What is metabolomics all about?

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Towards Personalized Medicine in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

Widely Targeted Metabolomic Analysis Provides New Insights into the Effect of Rootstocks on Citrus Fruit Quality

The use of different rootstocks has a significant effect on the content of flavor components and overall fruit quality. However, little information is available about the metabolic basis of the nutritional value of citrus plants. In this study, UPLC-MS/MS (ultra-performance liquid chromatography-tandem mass spectrometry) was performed to analyze the metabolites of three late-maturing hybrid mandarin varieties ('Gold Nugget', 'Tango' and 'Orah') grafted on four rootstocks ('Trifoliate orange', 'Carrizo citrange', 'Red tangerine' and 'Ziyang Xiangcheng'). A total of 1006 metabolites were identified through OPLS-DA (Orthogonal Partial Least Squares-Discriminant Analysis) analysis. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed the most critical pathways among the different pathways associated with genes grafted on the four rootstocks that were differentially activated, including tryptophan metabolism and sphingolipid metabolism in 'Gold Nugget'; tryptophan metabolism, phenylpropanoid biosynthesis and sphingolipid metabolism in 'Tango'; and pantothenate and CoA biosynthesis- and photosynthesis-related biosynthesis in 'Orah'. A considerable difference between the different rootstocks was also observed in the accumulation of lipids, phenolic acids and flavonoids; further analysis revealed that the rootstocks regulated specific metabolites, including deacetylnomylinic acid, sudachinoid A, amoenin evodol, rutaevin, cyclo (phenylalanine-glutamic acid), cyclo (proline-phenylalanine), 2-hydroxyisocaproic acid, and 2-hydroxy-3-phenylpropanoic acid. The results of this study provide a useful foundation for further investigation of rootstock selection for late-maturation hybrid mandarin varieties.

Plasma metabolites analysis of patients with papillary thyroid cancer: A preliminary untargeted 1H NMR-based metabolomics

Metabolomics plays a crucial role in identifying molecular biomarkers that can differentiate pathological conditions. In the case of thyroid cancer, it is essential to accurately diagnose malignancy from benignity to avoid unnecessary surgeries. The objective of this research was to apply untargeted NMR-based metabolomics in order to identify metabolic biomarkers that can distinguish between plasma samples of patients with papillary thyroid cancer (PTC) and multinodular goiter (MNG), as well as PTC and healthy individuals. The study included a cohort of 55 patients who were divided into three groups: PTC (n=20), MNG (n=16), and healthy (n=19). Plasma samples were collected from all participants and subjected to 1H NMR spectroscopy. Differential metabolites were identified using chemometric pattern recognition algorithms. The obtained metabolic profile had the potential to differentiate PTC from healthy plasma, but not from MNG. In patients diagnosed with PTC, a total of 18 compounds were discovered, revealing elevated levels of leucine, lysine, and 4-acetamidobutyric acid, while acetate, proline, acetoacetate, 3-hydroxybutyrate, glutamate, pyruvate, cystine, glutathione, asparagine, ethanolamine, histidine, tyrosine, myo-inositol, and glycerol along with a lipid compound were found to be lower in comparison to those of healthy individuals. According to the area under the curve (AUC) of the receiver operating characteristic curve, this particular profile exhibited an impressive capability of 85% to discern PTC from healthy subjects (AUC=0.853, sensitivity=78.95, specificity=84.21). The utilization of the 1H NMR-based metabolomics approach revealed considerable promise in the identification of PTC from healthy plasma specimens. The modifications noticed in the plasma metabolites have the potential to act as practical biomarkers that are non-invasive and could suggest transformations in the metabolic profile of thyroid tumors.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

UHPLC-qTOF-MS-Based Nontargeted Metabolomics to Characterize the Effects of Capsaicin on Plasma and Skin Metabolic Profiles of C57BL/6 Mice-An In vivo Experimental Study

Background

Capsaicin is the main compound found in chili pepper and has complex pharmacologic effects. This study aimed to elucidate the mechanism of the effect of capsaicin on physiological processes by analyzing changes in metabolites and metabolic pathways.

Methods

Female C57BL/6 mice were divided into two groups(n = 10/group) and fed with capsaicin-soybean oil solution(group T) or soybean oil(group C) for 6 weeks. Ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) based metabolomics was undertaken to assess plasma and skin metabolic profile changes and identify differential metabolites through multivariate analysis.

Results

According to the OPLS-DA score plots, the plasma and skin metabolic profiles in the group T and group C were significantly separated. In plasma, 38 significant differential metabolites were identified. KEGG pathway enrichment analysis revealed that the most significant plasma metabolic pathways included pyruvate metabolism and ABC transporters. In skin, seven significant differential metabolites were found. Four metabolic pathways with p values < 0.05 were detected, including sphingolipid metabolism, sphingolipid signaling pathway, apoptosis, and necroptosis.

Conclusion

These findings will provide metabolomic insights to assess the physiological functions of capsaicin and contribute to a better understanding of the potential effects of a capsaicin-rich diet on health.

Virulence factors of the genus Fusarium with targets in plants

Fusarium spp. comprise various species of filamentous fungi that cause severe diseases in plant crops of both agricultural and forestry interest. These plant pathogens produce a wide range of molecules with diverse chemical structures and biological activities. Genetic functional analyses of some of these compounds have shown their role as virulence factors (VF). However, their mode of action and contributions to the infection process for many of these molecules are still unknown. This review aims to analyze the state of the art in Fusarium VF, emphasizing their biological targets on the plant hosts. It also addresses the current experimental approaches to improve our understanding of their role in virulence and suggests relevant research questions that remain to be answered with a greater focus on species of agroeconomic importance. In this review, a total of 37 confirmed VF are described, including 22 proteinaceous and 15 non-proteinaceous molecules, mainly from Fusarium oxysporum and Fusarium graminearum and, to a lesser extent, in Fusarium verticillioides and Fusarium solani.

