Serum Metabolomic Profiles Research Articles

Serum metabolomic profiling of human gastric cancer and its relationship with the prognosis.

ObjectiveThis study was aimed to investigate serum metabolites in gastric cancer (GC) patients and their relationships with the prognosis of GC in order to find potential specific serum biomarkers for GC.MethodsBlood samples of 125 GC patients of unifocal GC at initial stage and 38 healthy people recruited in our hospital from September 2008 to August 2009 were analyzed by using high performance liquid chromatography coupled with electrospray ionization/quadrupole-time-of-flight mass spectrometry (HPLCESI/Q-TOFMS). Multiple statistical methods like principal component analysis (PCA), hierarchical clustering analysis, partial least squares discriminant analysis (PLS-DA), multivariate COX regression analysis, variance analysis and K-M survival curve were applied to analyze the raw obtained mass data in order to analyze the independent prognostic factors of GC. The structures of these metabolites were confirmed by comparing the m/z ratio and ion mode of with the data published from HMDB (www.hmdb.ca) databases.ResultsBy PLS-DA test, 16 serum metabolites in ESI+ mode of VIP>1 in both test group and validation group could definitely distinguish GC patients from healthy peoples (p<0.05). Multivariate COX regression analysis showed TNM staging, 2,4-hexadienoic acid, 4-methylphenyl dodecanoate and glycerol tributanoate were independent prognostic factors of GC (p<0.05). In the K-M survival analysis, the survival rate in high level group of the 3 selected serum metabolites together or alone was significant lower than in those in low level group (p<0.05).ConclusionLow serum levels of 2,4-hexadienoic acid, 4-methylphenyl dodecanoate and glycerol tributanoate may be important independent prognostic factors of GC.

Metabolome analysis reveals the association between the kynurenine pathway and human herpesvirus 6 encephalopathy in immunocompetent children

Human herpesvirus 6 (HHV-6) is the second most common causative pathogen of acute encephalopathy in immunocompetent children in Japan. Identification of biomarkers associated the pathophysiology is desirable to monitor disease severity, progression, and prognosis. To investigate potential biomarkers for HHV-6 encephalopathy, serum metabolome profiling was analyzed and candidate metabolites were investigated the function in the diseases. Pediatric patients with HHV-6 encephalopathy (n = 8), febrile seizure (n = 20), and febrile infection without febrile seizure (n = 7) were enrolled in this study, and serum metabolites were identified and quantified. For further analysis, serum samples of HHV-6 infected patients were analyzed by absolute quantification assay for kynurenine (KYN) and quinolinic acid (QUIN) in a total of 38 patients with or without encephalopathy. An in vitro blood–brain barrier (BBB) model was used to evaluate the effect of KYN and QUIN on BBB integrity because BBB damage induces brain edema. Metabolome profiling identified 159 metabolites in serum samples. The levels of KYN and QUIN, which belong to the tryptophan-KYN pathway, were significantly higher in the HHV-6 encephalopathy group than the other two groups. When quantified in the larger patient group, remarkably high levels of KYN and QUIN were observed exclusively in the encephalopathy group. Trans-endothelial electrical resistance of the BBB model was significantly decreased after QUIN treatment in culture. Metabolome analysis revealed that KYN and QUIN may be associated with the pathophysiology of HHV-6 encephalopathy. In particular, QUIN may damage BBB integrity.

