Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks

The role of salivary metabolomics in chronic periodontitis: bridging oral and systemic diseases

BACKGROUND

Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.

AIM OF REVIEW

In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.

Development and Validation of Targeted Metabolomics Methods Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for the Quantification of 235 Plasma Metabolites

Plasma contains metabolites with diverse physicochemical properties, ranging from highly polar to highly apolar, and concentrations spanning at least nine orders of magnitude. Plasma metabolome analysis is valuable for monitoring health and evaluating medical interventions but is challenging due to the metabolome's diversity and complexity. This study aims to develop and validate targeted LC-MS/MS methods for quantifying 235 mammalian metabolites from 17 compound classes in porcine plasma without prior derivatization. Utilizing reversed-phase and hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry, each analyte is identified and quantified using two selected reaction monitoring (SRM) transitions. Fast polarity switching and scheduled SRM enhance the metabolome coverage and throughput, enabling the analysis of one sample in about 40 min. A simple "dilute and shoot" sample preparation protocol was employed, with samples injected at two dilution levels to align metabolite concentrations within calibration curve ranges. Validation in porcine plasma included assessments of carryover, linearity, detection and quantification limits, repeatability and recovery. The method was further applied to plasma samples from various animal species, demonstrating its applicability to human and animal studies. This study establishes two robust LC-MS/MS methods for comprehensive porcine plasma metabolome quantification, advancing large-scale targeted metabolomics in biomedical research.

Metabolomics analyses and physical interventions in soccer: a systematic review

BACKGROUND

Soccer is the most recognized sports worldwide. It is a fertile ground for the use of metabolomics analyses, considering the multifactorial nature of soccer's physical demands on the body. Although scientific studies have tried using it to better understand the impacts of soccer into different contexts of the sport, no systematic review is available on metabolomics analyses in soccer athletes subjected to physical exertion interventions.

AIM OF REVIEW

Retrieve scientific articles that conducted metabolomics analyses on soccer athletes subjected to physical exertion interventions.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Initially, 271 studies were screened, and 48 were retrieved for abstract analysis. Of these, 26 met the eligibility criteria, but 5 failed to meet inclusion criteria. The 21 studies included in this systematic review demonstrate that responses from physical training or acute exercise sessions, followed by the effects of soccer matches, have been the primary focus of researchers to date, highlighting alterations on metabolites from the energy metabolism, immunological pathway, purines, tryptophan/phenylalanine metabolism, as well as oxidative species and antioxidant capacity. Other studies suggest, albeit preliminarily, that organic metabolites have the potential to distinguish soccer players' performance and physical fitness, as well as provide valuable insights into diet, physical condition, training load, and recovery throughout the season. Despite metabolomics great potential to understand physiological alterations provoked by soccer as shown by the included studies, future studies should consider female athletes, explore the cause-and-effect relationship between metabolites and soccer performance more deeply, and examine the effects of different training periodizations on these markers.

A Comprehensive Metabolomic and Microbial Analysis Following Dietary Amino Acid Reduction in Mice

Introduction: Nutritional metabolomics provides a comprehensive overview of the biochemical processes that are induced by dietary intake through the measurement of metabolite profiles in biological samples. However, there is a lack of deep phenotypic analysis that shows how dietary interventions influence the metabolic state across multiple physiologic sites. Dietary amino acids have emerged as important nutrients for physiology and pathophysiology given their ability to impact cell metabolism. Methods: The aim of the current study is to evaluate the effect of modulating amino acids in diet on the metabolome and microbiome of mice. Here, we report a comprehensive metabolite profiling across serum, liver, and feces, in addition to gut microbial analyses, following a reduction in either total dietary protein or diet-derived non-essential amino acids in mice. Results: We observed both distinct and overlapping patterns in the metabolic profile changes across the three sample types, with the strongest signals observed in liver and serum. Although amino acids and related molecules were the most commonly and strongly altered group of metabolites, additional small molecule changes included those related to glycolysis and the tricarboxylic acid cycle. Microbial profiling of feces showed significant differences in the abundance of select species across groups of mice. Conclusions: Our results demonstrate how changes in dietary amino acids influence the metabolic profiles across organ systems and the utility of metabolomic profiling for assessing diet-induced alterations in metabolism.

Investigating the involvement of the NLRP3/ASC/caspase-1 and NF-κb/MAPK pathways in the pathogenesis of gouty arthritis: Insights from irradiated and non-irradiated Trifolium alexandrium L. extracts and some metabolites

ETHNOPHARMACOLOGICAL RELEVANCE

Trifolium alexandrinum L. (TA), has traditionally been used in folk medicine for its anti-inflammatory properties against hyperuricemia and gout. However, the specific mechanisms of action of TA have not been thoroughly studied.

AIM OF THE WORK

This study aimed to evaluate the protective effects of irradiated (TR25) and non-irradiated (TR0) Trifolium alexandrinum L. aqueous extract (TAAE), along with two isolated compounds, caffeine (CAF) and saponin (SAP), in a rat model of acute gouty arthritis (GA).

MATERIALS AND METHODS

The GA model was established by injecting a monosodium urate (MSU) suspension into the knee joint. Synovial tissue pathology was assessed, and levels of TNF-α, IL-6, IL-1β, NF-κB, mTOR, AKT1, PI3K, NLRP3, and ASC were measured by ELISA. mRNA expression of ERK1, JNK, and p-38 MAPK was detected using qRT-PCR, and Caspase-1 protein expression was assessed by immunohistochemical analysis. Knee swelling, uric acid levels, liver and kidney function, and oxidative stress markers were also evaluated.

RESULTS

TAAE analysis identified 170 compounds, with 73 successfully identified using LC-HR-MS/MS, including caffeine citrate and theasapogenol B glycoside as the main constituents. The studied materials demonstrated significant protective effects against GA. TR25 administration significantly mitigated knee joint circumference compared to other treatments. It demonstrated potential in alleviating hyperuricemia, renal and hepatic impairments induced by MSU crystals. TR25 also alleviated oxidative stress and reduced levels of IL1β, IL-6, TNF-α, and NF-κB. Weak Caspase-1 immune-positive staining was observed in the TR25 group. TR25 decreased NLRP3 and ASC expression, reducing inflammatory cytokine levels in GA. It effectively inhibited the PI3K, AKT, and mTOR signaling pathways, promoting autophagy. Additionally, TR25 suppressed ERK1, JNK, and p-38 MAPK gene expression in synovial tissue. These effects were attributed to various components in TAAE, such as flavonoids, phenolic acids, tannins, alkaloids, and triterpenes.

