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.

     

Stability of targeted metabolite profiles of urine samples under different storage conditions

Introduction

Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS).

Objectives

We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics.

Methods

Pooled, fasting urine samples were collected and stored at ?80 °C (biobank standard), ?20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ? p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature.

Results

The concentrations of 63 investigated metabolites were stable at ?20 and 4 °C for up to 24 h when compared to samples immediately stored at ?80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E?04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature.

Conclusion

Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Reproducibility of non-fasting plasma metabolomics measurements across processing delays

Introduction

Processing delays after blood collection is a common pre-analytical condition in large epidemiologic studies. It is critical to evaluate the suitability of blood samples with processing delays for metabolomics analysis as it is a potential source of variation that could attenuate associations between metabolites and disease outcomes.

Objectives

We aimed to evaluate the reproducibility of metabolites over extended processing delays up to 48 h. We also aimed to test the reproducibility of the metabolomics platform.

Methods

Blood samples were collected from 18 healthy volunteers. Blood was stored in the refrigerator and processed for plasma at 0, 15, 30, and 48 h after collection. Plasma samples were metabolically profiled using an untargeted, ultrahigh performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) platform. Reproducibility of 1012 metabolites over processing delays and reproducibility of the platform were determined by intraclass correlation coefficients (ICCs) with variance components estimated from mixed-effects models.

Results

The majority of metabolites (approximately 70% of 1012) were highly reproducible (ICCs?≥?0.75) over 15-, 30- or 48-h processing delays. Nucleotides, energy-related metabolites, peptides, and carbohydrates were most affected by processing delays. The platform was highly reproducible with a median technical ICC of 0.84 (interquartile range 0.68–0.93).

Conclusion

Most metabolites measured by the UPLC–MS/MS platform show acceptable reproducibility up to 48-h processing delays. Metabolites of certain pathways need to be interpreted cautiously in relation to outcomes in epidemiologic studies with prolonged processing delays.

     

Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD)

Introduction

Both reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC–MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.

Objective

Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using design of experiments (DoE).

Methods

We iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD). Multivariate statistical analysis guided our decision process in the method optimizations.

Results

LC–MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5 % (p < 0.0001) over initial conditions with a 13.3 % increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8 and 57.3 %, with median metabolite response increases of 106.1 and 10.3 % (p < 0.0001 and p < 0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8 % response increase (p < 0.0001) over initial conditions.

Conclusions

The COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.

     

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.

     

Metabolomics profiling of concussion in adolescent male hockey players: a novel diagnostic method

Introduction

Concussions are a major health concern as they cause significant acute symptoms and in some athletes, long-term neurologic dysfunction. Diagnosis of concussion can be difficult, as are the decisions to stop play.

Objective

To determine if concussions in adolescent male hockey players could be diagnosed using plasma metabolomics profiling.

Methods

Plasma was obtained from 12 concussed and 17 non-concussed athletes, and assayed for 174 metabolites with proton nuclear magnetic resonance and direct injection liquid chromatography tandem mass spectrometry. Data were analysed with multivariate statistical analysis and machine learning.

Results

The estimated time from concussion occurrence to blood draw at the first clinic visit was 2.3 ± 0.7 days. Using principal component analysis, the leading 10 components, each containing 9 metabolites, were shown to account for 82 % of the variance between cohorts, and relied heavily on changes in glycerophospholipids. Cross-validation of the classifier using a leave-one out approach demonstrated a 92 % accuracy rate in diagnosing a concussion (P < 0.0001). The number of metabolites required to achieve the 92 % diagnostic accuracy was minimized from 174 to as few as 17 metabolites. Receiver operating characteristic analyses generated an area under the curve of 0.91, indicating excellent concussion diagnostic potential.

Conclusion

Metabolomics profiling, together with multivariate statistical analysis and machine learning, identified concussed athletes with >90 % certainty. Metabolomics profiling represents a novel diagnostic method for concussion, and may be amenable to point-of-care testing.

