The future of NMR-based metabolomics

Detection of 15N-labeled metabolites in microbial extracts using AI-designed broadband pulses for 1H, 15N heteronuclear NMR spectroscopy

Approximately 40% of bacterial and mammalian metabolites contain nitrogen-based chemical moieties such as amides, amines, and imines. The identification and quantification of these groups via 2D 1H,15N heteronuclear NMR spectroscopy have broadened the catalog of NMR-detected metabolites. However, these NMR experiments necessitate broadband radiofrequency (RF) pulses for inversion and refocusing operations to encompass the full range of 15N chemical shifts, a challenge that becomes increasingly apparent at high and ultra-high magnetic fields. Here, we show that a newly AI-designed broadband 15N universal 180° pulse for both inversion and refocusing incorporated in the 2D 1H, 15N heteronuclear single quantum coherence (2D 1H-15N BB-HSQC) experiment significantly enhances spectral sensitivity. We demonstrate the advantage of the new technique by analyzing the crude extract of Micromonospora sp. WMMC264, a microbial strain that produces siderophores for iron absorption from the environment. The implementation of the AI-designed pulse in the 2D 1H-15N BB-HSQC experiment will contribute to advancing the analysis of nitrogen-containing metabolites in biological fluids and cell extracts.

Xenometabolomics in Ecotoxicology: Concepts and Applications

Nontargeted high-resolution mass spectrometry (HRMS) allows for the characterization of a large fraction of the exposome, i.e., the entirety of chemicals an organism is exposed to, and helps detect important exogenous chemical compounds that could be key drivers of toxicological impact. Along with these chemical compounds occur endogenous metabolites that are essential for the health of the host organism. Chemical compounds derived from the biotransformation of xenobiotics present in the exposome are referred to as the xenometabolome, while endogenous metabolites derived from the host organism are referred to as the endometabolome. Recent advancements in HRMS technology allow for the detection of chemical features of biological and ecological importance in the context of chemical safety assessments with unprecedented sensitivity and resolution. In this perspective, we highlight the application of HRMS-based metabolomics of organisms in the context of ecotoxicology, the complexity of comprehensively characterizing the endometabolome, and distinguishing chemical compounds of the xenometabolome.

Glycerophospholipids Analysis Using Conventional 1H-31P HMBC and Its Application in Revealing the Characteristics of 18 Seaweeds

Glycerophospholipids (GPLs) are typical membrane lipids with important physiological and pharmacological functions, but little is known about their distribution characteristics in economic seaweeds. This study established a systematic evaluation method, including conventional 2D 1H-31P heteronuclear multiple bond correlation (HMBC) NMR for identifying GPLs and 1D 31P NMR for determining the contents, which were then applied to reveal subclasses characteristics of GPLs in 18 economic seaweeds in Asia. The results indicated that phosphatidylglycerol (PG) was the characteristic GPL subclass distributed in all of the 18 seaweeds (ranging from 0.08 to 1.75‰), generally with higher contents than that of known terrestrial plants. The characteristic chemical shifts of protons in the GPLs headgroup (H-1') together with protons in the backbone (H-3) were summarized through 1H-31P HMBC spectrum, which were helpful in assigning GPL subclasses.

Metabolomic signatures in liquid biopsy are associated with overall survival in metastatic melanoma patients treated with immune checkpoint inhibitor therapy

BACKGROUND

Immune checkpoint inhibitors (ICIs), such as anti-Cytotoxic T-Lymphocyte Antigen 4(CTLA-4) and anti-Programmed cell death protein 1 (PD-1) agents, have improved the prognosis of patients with metastatic melanoma. However, a proportion of patients develop resistance to these treatments, leading to a poor prognosis. Therefore, identifying potential non invasive and easy to measure biomarkers is crucial for guiding treatment strategies in patients with metastatic melanoma.

METHODS

A retrospective single-center study was conducted involving patients with metastatic stage IV melanoma who received first-line treatment with anti-CTL4 and/or anti-PD-1 agents. The patients were categorized into two groups on the basis of their 1-year overall survival (OS): those with good outcomes (long-term OS ≥ 1 year) and those with poor outcomes (short-term OS < 1 year). Peripheral metabolomics was performed using baseline sera from 132 patients via 600 MHz Nuclear Magnetic Resonance (NMR) spectroscopy. Enriched functional analysis was conducted to identify the metabolic pathways in which significant metabolites were involved.

RESULTS

Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing the metabolomics profiles of samples collected before ICI treatment revealed significantly different levels of metabolites between the two groups (long-term OS vs. short-term OS). Specifically, lactate, tryptophan and valine significantly predicted the OS of the whole study population subjected to ICI immunotherapy; alanine, asparagine, glutathione, histidine, isoleucine and phenylalanine significantly predicted the OS of patients treated with ipilimumab; glucose, glutamine, histidine and proline significantly predicted the OS of patients treated with nivolumab; and lactate, lysine and proline significantly predicted the OS of patients treated with ipilimumab plus nivolumab. Notably, tryptophan levels were correlated with treatment response in the overall patient group, whereas histidine and lactate levels were associated with response in patients treated with ipilimumab and with ipilimumab plus nivolumab, respectively. Interestingly, higher pretreatment levels of histidine were commonly found in long-term OS subgroups of patients treated with ipilimumab, nivolumab or ipilimumab plus nivolumab. Interestingly, considering only those metabolites that predict OS after univariate analysis, higher histidine, and lower lactate and proline levels resulted as associated with favorable OS in at least two patient cohorts.

CONCLUSIONS

Overall, this exploratory liquid biopsy study revealed a strong correlation between the pretreatment levels of some metabolites and the OS of patients with metastatic stage IV melanoma treated with anti-CTL4 and/or anti-PD-1 antibodies in the first-line setting and revealed the potential of these molecules to predict outcomes and define personalized management and treatment strategies.

Usnic acid impacts energy production and iron metabolism in Mycobacterium tuberculosis H37Rv

Mycobacterium tuberculosis has developed a wide array of response mechanisms to various stress factors. Usnic acid has been demonstrated to be a potent antimycobacterial agent that induces stress responses and growth inhibition in many mycobacterial species. Previous studies have shown that it alters the expression of stress-responsive sigma factors, as well as the metabolites and lipid profile in M. tuberculosis H37Ra. This study was designed to examine potential differences in the strain-specific susceptibility of the virulent H37Rv strain to usnic acid. By combining lipidomic and transcriptomic analyses, we uncovered the impact of usnic acid on bacterial metabolism. The observed downregulation of key lipid classes suggested reduced metabolic activity. The simultaneous elevation of mycobactins-siderophores used by members of the genus Mycobacterium to transport free extracellular iron ions into the cytoplasm-indicated the involvement of iron in the stress response generated by usnic acid. The repressed tricarboxylic acid (TCA) cycle and oxidative phosphorylation were compensated by the upregulation of alternative energy production pathways, such as cytochrome P450 and the ferredoxin reductase system. This indicates that mycobacteria may switch to alternative electron transport mechanisms under usnic acid stress using iron-sulfur clusters to generate energy. From a therapeutic perspective, the study highlights iron metabolism as an essential drug target in mycobacteria. Simultaneously, the results confirm the strain-specific metabolic response of sister strains against the same stressing agent.

