Metabolomics

Navigating liver health with metabolomics: A comprehensive review

Non-alcoholic fatty liver disease (NAFLD) is the main cause of chronic liver disease worldwide, affecting one-fourth of the world's population. With more than half of the world's population, the Asia-Pacific region contributed 62.6 % of liver-related fatal incidents in 2015. Currently, liver imaging techniques such as computed tomography (CT), nuclear magnetic resonance (NMR) spectroscopy, and ultrasound are non-invasive imaging methods to diagnose the disease. A liver biopsy is the gold standard test for establishing the definite diagnosis of non-alcoholic steatohepatitis (NASH). However, there are still significant problems with sample variability and the procedure's invasiveness. Numerous studies have indicated various non-invasive biomarkers for both fibrosis and steatosis to counter the invasiveness of diagnostic procedures. Metabolomics could be a promising method for detecting early liver diseases, investigating pathophysiology, and developing drugs. Metabolomics, when utilized with other omics technologies, can result in a deeper understanding of biological systems. Metabolomics has emerged as a prominent research topic, offering extensive opportunities to investigate biomarkers for liver diseases that are both sensitive and specific. In this review, we have described the recent studies involving the use of a metabolomics approach in the diagnosis of liver diseases, which would be beneficial for the early detection and treatment of liver diseases.

(Pre)Clinical Metabolomics Analysis

Metabolomics is the scientific field with the eager goal to comprehensively analyze the entirety of all small molecules of a biological system, i.e., the metabolome. Over the last few years, metabolomics has matured to become an analytical cornerstone of life science research across diverse fields, from fundamental biochemical applications to preclinical studies, including biomarker discovery and drug development. In this chapter, we provide an introduction to (pre)clinical metabolomics. We define key metabolomics aspects and provide the basis to thoroughly understand the relevance of this field in a biological and clinical context. We present and explain state-of-the-art analytical technologies devoted to metabolomic analysis as well as emerging technologies, discussing both strengths and weaknesses. Given the ever-increasing demand for handling complex datasets, the role of bioinformatics approaches in the context of metabolomic analysis is also illustrated.

Plasma metabolomic signatures after oral administration of ritonavir in COVID-19 treatment via chemometrics-assisted UPLC/Q-TOF/MS/MS

Understanding the pharmacodynamics of ritonavir through metabolomics offers insights into its side effects and helps in the development of safer therapies. This study aimed to investigate the effects of ritonavir treatment on the metabolic profiles of rabbits via a metabolomics approach, with the objective of elucidating its impact on various biochemical pathways and identifying relevant biomarkers. The rabbits were divided into control and ritonavir-treated groups, and their plasma samples were analyzed via ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF/MS/MS). Metabolites were identified on the basis of the masscharge ratio (m/z) and validated via XCMS software. Metabolites with a fold change ≥ 1.5 and P ≤ 0.01 were analyzed via principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) to distinguish between the groups. MetaboAnalyst 6.0 was used for pathway analysis to identify metabolic pathways affected by ritonavir. The PCA and OPLS-DA models revealed clear separation between the control and ritonavir-treated groups, with high R² and Q² values indicating robust model performance. Pathway analysis revealed that ritonavir treatment significantly affected several metabolic pathways, including those related to ether lipid, phenylalanine, sphingolipid, and glycerophospholipid metabolism. Particularly significant changes were observed in metabolites related to lipid metabolism, oxidative stress responses and cellular signaling. Ritonavir significantly impacts metabolic pathways, particularly those involved in lipid metabolism, and oxidative stress responses, which may influence immune responses and drug interactions. This study also highlights the potential of integrating metabolomics with personalized medicine approaches to optimize ritonavir treatment strategies and reduce adverse effects. These findings indicate that ritonavir significantly influences cellular homeostasis and metabolic processes in addition to its antiviral properties. This highlights the necessity of comprehending the metabolic effects of ritonavir to enhance its clinical application, especially in the management of COVID-19. Further research is warranted to explore these alterations and their implications for therapeutic strategies.

Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches.

A review on the novel biomarkers of systemic lupus erythematosus discovered via metabolomic profiling

Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease affecting various body organs and systems. The diagnosis of SLE and its complications is based on evident clinical symptoms, serological marker levels, and pathological findings. Some serological markers have a low sensitivity and specificity, and biopsy procedures are invasive in nature. Hence, metabolomics has emerged as a valuable tool for SLE screening and categorization. Its application has contributed significantly to identifying SLE pathogenesis, improving clinical diagnosis, and developing treatment approaches. This review provides an overview of the utilization of metabolomics in the study of SLE, focusing on advancements in understanding the disease's pathogenesis, aiding in diagnosis, and monitoring treatment efficacy.

Evaluating protocols for reproducible targeted metabolomics by NMR

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Metabolomic profiling of Fiore Sardo cheese: Investigation of the influence of thermal treatment and ripening time using univariate and multivariate classification techniques

The effect of different sub-pasteurization heat treatments and different ripening times was investigated in this work. The metabolite profiles of 95 cheese samples were analyzed using GC-MS in order to determine the effects of thermal treatment (raw milk, 57 °C and 68 °C milk thermization) and ripening time (105 and 180 days). ANOVA test on GC-MS peaks complemented with false discovery rate correction was employed to identify the compounds whose levels significantly varied over different ripening times and thermal treatments. The univariate t-test classifier and Partial Least Square Discriminant Analysis (PLS-DA) provided acceptable classification results, with an overall accuracy in cross-validation of 76% for the univariate model and 72% from the PLS-DA. The metabolites that mostly changed with ripening time were amino acids and one endocannabinoid (i.e., arachidonoyl amide), while compounds belonging to the classes of biogenic amines and saccharides resulted in being strongly affected by the thermization process.

