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

Introduction

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

Objectives

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

Methods

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

Results

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

Conclusion

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

     

<Superscript>1</Superscript>H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

Introduction

Gastric cancer (GC) is a malignant tumor worldwide. As primary pathway for metastasis, the lymphatic system is an important prognostic factor for GC patients. Although the metabolic changes of gastric cancer have been investigated in extensive studies, little effort focused on the metabolic profiling of lymph node metastasis (LNM)-positive or negative GC patients.

Objectives

We performed ¹H NMR spectrum of GC tissue samples with and without LNM to identify novel potential metabolic biomarkers in the process of LNM of GC.

Methods

¹H NMR-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of tissue samples from LNM-positive GC patients (n?=?40), LNM-negative GC patients (n?=?40) and normal controls (n?=?40).

Results

There was a clear separation between GC patients and normal controls, and 33 differential metabolites were identified in the study. Moreover, GC patients were also well-classified according to LNM-positive or negative. Totally eight distinguishing metabolites were selected in the metabolic profiling of GC patients with LNM-positive or negative, suggesting the metabolic dysfunction in the process of LNM. According to further validation and analysis, especially BCAAs metabolism (leucine, isoleucine, valine), GSH and betaine may be as potential factors of diagnose and prognosis of GC patients with or without LNM.

Conclusion

To our knowledge, this is the first metabolomics study focusing on LNM of GC. The identified distinguishing metabolites showed a promising application on clinical diagnose and therapy prediction, and understanding the mechanism underlying the carcinogenesis, invasion and metastasis of GC.

     

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.

     

Metabolic profiling of isolated mitochondria and cytoplasm reveals compartment-specific metabolic responses

Introduction

Subcellular compartmentalization enables eukaryotic cells to carry out different reactions at the same time, resulting in different metabolite pools in the subcellular compartments. Thus, mutations affecting the mitochondrial energy metabolism could cause different metabolic alterations in mitochondria compared to the cytoplasm. Given that the metabolite pool in the cytosol is larger than that of other subcellular compartments, metabolic profiling of total cells could miss these compartment-specific metabolic alterations.

Objectives

To reveal compartment-specific metabolic differences, mitochondria and the cytoplasmic fraction of baker’s yeast Saccharomyces cerevisiae were isolated and subjected to metabolic profiling.

Methods

Mitochondria were isolated through differential centrifugation and were analyzed together with the remaining cytoplasm by gas chromatography–mass spectrometry (GC–MS) based metabolic profiling.

Results

Seventy-two metabolites were identified, of which eight were found exclusively in mitochondria and sixteen exclusively in the cytoplasm. Based on the metabolic signature of mitochondria and of the cytoplasm, mutants of the succinate dehydrogenase (respiratory chain complex II) and of the F_OF₁-ATP-synthase (complex V) can be discriminated in both compartments by principal component analysis from wild-type and each other. These mitochondrial oxidative phosphorylation machinery mutants altered not only citric acid cycle related metabolites but also amino acids, fatty acids, purine and pyrimidine intermediates and others.

Conclusion

By applying metabolomics to isolated mitochondria and the corresponding cytoplasm, compartment-specific metabolic signatures can be identified. This subcellular metabolomics analysis is a powerful tool to study the molecular mechanism of compartment-specific metabolic homeostasis in response to mutations affecting the mitochondrial metabolism.

     

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.

     

Investigation of urinary volatomic alterations in head and neck cancer: a non-invasive approach towards diagnosis and prognosis

Introduction

Head and neck cancer (HNC), like many other forms of cancer, is usually detected in advanced stages, causing poor survival outcomes. Lack of specific and sensitive screening markers for early detection of HNC has worsened the scenario for the patients as well as the clinicians. Therefore, identification of efficient, noninvasive and affordable screening marker/methodology with high specificity and sensitivity is imminent need of situation.

Objectives

This study aims to identify and characterize urinary volatomic alterations specific to HNC.

Methods

Volatomic analysis of urine samples collected from HNC patients (n?=?29) and healthy controls (n?=?31) was performed using headspace solid phase microextraction coupled to gas chromatography mass spectrometry (GC–MS). Both univariate and multivariate statistical approaches were used to investigate HNC specific volatomic alterations.

Results

Statistical analysis revealed a total of 28 metabolites with highest contribution towards discrimination of HNC patients from healthy controls (VIP >1, p?<?0.05, Log₂ FC ≥0.58/≤?0.57). The discrimination efficiency and accuracy of urinary VOCs was ascertained by ROC curve analysis that allowed the identification of four metabolites viz. 2,6-dimethyl-7-octen-2-ol, 1-butanol, p-xylene and 4-methyl-2-heptanone with highest sensitivity and specificity to discriminate HNC patients from healthy controls. Further, the metabolic pathway analysis identified several dysregulated pathways in HNC patients and their detailed investigations could unravel novel mechanistic insights into the disease pathophysiology.

