Metabolomics analysis for biomarker discovery: advances and challenges

Multi-omics integration analysis: Tools and applications in environmental toxicology

Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.

V. S, Sekhar Mukhopadhyay C, Kashyap N, Sharma V, Singh N, Salajegheh Tazerji S, Kalantari R, Hajipour P, Singh Malik Y. Animal Wellness: The Power of Multiomics and Integrative Strategies: Multiomics in Improving Animal Health

The livestock industry faces significant challenges, with disease outbreaks being a particularly devastating issue. These diseases can disrupt the food supply chain and the livelihoods of those involved in the sector. To address this, there is a growing need to enhance the health and well-being of livestock animals, ultimately improving their performance while minimizing their environmental impact. To tackle the considerable challenge posed by disease epidemics, multiomics approaches offer an excellent opportunity for scientists, breeders, and policymakers to gain a comprehensive understanding of animal biology, pathogens, and their genetic makeup. This understanding is crucial for enhancing the health of livestock animals. Multiomic approaches, including phenomics, genomics, epigenomics, metabolomics, proteomics, transcriptomics, microbiomics, and metaproteomics, are widely employed to assess and enhance animal health. High-throughput phenotypic data collection allows for the measurement of various fitness traits, both discrete and continuous, which, when mathematically combined, define the overall health and resilience of animals, including their ability to withstand diseases. Omics methods are routinely used to identify genes involved in host-pathogen interactions, assess fitness traits, and pinpoint animals with disease resistance. Genome-wide association studies (GWAS) help identify the genetic factors associated with health status, heat stress tolerance, disease resistance, and other health-related characteristics, including the estimation of breeding value. Furthermore, the interaction between hosts and pathogens, as observed through the assessment of host gut microbiota, plays a crucial role in shaping animal health and, consequently, their performance. Integrating and analyzing various heterogeneous datasets to gain deeper insights into biological systems is a challenging task that necessitates the use of innovative tools. Initiatives like MiBiOmics, which facilitate the visualization, analysis, integration, and exploration of multiomics data, are expected to improve prediction accuracy and identify robust biomarkers linked to animal health. In this review, we discuss the details of multiomics concerning the health and well-being of livestock animals.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

The Application of Metabolomics in Hyperlipidemia: Insights into Biomarker Discovery and Treatment Efficacy Assessment

Hyperlipidemia is a lipid metabolism disorder that refers to increased levels of total triglycerides (TGs), cholesterol (TC), and low-density lipoprotein-cholesterol (LDL-C) and decreased levels of high-density lipoprotein-cholesterol (HDL-C). It is a major public health issue with increased prevalence and incidence worldwide. The ability to identify individuals at risk of this disorder before symptoms manifest will facilitate timely intervention and management to avert potential complications. This can be achieved by employing metabolomics as an early detection method for the diagnostic biomarkers of hyperlipidemia. Metabolomics is an analytical approach used to detect and quantify metabolites. This provides the ability to explain the metabolic processes involved in the development and progression of certain diseases. In recent years, interest in the use of metabolomics to identify disease biomarkers has increased, and several biomarkers have been discovered, such as docosahexaenoic acid, glycocholic acid, citric acid, betaine, and carnitine. This review discusses the primary metabolic alterations in the context of hyperlipidemia. Furthermore, we provide an overview of recent studies on the application of metabolomics to the assessment of the efficacy of traditional herbal products and common lipid-lowering medications.

Overview of Bovine Mastitis: Application of Metabolomics in Screening Its Predictive and Diagnostic Biomarkers

Bovine mastitis is an inflammatory disease of the mammary glands, and its pathogenesis and diagnosis are complicated. Through qualitative and quantitative analysis of small-molecule metabolites, the metabolomics technique plays an important role in finding biomarkers and studying the metabolic mechanism of bovine mastitis. Therefore, this paper reviews the predictive and diagnostic biomarkers of bovine mastitis that have been identified using metabolomics techniques and that are present in samples such as milk, blood, urine, rumen fluid, feces, and mammary tissue. In addition, the metabolic pathways of mastitis-related biomarkers in milk and blood were analyzed; it was found that the tricarboxylic acid (TCA) cycle was the most significant (FDR = 0.0015767) pathway in milk fluid, and glyoxylate and dicarboxylate metabolism was the most significant (FDR = 0.0081994) pathway in blood. The purpose of this review is to provide useful information for the prediction and early diagnosis of bovine mastitis.

Identification of pyruvic and maleic acid as potential markers for disease activity and prognosis in chronic urticaria

BACKGROUND

Population-based studies have highlighted the link between chronic urticaria (CU) and metabolic syndrome, and metabolic alterations have been revealed in CU. However, to our knowledge, a comprehensive metabolomics study on a large cohort of patients with CU has not been reported.

OBJECTIVE

We sought to explore the underlying metabolic subtypes and novel metabolite biomarkers for CU diagnosis and therapy.

METHODS

Plasma samples from 80 patients with CU and 82 healthy controls were collected for metabolomics quantification and bioinformatics analysis. Another independent cohort consisting of 144 patients with CU was studied to validate the findings. Bone marrow-derived mast cells and mice with IgE-induced passive cutaneous anaphylaxis were used for in vitro and in vivo experiments, respectively.

