Metabolomics

Alcohol-related liver disease (ALD) is a major cause of mortality and disability adjusted life years. It is not fully understood why a small proportion of patients develop progressive forms of ALD (e.g. fibrosis, cirrhosis). Differences in the metabolic processes could be behind the individual progression of ALD. Our aim was to examine differences in serum metabolome between patients with non-progressive ALD and patients with an early form of progressive ALD. The study had three study groups: progressive ALD (alcohol-related steatohepatitis or early-stage fibrosis, n=50), non-progressive ALD (simple steatosis, n=50) and healthy controls (n=32). Both ALD groups took part in a voluntary alcohol rehabilitation program. A non-targeted metabolomics analysis and targeted analysis of short chain fatty acids was done to the serum samples taken on the day of admission. We found 111 significantly (p<0.0005) altered identified metabolites between the study groups. Our main finding was that levels of glycine conjugated bile acids, glutamic acid, 7-methylguanine and several phosphatidylcholines were elevated in the progressive ALD group in comparison to both the non-progressive ALD group and the controls. Glycine conjugated bile acid, glutamic acid and 7-methylguanine also positively correlated with increased levels of aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, cell death biomarker M65, and liver stiffness. Our results indicate that the enterohepatic cycle of glycine conjugated bile acids as well as lipid and energy metabolism are altered in early forms of progressive ALD. These metabolic processes could be a target for preventing progression of ALD.

BACKGROUND: Erythrophagocytosis constitutes a major pathogenic mechanism of metabolic dysfunction associated fatty liver disease (MAFLD). Our previous research established a quantitative thin-layer chromatography (TLC) technique for sphingomyelin, revealing reduced levels in the red blood cells (erythrocytes) of patients with metabolic dysfunction associated fatty liver disease (MAFLD). This reduction was accompanied by erythrocyte sphingosine accumulation, a driver of pro-inflammatory erythrophagocytosis, though sphingosine 1-phosphate release remained stable. To better understand erythrocyte sphingosine metabolism, we adapted our quantitative TLC method to analyze sphingosine within the erythrocyte-conditioned media (ECM) of MAFLD patients. Methodology Separation was performed on 10X10cm Silica gel 60 F254 plates using a mobile phase of chloroform, methanol, acetic acid, and water (60:50:1:4 v/v/v/v). The dynamic range, linearity, and range of linearity were assessed by analysing sphingosine levels from 0.1 to 10microg/spot. We validated the system precision and sensitivity by performing triplicate analyses of sphingosine standards (1.25, 2.5, and microg). The limits of detection and quantification were derived from the calibration curve slope and standard deviation (3.3 XSD/slope for LOD; 10 XSD/slope for LOQ). Accuracy was assessed via recovery tests at 100%, 200%, and 300% of a 2.5microg load. We confirmed specificity by evaluating the retention factors against other lipid species. This protocol was applied to Folch-extracted lipids from the ECM (5 X 107 cells/ml) of four MAFLD patients and four healthy controls, spiked with 5microg of sphingosine. Findings The calibration model, based on combined Green and Blue color intensities, followed the linear equation y = -11.171x + 353.25(R2 = 0.94). Interday precision values were 0.21%, 1.65%, and 0.44%, while recovery rates (accuracy) ranged from 94.5% to 98.7%. The measured LOD and LOQ were 0.75microg and 1.21microg, respectively. The sensitivity was calculated at 90ng. Statistical analysis showed no significant variance in sphingosine concentrations in erythrocyte-conditioned media between the MAFLD group and the control group. Summary The described thin layer chromatography is accurate, precise, sensitive, with good limits of detection and quantification, and most importantly is low-cost and time-efficient. Using this method, we show that while erythrocytes of MAFLD patients exhibit sphingosine accumulation, the utilisation of exogenous sphingosine from their erythrocytes is not affected. This suggests that the metabolic shift may be driven by increased sphingosine supply from the plasma.

