Metabolomics biotechnology, applications, and future trends: a systematic review

Effects of processing technology on tea quality analyzed using high-resolution mass spectrometry-based metabolomics

Fixation is a crucial step in green tea processing that can impact quality. In this study, we explored the differences in the chemical components of steamed and fried green teas made from the same batch of fresh tea leaves using different fixing methods. Results showed that concentrations of sucrose and free amino acids were significantly higher in steamed green tea. Abundances of 12 compounds including purine nucleoside, pyrimidine nucleoside derivatives, and catechins were higher in fried green tea, while 34 compounds such as amino acids and their derivatives, benzofurans and flavonoids were higher in steamed green tea. Thus, steaming retained more compounds associated with sweet and fresh tastes, such as free amino acids, while frying produced more compounds with bitter tastes, such as catechin. This might explain why steamed green tea is mellower than fried tea.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Untargeted Metabolomics Based on Ultra-High-Performance Liquid Chromatography Coupled with Quadrupole Orbitrap High-Resolution Mass Spectrometry for Differential Metabolite Analysis of Pinelliae Rhizoma and Its Adulterants

The present study investigates the chemical composition variances among Pinelliae Rhizoma, a widely used Chinese herbal medicine, and its common adulterants including Typhonium flagelliforme, Arisaema erubescens, and Pinellia pedatisecta. Utilizing the non-targeted metabolomics technique of employing UHPLC-Q-Orbitrap HRMS, this research aims to comprehensively delineate the metabolic profiles of Pinelliae Rhizoma and its adulterants. Multivariate statistical methods including PCA and OPLS-DA are employed for the identification of differential metabolites. Volcano plot analysis is utilized to discern upregulated and downregulated compounds. KEGG pathway analysis is conducted to elucidate the differences in metabolic pathways associated with these compounds, and significant pathway enrichment analysis is performed. A total of 769 compounds are identified through metabolomics analysis, with alkaloids being predominant, followed by lipids and lipid molecules. Significant differential metabolites were screened out based on VIP > 1 and p-value < 0.05 criteria, followed by KEGG enrichment analysis of these differential metabolites. Differential metabolites between Pinelliae Rhizoma and Typhonium flagelliforme, as well as between Pinelliae Rhizoma and Pinellia pedatisecta, are significantly enriched in the biosynthesis of amino acids and protein digestion and absorption pathways. Differential metabolites between Pinelliae Rhizoma and Arisaema erubescens are mainly enriched in tyrosine metabolism and phenylalanine metabolism pathways. These findings aim to provide valuable data support and theoretical references for further research on the pharmacological substances, resource development and utilization, and quality control of Pinelliae Rhizoma.

Study of the mechanism underlying the anti-inflammatory effect of Miao medicine comprising raw and processed Radix Wikstroemia indica using the "sweat soaking method"

ETHNOPHARMACOLOGICAL RELEVANCE

To explore the differences in the anti-inflammatory efficacy and mechanisms of the Miao medicine, both raw and after processing, using the "sweat soaking method" of Radix Wikstroemia indica (RWI).

AIM OF THE STUDY

The purpose of this study was to explore the differences in the anti-inflammatory efficacy and mechanism of action before and after the processing of the Miao medicine (RWI) using the "sweat soaking method."

MATERIALS AND METHODS

Network pharmacology technology was used to construct the "drug-component target-pathway-disease" network, and the main anti-inflammatory pathways of RWI were identified. Rat models of collagen-induced arthritis were established. The changes in body weight, swelling rate of the foot pad and ankle joint, arthritis index, thymus index, spleen index, pathological changes of the ankle joint, and the content of inflammatory cytokines (IL-1β, IL-2, IL-6, IL-10, TNF-α, and NO) were used as indices to evaluate the effect of RWI on rats with collagen-induced arthritis before and after its processing. Plasma and urine samples were collected from the rats, and the potential biomarkers of, and metabolic pathways underlying the anti-inflammatory effects of RWI before and after processing were identified using 1H-Nuclear magnetic resonance metabolomics combined with a multivariate statistical analysis.

RESULTS

Eleven key anti-inflammatory targets of IL6, IL-1β, TNF, ALB, AKT1, IFNG, INS, STAT3, EGFR, TP53, and SRC were identified by network pharmacology. The PI3K-Akt signaling pathway, steroid hormone biosynthesis, arginine biosynthesis, arginine and proline metabolism, tryptophan metabolism, and other pathways were mainly involved in these effects. Pharmacodynamic studies found that both raw and processed RWI products downregulated inflammatory factors in rats with collagen-induced arthritis and alleviated the pathological changes. A total of 41 potential pathways for the anti-inflammatory effects of raw RWI products and 36 potential pathways for the anti-inflammatory effects of processed RWI products were identified by plasma and urine metabolomics. The common pathways of network pharmacology and metabolomics were steroid hormone biosynthesis, arginine biosynthesis, arginine and proline metabolism, and tryptophan metabolism.

CONCLUSIONS

The anti-inflammatory effect of RWI was mainly related to the regulation of steroid hormone biosynthesis, arginine biosynthesis, arginine and proline metabolism, and tryptophan metabolism. Finally, the "sweat soaking method" enhanced the anti-inflammatory effect of RWI.

Advances in tumor microenvironment and underlying molecular mechanisms of bladder cancer: a systematic review

Bladder cancer is one of the most frequent malignant tumors of the urinary system. The prevalence of bladder cancer among men and women is roughly 5:2, and both its incidence and death have been rising steadily over the past few years. At the moment, metastasis and recurrence of advanced bladder cancer-which are believed to be connected to the malfunction of multigene and multilevel cell signaling network-remain the leading causes of bladder cancer-related death. The therapeutic treatment of bladder cancer will be greatly aided by the elucidation of these mechanisms. New concepts for the treatment of bladder cancer have been made possible by the advancement of research technologies and a number of new treatment options, including immunotherapy and targeted therapy. In this paper, we will extensively review the development of the tumor microenvironment and the possible molecular mechanisms of bladder cancer.