Untargeted metabolomic analysis in naturally occurring canine diabetes mellitus identifies similarities to human Type 1 Diabetes

While predominant as a disease entity, knowledge voids exist regarding the pathogenesis of canine diabetes. To test the hypothesis that diabetic dogs have similar metabolomic perturbations to humans with type 1 diabetes (T1D), we analyzed serum metabolomic profiles of breed- and body weight-matched, diabetic (n = 6) and healthy (n = 6) dogs by liquid chromatography-mass spectrometry (LC-MS) profiling. We report distinct clustering of diabetic and control groups based on heat map analysis of known and unknown metabolites. Random forest classification identified 5/6 dogs per group correctly with overall out of bag error rate = 16.7%. Diabetic dogs demonstrated significant upregulation of glycolysis/gluconeogenesis intermediates (e.g., glucose/fructose, C6H12O6, keto-hexose, deoxy-hexose, (P < 0.01)), with significant downregulation of tryptophan metabolism metabolites (e.g., picolinic acid, indoxyl sulfate, anthranilate, (P < 0.01)). Multiple amino acids (AA), AA metabolites, and bile acids were also significantly lower in diabetic versus healthy dogs (P < 0.05) with the exception of the branched chain AA valine, which was elevated in diabetic animals (P < 0.05). Metabolomic profiles in diabetic versus healthy dogs shared similarities with those reported in human T1D (e.g., alterations in glycolysis/gluconeogensis metabolites, bile acids, and elevated branched chain AA). Further studies are warranted to evaluate the utility of canine diabetes to provide novel mechanistic insights to the human disorder.

A prospective study of serum metabolites and glioma risk.

Malignant glioma is one of the most lethal adult cancers, yet its etiology remains largely unknown. We conducted a prospective serum metabolomic analysis of glioma based on 64 cases and 64 matched controls selected from Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from collection of baseline fasting serum to diagnosis was nine years (inter-decile range 3-20 years). LC/MS-MS identified 730 known metabolites, and conditional logistic regression models estimated odds ratios for one-standard deviation differences in log-metabolite signals. Forty-three metabolites were associated with glioma at P<0.05. 2-Oxoarginine, cysteine, alpha-ketoglutarate, chenodeoxycholate and argininate yielded the strongest metabolite signals and were inversely related to overall glioma risk (0.0065≤P<0.0083). Also, seven xanthine metabolites related to caffeine metabolism were higher in cases than in controls (0.017≤P<0.042). Findings were mostly similar in high-grade glioma cases, although prominent inversely associated metabolites included the secondary bile acids glycocholenate sulfate and 3β-hydroxy-5-cholenoic acid, xenobiotic methyl 4-hydroxybenzoate sulfate, sex steroid 5alpha-pregnan-3beta, 20beta-diol-monosulfate, and cofactor/vitamin oxalate (0.0091≤P<0.021). A serum metabolomic profile of glioma identified years in advance of clinical diagnoses is characterized by altered signals in arginine/proline, antioxidant, and coffee-related metabolites. The observed pattern provides new potential leads regarding the molecular basis relevant to etiologic or sub-clinical biomarkers for glioma.