IMPORTANCE

A previous study on the influence of usnic acid on the avirulent H37Ra strain revealed that the early bacterial response was associated with redox homeostasis, lipid synthesis, and nucleic acid repair. The response of bacteria to antimicrobials is specific to each species and strain. Given the genetic and phenotypic differences between the avirulent H37Ra strain and the virulent H37Rv strain, we combined lipidomics and global transcriptomics to uncover the mechanism of action of usnic acid against H37Rv. The study identified strain-specific differences between the virulent H37Rv and avirulent H37Ra. The H37Ra strain exhibited increased metabolic activity, while the H37Rv strain showed a reduction in basic metabolic processes and activated alternative iron-dependent energy production. These differences highlight the varying susceptibility of sister strains within the same species to the same antibacterial agent.

Nuclear magnetic resonance-based metabolomics and risk of pancreatic cancer: a prospective analysis in the UK Biobank

BACKGROUND

Plasma metabolite levels in patients with pancreatic cancer (PC) have changed, but the relationship between the altered plasma metabolites and the risk for PC occurrence is not fully clear, as well as the predictive value of the specific metabolites.

METHODS

In this study, we obtained the metabolomics data of 243,145 people from the UK Biobank. An extreme gradient boosting (XGBoost) model, least absolute shrinkage and selection operator (Lasso) regression, and covariate-adjusted Cox proportional hazard regression models were used to evaluate the relationship between metabolites and PC risk. We also evaluated conventional risks, metabolites, and combination models for PC risk by comparing the area under the receiver operating characteristic curve (AUC).

RESULTS

The average follow-up time was 13.8 (± 2.1) years; 1,026 of 243,145 participants developed PC. Fourteen metabolites were significantly associated with PC, including glucose-related metabolites, lipids, lipoproteins, and amino acids. Increased PC risk was associated with citrate, glucose, and the percentage of triglycerides to total lipids in intermediate-density lipoprotein or small low-density lipoprotein. Glycine, histidine, cholesterol, and cholesterol ester subclasses were associated with lower PC risk. Predicting PC risk improved when the newly identified metabolites were added to conventional PC risk factors (AUC: 0.705 vs 0.711, p = 0.037). The Kaplan-Meier cumulative incidence curves showed that these metabolites were good predictors of PC risk (all log-rank p < 0.05).

CONCLUSION

We identified novel metabolites that were significantly associated with the occurrence of PC, which may aid in the early diagnosis of PC.

Polycystic Ovarian Syndrome: A Review of Multi-omics Analyses

Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities of young females, posing a grave public health challenge to the society. The objective of the present literature review is to analyze the enormous amount of information available by way of numerous multi-omic studies, and to explore a meaningful relationship between various factors such as genetic, proteomic, environmental etc. to understand the multifactorial metabolic disorder in a proper manner. Detailed literature search was done in various science article repositories and biomedical databases such as PubMed, Google Scholar, BioMed Central, Embase etc. by using several keywords in whole gamut of combinations. PCOS is a heritable disease. It manifests as a result of a combination of several intricately inter-linked symptoms such as anovulation, obesity, type II diabetes, hyperandrogenism, polycystic ovaries etc., the last one being the main manifestation of the disease, thus leading to infertility among several other complications. Such a multifactorial metabolic disorder with extreme symptomatic heterogeneity cannot be fully explained solely based on symptoms or genetic variations; thus, giving some space of thought to other factors such as epigenetic, microbiomic factors etc. playing a role in the causation of the disease. The present scientific survey of literature extensively reviews various aspects of PCOS by critically looking into the vast multi-omic data, and concluded with suggesting treatment options as well as lifestyle changes required to deal with the psychological/ emotional impacts of the condition on affected women.

Multi-omics in Allergic Rhinitis: Mechanism Dissection and Precision Medicine

Allergic rhinitis (AR) is a common chronic inflammatory airway disease caused by inhaled allergens, and its prevalence has increased in recent decades. AR not only causes nasal leakage, itchy nose, nasal congestion, sneezing, and allergic conjunctivitis but also induces asthma, as well as sleep disorders, anxiety, depression, memory loss, and other phenomena that seriously affect the patient's ability to study and work, lower their quality of life, and burden society. The current methods used to diagnose and treat AR are still far from ideal. Multi-omics technology can be used to comprehensively and systematically analyze the differentially expressed DNA, RNA, proteins, and metabolites and their biological functions in patients with AR. These capabilities allow for an in-depth understanding of the intrinsic pathogenic mechanism of AR, the ability to explore key cells and molecules that drive its progression, and to design personalized treatment for AR. This article summarizes the progress made in studying AR by use of genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in order to illustrate the important role of multi-omics technologies in facilitating the precise diagnosis and treatment of AR.

Unveiling the metabolomic profile of growth hormone deficiency children using NMR spectroscopy

INTRODUCTION

The diagnosis of Growth Hormone Deficiency (GHD) during childhood has been the subject of much controversy over the last few years. Aiming to accurate medical treatment, there is a need for biomarker discovery.

OBJECTIVE

To characterize the metabolic profile of GHD children, examine the effect of GH administration on the metabolic signature, and investigate the correlations between metabolites and IGF-1.

METHODS

Nuclear Magnetic Resonance (NMR)-based untargeted and targeted metabolomic approach applied to study the metabolic profiles of children with GHD. Plasma, serum, and urine samples were collected from twenty-two children diagnosed with GHD and forty-eight age matched controls from the Pediatric Endocrinology Unit of the University Hospital of Patras. Experimental data were examined by both multivariate and univariate statistical analysis.

RESULTS

The results of this pilot study revealed a different metabolic fingerprint of children with GHD in comparison to age-matched healthy individuals. However, the detected alterations in the metabolite patterns before and after GH treatment were subtle and of minor discriminative statistical power.

CONCLUSIONS

This study provides evidence that metabolome plays a pivotal role in GHD, but large-scale multicenter studies are warranted to validate the results.

α-methyltryptophan-mediated protection against diabetic nephropathy in db/db mice as studied with a metabolomics approach

Introduction

Diabetic nephropathy (DN), a major complication of diabetes, presents with poor clinical outcomes and affects patients throughout their lifetime. α-Methyltryptophan (α-MT) is a blocker of the amino acid transporter. SLC6A14 and also an inhibitor of indoleamine 2,3-dioxygenase-1 (IDO1).

Methods

In this study, we employed a nuclear magnetic resonance-based metabolomic approach to investigate the therapeutic effects of α-MT in a db/db mouse model of DN and explore the underlying molecular mechanisms.

Results

The results of the study demonstrated that α-MT significantly reduced the urinary excretion of albumin and creatinine, improved kidney function, and decreased renal fibrosis in db/db mice. Metabolomic analyses of kidney tissues and urine samples indicated that db/db mice displayed increased activity of the enzyme IDO1, and alongside pronounced metabolic disturbances. These disturbances are chiefly characterized by alterations in amino acid metabolism, energy production pathways, membrane biochemical features, and nicotinamide metabolism, all of which have been implicated in mTOR signaling and apoptotic pathways.

