoising model, loss function, data transformation, and training dataset on established metrics of image restoration when comparing denoised single frame images with corresponding six frame averages, considered as the ground truth. We further assess the restoration accuracy of six metrics of metabolic function from the denoised images relative to ground truth images. Using a novel algorithm based on deep denoising in the wavelet transform domain, we demonstrate optimal recovery of metabolic function metrics. Our results highlight the promise of denoising algorithms to recover diagnostically useful information from low SNR label-free two-photon images and their potential importance in the clinical translation of such imaging.

Regulatory mechanisms and metabolic changes of miRNA during leaf color change in the bud mutation branches of Acer pictum subsp. mono

Acer pictum subsp. mono is a colorful tree species with considerable ornamental and economic value. However, little is known about the metabolism and regulatory mechanism of leaf color change in A. p. subsp. mono. To reveal the molecular mechanism of leaf color change in A. p. subsp. mono, the present study examined the bud mutation branches and compared the metabolites of the red leaves (AR) of the bud mutation branches of A. p. subsp. mono with those of the green leaves (AG) of the wild-type branches. It was found that the chlorophyll and carotenoids content of the red leaves decreased significantly, while anthocyanins, and various antioxidant enzymes increased significantly compared with the green leaves. The glycosides cyanidin, pelargonidin, malvidin, petunidin, delphinidin, and peonidin were detected in AR by liquid chromatography-mass spectrometry. The cyanidin glycosides increased, and cyanidin 3-O-glycoside was significantly upregulated. We analyzed the transcriptome and small RNA of A. p. subsp. mono leaves and detected 4061 differentially expressed mRNAs and 116 differentially expressed miRNAs. Through miRNA-mRNA association analysis, five differentially expressed modules were found; one miRNA targeted three genes, and four miRNAs targeted a single gene. Among them, miR160b, miR6300, and miR396g were found to be the key miRNAs regulating stable anthocyanin accumulation in A. p. subsp. mono leaves. By revealing the physiological response of leaf color change and the molecular regulatory mechanism of the miRNA, this study provides new insight into the molecular regulatory mechanism of leaf color change, thereby offering a foundation for future studies.

Rapid LC-MS assay for targeted metabolite quantification by serial injection into isocratic gradients

Liquid chromatography mass spectrometry (LC-MS) has emerged as a mainstream strategy for metabolomics analyses. One advantage of LC-MS is that it can serve both as a biomarker discovery tool and as a platform for clinical diagnostics. Consequently, it offers an exciting opportunity to potentially transition research studies into real-world clinical tools. One important distinction between research versus diagnostics-based applications of LC-MS is throughput. Clinical LC-MS must enable quantitative analyses of target molecules in hundreds or thousands of samples each day. Currently, the throughput of these clinical applications is limited by the chromatographic gradient lengths, which-when analyzing complex metabolomics samples-are difficult to conduct in under ~ 3 min per sample without introducing serious quantitative analysis problems. To address this shortcoming, we developed sequential quantification using isotope dilution (SQUID), an analytical strategy that combines serial sample injections into a continuous isocratic mobile phase to maximize throughput. SQUID uses internal isotope-labelled standards to correct for changes in LC-MS response factors over time. We show that SQUID can detect microbial polyamines in human urine specimens (lower limit of quantification; LLOQ = 106 nM) with less than 0.019 normalized root mean square error. Moreover, we show that samples can be analyzed in as little as 57 s. We propose SQUID as a new, high-throughput LC-MS tool for quantifying small sets of target biomarkers across large cohorts.

Towards Non-Targeted Screening of Lipid Biomarkers for Improved Equine Anti-Doping

The current approach to equine anti-doping is focused on the targeted detection of prohibited substances. However, as new substances are rapidly being developed, the need for complimentary methods for monitoring is crucial to ensure the integrity of the racing industry is upheld. Lipidomics is a growing field involved in the characterisation of lipids, their function and metabolism in a biological system. Different lipids have various biological effects throughout the equine system including platelet aggregation and inflammation. A certain class of lipids that are being reviewed are the eicosanoids (inflammatory markers). The use of eicosanoids as a complementary method for monitoring has become increasingly popular with various studies completed to highlight their potential. Studies including various corticosteroids, non-steroidal anti-inflammatories and cannabidiol have been reviewed to highlight the progress lipidomics has had in contributing to the equine anti-doping industry. This review has explored the techniques used to prepare and analyse samples for lipidomic investigations in addition to the statistical analysis and potential for lipidomics to be used for a longitudinal assessment in the equine anti-doping industry.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Analysis of the Antioxidant Mechanism of Tamarix ramosissima Roots under NaCl Stress Based on Physiology, Transcriptomic and Metabolomic

