Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

Targeted and untargeted serum NMR metabolomics to reveal initial kidney disease in diabetes mellitus

Serum 1H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Plasma metabolomics identifies S-adenosylmethionine as a biomarker and potential therapeutic target for vascular aging in older adult males

Brachial-ankle pulse wave velocity (baPWV) is a noninvasive index of vascular aging. However, the metabolic profile underlying vascular aging has not yet been fully elucidated. The current study aimed to identify circulating markers of vascular aging as assessed by baPWV and to elucidate its mechanism from a metabolomic perspective in older adults. A total of 60 and 61 Chinese male participants aged ≥80 years were recruited to the metabolome and validation cohorts, respectively. The baPWV of participants was measured using an automatic waveform analyzer. Plasma metabolic profile was investigated using ultra-performance liquid chromatography coupled with triple quadrupole linear ion trap tandem mass spectrometry. Orthogonal partial least squares (OPLS) regression modeling established the association between metabolic profile and baPWV to determine important metabolites predictive of vascular aging. Additionally, an enzyme-linked immunosorbent assay was employed to validate the metabolites in plasma and culture media of vascular smooth muscle cells in vitro. OPLS modeling identified 14 and 22 metabolites inversely and positively associated with baPWV, respectively. These 36 biomarkers were significantly enriched in seven metabolite sets, especially in cysteine and methionine metabolism (p <0.05). Notably, among metabolites involved in cysteine and methionine metabolism, S-adenosylmethionine (SAM) level was inversely related to baPWV, with a significant correlation coefficient in the OPLS model (p <0.05). Furthermore, the relationship between SAM and vascular aging was reconfirmed in an independent cohort and at the cellular level in vitro. SAM was independently associated with baPWV after adjustments for clinical covariates (β = -0.448, p <0.001) in the validation cohort. In summary, plasma metabolomics identified an inverse correlation between SAM and baPWV in older males. SAM has the potential to be a novel biomarker and therapeutic target for vascular aging.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Cross-Modal Retrieval Between 13C NMR Spectra and Structures Based on Focused Libraries

Library matching by comparing carbon-13 nuclear magnetic resonance (13C NMR) spectra with spectral data in the library is a crucial method for compound identification. In our previous paper, we introduced a deep contrastive learning system called CReSS, which used a library that contained more structures. However, CReSS has two limitations: there were no unknown structures in the library, and a redundant library reduces the structure-elucidation accuracy. Herein, we replaced the oversize traditional libraries with focused libraries containing a small number of molecules. A previously generative model, CMGNet, was used to generate focused libraries for CReSS. The combined model achieved a Top-10 accuracy of 54.03% when tested on 6,471 13C NMR spectra. In comparison, CReSS with a random reference structure library achieved an accuracy of only 9.17%. Furthermore, to expand the advantages of the focused libraries, we proposed SAmpRNN, which is a recurrent neural network (RNN). With the large focused library amplified by SAmpRNN, the structure-identification accuracy of the model increased in 70.0% of the 30 random example cases. In general, cross-modal retrieval between 13C NMR spectra and structures based on focused libraries (CFLS) achieved high accuracy and provided more accurate candidate structures than traditional libraries for compound identification.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry

OBJECTIVE

This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC.

METHOD

This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models.

RESULTS

Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively.

CONCLUSION

The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification.

Metabolomics, Transcriptome and Single-Cell RNA Sequencing Analysis of the Metabolic Heterogeneity between Oral Cancer Stem Cells and Differentiated Cancer Cells

