Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Insight into metabolic dysregulation of polycystic ovary syndrome utilizing metabolomic signatures: a narrative review

Polycystic ovary syndrome (PCOS) is a complex multifactorial endocrinopathy affecting reproductive aged women globally, whose presentation is strongly influenced by genetic makeup, ethnic, and geographic diversity leaving these affected women substantially predisposed to reproductive and metabolic perturbations. Sophisticated techniques spanning genomics, proteomics, epigenomics, and transcriptomics have been harnessed to comprehensively understand the enigmatic pathophysiology of PCOS, however, conclusive markers for PCOS are still lacking today. Metabolomics represents a paradigm shift in biotechnological advances enabling the simultaneous identification and quantification of metabolites and the use of this approach has added yet another dimension to help unravel the strong metabolic component of PCOS. Reports dissecting the metabolic signature of PCOS have revealed disparate levels of metabolites such as pyruvate, lactate, triglycerides, free fatty acids, carnitines, branched chain and essential amino acids, and steroid intermediates in major biological compartments. These metabolites have been shown to be altered in women with PCOS overall, after phenotypic subgrouping, in animal models of PCOS, and also following therapeutic intervention. This review seeks to supplement previous reviews by highlighting the aforementioned aspects and to provide easy, coherent and elementary access to significant findings and emerging trends. This will in turn help to delineate the metabolic plot in women with PCOS in various biological compartments including plasma, urine, follicular microenvironment, and gut. This may pave the way to design additional studies on the quest of unraveling the etiology of PCOS and delving into novel biomarkers for its diagnosis, prognosis and management.

Eco-friendly synthesis of a porous reduced graphene oxide-polypyrrole-gold nanoparticle hybrid nanocomposite for electrochemical detection of methotrexate using a strip sensor

Chemotherapy is a crucial cancer treatment, but its effectiveness requires precise monitoring of drug concentrations in patients. This study introduces an innovative electrochemical strip sensor design to detect and continuously monitor methotrexate (MTX), a key chemotherapeutic drug. The sensor is crafted through an eco-friendly synthesis process that produces porous reduced graphene oxide (PrGO), which is then integrated with gold nanocomposites and polypyrrole (PPy) to boost the performance of a screen-printed carbon electrode (SPCE). Advanced techniques were employed for detailed characterization of the nanocomposites such as X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and BET analysis. The enhanced sensor exhibited a notable increase in the electrochemical oxidation signals of MTX, attributed to the improved electron transfer at the SPCE/PrGO-PPy-Au electrode interface. Superior electrochemical interfacial properties were well characterized with the techniques of cyclic voltammetry, electrochemical impedance spectroscopy, and square wave voltammetry. The sensor demonstrates an efficient electrochemical response toward the detection of MTX with a broad detection range from 130 nM to 1 μM, an impressively low detection limit of 0.4 nM in human serum, and a sensitivity of 24.1 μA μM-1. This combination highlights its exceptional performance in detecting analytes with high precision and sensitivity. The sensor exhibited a long-term continuous monitoring stability response to monitor the MTX drug in human serum for 4 hours. The sensor's high sensitivity, selectivity, reproducibility, and stability over time emphasize its potential as a valuable tool for the swift on-site testing of anticancer drugs in clinical and environmental settings.

Artificial intelligence in the management of metabolic disorders: a comprehensive review

This review explores the significant role of artificial intelligence (AI) in managing metabolic disorders like diabetes, obesity, metabolic dysfunction-associated fatty liver disease (MAFLD), and thyroid dysfunction. AI applications in this context encompass early diagnosis, personalized treatment plans, risk assessment, prevention, and biomarker discovery for early and accurate disease management. This review also delves into techniques involving machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and reinforcement learning associated with AI and their application in metabolic disorders. The following study also enlightens the challenges and ethical considerations associated with AI implementation, such as data privacy, model interpretability, and bias mitigation. We have reviewed various AI-based tools utilized for the diagnosis and management of metabolic disorders, such as Idx, Guardian Connect system, and DreaMed for diabetes. Further, the paper emphasizes the potential of AI to revolutionize the management of metabolic disorders through collaborations among clinicians and AI experts, the integration of AI into clinical practice, and the necessity for long-term validation studies. The references provided in the paper cover a range of studies related to AI, ML, personalized medicine, metabolic disorders, and diagnostic tools in healthcare, including research on disease diagnostics, personalized therapy, chronic disease management, and the application of AI in diabetes care and nutrition.