RESULTS

We observed clear metabolome differences between CU patients and healthy controls. Meanwhile, differential metabolites N6-acetyl-l-lysine, l-aspartate, maleic acid, and pyruvic acid were used to construct random forest classifiers and achieved area under receiver operating characteristic curve values greater than 0.85, suggesting their potential as diagnostic biomarkers of CU. More importantly, by exploring the underlying metabolic subtypes of CU, we found that the low abundance of pyruvic acid and maleic acid was significantly related to the activity of CU, poor efficacy of second-generation H1 antihistamines, and short relapse-free time. The results were validated in the independent cohort. Moreover, supplementation with pyruvate or maleate could significantly attenuate IgE-mediated mast cell activation in vitro and in vivo.

CONCLUSIONS

Plasma pyruvic acid and maleic acid may be effective biomarkers for predicting disease activity, therapeutic efficacy, and prognosis for patients with CU.

Integrated untargeted and targeted testicular metabolomics to reveal the regulated mechanism of Gushudan on the hypothalamic-pituitary-gonadal axis of kidney-yang-deficiency-syndrome rats

Modern studies have shown that neuroendocrine disorders caused by the dysfunction of the hypothalamic-pituitary-gonadal (HPG) axis are one of the important pathogenetic mechanisms of kidney-yang-deficiency-syndrome (KYDS). The preventive effect of Gushudan on KYDS has been reported, but its regulatory mechanisms on the HPG axis have not been elucidated. In this study, we developed an integrated untargeted and targeted metabolomics analysis strategy to investigate the regulatory mechanism of Gushudan on the HPG axis in rats with KYDS. In untargeted metabolomics, we screened 14 potential biomarkers such as glycine, lysine, and glycerol that were significantly associated with the HPG axis. To explore the effect of changes in the levels of potential biomarkers on KYDS, all of them were quantified in targeted metabolomics. With the quantitative results, correlations between potential biomarkers and testosterone, a functional indicator of the HPG axis, were explored. The results showed that oxidative stress, inflammatory response, and energy depletion, induced by metabolic disorders in rats, were responsible for the decrease in testosterone levels. Gushudan improves metabolic disorders and restores testosterone levels, thus restoring HPG axis dysfunction. This finding elucidates the special metabolic characteristics of KYDS and the therapeutic mechanism of Gushudan from a new perspective.

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

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.

Combination of histological and metabolomic assessments to evaluate the potential pharmacological efficacy of saikosaponin D

Saikosaponin D (SsD), a natural triterpenoid saponin compound, exhibits notable potential in suppressing tumor growth and inhibiting metastasis, particularly in breast cancer. However, its underlying mechanism of action for SsD remains unclear. In this study, a combination strategy to reveal the metabolism modulation of SsD on breast cancer was performed by integration of histopathological assessments and untargeted metabolomics analysis. Pathological evaluation of the efficacy of SsD from a visual and intuitive perspective. Accordingly, a non-targeted metabolomics study was used to investigate the pharmacological efficacy using a set of serum samples from mice before and after (0-30 days) modulated with SsD based on ultra-high performance liquid chromatography tandem orbitrap mass spectrometry to discover metabolite biomarkers for finding the key metabolic mechanism in a molecular perspective. As a result, 20 metabolites were selected as potential biomarkers for SsD efficacy evaluation with high sensitivity and specificity. These metabolites changes were involved in sphingolipid metabolism, glycerophospholipid metabolism, phenylalanine and tryptophan metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis pathways, suggesting that SsD exerted anti-breast cancer effects through the regulation of the underlying metabolism. In conclusion, we developed a new analysis strategy that effectively discovers tumor-progressing related metabolite biomarkers in serum for pharmacological efficacy evaluation.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Association of metabolomics with PD-1 inhibitor plus chemotherapy outcomes in patients with advanced non-small-cell lung cancer

BACKGROUND

Combining immune checkpoint inhibitors (ICIs) with chemotherapy has become a standard treatment for patients with non-small cell lung cancer (NSCLC) lacking driver gene mutations. Reliable biomarkers are essential for predicting treatment outcomes. Emerging evidence from various cancers suggests that early assessment of serum metabolites could serve as valuable biomarkers for predicting outcomes. This study aims to identify metabolites linked to treatment outcomes in patients with advanced NSCLC undergoing first-line or second-line therapy with programmed cell death 1 (PD-1) inhibitors plus chemotherapy.

METHOD

200 patients with advanced NSCLC receiving either first-line or second-line PD-1 inhibitor plus chemotherapy, and 50 patients undergoing first-line chemotherapy were enrolled in this study. The 200 patients receiving combination therapy were divided into a Discovery set (n=50) and a Validation set (n=150). These sets were further categorized into respond and non-respond groups based on progression-free survival PFS criteria (PFS≥12 and PFS<12 months). Serum samples were collected from all patients before treatment initiation for untargeted metabolomics analysis, with the goal of identifying and validating biomarkers that can predict the efficacy of immunotherapy plus chemotherapy. Additionally, the validated metabolites were grouped into high and low categories based on their medians, and their relationship with PFS was analyzed using Cox regression models in patients receiving combination therapy.

