Analytical techniques for metabolomic studies: a review

Advances in the synthesis and application of silver nanoparticles for laser mass spectrometry: A mini-review

Silver nanoparticles are used in laser mass spectrometry to replace organic matrices. Thanks to their unique properties, they enable effective desorption/ionization of samples of various polarities and ionization abilities. This review presents new methods for the synthesis of monoisotopic silver nanoparticles and the use of targets coated with these nanoparticles for qualitative and quantitative analyses of various small-molecule compounds. Additionally, the results of progress in the application of AgNPs for metabolomics analyses were presented.

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

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

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

Integration of transcriptome, volatile and non-volatile metabolite profile reveals characteristic aroma formation in Toona sinensis

Toona sinensis is renowned for its unique aroma, but the formation mechanism remains unclear. In this study, volatile and non-volatile metabolites were combined with transcriptomes to investigate the potential mechanism of aroma formation in T. sinensis buds (TSB) and microgreens (TSM). Volatile sulfur compounds (VSCs) and terpenes were the main volatiles of TSM and TSB, respectively. 20 volatiles were identified as potential biomarkers, mainly VSCs and terpenes. In VSC biosynthesis pathways, cysteine was primarily synthesized from serine transformation in TSM. S-(trans-l-propenyl)-l-cysteine was likely to be the main precursor of VSC biosynthesis in T. sinensis. Higher expression of lachrymatory-factor synthase (LFS) consuming more precursor (1-propenyl sulfenic acid) in TSB led to reduced accumulation of VSCs. Isopentenyl diphosphate isomerase (IDI) and mevalonate diphosphate decarboxylase (MPDC) might play crucial roles in T. sinensis terpene biosynthesis. This study provided valuable insights into the formation of characteristic aromas in T. sinensis.

A host-microbial metabolite interaction gut-on-a-chip model of the adult human intestine demonstrates beneficial effects upon inulin treatment of gut microbiome

Background: The gut and its microbiome have a major impact on many aspects of health and are therefore also an attractive target for drug- or food-based therapies. Here, we report on the added value of combining a microbiome screening model, the i-screen, with fresh intestinal tissue explants in a microfluidic gut-on-a-chip model, the Intestinal Explant Barrier Chip (IEBC). Methods: Adult human gut microbiome (fecal pool of 6 healthy donors) was cultured anaerobically in the i-screen platform for 24 h, without and with exposure to 4 mg/mL inulin. The i-screen cell-free culture supernatant was subsequently applied to the luminal side of adult human colon tissue explants (n = 3 donors), fixed in the IEBC, for 24 h and effects were evaluated. Results: The supplementation of the media with inulin promoted the growth of Anaerostipes, Bifidobacterium, Blautia, and Collinsella in the in vitro i-screen, and triggered an elevated production of butyrate by the microbiota. Human colon tissue exposed to inulin-treated i-screen cell-free culture supernatant or control i-screen cell-free culture supernatant with added short-chain fatty acids (SCFAs) showed improved tissue barrier integrity measured by a 28.2%-34.2% reduction in FITC-dextran 4000 (FD4) leakage and 1.3 times lower transport of antipyrine. Furthermore, the release of pro-inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α was reduced under these circumstances. Gene expression profiles confirmed these findings, but showed more profound effects for inulin-treated supernatant compared to SCFA-supplemented supernatant. Conclusion: The combination of i-screen and IEBC facilitates the study of complex intestinal processes such as host-microbial metabolite interaction and gut health.

Omics to Unveil Diabetes Mellitus Pathogenesis and Biomarkers: Focus on Proteomics, Lipidomics, and Metabolomics

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, resulting from either body's inability to produce or effectively utilize insulin. There are several types of DM, but the most common are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM). DM is a complex disease and a global health concern, and the current clinical markers, such as fasting glucose, are helpful in the diagnosis of DM, but are not specific and sensitive, especially when measured on the beginning of the pathogenesis. Therefore, there is a pressing need to discover new early biomarkers that can provide an early diagnosis. Omics is an important field for the discovery of potential new biomarkers, especially proteomics, metabolomics, and lipidomics, where techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance are utilized to identify novel DM biomarkers and their pathways. In this review, we report papers that applied omics in the context of DM to identify new markers and their relationship with this disease, with the aim of elucidating new diagnostic techniques for the main types of DM.