The role of the host-microbiome and metabolomics in sarcoidosis

Sarcoidosis is a complex inflammatory fibrotic disease that affects multiple organ systems. It is characterized by the infiltration of lymphocytes and mononuclear phagocytes, which form non-caseating granulomas in affected organs. The lungs and intrathoracic lymph nodes are the most commonly affected organs. The underlying cause of sarcoidosis is unknown, but it is believed to occur in genetically predisposed individuals who are exposed to pathogenic organisms, environmental contaminants, or self and non-self-antigens. Recent research has suggested that the microbiome may play a role in the development of respiratory conditions, including sarcoidosis. Additionally, metabolomic studies have identified potential biomarkers for monitoring sarcoidosis progression. This review will focus on recent microbiome and metabolomic findings in sarcoidosis, with the goal of shedding light on the pathogenesis and possible diagnostic and therapeutic approaches.

Metabolomics in severe traumatic brain injury: a scoping review

BACKGROUND

Diagnosis and prognostication of severe traumatic brain injury (sTBI) continue to be problematic despite years of research efforts. There are currently no clinically reliable biomarkers, though advances in protein biomarkers are being made. Utilizing Omics technology, particularly metabolomics, may provide new diagnostic biomarkers for sTBI. Several published studies have attempted to determine the specific metabolites and metabolic pathways involved; these studies will be reviewed.

AIMS

This scoping review aims to summarize the current literature concerning metabolomics in sTBI, review the comprehensive data, and identify commonalities, if any, to define metabolites with potential clinical use. In addition, we will examine related metabolic pathways through pathway analysis.

METHODS

Scoping review methodology was used to examine the current literature published in Embase, Scopus, PubMed, and Medline. An initial 1090 publications were identified and vetted with specific inclusion criteria. Of these, 20 publications were selected for further examination and summary. Metabolic data was classified using the Human Metabolome Database (HMDB) and arranged to determine the 'recurrent' metabolites and classes found in sTBI. To help understand potential mechanisms of injury, pathway analysis was performed using these metabolites and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database.

RESULTS

Several metabolites related to sTBI and their effects on biological pathways were identified in this review. Across the literature, proline, citrulline, lactate, alanine, valine, leucine, and serine all decreased in adults post sTBI, whereas both octanoic and decanoic acid increased. Hydroxy acids and organooxygen compounds generally increased following sTBI, while most carboxylic acids decreased. Pathway analysis showed significantly affected glycine and serine metabolism, glycolysis, branched-chain amino acid (BCAA) metabolism, and other amino acid metabolisms. Interestingly, no tricarboxylic acid cycle metabolites were affected.

CONCLUSION

Aside from a select few metabolites, classification of a metabolic profile proved difficult due to significant ambiguity between study design, sample size, type of sample, metabolomic detection techniques, and other confounding variables found in sTBI literature. Given the trends found in some studies, further metabolomics investigation of sTBI may be useful to identify clinically relevant metabolites.

Antimicrobial peptides from Bacillus spp. and strategies to enhance their yield

Antibiotic resistance is a growing concern that is affecting public health globally. The search for alternative antimicrobial agents has become increasingly important. Antimicrobial peptides (AMPs) produced by Bacillus spp. have emerged as a promising alternative to antibiotics, due to their broad-spectrum antimicrobial activity against resistant pathogens. In this review, we provide an overview of Bacillus-derived AMPs, including their classification into ribosomal (bacteriocins) and non-ribosomal peptides (lipopeptides and polyketides). Additionally, we delve into the molecular mechanisms of AMP production and describe the key biosynthetic gene clusters involved. Despite their potential, the low yield of AMPs produced under normal laboratory conditions remains a challenge to large-scale production. This review thus concludes with a comprehensive summary of recent studies aimed at enhancing the productivity of Bacillus-derived AMPs. In addition to medium optimization and genetic manipulation, various molecular strategies have been explored to increase the production of recombinant antimicrobial peptides (AMPs). These include the selection of appropriate expression systems, the engineering of expression promoters, and metabolic engineering. Bacillus-derived AMPs offer great potential as alternative antimicrobial agents, and this review provides valuable insights on the strategies to enhance their production yield, which may have significant implications for combating antibiotic resistance. KEY POINTS: • Bacillus-derived AMP is a potential alternative therapy for resistant pathogens • Bacillus produces two main classes of AMPs: ribosomal and non-ribosomal peptides • AMP yield can be enhanced using culture optimization and molecular approaches.

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

The Contribution of the Human Oral Microbiome to Oral Disease: A Review

The oral microbiome is an emerging field that has been a topic of discussion since the development of next generation sequencing and the implementation of the human microbiome project. This article reviews the current literature surrounding the oral microbiome, briefly highlighting most recent methods of microbiome characterization including cutting edge omics, databases for the microbiome, and areas with current gaps in knowledge. This article also describes reports on microorganisms contained in the oral microbiome which include viruses, archaea, fungi, and bacteria, and provides an in-depth analysis of their significant roles in tissue homeostasis. Finally, we detail key bacteria involved in oral disease, including oral cancer, and the current research surrounding their role in stimulation of inflammatory cytokines, the role of gingival crevicular fluid in periodontal disease, the creation of a network of interactions between microorganisms, the influence of the planktonic microbiome and cospecies biofilms, and the implications of antibiotic resistance. This paper provides a comprehensive literature analysis while also identifying gaps in knowledge to enable future studies to be conducted.

Metabolomics-A Tool to Find Metabolism of Endocrine Cancer

Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.