RESULTS

After the impact of chemotherapy was accounted for, two significant differential metabolites were identified in both the Discovery and Validation sets: N-(3-Indolylacetyl)-L-alanine and methomyl (VIP>1 and p<0.05). Notably, upregulation of both metabolites was observed in the group with a poorer prognosis. In the univariate analysis of PFS, lower levels of N-(3-Indolylacetyl)-L-alanine were associated with longer PFS (HR=0.59, 95% CI, 0.41 to 0.84, p=0.003), and a prolonged PFS was also indicated by lower levels of methomyl (HR=0.67, 95% CI, 0.47 to 0.96, p=0.029). In multivariate analyses of PFS, lower levels of N-(3-Indolylacetyl)-L-alanine were significantly associated with a longer PFS (HR=0.60, 95% CI, 0.37 to 0.98, p=0.041).

CONCLUSION

Improved outcomes were associated with lower levels of N-(3-Indolylacetyl)-L-alanine in patients with stage IIIB-IV NSCLC lacking driver gene mutations, who underwent first-line or second-line therapy with PD-1 inhibitors combined with chemotherapy. Further exploration of the potential predictive value of pretreatment detection of N-(3-Indolylacetyl)-L-alanine in peripheral blood for the efficacy of combination therapy is warranted.

STATEMENT

The combination of ICIs and chemotherapy has established itself as the new standard of care for first-line or second-line treatment in patients with advanced NSCLC lacking oncogenic driver alterations. Therefore, identifying biomarkers that can predict the efficacy and prognosis of immunotherapy plus chemotherapy is of paramount importance. Currently, the only validated predictive biomarker is programmed cell death ligand-1 (PD-L1), but its predictive value is not absolute. Our study suggests that the detection of N-(3-Indolylacetyl)-L-alanine in patient serum with untargeted metabolomics prior to combined therapy may predict the efficacy of treatment. Compared with detecting PD-L1 expression, the advantage of our biomarker is that it is more convenient, more dynamic, and seems to work synergistically with PD-L1 expression.

Challenges in the Metabolomics-Based Biomarker Validation Pipeline

As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.

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.

Understanding PFAS toxicity through cell culture metabolomics: Current applications and future perspectives

Per- and polyfluoroalkyl substances (PFAS), ubiquitous environmental contaminants, pose significant challenges to ecosystems and human health. While cell cultures have emerged as new approach methodologies (NAMs) in ecotoxicity research, metabolomics is an emerging technique used to characterize the small-molecule metabolites present in cells and to understand their role in various biological processes. Integration of metabolomics with cell cultures, known as cell culture metabolomics, provides a novel and robust tool to unravel the complex molecular responses induced by PFAS exposure. In vitro testing also reduces reliance on animal testing, aligning with ethical and regulatory imperatives. The current review summarizes key findings from recent studies utilizing cell culture metabolomics to investigate PFAS toxicity, highlighting alterations in metabolic pathways, biomarker identification, and the potential linkages between metabolic perturbations. Additionally, the paper discusses different types of cell cultures and metabolomics methods used for studies of environmental contaminants and particularly PFAS. Future perspectives on the combination of metabolomics with other advanced technologies, such as single-cell metabolomics (SCM), imaging mass spectrometry (IMS), extracellular flux analysis (EFA), and multi-omics are also explored, which offers a holistic understanding of environmental contaminants. The synthesis of current knowledge and identification of research gaps provide a foundation for future investigations that aim to elucidate the complexities of PFAS-induced cellular responses and contribute to the development of effective strategies for mitigating their adverse effects on human health.

Canine Mammary Tumors: Classification, Biomarkers, Traditional and Personalized Therapies

In recent years, many studies have focused their attention on the dog as a proper animal model for human cancer. In dogs, mammary tumors develop spontaneously, involving a complex interplay between tumor cells and the immune system and revealing several molecular and clinical similarities to human breast cancer. In this review, we summarized the major features of canine mammary tumor, risk factors, and the most important biomarkers used for diagnosis and treatment. Traditional therapy of mammary tumors in dogs includes surgery, which is the first choice, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these therapeutic strategies may not always be sufficient on their own; advancements in understanding cancer mechanisms and the development of innovative treatments offer hope for improved outcomes for oncologic patients. There is still a growing interest in the use of personalized medicine, which should play an irreplaceable role in the research not only in human cancer therapy, but also in veterinary oncology. Moreover, immunotherapy may represent a novel and promising therapeutic option in canine mammary cancers. The study of novel therapeutic approaches is essential for future research in both human and veterinary oncology.

Real-Time NMR-Based Drug Discovery to Identify Inhibitors against Fatty Acid Synthesis in Living Cancer Cells

Abnormal fatty acid metabolism is recognized as a key driver of tumor development and progression. Although numerous inhibitors have been developed to target this pathway, finding drugs with high specificity that do not disrupt normal cellular metabolism remains a formidable challenge. In this paper, we introduced a novel real-time NMR-based drug screening technique that operates within living cells. This technique provides a direct way to putatively identify molecular targets involved in specific metabolic processes, making it a powerful tool for cell-based drug screening. Using 2-13C acetate as a tracer, combined with 3D cell clusters and a bioreactor system, our approach enables real-time detection of inhibitors that target fatty acid metabolism within living cells. As a result, we successfully demonstrated the initial application of this method in the discovery of traditional Chinese medicines that specifically target fatty acid metabolism. Elucidating the mechanisms behind herbal medicines remains challenging due to the complex nature of their compounds and the presence of multiple targets. Remarkably, our findings demonstrate the significant inhibitory effect of P. cocos on fatty acid synthesis within cells, illustrating the potential of this approach in analyzing fatty acid metabolism events and identifying drug candidates that selectively inhibit fatty acid synthesis at the cellular level. Moreover, this systematic approach represents a valuable strategy for discovering the intricate effects of herbal medicine.