From discovery to translation: Endogenous substrates of OAT1 and OAT3 as clinical biomarkers for renal secretory function

The recent ICH M12 guidance on Drug Interaction Studies encourages the use of alternate approaches for predicting drug-drug interaction (DDI) potential of new chemical entities. One approach involves biomarkers, which are endogenous substrates of drug metabolizing enzymes and transporters (DMET) and can be used to assess the inhibitory potential of new chemical entities during Phase 1 clinical studies. Thus, biomarkers could potentially eliminate the need for dedicated DDI studies with exogenous probe substrates. Metabolomics, in conjunction with in vitro and/or in vivo preclinical models or clinical studies, can be used for biomarker discovery. We developed and applied a novel metabolomics-based DMET biomarker discovery (MDBD) approach to identify and qualify biomarkers of renal organic anion transporter 1 (OAT1) and OAT3. Untargeted metabolomics of pooled plasma and urine samples from a pharmacokinetic DDI study using the OAT1/3 inhibitor, probenecid, yielded 153 features identified as putative OAT1/3 biomarkers. Subsequently, in vitro transporter uptake assays using processed urine samples confirmed 57 of these features as OAT1 and/or OAT3 substrates. Finally, 23 features were clinically validated as OAT1/3 biomarkers through a detailed pharmacokinetic analysis (0-24 h) of plasma and urine samples. These biomarkers, either alone or as part of a panel, can predict OAT1/3-mediated DDIs and interindividual variability in the renal secretory clearance of organic anions across different populations, thereby enabling translational utility in clinical settings. The novel MDBD approach can be extended to discover biomarkers of other transporters and enzymes.

SUMMARY

Using clinical and mechanistic in vitro approaches, 23 endogenous substrates of OAT1/3 were identified as potential clinical biomarkers of renal secretary elimination of organic anions.

Using metabolomics to investigate the relationship between the metabolomic profile of the intestinal microbiota derivatives and mental disorders in inflammatory bowel diseases: a narrative review

Individuals with inflammatory bowel disease (IBD) are at a higher risk of developing mental disorders, such as anxiety and depression. The imbalance between the intestinal microbiota and its host, known as dysbiosis, is one of the factors, disrupting the balance of metabolite production and their signaling pathways, leading to disease progression. A metabolomics approach can help identify the role of gut microbiota in mental disorders associated with IBD by evaluating metabolites and their signaling comprehensively. This narrative review focuses on metabolomics studies that have comprehensively elucidated the altered gut microbial metabolites and their signaling pathways underlying mental disorders in IBD patients. The information was compiled by searching PubMed, Web of Science, Scopus, and Google Scholar from 2005 to 2023. The findings indicated that intestinal microbial dysbiosis in IBD patients leads to mental disorders such as anxiety and depression through disturbances in the metabolism of carbohydrates, sphingolipids, bile acids, neurotransmitters, neuroprotective, inflammatory factors, and amino acids. Furthermore, the reduction in the production of neuroprotective factors and the increase in inflammation observed in these patients can also contribute to the worsening of psychological symptoms. Analyzing the metabolite profile of the patients and comparing it with that of healthy individuals using advanced technologies like metabolomics, aids in the early diagnosis and prevention of mental disorders. This approach allows for the more precise identification of the microbes responsible for metabolite production, enabling the development of tailored dietary and pharmaceutical interventions or targeted manipulation of microbiota.

From Complexity to Clarity: Expanding Metabolome Coverage With Innovative Analytical Strategies

Metabolomics, a powerful discipline within systems biology, aims at comprehensive profiling of small molecules in biological samples. The challenges of biological sample complexity are addressed through innovative sample preparation methods, including solid-phase extraction and microextraction techniques, enhancing the detection and quantification of low-abundance metabolites. Advances in chromatographic separation, particularly liquid chromatography (LC) and gas chromatography (GC), coupled with high-resolution (HR) mass spectrometry (MS), have significantly improved the sensitivity, selectivity, and throughput of metabolomic studies. Cutting-edge techniques, such as ion-mobility mass spectrometry (IM-MS) and tandem MS (MS/MS), further expand the capacity for comprehensive metabolite profiling. These advanced analytical platforms each offer unique advantages for metabolomics, with continued technological improvements driving deeper insights into metabolic pathways and biomarker discovery. By providing a detailed overview of current trends and techniques, this review aims to offer valuable insights into the future of metabolomics in human health research and its translational potential in clinical settings. Toward the end, this review also highlights the biomedical applications of metabolomics, emphasizing its role in biomarker discovery, disease diagnostics, personalized medicine, and drug development.