Discussion

Administration of α-MT to db/db mice showed evidence of IDO1 inhibition and rectification of metabolic dysfunctions with concurrent suppression of mTOR signaling and apoptosis. These findings highlight the potential of α-MT as a promising therapeutic agent for diabetic nephropathy.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

Metabolomic characterization of MC3T3-E1pre-osteoblast differentiation induced by ipriflavone-loaded mesoporous nanospheres

This study reports on the metabolic changes accompanying the differentiation of MC3T3-E1 osteoprogenitor cells induced by mesoporous bioactive glass nanospheres (nMBG) loaded with ipriflavone (nMBG-IP). Ipriflavone (IP) is a synthetic isoflavone known for inhibiting bone resorption, maintaining bone density, and preventing osteoporosis. Delivering IP intracellularly is a promising strategy to modulate bone remodeling at significantly lower doses compared to free drug administration. Our results demonstrate that nMBG are efficiently internalized by pre-osteoblasts and, when loaded with IP, induce their differentiation. This differentiation process is accompanied by pronounced metabolic alterations, as monitored by NMR analysis of medium supernatants and cell extracts (exo- and endo-metabolomics, respectively). The main effects include an early-stage intensification of glycolysis and changes in several metabolic pathways, such as nucleobase metabolism, osmoregulatory and antioxidant pathways, and lipid metabolism. Notably, the metabolic impacts of nMBG-IP and free IP were very similar, whereas nMBG alone induced only mild changes in the intracellular metabolic profile without affecting the cells' consumption/secretion patterns or lipid composition. This finding indicates that the observed effects are primarily related to IP-induced differentiation and that nMBG nanospheres serve as convenient carriers with both efficient internalization and minimal metabolic impact. Furthermore, the observed link between pre-osteoblast differentiation and metabolism underscores the potential of utilizing metabolites and metabolic reprogramming as strategies to modulate the osteogenic process, for instance, in the context of osteoporosis and other bone diseases.

Exploration of plasma biomarkers for Alzheimer's disease by targeted lipid metabolomics based on nuclear magnetic resonance (NMR) spectroscopy

Alzheimer's disease (AD) is the most common cause of dementia, but the disease lacks convenient and cost-effective alternative biomarkers currently. We utilized targeted lipid metabolomics based on nuclear magnetic resonance (NMR) spectroscopy to identify plasma biomarkers in AD patients. Our study was a cross-sectional study that enrolled 58 AD patients and 40 matched health controls (HCs). Firstly, we identified plasma lipid metabolites that were significantly different between the two groups based on P < 0.05 and variable importance in the projection (VIP) > 1. Then we examined the correlation between the lipid metabolites and cognitive function using partial correlation analysis and assessed the diagnostic ability of the lipid metabolites using receiver operating characteristic (ROC) curves. Seventeen lipoproteins showed significant differences between AD patients and HCs among 114 lipid metabolites. All 17 lipoproteins were subtypes of low-density lipoprotein (LDL). Among them, LDL-3 particle number, LDL-3 apolipoprotein-B, LDL-3 phospholipids, LDL free cholesterol and LDL phospholipids were significantly correlated with cognitive function. The ROC curves showed that LDL-2 triglycerides (TG) and LDL-3 TG could significantly distinguish AD patients from HCs, with the area under the curve (AUC) above 0.7. In addition, we explored a strategy of combined diagnosis that significantly improved the diagnostic efficacy for AD (AUC = 0.879). Our study provides insight into the lipoprotein alterations associated with AD and potential biomarkers for its diagnosis and cognitive function assessment.

Metabolic and protein expression responses of Shewanella baltica in golden pomfret broths to slightly acidic electrolysed water

Shewanella baltica is a specific spoilage organism of golden pomfret. This study aims to explore the antibacterial mechanism of slightly acidic electrolysed water (SAEW) against S. baltica (strains ABa4, ABe2 and BBe1) in golden pomfret broths by metabolomics, proteomics and bioinformatics analyses. S. baltica was decreased by at least 3.94 log CFU/mL after SAEW treatment, and strain ABa4 had the highest resistance. Under SAEW stress, amino acids and organic acids in S. baltica decreased, and nucleotide related compounds degraded. Furthermore, 100 differentially expressed proteins (DEPs) were identified. Most DEPs of strains ABe2 and BBe1 were down-regulated, while some DEPs of strain ABa4 were up-regulated, especially those oxidative stress related proteins. These results suggest that the modes of SAEW against S. baltica can be traced to the inhibition of amino acid, carbon, nucleotide and sulphur metabolisms, and the loss of functional proteins for temperature regulation, translation, motility and protein folding.

Machine-learning-based plasma metabolomic profiles for predicting long-term complications of cirrhosis

BACKGROUND AND AIMS

The complications of liver cirrhosis occur after long asymptomatic stages of progressive fibrosis and are generally diagnosed late. We aimed to develop a plasma metabolomic-based score tool to predict these events.

APPROACH AND RESULTS

We enrolled 64,005 UK biobank participants with metabolomic profiles. Participants were randomly divided into the training (n=43,734) and validation cohorts (n=20,271). Liver cirrhosis complications were defined as hospitalization for liver cirrhosis or presentation with HCC. An interpretable machine-learning framework was applied to learn the metabolomic states extracted from 168 circulating metabolites in the training cohort. An integrated nomogram was developed and compared to conventional and genetic risk scores. We created 3 groups: low-risk, middle-risk, and high-risk through selected cutoffs of the nomogram. The predictive performance was validated through the area under a time-dependent receiver operating characteristic curve (time-dependent AUC), calibration curves, and decision curve analysis. The metabolomic state model could accurately predict the 10-year risk of liver cirrhosis complications in the training cohort (time-dependent AUC: 0.84 [95% CI: 0.82-0.86]), and outperform the fibrosis-4 index (time-dependent AUC difference: 0.06 [0.03-0.10]) and polygenic risk score (0.25 [0.21-0.29]). The nomogram, integrating metabolomic state, aspartate aminotransferase, platelet count, waist/hip ratio, and smoking status showed a time-dependent AUC of 0.930 at 3 years, 0.889 at 5 years, and 0.861 at 10 years in the validation cohort, respectively. The HR in the high-risk group was 43.58 (95% CI: 27.08-70.12) compared with the low-risk group.

CONCLUSIONS

We developed a metabolomic state-integrated nomogram, which enables risk stratification and personalized administration of liver-related events.

Untargeted Metabolic Phenotyping by LC-MS

Untargeted analysis by LC-MS is a valuable tool for metabolic profiling (metabonomics/metabolomics), and applications of this technology have grown rapidly over the past decade. LC-MS offers advantages of speed, sensitivity, relative ease of sample preparation, and large dynamic range compared to other platforms in this role. However, like any analytical approach, there are still drawbacks and challenges that have to be overcome, some of which are being addressed by advances in both column chemistries and instrumentation. In particular, the combination of LC-MS with ion mobility offers many new possibilities for improved analyte separation, detection, and structural identification. There are many untargeted LC-MS approaches which can be applied to metabolic phenotyping, and these usually need to be optimized for the type of sample, the nature of the study, or the biological question. Some of the main LC-MS approaches for untargeted metabolic phenotyping are described in detail in the following protocol.

Multi-biofluid metabolomics analysis of allergic respiratory rhinitis and asthma in early childhood

Background

Childhood rhinitis and asthma are allergic respiratory diseases triggered by common allergens, but they affect different parts of the respiratory system, leading to distinct symptoms. However, a comprehensive multi-biofluid metabolomics-based approach to uncover valuable insights into childhood allergies and allergen sensitization remains unaddressed.