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

Microbiota Implications in Endocrine-Related Diseases: From Development to Novel Therapeutic Approaches

This comprehensive review article delves into the critical role of the human microbiota in the development and management of endocrine-related diseases. We explore the complex interactions between the microbiota and the endocrine system, emphasizing the implications of microbiota dysbiosis for the onset and progression of various endocrine disorders. The review aims to synthesize current knowledge, highlighting recent advancements and the potential of novel therapeutic approaches targeting microbiota-endocrine interactions. Key topics include the impact of microbiota on hormone regulation, its role in endocrine pathologies, and the promising avenues of microbiota modulation through diet, probiotics, prebiotics, and fecal microbiota transplantation. We underscore the importance of this research in advancing personalized medicine, offering insights for more tailored and effective treatments for endocrine-related diseases.

Metabolomic Changes in Rat Serum after Chronic Exposure to Glyphosate-Based Herbicide

Glyphosate-based herbicides (GBHs) have gained extensive popularity in recent decades. For many years, glyphosate has been regarded as harmless or minimally toxic to mammals due to the absence of its primary target, the shikimic acid pathway in humans. Nonetheless, mounting evidence suggests that glyphosate may cause adverse health effects in humans via other mechanisms. In this study, we described the metabolomic changes in the serum of experimental rats exposed to chronic GBH using the highly sensitive LC-MS/MS technique. We investigated the possible relationship between chronic exposure to GBH and neurological disorders. Our findings suggest that chronic exposure to GBH can alter spatial learning memory and the expression of some important metabolites that are linked to neurophysiological disorders in young rats, with the female rats showing higher susceptibility compared to the males. This indicates that female rats are more likely to show early symptoms of the disorder on exposure to chronic GBH compared to male rats. We observed that four important metabolites (paraxanthine, epinephrine, L-(+)-arginine, and D-arginine) showed significant changes and involvement in neurological changes as suggested by ingenuity pathway analysis. In conclusion, our results indicate that chronic exposure to GBH can increase the risk of developing neurological disorders.

Intercropping of Narrow-Leafed Lupin (Lupinus angustifolius L.) and Barley (Hordeum vulgare L.) Affects the Flavonoid Composition of Both Crops

Barley (Hordeum vulgare L.) is a common cereal crop in agricultural production and is often included in legume-cereal intercropping. Flavonoids, a major class of secondary metabolites found in barley, are involved in plant defense and protection. However, the effect of intercropping on barley flavonoids remains unknown. Herein, an intercropping system involving barley and lupin (Lupinus angustifolius L.) was studied. Intercropping increased the level of luteolin in lupin roots. Lupin-barley intercropping considerably increased genistein, rutin, and apigenin in barley shoots. Genistein and apigenin were also detected in intercropped barley roots and rhizosphere soil. The three flavonoids have been reported as defense compounds, suggesting that lupin triggers a defense response in barley to strengthen its survival ability.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

CSF metabolomics alterations after aneurysmal subarachnoid hemorrhage: what do we know?

PURPOSE

The purpose of this mini review is to describe metabolomics in cerebrospinal fluid (CSF) and its potential in aneurysmal subarachnoid hemorrhage (aSAH). In brain injury, patients' micro dialysis enables detecting biochemical change in brain tissue. Indicators for ischemia were detected such as lactate, pyruvate, glucose, and glutamate. In aSAH patients, the pathophysiology and the factor for poor outcome are not completely understood yet. Routine use of biomarkers in CSF, particularly in aSAH patients, is still lacking.

METHODS

This mini review was performed on the role of metabolomics alterations after aneurysmal subarachnoid hemorrhage.

RESULTS

We identified five clinical studies that addressed metabolomics in patients with aneurysmal subarachnoid hemorrhage.

CONCLUSION

There is increasing evidence suggesting that biomarkers can give insight in the pathogenesis and can serve as an outcome predictor. In this mini review, we present a brief overview of metabolomics profiling in neuroscience and wish to discuss the predictive and therapeutic value in aSAH patients.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

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

Using untargeted metabolomics and volatile aroma compounds to predict expert sensory descriptors and consumer liking of beef loin steaks varying in quality grade, aging time, and degree of doneness

Precursors to flavor are important to its development, yet little is known about the intrinsic products of metabolism that influence flavor. Our objective was to use untargeted metabolomics and volatile aroma compounds to predict expert and consumer sensory traits. USDA Select and upper 2/3 Choice beef strip loins were wet aged for 10 or 20 d and then cut into steaks, vacuum-packaged, and frozen. Steaks were cooked to 63 °C, 71 °C, or 80 °C end-point internal steak temperature. USDA Choice steaks had more intense beef flavor identity, brown, roasted, fat-like, salty, sweet, sour, umami, buttery, and overall sweet flavors compared to USDA Select steaks (P < 0.05). Steaks cooked to 80 °C had more intense beef identity, brown, roasted, and umami flavors than steaks cooked to a lower degree of doneness. Steaks cooked to either 63 °C or 71 °C had more intense bloody, metallic, and sour flavors and were juicier, more tender, and had less connective tissue than steaks cooked to a higher degree of doneness. Volatile aroma compounds increased (P < 0.05) in Choice steaks aged for 20 d, while cooking steaks to 80 increased aldehydes, ketones, and pyrazines. Raw steaks had 69 small-molecule metabolomic compounds shared across all four quality grade x aging combinations, and discriminant analysis correctly categorized (P < 0.05) these metabolites. Metabolites and volatiles can be used to predict (r2 > 0.85) expert and consumer sensory panel descriptors and liking.