Abstract 2266: A prospective study of serum metabolites and glioma risk

Abstract Malignant glioma is one of the most devastating adult malignancies. Although there exists an urgent need for actionable leads regarding its prevention and early detection, the etiology of glioma remains largely unknown. We conducted a prospective serum metabolomic analysis of glioma based on 64 cases and individually matched controls selected from Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from collection of baseline fasting serum to diagnosis of glioma was nine years (interdecile range 3-20 years). We used ultra-high performance LC/MS which identified 730 known metabolites for analysis. Conditional logistic regression models examined odds ratios (ORs) and 95% confidence intervals for one-standard deviation differences in metabolite signals on a log-scale. Forty-three serum metabolites were associated with glioma at P&amp;lt;0.05. 2-Oxoarginine, cysteine, alpha-ketoglutarate, chenodeoxycholate and argininate yielded the strongest metabolite signals and were inversely associated overall risk of glioma (0.0065≤P&amp;lt;0.0083). Three serum acylcarnitines (0.016≤P&amp;lt;0.046), and seven xanthine (i.e., caffeine) metabolites (0.017≤P&amp;lt;0.042) were higher in cases than controls. Findings were mostly similar in high-grade glioma cases, although proportionally more metabolites showed positive risk associations such as N-acetylglutamate, ribonate, and 5-methyluridine (0.04≤P&amp;lt;0.02). Notably, xanthine compounds appeared unrelated to high-grade glioma. Prominent metabolite signals for inverse associations were the bile acids glycocholenate sulfate and 3β-hydroxy-5-cholenoic acid, xenobiotic methyl-4-hydroxybenzoate sulfate, sex steroid 5alpha-pregnan-3beta,20beta-diol monosulfate, and cofactor/vitamin oxalate. We found weaker risk associations for lower-grade glioma, of which none achieved significance at P&amp;lt;0.05. The metabolic sub-pathway analysis revealed associations for primary bile acid, urea cycle/arginine and proline, tocopherol, glycolysis/gluconeogenesis/pyruvate metabolites with overall glioma risk (0.005≤P&amp;lt;0.048); ascorbate and aldarate pathways were associated with high-grade glioma (P=0.02), whereas glycolysis/gluconeogenesis/pyruvate, eicosanoid and glutamate pathways were found to be associated with lower-grade disease (0.01≤P&amp;lt;0.04). These associations did not, however, reach the stringent Bonferroni significance threshold for correction of multiple comparisons. In conclusion, we identified a serum metabolomic profile of glioma, years in advance of clinical diagnoses, characterized by altered molecular signals in the arginine/proline, antioxidant, bile acid, and glycolytic pathways. The observed patterns provide potential new leads regarding the molecular basis relevant to etiologic or sub-clinical biomarkers for glioma. The findings require reexamination in larger and more diverse populations, including through glioma consortia. Citation Format: Jiaqi Huang, Stephanie J. Weinstein, Cari M. Kitahara, Edward Karoly, Joshua N. Sampson, Demetrius Albanes. A prospective study of serum metabolites and glioma risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2266. doi:10.1158/1538-7445.AM2017-2266

Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention.

Emerging evidence has linked the gut microbiome to human obesity. We performed a metagenome-wide association study and serum metabolomics profiling in a cohort of lean and obese, young, Chinese individuals. We identified obesity-associated gut microbial species linked to changes in circulating metabolites. The abundance of Bacteroides thetaiotaomicron, a glutamate-fermenting commensal, was markedly decreased in obese individuals and was inversely correlated with serum glutamate concentration. Consistently, gavage with B. thetaiotaomicron reduced plasma glutamate concentration and alleviated diet-induced body-weight gain and adiposity in mice. Furthermore, weight-loss intervention by bariatric surgery partially reversed obesity-associated microbial and metabolic alterations in obese individuals, including the decreased abundance of B. thetaiotaomicron and the elevated serum glutamate concentration. Our findings identify previously unknown links between intestinal microbiota alterations, circulating amino acids and obesity, suggesting that it may be possible to intervene in obesity by targeting the gut microbiota.

