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1.
Rachel A. Spicer Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):16
Introduction
Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.Objectives
(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.Methods
A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.Results
Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.Conclusion
Further efforts are required to improve data sharing in metabolomics.2.
Background
The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as ‘unknown compounds’. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.Aim of review
In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.Key scientific concepts of review
The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.3.
Emily G. Armitage Andrew D. Southam 《Metabolomics : Official journal of the Metabolomic Society》2016,12(9):146
Introduction
Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.Objectives
Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.Methods
A literature search of the current relevant primary research was performed.Results
Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.Conclusion
Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.4.
Anita H. Lewin Peter Silinski James Hayes Amanda Gilbert S. Wayne Mascarella Herbert H. Seltzman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):117
Introduction
Metabolomics analysis depends on the identification and validation of specific metabolites. This task is significantly hampered by the absence of well-characterized reference standards. The one-carbon carrier 10-formyltetrahydrofolate acts as a donor of formyl groups in anabolism, where it is a substrate in formyltransferase reactions in purine biosynthesis. It has been reported as an unstable substance and is currently unavailable as a reference standard for metabolomics analysis.Objectives
The current study was undertaken to provide the metabolomics community thoroughly characterized 10-formyltetrahydrofolate along with analytical methodology and guidelines for its storage and handling.Methods
Anaerobic base treatment of 5,10-methenyltetrahydrofolate chloride in the presence of antioxidant was utilized to prepare 10-formyltetrahydrofolate.Results
Pure 10-formyltetrahydrofolate has been prepared and physicochemically characterized. Conditions toward maintaining the stability of a solution of the dipotassium salt of 10-formyltetrahydrofolate have been determined.Conclusion
This study describes the facile preparation of pure (>90%) 10-formyltetrahydrofolate, its qualitative physicochemical characterization, as well as conditions to enable its use as a reference standard in physiologic samples.5.
Rachel Spicer Reza M. Salek Pablo Moreno Daniel Cañueto Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2017,13(9):106
Introduction
The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.Objectives
To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.Methods
The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.Results
A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.Conclusion
This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking.6.
M. G. L. Henquet M. Roelse R. C. H. de Vos A. Schipper G. Polder N. C. A. de Ruijter R. D. Hall M. A. Jongsma 《Metabolomics : Official journal of the Metabolomic Society》2016,12(7):115
Introduction
Metabolomics has become a valuable tool in many research areas. However, generating metabolomics-based biochemical profiles without any related bioactivity is only of indirect value in understanding a biological process. Therefore, metabolomics research could greatly benefit from tools that directly determine the bioactivity of the detected compounds.Objective
We aimed to combine LC–MS metabolomics with a cell based receptor assay. This combination could increase the understanding of biological processes and may provide novel opportunities for functional metabolomics.Methods
We developed a flow through biosensor with human cells expressing both the TRPV1, a calcium ion channel which responds to capsaicin, and the fluorescent intracellular calcium ion reporter, YC3.6. We have analysed three contrasting Capsicum varieties. Two were selected with contrasting degrees of spiciness for characterization by HPLC coupled to high mass resolution MS. Subsequently, the biosensor was then used to link individual pepper compounds with TRPV1 activity.Results
Among the compounds in the crude pepper fruit extracts, we confirmed capsaicin and also identified both nordihydrocapsaicin and dihydrocapsaicin as true agonists of the TRPV1 receptor. Furthermore, the biosensor was able to detect receptor activity in extracts of both Capsicum fruits as well as a commercial product. Sensitivity of the biosensor to this commercial product was similar to the sensory threshold of a human sensory panel.Conclusion
Our results demonstrate that the TRPV1 biosensor is suitable for detecting bioactive metabolites. Novel opportunities may lie in the development of a continuous functional assay, where the biosensor is directly coupled to the LC–MS.7.
Mark Daley Greg Dekaban Robert Bartha Arthur Brown Tanya Charyk Stewart Timothy Doherty Lisa Fischer Jeff Holmes Ravi S. Menon C. Anthony Rupar J. Kevin Shoemaker Douglas D. Fraser 《Metabolomics : Official journal of the Metabolomic Society》2016,12(12):185
Introduction
Concussions are a major health concern as they cause significant acute symptoms and in some athletes, long-term neurologic dysfunction. Diagnosis of concussion can be difficult, as are the decisions to stop play.Objective
To determine if concussions in adolescent male hockey players could be diagnosed using plasma metabolomics profiling.Methods
Plasma was obtained from 12 concussed and 17 non-concussed athletes, and assayed for 174 metabolites with proton nuclear magnetic resonance and direct injection liquid chromatography tandem mass spectrometry. Data were analysed with multivariate statistical analysis and machine learning.Results
The estimated time from concussion occurrence to blood draw at the first clinic visit was 2.3 ± 0.7 days. Using principal component analysis, the leading 10 components, each containing 9 metabolites, were shown to account for 82 % of the variance between cohorts, and relied heavily on changes in glycerophospholipids. Cross-validation of the classifier using a leave-one out approach demonstrated a 92 % accuracy rate in diagnosing a concussion (P < 0.0001). The number of metabolites required to achieve the 92 % diagnostic accuracy was minimized from 174 to as few as 17 metabolites. Receiver operating characteristic analyses generated an area under the curve of 0.91, indicating excellent concussion diagnostic potential.Conclusion
Metabolomics profiling, together with multivariate statistical analysis and machine learning, identified concussed athletes with >90 % certainty. Metabolomics profiling represents a novel diagnostic method for concussion, and may be amenable to point-of-care testing.8.