Understanding LC/MS-Based Metabolomics: A Detailed Reference for Natural Product Analysis

Liquid chromatography, when used in conjunction with mass spectrometry (LC/MS), is a powerful tool for conducting accurate and reproducible investigations of numerous metabolites in natural products (NPs). LC/MS has gained prominence in metabolomic research due to its high throughput, the availability of multiple ionization techniques and its ability to provide comprehensive metabolite coverage. This unique method can significantly influence various scientific domains. This review offers a comprehensive overview of the current state of LC/MS-based metabolomics in the investigation of NPs. This review provides a thorough overview of the state of the art in LC/MS-based metabolomics for the investigation of NPs. It covers the principles of LC/MS, various aspects of LC/MS-based metabolomics such as sample preparation, LC modes, method development, ionization techniques and data pre-processing. Moreover, it presents the applications of LC/MS-based metabolomics in numerous fields of NPs research such as including biomarker discovery, the agricultural research, food analysis, the study of marine NPs and microbiological research. Additionally, this review discusses the challenges and limitations of LC/MS-based metabolomics, as well as emerging trends and developments in this field.

Frailty and sarcopenia metabolomic signatures in kidney transplant candidates: the FRAILMar study

Background

Sarcopenia and frailty are often overlooked in assessing kidney transplant (KT) candidates with chronic kidney disease (CKD), potentially leading to poor post-transplant outcomes. This study aimed to identify metabolites associated with frailty and sarcopenia in KT candidates from the FRAILMar study.

Methods

Between June 2016 and June 2020, we evaluated frailty and sarcopenia in 173 KT candidates using the Physical Frailty Phenotype and EGWSOP-2 criteria, respectively. Seventy-five metabolic markers from targeted pathways, previously linked to CKD, sarcopenia or frailty, were measured in serum samples. These markers were analyzed using adjusted and weighted generalized linear models. Metabolomic data were integrated with multi-modal data, such as comorbidities, using a factor-based integration algorithm to identify metabolic phenotypes.

Results

Increased metabolites related to energy metabolism and essential amino acids were associated with frailty, mainly Krebs cycle intermediates. Sarcopenic KT candidates showed lower levels of aromatic amino acids, and lower protein/muscle metabolism, energy metabolism and neurotransmission compared with non-sarcopenic patients. Unsupervised multi-modal integration revealed a high-risk metabolic phenotype characterized by the presence of sarcopenia, diabetes mellitus and low body mass index, with alterations in branched-chain amino acids and high activity of lactate dehydrogenase enzyme.

Conclusions

Frailty and sarcopenia are common among KT candidates, and their metabolic status reveals notable disruptions in energy and amino acid metabolism. These findings highlight the value of a detailed metabolic assessment to more accurately evaluate patient health status prior to transplantation.

C. Calado C. Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint

Predicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast-Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.

A Systematic Review: Assessment of the Metabolomic Profile and Anti-Nutritional Factors of Cannabis sativa as a Feed Additive for Ruminants

Background:Cannabis sativa is a high-value crop that can be cultivated for ruminant's feed and medicinal purposes. The demand for Cannabis and Cannabis products has increased since the beginning of 21st century. Objectives: The increase in the production cost of high-protein feeds such as lucerne has led to an urgent need to investigate alternative high-protein sources. Methods: Cannabis has been identified as an alternative to lucerne due to its high protein content. Results: However, the cultivation and uses of Cannabis and its by-products in South Africa is limited due to the strict legislation. The metabolites and nutritional value of Cannabis are influenced by growing conditions and soil type. Furthermore, the available literature has shown that Cannabis contains anti-nutritional factors that may affect feed intake or bioavailability and digestibility. Conclusions: Therefore, it is crucial to employ a processing method that can reduce anti-nutritional factors to promote the feed intake and growth rate of sheep. Fermentation, as a processing method, can reduce anti-nutritional factors found in Cannabis, which will make it a palatable alternative feed supplement for ruminants such as Dorper sheep. Overall, this review paper aimed to examine the available literature on the use of Cannabis as an alternative high-protein feed supplement for Dorper sheep in South Africa.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

Monitoring central nervous system tumour metabolism using cerebrospinal fluid

Central nervous system (CNS) tumours are the most common cancer cause of death in under 40s in the UK, largely because they persist and recur and sometimes metastasise during treatment. Therefore, longitudinal monitoring of patients during and following treatment must be undertaken to understand the course of the disease and alter treatment plans reactively. This monitoring must be specific, sensitive, rapid, low cost, simple, and accepted by the patient. Cerebrospinal fluid (CSF) examination obtained following lumbar puncture, already a routine part of treatment in paediatric cases, could be better utilised with improved biomarkers. In this review, we discuss the potential for metabolites in the CSF to be used as biomarkers of CNS tumour remission, progression, response to drugs, recurrence and metastasis. We confer the clinical benefits and risks of this approach and conclude that there are many potential advantages over other tests and the required instrumentation is already present in UK hospitals. On the other hand, the approach needs more research investment to find more metabolite biomarkers, better understand their relation to the tumour, and validate those biomarkers in a standardised assay in order for the assay to become a clinical reality.