Aransay A, Martin JE, Sasselli IR, Carracedo A, Liz-Marzán LM. SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors

The tumor microenvironment (TME) is a dynamic pseudoorgan that shapes the development and progression of cancers. It is a complex ecosystem shaped by interactions between tumor and stromal cells. Although the traditional focus has been on the paracrine communication mediated by protein messengers, recent attention has turned to the metabolic secretome in tumors. Metabolic enzymes, together with exchanged substrates and products, have emerged as potential biomarkers and therapeutic targets. However, traditional techniques for profiling secreted metabolites in complex cellular contexts are limited. Surface-enhanced Raman scattering (SERS) has emerged as a promising alternative due to its nontargeted nature and simplicity of operation. Although SERS has demonstrated its potential for detecting metabolites in biological settings, its application in deciphering metabolic interactions within multicellular systems like the TME remains underexplored. In this study, we introduce a SERS-based strategy to investigate the secreted purine metabolites of tumor cells lacking methylthioadenosine phosphorylase (MTAP), a common genetic event associated with poor prognosis in various cancers. Our SERS analysis reveals that MTAP-deficient cancer cells selectively produce methylthioadenosine (MTA), which is taken up and metabolized by fibroblasts. Fibroblasts exposed to MTA exhibit: i) molecular reprogramming compatible with cancer aggressiveness, ii) a significant production of purine derivatives that could be readily recycled by cancer cells, and iii) the capacity to secrete purine derivatives that induce macrophage polarization. Our study supports the potential of SERS for cancer metabolism research and reveals an unprecedented paracrine crosstalk that explains TME reprogramming in MTAP-deleted cancers.

Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients

Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.

Biological biomarkers of oral cancer

The oral squamous cell carcinoma (OSCC) 5 year survival rate of 41% has marginally improved in the last few years, with less than a 1% improvement per year from 2005 to 2017, with higher survival rates when detected at early stages. Based on histopathological grading of oral dysplasia, it is estimated that severe dysplasia has a malignant transformation rate of 7%-50%. Despite these numbers, oral dysplasia grading does not reliably predict its clinical behavior. Thus, more accurate markers predicting oral dysplasia progression to cancer would enable better targeting of these lesions for closer follow-up, especially in the early stages of the disease. In this context, molecular biomarkers derived from genetics, proteins, and metabolites play key roles in clinical oncology. These molecular signatures can help predict the likelihood of OSCC development and/or progression and have the potential to detect the disease at an early stage and, support treatment decision-making and predict treatment responsiveness. Also, identifying reliable biomarkers for OSCC detection that can be obtained non-invasively would enhance management of OSCC. This review will discuss biomarkers for OSCC that have emerged from different biological areas, including genomics, transcriptomics, proteomics, metabolomics, immunomics, and microbiomics.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

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.

Attempts to Understand Oral Mucositis in Head and Neck Cancer Patients through Omics Studies: A Narrative Review

Oral mucositis (OM) is a common and clinically impactful side effect of cytotoxic cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy with or without concomitant chemotherapy. The etiology and pathogenic mechanisms of OM are complex, multifaceted and elicit both direct and indirect damage to the mucosa. In this narrative review, we describe studies that use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) in attempts to elucidate the biological pathways associated with the development or severity of OM. Integrating different omics into multi-omics approaches carries the potential to discover links among host factors (genomics), host responses (transcriptomics, metabolomics), and the local environment (microbiomics).

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Current Status of Omics Studies Elucidating the Features of Reproductive Biology in Blood-Feeding Insects

Female insects belonging to the genera Anopheles, Aedes, Glossina, and Rhodnius account for the majority of global vector-borne disease mortality. In response to mating, these female insects undergo several molecular, physiological, and behavioral changes. Studying the dynamic post-mating molecular responses in these insects that transmit human diseases can lead to the identification of potential targets for the development of novel vector control methods. With the continued advancements in bioinformatics tools, we now have the capability to delve into various physiological processes in these insects. Here, we discuss the availability of multiple datasets describing the reproductive physiology of the common blood-feeding insects at the molecular level. Additionally, we compare the male-derived triggers transferred during mating to females, examining both shared and species-specific factors. These triggers initiate post-mating genetic responses in female vectors, affecting not only their reproductive success but also disease transmission.

Targeted and untargeted urinary metabolic profiling of bladder cancer

Bladder cancer (BC) is frequent cancer affecting the urinary tract and is one of the most prevalent malignancies worldwide. No biomarkers that can be used for effective monitoring of therapeutic interventions for this cancer have been identified to date. This study investigated polar metabolite profiles in urine samples from 100 BC patients and 100 normal controls (NCs) using nuclear magnetic resonance (NMR) and two methods of high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS). Five urine metabolites were identified and quantified using NMR spectroscopy to be potential indicators of bladder cancer. Twenty-five LDI-MS-detected compounds, predominantly peptides and lipids, distinguished urine samples from BC and NCs individuals. Level changes of three characteristic urine metabolites enabled BC tumor grades to be distinguished, and ten metabolites were reported to correlate with tumor stages. Receiver-Operating Characteristics analysis showed high predictive power for all three types of metabolomics data, with the area under the curve (AUC) values greater than 0.87. These findings suggest that metabolite markers identified in this study may be useful for the non-invasive detection and monitoring of bladder cancer stages and grades.