     

Purging behaviors relate to impaired subjective sleep quality in female patients with anorexia nervosa: a prospective observational study

Background

We examined how purging behaviors relate to subjective sleep quality and sleep patterns and how symptoms of disordered eating behaviors relate to global sleep quality in female patients with anorexia nervosa (AN).

Methods

Participants were new consecutive female inpatients with a primary diagnosis of AN admitted to the Department of Psychosomatic Medicine at Kohnodai Hospital between June 26 and December 25, 2015. We recorded patients’ habitual eating behaviors, laxative overuse, or uretic misuse, and administered the Japanese versions of the Pittsburgh Sleep Quality Index (PSQI-J) and Center for Epidemiologic Studies Depression Scale. Raw PSQI-J data were used to determine sleep patterns (sleep-onset time, wake-up time, and sleep duration). To examine how purging behaviors related to sleep quality, we compared variables between AN restricting type (ANr) and AN binge-eating/purging type (ANbp). Spearman’s rank correlation analysis was used to examine which potential factors influence global PSQI-J score.

Results

Participants were 20 patients, of whom 12 had ANbp. Two ANr patients (25%) had global PSQI-J scores greater than 5, compared to 9 ANbp patients (75%; P < 0.05). Circadian rhythm disruption and abnormal sleep duration were significantly greater in ANbp patients than in ANr patients (P < 0.05). Global PSQI-J was significantly correlated with a diagnosis of ANbp (ρ = 0.525; P < 0.05), vomiting (ρ = 0.561; P < 0.05), and duration of illness (ρ = 0.536; P < 0.05).

Conclusions

ANbp patients had worse global sleep quality and greater disrupted sleep than did ANr patients. This suggests that treatments focusing on sleep would be useful, especially for ANbp patients. Furthermore, vomiting and duration of illness should be considered essential factors related to impaired global sleep quality.

Trial registration

Not applicable.

     

A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels

Introduction

Plasma triglyceride levels are a risk factor for coronary heart disease. Triglyceride metabolism is well characterized, but challenges remain to identify novel paths to lower levels. A metabolomics analysis may help identify such novel pathways and, therefore, provide hints about new drug targets.

Objectives

In an observational study, causal relationships in the metabolomics level of granularity are taken into account to distinguish metabolites and pathways having a direct effect on plasma triglyceride levels from those which are only associated with or have indirect effect on triglyceride.

Method

The analysis began by leveraging near-complete information from the genome level of granularity using the GDAG algorithm to identify a robust causal network over 122 metabolites in an upper level of granularity. Knowing the metabolomics causal relationships, we enter the triglyceride variable in the model to identify metabolites with direct effect on plasma triglyceride levels. We carried out the same analysis on triglycerides measured over five different visits spanning 24 years.

Result

Nine metabolites out of 122 metabolites under consideration influenced directly plasma triglyceride levels. Given these nine metabolites, the rest of metabolites in the study do not have a significant effect on triglyceride levels at significance level alpha = 0.001. Therefore, for the further analysis and interpretations about triglyceride levels, the focus should be on these nine metabolites out of 122 metabolites in the study. The metabolites with the strongest effects at the baseline visit were arachidonate and carnitine, followed by 9-hydroxy-octadecadenoic acid and palmitoylglycerophosphoinositol. The influence of arachidonate on triglyceride levels remained significant even at the fourth visit, which was 10 years after the baseline visit.

Conclusion

These results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels.

     

Comprehensive <Superscript>1</Superscript>H NMR metabolic profiling of body fluids for differentiation of meningitis in adults

Introduction

Meningitis, a morbidly infectious central nervous system pathology is accompanied by acute inflammation of the meninges, causing raised intracranial pressure linked with serious neurological sequelae.

Objective

To observe the variation in the metabolic profile, that may occur in serum and urine along with CSF in adults using ¹H NMR spectroscopy, with an attempt of appropriate and timely treatment regimen.

Methods

The ¹H NMR-based metabolomics has been performed in 115 adult subjects for differentiating bacterial meningitis (BM) and tubercular meningitis (TBM).

