Metabolomic Profiling Of Patients Research Articles

Metabolomic Profiling of Large Extracellular Vesicles in Patients Suffering from Small Cell Lung Cancer.

Large extracellular vesicles (lEV) offer a unique window into the metabolism of their cells of orign and dysregulation of lipid metabolism has been described in patients with small cell lung cancer (SCLC). Therefore, metabolomic profiling of patients' lEVs may offer insight into cancer metabolism as well as new potential biomarkers for monitoring disease progression. lEVs were isolated by differential centrifugation from the peripheral blood of SCLC patients and healthy controls. Targeted mass spectrometry was used to analyze the lipid composition of lEVs. After identifying relevant metabolites, biomarker and pathway analysis were conducted. SCLC patients exhibited a distinct metabolic profile compared to healthy controls. The metabolites TG(16:0:_38:3), TG(18:3_35:2), TG(16:0_40:7), Cer(d18:1/26:0), and CE(16:0) are not only able to discriminate between patients and control samples, but are also served as prognostic markers for survival. Patients with high concentrations of these metabolites showed significantly shorter survival times. Pathway analysis revealed alterations in 'sphingolipid metabolism', 'sphingolipid signaling pathway' and 'necroptosis'. Metabolic profiling of lEVs in SCLC patients is feasible and reveals a distinct metabolic profile. High concentrations of identified lipids are associated with poor prognosis.

Targeted metabolomic profiling of acute ST-segment elevation myocardial infarction

Myocardial infarction is a major cause of morbidity and mortality worldwide. Metabolomic investigations may be useful for understanding the pathogenesis of ST-segment elevation myocardial infarction (STEMI). STEMI patients were comprehensively examined via targeted metabolomic profiling, machine learning and weighted correlation network analysis. A total of 195 subjects, including 68 STEMI patients, 84 patients with stable angina pectoris (SAP) and 43 non-CVD patients, were enrolled in the study. Metabolomic profiling involving the quantitative analysis of 87 endogenous metabolites in plasma was conducted. This study is the first to perform targeted metabolomic profiling in patients with STEMI. We identified 36 significantly altered metabolites in STEMI patients. Increased levels of four amino acids, eight acylcarnitines, six metabolites of the NO–urea cycle and neurotransmitters, and three intermediates of tryptophan metabolism were detected. The following metabolites exhibited decreased levels: six amino acids, three acylcarnitines, three components of the NO–urea cycle and neurotransmitters, and three intermediates of tryptophan metabolism. We found that the significant changes in tryptophan metabolism observed in STEMI patients—the increase in anthranilic acid and tryptophol and decrease in xanthurenic acid and 3-OH-kynurenine—may play important roles in STEMI pathogenesis. On the basis of the differences in the constructed weighted correlation networks, new significant metabolite ratios were identified. Among the 22 significantly altered metabolite ratios identified, 13 were between STEMI patients and non-CVD patients, and 17 were between STEMI patients and SAP patients. Seven of these ratios were common to both comparisons (STEMI patients vs. non-CVD patients and STEMI patients vs. SAP patients). Additionally, two ratios were consistently observed among the STEMI, SAP and non-CVD groups (anthranilic acid: aspartic acid and GSG (glutamine: serine + glycine)). These findings provide new insight into the diagnosis and pathogenesis of STEMI.

Integrated proteomic and metabolomic profiling reveals novel insights on the inflammation and immune response in HFpEF