Targeted Metabolomic Biomarkers for Stroke Subtyping

BACKGROUND AND PURPOSE

Ischemic stroke is a heterogeneous disease with various etiologies. The current subtyping process is complicated, time-consuming, and costly. Metabolite-based biomarkers have the potential to improve classification and deliver optimal treatments. We here aimed to identify novel, targeted metabolomics-based biomarkers to discriminate between large-artery atherosclerosis (LAA) and cardioembolic (CE) stroke.

METHODS

We acquired serum samples and clinical data from a hospital-based acute stroke registry (ischemic stroke within 3 days from symptom onset). We included 346 participants (169 LAA, 147 CE, and 30 healthy older adults) and divided them into training and test sets. Targeted metabolomic analysis was performed using quantitative and quality-controlled liquid chromatography with tandem mass spectrometry. A multivariate regression model using metabolomic signatures was created that could independently distinguish between LAA and CE strokes.

RESULTS

The training set (n = 193) identified metabolomic signatures that were different in patients with LAA and CE strokes. Six metabolomic biomarkers, i.e., lysine, serine, threonine, kynurenine, putrescine, and lysophosphatidylcholine acyl C16:0, could discriminate between LAA and CE stroke after adjusting for sex, age, body mass index, stroke severity, and comorbidities. The enhanced diagnostic power of key metabolite combinations for discriminating between LAA and CE stroke was validated using the test set (n = 123).

CONCLUSIONS

We observed significant differences in metabolite profiles in LAA and CE strokes. Targeted metabolomics may provide enhanced diagnostic yield for stroke subtypes. The pathophysiological pathways of the identified metabolites should be explored in future studies.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Comparative putative metabolites profiling of Tachypleus gigas and Carcinoscorpius rotundicauda hemocytes stimulated with lipopolysaccharide

Horseshoe crabs are among the most studied invertebrates due to their unique, innate immune system and biological processes. The metabolomics study was conducted on lipopolysaccharide (LPS)-stimulated and non-stimulated hemocytes isolated from the Malaysian Tachypleus gigas and Carcinoscorpius rotundicauda. LC-TOF-MS, multivariate analyses, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) were included in this study to profile the metabolites. A total of 37 metabolites were identified to be differentially abundant and were selected based on VIP > 1. However, of the 37 putative metabolites, only 23 were found to be significant with ANOVA at p < 0.05. The metabolites were identified using several databases, and the literature review of the metabolites was reported in the manuscript. Thus, this study has provided further insights into the putative metabolites' presence in the hemocytes of horseshoe crabs that are stimulated and non-stimulated with LPS and their abundance in each species. Several putative metabolites showed they have medicinal values from previous studies.

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.

Microbiota-mediated metabolic perturbations in the gut and brain of mice after microplastic exposure

Microplastics (MPs), emerging environmental toxicants, have drawn attention because of their wide distribution in the environment. Exposure to MPs induces gut microbiota dysbiosis, intestinal barrier dysfunction, metabolic perturbations, and neurotoxicity in different rodents. However, the relationship between MPs, gut microbiota, and the metabolome of the gut and brain in mice remains unclear. In this study, female C57BL/6 mice were orally gavaged with vehicle, 200 nm MP, and 800 nm MP three times per week for four weeks. Cecal contents were collected for gut microbiota analysis using 16S rRNA gene sequencing. Intestinal and brain tissues from mice were used to determine metabolic profiles using liquid chromatography-mass spectrometry (LC-MS). The results showed that MP altered microbiota composition, accompanied by metabolic perturbations in the mouse gut and brain. Specifically, Firmicutes and Bacteroidetes were suggested to be important phyla for MP exposure, partially dominating further metabolite alterations. Simultaneously, MP-induced metabolic profiles were associated with energy homeostasis and bile acid, nucleotide, and carnitine metabolic pathways. The results of the mediation analysis further revealed an MP-microbiota-metabolite relationship. Our results indicate that MPs can induce gut dysbiosis and disturb metabolic dysfunction in the mouse brain and/or intestine. Integrative omics approaches have the potential to monitor MP-induced molecular responses in various organs and systematically elucidate the complex mechanisms of human health effects.

- Invited Review - Understanding the functionality of the rumen microbiota: searching for better opportunities for rumen microbial manipulation

Rumen microbiota play a central role in the digestive process of ruminants. Their remarkable ability to break down complex plant fibers and proteins, converting them into essential organic compounds that provide animals with energy and nutrition. Research on rumen microbiota not only contributes to improving animal production performance and enhancing feed utilization efficiency but also holds the potential to reduce methane emissions and environmental impact. Nevertheless, studies on rumen microbiota face numerous challenges, including complexity, difficulties in cultivation, and obstacles in functional analysis. This review provides an overview of microbial species involved in the degradation of macromolecules, the fermentation processes, and methane production in the rumen, all based on cultivation methods. Additionally, the review introduces the applications, advantages, and limitations of emerging omics technologies such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics, in investigating the functionality of rumen microbiota. Finally, the article offers a forward-looking perspective on the new horizons and technologies in the field of rumen microbiota functional research. These emerging technologies, with continuous refinement and mutual complementation, have deepened our understanding of rumen microbiota functionality, thereby enabling effective manipulation of the rumen microbial community.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium

BACKGROUND

The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies.

METHOD

We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure.

RESULT

Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle).