Background & Aims: Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) is the leading cause of chronic liver disease in children. However, accurate, noninvasive diagnostic tools remain limited. Current screening methods are invasive or lack sensitivity. Breath-based volatile organic compound (VOC) analysis offers a simple approach with potential for point of care screening. This study aimed to identify and validate breath VOC signatures of pediatric MASLD. Approach & Results: We conducted a prospective IRB approved cohort study at the Childrens Hospital of Philadelphia (CHOP). Children aged between 7 and 20 years with MASLD (n=22), as defined by hepatic steatosis either by liver biopsy or imaging and 1 cardiometabolic risk factor, and a control group without MASLD (n=20) were enrolled. Breath samples were collected using a standardized protocol and analyzed by untargeted comprehensive two-dimensional gas chromatography-mass spectrometry (GCGCMS). Machine learning and unsupervised clustering were applied to identify discriminatory VOCs and assess heterogeneity. Untargeted GCGCMS analysis identified a distinct breath VOC signature in children with MASLD compared with non MASLD controls. A Random Forest model achieved a sensitivity of 73% and specificity of 65%, with AUC of 0.84. The VOC 2,4-dimethyl-1-heptene demonstrated strong diagnostic performance in the discovery cohort with a sensitivity of 85%, specificity of 77% and an AUC of 0.81. Unsupervised clustering revealed four MASLD subgroups with distinct volatile phenotypes associated with differences in liver enzymes and metabolic parameters. External validation in a second pediatric cohort confirmed reproducible reductions in o/p-xylene in subjects with MASLD. Conclusions: Pediatric MASLD is associated with a reproducible breath VOC signature identified by untargeted GCGCMS. These findings support breath analysis as a scalable, noninvasive screening and stratification tool for pediatric MASLD and warrant validation in larger, longitudinal studies.

Physical inactivity and a weak fitness status have become a global health concern. Metabolomics, as an integrative systematic approach, might link to individuals fitness at the molecular level. In this study, we performed blood samples metabolomics analysis of a cohort of elderly people with different treatments. By defining two groups of fitness and corresponding metabolites profiles, we tested several machine learning classification approaches to identify key metabolite biomarkers, which showed robustly aspartate as a dominant negative marker of fitness. Following, the metabolomics data of the two groups were analyzed by a novel approach for metabolic network interaction termed COVRECON. Where we identified the enzyme AST as the most important metabolic regulation between the fit and the less fit groups. Routine blood tests in these two cohorts validated significant differences in AST and ALT. In summary, we combine machine learning classification and COVRECON to identify metabolomics biomarkers and causal processes for fitness of elderly people.

BackgroundCongenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. The prompt recognition of CHD can facilitate timely and appropriate intervention, reducing the probability of complications and enhancing the prognosis for impacted newborns. However, unlike other rare conditions routinely identified through federal and state newborn screening (NBS) programs, theres currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. ObjectiveThis study was set to identify reliable metabolite biomarkers with clinical relevance, with the aim to assess feasibility of screening and subtype classification of CHD utilizing the DBS newborn screening method. MethodsWe assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories (tetralogy of Fallot, inherited arrhythmia syndrome, neonatal cardiomyopathy). A robust, DBS-oriented metabolomic method, employing both global and targeted strategies based on liquid chromatography with tandem mass spectrometry (LC-MS/MS), was developed. To verify the reliability of this metabolic profiling, we conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records. Additionally, for hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Finally, logistic and LightGBM models were established to aid in CHD discrimination and classification. ResultsOur metabolomic workflow demonstrated consistent and reliable quantification of metabolites in DBS samples stored at the California Department of Public Health (CDPH) for up to 15 years. Through this process, we discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites as potential biomarkers for CHD assessment, and an additional twelve metabolites as potential markers for classifying different CHD subtypes within DBS samples. ConclusionsThis study represents the first attempt to validate metabolite profiling results using long-term storage DBS samples procured from the high-quality conditions of the CDPH biobank. The results unveil distinct metabolic discrepancies between various CHD subtypes and healthy controls. Furthermore, our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Background & Aims: Per- and polyfluoroalkyl substances (PFAS) are persistent endocrine-disrupting chemicals associated with metabolic dysfunction, including metabolic dysfunction-associated steatotic liver disease (MASLD). While PFAS perturb lipid and bile acid (BA) metabolism in a sex-specific manner, the underlying mechanisms remain unclear. We tested whether steroid hormones mediate PFAS-associated metabolic alterations. Methods: In 104 patients with biopsy-characterized MASLD, we performed sex-stratified analyses applied liquid chromatography coupled to mass spectrometry (LC-MS) for chemical analysis, integrating circulating steroids, PFAS exposure, hepatic lipidomics and BA profiles. Results: Steroid hormones were associated with MASLD severity in a sexually-dimorphic manner. Dihydrotestosterone showed consistent inverse associations with steatosis, fibrosis, necroinflammation and insulin resistance, particularly in females. PFAS exposure was associated with altered steroid profiles, predominantly indicating suppressed steroidogenesis in females. These PFAS-associated hormonal changes were linked to downstream alterations in hepatic lipids and BAs. Mediation analysis supported indirect effects of PFAS on metabolic pathways via steroids, including testosterone/epi-testosterone-mediated effects on ether phospholipids and estradiol-mediated effects on lithocholic acid. Females exhibited stronger PFAS-steroid-BA associations, whereas males showed weaker, lipid-centric effects. Conclusions: PFAS exposure is associated with sex-specific disruption of steroid hormone pathways that may link environmental exposure to lipid and BA dysregulation in MASLD. These findings identify steroid hormones as potential key mediators of PFAS-associated metabolic dysfunction and highlight sex as a critical determinant in environmental liver disease.