Yeast Solutions and Hyperpolarization Enable Real-Time Observation of Metabolized Substrates Even at Natural Abundance

Metabolic changes in an organism often occur much earlier than macroscopic manifestations of disease, such as invasive tumors. Therefore, noninvasive tools to monitor metabolism are fundamental as they provide insights into in vivo biochemistry. NMR represents one of the gold standards for such insights by observing metabolites. Using nuclear spin hyperpolarization greatly increases the NMR sensitivity, enabling μmol/L sensitivity with a time resolution of about one second. However, a metabolic phantom with reproducible, rapid, and human-like metabolism is needed to progress research in this area. Using baker's yeast as a convenient metabolic factory, we demonstrated in a single study that yeast cells provide a robust and rapidly metabolizing phantom for pyruvate and fumarate, including substrates with a natural abundance of 13C: we observed the production of ethanol, carbon dioxide, bicarbonate, lactate, alanine from pyruvate, malate, and oxaloacetate from fumarate. For observation, we hyperpolarized pyruvate and fumarate via the dissolution dynamic nuclear polarization (dDNP) technique to about 30% 13C polarization that is equivalent to 360,000 signal enhancement at 1 T and 310 K. Major metabolic pathways were observed using tracers at a natural abundance of 13C, demonstrating that isotope labeling is not always essential in vitro. Enriched [1-13C]pyruvate revealed minor lactate production, presumably via the D-lactate dehydrogenase (DLD) enzyme pathway, demonstrating the sensitivity gain using a dense yeast solution. We foresee that yeast as a metabolic factory can find application as an abundant MRI phantom standard to calibrate and optimize molecular MRI protocols. Our study highlights the potential of using hyperpolarization to probe the metabolism of yeast and other microorganisms even with naturally abundant substrates, offering valuable insights into their response to various stimuli such as drugs, treatment, nourishment, and genetic modification, thereby advancing drug development and our understanding of biochemical processes.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Unveiling the Metabolomic Profile of Oily Sensitive Skin: A Non-Invasive Approach

Skin barrier impairment is becoming increasingly common due to changes in lifestyle and modern living environments. Oily sensitive skin (OSS) is a condition that is characterized by an impaired skin barrier. Thus, examining the differences between OSS and healthy skin will enable a more objective evaluation of the characteristics of OSS and facilitate investigations of potential treatments. Initially, a self-assessment questionnaire was used to identify patients with OSS. Biophysical measurements and LAST scores were used to determine whether skin barrier function was impaired. Epidermal biophysical properties, including skin hydration, transepidermal water loss (TEWL), sebum content, erythema index (EI), and a* value, were measured with noninvasive instruments. We subsequently devised a noninvasive D-square sampling technique to identify changes in the skin metabolome in conjunction with an untargeted metabolomics analysis with an Orbitrap Q ExactiveTM series mass spectrometer. In the stratum corneum of 47 subjects, 516 skin metabolites were identified. In subjects with OSS, there was an increase in the abundance of 15 metabolites and a decrease in the abundance of 48 metabolites. The participants with OSS were found to have the greatest disruptions in sphingolipid and amino acid metabolism. The results revealed that an impaired skin barrier is present in patients with OSS and offers a molecular target for screening for skin barrier damage.

Biomarkers of insulin sensitivity/resistance

In recent years, remarkable advancements in elucidating the intricate molecular underpinnings of type 2 diabetes mellitus (T2D) have been achieved. Insulin resistance (IR) has been unequivocally acknowledged as the driving pathogenetic mechanism of T2D, preceding disease onset by several years. Nonetheless, diagnostic tools for ascertaining IR are lacking in current clinical practice, representing a critical unmet need; use of the hyperinsulinemic-euglycemic glucose clamp, widely accepted as the gold standard method for evaluating IR at present, is cumbersome in a clinical setting. Thus, the development of well-validated, reliable, and affordable biomarkers of IR has attracted considerable attention from the research community. The biomarkers under investigation can be divided into two major categories: (1) indices or ratios, comprising parameters obtained from a basic or comprehensive metabolic panel and/or derived from anthropometric measurements, and (2) circulating molecules implicated in pathophysiological processes associated with IR. Furthermore, numerous novel biomarkers, including markers of β-cell dysfunction, radiographic quantification of excess visceral adipose tissue, T2D prediction models, certain microRNAs and metabolomic biomarkers, have also provided promising preliminary results. This narrative review aims to present current evidence pertaining to the most notable and exciting biomarkers of IR that are under rigorous evaluation.

Precision Medicine in Bariatric Procedures

Obesity is a multi-factorial disease that is influenced by genetic, epigenetic, and environmental factors. Precision medicine is a practice wherein prevention and treatment strategies take individual variability into account. It involves using a variety of factors including deep phenotyping using clinical, physiologic, and behavioral characteristics, 'omics assays (eg, genomics, epigenomics, transcriptomics, and microbiomics among others), and environmental factors to devise practices that are individualized to subsets of patients. Personalizing the therapeutic modality to the individual can lead to enhanced effectiveness and tolerability. The authors review advances in precision medicine made in the field of bariatrics and discuss future avenues and challenges.

Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response

The intratumoral microbiome (TM) refers to the microorganisms in the tumor tissues, including bacteria, fungi, viruses, and so on, and is distinct from the gut microbiome and circulating microbiota. TM is strongly associated with tumorigenesis, progression, metastasis, and response to therapy. This paper highlights the current status of TM. Tract sources, adjacent normal tissue, circulatory system, and concomitant tumor co-metastasis are the main origin of TM. The advanced techniques in TM analysis are comprehensively summarized. Besides, TM is involved in tumor progression through several mechanisms, including DNA damage, activation of oncogenic signaling pathways (phosphoinositide 3-kinase [PI3K], signal transducer and activator of transcription [STAT], WNT/β-catenin, and extracellular regulated protein kinases [ERK]), influence of cytokines and induce inflammatory responses, and interaction with the tumor microenvironment (anti-tumor immunity, pro-tumor immunity, and microbial-derived metabolites). Moreover, promising directions of TM in tumor therapy include immunotherapy, chemotherapy, radiotherapy, the application of probiotics/prebiotics/synbiotics, fecal microbiome transplantation, engineered microbiota, phage therapy, and oncolytic virus therapy. The inherent challenges of clinical application are also summarized. This review provides a comprehensive landscape for analyzing TM, especially the TM-related mechanisms and TM-based treatment in cancer.

The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities

Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Impact of Serotonin Transporter Absence on Brain Insulin Receptor Expression, Plasma Metabolome Changes, and ADHD-like Behavior in Mice fed a Western Diet

The impaired function of the serotonin transporter (SERT) in humans has been linked to a higher risk of obesity and type 2 diabetes, especially as people age. Consuming a "Western diet" (WD), which is high in saturated fats, cholesterol, and sugars, can induce metabolic syndrome. Previous research indicated that mice carrying a targeted inactivation of the Sert gene (knockout, KO) and fed a WD display significant metabolic disturbances and behaviors reminiscent of ADHD. These abnormalities might be mediated via a dysfunction in insulin receptor (IR) signaling, which is also associated with adult ADHD. However, the impact of Sert deficiency on IR signaling and systemic metabolic changes has not been thoroughly explored. In this study, we conducted a detailed analysis of locomotor behavior in wild-type (WT) and KO mice fed a WD or control diet. We investigated changes in the blood metabolome and examined, via PCR, the expression of insulin receptor A and B isoforms and key regulators of their function in the brain. Twelve-month-old KO mice and their WT littermates were fed a WD for three weeks. Nuclear magnetic resonance spectroscopy analysis of plasma samples showed that KO mice on a WD had higher levels of lipids and lipoproteins and lower levels of glucose, lactate, alanine, valine, and isoleucine compared to other groups. SERT-KO mice on the control diet exhibited increased brain levels of both IR A and B isoforms, accompanied by a modest increase in the negative regulator ENPP. The KO mice also displayed anxiety-like behavior and reduced exploratory activity in an open field test. However, when the KO animals were fed a WD, the aberrant expression levels of IR isoforms in the KO mice and locomotor behavior were ameliorated indicating a complex interaction between genetic and dietary factors that might contribute to ADHD-like symptoms. Overall, our findings suggest that the lack of Sert leads to a unique metabolic phenotype in aged mice, characterized by dysregulated IR-related pathways. These changes are exacerbated by WD in the blood metabolome and are associated with behavioral abnormalities.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Egg white-derived peptides reduced blood glucose in high-fat-diet and low-dose streptozotocin-induced type 2 diabetic mice via regulating the hepatic gluconeogenic signaling and metabolic profile

Food proteins are considered an ideal source for the identification of bioactive peptides with the potential to intervene in nutrition-related chronic diseases such as cardiovascular disease, obesity, and diabetes. Egg white-derived peptides (EWPs) have been shown to improve glucose tolerance in insulin-resistant rats. However, underlying mechanisms are to be elucidated. Therefore, we hypothesized that EWP exerts a hypoglycemic effect by regulating hepatic glucose homeostasis. Our results showed that 7 weeks of EWP treatment reduced the fasting blood glucose in T2DM mice and the inhibition of the liver gluconeogenic pathway was involved in the mechanisms of actions. Using the untargeted metabolomics technique, we found that EWP treatment also altered the hepatic metabolic profile in T2DM mice, in which, the role of fatty acid esters of hydroxy fatty acids in mediating the hypoglycemic effect of EWPs might be pivotal.

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation

BACKGROUND

Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers.

METHODS

Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve.

RESULTS

In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics).

CONCLUSIONS

The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.

Integrated Metabolomics and Transcriptomics Analysis Reveals New Insights into Triterpene Biosynthesis in Rosa rugosa

Rosa rugosa is highly regarded for its aesthetic and therapeutic qualities. In particular, R. rugosa's flowers are known to produce essential oils containing a mixture of volatile terpenes, phenylpropanoids, and other compounds. Despite this, extensive research exists on volatile terpenes in flowers, while the knowledge of non-volatile terpenes in distinct tissues is still limited. Using UPLC-ESI-MS/MS, a comprehensive analysis of the terpene metabolites in five different tissues of R. rugosa was conducted. These metabolites accumulated in distinct tissues, and the majority of them were triterpenoids. Transcriptome data were collected from five tissues using RNA-seq. Transcriptomics and metabolomics were utilized to evaluate the triterpene biosynthesis pathway, resulting in new insights into its regulation and biosynthesis. The RrOSC10 was identified as a key enzyme in converting 2,3-oxidosqualene into α-amyrin, potentially contributing to the triterpene biosynthesis pathway. Furthermore, the expression of the RrOSC10 gene was upregulated by salinity for 0.5 h and 1 h, with subsequent downregulation at 2 h. This study lays a foundation for future research on the biosynthesis and accumulation of triterpenes in R. rugosa.

