Project description:ObjectiveSepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction.MethodPlasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, P < 0.05, and fold change (FC) > 1.5 or < 0.7. Pathway enrichment analysis further revealed associated metabolic pathways. In addition, we constructed a subgroup metabolic analysis between the survivors and non-survivors according to 28-day mortality in the cardiac dysfunction group.ResultsTwo metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism.ConclusionMetabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.
Project description:Selected Australian native fruits such as Davidson's plum, finger lime and native pepperberry have been reported to demonstrate potent antioxidant activity. However, comprehensive metabolite profiling of these fruits is limited, therefore the compounds responsible are unknown, and further, the compounds of nutritional value in these native fruits are yet to be described. In this study, untargeted and targeted metabolomics were conducted using the three fruits, together with assays to determine their antioxidant activities. The results demonstrate that targeted free and hydrolysed protein amino acids exhibited high amounts of essential amino acids. Similarly, important minerals like potassium were detected in the fruit samples. In antioxidant activity, Davidson's plum reported the highest activity in ferric reducing power (FRAP), finger lime in antioxidant capacity (ABTS), and native pepperberry in free radical scavenging (DPPH) and phosphomolybdenum assay. The compounds responsible for the antioxidant activity were tentatively identified using untargeted GC×GC-TOFMS and UHPLC-QqQ-TOF-MS/MS metabolomics. A clear discrimination into three clusters of fruits was observed using principal component analysis (PCA) and partial least squares (PLS) analysis. The correlation study identified a number of compounds that provide the antioxidant activities. GC×GC-TOFMS detected potent aroma compounds of limonene, furfural, and 1-R-α-pinene. Based on the untargeted and targeted metabolomics, and antioxidant assays, the nutritional potential of these Australian bush fruits is considerable and supports these indigenous fruits in the nutraceutical industry as well as functional ingredients for the food industry, with such outcomes benefiting Indigenous Australian communities.
Project description:Mitochondria are dynamic organelles that constantly alter their shape through the recruitment of specialized proteins, like mitofusin-2 (Mfn2) and dynamin-related protein 1 (Drp1). Mfn2 induces the fusion of nearby mitochondria, while Drp1 mediates mitochondrial fission. We previously found that the genetic or pharmacological activation of mitochondrial fusion was tumor suppressive against pancreatic ductal adenocarcinoma (PDAC) in several model systems. The mechanisms of how these different inducers of mitochondrial fusion reduce pancreatic cancer growth are still unknown. Here, we characterized and compared the metabolic reprogramming of these three independent methods of inducing mitochondrial fusion in KPC cells: overexpression of Mfn2, genetic editing of Drp1, or treatment with leflunomide. We identified significantly altered metabolites via robust, orthogonal statistical analyses and found that mitochondrial fusion consistently produces alterations in the metabolism of amino acids. Our unbiased methodology revealed that metabolic perturbations were similar across all these methods of inducing mitochondrial fusion, proposing a common pathway for metabolic targeting with other drugs.
Project description:Seminal extracellular vesicles (SemEVs) are repositories of biomolecules, including metabolites involved in the regulation of sperm function. The correlation between the metabolite profile of SemEVs and semen parameters, along with their role in regulating sperm function, is an unexplored area. This preliminary study evaluated the metabolomic content of SemEVs. Semen samples were obtained from 18 healthy men, and SemEVs were extracted from seminal plasma using the size exclusion chromatography qEV Gen 2-35 nm column coupled with an automatic fraction collector. The physical characterization of SemEVs was carried out with the ZetaView PMX-430-Z QUATT laser system. EV protein markers were detected using Western blot. In addition, these SemEVs were used for metabolomic profiling and functional bioinformatic analysis. The mean concentration of isolated SemEVs was 1.7 ± 1.1 × 1011/mL of seminal plasma, whereas SemEVs size and zeta potential were 129.5 ± 5.5 nm and -40.03 ± 3.99 mV, respectively. Western blot analysis confirmed the presence of EV specific markers such as CD81, ALIX, and TSG101. A total of 107 metabolites were identified using this untargeted metabolomic approach in SemEVs. Bioinformatics analysis further revealed that metabolites associated with tyrosine metabolism were highly enriched in these SemEVs. Ingenuity Pathway Analysis (IPA) also indicated that these metabolites present in SemEVs were involved in the regulation of the free radical scavenging pathway. Furthermore, our metabolomic results suggest that these SemEV-associated metabolites may play a pivotal role in the maintenance of seminal plasma redox homeostasis.
Project description:BackgroundChronic hepatopathies present a diagnostic challenge, with different diseases being associated with similar clinical and laboratory findings. Characterization of dogs with chronic hepatopathies can be difficult and require costly diagnostic procedures such as acquisition of a liver biopsy specimen. Noninvasive and inexpensive biomarkers that reliably characterize chronic hepatopathies such as chronic hepatitis or a congenital portosystemic vascular anomaly may decrease the need for costly or invasive diagnostic testing and guide novel therapeutic interventions.ObjectiveTo investigate differences in the serum metabolome among healthy dogs, dogs with congenital portosystemic shunts, and dogs with chronic hepatitis.AnimalsStored serum samples from 12 healthy dogs, 10 dogs with congenital portosystemic shunts, and 6 dogs with chronic hepatitis were analyzed.MethodsThe serum metabolome was analyzed with an untargeted metabolomics approach using gas chromatography-quadrupole time of flight mass spectrometry.ResultsPrincipal component analysis and heat dendrogram plots of the metabolomics data showed clustering among individuals in each group. Random forest analysis showed differences in the abundance of various metabolites including increased aromatic amino acids and xylitol in dogs with congenital portosystemic shunts. Based on the univariate statistics, 50 metabolites were significantly different among groups.Conclusions and clinical importanceThe serum metabolome varies among healthy dogs, dogs with congenital portosystemic shunts, and dogs with chronic hepatitis. Statistical analysis identified several metabolites that differentiated healthy dogs from dogs with vascular or parenchymal liver disease. Further targeted assessment of these metabolites is needed to confirm their diagnostic reliability.