There is a serious problem with soil salinization that affects the growth and development of plants. Tamarix ramosissima Ledeb (T. ramosissima), as a halophyte, is widely used for afforestation in salinized soils. At present, there are few reports on the antioxidant mechanism of T. ramosissima under NaCl stress. In this study, we learned about the superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities, and hydrogen peroxide (H₂O₂) and malondialdehyde (MDA) content changes in T. ramosissima. We also mined the relevant metabolic pathways in the antioxidant mechanism, candidate key genes, and their related differential metabolites and verified them using quantitative real-time PCR (qRT-PCR). The results show that the SOD, POD, and CAT activities, and the H₂O₂ and MDA content reached the highest values in the roots of T. ramosissima. Simultaneously, 92 differentially expressed genes (DEGs) related to antioxidant enzyme activities changed during 48 and 168 h of NaCl stress, and these DEGs were mainly upregulated in 168 h. Based on the association analysis of transcriptomic and metabolomic data, we found Unigene0089358 and Unigene0007782 as genes related to key enzymes in the flavonoid biosynthesis pathway. They were located in the upstream positive regulation at 48 and 168 h under NaCl stress, and their respective related metabolites (phloretin and pinocembrin) were involved in resistance to NaCl stress, and they were significantly correlated with their respective metabolites. In conclusion, at 48 and 168 h under NaCl stress, the roots of T. ramosissima resist NaCl stress by enhancing enzymatic and nonenzymatic antioxidant mechanisms, scavenging ROS generated by high-salt stress, alleviating NaCl toxicity, and maintaining the growth of T. ramosissima. This study provides genetic resources and a scientific theoretical basis for further breeding of salt-tolerant Tamarix plants and the molecular mechanism of antioxidants to alleviate NaCl toxicity.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.

Assessment of Digestion, Absorption, and Metabolism of Nanoencapsulated Phytochemicals Using In Vitro and In Vivo Models: A Perspective Paper

Nanoencapsulation delivery systems have been used to enhance the absorption and bioefficacy of phytochemicals. With modified physical and chemical properties, nanoencapsulated phytochemicals differ from their free forms in digestion, absorption, and metabolism. These pharmacokinetic processes can be assessed using a combination of various in vitro/in vivo models and analytical strategies, but each approach has its limitations. The correlation between current models and physiological conditions and their feasibility for nanoencapsulation systems require further validation. More detailed studies are still needed to clarify how nanoencapsulation affects the phytochemical and host interaction. Future investigations must take extra caution in model selection and result interpretation.

Impacts of Colored Light-Emitting Diode Illumination on the Reproductive Performance and Bioactive Constituents and the Molecular Mechanism of Hypothalamus Gland in Zi-geese

Goose is a seasonal breeding animal. Its reproduction is regulated by hypothalamus-pituitary-gonad axis and also affected by environmental factors such as light and location. Zi-goose is characterized with long egg-laying period and high egg-laying potential and belongs to the long-day type of seasonal breeding. In this study, the regulation mechanism of different lighting on reproductive performance of Zi-goose by using metabonomics analysis technology. In addition, 1,481 differential metabolites were screened out totally. 583 differential metabolites were identification in hypothalamus of Zi-goose. 196 differential metabolites were identification in pituitary of Zi-goose. 692 differential metabolites were identification in ovary of Zi-goose. Under red light condition for 12 h, expression of 433 differential metabolites were down-regulated and expression of 150 differential metabolites were up regulated in hypothalamus of Zi-goose, expression of 125 differential metabolites were down-regulated and expression of 71 differential metabolites were up-regulated in pituitary of Zi-goose, expression of 355 differential metabolites were down-regulated and expression of 337 differential metabolites were up-regulated in ovary of Zi-goose. 33 differential metabolites were closely associated with 1,264 transcripts and 400 homologous genes of related enzymes in hypothalamus of Zi-goose. 15 differential metabolites were closely associated with 163 transcripts and 47 homologous genes of related enzymes in pituitary of Zi-goose. 55 differential metabolites were closely associated with 1,255 transcripts and 360 homologous genes of related enzymes in ovary of Zi-goose. It was confirmed that four metabolic pathways were closely related to light regulation of reproductive performance of Zi-goose, namely GnRH signaling pathway, prolactin signaling pathway, thyroid hormone synthesis and ovarian steroidogenesis. Typical differential metabolites of arachidonic acid, glucose-6-phosphate, progesterone, glutathione, oxidized glutathione, testosterone, deoxyepiandrosterone and their related protein genes would play an important role in light regulation of reproductive performance of Zi-goose.