CONCLUSION

These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

Distinct Metabolic Profiles of Ocular Hypertensives in Response to Hypoxia

Glaucoma is a neurodegenerative disease that affects the retinal ganglion cells (RGCs). The main risk factor is elevated intraocular pressure (IOP), but the actual cause of the disease remains unknown. Emerging evidence indicates that metabolic dysfunction plays a central role. The aim of the current study was to determine and compare the effect of universal hypoxia on the metabolomic signature in plasma samples from healthy controls (n = 10), patients with normal-tension glaucoma (NTG, n = 10), and ocular hypertension (OHT, n = 10). By subjecting humans to universal hypoxia, we aim to mimic a state in which the mitochondria in the body are universally stressed. Participants were exposed to normobaric hypoxia for two hours, followed by a 30 min recovery period in normobaric normoxia. Blood samples were collected at baseline, during hypoxia, and in recovery. Plasma samples were analyzed using a non-targeted metabolomics approach based on liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). Multivariate analyses were conducted using principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), and univariate analysis using the Wilcoxon signed-rank test and false discovery rate (FDR) correction. Unique metabolites involved in fatty acid biosynthesis and ketone body metabolism were upregulated, while metabolites of the kynurenine pathway were downregulated in OHT patients exposed to universal hypoxia. Differential affection of metabolic pathways may explain why patients with OHT initially do not suffer or are more resilient from optic nerve degeneration. The metabolomes of NTG and OHT patients are regulated differently from control subjects and show dysregulation of metabolites important for energy production. These dysregulated processes may potentially contribute to the elevation of IOP and, ultimately, cell death of the RGCs.

Integrating metabolomics, 16S rRNA sequencing, network pharmacology, and metorigin to explore the mechanism of Cinnamomi Cortex in treating chronic atrophic gastritis rats

BACKGROUND

Cinnamomi cortex called as Rougui (RG) in Chinese was a widely used food-medicine homology. RG has the potential to treat chronic atrophic gastritis (CAG), a disease with widespread impact in the Chinese population.

PURPOSE

This study aimed to explore its mechanism against CAG based on amalgamated strategies.

METHODS

Network pharmacology was used to predict the potential effective components and the core targets of RG against CAG based on the comprehensive chemical characterization using UHPLC-Q/TOF MS (ultra high performance liquid chromatogramphy-quadrupole/time-of-flight mass spectrometry). The CAG animals model were further used to validate its pharmacodynamics, of which gut microbiota of caecal contents were analyzed by integrating metabolomics, 16S rRNA sequencing, Metorigin metabolite traceability analysis and molecular docking to explore its action mechanism.

RESULTS

Network pharmacology firstly predicted the efficacy of RG was attributed to four effective components and seven targets. Metabolomics of caecal contents in CAG rats revealed primary bile acid biosynthesis was its targeted metabolic pathway associated with the metabolism of gut microbiota coupled with Metorigin traceability analysis. 16S rRNA sequencing showed that RG treated CAG by regulating the imbalance of gut microbiota. Molecular docking further confirmed that the effective components of RG could intervene with potential targets, metorigin analysis pathway, and key enzymes of gut microbiota metabolic pathways.

CONCLUSION

Our results proved that RG exerted favorable effect on CAG. The four active ingredients (quercetin, kaempferol, oleic acid, and (-)-epicatechin) of RG were the key to exert drug effect, which could targeted the core target of CAG, primary bile acid biosynthesis and intestinal flora metabolic pathways.

Identification of Novel Biomarkers for Early Diagnosis of Atherosclerosis Using High-Resolution Metabolomics

Atherosclerosis (AS) is a metabolic disorder and the pre-stage of several cardiovascular diseases, including myocardial infarction, stroke, and angina pectoris. Early detection of AS can provide the opportunity for effective management and better clinical results, along with the prevention of further progression of the disease. In the current study, an untargeted and targeted metabolomic approach was used to identify possible metabolic signatures that have altered levels in AS patients. A total of 200 serum samples from individuals with AS and normal were analyzed via liquid chromatography-high-resolution mass spectrometry. Univariate and multivariate analysis approaches were used to identify differential metabolites. A group of metabolites associated with bile acids, amino acids, steroid hormones, and purine metabolism were identified that are capable of distinguishing AS-risk sera from normal. Further, the targeted metabolomics approach confirmed that six metabolites, namely taurocholic acid, cholic acid, cortisol, hypoxanthine, trimethylamine N-oxide (TMAO), and isoleucine, were found to be significantly upregulated, while the concentrations of glycoursodeoxycholic acid, glycocholic acid, testosterone, leucine, methionine, phenylalanine, tyrosine, and valine were found to be significantly downregulated in the AS-risk sera. The receiver operating characteristic curves of three metabolites, including cortisol, hypoxanthine, and isoleucine, showed high sensitivity and specificity. Taken together, these findings suggest cortisol, hypoxanthine, and isoleucine as novel biomarkers for the early and non-invasive detection of AS. Thus, this study provides new insights for further investigations into the prevention and management of AS.

A Multi-Omics Approach Revealed Common Dysregulated Pathways in Type One and Type Two Endometrial Cancers

Endometrial cancer (EC) is the most frequent gynecologic cancer in postmenopausal women. Pathogenetic mechanisms that are related to the onset and progression of the disease are largely still unknown. A multi-omics strategy can help identify altered pathways that could be targeted for improving therapeutical approaches. In this study we used a multi-omics approach on four EC cell lines for the identification of common dysregulated pathways in type 1 and 2 ECs. We analyzed proteomics and metabolomics of AN3CA, HEC1A, KLE and ISHIKAWA cell lines by mass spectrometry. The bioinformatic analysis identified 22 common pathways that are in common with both types of EC. In addition, we identified five proteins and 13 metabolites common to both types of EC. Western blotting analysis on 10 patients with type 1 and type 2 EC and 10 endometria samples confirmed the altered abundance of NPEPPS. Our multi-omics analysis identified dysregulated proteins and metabolites involved in EC tumor growth. Further studies are needed to understand the role of these molecules in EC. Our data can shed light on common pathways to better understand the mechanisms involved in the development and growth of EC, especially for the development of new therapies.

