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.

     

The dental calculus metabolome in modern and historic samples

Introduction

Dental calculus is a mineralized microbial dental plaque biofilm that forms throughout life by precipitation of salivary calcium salts. Successive cycles of dental plaque growth and calcification make it an unusually well-preserved, long-term record of host-microbial interaction in the archaeological record. Recent studies have confirmed the survival of authentic ancient DNA and proteins within historic and prehistoric dental calculus, making it a promising substrate for investigating oral microbiome evolution via direct measurement and comparison of modern and ancient specimens.

Objective

We present the first comprehensive characterization of the human dental calculus metabolome using a multi-platform approach.

Methods

Ultra performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS) quantified 285 metabolites in modern and historic (200 years old) dental calculus, including metabolites of drug and dietary origin. A subset of historic samples was additionally analyzed by high-resolution gas chromatography–MS (GC–MS) and UPLC–MS/MS for further characterization of metabolites and lipids. Metabolite profiles of modern and historic calculus were compared to identify patterns of persistence and loss.

Results

Dipeptides, free amino acids, free nucleotides, and carbohydrates substantially decrease in abundance and ubiquity in archaeological samples, with some exceptions. Lipids generally persist, and saturated and mono-unsaturated medium and long chain fatty acids appear to be well-preserved, while metabolic derivatives related to oxidation and chemical degradation are found at higher levels in archaeological dental calculus than fresh samples.

Conclusions

The results of this study indicate that certain metabolite classes have higher potential for recovery over long time scales and may serve as appropriate targets for oral microbiome evolutionary studies.

     

Exploration of serum biomarkers for predicting the response to Inchinkoto (ICKT), a Japanese traditional herbal medicine

Introduction

In patients with obstructive jaundice, biliary drainage sometimes fails to result in improvement. A pharmaceutical-grade choleretic herbal medicine, Inchinkoto (ICKT), has been proposed to exert auxiliary effects on biliary drainage; however, its effects are variable among patients.

Objectives

The aim of this study is to explore serum biomarkers that are associated with pharmaceutical efficacy of ICKT.

Methods

Obstructive jaundice patients who underwent external biliary decompression were enrolled (n?=?37). ICKT was given orally 3 times a day at daily dose of 7.5 g. Serum and bile samples were collected before, 3 h after, and 24 h after ICKT administration. The concentrations of total bilirubin, direct bilirubin, and total bile acid in bile specimens were measured. Metabolites in serum samples were comprehensively profiled using LC–MS/MS and GC–MS/MS. Pharmacokinetic analysis of major ICKT components was also performed.

Results

ICKT administration significantly decreased serum ALT and increased bile volume after 24 h. The serum concentrations of ICKT components were not well correlated with the efficacy of ICKT. However, the ratio of 2-hydroxyisobutyric acid to arachidonic acid and the ratio of glutaric acid to niacinamide, exhibited good performance as biomarkers for the efficacy of ICKT on bile flow and ALT, respectively. Additionally, comprehensive correlation analysis revealed that serum glucuronic acid was highly correlated with serum total bilirubin, suggesting that this metabolite may be deeply involved in the pathogenesis of jaundice.

Conclusions

The present study indicates that ICKT is efficacious and provides candidates for predicting ICKT efficacy. Further validation studies are warranted.

     

Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

Background

Intestinal bacteria are known to regulate bile acid (BA) homeostasis via intestinal biotransformation of BAs and stimulation of the expression of fibroblast growth factor 19 through intestinal nuclear farnesoid X receptor (FXR). On the other hand, BAs directly regulate the gut microbiota with their strong antimicrobial activities. It remains unclear, however, how mammalian BAs cross-talk with gut microbiome and shape microbial composition in a dynamic and interactive way.

Results

We quantitatively profiled small molecule metabolites derived from host-microbial co-metabolism in mice, demonstrating that BAs were the most significant factor correlated with microbial alterations among all types of endogenous metabolites. A high-fat diet (HFD) intervention resulted in a rapid and significant increase in the intestinal BA pool within 12 h, followed by an alteration in microbial composition at 24 h, providing supporting evidence that BAs are major dietary factors regulating gut microbiota. Feeding mice with BAs along with a normal diet induced an obese phenotype and obesity-associated gut microbial composition, similar to HFD-fed mice. Inhibition of hepatic BA biosynthesis under HFD conditions attenuated the HFD-induced gut microbiome alterations. Both inhibition of BAs and direct suppression of microbiota improved obese phenotypes.

Conclusions

Our study highlights a liver–BA–gut microbiome metabolic axis that drives significant modifications of BA and microbiota compositions capable of triggering metabolic disorders, suggesting new therapeutic strategies targeting BA metabolism for metabolic diseases.

     

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.

