Measuring the metabolome: current analytical technologies

Differentiation of three commercial tuna species through GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics and chemometrics

Food fraud is common in the tuna industry because of the economic benefits involved. Ensuring the authenticity of tuna species is crucial for protecting both consumers and tuna stocks. In this study, GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics were used to investigate the metabolite profiles of three commercial tuna species (skipjack tuna, bigeye tuna and yellowfin tuna). A total of 22 and 77 metabolites were identified with high confidence using GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry, respectively. Further screening via chemometrics revealed that 38 metabolites could potentially serve as potential biomarkers. Hierarchical cluster analysis showed that the screened metabolite biomarkers successfully distinguished the three tested tuna species. Furthermore, a total of 27 metabolic pathways were identified through enrichment analysis based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways.

Development of metabolomic biomarkers to discriminate the geographical origin of Korean and Russian snow crabs using CE-TOF/MS

The quantity of snow crabs (Chionoecetes opilio) harvested in Korea is subject to seasonal restrictions; therefore, snow crabs are imported from Russia. Metabolites in snow crabs from two geographic origins were compared. The metabolites were subjected to metabolomic analysis to prevent fraudulent sales of marine products from a particular country. Capillary electrophoresis time-of-flight mass spectrometry was used. Seventy-seven target metabolites were identified using a mass spectral library. Through orthogonal partial least squares discriminant analysis, the top 25 biomarker candidates were evaluated based on p-values and fold changes. A total of 246 peaks (187 and 59 in the cation and anion modes, respectively) were identified. Among the biomarker candidates, 2-oxovaleric acid, asymmetric dimethylarginine, hypotaurine, and allo-threonine were selected as final biomarkers to unequivocally determine the geographic origin. Overall, metabolic analyses allowed us to differentiate snow crabs from different geographic origins. This method could also be extended of other marine products.

Betaine and I-LG may have a predictive value for ATB: A causal study in a large European population

PURPOSE

To analyze the causal relationship between 486 human serum metabolites and the active tuberculosis (ATB) in European population.

METHODS

In this study, the causal relationship between human serum metabolites and the ATB was analyzed by integrating the genome-wide association study (GWAS). The 486 human serum metabolites were used as the exposure variable, three different ATB GWAS databases in the European population were set as outcome variables, and single nucleotide polymorphisms were used as instrumental variables for Mendelian Randomization. The inverse variance weighting was estimated causality, the MR-Egger intercept to estimate horizontal pleiotropy, and the combined effects of metabolites were also considered in the meta-analysis. Furthermore, the web-based MetaboAnalyst 6.0 was engaged for enrichment pathway analysis, while R (version 4.3.2) software and Review Manager 5.3 were employed for statistical analysis.

RESULTS

A total of 21, 17, and 19 metabolites strongly associated with ATB were found in the three databases after preliminary screening (P < 0.05). The intersecting metabolites across these databases included tryptophan, betaine, 1-linoleoylglycerol (1-monolinolein) (1-LG), 1-eicosatrienoylglycerophosphocholine, and oleoylcarnitine. Among them, betaine (I2 = 24%, P = 0.27) and 1-LG (I2 = 0%, P = 0.62) showed the lowest heterogeneity among the different ATB databases. In addition, the metabolic pathways of phosphatidylethanolamine biosynthesis (P = 0.0068), methionine metabolism (P = 0.0089), betaine metabolism (P = 0.0205) and oxidation of branched-chain fatty acids (P = 0.0309) were also associated with ATB.

CONCLUSION

Betaine and 1-LG may be biomarkers or auxiliary diagnostic tools for ATB. They may provide new guidance for medical practice in the early diagnosis and surveillance of ATB. In addition, by interfering with phosphatidylethanolamine biosynthesis, methionine metabolism, betaine metabolism, oxidation of branched-chain fatty acids, and other pathways, it is helpful to develop new anti-tuberculosis drugs and explore the virulence or pathogenesis of ATB at a deeper level, providing an effective reference for future studies.

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

Integrating metabolomics into reproduction: Sperm metabolism and fertility enhancement in pigs

The last decades of research have revealed that many other factors besides gamete genomes are able to determine the reproductive outcomes. Indeed, paternal factors have been observed to be capable of modulating multiple crucial features of the reproductive process, such as sperm physiology, the maternal environment and, even, the offspring health. These recent advances have been encompassed with the emergence of OMICS technologies, as they comprehensively characterise the molecular composition of biological systems. The present narrative review aimed to take a closer look at the potential of these technologies in the field of reproductive biology. This literature revision shows that most studies up to date have followed a non-targeted approach to screen mammalian seminal plasma (SP) and sperm metabolite composition through different metabolome platforms. These studies have proposed metabolites of multiple natures as potential in vivo fertility biomarkers. Yet, targeted approaches can be used to answer specific biological question, and their power is exemplified herein. For instance, metabolomic studies have uncovered not only that glycolysis is the main ATP energy source of pig sperm, but also that sperm metabolism can trigger DNA damage, hence compromise embryo development. In conclusion, this review shows the potential of both non-targeted and targeted metabolomics for the discovery of cell pathways that govern the reproductive process. Understanding these systems could help make progress in different areas, including livestock efficient breeding, the improvement of artificial reproductive technologies, and the development of biomarkers for infertility detection.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study

BACKGROUND

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.

