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.

     

Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis

Introduction

Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries.

Objectives

The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties.

Methods

The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333).

Results

Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections.

Conclusions

In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.

     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.

     

Diagnosis of <Emphasis Type="Italic">Clostridium difficile</Emphasis> infection using an UPLC–MS based metabolomics method

Introduction

The fecal metabolome of Clostridium difficile (CD) infection is far from being understood, particularly its non-volatile organic compounds. The drawbacks of current tests used to diagnose CD infection hinder their application.

Objective

The aims of this study were to find new characteristic fecal metabolites of CD infection and develop a metabolomics model for the diagnosis of CD infection.

Methods

Ultra-performance liquid chromatography-mass spectrometry (UPLC–MS) was used to characterize the fecal metabolome of CD positive and negative diarrhea and healthy control stool samples.

Results

Diarrhea and healthy control samples showed distinct clusters in the principal components analysis score plot, and CD positive group and CD negative group demonstrated clearer separation in a partial least squares discriminate analysis model. The relative abundance of sphingosine, chenodeoxycholic acid, phenylalanine, lysophosphatidylcholine (C16:0), and propylene glycol stearate was higher, and the relative abundance of fatty amide, glycochenodeoxycholic acid, tyrosine, linoleyl carnitine, and sphingomyelin was lower in CD positive diarrhea groups, than in the CD negative group. A linear discriminant analysis model based on capsiamide, dihydrosphingosine, and glycochenodeoxycholic acid was further constructed to identify CD infection in diarrhea. The leave-one-out cross-validation accuracy and area under receiver operating characteristic curve for the training set/external validation set were 90.00/78.57%, and 0.900/0.7917 respectively.

Conclusions

Compared with other hospital-onset diarrhea, CD diarrhea has distinct fecal metabolome characteristics. Our UPLC–MS metabolomics model might be useful tool for diagnosing CD diarrhea.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation

Introduction

Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.

Objectives

This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.

Methods

Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.

Results

In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.

Conclusions

The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.

     

MIDAS-G: a computational platform for investigating fragmentation rules of tandem mass spectrometry in metabolomics

Introduction

Tandem mass spectrometry (MS/MS) has been widely used for identifying metabolites in many areas. However, computationally identifying metabolites from MS/MS data is challenging due to the unknown of fragmentation rules, which determine the precedence of chemical bond dissociation. Although this problem has been tackled by different ways, the lack of computational tools to flexibly represent adjacent structures of chemical bonds is still a long-term bottleneck for studying fragmentation rules.

Objectives

This study aimed to develop computational methods for investigating fragmentation rules by analyzing annotated MS/MS data.

Methods

We implemented a computational platform, MIDAS-G, for investigating fragmentation rules. MIDAS-G processes a metabolite as a simple graph and uses graph grammars to recognize specific chemical bonds and their adjacent structures. We can apply MIDAS-G to investigate fragmentation rules by adjusting bond weights in the scoring model of the metabolite identification tool and comparing metabolite identification performances.

Results

We used MIDAS-G to investigate four bond types on real annotated MS/MS data in experiments. The experimental results matched data collected from wet labs and literature. The effectiveness of MIDAS-G was confirmed.

Conclusion

We developed a computational platform for investigating fragmentation rules of tandem mass spectrometry. This platform is freely available for download.

     

Serum metabolomics using ultra performance liquid chromatography coupled to mass spectrometry in lactating dairy cows following a single dose of sporidesmin

Introduction

Photosensitization is a common clinical sign in cows suffering from liver damage caused by the mycotoxin sporidesmin. This disease, called facial eczema (FE), is of major importance in New Zealand. Current techniques for diagnosing animals with subclinical sporidesmin-induced liver damage (i.e. without photosensitization) are nonspecific. In addition, little is known of the mechanisms involved in sporidesmin resistance, nor the early effects seen following low-dose sporidesmin intoxication.

Objective

The objective of this study was to identify individual metabolites or metabolic profiles that could be used as serum markers for early stage FE in lactating cows.

Methods

Results are presented from a 59-day sporidesmin challenge in Friesian-cross dairy cows. Serum metabolite profiles were obtained using reversed phase ultra-performance liquid chromatography (UPLC) electrospray ionization mass spectrometry (MS) and UPLC tandem MS. Multivariate and time series analyses were used to assess the data.

Results

Statistical analysis, both with and without the temporal component, could distinguish the profiles of animals with clinical signs from the others, but not those affected subclinically. An increase in the concentrations of a combination of taurine- and glycine-conjugated secondary bile acids (BAs) was the most likely cause of the separation. This is the first time that MS methods have been applied to FE and that bile acids changes have been detected in cattle exposed to sporidesmin.

