Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome

Background

Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.

Aim

Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.

Methods

Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.

Results

Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.

Conclusion

Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.

     

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

Introduction

Metabolomics is the emerging member of “omics” sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

Objectives

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

Methods

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

Results

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

Conclusion

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Introduction

Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.

Objectives

To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.

Methods

PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.

Results

Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.

Conclusion

Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.

     

High resolution metabolomic analysis of ASD human brain uncovers novel biomarkers of disease

Introduction

Autism spectrum disorders (ASD) is a group of neurodevelopmental disorders believed to have a multifactorial basis. Presently, diagnosis is based on behavioral and developmental signs in children before the age of 3 and no reliable clinical biomarkers are available for early detection.

Objectives

This study aimed to biochemically profile the cerebellum from post-mortem human brain from ASD sufferers (n = 11) and compare their profiles to that of age-matched controls (n = 11) with no known brain disorder.

Methods

Using liquid chromatography combined with LTQ-Orbitrap mass spectrometry we detected 14,328 features in ESI+ mode in polar extracts of post-mortem brain.

Results

Of these only 37 were found to be statistically significantly different between ASD and controls (p < 0.05; fdr < 0.05). A panel of four features had a predictive power of 96.64 %, following statistical cross validation, for ASD detection. This model produced an AUC = 0.874 (CI 0.768–0.944) and a Fisher’s exact score of p = 4.50E?29.

Conclusion

Whilst at this time we were unable to chemically identify the four features of interest we believe that this study underscores the potential value of high resolution metabolomics for the study of ASD. Further characterization of the polar metabolome of post mortem ASD brains could lead to the identification of potential biomarkers and novel therapeutics for the disease. The development of accurate biomarkers could assist in the early detection of ASD and promote early intervention strategies to improve outcome.

     

Plasma metabolomics in calves with acute bronchopneumonia

Background

Bovine respiratory disease is one of the main health issues in dairy calves. Inflammatory lung diseases are highly complex with respect to pathogenesis and relationships between inflammation, clinical disease and response to treatment. Metabolomics may offer the potential to identify biomarkers that define calf bronchopneumonia in terms of combined clinical, physiological and patho-biological abnormalities. While metabolomic studies are often encountered in childhood pneumonia, there is no knowledge related to the same approach to calf pneumonia.

Objective

The aim of this first study was to reveal the new potential biomarkers for acute calf bronchopneumonia by single proton (¹H) Nuclear magnetic resonance (NMR) based quantitative metabolomics.

Methods

Fifty dairy calves with acute bronchopneumonia presented for treatment to the teaching hospital, and ten healthy dairy calves belonging the teaching farm were used. Laboratory (hematological: complete blood count and blood gas analysis, and biochemical analysis related to health profile) were performed. NMR spectra of the all samples (50 diseased + 10 healthy water soluble extracts, 50 diseased + 10 healthy lipid extracts) were acquired using a standard Nuclear Overhauser Effect Spectroscopy pulse sequence.

Results

NMR based metabolomics analysis showed that calves suffering from bronchopneumonia and healthy calves have two different and distinguishable metabolic fingerprints using both water soluble and lipid extracts. Alterations in metabolites, increases in 2-methyl glutarate, phenylalanine, phosphatidylcholine, and decreases in ethanol, dimethylsulfone, propionate, acetate, allantoin, free cholesterol, cholesterol (–C18), were meaningful for pathogenic mechanisms of calf bronchopneumonia.

Conclusion

The NMR based metabolomics may contribute to better understanding bronchopneumonia in calves.

     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

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.

     

A putative urinary biosignature for diagnosis and follow-up of tuberculous meningitis in children: outcome of a metabolomics study disclosing host–pathogen responses

Introduction

Tuberculous meningitis (TBM) is a severe manifestation of tuberculosis, presenting with high morbidity and mortality in children. Existing diagnostic methods for TBM are invasive and time-consuming and the need for highly sensitive and selective diagnosis remains high on the TBM agenda.

Objective

Our aim was to exploit metabolomics as an approach to identify metabolites as potential diagnostic predictors for children with TBM through a non-invasive means.

Methods

Urine samples selected for this study were from three paediatric groups: patients with confirmed TBM (n = 12), patients clinically suspected with TBM but later confirmed to be negative (n = 19) and age-matched controls (n = 29). Metabolomics data were generated through gas chromatography–mass spectrometry analysis and important metabolites were identified according to standard statistical procedures used for metabolomics data.

