Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity

Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

The Metabolic Signature of Cardiorespiratory Fitness: A Systematic Review

Background Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies. Objective This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids. Methods PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS. Results Twenty-two studies were included and 667 metabolites, measured in plasma (n = 619), serum (n = 18), skeletal muscle (n = 16), urine (n = 11), or sweat (n = 3), were identified. Lipids were the metabolites most commonly positively (n = 174) and negatively (n = 274) associated with CRF. Specific circulating glycerophospholipids (n = 85) and cholesterol esters (n = 17) were positively associated with CRF, while circulating glycerolipids (n = 152), glycerophospholipids (n = 42), acylcarnitines (n = 14), and ceramides (n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF (n = 15). Conclusions Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches. PROSPERO Registration Number CRD42020214375.

Integration of Targeted Gene Expression Profiling and FDG-PET Radiomics Uncovers Radiometabolic Signatures Associated with Outcome in Diffuse Large B-Cell Lymphoma

Metabolic rewiring is a hallmark of cancer and a predominant feature of aggressive lymphoproliferative disorders such as diffuse large B-cell lymphomas (DLBCL), which need a reshaped metabolism in order to meet the increased demands related to rapid cell proliferation. Emerging evidence indicates that chemoresistance is closely related to altered metabolism in cancer. However, the relationship between metabolic rewiring and chemoresistance in lymphoma is yet to be elucidated. Radiomic analysis applied to functional imaging with fluoroedoxyglucose positron emission tomography (FDG-PET) provides a unique opportunity to explore DLBCL metabolism. In this study we hypothesized that distinct gene expression (GEP) signatures might be correlated with specific FDG-PET radiomics signatures, which in turn could be associated with resistance to standard chemoimmunotherapy and DLBCL outcome. First, we retrospectively analyzed a discovery cohort of 48 consecutive DLBCL patients (pts) treated at our center with standard first line R-CHOP/R-CHOP-like chemoimmunotherapy from 2010 to 2018, with available formalin-fixed paraffin embedded (FFPE) tissue from the initial diagnostic biopsy and FDG-PET radiomics data extracted from the same target lesion. Median follow-up was 55 months (range 18-110). We profiled this cohort with targeted-GEP (T-GEP) (NanoString platform), using a custom panel to define the cell of origin (COO) and MYC/BCL-2 levels, and a dedicated panel comprising 180 genes encompassing the most relevant cancer metabolism pathways. By applying the maxstat package we found that a 6-gene metabolic signature was strongly associated with outcome and outperformed the COO, the MYC/BCL-2 status and the International Prognostic Index (IPI) score for progression free survival (PFS) and overall survival (OS) in multivariate analysis. The 6-gene metabolic signature included genes regulating oxidative metabolism and fatty acid oxidation (SCL25A1, PDK4, PDPR) which were upregulated, and was inversely associated with genes involved in glycolytic pathways (MAP2K1, HIF1A, GBE1) which were downregulated. Notably 5-year PFS and OS were 100% and 95% in metabolic signature (met-Sig) low pts vs 24% and 45% in met-Sig high pts respectively (p<0.0001 for PFS and OS). There was no significant association between the COO, MYC/BCL-2 levels, standardized uptake value (SUV), and the 6-gene signature. The prognostic value of the 6-gene signature for OS was validated in 2 large publicly available cohorts of 469 (Sha et al. J Clin Oncol 2019) and 233 (Lenz et al. N Eng J Med 2005) pts. Next, we integrated PET radiomics and T-GEP data. Radiomics analysis (LifeX package) was performed by applying regions of interest semi-automatically, using a 25% SUV max threshold for segmentation. Fifty-five radiomic features (RFs) were extracted and 10 RFs significantly correlated either positively or negatively with the T-GEP metabolic signature (Spearman). After stability evaluation, applying a stepwise feature selection procedure, 4 RFs (Histo Curtosis, Histo Energy, Shape Sphericity, NGLDM Contrast) were used to generate a radiomic signature (hereafter called radiometabolic signature) characterized by the most significant correlation with both the metabolic T-GEP signature (r=0.43, p=0.0027) and PFS (p=0.004). These results (obtained analyzing the lesion of the initial diagnostic biopsy), were confirmed using different target lesions (i.e. the most FDG-avid and the largest lesion), and were validated in a second independent cohort of 64 patients (validation cohort) treated at our center in the same period of time (with no FFPE tissue available). A multivariate analysis performed in the whole cohort of 112 pts (discovery + validation) indicated that the radiometabolic signature retained independent prognostic value in relation to the IPI score and metabolic tumor volume. The robustness of the radiometabolic signature was further confirmed by using a second segmentation method (fixed 2.5 SUV max threshold). These data indicate that oxidative metabolic rewiring could be a powerful adverse prognostic predictor, suggesting the possibility of targeting oxidative metabolism to overcome chemorefractoriness in DLBCL. This study provides the proof of principle for the use of FDG-PET radiomics as a tool for non-invasive assessment of cancer metabolism, and for predicting metabolic vulnerabilities in DLBCL. Figure 1 Figure 1. Disclosures Tarella: ADC-THERAPEUTICS: Other: ADVISORY BOARD; Abbvie: Other: ADVISORY BOARD. Pileri: CELGENE: Other: ADVISORY BOARD; ROCHE: Other: ADVISORY-BOARD; NANOSTRING: Other: ADVISORY BOARD. Derenzini: TAKEDA: Research Funding; BEIGENE: Other: ADVISORY BOARD; ASTRA-ZENECA: Consultancy, Other: ADVISORY-BOARD; TG-THERAPEUTICS: Research Funding; ADC-THERAPEUTICS: Research Funding.

