Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Multifunctional dietary approach reduces intestinal inflammation in relation with changes in gut microbiota composition in subjects at cardiometabolic risk: the SINFONI project

The development of cardiometabolic (CM) diseases is associated with chronic low-grade inflammation, partly linked to alterations of the gut microbiota (GM) and reduced intestinal integrity. The SINFONI project investigates a multifunctional (MF) nutritional strategy's impact combining different bioactive compounds on inflammation, GM modulation and CM profile. In this randomized crossover-controlled study, 30 subjects at CM-risk consumed MF cereal-products, enriched with polyphenols, fibers, slowly-digestible starch, omega-3 fatty acids or Control cereal-products (without bioactive compounds) for 2 months. Metabolic endotoxemia (lipopolysaccharide (LPS), lipopolysaccharide-binding protein over soluble cluster of differentiation-14 (LBP/sCD14), systemic inflammation and cardiovascular risk markers, intestinal inflammation, CM profile and response to a one-week fructose supplementation, were assessed at fasting and post mixed-meal. GM composition and metabolomic analysis were conducted. Mixed linear models were employed, integrating time (pre/post), treatment (MF/control), and sequence/period. Compared to control, MF intervention reduced intestinal inflammation (fecal calprotectin, p = 0.007) and endotoxemia (fasting LPS, p < 0.05), without alteration of systemic inflammation. MF decreased serum branched-chain amino acids compared to control (p < 0.05) and increased B.ovatus, B.uniformis, A.butyriciproducens and unclassified Christensenellaceae.CAG-74 (p < 0.05). CM markers were unchanged. A 2-month dietary intervention combining multiple bioactive compounds improved intestinal inflammation and induced GM modulation. Such strategy appears as an effective strategy to target low-grade inflammation through multi-target approach.

Applying an NMR-based metabolomic workflow to unveil strawberry molecular mechanisms in vernalization

Nuclear Magnetic Resonance (NMR) is a mature technique in metabolomics due to its non-invasive, highly reproducible, and inherently quantitative nature. However, difficulties in data analysis hinder its standardization in research. Herein, we propose an NMR-based metabolomic workflow that comprises data preprocessing, metabolite annotation, and data analysis. In this work, we apply such workflow to study vernalization, which is a critical process for crop development with largely unknown molecular mechanisms. Our findings suggest that sugar mobility, accessibility, and increased photosynthetic activity support plant viability post-vernalization. In other words, these processes ensure successful transplanting of the plant, highlighting the importance of sufficient cold exposure for flowering, fruiting, and ripening. This study demonstrates that the proposed workflow is suitable to capture metabolic changes in plant development. Such methodology underscores the potential of NMR-based metabolomics as a powerful tool for crop monitoring, aiding in improved agricultural practices and yield optimization.

Disclose ATP-synthase as a protein target of the antimicrobial peptide RiLK1 in Escherichia coli: An alternative receptor-mediated bactericidal mechanism

In this study, we investigated the mechanism of action of the 10-aminoacid RiLK1 peptide against Escherichia coli (strain ATCC 25922), both in vitro and in contaminated meat matrices. Therefore, a mass spectrometry-based functional proteomics platform was employed to identify specific molecular targets of RiLK1 in a membrane protein-enriched E. coli lysate and to obtain information on their interaction mechanism. This target deconvolution approach combines MS-limited proteolysis techniques, like Drug Affinity Responsive Target Stability (DARTS) and targeted-limited Proteolysis coupled with Mass Spectrometry (t-LiP-MS). The b and δ subunits of the multimeric enzymatic complex ATP synthase, the smallest known biological nanomotor found in all cells, were identified as the relevant RiLK1 membrane protein targets. Extensive molecular docking and biochemical analyses validated and improved the suggested interaction profile. These unique findings could rationally explain the relevant RiLK1 bactericidal effects against E. coli strains, suggesting its potential application in food safety and preservation.

Rapid prediction of cervical cancer and high-grade precursor lesions: An integrated approach using low-field 1H NMR and chemometric analysis

Cervical cancer (CC) is a significant cause of morbidity and mortality in women, often preceded by high-grade cervical intraepithelial lesions (HSIL). Although conventional cytology (Pap smear) is widely used for screening, its sensitivity limitations and high false-positive rate reinforce the need for complementary methods. This study investigated the feasibility of low-field 1H NMR spectroscopy combined with chemometric modeling to differentiate healthy individuals (CON) from patients with HSIL and CC. Principal Component Analysis (PCA) was applied to explore metabolic patterns and identify relevant spectral variables in group differentiation. PCA1 highlighted the separation between CC and the other groups, while PCA2 and PCA3 evidenced intermediate metabolic characteristics in HSIL, reinforcing its role as a transition stage. Three classification scenarios were evaluated using Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA): (1) CON as target class, HSIL/CC as outclass classes; (2) HSIL as target class, CON as outclass class; and (3) CC as target class, CON as outclass class. Calibration was optimal (100 % SEN, SPE, ACC, MCC), and prediction showed higher efficacy in detecting CC (SPE = 100 %, MCC = 70 %), indicating that the model was more efficient in screening cervical cancer cases. Furthermore, low-field 1H NMR has demonstrated potential as a metabolomic screening tool. It is a promising alternative due to its greater accessibility, lower operational cost, and non-invasive nature, complementing traditional methods of early detection of tumors and cervical lesions.

Italian White Truffle (Tuber magnatum Pico): Discovery of new molecules through untargeted UHPLC-QTOF-MS analysis

The Italian white truffle (Tuber magnatum Pico) is worldwide recognized as one of the most expensive edible mushrooms. Its chemical characterization has not yet been properly investigated in scientific literature. The aim of this study was to explore, for the first time, the truffle polar metabolic profile using an untargeted approach through UHPLC-ESI-QTOF-MS data-dependent acquisition. The total features list was filtered and molecules were annotated according to the MS spectra correspondence score with a threshold of 60 %. This led to a total of 412 annotated compounds mainly referred to amino acids, B-vitamins, carbohydrates and fatty acids derivatives. A targeted analysis was applied to four annotated molecules never detected in truffle before, being riboflavin, azelaic, 2-isopropylmalic and 3-isopropylmalic acids. Some constituents of the extracts were semi-quantitatively analysed by 1H NMR spectroscopy. Results place a first brick in white truffle characterization, paving the way towards authenticity biomarkers detection useful in frauds prevention.

A nuclear magnetic resonance spectroscopy metabolomic approach to renal dysfunction in canine leishmaniasis

Chronic kidney disease (CKD) is a major complication and the leading cause of mortality in canine leishmaniasis (CanL). The kidneys are essential for numerous metabolic processes, and specific metabolites may serve as predictive biomarkers of kidney function. Nuclear Magnetic Resonance (NMR) spectroscopy is a prominent analytical tool in metabolomics, capable of identifying metabolites in urine. This study aim to identify distinct patterns in the NMR spectra of urine samples from dogs with CKD in CanL, reflecting the underlying metabolic profiles Fifty-five dogs were divided into three groups: 14 healthy control dogs (CG), 33 dogs with CKD secondary to leishmaniasis, and 8 dogs with CKD unrelated to leishmaniasis. CanL dogs were classified according to the International Renal Interest Society (IRIS) staging system: stage 1 (15 dogs), stage 2 (10 dogs), stage 3 (6 dogs), and stage 4 (2 dogs); and by LeishVet guidelines: stage I (5 dogs), stage II (4 dogs), stage III (14 dogs), and stage IV (10 dogs). Among dogs with CKD alone, one dog was in IRIS stage 1, two in stage 2, one in stage 3, and four in stage 4. Low-field proton nuclear magnetic resonance (1H NMR) spectroscopy and multivariate analysis were used to classify urine samples. Statistical analysis was conducted on hematology, urine and plasma samples from studied dogs. Using 1H NMR spectroscopy to classify urine samples from dogs with CKD, both with and without leishmaniasis, revealed distinct spectral patterns between the different groups. In conclusion, low-field 1H NMR spectroscopy demonstrated that CKD presents a distinct metabolic profile compared to kidney damage secondary to leishmaniasis.

