High-fat and high-carbohydrate diets cause a number of metabolic disorders in mammals. However, little is known about metabolomic changes caused by dietary imbalances in fish.
Objectives
The objective of this study was to assess the impacts of high-fat diet (HFD), high-carbohydrate diet (HCD) and high-fat-high-carbohydrate diet (HFHCD) on metabolites in a farmed cyprinid fish Megalobrama amblycephala.
Methods
We have employed the 1H NMR-based metabolomic approach to measure the concentrations of metabolites in plasma and liver of four different diet groups: HFD, HCD, HFHCD and control. Multivariate statistical analyses were used to determine significantly changed metabolites between all group-pairs.
Results
All three test diets have affected metabolic profiles, phenotypes and clinical chemistry. High-fat diets (HFD, HFHCD) resulted in a higher average weight than HCD, but high-carbohydrate diets (HCD, HFHCD) caused signs of liver damage. HCD has resulted in elevated metabolites in energy pathways, leading to further disturbances in creatine pathway. Excess of carbohydrate and lipid metabolism products in the HFHCD group appears to have caused “congestion” of the TCA cycle, causing a significant decline in the numbers of amino acids entering the cycle, which in turn resulted in elevated levels of seven amino acids in this group. Gut microbiota metabolites (TMA) exhibited a strong positive correlation with the carbohydrate content and a negative correlation with the fat content in diets.
Conclusion
These results provide an important insight into the diet-affected metabolic disorders that often lead to financial losses in the aquaculture of Megalobrama amblycephala.
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.
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.
Boiling ethanol extraction is a frequently used method for metabolomics studies of biological samples. However, the stability of several central carbon metabolites, including nucleotide triphosphates, and the influence of the cellular matrix on their degradation have not been addressed.
Objectives
To study how a complex cellular matrix extracted from yeast (Saccharomyces cerevisiae) may affect the degradation profiles of nucleotide triphosphates extracted under boiling ethanol conditions.
Methods
We present a double-labelling LC–MS approach with a 13C-labeled yeast cellular extract as complex surrogate matrix, and 13C15N-labeled nucleotides as internal standards, to study the effect of the yeast matrix on the degradation of nucleotide triphosphates.
Results
While nucleotide triphosphates were degraded to the corresponding diphosphates in pure solutions, degradation was prevented in the presence of the yeast matrix under typical boiling ethanol extraction conditions.
Conclusions
Extraction of biological samples under boiling ethanol extraction conditions that rapidly inactivate enzyme activity are suitable for labile central energy metabolites such as nucleotide triphosphates due to the stabilizing effect of the yeast matrix. The basis of this phenomenon requires further study.
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.
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 FOF1-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.
The chemical sensitivity of urine metabolomics analysis is greatly compromised due to the large amounts of inorganic salts in urine (NaCl, KCl), which are detrimental to analytical instrumentation, e.g. chromatographic columns or mass spectrometers. Traditional desalting approaches applied to urine pretreatment suffer from the chemical losses, which reduce the information depth of analysis.
Objectives
We aimed to test a simple approach for the simultaneous preconcentration and desalting of organic solutes in urine based on the collection of induced bursting bubble aerosols above the surface of urine samples.
Method
Bursting bubbles were generated at ambient conditions by feeding gas through an air diffuser at the bottom of diluted (200 times in ultrapure water) urine solution (50–500 mL). Collected aerosols were analyzed by the direct-infusion electrospray ionization mass spectrometry (ESI–MS).
Results
The simultaneous preconcentration (ca. 6–12 fold) and desalting (ca. six–tenfold) of organic solutes in urine was achieved by the bursting bubble sample pretreatment, which allowed ca. three-times higher number of identified urine metabolites by high-resolution MS analysis. No chemical losses due to bubbling were observed. The increased degree of MS data clustering was demonstrated on the principal component analysis of data sets from the urine of healthy people and from the urine people with renal insufficiency. At least ten times higher sensitivity of trace drug detection in urine was demonstrated for clenbuterol and salbutamol.