New Developments in Hepatorenal Syndrome

Hepatorenal syndrome (HRS) continues to be one of the major complications of decompensated cirrhosis, leading to death in the absence of liver transplantation. Challenges in precisely evaluating renal function in the patient with cirrhosis remain because of the limitations of serum creatinine (Cr) alone in estimating glomerular filtration rate (GFR); current GFR estimating models appear to underestimate renal dysfunction. Newer models incorporating renal biomarkers, such as the Cr-Cystatin C GFR Equation for Cirrhosis appear to estimate measured GFRmore accurately. A major change in the diagnostic criteria for HRS based on dynamic serial changes in serum Cr that regard HRS type 1 as a special form of acute kidney injury promises the possibility of earlier identification of renal dysfunction in patients with cirrhosis. The diagnostic criteria of HRS still include the exclusion of other causes of kidney injury. Renal biomarkers have been disappointing in assisting with the differentiation of HRS from prerenal azotemia and other kidney disorders. Serum metabolomic profiling may be a more powerful tool to assess renal dysfunction, although the practical clinical significance ofthis remains unclear. As a result of the difficulties of assessing renal function in cirrhosis and the varying HRS diagnostic criteria and the rigor with which they are applied, the precise incidence and prevalence of HRS is unknown, but it is likely that HRS occurs more commonly than expected. The pathophysiology of HRS is rooted firmly in the setting of progressive reduction in renal blood flow as a result of portal hypertension and splanchnic vasodilation. Progressive marked renal cortical ischemia in patients with cirrhosis parallels the evolution of diuretic-sensitive ascites to diuretic-refractory ascites and HRS, a recognized continuum of renal dysfunction in cirrhosis. Alterations in nitrous oxide production, both increased and decreased, may play a major role in the pathophysiology of this evolution. The inflammatory cascade, triggered by bacterial translocation and endotoxemia, increasingly recognized as important in the manifestation of acute-on-chronic liver failure, also may play a significant role in the pathophysiology of HRS. The mainstay of treatment remains vasopressor therapy with albumin in an attempt to reverse splanchnic vasodilation and improve renal blood flow. Several meta-analyses have confirmed thevalue of vasopressors, chiefly terlipressin and noradrenaline, in improving renal function and reversing HRS type 1. Other interventions such as renal replacement therapy, transjugular intrahepatic portosystemic shunt, and artificial liver support systems have a very limited role in improving outcomes in HRS. Liver transplantation remainsthe definitive treatment for HRS. The frequency of simultaneous liver-kidney transplantation has increased dramatically in the Model for End-stage Liver Disease era, with changes in organ allocation policies. This has resulted in a more urgent need to predict native kidney recovery from HRS after liver transplantation alone, to avoid unnecessary simultaneous liver-kidney transplantation.

Serum Metabolomic Profiles for Breast Cancer Diagnosis, Grading and Staging by Gas Chromatography-Mass Spectrometry

Detection of metabolic signature for breast cancer (BC) has the potential to improve patient prognosis. This study identified potentially significant metabolites differentiating between breast cancer patients and healthy controls to help in diagnosis, grading, staging and determination of neoadjuvant status. Serum was collected from 152 pre-operative breast cancer (BC) patients and 155 healthy controls in this case-controlled study. Gas chromatography-mass spectrometry (GC-MS) was used to obtain metabolic profiles followed by chemometric analysis with the identification of significantly differentiated metabolites including 7 for diagnosis, 18 for grading, 23 for staging, 15 for stage III subcategory and 10 for neoadjuvant status (p-value < 0.05). Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between healthy controls and BC patients with a sensitivity of 96% and specificity of 100% on external validation. Models for grading, staging and neoadjuvant status were built with Decision Tree Algorithm with predictive accuracy of 71.5%, 71.3% and 79.8% respectively. Pathway analysis revealed increased glycolysis, lipogenesis, and production of volatile organic metabolites indicating the metabolic alterations in breast cancer.

Identification of potential diagnostic biomarkers of acute pancreatitis by serum metabolomic profiles

Acute pancreatitis (AP) is defined as an acute inflammation of pancreas that may cause damage to other tissues and organs depending upon the severity of symptoms. The diagnosis of AP is usually made by detection of raised circulating pancreatic enzyme levels, but there are occasional false positive and false negative diagnoses and such tests are often normal in delayed presentations. More accurate biomarkers would help in such situations. In this study, the global metabolites' changes of AP patients (APP) were profiled by using gas chromatography-mass spectrometry (GC-MS). Multivariate pattern recognition techniques were used to establish the classification models to distinguish APP from healthy participants (HP). Some significant metabolites including 3-hydroxybutyric acid, phosphoric acid, glycerol, citric acid, d-galactose, d-mannose, d-glucose, hexadecanoic acid and serotonin were selected as potential biomarkers for helping clinical diagnosis of AP. Furthermore, the metabolite changes in APP with severe and mild symptoms were also analyzed. Based on the selected biomarkers, some relevant pathways were also identified. Our results suggested that GC-MS based serum metabolomics method can be used in the clinical diagnosis of AP by profiling potential biomarkers.

Serum Metabolomic Profiling of Piglets Infected with Virulent Classical Swine Fever Virus.