Results

A global metabolite profile that characterized TBM was developed from the data, reflecting the host and microbial responses. Nine different logistic regression models were fitted to selected metabolites for the best combination as predictors for TBM. Four metabolites—methylcitric, 2-ketoglutaric, quinolinic and 4-hydroxyhippuric acids—showed excellent diagnostic ability and provided prognostic insight into our TBM patients.

Conclusions

This study is the first to illustrate holistically the metabolic complexity of TBM and provided proof-of-concept that a biosignature of urinary metabolites can be defined for non-invasive diagnosis and prognosis of paediatric TBM patients. The biosignature should be developed and validated through future prospective studies to generate a medical algorithm for diagnosis in the initial stages of the disease and for monitoring of treatment strategies.

     

Non-invasive urinary metabolomic profiles discriminate biliary atresia from infantile hepatitis syndrome

Introduction

Neonatal cholestatic disorders are a group of hepatobiliary diseases occurring in the first 3 months of life. The most common causes of neonatal cholestasis are infantile hepatitis syndrome (IHS) and biliary atresia (BA). The clinical manifestations of the two diseases are too similar to distinguish them. However, early detection is very important in improving the clinical outcome of BA. Currently, a liver biopsy is the only proven and effective method used to differentially diagnose these two similar diseases in the clinic. However, this method is invasive. Therefore, sensitive and non-invasive biomarkers are needed to effectively differentiate between BA and IHS. We hypothesized that urinary metabolomics can produce unique metabolite profiles for BA and IHS.

Objectives

The aim of this study was to characterize urinary metabolomic profiles in infants with BA and IHS, and to identify differences among infants with BA, IHS, and normal controls (NC).

Methods

Urine samples along with patient characteristics were obtained from 25 BA, 38 IHS, and 38 NC infants. A non-targeted gas chromatography–mass spectrometry (GC–MS) metabolomics method was used in conjunction with orthogonal partial least squares discriminant analysis (OPLS-DA) to explore the metabolomic profiles of BA, IHS, and NC infants.

Results

In total, 41 differentially expressed metabolites between BA vs. NC, IHS vs. NC, and BA vs. IHS were identified. N-acetyl-d-mannosamine and alpha-aminoadipic acid were found to be highly accurate at distinguishing between BA and IHS.

Conclusions

BA and IHS infants have specific urinary metabolomic profiles. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be used to discriminate BA from IHS.

     

Hyper response to ovarian stimulation affects the follicular fluid metabolomic profile of women undergoing IVF similarly to polycystic ovary syndrome

Introduction

During in vitro fertilization (IVF), the hyper response to controlled ovarian stimulation (COS) is a common characteristic among patients diagnosed with polycystic ovary syndrome (PCOS), although non-diagnosed patients may also demonstrate this response.

Objectives

In an effort to investigate follicular metabolic characteristics associated with hyper response to COS, the present study analyzed follicular fluid (FF) samples from patients undergoing IVF.

Methods

FF samples were obtained from patients with PCOS and hyper response during IVF (PCOS group, N?=?15), patients without PCOS but with hyper response during IVF (HR group, N?=?44), and normo-responder patients receiving IVF (control group, N?=?22). FF samples underwent Bligh and Dyer extraction, followed by metabolomic analysis by ultra-performance liquid chromatography mass spectrometry, considering two technical replicates. Clinical data was analyzed by ANOVA and chi-square tests. The metabolomic dataset was analyzed by multivariate statistics, and the significance of biomarkers was confirmed by ANOVA.

Results

Clinical data showed differences regarding follicles production, oocyte and embryo quality. From the 15 proposed biomarkers, 14 were of increased abundance in the control group and attributed as fatty acids, diacylglycerol, triacylglycerol, ceramide, ceramide-phosphate, phosphatidylcholine, and sphingomyelin. The PCOS patients showed increased abundance of a metabolite of m/z 144.0023 that was not attributed to a class.

Conclusion

The clinical and metabolic similarities observed in the FF of hyper responders with and without PCOS diagnosis indicate common biomarkers that could assist on the development of accessory tools for assessment of IVF parameters.

     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

Monitoring cancer prognosis,diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

Objectives

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.

Methods

A literature search of the current relevant primary research was performed.