No skin off your back: the sampling and extraction of sebum for metabolomics

INTRODUCTION

Sebum-based metabolomics (a subset of "sebomics") is a developing field that involves the sampling, identification, and quantification of metabolites found in human sebum. Sebum is a lipid-rich oily substance secreted by the sebaceous glands onto the skin surface for skin homeostasis, lubrication, thermoregulation, and environmental protection. Interest in sebomics has grown over the last decade due to its potential for rapid analysis following non-invasive sampling for a range of clinical and environmental applications.

OBJECTIVES

To provide an overview of various sebum sampling techniques with their associated challenges. To evaluate applications of sebum for clinical research, drug monitoring, and human biomonitoring. To provide a commentary of the opportunities of using sebum as a diagnostic biofluid in the future.

METHODS

Bibliometric analyses of selected keywords regarding skin surface analysis using the Scopus search engine from 1960 to 2022 was performed on 12th January 2023. The published literature was compartmentalised based on what the work contributed to in the following areas: the understanding about sebum, its composition, the analytical technologies used, or the purpose of use of sebum. The findings were summarised in this review.

RESULTS

Historically, about 15 methods of sampling have been used for sebum collection. The sample preparation approaches vary depending on the analytes of interest and are summarised. The use of sebum is not limited to just skin diseases or drug monitoring but also demonstrated for other systemic disease. Most of the work carried out for untargeted analysis of metabolites associated with sebum has been in the recent two decades.

CONCLUSION

Sebum has a huge potential beyond skin research and understanding how one's physiological state affects or reflects on the skin metabolome via the sebaceous glands itself or by interactions with sebaceous secretion, will open doors for simpler biomonitoring. Sebum acts as a sink to environmental metabolites and has applications awaiting to be explored, such as biosecurity, cross-border migration, localised exposure to harmful substances, and high-throughput population screening. These applications will be possible with rapid advances in volatile headspace and lipidomics method development as well as the ability of the metabolomics community to annotate unknown species better. A key issue with skin surface analysis that remains unsolved is attributing the source of the metabolites found on the skin surface before meaningful biological interpretation.

Matrix Effects in GC–MS Profiling of Common Metabolites after Trimethylsilyl Derivatization

Metabolite profiling using gas chromatography coupled to mass spectrometry (GC–MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC–MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC–MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.

LC-IMS-HRMS for identification of biomarkers in untargeted metabolomics: The effects of pterostilbene and resveratrol consumption in liver steatosis, animal model

Untargeted metabolomics with the combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) was applied to investigate the impact of resveratrol and pterostilbene supplementation on the metabolic fingerprint of the Wistar rats liver with induced liver steatosis. RP-LC and HILIC in both ionisation modes were employed to analyse the liver samples (n = 40) from Wistar rats fed with a high-fat and high-fructose diet, supplemented or not with resveratrol and pterostilbene. After univariate and multivariate statistical analysis, 34 metabolites were highlighted in the different diets and elucidated. Despite the structural similarity, different alterations in liver metabolism were observed by the supplementations. Resveratrol treatment was characterised by the alteration in metabolism of 17 lysophospholipids, while pterostilbene affected some vitamins and derivatives, among others. IMS has demonstrated great potential in the elucidation process thanks to the additional structural descriptor the CCS (Ų), providing more confidence in the identification.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

Discovery of Food Intake Biomarkers Using Metabolomics

Due to the high impact of diet exposure on health, it is crucial the generation of robust data of regular dietary intake, hence improving the accuracy of dietary assessment. The metabolites derived from individual food or group of food have great potential to become biomarkers of food intake (BFIs) and provide more objective food consumption measurements.Herein, it is presented an untargeted metabolomic workflow for the discovery BFIs in blood and urine samples, from the study design to the biomarker identification. Samples are analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). A wide variety of compounds are covered by separate analyses of medium to nonpolar molecules and polar metabolites based on two LC separations as well as both positive and negative electrospray ionization. The main steps of data treatment of the comprehensive data sets and statistical analysis are described, as well as the principal considerations for the BFI identification.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Omics technologies to understand drug toxicity mechanisms

Drug side effects are an important study subject in pharmacology. Recent omics technologies provide a range of omics data and help to understand the biological mechanisms involved in drug effects. These modern technologies provide significant support to all biological disciplines, including drug toxicology. In this review, we provide an overview the use of omics applications to understand drug side effects at the molecular level. We discuss by available omics technologies, their possible uses, as well as their advantages and limitations.