Cold-hot Janus electrochemical aptamer-based sensor for calibration-free determination of biomolecules

Real-time, high-frequency measurements of pharmaceuticals, metabolites, exogenous antigens, and other biomolecules in biological samples can provide critical information for health management and clinical diagnosis. Electrochemical aptamer-based (EAB) sensor is a promising analytical technique capable of achieving these goals. However, the issues of insufficient sensitivity, frequent calibration and lack of adapted portable electrochemical device limit its practical application in immediate detection. In response we have fabricated an on-chip-integrated, cold-hot Janus EAB (J-EAB) sensor based on the thermoelectric coolers (TECs). Attributed to the Peltier effect, the enhanced/suppressed current response can be generated simultaneously on cold/hot sides of the J-EAB sensor. The ratio of the current responses on the cold and hot sides was used as the detection signal, enabling rapid on-site, calibration-free determination of small molecules (procaine) as well as macromolecules (SARS-CoV-2 spike protein) in single step, with detection limits of 1 μM and 10 nM, respectively. We have further demonstrated that the J-EAB sensor is effective in improving the ease and usability of the actual detection process, and is expected to provide a universal, low-cost, fast and easy potential analytical tool for other clinically important biomarkers, drugs or pharmaceutical small molecules.

CMOS Point-of-Care Diagnostics Technologies: Recent Advances and Future Prospects

This review provides a comprehensive overview of point-of-care (PoC) devices across several key diagnostic applications, including blood analysis, infectious disease detection, neural interfaces, and commercialized integrated circuits (ICs). In the blood analysis section, the focus is on biomarkers such as glucose, dopamine, and aptamers, and their respective detection techniques. The infectious disease section explores PoC technologies for detecting pathogens, RNA, and DNA, highlighting innovations in molecular diagnostics. The neural interface section reviews advancements in neural recording and stimulation for therapeutic applications. Finally, a survey of commercialized ICs from companies such as Abbott and Medtronic is presented, showcasing existing PoC devices already in widespread clinical use. This review emphasizes the role of complementary metal-oxide-semiconductor (CMOS) technology in enabling compact, efficient diagnostic systems and offers insights into the current and future landscape of PoC devices.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities' expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Identification of Chagas disease biomarkers using untargeted metabolomics

Untargeted metabolomic analysis is a powerful tool used for the discovery of novel biomarkers. Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical disease that affects 6-7 million people with approximately 30% developing cardiac manifestations. The most significant clinical challenge lies in its long latency period after acute infection, and the lack of surrogate markers to predict disease progression or cure. In this cross-sectional study, we analyzed sera from 120 individuals divided into four groups: 31 indeterminate CD, 41 chronic chagasic cardiomyopathy (CCC), 18 Latin Americans with other cardiomyopathies and 30 healthy volunteers. Using a high-throughput panel of 986 metabolites, we identified three distinct profiles among individuals with cardiomyopathy, indeterminate CD and healthy volunteers. After a more stringent analysis, we identified some potential biomarkers. Among peptides, phenylacetylglutamine and fibrinopeptide B (1-13) exhibited an increasing trend from controls to ICD and CCC. Conversely, reduced levels of bilirubin and biliverdin alongside elevated urobilin correlated with disease progression. Finally, elevated levels of cystathionine, phenol glucuronide and vanillactate among amino acids distinguished CCC individuals from ICD and controls. Our novel exploratory study using metabolomics identified potential biomarker candidates, either alone or in combination that if confirmed, can be translated into clinical practice.

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

A multidimensional strategy for characterization, distinction, and quality control of two Clinopodium medicinal plants

ETHNOPHARMACOLOGICAL RELEVANCE

Clinopodium chinense Kuntze (CC) and Clinopodium polycephalum (Vaniot) C. Y. Wu & S. J. Hsuan (CP) are both included in the Pharmacopoeia of the People's Republic of China (edition 2020) as the legitimate source of "Duan Xue Liu" (DXL), which is a crucial traditional Chinese medicine used as a clinical remedy for bleeding diseases. However, the differences in plant endogenous metabolites and bioactivities between CC and CP are still unclear.

AIM OF THE STUDY

This study aims to provide a scientific basis to investigate the differences between CC and CP ensuring the efficient and safe use of DXL.

MATERIALS AND METHODS

A multidimensional strategy including plant metabolomics, digital reference standard (DRS) analyzer, and biological activities assay was creatively constructed for the characterization, distinction, and quality control of CC and CP.

RESULTS

There were apparent differences in the metabolites between CC and CP. 7 compounds contributing to the differences were successfully identified. On that basis, linear calibration using two reference substances (LCTRS) methods was proved as a more accurate and specific quality analysis method for CC and CP. In addition, bioactivity assays showed that both CC and CP exhibited obvious hemostatic activity, while CC showed greater potential to resist inflammation and free radicals.