CONCLUSION

Importantly, irradiation (25 KGy) enhanced the antioxidant effects and phtchemical contents of TAAE. Additionally, TR0, TR25, CAF, and SAP exhibited promising protective effects against GA, suggesting their therapeutic potential for managing this condition. These effects were likely mediated through modulation of the NLRP3/ASC/Caspase-1 and ERK/JNK/p-38 MAPK signaling pathways, as well as regulation of the PI3K/AKT/mTOR pathway. Further research is warranted to fully elucidate the underlying mechanisms and optimize their clinical applications.

Rapid LA-REIMS-based metabolic fingerprinting of serum discriminates aflatoxin-exposed from non-exposed pregnant women: a prospective cohort from the Butajira Nutrition, Mental Health, and Pregnancy (BUNMAP) Study in rural Ethiopia

To date, the changes in maternal metabolic response associated with prenatal aflatoxin exposure remain largely unknown. This study investigated the effects of prenatal aflatoxin exposure on the maternal serum metabolome in rural Ethiopia. A total of 309 pregnant women were enrolled prospectively, and their serum aflatoxin concentrations were measured using targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Serum metabolic fingerprints were obtained using laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS), followed by combination of univariate and multivariate statistical modelling to evaluate changes in circulating metabolic features between aflatoxin-exposed and unexposed mothers and to select discriminatory metabolic features. The analysis revealed that 81.8% of women were exposed to aflatoxins, with a median concentration of 12.9 pg/mg albumin. The orthogonal partial least square discriminant analysis (OPLS-DA) regression model demonstrated significant disparities in the serum metabolome when comparing Ethiopian pregnant women with low vs high aflatoxin exposure. Thirty-two differentially expressed metabolic features were identified, affecting aminoacyl-tRNA biosynthesis pathway. Several discriminatory metabolites have been identified, including glutamine, tryptophan, tyrosine, carnosine, and 1-methylnicotinamide. In conclusion, our findings indicate that aflatoxin exposure during pregnancy have shown disparities in the maternal serum metabolome, primarily affecting protein synthesis. Further research is needed to identify specific metabolite biomarkers and elucidate the underlying mechanisms.

Volatile Metabolite Profiles of Robusta Green Bean Coffee From Different Geographical Origins in West Java and Their Correlation With Temperature, Rainfall, and Altitudes Using SPME GC-MS-Based Metabolomics

The chemical composition, including volatile metabolites of green coffee beans, is influenced by geographic origin. The aim of this study was to reveal the volatile metabolite profile of a single variety of Robusta green bean coffee from five major plantation regions in West Java and to correlate these profiles with temperature, rainfall, and altitude. By using solid phase micro extractions and gas chromatography-mass spectrometry, 143 different volatile compounds were detected, with aromatic hydrocarbon, alcohols, monoterpene, pyrazines, sesquiterpenes, carboxylic acids, and terpene the most dominant. Principal component analysis (PCA) indicated 64.3% variability, showing that the metabolite profile of Robusta green coffee from the Bogor region was distinctly different from those in Ciamis, Kuningan, Sumedang, and Tasikmalaya, which were more similar to each other. Metabolites such as benzaldehyde, isovaleric acid, toluene, diisobutyl succinate, 1-heptene, 4-dodecene, caffeine, acetic acid, and methyl benzoate were identified as key discriminants, with a VIP score greater than 1.5. Temperature increases were linked to higher levels of isovaleric acid, diisobutyl succinate, 4-dodecene, toluene, and acetic acid, while other discriminant metabolites declined. Increased rainfall was associated with higher levels of benzaldehyde, 1-heptene, caffeine, and methyl benzoate, but lower levels of the other discriminants. Altitude had a positive correlation with methyl benzoate and 1-heptene, and a negative correlation with isovaleric acid and 4-dodecene, with weaker correlations for other compounds. In summary, Robusta green coffee beans from different regions of West Java can be distinguished by their volatile metabolites. Bogor green coffee beans had higher levels of benzaldehyde, 1-heptene, caffeine, and methyl benzoate, Kuningan beans had more diisobutyl succinate and 4-dodecene, Ciamis beans had higher levels of isovaleric acid, diisobutyl succinate, and 4-dodecene, while Sumedang and Tasikmalaya beans were similar, with higher levels of isovaleric acid, diisobutyl succinate, 4-dodecene, toluene, and acetic acid. This difference is related to the climatic factors of temperature and rainfall, as well as the altitude at which Robusta coffee is grown.

Physiological and differential protein expression analyses of the calcium stress response in the Drynaria roosii rhizome

High concentration Ca2+ in karst soil is harmful to agriculture. Some dominant plants can adapt well to karst soil, but how Ca2+ affect plant is unknown. Drynaria roosii is a Ca2+-tolerant fern and its dry rhizome is a common Chinese medicine of Miao nationality in Guizhou, China. This study analyzed the physiological and proteomic characteristics of the rhizome of D. roosii under calcium stress. Physiological results indicated that calcium stress may lead to osmotic stress. Proteomic results showed that 147 differentially expressed proteins (96 increased, 51decreased) were identified under calcium stress, and these proteins mainly involved in signal transduction, protein translation, material transport, antioxidant defense and secondary metabolism. This study will lay a foundation for studying the calcium adaptation mechanism of D. roosii at the molecular level.

Saliva metabolomics: concepts and applications in oral disorders

OBJECTIVES

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies.

MATERIAL AND METHODS

This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages.

RESULTS

Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline.

CONCLUSIONS

Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories.

CLINICAL RELEVANCE

Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

The potential protective effect of 3-Hydroxybutyrate against aortic dissection: a mendelian randomization analysis

BACKGROUND

3-Hydroxybutyrate, also called β-hydroxybutyrate, is a significant constituent of ketone bodies. Previous observational and experimental studies have suggested that ketogenic diet, especially 3-hydroxybutyrate, may have a protective effect against cardiovascular disease. However, the relationship between ketone bodies, especially 3-hydroxybutyrate, and aortic dissection remains uncertain.

MATERIALS AND METHODS

Publicly accessible data from genome-wide association study (GWAS) was utilized to obtain information on ketone bodies, including 3-hydroxybutyrate, acetoacetate and acetone as exposure respectively, while GWAS data on aortic dissection was used as outcome. Subsequently, two-sample Mendelian randomization (MR) analysis was conducted to examine the potential relationship between ketone bodies and aortic dissection. Then, reverse and multivariate Mendelian randomization analyses were performed. Additionally, sensitivity tests were conducted to assess the robustness of MR study.

