Metabolomic Fingerprint of Heart Failure with Preserved Ejection Fraction

BackgroundHeart failure (HF) with preserved ejection fraction (HFpEF) is increasingly recognized as an important clinical entity. Preclinical studies have shown differences in the pathophysiology between HFpEF and HF with reduced ejection fraction (HFrEF). Therefore, we hypothesized that a systematic metabolomic analysis would reveal a novel metabolomic fingerprint of HFpEF that will help understand its pathophysiology and assist in establishing new biomarkers for its diagnosis.ConclusionsThe metabolomics approach employed in this study identified a unique metabolomic fingerprint of HFpEF that is distinct from that of HFrEF. This metabolomic fingerprint has been utilized to identify two novel panels of metabolites that can separate HFpEF patients from both non-HF controls and HFrEF patients.

Clinical Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02052804","term_id":"NCT02052804"}}NCT02052804     

Accurate,Fully-Automated NMR Spectral Profiling for Metabolomics

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person’s biofluids, which means such diseases can often be readily detected from a person’s “metabolic profile"—i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person’s metabolic profile. Given a 1D ¹H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the “signatures” of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively—with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.     

Metabolomic prediction of endometrial cancer

Introduction

Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.

Objective

To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.

Methods

We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I–II) and 10 late-stage (FIGO stages III–IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n?=?69) and an independent validation group (n?=?47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.

Results

A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI)?=?0.826 (0.706–0.946) and a sensitivity?=?82.6%, and specificity?=?70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI)?=?0.819 (0.689–0.95) and a sensitivity?=?72.2% and specificity?=?79.2% in the validation group.

Conclusions

EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

     

Identification of candidate biomarkers of brain damage in a mouse model of closed head injury: a metabolomic pilot study

We aim to identify candidate brain biomarkers for, and to elucidate the pathophysiology of closed traumatic brain injury (TBI). Nuclear magnetic resonance (NMR) based metabolomic analysis was performed on the whole brain of mice undergoing TBI using a validated technique. There were 10 TBI mice compared to 8 sham operated controls. A total of 45 metabolites were evaluated. There was a statistically significant alteration in concentrations of 29 metabolites in TBI brains as compared to controls (FDR <0.05). Profound disturbances of several metabolic pathways (FDR <1E-07), including pathways associated with purine, alanine, aspartate and glutamine and glutathione metabolism were observed. Also, a significant elevation in glutamate (the main excitatory neurotransmitter) and depression of GABA (the main inhibitory neurotransmitter) was observed. Four metabolites, ADP, AMP, NAD+, and IMP were the most important indicators of TBI, relative to normal controls. All were elevated in the TBI mice. A combination of these 4 biomarkers produced a perfect predictor of TBI status, AUC (95 % CI) = 1.0 (1.0, 1.0). We also detected significant disturbances in mitochondrial function, energy metabolism, neurotransmitter metabolism and other important biochemical pathways in TBI mouse brains. Further studies to assess the utility of metabolomics to detect and classify the severity of and assess the prognosis of TBI is warranted.     

Serum metabolomic markers for traumatic brain injury: a mouse model

Introduction

Traumatic brain injury (TBI) is physical injury to brain tissue that temporarily or permanently impairs brain function.

Objectives

Evaluate the use of metabolomics for the development of biomarkers of TBI for the diagnosis and timing of injury onset.

Methods

A validated model of closed injury TBI was employed using 10 TBI mice and 8 sham operated controls. Quantitative LC–MS/MS metabolomic analysis was performed on the serum.

Results

Thirty-six (24.0 %) of 150 metabolites were altered with TBI. Principal component analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) analyses revealed clear segregation between TBI versus control sera. The combination of methionine sulfoxide and the lipid PC aa C34:4 accurately diagnosed TBI, AUC (95 % CI) 0.85 (0.644–1.0). A combination of metabolite markers were highly accurate in distinguishing early (4 h post TBI) from late (24 h) TBI: AUC (95 % CI) 1.0 (1.0–1.0). Spermidine, which is known to have an antioxidant effect and which is known to be metabolically disrupted in TBI, was the most discriminating biomarker based on the variable importance ranking in projection (VIP) plot. Several important metabolic pathways were found to be disrupted including: pathways for arginine, proline, glutathione, cysteine, and sphingolipid metabolism.