Discrimination of blood metabolomics profiles in neonates with idiopathic polyhydramnios

This study aimed to compare the blood metabolic status of neonates with idiopathic polyhydramnios (IPH) and those with normal amniotic fluid, and to explore the relationship between IPH and fetal health. Blood metabolites of 32 patients with IPH and 32 normal controls admitted to the Sixth Affiliated Hospital of Sun Yat-sen University between January 2017 and December 2022 were analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Orthogonal partial least squares discriminant analysis (OPLS-DA) and metabolite enrichment analyses were performed to identify the differential metabolites and metabolic pathways. There was a significant difference in the blood metabolism between newborns with IPH and those with normal amniotic fluid. Six discriminant metabolites were identified: glutamate, serine, asparagine, aspartic acid, homocysteine, and phenylalanine. Differential metabolites were mainly enriched in two pathways: aminoacyl-tRNA biosynthesis, and alanine, aspartate, and glutamate metabolism.

CONCLUSIONS

This study is the first to investigate metabolomic profiles in newborns with IPH and examine the correlation between IPH and fetal health. Differential metabolites and pathways may affect amino acid synthesis and the nervous system. Continuous attention to the development of the nervous system in children with IPH is necessary.

WHAT IS KNOWN

• There is an increased risk of adverse pregnancy outcomes with IPH, such as perinatal death, neonatal asphyxia, neonatal intensive care admission, cesarean section rates, and postpartum hemorrhage. • Children with a history of IPH have a higher proportion of defects than the general population, particularly central nervous system problems, neuromuscular disorders, and other malformations.

WHAT IS NEW

• In neonates with IPH, six differential metabolites were identified with significant differences and good AUC values using LC-MS/MS analysis: glutamic acid, serine, asparagine, aspartic acid, homocysteine, and phenylalanine, which were mainly enriched in two metabolic pathways: aminoacyl-tRNA biosynthesis and alanine, aspartate, and glutamate metabolism. • These differential metabolites and pathways may affect amino acid synthesis and development of the nervous system in neonates with IPH.

Neuronal progenitor cells-based metabolomics study reveals dysregulated lipid metabolism and identifies putative biomarkers for CLN6 disease

Neuronal ceroid lipofuscinosis 6 (CLN6) is a rare and fatal autosomal recessive disease primarily affecting the nervous system in children. It is caused by a pathogenic mutation in the CLN6 gene for which no therapy is available. Employing an untargeted metabolomics approach, we analyzed the metabolic changes in CLN6 subjects to see if this system could potentially yield biomarkers for diagnosis and monitoring disease progression. Neuronal-like cells were derived from human fibroblast lines from CLN6-affected subjects (n = 3) and controls (wild type, n = 3). These were used to assess the potential of a neuronal-like cell-based metabolomics approach to identify CLN6 distinctive and specific biomarkers. The most impacted metabolic profile is associated with sphingolipids, glycerophospholipids metabolism, and calcium signaling. Over 2700 spectral features were screened, and fifteen metabolites were identified that differed significantly between both groups, including the sphingolipids C16 GlcCer, C24 GlcCer, C24:1 GlcCer and glycerophospholipids PG 40:6 and PG 40:7. Of note, these fifteen metabolites were downregulated in the CLN6 disease group. This study is the first to analyze the metabolome of neuronal-like cells with a pathogenic mutation in the CLN6 gene and to provide insights into their metabolomic alterations. This could allow for the development of novel biomarkers for monitoring CLN6 disease.

Metabolomics and molecular networking approach for exploring the anti-diabetic activity of medicinal plants

Metabolomics and molecular networking approaches have expanded rapidly in the field of biological sciences and involve the systematic identification, visualization, and high-throughput characterization of bioactive metabolites in natural products using sophisticated mass spectrometry-based techniques. The popularity of natural products in pharmaceutical therapies has been influenced by medicinal plants with a long history of ethnobotany and a vast collection of bioactive compounds. Here, we selected four medicinal plants Cleistocalyx operculatus, Terminalia chebula, Ficus lacor, and Ficus semicordata, the biochemical characteristics of which remain unclear owing to the inherent complexity of their plant metabolites. In this study, we aimed to evaluate the potential of these aforementioned plant extracts in inhibiting the enzymatic activity of α-amylase and α-glucosidase, respectively, followed by the annotation of secondary metabolites. The methanol extract of Ficus semicordata exhibited the highest α-amylase inhibition with an IC50 of 46.8 ± 1.8 μg mL-1, whereas the water fraction of Terminalia chebula fruits demonstrated the most significant α-glucosidase inhibition with an IC50 value of 1.07 ± 0.01 μg mL-1. The metabolic profiling of plant extracts was analyzed through Liquid Chromatography-Mass Spectrometry (LC-HRMS) of the active fractions, resulting in the annotation of 32 secondary metabolites. Furthermore, we applied the Global Natural Product Social Molecular Networking (GNPS) platform to evaluate the MS/MS data of Terminalia chebula (bark), revealing that there were 205 and 160 individual ion species observed as nodes in the methanol and ethyl acetate fractions, respectively. Twenty-two metabolites were tentatively identified from the network map, of which 11 compounds were unidentified during manual annotation.

Anti-Inflammatory Activity of Lauraceae Plant Species and Prediction Models Based on Their Metabolomics Profiling Data

The Lauraceae is a botanical family known for its anti-inflammatory potential. However, several species have not yet been studied. Thus, this work aimed to screen the anti-inflammatory activity of this plant family and to build statistical prediction models. The methodology was based on the statistical analysis of high-resolution liquid chromatography coupled with mass spectrometry data and the ex vivo anti-inflammatory activity of plant extracts. The ex vivo results demonstrated significant anti-inflammatory activity for several of these plants for the first time. The sample data were applied to build anti-inflammatory activity prediction models, including the partial least square acquired, artificial neural network, and stochastic gradient descent, which showed adequate fitting and predictive performance. Key anti-inflammatory markers, such as aporphine and benzylisoquinoline alkaloids were annotated with confidence level 2. Additionally, the validated prediction models proved to be useful for predicting active extracts using metabolomics data and studying their most bioactive metabolites.

Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer

BACKGROUND

Anti-folate drug pemetrexed is a vital chemotherapy medication for non-small cell lung cancer (NSCLC). Its response varies widely and often develops resistance to the treatment. Therefore, it is urgent to identify biomarkers and establish models for drug efficacy evaluation and prediction for rational drug use.