BackgroundThe precise mechanisms leading to the development of heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. In this study, an integrative approach utilizing untargeted proteomics and metabolomics was employed to delineate the altered proteomic and metabolomic profiles in patients with HFpEF compared to healthy controls.Materials and methodsData were collected from a prospective cohort consisting of 30 HFpEF participants and 30 healthy controls, matched by gender and age. plasma samples were analyzed by multi-omics platforms. The quantification of plasma proteins and metabolites was performed using data-independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively. Additionally, Proteomic and metabolomic results were analyzed separately and integrated using correlation and pathway analysis. This was followed by the execution of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies to elucidate the biological relevance of the observed results.ResultsA total of 46 significantly differentially expressed proteins (DEPs) and 102 differentially expressed metabolites (DEMs) were identified. Then, GO and KEGG pathway enrichment analyses were performed by DEPs and DEMs. Integrated analysis of proteomics and metabolomics has revealed Tuberculosis and African trypanosomiasis pathways that are significantly enriched and the DEPs and DEMs enriched within them, are associated with inflammation and immune response.ConclusionsIntegrated proteomic and metabolomic analyses revealed distinct inflammatory and immune response pathways in HFpEF, highlighting novel therapeutic avenues.

Plasma Metabolite Profiling Identifies Nondiabetic Chronic Pancreatitis Patients With Metabolic Alterations Progressing to Prediabetes Before HbA1c.

Diabetes (T3cDM) secondary to chronic pancreatitis (CP) arises due to endocrine dysfunction and metabolic dysregulations. Currently, diagnostic tests are not available to identify patients who may progress from normoglycemia to hyperglycemia in CP. We conducted plasma metabolomic profiling to diagnose glycemic alterations early in the course of disease. Liquid chromatography-tandem mass spectrometry was used to generate untargeted, targeted plasma metabolomic profiles in patients with CP, controls (n = 445) following TRIPOD guidelines. Patients were stratified based on glucose tolerance tests following ADA guidelines. Multivariate analysis was performed using partial least squares discriminant analysis to assess discriminatory ability of metabolites among stratified groups. COMBIROC and logistic regression were used to derive biomarker signatures. AI-ML tool (Rapidminer) was used to verify these preliminary results. Ceramide, lysophosphatidylethanolamine, phosphatidylcholine, lysophosphatidic acid (LPA), phosphatidylethanolamine, carnitine, and lysophosphatidylcholine discriminated T3cDM CP patients from healthy controls with AUC 93% (95% CI 0.81-0.98, P < 0.0001), and integration with pancreatic morphology improved AUC to 100% (95% CI 0.93-1.00, P < 0.0001). LPA, phosphatidylinositol, and ceramide discriminated nondiabetic CP with glycemic alterations (pre-diabetic CP); AUC 66% (95% CI 0.55-0.76, P = 0.1), and integration enhanced AUC to 74% (95% CI 0.55-0.88, P = 0.86). T3cDM was distinguished from prediabetic by LPA, phosphatidylinositol, and sphinganine (AUC 70%; 95% CI 0.54-0.83, P = 0.08), and integration improved AUC to 83% (95% CI 0.68-0.93, P = 0.05). CombiROC cutoff identified 75% and 78% prediabetes in validation 1 and 2 cohorts. Random forest algorithm assessed performance of integrated panel demonstrating AUC of 72% in predicting glycemic alterations. We report for the first time that a panel of metabolites integrated with pancreatic morphology detects glycemia progression before HbA1c in patients with CP.

Metabolomic Profiles in Patients with Cervical Cancer Undergoing Cisplatin and Radiation Therapy.

This study was aimed to evaluate endogenous metabolic changes before and after cisplatin and radiation therapy in patients with cervical cancer via untargeted metabolomic analysis using plasma samples. A total of 13 cervical cancer patients were enrolled in this study. Plasma samples were collected from each patient on two occasions: approximately one week before therapy (P1) and after completion of cisplatin and radiation therapy (P2). Of the 13 patients, 12 patients received both cisplatin and radiation therapy, whereas one patient received radiation therapy alone. The samples were analyzed using the Ultimate 3000 coupled with Q ExactiveTM Focus Hybrid Quadrupole-OrbitrapTM mass spectrometry (Thermo Fisher Scientific, Waltham, MA, USA). Chromatographic separation utilized a Kinetex C18 column 2.1×100 mm (2.6 μm) (Phenomenex, Torrance, CA, USA), and the temperature was maintained at 40°C. Following P2, there were statistically significant increases in the concentrations of indoxyl sulfate, phenylacetylglutamine, Lysophosphatidyethanolamine (LysoPE) (18:1), and indole-3-acetic acid compared with the concentrations observed at P1. Specifically, in the human papillomavirus (HPV) noninfection group, indoxyl sulfate, LysoPE (18:1), and phenylacetylglutamine showed statistically significant increases at P2 compared with P1. No significant changes in metabolite concentrations were observed in the HPV infection group. Indoxyl sulfate, LysoPE (18:1), phenylacetylglutamine, and indole-3-acetic acid were significantly increased following cisplatin and radiation therapy.

Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension.

Endocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing's syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT. Retrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus. After adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites - 18 in PPGL, 15 in CS, and 23 in PA - were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites. Our study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL's metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.

Analysis of alterations in serum vitamins and correlations with gut microbiome, microbial metabolomics in patients with sepsis

BackgroundVitamins are essential micronutrients that play key roles in many biological pathways associated with sepsis. The gut microbiome plays a pivotal role in the progression of sepsis and may contribute to the onset of multi-organ dysfunction syndrome (MODS). The aim of this study was to investigate the changes in serum vitamins, and their correlation with intestinal flora and metabolomic profiles in patients with sepsis. MethodsThe serum levels of vitamins were determined by Ultra Performance Liquid Chromatography (UPLC). 16S rRNA gene sequencing and Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) targeted metabolomics were used for microbiome and metabolome analysis. ResultsIn the training cohort: After univariate, multivariate (OPLS-DA) and Spearman analyses, it was concluded that vitamin levels of 25 (OH) VD3 and (VD2 + VD3), as well as vitamins A and B9, differed significantly among healthy controls (HC), non-septic critical patients (NS), and sepsis patients (SS) (P < 0.05). The validation cohort confirmed the differential vitamin findings from the training cohort. Moreover, analyses of gut flora and metabolites in septic patients and healthy individuals revealed differential flora, metabolites, and metabolic pathways that were linked to alterations in serum vitamin levels. We found for the first time that vitamin B9 was negatively correlated with g_Sellimonas. ConclusionSepsis patients exhibited significantly lower levels of 25 (OH) VD3 and (VD2 + VD3), vitamins A and B9, which hold potential as predictive markers for sepsis prognosis. The changes in these vitamins may be associated with inflammatory factors, oxidative stress, and changes in gut flora.

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study.

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.

Metabolomic profile of patients on levothyroxine treatment for hypothyroidism.

Hypothyroidism is clinically characterized by a decrease in levels of the circulating thyroid hormones namely thyroxine and triiodothyronine. The main treatment for hypothyroidism is thyroid hormone replacement using levothyroxine to normalize serum thyroid hormone levels. In this study, we explored the metabolic changes in the plasma of patients with hypothyroidism after reaching a euthyroid state with levothyroxine treatment. Plasma samples from 18 patients diagnosed as overt hypothyroidism were collected before and after levothyroxine treatment upon reaching a euthyroid state and were analyzed by high-resolution mass spectrometry-based metabolomics. Multivariate and univariate analyses evaluated data to highlight potential metabolic biomarkers. Liquid chromatography-mass spectrometry-based metabolomics revealed a significant decrease in the levels of ceramide, phosphatidylcholine, triglycerides, acylcarnitine, and peptides after levothyroxine treatment; this could indicate a change in the fatty acid transportation system and an enhanced β-oxidation, compared with a hypothyroid state. At the same time, the decrease in the peptides suggested a shift in protein synthesis. In addition, there was a considerable rise in glycocholic acid following therapy, suggesting the involvement of thyroid hormones in stimulating bile acid production and secretion. A metabolomic analysis of patients with hypothyroidism revealed significant changes in several metabolites and lipids after treatment. This study showed the value of the metabolomics technique in providing a complementary understanding of the pathophysiology of hypothyroidism and as a crucial tool for examining the molecular impact of levothyroxine treatment on hypothyroidism. It was an important tool for investigating the therapeutic effects of levothyroxine on hypothyroidism at the molecular level.