Development of a point-of-care colorimetric metabolomic sensor platform

Metabolomics is the large-scale study of small molecule metabolites within a biological system. It has applications in measuring dietary intake, predicting heart disease risk, and diagnosing cancer. Metabolites are often measured using high-end analytical tools such as mass spectrometers or large spectrophotometers. However, due to their size, cost, and need for skilled operators, using such equipment at the bedside is not practical. To address this issue, we have developed a low-cost, portable, optical color sensor platform for metabolite detection. This platform includes LEDs, sensors, microcontrollers, a power source, and a Bluetooth chip enclosed within a 3D-printed light-tight case. We evaluated the color sensor's performance using both a range of dyed water samples as well as well-established colorimetric reactions for specific metabolite detection. The sensor accurately measured creatinine, L-carnitine, ascorbate, and succinate well within normal human urine levels with accuracy and sensitivity equal to or better than a standard laboratory spectrophotometer. Our color sensor offers a cost-effective, portable alternative for measuring metabolites via colorimetric assays, thereby enabling low-cost, point-of-care metabolite testing.

Metabolomic pathways in food allergy

Food allergy (FA) is a widespread issue, affecting as many as 10% of the population. Over the past two to three decades, the prevalence of FA has been on the rise, particularly in industrialized and westernized countries. FA is a complex, multifactorial disease mediated by type 2 immune responses and involving environmental and genetic factors. However, the precise mechanisms remain inadequately understood. Metabolomics has the potential to identify disease endotypes, which could beneficially promote personalized prevention and treatment. A metabolome approach would facilitate the identification of surrogate metabolite markers reflecting the disease activity and prognosis. Here, we present a literature overview of recent metabolomic studies conducted on children with FA.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Pubertal Girls With Overweight/Obesity Have Higher Androgen Levels-Can Metabolomics Tell us Why?

CONTEXT

Pubertal girls with higher total body fat (TBF) demonstrate higher androgen levels. The cause of this association is unknown but is hypothesized to relate to insulin resistance.

OBJECTIVE

This work aimed to investigate the association between higher TBF and higher androgens in pubertal girls using untargeted metabolomics.

METHODS

Serum androgens were determined using a quantitative mass spectrometry (MS)-based assay. Metabolomic samples were analyzed using liquid chromatography high-resolution MS. Associations between TBF or body mass index (BMI) z score (exposure) and metabolomic features (outcome) and between metabolomic features (exposure) and serum hormones (outcome) were examined using gaussian generalized estimating equation models with the outcome lagged by one study visit. Benjamini-Hochberg false discovery rate (FDR) adjusted P values were calculated to account for multiple testing. RaMP-DB (relational database of metabolomic pathways) was used to conduct enriched pathway analyses among features nominally associated with body composition or hormones.

RESULTS

Sixty-six pubertal, premenarchal girls (aged 10.9 ± 1.39 SD years; 60% White, 24% Black, 16% other; 63% normal weight, 37% overweight/obese) contributed an average of 2.29 blood samples. BMI and TBF were negatively associated with most features including raffinose (a plant trisaccharide) and several bile acids. For BMI, RaMP-DB identified many enriched pathways related to bile acids. Androstenedione also showed strong negative associations with raffinose and bile acids.

CONCLUSION

Metabolomic analyses of samples from pubertal girls did not identify an insulin resistance signature to explain the association between higher TBF and androgens. Instead, we identified potential novel signaling pathways that may involve raffinose or bile acid action at the adrenal gland.

Integrative metabolomic and transcriptomic analyses reveals the accumulation patterns of key metabolites associated with flavonoids and terpenoids of Gynostemma pentaphyllum (Thunb.) Makino

Gynostemma pentaphyllum (Thunb.) Makino (G. pentaphyllum) is a medicinal and edible plant with multiple functions of liver protection, anti-tumor, anti-inflammation, balancing blood sugar and blood lipids. The nutritional value of the G. pentaphyllum plant is mainly due to its rich variety of biologically active substances, such as flavonoids, terpenes and polysaccharides. In this study, we performed a comprehensive analysis combining metabolomics and root, stem and leaf transcriptomic data of G. pentaphyllum. We used transcriptomics and metabolomics data to construct a dynamic regulatory network diagram of G. pentaphyllum flavonoids and terpenoids, and screened the transcription factors involved in flavonoids and terpenoids, including basic helix-loop-helix (bHLH), myb-related, WRKY, AP2/ERF. Transcriptome analysis results showed that among the DEGs related to the synthesis of flavonoids and terpenoids, dihydroflavonol 4-reductase (DFR) and geranylgeranyl diphosphate synthases (GGPPS) were core genes. This study presents a dynamic image of gene expression in different tissues of G. pentaphyllum, elucidating the key genes and metabolites of flavonoids and terpenoids. This study is beneficial to a deeper understanding of the medicinal plants of G. pentaphyllum, and also provides a scientific basis for further regulatory mechanisms of plant natural product synthesis pathways and drug development.

A current review of U.S. beef flavor I: Measuring beef flavor

Historically, consumer acceptance of beef was determined by tenderness. Developments in genetics and management over the last couple of decades have improved tenderness to the point that it is secondary to other factors in beef's taste. Flavor, however, is an extraordinarily complex taste attribute dependent on biological sensors in the mouth, sinus cavity, and jaws. The culinary industry has recently focused on innovative ways to give consumers new products satisfying their curiosity about different foods, especially proteins. Competition from plant-based, cell-based, and even other animal-based proteins provides diversity in consumers' ability to select a protein that satisfies their desire to include unique products in their diet. Consequently, the beef industry has focused on flavor for the last 10 to 15 years to determine whether it can provide the guardrails for beef consumption in the future. The U.S. beef industry formed a Flavor Working Group in 2012 composed of the authors listed here to investigate new and innovative ways to manage and measure beef flavor. The results of this working group have resulted in dozens of papers, presentations, abstracts, and symposia. The objective of this manuscript is to summarize the research developed by this working group and by others worldwide that have investigated methodologies that measure beef flavor. This paper will describe the strengths of the research in beef flavor measurement and point out future needs that might be identified as technology advances.