METHODS

A total of 360 subjects were screened and 323 subjects were recruited. Using metabolomics in combination with machine learning methods, we are trying to select potential biomarkers to diagnose NSCLC and evaluate the efficacy of pemetrexed in treating NSCLC. Furtherly, we measured the concentration of eight metabolites in the tryptophan metabolism pathway in the validation set containing 201 subjects using a targeted metabolomics method with UPLC-MS/MS.

RESULTS

In the discovery set containing 122 subjects, the metabolic profile of healthy controls (H), newly diagnosed NSCLC patients (ND), patients who responded well to pemetrexed treatment (S) and pemetrexed-resistant patients (R) differed significantly on the PLS-DA scores plot. Pathway analysis showed that glycine, serine and threonine metabolism occurred in every two group comparisons. TCA cycle, pyruvate metabolism and glycerolipid metabolism are the most significantly changed pathways between ND and H group, pyruvate metabolism was the most altered pathway between S and ND group, and tryptophan metabolism was the most changed pathway between S and R group. We found Random forest method had the maximum area under the curve (AUC) and can be easily interpreted. The AUC is 0.981 for diagnosing patients with NSCLC and 0.954 for evaluating pemetrexed efficiency.

CONCLUSION

We compared eight mathematical models to evaluate pemetrexed efficiency for treating NSCLC. The Random forest model established with metabolic markers tryptophan, kynurenine and xanthurenic acidcan accurately diagnose NSCLC and evaluate the response of pemetrexed.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

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

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

The overshadowed role of electron ionization-mass spectrometry in analytical biotechnology

Target and untargeted analysis of several compounds are crucial methods in important areas such as omics sciences. Gas chromatography coupled to mass spectrometry (GC-MS) is widely used for volatile and thermally stable compounds. In this case, the electron ionization technique (EI) is preferable as it produces highly fragmented and reproducible spectra comparable to spectral libraries. However, only a fraction of target compounds is analyzable by GC without chemical derivatization. Therefore, liquid chromatography (LC) coupled with MS is the most used technique. Contrary to EI, electrospray ionization does not produce reproducible spectra. That is why researchers have been working on interfaces between LC and EI-MS to bridge the gap between those techniques. This short review will discuss advancements, applications, and perspectives on biotechnological analysis.

Authentication of milk thistle commercial products using UHPLC-QTOF-ESI + MS metabolomics and DNA metabarcoding

BACKGROUND

Milk thistle is one of the most popular hepatoprotectants, and is often sold in combination with other ingredients. Botanical supplements are known to be vulnerable to contamination and adulteration, and emerging technologies show promise to improve their quality control.

METHODS

Untargeted and semi-targeted metabolomics based on UHPLC-QTOF-ESI⁺MS techniques, UV spectrometry, and DNA metabarcoding using Illumina MiSeq were used to authenticate eighteen milk thistle botanical formulations (teas, capsules, tablets, emulsion).

RESULTS

Untargeted metabolomics separated 217 molecules and by multivariate analysis the discrimination between the different preparations was established. The semi-targeted metabolomics focused on 63 phytochemicals, mainly silymarin flavonolignans and flavonoids, that may be considered as putative biomarkers of authenticity. All formulations contained molecules from silymarin complexes at different levels. The quantitative evaluation of silybins was done using in parallel UV spectrometry and UHPLC-QTOF-ESI⁺MS and their correlations were compared. DNA metabarcoding detected milk thistle in eleven out of sixteen retained preparations, whereas two others had incomplete evidence of milk thistle despite metabolomics validating specific metabolites, e.g., silymarin complex, identified and quantified in all samples. Meanwhile, the DNA metabarcoding provided insights into the total species composition allowing the interpretation of the results in a broad context.

CONCLUSION

Our study emphasizes that combining spectroscopic, chromatographic, and genetic techniques bring complementary information to guarantee the quality of the botanical formulations.

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Metabolomics and Network Analyses Reveal Phenylalanine and Tyrosine as Signatures of Anthracycline-Induced Hepatotoxicity

The chemotherapy drug doxorubicin (DOX) is an anthracycline with over 30% incidence of liver injury in breast cancer patients, yet the mechanism of its hepatotoxicity remains unclear. To identify potential biomarkers for anthracycline-induced hepatotoxicity (AIH), we generated clinically-relevant mouse and rat models administered low-dose, long-term DOX. These models exhibited significant liver damage but no decline in cardiac function. Through untargeted metabolic profiling of the liver, we identified 27 differential metabolites in a mouse model and 28 in a rat model. We then constructed a metabolite-metabolite network for each animal model and computationally identified several potential metabolic markers, with particular emphasis on aromatic amino acids, including phenylalanine, tyrosine, and tryptophan. We further performed targeted metabolomics analysis on DOX-treated 4T1 breast cancer mice for external validation. We found significant (p < 0.001) reductions in hepatic levels of phenylalanine and tyrosine (but not tryptophan) following DOX treatment, which were strongly correlated with serum aminotransferases (ALT and AST) levels. In summary, the results of our study present compelling evidence supporting the use of phenylalanine and tyrosine as metabolic signatures of AIH.

Maximizing the value of twin studies in health and behaviour

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

A Novel Metabolic Score for Predicting the Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease

Background

Chronic obstructive pulmonary disease (COPD) has higher mortality when developing to acute exacerbation (AECOPD); hence, the early intervention of COPD is critical for preventing AECOPD. Exploring the serum metabolites associated with acute exacerbation in patients with COPD will contribute to the early intervention of COPD.

Methods

In the study, a non-targeted metabolomics strategy combined with multivariate statistical methods was performed to explore the metabolic profiling of COPD developing acute exacerbation, to screen the potential metabolites associated with AECOPD and to analyze the potential value of these metabolites in predicting the development of COPD.