DA-SRN: Omics data analysis based on the sample network optimization for complex diseases

Effective biomarker identification and accurate sample label prediction are still challenging for complex diseases. Patient similarity network (PSN) analysis is a powerful tool in disease omics data analysis. The topology of PSN can reflect the discriminative ability of the corresponding feature space on which the sample network is built. In this study, a novel omics data analysis method based on the sample reference network (DA-SRN) is proposed to identify the potential biomarkers and predict the sample categories. DA-SRN defines the informative features and the sample reference network in optimizing the network structure by genetic algorithm. It labels the samples based on the graph neural network, the reference network and the selected informative features. DA-SRN was compared with nine efficient omics data analysis methods on the genomics, metabolomics and transcriptomics datasets to show its validation. The comparison results showed that it outperformed the other methods in area under receiver operating characteristic curve (AUROC), sensitivity, specificity and area under precision-recall curve (AUPRC) in most cases. Besides, the important metabolites identified by DA-SRN for the type 2 diabetes (T2D) metabolomics data were further examined. The pathway analysis revealed the close relationships between the identified metabolites and the critical metabolic pathways related to the occurrence and development of T2D. The experimental results illustrate that DA-SRN can extract the valuable information from the complex omics data by analyzing the sample relationship, and is promising in biomarker identification and sample discrimination for complex diseases.

Uncovering anti-influenza mechanism of Ophiocordyceps sinensis using network pharmacology, molecular pharmacology, and metabolomics

Ophiocordyceps sinensis is a precious Chinese traditional herb with a long medicinal history. This study used UPLC-MS metabolomics to explore and compare the metabolic profiles of the stroma (OSBSz), sclerotium (OSBSh), and mycelium (OSBS) of O sinensis to analyze their differential metabolites and identified potential active components. Then combined with network pharmacology and molecular docking to explore the mechanism of differential metabolites with anti-influenza properties. The results indicate that the stroma, sclerotium, and mycelium showed significant differences in metabolites. The key pathways for differential metabolites were butanoate metabolism, thiamin metabolism, alanine, aspartate and glutamate metabolism, citrate cycle, and arginine biosynthesis. Protein-protein interaction analysis identified potential targets, including SRC, RHOA, HSP90AA1, VEGFA, ITGB1, PRKCA, and ITGA1, and the key protective pathways in-volved PI3K-Akt, HIF-1, influenza A, and Coronavirus disease 2019. The molecular docking results showed that the core metabolite D-(-)-glutamine has high binding affinity with SRC, RHOA, and EGFR, re-flecting the multi-component and multi-target network system of O sinensis. In short, the combination of metabonomics, network pharmacology and macromolecular docking technology provides a new way to explore the anti-influenza research of O sinensis. This is undoubtedly an important theoretical support for the clinical application of O sinensis in the future.

Proteomics-Driven Biomarkers in Pancreatic Cancer

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

Simultaneous quantitation of multiple myeloma related dietary metabolites in serum using HILIC-LC-MS/MS

Background

Recent studies from targeted and untargeted metabolomics have consistently revealed that diet-related metabolites, including carnitine (C0), several species of acylcarnitines (AcyCNs), amino acids, ceramides, and lysophosphatidylcholines (LPCs) may serve as potential multiple myeloma (MM) biomarkers. However, most of these approaches had some intrinsic limitations, namely low reproducibility and compromising the accuracy of the results.

Objective

This study developed and validated a precise, efficient, and reliable liquid chromatography tandem mass spectrometric (LC-MS/MS) method for measuring these 28 metabolic risk factors in human serum.

Design

This method employed isopropanol to extract the metabolites from serum, gradient elution on a hydrophilic interaction liquid chromatographic column (HILIC) for chromatographic separation, and multiple reaction monitor (MRM) mode with positive electrospray ionization (ESI) for mass spectrometric detection.

Results

The correlation coefficients of linear response for this method were more than 0.9984. Analytical recoveries ranged from 91.3 to 106.3%, averaging 99.5%. The intra-run and total coefficients of variation were 1.1-5.9% and 2.0-9.6%, respectively. We have simultaneously determined the serological levels of C0, several subclasses of AcyCNs, amino acids, ceramides, and LPCs within 15 min for the first time.

Conclusion

The established LC-MS/MS method was accurate, sensitive, efficient, and could be valuable in providing insights into the association between diet patterns and MM disease and added value in further clinical research.