Results

The discriminant function analysis (DFA) of the three bio-fluids collectively identified 3-hydroxyisovalerate, lactate, glucose, formate, valine, alanine, ketonic bodies, malonate and choline containing compounds (choline and GPC) as significant metabolites among cases versus control group. The differentiation of bacterial meningitis and tuberculous meningitis (BM vs. TBM) can be done on the basis of identification of 3-hydroxyisovalerate, isobutyrate and formate in case of CSF (with a correct classification of 78 %), alanine in serum (correct classification 60 %), valine and acetone in case of urine (correct classification 89.1 %). The NMR spectral bins based orthogonal signal correction principal component analysis score plots of significant metabolites obtained from DFA also provided group classification among cases versus control group in CSF, serum and urine samples. The variable importance in projection scores also identified similar significant metabolites as obtained from DFA, collectively in CSF, serum and urine samples, responsible for differentiation of meningitis.

Conclusion

The CSF contained metabolites which are formed during infection and inflammation, and these were also found in significant quantity in serum and urine samples.

     

Weight loss and weight maintenance obtained with or without GLP-1 analogue treatment decrease branched chain amino acid levels

Introduction

Increased levels of circulating branched chain amino acids (BCAAs), as well as phenylalanine, and tyrosine have been suggested to be involved in the pathogenesis of insulin resistance and type 2 diabetes. However, it is unknown how these metabolites are affected by weight loss, and during weight-maintaining treatment with glucagon-like peptide-1 receptor agonist (GLP-1 RA).

Objective

We aimed to characterize changes in metabolites related to protein turnover and glycolysis after a weight loss intervention followed by long term weight maintenance with/without GLP-1 RA.

Methods

Fifty-eight obese individuals underwent a diet-induced 12 % body weight loss during 8 weeks. Participants were randomized to weight maintenance with or without administration of the GLP-1 RA liraglutide (1.2 mg/day) for 52 weeks. Metabolomic profiling by high-throughput proton nuclear magnetic resonance spectroscopy was used for quantification of metabolites.

Results

The weight loss was maintained in both groups and was associated with 9–20 % decreases in plasma concentrations of alanine, phenylalanine, histidine, tyrosine and the BCAAs leucine, isoleucine and valine (p < 0.05). Plasma citrate levels increased during weight loss (p = 5.2 × 10^?15) and showed inverse correlation with insulin resistance measured by HOMA–IR levels (r = ?0.318, p = 0.025). Valine concentrations were lower in the control group compared to the GLP-1RA group during weight maintenance (p = 0.005).

Conclusion

Weight loss is associated with marked changes in plasma concentrations of eight amino acids and glycolysis-related metabolites. Levels of the suggested type 2 diabetes risk markers (BCAAs) remain low during long-term weight maintenance.

     

Corneal Collagen Cross-Linking for the Management of Mycotic Keratitis

Purpose

To evaluate the efficiency of corneal collagen cross-linking (CXL) in addition to topical voriconazole in cases with mycotic keratitis.

Design

Retrospective case series in a tertiary university hospital.

Participants

CXL was performed on 13 patients with mycotic keratitis who presented poor or no response to topical voriconazole treatment.

Methods

The clinical features, symptoms, treatment results and complications were recorded retrospectively. The corneal infection was graded according to the depth of infection into the stroma (from grade 1 to grade 3). The visual analogue scale was used to calculate the pain score before and 2 days after surgery.

Main Outcome Measures

Grade of the corneal infection.

Results

Mean age of 13 patients (6 female and 7 male) was 42.4 ± 17.7 years (20–74 years). Fungus was demonstrated in culture (eight patients) or cytological examination (five patients). Seven of the 13 patients (54%) were healed with topical voriconazole and CXL adjuvant treatment in 26 ± 10 days (15–40 days). The remaining six patients did not respond to CXL treatment; they initially presented with higher grade ulcers. Pre- and post-operative pain score values were 8 ± 0.8 and 3.5 ± 1, respectively (p < 0.05).