Redox Regulation of Xenobiotics by Reactive Sulfur and Supersulfide Species

Significance: Routine exposure to xenobiotics is unavoidable during our lifetimes. Certain xenobiotics are hazardous to human health, and are metabolized in the body to render them less toxic. During this process, several detoxification enzymes cooperatively metabolize xenobiotics. Glutathione (GSH) conjugation plays an important role in the metabolism of electrophilic xenobiotics. Recent Advances: Recent advances in reactive sulfur and supersulfide (RSS) analyses showed that persulfides and polysulfides bound to low-molecular-weight thiols, such as GSH, and to protein thiols are abundant in both eukaryotes and prokaryotes. The highly nucleophilic nature of hydropersulfides and hydropolysulfides contributes to cell protection against oxidative stress and electrophilic stress. Critical Issues: In contrast to GSH conjugation to electrophiles that is aided by glutathione S-transferase (GST), persulfides and polysulfides can directly form conjugates with electrophiles without the catalytic actions of GST. The polysulfur bonds in the conjugates are further reduced by perthioanions and polythioanions derived from RSS to form sulfhydrated metabolites that are no longer electrophilic but rather nucleophilic, and differ from metabolites that are formed via GSH conjugation. Future Directions: In view of the abundance of RSS in cells and tissues, metabolism of xenobiotics that is mediated by RSS warrants additional investigations, such as studies of the impact of microbiota-derived RSS on xenobiotic metabolism. Metabolites formed from reactions between electrophiles and RSS may be potential biomarkers for monitoring exposure to electrophiles and for studying their metabolism by RSS. Antioxid. Redox Signal. 40, 679-690.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

The geomicrobiology of limestone, sulfuric acid speleogenetic, and volcanic caves: basic concepts and future perspectives

Caves are ubiquitous subterranean voids, accounting for a still largely unexplored surface of the Earth underground. Due to the absence of sunlight and physical segregation, caves are naturally colonized by microorganisms that have developed distinctive capabilities to thrive under extreme conditions of darkness and oligotrophy. Here, the microbiomes colonizing three frequently studied cave types, i.e., limestone, sulfuric acid speleogenetic (SAS), and lava tubes among volcanic caves, have comparatively been reviewed. Geological configurations, nutrient availability, and energy flows in caves are key ecological drivers shaping cave microbiomes through photic, twilight, transient, and deep cave zones. Chemoheterotrophic microbial communities, whose sustenance depends on nutrients supplied from outside, are prevalent in limestone and volcanic caves, while elevated inorganic chemical energy is available in SAS caves, enabling primary production through chemolithoautotrophy. The 16S rRNA-based metataxonomic profiles of cave microbiomes were retrieved from previous studies employing the Illumina platform for sequencing the prokaryotic V3-V4 hypervariable region to compare the microbial community structures from different cave systems and environmental samples. Limestone caves and lava tubes are colonized by largely overlapping bacterial phyla, with the prevalence of Pseudomonadota and Actinomycetota, whereas the co-dominance of Pseudomonadota and Campylobacterota members characterizes SAS caves. Most of the metataxonomic profiling data have so far been collected from the twilight and transient zones, while deep cave zones remain elusive, deserving further exploration. Integrative approaches for future geomicrobiology studies are suggested to gain comprehensive insights into the different cave types and zones. This review also poses novel research questions for unveiling the metabolic and genomic capabilities of cave microorganisms, paving the way for their potential biotechnological applications.

Breast Cancer Exposomics

We are exposed to a mixture of environmental man-made and natural xenobiotics. We experience a wide spectrum of environmental exposure in our lifetime, including the effects of xenobiotics on gametogenesis and gametes that undergo fertilization as the starting point of individual development and, moreover, in utero exposure, which can itself cause the first somatic or germline mutation necessary for breast cancer (BC) initiation. Most xenobiotics are metabolized or/and bioaccumulate and biomagnify in our tissues and cells, including breast tissues, so the xenobiotic metabolism plays an important role in BC initiation and progression. Many considerations necessitate a more valuable explanation regarding the molecular mechanisms of action of xenobiotics which act as genotoxic and epigenetic carcinogens. Thus, exposomics and the exposome concept are based on the diversity and range of exposures to physical factors, synthetic chemicals, dietary components, and psychosocial stressors, as well as their associated biologic processes and molecular pathways. Existing evidence for BC risk (BCR) suggests that food-borne chemical carcinogens, air pollution, ionizing radiation, and socioeconomic status are closely related to breast carcinogenesis. The aim of this review was to depict the dynamics and kinetics of several xenobiotics involved in BC development, emphasizing the role of new omics fields related to BC exposomics, such as environmental toxicogenomics, epigenomics and interactomics, metagenomics, nutrigenomics, nutriproteomics, and nutrimiRomics. We are mainly focused on food and nutrition, as well as endocrine-disrupting chemicals (EDCs), involved in BC development. Overall, cell and tissue accumulation and xenobiotic metabolism or biotransformation can lead to modifications in breast tissue composition and breast cell morphology, DNA damage and genomic instability, epimutations, RNA-mediated and extracellular vesicle effects, aberrant blood methylation, stimulation of epithelial-mesenchymal transition (EMT), disruption of cell-cell junctions, reorganization of the actin cytoskeleton, metabolic reprogramming, and overexpression of mesenchymal genes. Moreover, the metabolism of xenobiotics into BC cells impacts almost all known carcinogenic pathways. Conversely, in our food, there are many bioactive compounds with anti-cancer potential, exerting pro-apoptotic roles, inhibiting cell cycle progression and proliferation, migration, invasion, DNA damage, and cell stress conditions. We can conclude that exposomics has a high potential to demonstrate how environmental exposure to xenobiotics acts as a double-edged sword, promoting or suppressing tumorigenesis in BC.

Metabolites: a converging node of host and microbe to explain meta-organism

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification

MOTIVATION

Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.