The Metabolic Profile of Plasma During Epileptogenesis in a Rat Model of Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) arises mostly because of an initial injury. Certain stimuli can make a normal brain prone to repeated, spontaneous seizures via a process called epileptogenesis. This study examined the plasma metabolomics profile in rats with the induced TLE to identify feasible biomarkers that can distinguish progression of epileptogenesis in three different time points and reveal the underlying mechanisms of epileptogenesis. Status epilepticus (SE) was induced by repetitive intraperitoneal injections of low-dose lithium chloride-pilocarpine hydrocholoride. Blood samples were collected 48 h, 1 week, and 6 weeks after SE, respectively. Plasma metabolites were analyzed by nuclear magnetic resonance (NMR) spectrometry. Statistical analysis was performed using MetaboAnalyst 6.0. An orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to represent variations between the TLE model groups and respective controls. Volcano plot analysis was used to identify key features, applying a fold-change criterion of 1.5 and a t-test threshold of 0.05. 48 h after SE, dimethyl sulfone (DMSO2) and creatinine levels were decreased, whereas glycine and creatine levels were increased. The only metabolite that changed 1 week after SE was pyruvic acid, which was increased compared to its control level. Lactic acid, pyruvic acid, and succinic acid levels were increased 6 weeks after SE. The identified metabolites were especially related to the tricarboxylic acid cycle and glycine, serine, and threonine metabolism. The results illustrate that distinct plasma metabolites can function as phase-specific biomarkers in TLE and reveal new insights into the mechanisms underlying SE.

Association of nuclear magnetic resonance-based metabolomics with bone health in the UK Biobank

OBJECTIVES

The study aimed to explore associations of metabolomic data based on nuclear magnetic resonance (NMR) with the risk of fractures and bone mineral density (BMD).

METHODS

We included 69,963 participants without fractures at baseline in the UK Biobank. Cox proportional hazard models were used to estimate the associations of metabolomic biomarkers measured by NMR technology with the risk of all fractures and hip fracture. We used principal component analysis (PCA) to obtain uncorrelated principal components (PC), which were further used to estimate the associations of each PC with BMD, all fractures, and hip fracture separately.

RESULTS

During a median follow-up of 12.6 years, 3840 incidents of all fractures and 666 incidents of hip fracture were documented. Ninety-four of the 143 metabolomic biomarkers were significantly associated with incident all fractures, and 81 were significantly associated with incident hip fracture. The very low-density lipoprotein (VLDL) subclasses in different lipid constituents were associated with increased BMD at multiple sites, whereas high-density lipoprotein (HDL) subclasses were associated with decreased BMD. Higher concentrations of small (HR per SD increment: 0.92; 95 % CI: 0.88-0.97), medium (HR per SD increment: 0.91; 95 % CI: 0.88-0.94), and large (HR per SD increment: 0.93; 95 % CI: 0.90-0.96) low-density lipoprotein (LDL) particles were associated with a lower risk of all fractures. Similarly, higher VLDL subclasses (excluding very small VLDL particles) were associated with a lower risk of all fractures. Besides, higher levels of lipid constituents (including total lipids, cholesteryl esters, cholesterol, and free cholesterol) of very large and large HDL were associated with an increased risk of all fractures. PC1 (mainly contributed by lipid subclasses of LDL and VLDL), which explained the most variance of individual biomarkers, showed a negative association with the risk of all fractures (P = 7.80E-08). Similar associations were observed for hip fracture.

CONCLUSIONS

Higher levels of large and very large HDL were associated with an increased risk of fractures, whereas higher lipid subclasses of LDL and VLDL were associated with a lower risk of fracture. Higher levels of VLDL subclasses in different lipid constituents were associated with increased BMD at multiple sites, while higher level of HDL was associated with decreased BMD.

Beneficial effects of Glc-1,6-P2 modulation on mutant phosphomannomutase-2

The metabolite Glucose-1,6-bisphosphate (Glc-1,6-P2) plays a vital role in human metabolism, and is a crucial activator and stabilizer for phosphomannomutase-2 (PMM2) - mutations within this protein propagate the most common congenital disorder of glycosylation (PMM2-CDG). In vivo, Glc-1,6-P2 is hydrolysed by phosphomannomutase-1 (PMM1), predominantly in the brain, under the influence of inosine monophosphate (IMP). In the present study, we employed knock-out PMM1 in Arg141His/Phe119LeuPMM2 patient-derived fibroblasts and investigated the phenotypic improvement. Increased Glc-1,6-P2 was associated with glycosylation enhancement, confirmed by glycan profiling. Previously identified PMM2-CDG biomarkers, such as LAMP1, PTX3 and lysosomal enzymes showed empirical imrovement- these findings were corroborated by metabolomic and proteomic analysis. Moreover, our results support the potential of Glc-1,6-P2 modulation for PMM2-CDG, potentiating novel perspectives in drug discovery.

Estimation of postmortem interval under different ambient temperatures based on multi-organ metabolomics and machine learning algorithm

In forensic practice, the estimation of postmortem interval has been a persistent challenge. Recently, there has been an increasing utilization of metabolomics techniques combined with machine learning methods for postmortem interval estimation. When examining metabolite changes from a global perspective, rather than relying on specific substance changes, estimating postmortem interval through machine learning methods is more precise and entails fewer errors. Prior studies have investigated the use of metabolomics to estimate postmortem interval. Nevertheless, most of them focused on analyzing the metabolomic properties of a single organ or biofluid concerning a specific temperature. In this study, we employ the GC-MS platform to identify metabolites in the liver, kidney, and quadriceps femoris muscle of mechanically suffocated Sprague Dawley rats at various temperatures. Multivariable statistical analysis was used to determine differential compounds from the original data. The machine learning method was used to establish models for the estimation of postmortem interval under various ambient temperatures. As indicated by the results, liver, kidney, and quadriceps femoris muscle samples were screened for 24, 18, and 19 differential metabolites respectively, associated with postmortem interval under various ambient temperatures. Based on the metabolites listed above, the support vector regression models were established by utilizing single-organ and multi-organ metabolomics data for postmortem interval estimation. The multi-organ model showed a higher estimation accuracy. Also, a comprehensive generalization postmortem interval estimation model was established with multi-organ metabolomics data and temperature variables, which can be used for the postmortem interval estimation within the temperature range of 5-35℃. These results demonstrate that a multi-organ model utilizing metabolomics techniques can accurately estimate the postmortem interval under various ambient temperatures. Meanwhile, this research establishes a strong foundation for the practical application of metabolomics in postmortem interval estimation.

Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood

BACKGROUND

Metabolic profiling of blood metabolites, particularly in plasma and serum, is vital for studying human diseases, human conditions, drug interventions and toxicology. The clinical significance of blood arises from its close ties to all human cells and facile accessibility. However, patient-specific variables such as age, sex, diet, lifestyle and health status, along with pre-analytical conditions (sample handling, storage, etc.), can significantly affect metabolomic measurements in whole blood, plasma, or serum studies. These factors, referred to as confounders, must be mitigated to reveal genuine metabolic changes due to illness or intervention onset.

REVIEW OBJECTIVE

This review aims to aid metabolomics researchers in collecting reliable, standardized datasets for NMR-based blood (whole/serum/plasma) metabolomics. The goal is to reduce the impact of confounding factors and enhance inter-laboratory comparability, enabling more meaningful outcomes in metabolomics studies.

KEY CONCEPTS

This review outlines the main factors affecting blood metabolite levels and offers practical suggestions for what to measure and expect, how to mitigate confounding factors, how to properly prepare, handle and store blood, plasma and serum biosamples and how to report data in targeted NMR-based metabolomics studies of blood, plasma and serum.

Nebulized riclinoctaose mitigates ovalbumin-induced allergic asthma by attenuating mast cell activation

Allergic asthma is an inflammatory airway disease in which mast cells play a key role in its pathogenesis. Riclinoctaose (Rios), an octasaccharide composed of glucose and galactose, has been reported to modulate macrophage polarization in renal ischemia-reperfusion injury. This study investigates the effects of nebulized Rios in an ovalbumin (OVA)-induced allergic asthma model. Nebulized Rios significantly reduced airway hyperresponsiveness (AHR), inflammatory cell infiltration, pulmonary fibrosis, and mucus production in OVA-treated mice. Additionally, Rios suppressed lung inflammatory cytokine production and protected against OVA-induced oxidative injury. In bronchoalveolar lavage fluid (BALF), Rios decreased the number of neutrophils and macrophages, as well as serum immunoglobulin E (IgE) and interleukin-4 (IL-4) levels. Metabolomics analysis using 1H NMR showed significant changes in the lung metabolic profile of OVA-induced asthma mice, which were partially reversed by Rios treatment. Confocal laser microscopy (CLM) revealed that fluorescently labeled Rios specifically binds to mast cell membranes. In vitro, Rios reduced degranulation and inflammatory responses in mast cells RBL-2H3 following anti-Dinitrophenyl-Immunoglobulin E (anti-DNP-IgE) sensitization and anti-Dinitrophenyl-Bovine Serum Albumin (anti-DNP-BSA) stimulation. These results suggest that Rios has therapeutic potential for allergic asthma, likely through the modulation of mast cell degranulation and mediator release.