13C based Isotopic tracers of the media components can be used to kinetically track their contribution in the cell systems. A tracer ([U-13C6] glucose) was used to monitor its contribution into the central carbon metabolic pathways of human lung carcinoma (A549) cells by Gas chromatography-mass spectrometry (GC-MS) based mass isotopomer analysis. Calculated average 13C of methanolic extracts (glycerol: 5.46{+/-}3.53 % and lactate: 74.4{+/-}2.65 %), protein acid hydrolysates (serine: 4.51{+/-}0.21 %, glycine: 2.44{+/-}0.31 %, alanine: 24.56{+/-}0.59 %, glutamate: 8.81{+/-}0.85 %, proline: 6.96{+/-}0.53 % and aspartate: 10.72{+/-}0.95 %) and the culture filtrate (glycerol: 43.14{+/-}1.45 % and lactate: 81.67{+/-}0.91 %), showed significant contribution of 13C glucose. We observed the Warburg effect with higher levels of 13C lactate in the culture filtrate. 13C glycerol levels in culture supernatant showed significant increase with time and amino acids of glucogenic origin also contributed cellular protein biomass. The workflow adopted in this study for 13C analysis could be useful for the metabolic phenotyping of other mammalian cell systems under normal and perturbed (cancer and infection) conditions.

The intestinal microbiome is composed of myriad microbial species that produce metabolites that impact host health. While loss of bacterial species and beneficial metabolites from the fecal microbiome is associated with development of a range of diseases and medical complications, there are currently no diagnostic tests that rapidly identify individuals with microbiome deficiencies. Reduced concentrations of fecal butyrate and deoxycholic acid are associated with adverse clinical outcomes and result from the loss of a subset of health-associated bacterial species. We present a rapid diagnostic test based on 3-nitrophenylhydrazine derivatization and ultrahigh-performance liquid chromatography-mass spectrometry that measures fecal butyrate and deoxycholic acid concentrations as markers of microbiome function. A matrix-matched calibration curve was developed using a simulated fecal mixture to optimize accuracy and facilitate adherence to clinical laboratory regulations. The assay showed an analytical measurement range from 4.3-3030.1 {micro}M (LLOQ = 9.75 {micro}M) for butyrate and from 0.9- 64.9 {micro}M (LLOQ = 0.9 {micro}M) for deoxycholic acid. Precision demonstrated a coefficient of variation <15% at all QC levels tested. The assay can be performed in under an hour from extraction to provision of quantitative results, enabling the rapid identification of patients with defective microbiome function.