Structural Investigation of Betulinic Acid Plasma Metabolites by Tandem Mass Spectrometry

Betulinic acid (BA) has been extensively studied in recent years mainly for its antiproliferative and antitumor effect in various types of cancers. Limited data are available regarding the pharmacokinetic profile of BA, particularly its metabolic transformation in vivo. In this study, we present the screening and structural investigations by ESI Orbitrap MS in the negative ion mode and CID MS/MS of phase I and phase II metabolites detected in mouse plasma after the intraperitoneal administration of a nanoemulsion containing BA in SKH 1 female mice. Obtained results indicate that the main phase I metabolic reactions that BA undergoes are monohydroxylation, dihydroxylation, oxidation and hydrogenation, while phase II reactions involved sulfation, glucuronidation and methylation. The fragmentation pathway for BA and its plasma metabolites were elucidated by sequencing of the precursor ions by CID MS MS experiments.

Sánchez B, León C, Nozal L, Mora-Rodríguez JM, Castro F, Crego AL, Díaz-Laviada I. Metabolic fingerprinting of chemotherapy-resistant prostate cancer stem cells. An untargeted metabolomic approach by liquid chromatography-mass spectrometry

Chemoresistance is one of the most important challenges in cancer therapy. The presence of cancer stem cells within the tumor may contribute to chemotherapy resistance since these cells express high levels of extrusion pumps and xenobiotic metabolizing enzymes that inactivate the therapeutic drug. Despite the recent advances in cancer cell metabolism adaptations, little is known about the metabolic adaptations of the cancer stem cells resistant to chemotherapy. In this study, we have undertaken an untargeted metabolomic analysis by liquid chromatography–high-resolution spectrometry combined with cytotoxicity assay, western blot, quantitative real-time polymerase chain reaction (qPCR), and fatty acid oxidation in a prostate cancer cell line resistant to the antiandrogen 2-hydroxiflutamide with features of cancer stem cells, compared to its parental androgen-sensitive cell line. Metabolic fingerprinting revealed 106 out of the 850 metabolites in ESI+ and 67 out of 446 in ESI- with significant differences between the sensitive and the resistant cell lines. Pathway analysis performed with the unequivocally identified metabolites, revealed changes in pathways involved in energy metabolism as well as posttranscriptional regulation. Validation by enzyme expression analysis indicated that the chemotherapy-resistant prostate cancer stem cells were metabolically dormant with decreased fatty acid oxidation, methionine metabolism and ADP-ribosylation. Our results shed light on the pathways underlying the entry of cancer cells into dormancy that might contribute to the mechanisms of drug resistance.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

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

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

Metabolomic profiling of intrauterine growth-restricted preterm infants: a matched case-control study

BACKGROUND

The biochemical variations occurring in intrauterine growth restriction (IUGR), when a fetus is unable to achieve its genetically determined potential, are not fully understood. The aim of this study is to compare the urinary metabolomic profile between IUGR and non-IUGR very preterm infants to investigate the biochemical adaptations of neonates affected by early-onset-restricted intrauterine growth.

METHODS

Neonates born <32 weeks of gestation admitted to neonatal intensive care unit (NICU) were enrolled in this prospective matched case-control study. IUGR was diagnosed by an obstetric ultra-sonographer and all relevant clinical data during NICU stay were captured. For each subject, a urine sample was collected within 48 h of life and underwent untargeted metabolomic analysis using mass spectrometry ultra-performance liquid chromatography. Data were analyzed using multivariate and univariate statistical analyses.

RESULTS

Among 83 enrolled infants, 15 IUGR neonates were matched with 19 non-IUGR controls. Untargeted metabolomic revealed evident clustering of IUGR neonates versus controls showing derangements of pathways related to tryptophan and histidine metabolism and aminoacyl-tRNA and steroid hormones biosynthesis.

CONCLUSIONS

Neonates with IUGR showed a distinctive urinary metabolic profile at birth. Although results are preliminary, metabolomics is proving to be a promising tool to explore biochemical pathways involved in this disease.

IMPACT

Very preterm infants with intrauterine growth restriction (IUGR) have a distinctive urinary metabolic profile at birth. Metabolism of glucocorticoids, sexual hormones biosynthesis, tryptophan-kynurenine, and methionine-cysteine pathways seem to operate differently in this sub-group of neonates. This is the first metabolomic study investigating adaptations exclusively in extremely and very preterm infants affected by early-onset IUGR. New knowledge on metabolic derangements in IUGR may pave the ways to further, more tailored research from a perspective of personalized medicine.

Untargeted metabolomic analysis of thoracic blood from badgers indicate changes linked to infection with bovine tuberculosis (Mycobacterium bovis): a pilot study

INTRODUCTION

Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB) in cattle, represents a major disease burden to UK cattle farming, with considerable costs associated with its control. The European badger (Meles meles) is a known wildlife reservoir for bTB and better knowledge of the epidemiology of bTB through testing wildlife is required for disease control. Current tests available for the diagnosis of bTB in badgers are limited by cost, processing time or sensitivities.