Urinary metabolomics identified metabolic disturbance associated with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder. Nevertheless, its accurate mechanisms remain unclear. Metabolomics is a powerful technique to identify small molecules that could be used to discover pathogenesis and therapeutical targets of disease. In the present study, a urinary untargeted metabolomics combined with targeted quantification analysis was performed to uncover metabolic disturbance associated with PCOS. A total of thirty-eight metabolites were obtained between PCOS patients and healthy controls, which were mainly involved in lipids (39.5%), organic acids and derivatives (23.7%), and organic oxygen compounds (18.4%). Based on enrichment analysis, fourteen metabolic pathways were found to be perturbed in PCOS, particularly glycerophospholipid metabolism and tryptophan metabolism. Targeted quantification profiling of tryptophan metabolism demonstrated that seven compounds (tryptophan, kynurenine, kynurenic acid, quinolinic acid, xanthurenic acid, 3-hydroxyanthranilic acid and 3-hydroxykynurenine) were up-regulated in PCOS. And these tryptophan-kynurenine metabolites showed significant correlations with PCOS clinical features, such as positively associated with testosterone, free androgen index, and the ratio of luteinizing hormone to follicle stimulating hormone. Thus, this study disclosed urinary metabolome changes associated with PCOS, and might provide new insights into PCOS pathogenesis elucidation and therapeutical target development.

Serum metabolomic profiling reveals potential biomarkers in assessing the management of women with polycystic ovary syndrome: a randomized controlled trial

BACKGROUND

As one of the most common endocrinal disorders for women at childbearing age, the diagnostic criteria of polycystic ovary syndrome (PCOS) have been defined differently among different international health organizations. Phenotypic heterogeneity of PCOS also brings about difficulties for its diagnosis and management assessment. Therefore, more efficient biomarkers representing the progression of PCOS are expected to be integrated into the monitoring of management process using metabolomic approaches.

METHODS

In this prospective randomized controlled trial, 117 PCOS patients were enrolled from December 2016 to September 2017. Classical diagnostic parameters, blood glucose, and metabolome were measured in these patients before and at 2 months and 3 months of different medical interventions. The receiver operating characteristic (ROC) curves were built based on multivariate statistical analysis using data at baseline and 3 months' management, and combinational biomarkers with appreciable sensitivity and specificity were selected, which then validated with data collected at 2 months.

RESULTS

A set of metabolites including glutamic acid, aspartic acid, 1-methylnicotinamide, acetylcarnitine, glycerophosphocholine, and oleamide were filtered out with high performance in representing the improvement through 3-month management of PCOS with high sensitivity and specificity in ROC analysis and validation with other two groups showed an appreciable area under the curve over 0.96.

CONCLUSIONS

The six metabolites were representative of the remission of PCOS through medical intervention, making them a set of potential biomarkers for assessing the outcome of PCOS management.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03264638.

You Are What You Eat: Application of Metabolomics Approaches to Advance Nutrition Research

A healthy condition is defined by complex human metabolic pathways that only function properly when fully satisfied by nutritional inputs. Poor nutritional intakes are associated with a number of metabolic diseases, such as diabetes, obesity, atherosclerosis, hypertension, and osteoporosis. In recent years, nutrition science has undergone an extraordinary transformation driven by the development of innovative software and analytical platforms. However, the complexity and variety of the chemical components present in different food types, and the diversity of interactions in the biochemical networks and biological systems, makes nutrition research a complicated field. Metabolomics science is an "-omic", joining proteomics, transcriptomics, and genomics in affording a global understanding of biological systems. In this review, we present the main metabolomics approaches, and highlight the applications and the potential for metabolomics approaches in advancing nutritional food research.

UPLC/MS-based untargeted metabolomics reveals the changes of metabolites profile of Salvia miltiorrhiza bunge during Sweating processing

Salvia miltiorrhiza has numerous compounds with extensive clinical application. "Sweating", a processing method of Traditional Chinese Medicine (TCM), results in great changes in pharmacology and pharmacodynamics. Previously, chromatogram of 10 characteristic metabolites in S. miltiorrhiza showed a significant difference after "Sweating". Due to the complexity of TCM, changes in metabolites should be investigated metabolome-wide after "Sweating". An untargeted UPLC/MS-based metabolomics was performed to discover metabolites profile variation of S. miltiorrhiza after "Sweating". Multivariate analysis was conducted to screen differential metabolites. Analysis indicated distinct differences between sweated and non-sweated samples. 10,108 substance peaks had been detected altogether, and 4759 metabolites had been identified from negative and positive ion model. 287 differential metabolites were screened including 112 up-regulated and 175 down-regulated and they belong to lipids and lipid-like molecules, and phenylpropanoid and polyketides. KEGG analysis showed the pathway of linoleic acid metabolism, and glyoxylate and dicarboxylate metabolism were mainly enriched. 31 and 49 identified metabolites were exclusively detected in SSM and NSSM, respectively, which mainly belong to carboxylic acids and derivatives, polyketides and fatty acyls. By mapping tanshinones and salvianolic acids to 4759 identified metabolites library, 23 characteristic metabolites had been identified, among which 11 metabolites changed most. We conclude that "Sweating'' has significant effect on metabolites content and composition of S. miltiorrhiza.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection

MOTIVATION

Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning.

RESULTS

Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output.

AVAILABILITY AND IMPLEMENTATION

An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

UHPLC-HRMSn Analysis Reveals the Dynamic Metabonomic Responses of Salvia miltiorrhiza Hairy Roots to Polysaccharide Fraction from Trichoderma atroviride

We have previously reported that Trichoderma atroviride, an endophytic fungus isolated from S. miltiorrhiza, promotes S. miltiorrhiza hairy root growth and significantly stimulates the biosynthesis of tanshinones specifically the polysaccharide fraction (PSF). However, this study only focused exclusively on six metabolites whilst ignoring changes to the whole metabolite composition of the S. miltiorrhiza hairy roots. In the present study, the dynamic metabonomic responses of S. miltiorrhiza hairy roots were investigated using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMSⁿ). UHPLC-HRMS typical total ions chromatograms (TICs) of PSF-treated hairy root samples were different from the control. Moreover, the results of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA) indicated that PSF-treated samples were significantly different from the control. Through the analysis of PLS-DA, a total of 114 and 99 differential metabolites were found from the positive and negative models respectively and a total of 33 differential metabolites were identified. Thus, S. miltiorrhiza hairy roots had been induced to regulate the metabolic profiling in response to PSF and the changes of the metabolic profiling contributed to promoting the biosynthesis of tanshinones notably whilst the biosynthesis of phenolic acids were slightly inhibited.

1,4-Dioxane exposure induces kidney damage in mice by perturbing specific renal metabolic pathways: An integrated omics insight into the underlying mechanisms

1,4-Dioxane (dioxane), an industrial solvent widely detected in environmental and biological matrices, has potential nephrotoxicity. However, the underlying mechanism by which dioxane induces kidney damage remains unclear. In this study, we used an integrated approach, combining kidney transcriptomics and urine metabolomics, to explore the mechanism for the toxic effects of dioxane on the mouse kidney. Transcriptomics profiling showed that exposure to 0.5 mg/L dioxane induced perturbations of multiple signaling pathways in kidneys, such as MAPK and Wnt, although no changes in oxidative stress indicators or anatomical pathology were observed. Exposure to 500 mg/L dioxane significantly disrupted various metabolic pathways, concomitantly with observed renal tissue damage and stimulated oxidant defense system. Urine metabolomic analysis using NMR indicated that exposure to dioxane gradually altered the metabolic profile of urine. Within the full range of altered metabolites, the metabolic pathway containing glycine, serine and threonine was the most significantly altered pathway at the early stage of exposure (3 weeks) in both 0.5 and 500 mg/L dioxane-treated groups. However, with prolonged exposure (9 and 12 weeks), the level of taurine significantly decreased after treatment of 0.5 mg/L dioxane, while exposure to 500 mg/L dioxane significantly increased glutathione levels in urine and decreased arginine metabolism. Furthermore, integrated omics analysis showed that 500 mg/L dioxane exposure induced arginine deficiency by perturbing several genes involved in renal arginine metabolism. Shortage of arginine coupled with increased oxidative stress could lead to renal dysfunction. These findings offer novel insights into the toxicity of dioxane.

Co-Culture Systems for the Production of Secondary Metabolites: Current and Future Prospects

Microorganisms are the great sources of Natural Products (NPs); these are imperative to their survival apart from conferring competitiveness amongst each other within their environmental niches. Primary and secondary metabolites are the two major classes of NPs that help in cell development, where antimicrobial activity is closely linked with secondary metabolites. To capitalize on the effects of secondary metabolites, co-culture methods have been often used to develop an artificial microbial community that promotes the action of these metabolites. Different analytical techniques will subsequently be employed based on the metabolite specificity and sensitivity to further enhance the metabolite induction. Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography (GC)-MS are commonly used for metabolite separation while Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) have been used as tools to elucidate the structure of compounds. This review intends to discuss current systems in use for co-culture in addition to its advantages, with discourse into the investigation of specific techniques in use for the detailed study of secondary metabolites. Further advancements and focus on co-culture technologies are required to fully realize the massive potential in synthetic biological systems.