Background and Purposealkaptonuria (AKU) is an inherited disorder of tyrosine metabolism caused by lack of the enzyme homogentisate 1,2-dioxygenase (HGD). The primary biochemical consequence of HGD-deficiency is increased circulating homogentisic acid (HGA), which is central to AKU disease pathology. The aim of this study was to investigate the wider metabolic consequences of targeted Hgd disruption. Experimental Approachthe first metabolomic analysis of the Hgd-/- AKU mouse model was performed. Urinary metabolites altered in Hgd-/- were further validated by showing that the HGA-lowering drug nitisinone reversed their direction of alteration in AKU Key Resultscomparison of Hgd-/- (AKU) versus Hgd+/- (heterozygous control) urine revealed increases in HGA and a group of 8 previously unreported HGA-derived transformation products from phase I and II metabolism. HGA biotransformation products HGA-sulfate, HGA-glucuronide, HGA-hydrate and hydroxymethyl-HGA were also decreased in urine from both mice and patients with AKU on the HGA-lowering agent nitisinone. Hgd knockout also revealed a host of previously unrecognised associations between tyrosine, purine and TCA cycle metabolic pathways. Conclusion and ImplicationsAKU is rare, but our findings further what is currently understood about tyrosine metabolism more generally, and show for the first time that phase I and II detoxification is recruited to prevent accumulation of endogenously-produced metabolites in inborn errors of metabolism. The data highlight the misconception that phase I and II metabolic biotransformations are reserved solely for drug clearance; these are ancient mechanisms, which represent new potential treatment targets in inherited metabolic diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/044347v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@12b0b2forg.highwire.dtl.DTLVardef@eb4bacorg.highwire.dtl.DTLVardef@1b511e1org.highwire.dtl.DTLVardef@a8ef8e_HPS_FORMAT_FIGEXP M_FIG C_FIG Bullet point summaryWhat is already known O_LIIncreased circulating homogentisic acid is central to disease pathology in the inherited metabolic disease alkaptonuria C_LIO_LIThe Hgd knockout mouse, created in our laboratory, accurately models human alkaptonuria C_LI What this study adds O_LIPhase I and II biotransformations are recruited in alkaptonuria for detoxification of homogentisic acid C_LIO_LIThese data challenge misconceptions that phase I and II metabolism is solely for drug clearance C_LI Clinical significance O_LIPhase I and II metabolic processes represent new treatment targets in inherited metabolic diseases C_LIO_LIThe molecular pathology of AKU extends much further than the known alteration to tyrosine metabolism C_LI

BackgroundDespite over three decades of research, the use of peripheral bile acid concentrations or proportions as biomarkers for human liver injury remain inconclusive due to variable and inconsistent findings. ObjectiveThe aim of this systematic review and meta-analysis was to identify factors contributing to the variability in published bile acid research and propose recommendations to enhance the robustness and reproducibility of future studies. MethodsA search of the PubMed database and a systematic manual screening of references until May 2024 for studies reporting peripheral bile acid concentrations in humans was conducted. English-language studies reporting mean or median concentrations of at least one of 15 predetermined circulating bile acids in human cohorts were included. The exclusion criteria were editorials, commentaries, letters to the editor, conference proceedings, abstracts, and monographs. Raw bile acid concentrations, subject demographics (number, average age, sex distribution, health status, fasted/fed status), the blood matrix analysed, the matrix volume analysed, the bile acid extraction process, and analytical technique when available were extracted by a single observer. Results65 studies involving 215 cohorts were selected. Bile acid concentrations in normal cohorts exhibit large intervariability. The analytical technique used to measure bile acid concentrations, the fasted/fed status of patients at the time of sampling, the choice of blood collection matrix, the starting volume of this matrix, and the choice of protein precipitation solvent are found to be determinants of this variability. LimitationsOnly mean or median bile acid concentrations in study cohorts were extracted from studies and compared since bile acid concentrations are rarely reported in individual subjects. Analysing mean or median bile acid concentrations in study cohorts may not give a true sense of bile acid concentrations and therefore their determinants. DiscussionExperimental, analytical and biological sources of mean peripheral bile acid concentration variability were identified. These must be standardised across future studies to clarify the potential of peripheral bile acids as biomarkers.