Conclusion

Overall, this study provides valuable fingerprint of the volatile profile of HNC patients, which in turn, might help in improving the current understanding of this form of cancer and lead to the development of non-invasive approaches for HNC diagnosis.

     

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.

     

Metabolic profiling of <Emphasis Type="Italic">Daphnia magna</Emphasis> exposed to environmental stressors by GC–MS and chemometric tools

Introduction

Climate change is a major concern for the scientific community, demanding novel information about the effects of environmental stressors on living organisms. Metabolic profiling is required for achieving the most extensive possible range of compounds and their concentration changes on stressed conditions.

Objectives

Individuals of the crustacean species Daphnia magna were exposed to three different abiotic factors linked to global climate change: high salinity, high temperature levels and hypoxia. Advanced chemometric tools were used to characterize the metabolites affected by the exposure.

Method

An exploratory analysis of gas chromatography-mass spectrometry (GC–MS) data was performed to discriminate between control and exposed daphnid samples. Due to the complexity of these GC–MS data sets, a comprehensive untargeted analysis of the full scan data was performed using multivariate curve resolution-alternating least squares (MCR-ALS) method. This approach enabled to resolve most of the metabolite signals from interference peaks caused by derivatization reactions. Metabolites with significant changes in their peak areas were tentatively identified and the involved metabolic pathways explored.

Results

D. magna metabolic biomarkers are proposed for the considered physical factors. Metabolites related with energy metabolic pathways including some amino acids, carbohydrates, organic acids and nucleosides were identified as potential biomarkers of the investigated treatments.

Conclusions

The proposed untargeted GC–MS metabolomics strategy and multivariate data analysis tools were useful to investigate D. magna metabolome under environmental stressed conditions.

     

Uncovering the metabolic response of abalone (<Emphasis Type="Italic">Haliotis midae)</Emphasis> to environmental hypoxia through metabolomics

Introduction

Oxygen is essential for metabolic processes and in the absence thereof alternative metabolic pathways are required for energy production, as seen in marine invertebrates like abalone. Even though hypoxia has been responsible for significant losses to the aquaculture industry, the overall metabolic adaptations of abalone in response to environmental hypoxia are as yet, not fully elucidated.

Objective

To use a multiplatform metabolomics approach to characterize the metabolic changes associated with energy production in abalone (Haliotis midae) when exposed to environmental hypoxia.

Methods

Metabolomics analysis of abalone adductor and foot muscle, left and right gill, hemolymph, and epipodial tissue samples were conducted using a multiplatform approach, which included untargeted NMR spectroscopy, untargeted and targeted LC–MS spectrometry, and untargeted and semi-targeted GC-MS spectrometric analyses.

Results

Increased levels of anaerobic end-products specific to marine animals were found which include alanopine, strombine, tauropine and octopine. These were accompanied by elevated lactate, succinate and arginine, of which the latter is a product of phosphoarginine breakdown in abalone. Primarily amino acid metabolism was affected, with carbohydrate and lipid metabolism assisting with anaerobic energy production to a lesser extent. Different tissues showed varied metabolic responses to hypoxia, with the largest metabolic changes in the adductor muscle.

Conclusions

From this investigation, it becomes evident that abalone have well-developed (yet understudied) metabolic mechanisms for surviving hypoxic periods. Furthermore, metabolomics serves as a powerful tool for investigating the altered metabolic processes in abalone.

     

Serum metabolites associated with feed efficiency in black angus steers

Introduction

Improving feed utilization in cattle is required to reduce input costs, increase production, and ultimately improve sustainability of the beef cattle industry. Characterizing metabolic differences between efficient and non-efficient animals will allow stakeholders to identify more efficient cattle during backgrounding.

Objectives

This study used an untargeted metabolomics approach to determine differences in serum metabolites between animals of low and high residual feed intake.

Methods

Residual feed intake was determined for 50 purebred Angus steers and 29 steers were selected for the study steers based on low versus high feed efficiency. Blood samples were collected from steers and analyzed using untargeted metabolomics via mass spectrometry. Metabolite data was analyzed using Metaboanalyst, visualized using orthogonal partial least squares discriminant analysis, and p-values derived from permutation testing. Non-esterified fatty acids, urea nitrogen, and glucose were measured using commercially available calorimetric assay kits. Differences in metabolites measured were grouped by residual feed intake was measured using one-way analysis of variance in SAS 9.4.

Results

Four metabolites were found to be associated with differences in feed efficiency. No differences were found in other serum metabolites, including serum urea nitrogen, non-esterified fatty acids, and glucose.

Conclusions

Four metabolites that differed between low and high residual feed intake have important functions related to nutrient utilization, among other functions, in cattle. This information will allow identification of more efficient steers during backgrounding.