METHODS

To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.

RESULTS

A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.

CONCLUSION

These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

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.

Structural elucidation of derivatives of polyfunctional metabolites after methyl chloroformate derivatization by high-resolution mass spectrometry gas chromatography. Application to microbiota metabolites

Metabolomics has become an essential discipline in the study of microbiome, emerging gas chromatography coupled to mass spectrometry as the most mature, robust, and reproducible analytical technique. Silylation is the most widely used chemical derivatization strategy, although it has some limitations. In this regard, alkylation by alkyl chloroformate offers some advantages, such as a rapid reaction, milder conditions, better reproducibility, and the generation of more stable derivatives. However, commercial spectral libraries do not include many of the alkyl derivatives, mainly for polyfunctional metabolites, which can form multiple derivatives. That introduces a huge bias in untargeted metabolomics leading to common errors such as duplicates, unknowns, misidentifications, wrong assignations, and incomplete results from which non-reliable findings and conclusions will be retrieved. For this reason, the purpose of this study is to overcome these shortcomings and to expand the knowledge of metabolites in general and especially those closely related to the gut microbiota through the thorough study of the reactivity of the different functional groups in real matrix derivatized by methyl chloroformate, a common representative alkylation reagent. To this end, a systematic workflow has been developed based on exhaustive structural elucidation, along with computational simulation, and taking advantage of the high sensitivity and high-resolution gas chromatography-mass spectrometry. Several empirical rules have been established according to chemically different entities (free fatty acids, amino acids, polyols, sugars, amines, and polyfunctional groups, etc.) to predict the number of derivatives formed from a single metabolite, as well as their elution order and structure. In this work, some methyl chloroformate derivatives not previously reported as well as the mechanisms to explain them are given. Extremely important is the interconversion of E- and Z- geometric isomers of unsaturated dicarboxylic acids (case of fumaric-maleic and case of citraconic-mesaconic acids), or the formation of cycled derivatives for amino acids, as well as common metabolites, as in the case of serine and cysteine, and many others.

Identification of primary metabolites in fungal species of Trichophyton mentagrophyte and Trichophyton rubrum by NMR spectroscopy

BACKGROUND

Superficial mycoses are fungal infections limited to the outermost layers of the skin and its appendages. The chief causative agents of these mycoses are dermatophytes and yeasts. The diagnosis of dermatophytosis can be made by direct mycological examination with potassium hydroxide (10%-30%) of biological material obtained from patients with suspected mycosis, providing results more rapid than fungal cultures, which may take days or weeks. This information, together with clinical history and laboratory diagnosis, ensures that the appropriate treatment is initiated promptly. However, false negative results are obtained in 5%-15%, by conventional methods of diagnosis of dermatophytosis.

OBJECTIVES

To study the metabolic profiles of the commonly occurring dermatophytes by NMR spectroscopy.

PATIENTS/MATERIALS

We have used 1D and 2D Nuclear Magnetic Resonance (NMR) experiments along with Human Metabolome Database (HMDB) and Chenomx database search for identification of primary metabolites in the methanol extract of two fungal species: Trichophyton mentagrophyte (T. mentagrophyte) and Trichophyton rubrum (T. rubrum). Both standard strains and representative number of clinical isolates of these two species were investigated. Further, metabolic profiles obtained were analysed using multivariate analysis.

RESULTS

We have identified 23 metabolites in the T. mentagrophyte and another 23 metabolites in T. rubrum. Many important metabolites like trehalose, proline, mannitol, acetate, GABA and several other amino acids were detected, which provide the necessary components for fungal growth and metabolism. Altered metabolites were defined between Trichophyton mentagrophyte and T. rubrum strains.

CONCLUSION

We have detected many metabolites in the two fungal species T. mentagrophyte and T. rubrum by using NMR spectroscopy. NMR spectroscopy provides a holistic snapshot of the metabolome of an organism. Key metabolic differences were identified between the two fungal strains. We need to perform more studies on metabolite profiling of the samples from these species for their rapid diagnosis and prompt treatment.