Urinary metabolomic biomarker candidates for skeletal muscle wasting in patients with rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease that affects the joints, leading to chronic synovial inflammation and local tissue destruction. Extra-articular manifestations may also occur, such as changes in body composition. Skeletal muscle wasting is often observed in patients with RA, but methods for assessing loss of muscle mass are expensive and not widely available. Metabolomic analysis has shown great potential for identifying changes in the metabolite profile of patients with autoimmune diseases. In this setting, urine metabolomic profiling in patients with RA may be a useful tool to identify skeletal muscle wasting. Patients aged 40-70years with RA have been recruited according to the 2010 ACR/EULAR classification criteria. Further, the Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP) determined the disease activity. The muscle mass was measured by Dual X-ray absorptiometry (DXA) to generate the appendicular lean mass index (ALMI) by summing the lean mass measurements for both arms and legs and dividing them by height squared (kg/height2 ). Finally, urine metabolomic analysis by 1 H nuclear magnetic resonance (1 H-NMR) spectroscopy was performed and the metabolomics data set analysed using the BAYESIL and MetaboAnalyst software packages. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the 1 H-NMR data, followed by Spearman's correlation analysis. The combined receiver operating characteristic curve (ROC) was calculated, as well as the logistic regression analyses to establish a diagnostic model. The significance level at P<0.05 was set for all analyses. The total set of subjects investigated included 90 patients with RA. Most patients were women (86.7%), with a mean age of 56.5±7.3years old and a median DAS28-CRP of 3.0 (IQR 1.0-3.0). Fifteen metabolites were identified in the urine samples with high variable importance in projection (VIP scores) by MetaboAnalyst. Of these, dimethylglycine (r=0.205; P=0.053), oxoisovalerate (r=-0.203; P=0.055), and isobutyric acid (r=-0.249; P=0.018) were significantly correlated with ALMI. Based on the low muscle mass (ALMI ≤6.0kg/m2 for women and ≤8.1kg/m2 for men) a diagnostic model have been established with dimethylglycine (area under the curve [AUC]=0.65), oxoisovalerate (AUC=0.49), and isobutyric acid (AUC=0.83) with significant sensitivity and specificity. Isobutyric acid, oxoisovalerate, and dimethylglycine from urine samples were associated with low skeletal muscle mass in patients with RA. These findings suggest that this group of metabolites may be further tested as biomarkers for identification of skeletal muscle wasting.

Relationship between 4-Hydroxynonenal (4-HNE) as Systemic Biomarker of Lipid Peroxidation and Metabolomic Profiling of Patients with Prostate Cancer.

An oxidative degradation product of the polyunsaturated fatty acids, 4-hydroxynonenal (4-HNE), is of particular interest in cancer research due to its concentration-dependent pleiotropic activities affecting cellular antioxidants, metabolism, and growth control. Although an increase in oxidative stress and lipid peroxidation was already associated with prostate cancer progression a few decades ago, the knowledge of the involvement of 4-HNE in prostate cancer tumorigenesis is limited. This study investigated the appearance of 4-HNE-protein adducts in prostate cancer tissue by immunohistochemistry using a genuine 4-HNE monoclonal antibody. Plasma samples of the same patients and samples of the healthy controls were also analyzed for the presence of 4-HNE-protein adducts, followed by metabolic profiling using LC-ESI-QTOF-MS and GC-EI-Q-MS. Finally, the analysis of the metabolic pathways affected by 4-HNE was performed. The obtained results revealed the absence of 4-HNE-protein adducts in prostate carcinoma tissue but increased 4-HNE-protein levels in the plasma of these patients. Metabolomics revealed a positive association of different long-chain and medium-chain fatty acids with the presence of prostate cancer. Furthermore, while linoleic acid positively correlated with the levels of 4-HNE-protein adducts in the blood of healthy men, no correlation was obtained for cancer patients indicating altered lipid metabolism in this case. The metabolic pathway of unsaturated fatty acids biosynthesis emerged as significantly affected by 4-HNE. Overall, this is the first study linking 4-HNE adduction to plasma proteins with specific alterations in the plasma metabolome of prostate cancer patients. This study revealed that increased 4-HNE plasma protein adducts could modulate the unsaturated fatty acids biosynthesis pathway. It is yet to be determined if this is a direct result of 4-HNE or whether they are produced by the same underlying mechanisms. Further mechanistic studies are needed to grasp the biological significance of the observed changes in prostate cancer tumorigenesis.