RESULTS

Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification.

AVAILABILITY AND IMPLEMENTATION

The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.

Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study

Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.

Association between plasma L-carnitine levels and mitochondrial DNA copy number

Mitochondria are key cytoplasmic organelles in eukaryotic cells that generate adenosine triphosphate (ATP) through the electron transport chain and oxidative phosphorylation. Mitochondrial DNA (mtDNA) copy number (mtDNAcn) is considered a biomarker for both mitochondrial quantity and function as well as cellular oxidative stress level. Previous epidemiologic findings revealed that weight gain, higher body mass index (BMI), smoking, and high insulinemic potential of lifestyle were associated with lower leukocyte mtDNAcn. Carnitines are a group of compounds that play a critical role in energy production. We quantified the associations of plasma L-carnitine levels with leukocyte mtDNAcn. We then examined the association between mtDNAcn and L-carnitine (HMDB0000062) in 538 U.S. men without cancers, diabetes, or cardiovascular disease at blood collection from the Health Professionals Follow-Up Study (HPFS). We found a significant inverse association between L-carnitine and mtDNAcn (ρ = -0.1, P = 0.02). This implies that the carnitine metabolic pathway may be associated with mitochondrial function and oxidative stress.

Oilomics: An important branch of foodomics dealing with oil science and technology

Oil is one of three nutritious elements. The application of omics techniques in the field of oil science and technology is attracted increasing attention. Oilomics, which emerged as an important branch of foodomics, has been widely used in various aspects of oil science and technology. However, there are currently no articles systematically reviewing the application of oilomics. This paper aims to provide a critical overview of the advantages and value of oilomics technology compared to traditional techniques in various aspects of oil science and technology, including oil nutrition, oil processing, oil quality, safety, and traceability. Moreover, this article intends to review major issues in oilomics and give a comprehensive, critical overview of the current state of the art, future challenges and trends in oilomics, with a view to promoting the optimal application and development of oilomics technology in oil science and technology.

Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids

BACKGROUND

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear.

METHODS

We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD.

RESULTS

A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another.

CONCLUSIONS

Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.

MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.

Multi-omics in HIV: searching insights to understand immunological non-response in PLHIV

Antiretroviral therapy (ART) induces persistent suppression of HIV-1 replication and gradual recovery of T-cell counts, and consequently, morbidity and mortality from HIV-related illnesses have been significantly reduced. However, in approximately 30% of people living with HIV (PLHIV) on ART, CD4+ T-cell counts fail to normalize despite ART and complete suppression of HIV viral load, resulting in severe immune dysfunction, which may represent an increased risk of clinical progression to AIDS and non-AIDS events as well as increased mortality. These patients are referred to as "immune inadequate responders", "immunodiscordant responders" or "immune nonresponders (INR)". The molecular mechanisms underlying poor CD4+ T-cell recovery are still unclear. In this sense, the use of omics sciences has shed light on possible factors involved in the activity and metabolic dysregulation of immune cells during the failure of CD4+ T-cell recovery in INR. Moreover, identification of key molecules by omics approaches allows for the proposal of potential biomarkers or therapeutic targets to improve CD4+ T-cell recovery and the quality of life of these patients. Hence, this review aimed to summarize the information obtained through different omics concerning the molecular factors and pathways associated with the INR phenotype to better understand the complexity of this immunological status in HIV infection.

Differential Responses of Physiological Parameters, Production Traits, and Blood Metabolic Profiling between First- and Second-Parity Holstein Cows in the Comparison of Spring versus Summer Seasons

Heat stress (HS) negatively influences cows' welfare and productivity. Therefore, a better understanding of the physiological and molecular mechanisms of HS responses from multiple parities is paramount for the development of effective management and breeding strategies. In comparison with first-parity cows in the spring (Spring-1), first-parity cows in the summer (Summer-1) had a significantly higher rectal temperature (RT), respiration rate (RR), drooling score (DS), and daily activity (DA), while lower (P < 0.05) daily rumination (DR), seven-day average milk yield (7AMY), milk yield on sampling day (MY_S), milk yield on test day (MY_T), and lactose percentage (LP) were observed. When comparing the spring (Spring-2) and summer (Summer-2) of the second-parity cows, significant differences were also found in RT, RR, DS, DA, and DR (P < 0.05), corresponding to similar trends with the first parity while having smaller changes. Moreover, significantly negative impacts on performance traits were only observed on fat percentage (FP) and LP. These results showed that there were different biological responses between first- and second-parity Holstein cows. Further, 18 and 17 metabolites were involved in the seasonal response of first- and second-parity cows, respectively. Nine differential metabolites were shared between the two parities, and pathway analyses suggested that cows had an inhibited tricarboxylic acid cycle, increased utilization of lipolysis, and a dysregulated gut microbiome during the summer. The metabolites identified exclusively for each parity highlighted the differences in microbial response and host amino acid metabolism between two parities in response to HS. Moreover, glucose, ethanol, and citrate were identified as potential biomarkers for distinguishing individuals between Spring-1 and Summer-1. Ethanol and acetone were better predictors for distinguishing individuals between Spring-2 and Summer-2. Taken together, the present study demonstrated the impact of naturally induced HS on physiological parameters, production traits, and the blood metabolome of Holstein cows. There are different biological responses and regulation mechanisms between first- and second-parity Holstein cows.