Conclusions

It is well known that BA concentrations increase during cholestasis due to damage to bile ducts and leakage of the bile. This is the first study to investigate metabolomic changes in serum following a sporidesmin challenge. Further work to establish the significance of the elevation of individual BAs concentrations in the serum of early-stage sporidesmin-poisoned cows is necessary.

     

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.

     

Metabolomics analysis of oil palm (<Emphasis Type="Italic">Elaeis guineensis</Emphasis>) leaf: evaluation of sample preparation steps using UHPLC–MS/MS

Introduction

Metabolomics analysis of oil palm leaves is a promising strategy to prospect new added-value compounds of this underutilized oil industry by-product. Although previous studies had reported some metabolites identified in this matrix, they had been focused on few compounds using conventional analytical techniques.

Objectives

This study aimed to develop a new high throughput method based on metabolomics able to detect a wide range of metabolites classes in Elaeis guineensis leaves. Furthermore, we investigate the effects caused by harvesting/sample preparation steps for the metabolites identification.

Method

Metabolites analyses were performed by ultra-high liquid chromatography—mass spectrometry (UHPLC–MS) using both ionization modes, ESI(+)–MS and ESI(?)–MS. ANOVA simultaneous component analysis (ASCA) of the resulting complex multivariate dataset was applied to evaluate metabolic alterations. Identification of major metabolites was performed by high resolution mass spectrometry and MS/MS experiments.

Result

A high throughput method based on UHPLC–MS was successfully developed to E. guineensis leaves, detecting from polar to non-polar acid and basic metabolites. According to ASCA, oil palm leaves metabolic fingerprintings have shown influence of transportation/storage and extraction solvent used chosen. In fact, the most significant effect is due to the solvent composition. A total of thirteen metabolites were assigned based on HRMS and MS/MS experiments. However, only seven metabolites identified were previously reported, which represents a potential field to discover new metabolites.

Conclusion

Sample preparation steps should be carefully performed in metabolomics studies, because metabolites will be extracted and identified based on transport and solvent of extraction conditions. In this study, we established a reliable analytical protocol, from sample preparation to data analyses, to prospect new metabolites in oil palm leaves. This protocol could be further applied to similar oil-bearing crops.

     

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

Introduction

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

Objective

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

Methods

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

Results

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC–MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc.

Conclusions

Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.

     

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.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling 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.

     

Metabolic fingerprinting analysis of oil palm reveals a set of differentially expressed metabolites in fatal yellowing symptomatic and non-symptomatic plants

Introduction

Oil palm (E. guineensis), the most consumed vegetable oil in the world, is affected by fatal yellowing (FY), a condition that can lead to the plant’s death. Although studies have been performed since the 1980s, including investigations of biotic and abiotic factors, FY’s cause remains unknown and efforts in researches are still necessary.

Objectives

This work aims to investigate the metabolic expression in plants affected by FY using an untargeted metabolomics approach.

Method

Metabolic fingerprinting analysis of oil palm leaves was performed using ultra high liquid chromatography–electrospray ionization–mass spectrometry (UHPLC–ESI–MS). Chemometric analysis, using principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), was applied to data analysis. Metabolites identification was performed by high resolution mass spectrometry (HRMS), MS/MS experiments and comparison with databases and literature.

Results

Metabolomics analysis based on MS detected more than 50 metabolites in oil palm leaf samples. PCA and PLS-DS analysis provided group segregation and classification of symptomatic and non-symptomatic FY samples, with a great external validation of the results. Nine differentially expressed metabolites were identified as glycerophosphorylcholine, arginine, asparagine, apigenin 6,8-di-C-hexose, tyramine, chlorophyllide, 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine, proline and malvidin 3-glucoside-5-(6″-malonylglucoside). Metabolic pathways and biological importance of those metabolites were assigned.

Conclusion

Nine metabolites were detected in a higher concentration in non-symptomatic FY plants. Seven are related to stress factors i.e. plant defense and nutrient absorption, which can be affected by the metabolic depression of these compounds. Two of those metabolites (glycerophosphorylcholine and 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine) are presented as potential biomarkers, since they have no known direct relation to plant stress.

     

Polycystic ovary syndrome in Indian women: a mass spectrometry based serum metabolomics approach

Introduction

Polycystic ovary syndrome (PCOS) is a complex, heterogeneous endocrinological disorder with uncertain pathogenesis and is very common in women of reproductive age. There are few reports of utilizing metabolomics approach to understand the complex pathophysiology of PCOS. However, excluding one previous NMR-based metabolomics study, none of the study was conducted in Indian population.

Objective

The study aims to compare the serum metabolomic profile of PCOS women with controls from the Eastern region of India.