Methods

Seventy-six children, comprising 26 with rhinitis, 26 with asthma, and 24 healthy controls, were enrolled. Fecal, blood, and urine metabolomic analyses using 1H nuclear magnetic resonance (NMR) spectroscopy were conducted. An integrative analysis of their associations with allergen-specific IgE levels in the context of allergic rhinitis and asthma were also assessed.

Results

The analysis of 228 various biofluid samples revealed strong positive correlations between stool and blood metabolites, while blood metabolites exhibited negative correlations with most urine metabolites. Five and 19 metabolites were significantly different in children with rhinitis and asthma, respectively (P < 0.05). Among them, blood isovaleric acid correlated positively with stool IgE levels in rhinitis, while stool butyric acid and acetic acid in asthma exhibited strong negative correlations with total serum and mite allergen-specific IgE levels (P < 0.01). Blood metabolic profiles appeared to have the highest area under the curve (AUC) of 0.732 for rhinitis, whereas stool metabolic profiles had the highest AUC of 0.799 for asthma.

Conclusions

Multiple biofluid metabolomics provides comprehensive insights into childhood allergies, with blood profiles associated with allergic rhinitis and fecal profiles linked to asthma. Their short-chain fatty acid metabolites related to IgE levels emphasize the significant role of the gut microbiota in childhood rhinitis and asthma.

Metabolomic signature of pediatric diabetic ketoacidosis: key metabolites, pathways, and panels linked to clinical variables

BACKGROUND

Diabetic ketoacidosis (DKA) is a serious complication of type 1 diabetes (T1D), arising from relative insulin deficiency and leading to hyperglycemia, ketonemia, and metabolic acidosis. Early detection and treatment are essential to prevent severe outcomes. This pediatric case-control study utilized plasma metabolomics to explore metabolic alterations associated with DKA and to identify predictive metabolite patterns.

METHODS

We examined 34 T1D participants, including 17 patients admitted with severe DKA and 17 age- and sex-matched individuals in insulin-controlled states. A total of 215 plasma metabolites were analyzed using proton nuclear magnetic resonance and direct-injection liquid chromatography/mass spectrometry. Multivariate statistical methods, machine learning techniques, and bioinformatics were employed for data analysis.

RESULTS

After adjusting for multiple comparisons, 65 metabolites were found to differ significantly between the groups (28 increased and 37 decreased). Metabolomics profiling demonstrated 100% accuracy in differentiating severe DKA from insulin-controlled states. Random forest analysis indicated that classification accuracy was primarily influenced by changes in ketone bodies, acylcarnitines, and phosphatidylcholines. Additionally, groups of metabolites (ranging in number from 8 to 18) correlated with key clinical and biochemical variables, including pH, bicarbonate, glucose, HbA1c, and Glasgow Coma Scale scores.

CONCLUSIONS

These findings underscore significant metabolic disturbances in severe DKA and their associations with critical clinical indicators. Future investigations should explore if metabolic alterations in severe DKA can identify patients at increased risk of complications and/or guide future therapeutic interventions.

A-SIMA/A-MAP: a comprehensive toolkit for NMR-based metabolomics analysis

INTRODUCTION

Metabolomics is the comprehensive study of small molecules in biological systems. It has recently garnered attention for its wide variety of applications such as diseases, drug treatments, agriculture, and more. As the interest in metabolomics grow, meeting the demands of cutting-edge research requires software tools that not only advance analytical capabilities, but also prioritize user-friendly features.

OBJECTIVES

In response to this need, we present two new computer programs, A-SIMA: Advanced-Software for Interactive Metabolite Analysis and A-MAP: A Multivariate Analysis Program. These tools aim to introduce new capabilities for metabolite identification and data analysis, and thereby advancing the computational methodology in NMR-based metabolomics.

METHODS

A-SIMA is designed with an easy-to-use graphical user interface which allows users to perform metabolite identification on 1D and 2D NMR data effortlessly with complete control over the identification process. Similarly, A-MAP facilitates multivariate statistical analysis of metabolite data through a straightforward process. It offers analysis options such as Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis using regions of interests as inputs.

RESULTS

Both A-SIMA and A-MAP are pre-built in the POKY suite, available at https://poky.clas.ucdenver.edu , with tutorial videos on YouTube for guidance on not only the programs, but also installation. The POKY suite is a software program for NMR biomolecular analysis. With the addition of these programs in POKY, researchers and professionals can experience a fully integrated process for every step of their metabolite analysis. Data can also be easily exported from these programs to be applied elsewhere.

CONCLUSION

The introduction of A-SIMA and A-MAP can be promising tools that can lead significant advancements in metabolomics research. These tools offer enhanced capabilities for metabolite analysis and statistical modelling in a user-friendly manner. Their integration into the POKY suite ensures accessibility, usability, and efficiency.

The exometabolome as a hidden driver of bacterial virulence and pathogenesis

The traditional view of metabolism as merely supplying energy and biosynthetic precursors is undergoing a paradigm shift. Metabolic dynamics not only regulates gene expression but also orchestrates cellular processes with remarkable precision. Most bacterial pathogens exhibit exceptional metabolic plasticity, enabling them to adapt to diverse environments, including hostile conditions within a host. While the role of intracellular bacterial metabolism in pathogen-host interactions has been extensively studied, the contributions of the extracellularly released or secreted bacterial metabolites (referred to here as the bacterial 'exometabolome') to metabolic adaptations and disease pathogenesis remain largely unexplored. In this review, we highlight the significant and intriguing roles of bacterial exometabolomes in drug tolerance, immune suppression, and disease pathogenesis, opening a new frontier in our understanding of bacterial-host interactions.

Metabolic reprogramming in sepsis-associated acute kidney injury: insights from lipopolysaccharide-induced oxidative stress and amino acid dysregulation

BACKGROUND

Sepsis-associated acute kidney injury (SA-AKI) stands out as a critical health issue due to its high mortality and morbidity rates. This study aimed to comprehensively investigate the biochemical and metabolic alterations induced by lipopolysaccharide (LPS) in human embryonic kidney cells (HEK-293) using an in vitro model.

METHODS AND RESULTS

The study investigated the impact of LPS on HEK-293 cells by evaluating cytotoxicity using the MTT assay, analyzing apoptosis, cell cycle progression, and oxidative stress via flow cytometry, measuring TNF-α levels through ELISA, and assessing amino acid metabolism with LC-MS/MS. The findings demonstrated that LPS significantly reduced cell viability in a dose-dependent manner, increased apoptotic cell populations, induced DNA damage by arresting the cell cycle in the Sub-G1 phase, and activated oxidative stress pathways. Notably, elevated reactive oxygen species (ROS) production and increased secretion of the pro-inflammatory cytokine TNF-α highlighted LPS's inflammatory and cytotoxic effects. Furthermore, systematic analysis revealed LPS-induced disruptions in amino acid metabolism, including marked reductions in alanine, arginine, and aspartic acid levels. KEGG pathway analysis identified significant metabolic alterations in pathways such as the urea cycle, TCA cycle, and glutathione metabolism. Interestingly, elevated citrulline levels suggested a potential adaptive mechanism to counteract LPS-induced inflammation and oxidative stress. Additionally, ROC analysis identified cystine as a highly reliable biomarker, with an AUC value of 1.00, emphasizing its critical role in metabolic reprogramming associated with SA-AKI.