Understanding the distinct metabolic characteristics of cancer stem cells (CSC) may allow us to better cope with the clinical challenges associated with them. In this study, OSCC cell lines (CAL27 and HSC3) and multicellular tumor spheroid (MCTS) models were used to generate CSC-like cells. Quasi-targeted metabolomics and RNA sequencing were used to explore altered metabolites and metabolism-related genes. Pathview was used to display the metabolites and transcriptome data in a KEGG pathway. The single-cell RNA sequencing data of six patients with oral cancer were analyzed to characterize in vivo CSC metabolism. The results showed that 19 metabolites (phosphoethanolamine, carbamoylphosphate, etc.) were upregulated and 109 metabolites (2-aminooctanoic acid, 7-ketocholesterol, etc.) were downregulated in both MCTS cells. Integration pathway analysis revealed altered activity in energy production (glycolysis, citric cycle, fatty acid oxidation), macromolecular synthesis (purine/pyrimidine metabolism, glycerophospholipids metabolism) and redox control (glutathione metabolism). Single-cell RNA sequencing analysis confirmed altered glycolysis, glutathione and glycerophospholipid metabolism in in vivo CSC. We concluded that CSCs are metabolically inactive compared with differentiated cancer cells. Thus, oral CSCs may resist current metabolic-related drugs. Our result may be helpful in developing better therapeutic strategies against CSC.

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.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry

OBJECTIVE

This study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR).

METHOD

This study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25-40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models.

RESULTS

A total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively.

CONCLUSION

The urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Algal nutraceuticals: A perspective on metabolic diversity, current food applications, and prospects in the field of metabolomics

The current consumers' demand for food naturalness is urging the search for new functional foods of natural origin with enhanced health-promoting properties. In this sense, algae constitute an underexplored biological source of nutraceuticals that can be used to fortify food products. Both marine macroalgae (or seaweeds) and microalgae exhibit a myriad of chemical constituents with associated features as a result of their primary and secondary metabolism. Thus, primary metabolites, especially polysaccharides and phycobiliproteins, present interesting properties to improve the rheological and nutritional properties of food matrices, whereas secondary metabolites, such as polyphenols and xanthophylls, may provide interesting bioactivities, including antioxidant or cytotoxic effects. Due to the interest in algae as a source of nutraceuticals by the food and related industries, novel strategies should be undertaken to add value to their derived functional components. As a result, metabolomics is considered a high throughput technology to get insight into the full metabolic profile of biological samples, and it opens a wide perspective in the study of algae metabolism, whose knowledge is still little explored. This review focuses on algae metabolism and its applications in the food industry, paying attention to the promising metabolomic approaches to be developed aiming at the functional characterization of these organisms.

1H NMR-Based Metabolomics to Assess the Impact of Soil Type on the Chemical Composition of Nero d'Avola Red Wines

In this study, the soil effect on the micro-component composition of Nero d'Avola wines obtained from different locations was investigated through ¹H NMR-based metabolomics. Two different approaches were applied: the targeted (TA) and the non-targeted one (NTA). The former differentiated the wines by profiling (i.e., by identifying and quantifying) a number of different metabolites. The latter provided wine fingerprinting by processing the entire spectra with multivariate statistical analysis. NTA also allowed investigation of the hydrogen bond network inside wines via the analysis of ¹H NMR chemical shift dispersions. Results showed that the differences among wines were due not only to the concentrations of various analytes but also to the characteristics of the H-bond network where different solutes were involved. The H-bond network affects both gustatory and olfactory perceptions by modulating the way how solutes interact with the human sensorial receptors. Moreover, the aforementioned H-bond network is also related to the soil properties from which the grapes were taken. Therefore, the present study can be considered a good attempt to investigate terroir, i.e., the relationship between wine quality and soil characteristics.

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

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

Small molecule bio-signature in childhood intra-thoracic tuberculosis identified by metabolomics