Metabolomic Profiling in Patients with Different Hemodynamic Subtypes of Severe Aortic Valve Stenosis.

Severe aortic stenosis (AS) is a common pathological condition in an ageing population imposing significant morbidity and mortality. Based on distinct hemodynamic features, i.e., ejection fraction (EF), transvalvular gradient and stroke volume, four different AS subtypes can be distinguished: (i) normal EF and high gradient, (ii) reduced EF and high gradient, (iii) reduced EF and low gradient, and (iv) normal EF and low gradient. These subtypes differ with respect to pathophysiological mechanisms, cardiac remodeling, and prognosis. However, little is known about metabolic changes in these different hemodynamic conditions of AS. Thus, we carried out metabolomic analyses in serum samples of 40 AS patients (n = 10 per subtype) and 10 healthy blood donors (controls) using ultrahigh-performance liquid chromatography-tandem mass spectroscopy. A total of 1293 biochemicals could be identified. Principal component analysis revealed different metabolic profiles in all of the subgroups of AS (All-AS) vs. controls. Out of the determined biochemicals, 48% (n = 620) were altered in All-AS vs. controls (p < 0.05). In this regard, levels of various acylcarnitines (e.g., myristoylcarnitine, fold-change 1.85, p < 0.05), ketone bodies (e.g., 3-hydroxybutyrate, fold-change 11.14, p < 0.05) as well as sugar metabolites (e.g., glucose, fold-change 1.22, p < 0.05) were predominantly increased, whereas amino acids (e.g., leucine, fold-change 0.8, p < 0.05) were mainly reduced in All-AS. Interestingly, these changes appeared to be consistent amongst all AS subtypes. Distinct differences between AS subtypes were found for metabolites belonging to hemoglobin metabolism, diacylglycerols, and dihydrosphingomyelins. These findings indicate that relevant changes in substrate utilization appear to be consistent for different hemodynamic subtypes of AS and may therefore reflect common mechanisms during AS-induced heart failure. Additionally, distinct metabolites could be identified to significantly differ between certain AS subtypes. Future studies need to define their pathophysiological implications.

Distinct transcriptomic and metabolomic profiles characterize NSAID-induced urticaria/angioedema patients undergoing aspirin desensitization