Mechanism of Curcuma wenyujin Rhizoma on Acute Blood Stasis in Rats Based on a UPLC-Q/TOF-MS Metabolomics and Network Approach

Rhizome of Curcuma wenyujin, which is called EZhu in China, is a traditional Chinese medicine used to treat blood stasis for many years. However, the underlying mechanism of EZhu is not clear at present. In this study, plasma metabolomics combined with network pharmacology were used to elucidate the therapeutic mechanism of EZhu in blood stasis from a metabolic perspective. The results showed that 26 potential metabolite markers of acute blood stasis were screened, and the levels were all reversed to different degrees by EZhu preadministration. Metabolic pathway analysis showed that the improvement of blood stasis by Curcuma wenyujin rhizome was mainly related to lipid metabolism (linoleic acid metabolism, ether lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism) and amino acid metabolisms (tryptophan metabolism, lysine degradation). The component-target-pathway network showed that 68 target proteins were associated with 21 chemical components in EZhu. Five metabolic pathways of the network, including linoleic acid metabolism, sphingolipid metabolism, glycerolipid metabolism, arachidonic acid metabolism, and steroid hormone biosynthesis, were consistent with plasma metabolomics results. In conclusion, plasma metabolomics combined with network pharmacology can be helpful to clarify the mechanism of EZhu in improving blood stasis and to provide a literature basis for further research on the therapeutic mechanism of EZhu in clinical practice.

Interpretation of Euphorbia Kansui Stir-Fried with Vinegar Treating Malignant Ascites by a UPLC-Q-TOF/MS Based Rat Serum and Urine Metabolomics Strategy Coupled with Network Pharmacology

Euphorbia kansui stir-fried with vinegar (V-kansui) has promising biological activities toward treating malignant ascites with reduced toxicity compared to crude kansui. But the mechanism concerning promoting the excretion of ascites has not been systematically studied. The purpose of this paper was to investigate the possible mechanism of V-kansui in treating malignant ascites, including metabolic pathways and molecular mechanism using an integrated serum and urine metabolomics coupled with network pharmacology. Serum and urine samples of rats were collected and analyzed by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). A comparison with crude kansui was also made to demonstrate the feasibility of processing. Principle component analysis (PCA) and orthogonal partial least square discriminate analysis (OPLS-DA) were conducted to discriminate the groups, search important variables and reveal the possible pathways. A compound-target-metabolite network was finally constructed to identify the crucial targets to further understand the molecular mechanism. Sixteen significant metabolites contributing to the discrimination of model and control groups were tentatively screened out. They were mainly involved in the arachidonic acid metabolism, steroid hormone biosynthesis and primary bile acid to possibly reduce inflammatory and modulate the renin-angiotensin-aldosterone system to achieve treating malignant ascites. A bio-network starting from the compounds and ending in the metabolites was constructed to elucidate the molecular mechanism. HSP90AA1, ANXA2, PRDX6, PCNA, SOD2 and ALB were identified as the potential key targets that were responsible for the treatment of malignant ascites by the parameter combining the average shortest path length and betweenness centrality. The correlated 17 compounds were considered as the potential active ingredients in V-kansui. In addition, the metabolomics showed that the effect of V-kansui was almost in accordance with crude kansui. These results systematically revealed the mechanism of V-kansui against malignant ascites for the first time using metabolomics coupled with network pharmacology. V-kansui could be a promising safe and therapeutic medicine for the excretion of ascites.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Classification of samples from NMR-based metabolomics using principal components analysis and partial least squares with uncertainty estimation

Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure ( https://doi.org/10.1016/j.chemolab.2016.12.010 ) with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing. Graphical abstract ᅟ.

LC-MS based metabolic and metabonomic studies of Panax ginseng

INTRODUCTION

Panax ginseng has received much attention as a valuable health supplement with medicinal potential. Its chemical diversity and multiple pharmacological properties call for comprehensive methods to better understand the effects of ginseng and ginsenosides. Liquid chromatography-mass spectrometry (LC-MS) based metabonomic approaches just fit the purpose.

OBJECTIVE

Aims to give a review of recent progress on LC-MS based pharmacokinetic, metabolic, and phytochemical metabolomic studies of ginseng, and metabonomic studies of ginseng intervention effects.

METHODS

The review has four sections: the first section discusses metabolic studies of ginsenosides based on LC-MS, the second focuses on ginsenoside-drug interactions and pharmacokinetic interaction between herb compounds based on LC-MS, the third is phytochemical metabolomic studies of ginseng based on LC-MS, and the fourth deals with metabonomic studies of ginseng intervention effects based on LC-MS.