As science continues to push the frontiers on the length of space flight, we begin to experience an enhanced need for food that is nutritionally dense. Nasa-grants have worked to explore the ability of growing superfoods in space on aircrafts. However, it is paramount to ensure the physiological health of the plants being grown in space flight, perform relatively similar to the plants that grow on earth. An aspect which needs to be further probed is the comparison of microgravity and gravity on the plants seeking to be grown in space flight. Within this NASA-funded research, we grew a superfood vegetable, Kale. The kale was grown in simulated microgravity environments to mimic life in outer space. This simulation was induced by means of a 2-D clinostat. Additionally, kale was grown in gravity conditions as well. We used multivariate statistical tools such as Principal Component Analysis (PCA), together with differential-expression visualizations like volcano plots, to summarize complex data rendered from LC-MS. In this study, we used these approaches to examine how horizontal and vertical orientations both in static and rotating configurations simulate aspects of simulated-microgravity and influence metabolic responses; this combined analysis provided a broad perspective on gravity-related metabolic adaptations and points to potential molecular markers that could guide future research on spaceflight health and countermeasure development.

Biological sex is one of the major anthropometric factors which influences physiology, metabolism and health status. We have investigated the effect of sexual dimorphism on the blood lipidome profile in three large population level studies - the Alzheimers disease neuroimaging initiative - ADNI (n =806), the GeneBank Functional Cardio-Metabolomics cohort (n= 1015) and the Genetics of Lipid lowering Drugs and Diet Network - GOLDN (n=422). In total, 355 unique lipids from 15 lipid classes were detected across all three studies using LC-MS. Sixty percent of these lipids differed between men and women in all three cohorts, and up to 87% of all lipids demonstrated sex differences in at least one cohort. ChemRICH enrichment statistics on lipid classes showed that phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, ceramides, sphingomyelins and cholesterol esters were found at higher levels in female subjects while triacylglycerols and lysophosphatidylcholines were found at higher levels in male participants across the three cohorts. This strong sex effect on the blood lipidome suggests that specific regulatory mechanisms may exist that regulate lipid metabolism in a different manner between men and women. Cohort studies involving blood lipidomics should consider separate analyses for male and female participants instead of combined analyses treating sex as a confounding factor.

BackgroundThe metabolome of COVID-19 patients has been studied sparsely, with most research focusing on a limited number of plasma metabolites or small cohorts. This is the first study to test saliva metabolites in COVID-19 patients in a comprehensive way, revealing significant changes linked to disease severity and highlighting saliva is potential as a non-invasive diagnostic tool. MethodsWe included 30 asymptomatic subjects with no prior COVID-19 infection or vaccination, 102 patients with mild SARS-CoV-2 infection, and 61 hospitalized patients with confirmed SARS-CoV-2 status. Saliva samples were analyzed using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS/MS) in positive and negative ionization modes. ResultsSignificant changes in metabolites were identified in COVID-19 patients, with distinct patterns based on disease severity. Healthy individuals exhibited a well-regulated bacterial network, while severe cases showed disordered microbial networks. Elevated dipeptides such as Val-Glu and Met-Gln in moderate cases suggest specific protease activity related to SARS-CoV-2. Increased acetylated amino acids like N-Acetylserine and N-Acetylhistidine indicate potential biomarkers for stress and disease severity. Bacterial metabolites, including muramic acid and indole-3-carboxaldehyde, were higher in mild-moderate cases, indicating oral microbiota changes. In severe cases, polyamines and organ damage-related metabolites, such as N-acetylspermine and 3-methylcytidine, were significantly increased. Interestingly, metabolites reduced in moderate cases were elevated in severe cases. ConclusionsSaliva metabolomics offers insight into disease progression and potential biomarkers for COVID-19.