     

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.

     

Habitual sleep and human plasma metabolomics

Introduction

Sleep plays an important role in cardiometabolic health. The sleep-wake cycle is partially driven by the endogenous circadian clock, which governs a range of metabolic pathways. The association between sleep and cardiometabolic health may be mediated by alterations of the human metabolome.

Objectives

To better understand the biological mechanism underlying the association between sleep and health, we examined human plasma metabolites in relation to sleep duration and sleep timing.

Methods

Using an untargeted approach, 329 fasting plasma metabolites were measured in 277 Chinese participants. We measured sleep timing (midpoint between bedtime and wake up time) using repeated time-use surveys (4 weeks during 1 year) and previous night sleep duration from questionnaires completed before sample donation.

Results

We found 64 metabolites that were associated with sleep timing with a false discovery rate of 0.2 or lower, after adjusting for potential confounders. Notably, we found that later sleep timing was associated with higher levels of multiple metabolites in amino acid metabolism, including branched chain amino acids and their gamma-glutamyl dipeptides. We also found widespread associations between sleep timing and numerous metabolites in lipid metabolism, including bile acids, carnitines and fatty acids. In contrast, previous night sleep duration was not associated with plasma metabolites in our study.

Conclusion

Sleep timing was associated with a large number of metabolites across a variety of biochemical pathways. Some metabolite associations are consistent with a relationship between late chronotype and adverse effects on cardiometabolic health.

     

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.

     

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.

     

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.

     

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.

     

Coupling solid phase microextraction to complementary separation platforms for metabotyping of <Emphasis Type="Italic">E. coli</Emphasis> metabolome in response to natural antibacterial agents

Introduction

Essential oils are known to possess antimicrobial activity; thus, their use has played an important role over the years in medicine and for food preservation purposes.

Objective

The effect of clove oil and its major constituents as bactericidal agents on the global metabolic profiling of E. coli bacteria was assessed by means of metabolic alterations, using solid phase microextraction (SPME) as a sample preparation method coupled to complementary analytical platforms.

Method

E. Coli cultures treated with clove oil and its major individual components were sampled by HS-SPME-GCxGC-ToF/MS and SPME-UPLC–MS. Full factorial design was applied in order to estimate the most effective antibacterial agent towards E. coli. Central composite design and factorial design were applied to investigate parameters influencing metabolite coverage and efficiency by SPME.

Results

The metabolic profile, including 500 metabolites identified by LC–MS and 789 components detected by GCxGC-ToF/MS, 125 of which were identified as dysregulated metabolites, revealed changes in the metabolome provoked by the antibacterial activity of clove oil, and in particular its major constituent eugenol. Analyses of individual components selected using orthogonal projections to latent structures discriminant analysis showed a neat differentiation between control samples in comparison to treated samples in various sets of metabolic pathways.

Conclusions

The combination of a sample preparation method capable of providing cleaner extracts coupled to different analytical platforms was successful in uncovering changes in metabolic pathways associated with lipids biodegradation, changes in the TCA cycle, amino acids, and enzyme inhibitors in response to antibacterial treatment.

     

Ethnicity influences gut metabolites and microbiota of the tribes of Assam,India

Introduction

The human gut microbes and their metabolites are involved in multiple host metabolic pathways. Dysbiosis in the gut microbiota and altered metabolite profiles were reported in diseased state. In a region like Assam, where 12.4% of the populations are tribal population, evaluating the influence of ethnicity on gut microbiota and metabolites has become important to further differentiate it from the diseased state.

Objective

To study the influence of ethnicity on fecal metabolite profile and their association with the gut microbiota composition.

Methods

In this study, we determined the untargeted fecal metabolites from five ethnic groups of Assam (Tai-Aiton, Bodo, Karbi, Tea-tribe and Tai-Phake) using GC–MS and compared them among the tribes for common and unique metabolites. Metabolites of microbial origin were related with the available metagenomic data on gut bacterial profiles of the same ethnic groups and functional analysis were carried out based on HMDB.

Results

The core fecal metabolite profile of the Tea-tribe contained aniline, benzoate and acetaldehyde. PLS-DA based on the metabolites suggested that the individuals grouped based on their ethnicity. PCA plot of the data on bacterial abundance at the level of genus indicated clustering of individuals based on ethnicity. Positive correlations were observed between propionic acid and the genus Clostridium (R?=?0.43 and p?=?0.03), butyric acid and the genus Lactobacillus (R?=?0.45 and p?=?0.024), acetic acid and the genus Bacteroides (R?=?0.63 and p?=?0.001) and methane and the genus Escherichia (R?=?0.58 and p?=?0.002).

Conclusion

Results of this study indicated that ethnicity influences both gut bacterial profile and their metabolites.

     

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.