Conclusion
Our results indicate the high versatility of bubble bursting as a simple pretreatment approach to enhance the chemical depth and sensitivity of urine analysis. The approach could be attractive for personalized medicine as well as for the diagnostics of renal disorders of different etiology (diabetic nephropathy, chronic renal failure, transplant-associated complications, oncological disorders).
Graphical Abstract
Urine desalting and preconcentration in bursting bubbles.
Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.
Objective
To introduce a software tool for the identification of isotopologues from mass spectrometry data.
Methods
DeltaMS relies on XCMS peak detection and X13CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X13CMS.
Results
To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures.
Conclusion
DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.
In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC–MS, LC–MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.
Aim of review
(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.
Key message
Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.
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.
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.
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.
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.
Menopause is associated with increased abdominal fat and increased risk of developing diabetes and cardiovascular disease.
Objectives
The present study evaluated the plasma metabolic response in relation to insulin sensitivity after weight loss via diet intervention.
Methods
This work includes two studies; i) Ten women on a 5 weeks Paleolithic-type diet (PD, 30 energy percent (E%) protein, 40 E% fat, 30 E% carbohydrates), ii) 55 women on 6 months of either PD or Nordic Nutrition Recommendations diet (NNR, 15 E% protein, 30 E% fat, and 55 E% carbohydrates). Plasma metabolic profiles were acquired at baseline and post diet using gas chromatography time-of-flight/mass spectrometry and investigated in relation to insulin sensitivity using multivariate bioinformatics.
Results
Both the PD and NNR diet resulted in significant weight loss, reduced waist circumference, improved serum lipid profiles, and improved insulin sensitivity. We detected a baseline metabolic profile that correlated significantly with insulin sensitivity, and of which components increased significantly in the PD group compared to NNR. Specifically, a significant increase in myo-inositol (MI), a second messenger of insulin action, and β-hydroxybutyric acid (β-HB) increased while dihomo-gamma-linoleic acid (DGLA) decreased in PD compared to NNR, which correlated with improved insulin sensitivity. We also detected a significant decrease in tyrosine and tryptophan, potential markers of insulin resistance when elevated in the circulation, with the PD but not the NNR.
Conclusions
Using metabolomics, we detected changes in the plasma metabolite profiles associated with weight loss in postmenopausal women by different diets. The metabolic profiles following 6 months of PD were linked to beneficial effects on insulin sensitivity compared to NNR.
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.
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.
The prevalence of metabolic syndrome (MS) increased in recent years in both adolescents and children groups. The aim of the study is evaluating the relationship between insulin and uric acid (UA) level in MS in adolescents
Materials and Methods
we studied 120 adolescence aged 10 to 19 in two groups: control group without metabolic syndrome and case group with metabolic syndrome. The Criteria of ATP III was considered as a diagnosis factor for metabolic syndrome.
Discussion
Various studies have been conducted in various populations to evaluate the relationship between UA level and MS in adolescents. Abdominal obesity, low HDL, hypertriglyceridemia and hypertension are associated with high UA level. In their analysis, the MS OR in UA level?4.9, 4.9-5.8 and ?5.8 mg/dl was 1, 2.53 and 9.03, respectively, which were higher than our findings in current study. Hyperinsulinemia caused by insulin resistance is one of the complications associated with MS, which puts individuals at risk of diabetes and cardiovascular events.
Results
Uric acid level in the Case group was significantly higher than the control group (p = 0.0001, 43.8±1.4 vs. 4.1±1 mg/dl, respectively). Insulin level was significantly higher in the case group in compare to the control group (p = 0.008, 9.8± 5.3 vs. 12.2±6 μU/ml, respectively).
Conclusion
The findings of this case-control study showed that adolescents with metabolic syndrome have a higher uric acid and insulin level in compare to normal subjects. We hypothesis that increase in serum insulin and uric acid level can be a risk factor in the development of metabolic syndrome.
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.
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.
Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies.
Materials and Methods
The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow.
Conclusions
Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.