Classical swine fever (CSF) is a highly contagious swine infectious disease and causes significant economic losses for the pig industry worldwide. The objective of this study was to determine whether small molecule metabolites contribute to the pathogenesis of CSF. Birefly, serum metabolomics of CSFV Shimen strain-infected piglets were analyzed by ultraperformance liquid chromatography/electrospray ionization time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) in combination with multivariate statistical analysis. In CSFV-infected piglets at days 3 and 7 post-infection changes were found in metabolites associated with several key metabolic pathways, including tryptophan catabolism and the kynurenine pathway, phenylalanine metabolism, fatty acid and lipid metabolism, the tricarboxylic acid and urea cycles, branched-chain amino acid metabolism, and nucleotide metabolism. Several pathways involved in energy metabolism including fatty acid biosynthesis and β-oxidation, branched-chain amino acid metabolism, and the tricarboxylic acid cycle were significantly inhibited. Changes were also observed in several metabolites exclusively associated with gut microbiota. The metabolomic profiles indicate that CSFV-host gut microbiome interactions play a role in the development of CSF.

Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample.

Fatty liver disease (FLD) is an important intermediate trait along the cardiometabolic disease spectrum and strongly associates with type 2 diabetes. Knowledge of biological pathways implicated in FLD is limited. An untargeted metabolomic approach might unravel novel pathways related to FLD. In a population-based sample (n=555) from Northern Germany, liver fat content was quantified as liver signal intensity using magnetic resonance imaging. Serum metabolites were determined using a non-targeted approach. Partial least squares regression was applied to derive a metabolomic score, explaining variation in serum metabolites and liver signal intensity. Associations of the metabolomic score with liver signal intensity and FLD were investigated in multivariable-adjusted robust linear and logistic regression models, respectively. Metabolites with a variable importance in the projection >1 were entered in in silico overrepresentation and pathway analyses. In univariate analysis, the metabolomics score explained 23.9% variation in liver signal intensity. A 1-unit increment in the metabolomic score was positively associated with FLD (n=219; odds ratio: 1.36; 95% confidence interval: 1.27-1.45) adjusting for age, sex, education, smoking and physical activity. A simplified score based on the 15 metabolites with highest variable importance in the projection statistic showed similar associations. Overrepresentation and pathway analyses highlighted branched-chain amino acids and derived gamma-glutamyl dipeptides as significant correlates of FLD. A serum metabolomic profile was associated with FLD and liver fat content. We identified a simplified metabolomics score, which should be evaluated in prospective studies.

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination.

This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis−mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

Effect of 12‐week tocotrienol supplementation on postmenopausal women with low bone mass: a serum metabolomics profile

Osteoporosis is a major health problem in postmenopausal women. This study evaluated the effects of 12‐week tocotrienol (TT) supplementation on serum metabolites in postmenopausal osteopenic women. Eighty nine participants (59.7±6.8 yr, BMI 28.7±5.7 kg/m2) were assigned to 3 treatments: placebo (860 mg olive oil/day), TT300 (300 mg TT/day), and TT600 (600 mg TT/day) for 12 weeks. TT consisted of 90% d‐TT and 10% g‐TT. After 12 weeks, 2 participants dropped out (3% attrition rate, one from placebo and one from TT600) due to non‐compliance. Pill compliance rates for placebo, TT300, and TT600 were 93%, 92%, and 91%, respectively. In this serum metabolomics study, we only evaluated the placebo and TT600 at baseline and 12 weeks. At baseline, there were no differences in any demographic parameters, comprehensive metabolic panel (CMP), and oxidative stress biomarker (8‐OHdG) between the placebo and TT600 groups. At 12 weeks, the TT600 group had significantly lower 8‐OHdG than the placebo group, whereas no differences in any CMP parameters were observed. Metabolomics analysis of serum samples revealed that 48 biochemicals were higher and 65 biochemicals were lower in the TT600 group at 12 weeks, compared to baseline at 0 week. The results confirmed higher tocotrienols as expected, lower catabolites of tryptophan, and several changes in lysophospholipids and acylcarnitines in subjects given TT600. The results showed that 12‐week TT supplementation modulated serum metabolite profiles in postmenopausal, osteopenic women, a response potentially linked to reduced inflammation and oxidative stress.Support or Funding InformationAmerican River Nutrition, Inc., Hadley, MA.