Exercise Attenuates Skeletal Muscle Atrophy in Senescent SAMP8 Mice: Metabolic Insights from NMR-Based Metabolomics

Age-related skeletal muscle atrophy is a major health concern in the elderly, contributing to reduced mobility, increased risk of falls, and metabolic dysfunction. The senescence-accelerated prone 8 (SAMP8) mouse model, known for its rapid aging and early cognitive decline, serves as an essential model for studying age-related muscle degeneration. While previous studies have shown that exercise attenuates muscle atrophy by promoting regeneration and improving strength, the underlying metabolic mechanisms remain poorly understood. This study used the SAMP8 model to evaluate the effects of exercise on muscle atrophy and associated metabolic changes. Our results show that exercise promoted muscle growth by reducing body weight, increasing skeletal muscle mass, and decreasing fat accumulation. Furthermore, exercise improved grip strength, muscle tone, and muscle fiber cross-sectional area, thereby preserving muscle functionality. NMR-based metabolomic analysis identified key metabolic pathways modulated by exercise, including glycine, serine, and threonine metabolism; alanine, aspartate, and glutamate metabolism; pyruvate metabolism; and taurine and hypotaurine metabolism. These findings underscore the therapeutic potential of exercise in combating age-related muscle wasting and elucidate the metabolic pathways underlying its benefits.

Pharmacometabolomics: An emerging platform for understanding the pathophysiological processes and therapeutic interventions

Pharmacometabolomics has emerged as a new subclass of metabolomics, aiming to predict an individual's response to a drug or optimize therapy based on prior information on an individual's metabolic profile. Pharmacometabolomics is being explored in drug discovery, biomarker identification, disease diagnosis, monitoring of disease progression, and therapeutic intervention. The time points-based sample collection is essential to measure the response of individuals to pathophysiological processes and therapeutic interventions. Analytical techniques such as NMR, LC-MS, and GC-MS have been employed to assess a huge number of metabolites present in biological systems. NMR has an advantage over other analytical techniques as it provides a snapshot of tissue and biological fluids, however, it requires higher magnetic fields to achieve better resolution. GC-MS could cover a wide range of metabolites due to high resolution but requires derivatization for certain metabolites. LC-MS is equally competitive and separates a wide range of metabolites with diverse polarities but requires extensive method development. Several platforms have been developed to analyze the analytical data and provide meaningful results via data reduction methods. PCA and PLS-DA are the most common methods for reduction dimensionality through simplified multivariate data modeling. This manuscript brings insights into the overview of pharmacometabolomics experimental design and the application of various analytical techniques and multivariate statistical analysis in the various fields of medical research.

Serum Metabolomic Profiling in Healthy Dogs Supplemented with Increasing Levels of Purified Beta-1,3/1,6-Glucans

Metabolomics has proven to be an effective tool for elucidating mechanisms and assessing the effectiveness of dietary interventions in canine and feline nutrition. In this context, the present study aimed to perform a metabolomic analysis of the serum of dogs supplemented with increasing levels of beta-1,3/1,6-glucans to generate evidence and gain a deeper understanding of the metabolic responses associated with this supplementation. Eight dogs were evenly assigned to two balanced 4 × 4 Latin squares. Four diets were tested, differing only in beta-glucan content (0.0%, 0.07%, 0.14%, and 0.28%), and the dogs were fed according to their individual maintenance energy requirements. Each experimental period lasted 35 days. On day 35, 5 mL of blood was collected via jugular venipuncture to obtain serum for metabolomic analysis. Nuclear magnetic resonance analysis identified 12 key serum metabolites. Hierarchical heat map analysis revealed differences in metabolite intensity between treatments (p < 0.05). Additionally, the most relevant metabolic pathways were phenylalanine, tyrosine, and tryptophan metabolism; alanine, aspartate, and glutamate metabolism; and glyoxylate and dicarboxylate metabolism. This study demonstrated that increasing levels of purified beta-1,3/1,6-glucans from Saccharomyces cerevisiae modulated key metabolic pathways in dogs, particularly those related to amino acid, lipid and energy metabolisms, and gut microbiota. These findings provide insights into the mechanisms by which beta-glucans influence canine health.

Quantitative Nuclear Magnetic Resonance for Small Biological Molecules in Complex Mixtures: Practical Guidelines and Key Considerations for Non-Specialists

Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical approach that enables both the structural determination and precise quantification of small molecules, such as metabolites. However, achieving precise quantification with NMR involves more than simply comparing integrals derived from NMR peaks to a concentration reference; quantitative NMR (qNMR) is a distinct and specialized application within the field. To obtain absolute quantitative results, spectra must be acquired under strict experimental conditions. Unfortunately, these acquisition parameters can be challenging to implement experimentally and often require trade-offs that compromise high throughput or practicality. In such situations, alternative strategies based on relative quantification and advanced software tools offer valuable solutions. This review aims to provide non-specialists with the key concepts and methodologies required for accurate NMR-based quantification in biomedical research, focusing on practical guidelines and experimental considerations. Unlike prior reviews, it prioritizes accessibility and practical implementation for researchers outside the field, emphasizing key experimental workflows and applications in biological and clinical studies. It clarifies the distinctions between absolute and relative concentration determinations and emphasizes the critical importance of sample preparation, pulse sequence selection, and rigorous control of experimental parameters. Recent technological advancements, such as high-field spectrometers and cryoprobes, have significantly enhanced the sensitivity and accuracy of NMR, enabling the reliable detection of low-concentration metabolites. Quantitative NMR thus offers critical potential in elucidating metabolic processes, supporting drug development, and aiding disease diagnosis.

Acute changes in urinary metabolites: vinyasa yoga compared to cycle ergometer exercise

Introduction

Increased interest in unconventional exercise such as vinyasa yoga has outpaced our understanding of the physiological response to yoga exercise. The objective of the current study was to evaluate changes in urinary metabolites (i.e., alanine, phenylalanine, glycine, choline, taurine, creatinine, creatine, dimethylamine, citrate, pyruvate, acetate, and beta-hydroxybutyrate) elicited by vinyasa yoga compared to moderate intensity aerobic exercise in young healthy adults.

Methods

Twelve participants, six women and six men, completed a vinyasa yoga exercise session (VY) and a moderate intensity cycle ergometer exercise session (ME) in a sequential fashion. The intensity of the ME was matched to heart rate and rating of perceived exertion elicited during the initial VY. Urine samples were collected at baseline and following the completion of each of VY and ME. Metabolite concentrations after each exercise were normalized to their baseline levels to obtain a relative exercise-induced change in concentration. We hypothesized that activation of large muscle groups in the lower extremities would foster greater ME-induced alterations in metabolites.

Results

Exercise-induced changes in urinary concentrations of phenylalanine, creatinine, creatine, glycine, choline, taurine, dimethylamine, citrate, pyruvate, alanine, and beta-hydroxybutyrate were greater in ME compared to VY (P < 0.05). There was no difference between the exercise-induced changes in lactate between groups (P < 0.05).

Discussion

The results of this study demonstrate that ME promotes more robust changes in urinary metabolites compared to VY. These differences may be due to a greater localized workload on the large muscle groups of the lower extremities during ME, and potentially highlight the distributed metabolic demand of VY.