Results

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

Conclusion

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

     

Metabolomics reveals dose effects of low-dose chronic exposure to uranium in rats: identification of candidate biomarkers in urine samples

Introduction

Data are sparse about the potential health risks of chronic low-dose contamination of humans by uranium (natural or anthropogenic) in drinking water. Previous studies report some molecular imbalances but no clinical signs due to uranium intake.

Objectives

In a proof-of-principle study, we reported that metabolomics is an appropriate method for addressing this chronic low-dose exposure in a rat model (uranium dose: 40 mg L^?1; duration: 9 months, n = 10). In the present study, our aim was to investigate the dose–effect pattern and identify additional potential biomarkers in urine samples.

Methods

Compared to our previous protocol, we doubled the number of rats per group (n = 20), added additional sampling time points (3 and 6 months) and included several lower doses of natural uranium (doses used: 40, 1.5, 0.15 and 0.015 mg L^?1). LC–MS metabolomics was performed on urine samples and statistical analyses were made with SIMCA-P+ and R packages.

Results

The data confirmed our previous results and showed that discrimination was both dose and time related. Uranium exposure was revealed in rats contaminated for 9 months at a dose as low as 0.15 mg L^?1. Eleven features, including the confidently identified N1-methylnicotinamide, N1-methyl-2-pyridone-5-carboxamide and 4-hydroxyphenylacetylglycine, discriminated control from contaminated rats with a specificity and a sensitivity ranging from 83 to 96 %, when combined into a composite score.

Conclusion

These findings show promise for the elucidation of underlying radiotoxicologic mechanisms and the design of a diagnostic test to assess exposure in urine, in a dose range experimentally estimated to be above a threshold between 0.015 and 0.15 mg L^?1.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

Background

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

Objective

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

Methods

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

Results

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

Conclusion

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

     

Identification of serum metabolic markers for diagnosis of women with dormant genital tuberculosis

Introduction

Genital tuberculosis (GTB) in women is one of the common causes of infertility in emerging countries. As an intracellular pathogen, Mycobacterium tuberculosis in the endometrium significantly alters the host metabolism in dormant GTB cases. Nuclear magnetic resonance (NMR) based metabolic profiling has emerged as a useful tool for identification of biomarkers in biological fluids.

Objective

To investigate NMR based serum metabolic profile of dormant GTB women as compared to controls.

Methods

Dormant GTB women (n = 26) and unexplained infertile women (controls; n = 26), healthy proven fertile women undergoing voluntary sterilization (n = 25) and women undergoing recurrent spontaneous miscarriage (RSM) (n = 27) were included in the study. 700 MHz proton NMR spectra of serum collected from these patients were recorded. Multivariate analysis including principal component analysis, partial least squares discriminant analysis and orthogonal projection to latent structure-discriminant analysis was applied to all the spectra. Association of dysregulated serum metabolites with our earlier findings related to altered endometrial tissue metabolites in dormant GTB women was studied using multiple correlation analysis.

Results

This study indicates a clear metabolic differentiation between women with dormant GTB and controls. Metabolites including 3-hydroxybutyrate, succinate, citrate, acetate, l-glutamine, l-lysine, glutamate, l-threonine and 1-methyl histidine were found to be significantly upregulated in serum of women with dormant GTB compared with controls. Pearson’s correlation analysis showed a significant correlation between the expression of endometrial tissue and serum metabolites.

Conclusions

The set of identified metabolites may be considered as candidate markers for the diagnosis of dormant GTB and help clinicians in early therapeutic management.

     

Italian cohort of patients affected by inflammatory bowel disease is characterised by variation in glycerophospholipid,free fatty acids and amino acid levels

Background

Inflammatory bowel disease is a group of pathologies characterised by chronic inflammation of the intestine and an unclear aetiology. Its main manifestations are Crohn’s disease and ulcerative colitis. Currently, biopsies are the most used diagnostic tests for these diseases and metabolomics could represent a less invasive approach to identify biomarkers of disease presence and progression.

Objectives

The lipid and the polar metabolite profile of plasma samples of patients affected by inflammatory bowel disease have been compared with healthy individuals with the aim to find their metabolomic differences. Also, a selected sub-set of samples was analysed following solid phase extraction to further characterise differences between pathological samples.