CONCLUSIONS

This study provides critical insights into the molecular pathophysiology of SA-AKI and emphasizes the promise of metabolomic approaches in the early diagnosis of sepsis-related complications and the development of targeted therapies.

Current status of serum metabolites biomarkers for polyps and colorectal cancer: a systematic review

Background

Early detection of colorectal cancer (CRC) is crucial to enhance the disease treatment and prognosis of patients. Colonoscopy remains the gold standard for CRC detection; however, it requires trained personnel with expensive tools. Currently, serum metabolites have been discovered to be used to discriminate patients with polyps and CRC. This study aimed to identify the most commonly detected predictive serum metabolites for polyps and CRC.

Methods

A systematic search of the Web of Science, PubMed, and Cochrane Library databases was conducted using PRISMA guidelines. Ten studies investigating serum metabolite biomarkers of CRC and polyps using different analytical platforms and study populations were included. QUADOMICS tool was used to analyse the quality of the included studies. All reported metabolites were then enriched into the pathways using MetaboAnalyst 5.0.

Results

We found that several potential signature metabolites overlapped between studies, including tyrosine, lysine, cystine, arabinose, and lactate for CRC and lactate and glutamate for polyps. The most affected pathways related to CRC were the urea cycle, glutathione metabolism, purine metabolism, glutamate metabolism, and ammonia recycling. In contrast, those affected in the polyps were the urea cycle, glutamate metabolism, glutathione metabolism, arginine and proline metabolism, and carnitine synthesis.

Conclusions

This review has found commonly detected serum metabolites for polyps and CRC with huge potential to be used in clinical settings. However, the differences between altered pathways in polyps and CRC, other external factors, and their effects on the regulation level, sensitivity, and specificity of each identified metabolite remained unclear, which could benefit from a further extensive cohort study and well-defined analysis equipment.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

PURPOSE

To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression.

DESIGN

Multicenter, multiethnic cohort study.

PARTICIPANTS

This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort.

METHODS

Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts.

MAIN OUTCOME MEASURES

DR development and progression and retinal microvascular damage.

RESULTS

Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05).

CONCLUSIONS

Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Membrane-based preparation for mass spectrometry imaging of cultures of bacteria

The study of the dialogue between microorganisms at the molecular level is becoming essential to understand their relationship (antagonist, neutral, or beneficial interactions) and its impact on the organization of the microbial community. Mass spectrometry imaging (MSI) with matrix-assisted laser desorption/ionization (MALDI) is a technique that reveals the spatial distribution of molecules on a sample surface that may be involved in interactions between organisms. An experimental limitation to perform MALDI MSI is a flat sample surface, which in many cases could not be achieved for bacterial colonies such as filamentous bacteria (e.g., Streptomyces). In addition, sample heterogeneity affects sample dryness and MALDI matrix deposition prior to MSI. To avoid such problems, we introduce an additional step in the sample preparation. A polymeric membrane is interposed between the microorganisms and the agar-based culture medium, allowing the removal of bacterial colonies prior to MSI of the homogeneous culture medium. A proof of concept was evaluated on Streptomyces ambofaciens (a soil bacterium) cultures on solid media. As the mycelium was removed at the same time as the polymeric membrane, the metabolites released into the medium were spatially resolved by MALDI MSI. In addition, extraction of the recovered mycelium from the membrane confirmed the identification of the metabolites by ESI MS/MS analysis. This approach allows both the spatial distribution of metabolites produced by microorganisms in an agar medium to be studied under well-controlled sample preparation and their structure to be elucidated. This capability is illustrated using desferrioxamine E, a siderophore produced by S. ambofaciens.

Therapeutic potential of β-hydroxybutyrate in the management of pancreatic neoplasms: exploring novel diagnostic and treatment strategies

Pancreatic neoplasm, a highly aggressive and often fatal cancer, poses challenges due to late detection and nonspecific symptoms. Therefore, both early diagnosis and appropriate therapeutic approaches are necessary to augment the condition of these patients. Cancer cells undergo metabolic deregulation, which enables their proliferation, survival, and invasion. As a result, it is crucial to focus on the metabolic pathways in prevalent cancers and explore treatment strategies that target these pathways to control tumor growth effectively. This is particularly relevant in cancers like pancreatic cancer, which undergo numerous metabolic alterations. The ketogenic regimen, characterized by low carbohydrate and protein contents and high-fat sources, does not involve caloric restriction. This allows for the induction of ketogenesis and an increase in ketone bodies, while insulin and glucose levels remain low even after meals. This unique metabolic state may influence the tumor microenvironment. Given the lack of unanimous agreement on the precise role and mechanism of the ketogenic diet, this review aims to clarify the diagnostic value and accuracy of ketone bodies in various types of pancreatic tumors and explore the potential anti-cancer effects of the ketogenic diet when used alone or in conjunction with chemotherapy, also to determine the potential of the ketogenic diet to be used as adjuvant therapy. The outcomes of this study are instrumental in enhancing our understanding of the benefits and drawbacks associated with employing this diet for the management and diagnosis of pancreatic cancer.

Nuclear Magnetic Resonance Analysis Implicates Sex-Specific Dysregulation of the Blood Lipids in Alzheimer's Disease: A Retrospective Health-Controlled Study

OBJECTIVE

The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer's disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers.

METHODS

Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy.

RESULTS

Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05).

CONCLUSION

Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields

The field of metabolomics, which is quintessential in today's omics research, involves the large-scale detection, identification, and quantification of small-molecule metabolites in a wide range of biological samples. Nuclear magnetic resonance spectroscopy (NMR) has emerged as a powerful tool for metabolomics due to its high resolution, reproducibility, and exceptional quantitative nature. One of the key bottlenecks of metabolomics studies, however, remains the accurate and automated analysis of the resulting NMR spectra with good accuracy and minimal human intervention. Here, we present the COLMAR1d platform, consisting of a public web server and an optimized database, for one-dimensional (1D) NMR-based metabolomics analysis to address these challenges. The COLMAR1d database comprises more than 480 metabolites from GISSMO enabling a database query of spectra measured at arbitrary magnetic field strengths, as is demonstrated for spectra acquired between 1H resonance frequencies of 80 MHz and 1.2 GHz of mouse serum, DMEM cell growth medium, and wine. COLMAR1d combines the GISSMO metabolomics database concept with the latest tools for automated processing, spectral deconvolution, database querying, and globally optimized mixture analysis for improved accuracy and efficiency. By leveraging advanced computational algorithms, COLMAR1d offers a user-friendly, automated platform for quantitative 1D NMR-based metabolomics analysis allowing a wide range of applications, including biomarker discovery, metabolic pathway elucidation, and integration with multiomics strategies.