SAA1 as a Potential Early Diagnostic Biomarker for Sepsis Through Integrated Proteomics and Metabolomics

Sepsis is characterised by fatal organ dysfunction resulting from a dysfunctional host response to infection, imposing a substantial economic burden on families and society. Therefore, identifying biomarkers for early sepsis diagnosis and improving patient prognosis are critical. This study recruited 59 sepsis patients and 35 healthy volunteers from the Department of Critical Care Medicine at Harbin Medical University Affiliated First Hospital between March and December 2021. Through a combination of non-targeted and targeted proteomics and metabolomics sequencing, along with various analytical methods, we initially identified and validated serum amyloid A1 (SAA1) as a diagnostic biomarker for sepsis. Our study found that SAA1 was significantly elevated in the sepsis group, demonstrating its diagnostic value for sepsis (AUC: 0.95, 95% CI: 0.88-1). Additionally, a positive correlation was observed between SAA1 and disease severity, as indicated by the Sequential Organ Failure Assessment (SOFA) score (R = 0.51, p = 0.004) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (R = 0.52, p = 0.003). This study suggests that SAA1 is a potentially effective and reliable marker for diagnosing sepsis and predicting its severity.

Effects of Skim Milk Whey-Derived Proteins on Plasma, Urine, and Gut Metabolites in Preterm Piglets as a Model for Infants

This study investigates the metabolic impact of skim milk whey-derived protein concentrate (SPC) for infant formula, including its heat-treated (HT-SPC) and stored (HTS-SPC) variants, on the plasma, urine, and gut metabolites of newborn piglets, compared to conventional whey protein concentrate (WPC). Preterm piglets were fed formula containing WPC, SPC, HT-SPC, or HT-SPC, HTS-SPC for 5 days. Metabolomic analysis of plasma, urine, and colon content was performed using 1H NMR. Relative to WPC, SPC mainly affected colon content metabolites, increasing 19 metabolites in the colon and tyrosine in plasma, while decreasing pyruvate in colon content and glycine in plasma. Heat-treatment and storage of SPC led to increased metabolite concentrations in colon contents and urine. Notably, significant correlations between gut metabolites and abundant gut bacteria genes were observed only in the SPC-fed pigs. SPC induced higher branched chain amino acid concentrations in the gut, but had minimal effects on plasma and urinary metabolites, likely due to differences in dietary proteins and in microbiota metabolism. While the clinical effects of SPC-induced gut branched chain amino acids remain unclear, the results from our study suggest that SPC-based infant formula is metabolically safe for sensitive newborns, comparable to WPC-based formulas.

Liver gene expression and its rewiring in hepatic steatosis are controlled by PI3Kα-dependent hepatocyte signaling

Insulin and other growth factors are key regulators of liver gene expression, including in metabolic diseases. Most of the phosphoinositide 3-kinase (PI3K) activity induced by insulin is considered to be dependent on PI3Kα. We used mice lacking p110α, the catalytic subunit of PI3Kα, to investigate its role in the regulation of liver gene expression in health and in metabolic dysfunction-associated steatotic liver disease (MASLD). The absence of hepatocyte PI3Kα reduced maximal insulin-induced PI3K activity and signaling, promoted glucose intolerance in lean mice and significantly regulated liver gene expression, including insulin-sensitive genes, in ad libitum feeding. Some of the defective regulation of gene expression in response to hepatocyte-restricted insulin receptor deletion was related to PI3Kα signaling. In addition, though PI3Kα deletion in hepatocytes promoted insulin resistance, it was protective against steatotic liver disease in diet-induced obesity. In the absence of hepatocyte PI3Kα, the effect of diet-induced obesity on liver gene expression was significantly altered, with changes in rhythmic gene expression in liver. Altogether, this study highlights the specific role of p110α in the control of liver gene expression in physiology and in the metabolic rewiring that occurs during MASLD.

Deep learning and its applications in nuclear magnetic resonance spectroscopy

Nuclear Magnetic Resonance (NMR), as an advanced technology, has widespread applications in various fields like chemistry, biology, and medicine. However, issues such as long acquisition times for multidimensional spectra and low sensitivity limit the broader application of NMR. Traditional algorithms aim to address these issues but have limitations in speed and accuracy. Deep Learning (DL), a branch of Artificial Intelligence (AI) technology, has shown remarkable success in many fields including NMR. This paper presents an overview of the basics of DL and current applications of DL in NMR, highlights existing challenges, and suggests potential directions for improvement.

Proton-Nuclear Magnetic Resonance Metabolomics of Gingival Crevicular Fluid During Orthodontic Tooth Movement With Aligners

OBJECTIVES

To determine the correlation between orthodontic tooth movement and a pre-defined set of gingival crevicular fluid (GCF) metabolites through proton nuclear magnetic resonance (1H-NMR) spectroscopy.

MATERIALS AND METHODS

A clinical randomised prospective split-mouth study comparing the GCF metabolites around stationary and moving second maxillary molars. Twenty-four healthy subjects diagnosed with dental class II malocclusion undergoing orthodontic clear aligner treatment (CAT) were enrolled. GCF samples from the mesial and distal sulcus of second molars under stationary conditions or under 1 N of distalising force were harvested at baseline, 1 h, 7 days and 21 days after the application of CAT. 1H-NMR was utilised for GCF sample analysis. The 2-dimensional total correlation spectroscopy spectral signature of 35 known GCF metabolites was compared in moving and stationary teeth. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), variable importance in projection (VIP) score and area under the curve (AUC) were computed utilising MetaboAnalyst 5.0 software.

RESULTS

VIP-score values showed statistically significant differences between the metabolites involved in moving and stationary molars (p < 0.05). PCA and PLS-DA results showed potential differences between the metabolite clusters. The variation of the 1H-NMR signals of Glutamine, Uracil, N-Acetylneuraminate and alpha-ketoglutarate contributes primarily to the variance across metabolites in moving versus stationary teeth at 1 h, 7 days and 21 days.

CONCLUSION

High values of Glutamine and low values of Uracil, N-Acetylneurinamate and alpha-ketoglutarate could be utilised to predict the progress of orthodontic tooth movement over time. Knowledge of metabolites predictive of tooth movement could contribute to the design of tailored orthodontic treatment planning, reducing time, costs and side-effects.

Biomarkers of endometriosis

Endometriosis represents a diverse disease characterized by three distinct phenotypes: superficial peritoneal lesions, ovarian endometriomas, and deep infiltrating endometriosis. The most widely accepted pathophysiological hypothesis for endometriosis is rooted in retrograde menstruation, a phenomenon observed in most patients. Endometriosis is closely linked to infertility, but having endometriosis does not necessarily imply infertility. The disease can impact fertility through various mechanisms affecting the pelvic cavity, ovaries, and the uterus itself. MicroRNAs (miRNAs) indeed represent a fascinating and essential component of the regulatory machinery within cells. Discovered in the early 1990s, miRNAs have since been identified as critical players in gene expression control. Unfortunately, ovarian endometrioma is a common gynecologic disorder for which specific serum markers are currently lacking. Some have examined urocortin for its ability to differentiate endometriomas from other benign ovarian cysts. Another potential marker, Cancer Antigen 125 (CA-125) is a well-established indicator for epithelial cell ovarian cancer and its levels can be elevated in conditions such as endometriosis. CA-125 is derived from coelomic epithelia, including the endometrium, fallopian tube, ovary, and peritoneum. In this review we examine the pathophysiologic basis for endometriosis and highlight potential markers to more fully characterize the underlying biochemical processes linked to this multifaceted disease state.

Spectroscopic Nuclear Magnetic Resonance and Fourier Transform-Infrared Approach Used for the Evaluation of Healing After Surgical Interventions for Patients with Colorectal Cancer: A Pilot Study

Background/Objectives: Colorectal cancer (CRC) is one of the most common and deadly types of cancer. Compared with the classical histopathological approach, this study discusses the application of 1H NMR and FT-IR techniques for the fast evaluation degree of healing of patients with CRC after surgical intervention. Methods: Native and deproteinized blood plasma collected from 10 patients with confirmed CRC and 20 healthy volunteers were analyzed using 1H NMR T2 distributions and FT-IR spectra measured for samples collected before and 7 days after surgery. The average FT-IR spectrum from 20 healthy volunteers is also presented. Principal component analysis (PCA) was performed on the FT-IR spectra. The results were used for further statistical analysis using receiver operating characteristic (ROC) and area under the curve (AUC) and to produce a series of prediction maps using a machine learning library. Results: Both experimental methods combined with analysis methods demonstrated that the native blood plasma samples can be better used to predict the CRC patients' evolution 7 days after surgery. Three patients showed a significant evolution by 1H NMR T2 distribution, correlated to the observation of FT-IR-PCA analysis. Maps of medical state probability were generated using a trained machine learning-based ANN. Conclusions: The experimental measurements combined with an advanced statistical analysis and machine learning were successfully used and show that the healing process of patients with CRC is not linear, from the preoperative state to the state associated with healthy volunteers, but passes through a distinct healing state.