CONCLUSION

In summary, it was the first time to investigate the chemical constituents and bioactivities differences between CC and CP with the help of plant metabolomics, DRS study, and biological activity assays. These two plants were significantly separated in the integrated analysis, suggesting that we should pay attention to the distinction to prevent unexpected risks caused by medicinal materials.

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.

Advancements in magnetic nanoparticle-based biosensors for point-of-care testing

The significance of point-of-care testing (POCT) in early clinical diagnosis and personalized patient care is increasingly recognized as a crucial tool in reducing disease outbreaks and improving patient survival rates. Within the realm of POCT, biosensors utilizing magnetic nanoparticles (MNPs) have emerged as a subject of substantial interest. This review aims to provide a comprehensive evaluation of the current landscape of POCT, emphasizing its growing significance within clinical practice. Subsequently, the current status of the combination of MNPs in the Biological detection has been presented. Furthermore, it delves into the specific domain of MNP-based biosensors, assessing their potential impact on POCT. By combining existing research and spotlighting pivotal discoveries, this review enhances our comprehension of the advancements and promising prospects offered by MNP-based biosensors in the context of POCT. It seeks to facilitate informed decision-making among healthcare professionals and researchers while also promoting further exploration in this promising field of study.

Analysis and Characterization of the Extracellular Vesicles Released in Non-Cancer Diseases Using Matrix-Assisted Laser Desorption Ionization/Mass Spectrometry

The extracellular vesicles (EVs) released by cells play a crucial role in intercellular communications and interactions. The direct shedding of EVs from the plasma membrane represents a fundamental pathway for the transfer of properties and information between cells. These vesicles are classified based on their origin, biogenesis, size, content, surface markers, and functional features, encompassing a variety of bioactive molecules that reflect the physiological state and cell type of origin. Such molecules include lipids, nucleic acids, and proteins. Research efforts aimed at comprehending EVs, including the development of strategies for their isolation, purification, and characterization, have led to the discovery of new biomarkers. These biomarkers are proving invaluable for diagnosing diseases, monitoring disease progression, understanding treatment responses, especially in oncology, and addressing metabolic, neurological, infectious disorders, as well as advancing vaccine development. Matrix-Assisted Laser Desorption Ionization (MALDI)/Mass Spectrometry (MS) stands out as a leading tool for the analysis and characterization of EVs and their cargo. This technique offers inherent advantages such as a high throughput, minimal sample consumption, rapid and cost-effective analysis, and user-friendly operation. This review is mainly focused on the primary applications of MALDI-time-of-flight (TOF)/MS in the analysis and characterization of extracellular vesicles associated with non-cancerous diseases and pathogens that infect humans, animals, and plants.

Biochemical diagnosis of congenital disorders of glycosylation

Congenital disorders of glycosylation (CDG) are one of the fastest growing groups of inborn errors of metabolism, comprising over 160 described diseases to this day. CDG are characterized by a dysfunctional glycosylation process, with molecular defects localized in the cytosol, the endoplasmic reticulum, or the Golgi apparatus. Depending on the CDG, N-glycosylation, O-glycosylation and/or glycosaminoglycan synthesis can be affected. Various proteins, lipids, and glycosylphosphatidylinositol anchors bear glycan chains, with potential impacts on their folding, targeting, secretion, stability, and thus, functionality. Therefore, glycosylation defects can have diverse and serious clinical consequences. CDG patients often present with a non-specific, multisystemic syndrome including neurological involvement, growth delay, hepatopathy and coagulopathy. As CDG are rare diseases, and typically lack distinctive clinical signs, biochemical and genetic testing bear particularly important and complementary diagnostic roles. Here, after a brief introduction on glycosylation and CDG, we review historical and recent findings on CDG biomarkers and associated analytical techniques, with a particular emphasis on those with relevant use in the specialized clinical chemistry laboratory. We provide the reader with insights and methods which may help them properly assist the clinician in navigating the maze of glycosylation disorders.