The Effects of Single and Combined Stressors on Daphnids-Enzyme Markers of Physiology and Metabolomics Validate the Impact of Pollution

The continuous global increase in population and consumption of resources due to human activities has had a significant impact on the environment. Therefore, assessment of environmental exposure to toxic chemicals as well as their impact on biological systems is of significant importance. Freshwater systems are currently under threat and monitored; however, current methods for pollution assessment can neither provide mechanistic insight nor predict adverse effects from complex pollution. Using daphnids as a bioindicator, we assessed the impact in acute exposures of eight individual chemicals and specifically two metals, four pharmaceuticals, a pesticide and a stimulant, and their composite mixture combining phenotypic, biochemical and metabolic markers of physiology. Toxicity levels were in the same order of magnitude and significantly enhanced in the composite mixture. Results from individual chemicals showed distinct biochemical responses for key enzyme activities such as phosphatases, lipase, peptidase, β-galactosidase and glutathione-S-transferase. Following this, a more realistic mixture scenario was assessed with the aforementioned enzyme markers and a metabolomic approach. A clear dose-dependent effect for the composite mixture was validated with enzyme markers of physiology, and the metabolomic analysis verified the effects observed, thus providing a sensitive metrics in metabolite perturbations. Our study highlights that sensitive enzyme markers can be used in advance on the design of metabolic and holistic assays to guide the selection of chemicals and the trajectory of the study, while providing mechanistic insight. In the future this could prove to become a useful tool for understanding and predicting freshwater pollution.

Exact mass GC-MS analysis: Protocol, database, advantages and application to plant metabolic profiling

Plant metabolomics has been used widely in plant physiology, in particular to analyse metabolic responses to environmental parameters. Derivatization (via trimethylsilylation and methoximation) followed by GC-MS metabolic profiling is a major technique to quantify low molecular weight, common metabolites of primary carbon, sulphur and nitrogen metabolism. There are now excellent opportunities for new generation analyses, using high resolution, exact mass GC-MS spectrometers that are progressively becoming relatively cheap. However, exact mass GC-MS analyses for routine metabolic profiling are not common, since there is no dedicated available database. Also, exact mass GC-MS is usually dedicated to structural resolution of targeted secondary metabolites. Here, we present a curated database for exact mass metabolic profiling (made of 336 analytes, 1064 characteristic exact mass fragments) focused on molecules of primary metabolism. We show advantages of exact mass analyses, in particular to resolve isotopic patterns, localise S-containing metabolites, and avoid identification errors when analytes have common nominal mass peaks in their spectrum. We provide a practical example using leaves of different Arabidopsis ecotypes and show how exact mass GC-MS analysis can be applied to plant samples and identify metabolic profiles.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Consideration of metabolomics and transcriptomics data in the context of using avian embryos for toxicity testing

Early-life stage (ELS) avian toxicity tests have been proposed as a more ethical alternative to traditional standardized tests with adult birds. At the same time, 'omics approaches are gaining traction in the field of avian toxicology, but little has been done to characterize the metabolome and transcriptome at different life stages. The present study uses 'omics data from toxicity tests of 8 environmental chemicals in ELS and adult Japanese quail (Coturnix japonica) to address this data gap. Previous analyses of these data focused on responses to each of the individual chemicals. Here, we consider data from all studies to describe variation in the metabolome and transcriptome between life stages and across independent experiments, irrespective of chemical treatment. Of the 230 metabolites detected in liver, 163 were shared between the two life stages. However, many of the targeted bile acids that were present in the adult liver were absent from ELS samples. For the transcriptome, >90% of the 18,364 detected transcripts were common to both life stages. Based on the 213 genes solely detected in ELS liver, the neuroactive ligand-receptor interaction pathway was significantly enriched. Multivariate and hierarchical clustering analyses revealed that variability among independent experiments was higher for the adult than the ELS studies at both the metabolomic and transcriptomic levels. Our results indicate concordance of the two approaches, with less variation between independent experiments in the ELS metabolome and transcriptome than in adults, lending support for the use of ELS as an alternative toxicity testing strategy.

The expression pattern of Immune checkpoints after chemo/radiotherapy in the tumor microenvironment

As a disease with the highest disease-associated burden worldwide, cancer has been the main subject of a considerable proportion of medical research in recent years, intending to find more effective therapeutic approaches with fewer side effects. Combining conventional methods with newer biologically based treatments such as immunotherapy can be a promising approach to treating different tumors. The concept of "cancer immunoediting" that occurs in the field of the tumor microenvironment (TME) is the aspect of cancer therapy that has not been at the center of attention. One group of the role players of the so-called immunoediting process are the immune checkpoint molecules that exert either co-stimulatory or co-inhibitory effects in the anti-tumor immunity of the host. It involves alterations in a wide variety of immunologic pathways. Recent studies have proven that conventional cancer therapies, such as chemotherapy, radiotherapy, or a combination of them, i.e., chemoradiotherapy, alter the "immune compartment" of the TME. The mentioned changes encompass a wide range of variations, including the changes in the density and immunologic type of the tumor-infiltrating lymphocytes (TILs) and the alterations in the expression patterns of the different immune checkpoints. These rearrangements can have either anti-tumor immunity empowering or immune attenuating sequels. Thus, recognizing the consequences of various chemo(radio)therapeutic regimens in the TME seems to be of great significance in the evolution of therapeutic approaches. Therefore, the present review intends to summarize how chemo(radio)therapy affects the TME and specifically some of the most important, well-known immune checkpoints' expressions according to the recent studies in this field.

Interactions between Medical Plant-Derived Bioactive Compounds: Focus on Antimicrobial Combination Effects

It is accepted that the medicinal use of complex mixtures of plant-derived bioactive compounds is more effective than purified bioactive compounds due to beneficial combination interactions. However, synergy and antagonism are very difficult to study in a meticulous fashion since most established methods were designed to reduce the complexity of mixtures and identify single bioactive compounds. This study represents a critical review of the current scientific literature on the combined effects of plant-derived extracts/bioactive compounds. A particular emphasis is provided on the identification of antimicrobial synergistic or antagonistic combinations using recent metabolomics methods and elucidation of approaches identifying potential mechanisms that underlie their interactions. Proven examples of synergistic/antagonistic antimicrobial activity of bioactive compounds are also discussed. The focus is also put on the current challenges, difficulties, and problems that need to be overcome and future perspectives surrounding combination effects. The utilization of bioactive compounds from medicinal plant extracts as appropriate antimicrobials is important and needs to be facilitated by means of new metabolomics technologies to discover the most effective combinations among them. Understanding the nature of the interactions between medicinal plant-derived bioactive compounds will result in the development of new combination antimicrobial therapies.