     

Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

Introduction

Ketosis is a prevalent metabolic disease of transition dairy cows that affects milk yield and the development of other periparturient diseases.

Objectives

The objective of this study was to retrospectively metabotype the serum of dairy cows affected by ketosis before clinical signs of disease, during the diagnosis of ketosis, and after the diagnosis of disease and identify potential predictive and diagnostic serum metabolite biomarkers for the risk of ketosis.

Methods

Targeted metabolomics was used to identify and quantify 128 serum metabolites in healthy (CON, n?=?20) and ketotic (n?=?6) cows by DI/LC-MS/MS at ?8 and ?4 weeks prepartum, during the disease week, and at +4 and +8 weeks after parturition.

Results

Significant changes were detected in the levels of several metabolite groups including amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines in the serum of ketotic cows during all time points studied.

Conclusions

Results of this study support the idea that ketosis is preceded and associated and followed by alterations in multiple metabolite groups. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker model with very high sensitivity and specificity were identified. Overall, these findings throw light on the pathobiology of ketosis and some of the metabolites identified might serve as predictive biomarkers for the risk of ketosis. The data must be considered as preliminary given the lower number of ketotic cows in this study and more research with a larger cohort of cows is warranted to validate the results.

     

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.

     

Urine metabolic fingerprinting can be used to predict the risk of metritis and highlight the pathobiology of the disease in dairy cows

Introduction

Metritis is an uterine pathology that causes economic losses for the dairy industry. It is associated with lower reproductive efficiency, increased culling rates, decreased milk production and increased veterinary costs.

Objectives

To gain a more detailed view of the urine metabolome and to detect metabolite signature in cows with metritis. In addition, we aimed to identify early metabolites which can help to detect cows at risk to develop metritis in the future.

Methods

We used nuclear magnetic resonance spectroscopy starting at 8 and 4 weeks prior to the expected day of parturition, during the week of diagnosis of metritis, and at 4 and 8 weeks after diagnosis of metritis in Holstein dairy cows.

Results

At 8 weeks before parturition, pre-metritic cows had a total of 30 altered metabolites. Interestingly, 28 of them increased in urine when compared with control cows (P?<?0.05). At 4 weeks before parturition, 34 metabolites were altered. At the week of diagnosis of metritis a total of 20 metabolites were altered (P?<?0.05). The alteration continued at 4 and 8 weeks after diagnosis.

Conclusions

The metabolic fingerprints in the urine of pre-metritic and metritic cows point toward excretion of multiple amino acids, tricarboxylic acid cycle metabolites and monosaccharides. Combination of galactose, leucine, lysine and panthotenate at 8 weeks before parturition might serve as predictive biomarkers for metritis.

     

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.

     

A targeted metabolomic procedure for amino acid analysis in different biological specimens by ultra-high-performance liquid chromatography–tandem mass spectrometry

Introduction

Amino acid analysis in biological fluids is essential for the study of inborn errors of metabolism (IEM) and other diseases.

Objectives

Our aim was to develop a UPLC-MS/MS procedure for the analysis of 25 amino acids and identification of 17 related compounds.

Methods

Sample treatment conditions were optimized for plasma, urine, cerebrospinal fluid (CSF) and dried blood spots. Amino acids and related compounds were analyzed on a Waters ACQUITY UPLC H-class instrument with a reversed-phase C-18 column using water and acetonitrile with 0.1% formic acid as the mobile phases (run time?=?9 min). The detection was performed with a Waters Xevo TQD triple-quadrupole mass spectrometer using positive electrospray ionization in the multiple reaction monitoring mode.

Results

The method linearity, intra-assay and inter-assay precision, detection limit, quantification limit and trueness analysis displayed adequate results in both physiological and pathological conditions. Method comparison was performed between UPLC-MS/MS and ion exchange chromatography (IEC) with ninhydrin derivatization, and the methods showed good agreement, except for 4-hydroxyproline, aspartate and citrulline. Paediatrics age-related reference values in plasma, urine and CSF were established and patients with different IEM were easily identified.

Conclusion

We report a modified UPLC-MS/MS procedure for the analysis of 42 amino acids and related compounds in different specimens. The method is fast, sensitive and robust, and it has been validated to be an alternative to the traditional IEC procedure as the routine method used in metabolic laboratories. The method greatly decreases the run time of the analysis while displaying good metrological results.

     

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.

     

Cinnamon: does it hold its promises in cows? Using non-targeted blood serum metabolomics profiling to test the effects of feeding cinnamon to dairy cows undergoing lactation-induced insulin resistance

Introduction

Cinnamon exerts insulin-enhancing activity in vitro and was demonstrated to improve blood glucose and lipid profiles in several human studies. Such effects may have an impact on metabolically stressed cows.

Objective

To study the effects of cinnamon supplementation during the transition from late pregnancy to early lactation on the metabolism in dairy cows.