Global Profiling of Urinary Mercapturic Acids Using Integrated Library-Guided Analysis

Urinary mercapturic acids (MAs) are often used as biomarkers for monitoring human exposures to occupational and environmental xenobiotics. In this study, we developed an integrated library-guided analysis workflow using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. This method includes expanded assignment criteria and a curated library of 220 MAs and addresses the shortcomings of previous untargeted approaches. We employed this workflow to profile MAs in the urine of 70 participants─40 nonsmokers and 30 smokers. We found approximately 500 MA candidates in each urine sample, and 116 MAs from 63 precursors were putatively annotated. These include 25 previously unreported MAs derived mostly from alkenals and hydroxyalkenals. Levels of 68 MAs were comparable in nonsmokers and smokers, 2 MAs were higher in nonsmokers, and 46 MAs were elevated in smokers. These included MAs of polycyclic aromatic hydrocarbons and hydroxyalkenals and those derived from toxicants present in cigarette smoke (e.g., acrolein, 1,3-butadiene, isoprene, acrylamide, benzene, and toluene). Our workflow allowed profiling of known and unreported MAs from endogenous and environmental sources, and the levels of several MAs were increased in smokers. Our method can also be expanded and applied to other exposure-wide association studies.

Comparison of the Effect of Phospholipid Extracts from Salmon and Silver Carp Heads on High-Fat-Diet-Induced Metabolic Syndrome in C57BL/6J Mice

Metabolic syndrome (MetS) is a global health problem, and EPA/DHA-enriched phospholipids (EPA/DHA-PLs) have been found to have positive effects on MetS improvement. Currently, research on EPA/DHA-PL mainly focuses on special and rare seafood, such as phospholipids derived from krill, sea cucumber, squid, and fish roe. However, it has been recently demonstrated that abundant EPA/DHA-PL can also be found in bulk fish and its by-products. Nonetheless, there is still limited research on the biological activities of EPA/DHA-PL derived from these sources. The aim of this study was to investigate the effect of phospholipid extracts from the heads of salmon and silver carp (S-PLE and SC-PLE) on the high-fat-diet-induced MetS in C57/BL mice. After an 8-week intervention, both SC-PLE and S-PLE had a significant ameliorating effect on MetS. Moreover, SC-PLE was more effective than S-PLE in reducing liver inflammation and fasting glucose. Both of the PL extracts were able to regulate the expression of key genes in lipid synthesis, fatty acid β-oxidation, and insulin signaling pathways. Compared with S-PLE, dietary SC-PLE had a greater influence on liver metabolomics. Pathway enrichment analysis showed that the differential metabolites of SC-PLE were mainly involved in arachidonic acid metabolism and glutathione metabolism. The results indicated that the different metabolic regulation methods of S-PLE and SC-PLE could be related to their variant molecular composition in EPA/DHA-PL.

Integration of Volatilomics and Metabolomics Unveils Key Flavor-Related Biological Pathways in Different Carambola Cultivars

Carambola is a tropical fruit that is highly sought after by consumers due to its unique flavor, star shape, and nutritional value. Enhancing the flavor quality of this fruit can increase the consumer acceptance and market demand. However, flavor is an intrinsic characteristic of fruits. Its decoding requires in-depth knowledge based on recognizing key biological pathways relevant to flavor formation and development. In this study, the volatile and non-volatile metabolites contributing to the flavor variation of five carambola cultivars were investigated by a novel strategy combining GC-MS/O-based volatilomics with LC-MS-based metabolomics. Several significant flavor-related pathways, involving biosynthesis or metabolism of amino acids, terpenoids, fatty acids, sugar and organic acid, and flavonoids were identified based on the enrichment analysis of important volatile and non-volatile metabolites. The results indicated that there were metabolites in the flavor-related pathways being up- or downregulated, leading to the differences in flavor traits of different carambola cultivars. This study could provide a valuable reference for breeders and researchers of interest in the mechanisms underlying the regulation of flavor, which would ultimately lead to the creation of carambola cultivars with more attractive flavor profiles and pleasurable consuming experiences.

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.

Potential Diagnostic Biomarker Detection for Prostate Cancer Using Untargeted and Targeted Metabolomic Profiling

Prostate cancer (PCa) remains one of the leading causes of cancer mortality in men worldwide, currently lacking specific, early detection and staging biomarkers. In this regard, modern research focuses efforts on the discovery of novel molecules that could represent potential future non-invasive biomarkers for the diagnosis of PCa, as well as therapeutic targets. Mounting evidence shows that cancer cells express an altered metabolism in their early stages, making metabolomics a promising tool for the discovery of altered pathways and potential biomarker molecules. In this study, we first performed untargeted metabolomic profiling on 48 PCa plasma samples and 23 healthy controls using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-[ESI+]-MS) for the discovery of metabolites with altered profiles. Secondly, we selected five molecules (L-proline, L-tryptophan, acetylcarnitine, lysophosphatidylcholine C18:2 and spermine) for the downstream targeted metabolomics and found out that all the molecules, regardless of the PCa stage, were decreased in the PCa plasma samples when compared to the controls, making them potential biomarkers for PCa detection. Moreover, spermine, acetylcarnitine and L-tryptophan had very high diagnostic accuracy, with AUC values of 0.992, 0.923 and 0.981, respectively. Consistent with other literature findings, these altered metabolites could represent future specific and non-invasive candidate biomarkers for PCa detection, which opens novel horizons in the field of metabolomics.