MATERIALS AND METHODS

We assessed the ability of flow infusion electrospray-high-resolution mass spectrometry (FIE-HRMS) to determine potential differences between infected and non-infected badgers based on thoracic blood samples obtained from badgers found dead in Wales. Thoracic blood samples were autoclaved for handling in a containment level 2 (CL2) hazard laboratory.

RESULTS

Here we show the major differences associated with with M. bovis infection were changes to folate, pyrimidine, histidine, glycerophospholipid and phosphonate metabolism.

CONCLUSIONS

Our studies have indicated differences in the metabolomic signature of badgers found dead in relation to their infection status, suggesting metabolomics could hold potential for developing novel diagnostics for bTB in badgers. As well as highlighting a potential way to handle samples containing a highly pathogenic agent at CL2 for metabolomics studies.

Cell Metabolomics Study on Synergistic anti-Hepatocellular Carcinoma Effect of Aidi Injection Combined with Doxorubicin

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the second most common cause of cancer deaths. This study aimed to explore the inhibitory effect and mechanism of Aidi injection (ADI) combined with doxorubicin (DOX) in HCC treatment. The drug concentrations in combined threapy was determined by investigating the effect of various concentrations of ADI and DOX on the viability of H22 cells. The combination index (CI) was also calculated. A metabolomic strategy based on ultrahigh performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) platform was established to analyze the metabolites. As a result, the CI values were less than 1, indicating that the combination of ADI and DOX exerted a synergistic effect on HCC treatment. The combination of 40‰ ADI and 1 μmol/L DOX had the strongest inhibitory effect and was used for subsequent metabolomic analysis. A total of 19 metabolic markers were obtained in metabolomic analysis, including amino acids (L-glutamic acid, L-arginine, and L-tyrosine), organic acids (succinic acid and citric acid), adenosine, and hypoxanthine , etc. Compared with the treatment using DOX or ADI alone, the combined therapy further regulated the levels of metabolic markers in HCC, which may be the reason for the synergistic effect. Seven metabolic pathways were significantly enriched, including phenylalanine, tyrosine and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, phenylalanine metabolism, arginine biosynthesis, tricarboxylic acid (TCA) cycle, and purine metabolism. These findings demonstrated that ADI combined with DOX can effectively inhibit the viability of H22 cells, which may exert a synergistic anti-tumor effect by balancing the metabolism of amino acids and energy-related substances.

Non-Invasive Identification of Sex in Cultured Bovine Embryos by UHPLC-MS/MS Metabolomics

INTRODUCTION

Different gene expression between male and female bovine embryos leads to metabolic differences.

OBJECTIVE

We used UHPLC-MS/MS to identify sex metabolite biomarkers in embryo culture medium (CM).

METHODS

Embryos were produced in vitro under highly variable conditions, i.e., fertilized with 7 bulls, two breeds, and cultured with BSA or BSA + serum until Day-6. On Day-6, embryos were cultured individually for 24 h. CM of Day-7 embryos (86 female and 81 male) was collected, and Day-6 and Day-7 embryonic stages recorded.

RESULTS

A study by sample subsets with fixed factors (culture, bull breed, and Day-6 and Day-7 stages) tentatively identified 31 differentially accumulated metabolites through 182 subsets. Day-6 and Day-7 stage together affected 13 and 11 metabolites respectively, while 19 metabolites were affected by one or another stage and/or day. Culture supplements and individual bull changed 19 and 15 metabolites, respectively. Single bull exerted the highest influence (20 metabolites with the significantly highest p values). Lipid (93 subsets; 11 metabolites) and amino acid (55 subsets; 13 metabolites) were the most relevant classes for sex identification.

CONCLUSIONS

Single biomarker led to inefficient sex diagnosis, while metabolite combinations accurately identified sex. Our study is a first in non-invasive sex identification in cattle by overcoming factors that induce metabolic variation.

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.

Predictive value of serum bile acids as metabolite biomarkers for liver cirrhosis: a systematic review and meta-analysis

INTRODUCTION

A large number of studies have explored the potential biomarkers for detecting liver cirrhosis in an early stage, yet consistent conclusions are still warranted.

OBJECTIVES

To conduct a review and a meta-analysis of the existing studies that test the serum level of bile acids in cirrhosis as the potential biomarkers to predict cirrhosis.