RESULTS

The inverse-variance weighted (IVW) method of Mendelian randomization analysis of gene prediction observed a negative correlation between 3-hydroxybutyrate and risk of aortic dissection (OR 0.147, 95% CI 0.053-0.410). Furthermore, consistent findings were obtained through the implementation of the weighted median, simple mode, Mendelian randomization-Egger (MR-Egger), and weighted mode methods. After adjusting acetoacetate (OR 0.143, 95% CI 0.023-0.900) or acetone (OR 0.100, 95% CI 0.025-0.398), MR analysis of gene prediction still observed a negative correlation between 3-hydroxybutyrate and risk of aortic dissection. No indications of heterogeneity or pleiotropy among the SNPs were detected.

CONCLUSION

The findings from the MR analysis demonstrated that genetically predicted 3-hydroxybutyrate exhibits a protective effect against aortic dissection.

Cellular metabolomics study of the antitumor mechanism of Sijunzi decoction combined with mitomycin C

Mitomycin C (MMC) has an antitumor effect and is considered as a broad-spectrum antibiotic. Sijunzi Decoction (SJZD), a well-known ancient Chinese prescription, is widely used in the treatment of cancer when combined with chemotherapy drugs. Studies have shown that SJZD can be combined with other drugs to enhance the therapeutic effect against cancer and inhibit the toxicity of chemotherapy drugs, but the specific mechanism is not clear. Thus, we hope to further explore the antitumor mechanism of combined SJZD and MMC. 3-(4,5-Dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide assay, flow cytometry, western blot, 1H NMR and HPLC-MS were used to study the mechanism at the cellular level. The results show that the combined administration can have a more significant effect on inhibiting the proliferation of cancer cells, promoting their apoptosis. Based on metabolomics, 38 biomarkers were found in the MMC group and 43 biomarkers were found in the combined administration group. Among them, 18 unique biomarkers were discovered in the combined administration group. Studies have shown that the antitumor mechanism of combined administration is related to amino acid metabolism, energy metabolism, lipid metabolism and nucleotide metabolism, among which amino acid metabolism is the most important. In addition, SJZD achieves the effect of toxin reduction and efficiency enhancement by improving the body's immunity and improving the oxidative stress environment.

Maize Grain Metabolite Profiling by NMR: Effects of Growing Year, Variety, and Cropping System

Considering that maize (Zea mays L.) is a staple food for a large segment of the population worldwide, many attempts have been made to improve the nutritional value of its grain and at the same time to achieve sustainable cropping systems. The present study aimed to characterize the composition and nutritional value of maize grain as influenced by cropping system, genetic background (variety), and growing year using untargeted NMR metabolomics. The composition of both water- (sugars and polyols, organic acids, and amino acids) and liposoluble metabolites (free and esterified fatty acids, sterols, and lipids) extracted from the maize grain was determined. Multivariate statistical analyses (PCA and ANOVA) pointed to the growing year and the variety as the most important random and fixed factors, respectively, influencing the metabolite profile. The samples were separated along PC1 and PC3 according to the growing year and the variety, respectively. A higher content of citric acid and diunsaturated fatty acids and a lower content of tyrosine, trigonelline, and monounsaturated fatty acids was observed in the organic with respect to the conventional variety. The effect of the cropping system was overwhelmed by the random effect of the growing year. The results provide novel knowledge on the influence of agronomic practices on maize micronutrient contents.

Association between the third trimester maternal serum metabolome and child growth and development through the first year of life

Evidence suggests that maternal metabolome may be associated with child health outcomes. We analyzed the association between the maternal metabolome between 28-35 gestational weeks and child growth and development during the first year. A prospective cohort of 98 mother-child dyads was followed at birth, 1, 6, and 12 months. Maternal serum samples were collected for targeted LC-MS/MS analysis, which measured 132 metabolites. The child's growth and development were assessed at each time-point. Z-scores were calculated based on WHO growth standards, and the domains of development were assessed using the Ages and Stages Questionnaires (ASQ-3). Multiple linear mixed-effects models were performed and confounders were identified using a Diagram Acyclic Graph. The Benjamini-Hochberg correction was used for multiple comparison adjustments. We found a positive association between lysophosphatidylcholines (14:0; 16:0; 16:1; 17:0; 18:0; 18:1; 18:2; 20:4) with the z-score of weight-for-age, and lysophosphatidylcholines (14:0; 16:0; 16:1; 18:0) and taurine with the z-score of weight-for-length, and lysophosphatidylcholines (14:0; 16:0; 16:1; 17:0; 18:0; 18:1; 18:2; 20:4) and glycine with the z-score of BMI-for-age. The leucine, methionine, tryptophan, and valine were negatively associated with the fine motor skills domain. We observed an association between maternal metabolome and the growth and child's development throughout the first year.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

The Omics Revolution in Understanding Chicken Reproduction: A Comprehensive Review

Omics approaches have significantly contributed to our understanding of several aspects of chicken reproduction. This review paper gives an overview of the use of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics to elucidate the mechanisms of chicken reproduction. Genomics has transformed the study of chicken reproduction by allowing the examination of the full genetic makeup of chickens, resulting in the discovery of genes associated with reproductive features and disorders. Transcriptomics has provided insights into the gene expression patterns and regulatory mechanisms involved in reproductive processes, allowing for a better knowledge of developmental stages and hormone regulation. Furthermore, proteomics has made it easier to identify and quantify the proteins involved in reproductive physiology to better understand the molecular mechanisms driving fertility, embryonic development, and egg quality. Metabolomics has emerged as a useful technique for understanding the metabolic pathways and biomarkers linked to reproductive performance, providing vital insights for enhancing breeding tactics and reproductive health. The integration of omics data has resulted in the identification of critical molecular pathways and biomarkers linked with chicken reproductive features, providing the opportunity for targeted genetic selection and improved reproductive management approaches. Furthermore, omics technologies have helped to create biomarkers for fertility and embryonic viability, providing the poultry sector with tools for effective breeding and reproductive health management. Finally, omics technologies have greatly improved our understanding of chicken reproduction by revealing the molecular complexities that underpin reproductive processes.

Climate-Affected Australian Tropical Montane Cloud Forest Plants: Metabolomic Profiles, Isolated Phytochemicals, and Bioactivities

The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation's total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and the WTWHA is no exception. The current average annual temperature of WTWHA in northeast Queensland is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species, endemic to tropical montane cloud forests (TMCF) within the WTWHA, which are already experiencing climate change threats. Some of these plants are used in herbal medicines. This study comprehensively reviewed the metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses the following: (i) recent developments in plant metabolomics studies that can be applied to study and better understand the interactions of wet tropics plants with climatic stress, (ii) medicinal plants and isolated phytochemicals with structural diversity, and (iii) reported biological activities of crude extracts and isolated compounds.

Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis

Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.

COVID-19 and Comorbidities: What Has Been Unveiled by Metabolomics?

The COVID-19 pandemic has brought about diverse impacts on the global population. Individuals with comorbidities were more susceptible to the severe symptoms caused by the virus. Within the crisis scenario, metabolomics represents a potential area of science capable of providing relevant information for understanding the metabolic pathways associated with the intricate interaction between the viral disease and previous comorbidities. This work aims to provide a comprehensive description of the scientific production pertaining to metabolomics within the specific context of COVID-19 and comorbidities, while highlighting promising areas for exploration by those interested in the subject. In this review, we highlighted the studies of metabolomics that indicated a variety of metabolites associated with comorbidities and COVID-19. Furthermore, we observed that the understanding of the metabolic processes involved between comorbidities and COVID-19 is limited due to the urgent need to report disease outcomes in individuals with comorbidities. The overlap of two or more comorbidities associated with the severity of COVID-19 hinders the comprehension of the significance of each condition. Most identified studies are observational, with a restricted number of patients, due to challenges in sample collection amidst the emergent situation.

The role of metabolomics in informing strategies for improving photosynthesis

Photosynthesis plays a vital role in acclimating to and mitigating climate change, providing food and energy security for a population that is constantly growing, and achieving an economy with zero carbon emissions. A thorough comprehension of the dynamics of photosynthesis, including its molecular regulatory network and limitations, is essential for utilizing it as a tool to boost plant growth, enhance crop yields, and support the production of plant biomass for carbon storage. Photorespiration constrains photosynthetic efficiency and contributes significantly to carbon loss. Therefore, modulating or circumventing photorespiration presents opportunities to enhance photosynthetic efficiency. Over the past eight decades, substantial progress has been made in elucidating the molecular basis of photosynthesis, photorespiration, and the key regulatory mechanisms involved, beginning with the discovery of the canonical Calvin-Benson-Bassham cycle. Advanced chromatographic and mass spectrometric technologies have allowed a comprehensive analysis of the metabolite patterns associated with photosynthesis, contributing to a deeper understanding of its regulation. In this review, we summarize the results of metabolomics studies that shed light on the molecular intricacies of photosynthetic metabolism. We also discuss the methodological requirements essential for effective analysis of photosynthetic metabolism, highlighting the value of this technology in supporting strategies aimed at enhancing photosynthesis.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Profiling canine mammary tumors: A potential model for studying human breast cancer

Despite all clinical progress recorded in the last decades, human breast cancer (HBC) remains a major challenge worldwide both in terms of its incidence and its management. Canine mammary tumors (CMTs) share similarities with HBC and represent an alternative model for HBC. The utility of the canine model in studying HBC relies on their common features, include spontaneous development, subtype classification, mutational profile, alterations in gene expression profile, and incidence/prevalence. This review describes the similarities between CMTs and HBC regarding genomic landscape, microRNA expression alteration, methylation, and metabolomic changes occurring during mammary gland carcinogenesis. The primary purpose of this review is to highlight the advantages of using the canine model as a translational animal model for HBC research and to investigate the challenges and limitations of this approach.

Sample Preparation from Plant Tissue for Gas Chromatography-Mass Spectrometry (GC-MS)we

Metabolites are intermediate products formed during metabolism. Metabolites play different roles, including providing energy, supporting structure, transmitting signals, catalyzing reactions, enhancing defense, and interacting with other species. Plant metabolomics research aims to detect precisely all metabolites found within tissues of plants through GC-MS. This chapter primarily focuses on extracting metabolites using chemicals such as methanol, chloroform, ribitol, MSTFA, and TMCS. The metabolic analysis method is frequently used according to the specific kind of sample or matrix being investigated and the analysis objective. Chromatography (LC, GC, and CE) with mass spectrometry and NMR spectroscopy is used in modern metabolomics to analyze metabolites from plant samples. The most frequently used method for metabolites analysis is the GC-MS. It is a powerful technique that combines gas chromatography's separation capabilities with mass spectrometry, offering detailed information, including structural identification of each metabolite. This chapter contains an easy-to-follow guide to extract plant-based metabolites. The current protocol provides all the information needed for extracting metabolites from a plant, precautions, and troubleshooting.

Molecular insights and omics-based understanding of plant-microbe interactions under drought stress

The detrimental effects of adverse environmental conditions are always challenging and remain a major concern for plant development and production worldwide. Plants deal with such constraints by physiological, biochemical, and morphological adaptations as well as acquiring mutual support of beneficial microorganisms. As many stress-responsive traits of plants are influenced by microbial activities, plants have developed a sophisticated interaction with microbes to cope with adverse environmental conditions. The production of numerous bioactive metabolites by rhizospheric, endo-, or epiphytic microorganisms can directly or indirectly alter the root system architecture, foliage production, and defense responses. Although plant-microbe interactions have been shown to improve nutrient uptake and stress resilience in plants, the underlying mechanisms are not fully understood. "Multi-omics" application supported by genomics, transcriptomics, and metabolomics has been quite useful to investigate and understand the biochemical, physiological, and molecular aspects of plant-microbe interactions under drought stress conditions. The present review explores various microbe-mediated mechanisms for drought stress resilience in plants. In addition, plant adaptation to drought stress is discussed, and insights into the latest molecular techniques and approaches available to improve drought-stress resilience are provided.

Targeted Metabolomics Study on the Effect of Vinegar Processing on the Chemical Changes and Antioxidant Activity of Angelica sinensis

Angelica sinensis (Oliv.) Diels (A. sinensis) has a long processing history. In order to obtain a more valuable composition and higher antioxidant behavior, it is often processed by stir-frying and vinegar treatment. However, the underlying mechanism of chemical changes remains ambiguous. Using UPLC-QQQ-MS/MS alongside targeted metabolomics techniques, this study probed the variances between crude and processed A. sinensis. We identified 1046 chemical components in total, 123 differential components in stir-fried A. sinensis, and 167 in vinegar-treated ones were screened through multivariate statistical analysis. Moreover, 83 significant compounds, encompassing amino acids, phenolic acids, etc., were identified across both processing methods. The in vitro antioxidant activities of these A. sinensis forms were assessed, revealing a positive correlation between most of the unique components emerging after processing and the antioxidant capabilities. Notably, post-processing, the chemical composition undergoes significant alterations, enhancing the antioxidant activity. Specific compounds, including 4-hydroxybenzaldehyde, syringetin-3-O-glucoside, and salicylic acid, greatly influence antioxidant activity during processing.