The diagnosis of pediatric tuberculosis (TB) remains a major challenge, hence the evaluation of new tools for improved diagnostics is urgently required. We investigated the serum metabolic profile of children with culture-confirmed intra-thoracic TB (ITTB) (n = 23) and compared it with those of non-TB controls (NTCs) (n = 13) using proton NMR spectroscopy-based targeted and untargeted metabolomics approaches. In targeted metabolic profiling, five metabolites (histidine, glycerophosphocholine, creatine/phosphocreatine, acetate, and choline) differentiated TB children from NTCs. Additionally, seven discriminatory metabolites (N-α-acetyl-lysine, polyunsaturated fatty acids, phenylalanine, lysine, lipids, glutamate + glutamine, and dimethylglycine) were identified in untargeted metabolic profiling. The pathway analysis revealed alterations in six metabolic pathways. The altered metabolites were associated with impaired protein synthesis, hindered anti-inflammatory and cytoprotective mechanisms, abnormalities in energy generation processes and membrane metabolism, and deregulated fatty acid and lipid metabolisms in children with ITTB. The diagnostic significance of the classification models obtained from significantly distinguishing metabolites showed sensitivity, specificity, and area under the curve of 78.2%, 84.6%, and 0.86, respectively, in the targeted profiling and 92.3%, 100%, and 0.99, respectively, in the untargeted profiling. Our findings highlight detectable metabolic changes in childhood ITTB; however, further validation is warranted in a large cohort of the pediatric population.

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

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

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

Spatial distribution of metabolites in the retina and its relevance to studies of metabolic retinal disorders

INTRODUCTION

The primate retina has evolved regional specialisations for specific visual functions. The macula is specialised towards high acuity vision and is an area that contains an increased density of cone photoreceptors and signal processing neurons. Different regions in the retina display unique susceptibility to pathology, with many retinal diseases primarily affecting the macula.

OBJECTIVES

To better understand the properties of different retinal areas we studied the differential distribution of metabolites across the retina.

METHODS

We conducted an untargeted metabolomics analysis on full-thickness punches from three different regions (macula, temporal peri-macula and periphery) of healthy primate retina.

RESULTS

Nearly half of all metabolites identified showed differential abundance in at least one comparison between the three regions. Furthermore, mapping metabolomics results from macula-specific eye diseases onto our region-specific metabolite distributions revealed differential abundance defining systemic metabolic dysregulations that were region specific.

CONCLUSIONS

The unique metabolic phenotype of different retinal regions is likely due to the differential distribution of different cell types in these regions reflecting the specific metabolic requirements of each cell type. Our results may help to better understand the pathobiology of retinal diseases with region specificity.

Targeting mitochondrial impairment for the treatment of cardiovascular diseases: From hypertension to ischemia-reperfusion injury, searching for new pharmacological targets

Mitochondria and mitochondrial proteins represent a group of promising pharmacological target candidates in the search of new molecular targets and drugs to counteract the onset of hypertension and more in general cardiovascular diseases (CVDs). Indeed, several mitochondrial pathways result impaired in CVDs, showing ATP depletion and ROS production as common traits of cardiac tissue degeneration. Thus, targeting mitochondrial dysfunction in cardiomyocytes can represent a successful strategy to prevent heart failure. In this context, the identification of new pharmacological targets among mitochondrial proteins paves the way for the design of new selective drugs. Thanks to the advances in omics approaches, to a greater availability of mitochondrial crystallized protein structures and to the development of new computational approaches for protein 3D-modelling and drug design, it is now possible to investigate in detail impaired mitochondrial pathways in CVDs. Furthermore, it is possible to design new powerful drugs able to hit the selected pharmacological targets in a highly selective way to rescue mitochondrial dysfunction and prevent cardiac tissue degeneration. The role of mitochondrial dysfunction in the onset of CVDs appears increasingly evident, as reflected by the impairment of proteins involved in lipid peroxidation, mitochondrial dynamics, respiratory chain complexes, and membrane polarization maintenance in CVD patients. Conversely, little is known about proteins responsible for the cross-talk between mitochondria and cytoplasm in cardiomyocytes. Mitochondrial transporters of the SLC25A family, in particular, are responsible for the translocation of nucleotides (e.g., ATP), amino acids (e.g., aspartate, glutamate, ornithine), organic acids (e.g. malate and 2-oxoglutarate), and other cofactors (e.g., inorganic phosphate, NAD⁺, FAD, carnitine, CoA derivatives) between the mitochondrial and cytosolic compartments. Thus, mitochondrial transporters play a key role in the mitochondria-cytosol cross-talk by leading metabolic pathways such as the malate/aspartate shuttle, the carnitine shuttle, the ATP export from mitochondria, and the regulation of permeability transition pore opening. Since all these pathways are crucial for maintaining healthy cardiomyocytes, mitochondrial carriers emerge as an interesting class of new possible pharmacological targets for CVD treatments.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Teaser

Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

COVID-19 currently represents one of the major health challenges worldwide. Albeit its infectious character, with onset affectation mainly at the respiratory track, it is clear that the pathophysiology of COVID-19 has a systemic character, ultimately affecting many organs. This feature enables the possibility of investigating SARS-CoV-2 infection using multi-omic techniques, including metabolomic studies by chromatography coupled to mass spectrometry or by nuclear magnetic resonance (NMR) spectroscopy. Here we review the extensive literature on metabolomics in COVID-19, that unraveled many aspects of the disease including: a characteristic metabotipic signature associated to COVID-19, discrimination of patients according to severity, effect of drugs and vaccination treatments and the characterization of the natural history of the metabolic evolution associated to the disease, from the infection onset to full recovery or long-term and long sequelae of COVID.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

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.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques

Mass spectrometry (MS) has become the central technique that is extensively used for the analysis of molecular structures of unknown compounds in the gas phase. It manipulates the molecules by converting them into ions using various ionization sources. With high-resolution MS, accurate molecular weights (MW) of the intact molecular ions can be measured so that they can be assigned a molecular formula with high confidence. Furthermore, the application of tandem MS has enabled detailed structural characterization by breaking the intact molecular ions and protonated or deprotonated molecules into key fragment ions. This approach is not only used for the structural elucidation of small molecules (MW < 2000 Da), but also crucial biopolymers such as proteins and polypeptides; therefore, MS has been extensively used in multiomics studies for revealing the structures and functions of important biomolecules and their interactions with each other. The high sensitivity of MS has enabled the analysis of low-level analytes in complex matrices. It is also a versatile technique that can be coupled with separation techniques, including chromatography and ion mobility, and many other analytical instruments such as NMR. In this review, we aim to focus on the technical advances of MS-based structural elucidation methods over the past five years, and provide an overview of their applications in complex mixture analysis. We hope this review can be of interest for a wide range of audiences who may not have extensive experience in MS-based techniques.

On the Relevance of Soft Tissue Sarcomas Metabolic Landscape Mapping

Soft tissue sarcomas (STS) prognosis is disappointing, with current treatment strategies being based on a "fit for all" principle and not taking distinct sarcoma subtypes specificities and genetic/metabolic differences into consideration. The paucity of precision therapies in STS reflects the shortage of studies that seek to decipher the sarcomagenesis mechanisms. There is an urge to improve STS diagnosis precision, refine STS classification criteria, and increase the capability of identifying STS prognostic biomarkers. Single-omics and multi-omics studies may play a key role on decodifying sarcomagenesis. Metabolomics provides a singular insight, either as a single-omics approach or as part of a multi-omics strategy, into the metabolic adaptations that support sarcomagenesis. Although STS metabolome is scarcely characterized, untargeted and targeted metabolomics approaches employing different data acquisition methods such as mass spectrometry (MS), MS imaging, and nuclear magnetic resonance (NMR) spectroscopy provided important information, warranting further studies. New chromatographic, MS, NMR-based, and flow cytometry-based methods will offer opportunities to therapeutically target metabolic pathways and to monitorize the response to such metabolic targeting therapies. Here we provide a comprehensive review of STS omics applications, comprising a detailed analysis of studies focused on the metabolic landscape of these tumors.

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%.

NMR as powerful technology for non-invasively monitoring cell health and expansion during bioprocessing

Over the last decades, the success of advanced cell therapies and the increasing production volumes of vaccines, proteins or viral vectors have raised the need of robust cell-based manufacturing processes for ensuring product quality and satisfying GMP requirements. The cultivation process of cells needs to be highly controlled for improved productivity, reduced variability and optimized bioprocesses. Cell cultures can be easily monitored using different technologies, which could deliver direct or indirect assessment of the cells' viability. Among these techniques, Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technology which permits the evaluation and the identification of key endogenous metabolites. NMR can provide information on the cell metabolic pathways, on the bioprocesses and is also capable to quickly test for impurities. In this study, NMR was successfully used as a technology for monitoring cell viability and expansion in different supports for cell growth (including bioreactors), in order to predict the bioprocess output and for the early identification of key metabolites linked to cell starvation. This investigation will allow the timely control of culture conditions and favour the optimization of the bioprocesses. This article is protected by copyright. All rights reserved.