METHODS

Six databases had been searched from inception date to April 12, 2021. Screening and selection of the records were based on the inclusion criteria. The risk of bias was assessed with the Newcastle-Ottawa quality assessment scale (NOS). Mean difference (MD) and confidence intervals 95% (95% CI) were calculated by using the random effect model for the concentrations of bile acids in the meta-analysis, and I² statistic was used to measure studies heterogeneity. This study was registered on PROSPERO.

RESULTS

A total of 1583 records were identified and 31 studies with 2679 participants (1263 in the cirrhosis group, 1416 in the healthy control group) were included. The quality of included studies was generally high, with 25 studies (80.6%) rated over 7 stars. A total of 45 bile acids or their ratios in included studies were extracted. 36 increased in the cirrhosis group compared with those of the healthy controls by a qualitative summary, 5 decreased and 4 presented with mixing results. The result of meta-analysis among 12 studies showed that 13 bile acids increased, among which four primary conjugated bile acids showed the most significant elevation in the cirrhosis group: GCDCA (MD = 11.38 μmol/L, 95% CI 8.21-14.55, P < 0.0001), GCA (MD = 5.72 μmol/L, 95% CI 3.47-7.97, P < 0.0001), TCDCA (MD = 3.57 μmol/L, 95% CI 2.64-4.49, P < 0.0001) and TCA (MD = 2.14 μmol/L, 95% CI 1.56-2.72, P < 0.0001). No significant differences were found between the two groups in terms of DCA (MD = - 0.1 μmol/L, 95% CI - 0.18 to - 0.01, P < 0.0001) and LCA (MD = - 0.01 μmol/L, 95% CI - 0.01 to - 0.02, P < 0.0001), UDCA (MD = - 0.14 μmol/L, 95% CI - 0.04 to - 0.32, P < 0.0001), and TLCA (MD = 0 μmol/L, 95% CI 0-0.01, P < 0.0001). Subgroup analysis in patients with hepatitis B cirrhosis showed similar results.

CONCLUSION

Altered serum bile acids profile seems to be associated with cirrhosis. Some specific bile acids (GCA, GCDCA, TCA, and TCDCA) may increase with the development of cirrhosis, which possibly underlay their potential role as predictive biomarkers for cirrhosis. Yet this predictive value still needs further investigation and validation in larger prospective cohort studies.

Comprehensive Analysis of Metabolic Changes in Male Mice Exposed to Sodium Valproate Based on GC-MS Analysis

Purpose

Sodium valproate (VPA) is the most widely used broad-spectrum antiepileptic first-line drug in clinical practice and is effective against various types of epilepsy. However, VPA can induce severe cardiotoxicity, nephrotoxicity, hepatotoxicity, and neurotoxicity, which limits its use. Metabolomic studies of VPA-induced toxicity have focused primarily on changes in serum and urine metabolites but have not evaluated changes in major organs or tissues.

Methods

Central target tissues (intestine, lung, liver, hippocampus, cerebral cortex, inner ear, spleen, kidney, heart, and serum) were analyzed using gas chromatography mass spectrometry to comprehensively evaluate VPA toxicity in mouse models.

Results

Multivariate analyses, including orthogonal projections of the latent structure and Student’s t test, indicated that depending on the matrix used in the study (the intestine, lung, liver, hippocampus, cerebral cortex, inner ear, spleen, kidney, heart or serum) the number of metabolites differed, the lung being the poorest and the kidney the richest in number.

Conclusion

These metabolites were closely related and were found to participate in 12 key pathways related to amino acid, fatty acid, and energy metabolism, revealing that the toxic mechanism of VPA may involve oxidative stress, inflammation, amino acid metabolism, lipid metabolism, and energy disorder.

PeakBot: Machine learning based chromatographic peak picking

Motivation

Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task.

Results

A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.

Availability and implementation

The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Multifunctions and Future Prospects of Endophytes and Their Metabolites in Plant Disease Management

Endophytes represent a ubiquitous and magical world in plants. Almost all plant species studied by different researchers have been found to harbor one or more endophytes, which protect host plants from pathogen invasion and from adverse environmental conditions. They produce various metabolites that can directly inhibit the growth of pathogens and even promote the growth and development of the host plants. In this review, we focus on the biological control of plant diseases, aiming to elucidate the contribution and key roles of endophytes and their metabolites in this field with the latest research information. Metabolites synthesized by endophytes are part of plant disease management, and the application of endophyte metabolites to induce plant resistance is very promising. Furthermore, multi-omics should be more fully utilized in plant–microbe research, especially in mining novel bioactive metabolites. We believe that the utilization of endophytes and their metabolites for plant disease management is a meaningful and promising research direction that can lead to new breakthroughs in the development of more effective and ecosystem-friendly insecticides and fungicides in modern agriculture.