Microfluidics enabled multi-omics triple-shot mass spectrometry for cell-based therapies

In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all "omes," potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Mass Spectrometric Identification of Metabolites after Magnetic-Pulse Treatment of Infected Pyrus communis L. Microplants

The major goal of this study is to create a venue for further work on the effect of pulsed magnetic fields on plant metabolism. It deals with metabolite synthesis in the aforementioned conditions in microplants of Pyrus communis L. So far, there have been glimpses into the governing factors of plant biochemistry in vivo, and low-frequency pulsed magnestatic fields have been shown to induce additional electric currents in plant tissues, thus perturbing the value of cell membrane potential and causing the biosynthesis of new metabolites. In this study, sixty-seven metabolites synthesized in microplants within 3-72 h after treatment were identified and annotated. In total, thirty-one metabolites were produced. Magnetic-pulse treatment caused an 8.75-fold increase in the concentration of chlorogenic acid (RT = 8.33 ± 0.0197 min) in tissues and the perturbation of phenolic composition. Aucubin, which has antiviral and antistress biological activity, was identified as well. This study sheds light on the effect of magnetic fields on the biochemistry of low-molecular-weight metabolites of pear plants in vitro, thus providing in-depth metabolite analysis under optimized synthetic conditions. This study utilized high-resolution gas chromatography-mass spectrometry, metabolomics methods, stochastic dynamics mass spectrometry, quantum chemistry, and chemometrics, respectively. Stochastic dynamics uses the relationships between measurands and molecular structures of silylated carbohydrates, showing virtually identical mass spectra and comparable chemometrics parameters.

From Nontargeted to Targeted Analysis: Feature Selection in the Differentiation of Truffle Species (Tuber spp.) Using 1H NMR Spectroscopy and Support Vector Machine

The price of different truffle types varies according to their culinary value, sometimes by more than a factor of 10. Nonprofessionals can hardly distinguish visually the species within the white or black truffles, making the possibility of food fraud very easy. Therefore, the identification of different truffle species (Tuber spp.) is an analytical task that could be solved in this study. The polar extract from a total of 80 truffle samples was analyzed by 1H NMR spectroscopy in combination with chemometric methods covering five commercially relevant species. All classification models were validated applying a repeated nested cross-validation. In direct comparison, the two very similar looking and closely related black representatives Tuber melanosporum and Tuber indicum could be classified 100% correctly. The most expensive truffle Tuber magnatum could be distinguished 100% from the other relevant white truffle Tuber borchii. In addition, signals for a potential Tuber borchii and a potential Tuber melanosporum marker for targeted approaches could be detected, and the corresponding molecules were identified as betaine and ribonate. A model covering all five truffle species Tuber aestivum, Tuber borchii, Tuber indicum, Tuber magnatum, and Tuber melanosporum was able to correctly discriminate between each of the species.

Dynamics of Organic Acids during the Droplet-Vitrification Cryopreservation Procedure Can Be a Signature of Oxidative Stress in Pogostemon yatabeanus

Cryopreservation in liquid nitrogen (LN, -196 °C) is a unique option for the long-term conservation of threatened plant species with non-orthodox or limitedly available seeds. In previous studies, a systematic approach was used to develop a droplet-vitrification (DV) cryopreservation protocol for Postemon yatabeanus shoot tips that includes preculture with 10% sucrose, osmoprotection with C4-35%, cryoprotection with A3-80% vitrification solution, and a three-step regrowth starting with the ammonium-free medium. The tricarboxylic acid (TCA) cycle is a crucial component of plant cell metabolism as it is involved in redox state regulation and energy provision. We hypothesized that organic acids (OAs) associated with the TCA and its side reactions indirectly indicate metabolism intensity and oxidative stress development in shoot tips under the cryopreservation procedure. In this study, the contents of 14 OAs were analyzed using gas chromatography-tandem mass spectrometry (GC-MS/MS) in P. yatabeanus shoot tips in a series of treatments including individual steps of the DV procedure, additional stress imposed by non-optimum protocol conditions (no preculture, no osmoprotection, various vitrification solution composition, using vials instead of aluminum foils, etc.) and regrowth on different media with or without ammonium or growth regulators. The possible relation of OA content with the total cryoprotectant (CPA) concentration and shoot tips regeneration percentage was also explored. Regeneration of cryopreserved shoot tips reduced in descending order as follows: standard protocol condition (91%) > non-optimum vitrification solution (ca. 68%) > non-optimum preculture (60-62%) > regrowth medium (40-64%) > no osmoprotection, cryopreservation in vials (28-30%). Five OAs (glycolic, malic, citric, malonic, and lactic) were the most abundant in the fresh (control) shoot tips. The dynamic pattern of OAs during the DV procedure highly correlated (r = 0.951) with the total CPA concentration employed: it gradually increased through the preculture, osmoprotection, and cryoprotection, peaked at cooling/rewarming (6.38-fold above control level), and returned to the fresh control level after 5 days of regrowth (0.89-fold). The contents of four OAs (2-hydroxybutyric, 3-hydroxypropionic, lactic, and glycolic) showed the most significant (10-209-fold) increase at the cooling/rewarming step. Lactic and glycolic acids were the major OAs at cooling/rewarming, accounting for 81% of the total OAs content. The OAs were categorized into three groups based on their dynamics during the cryopreservation protocol, and these groups were differently affected by protocol step modifications. However, there was no straightforward relationship between the dynamics of OAs and shoot tip regeneration. The results suggest that active modulation of OAs metabolism may help shoot tips to cope with osmotic stress and the chemical cytotoxicity\ of CPAs. Further intensive studies are needed to investigate the effect of cryopreservation on cell primarily metabolism and identify oxidative stress-related biomarkers in plant materials.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