Conclusions

The current study suggests that adjunctive CXL treatment is effective in patients with small and superficial mycotic ulcers. These observations require further research by large randomized clinical trials.

     

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.

     

Selective oxidation of UDP-glucose to UDP-glucuronic acid using permeabilized <Emphasis Type="Italic">Schizosaccharomyces pombe</Emphasis> expressing human UDP-glucose 6-dehydrogenase

Objectives

To use permeabilized cells of the fission yeast, Schizosaccharomyces pombe, that expresses human UDP-glucose 6-dehydrogenase (UGDH, EC 1.1.1.22), for the production of UDP-glucuronic acid from UDP-glucose.

Results

In cell extracts no activity was detected. Therefore, cells were permeabilized with 0.3 % (v/v) Triton X-100. After washing away all low molecular weight metabolites, the permeabilized cells were directly used as whole cell biocatalyst. Substrates were 5 mM UDP-glucose and 10 mM NAD⁺. Divalent cations were not added to the reaction medium as they promoted UDP-glucose hydrolysis. With this reaction system 5 mM UDP-glucose were converted into 5 mM UDP-glucuronic acid within 3 h.

Conclusions

Recombinant permeabilized cells of S. pombe can be used to synthesize UDP-glucuronic acid with 100 % yield and selectivity.

     

Quantitative metabolome profiling reveals the involvement of the kynurenine pathway in influenza-associated encephalopathy

Introduction

Influenza-associated encephalopathy is a serious complication of influenza and is the most common form of acute encephalitis/encephalopathy in Japan. The number of reports from other countries is increasing, reflecting international recognition and concern.

Objectives

Identification of a specific biomarker could provide important clues about the pathophysiology of influenza-associated encephalopathy.

Methods

During the 2009–2011 flu seasons, 34 pediatric patients hospitalized with influenza complications, including influenza-associated encephalopathy, were enrolled in the study. Serum samples were collected during the acute and convalescent phases of disease. Patients were classified into encephalopathy (n = 12) and non-encephalopathy (n = 22) groups. Serum metabolites were identified and quantified by capillary electrophoresis coupled with time-of-flight mass spectrometry. Quantified data were evaluated for comparative analysis. Subsequently, a total of 55 patients with or without encephalopathy were enrolled for absolute quantification of serum kynurenine and quinolinic acid.

Results

Based on m/z values and migration times, 136 metabolites were identified in serum samples. During the acute phase of disease, three metabolites (succinic acid, undecanoic acid, and kynurenine) were significantly higher, and two other metabolites (decanoic acid and cystine) were significantly lower, in the encephalopathy group compared to the non-encephalopathy group (p = 0.012, 0.022, 0.044, 0.038, 0.046, respectively). In a larger patient group, serum kynurenine and its downstream product in tryptophan metabolism, quinolinic acid, a known neurotoxin, were significantly higher in the encephalopathy than the non-encephalopathy without febrile seizure group.

Conclusion

Comprehensive metabolite profiles revealed five metabolites as potential biomarkers for influenza-associated encephalopathy; the tryptophan–kynurenine metabolic process could be associated with its pathophysiology.

     

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.

     

Metabolomic analysis of tomato seed germination

Introduction

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

Objective

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

Methods

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

Results

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

Conclusions

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.

     

Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose

Introduction

Fasting metabolite profiles have been shown to distinguish type 2 diabetes (T2D) patients from normal glucose tolerance (NGT) individuals.

Objectives

We investigated whether, besides fasting metabolite profiles, postprandial metabolite profiles associated with T2D can stratify individuals with impaired fasting glucose (IFG) by their similarities to T2D.

Methods

Three groups of individuals (age 45–65 years) without any history of IFG or T2D were selected from the Netherlands Epidemiology of Obesity study and stratified by baseline fasting glucose concentrations (NGT (n?=?176), IFG (n?=?186), T2D (n?=?171)). 163 metabolites were measured under fasting and postprandial states (150 min after a meal challenge). Metabolite profiles specific for a high risk of T2D were identified by LASSO regression for fasting and postprandial states. The selected profiles were utilised to stratify IFG group into high (T2D probability?≥?0.7) and low (T2D probability?≤?0.5) risk subgroups. The stratification performances were compared with clinically relevant metabolic traits.