Speciomics as a concept involving chemical speciation and omics

The study of chemical speciation and the refinement and expansion of omics-based methods are both consolidated and highly active research fields. Although well established, such fields are extremely dynamic and are driven by the emergence of new strategies and improvements in instrumentation. In the case of omics-based studies, subareas including lipidomics, proteomics, metallomics, metabolomics and foodomics have emerged. Here, speciomics is being proposed as an "umbrella" term, that incorporates all of these subareas, to capture studies where the evaluation of chemical species is carried out using omics approaches. This paper contextualizes both speciomics and the speciome, and reviews omics applications used for species identification through examination of proteins, metalloproteins, metabolites, and nucleic acids. In addition, some implications from such studies and a perspective for future development of this area are provided. SIGNIFICANCE: The synergic effect between chemical speciation and omics is highlighted in this work, demonstrating an emerging area of research with a multitude of possibilities in terms of applications and further developments. This work not only defines and contextualizes speciomics and individual speciomes, but also demonstrates with some examples the great potential of this new interdisciplinary area of research.

Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.

Metabotyping the Welsh population of badgers based on thoracic fluid

INTRODUCTION

The European badger (Meles meles) is a known wildlife reservoir for bovine tuberculosis (bTB) and a better understanding of the epidemiology of bTB in this wildlife species is required for disease control in both wild and farmed animals. Flow infusion electrospray-high-resolution mass spectrometry (FIE-HRMS) may potentially identify novel metabolite biomarkers based on which new, rapid, and sensitive point of care tests for bTB infection could be developed.

OBJECTIVES

In this foundational study, we engaged on assessing the baseline metabolomic variation in the non-bTB infected badger population ("metabotyping") across Wales.

METHODS

FIE-HRMS was applied on thoracic fluid samples obtained by post-mortem of bTB negative badgers (n = 285) which were part of the Welsh Government 'All Wales Badger Found Dead' study.

RESULTS

Using principal component analysis and partial least squares-discriminant analyses, the major sources of variation were linked to sex, and to a much lesser extent age, as indicated by tooth wear. Within the female population, variation was seen between lactating and non-lactating individuals. No significant variation linked to the presence of bite wounds, obvious lymphatic lesions or geographical region of origin was observed.

CONCLUSION

Future metabolomic work when making comparisons between bTB infected and non-infected badger samples will only need be sex-matched and could focus on males only, to avoid lactation bias.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

Bacterial Metabolomics: Sample Preparation Methods

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important "omics" tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Salivary Metabolomics for Oral Squamous Cell Carcinoma Diagnosis: A Systematic Review

Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer in which the consumption of tobacco and alcohol is considered to be the main aetiological factor. Salivary metabolome profiling could identify novel biochemical pathways involved in the pathogenesis of various diseases. This systematic review was designed to answer the question “Are salivary metabolites reliable for the diagnosis of oral squamous cell carcinoma?”. Following the inclusion and exclusion criteria, nineteen studies were included (according to PRISMA statement guidelines). In all included studies, the diagnostic material was unstimulated whole saliva, whose metabolome changes were determined by different spectroscopic methods. At the metabolic level, OSCC patients differed significantly not only from healthy subjects but also from patients with oral leukoplakia, lichen planus or other oral potentially malignant disorders. Among the detected salivary metabolites, there were the indicators of the impaired metabolic pathways, such as choline metabolism, amino acid pathways, polyamine metabolism, urea cycle, creatine metabolism, glycolysis or glycerolipid metabolism. In conclusion, saliva contains many potential metabolites, which can be used reliably to early diagnose and monitor staging in patients with OSCC. However, further investigations are necessary to confirm these findings and to identify new salivary metabolic biomarkers.

Metabolomic Insights Into Endophyte-Derived Bioactive Compounds

Among the various plant-associated microbiota, endophytes (the microbial communities inhabiting plant endosphere without causing disease symptoms) exhibit the most intimate and specific association with host plants. Endophytic microbes influence various aspects of plant responses (such as increasing availability of nutrients, tolerance against biotic and abiotic stresses, etc.) by modulating the primary and secondary metabolism of the host. Besides, endophytic microbes produce a diverse array of bioactive compounds, which have potential applications in the pharmaceutical, food, and cosmetic industries. Further, there is sufficient evidence for endophyte-derived plant metabolites, which could be pursued as alternative sources of commercially important plant metabolites. The field of bioprospecting, the discovery of novel chemistries, and endophyte-mediated production of plant metabolites have witnessed a boom with the advent of omics technologies (especially metabolomics) in endophyte research. The high throughput study of small metabolites at a particular timepoint or tissue forms the core of metabolomics. Being downstream to transcriptome and proteome, the metabolome provides the most direct reflection of the phenotype of an organism. The contribution of plant and microbial metabolomics for answering fundamental questions of plant-endophyte interaction, such as the effect of endophyte inoculation on plant metabolome, composition of metabolites on the impact of environmental stressors (biotic and abiotic), etc., have also been discussed.