Metabolome Mining of Curcuma longa L. Using HPLC-MS/MS and Molecular Networking

Turmeric, Curcuma longa L., is a type of medicinal plant characterized by its perennial nature and rhizomatous growth. It is a member of the Zingiberaceae family and is distributed across the world's tropical and subtropical climates, especially in South Asia. Its rhizomes have been highly valued for food supplements, spices, flavoring agents, and yellow dye in South Asia since ancient times. It exhibits a diverse array of therapeutic qualities that encompass its ability to combat diabetes, reduce inflammation, act as an antioxidant, exhibit anticancer properties, and promote anti-aging effects. In this study, organic extracts of C. longa rhizomes were subjected to HPLC separation followed by ESI-MS and low-energy tandem mass spectrometry analyses. The Global Natural Product Social Molecular Networking (GNPS) approach was utilized for the first time in this ethnobotanically important species to conduct an in-depth analysis of its metabolomes based on their fragments. To sum it up, a total of 30 metabolites including 16 diarylheptanoids, 1 diarylpentanoid, 3 bisabolocurcumin ethers, 4 sesquiterpenoids, 4 cinnamic acid derivatives, and 2 fatty acid derivatives were identified. Among the 16 diarylheptanoids identified in this study, 5 of them are reported for the first time in this species.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R² > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

Physiological response in E. coli to YdgR overexpression depends on whether the protein has an intact function

Membrane transport proteins are essential for the transport of a wide variety of molecules across the cell membrane to maintain cellular homeostasis. Generally, these transport proteins can be overexpressed in a suitable host (bacteria, yeast, or mammalian cells), and it is well documented that overexpression of membrane proteins alters the global metabolomic and proteomic profiles of the host cells. In the present study, we investigated the physiological consequences of overexpression of a membrane transport protein YdgR that belongs to the POT/PTR family from E. coli by using the lab strain BL21 (DE3)pLysS in its functional and attenuated mutant YdgR-E33Q. We found significant differences between the omics (metabolomics and proteomics) profiles of the cells expressing functional YdgR as compared to cells expressing attenuated YdgR, e.g., upregulation of several uncharacterized y-proteins and enzymes involved in the metabolism of peptides and amino acids. Furthermore, molecular network analysis suggested a relatively higher presence of proline-containing tripeptides in cells expressing functional YdgR. We envisage that an in-depth investigation of physiological alterations due to protein over-expression may be used for the deorphanization of the y-gene transportome.

Protonated Forms of Naringenin and Naringenin Chalcone: Proteiform Bioactive Species Elucidated by IRMPD Spectroscopy, IMS, CID-MS, and Computational Approaches

Naringenin (Nar) and its structural isomer, naringenin chalcone (ChNar), are two natural phytophenols with beneficial health effects belonging to the flavonoids family. A direct discrimination and structural characterization of the protonated forms of Nar and ChNar, delivered into the gas phase by electrospray ionization (ESI), was performed by mass spectrometry-based methods. In this study, we exploit a combination of electrospray ionization coupled to (high-resolution) mass spectrometry (HR-MS), collision-induced dissociation (CID) measurements, IR multiple-photon dissociation (IRMPD) action spectroscopy, density functional theory (DFT) calculations, and ion mobility-mass spectrometry (IMS). While IMS and variable collision-energy CID experiments hardly differentiate the two isomers, IRMPD spectroscopy appears to be an efficient method to distinguish naringenin from its related chalcone. In particular, the spectral range between 1400 and 1700 cm^-1 is highly specific in discriminating between the two protonated isomers. Selected vibrational signatures in the IRMPD spectra have allowed us to identify the nature of the metabolite present in methanolic extracts of commercial tomatoes and grapefruits. Furthermore, comparisons between experimental IRMPD and calculated IR spectra have clarified the geometries adopted by the two protonated isomers, allowing a conformational analysis of the probed species.