MetAssimulo 2.0: a web app for simulating realistic 1D and 2D metabolomic 1H NMR spectra

MOTIVATION

Metabolomics extensively utilizes nuclear magnetic resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both 1D and 2D NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labeled data. In this context, simulation of spectral data becomes a tractable solution for algorithm development.

RESULTS

Here, we introduce MetAssimulo 2.0, a comprehensive upgrade of the MetAssimulo 1.b metabolomic 1H NMR simulation tool, reimplemented as a Python-based web application. Where MetAssimulo 1.0 only simulated 1D 1H spectra of human urine, MetAssimulo 2.0 expands functionality to urine, blood, and cerebral spinal fluid, enhancing the realism of blood spectra by incorporating a broad protein background. This enhancement enables a closer approximation to real blood spectra, achieving a Pearson correlation of approximately 0.82. Moreover, this tool now includes simulation capabilities for 2D J-resolved (J-Res) and Correlation Spectroscopy spectra, significantly broadening its utility in complex mixture analysis. MetAssimulo 2.0 simulates both single, and groups, of spectra with both discrete (case-control, e.g. heart transplant versus healthy) and continuous (e.g. body mass index) outcomes and includes inter-metabolite correlations. It thus supports a range of experimental designs and demonstrating associations between metabolite profiles and biomedical responses.By enhancing NMR spectral simulations, MetAssimulo 2.0 is well positioned to support and enhance research at the intersection of deep learning and metabolomics.

AVAILABILITY AND IMPLEMENTATION

The code and the detailed instruction/tutorial for MetAssimulo 2.0 is available at https://github.com/yanyan5420/MetAssimulo_2.git. The relevant NMR spectra for metabolites are deposited in MetaboLights with accession number MTBLS12081.

Urinary Metabolomics of Plastic Manufacturing Workers: A Pilot Study

The plastic manufacturing industry has a crucial role in the global economy with a significant impact in a wide range of fields. The chemical risk to which workers are potentially exposed is difficult to characterize and strictly related to both the products and processes adopted. Among the chemicals used, we can cite styrene, phenol, butadiene and phthalates, but nano- and microplastic particles can also be released in the work environment. In this pilot study, we present for the first time an NMR-based metabolomic approach for assessing urinary profiles of workers employed in a plastic manufacturing company. Urine samples from twelve workers and thirteen healthy volunteers were collected and analyzed by NMR spectroscopy. Forty-six urinary metabolites belonging to different chemical classes were univocally identified and quantified. The dataset so obtained was then subjected to multivariate statistical analysis to characterize each profile and highlight any differences. An alteration in some metabolites involved in several pathways, such as amino acid metabolism and NAD metabolism, was found, and a strong impact on gut microflora was also speculated. Ultimately, our work has the objective of adding a tile to the knowledge of biological effects possibly related to occupational exposure even if it is below the threshold limit values.

Metabolomic study for the identification of symptomatic carotid plaque biomarkers

Carotid artery stenosis is mainly produced due to the progressive accumulation of atherosclerotic plaque in the vascular wall. The atherosclerotic plaque is characterized by the accumulation of lipids, low density proteins, expression of chemokines and adhesion molecules, and migration of monocytes and lymphocytes into the plaque. Its rupture can produce stroke, but embolic propensity depends principally on the composition and vulnerability of plaque rather than the severity of stenosis. It is important, then, to ascertain which patients with carotid artery stenosis have a greater risk of developing neurological symptomatology. Here, we present a metabolomic study by using nuclear magnetic resonance (NMR) spectroscopy in atheroma plaque and serum samples from patients with recently symptomatic and asymptomatic carotid stenosis to search for metabolites that could be used as biomarkers associated with plaque vulnerability and subsequent risk of rupture. Thirty-eight atheromatous plaque samples (24 asymptomatic patients and 14 symptomatic) and 70 serum samples (43 asymptomatic and 27 symptomatic) were studied by NMR spectroscopy. The data were analysed using multivariate statistics (PLS-DA) to determine a model to discriminate between symptomatic and asymptomatic samples (atheroma plaques and sera). The calculated PLS-DA models showed a 100 % sensitivity and a 96.6 % specificity for the cross validation to discriminate between symptomatic and asymptomatic plaques, and 88.37 % sensitivity and 77.78 % specificity when serum samples were analysed. According to the results of our multivariate and univariate analysis, the most discriminative metabolites for plaque vulnerability were threonine in serum samples, and glutamate in plaque samples. Also, an analysis of the main metabolic pathways involved in plaque vulnerability revealed that d-glutamine and d-glutamate metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis were the most affected pathways in plaque and serum, respectively.

Metabolomic Biomarkers of Pulmonary Fibrosis in COVID-19 Patients One Year After Hospital Discharge

Coronavirus disease 2019 (COVID-19) global pandemic has affected more than 600 million people up to date. The symptomatology and severity of COVID-19 are very broad, and there are still concerns about the long-term sequelae that it can have on discharged patients. The development of pulmonary fibrotic sequelae after this infection is especially worrying. Our aim was to determine if there was a metabolomic signature that could predict the development of pulmonary fibrotic sequelae. It is a multicenter prospective observation subcohort based on the COVID-FIBROTIC study. A metabolomic analysis was performed by nuclear magnetic resonance (NMR) on serum samples from patients admitted with bilateral COVID-19 pneumonia collected 2 months after hospital discharge. One year after admission, clinical, functional and radiological data were collected from these same patients. Finally, 109 patients (mean age 57.68 [DS14.03], 65.13% male) were available. Fibrotic sequelae 1 year after COVID-19 disease were found in 33% of them. Based on the NMR analysis of the serum samples, it was possible to distinguish with 80.82% of sensitivity, 72.22% of specificity and 0.83 of an area under the curve (AUC) value which patients would have radiological signs of pulmonary fibrotic pattern 1 year after sample collection. According to the metabolites participating in the discriminative model and the univariate statistics, glucose, valine, and fatty acids (═CH-CH2-CH═) were suggested as potential biomarkers of the development of pulmonary fibrotic sequelae after COVID-19. TRIAL REGISTRATION NUMBER: clinicaltrials.gov NCT04409275 (June 1, 2020).

FECAL CALPROTECTIN AND INTESTINAL METABOLITES: WHAT IS THEIR IMPORTANCE IN THE ACTIVITY AND DIFFERENTIATION OF PATIENTS WITH INFLAMMATORY BOWEL DISEASES?

BACKGROUND

Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), lacks a known etiology. Although clinical symptoms, imaging, and colonoscopy are common diagnostic tools, fecal calprotectin (FC) serves as a widely used biomarker to track disease activity. Metabolomics, within the omics sciences, holds promise for identifying disease progression biomarkers. This approach involves studying metabolites in biological media to uncover pathological factors.

AIMS

The purpose of this study was to explore fecal metabolomics in IBD patients, evaluate its potential in differentiating subtypes, and assess disease activity using FC.

METHODS

Cross-sectional study including IBD patients, clinical data, and FC measurements (=200 μg/g as an indicator of active disease).

RESULTS

Fecal metabolomics utilized chromatography mass spectrometry/solid phase microextraction with MetaboAnalyst 5.0 software for analysis. Of 52 patients (29 UC, 23 CD), 36 (69.2%) exhibited inflammatory activity. We identified 56 fecal metabolites, with hexadecanoic acid, squalene, and octadecanoic acid notably distinguishing CD from UC. For UC, octadecanoic and hexadecanoic acids correlated with disease activity, whereas octadecanoic acid was most relevant in CD.

CONCLUSIONS

These findings highlight the potential of metabolomics as a noninvasive complement for evaluating IBD, aiding diagnosis, and assessing disease activity.