NMR Metabolomics Assessment of Osteogenic Differentiation of Adipose-Tissue-Derived Mesenchymal Stem Cells

This Article presents, for the first time to our knowledge, an untargeted nuclear magnetic resonance (NMR) metabolomic characterization of the polar intracellular metabolic adaptations of human adipose-derived mesenchymal stem cells during osteogenic differentiation. The use of mesenchymal stem cells (MSCs) for bone regeneration is a promising alternative to conventional bone grafts, and untargeted metabolomics may unveil novel metabolic information on the osteogenic differentiation of MSCs, allowing their behavior to be understood and monitored/guided toward effective therapies. Our results unveiled statistically relevant changes in the levels of just over 30 identified metabolites, illustrating a highly dynamic process with significant variations throughout the whole 21-day period of osteogenic differentiation, mainly involving amino acid metabolism and protein synthesis; energy metabolism and the roles of glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation; cell membrane metabolism; nucleotide metabolism (including the specific involvement of O-glycosylation intermediates and NAD⁺); and metabolic players in protective antioxidative mechanisms (such as glutathione and specific amino acids). Different metabolic stages are proposed and are supported by putative biochemical explanations for the metabolite changes observed. This work lays the groundwork for the use of untargeted NMR metabolomics to find potential metabolic markers of osteogenic differentiation efficacy.

Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils

Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL).

Activity-differential search for amyloid-β aggregation inhibitors using LC-MS combined with principal component analysis

Aggregation of amyloid β42 (Aβ42) is one of the hallmarks of Alzheimer's disease (AD). Inhibition of Aβ42 aggregation is thus a promising approach for AD therapy. Kampo medicine has been widely used to combat dementias such as AD. Crude drug known as Shoyaku is an ingredient of Kampo that could have potential as a natural source of novel drugs. However, given that a mixture of compounds, rather than singular compounds, could contribute to the biological functions of crude drug, there are very limited studies on the structure and mechanism of each constituent in crude drug which may have anti-Aβ42 aggregation properties. Herein we provide an efficient method, using LC-MS combined with principal component analysis (PCA), to search for activity-dependent compounds that inhibit Aβ42 aggregation from 46 crude drug extracts originating from 18 plants. Only 5 extracts (Kakou, Kayou, Gusetsu, Rensu, and Renbou) from lotus demonstrated differentially inhibitory activities depending on the part of the plant from which they are derived (e.g. petiole, leaf, root node, stamen, and receptacle, respectively). To compare the anti-aggregative properties of compounds of active crude drug with those of inactive crude drug, these extracts were subjected to LC-MS measurement, followed by PCA. From 12 candidate compounds identified from the analysis, glucuronized and glucosidized quercetin, as well as 6 flavonoids (datiscetin, kaempferol, morin, robinetin, quercetin, and myricitrin), including catechol or flatness moiety suppressed Aβ42 aggregation, whereas curcumol, a sesquiterpene, did not. In conclusion, this study offers a new activity-differential methodology to identify bioactive natural products in crude drugs that inhibit Aβ42 aggregation and that could be applied to future AD therapies.

Predictive value of liver cirrhosis using metabolite biomarkers of bile acid in the blood: A protocol for systematic review and meta-analysis

BACKGROUND

Previous studies have indicated that the changes of bile acids are associated with liver cirrhosis. The objective of our study is to perform a systematic review to explore the relationship between bile acids and the pathologic process of cirrhosis, and to find minimally invasive, accurate and reliable potential biomarkers for predicting cirrhosis.

METHODS

EMBASE, the Cochrane Library, PubMed, Web of Science, WanFang Data and Chinese National Knowledge Infrastructure (CNKI) will be searched, using the search strategy of liver cirrhosis, bile acids and metabolomic. The screening process will be conducted strictly based on inclusion and exclusion criteria. Clinical studies based on human including randomized controlled trial, cohort study and case control study will be included without restriction of time. Cochrane collaboration's tool for assessing risk of bias and Newcastle-Ottawa Scale (NOS) will be applied to assess the risk of bias to randomized controlled trial and observational study, respectively. The bile acids and their concentrate which are different between liver cirrhosis and control group will be the mainly outcome. A qualitative analysis will be performed to profile the trajectory change of bile acids, then the meta-analysis will be done for quantitative analysis.

RESULTS

The bile acids profile of liver cirrhosis that has potential predictive value for cirrhosis will be identified.

CONCLUSION

The conclusion of this systematic review will finding potential biomarkers for predicting cirrhosis.

ETHICS AND DISSEMINATION

This systematic review is based on published researches, so there is no ethical approval required. We intend to disseminate our findings in a peer-reviewed journal.