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Metabolic Regulation of Hepatitis B Virus Infection in HBV-Transgenic Mice

Hepatitis B virus (HBV) infection is a worldwide health burden. Metabolomics analysis has revealed HBV-induced metabolism dysregulation in liver tissues and hepatocytes. However, as an infectious disease, the tissue-specific landscape of metabolic profiles of HBV infection remains unclear. To fill this gap, we applied untargeted nuclear magnetic resonance (NMR) metabolomic analysis of the heart, liver, spleen, lung, kidney, pancreas, and intestine (duodenum, jejunum, ileum) in HBV-transgenic mice and their wild-type littermates. Strikingly, we found systemic metabolic alterations induced by HBV in liver and extrahepatic organs. Significant changes in metabolites have been observed in most tissues of HBV-transgenic mice, except for ileum. The metabolic changes may provide novel therapeutic targets for the treatment of HBV infection. Moreover, tissue-specific metabolic profiles could speed up the study of HBV induced systemic metabolic reprogramming, which could help follow the progression of HBV infection and explain the underlying pathogenesis.

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data.

Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks.

Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.

In vivo mechanotransduction: Effect of acute exercise on the metabolomic profiles of mouse synovial fluid

Objective

Exercise is known to induce beneficial effects in synovial joints. However, the mechanisms underlying these are unclear. Synovial joints experience repeated mechanical loading during exercise. These mechanical stimuli are transduced into biological responses through cellular mechanotransduction. Mechanotransduction in synovial joints is typically studied in tissues. However, synovial fluid directly contacts all components of the joint, and thus may produce a whole-joint picture of the mechanotransduction response to loading. The objective of this study was to determine if metabolic phenotypes are present in the synovial fluid after acute exercise as a first step to understanding the beneficial effects of exercise on the joint.

Material and methods

Mice underwent a single night of voluntary wheel running or standard housing and synovial fluid was harvested for global metabolomic profiling by LC-MS. Hierarchical unsupervised clustering, partial least squares discriminant, and pathway analysis provided insight into exercise-induced mechanotransduction.

Results

Acute exercise produced a distinct metabolic phenotype in synovial fluid. Mechanosensitive metabolites included coenzyme A derivatives, prostaglandin derivatives, phospholipid species, tryptophan, methionine, vitamin D3, fatty acids, and thiocholesterol. Enrichment analysis identified several pathways previously linked to exercise including amino acid metabolism, inflammatory pathways, citrulline-nitric oxide cycle, catecholamine biosynthesis, ubiquinol biosynthesis, and phospholipid metabolism.

Conclusion

To our knowledge, this is the first study to investigate metabolomic profiles of synovial fluid during in vivo mechanotransduction. These profiles indicate that exercise induced stress-response processes including both pro- and anti-inflammatory pathways. Further research will expand these results and define the relationship between the synovial fluid and the serum.

Metabolomic alteration induced by psychotropic drugs: Short-term metabolite profile as a predictor of weight gain evolution

Psychotropic drugs can induce strong metabolic adverse effects, potentially increasing morbidity and/or mortality of patients. Metabolomic profiling, by studying the levels of numerous metabolic intermediates and products in the blood, allows a more detailed examination of metabolism dysfunctions. We aimed to identify blood metabolomic markers associated with weight gain in psychiatric patients. Sixty-two patients starting a treatment known to induce weight gain were recruited. Two hundred and six selected metabolites implicated in various pathways were analyzed in plasma, at baseline and after 1 month of treatment. Additionally, 15 metabolites of the kynurenine pathway were quantified. This latter analysis was repeated in a confirmatory cohort of 24 patients. Among the 206 metabolites, a plasma metabolomic fingerprint after 1 month of treatment embedded 19 compounds from different chemical classes (amino acids, acylcarnitines, carboxylic acids, catecholamines, nucleosides, pyridine, and tetrapyrrole) potentially involved in metabolic disruption and inflammation processes. The predictive potential of such early metabolite changes on 3 months of weight evolution was then explored using a linear mixed-effects model. Of these 19 metabolites, short-term modifications of kynurenine, hexanoylcarnitine, and biliverdin, as well as kynurenine/tryptophan ratio at 1 month, were associated with 3 months weight evolution. Alterations of the kynurenine pathway were confirmed by quantification, in both exploratory and confirmatory cohorts. Our metabolomic study suggests a specific metabolic dysregulation after 1 month of treatment with psychotropic drugs known to induce weight gain. The identified metabolomic signature could contribute in the future to the prediction of weight gain in patients treated with psychotropic drugs.

Review on in vivo profiling of drug metabolites with LC-MS/MS in the past decade

Metabolite profiling is an indispensable part of drug discovery and development, enabling a comprehensive understanding of the drug's metabolic behavior. Liquid chromatography-mass spectrometry facilitates metabolite profiling by reducing sample complexity and providing high sensitivity. This review discusses the in vivo metabolite profiling involving LC-MS/MS and the utilization of QTOF, QQQ mass analyzers with a particular emphasis on a mass filter. Further, a summary of sample extraction procedures in biological matrices such as plasma, urine, feces, serum and hair as in vivo samples are outlined. toward the end, we present 15 case studies in biological matrices and their LC-MS/MS conditions to understand the metabolic disposition.