Transcriptomic and Metabolomic Analyses Reveal Differences in Flavonoid Pathway Gene Expression Profiles between Two Dendrobium Varieties during Vernalization

Dendrobium (Orchidaceae, Epidendoideae) plants have flowers with a wide variety of colors that persist for a long period throughout the year. The yellow coloration of Dendrobium flowers is mainly determined by the flavonol pathway and the flavone pathway, but the relevant biosynthesis mechanisms during vernalization remain unclear. To explore the similarities and differences in flavonoid biosynthesis in different tissues during vernalization, we selected two species of Dendrobium for a flower color study: Dendrobium capillipes Rchb (which has yellow flowers) and Dendrobium nobile Lindl (which has white flowers). We collected a total of 36 samples from six tissue types and both Dendrobium species during vernalization and subjected the samples to metabolic profiling and transcriptome sequencing. A total of 31,504 differentially expressed genes (DEGs) were identified between different tissues of the two Dendrobium species by transcriptomic analysis. However, many differentially accumulated metabolites (DAMs) and DEGs were enriched not only in the general pathway of "flavonoid biosynthesis" but also in multiple subpathways of "flavone and flavonol biosynthesis". According to a combined transcriptome and metabolome analysis, Putrescine hydroxycinnamoyl transferase 1 (LOC110093422) may be the main gene responsible for the differences in flavonoid accumulation during vernalization, which is closely associated with yellow flowers. Taken together, the results of our study preliminarily revealed the metabolites responsible for and the key genes regulating flavonoid biosynthesis during vernalization. These results provide a basis for the further study of the molecular mechanism of flavonoid synthesis during vernalization.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

HPLC-based metabolomics of Dendrobium officinale revealing its antioxidant ability

Dendrobium officinale is an orchid with medicinal and nutritional properties that has received increasing attention because of its health benefits; however, there is limited information about the metabolic basis of these properties. In this report, secondary metabolites and the antioxidant activity of D. officinale stem samples from three provenances were analyzed, using a UHPLC-QqQ-MS/MS-based metabolomics approach. In total, 411 metabolites were identified including 8 categories such as flavonoids and phenolic acids, 136 of which were differential metabolites. These differentially accumulated metabolites (DAMs) were mainly enriched in secondary metabolic pathways such as flavone, flavonol, tropane, piperidine, pyridine, isoquinoline alkaloid biosynthesis and tyrosine metabolism. The metabolomic profiling suggested that the quantity and content of flavonoid compounds accounted for the highest proportion of total metabolites. Hierarchical cluster analysis (HCA) showed that the marker metabolites of D. officinale from the three provenances were mainly flavonoids, alkaloids and phenolic acids. Correlation analysis identified that 48 differential metabolites showed a significant positive correlation with antioxidant capacity (r ³ 0.8 and p < 0.0092), and flavonoids were the main factors affecting the different antioxidant activities. It is worth noting that quercetin-3-O-sophoroside-7-O-rhamnoside and dihydropinosylvin methyl ether might be the main compounds causing the differences in antioxidant capacity of Yunnan provenance (YN), Zhejiang provenance (ZJ), and Guizhou provenance (GZ). These finding provides valuable information for screening varieties, quality control and product development of D. officinale.

Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Background

To explore potential metabolomics biomarker in predicting the efficiency of the chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC).

Methods

A total of 83 eligible patients were assigned to receive chemo-immunotherapy. Serum samples were prospectively collected before the treatment to perform metabolomics profiling analyses under the application of gas chromatography mass spectrometry (GC-MS). The key metabolites were identified using projection to latent structures discriminant analysis (PLS-DA). The key metabolites were used for predicting the chemo-immunotherapy efficiency in advanced NSCLC patients.

Results

Seven metabolites including pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate were identified as the key metabolites to the chemo-immunotherapy response. The receiver operating characteristic curves (AUC) were 0.79 (95% CI: 0.69-0.90), 0.60 (95% CI: 0.48-0.73), 0.69 (95% CI: 0.57-0.80), 0.63 (95% CI: 0.51-0.75), 0.60 (95% CI: 0.48-0.72), 0.56 (95% CI: 0.43-0.67), and 0.67 (95% CI: 0.55-0.80) for the key metabolites, respectively. A binary logistic regression was used to construct a combined biomarker model to improve the discriminating efficiency. The AUC was 0.86 (95% CI: 0.77-0.94) for the combined biomarker model. Pathway analyses showed that urea cycle, glucose-alanine cycle, glycine and serine metabolism, alanine metabolism, and glutamate metabolism were the key metabolic pathway to the chemo-immunotherapy response in patients with advanced NSCLC.

Conclusion

Metabolomics analyses of key metabolites and pathways revealed that GC-MS could be used to predict the efficiency of chemo-immunotherapy. Pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate played a central role in the metabolic of PD patients with advanced NSCLC.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

1 H-NMR Metabolomics Profiling and Volatile Content of 'Hoja Santa' (Piper auritum Kunth): A Millenary Edible Plant Consumed in Mexico

The leaves of Piper auritum Kunth ('Hoja Santa') have been consumed for centuries by native people of central and southern Mexico as a fresh vegetable or condiment. Herein we present the result of the ¹ H-NMR metabolomics profiling of three accessions of P. auritum harvested in three different provinces of Mexico (Puebla, Tlaxcala, and Oaxaca). The volatile content associated with the flavoring properties of the plant was also determined by GC/MS. The non-targeted metabolome of these samples revealed that P. auritum is a source of free essential amino acids such as isoleucine, leucine, threonine, valine, histidine, phenylalanine, and tryptophan as well as organic acids, free monosaccharides, and valuable nutraceuticals such as trigonelline, Myo-inositol, betaine, and choline. Principal component analysis and orthogonal partial least squares discriminated analysis of the metabolites found in P. auritum revealed trigonelline as the main differential compound found in the three studied accessions, suggesting this metabolite as a possible chemical marker. According to these statistical approaches, 60 % of the differential metabolites were provided by Oaxaca samples, suggesting that leaves harvested in this province have better (p<0.05) nutritional properties than the other samples analyzed. Nevertheless, the high abundance of the anti-nutrient safrole (90 %) in the volatile fraction, advises the potential toxicity of P. auritum consumed in Oaxaca. On the other hand, samples harvested in the northern highlands of Puebla, contained the lowest levels of safrole (30 %) and acceptable levels of nutrients and nutraceuticals including choline. From the three groups of studied plants, those harvested in the northern highlands from Puebla, could be considered safer for human consumption than the other analyzed accessions.