Results

Serum lysine, glutamine, 3-hydroxybutyrate, pyruvate and glutamate levels were significantly higher, while 1-methylhistidine, isoleucine, choline, valine, alanine, histidine and leucine levels were significantly lower in AECOPD patients, compared with stable COPD patients after normalization based on the healthy controls. Moreover, eight metabolic pathways were significantly altered (P<0.05) in the serum of AECOPD patients compared with the stable COPD population, including purine metabolism, glutamine and glutamate metabolism, arginine biosynthesis, butyrate metabolism, ketone body synthesis and degradation, and linoleic acid metabolism. In addition, the correlation analysis between metabolites and AECOPD patients demonstrated that an M-score based on a weighted sum of concentrations of four metabolites including pyruvate, isoleucine, 1-methylhistidine and glutamine were significantly associated with the acute exacerbation of pulmonary ventilation function in COPD patients.

Conclusion

Altogether, the metabolite score based on a weighted sum of concentrations of four serum metabolites was associated with an increased risk of COPD developing acute exacerbation, which will provide a new insight for the understanding of COPD development.

Metabolomics Analysis Reveals Molecular Signatures of Metabolic Complexity in Children with Hypercholesterolemia

Despite the importance of hypercholesterolemia in children, it is overlooked, and there are currently few metabolomics-based approaches available to understand its molecular mechanisms. Children from a birth cohort had their cholesterol levels measured with the aim of identifying the metabolites for the molecular biological pathways of childhood hypercholesterolemia. One hundred and twenty-five children were enrolled and stratified into three groups according to cholesterol levels (acceptable, <170 mg/dL, n = 42; borderline, 170–200 mg/dL, n = 52; and high, >200 mg/dL, n = 31). Plasma metabolomic profiles were obtained by using 1H-nuclear magnetic resonance (NMR) spectroscopy, and partial least squares-discriminant analysis (PLS-DA) was applied using the MetaboAnalyst 5.0 platform. Metabolites significantly associated with different cholesterol statuses were identified, and random forest classifier models were used to rank the importance of these metabolites. Their associations with serum lipid profile and functional metabolic pathways related to hypercholesterolemia were also assessed. Cholesterol level was significantly positively correlated with LDL-C and Apo-B level, as well as HDL-C and Apo-A1 level separately, whereas HDL-C was negatively correlated with triglyceride level (p < 0.01). Eight metabolites including tyrosine, glutamic acid, ornithine, lysine, alanine, creatinine, oxoglutaric acid, and creatine were significantly associated with the different statuses of cholesterol level. Among them, glutamic acid and tyrosine had the highest importance for different cholesterol statuses using random forest regression models. Carbohydrate and amino acid metabolisms were significantly associated with different cholesterol statuses, with glutamic acid being involved in all amino acid metabolic pathways (FDR-adjusted p < 0.01). Hypercholesterolemia is a significant health concern among children, with up to 25% having high cholesterol levels. Glutamic acid and tyrosine are crucial amino acids in lipid metabolism, with glutamic-acid-related amino acid metabolism playing a significant role in regulating cholesterol levels.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics

A number of studies have assessed the impact of SARS-CoV-2 infection and COVID-19 severity on the metabolome of exhaled air, saliva, plasma, and urine to identify diagnostic and prognostic biomarkers. In spite of the richness of the literature, there is no consensus about the utility of metabolomic analyses for the management of COVID-19, calling for a critical assessment of the literature. We identified mass spectrometric metabolomic studies on specimens from SARS-CoV2-infected patients and subjected them to a cross-study comparison. We compared the clinical design, technical aspects, and statistical analyses of published studies with the purpose to identify the most relevant biomarkers. Several among the metabolites that are under- or overrepresented in the plasma from patients with COVID-19 may directly contribute to excessive inflammatory reactions and deficient immune control of SARS-CoV2, hence unraveling important mechanistic connections between whole-body metabolism and the course of the disease. Altogether, it appears that mass spectrometric approaches have a high potential for biomarker discovery, especially if they are subjected to methodological standardization.

Global warming and glyphosate toxicity (I): Adult zebrafish modelling with behavioural, immunohistochemical and metabolomic approaches

Global warming further increases the toxic threat of environmental pollutants on organisms. In order to reveal the dimensions of this threat more clearly, it is of great importance that the studies be carried out with temperature differences as close as possible to the temperature values that will represent the global climate projection. In our study, how the toxicity of glyphosate, which is widely used around the world, on zebrafish changes with temperature increases of 0.5° was investigated on behavioral and molecular basis. For this purpose, adult zebrafish were exposed to glyphosate at concentrations of 1 ppm and 5 ppm for 96 h in four environments with a temperature difference of 0.5° (28.5; 29.0; 29.5; 30.0 °C). At the end of the exposure, half of the zebrafish were sampled and remaining half were left for a 10-day recovery process. At the end of the trials, zebrafish were subjected to circadian rhythm and anxiety tests. In addition, histopathological, immunohistochemical and metabolome analyses were performed on brain tissues. As a result, it has been detected that anxiety and circadian rhythm were disrupted in parallel with the increased temperature and glyphosate concentration, and increased histopathological findings and 5-HT4R and GNAT2 immunopositivity in the brain. As a result of metabolome analysis, more than thirty annotated metabolites have been determined due to the synergistic effect of temperature increase and glyphosate exposure. As a conclusion, it was concluded that even a temperature increase of 0.5° caused an increasing effect of glyphosate toxicity in the zebrafish model.

Metabolomics for biomarker discovery in schistosomiasis: A systematic scoping review

Background

Metabolomic based approaches are essential tools in the discovery of unique biomarkers for infectious diseases via high-throughput global assessment of metabolites and metabolite pathway dysregulation. This in-turn allows the development of diagnostic tools and provision of therapeutics. In this review, we aimed to give an overview of metabolite biomarkers and metabolic pathway alterations during Schistosoma haematobium and Schistosoma mansoni infections.

Methods

We conducted the review by systematically searching electronic databases and grey literature to identify relevant metabolomics studies on schistosomiasis. Arksey and O’Malley methodology for conducting systematic scoping reviews was applied. A narrative summary of results was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping review guidelines.