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism", "Glycerolipid metabolism" and "Arginine and proline metabolism". We propose a three-metabolite diagnostic panel-comprising isoleucine, TMAO, and glucose-that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

Perturbations in mitochondrial metabolism associated with defective cardiolipin biosynthesis: An in-organello real-time NMR study

Mitochondria are central to cellular metabolism; hence, their dysfunction contributes to a wide array of human diseases. Cardiolipin, the signature phospholipid of the mitochondrion, affects proper cristae morphology, bioenergetic functions, and metabolic reactions carried out in mitochondrial membranes. To match tissue-specific metabolic demands, cardiolipin typically undergoes an acyl tail remodeling process with the final step carried out by the phospholipid-lysophospholipid transacylase tafazzin. Mutations in tafazzin are the primary cause of Barth syndrome. Here, we investigated how defects in cardiolipin biosynthesis and remodeling impacts metabolic flux through the TCA cycle and associated yeast pathways. Nuclear magnetic resonance was used to monitor in real-time the metabolic fate of 13C3-pyruvate in isolated mitochondria from three isogenic yeast strains. We compared mitochondria from a WT strain to mitochondria from a Δtaz1 strain that lacks tafazzin and contains lower amounts of unremodeled cardiolipin and mitochondria from a Δcrd1 strain that lacks cardiolipin synthase and cannot synthesize cardiolipin. We found that the 13C-label from the pyruvate substrate was distributed through twelve metabolites. Several of the metabolites were specific to yeast pathways including branched chain amino acids and fusel alcohol synthesis. While most metabolites showed similar kinetics among the different strains, mevalonate concentrations were significantly increased in Δtaz1 mitochondria. Additionally, the kinetic profiles of α-ketoglutarate, as well as NAD+ and NADH measured in separate experiments, displayed significantly lower concentrations for Δtaz1 and Δcrd1 mitochondria at most time points. Taken together, the results show how cardiolipin remodeling influences pyruvate metabolism, tricarboxylic acid cycle flux, and the levels of mitochondrial nucleotides.

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

Analyzing the differences and correlations between key metabolites and dominant microorganisms in different regions of Daqu based on off-target metabolomics and high-throughput sequencing

Daqu is usually produced in an open environment, which makes its quality unstable. The microbial community of Daqu largely determines its quality. Therefore, in order to improve the fermentation characteristics of Daqu, samples were collected from Jinsha County (MT1), Xishui County (MT2), and Maotai Town (MT3) in Guizhou Province to explore the microbial diversity of Daqu and its impact on Daqu's metabolites.Off-target metabolomics was used to detect metabolites, and high-throughput sequencing was used to detect microorganisms. Metabolomics results revealed that MT1 and MT2 had the highest relative fatty acid content, whereas MT3 had the highest organooxygen compound content. Principal component analysis and partial least squares discriminant analysis revealed significant differences in the metabolites among the three groups, followed by the identification of 33 differential metabolites (key metabolites) filtered using the criteria of variable importance in projection >1 and p < 0.001. According to the microbiological results, Proteobacteria was the dominant bacteria phylum in three samples. Gammaproteobacteria was the dominant class in MT1(26.84 %) and MT2(36.54 %), MT3 is Alphaproteobacteria(25.66 %). And Caulobacteraceae was the dominant family per the abundance analysis, MTI was 24.32 %, MT2 and MT3 were 33.71 % and 24.40 % respectively. Three samples dominant fungi phylum were Ascomycota, and dominant fungi family were Thermoascaceae. Pseudomonas showed a significant positive connection with various fatty acyls, according to correlation analyses between dominant microorganisms (genus level) and key metabolites. Fatty acyls and Thermomyces showed a positive correlation, but Thermoascus had the reverse relation. These findings offer a theoretical framework for future studies on the impact of metabolites on Baijiu quality during fermentation.

Applicability of metabolomics to improve sustainable grapevine production

Metabolites represent the end product of gene expression, protein interaction and other regulatory mechanisms. The metabolome reflects a biological system's response to genetic and environmental changes, providing a more accurate description of plants' phenotype than the transcriptome or the proteome. Grapevine (Vitis vinifera L.), established for the production of wine grapes, table grapes, and raisins, holds immense agronomical and economic significance not only in the Mediterranean region but worldwide. As all plants, grapevines face the adverse impact of biotic and abiotic stresses that negatively affect multiple stages of grape and wine industry, including plant and berry development pre- and post-harvest, fresh grapes processing and consequently wine quality. In the present review we highlight the applicability of metabolome analysis in the understanding of the mechanisms involved in grapevine response and acclimatization upon the main biotic and abiotic constrains. The metabolome of induced morphogenic processes such as adventitious rooting and somatic embryogenesis is also explored, as it adds knowledge on the physiological and molecular phenomena occurring in the explants used, and on the successfully propagation of grapevines with desired traits. Finally, the microbiome-induced metabolites in grapevine are discussed in view of beneficial applications derived from the plant symbioses.

Metabolomic profiling of elite female soccer players: urinary biomarkers over a championship season

INTRODUCTION

In soccer, most studies evaluate metabolic profile changes in male athletes, often using data from a single match. Given the current landscape of women's soccer and the effects of biological sex on the physiological response and adaptation to exercise, more studies targeting female athletes and analyzing pre- and post-game moments throughout the season are necessary.

OBJECTIVES

To describe the metabolomics profile of female soccer athletes from an elite team in Brazil. The study observed the separation of groups in three pre- and post-game moments and identified the discriminating metabolites.

METHODS

The study included 14 female soccer athletes. Urine samples were collected and analyzed using Nuclear Magnetic Resonance in pre-game and immediate post-game moments over three national championship games. The metabolomics data were then used to generate OPLS-DA and VIP plots.

RESULTS

Forty-three metabolites were identified in the samples. OPLS-DA analyses demonstrated a progressive separation between pre-post conditions, as supported by an increasing Q2 value (0.534, 0.625, and 0.899 for games 1, 2 and 3, respectively) and the first component value (20.2% and 19.1% in games 1 and 2 vs. 29.9% in game 3). Eight out of the fifteen most discriminating metabolites appeared consistently across the three games: glycine, formate, citrate, 3-hydroxyvalerate, glycolic acid, trimethylamine, urea, and dimethylglycine.

CONCLUSION

The main difference between the three games was the increasing separation between groups throughout the championship. Since the higher VIP-scores metabolites are linked to energy and protein metabolism, this separation may be attributed several factors, one being the accumulation of fatigue.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Nonlinear relationships of circulating polyunsaturated fatty acids with the complications of liver cirrhosis: A prospective, longitudinal cohort study

BACKGROUND & AIMS

The role of circulating polyunsaturated fatty acids (PUFAs) in preventing liver cirrhosis complications remains unclear.

METHODS

Between 2006 and 2010, 273,834 UK Biobank participants with plasma PUFA quantification data were enrolled and followed up until October 31, 2022. Plasma PUFAs were quantified using a high-throughput nuclear magnetic resonance-based metabolic profiling platform. Liver cirrhosis complications were defined as hospitalization for liver cirrhosis or presentation with hepatocellular carcinoma.