RESULTS

LC-MS based metabonomic research on ginseng include analysis of single ginsenoside and total ginsenosides. The theory of multi-components and multi-targeted mechanisms helps to explain ginseng effects.

CONCLUSION

LC-MS based metabonomics is a promising way to comprehensively assess ginseng. It is valuable for quality control and mechanism studies of ginseng.

Metabolomics and Precision Medicine in Trauma: The State of the Field

Trauma is a major problem in the United States. Mortality from trauma is the number one cause of death under the age of 45 in the United States and is the third leading cause of death for all age groups. There are approximately 200,000 deaths per year due to trauma in the United States at a cost of over $671 billion in combined healthcare costs and lost productivity. Unsurprisingly, trauma accounts for approximately 30% of all life-years lost in the United States. Due to immense development of trauma systems, a large majority of trauma patients survive the injury, but then go on to die from complications arising from the injury. These complications are marked by early and significant metabolic changes accompanied by inflammatory responses that lead to progressive organ failure and, ultimately, death. Early resuscitative and surgical interventions followed by close monitoring to identify and rescue treatment failures are key to successful outcomes. Currently, the adequacy of resuscitation is measured using vital signs, noninvasive methods such as bedside echocardiography or stroke volume variation, and other laboratory endpoints of resuscitation, such as lactate and base deficit. However, these methods may be too crude to understand cellular and subcellular changes that may be occurring in trauma patients. Better diagnostic and therapeutic markers are needed to assess the adequacy of interventions and monitor responses at a cellular and subcellular level and inform clinical decision-making before complications are clinically apparent. The developing field of metabolomics holds great promise in the identification and application of biochemical markers toward the clinical decision-making process.

1H NMR based metabolite profiling for optimizing the ethanol extraction of Wolfiporia cocos

Metabolite profiling of Wolfiporia cocos (family: Polyporaceae) had been much advancement in recent days, and its analysis by nuclear magnetic resonance (NMR) spectroscopy has become well established. However, the highly important trait of W. cocos still needs advanced protocols despite some standardization. Partial least squares discriminant analysis (PLS-DA) was used as the multivariate statistical analysis of the ¹H NMR data set. The PLS-DA model was validated, and the key metabolites contributing to the separation in the score plots of different ethanol W. cocos extract. ¹H NMR spectroscopy of W. cocos identified 33 chemically diverse metabolites in D₂O, consisting of 13 amino acids, 11 organic acids 2 sugars, 3 sugar alcohols, 1 nucleoside, and 3 others. Among these metabolites, the levels of tyrosine, proline, methionine, sarcosine, choline, acetoacetate, citrate, 4-aminobutyrate, aspartate, maltose, malate, lysine, xylitol, lactate threonine, leucine, valine, isoleucine, uridine, guanidoacetate, arabitol, mannitol, glucose, and betaine were increased in the 95% ethanol extraction sample compared with the levels in other samples, whereas level of acetate, phenylalanine, alanine, succinate, and fumarate were significantly increased in the 0% ethanol extraction sample. A biological triterpenoid, namely pachymic acid, was detected from different ethanol P. cocos extract using ¹H-NMR spectra were found in CDCl₃. This is the first report to perform the metabolomics profiling of different ethanol W. cocos extract. These researches suggest that W. cocos can be used to obtain substantial amounts of bioactive ingredients for use as potential pharmacological and nutraceuticals agents.

Comparative Metabolomics Analysis of Cervicitis in Human Patients and a Phenol Mucilage-Induced Rat Model Using Liquid Chromatography Tandem Mass Spectrometry

Cervicitis is an exceedingly common gynecological disorder that puts women at high risk of sexually transmitted infections and induces a series of reproductive system diseases. This condition also has a significant impact on quality of life and is commonly misdiagnosed in clinical practice due to its complicated pathogenesis. In the present study, we performed non-targeted plasma metabolomics analysis of cervicitis in both plasma samples obtained from human patients and plasma samples from a phenol mucilage induced rat model of cervicitis, using ultra-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. In addition to differences in histopathology, we identified differences in the metabolic profile between the cervicitis and control groups using unsupervised principal component analysis and orthogonal projections to latent structures discriminant analysis. These results demonstrated changes in plasma metabolites, with 27 and 22 potential endogenous markers identified in rat and human samples, respectively. The metabolic pathway analysis showed that linoleic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism are key metabolic pathways involved in cervicitis. This study showed the rat model was successfully created and applied to understand the pathogenesis of cervicitis.