DNA damage in inhabitants exposed to heavy metals near Hudiara drain, Lahore, Pakistan

The current study was conducted on the inhabitants living in the area adjacent to the Hudiara drain using bore water and vegetables adjacent to the Hudiara drain. Toxic heavy metals badly affect human health because of industrial environmental contamination. Particularly hundreds of millions of individuals globally have faced the consequences of consuming water and food tainted with pollutants. Concentrations of heavy metals in human blood were elevated in Hudiara drainings in Lahore city, Pakistan, due to highly polluted industrial effluents. The study determined the health effects of high levels of heavy metals (Cd, Cu, Zn, Fe, Pb, Ni, Hg, Cr) on residents of the Hudiara draining area, including serum MDA, 8-Isoprostane, 8-hydroxyguanosine, and creatinine levels. An absorption spectrophotometer was used to determine heavy metals in wate water, drinking water, soil, plants and human beings blood sampleas and ELISA kits were used to assess the level of 8-hydroxyguanosine, MDA, 8-Isoprostane in plasma serum creatinine level. Waste water samples, irrigation water samples, drinking water samples, Soil samples, Plants samples and blood specimens of adult of different weights and ages were collected from the polluted area of the Hudiara drain (Laloo and Mohanwal), and control samples were obtained from the unpolluted site Sheiikhpura, 60 km away from the site. Toxic heavy metals in blood damage the cell membrane and DNA structures, increasing the 8-hydroxyguanosine, MDA, creatinine, and 8-Isoprostane. Toxic metals contaminated bore water and vegetables, resulting in increased levels of creatinine, MDA, Isoprostane, and 8-hydroxy-2-guanosine in the blood of inhabitants from the adjacent area Hudiara drain compared to the control group. In addition,. This study also investigated heavy metal concentrations in meat and milk samples from buffaloes, cows, and goats. In meat, cow samples showed the highest Cd, Cu, Fe and Mn concentrations. In milk also, cows exhibited elevated Cu and Fe levels compared to goats. The results highlight species-specific variations in heavy metal accumulation, emphasizing the need for targeted monitoring to address potential health risks. The significant difference between the two groups i.e., the control group and the affected group, in all traits of the respondents (weight, age, heavy metal values MDA, 8-Isoprostane, 8-hydroxyguaniosine, and serum creatinine level). Pearson's correlation coefficient was calculated. The study has shown that the level of serum MDA, 8-Isoprostane, 8-hydroxyguaniosine, or creatinine has not significantly correlated with age, so it is independent of age. This study has proved that in Pakistan, the selected area of Lahore in the villages of Laloo and Mohanwal, excess of heavy metals in the human body damages the DNA and increases the level of 8-Isoprostane, MDA, creatinine, and 8-hydroxyguaniosine. As a result, National and international cooperation must take major steps to control exposure to heavy metals.

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.

Metabolomics: Challenges and Opportunities in Systems Biology Studies

Metabolomics can provide diagnostic, prognostic, and therapeutic biomarker profiles of individual patients because a large number of metabolites can be simultaneously measured in biological samples in an unbiased manner. Minor stimuli can result in substantial alterations, making it a valuable target for analysis. Due to the complexity and sensitivity of the metabolome, studies must be devised to maintain consistency, minimize subject-to-subject variation, and maximize information recovery. This effort has been aided by technological advances in experimental design, rodent models, and instrumentation. Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy of biofluids, such as plasma, urine, and faeces provide the opportunity to identify biomarker change patterns that reflect the physiological or pathological status of an individual patient. Metabolomics has the ultimate potential to be useful in a clinical context, where it could be used to predict treatment response and survival and for early disease diagnosis. During drug treatment, an individual's metabolic status could be monitored and used to predict deleterious effects. Therefore, metabolomics has the potential to improve disease diagnosis, treatment, and follow-up care. In this chapter, we demonstrate how a metabolomics study can be used to diagnose a disease by classifying patients as either healthy or pathological, while accounting for individual variation.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

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.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Urine Metabolites and Bioactive Compounds from Functional Food: Applications of Liquid Chromatography Mass Spectrometry

Bioactive compounds in functional foods, medicinal plants and others are considered attractive value-added molecules based on their wide range of bioactivity. It is clear that an important role is occupied by polyphenol, phenolic compounds and others. Urine is an effective biofluid to evaluate and monitor alterations in homeostasis and other processes related to metabolism. The current review provides a detailed description of the formation of urine in human body, various aspects relevant to sampling and analysis of urinary metabolites before presenting recent developments leveraging on metabolite profiling of urine. For the profiling of small molecules in urine, advancement of liquid chromatography mass tandem spectrometry (LC/MS/MS), establishment of standardized chemical fragmentation libraries, computational resources, data-analysis approaches with pattern recognition tools have made it an attractive option. The profiling of urinary metabolites gives an overview of the biomarkers associated with the diet and evaluates its biological effects. Metabolic pathways such as glycolysis, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine metabolism and others can be evaluated. Finally, a combination of metabolite profiling with chemical standardization and bioassay in functional food and medicinal plants will likely lead to the identification of new biomarkers and novel biochemical insights.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