Altered Urinary Metabolomics in Hereditary Angioedema

Hereditary angioedema (HAE) is a rare and potentially life-threatening disease with heterogeneous clinical symptoms. The metabolomic profile of HAE remains unknown. Uncovering the metabolic signatures of HAE may provide inspiration for a comprehensive understanding of HAE pathogenesis and may help explore potential new metabolic biomarkers. We performed a comprehensive metabolic analysis using high-performance liquid chromatography−tandem mass spectrometry (HPLC-MS/MS). Urine samples from 34 HAE patients and 82 healthy controls (HCs) were collected to characterize the metabolic signatures associated with HAE. The metabolomes of HAE patients carrying different mutation types were also compared. A total of 795 metabolites were accurately detected and quantified. We considered 73 metabolites as differential metabolites in HAE patients (with an importance in projection (VIP) value > 1.0, q-value < 0.05, and fold change (FC) ≥ 1.2 or FC ≤ 0.8). Several metabolites associated with riboflavin metabolism, the citrate cycle, oxidative stress, and inflammation, including xanthine, oxypurinol, vitamin B2, and isocitrate, were significantly altered in HAE patients. No significantly different metabolites were found in HAE patients carrying different mutation types. The present study highlights that metabolic disturbances in the purine metabolism, riboflavin metabolism, and TCA cycle may be involved in the pathogenesis of HAE. Although biochemical significance requires further experimental verification, these findings may help to identify novel candidate metabolite biomarkers associated with HAE.

Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours

One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps Sinensis

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.

Alteration of Bile Acids and Omega-6 PUFAs Are Correlated With the Progression and Prognosis of Drug-Induced Liver Injury

Background & Aims

Drug-induced liver injury (DILI) is one of the leading causes of liver failure with some of the patients progressed to chronic DILI. The mechanisms underlying the severity and chronicity of DILI are poorly elucidated and the biomarkers are limited. Metabolites and gut microbiota played a crucial role in the development of various liver diseases. Herein, a systematic analysis of serum metabolites and gut microbiota was performed in DILI patients, aiming to identify metabolites correlated with the progression and clinical prognosis of DILI.

Methods

Various serum metabolites were quantitated using a metabolite array technology in this prospective study. Gut microbiome compositions and the expression profiles of liver genes were determined in patients with DILI and healthy controls.

Results

Metabolomic analysis revealed that bile acids (BAs) and polyunsaturated fatty acids (PUFAs) were closely related to DILI severity and chronicity respectively. The ratios of serum primary/secondary BAs and omega-6/omega-3 PUFAs were elevated in DILI patients. A model established by adrenic acid (AdA) and aspartic acid (Asp) exerts good performance for predicting the chronicity of DLIL. Hepatic transcriptome revealed enhanced expression of PUFA peroxidation and supressed expression of BA synthesis related genes in DILI patients. In addition, Lactic acid bacteria and BA converting bacteria were increased in gut of DILI patients. Besides, elevated serum malondialdehyde (MDA) and fibroblast growth factor 19 (FGF19) was observed in DILI patients.

Conclusion

BAs and PUFAs could be potent markers for the severity and chronicity of DILI respectively. The panel of AdA and Asp could be ideal predictive model for the risk of chronicity at the acute stage of DILI. Gut microbiota might act as a negative feedback mechanism to maintain the homeostasis of BAs and PUFAs via FGF19 signalling and PUFA saturation, respectively. Our study revealed novel biomarkers for severe and chronic DILI and provided new therapeutic targets for DILI.

Transcriptome and metabolome profiling unveil the accumulation of flavonoids in Dendrobium officinale

Dendrobium officinale is a Chinese herbal medicine with a long history of use in China. Flavonoids are known to be an important secondary metabolite in Dendrobium officinale, but very little is known about their molecular regulation mechanism in D. officinale. In this study, we collected one to four years old D. officinale stems for the purpose of RNA-sequencing and mass spectrometry data collection. The results showed that metabolome analysis detected 124 different flavonoid metabolites of which flavonol metabolites were significantly increased in biennial samples. In the transcriptome analysis, 30 different genes involved in the synthesis of flavonoid were identified. The key genes FLS (LOC110101392, LOC110107557, LOC110114894) that regulate the synthesis of flavonols are highly expressed in biennial samples. The present study contributes a new insight into the molecular mechanism of flavonoid accumulation in D. officinale.