Lipidomics to investigate the pharmacologic mechanisms of ginkgo folium in the hyperuricemic rat model

Hyperuricemia caused by purine metabolic abnormalities is reported to have close correlation with lipid metabolic disorders. Ginkgo folium, a frequently-used lipid-lowering medicine, has significant anti-hyperuricemia effects. However, it is poorly known about the interaction between lowering uric acid and regulation of lipid metabolic disorders. In this study, hyperuricemic rat model was induced by orally administration with fructose. Ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) combined with pattern recognition approaches were used to determine different lipid metabolites in serum of control group, model group, and different doses of ginkgo folium groups. Principal component analysis (PCA) was applied to analyze the MS data to assess the establishment of model, partial least squares-discriminate analysis (PLS-DA) and independent samples T-test were performed to indicate the differences between different groups of rats and to find biomarkers. Metabolomics pathway analysis (MetPA) was introduced to reveal the pharmacologic mechanisms of ginkgo folium. 19 potential biomarkers associated with hyperuricemia were found. After intervened by ginkgo folium, these biomarkers were returning to normal level. Among these biomarkers, 13 lipid biomarkers were significantly reversed. Ginkgo filum can lower uric acid via adjusting back the level of PCs and LPCs, which suggested that its treatment mechanisms may be related to reducing the activity of PLA2. In sum, the lipidomics analysis in the system level have enhanced our understanding to pathogenesis of hyperuricemia and the results suggested that ginkgo folium could alleviate the abnormal metabolic status of hyperuricemia. These results demonstrated a new mechanism for lowering uric acid, which was helpful to the early treatment for hyperuricemia.

Patchouli oil ameliorates acute colitis: A targeted metabolite analysis of 2,4,6-trinitrobenzenesulfonic acid-induced rats

The incidence of inflammatory bowel disease (IBD), characterized by chronic, relapsing intestinal inflammation, has continually increased in recent years. A previous study by our group identified five potential metabolic markers possibly associated with the pathology of 2,4,6-trinitrobenzenesulfonic acid (TNBS)-induced IBD in rats. The present study aimed to examine the potential therapeutic effects of the essential oil of Pogostemon cablin (also known as patchouli; PO) on TNBS-induced rats and investigate the concomitant metabolic changes by targeting the previously identified potential markers. Pogostemon cablin is widely used to treat gastrointestinal diseases, including IBD, in China. The results of the present study showed that PO (270 mg/kg, rectal instillation) significantly alleviated colonic damage and reduced disease activity indicators and colonic myeloperoxidase in TNBS-induced rats. In addition, a targeted metabolic profiling study identified that four metabolites were elevated in the urine of the animals in the TNBS group, which were significantly inhibited by treatment with PO: Two tryptophan metabolites [4-(2-aminophenyl)-2,4-dioxobutanoic acid and 4,6-cihydroxyquinoline] and two gut microbial metabolites (phenylacetylglycine and p-cresol glucuronide). Taken together, these findings suggested that PO ameliorated the symptoms of TNBS-induced IBD and reversed the metabolic changes potentially associated with TNBS-induced IBD in rats.

Origins, Phytochemistry, Pharmacology, Analytical Methods and Safety of Cortex Moutan (Paeonia suffruticosa Andrew): A Systematic Review

Cortex Moutan (CM), a well-known traditional Chinese medicine, is commonly used for treating various diseases in China and other eastern Asian countries. Recorded in Pharmacopeias of several countries, CM is now drawing increasing attention and under extensive studies in various fields. Phytochemical studies indicate that CM contains many valuable secondary metabolites, such as monoterpene glycosides and phenols. Ample evidence from pharmacological researches suggest that CM has a wide spectrum of activities, such as anti-inflammatory, anti-oxidant, anti-tumor, anti-diabetic, cardiovascular protective, neuroprotective, hepatoprotective effects. Moreover, various analytical methods were established for the quality evaluation and safety control of CM. This review synopsizes updated information concerning the origins, phytochemistry, pharmacology, analytical method and safety of CM, aiming to provide favorable references for modern CM research and application. In conclusion, continuing pharmacological investigations concerning CM should be conducted to unravel its pharmacological mechanisms. Further researches are necessary to obtain comprehensive and applicable analytical approach for quality evaluation and establish harmonized criteria of CM.