Urinary Metabolic Profiling During Epileptogenesis in Rat Model of Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Background/Objectives: Temporal lobe epilepsy (TLE) often develops following an initial brain injury, where specific triggers lead to epileptogenesis-a process transforming a healthy brain into one prone to spontaneous, recurrent seizures. Although electroencephalography (EEG) remains the primary diagnostic tool for epilepsy, it cannot predict the risk of epilepsy after brain injury. This limitation highlights the need for biomarkers, particularly those measurable in peripheral samples, to assess epilepsy risk. This study investigated urinary metabolites in a rat model of TLE to identify biomarkers that track epileptogenesis progression across the acute, latent, and chronic phases and elucidate the underlying mechanisms. Methods: Status epilepticus (SE) was induced in rats using repeated intraperitoneal injections of lithium chloride-pilocarpine hydrochloride. Urine samples were collected 48 h, 1 week, and 6 weeks after SE induction. Nuclear magnetic resonance spectrometry was used for metabolomic analysis, and statistical evaluations were performed using MetaboAnalyst 6.0. Differences between epileptic and control groups were represented using the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Volcano plot analysis identified key metabolic changes, applying a fold-change threshold of 1.5 and a p-value < 0.05. Results: The acute phase exhibited elevated levels of acetic acid, dihydrothymine, thymol, and trimethylamine, whereas glycolysis and tricarboxylic acid cycle metabolites, including pyruvic and citric acids, were reduced. Both the acute and latent phases showed decreased theobromine, taurine, and allantoin levels, with elevated 1-methylhistidine in the latent phase. The chronic phase exhibited reductions in pimelic acid, tiglylglycine, D-lactose, and xanthurenic acid levels. Conclusions: These findings highlight stage-specific urinary metabolic changes in TLE, suggesting distinct metabolites as biomarkers for epileptogenesis and offering insights into the mechanisms underlying SE progression.

Pure Shift NMR with Solvent Suppression: A Robust and General Method for Determining Quantitative Metabolic Profiles in Biofluids

Ultrahigh-resolution pure shift NMR has recently been shown as a promising approach for providing quantitative metabolic profiles that can be used to study the metabolic footprint left by cancer cells in their aqueous growth medium. In this approach, a library of reference 1H pure shift spectra with water suppression was implemented to determine metabolite concentrations from the NOESY-presat-PSYCHE-SAPPHIRE spectrum recorded on the extracellular medium. This achievement clearly called for a generalization of a quantification method relying on ultrahigh-resolution data to other biological samples of interest (urine, plasma, tissue extracts, etc.), which requires evaluating the robustness of the analytical workflow. We have first addressed the influence of sample preparation on the quality of metabolite quantification. The quantification performed on a model mixture of metabolites prepared under different conditions shows good linearity, trueness, and precision, which highlights the high reproducibility of the proposed analytical protocol regardless of the physicochemical conditions in the sample. Second, we have successfully implemented this quantification protocol to determine metabolite levels in real urine and plasma samples, thereby paving the way for the use of the library of pure shift reference spectra for accurate and quantitative metabolic profiling of a broad range of aqueous samples.

Toxicity of Pentachlorophenol Exposure on Male and Female Heteropneustes fossilis Investigated Using NMR-Based Metabolomics Approach

Pentachlorophenol (PCP) is one of the most common chlorophenols utilized in numerous industrial processes, including the production of dyes, pesticides, wood preservatives, disinfectants, antiseptics, and medicines because it has fungicidal and bactericidal characteristics. Previous studies on catfish (Heteropneustes fossilis) revealed that PCP acts as a potent endocrine disruptor and also causes behavioral changes in a concentration-dependent manner. However, the toxicological effects of PCP have not been compared between male and female catfish. The present study aims to investigate the toxic effects of PCP on catfish through histopathological changes, oxidative stress, and serum metabolomics after 60 days of exposure. Chronic exposure to sublethal concentrations of PCP resulted in significant histopathological alterations in the liver and gonad, including leukocyte infiltration, hepatocyte degeneration, follicular layer dissolution, and abnormal sperm distribution. Increased levels of lipid peroxidation and hydrogen peroxide, along with decreased antioxidant enzyme activity, were observed in PCP-exposed groups. A 1H NMR-based metabolomics approach was employed to investigate the toxic effects of PCP on catfish serum, revealing alterations in various metabolites, including amino acids, organic acids, glucose, cholesterol, and neurotransmitters, in a dose-dependent manner. Multivariate partial least-squares discriminant analysis (PLS-DA) identified metabolic changes associated with oxidative stress, disruption in hormone synthesis and reproduction, disturbance in osmoregulation and membrane stabilization, energy metabolism disorder, amino acid metabolism disorder, and neurotransmitter imbalance in PCP-exposed catfish. This study demonstrates the efficacy of metabolomics in elucidating the toxicity and underlying mechanisms of wood preservatives like PCP, providing valuable insights for risk assessment in toxicology research. Overall, these findings contribute to our understanding of the toxicological effects of PCP exposure on aquatic organisms and highlight the potential of histology, oxidative stress, and metabolomics in assessing environmental contaminants' risks.

Altered hepatic metabolism mediates sepsis preventive effects of reduced glucose supply in infected preterm newborns

Preterm infants are susceptible to neonatal sepsis, a syndrome of pro-inflammatory activity, organ damage, and altered metabolism following infection. Given the unique metabolic challenges and poor glucose regulatory capacity of preterm infants, their glucose intake during infection may have a high impact on the degree of metabolism dysregulation and organ damage. Using a preterm pig model of neonatal sepsis, we previously showed that a drastic restriction in glucose supply during infection protects against sepsis via suppression of glycolysis-induced inflammation, but results in severe hypoglycemia. Now we explored clinically relevant options for reducing glucose intake to decrease sepsis risk, without causing hypoglycemia and further explore the involvement of the liver in these protective effects. We found that a reduced glucose regime during infection increased survival via reduced pro-inflammatory response, while maintaining normoglycemia. Mechanistically, this intervention enhanced hepatic oxidative phosphorylation and possibly gluconeogenesis, and dampened both circulating and hepatic inflammation. However, switching from a high to a reduced glucose supply after the debut of clinical symptoms did not prevent sepsis, suggesting metabolic conditions at the start of infection are key in driving the outcome. Finally, an early therapy with purified human inter-alpha inhibitor protein, a liver-derived anti-inflammatory protein, partially reversed the effects of low parenteral glucose provision, likely by inhibiting neutrophil functions that mediate pathogen clearance. Our findings suggest a clinically relevant regime of reduced glucose supply for infected preterm infants could prevent or delay the development of sepsis in vulnerable neonates.

Metabolic signature of renal cell carcinoma tumours and its correlation with the urinary metabolome

INTRODUCTION

Despite considerable advances in cancer research, the increasing prevalence and high mortality rate of clear cell renal cell carcinoma (ccRCC) remain a significant challenge. A more detailed comprehension of the distinctive metabolic characteristics of ccRCC is vital to enhance diagnostic, prognostic, and therapeutic strategies.

OBJECTIVES

This study aimed to investigate the metabolic signatures of ccRCC tumours and, for the first time, their correlation with the urinary metabolome of the same patients.

METHODS

We applied a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach to analyse matched tissue and urine samples from a cohort of 18 ccRCC patients and urine samples from 18 cancer-free controls. Multivariate and univariate statistical methods, as well as pathway and correlation analyses, were performed to assess metabolic dysregulations and correlations between tissue and urine.

RESULTS

The results showed a ccRCC metabolic signature characterized by reprogramming in amino acid, energy, and sugar and inositol phosphate metabolisms. Our study identified, for the first time, significantly decreased levels of asparagine, proline, gluconate, 3-aminoisobutanoate, 4-aminobutanoate and urea in ccRCC tumours, highlighting the involvement of arginine biosynthesis, β-alanine metabolism and purine and pyrimidine metabolism in ccRCC. The correlations between tissue and urine metabolomes provide evidence for the potential usefulness of urinary metabolites in understanding systemic metabolic changes driven by RCC tumours.

CONCLUSIONS

These findings significantly advance our understanding of metabolic reprogramming in ccRCC and the systemic metabolic changes associated with the disease. Future research is needed to validate these findings in larger cohorts and to determine their potential implications for diagnosis and targeted therapies.