Review of the recent developments in metabolomics-based phytochemical research

Phytochemicals are important bioactive components present in natural products. Although the health benefits of many food products are well-known and accepted as a common knowledge, the identity of the main bioactive molecules and the mechanism by which they interact in the body of human are often unknown. It was only in the last 30 years when the field of metabolomics had matured that the identification of such molecules with bioactivity has been made possible through the development of instruments to separate and computational techniques to characterize complex samples. This in turn has enabled in vitro studies to quantify the biological activity of the respective phytochemical either in mice models or in humans. In this review, the importance of key dietary phytochemicals such as phenolic acids, flavonoids, carotenoids, resveratrol, curcumin, and capsaicinoids are discussed together with their potential functions for human health. Untargeted metabolomics, in particular, liquid chromatography mass spectrometry, is the most used method to isolate, identify and profile bioactive compounds in the study of phytochemicals in foods. The application of metabolomics in drug discovery is a common practice nowadays and has boosted the drug and/or supplement manufacturing sector.HighlightsPhytochemicals are beneficial compounds for human healthPhytochemicals are plant-based bioactive and obtainable from natural productsUntargeted metabolomics has boosted the discovery of phytochemicals from foodTargeted metabolomics is key in the authentication and screening of phytochemicalsMetabolomics of phytochemicals is reshaping the road to drug and supplement manufacture.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Updates on the Functions and Molecular Mechanisms of the Genes Involved in Aspergillus flavus Development and Biosynthesis of Aflatoxins

Aspergillus flavus (A. flavus) is a ubiquitous and opportunistic fungal pathogen that causes invasive and non-invasive aspergillosis in humans and animals. This fungus is also capable of infecting a large number of agriculture crops (e.g., peanuts, maze, cotton seeds, rice, etc.), causing economic losses and posing serious food-safety concerns when these crops are contaminated with aflatoxins, the most potent naturally occurring carcinogens. In particular, A. flavus and aflatoxins are intensely studied, and they continue to receive considerable attention due to their detrimental effects on humans, animals, and crops. Although several studies have been published focusing on the biosynthesis of the aforementioned secondary metabolites, some of the molecular mechanisms (e.g., posttranslational modifications, transcription factors, transcriptome, proteomics, metabolomics and transcriptome, etc.) involved in the fungal development and aflatoxin biosynthesis in A. flavus are still not fully understood. In this study, a review of the recently published studies on the function of the genes and the molecular mechanisms involved in development of A. flavus and the production of its secondary metabolites is presented. It is hoped that the information provided in this review will help readers to develop effective strategies to reduce A. flavus infection and aflatoxin production.

The potential of nuclear magnetic resonance (NMR) in metabolomics and lipidomics of microalgae- a review

Microalgae biotechnology has made it possible to derive secondary bioactive metabolites from microalgae strains that have opened up their entire potential to uncover a wide range of novel metabolic capabilities and turn these into bio-products for the development of sustainable bio-refineries. Nuclear Magnetic Resonance Technology (NMR) has been one of the most successful and functional research technology over the past two decades to analyse the composition, structure and functionality of distinct metabolites in the different microalgae strains. This technology offers qualitative as well as quantitative knowledge about the endogenous metabolites and lipids of low molecular mass to offer a good picture of the physiological state of biological samples in metabolomics and lipidomics studies. Henceforth, this review is aimed at introducing the metabolomics and lipidomics studies into the field of NMR technology and also highlights the protocols for the isolation and metabolic measurements of metabolites from microalgae that should be redirected to resource recovery and value-added products with a systematic and holistic approach for scalability or sustainability.

Phospholipid-Gold Nanorods Induce Energy Crisis in MCF-7 Cells: Cytotoxicity Evaluation Using LC-MS-Based Metabolomics Approach

Phospholipid-modified gold nanorods (phospholipid-GNRs) have demonstrated drastic cytotoxicity towards MCF-7 breast cancer cells compared to polyethylene glycol-coated GNRs (PEG-GNRs). In this study, the mechanism of cytotoxicity of phospholipid-GNRs towards MCF-7 cells was investigated using mass spectrometry-based global metabolic profiling and compared to PEGylated counterparts. The results showed that when compared to PEG-GNRs, phospholipid-GNRs induced significant and more pronounced impact on the metabolic profile of MCF-7 cells. Phospholipid-GNRs significantly decreased the levels of metabolic intermediates and end-products associated with cellular energy metabolisms resulting in dysfunction in TCA cycle, a reduction in glycolytic activity, and imbalance of the redox state. Additionally, phospholipid-GNRs disrupted several metabolism pathways essential for the normal growth and proliferation of cancer cells including impairment in purine, pyrimidine, and glutathione metabolisms accompanied by lower amino acid pools. On the other hand, the effects of PEG-GNRs were limited to alteration of glycolysis and pyrimidine metabolism. The current work shed light on the importance of metabolomics as a valuable analytical approach to explore the molecular effects of GNRs with different surface chemistry on cancer cell and highlights metabolic targets that might serve as promising treatment strategy in cancer.

Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley

Barley is the most common source for malt to be used in brewing beer and other alcoholic beverages. This involves converting the starch of barley into fermentable sugars a process that involves malting, that is germinating of the grains, and mashing, which is an enzymatic process. Numerous metabolic processes are involved in germination, where distinct and time-dependent alterations at the metabolite levels happen. In this study, 2,628 plots of 565 spring malting barley lines from Nordic Seed A/S were investigated. Phenotypic records were available for six malting quality (MQ) traits: filtering speed (FS), wort clearness (WCL), extract yield (EY), wort color (WCO), beta glucan (BG), and wort viscosity (WV). Each line had a set of dense genomic markers. In addition, 24,018 metabolomic features (MFs) were obtained for each sample from nuclear magnetic resonance (NMR) spectra for wort samples produced from each experimental plot. The genetic variation in the MFs was investigated using a univariate model, and the relationship between MFs and the MQ traits was studied using a bivariate model. Results showed that a total of 8,604 MFs had heritability estimates significantly larger than 0 and for all MQ traits, there were genetic correlations with up to 86.77% and phenotypic correlations with up to 90.07% of the significant heritable MFs. In conclusion, around one third of all MFs were significantly heritable, among which a considerable proportion had significant additive genetic and/or phenotypic correlations with the MQ traits (WCO, WV, and BG) in spring barley. The results from this study indicate that many of the MFs are heritable and MFs have great potential to be used in breeding barley for high MQ.

