Metabolomics of the human aqueous humor

Introduction

The optical elements of the eye—cornea, lens, and vitreous humor—are avascular tissues, and their nutrition and waste removal are provided by aqueous humor (AH). The AH production occurs through the active secretion and the passive diffusion/ultrafiltration of blood plasma. The comparison of the metabolomic profiles of AH and plasma is important for understanding of the mechanisms of biochemical processes and metabolite transport taking place in vivo in ocular tissues.

Objectives

The work is aimed at the determination of concentrations of a wide range of most abundant metabolites in the human AH, the comparison of the metabolomic profiles of AH and serum, and the analysis of the post-mortem metabolomic changes in these two biological fluids.

Methods

The quantitative metabolomic profiling was carried out with the use of two independent methods—high-frequency ¹H NMR spectroscopy and HPLC with high-resolution ESI-MS detection.

Results

The concentrations of 71 most abundant metabolites in blood serum and AH from living patients and human cadavers have been measured. It has been found that the level of ascorbate in AH is by two orders of magnitude higher than that in serum; the levels of other metabolites are either similar to that in serum, or differ from that by a factor of 2–5. The post-mortem metabolomic composition of both serum and AH undergoes rapid and strong changes.

Conclusion

The differences between the metabolomic profiles of AH and serum for majority of metabolites can be attributed to the metabolic activity of the ocular tissues leading to the lack or excess of some metabolites, while the high concentration of ascorbate in AH demonstrates the activity of ascorbate-specific pumps at the blood-aqueous border. The post-mortem metabolomic changes are caused by the disruption of the major biochemical cycles and cell lysis. These changes should be taken into account in the analysis of disease-induced changes in post-mortem samples of the ocular tissues.

     

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

Introduction

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

Objectives

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

Methods

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using ¹H nuclear magnetic resonance (¹H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

Results

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

Conclusion

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

     

A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

Introduction

Chromosomal anomalies (CA) are the most frequent fetal anomalies.

Objective

To evaluate the diagnostic performance of a machine learning ensemble model based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester .

Methods

This is a case-control pilot study. Metabolomic profiles have been obtained on serum of 328 mothers (220 controls and 108 cases), using gas chromatography coupled to mass spectrometry. Eight machines learning and classification models were built and optimized. An ensemble model was built using a voting scheme. All samples were randomly divided into two sets. One was used as training set, the other one for diagnostic performance assessment.

Results

Ensemble machine learning model correctly classified all cases and controls. The accuracy was the same for trisomy 21 and 18; also, the other CA were correctly detected. Elaidic, stearic, linolenic, myristic, benzoic, citric and glyceric acid, mannose, 2-hydroxy butyrate, phenylalanine, proline, alanine and 3-methyl histidine were selected as the most relevant metabolites in class separation.

Conclusion

The proposed model, based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester, correctly identifies all the cases of chromosomal abnormalities. Overall, this preliminary analysis appeared suggestive of a metabolic environment conductive to increased oxidative stress and a disturbance in the fetal central nervous system development. Maternal serum metabolomics can be a promising tool in the screening of chromosomal defects. Moreover, metabolomics allows to extend our knowledge about biochemical alterations caused by aneuploidies and responsible for the observed phenotypes.

     

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.

     

Post-mortem changes in the metabolomic compositions of rabbit blood,aqueous and vitreous humors

Introduction

The analysis of post-mortem metabolomic changes in biological fluids opens the way to develop new methods for the estimation of post-mortem interval (PMI). It may also help in the analysis of disease-induced metabolomic changes in human tissues when the postoperational samples are compared to the post-mortem samples from healthy donors.

Objectives

The goals of this study are to observe and classify the post-mortem changes occurring in the rabbit blood, aqueous and vitreous humors (AH and VH), to identify the potential PMI markers among a wide range of metabolites, and also to determine which biological fluid—blood, AH or VH—is more suitable for the PMI estimation.

Methods

The quantitative metabolomic profiling of samples of the rabbit serum, AH and VH taken at different PMIs has been performed with the combined use of high-frequency NMR and high-resolution LC–MS methods.

Results

The quantitative levels of 61 metabolites in the rabbit serum, AH and VH at different PMIs have been measured. It has been found that the post-mortem metabolomic changes in AH and VH proceed slower than in blood, and the data scattering is lower. Among the metabolites whose concentrations increase with time, the most significant and linear growth is found for hypoxanthine, choline and glycerol.

Conclusion

The obtained results suggest that the ocular fluids AH and VH may have some advantages over blood serum for the search of potential biochemical markers for the PMI estimation. Among the compounds studied in the present work, hypoxanthine, choline and glycerol give the biggest promise as the potential PMI biomarkers.