BackgroundHepatocellular carcinoma (HCC) is an increasingly common and one of the leading causes of cancer mortality worldwide. Only a small percentage of HCC patients are eligible to curative treatment. There is a need for a point of care, early diagnostic or screening tool. It is not clear whether exhaled volatile organic compounds (VOCs) could fulfil those needs. HypothesisWe postulate that exhaled VOCs can identify potential biomarkers for non-invasive detection of HCC. AimsThis systematic review aims to critically review the current knowledge regarding the exhaled VOCs linked to HCC detection. MethodsA systematic electronic search was conducted. Search strategy included all studied published until the 24th of March 2021 using a combination of relevant keywords. ResultsThe search yielded 6 publications using the PRISMA pathway. Two of the studies described in vitro experiments, and four clinical studies were conducted on small groups of patients. Overall, 42 headspace gases were analysed in the in vitro studies. Combined, the clinical studies included 164 HCC patients and 260 controls. The studies reported potential role for a combination of VOCs in the diagnosis of HCC. However, only limonene, acetaldehyde and ethanol could be traced back to their biological pathways using KEGG pathway enrichment analysis. ConclusionsAlthough there appears to be promise in VOCs research associated with HCC, there is no single volatile biomarker in exhaled breath attributed to HCC and data from extracted studies indicates a lack of standardization. Large population studies are required to verify the existence of VOCs linked to HCC.

Summary/AbstractNon-small cell lung cancer (NSCLC) is among the most malignant tumors with high propensity for metastasis and is the leading cause of cancer-related death globally. Most patients present with regional and distant metastasis, associated with poor prognosis. Lipids may play an essential role in either activating or inhibiting detachment-induced apoptosis (anoikis), where the latter is a crucial mechanism to prevent metastasis, and it may have a cross-talk with autophagy. Autophagy has been shown to be induced in various human cancer metastasis, modulating tumor cell motility and invasion, cancer cell differentiation, resistance to anoikis, and epithelial to mesenchymal transition. Hence, it may play a crucial role in the transition of benign to malignant phenotypes, the core of metastasis initiation. Here, we provide a method we have established in our laboratory for detecting of lipids in attached and detached non-small lung cancer cells and show how to analyze lipidomics data to find its correlation with autophagy-related pathways. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/573842v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1127909org.highwire.dtl.DTLVardef@1c86359org.highwire.dtl.DTLVardef@f29722org.highwire.dtl.DTLVardef@a05a7d_HPS_FORMAT_FIGEXP M_FIG C_FIG Workflow of A549 attached and detached cell culture. Cells are collected, lipids are extracted and subjected to LC/MS processing. Next, lipidomics resulting data is analyzed through various steps involving utilization of Metaboanlyst, HDMB, Swiss Target Prediction, and String.

Heavy isotopes are discriminated by biological systems due to kinetic isotopic effects at the biochemical/metabolic levels. How these heavy isotopes are enriched or depleted over a long term is unclear, but artificial manipulation of heavy isotope content in various organisms has produced significant impacts on biological functions, suggesting the origin may arise with intrinsic mechanisms for a functional outcome. Our previous study has revealed an age-associated decline in metabolite heavy isotope content (HIC) in the budding yeast, which could be reversed in part by supplementing heavy water, and consequently, also increased yeast lifespans. In the current study, we report a similar age-dependent decline in HIC from three types of mouse tissues: brain, heart, and skeletal muscles. Furthermore, individual tissues exhibited different patterns of HIC change over age, which appeared to match their development and maturation timelines. These results have demonstrated that age-dependent decline in HIC also exists in mammals, which is likely a traceable feature of development and perhaps aging. Thus, we believe that reversing the decline in HIC could have the potential to extend the healthspan of humans.

The pentose phosphate pathway (PPP) plays a key role in the cellular regulation of immune cell function; however, little is known about the interplay of metabolic adjustments in granulocytes, especially regarding the non-oxidative PPP. For the determination of metabolic mechanisms within glucose metabolism, we propose a novel Bayesian 13C-Metabolic flux analysis based on ex-vivo parallel tracer experiments with [1,2-13C]glucose, [U-13C]glucose, and [4,5,6-13C]glucose and gas chromatography-mass spectrometry labeling measurements of metabolic fragments including sugar phosphates. With this approach we obtained precise flux distributions and their joint confidence regions, which showed that phagocytic stimulation reversed the direction of non-oxidative PPP net fluxes from ribose-5-phosphate biosynthesis towards glycolytic pathways. This process was closely associated with the up-regulation of the oxidative PPP to promote the oxidative burst. The estimated fluxes showed strong pairwise inter-relations forming a single line in several cases. This behavior could be explained with a three-dimensional permissible space derived from stoichiometric-flux-constraint analysis and enabled a principal component analysis detecting only three distinct axes of coordinated flux changes that were sufficient to explain all flux observations.

BackgroundFatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting. MethodsHigh-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability. ResultsHere we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia. ConclusionsFatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.

ABSTRACTSO_ST_ABSIntroductionC_ST_ABSHypothyroidism is a prevalent disorder that affect all systems of body, including the cardiovascular system and control of cardiac function. However, much remains unknown about the metabolic pathways and disorders in the heart induced by hypothyroidism, as well as the metabolic mechanisms involved. ObjectivesThis work aims to investigate the impacts of hypothyroidism on the heart and explore its underlying mechanisms through untargeted metabolomics analysis. MethodsAn experimental model of hypothyroidism was established by giving water containing 0.1% methimazole and 1% potassium perchlorate to 12-week-old C57BL/6J mice for six consecutive weeks. ResultsAfter treatment, hypothyroidism significantly reduced mices heart weight and heart rate. Metabolomics analysis revealed that the different metabolites (DEMs) primarily concentrated in Phospholipids, Lipids or Lipids-like, Carbohydrates and Carbohydrate conjugates. These specific metabolites were mainly enriched in pathways related to lipid metabolism. The hearts of hypothyroid mice exhibited significantly lower levels of lipoyl-carnitine compared to controls, along with markedly higher levels of glycolysis and pentose phosphate pathway (PPP) intermediates. Furthermore, carnitine palmitoyltransferase 1B (CPT1B) and carnitine palmitoyltransferase 2 (CPT2) expressions were significantly decreased in the hypothyroid mouse heart, while lactate dehydrogenase A (LDHA) expressions was significantly increased, according to the immunoblotting assay. ConclusionsResearch indicates that hypothyroidism triggers disruptions in both cardiac energy metabolism and phospholipid metabolism in adult mice, which in turn results in myocardial atrophy. These findings could pave the way for novel therapeutic strategies aimed at treating hypothyroidism-induced myocardial atrophy.

Brazil has the second highest COVID-19 death rate while Rio de Janeiro is among the states with the highest rate in the country. Although effective vaccines have been developed, it is anticipated that the ongoing COVID-19 pandemic will transition into an endemic state. Under this scenario, it is worrisome that the underlying molecular mechanisms associated with the disease clinical evolution from mild to severe, as well as the mechanisms leading to long COVID are not yet fully understood. In this study, 1H Nuclear Magnetic Resonance spectroscopy and Liquid Chromatography-Mass spectrometry-based metabolomics were used to identify potential pathways and metabolites involved in COVID-19 pathophysiology and disease outcome. We prospectively enrolled 35 severe RT-PCR confirmed COVID-19 cases within 72 hours from intensive care unit admission, between April and July 2020 from two reference centers in Rio de Janeiro, and 12 samples from non-infected control subjects. Of the 35 samples from COVID-19 patients, 18 were from survivors and 17 from non-survivors. We observed that patients with severe COVID-19 had their plasma metabolome significantly changed if compared to control subjects. We observed lower levels of glycerophosphocholine and other choline-related metabolites, serine, glycine, and betaine, indicating a dysregulation in methyl donors and one-carbon metabolism. Importantly, non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid and N-acetylserine compared to survivors and controls, reflecting uncontrolled inflammation, liver and kidney dysfunction, and insulin resistance in these patients. Lipoprotein dynamics and amino acid metabolism were also altered in severe COVID-19 subjects. Several changes were greater in women, thus patients sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. The incidence of severe outcome after hospital discharge is very high in Brazil, thus these metabolic alterations may be used to monitor patients organs and tissues and to understand the pathophysiology of long-post COVID-19.