Project description:Obstructive sleep apnea (OSA) has been demonstrated to be associated with liver injury. Nevertheless, the mechanisms linking the two disorders remain largely unexplored to date. Based on UHPLC/Q-TOF MS platform, the present study aimed to study the hepatic metabolomic profiling in a chronic intermittent hypoxia (CIH) mouse model to identify altered metabolites and related metabolic pathways. C57BL/6 Mice (n = 12 each group) were exposed to intermittent hypoxia or control conditions (room air) for 12 weeks. At the end of the exposure, liver enzymes and histological changes were assessed. Untargeted metabolomics approach by UHPLC/Q-TOF MS and orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to screen altered metabolites in mice liver. Bioinformatics analyses were applied to identify the related metabolic pathways. CIH treatment caused a remarkable liver injury in mice. A total of 27 differential metabolites in negative ion mode and 44 in positive ion mode were identified between the two groups. These metabolites were correlated to multiple biological and metabolic processes, including various amino acid metabolism, membrane transport, lipid metabolism, carbohydrate metabolism, nucleotide metabolism, ferroptosis, etc. three differential metabolites including glutathione, glutathione disulfide, arachidonic acid (peroxide free) were identified in the ferroptosis pathway. CIH was associated with a significant metabolic profiling change in mice liver. The metabolites in amino acid metabolism, membrane transport, lipid metabolism, carbohydrate metabolism, nucleotide metabolism, and ferroptosis played an important role in CIH-induced liver injury. These findings contribute to a better understanding of the mechanisms linking OSA and liver injury and help identify potential therapeutic targets.
Project description:Sphagnum mosses dominate peatlands by employing harsh ecosystem tactics to prevent vascular plant growth and microbial degradation of these large carbon stores. Knowledge about Sphagnum-produced metabolites, their structure and their function, is important to better understand the mechanisms, underlying this carbon sequestration phenomenon in the face of climate variability. It is currently unclear which compounds are responsible for inhibition of organic matter decomposition and the mechanisms by which this inhibition occurs. Metabolite profiling of Sphagnum fallax was performed using two types of mass spectrometry (MS) systems and 1H nuclear magnetic resonance spectroscopy (1H NMR). Lipidome profiling was performed using LC-MS/MS. A total of 655 metabolites, including one hundred fifty-two lipids, were detected by NMR and LC-MS/MS-329 of which were novel metabolites (31 unknown lipids). Sphagum fallax metabolite profile was composed mainly of acid-like and flavonoid glycoside compounds, that could be acting as potent antimicrobial compounds, allowing Sphagnum to control its environment. Sphagnum fallax metabolite composition comparison against previously known antimicrobial plant metabolites confirmed this trend, with seventeen antimicrobial compounds discovered to be present in Sphagnum fallax, the majority of which were acids and glycosides. Biological activity of these compounds needs to be further tested to confirm antimicrobial qualities. Three fungal metabolites were identified providing insights into fungal colonization that may benefit Sphagnum. Characterizing the metabolite profile of Sphagnum fallax provided a baseline to understand the mechanisms in which Sphagnum fallax acts on its environment, its relation to carbon sequestration in peatlands, and provide key biomarkers to predict peatland C store changes (sequestration, emissions) as climate shifts.
Project description:Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.
Project description:BackgroundFood allergy (FA) affects an increasing proportion of children for reasons that remain obscure. Novel disease biomarkers and curative treatment options are strongly needed.ObjectiveWe sought to apply untargeted metabolomic profiling to identify pathogenic mechanisms and candidate disease biomarkers in patients with FA.MethodsMass spectrometry-based untargeted metabolomic profiling was performed on serum samples of children with either FA alone, asthma alone, or both FA and asthma, as well as healthy pediatric control subjects.ResultsIn this pilot study patients with FA exhibited a disease-specific metabolomic signature compared with both control subjects and asthmatic patients. In particular, FA was uniquely associated with a marked decrease in sphingolipid levels, as well as levels of a number of other lipid metabolites, in the face of normal frequencies of circulating natural killer T cells. Specific comparison of patients with FA and asthmatic patients revealed differences in the microbiota-sensitive aromatic amino acid and secondary bile acid metabolism. Children with both FA and asthma exhibited a metabolomic profile that aligned with that of FA alone but not asthma. Among children with FA, the history of severe systemic reactions and the presence of multiple FAs were associated with changes in levels of tryptophan metabolites, eicosanoids, plasmalogens, and fatty acids.ConclusionsChildren with FA have a disease-specific metabolomic profile that is informative of disease mechanisms and severity and that dominates in the presence of asthma. Lower levels of sphingolipids and ceramides and other metabolomic alterations observed in children with FA might reflect the interplay between an altered microbiota and immune cell subsets in the gut.