Prospective serum metabolomic profile of prostate cancer by size and extent of primary tumor.

Two recent investigations found serum lipid and energy metabolites related to aggressive prostate cancer up to 20 years prior to diagnosis. To elucidate whether those metabolomic profiles represent etiologic or tumor biomarker signals, we prospectively examined serum metabolites of prostate cancer cases by size and extent of primary tumors in a nested case-control analysis in the ATBC Study cohort that compared cases diagnosed with T2 (n = 71), T3 (n = 51), or T4 (n = 15) disease to controls (n = 200). Time from fasting serum collection to diagnosis averaged 10 years (range 1–20). LC/MS-GC/MS identified 625 known compounds, and logistic regression estimated odds ratios (ORs) associated with one-standard deviation differences in log-metabolites. N-acetyl-3-methylhistidine, 3-methylhistidine and 2′-deoxyuridine were elevated in men with T2 cancers compared to controls (ORs = 1.38–1.79; 0.0002 ≤ p ≤ 0.01). By contrast, four lipid metabolites were inversely associated with T3 tumors: oleoyl-linoleoyl-glycerophosphoinositol (GPI), palmitoyl-linoleoyl-GPI, cholate, and inositol 1-phosphate (ORs = 0.49–0.60; 0.000017 ≤ p ≤ 0.003). Secondary bile acid lipids, sex steroids and caffeine-related xanthine metabolites were elevated, while two Krebs cycle metabolites were decreased, in men diagnosed with T4 cancers. Men with T2, T3, and T4 prostate cancer primaries exhibit qualitatively different metabolite profiles years in advance of diagnosis that may represent etiologic factors, molecular patterns reflective of distinct primary tumors, or a combination of both.

Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population.

Purpose: Detecting signals of micrometastatic disease in patients with early breast cancer (EBC) could improve risk stratification and allow better tailoring of adjuvant therapies. We previously showed that postoperative serum metabolomic profiles were predictive of relapse in a single-center cohort of estrogen receptor (ER)-negative EBC patients. Here, we investigated this further using preoperative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial.Experimental Design: Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or ≥6 years clinical follow-up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated on the basis of the likelihood of the sample being misclassified as metastatic.Results: In the training set, the RF model separated EBC from MBC with a discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an AUC of 0.747 in ROC analysis. Accuracy was maximized at 71.3% (sensitivity, 70.8%; specificity, 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of Adjuvant! Online risk of relapse score.Conclusions: In a multicenter group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathologic risk factors. Clin Cancer Res; 23(6); 1422-31. ©2017 AACR.