Exploring the chemotypes underlying important agronomic and consumer traits in cassava (Manihot esculenta crantz)

A broad diversity of phenotypes are available within the cassava germplasm collections. The phenotypes include improved nutritional, starch or culinary root quality as well as abiotic and biotic resistance properties. Some of these traits can be found naturally occurring in cassava landraces, whereas others are the result of targeted breeding efforts. For future breeding programmes it is important to know the underlying mechanisms of these desirable traits. Metabolomics can assist in the elucidation of these mechanisms by measuring the end products of the cellular processes conferring the traits of interest. The present study focused on the comparison of two or more variants of the same trait such as high and low culinary quality or resistance and susceptibility to thrips. Overall, eight different traits were assessed. Results showed that amino acids and umami compounds were associated with superior culinary attributes and the phenylpropanoid superpathway plays an important role in pest resistance. Furthermore, the data highlighted a low chemodiversity in African cassavas and that the source-sink relation was still active at the harvest stage.

DNA Methylation Associated with Mitochondrial Dysfunction in a South African Autism Spectrum Disorder Cohort

Autism spectrum disorder (ASD) is characterized by phenotypic heterogeneity and a complex genetic architecture which includes distinctive epigenetic patterns. We report differential DNA methylation patterns associated with ASD in South African children. An exploratory whole‐epigenome methylation screen using the Illumina 450 K MethylationArray identified differentially methylated CpG sites between ASD and controls that mapped to 898 genes (P ≤ 0.05) which were enriched for nine canonical pathways converging on mitochondrial metabolism and protein ubiquitination. Targeted Next Generation Bisulfite Sequencing of 27 genes confirmed differential methylation between ASD and control in our cohort. DNA pyrosequencing of two of these genes, the mitochondrial enzyme Propionyl‐CoA Carboxylase subunit Beta (PCCB) and Protocadherin Alpha 12 (PCDHA12), revealed a wide range of methylation levels (9–49% and 0–54%, respectively) in both ASD and controls. Three CpG loci were differentially methylated in PCCB (P ≤ 0.05), while PCDHA12, previously linked to ASD, had two significantly different CpG sites (P ≤ 0.001) between ASD and control. Differentially methylated CpGs were hypomethylated in ASD. Metabolomic analysis of urinary organic acids revealed that three metabolites, 3‐hydroxy‐3‐methylglutaric acid (P = 0.008), 3‐methyglutaconic acid (P = 0.018), and ethylmalonic acid (P = 0.043) were significantly elevated in individuals with ASD. These metabolites are directly linked to mitochondrial respiratory chain disorders, with a putative link to PCCB, consistent with impaired mitochondrial function. Our data support an association between DNA methylation and mitochondrial dysfunction in the etiology of ASD. Autism Res 2020, 13: 1079‐1093. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Lay Summary

Epigenetic changes are chemical modifications of DNA which can change gene function. DNA methylation, a type of epigenetic modification, is linked to autism. We examined DNA methylation in South African children with autism and identified mitochondrial genes associated with autism. Mitochondria are power‐suppliers in cells and mitochondrial genes are essential to metabolism and energy production, which are important for brain cells during development. Our findings suggest that some individuals with ASD also have mitochondrial dysfunction.

Multimodal Integrated Sensor Platform for Rapid Biomarker Detection

Precision metabolomics and quantification for cost-effective rapid diagnosis of disease are the key goals in personalized medicine and point-of-care testing. At present, patients are subjected to multiple test procedures requiring large laboratory equipment. Microelectronics has already made modern computing and communications possible by integration of complex functions within a single chip. As More than Moore technology increases in importance, integrated circuits for densely patterned sensor chips have grown in significance. Here, we present a versatile single complementary metal-oxide-semiconductor chip forming a platform to address personalized needs through on-chip multimodal optical and electrochemical detection that will reduce the number of tests that patients must take. The chip integrates interleaved sensing subsystems for quadruple-mode colorimetric, chemiluminescent, surface plasmon resonance, and hydrogen ion measurements. These subsystems include a photodiode array and a single photon avalanche diode array with some elements functionalized to introduce a surface plasmon resonance mode. The chip also includes an array of ion sensitive field-effect transistors. The sensor arrays are distributed uniformly over an active area on the chip surface in a scalable and modular design. Bio-functionalization of the physical sensors yields a highly selective simultaneous multiple-assay platform in a disposable format. We demonstrate its versatile capabilities through quantified bio-assays performed on-chip for glucose, cholesterol, urea, and urate, each within their naturally occurring physiological range.

Investigating Causality Between Blood Metabolites and Emotional and Behavioral Responses to Traumatic Stress: a Mendelian Randomization Study

To investigate the causal relationship between blood metabolites and traits related to trauma-response, we combined genome-wide and metabolome-wide datasets generated from large-scale cohorts. Five trauma-response traits ascertained in the UK Biobank (52,816 < N < 117,900 individuals) were considered: (i) "Avoided activities/situations because of previous stressful experience" (Avoidance); (ii) "Felt distant from other people" (Distant); (iii) "Felt irritable/had angry outbursts" (Irritable); (iv) "Felt very upset when reminded of stressful experience" (Upset); (v) "Repeated disturbing thoughts of stressful experience". These were investigated with respect to 52 blood metabolites tested in a previous genome-wide-association study (N = 24,925 European-ancestry individuals). Linkage disequilibrium score regression, polygenic risk scoring (PRS), and Mendelian randomization were applied to the datasets. We observed that 14 metabolites were genetically correlated with trauma-response traits (p < 0.05). High-resolution PRS of 4 metabolites (citrate; glycoprotein acetyls; concentration of large very-low-density lipoproteins (VLDL) particles (LVLDLP); total cholesterol in medium particles of VLDL (MVLDLC)) were associated with trauma-response traits (false discovery rate Q < 10%). These genetic associations were partially due to causal relationships (Citrate→Upset β = - 0.058, p = 9.1 × 10^-4; Glycoproteins→Avoidance β = 0.008, p = 0.003; LVLDLP→Distant β = 0.008, p = 0.022; MVLDLC→Avoidance β = 0.019, p = 3 × 10^-4). No reverse associations were observed. In conclusion, our study supports causal relationships between certain blood metabolites and emotional and behavioral responses to traumatic experiences.