Methods

Twenty-four Holstein cows (n?=?8/group) were assigned to either the control group (CTR; without supplementation) or the supplementation groups [supplemental cinnamon at 20 (LCIN) or 40 (HCIN) g/cow per day (d)] from 28 d before calving until 21 d thereafter. Blood samples were assayed for glucose, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin; an index estimating insulin sensitivity (RQUICKI) was calculated. The serum metabolome was characterized in the samples collected from d 14 using a non-targeted approach.

Results

The serum concentrations of glucose and insulin did not differ among groups and followed a similar pattern over time. The serum NEFA concentrations were greater in LCIN (d 2, 7, and 14) and HCIN (d 14) than in CTR. On d 14 and 21, LCIN and HCIN had greater serum BHBA concentrations than CTR cows. The top 10 metabolites identified with significantly higher levels in the supplemented than the CTR cows were related to fatty acid metabolism.

Conclusion

The data suggest lipolytic and ketogenic effects of cinnamon supplementation in dairy cows during the transition from late gestation to early lactation. The fatty acid metabolites found elevated in the supplemented cows point towards impaired mitochondrial fatty acid β-oxidation.

     

Prepartal overfeeding alters the lipidomic profiles in the liver and the adipose tissue of transition dairy cows

Introduction

Physiological adaptations in the energy metabolism of dairy cows during the periparturient period are partly mediated by insulin resistance (IR), which may subsequently induce metabolic disorders postpartum. The molecular mechanisms underlying IR in dairy cows are largely unknown.

Objective

This study aimed to find a novel insight into the molecular mechanisms underlying IR in dairy cows during the periparturient period by analyzing the effects of prepartal overfeeding on the lipidomic profiles in the liver and adipose tissue (AT).

Methods

Sixteen cows were allocated to controlled-energy and high-energy feeding groups. Lipidomic profiling was conducted on liver and adipose tissue samples collected at 8 days prior to the predicted parturition, and 1 day (only AT) and 9 days after the actual parturition.

Results

Five ceramides (Cers) were identified to be significantly increased by prepartal overfeeding in AT in the analysis of the variance between groups within time points. Principal component-linear discriminant analysis showed that lipidomic profiles between the feeding groups were mainly characterized by phosphatidylcholines (PC), phosphatidylethanolamines (PE), lysophophosphatidylcholines (LysoPC), and lysophosphatidylethanolamines (LysoPE) in the liver, and by Cer, PE, and phosphatidylinositols (PI) in AT. Lipid class levels indicated that prepartal overfeeding elevated the concentration of PE, PI, LysoPC, LysoPE, and sphingomyelin in the liver, and increased the concentration of Cer in AT during the periparturient period.

Conclusion

Prepartal overfeeding significantly altered the concentrations of various sphingolipids, phospholipids, and lysophospholipids in the liver and AT of dairy cows during the periparturient period.

     

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.

     

GC–MS analysis of the ruminal metabolome response to thiamine supplementation during high grain feeding in dairy cows

Introduction

Thiamine is known to attenuate high-concentrate diet induced subacute ruminal acidosis (SARA) in dairy cows, however, the underlying mechanisms remain unclear.

Objectives

The major objective of this study was to investigate the metabolic mechanisms of thiamine supplementation on high-concentrate diet induced SARA.

Methods

Six multiparous, rumen-fistulated Holstein cows were used in a replicated 3?×?3 Latin square design. The treatments included a control diet (CON; 20% starch, dry matter basis), a SARA-inducing diet (SAID; 33.2% starch, dry matter basis) and SARA-inducing diet supplemented with 180 mg of thiamine/kg of dry matter intake (SAID?+?T). On d21 of each period, ruminal fluid samples were collected at 3 h post feeding, and GC/MS was used to analyze rumen fluid samples.

Results

PCA and OPLS-DA analysis demonstrated that the ruminal metabolite profile were different in three treatments. Compared with CON treatment, SAID feeding significantly decreased rumen pH, acetate, succinic acid, increased propionate, pyruvate, lactate, glycine and biogenic amines including spermidine and putrescine. Thiamine supplementation significantly decreased rumen content of propionate, pyruvate, lactate, glycine and spermidine; increase rumen pH, acetate and some medium-chain fatty acids. The enrichment analysis of different metabolites indicated that thiamine supplementation mainly affected carbohydrates, amino acids, pyruvate and thiamine metabolism compared with SAID treatment.

Conclusions

These findings revealed that thiamine supplementation could attenuate high-concentrate diet induced SARA by increasing pyruvate formate-lyase activity to promote pyruvate to generate acetyl-CoA and inhibit lactate generation. Besides, thiamine reduced biogenic amines to alleviate ruminal epithelial inflammatory response.

     

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.

     

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.

     

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.