Highlights of This Issue

Detection of signals of micrometastatic disease in early breast cancer may improve risk stratification, and serum metabolomic profiles of early breast cancer patients have been shown to be predictive of relapse in a single institution series. Taking samples from a multicentre, phase III adjuvant breast cancer trial in premenopausal women, Hart and colleagues used NMR spectra of preoperative serum samples to build a model that identified women at higher risk of relapse, independent of standard risk factors. Metabolomic analysis has the potential to improve disease staging and prognostication and allow better tailoring of adjuvant therapies.MERTK tyrosine kinase is a potential therapeutic target in acute leukemia. DeRyckere and colleagues developed a series of novel, orally bioavailable MERTK-selective tyrosine kinase inhibitors and now report robust activity mediated by UNC2025, a third-generation compound suitable for clinical development in preclinical acute leukemia models. UNC2025 had antileukemia activity in approximately 30% of 260 freshly isolated leukemia patient samples and had therapeutic activity alone and in combination with cytotoxic chemotherapy in cell line and patient-derived xenografts, suggesting that a large fraction of patients with acute leukemia could benefit from treatment with a MERTK-targeted therapy.CD38 expression is associated with poor prognosis in chronic lymphocytic leukemia (CLL). CD38 is an ectoenzyme that cooperates in migration, adhesion, and invasion through its association with CXCR4, MMP9, and CD49d. To explore its potential as a drug target in CLL, Matas-Céspedes and colleagues analyzed the effect of the anti-CD38 monoclonal antibody daratumumab. The authors demonstrate a dual effect of daratumumab by erradicating the tumor cell both in vitro and in vivo, and by hampering CLL cell adhesion, migration, and homing to secondary organs. These results provide scientific rationale for the use of daratumumab in poor prognosis CD38+CLL.The role of neutrophils in the tumor microenvironment remains controversial. To explore their role in gastric cancer development, Li and colleagues analyzed the distribution and clinical relevance of neutrophils in different microanatomical areas and peripheral blood and assessed the regulation and function of neutrophils by both in vitro and in vivo studies. Their results provided direct evidence supporting the pivotal role of IL-17+ neutrophils in promoting angiogenesis during gastric cancer progression and revealed a fine-tuned collaboration between cancer cells and immune cells in distinct tumor milieus, potentially providing future therapeutic avenues against cancer.

Cinnamon: does it hold its promises in cows? Using non-targeted blood serum metabolomics profiling to test the effects of feeding cinnamon to dairy cows undergoing lactation-induced insulin resistance

Cinnamon exerts insulin-enhancing activity in vitro and was demonstrated to improve blood glucose and lipid profiles in several human studies. Such effects may have an impact on metabolically stressed cows. To study the effects of cinnamon supplementation during the transition from late pregnancy to early lactation on the metabolism in dairy cows. Twenty-four Holstein cows (n = 8/group) were assigned to either the control group (CTR; without supplementation) or the supplementation groups [supplemental cinnamon at 20 (LCIN) or 40 (HCIN) g/cow per day (d)] from 28 d before calving until 21 d thereafter. Blood samples were assayed for glucose, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin; an index estimating insulin sensitivity (RQUICKI) was calculated. The serum metabolome was characterized in the samples collected from d 14 using a non-targeted approach. The serum concentrations of glucose and insulin did not differ among groups and followed a similar pattern over time. The serum NEFA concentrations were greater in LCIN (d 2, 7, and 14) and HCIN (d 14) than in CTR. On d 14 and 21, LCIN and HCIN had greater serum BHBA concentrations than CTR cows. The top 10 metabolites identified with significantly higher levels in the supplemented than the CTR cows were related to fatty acid metabolism. The data suggest lipolytic and ketogenic effects of cinnamon supplementation in dairy cows during the transition from late gestation to early lactation. The fatty acid metabolites found elevated in the supplemented cows point towards impaired mitochondrial fatty acid β-oxidation.

Near-Complete Correction of Profound Metabolomic Impairments Corresponding to Functional Benefit in MPS IIIB Mice after IV rAAV9-hNAGLU Gene Delivery

Mucopolysaccharidosis (MPS) IIIB is a lysosomal storage disease with complex CNS and somatic pathology due to a deficiency in α-N-acetylglucosaminidase (NAGLU). Using global metabolic profiling by mass spectrometry targeting 361 metabolites, this study detected significant decreases in 225 and increases in six metabolites in serum samples from 7-month-old MPS IIIB mice, compared to wild-type (WT) mice. The metabolic disturbances involve virtually all major pathways of amino acid, peptide (58/102), carbohydrate (18/28), lipid (111/139), nucleotide (12/24), energy (2/9), vitamin and cofactor (11/16), and xenobiotic (11/28) metabolism. Notably, the reduced metabolites included eight essential amino acids, vitamins (C, E, B2, and B6), and neurotransmitters (serotonin, glutamate, aspartate, tryptophan, and N-acetyltyrosine). The metabolic impairments appear to emerge early during disease progression before the age of 2months. Importantly, the restoration of NAGLU activity with an intravenous (i.v.) injection of rAAV9-hNAGLU vector led to near-complete correction of all serum metabolite abnormalities, with 201 (87%) metabolites normalized and 30 (13%) over-corrected. While the mechanisms are unclear, our data demonstrate that the lack of NAGLU activity triggers profound functional metabolic disturbances in MPS IIIB. These metabolic impairments respond well to a systemic rAAV9-hNAGLU gene delivery, supporting the surrogate biomarker potential of serum metabolomic profiles for MPS IIIB therapies.