     

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.

     

Metabolic characterization of hepatitis B virus-related liver cirrhosis using NMR-based serum metabolomics

Introduction

Liver cirrhosis (LC) is an advanced liver disease that can develop into hepatocellular carcinoma. Hepatitis B virus (HBV) infection is one of the main causes of LC. Therefore, there is an urgent need for developing a new method to monitor the progression of HBV-related LC (HBV-LC).

Objectives

In this study, we attempted to examine serum metabolic changes in healthy individuals as well as patients with HBV and HBV-LC. Furthermore, potential metabolite biomarkers were identified to evaluate patients progressed from health to HBV-LC.

Methods

Metabolic profiles in the serum of healthy individuals as well as patients with HBV and HBV-LC were detected using an NMR-based metabolomic approach. Univariate and multivariate analyses were conducted to analyze serum metabolic changes during HBV-LC progression. Moreover, potential metabolite biomarkers were explored by receiver operating characteristic curve analysis.

Results

Serum metabolic changes were closely associated with the progression of HBV-LC, mainly involving energy metabolism, protein metabolism, lipid metabolism and microbial metabolism. Serum histidine was identified as a potential biomarker for HBV patients. Acetate, formate, pyruvate and glutamine in the serum were identified as a potential biomarker panel for patients progressed from HBV to HBV-LC. In addition, phenylalanine, unsaturated lipid, n-acetylglycoprotein and acetone in the serum could be considered as a potential common biomarkers panel for these patients.

Conclusion

NMR-based serum metabolomic approach could be a promising tool to monitor the progression of liver disease. Different metabolites may reflect different stages of liver disease.

     

Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

Introduction

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

Objectives

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

Methods

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC–MS based untargeted and stable isotope assisted metabolomic techniques.

Results

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

Conclusion

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

     

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Introduction

Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis.

Objectives

The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles.

Methods

Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates.

Results

Baroni-Urbani–Buser (BUB) and Hawkins–Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis.

Conclusion

Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.

     

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.

     

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.

     

Chilling slows anaerobic metabolism to improve anoxia tolerance of insects

Background

Insects are renowned for their ability to survive anoxia. Anoxia tolerance may be enhanced during chilling through metabolic suppression.

Aims

Here, the metabolomic response of insects to anoxia, both with and without chilling, for different durations (12–36 h) was examined to assess the potential cross-tolerance mechanisms.

Results

Chilling during anoxia (cold anoxia) significantly improved survival relative to anoxia at warmer temperatures. Reduced intermediate metabolites and increased lactic acid, indicating a switch to anaerobic metabolism, were characteristic of larvae in anoxia.

Conclusions

Anoxia tolerance was correlated survival improvements after cold anoxia were correlated with a reduction in anaerobic metabolism.

     

Effect of everolimus on the glucose metabolic pathway in mouse skeletal muscle cells (C2C12)

Introduction

Everolimus selectively inhibits mammalian target of rapamycin complex 1 (mTORC1) and exerts an antineoplastic effect. Metabolic disturbance has emerged as a common and unique side effect of everolimus.

Objectives

We used targeted metabolomic analysis to investigate the effects of everolimus on the intracellular glycometabolic pathway.

Methods

Mouse skeletal muscle cells (C2C12) were exposed to everolimus for 48 h, and changes in intracellular metabolites were determined by capillary electrophoresis time-of-flight mass spectrometry. mRNA abundance, protein expression and activity were measured for enzymes involved in glycometabolism and related pathways.

Results

Both extracellular and intracellular glucose levels increased with exposure to everolimus. Most intracellular glycometabolites were decreased by everolimus, including those involved in glycolysis and the pentose phosphate pathway, whereas no changes were observed in the tricarboxylic acid cycle. Everolimus suppressed mRNA expression of enzymes related to glycolysis, downstream of mTOR signaling enzymes and adenosine 5′-monophosphate protein kinases. The activity of key enzymes involved in glycolysis and the pentose phosphate pathway were decreased by everolimus. These results show that everolimus impairs glucose utilization in intracellular metabolism.

Conclusions

The present metabolomic analysis indicates that everolimus impairs glucose metabolism in muscle cells by lowering the activities of glycolysis and the pentose phosphate pathway.

     

A clustering-based preprocessing method for the elimination of unwanted residuals in metabolomic data

Introduction

The metabolome of a biological system is affected by multiple factors including factor of interest (e.g. metabolic perturbation due to disease) and unwanted factors or factors which are not primarily the focus of the study (e.g. batch effect, gender, and level of physical activity). Removal of these unwanted data variations is advantageous, as the unwanted variations may complicate biological interpretation of the data.