Mitochondrial and metabolic features of salugenesis and the healing cycle

Pathogenesis and salugenesis are the first and second stages of the two-stage problem of disease production and health recovery. Salugenesis is the automatic, evolutionarily conserved, ontogenetic sequence of molecular, cellular, organ system, and behavioral changes that is used by living systems to heal. It is a whole-body process that begins with mitochondria and the cell. The stages of salugenesis define a circle that is energy- and resource-consuming, genetically programmed, and environmentally responsive. Energy and metabolic resources are provided by mitochondrial and metabolic transformations that drive the cell danger response (CDR) and create the three phases of the healing cycle: Phase 1-Inflammation, Phase 2-Proliferation, and Phase 3-Differentiation. Each phase requires a different mitochondrial phenotype. Without different mitochondria there can be no healing. The rise and fall of extracellular ATP (eATP) signaling is a key driver of the mitochondrial and metabolic reprogramming required to progress through the healing cycle. Sphingolipid and cholesterol-enriched membrane lipid rafts act as rheostats for tuning cellular sensitivity to purinergic signaling. Abnormal persistence of any phase of the CDR inhibits the healing cycle, creates dysfunctional cellular mosaics, causes the symptoms of chronic disease, and accelerates the process of aging. New research reframes the rising tide of chronic disease around the world as a systems problem caused by the combined action of pathogenic triggers and anthropogenic factors that interfere with the mitochondrial functions needed for healing. Once chronic pain, disability, or disease is established, salugenesis-based therapies will start where pathogenesis-based therapies end.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

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

Liquid chromatography and differential mobility spectrometry-data-independent mass spectrometry for comprehensive multidimensional separations in metabolomics

The benefits of combining drift time ion mobility (DTIMS) with liquid chromatography-high-resolution mass spectrometry (HRMS) have been reported for metabolomics but the use of differential time mobility spectrometry (DMS) is less obvious due to the need for rapid scanning of the DMS cell. Drift DTIMS provides additional precursor ion selectivity and collisional cross-section information but the separation resolution between analytes remains cell- and component-dependent. With DMS, the addition of 2-propanol modifier can improve the selectivity but on cost of analyte MS response. In the present work, we investigate the liquid chromatography-mass spectrometry (LC-MS) analysis of a mix of 50 analytes, representative for urine and plasma metabolites, using scanning DMS with the single modifiers cyclohexane (Ch), toluene (Tol), acetonitrile (ACN), ethanol (EtOH), and 2-propanol (IPA), and a binary modifier mixture (cyclohexane/2-propanol) with emphasis on selectivity and signal sensitivity. 1.5% IPA in the N₂ stream was found to suppress the signal of 50% of the analytes which could be partially recovered with the use of IPA to 0.05% as a Ch/IPA mixture. The potential to use the separation voltage/compensation voltage/modifier (SV/CoV/Mod) feature as an additional analyte identifier for qualitative analysis is also presented and applied to a data-independent LCxDMS-SWATH-MS workflow for the analysis of endogenous metabolites and drugs of abuse in human urine samples from traffic control.

Abnormal fatty acid metabolism and ceramide expression may discriminate myocardial infarction from strangulation death: A pilot study

Determining myocardial infarction (MI) and mechanical asphyxia (MA) was one of the most challenging tasks in forensic practice. The present study aimed to investigate the potential of fatty acid (FAs) metabolism, and lipid alterations in determining MI and MA. MA and MI mouse models were constructed, and metabolic profiles were obtained by LC-MS-based untargeted metabolomics. The metabolic alterations were explored using the PCA, OPLS-DA, the Wilcoxon test, and fold change analysis. The contents of lipid droplets (LDs) were detected by the transmission scanning electron microscope and Oil red O staining. The immunohistochemical assay was performed to detect CD36 and dysferlin. The ceramide was assessed by LC-MS. PCA showed considerable differences in the metabolite profiles, and the well-fitting OPLS-DA model was developed to screen differential metabolites. Thereinto, 9 metabolites in the MA were reduced, while metabolites were up- and down-regulated in MI. The increased CD36 suggested that MI and MA could enhance the intake of FAs and disturb energy metabolism. The increased LDs, decreased dysferlin, and increased ceramide (C18:0, C22:0, and C24:0) were observed in MI groups, confirming the lipid deposition. The present study indicated significant differences in myocardial FAs metabolism and lipid alterations between MI and MA, suggesting that FAs metabolism and related proteins, certain ceramide may harbor the potential as biomarkers for discrimination of MI and MA.

Untargeted Metabolomics by Liquid Chromatography-Mass Spectrometry in Biomedical Research

Metabolomics, alone or in combination with other omics sciences, has shown great relevance in a large number of investigations in different branches of biomedicine, often providing novel discoveries and helping to expand the knowledge. Metabolomics analyses are carried out using different techniques, but in this chapter, we focus on liquid chromatography coupled to high-resolution mass spectrometry. The designated methodology consists of an untargeted approach for the analysis of plasma samples. The use of this method, with a reverse-phase column and electrospray ionization in positive mode, covers the detection of a broad range of metabolites, mainly of nonpolar and of intermediate polarity. This chapter also reviews the mass fragmentation spectra for the identification of bile acids, acylcarnitines, and glycerophospholipids.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.