Results

Two metabolite profiles specific for T2D (n_fasting = 12 metabolites, n_postprandial = 4 metabolites) were identified, with all four postprandial metabolites also being identified in the fasting state. Stratified by the postprandial profile, the high-risk subgroup of IFG individuals (n?=?72) showed similar glucose concentrations to the low-risk subgroup (n?=?57), yet a higher BMI (difference: 3.3 kg/m² (95% CI 1.7–5.0)) and postprandial insulin concentrations (21.5 mU/L (95% CI 1.8–41.2)).

Conclusion

Postprandial metabolites identified T2D patients as good as fasting metabolites and exhibited enhanced signals for IFG stratification, which offers a proof of concept that metabolomics research should not focus on the fasting state alone.

     

Recent advances in human sweat metabolomics for lung cancer screening

Introduction

Lung cancer is the leading cause of cancer related mortality owing to the advanced stage it is usually detected because the available diagnostic tests are expensive and invasive; therefore, they cannot be used for general screening.

Objectives

To increase robustness of previous biomarker panels—based on metabolites in sweat samples—proposed by the authors, new samples were collected within different intervals (4 months and 2 years), analyzed at different times (2012 and 2014, respectively) by different analysts to discriminate between LC patients and smokers at risk factor.

Methods

Sweat analysis was carried out by LC–MS/MS with minimum sample preparation and the generated analytical data were then integrated to minimize variability in statistical analysis.

Results

Panels with capability to discriminate LC patients from smokers at risk factor were obtained taken into account the variability between both cohorts as a consequence of the different intervals for samples collection, the times at which the analyses were carried out and the influence of the analyst. Two panels of metabolites using the PanelomiX tool allow reducing false negatives (95 % specificity) and false positives (95 % sensitivity). The first panel (96.9 % specificity and 83.8 % sensitivity) is composed by monoglyceride MG(22:2), muconic, suberic and urocanic acids, and a tetrahexose; the second panel (81.2 % specificity and 97.3 % sensitivity) is composed by the monoglyceride MG(22:2), muconic, nonanedioic and urocanic acids, and a tetrahexose.

Conclusion

The study has allowed obtaining a prediction model more robust than that obtained in the previous study from the authors.

     

Strategies to assess and optimize stability of endogenous amines during cerebrospinal fluid sampling

Introduction

Metabolic profiling of cerebrospinal fluid (CSF) is a promising technique for studying brain diseases. Measurements should reflect the in vivo situation, so ex vivo metabolism should be avoided.

Objective

To investigate the effects of temperature (room temperature vs. 4 °C), centrifugation and ethanol, as anti-enzymatic additive during CSF sampling on concentrations of glutamic acid, glutamine and other endogenous amines.

Methods

CSF samples from 21 individuals were processed using five different protocols. Isotopically-labeled alanine, isoleucine, glutamine, glutamic acid and dopamine were added prior to sampling to trace any degradation. Metabolomics analysis of endogenous amines, isotopically-labeled compounds and degradation products was performed with a validated LC–MS method.

Results

Thirty-six endogenous amines were quantified. There were no statistically significant differences between sampling protocols for 31 out of 36 amines. For GABA there was primarily an effect of temperature (higher concentrations at room temperature than at 4 °C) and a small effect of ethanol (lower concentrations if added) due to possible degradation. O-phosphoethanolamine concentrations were also lower when ethanol was added. Degradation of isotopically-labeled compounds (e.g. glutamine to glutamic acid) was minor with no differences between protocols.

Conclusion

Most amines can be considered stable during sampling, provided that samples are cooled immediately to 4 °C, centrifuged, and stored at ??80 °C within 2 h. The effect of ethanol addition for more unstable metabolites needs further investigation. This was the first time that labeled compounds were used to monitor ex vivo metabolism during sampling. This is a useful strategy to study the stability of other metabolites of interest.