There is limited data on the mechanisms of aspirin desensitization in patients with nonsteroidal anti-inflammatory drug (NSAID)-induced urticaria/angioedema (NIUA). We sought to characterize the transcriptomic and metabolomic profiles of patients with NIUA undergoing aspirin desensitization. PBMCs and plasma were separated from the blood of patients with NIUA undergoing aspirin desensitization for coronary artery disease and NSAID-tolerant controls. RNA was isolated from PBMCs and subjected to messenger RNA (mRNA)- and long noncoding RNA (lncRNA)-sequencing. Plasma samples were analyzed using LC-MS/MS for metabolite shifts using a semitargeted metabolomics panel. Eleven patients with NIUA and 10 healthy controls were recruited. The mRNA gene profiles of predesensitization versus postdesensitization and healthy control versus postdesensitization did not differ significantly. However, we identified 739 mRNAs and 888 lncRNAs as differentially expressed from preaspirin desensitization patients and controls. A12-mRNA gene signature was trained using a machine learning algorithm to distinguish between controls, postdose, and predose samples. Ingenuity Pathway Analysis identified 5 canonical pathways that were significantly enriched in preaspirin desensitization samples. IL-22 was the most upregulated pathway. To investigate the potential regulatory roles of the differentially expressed lncRNA on the mRNAs, 9lncRNAs and 12 mRNAs showed significantly correlated expression patterns in the IL-22 pathway. To validate the transcriptomics data, IL-22 was measured in the plasma samples of the subjects using ELISA. IL-22 was significantly higher in preaspirin desensitization patients compared with controls. In parallel, metabolomic analysis revealed stark differences in plasma profiles of preaspirin desensitization patients and healthy controls. In particular, 2-hydroxybenzoic acid (salicylic acid) was significantly lower in preaspirin desensitization patients compared with healthy controls. This is the first study to combine both transcriptomic and metabolomic approaches in patients with NIUA, which contributes to a deeper understanding about the pathogenesis of NIUA and may potentially pave the way toward a molecular diagnosis of NSAID hypersensitivity.

Metabolomics of clinical samples reveal the treatment mechanism of lanthanum hydroxide on vascular calcification in chronic kidney disease.

Previous studies showed that lanthanum hydroxide (LH) has a therapeutic effect on chronic kidney disease (CKD) and vascular calcification, which suggests that it might have clinical value. However, the target and mechanism of action of LH are unclear. Metabolomics of clinical samples can be used to predict the mechanism of drug action. In this study, metabolomic profiles in patients with end-stage renal disease (ESRD) were used to screen related signaling pathways, and we verified the influence of LH on the ROS-PI3K-AKT-mTOR-HIF-1α signaling pathway by western blotting and quantitative real-time RT-qPCR in vivo and in vitro. We found that ROS and SLC16A10 genes were activated in patients with ESRD. The SLC16A10 gene is associated with six significant metabolites (L-cysteine, L-cystine, L-isoleucine, L-arginine, L-aspartic acid, and L-phenylalanine) and the PI3K-AKT signaling pathway. The results showed that LH inhibits the ESRD process and its cardiovascular complications by inhibiting the ROS-PI3K-AKT-mTOR-HIF-1α signaling pathway. Collectively, LH may be a candidate phosphorus binder for the treatment of vascular calcification in ESRD.

Metabolomic Profiling in Patients with Heart Failure and Exercise Intolerance: Kynurenine as a Potential Biomarker.

Aims: Metabolic and structural perturbations in skeletal muscle have been found in patients with heart failure (HF) both with preserved (HFpEF) and reduced (HFrEF) ejection fraction in association with reduced muscle endurance (RME). We aimed in the current study to create phenotypes for patients with RME and HFpEF compared to RME HFrEF according to their metabolomic profiles and to test the potential of Kynurenine (Kyn) as a marker for RME. Methods: Altogether, 18 HFrEF, 17 HFpEF, and 20 healthy controls (HC) were prospectively included in the current study. The following tests were performed on all participants: isokinetic muscle function tests, echocardiography, spiroergometry, and varied blood tests. Liquid chromatography tandem mass spectrometry was used to quantify metabolites in serum. Results: Except for aromatic and branched amino acids (AA), patients with HF showed reduced AAs compared to HC. Further perturbations were elevated concentrations of Kyn and acylcarnitines (ACs) in HFpEF and HFrEF patients (p < 0.05). While patients with HFpEF and RME presented with reduced concentrations of ACs (long- and medium-chains), those with HFrEF and RME had distorted AAs metabolism (p < 0.05). With an area under the curve (AUC) of 0.83, Kyn shows potential as a marker in HF and RME (specificity 70%, sensitivity 83%). In a multiple regression model consisting of short-chain-ACs, spermine, ornithine, glutamate, and Kyn, the latest was an independent predictor for RME (95% CI: −13.01, −3.30, B: −8.2 per 1 µM increase, p = 0.001). Conclusions: RME in patients with HFpEF vs. HFrEF proved to have different metabolomic profiles suggesting varied pathophysiology. Kyn might be a promising biomarker for patients with HF and RME.