Results

Twelve articles included in the review identified 127 metabolites, whose concentrations were considerably altered during S. mansoni and S. haematobium infections. The metabolites were assigned to metabolic pathways involved in energy (34.6%), gut microbial (11.0%), amino acid (25.2%), nucleic acids (6.3%), immune proteins (8.7%) hormones (2.4%) and structural proteins/lipids (11.8%). Energy related metabolic pathways were the most affected during schistosome infections with metabolites such as succinate, citrate, aconitate and fumarate of the tricarbocylic acid cycle being significantly altered in organ, serum and plasma samples. Amino acid metabolism was also impacted during schistosome infections as phenylacetylglycine, alanine, taurine, 2-oxoisocaproate and 2-oxoisovalerate emerged as potent biomarkers. Elevated structural proteins such as actin, collagen and keratin concentrations were identified as biomarkers of liver fibrosis, a common pathological feature in chronic schistosomiasis infections. Hippurate was a major metabolite biomarker in the gut microbial related pathway.

Conclusions

The analysis of the literature revealed that energy related metabolic pathways are considerably altered during S. mansoni and S. haematobium infections. Therefore, their metabolites may provide biomarkers for diagnosis and prognosis in addition to providing therapeutics for parasitic infections. This scoping review has identified a need to replicate more schistosomiasis metabolomic studies in humans to complement animal-model based studies.

The Impact Evaluation of Acid Mine Drainage on Zebrafish (Danio rerio) and Water Fleas (Daphnia magna) in the Vicinity of the Geum River Basin in Korea

Heavy metals, such as copper, lead, and cadmium, carried by acid mine drainage are pollutants of the aquatic ecosystem, posing a significant health risk to the water resource for humans. Environmental technologies to reduce metal contamination are applied for post-mining prevention and improvement. Despite detailed pollution management, water contaminated by heavy metals still flows into the natural water system. This study investigated the impact of drainage discharged from abandoned mines near the major river in South Korea on aquatic organisms. The toxicity of the field water showed a more significant effect than observed through the experiment for each heavy-metal concentration. Various toxic substances coexisted in the field water around the mine, such that the overall toxic intensity was high even when the concentration of each heavy metal was low. As a result, the inhibition of activity of aquatic organisms was observed at low individual concentrations, and further investigation on the effect of long-term exposure to trace amounts of heavy metals is required.

Metabolomic and elemental profiling of blood serum in bladder cancer

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

Extracellular Vesicles from Animal Milk: Great Potentialities and Critical Issues

Other than representing the main source of nutrition for newborn mammals, milk delivers a sophisticated signaling system from mother to child that promotes postnatal health. The bioactive components transferred through the milk intake are important for the development of the newborn immune system and include oligosaccharides, lactoferrin, lysozyme, α-La, and immunoglobulins. In the last 15 years, a pivotal role in this mother-to-child exchange has been attributed to extracellular vesicles (EVs). EVs are micro- and nanosized structures enclosed in a phospholipidic double-layer membrane that are produced by all cell types and released in the extracellular environment, reaching both close and distant cells. EVs mediate the intercellular cross-talk from the producing to the receiving cell through the transfer of molecules contained within them such as proteins, antigens, lipids, metabolites, RNAs, and DNA fragments. The complex cargo can induce a wide range of functional modulations in the recipient cell (i.e., anti-inflammatory, immunomodulating, angiogenetic, and pro-regenerative modulations) depending on the type of producing cells and the stimuli that these cells receive. EVs can be recovered from every biological fluid, including blood, urine, bronchoalveolar lavage fluid, saliva, bile, and milk, which is one of the most promising scalable vesicle sources. This review aimed to present the state-of-the-art of animal-milk-derived EV (mEV) studies due to the exponential growth of this field. A focus on the beneficial potentialities for human health and the issues of studying vesicles from milk, particularly for the analytical methodologies applied, is reported.

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.

Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer

Bladder cancer (BC) is a common urological cancer of high mortality and recurrence rates. Currently, cystoscopy is performed as standard examination for the diagnosis and subsequent monitoring for recurrence of the patients. Frequent expensive and invasive procedures may deterrent patients from regular follow-up screening, therefore it is important to look for new non-invasive methods to aid in the detection of recurrent and/or primary BC. In this study, ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry was employed for non-targeted metabolomic profiling of 200 human serum samples to identify biochemical signatures that differentiate BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation revealed twenty-seven metabolites that differentiate between BC patients from NCs. Abundances of these metabolites displayed statistically significant differences in two independent training and validation sets. Twenty-three serum metabolites were also found to be distinguishing between low- and high-grade of BC patients and controls. Thirty-seven serum metabolites were found to differentiate between different stages of BC. The results suggest that measurement of serum metabolites may provide more facile and less invasive diagnostic methodology for detection of bladder cancer and recurrent disease management.

Changes in serum metabolomics in idiopathic pulmonary fibrosis and effect of approved antifibrotic medication

Idiopathic pulmonary fibrosis (IPF) is a progressive disease with significant mortality and morbidity. Approval of antifibrotic therapy has ameliorated disease progression, but therapy response is heterogeneous and to date, adequate biomarkers predicting therapy response are lacking. In recent years metabolomic technology has improved and is broadly applied in cancer research thus enabling its use in other fields. Recently both aberrant metabolic and lipidomic pathways have been described to influence profibrotic responses. We thus aimed to characterize the metabolomic and lipidomic changes between IPF and healthy volunteers (HV) and analyze metabolomic changes following treatment with nintedanib and pirfenidone. We collected serial serum samples from two IPF cohorts from Germany (n = 122) and Spain (n = 21) and additionally age-matched healthy volunteers (n = 16). Metabolomic analysis of 630 metabolites covering 14 small molecule and 12 different lipid classes was carried out using flow injection analysis tandem mass spectrometry for lipids and liquid chromatography tandem mass spectrometry for small molecules. Levels were correlated with survival and disease severity. We identified 109 deregulated analytes in IPF compared to HV in cohort 1 and 112 deregulated analytes in cohort 2. Metabolites which were up-regulated in both cohorts were mainly triglycerides while the main class of down-regulated metabolites were phosphatidylcholines. Only a minority of de-regulated analytes were small molecules. Triglyceride subclasses were inversely correlated with baseline disease severity (GAP-score) and a clinical compound endpoint of lung function decline or death. No changes in the metabolic profiles were observed following treatment with pirfenidone. Nintedanib treatment induced up-regulation of triglycerides and phosphatidylcholines. Patients in whom an increase in these metabolites was observed showed a trend towards better survival using the 2-years composite endpoint (HR 2.46, p = 0.06). In conclusion, we report major changes in metabolites in two independent cohorts testing a large number of patients. Specific lipidic metabolite signatures may serve as biomarkers for disease progression or favorable treatment response to nintedanib.