Effect of Different Processing Methods on the Chemical Constituents of Scrophulariae Radix as Revealed by 2D NMR-Based Metabolomics

Scrophulariae Radix (SR) is one of the oldest and most frequently used Chinese herbs for oriental medicine in China. Before clinical use, the SR should be processed using different methods after harvest, such as steaming, “sweating”, and traditional fire-drying. In order to investigate the difference in chemical constituents using different processing methods, the two-dimensional (2D) ¹H-¹³C heteronuclear single quantum correlation (¹H-¹³C HSQC)-based metabolomics approach was applied to extensively characterize the difference in the chemical components in the extracts of SR processed using different processing methods. In total, 20 compounds were identified as potential chemical markers that changed significantly with different steaming durations. Seven compounds can be used as potential chemical markers to differentiate processing by sweating, hot-air drying, and steaming for 4 h. These findings could elucidate the change of chemical constituents of the processed SR and provide a guide for the processing. In addition, our protocol may represent a general approach to characterizing chemical compounds of traditional Chinese medicine (TCM) and therefore might be considered as a promising approach to exploring the scientific basis of traditional processing of TCM.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Blood Metabolomics Analysis Identifies Differential Serum Metabolites in Elite and Sub-elite Swimmers

Objective: Metabolites in body fluids, such as lactate, glucose, and creatinine, have been measured by conventional methods to evaluate physical function and performance or athletic status. The objectives of the current study were to explore the novel metabolite biomarkers in professional swimmers with different competition levels using nuclear magnetic resonance (NMR) metabolomics, and try to establish a model to identify the athletic status or predict the competitive potential.

Methods: Serum samples were collected from 103 elite and 84 sub-elite level Chinese professional swimmers, and were profiled by NMR analysis.

Results: Out of the thirty-six serum metabolites profiled, ten were associated with the athletic status of swimmers (with p < 0.05). When compared with sub-elite swimmers, elite swimmers had higher levels of high-density lipoprotein (HDL), unsaturated fatty acid, lactic acid, and methanol. Elite swimmers had lower levels of isoleucine, 3-hydroxybutyric acid, acetoacetate, glutamine, glycine, and α-glucose. A model with four metabolites, including HDL, glutamine, methanol, and α-glucose, was established to predict athletic status by adjusting with different covariates. The area under the curve (AUC) of the best model was 0.904 (95% CI: 0.862-0.947), with a sensitivity and specificity of 75.5 and 90.2%, respectively.

Conclusion: We have identified ten metabolite biomarkers with differentially expressed levels between elite and sub-elite swimmers, the differences could result from genetic or sports level between the two cohorts. A model with four metabolites has successfully differentiated professional swimmers with different competitive levels.

Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets

Simple Summary

Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs.

Abstract

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Untargeted 1H-NMR Urine Metabolomic Analysis of Preterm Infants with Neonatal Sepsis

One of the most critical medical conditions occurring after preterm birth is neonatal sepsis, a systemic infection with high rates of morbidity and mortality, chiefly amongst neonates hospitalized in Neonatal Intensive Care Units (NICU). Neonatal sepsis is categorized as early-onset sepsis (EOS) and late-onset sepsis (LOS) regarding the time of the disease onset. The accurate early diagnosis or prognosis have hurdles to overcome, since there are not specific clinical signs or laboratory tests. Herein, a need for biomarkers presents, with the goals of aiding accurate medical treatment, reducing the clinical severity of symptoms and the hospitalization time. Through nuclear magnetic resonance (NMR) based metabolomics, we aim to investigate the urine metabolomic profile of septic neonates and reveal those metabolites which could be indicative for an initial discrimination between the diseased and the healthy ones. Multivariate and univariate statistical analysis between NMR spectroscopic data of urine samples from neonates that developed EOS, LOS, and a healthy control group revealed a discriminate metabolic profile of septic newborns. Gluconate, myo-inositol, betaine, taurine, lactose, glucose, creatinine and hippurate were the metabolites highlighted as significant in most comparisons.