Methods

PCOS women (n?=?35) and healthy control women (n?=?30) undergoing tubal ligation were recruited for this study. Serum metabolic profiles were generated using liquid chromatography–tandem mass spectrometry (LC-MS/MS) and gas chromatography–mass spectrometry (GC-MS). Multivariate statistical analysis was applied to spectral data obtained from both the LC-MS/MS and GC/MS.

Results

Nine metabolites were identified to be most significantly dysregulated in sera of PCOS women; however, few other identified metabolites were also altered but with lesser significance. Amongst these metabolites, riboflavin, sucrose, adenine and N-acetyl glycine, phosphoric acid and cortisol were down-regulated, whereas, thymine, cystathionine, and phenylalanine were up-regulated in PCOS when compared with controls. The observed changes in metabolite expression suggested alterations in aminoacyl-tRNA biosynthesis, metabolism of nitrogen, alanine-aspartate-glutamate, galactose, glycine-serine-threonine, and pyrimidine-purine among several metabolic pathways possibly implicated in these PCOS women.

Conclusion

The altered metabolites identified in PCOS women of Eastern Indian population, provide insight into current perceptive of the disease pathology, metabolic involvements, and may be considered as putative markers of PCOS.

     

Extending our tools and resources in the non-conventional industrial yeast <Emphasis Type="Italic">Xanthophyllomyces dendrorhous</Emphasis> through the application of metabolite profiling methodologies

Introduction

Xanthophyllomyces dendrorhous is a non-conventional industrial yeast. It has the unique ability among yeasts to produce geranylgeranyl pyrophosphate derived terpenoids such as carotenoids and in particular the high value pigment astaxanthin.

Objective

In order to fully exploit the industrial potential of Xanthophyllomyces using modern industrial biotechnology approaches the further development of “omic” resources in this organism are required to build on the now sequenced and annotated genome. To contribute to this goal, the present study has developed and implemented an efficient metabolite profiling system comprised of, quenching, extraction and associated GC–MS and UPLC analysis.

Method

Four quenching methods and five extraction methods compatible with GC–MS and UPLC profiling were tested and validated by analysing steady state metabolite changes of Xanthophyllomyces cultivated at laboratory scale in liquid shake culture at lag, exponential and early and late stationary phases.

Results

A customised Automated Mass Spectral Deconvolution and Identification System (AMDIS) library has been created for Xanthophyllomyces, over 400 compounds are present in the library of which 78 are detected and quantified routinely in polar and non-polar derived extracts. A preliminary biochemical network has been constructed. Over a standardised laboratory growth cycle, changes in metabolite levels have been determined to create reference point for future strain improvement approaches and the initial biochemical network construction. Correlation analysis has illustrated that astaxanthin formation correlates positively with different sectors of intermediary metabolism (e.g. the TCA cycle intermediates and amino acid formation), “short” saturated fatty acids and β-carotene, while other metabolites are reduced in response to astaxanthin production. These sectors of intermediary metabolism offer potential future targets for the manipulation resulting in the generation of strains with improved titres of given terpenoids.

Discussion

In summary a robust metabolite profiling system for Xanthophyllomyces is in place to further our understanding and potential exploitation of this underutilised industrial yeast.

     

Comparative metabolome analysis of ruminal changes in Holstein dairy cows fed low- or high-concentrate diets

Introduction

Currently, information on the comprehensive changes in the ruminal metabolites of dairy cows fed high-concentrate diet is limited.

Objectives

This study aimed to compare the composition of whole-ruminal metabolites in dairy cows that were fed a low concentrate diet or a high concentrate diet using modern metabolome analysis.

Methods

Cows were fed a low-concentrate diet (LC; 40% concentrate feeds, dry matter (DM) basis) or a high-concentrate diet (HC; 70% concentrate feeds, DM basis). GC/MS was used to analyze rumen fluid samples.

Results

As compared with the LC group, HC diet significantly increased the concentration of bacterial degradation products (included xanthine, hypoxanthine, uracil, etc.), some toxic compounds (included lipopolysaccharide, biogenic amines, ethanolamine, etc.) and 15 amino acids (included alanine, leucine, glycine, etc.). The enrichment analysis of differentially expressed metabolites indicated that three pathways, including aminoacyl-tRNA biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and valine, leucine and isoleucine biosynthesis, were significantly enriched after the diet treatments. Correlation network analysis revealed that HC diets altered the ruminal metabolic pattern, and the metabolites in the HC group were more complicated than those in the LC group. The correlations between ruminal metabolites and blood parameters were mainly centralized in the ruminal metabolites and albumin (40 metabolites), followed by globulin (18 metabolites) and total protein (6 metabolites).

Conclusions

These findings revealed that HC feeding altered the concentrations of ruminal metabolites as well as the metabolic pattern, and the rumen metabolism could be reflected by blood metabolism to a certain degree.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.