GC-MS Based Metabolomics Analysis to Evaluate Short-Term Effect of Tumor Removal on Patients with Early-Stage Breast Cancer.

In this study, it was aimed to demonstrate the short-term effect of breast cancer surgery and tumor removal on the metabolomic profiles of patients with early-stage breast cancer. This cohort consisted of 18 early-stage breast carcinoma patients who had breast cancer surgery to remove tumor and surrounding tissues. The blood samples obtained preoperatively and 24h after surgery were used in this investigation. Gas chromatography-mass spectrometry (GC-MS) based metabolomic analysis was performed to determine the metabolites. The GC-MS-based metabolomics profile enabled the identification of 162 metabolites in the plasma samples. Postoperatively, glyceric acid, phosphoric acid, O-phosphocolamine, 2-hydroxyethyliminodiacetic acid, N-acetyl-D-mannosamine, N-acetyl-5-hydroxytryptamine, methyl stearate, methyl oleate, iminodiacetic acid, glycerol 1-phosphate, β-glycerol phosphate and aspartic acid were found to be significantly increased (P < 0.05 for all), whereas saccharic acid, leucrose, gluconic acid, citramalic acid and acetol were significantly decreased (P < 0.05 for all). Breast cancer surgery and tumor removal has an impact on the metabolomic profiles of patients with early-stage breast cancer. These findings can be used for understanding the pathogenesis of breast cancer biology and screening the success of the surgery.

Palmitic Acid, A Critical Metabolite, Aggravates Cellular Senescence Through Reactive Oxygen Species Generation in Kawasaki Disease.

Coronary artery lesions (CALs) are severe complications of Kawasaki disease (KD), resulting in stenosis and thrombogenesis. Metabolomic profiling of patients’ plasma could assist in elucidating the pathogenesis of CALs and identifying diagnostic biomarkers, which are imperative for clinical treatment. The metabolic profiles between KD patients with CALs and without CALs (non-coronary artery lesion, or NCAL, group) indicated the most significantly differentially expressed metabolite, palmitic acid (PA), showed the most massive fold change at 9.879. Furthermore, PA was proven to aggravate endothelial cellular senescence by increasing the generation of reactive oxygen species (ROS) in KD, and those two phenotypes were confirmed to be enriched among the differentially expressed genes between KD and normal samples from GEO datasets. Collectively, our findings indicate that cellular senescence may be one of the mechanisms of vascular endothelial damage in KD. PA may be a biomarker and potential therapeutic target for predicting the occurrence of CALs in KD patients. All things considered, our findings confirm that plasma metabolomics was able to identify promising biomarkers and potential pathogenesis mechanisms in KD. To conclude, Palmitic acid could be a novel target in future studies of CALs in patients with KD.

P702 Faecal Microbiota Transplantation Sculpts the faecal and mucosal microbial and metabolomic profiles in patients with Ulcerative Colitis