Evan L. Pannkuk Evagelia C. Laiakis Tytus D. Mak Giuseppe Astarita Simon Authier Karen Wong Albert J. FornaceJr. 《Metabolomics : Official journal of the Metabolomic Society》2016,12(5):80
Introduction
Due to dangers associated with potential accidents from nuclear energy and terrorist threats, there is a need for high-throughput biodosimetry to rapidly assess individual doses of radiation exposure. Lipidomics and metabolomics are becoming common tools for determining global signatures after disease or other physical insult and provide a “snapshot” of potential cellular damage.Objectives
The current study assesses changes in the nonhuman primate (NHP) serum lipidome and metabolome 7 days following exposure to ionizing radiation (IR).Methods
Serum sample lipids and metabolites were extracted using a biphasic liquid–liquid extraction and analyzed by ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry. Global radiation signatures were acquired in data-independent mode.Results
Radiation exposure caused significant perturbations in lipid metabolism, affecting all major lipid species, including free fatty acids, glycerolipids, glycerophospholipids and esterified sterols. In particular, we observed a significant increase in the levels of polyunsaturated fatty acids (PUFA)-containing lipids in the serum of NHPs exposed to 10 Gy radiation, suggesting a primary role played by PUFAs in the physiological response to IR. Metabolomics profiling indicated an increase in the levels of amino acids, carnitine, and purine metabolites in the serum of NHPs exposed to 10 Gy radiation, suggesting perturbations to protein digestion/absorption, biological oxidations, and fatty acid β-oxidation.Conclusions
This is the first report to determine changes in the global NHP serum lipidome and metabolome following radiation exposure and provides information for developing metabolomic biomarker panels in human-based biodosimetry.9.
Sven Zukunft Cornelia Prehn Cornelia Röhring Gabriele Möller Martin Hrabě de Angelis Jerzy Adamski Janina Tokarz 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):18
Introduction
Global metabolomics analyses using body fluids provide valuable results for the understanding and prediction of diseases. However, the mechanism of a disease is often tissue-based and it is advantageous to analyze metabolomic changes directly in the tissue. Metabolomics from tissue samples faces many challenges like tissue collection, homogenization, and metabolite extraction.Objectives
We aimed to establish a metabolite extraction protocol optimized for tissue metabolite quantification by the targeted metabolomics AbsoluteIDQ? p180 Kit (Biocrates). The extraction method should be non-selective, applicable to different kinds and amounts of tissues, monophasic, reproducible, and amenable to high throughput.Methods
We quantified metabolites in samples of eleven murine tissues after extraction with three solvents (methanol, phosphate buffer, ethanol/phosphate buffer mixture) in two tissue to solvent ratios and analyzed the extraction yield, ionization efficiency, and reproducibility.Results
We found methanol and ethanol/phosphate buffer to be superior to phosphate buffer in regard to extraction yield, reproducibility, and ionization efficiency for all metabolites measured. Phosphate buffer, however, outperformed both organic solvents for amino acids and biogenic amines but yielded unsatisfactory results for lipids. The observed matrix effects of tissue extracts were smaller or in a similar range compared to those of human plasma.Conclusion
We provide for each murine tissue type an optimized high-throughput metabolite extraction protocol, which yields the best results for extraction, reproducibility, and quantification of metabolites in the p180 kit. Although the performance of the extraction protocol was monitored by the p180 kit, the protocol can be applicable to other targeted metabolomics assays.10.