Urinary metabolic modulation in human participants residing in Siachen: a 1H NMR metabolomics approach

The main physiological challenge in high altitude environment is hypoxia which affects the aerobic metabolism reducing the energy supply. These changes may further progress toward extreme environment-related diseases. These are further reflected in changes in small molecular weight metabolites and metabolic pathways. In the present study, metabolic changes due to chronic environmental hypoxia were assessed using 1H NMR metabolomics by analysing the urinary metabolic profile of 70 people at sea level and 40 people at Siachen camp (3700 m) for 1 year. Multivariate statistical analysis was carried out, and PLSDA detected 15 metabolites based on VIP score > 1. ROC analysis detected cis-aconitate, Nicotinamide Mononucleotide, Tyrosine, Choline and Creatinine metabolites with a high range of sensitivity and specificity. Pathway analysis revealed 16 pathways impact > 0.05, and phenylalanine tyrosine and tryptophan biosynthesis was the most prominent altered pathway indicating metabolic remodelling to meet the energy requirements. TCA cycle, Glycine serine and Threonine metabolism, Glutathione metabolism and Cysteine alterations were other metabolic pathways affected during long-term high-altitude hypoxia exposure. Present findings will help unlock a new dimension for the potential application of NMR metabolomics to address extreme environment-related health problems, early detection and developing strategies to combat high altitude hypoxia.

Metabolomics Analysis and Antioxidant Potential of Endophytic Diaporthe fraxini ED2 Grown in Different Culture Media

Endophytic fungi are a promising source of bioactive metabolites with a wide range of pharmacological activities. In the present study, MS-based metabolomics was conducted to study the metabolomes variations of endophytic Diaporthe fraxini ED2 grown in different culture media. Total phenolic content (TPC), total flavonoid content (TFC), 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, 2,2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), and ferric reducing antioxidant power (FRAP) assays were conducted to assess the antioxidant potential of the fungal extracts. Multivariate data analysis (MVDA) was employed in data analysis and interpretation to elucidate the complex metabolite profile. The supplemented culture medium of D. fraxini fungal extract stimulated the production of metabolites not occurring in the normal culture medium. Antioxidant activity studies revealed the potential of supplemented cultured fungal extract of D. fraxini as a source of antioxidants. The present findings highlight that fungal culture medium supplementation is an effective approach to unravelling the hidden metabolome in plant-associated fungal diversity.

RawHummus: an R Shiny app for automated raw data quality control in metabolomics

MOTIVATION

Robust and reproducible data is essential to ensure high-quality analytical results and is particularly important for large-scale metabolomics studies where detector sensitivity drifts, retention time and mass accuracy shifts frequently occur. Therefore, raw data need to be inspected before data processing to detect measurement bias and verify system consistency.

RESULTS

Here, we present RawHummus, an R Shiny app for an automated raw data quality control (QC) in metabolomics studies. It produces a comprehensive QC report, which contains interactive plots and tables, summary statistics and detailed explanations. The versatility and limitations of RawHummus are tested with 13 metabolomics/lipidomics datasets and 1 proteomics dataset obtained from 5 different liquid chromatography mass spectrometry platforms.

AVAILABILITY AND IMPLEMENTATION

RawHummus is released on CRAN repository (https://cran.r-project.org/web/packages/RawHummus), with source code being available on GitHub (https://github.com/YonghuiDong/RawHummus). The web application can be executed locally from the R console using the command 'runGui()'. Alternatively, it can be freely accessed at https://bcdd.shinyapps.io/RawHummus/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Therapeutic Approaches to Amyotrophic Lateral Sclerosis from the Lab to the Clinic

Amyotrophic Lateral Sclerosis (ALS) is a terminal neuro-degenerative disorder that is clinically recognized as a gradual degeneration of the upper and lower motor neurons, with an average duration of 3 to 5 years from initiation of symptoms to death. The mechanisms underlying the pathogenesis and progression of the disease are multifactorial. Therefore, to find effective treatments, it is necessary to understand this heterogeneity underlying the progression of ALS. Recent developments in gene therapy have opened a new avenue to treat this condition, especially for the characterized genetic types. Gene therapy methods have been studied in a variety of pre-clinical settings and clinical trials, and they may be a promising path for developing an effective and safe ALS cure. A growing body of evidence demonstrates abnormalities in energy metabolism at the cellular and whole-body level in animal models and in people living with ALS. The use and incorporation of high-throughput "omics" methods has radically transformed our thought about ALS, strengthening our understanding of the disease's dynamic molecular architecture, differentiating distinct patient subtypes, and creating a reasonable basis for the identification of biomarkers and novel individualised treatments. Future clinical and laboratory trials would also focus on the diverse relationships between metabolism and ALS to address the issue of whether targeting deficient metabolism in ALS is an effective way to change disease progression. In this review, we focus on the detailed pathogenesis of ALS and highlight principal genes, i.e., SOD1, TDP-43, C9orf72, and FUS, targeted therapeutic approaches of ALS. An attempt is made to provide up-to-date information on clinical outcomes, including various biomarkers which are thought to be important players in early ALS detection.