Abstract Background Gut microbiota modulation via faecal microbiota transplantation (FMT)is known to induce long lasting clinical as well as endoscopic remission in patients with UC. The present study aims to identify microbial and metabolomic changes in the faecal as well mucosal niches, in response to FMT in patients with UC. Methods 28 patients with mild-moderate UC and 16 non-IBD controls were enrolled. Patients were given a weekly infusion of pooled-multidonor-FMT, for 8 weeks, while maintaining a uniform dietary pattern. We collected paired stool and recto-sigmoidal biopsysamples from patients with UC pre-FMT (n=28), post-FMT (n=10) and controls (n=16). 16S (V3-V4) rRNA sequencing and LC-MS based untargeted metabolomics was performed. α and β diversity analysis and differential abundance (DA) analysis of microbiota was performed using DeSeq2 R package. Differential metabolite peaks between the three groups were identified via volcano plot and annotated using LipidMaps, HMDB, Kegg and Metlin databases. (Fig.1) Results α-diversity of patients with UC was not significantly different from that of controls, in both sample matrices. Post-FMT α-diversity of faecal samples differed significantly from the Pre-FMT group, however in mucosal tissue, the difference was non-significant. β-diversity indices were significant in UC pre-FMT vs post-FMT populations for both matrices (Fig.2A and B). DA analysis identified faecal and mucosal controls and UC post-FMT samples to be significantly similar [ANOSIM R=0.07; p=0.1 (faecal) and R=0.02; p=0.315 (mucosal)]. Analysis of core microbiota revealed diminished faecal microbial core in UC, with no major UC-associated loss of mucosal core members, showing relatively conserved nature of mucosal microbiota. FMT restored microbial core along both niches. UC pre-FMT samples displayed increased abundance of Firmicutes (Limosilactobacillus, Lactobacillus, Streptococcus, Veilonella and Sphingomonas) and Proteobacteria (Methylobacterium) in faecal samples, while mucosa-associated microbiota displayed increased abundances of Firmicutes (Streptococcus, Limosilactobacillus, Ligilactobacillus, Eubacterium, Enterococcus, etc), Bacteroidetes (Parabacteroides and Alistipes) and Proteobacteria (Burkholderia)(Fig.3). Distinct functional classes of metabolites, including bacterial cell wall and QS molecules, membrane lipids, metabolism of vitamins, long and medium chain fatty acids, bile acids, and tryptophan etc, were also found to be altered between the three groups in both sample matrices(Fig.4) Conclusion FMT efficiently restores beneficial bacterial populations and related metabolic pathways in faecal as well the relatively reserved mucosal microbial niches, which may pave way for induction of remission in patients with UC.

Exercise-Induced Plasma Metabolomic Profiles in Patients With Peripheral Arterial Disease.

Aim: A better knowledge of the biological consequences in the blood of these exercise-induced ischemic events in lower extremity artery disease (LEAD) may improve the prospects of disease management. We explored the preminus postexercise metabolomic difference in 39 patients with LEAD referred for a treadmill oximetry test [transcutaneous oximetry (TcPO2)].Methods: Ischemia was estimated through the sum of decrease from rest of oxygen pressure (DROPs) (limb TcPO2 changes minus chest TcPO2 changes) at buttocks, thighs, and calves regions. Targeted metabolomic analyses measuring 188 metabolites were performed on a few microliters blood samples taken at the earlobe at rest and 3 min after exercise.Results: Maximum walking distance (MWD) was 290 m (120–652 m) and ankle brachial index (ABI) was 0.67 ± 0.17. Supervised paired partial least squares discriminant analysis based on 23,345 models showed good predictive performance for test sets with a median area under the receiver operating characteristic (AUROC) curve value of 0.99 and a p-value of 0.00049. The best discriminant metabolites contributing to the model included a subset of 71 (47%) of the 150 accurately measured metabolites in the plasma, comprising 3 acylcarnitines, 3 amino acids, 5 biogenic amines, 9 sphingomyelin, 7 lysophosphatidylcholines, and 44 phosphatidylcholines. In addition, 16 of these metabolites were found to correlate with one or more severity scores of the LEAD.Conclusion: Our results provide new insights into the biological changes that accompany exercise in LEAD and contribute to a better understanding of walking impairment pathophysiology in LEAD, highlighting new candidate biomarkers.

Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps.

BACKGROUNDColorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors.AIMTo characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP).METHODSProton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index.RESULTSNMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified.CONCLUSIONIn conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.