Shayne Mason A. Marceline Tutu van Furth Regan Solomons Ron A. Wevers Mari van Reenen Carolus J. Reinecke 《Metabolomics : Official journal of the Metabolomic Society》2016,12(7):110
Introduction
Tuberculous meningitis (TBM) is a severe manifestation of tuberculosis, presenting with high morbidity and mortality in children. Existing diagnostic methods for TBM are invasive and time-consuming and the need for highly sensitive and selective diagnosis remains high on the TBM agenda.Objective
Our aim was to exploit metabolomics as an approach to identify metabolites as potential diagnostic predictors for children with TBM through a non-invasive means.Methods
Urine samples selected for this study were from three paediatric groups: patients with confirmed TBM (n = 12), patients clinically suspected with TBM but later confirmed to be negative (n = 19) and age-matched controls (n = 29). Metabolomics data were generated through gas chromatography–mass spectrometry analysis and important metabolites were identified according to standard statistical procedures used for metabolomics data.Results
A global metabolite profile that characterized TBM was developed from the data, reflecting the host and microbial responses. Nine different logistic regression models were fitted to selected metabolites for the best combination as predictors for TBM. Four metabolites—methylcitric, 2-ketoglutaric, quinolinic and 4-hydroxyhippuric acids—showed excellent diagnostic ability and provided prognostic insight into our TBM patients.Conclusions
This study is the first to illustrate holistically the metabolic complexity of TBM and provided proof-of-concept that a biosignature of urinary metabolites can be defined for non-invasive diagnosis and prognosis of paediatric TBM patients. The biosignature should be developed and validated through future prospective studies to generate a medical algorithm for diagnosis in the initial stages of the disease and for monitoring of treatment strategies.11.
Saleh Alseekh Luisa Bermudez Luis Alejandro de Haro Alisdair R. Fernie Fernando Carrari 《Metabolomics : Official journal of the Metabolomic Society》2018,14(11):148
Background
Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.Aim of Review
We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.Key Scientific Concepts of Review
Translational metabolomics applied to crop breeding programs.12.
Abdullah Basoglu Nuri Baspinar Leonardo Tenori Alessia Vignoli Ramazan Yildiz 《Metabolomics : Official journal of the Metabolomic Society》2016,12(8):128
Background
Bovine respiratory disease is one of the main health issues in dairy calves. Inflammatory lung diseases are highly complex with respect to pathogenesis and relationships between inflammation, clinical disease and response to treatment. Metabolomics may offer the potential to identify biomarkers that define calf bronchopneumonia in terms of combined clinical, physiological and patho-biological abnormalities. While metabolomic studies are often encountered in childhood pneumonia, there is no knowledge related to the same approach to calf pneumonia.Objective
The aim of this first study was to reveal the new potential biomarkers for acute calf bronchopneumonia by single proton (1H) Nuclear magnetic resonance (NMR) based quantitative metabolomics.Methods
Fifty dairy calves with acute bronchopneumonia presented for treatment to the teaching hospital, and ten healthy dairy calves belonging the teaching farm were used. Laboratory (hematological: complete blood count and blood gas analysis, and biochemical analysis related to health profile) were performed. NMR spectra of the all samples (50 diseased + 10 healthy water soluble extracts, 50 diseased + 10 healthy lipid extracts) were acquired using a standard Nuclear Overhauser Effect Spectroscopy pulse sequence.Results
NMR based metabolomics analysis showed that calves suffering from bronchopneumonia and healthy calves have two different and distinguishable metabolic fingerprints using both water soluble and lipid extracts. Alterations in metabolites, increases in 2-methyl glutarate, phenylalanine, phosphatidylcholine, and decreases in ethanol, dimethylsulfone, propionate, acetate, allantoin, free cholesterol, cholesterol (–C18), were meaningful for pathogenic mechanisms of calf bronchopneumonia.Conclusion
The NMR based metabolomics may contribute to better understanding bronchopneumonia in calves.13.
Nguyen Phuoc Long Sang Jun Yoon Nguyen Hoang Anh Tran Diem Nghi Dong Kyu Lim Yu Jin Hong Soon-Sun Hong Sung Won Kwon 《Metabolomics : Official journal of the Metabolomic Society》2018,14(8):109
Introduction
Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.Objectives
In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.Methods
PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement.Results and Conclusion
The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43–1.00, specificity: 0.73–1.00). The effects of patients’ bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.14.
Introduction
Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.Objectives
Assessment of the quality of raw data processing in untargeted metabolomics.Methods
Five published untargeted metabolomics studies, were reanalyzed.Results
Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.Conclusion
Incomplete raw data processing shows unexplored potential of current and legacy data.15.
Seth D. Rhoades Aalim M. Weljie 《Metabolomics : Official journal of the Metabolomic Society》2016,12(12):183
Introduction
Both reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC–MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.Objective
Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using design of experiments (DoE).Methods
We iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD). Multivariate statistical analysis guided our decision process in the method optimizations.Results
LC–MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5 % (p < 0.0001) over initial conditions with a 13.3 % increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8 and 57.3 %, with median metabolite response increases of 106.1 and 10.3 % (p < 0.0001 and p < 0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8 % response increase (p < 0.0001) over initial conditions.Conclusions
The COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.16.