A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics

Humans are exposed to an ever-increasing number of environmental toxicants, some of which have gradually been elucidated to be important risk factors for metabolic diseases, such as diabetes and obesity. These metabolism-sensitive diseases typically occur when key metabolic and signaling pathways were disrupted, which can be influenced by the exposure to contaminants such as endocrine disrupting chemicals (EDCs), along with genetic and lifestyle factors. This promotes the concept and research on environmental metabolism disrupting chemicals (MDCs). In addition, identifying endogenous biochemical markers of effect linked to disease states is becoming an important tool to screen the biological targets following environmental contaminant exposure, as well as to provide an overview of toxicity risk assessment. As such, the current review aims to contribute to the further understanding of exposome and human health and disease by characterizing environmental exposure and effect metabolic biomarkers. We summarized MDC-associated metabolic biomarkers in laboratory animal and human cohort studies using high throughput targeted and nontargeted metabolomics techniques. Contaminants including heavy metals and organohalogen compounds, especially EDCs, have been repetitively associated with metabolic disorders, whereas emerging contaminants such as perfluoroalkyl substances and microplastics have also been found to disrupt metabolism. In addition, we found major limitations in the effective identification of metabolic biomarkers especially in human studies, toxicological research on the mixed effect of environmental exposure has also been insufficient compared to the research on single chemicals. Thus, it is timely to call for research efforts dedicated to the study of combined effect and metabolic alterations for the better assessment of exposomic toxicology and health risks. Moreover, advanced computational and prediction tools, further validation of metabolic biomarkers, as well as systematic and integrative investigations are also needed in order to reliably identify novel biomarkers and elucidate toxicity mechanisms, and to further utilize exposome and metabolome profiling in public health and safety management.

Recent Developments in Metabolomics Studies of Endophytic Fungi

Endophytic fungi are microorganisms that colonize living plants' tissues without causing any harm. They are known as a natural source of bioactive metabolites with diverse pharmacological functions. Many structurally different chemical metabolites were isolated from endophytic fungi. Recently, the increasing trends in human health problems and diseases have escalated the search for bioactive metabolites from endophytic fungi. The conventional bioassay-guided study is known as laborious due to chemical complexity. Thus, metabolomics studies have attracted extensive research interest owing to their potential in dealing with a vast number of metabolites. Metabolomics coupled with advanced analytical tools provides a comprehensive insight into systems biology. Despite its wide scientific attention, endophytic fungi metabolomics are relatively unexploited. This review highlights the recent developments in metabolomics studies of endophytic fungi in obtaining the global metabolites picture.

Cross-Modal Retrieval between 13C NMR Spectra and Structures for Compound Identification Using Deep Contrastive Learning

Library matching using carbon-13 nuclear magnetic resonance (¹³C NMR) spectra has been a popular method adopted in compound identification systems. However, the usability of existing approaches has been restricted as enlarging a library containing both a chemical structure and spectrum is a costly and time-consuming process. Therefore, we propose a fundamentally different, novel approach to match ¹³C NMR spectra directly against a molecular structure library. We develop a cross-modal retrieval between spectrum and structure (CReSS) system using deep contrastive learning, which allows us to search a molecular structure library using the ¹³C NMR spectrum of a compound. In the test of searching 41,494 ¹³C NMR spectra against a reference structure library containing 10.4 million compounds, CReSS reached a recall@10 accuracy of 91.64% and a processing speed of 0.114 s per query spectrum. When further incorporating a filter with a molecular weight tolerance of 5 Da, CReSS achieved a new remarkable recall@10 of 98.39%. Furthermore, CReSS has potential in detecting scaffolds of novel structures and demonstrates great performance for the task of structural revision. CReSS is built and developed to bridge the gap between ¹³C NMR spectra and structures and could be generally applicable in compound identification.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement

"Omics" techniques (including genomics, transcriptomics, metabolomics, proteomics, and metagenomics) have been employed with huge success in the improvement of agricultural crops. As marine aquaculture of macroalgae expands globally, biologists are working to domesticate species of macroalgae by applying these techniques tested in agriculture to wild macroalgae species. Metabolomics has revealed metabolites and pathways that influence agriculturally relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in metagenomics have improved understanding of host-symbiont interactions and the potential for microbial organisms to improve crop outcomes. There is much room in the field of macroalgal biology for further research toward improvement of macroalgae cultivars in aquaculture using metabolomic and metagenomic analyses. To this end, this review discusses the application and necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance seaweed farming worldwide.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

An integrative approach based on GC-qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkers

INTRODUCTION

Globally, breast cancer (BC) is leading at the top of women's diseases and, as a multifactorial disease, there is the need for the development of new approaches to aid clinicians on monitoring BC treatments. In this sense, metabolomic studies have become an essential tool allowing the establishment of interdependency among metabolites in biological samples.

OBJECTIVE

The combination of nuclear magnetic resonance (NMR) and gas chromatography-quadrupole mass spectrometry (GC-qMS) based metabolomic analyses of urine and breast tissue samples from BC patients and cancer-free individuals was used.

METHODS

Multivariate statistical tools were used in order to obtain a panel of metabolites that could discriminate malignant from healthy status assisting in the diagnostic field. Urine samples (n = 30), cancer tissues (n = 30) were collected from BC patients, cancer-free tissues were resected outside the tumor margin from the same donors (n = 30) while cancer-free urine samples (n = 40) where obtained from healthy subjects and analysed by NMR and GC-qMS methodologies.

RESULTS

The orthogonal partial least square discriminant analysis model showed a clear separation between BC patients and cancer-free subjects for both classes of samples. Specifically, for urine samples, the goodness of fit (R²Y) and predictive ability (Q²) was 0.946 and 0.910, respectively, whereas for tissue was 0.888 and 0.813, revealing a good predictable accuracy. The discrimination efficiency and accuracy of tissue and urine metabolites was ascertained by receiver operating characteristic curve analysis that allowed the identification of metabolites with high sensitivity and specificity. The metabolomic pathway analysis identified several dysregulated pathways in BC, including those related with lactate, valine, aspartate and glutamine metabolism. Additionally, correlations between urine and tissue metabolites were investigated and five metabolites (e.g. acetone, 3-hexanone, 4-heptanone, 2-methyl-5-(methylthio)-furan and acetate) were found to be significant using a dual platform approach.

CONCLUSION

Overall, this study suggests that an improved metabolic profile combining NMR and GC-qMS may be useful to achieve more insights regarding the mechanisms underlying cancer.

Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease

The high morbidity and mortality of neuroinflammatory diseases drives significant interest in understanding the underlying mechanisms involved in the innate and adaptive immune response of the central nervous system (CNS). Diagnostic biomarkers are important to define treatable neuroinflammation. Metabolomics is a rapidly evolving research area offering novel insights into metabolic pathways, and elucidation of reliable metabolites as biomarkers for diseases. This review focuses on the emerging literature regarding the detection of neuroinflammation using cerebrospinal fluid (CSF) metabolomics in human cohort studies. Studies of classic neuroinflammatory disorders such as encephalitis, CNS infection and multiple sclerosis confirm the utility of CSF metabolomics. Additionally, studies in neurodegeneration and neuropsychiatry support the emerging potential of CSF metabolomics to detect neuroinflammation in common CNS diseases such as Alzheimer's disease and depression. We demonstrate metabolites in the tryptophan-kynurenine pathway, nitric oxide pathway, neopterin and major lipid species show moderately consistent ability to differentiate patients with neuroinflammation from controls. Integration of CSF metabolomics into clinical practice is warranted to improve recognition and treatment of neuroinflammation.