Rhododendron chrysanthum's Primary Metabolites Are Converted to Phenolics More Quickly When Exposed to UV-B Radiation

The plant defense system is immediately triggered by UV-B irradiation, particularly the production of metabolites and enzymes involved in the UV-B response. Although substantial research on UV-B-related molecular responses in Arabidopsis has been conducted, comparatively few studies have examined the precise consequences of direct UV-B treatment on R. chrysanthum. The ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) methodology and TMT quantitative proteomics are used in this study to describe the metabolic response of R. chrysanthum to UV-B radiation and annotate the response mechanism of the primary metabolism and phenolic metabolism of R. chrysanthum. The outcomes demonstrated that following UV-B radiation, the primary metabolites (L-phenylalanine and D-lactose*) underwent considerable changes to varying degrees. This gives a solid theoretical foundation for investigating the use of precursor substances, such as phenylalanine, to aid plants in overcoming abiotic stressors. The external application of ABA produced a considerable increase in the phenolic content and improved the plants' resistance to UV-B damage. Our hypothesis is that externally applied ABA may work in concert with UV-B to facilitate the transformation of primary metabolites into phenolic compounds. This hypothesis offers a framework for investigating how ABA can increase a plant's phenolic content in order to help the plant withstand abiotic stressors. Overall, this study revealed alterations and mechanisms of primary and secondary metabolic strategies in response to UV-B radiation.

Metabolome expression in Eucryphia cordifolia populations: Role of seasonality and ecological niche centrality hypothesis

The ecological niche centrality hypothesis states that population abundance is determined by the position in the ecological niche, expecting higher abundances towards the center of the niche and lower at the periphery. However, the variations in the conditions that favor the persistence of populations between the center and the periphery of the niche can be a surrogate of stress factors that are reflected in the production of metabolites in plants. In this study we tested if metabolomic similarity and diversity in populations of the tree species Eucryphia cordifolia Cav. vary according to their position with respect to the structure of the ecological niche. We hypothesize that populations growing near the centroid should exhibit lower metabolites diversity than plants growing at the periphery of the niche. The ecological niche of the species was modeled using correlative approaches and bioclimatic variables to define central and peripheral localities from which we chose four populations to obtain their metabolomic information using UHPLC-DAD-QTOF-MS. We observed that populations farther away from the centroid tend to have higher metabolome diversity, thus supporting our expectation of the niche centrality hypothesis. Nonetheless, the Shannon index showed a marked variation in metabolome diversity at the seasonal level, with summer and autumn being the periods with higher metabolite diversity compared to winter and spring. We conclude that both the environmental variation throughout the year in combination with the structure of the ecological niche are relevant to understand the variation in expression of metabolites in plants.

The changes of gut microbiota and metabolites in different drug-induced liver injuries

The increasing incidence of drug-induced liver injury (DILI) has become a major concern. Gut microbiota, as another organ of the human body, has been studied in various tumors, cardiovascular metabolic diseases, inflammatory bowel disease and human immunity. The studies mentioned above have confirmed its important impact on the occurrence and development of DILI. The gut-liver axis explains the close relationship between the gut and the liver, and it may be a pathway by which gut microbes contribute to DILI. In addition, the interaction between drugs and gut microbes affects both separately, which in turn may have positive or negative effects on the body, including DILI. There are both common and specific changes in liver injury caused by different drugs. The alteration of metabolites in DILI is also a new direction of therapeutic exploration. The application of microbiomics, metabolomics and other multi-omics to DILI has also explored new ideas for DILI. In this review, we conclude the alterations of gut microbes and metabolites under different DILI, and the significance of applying gut microbiome-metabolomics to DILI, so as to explore the metabolic characteristics of DILI and possible novel metabolic biomarkers.

Food Monitoring: Limitations of Accelerated Storage to Predict Molecular Changes in Hazelnuts (Corylus avellana L.) under Realistic Conditions Using UPLC-ESI-IM-QTOF-MS

Accelerated storage is routinely used with pharmaceuticals to predict stability and degradation patterns over time. The aim of this is to assess the shelf life and quality under harsher conditions, providing crucial insights into their long-term stability and potential storage issues. This study explores the potential of transferring this approach to food matrices for shelf-life estimation. Therefore, hazelnuts were stored under accelerated short-term and realistic long-term conditions. Subsequently, they were analyzed with high resolution mass spectrometry, focusing on the lipid profile. LC-MS analysis has shown that many unique processes take place under accelerated conditions that do not occur or occur much more slowly under realistic conditions. This mainly involved the degradation of membrane lipids such as phospholipids, ceramides, and digalactosyldiacylglycerides, while oxidation processes occurred at different rates in both conditions. It can be concluded that a food matrix is far too complex and heterogeneous compared to pharmaceuticals, so that many more processes take place during accelerated storage, which is why the results cannot be used to predict molecular changes in hazelnuts stored under realistic conditions.

Serum metabolomics analysis of biomarkers and metabolic pathways in patients with colorectal cancer associated with spleen-deficiency and qi-stagnation syndrome or damp-heat syndrome: a prospective cohort study

Objective

To profile the serum metabolites and metabolic pathways in colorectal cancer (CRC) patients associated with spleen-deficiency and qi-stagnation syndrome (SDQSS) or damp-heat syndrome (DHS).

Methods

From May 2020 to January 2021, CRC patients diagnosed with traditional Chinese medicine (TCM) syndromes of SDQSS or DHS were enrolled. The clinicopathological data of the SDQSS and DHS groups were compared. The serum samples were analyzed by liquid chromatography-mass spectrometry (LC-MS). The variable importance in the projection >1, fold change ≥3 or ≤0.333, and P value ≤0.05 were used to identify differential metabolites between the two groups. Furthermore, areas under the receiver operating characteristic (ROC) curve > 0.9 were applied to select biomarkers with good predictive performance. The enrichment metabolic pathways were searched through the database of Kyoto Encyclopedia of Genes and Genomes.

Results

60 CRC patients were included (30 SDQSS and 30 DHS). The level of alanine aminotransferase was marginally significantly higher in the DHS group than the SDQSS group (P = 0.051). The other baseline clinicopathological characteristics were all comparable between the two groups. 23 differential serum metabolites were identified, among which 16 were significantly up-regulated and 7 were significantly down-regulated in the SDQSS group compared with the DHS group. ROC curve analysis showed that (S)-3-methyl-2-oxopentanoic acid, neocembrene, 1-aminocyclopropanecarboxylic acid, 3-methyl-3-hydroxypentanedioate, and nicotine were symbolic differential metabolites with higher predictive power. The top five enrichment signalling pathways were valine, leucine and isoleucine biosynthesis; lysosome; nicotine addiction; fructose and mannose metabolism; and pertussis.