Metabolomic Identification of Subtypes of Nonalcoholic Steatohepatitis

Nonalcoholic fatty liver disease (NAFLD) is a consequence of defects in diverse metabolic pathways that involve hepatic accumulation of triglycerides. Features of these aberrations might determine whether NAFLD progresses to nonalcoholic steatohepatitis (NASH). We investigated whether the diverse defects observed in patients with NAFLD are caused by different NAFLD subtypes with specific serum metabolomic profiles, and whether these can distinguish patients with NASH from patients with simple steatosis. We collected liver and serum from methionine adenosyltransferase 1a knockout (MAT1A-KO) mice, which have chronically low levels of hepatic S-adenosylmethionine (SAMe) and spontaneously develop steatohepatitis, as well as C57Bl/6 mice (controls); the metabolomes of all samples were determined. We also analyzed serum metabolomes of 535 patients with biopsy-proven NAFLD (353 with simple steatosis and 182 with NASH) and compared them with serum metabolomes of mice. MAT1A-KO mice were also given SAMe (30 mg/kg/day for 8 weeks); liver samples were collected and analyzed histologically for steatohepatitis. Livers of MAT1A-KO mice were characterized by high levels of triglycerides, diglycerides, fatty acids, ceramides, and oxidized fatty acids, as well as low levels of SAMe and downstream metabolites. There was a correlation between liver and serum metabolomes. We identified a serum metabolomic signature associated with MAT1A-KO mice that also was present in 49% of the patients; based on this signature, we identified 2 NAFLD subtypes. We identified specific panels of markers that could distinguish patients with NASH from patients with simple steatosis for each subtype of NAFLD. Administration of SAMe reduced features of steatohepatitis in MAT1A-KO mice. In an analysis of serum metabolomes of patients with NAFLD and MAT1A-KO mice with steatohepatitis, we identified 2 major subtypes of NAFLD and markers that differentiate steatosis from NASH in each subtype. These might be used to monitor disease progression and identify therapeutic targets for patients.

Serum Metabolomic Profiling Identifies Characterization of Non-Obstructive Azoospermic Men.

Male infertility is considered a common health problem, and non-obstructive azoospermia with unclear pathogenesis is one of the most challenging tasks for clinicians. The objective of this study was to investigate the differential serum metabolic pattern in non-obstructive azoospermic men and to determine potential biomarkers related to spermatogenic dysfunction. Serum samples from patients with non-obstructive azoospermia (n = 22) and healthy controls (n = 31) were examined using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Serum metabolomic profiling could differentiate non-obstructive azoospermic patients from healthy control subjects. A total of 24 metabolites were screened and identified as potential markers, many of which are involved in energy production, oxidative stress and cell apoptosis in spermatogenesis. Moreover, the results showed that various metabolic pathways, including d-glutamine and d-glutamate metabolism, taurine and hypotaurine metabolism, pyruvate metabolism, the citrate cycle and alanine, aspartate and glutamate metabolism, were disrupted in patients with non-obstructive azoospermia. Our results indicated that the serum metabolic disorders may contribute to the etiology of non-obstructive azoospermia. This study suggested that serum metabolomics could identify unique metabolic patterns of non-obstructive azoospermia and provide novel insights into the pathogenesis underlying male infertility.