Seasonal variations in the metabolomic profile of the ovarian follicle components in Italian Mediterranean Buffaloes

The aim of this work was to evaluate the seasonal effect on the metabolomic profile of the ovarian follicle in Italian Mediterranean buffalo to unravel the causes of the reduced competence during the non-breeding season (NBS). Samples of follicular fluid, follicular cells, cumulus cells and oocytes were collected from abattoir-derived ovaries during breeding season (BS) and NBS and analyzed by 1H Nuclear Magnetic Resonance. The Orthogonal Projections to Latent Structures of the Discriminant Analysis showed clear separation into seasonal classes and Variable Importance in Projection method identified differentially abundant metabolites between seasons. Seasonal differences were recorded in metabolite content in all analyzed components suggesting that the decreased oocyte competence during NBS may be linked to alteration of several metabolic pathways. The pathway enrichment analysis revealed that differences in the metabolites between the seasons were linked to glutathione, energy generating and amino acid metabolism and phospholipid biosynthesis. The current work allows the identification of potential positive competence markers in the follicular fluid as glutathione, glutamate, lactate and choline, and negative markers like leucine, isoleucine and β-hydroxybutyrate. These results form a major basis to develop potential strategies to optimize the follicular environment and the IVM medium to improve the competence of oocytes during the NBS.

Development and Application of Whole-Cell Biosensors for the Detection of Gallic Acid

Gallic acid is a prevalent secondary plant metabolite distinguished as one of the most effective free-radical scavengers among phenolic acids. This compound is also known for its cytotoxic, anti-inflammatory, and antimicrobial activities. Bulk quantities of gallic acid are conventionally produced by acid hydrolysis of tannins, a costly and environmentally hazardous process. With the aim to develop more sustainable approaches, microbial bioproduction strategies have been attempted recently. To advance synthetic biology and metabolic engineering of microorganisms for gallic acid production, we characterize here a transcription factor-based inducible system PpGalR/P_{PP_RS13150} that responds to the extracellular gallic acid in a dose-dependent manner in Pseudomonas putida KT2440. Surprisingly, this compound does not mediate induction when PpGalR/P_{PP_RS13150} is used in non-native host background. We show that the activation of the inducible system requires gallate dioxygenase activity encoded by galA gene. The 4-oxalomesaconic acid, an intermediate of gallic acid-metabolism, is identified as the effector molecule that interacts with the transcription factor GalR mediating activation of gene expression. Introduction of galA gene along galR enables development of biosensors suitable for detection and monitoring of gallic acid extracellularly using non-native hosts such as E. coli and C. necator. Moreover, the P. putida-based biosensor's applicability is demonstrated by detecting and measuring gallic acid in extracts of Camellia sinensis leaves. This study reports the strategy, which can be applied for developing gallic acid biosensors using bacterial species outside Pseudomonas genus.

Linking processes to community functions—insights into litter decomposition combining fungal metatranscriptomics and environmental NMR profiling

In forest ecosystems, decomposition is essential for carbon and nutrient cycling and therefore a key process for ecosystem functioning. During the decomposition process, litter chemistry, involved decomposer organisms, and enzymatic activity change interdependently. Chemical composition of the litter is the most complex and dynamic component in the decomposition process and therefore challenging to assess holistically. In this study, we aimed to characterize chemical shifts during decomposition and link them to changes in decomposer fungal activity. We characterized the chemical composition of freshly fallen autumn leaves of European beech (Fagus sylvatica) and the corresponding leaf litter after 1 year of decomposition by proton nuclear magnetic resonance spectroscopy. We further tested the applicability of spiking experiments for qualitative and quantitative characterization of leaves and litter chemistry. The composition and transcriptional activity of fungal communities was assessed by high-throughput Illumina sequencing in the same litter samples. We were able to distinguish freshly fallen leaves from 1-year-old litter based on their chemical composition. Chemical composition of leaves converged among regions with progressing decomposition. Fungal litter communities differed in composition among regions, but they were functionally redundant according to the expression of genes encoding litter degrading enzymes (CAZymes). Fungi of the saprotrophic genera Mycena and Chalara correlated with transcription of litter-degrading CAZymes in 1-year-old litter. Forestry measures influenced the diversity and transcription rate of the detected CAZymes transcripts in litter. Their expression was primarily predicted by composition of the soluble chemical fraction of the litter. Environmental NMR fingerprints thus proved valuable for inferring ecological contexts. We propose and discuss a holistic framework to link fungal activity, enzyme expression, and chemical composition.

Comprehensive serum lipidomic analyses reveal potential biomarkers for malignant breast cancer: A case-control study

BACKGROUND

Breast cancer is the most worldwide commonly found malignancy among women. The evidence for lipidomic studies of breast cancer in the Chinese population is relatively limited.