Conclusion

Our study identifies the differential metabolites and characteristic metabolic pathways among CRC patients with SDQSS or DHS, offering the possibility of accurate and objective syndrome differentiation and TCM treatment for CRC patients.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Exploring the effect of the Uyghur medicine Munziq Balgam on a collagen-induced arthritis rat model by UPLC-MS/MS-based metabolomics approach

ETHNOPHARMACOLOGICAL RELEVANCE

Munziq Balgam (MBm) is a classic preparation of a traditional Uyghur medicine used for many years to treat abnormal body fluid diseases. The formula, as an in-hospital preparation, has already been used in the Hospital of Xinjiang Traditional Uyghur Medicine to treat rheumatoid arthritis (RA) with significant clinical effects.

AIM OF THE STUDY

This study intends to reveal the intervention effect of MBm on collagen-induced arthritis (CIA) rats, discover the potential biomarkers with efficacy, and explore the mechanisms of metabolic regulation by using metabolomics method.

MATERIAL AND METHODS

Sprague Dawley (SD) rats were randomly divided into five groups: blank group, CIA model group, Munziq Balgam nomal-dosage, Munziq Balgam high-dosage group and control group. Body weight, paw swelling, arthritis index, immune indices and histopathological experiments were carried out. Plasma from rats were detected by UPLC-MS/MS. Metabolomics of plasma was performed to analyze metabolic profiles, potential biomarkers, and metabolic pathways of MBm for CIA rats. The main metabolic result of Uyghur medicine MBm was compared with that of Zhuang medicine Longzuantongbi granules (LZTBG) to explore the characteristics of two ethnic medicines from different regions for RA.

RESULTS

MBm could significantly alleviate symptoms of CIA rats by relieving arthritis symptoms on paw redness and swelling, inflammatory cell infiltration, synovial hyperplasia, pannus, cartilage and bone tissue destruction, as well as inhibiting the expression of IL-1β, IL-6, TNF-α, UA and ALP. Linoleic acid, alpha-linolenic acid, pantothenate and CoA biosynthesis, achidonic acid, gycerophospholipid, sphingolipid metabolism, primary bile acid biosynthesis, porphyrin and chlorophyll metabolism and fatty acid degradation served as the main nine pathways of the interventional effect of MBm on CIA rats. Twenty-three different metabolites were screened out and strongly associated with the indicator makes of RA. Eight potential efficacy-related biomarkers were finally discovered in metabolic pathway network (phosphatidylcholine, bilirubin, sphinganine 1-phosphate, phytosphingosine, SM (d18:1/16:0), pantothenic acid, l-palmitoylcarnitine, chenodeoxycholate). Three metabolites (chenodeoxycholate, hyodeoxycholic acid and O-palmitoleoylcarnitine) were changed in both the metabolic study of MBm and LZTBG intervention effects on CIA rats. Additionally, MBm and LZTBG shared the same 6 metabolic pathways including linoleic acid, alpha-linolenic acid, pantothenate and CoA biosynthesis, achidonic acid, gycerophospholipid, and primary bile acid biosynthesis.

CONCLUSION

The study suggested that MBm may effectively alleviate RA by regulating inflammation, immunity-related pathways and multiple targets. Metabolomics analysis showed that MBm (Xinjiang, the north of China) and LZTBG (Guangxi, the south of China), two ethnic medicines from different regions in China, share common metabolites and pathways but also have distinct differences in their interventions for RA.

Widely Targeted HPLC-MS/MS Metabolomics Analysis Reveals Natural Metabolic Insights in Insects

Insect metabolites play vital roles in regulating the physiology, behavior, and numerous adaptations of insects, which has contributed to them becoming the largest class of Animalia. However, systematic metabolomics within the insects is still unclear. The present study performed a widely targeted metabolomics analysis based on the HPLC-MS/MS technology to construct a novel integrated metabolic database presenting comprehensive multimetabolite profiles from nine insect species across three metamorphosis types. A total of 1442 metabolites were identified, including amino acids and their metabolites, organic acids and their derivatives, fatty acids (FAs), glycerophospholipids (GPs), nucleotides and their metabolites, and benzene and its substituted derivatives. Among them, 622 metabolites were used to generate a 0 and 1 matrix based on their presence or absence, and these metabolites were enriched in arachidonic acid metabolism, tyrosine metabolism, phenylalanine metabolism, and insect hormone biosynthesis pathways. Our study revealed that there is a high coincidence between the evolutionary relationships of the species and the hierarchical cluster based on the types of metabolites, while the quantities of the metabolites show a high diversity among species. The metabolome of the nine representative insects provides an important platform for implementing the analysis of insect systemic metabolites and biological events at the metabolic level.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

Untargeted metabolomic analysis to explore the impact of soil amendments in a non-conventional wastewater treatment

As non-conventional wastewater treatment, vegetation filters make the most of the natural attenuation processes that occur in soil to remove contaminants, while providing several environmental benefits. However, this practice may introduce contaminants of emerging concern (CECs) and their transformation products (TPs) into the environment. A potential improvement to the system was tested using column experiments containing soil (S) and soil amended with woodchips (SW) or biochar (SB) irrigated with synthetic wastewater that included 11 selected CECs. This study evaluated: i) known CECs attenuation and ii) unknown metabolites formation. Known CECs attenuation was assessed by total mass balance by considering both water and soil media. An untargeted metabolomic strategy was developed to assess the formation of unknown metabolites and to identify them in water samples. The results indicated that SB enhanced CECs attenuation and led to the formation of fewer metabolites. Sorption and biodegradation processes were favored by the bigger surface area of particles in SB column, especially for compounds with negative charges. Incorporating woodchips into soil shortened retention times in the column, which reduced attenuation phenomena and resulted in the formation of significantly more metabolites. Incomplete biodegradation reactions, fostered by shorter retention times in SW column could mainly explain these results.

NMR-based metabolomics applied to ecotoxicology with zebrafish (Danio rerio) as a prominent model for metabolic profiling and biomarker discovery: Overviewing the most recent approaches

Metabolomics is an innovative approach used in the medical, toxicological, and biological sciences. As an interdisciplinary topic, metabolomics and its relation with the environment and toxicological research are extensive. The use of substances, such as drugs and pesticides, contributes to the continuous releasing of xenobiotics into the environment, harming organisms and their habitats. In this context, fish are important bioindicators of the environmental condition and have often been used as model species. Among them, zebrafish (Danio rerio) presents itself as a versatile and straightforward option due to its unique attributes for research. Zebrafish proves to be a valuable model for toxicity assays and also for metabolomics profiling by analytical tools. Thus, NMR-based metabolomics associated with statistical analysis can reasonably assist researchers in critical factors related to discovering and validating biomarkers through accurate diagnosis. Therefore, this review aimed to report the studies that applied zebrafish as a model for (eco)toxicological assays and essentially utilized NMR-based metabolomics analysis to assess the biochemical profile and thus suggest the potential biological marker.