Objectives

We aim to develop a new unwanted variations elimination (UVE) method called clustering-based unwanted residuals elimination (CURE) to reduce metabolic variation caused by unwanted/hidden factors in metabolomic data.

Methods

A mean-centered metabolomic dataset can be viewed as a combination of a studied factor matrix and a residual matrix. The CURE method assumes that the residual should be normally distributed if it only contains inter-individual variation. However, if the residual forms multiple clusters in feature subspace of principal components analysis or partial least squares discriminant analysis, the residual may contain variation due to unwanted factors. This unwanted variation is removed by doing K-means data clustering and removal of means for each cluster from the residuals. The process is iterated until the residual no longer forms multiple clusters in feature subspace.

Results

Three simulated datasets and a human metabolomic dataset were used to demonstrate the performance of the proposed CURE method. CURE was found able to remove most of the variations caused by unwanted factors, while preserving inter-individual variation between samples.

Conclusion

The CURE method can effectively remove unwanted data variation, and can serve as an alternative UVE method for metabolomic data.

     

Alterations in lipid,amino acid,and energy metabolism distinguish Crohn’s disease from ulcerative colitis and control subjects by serum metabolomic profiling

Introduction

Biomarkers are needed in inflammatory bowel disease (IBD) to help define disease activity and identify underlying pathogenic mechanisms. We hypothesized that serum metabolomics, which produces unique metabolite profiles, can aid in this search.

Objectives

The aim of this study was to characterize serum metabolomic profiles in patients with IBD, and to assess for differences between patients with ulcerative colitis (UC), Crohn’s disease (CD), and non-IBD subjects.

Methods

Serum samples from 20 UC, 20 CD, and 20 non-IBD control subjects were obtained along with patient characteristics, including medication use and clinical disease activity. Non-targeted metabolomic profiling was performed using ultra-high performance liquid chromatography/mass spectrometry (UPLC-MS/MS) optimized for basic or acidic species and hydrophilic interaction liquid chromatography (HILIC/UPLC-MS/MS).

Results

In total, 671 metabolites were identified. Comparing IBD and control subjects revealed 173 significantly altered metabolites (27 increased and 146 decreased). The majority of the alterations occurred in lipid-, amino acid-, and energy-related metabolites. Comparing only CD and control subjects revealed 286 significantly altered metabolites (54 increased and 232 decreased), whereas comparing UC and control subjects revealed only five significantly altered metabolites (all decreased). Hierarchal clustering using significant metabolites separated CD from UC and control subjects.

Conclusions

We demonstrate that a number of lipid-, amino acid-, and tricarboxylic acid cycle-related metabolites were significantly altered in IBD patients, more specifically in CD. Therefore, alterations in lipid and amino acid metabolism and energy homeostasis may play a key role in the pathogenesis of CD.

     

Quantitative metabolomic analysis of the human cornea and aqueous humor

Introduction

Cornea is the outermost part of the eye supplied mostly by aqueous humor (AH). Therefore, the comparison of the metabolomic compositions of AH and cornea may help to determine which compounds are produced inside the cornea, and which penetrate into cornea from AH for intra-corneal consumption. Keratoconus (KC) is the most common form of the cornea dystrophy, and the analysis of KC corneas can unravel the metabolomic changes occurring in AH and cornea of KC patients.

Objectives

The work is aimed at the determination of concentrations of a wide range of metabolites in the human cornea and AH, the comparison of the metabolomic profiles of cornea and AH, and the comparison of the metabolomic compositions of samples taken from KC patients and normal donors (post-mortem).

Methods

The quantitative metabolomic profiling was carried out with the use of two independent methods—high-frequency ¹H NMR spectroscopy and HPLC with high-resolution ESI-MS detection.

Results

The concentrations of 71 most abundant metabolites in cornea and AH from keratoconus patients and from human cadavers have been measured. It is found that the concentrations of purines and organic acids in cornea are significantly higher than in AH. The KC corneas are characterized by the enhanced levels of acetate and citrate, and also by low values of GSH/GSSG ratios.

Conclusion

A significant difference in the metabolomic compositions of the human AH and cornea has been revealed. The concentrations of glucose and some amino acids in cornea are significantly lower than in AH, indicating their fast consumption inside the cornea. The high levels of organic acids, purines and GSH in cornea should be attributed to their production in the cornea. The enhanced levels of acetate and citrate as well as the low values of GSH/GSSG ratios in KC corneas are the indicators of the oxidative stress.

     

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.

     

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.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

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.