OBJECTIVE

Our current study aimed to identify peripheral lipids capable of distinguishing adults with and without malignant breast cancer in a Chinese population and to explore the potential lipid metabolism pathways implicated in breast cancer.

METHODS

Lipidomics was performed with an Ultimate 3000 UHPLC system coupled with a Q-Exactive HF MS platform by using the serum of 71 female patients with malignant breast cancer and 92 age-matched (± 2 years) healthy women. The data were uploaded to and processed by the specialized online software Metaboanalyst 5.0. Both univariate and multivariate analyses were carried out for potential biomarker screening. Areas under the receiver-operating characteristic (ROC) curves (AUCs) of identified differential lipids were obtained for evaluating their classification capacity.

RESULTS

A total of 47 significantly different lipids were identified by applying the following criteria: false discovery rate-adjusted P < 0.05, variable importance in projection ⩾ 1.0, and fold change ⩾ 2.0 or ⩽ 0.5. Among them, 13 lipids were identified as diagnostic biomarkers with the area under curve (AUC) greater than 0.7. Multivariate ROC curves indicated that AUCs greater than 0.8 could be achieved with 2-47 lipids.

CONCLUSIONS

Using an untargeted LC-MS-based metabolic profiling approach, our study provides preliminary evidence that extensive dysregulations of OxPCs, PCs, SMs and TAGs were involved in the pathological processes of breast cancer. We provided clues for furtherly investigating the role of lipid alterations in the pathoetiology of breast cancer.

Pediatric Population with Down Syndrome: Obesity and the Risk of Cardiovascular Disease and Their Assessment Using Omics Techniques-Review

People with Down syndrome (PWDS) are more at risk for developing obesity, oxidative stress disorders, metabolic disorders, and lipid and carbohydrate profile disorders than the general population. The presence of an additional copy of genes on chromosome 21 (i.e., the superoxide dismutase 1 gene (SOD1) and gene coding for the cystathionine β-synthase (CBS) enzyme) raises the risk for cardiovascular disease (CVD). As a result of disorders in metabolic processes and biochemical pathways, theoretically protective factors (low homocysteine level, high SOD1 level) do not fulfil their original functions. Overexpression of the CBS gene leads to the accumulation of homocysteine-a CVD risk factor. An excessive amount of protective SOD1, in the case of a lack of compensatory increase in the activity of catalase and peroxidase, leads to intensifying free radical processes. The occurrence of metabolic disorders and the amplified effect of oxidative stress carries higher risk of exposure of people with DS to CVD. At present, classic predispositions are known, but it is necessary to identify early risk factors in order to be able to employ CVD and obesity prophylaxis. Detailed determination of the metabolic and lipid profile may provide insight into the molecular mechanisms underlying CVD.

Antiviral COVID-19 protein and molecular docking: In silico characterization of various antiviral compounds extracted from Arisaema jacquemontii Blume

Arisaema jacquemontii Blume is a highly medicinal and poisonous plant belong to the family Araceae. It is used to treat several deadly diseases, including viral infections. It has antioxidant, anti-cancerous, antimalarial, anti-vermicidal, and antiviral activities. Therefore, five parts of the Arisaema jacquemontii Blume plant, such as leaf, seed, stem, pulp, and rhizome extract, were evaluated for metabolic and in silico characterization of probable compounds using gas chromatography-mass spectrometry (GC-MS) analysis. A total of 22 compounds were isolated from the methanolic extracts of A. jacquemontii Blume. A selected antiviral COVID-19 protein i.e., protease (6LU7) was docked against the obtained compounds. Different affinities were obtained through various compounds. The best results were shown by three different compounds identified in the rhizome. The maximum binding affinity of these compounds is 8.1 kJ/mol. Molecular docking (MD) indicate that these molecules have the highest binding energies and hydrogen bonding interactions. The binding mode of interaction was discovered to be reasonably effective for counteracting the SARS virus COVID-19. The findings of this study could be extremely useful in the development of more phytochemical-based COVID-19 therapeutics.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted ¹³C and ¹H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted ¹³C and ¹H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of ¹H and ¹³C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data

INTRODUCTION

Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis.

OBJECTIVES

Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data.

METHODS

A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively.

RESULTS

The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB.

CONCLUSION

Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer.

Ecosystem-specific microbiota and microbiome databases in the era of big data

The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.