Metabolomic profile of severe COVID-19 and a signature predictive of progression towards severe disease status: a prospective cohort study (METCOVID)

Profound metabolomic alterations occur during COVID-19. Early identification of the subset of hospitalised COVID-19 patients at risk of developing severe disease is critical for optimal resource utilization and prompt treatment. This work explores the metabolomic profile of hospitalised adult COVID-19 patients with severe disease, and establishes a predictive signature for disease progression. Within 48 hours of admission, serum samples were collected from 148 hospitalised patients for nuclear magnetic resonance (NMR) spectroscopy. Lipoprotein profiling was performed using the 1H-NMR-based Liposcale test, while low molecular weight metabolites were analysed using one-dimensional Carr-Purcell-Meiboom-Gill pulse spectroscopy and an adaptation of the Dolphin method for lipophilic extracts. Severe COVID-19, per WHO's Clinical Progression Scale, was characterized by altered lipoprotein distribution, elevated signals of glyc-A and glyc-B, a shift towards a catabolic state with elevated levels of branched-chain amino acids, and accumulation of ketone bodies. Furthermore, COVID-19 patients initially presenting with moderate disease but progressing to severe stages exhibited a distinct metabolic signature. Our multivariate model demonstrated a cross-validated AUC of 0.82 and 72% predictive accuracy for severity progression. NMR spectroscopy-based metabolomic profiling enables the identification of moderate COVID-19 patients at risk of disease progression, aiding in resource allocation and early intervention.

Ketone Bodies Are Potential Prognostic Biomarkers in Relapsed/Refractory Diffuse Large B-Cell Lymphoma: Results from the R2-GDP-GOTEL Trial

Background: Patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who are ineligible for high-dose chemotherapy have limited treatment options and poor life expectancy. The purpose of this study is to identify a serum metabolomic profile that may be predictive of outcome in patients with R/R-DLBCL. Methods: This study included 69 R/R DLBCL patients from the R2-GDP-GOTEL trial (EudraCT 2014-001620-299). Serum samples were collected at baseline, and the mean length of follow-up was 41 months. Serum metabolites were analyzed by nuclear magnetic resonance (NMR). Metabolites were correlated with treatment response, progression-free survival (PFS), and overall survival (OS). Results: Serum levels of 3-hydroxybutyrate (3OHB) and acetone were significantly (p < 0.001) associated with PFS (3OHB: hazard ratio [HR] 7.7, 95% confidence interval [CI] 2.5-24.1; acetone: HR 9.32, 95% CI 2.75-31.6) and OS (3OHB: HR 9.32, 95% CI 2.75-31.6; acetone: HR 1.92, 95% CI 1.36-2.69). Serum values of 141 µM for 3OHB and 40 µM for acetone were the optimal cutoffs associated with the survival outcomes. Elevated 3OHB levels (>141 μM) were specific to the ABC subtype of DLBCL, while acetone levels were elevated in both types of DLCBL but more pronounced in ABC cases. In a multivariate survival analysis, including the International Prognostic Index (IPI) score and refractoriness status (R/R), 3OHB and acetone remained significant. To aid oncologists employing the R2-GDP regime, we constructed PFS and OS nomograms for R/R-DLBCL risk stratification, incorporating 3OHB levels or acetone levels, IPI score, and refractoriness status. The nomogram with 3OHB and refractoriness status showed a time-dependent AUC of 0.86 for 6-month PFS and 0.84 for 12-month OS. These nomograms provide a comprehensive tool for individualized risk assessment and treatment optimization. Conclusions: The ketone bodies 3OHB and acetone are potential prognostic biomarkers of poor outcome in R/R DLBCL patients treated with the R2-GDP regimen, independently of IPI score and chemorefractoriness status.

Characterization of Metabolic Patterns in Mouse Spermatogenesis and Its Clinical Implications in Humans

Spermatogenesis is a complex process requiring precisely controlled metabolic adaptations. Although the genetic and cellular aspects of spermatogenesis have been extensively studied, the underlying metabolic mechanisms remain largely underexplored. In this study, we utilized STA-PUT technology to separate three key cell types involved in mouse spermatogenesis: pachytene spermatocytes (PAC), round spermatids (RS), and elongated spermatids (ES). A comprehensive untargeted metabolomic analysis revealed significant metabolic changes during spermatogenesis, such as reduced methylation-related metabolites and increased glycolytic intermediates and TCA cycle metabolites during ES. Moreover, metabolic differences between germ cells and somatic cells (Leydig and Sertoli cells) were highlighted, particularly in steroidogenesis and lipid metabolism. To investigate clinical relevance, we analyzed human seminal plasma. Samples from individuals with azoospermia displayed significant metabolic abnormalities, including reduced methionine, tryptophan, and arginine, which play vital roles in sperm development. Pathway enrichment analysis revealed disturbances in the metabolism of nucleotide, amino acid, and energy in azoospermia, suggesting potential biomarkers of male infertility. Our findings provide a comprehensive metabolic profile of spermatogenesis and suggest that metabolic alterations may be significant contributors to male infertility, particularly in cases of azoospermia.

Metabolic Heterogeneity in Diffuse Large B-Cell Lymphoma Cells Reveals an Innovative Antimetabolic Combination Strategy

Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, characterized by aggressive and heterogeneous tumors originating from B-cells. Especially in patients with relapsed or refractory (R/R) disease, DLBCL remains a challenging cancer to treat. Metabolic reprogramming is a hallmark of malignant cells. Our research focuses on developing strategies to enhance clinical outcomes for R/R DLBCL patients by targeting metabolic vulnerabilities. Methods: We investigated the effects of combining metformin and L-asparaginase, two FDA-approved antimetabolic drugs, on DLBCL cell metabolism and survival. Nuclear magnetic resonance (NMR) spectroscopy was employed to assess metabolic disturbances induced by the drug combination. The impact on lipid metabolism, glycolysis, glutaminolysis, the tricarboxylic acid (TCA) cycle, and antioxidant responses was examined. Induction of apoptosis was evaluated by FACS analysis. Results: The combination of metformin and L-asparaginase strongly sensitized DLBCL cells to apoptosis, independently of their oxidative phosphorylation (OxPhos) or BCR/glycolytic status. NMR spectroscopy revealed that this combination induces broader metabolic disturbances than either drug alone. It disrupts lipid metabolism by altering levels of phospholipids, cholesterol, and fatty acids. Additionally, it counteracts the pro-glycolytic effect of metformin, decreases glycolysis, and reduces glutaminolysis. It also affects the TCA cycle and antioxidant responses, critical for cellular energy production and redox balance. Furthermore, this combination interferes with two key cancer survival pathways, mTORC1 and MAPK signaling. Importantly, proof of principle for its beneficial effect was demonstrated in DLBCL patients. Conclusions: Combining metformin and L-asparaginase affects DLBCL cell survival by targeting multiple metabolic pathways and may represent a novel therapeutic approach for R/R DLBCL patients.

Serum metabolomic signatures of patients with rare neurogenetic diseases: an insight into potential biomarkers and treatment targets

Introduction

To further advance our understanding of Muscular Dystrophies (MDs) and Spinocerebellar Ataxias (SCAs), it is necessary to identify the biological patterns associated with disease pathology. Although progress has been made in the fields of genetics and transcriptomics, there is a need for proteomics and metabolomics studies. The present study aimed to be the first to document serum metabolic signatures of MDs (DMD, BMD, and LGMD 2A) SCAs (SCA 1-3), from a South Asian perspective.

Methods

A total of 28 patients (SCA 1-10, SCA 2-2, SCA 3-2, DMD-10, BMD-2, LGMD-2) and eight controls (aged 8-65 years) were included. Metabolomic analysis was performed by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS), with support from the Houston Omics Collaborative.

Results and discussion

Amino acid metabolism was the primary altered super pathway in DMD followed by carbohydrate metabolism and lipid metabolism. In contrast, BMD and LGMD 2A exhibited a more prominent alteration in lipid metabolism followed by amino acid metabolism. In SCAs, primarily lipid, amino acid, peptide, nucleotide, and xenobiotics pathways are affected. Our findings offer new insights into the variance of metabolite levels in MD and SCA, with substantial implications for pathology, drug development, therapeutic targets and clinical management. Intriguingly, this study identified two novel metabolites associated with SCA. This pilot cross-sectional study warrants further research involving larger groups of participants, to validate our findings.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

Time-Course physiopathology of Porthidium lansbergii lansbergii Envenomation in Swiss Webster Mice: Insights into Systemic Manifestations

OBJECTIVE

The expansion of human activities in northern Colombia has increased human-snake encounters, particularly with venomous Porthidium lansbergii lansbergii. Given the limited knowledge of systemic envenomation effects and previous studies focusing only on early murine symptoms, this investigation aimed to describe the time-course physiopathology of P. lansbergii lansbergii envenomation following intramuscular injection in vivo.

METHODS

Venom was inoculated in the gastrocnemius muscles of Swiss Webster mice, and blood, urine, and tissue samples were taken at different times to evaluate lethality and biochemical markers of renal function and oxidative stress.