Rachael Hough Debra Archer Christopher Probert 《Metabolomics : Official journal of the Metabolomic Society》2018,14(2):19
Introduction
Disturbance to the hindgut microbiota can be detrimental to equine health. Metabolomics provides a robust approach to studying the functional aspect of hindgut microorganisms. Sample preparation is an important step towards achieving optimal results in the later stages of analysis. The preparation of samples is unique depending on the technique employed and the sample matrix to be analysed. Gas chromatography mass spectrometry (GCMS) is one of the most widely used platforms for the study of metabolomics and until now an optimised method has not been developed for equine faeces.Objectives
To compare a sample preparation method for extracting volatile organic compounds (VOCs) from equine faeces.Methods
Volatile organic compounds were determined by headspace solid phase microextraction gas chromatography mass spectrometry (HS-SPME-GCMS). Factors investigated were the mass of equine faeces, type of SPME fibre coating, vial volume and storage conditions.Results
The resultant method was unique to those developed for other species. Aliquots of 1000 or 2000 mg in 10 ml or 20 ml SPME headspace were optimal. From those tested, the extraction of VOCs should ideally be performed using a divinylbenzene-carboxen-polydimethysiloxane (DVB-CAR-PDMS) SPME fibre. Storage of faeces for up to 12 months at ? 80 °C shared a greater percentage of VOCs with a fresh sample than the equivalent stored at ? 20 °C.Conclusions
An optimised method for extracting VOCs from equine faeces using HS-SPME-GCMS has been developed and will act as a standard to enable comparisons between studies. This work has also highlighted storage conditions as an important factor to consider in experimental design for faecal metabolomics studies.17.
Sonia Liggi Christine Hinz Zoe Hall Maria Laura Santoru Simone Poddighe John Fjeldsted Luigi Atzori Julian L. Griffin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):52
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.18.
Fernanda Bertuccez Cordeiro Thais Regiani Cataldi Lívia do Vale Teixeira da Costa Beatriz Zappellini de Souza Daniela Antunes Montani Renato Fraietta Carlos Alberto Labate Agnaldo Pereira Cedenho Edson Guimarães Lo Turco 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):120
Introduction
Endometriosis is an estrogen-dependent gynecological disease that causes infertility, and potential metabolomic biomarkers related to ovarian endometriosis and poor outcomes after assisted reproductive treatments are still lacking.Objectives
The present study analyzed the metabolomic profiling of follicular fluid samples from 40 patients undergoing in vitro fertilization.Methods
The follicular fluid samples were classified as controls (n = 22) and endometriosis patients (n = 18). The samples were submitted to Bligh and Dyer protocol followed by metabolomics analysis by ultra-performance liquid chromatography mass spectrometry. Clinical data was assessed by Students’ T-test and metabolomics data was analyzed by multivariate statistics by MetaboAnalyst 3.0 to obtain intrinsic characteristics that allowed for groups discrimination. The Receiver Operating Characteristic curve was carried out for the proposed biomarkers, aiming to determine their specificity and sensitivity, as a set and individually.Results
From the metabolomic analysis, 20 ion masses were selected as potential biomarkers from principal component analysis, which showed that all biomarkers were more abundant in the endometriosis group when compared to controls. Tentative attribution was performed by lipid maps database, demonstrating that these potential biomarkers correspond to fatty acids, carnitines, monoacylglycerols, lysophosphatidic acids, lysophosphatidylglycerols, diacylglycerols, lysophosphatidylcholines, phosphatidylserine, lysophosphatidylinositols and Phosphatidic Acid.Conclusion
The use of mass spectrometry-based metabolomics allowed for the identification of effective biomarkers for ovarian endometriosis, which may contribute for a better comprehension of the disease and how it affects the ovary, as well as assisting in the development of accessory tools for endometriosis diagnosis and infertility management.19.
Farhana R. Pinu Ninna Granucci James Daniell Ting-Li Han Sonia Carneiro Isabel Rocha Jens Nielsen Silas G. Villas-Boas 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):43
Introduction
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.20.
Heun-Sik Lee Tao Xu Young Lee Nam-Hee Kim Yeon-Jung Kim Jeong-Min Kim Sang Yun Cho Kwang-Youl Kim Moonsuk Nam Jerzy Adamski Karsten Suhre Wolfgang Rathmann Annette Peters Rui Wang-Sattler Bok-Ghee Han Bong-Jo Kim 《Metabolomics : Official journal of the Metabolomic Society》2016,12(12):178