Colon Cancer: From Epidemiology to Prevention

Colorectal cancer (CRC) is one of the most prevalent cancers affecting humans, with a complex genetic and environmental aetiology. Unlike cancers with known environmental, heritable, or sex-linked causes, sporadic CRC is hard to foresee and has no molecular biomarkers of risk in clinical use. One in twenty CRC cases presents with an established heritable component. The remaining cases are sporadic and associated with partially obscure genetic, epigenetic, regenerative, microbiological, dietary, and lifestyle factors. To tackle this complexity, we should improve the practice of colonoscopy, which is recommended uniformly beyond a certain age, to include an assessment of biomarkers indicative of individual CRC risk. Ideally, such biomarkers will be causal to the disease and potentially modifiable upon dietary or therapeutic interventions. Multi-omics analysis, including transcriptional, epigenetic as well as metagenomic, and metabolomic profiles, are urgently required to provide data for risk analyses. The aim of this article is to provide a perspective on the multifactorial derailment of homeostasis leading to the initiation of CRC, which may be explored via multi-omics and Gut-on-Chip analysis to identify much-needed predictive biomarkers.

Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View

Abiotic stresses rewire plant central metabolism to maintain metabolic and energy homeostasis. Metabolites involved in the plant central metabolic network serve as a hub for regulating carbon and energy metabolism under various stress conditions. In this review, we introduce recent metabolomics techniques used to investigate the dynamics of metabolic responses to abiotic stresses and analyze the trend of publications in this field. We provide an updated overview of the changing patterns in central metabolic pathways related to the metabolic responses to common stresses, including flooding, drought, cold, heat, and salinity. We extensively review the common and unique metabolic changes in central metabolism in response to major abiotic stresses. Finally, we discuss the challenges and some emerging insights in the future application of metabolomics to study plant responses to abiotic stresses.

Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit

The AbsoluteIDQ p400 HR kit is a commercial product for targeted metabolomics. While the kit has been validated for human plasma and serum, adherent cell lysates have not yet been evaluated. We have optimized the detection of polar and lipid metabolites in cell lysates using the kit to enable robust and repeatable analysis of the detected metabolites. Parameters optimized include total cell mass, loading volume and extraction solvent. We present a cell preparation and analytical method and report on the performance of the kit with regard to detectability of the targeted metabolites and their repeatability. The kit can be successfully used for a relative quantification analysis of cell lysates from adherent cells although validated only for human plasma and serum. Most metabolites are below the limit of the Biocrates' set quantification limits and we confirmed that this relative quantification can be used for further statistical analysis. Using this approach, up to 45% of the total metabolites in the kit can be detected with a reasonable analytical performance (lowest median RSD 9% and 13% for LC and FIA, respectively) dependent on the method used. We recommend using ethanol as the extraction solvent for cell lysates of osteosarcoma cell lines for the broadest metabolite coverage and 25 mg of cell mass with a loading volume of 20 µL per sample.

A Review of Metabolomic Profiling in Rheumatoid Arthritis: Bringing New Insights in Disease Pathogenesis, Treatment and Comorbidities

Metabolomic analysis provides a wealth of information that can be predictive of distinctive phenotypes of pathogenic processes and has been applied to better understand disease development. Rheumatoid arthritis (RA) is an autoimmune disease with the establishment of chronic synovial inflammation that affects joints and peripheral tissues such as skeletal muscle and bone. There is a lack of useful disease biomarkers to track disease activity, drug response and follow-up in RA. In this review, we describe potential metabolic biomarkers that might be helpful in the study of RA pathogenesis, drug response and risk of comorbidities. TMAO (choline and trimethylamine oxide) and TCA (tricarboxylic acid) cycle products have been suggested to modulate metabolic profiles during the early stages of RA and are present systemically, which is a relevant characteristic for biomarkers. Moreover, the analysis of lipids such as cholesterol, FFAs and PUFAs may provide important information before disease onset to predict disease activity and treatment response. Regarding therapeutics, TNF inhibitors may increase the levels of tryptophan, valine, lysine, creatinine and alanine, whereas JAK/STAT inhibitors may modulate exclusively fatty acids. These observations indicate that different disease modifying antirheumatic drugs have specific metabolic profiles and can reveal differences between responders and non-responders. In terms of comorbidities, physical impairment represented by higher fatigue scores and muscle wasting has been associated with an increase in urea cycle, FFAs, tocopherols and BCAAs. In conclusion, synovial fluid, blood and urine samples from RA patients seem to provide critical information about the metabolic profile related to drug response, disease activity and comorbidities.

Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis

LC–MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.

High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS

Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.

Metabolomics facilitate the personalized management in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Urinary Metabolic Markers of Bladder Cancer: A Reflection of the Tumor or the Response of the Body?