Comparison of chromatographic conditions for the targeted tandem mass spectrometric determination of 354 mammalian metabolites

Metabolomics is becoming increasingly popular in livestock research, but no single analytical method can cover the entire metabolome. As such, we compared similar and complementary chromatographic methods with respect to analyte coverage and chromatographic properties of mammalian metabolites. We investigated 354 biologically relevant primary metabolites from 19 compound classes including amino acids, bile acids, biogenic amines, carboxylic acids, lipids, nucleotides and sugars. A total of 2063 selected reaction monitoring transitions were optimized on a triple quadrupole mass spectrometer. We then determined the retention profiles and peak parameters of our compounds using an anion exchange chromatography (AIC), three reversed-phase (RP) and three hydrophilic interaction liquid chromatography (HILIC) methods. On average, HILIC methods covered 54% of all metabolites with retention factors >1, while average RP coverage was 41%. In contrast to RP, HILIC methods could also retain polar metabolites such as amino acids and biogenic amines. Carboxylic acids, nucleotides, and sugar related compounds were best separated by AIC or zwitterionic pHILIC with alkaline eluents. Combining two complementary HILIC and RP methods increased the library coverage to 92%. By further including important short chain fatty acids, a combination of HILIC, RP and AIC methods achieved a coverage of 97%. The resulting dataset of LC and MS/MS parameters will facilitate the development of tailor-made quantitative targeted LC-MS/MS methods to investigate the mammalian metabolome.

Plant and microbial sciences as key drivers in the development of metabolomics research

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Untargeted profiling of secondary metabolites and phytotoxins associated with stemphylium blight of lentil

Stemphylium botryosum alters lentil secondary metabolism and differentially affects resistant and susceptible genotypes. Untargeted metabolomics identifies metabolites and their potential biosynthetic pathways that play a crucial role in resistance to S. botryosum. The molecular and metabolic processes that mediate resistance to stemphylium blight caused by Stemphylium botryosum Wallr. in lentil are largely unknown. Identifying metabolites and pathways associated with Stemphylium infection may provide valuable insights and novel targets to breed for enhanced resistance. The metabolic changes following infection of four lentil genotypes by S. botryosum were investigated by comprehensive untargeted metabolic profiling employing reversed-phase or hydrophilic interaction liquid chromatography (HILIC) coupled to a Q-Exactive mass spectrometer. At the pre-flowering stage, plants were inoculated with S. botryosum isolate SB19 spore suspension and leaf samples were collected at 24, 96 and 144 h post-inoculation (hpi). Mock-inoculated plants were used as negative controls. After analyte separation, high-resolution mass spectrometry data was acquired in positive and negative ionization modes. Multivariate modeling revealed significant treatment, genotype and hpi effects on metabolic profile changes that reflect lentil response to Stemphylium infection. In addition, univariate analyses highlighted numerous differentially accumulated metabolites. By contrasting the metabolic profiles of SB19-inoculated and mock-inoculated plants and among lentil genotypes, 840 pathogenesis-related metabolites were detected including seven S. botryosum phytotoxins. These metabolites included amino acids, sugars, fatty acids and flavonoids in primary and secondary metabolism. Metabolic pathway analysis revealed 11 significant pathways including flavonoid and phenylpropanoid biosynthesis, which were affected upon S. botryosum infection. This research contributes to ongoing efforts toward a comprehensive understanding of the regulation and reprogramming of lentil metabolism under biotic stress, which will provide targets for potential applications in breeding for enhanced disease resistance.

1 H-Nuclear Magnetic Resonance Metabolomics Analysis of Arabidopsis thaliana Exposed to Perfluorooctanoic Acid and Perfluoroctanesulfonic Acid

Perfluorinated alkyl substances (PFAS) are ubiquitous environmental contaminants that are widely used in consumer products and fire suppression foams. The presence of PFAS in ground and surface water can create a route for PFAS to enter the soil, exposing ecosystems (including agroecosystems), where they will move through the food web via biomagnification. The toxicity of PFAS to plants, particularly in agricultural ecosystems, is of emerging concern due to the application of biosolids that are often contaminated with PFAS. Nevertheless, due to the low concentrations of PFAS in most agricultural soils, the direct impact of PFAS on plant health is not well understood. We used ¹ H-nuclear magnetic resonance (NMR) metabolomics to explore the effects of exposure of two key PFAS, perfluorooctanoic acid and perfluorooctanesulfonic acid, on Arabidopsis thaliana, a model organism. We found that Arabidopsis exhibited an accumulation of multiple metabolites, including soluble sugars (glucose and sucrose), multiple amino acids, and tri-carboxylic acid (TCA) cycle intermediates, suggesting that PFAS exposure impacts the metabolism of plants by causing an accumulation of stress-related amino acids and soluble sugars that drives increased activity of the TCA cycle. The present study shows that ¹ H-NMR metabolomics is a viable tool for investigating changes in the metabolic profile of plants exposed to PFAS and can be used to illuminate the stress response of plants in a high-throughput, nonbiased manner. Environ Toxicol Chem 2023;42:663-672. © 2022 SETAC.

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

Filling gaps in animal welfare assessment through metabolomics

Sustainability has become a central issue in Italian livestock systems driving food business operators to adopt high standards of production concerning animal husbandry conditions. Meat sector is largely involved in this ecological transition with the introduction of new label claims concerning the defense of animal welfare (AW). These new guarantees referred to AW provision require new tools for the purpose of authenticity and traceability to assure meat supply chain integrity. Over the years, European Union (EU) Regulations, national, and international initiatives proposed provisions and guidelines for assuring AW introducing requirements to be complied with and providing tools based on scoring systems for a proper animal status assessment. However, the comprehensive and objective assessment of the AW status remains challenging. In this regard, phenotypic insights at molecular level may be investigated by metabolomics, one of the most recent high-throughput omics techniques. Recent advances in analytical and bioinformatic technologies have led to the identification of relevant biomarkers involved in complex clinical phenotypes of diverse biological systems suggesting that metabolomics is a key tool for biomarker discovery. In the present review, the Five Domains model has been employed as a vademecum describing AW. Starting from the individual Domains-nutrition (I), environment (II), health (III), behavior (IV), and mental state (V)-applications and advances of metabolomics related to AW setting aimed at investigating phenotypic outcomes on molecular scale and elucidating the biological routes most perturbed from external solicitations, are reviewed. Strengths and weaknesses of the current state-of-art are highlighted, and new frontiers to be explored for AW assessment throughout the metabolomics approach are argued. Moreover, a detailed description of metabolomics workflow is provided to understand dos and don'ts at experimental level to pursue effective results. Combining the demand for new assessment tools and meat market trends, a new cross-strategy is proposed as the promising combo for the future of AW assessment.