Genotypic Variation in the Root and Shoot Metabolite Profiles of Wheat (Triticum aestivum L.) Indicate Sustained, Preferential Carbon Allocation as a Potential Mechanism in Phosphorus Efficiency

Changes in the levels of plant metabolites in response to nutrient deficiency is indicative of how plants utilize scarce resources. In this study, changes in the metabolite profile of roots and shoots of wheat genotypes differing in phosphorus use efficiency (PUE) was investigated. Under low P supply and at 28 days after sowing (DAS), the wheat breeding line, RAC875 (P efficient) produced 42% more shoot biomass than the wheat variety, and Wyalkatchem (P inefficient). Significant changes in the metabolite profile in leaves and roots were observed under low P supply and significant genotypic variation was evident. Under low P supply, an increase in raffinose and 1-kestose was evident in roots of both wheat genotypes, with RAC875 accumulating more when compared to Wyalkatchem. There was no significant increase in raffinose and 1-kestose in leaves when plants were grown under P deficiency. P deficiency had no significant impact on the levels of sucrose, maltose, glucose and fructose in both genotypes, and while phosphorylated sugars (glucose-6-P and fructose-6-P) remained unchanged in RAC875, in Wyalkatchem, glucose-6-P significantly decreased in roots, and fructose-6-P significantly decreased in both leaves and roots. Glycerol-3-P decreased twofold in roots of both wheat genotypes in response to low P. In roots, RAC875 exhibited significantly lower levels of fumarate, malate, maleate and itaconate than Wyalkatchem, while low P enhanced organic acid exudation in RAC875 but not in Wyalkatchem. RAC875 showed greater accumulation of aspartate, glutamine and β-alanine in leaves than Wyalkatchem under low P supply. Greater accumulation of raffinose and 1-kestose in roots and aspartate, glutamine and β-alanine in leaves appears to be associated with enhanced PUE in RAC875. Glucose-6-P and fructose-6-P are important for glycolysis, thus maintaining these metabolites would enable RAC875 to maintain carbohydrate metabolism and shoot biomass under P deficiency. The work presented here provides evidence that differences in metabolite profiles can be observed between wheat varieties that differ in PUE and key metabolic pathways are maintained in the efficient genotype to ensure carbon supply under P deficiency.

Metabolomic variability of four macroalgal species of the genus Lobophora using diverse approaches

Among comparative metabolomic studies used in marine sciences, only few of them are dedicated to macroalgae despite their ecological importance in marine ecosystems. Therefore, experimental data are needed to assess the scopes and limitations of different metabolomic techniques applied to macroalgal models. Species of the genus Lobophora belong to marine brown algae (Family: Dictyotaceae) and are widely distributed, especially in tropical coral reefs. The species richness of this genus has only been unveiled recently and it includes species of diverse morphologies and habitats, with some species interacting with corals. This study aims to assess the potential of different metabolomic fingerprinting approaches in the discrimination of four well known Lobophora species (L. rosacea, L. sonderii, L. obscura and L. monticola). These species present distinct morphologies and are found in various habitats in the New Caledonian lagoon (South-Western Pacific). We compared and combined different untargeted metabolomic techniques: liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (1H-NMR) and gas chromatography (GC-MS). Metabolomic separations were observed between each Lobophora species, with significant differences according to the techniques used. LC-MS was the best approach for metabotype distinction but a combination of approaches was also useful and allowed identification of chemomarkers for some species. These comparisons provide important data on the use of metabolomic approaches in the Lobophora genus and will pave the way for further studies on the sources of metabolomic variations for this ecologically important macroalgae.

Metabolomics Profiling before, during, and after the Beijing Olympics: A Panel Study of Within-Individual Differences during Periods of High and Low Air Pollution

BACKGROUND

The metabolome is a collection of exogenous chemicals and metabolites from cellular processes that may reflect the body's response to environmental exposures. Studies of air pollution and metabolomics are limited.

OBJECTIVES

To explore changes in the human metabolome before, during, and after the 2008 Beijing Olympics Games, when air pollution was high, low, and high, respectively.

METHODS

Serum samples were collected before, during, and after the Olympics from 26 participants in an existing panel study. Gas and ultra-high performance liquid chromatography/mass spectrometry were used in metabolomics analysis. Repeated measures ANOVA, network analysis, and enrichment analysis methods were employed to identify metabolites and classes associated with air pollution changes.

RESULTS

A total of 886 molecules were measured in our metabolomics analysis. Network partitioning identified four modules with 65 known metabolites that significantly changed across the three time points. All known molecules in the first module ([Formula: see text]) were lipids (e.g., eicosapentaenoic acid, stearic acid). The second module consisted primarily of dipeptides ([Formula: see text], e.g., isoleucylglycine) plus 8 metabolites from four other classes (e.g., hypoxanthine, 12-hydroxyeicosatetraenoic acid). Most of the metabolites in Modules 3 (19 of 23) and 4 (5 of 5) were unknown. Enrichment analysis of module-identified metabolites indicted significantly overrepresented pathways, including long- and medium-chain fatty acids, polyunsaturated fatty acids (n3 and n6), eicosanoids, lysolipid, dipeptides, fatty acid metabolism, and purine metabolism [(hypo) xanthine/inosine-containing pathways].