Exploration of the Amino Acid Metabolic Signature in Anthracycline-induced Cardiotoxicity Using an Optimized Targeted Metabolomics Approach Based on UPLC-MS/MS

Background: Although anthracyclines improve the long-term survival rate of patients with cancer, severe and irreversible myocardial damage limits their clinical application. Amino acids (AAs) play critical roles in protein synthesis, energy generation, and metabolism, as well as maintenance of the normal structure of cardiomyocytes. Conversely, AA metabolism in cardiomyocytes can be altered under pathological conditions. Therefore, exploring the AA metabolic signature in anthracycline-induced cardiotoxicity (AIC) is important for identifying novel mechanisms.Methods: We established mouse and cellular models of Adriamycin (ADR)-induced cardiac injury. Using a targeted AA metabolomics approach based on ultra-performance lipid chromatography–tandem mass spectrometry (UPLC-MS/MS), we quantified more than 120 AA metabolites through derivatization-assisted sensitivity enhancement with 5-aminoisoquinolyl-N-hydroxysuccinimidyl carbamate (5-AIQC). The AA metabolic signatures in the sera of AIC mice and supernatant samples of ADR-treated H9c2 cardiomyocytes were analyzed. Results: The levels of 14 AA metabolites were altered in ADR-treated mice (p < 0.05). l-2-aminoadipic acid (2-AA) was one of the most suppressed metabolites in AIC. Pre-treatment with 2-AA failed to alter ADR-induced cardiac function impairment, but it exacerbated the ADR-induced decrease of left ventricular anterior wall thickness, indicating that 2-AA might contribute to AIC. Via bioinformatics analysis, we identified nine differential AA metabolites in mice, namely l-glutamic acid, l-lysine, l-serine, l-tryptophan, l-methionine, l-histidine, l-asparagine, l-tyrosine, and O-phosphorylethanolamine, and five differential AA metabolites in ADR-treated H9c2 cardiomyocytes, specifically l-tyrosine, l-alanine, l-glutamine, l-serine, and l-glutamic acid. Three AAs with increased levels (l-glutamate, l-serine, and l-tyrosine) overlapped in the two models, suggesting a possible mechanism of AA metabolic impairment during AIC. The metabolic pathways perturbed by AIC involved aminoacyl-tRNA biosynthesis and alanine, aspartate, and glutamate metabolism. Conclusions: These data indicate that a targeted AA metabolomics approach based on UPLC-MS/MS can be used to explore the AA metabolic signature and identify novel mechanisms of AIC, which may provide new clues for the prevention and treatment of this condition in the early clinical stage.

Oncogene-mediated metabolic gene signature predicts breast cancer outcome

Breast cancer remains the second most lethal cancer among women in the United States and triple-negative breast cancer is the most aggressive subtype with limited treatment options. Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. In this study, we demonstrate that Trop2 is overexpressed in triple-negative breast cancer (TNBC), and downregulation of Trop2 delays TNBC cell and tumor growth supporting the oncogenic role of Trop2 in breast cancer. Through proteomic profiling, we discovered a metabolic signature comprised of TALDO1, GPI, LDHA, SHMT2, and ADK proteins that were downregulated in Trop2-depleted breast cancer tumors. The identified oncogene-mediated metabolic gene signature is significantly upregulated in TNBC patients across multiple RNA-expression clinical datasets. Our study further reveals that the metabolic gene signature reliably predicts poor survival of breast cancer patients with early stages of the disease. Taken together, our study identified a new five-gene metabolic signature as an accurate predictor of breast cancer outcome.

Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.

Intensive lactation among women with recent gestational diabetes significantly alters the early postpartum circulating lipid profile: the SWIFT study

Background Women with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D). It is estimated that 20-50% of women with GDM history will progress to T2D within 10 years after delivery. Intensive lactation could be negatively associated with this risk, but the mechanisms behind a protective effect remain unknown. Methods In this study, we utilized a prospective GDM cohort of 1010 women without T2D at 6-9 weeks postpartum (study baseline) and tested for T2D onset up to 8 years post-baseline (n=980). Targeted metabolic profiling was performed on fasting plasma samples collected at both baseline and follow-up (1-2 years post-baseline) during research exams in a subset of 350 women (216 intensive breastfeeding, IBF vs. 134 intensive formula feeding or mixed feeding, IFF/Mixed). The relationship between lactation intensity and circulating metabolites at both baseline and follow-up were evaluated to discover underlying metabolic responses of lactation and to explore the link between these metabolites and T2D risk. Results We observed that lactation intensity was strongly associated with decreased glycerolipids (TAGs/DAGs) and increased phospholipids/sphingolipids at baseline. This lipid profile suggested decreased lipogenesis caused by a shift away from the glycerolipid metabolism pathway towards the phospholipid/sphingolipid metabolism pathway as a component of the mechanism underlying the benefits of lactation. Longitudinal analysis demonstrated that this favorable lipid profile was transient and diminished at 1-2 years postpartum, coinciding with the cessation of lactation. Importantly, when stratifying these 350 women by future T2D status during the follow-up (171 future T2D vs. 179 no T2D), we discovered that lactation induced robust lipid changes only in women who did not develop incident T2D. Subsequently, we identified a cluster of metabolites that strongly associated with future T2D risk from which we developed a predictive metabolic signature with a discriminating power (AUC) of 0.78, superior to common clinical variables (i.e., fasting glucose, AUC 0.56 or 2-h glucose, AUC 0.62). Conclusions In this study, we show that intensive lactation significantly alters the circulating lipid profile at early postpartum and that women who do not respond metabolically to lactation are more likely to develop T2D. We also discovered a 10-analyte metabolic signature capable of predicting future onset of T2D in IBF women. Our findings provide novel insight into how lactation affects maternal metabolism and its link to future diabetes onset. Trial registration ClinicalTrials.gov NCT01967030.