This work will review the metabolic information that various studies have obtained in recent years on bladder cancer, with particular attention to discovering biomarkers in urine for the diagnosis and prognosis of this disease. In principle, they would be capable of complementing cystoscopy, an invasive but nowadays irreplaceable technique or, in the best case, of replacing it. We will evaluate the degree of reproducibility that the different experiments have shown in the indication of biomarkers, and a synthesis will be attempted to obtain a consensus list that is more likely to become a guideline for clinical practice. In further analysis, we will inquire into the origin of these dysregulated metabolites in patients with bladder cancer. For this purpose, it will be helpful to compare the imbalances measured in urine with those known inside tumor cells or tissues. Although the urine analysis is sometimes considered a liquid biopsy because of its direct contact with the tumor in the bladder wall, it contains metabolites from all organs and tissues of the body, and the tumor is separated from urine by the most impermeable barrier found in mammals. The distinction between the specific and systemic responses can help understand the disease and its consequences in more depth.

Metabolomics and cytokine profiling of mesenchymal stromal cells identify markers predictive of T-cell suppression

BACKGROUND AIMS

Mesenchymal stromal cells (MSCs) have shown great promise in the field of regenerative medicine, as many studies have shown that MSCs possess immunomodulatory function. Despite this promise, no MSC therapies have been licensed by the Food and Drug Administration. This lack of successful clinical translation is due in part to MSC heterogeneity and a lack of critical quality attributes. Although MSC indoleamine 2,3-dioxygnease (IDO) activity has been shown to correlate with MSC function, multiple predictive markers may be needed to better predict MSC function.

METHODS

Three MSC lines (two bone marrow-derived, one induced pluripotent stem cell-derived) were expanded to three passages. At the time of harvest for each passage, cell pellets were collected for nuclear magnetic resonance (NMR) and ultra-performance liquid chromatography mass spectrometry (MS), and media were collected for cytokine profiling. Harvested cells were also cryopreserved for assessing function using T-cell proliferation and IDO activity assays. Linear regression was performed on functional data against NMR, MS and cytokines to reduce the number of important features, and partial least squares regression (PLSR) was used to obtain predictive markers of T-cell suppression based on variable importance in projection scores.

RESULTS

Significant functional heterogeneity (in terms of T-cell suppression and IDO activity) was observed between the three MSC lines, as were donor-dependent differences based on passage. Omics characterization revealed distinct differences between cell lines using principal component analysis. Cell lines separated along principal component one based on tissue source (bone marrow-derived versus induced pluripotent stem cell-derived) for NMR, MS and cytokine profiles. PLSR modeling of important features predicted MSC functional capacity with NMR (R² = 0.86), MS (R² = 0.83), cytokines (R² = 0.70) and a combination of all features (R² = 0.88).

CONCLUSIONS

The work described here provides a platform for identifying markers for predicting MSC functional capacity using PLSR modeling that could be used as release criteria and guide future manufacturing strategies for MSCs and other cell therapies.

Metabolomics as a Tool to Investigate HIV/TB Co-Infection

The HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome) and tuberculosis (TB) pandemics are perpetuated by a significant global burden of HIV/TB co-infection. The synergy between HIV and Mycobacterium tuberculosis (Mtb) during co-infection of a host is well established. While this synergy is known to be driven by immunological deterioration, the metabolic mechanisms thereof remain poorly understood. Metabolomics has been applied to study various aspects of HIV and Mtb infection separately, yielding insights into infection- and treatment-induced metabolic adaptations experienced by the host. Despite the contributions that metabolomics has made to the field, this approach has not yet been systematically applied to characterize the HIV/TB co-infected state. Considering that limited HIV/TB co-infection metabolomics studies have been published to date, this review briefly summarizes what is known regarding the HIV/TB co-infection synergism from a conventional and metabolomics perspective. It then explores metabolomics as a tool for the improved characterization of HIV/TB co-infection in the context of previously published human-related HIV infection and TB investigations, respectively as well as for addressing the gaps in existing knowledge based on the similarities and deviating trends reported in these HIV infection and TB studies.

Assessment of XCMS Optimization Methods with Machine-Learning Performance

The metabolomics field is under rapid development. In particular, biomarker identification and pathway analysis are growing, as untargeted metabolomics is usable for discovery research. Frequently, new processing and statistical strategies are proposed to accommodate the increasing demand for robust and standardized data. One such algorithm is XCMS, which processes raw data into integrated peaks. Multiple studies have tried to assess the effect of optimizing XCMS parameters, but it is challenging to quantify the quality of the XCMS output. In this study, we investigate the effect of two automated optimization tools (Autotuner and isotopologue parameter optimization (IPO)) using the prediction power of machine learning as a proxy for the quality of the data set. We show that optimized parameters outperform default XCMS settings and that manually chosen parameters by liquid chromatography-mass spectrometry (LC-MS) experts remain the best. Finally, the machine-learning approach of quality assessment is proposed for future evaluations of newly developed optimization methods because its performance directly measures the retained signal upon preprocessing.

Defining NASH from a Multi-Omics Systems Biology Perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.