Genetic and Metabolite Variability among Commercial Varieties and Advanced Lines of Vicia faba L

Vicia faba L. (faba bean) is one of the most promising pulse crops due to its nutritional value and high nitrogen fixation capacity. The aim of the present study was to compare the genetic diversity and the seed metabolite profiles of five genetic materials of faba bean. Specifically, three newly developed advanced lines (KK18, KK14 and KK10) and two commercial cultivars (POLIKARPI and TANAGRA), were evaluated for this purpose. Genetic diversity among populations was assessed by SCoT molecular markers. Through UPGMA dendrogram, genetic distances between populations were estimated. Untargeted metabolomics analysis of the seeds was performed employing GC/EI/MS. The cultivar POLYKARPI exhibited the highest polymorphism. All varieties showed a higher within-cultivars and advanced lines variability than between. POLYKARPI and KK14 had the lowest genetic distances, while KK18 and TANAGRA presented the highest ones. The advanced line KK18 displayed the best nutritional profile, the highest concentration of desirable metabolites (lactic acid and trehalose), the lowest concentration of anti-nutritional factors (oxalic acid) and the lowest concentration of saturated fatty acids (palmitic and stearic acid). According to the results of the present study, KK18 line is a very promising material for further exploration and utilization in breeding programs.

Metabolite fingerprinting: A powerful metabolomics approach for marker identification and functional gene annotation

Non-targeted metabolome approaches aim to detect metabolite markers related to stress, disease, developmental or genetic perturbation. In the later context, it is also a powerful means for functional gene annotation. A prerequisite for non-targeted metabolome analyses are methods for comprehensive metabolite extraction. We present three extraction protocols for a highly efficient extraction of metabolites from plant material with a very broad metabolite coverage. The presented metabolite fingerprinting workflow is based on liquid chromatography high resolution accurate mass spectrometry (LC-HRAM-MS), which provides suitable separation of the complex sample matrix for the analysis of compounds of different polarity by positive and negative electrospray ionization and mass spectrometry. The resulting data sets are then analyzed with the software suite MarVis and the web-based interface MetaboAnalyst. MarVis offers a straightforward workflow for statistical analysis, data merging as well as visualization of multivariate data, while MetaboAnalyst is used in our hands as complementary software for statistics, correlation networks and figure generation. Finally, MarVis provides access to species-specific metabolite and pathway data bases like KEGG and BioCyc and to custom data bases tailored by the user to connect the identified markers or features with metabolites. In addition, identified marker candidates can be interactively visualized and inspected in metabolic pathway maps by KEGG pathways for a more detailed functional annotation and confirmed by mass spectrometry fragmentation experiments or coelution with authentic standards. Together this workflow is a valuable toolbox to identify novel metabolites, metabolic steps or regulatory principles and pathways.

Metabolomics-based study of potential biomarkers of sepsis

The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.

Metabolomics as a potential tool for monitoring patients with aneurysmal subarachnoid hemorrhage

Metabolomics has evolved into a particularly useful tool to study interactions between metabolites and serves as an aid in unraveling the complexity of entire metabolomes. Nonetheless, it is increasingly viewed as a methodology with practical applications in the clinical setting, where identifying and quantifying biomarkers of interest could prove useful for diagnostics. Starting from a concise overview of the most prominent analytical techniques employed in metabolomics, herein we present a review of its application in studies of brain metabolism and cerebrovascular diseases, paying most attention to its uses in researching aneurysmal subarachnoid hemorrhage. Both animal models and human studies are considered, and metabolites identified as potential biomarkers are highlighted.

Accelerated Solvent Extraction of Phenols from Lyophilised Ground Grape Skins and Seeds

The efficient extraction of phenols from grapes is an important step for their reliable quantification. The aim was to optimise the lyophilisation process and the extraction of phenols from grape skins and seeds. The phenol extraction yield from lyophilised tissues was investigated with different accelerated solvent extraction (ASE) operating conditions. Skins and seeds were separated from frozen berries and lyophilised without being ground. The weight loss during lyophilisation was followed daily. Phenols were extracted from lyophilised, cryo-ground seeds and skins with ASE at room temperature and 10.3 MPa using 80% aqueous acetone and 60% aqueous methanol. The effects of ASE operational parameters (the number of extraction cycles (ECs) and static time (ST) duration) were investigated. The yield of extracted phenols was evaluated spectrophotometrically by determining total phenolic index at 280 nm (TPI). The weight of skins and seeds significantly dropped after 24 h of lyophilisation and continued to decrease, although not significantly, up until the 9th day. The optimal lyophilisation time was estimated to be 3 days and 5 days for skins and seeds, respectively. The phenol extraction yield was significantly affected after changes of ASE conditions. Based on TPI, the optimal ASE conditions were as follows: (i) lyophilised seeds—eight ECs with 10 min ST using aqueous acetone and then four ECs with 20 min ST using aqueous methanol; (ii) lyophilised skins—eight ECs with 1 min ST using aqueous acetone and then one EC with 20 min ST using aqueous methanol.

Unveiling Chemical Interactions Between Plants and Fungi Using Metabolomics Approaches

Metabolomics has been extensively used in clinical studies in the search for new biomarkers of human diseases. However, this approach has also been highlighted in agriculture and biological sciences, once metabolomics studies have been assisting researchers to deduce new chemical mechanisms involved in biological interactions that occur between microorganisms and plants. In this sense, the knowledge of the biological role of each metabolite (virulence factors, signaling compounds, antimicrobial metabolites, among others) and the affected biochemical pathways during the interaction contribute to a better understand of different ecological relationships established in nature. The current chapter addresses five different applications of the metabolomics approach in fungal-plant interactions research: (1) Discovery of biomarkers in pathogen-host interactions, (2) plant diseases diagnosis, (3) chemotaxonomy, (4) plant defense, and (5) plant resistance; using mass spectrometry and/or nuclear magnetic resonance spectroscopy, which are the techniques most used in metabolomics.