Acute Pancreatitis: Diagnosis and Treatment

Acute pancreatitis is a common indication for hospital admission, increasing in incidence, including in children, pregnancy and the elderly. Moderately severe acute pancreatitis with fluid and/or necrotic collections causes substantial morbidity, and severe disease with persistent organ failure causes significant mortality. The diagnosis requires two of upper abdominal pain, amylase/lipase ≥ 3 ×upper limit of normal, and/or cross-sectional imaging findings. Gallstones and ethanol predominate while hypertriglyceridaemia and drugs are notable among many causes. Serum triglycerides, full blood count, renal and liver function tests, glucose, calcium, transabdominal ultrasound, and chest imaging are indicated, with abdominal cross-sectional imaging if there is diagnostic uncertainty. Subsequent imaging is undertaken to detect complications, for example, if C-reactive protein exceeds 150 mg/L, or rarer aetiologies. Pancreatic intracellular calcium overload, mitochondrial impairment, and inflammatory responses are critical in pathogenesis, targeted in current treatment trials, which are crucially important as there is no internationally licenced drug to treat acute pancreatitis and prevent complications. Initial priorities are intravenous fluid resuscitation, analgesia, and enteral nutrition, and when necessary, critical care and organ support, parenteral nutrition, antibiotics, pancreatic exocrine and endocrine replacement therapy; all may have adverse effects. Patients with local complications should be referred to specialist tertiary centres to guide further management, which may include drainage and/or necrosectomy. The impact of acute pancreatitis can be devastating, so prevention or reduction of the risk of recurrence and progression to chronic pancreatitis with an increased risk of pancreas cancer requires proactive management that should be long term for some patients.

Sex differences in the human metabolome

BACKGROUND

The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved.

MAIN BODY

This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account.

CONCLUSIONS

The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Metabolomic Workflow for the Accurate and High-Throughput Exploration of the Pathways of Tryptophan, Tyrosine, Phenylalanine, and Branched-Chain Amino Acids in Human Biofluids

The modulation of host and dietary metabolites by gut microbiota (GM) is important for maintaining correct host physiology and in the onset of various pathologies. An ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method was developed for the targeted quantitation in human plasma, serum, and urine of 89 metabolites resulting from human-GM cometabolism of dietary essential amino acids tryptophan, tyrosine, and phenylalanine as well as branched-chain amino acids. Ninety-six-well plate hybrid-SPE enables fast clean-up of plasma and serum. Urine was diluted and filtered. A 15 min cycle enabled the acquisition of 96 samples per day, with most of the metabolites stable in aqueous solution for up to 72 h. Calibration curves were specifically optimized to cover expected concentrations in biological fluids, and limits of detection were at the order of ppb. Matrix effects were in acceptable ranges, and analytical recoveries were in general greater than 80%. Inter and intraday precision and accuracy were satisfactory. We demonstrated its application in plasma and urine samples obtained from the same individual in the frame of an interventional study, allowing the quantitation of 51 metabolites. The method could be considered the reference for deciphering changes in human-gut microbial cometabolism in health and disease. Data are available via Metabolights with the identifier MTBLS4399.

Global metabolome profiling of COVID-19 respiratory specimen using high-resolution mass spectrometry (HRMS)

Here we describe a protocol for identifying metabolites in respiratory specimens of patients that are SARS-CoV-2 positive, SARS-CoV-2 negative, or H1N1 positive. This protocol provides step-by-step instructions on sample collection from patients, followed by metabolite extraction. We use ultra-high-pressure liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) for data acquisition and describe the steps for data analysis. The protocol was standardized with specific customization for SARS-CoV-2-containing respiratory specimens. For complete details on the use and execution of this protocol, please refer to Maras et al. (2021).

Harnessing Metabolomics to Advance Epilepsy Research

Metabolomics is the laboratory analysis and scientific study of the metabolome—that is, the entire collection of small molecule chemicals in an organism. The metabolome represents the functional state of an organism and provides a multifaceted readout of the aggregate activity of endogenous (cellular) and exogenous (environmental) processes. In this review, we discuss how the integrative and dynamic properties of the metabolome create unique opportunities to study complex pathologies that evolve and oscillate over time, like epilepsy. We explain how the scientific progress and clinical applications of metabolomics remain hampered by biological and technical challenges, and we propose best practices to overcome these challenges so that metabolomics can be used in a rigorous and effective manner to further epilepsy research.

Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories

In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.