RESULTS

This study reports the first intramuscular LD50 for P. lansbergii lansbergii venom at 24.83 mg/Kg. Administering 80% of this LD50 induced early signs of renal injury, evidenced by urinary biomarkers over 24 h. The antioxidant activity was found at low levels in kidney tissue throughout the evaluated time post-envenomation. Malondialdehyde activity increased at the earliest point, while proinflammatory activity increased later. Urine metabolomics revealed elevated taurine and allantoin in the envenomed groups.

DISCUSSION

Compensatory mechanisms in response to oxidative stress and tissue damage induced by the venom were evident in the envenomed mice over the evaluated time. However, histological analysis revealed evidence of pro-inflammatory processes occurring only at early times. Metabolomic analyses of urine samples identified taurine as a potential early biomarker of elevated oxidative stress and protein and creatinine levels.

CONCLUSIONS

P. lansbergii lansbergii venom induces alterations in murine renal tissue, affecting urinary biomarkers of kidney function within hours post-envenomation. Delayed proinflammatory effects may suggest an antioxidant imbalance in the envenomed mice, with unknown long-term effects. Further research on the role of oxidative stress and inflammation in renal structure and function following envenomation is necessary, emphasizing the need for prompt clinical management.

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.

Comparative lipidomic and metabolomic profiling of mdx and severe mdx-apolipoprotein e-null mice

Despite its notoriously mild phenotype, the dystrophin-deficient mdx mouse is the most common model of Duchenne muscular dystrophy (DMD). By mimicking a human DMD-associated metabolic comorbidity, hyperlipidemia, in mdx mice by inactivating the apolipoprotein E gene (mdx-ApoE) we previously reported severe myofiber damage exacerbation via histology with large fibro-fatty infiltrates and phenotype humanization with ambulation dysfunction when fed a cholesterol- and triglyceride-rich Western diet (mdx-ApoEW). Herein, we performed comparative lipidomic and metabolomic analyses of muscle, liver and serum samples from mdx and mdx-ApoEW mice using solution and high-resolution-magic angle spinning (HR-MAS) 1H-NMR spectroscopy. Compared to mdx and regular chow-fed mdx-ApoE mice, we observed an order of magnitude increase in lipid deposition in gastrocnemius muscle of mdx-ApoEW mice including 11-fold elevations in -CH3 and -CH2 lipids, along with pronounced elevations in serum cholesterol, fatty acid, triglyceride and phospholipids. Hepatic lipids were also elevated but did not correlate with the extent of muscle lipid infiltration or differences in serum lipids. This study provides the first lipometabolomic signature of severe mdx lesions exacerbated by high circulating lipids and lends credence to claims that the liver, the main regulator of whole-body lipoprotein metabolism, may play only a minor role in this process.

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.

1H NMR-Based Metabolomic Signatures in Rodent Models of Sporadic Alzheimer's Disease and Metabolic Disorders

Alzheimer's disease (AD) is a chronic neurological disorder that impacts the elderly population all over the globe. Evidence suggests association between AD and metabolic disorders such as diabetes mellitus (DM) and obesity (OB). The present study is an attempt to evaluate metabolic alterations in the serum and brain through NMR spectroscopy with the aim to identify shared metabolic signatures. AD was induced in rats by stereotactic intracerebroventricular injection of oligomerized Aβ-42 peptide into the brain. DM and OB were induced by intraperitoneal injection of streptozotocin and feeding rats on a high-fat diet, respectively. The metabolic alterations obtained through 1H NMR spectroscopy were further subjected to multivariate analysis by principal component analysis and partial least-squares discrimination for identification of metabolic signatures. In the serum, the levels of lactate and betaine were increased in AD, DM, and OB rats. On the other hand, the metabolite profile of brain indicated increase in the levels of lactate, N-acetylaspartate, and creatinine in AD, DM, and OB rats. Additionally, the concentration of neurochemicals such as glutamate, GABA, N-acetylglutamate, and myo-inositol were also elevated. The alterations in neurotransmitters and cerebral energy metabolism were accompanied by deficits in cognition assessed by Morris water maze in AD, DM, and OB rats. The perturbed metabolic profiles were accompanied by the presence of pathogenic amyloid deposits visualized by Congo red stain in the brains of AD, DM, and OB rats. Overall, the study identifies common metabolic signatures in AD, DM, and OB that may be involved in etiopathogenesis and also suggests linkages between these three conditions.

HRMAS 1H NMR and CPMAS 13C NMR spectroscopies coupled with chemometrics for the metabolomic investigation of commercial teas

In this study, the metabolome of different types of tea (i.e., black, green and earl grey) is explored by means of HRMAS 1H (i.e., semisolid state) NMR and CPMAS 13C (i.e., solid state) NMR spectroscopies. By elaborating the metabolomic data with unsupervised and supervised chemometric tools (PCA, PLS-DA), it was possible to set up classification models with the aim to discriminate the different types of tea as based on differences in their chemical composition. Both the applications of the NMR spectroscopies also allowed to obtain information about the metabolic biomarkers leading the differentiation among teas. These were mainly represented by phenolic compounds. Also, some non-phenolic compounds, such as amino acids, carbohydrates, and terpenoids, played important roles in shaping tea quality. The findings of this study provided useful insights into the application of solid and semisolid state NMR spectroscopies, in combination with chemometrics, in the context of food authentication and traceability.

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

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

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90 %), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.

The Effectiveness of the Low-Glycemic and Insulinemic (LOGI) Regimen in Maintaining the Benefits of the VLCKD in Fibromyalgia Patients

Background: Fibromyalgia (FM) is a chronic disorder that causes damage to the neuro-muscular system and alterations in the intestinal microbiota and affects the psychological state of the patient. In our previous study, we showed that 22 women patients subjected to a specific very low-carbohydrate ketogenic therapy (VLCKD) showed an improvement in clinical scores as well as neurotransmission-related and psychological dysfunctions and intestinal dysbiosis. Furthermore, NMR metabolomic data showed that changes induced by VLCKD treatment were evident in all metabolic pathways related to fibromyalgia biomarkers. Methods: Based on this evidence, we extend our investigation into dietary interventions for fibromyalgia by evaluating the impact of transitioning from a VLCKD to a low-glycemic insulinemic (LOGI) diet over an additional 45-day period. Therefore, participants initially following a VLCKD were transitioned to the LOGI diet after 45 days to determine whether the improvements in FM symptoms and metabolic dysfunctions achieved through VLCKD could be sustained with LOGI. Results: Our findings suggested that while VLCKD serves as an effective initial intervention for correcting metabolic imbalances and alleviating FM symptoms, transitioning to a LOGI diet offers a practical and sustainable dietary strategy. This transition preserves clinical improvements and supports long-term adherence and quality of life, underscoring the importance of adaptable nutritional therapies in chronic disease management. Control patients who adhered only to the LOGI diet for 90 days showed only modest improvement in clinical and psychological conditions, but not elimination of fibromyalgia symptoms. Conclusions: In conclusion the LOGI diet is an excellent alternative to maintain the results obtained from the regime VLCKD.

Impact of a Withania somnifera and Bacopa monnieri Formulation on SH-SY5Y Human Neuroblastoma Cells Metabolism Through NMR Metabolomic

Objectives: This study investigates the effectiveness of an herbal formulation, STRESSLESS (ST-65), which combines ashwagandha (Withania somnifera) and bacopa (Bacopa monnieri), on SH-SY5Y human neuroblastoma cells. Given the rising interest in natural compounds for neuroprotection and stress alleviation, we aimed to explore the cellular and molecular effects of this formulation. Methods: Utilizing a nuclear magnetic resonance (NMR) metabolomic approach and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS), we identified key bioactive compounds in ST-65, including withanolides from ashwagandha and bacosides from bacopa. Results: Our findings indicate that ST-65 treatment significantly alters the metabolic profile of SH-SY5Y cells. Key changes included increased levels of metabolites linked to neuroprotection, energy metabolism, and antioxidant defense. Notable enhancements were observed in specific amino acids and neuroprotective compounds, suggesting activation of neuroprotective mechanisms and mitigation of stress-induced damage. Conclusions: The study reveals a complex phyto-chemical profile of ST-65 and underscores its potential as a natural active agent for addressing stress-related neurodegenerative conditions. These insights into neuronal mechanisms provide a foundation for further exploration of herbal formulations in neuroprotection.