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

Metabolomic Analysis Reveals Potential Biomarkers and the Underlying Pathogenesis Involved in Mycoplasma Pneumoniae Pneumonia

Although previous studies have reported the use of metabolomics for infectious diseases, little is known about the potential function of plasma metabolites in children infected with Mycoplasma pneumoniae (MP). Here, a combination of liquid chromatography-quadrupole time-of-flight mass spectrometry and random forest-based classification model was used to provide a broader range of applications in MP diagnosis. In the training cohort, plasma from 63 MP pneumonia children (MPPs), 37 healthy controls (HC) and 29 infectious disease controls (IDC) was collected. After multivariate analyses, 357 metabolites were identified to be differentially expressed among MPP, HC and IDC groups, and 3 metabolites (568.5661, 459.3493 and 411.3208) had high diagnostic values. In an independent cohort with 57 blinded subjects, samples were successfully classified into different groups, demonstrating the reliability of these biomarkers for distinguishing MPPs from controls. A metabolomic signature analysis identified major classes of glycerophospholipids, sphingolipids and fatty acyls were increased in MPPs. These markedly altered metabolites are mainly involved in glycerophospholipid and sphingolipid metabolism. As the ubiquitous building blocks of eukaryotic cell membranes, dysregulated lipid metabolism indicates damage of the cellular membrane and the activation of immunity in MPPs. Moreover, lipid metabolites, differentially expressed between severe and mild MPPs, were correlated with the markers of extrapulmonary complications, suggesting that they may be involved in MPP disease severity. These findings may offer new insights into biomarker selection and the pathogenesis of MPP in children.

Inborn Errors of Metabolism Screening in Neonates: Current Perspective with Diagnosis and Therapy

Inborn errors of metabolism (IEMs) are rare hereditary or acquired disorders resulting from an enzymatic deformity in biochemical and metabolic pathways influencing proteins, fats, carbohydrate metabolism, or hampered some organelle function. Even though individual IEMs are uncommon, together, they represent a diverse class of genetic diseases, with new issues and disease mechanisms being portrayed consistently. IEM includes the extraordinary multifaceted nature of the fundamental pathophysiology, biochemical diagnosis, molecular level investigation, and complex therapeutic choices. However, due to the molecular, biochemical, and clinical heterogeneity of IEM, screening alone will not detect and diagnose all illnesses included in newborn screening programs. Early diagnosis prevents the emergence of severe clinical symptoms in the majority of IEM cases, lowering morbidity and death. The appearance of IEM disease can vary from neonates to adult people, with the more serious conditions showing up in juvenile stages along with significant morbidity as well as mortality. Advances in understanding the physiological, biochemical, and molecular etiologies of numerous IEMs by means of modalities, for instance, the latest molecular-genetic technologies, genome engineering knowledge, entire exome sequencing, and metabolomics, have prompted remarkable advancement in detection and treatment in modern times. In this review, we analyze the biochemical basis of IEMs, clinical manifestations, the present status of screening, ongoing advances, and efficiency of diagnosis in treatment for IEMs, along with prospects for further exploration as well as innovation.

Endogenous metabolic markers for predicting the activity of dihydropyrimidine dehydrogenase

Five‐fluorouracil (5‐FU) is a chemotherapeutic agent that is mainly metabolized by the rate‐limiting enzyme dihydropyrimidine dehydrogenase (DPD). The DPD enzyme activity deficiency involves a wide range of severities. Previous studies have demonstrated the effect of a DPYD single nucleotide polymorphism on 5‐FU efficacy and highlighted the importance of studying such genes for cancer treatment. Common polymorphisms of DPYD in European ancestry populations are less frequently present in Koreans. DPD is also responsible for the conversion of endogenous uracil (U) into dihydrouracil (DHU). We quantified U and DHU in plasma samples of healthy male Korean subjects, and samples were classified into two groups based on DHU/U ratio. The calculated DHU/U ratios ranged from 0.52 to 7.12, and the two groups were classified into the 10th percentile and 90th percentile for untargeted metabolomics analysis using liquid chromatography‐quantitative time‐of‐flight‐mass spectrometry. A total of 4440 compounds were detected and filtered out based on a coefficient of variation below 30%. Our results revealed that six metabolites differed significantly between the high activity group and low activity group (false discovery rate q‐value < 0.05). Uridine was significantly higher in the low DPD activity group and is a precursor of U involved in pyrimidine metabolism; therefore, we speculated that DPD deficiency can influence uridine levels in plasma. Furthermore, the cutoff values for detecting DPD deficient patients from previous studies were unsuitable for Koreans. Our metabolomics approach is the first study that reported the DHU/U ratio distribution in healthy Korean subjects and identified a new biomarker of DPD deficiency.

Blood biomarkers for assessment of mitochondrial dysfunction: An expert review

Although mitochondrial dysfunction is the known cause of primary mitochondrial disease, mitochondrial dysfunction is often difficult to measure and prove, especially when biopsies of affected tissue are not available. In order to identify blood biomarkers of mitochondrial dysfunction, we reviewed studies that measured blood biomarkers in genetically, clinically or biochemically confirmed primary mitochondrial disease patients. In this way, we were certain that there was an underlying mitochondrial dysfunction which could validate the biomarker. We found biomarkers of three classes: 1) functional markers measured in blood cells, 2) biochemical markers of serum/plasma and 3) DNA markers. While none of the reviewed single biomarkers may perfectly reveal all underlying mitochondrial dysfunction, combining biomarkers that cover different aspects of mitochondrial impairment probably is a good strategy. This biomarker panel may assist in the diagnosis of primary mitochondrial disease patients. As mitochondrial dysfunction may also play a significant role in the pathophysiology of multifactorial disorders such as Alzheimer's disease and glaucoma, the panel may serve to assess mitochondrial dysfunction in complex multifactorial diseases as well and enable selection of patients who could benefit from therapies targeting mitochondria.