Analysis of Metabolites and Metabolic Pathways of Three Chinese Jujube Cultivar

Most studies on metabolites in jujube fruits focus on specific types of metabolites, but there are only a few comprehensive reports on the metabolites in jujube fruits. In order to understand the variance of metabolites in fruits of different jujube varieties. The objective of this study was to explore the metabolic components of jujube fruit by comparing three cultivars, namely Linyi LiZao (LZ), Jiaocheng SuantianZao (STZ), and Xianxian Muzao (MZ). The metabolites present in the fruits of these three cultivars were evaluated and compared. The results revealed the detection of 1059 metabolites across the three jujube varieties, with each cultivar exhibiting distinct metabolic characteristics. Notably, MZ exhibited a higher abundance of six metabolite classes, namely amino acids and derivatives, flavonoids, lipids, organic acids, phenolic acids, and terpenoids, compared to LZ. Conversely, LZ exhibited higher concentrations of alkaloids, lignans, coumarins, nucleotides, and their derivatives compared to the other two cultivars. In terms of STZ, its content of amino acids and derivatives, lignans and coumarins, organic acids, and phenolic acids was largely similar to that of LZ. However, the content of alkaloids, nucleotides, and their derivatives, and terpenoids was significantly higher in STZ compared to LZ. Additionally, STZ exhibited lower levels of flavonoids and lipids compared to LZ. Moreover, MZ was found to be less nutritionally rich than STZ, except for lignans and coumarins, as it displayed lower levels of all the metabolites. KEGG pathway enrichment analysis revealed six significantly different metabolic pathways (p < 0.05) between LZ and MZ, including arginine and proline metabolism, sphingolipid metabolism, flavonoid biosynthesis, glutathione metabolism, glycerophospholipid metabolism, and cysteine and methionine metabolism. The metabolites in STZ and MZ exhibited three significantly different pathways (p < 0.05), primarily associated with flavonoid biosynthesis, arginine and proline metabolism, and sphingolipid metabolism. The significantly differential metabolites between LZ and STZ were observed in the phenylpropionic acid biosynthesis pathway and the ubiquinone and other terpenoid-quinone biosynthesis pathways. LZ showed a closer relationship with STZ than with MZ. STZ and LZ exhibited higher medicinal values, while LZ had lower acidity and MZ displayed better antioxidant activity. This study presents the first thorough analysis of metabolites in LZ, STZ, and MZ cultivars, which can serve as a theoretical basis for quality analysis, functional research, and classification processing of jujube fruit.

Metabolomic analysis of Mycobacterium tuberculosis reveals metabolic profiles for identification of drug-resistant tuberculosis

The detection of pre-extensively (pre-XDR) and extensively drug-resistant tuberculosis (XDR-TB) is challenging. Drug-susceptibility tests for some anti-TB drugs, especially ethambutol (ETH) and ethionamide (ETO), are problematic due to overlapping thresholds to differentiate between susceptible and resistant phenotypes. We aimed to identify possible metabolomic markers to detect Mycobacterium tuberculosis (Mtb) strains causing pre-XDR and XDR-TB. The metabolic patterns of ETH- and ETO-resistant Mtb isolates were also investigated. Metabolomics of 150 Mtb isolates (54 pre-XDR, 63 XDR-TB and 33 pan-susceptible; pan-S) were investigated. Metabolomics of ETH and ETO phenotypically resistant subgroups were analyzed using UHPLC-ESI-QTOF-MS/MS. Orthogonal partial least-squares discriminant analysis revealed distinct separation in all pairwise comparisons among groups. Two metabolites (meso-hydroxyheme and itaconic anhydride) were able to differentiate the pre-XDR and XDR-TB groups from the pan-S group with 100% sensitivity and 100% specificity. In comparisons of the ETH and ETO phenotypically resistant subsets, sets of increased (ETH = 15, ETO = 7) and decreased (ETH = 1, ETO = 6) metabolites specific for the resistance phenotype of each drug were found. We demonstrated the potential for metabolomics of Mtb to differentiate among types of DR-TB as well as between isolates that were phenotypically resistant to ETO and ETH. Thus, metabolomics might be further applied for DR-TB diagnosis and patient management.

The toxicity of nanoparticles and their interaction with cells: an in vitro metabolomic perspective

Nowadays, nanomaterials (NMs) are widely present in daily life due to their significant benefits, as demonstrated by their application in many fields such as biomedicine, engineering, food, cosmetics, sensing, and energy. However, the increasing production of NMs multiplies the chances of their release into the surrounding environment, making human exposure to NMs inevitable. Currently, nanotoxicology is a crucial field, which focuses on studying the toxicity of NMs. The toxicity or effects of nanoparticles (NPs) on the environment and humans can be preliminary assessed in vitro using cell models. However, the conventional cytotoxicity assays, such as the MTT assay, have some drawbacks including the possibility of interference with the studied NPs. Therefore, it is necessary to employ more advanced techniques that provide high throughput analysis and avoid interferences. In this case, metabolomics is one of the most powerful bioanalytical strategies to assess the toxicity of different materials. By measuring the metabolic change upon the introduction of a stimulus, this technique can reveal the molecular information of the toxicity induced by NPs. This provides the opportunity to design novel and efficient nanodrugs and minimizes the risks of NPs used in industry and other fields. Initially, this review summarizes the ways that NPs and cells interact and the NP parameters that play a role in this interaction, and then the assessment of these interactions using conventional assays and the challenges encountered are discussed. Subsequently, in the main part, we introduce the recent studies employing metabolomics for the assessment of these interactions in vitro.

Animal sourced biopolymer for mitigating xenobiotics and hazardous materials

Over the past several decades, xenobiotic chemicals have badly affected the environment including human health, ecosystem and environment. Animal-sourced biopolymers have been employed for the removal of heavy metals and organic dyes from the contaminated soil and waste waters. Animal-sourced biopolymers are biocompatible, cost-effective, eco-friendly, and sustainable in nature which make them a favorable choice for the mitigation of xenobiotic and hazardous compounds. Chitin/chitosan, collagen, gelatin, keratin, and silk fibroin-based biopolymers are the most commonly used biopolymers. This chapter reviews the current challenge faced in applying these animal-based biopolymers in eliminating/neutralizing various recalcitrant chemicals and dyes from the environment. This chapter ends with the discussion on the recent advancements and future development in the employability of these biopolymers in such environmental applications.