CONCLUSIONS

We identified two major metabolic signatures: one consisting of lipids, and a second that included dipeptides, polyunsaturated fatty acids, taurine, and xanthine. Metabolites in both groups decreased during the 2008 Beijing Olympics, when air pollution was low, and increased after the Olympics, when air pollution returned to normal (high) levels. https://doi.org/10.1289/EHP3705.

Metabolic pathways modulate the neuronal toxicity associated with fragile X-associated tremor/ataxia syndrome

Fragile X-associated tremor/ataxia syndrome (FXTAS) is an adult-onset neurodegenerative disorder that affects premutation carriers (55-200 CGG repeats) of the fragile X mental retardation 1 (FMR1) gene. Much remains unknown regarding the metabolic alterations associated with FXTAS, especially in the brain, and the most affected region, the cerebellum. Investigating the metabolic changes in FXTAS will aid in the identification of biomarkers as well as in understanding the pathogenesis of disease. To identify the metabolic alterations associated with FXTAS, we took advantage of our FXTAS mouse model that expresses 90 CGG repeats in cerebellar Purkinje neurons and exhibits the key phenotypic features of FXTAS. We performed untargeted global metabolic profiling of age-matched control and FXTAS mice cerebella at 16-20 weeks and 55 weeks. Out of 506 metabolites measured in cerebellum, we identified 186 metabolites that demonstrate significant perturbations due to the (CGG)90 repeat (P<0.05) and found that these differences increase dramatically with age. To identify key metabolic changes in FXTAS pathogenesis, we performed a genetic screen using a Drosophila model of FXTAS. Out of 28 genes that we tested in the fly, 8 genes showed significant enhanced neuronal toxicity associated with CGG repeats, such as Schlank (ceramide synthase), Sk2 (sphingosine kinase) and Ras (IMP dehydrogenase). By combining metabolic profiling with a Drosophila genetic screen to identify genetic modifiers of FXTAS, we demonstrate an effective method for functional validation of high-throughput metabolic data and show that sphingolipid and purine metabolism are significantly perturbed in FXTAS pathogenesis.

Application of metabolomics and molecular networking in investigating the chemical profile and antitrypanosomal activity of British bluebells (Hyacinthoides non-scripta)

Bulb, leaf, scape and flower samples of British bluebells (Hyacinthoides non-scripta) were collected regularly for one growth period. Methanolic extracts of freeze-dried and ground samples showed antitrypanosomal activity, giving more than 50% inhibition, for 20 out of 41 samples. High-resolution mass spectrometry was used in the dereplication of the methanolic extracts of the different plant parts. The results revealed differences in the chemical profile with bulb samples being distinctly different from all aerial parts. High molecular weight metabolites were more abundant in the flowers, shoots and leaves compared to smaller molecular weight ones in the bulbs. The anti-trypanosomal activity of the extracts was linked to the accumulation of high molecular weight compounds, which were matched with saponin glycosides, while triterpenoids and steroids occurred in the inactive extracts. Dereplication studies were employed to identify the significant metabolites via chemotaxonomic filtration and considering their previously reported bioactivities. Molecular networking was implemented to look for similarities in fragmentation patterns between the isolated saponin glycoside at m/z 1445.64 [M + formic-H]- equivalent to C64H104O33 and the putatively found active metabolite at m/z 1283.58 [M + formic-H]- corresponding to scillanoside L-1. A combination of metabolomics and bioactivity-guided approaches resulted in the isolation of a norlanostane-type saponin glycoside with antitrypanosomal activity of 98.9% inhibition at 20 µM.

Effects of Repeated Sublethal External Exposure to Deep Water Horizon Oil on the Avian Metabolome

We assessed adverse effects of external sublethal exposure of Deepwater Horizon, Mississippi Canyon 252 oil on plasma and liver metabolome profiles of the double-crested cormorant (Phalacrocorax auritus), a large (1.5 to 3.0 kg) diving waterbird common in the Gulf of Mexico. Metabolomics analysis of avian plasma showed significant negative effects on avian metabolic profiles, in some cases after only two external exposures (26 g cumulative) to oil. We observed significant (p < 0.05) changes in intermediate metabolites of energy metabolism and fatty acid and amino acid metabolic pathways in cormorants after repeated exposure to oil. Exposure to oil increased several metabolites (glycine, betaine, serine and methionine) that are essential to the one-carbon metabolism pathway. Lipid metabolism was affected, causing an increase in production of ketone bodies, suggesting lipids were used as an alternative energy source for energy production in oil exposed birds. In addition, metabolites associated with hepatic bile acid metabolism were affected by oil exposure which was correlated with changes observed in bile acids in exposed birds. These changes at the most basic level of phenotypic expression caused by sublethal exposure to oil can have effects that would be detrimental to reproduction, migration, and survival in avian species.

Understanding Camellia sinensis using Omics Technologies along with Endophytic Bacteria and Environmental Roles on Metabolism: A Review

Camellia sinensis is the most consumed beverage worldwide. It contains a wide variety of secondary metabolites, such as alkaloids, saponins, tannins, catechins, and polyphenols, generated through a condensation reaction of cinnamic acid with three malonyl-CoA groups. In addition to the metabolic processes occurring within this plant, there are also some plant-associated bacterial endophytes. These bacteria reside in the living tissues of the host plants without causing any harmful effect to them, thereby stimulating secondary metabolite production with a diverse range of biological effects. Omics technologies reveal understanding of the biological phenomena of transcriptomics, proteomics, and metabolomics. In this sense, the present review aims to provide a comprehensive review of various methods used to identify distinct plant compounds, namely transcriptomic, proteomic, and metabolomic analysis. The role of endophytic bacteria in C. sinensis metabolism, and C. sinensis antioxidant and antimicrobial effects, are also carefully highlighted.