Urinary signature of pig carcasses with boar taint by liquid chromatography-high-resolution mass spectrometry

Boar taint is an offensive odour that can occur while cooking pork or pork products and is identified in some uncastrated male pigs that have reached puberty. It is widely held that boar taint is the result of the accumulation in back fat of two malodorous compounds: androstenone and skatole. The purpose of this study is to assess a mass spectrometry-based metabolomics strategy to investigate the metabolic profile of urine samples from pig carcasses presenting low (untainted) and high (tainted) levels of androstenone and skatole in back fat. Urine samples were analysed by LC-ESI(+)-HRMS. Discrimination between tainted and untainted animals was observed by the application of multivariate statistical analysis, which allowed candidate urinary biomarkers to be highlighted. These urinary metabolites were positively correlated to androstenone and skatole levels in back fat. Therefore, the study suggests that the measurement of these urinary metabolites might provide information with regard to androstenone and skatole levels in live pigs.

Screening and verifying endometrial carcinoma diagnostic biomarkers based on a urine metabolomic profiling study using UPLC-Q-TOF/MS

BACKGROUND

Endometrial carcinoma (EOC) is a gynecological disease with one of the highest worldwide incidences. Due to the lack of typical clinical symptoms and limited sensitive screening methods used to diagnose endometrial carcinoma, the disease is easily neglected before patients are aware of its presence. Therefore, EOC results in serious impacts on women's lives and health. We screened diagnostic biomarkers of EOC with a noninvasive method that compared healthy individuals and endometrial hyperplasia (EOH) patients.

METHODS

The morning urine of 25 healthy individuals, 25 patients with EOC and 10 patients with EOH were analyzed using an ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) platform. Metabolomics data were used to screen the different metabolites according to principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) analyses. Furthermore, the screened biomarkers of the newly diagnosed EOC and EOH candidates and healthy individuals were verified using the predictive model of the support vector machine (SVM) to obtain EOC diagnostic biomarkers.

RESULTS

An EOC diagnostic biomarker group was found according to the metabolomics method. Five diagnostic biomarkers, including porphobilinogen, acetylcysteine, N-acetylserine, urocanic acid and isobutyrylglycine, were significantly changed in the EOC patients. Among them, porphobilinogen and acetylcysteine were significantly down-regulated, while N-acetylserine, urocanic acid and isobutyrylglycine were significantly up-regulated.

CONCLUSIONS

Disturbances in these biomarkers have negative impacts on the body's metabolic functioning. The EOC diagnostic biomarker group can provide a clinical reference for diagnosing EOC and insight into the diagnosis of other diseases in the clinic.

Metabolomics Analysis of the Larval Head of the Silkworm, Bombyx mori

The head, which performs many biological functions, is the most complicated structure of an insect. Development, locomotor behavior, food intake, environmental sensing, and signal transduction are all controlled by the insect’s head. As a well-studied insect in Lepidoptera, the silkworm head has an additional function of spinning silk fibers. To understand which molecules are involved in these physiological activities, we performed a metabolomics analysis of silkworm heads. By integrating GC-MS and LC-MS/MS, 90 metabolites were identified in the larval heads of silkworms. These were classified into 13 categories, including amino acids, sugars, organic acids, nucleotides, alcohols, and fatty acids. Informatics analysis revealed that these metabolites are involved in cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, organismal systems, and other pathways. The identified metabolites and pathways are involved in biological processes such as signal transduction, carbohydrate metabolism, endocrine activities, and sensory activities; reflecting the functions of various organs in silkworm heads. Thus, our findings provide references which elucidate the potential functions of the silkworm head and will be of great value for the metabolomics research of silkworms and other insects.

The fungicide triadimefon affects beer flavor and composition by influencing Saccharomyces cerevisiae metabolism

Despite the fact that beer is produced on a large scale, the effects of pesticide residues on beer have been rarely investigated. In this study, we used micro-brewing settings to determine the effect of triadimefon on the growth of Saccharomyces cerevisiae and beer flavor. The yeast growth in medium was significantly inhibited (45%) at concentrations higher than 5 mg L(-1), reaching 80% and 100% inhibition at 10 mg L(-1) and 50 mg L(-1), respectively. There were significant differences in sensory quality between beer samples fermented with and without triadimefon based on data obtained with an electronic tongue and nose. Such an effect was most likely underlain by changes in yeast fermentation activity, including decreased utilization of maltotriose and most amino acids, reduced production of isobutyl and isoamyl alcohols, and increased ethyl acetate content in the fungicide treated samples. Furthermore, yeast metabolic profiling by phenotype microarray and UPLC/TOF-MS showed that triadimefon caused significant changes in the metabolism of glutathione, phenylalanine and sphingolipids, and in sterol biosynthesis. Thus, triadimefon negatively affects beer sensory qualities by influencing the metabolic activity of S. cerevisiae during fermentation, emphasizing the necessity of stricter control over fungicide residues in brewing by the food industry.