Methods

A total of 200 plasma samples were analysed using drift tube ion mobility coupled with time of flight mass spectrometry and liquid chromatography for the lipid metabolite profile analysis, while liquid chromatography coupled with triple quadrupole mass spectrometry was used for the polar metabolite profile analysis.

Results

Variations in the lipid profile between inflammatory bowel disease and healthy individuals were highlighted. Phosphatidylcholines, lyso-phosphatidylcholines and fatty acids were significantly changed among pathological samples suggesting changes in phospholipase A₂ and arachidonic acid metabolic pathways. Variations in the levels of cholesteryl esters and glycerophospholipids were also found. Furthermore, a decrease in amino acids levels suggests mucosal damage in inflammatory bowel disease.

Conclusions

Given good statistical results and predictive power of the model produced in our study, metabolomics can be considered as a valid tool to investigate inflammatory bowel disease.

     

Lung injury-induced skeletal muscle wasting in aged mice is linked to alterations in long chain fatty acid metabolism

Introduction

Older patients are more likely to acquire and die from acute respiratory distress syndrome (ARDS) and muscle weakness may be more clinically significant in older persons. Recent data implicate muscle ring finger protein 1 (MuRF1) in lung injury-induced skeletal muscle atrophy in young mice and identify an alternative role for MuRF1 in cardiac metabolism regulation through inhibition of fatty acid oxidation.

Objectives

To develop a model of lung injury-induced muscle wasting in old mice and to evaluate the skeletal muscle metabolomic profile of adult and old acute lung injury (ALI) mice.

Methods

Young (2 month), adult (6 month) and old (20 month) male C57Bl6 J mice underwent Sham (intratracheal H₂O) or ALI [intratracheal E. coli lipopolysaccharide (i.t. LPS)] conditions and muscle functional testing. Metabolomic analysis on gastrocnemius muscle was performed using gas chromatography-mass spectrometry (GC–MS).

Results

Old ALI mice had increased mortality and failed to recover skeletal muscle function compared to adult ALI mice. Muscle MuRF1 expression was increased in old ALI mice at day 3. Non-targeted muscle metabolomics revealed alterations in amino acid biosynthesis and fatty acid metabolism in old ALI mice. Targeted metabolomics of fatty acid intermediates (acyl-carnitines) and amino acids revealed a reduction in long chain acyl-carnitines in old ALI mice.

Conclusion

This study demonstrates age-associated susceptibility to ALI-induced muscle wasting which parallels a metabolomic profile suggestive of altered muscle fatty acid metabolism. MuRF1 activation may contribute to both atrophy and impaired fatty acid oxidation, which may synergistically impair muscle function in old ALI mice.

     

Characterisation of the main drivers of intra- and inter- breed variability in the plasma metabolome of dogs

Introduction

Dog breeds are a consequence of artificial selection for specific attributes. These closed genetic populations have metabolic and physiological characteristics that may be revealed by metabolomic analysis.

Objectives

To identify and characterise the drivers of metabolic differences in the fasted plasma metabolome and then determine metabolites differentiating breeds.

Methods

Fasted plasma samples were collected from dogs maintained under two environmental conditions (controlled and client-owned at home). The former (n = 33) consisted of three breeds (Labrador Retriever, Cocker Spaniel and Miniature Schnauzer) fed a single diet batch, the latter (n = 96), client-owned dogs consisted of 9 breeds (Beagle, Chihuahua, Cocker Spaniel, Dachshund, Golden Retriever, Greyhound, German Shepherd, Labrador Retriever and Maltese) consuming various diets under differing feeding regimens. Triplicate samples were taken from Beagle (n = 10) and Labrador Retriever (n = 9) over 3 months. Non-targeted metabolite fingerprinting was performed using flow infusion electrospray-ionization mass spectrometry which was coupled with multivariate data analysis. Metadata factors including age, gender, sexual status, weight, diet and breed were investigated.

Results

Breed differences were identified in the plasma metabolome of dogs housed in a controlled environment. Triplicate samples from two breeds identified intra-individual variability, yet breed separation was still observed. The main drivers of variance in dogs maintained in the home environment were associated with breed and gender. Furthermore, metabolite signals were identified that discriminated between Labrador Retriever and Cocker Spaniels in both environments.

Conclusion

Metabolite fingerprinting of plasma samples can be used to investigate breed differences in client-owned dogs, despite added variance of diet, sexual status and environment.