Conclusion

Using serum metabolomic analysis we were able to identify novel putative serum biomarkers of TBI. They were accurate for detecting and determining the timing of TBI. In addition, pathway analysis provided important insights into the biochemical mechanisms of brain injury. Potential clinical implications for diagnosis, timing, and monitoring brain injury are discussed.

     

Succinate and malate improve development of hamster eight-cell embryos in vitro: Confirmation of viability by embryo transfer

The in vitro development of hamster preimplantation embryos is supported by non-glucose energy substrates. To investigate the importance of embryonic metabolism, influence of succinate and malate on the development of hamster 8-cell embryos to blastocysts was examined using a chemically defined protein-free modified hamster embryo culture medium-2 (HECM-2m). There was a dose-dependent influence of succinate on blastocyst development; 0.5 mM succinate was optimal (85.1% ± 3.9 vs. 54.5% ± 3.5). In succinate-supplemented HECM-2m, blastocyst development was reduced by omission of lactate (68.5% ± 7.2), but not pyruvate (85.8% ± 6.2) or glutamine (84.1% ± 2.1). Succinate along with either glutamine or lactate or pyruvate poorly supported blastocyst development (28%–58%). Malate also stimulated blastocyst development; 0.01 mM malate was optimal (86.3% ± 2.8). Supplementation of both succinate and malate to HECM-2m supported maximal (100%) blastocyst development, which was inhibited 4-fold by the addition of glucose/phosphate. The mean cell numbers (MCN) of blastocysts cultured in succinate-supplemented HECM-2m was higher (28.3 ± 1.1) than it was for those cultured in the absence of glutamine or pyruvate (range 20–24). The MCN was the highest (33.4 ± 1.6) for blastocysts cultured in succinate-malate-supplemented HECM-2m followed by those in succinate (28.3 ± 1.1) or malate (24.7 ± 0.5) supplemented HECM-2m. Embryo transfer experiments showed that 29.8% (±4.5) of transferred blastocysts cultured in succinate-malate-supplemented HECM-2m produced live births, similar (P > 0.1) to the control transfers of freshly recovered 8-cells (33.5% ± 2.0) or blastocysts (28.9% ± 3.0). These data show that supplementation of succinate and malate to HECM-2m supports 100% development of hamster 8-cell embryos to high quality viable blastocysts and that non-glucose oxidizable energy substrates are the most preferred components in hamster embryo culture medium. Mol. Reprod. Dev. 47:440–447, 1997. © 1997 Wiley-Liss, Inc.     

The human serum metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.     

The Metabolomic Profile of Umbilical Cord Blood in Neonatal Hypoxic Ischaemic Encephalopathy

Background

Hypoxic ischaemic encephalopathy (HIE) in newborns can cause significant long-term neurological disability. The insult is a complex injury characterised by energy failure and disruption of cellular homeostasis, leading to mitochondrial damage. The importance of individual metabolic pathways, and their interaction in the disease process is not fully understood. The aim of this study was to describe and quantify the metabolomic profile of umbilical cord blood samples in a carefully defined population of full-term infants with HIE.

Methods and Findings

The injury severity was defined using both the modified Sarnat score and continuous multichannel electroencephalogram. Using these classification systems, our population was divided into those with confirmed HIE (n = 31), asphyxiated infants without encephalopathy (n = 40) and matched controls (n = 71). All had umbilical cord blood drawn and biobanked at −80°C within 3 hours of delivery. A combined direct injection and LC-MS/MS assay (AbsolutIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria) was used for the metabolomic analyses of the samples. Targeted metabolomic analysis showed a significant alteration between study groups in 29 metabolites from 3 distinct classes (Amino Acids, Acylcarnitines, and Glycerophospholipids). 9 of these metabolites were only significantly altered between neonates with Hypoxic ischaemic encephalopathy and matched controls, while 14 were significantly altered in both study groups. Multivariate Discriminant Analysis models developed showed clear multifactorial metabolite associations with both asphyxia and HIE. A logistic regression model using 5 metabolites clearly delineates severity of asphyxia and classifies HIE infants with AUC = 0.92. These data describe wide-spread disruption to not only energy pathways, but also nitrogen and lipid metabolism in both asphyxia and HIE.

Conclusion

This study shows that a multi-platform targeted approach to metabolomic analyses using accurately phenotyped and meticulously biobanked samples provides insight into the pathogenesis of perinatal asphyxia. It highlights the potential for metabolomic technology to develop a diagnostic test for HIE.