RESULTS

During a median follow-up of 13.9 years, 2026 participants developed liver cirrhosis complications. Total plasma PUFAs, omega-3 PUFAs, docosahexaenoic acid (DHA), omega-6 PUFAs, and linoleic acid (LA) were inversely associated with the risk of liver cirrhosis complications, whereas the plasma omega-6/omega-3 ratio was positively associated. Nonparametrically restricted cubic spline regression showed nonlinear associations of plasma PUFAs with liver cirrhosis complications. The inflection points were 4.78 mmol/L for total PUFAs, 0.73 mmol/L for omega-3 PUFAs, 0.25 mmol/L for DHA, 4.07 mmol/L for omega-6 PUFAs, and 2.99 mmol/L for LA. Plasma omega-3 PUFAs were negatively associated with the risk of liver cirrhosis complications when omega-3 PUFAs were <0.73 mmol/L (adjusted hazard ratio [HR], 0.11 [0.08-0.16]), whereas the association was inverted when omega-3 PUFAs were ≥0.73 mmol/L (adjusted HR, 1.87 [1.20-2.92]).

CONCLUSIONS

The protective effect of plasma omega-3 PUFAs on liver cirrhosis complications is reversed after passing the corresponding inflection point, suggesting an optimal dietary omega-3 PUFA supplementation dose.

Assessing inorganic nanoparticle toxicity through omics approaches

In the last two decades, the development of nanotechnology has resulted in inorganic nanoparticles playing crucial roles in key industries, ranging from healthcare to energy technologies. For instance, gold and silver nanoparticles are widely used in rapid COVID-19 and flu tests, titania and zinc oxide nanoparticles are commonly found in cosmetic products, and superparamagnetic iron oxide nanoparticles have been clinically exploited as contrast agents and anti-anemia medicines. As a result, human exposure to nanomaterials is continuously increasing, raising concerns about their potential adverse health effects. Historically, the study of nanoparticle toxicity has largely relied on macroscopic observations obtained in different in vitro and in vivo models, resulting in readouts such as median lethal dose, biodistribution profile, and/or histopathological assessment. In recent years, omics methodologies, including transcriptomics, epigenomics, proteomics, metabolomics, and lipidomics, are increasingly used to characterize the biological interactions of nanomaterials, providing a better and broader understanding of their impact and mechanisms of toxicity. These approaches have been able to identify important genes and gene products that mediate toxicological effects, as well as endogenous functions and pathways dysregulated by nanoparticles. Omics methods improve our understanding of nanoparticle biology, and unravel mechanistic insights into nanomedicine-based therapies. This review aims to provide a deeper understanding and new perspectives of omics approaches to characterize the toxicity and biological interactions of inorganic nanoparticles, and improve the safety of nanoparticle applications.

Enhancement of weak signals by applying a suppression method to high-intense methyl and methylene signals of lipids in NMR spectroscopy

Lipids play crucial roles in human biology, serving as energy stores, cell membranes, hormone production, and signaling molecules. Accordingly, their study under lipidomics has advanced the study of living organisms. 1-Dimensional (D) and 2D NMR methods, particularly 1D 1H and 2D 1H-1H Total Correlation Spectroscopy (TOCSY), are commonly used in lipidomics for quantification and structural identification. However, these NMR methods suffer from low sensitivity, especially in cases of low concentrated molecules such as protons attached to hydroxy, esters, aliphatic, or aromatic unsaturated carbons. Such molecules are common in complex mixtures such as dairy products and plant oils. On the other hand, lipids have highly populated fractions of methyl and methylene groups that result in intense peaks that overwhelm lower peaks and cause inhomogeneities in 2D TOCSY spectra. In this study, we applied a method of suppression to suppress these intense peaks of methyl and methylene groups to detect weaker peaks. The suppression method was investigated on samples of cheese, butter, a mixture of lipids, coconut oil, and olive oil. A significant improvement in peak sensitivity and visibility of cross-peaks was observed, leading to enhanced comparative quantification and structural identification of a greater number of lipids. Additionally, the enhanced sensitivity reduced the time required for the qualitative and comparative quantification of other lipid compounds and components. This, in turn, enables faster and more reliable structural identification and comparative quantification of a greater number of lipids. Additionally, it reduces the time required for the qualitative, and comparative quantification due to the enhancement of sensitivity.

Untargeted stimulated and unstimulated salivary metabolomics and saliva flow rate in children

OBJECTIVE

The present study aimed to determine the salivary flow and metabolomic profile of stimulated and unstimulated saliva in children.

MATERIALS AND METHODS

Children who attended the Pediatric Dentistry Clinic of the State University of Rio de Janeiro -UERJ between 3 and 12 years of age were selected. Unstimulated and stimulated whole saliva, using mechanical stimulus, were collected. The samples were centrifuged at 12,000 g, 4oC for 1 h. The 1H- NMR spectra were acquired in 500 MHz equipment. The data were extracted into 0.03 ppm buckets in AMIX, and multivariate analysis (PLS-DA and O-PLS-DA) was performed in Metaboanalyst 2.0. For other analyses, such as salivary flow, the data was tabulated in the SPSS 20.0 statistical package, analyzed descriptively, and after applying the Wilcoxon test. The interval of confidence was set at 95%.

RESULTS

The mean age was 7.5 (± 1.94), and 47.0% (n = 31) were female, 63.6% (n = 42). The median flow rate for stimulated saliva was 0.74 (IC 0.10-2.40) and was statistically higher (p < 0.001; Wilcoxon test) than unstimulated was 0.39 (IC 0.00-1.80). Children older than seven years old also presented a higher difference between unstimulated and stimulated saliva (p = 0.003; Mann-Whitney test). The PLS-DA and O-PLS-DA demonstrated a different profile in stimulated and unstimulated saliva. Acetate, glucose, propionate, and lysine were higher in the unstimulated whole saliva than in stimulated saliva. Isoleucine, N-acetyl sugar, hydroxybutyrate, glutamate, leucine, propionate, butyrate, valine, isoleucine, succinate, saturated fatty acid, and histidine were found in greater amounts in the saliva of patients with stimulated saliva.

CONCLUSION

The stimulated saliva presented a higher flow rate, and older children exhibited a higher flow rate resulting from it's the stimulus. The mechanical stimulus increased the levels of the major metabolites.

Discovery and Validation of Potential Serum Biomarkers for Heart Failure by Untargeted Metabolomics

Detection of biomarkers was extremely important for the early diagnosis, prognosis, and therapy optimization of diseases. The purpose of this study was to investigate the differences in serum metabolites between patients with heart failure (HF) and healthy control (HC) and to diagnose HF qualitatively. In this study, serum samples from 83 patients with HF and 35 HCs were used as the research subjects for untargeted metabolomic analysis using ultraperformance liquid chromatography combined with quadrupole-time of flight mass spectrometry (UPLC-QTOF/MS) technology. Potential biomarkers were screened and validated using the orthogonal partial least squares discriminant analysis (OPLS-DA), random forest (RF), binary logistic regression (BLR), and receiver operating characteristic (ROC) analysis. The results indicated that a total of 43 metabolites were considered as differentially expressed metabolites (DEMs). Among these DEMs, glycodeoxycholate was identified as a specific biomarker of HF. A ROC curve analysis for HC versus HF discrimination showed an area under the ROC curve (AUC) of 0.9853 (95% CI: 0.9859-1.0000), a sensitivity of 95%, and a specificity of 100%. Hence, glycodeoxycholate might serve as a potential biomarker for HF. Furthermore, the amino acid metabolism was screened as the most significantly altered pathway in patients with HF. By identifying serum biomarkers and analyzing metabolic pathways, our study provided opportunities to enhance the understanding of the pathogenesis and early diagnosis of HF.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Keratin from Animal By-Products: Structure, Characterization, Extraction and Application-A Review

Keratin is a structural fibrous protein and the core constituent of animal by-products from livestock such as wool, feathers, hooves, horns, and pig bristles. This natural polymer is also the main component of human hair and is present at an important percentage in human and animal skin. Significant amounts of keratin-rich animal tissues are discarded worldwide each year, ca. 12 M tons, and the share used for keratin extraction and added-value applications is still very low. An important stream of new potential raw materials, represented by animal by-products and human hair, is thus being lost, while a large-scale valorization could contribute to a circular bioeconomy and to the reduction in the environmental fingerprint of those tissues. Fortunately, scientific research has made much important progress in the last 10-15 years in the better understanding of the complex keratin architecture and its variability among different animal tissues, in the development of tailored extraction processes, and in the screening of new potential applications. Hence, this review aims at a discussion of the recent findings in the characterization of keratin and keratin-rich animal by-product structures, as well as in keratin recovery by conventional and emerging techniques and advances in valorization in several fields.

Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows

The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.

Using Synthetic Biology to Understand the Function of Plant Specialized Metabolites

Plant specialized metabolites (PSMs) are variably distributed across taxa, tissues, and ecological contexts; this variability has inspired many theories about PSM function, which, to date, remain poorly tested because predictions have outpaced the available data. Advances in mass spectrometry-based metabolomics have enabled unbiased PSM profiling, and molecular biology techniques have produced PSM-free plants; the combination of these methods has accelerated our understanding of the complex ecological roles that PSMs play in plants. Synthetic biology techniques and workflows are producing high-value, structurally complex PSMs in quantities and purities sufficient for both medicinal and functional studies. These workflows enable the reengineering of PSM transport, externalization, structural diversity, and production in novel taxa, facilitating rigorous tests of long-standing theoretical predictions about why plants produce so many different PSMs in particular tissues and ecological contexts. Plants use their chemical prowess to solve ecological challenges, and synthetic biology workflows are accelerating our understanding of these evolved functions.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

A convenient method for the relative and absolute quantification of lipid components in liposomes by 1H- and 31P NMR-spectroscopy

OBJECTIVE

Liposomes are promising delivery systems for pharmaceutical applications and have been used in medicine in the recent past. Preparation of liposomes requires reliable characterization and quantification of the phospholipid components for which the traditional cumbersome molybdate method is used frequently. The objective was to improve relative and absolute quantification of lipid components from liposomes.

METHODS

A reliable method for quantification of lipid composition in liposome formulations in the 1-10 μmol range with 1H- and 31P NMR spectroscopy at 600 MHz has been developed. The method is based on three crystalline small-molecule standards (Ph3PO4, (Tol)3PO4, and Ph3PO) in CDCl3.

RESULTS

Excellent calibration linearity and chemical stability of the standards was observed. The method was tested in blind fashion on liposomes containing POPC, PEG-ceramide and a pH-sensitive trans-aminocyclohexanol-based amphiphile (TACH).1 Relative quantification (percentage of components) as well as determination of absolute lipid amount was possible with excellent reproducibility with an average error of 5%. Quantification (triplicate) was accomplished in 15 min based on 1H NMR and in 1 h based on 31P NMR. Very little change in mixture composition was observed over multiple preparative steps.

CONCLUSION

Liposome preparations containing POPC, POPE, DOPC, DPPC, TACH, and PEG-ceramide can be reliably characterized and quantified by 1H NMR and 31P NMR spectroscopy at 600 MHz in the μmol range.

Perturbations in mitochondrial metabolism associated with defective cardiolipin biosynthesis: An in-organello real-time NMR study

Mitochondria are central to cellular metabolism; hence, their dysfunction contributes to a wide array of human diseases including cancer, cardiopathy, neurodegeneration, and heritable pathologies such as Barth syndrome. Cardiolipin, the signature phospholipid of the mitochondrion promotes proper cristae morphology, bioenergetic functions, and directly affects metabolic reactions carried out in mitochondrial membranes. To match tissue-specific metabolic demands, cardiolipin typically undergoes an acyl tail remodeling process with the final step carried out by the phospholipid-lysophospholipid transacylase tafazzin. Mutations in the tafazzin gene are the primary cause of Barth syndrome. Here, we investigated how defects in cardiolipin biosynthesis and remodeling impact metabolic flux through the tricarboxylic acid cycle and associated pathways in yeast. Nuclear magnetic resonance was used to monitor in real-time the metabolic fate of 13C3-pyruvate in isolated mitochondria from three isogenic yeast strains. We compared mitochondria from a wild-type strain to mitochondria from a Δtaz1 strain that lacks tafazzin and contains lower amounts of unremodeled cardiolipin, and mitochondria from a Δcrd1 strain that lacks cardiolipin synthase and cannot synthesize cardiolipin. We found that the 13C-label from the pyruvate substrate was distributed through about twelve metabolites. Several of the identified metabolites were specific to yeast pathways, including branched chain amino acids and fusel alcohol synthesis. Most metabolites showed similar kinetics amongst the different strains but mevalonate and α-ketoglutarate, as well as the NAD+/NADH couple measured in separate nuclear magnetic resonance experiments, showed pronounced differences. Taken together, the results show that cardiolipin remodeling influences pyruvate metabolism, tricarboxylic acid cycle flux, and the levels of mitochondrial nucleotides.

SERSomes for metabolic phenotyping and prostate cancer diagnosis

Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.

Recent advances and challenges in the analysis of natural toxins

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

Advances in Molecular and Genetic Technologies and the Problems Related to Their Application in Personalized Medicine

Advances in the global personalized medicine market are directly related to innovations and developments in molecular and genetic technologies. This review focuses on the key trends in the development of these technologies in the healthcare sector. The existing global developments having an impact on the evolution of the personalized medicine market are reviewed. Efficient measures to support the development of molecular and genetic technologies are proposed.

NMR-based metabolomic analysis of plasma from elderly patients with CVD before and after using contrast media

Contrast-induced acute kidney injury (CI-AKI) is a growingly common kidney problem caused by medical procedures involving contrast media (CM), especially in older patients with existing health issues. It is crucial to pinpoint potential biomarkers for the early detection of CI-AKI. Previously, we observed that iodixanol affects glucose, choline, and glutathione metabolism in endothelial cells under laboratory conditions. In this study, we used 1H NMR-based metabolomics to examine the metabolic changes in the blood plasma of elderly patients with cardiovascular disease (CVD) before and after receiving iodixanol. We identified altered metabolites in plasma 24 and 48 h after iodixanol injection compared to levels before injection. Notably, metabolites such as glucose, unsaturated fatty acids (UFA), low-density lipoprotein (LDL)/very low-density lipoprotein (VLDL), pyruvate, choline, and glycine showed potential as biomarkers at 24 h post-injection compared to levels before injection. Similarly, glucose, pyruvate, lactate, choline, and glycine in plasma could serve as potential biomarkers at 48 h post-injection. Iodixanol notably affected pathways related to glycolysis, fatty acid breakdown, and amino acid metabolism according to our metabolic pathway analysis. The altered levels of specific metabolites in plasma could be indicative of CM-induced kidney injury. Overall, this research aids in understanding the physiological mechanisms involved and in identifying early biomarkers and prevention strategies for CI-AKI.