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Croatian Medical Journal Dec 2012Technological innovations and translation of basic discoveries to clinical practice drive advances in medicine. Today's innovative technologies enable comprehensive... (Review)
Review
Technological innovations and translation of basic discoveries to clinical practice drive advances in medicine. Today's innovative technologies enable comprehensive screening of the genome, transcriptome, proteome, and metabolome. The detailed knowledge, converged in the integrated "omics" (genomics, transcriptomics, proteomics, and metabolomics), holds an immense potential for understanding mechanism of diseases, facilitating their early diagnostics, selecting personalized therapeutic strategies, and assessing their effectiveness. Metabolomics is the newest "omics" approach aimed to analyze large metabolite pools. The next generation of metabolomic screening requires technologies for high throughput and robust monitoring of metabolite levels and their fluxes. In this regard, stable isotope 18O-based metabolite tagging technology expands quantitative measurements of metabolite levels and turnover rates to all metabolites that include water as a reactant, most notably phosphometabolites. The obtained profiles and turnover rates are sensitive indicators of energy and metabolic imbalances like the ones created by genetic deficiencies, myocardial ischemia, heart failure, neurodegenerative disorders, etc. Here we describe and discuss briefly the potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic.
Topics: Diagnosis; Disease; Humans; Metabolome; Metabolomics; Oxygen Isotopes; Precision Medicine; Therapeutics
PubMed: 23275318
DOI: 10.3325/cmj.2012.53.529 -
Current Opinion in Chemical Biology Aug 2023With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing... (Review)
Review
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
Topics: Metabolomics; Metabolome; Machine Learning
PubMed: 37207402
DOI: 10.1016/j.cbpa.2023.102324 -
Nature Metabolism Jun 2023Tumour metabolism is controlled by coordinated changes in metabolite abundance and gene expression, but simultaneous quantification of metabolites and transcripts in... (Meta-Analysis)
Meta-Analysis
Tumour metabolism is controlled by coordinated changes in metabolite abundance and gene expression, but simultaneous quantification of metabolites and transcripts in primary tissue is rare. To overcome this limitation and to study gene-metabolite covariation in cancer, we assemble the Cancer Atlas of Metabolic Profiles of metabolomic and transcriptomic data from 988 tumour and control specimens spanning 11 cancer types in published and newly generated datasets. Meta-analysis of the Cancer Atlas of Metabolic Profiles reveals two classes of gene-metabolite covariation that transcend cancer types. The first corresponds to gene-metabolite pairs engaged in direct enzyme-substrate interactions, identifying putative genes controlling metabolite pool sizes. A second class of gene-metabolite covariation represents a small number of hub metabolites, including quinolinate and nicotinamide adenine dinucleotide, which correlate to many genes specifically expressed in immune cell populations. These results provide evidence that gene-metabolite covariation in cellularly heterogeneous tissue arises, in part, from both mechanistic interactions between genes and metabolites, and from remodelling of the bulk metabolome in specific immune microenvironments.
Topics: Humans; Metabolomics; Metabolome; Neoplasms; Gene Expression Profiling; Transcriptome; Tumor Microenvironment
PubMed: 37337120
DOI: 10.1038/s42255-023-00817-8 -
Seminars in Reproductive Medicine Mar 2014Preimplantation embryo metabolism demonstrates distinctive characteristics associated with the developmental potential of embryos. On this basis, metabolite content of... (Review)
Review
Preimplantation embryo metabolism demonstrates distinctive characteristics associated with the developmental potential of embryos. On this basis, metabolite content of culture media was hypothesized to reflect the implantation potential of individual embryos. This hypothesis was tested in consecutive studies reporting a significant association between culture media metabolites and embryo development or clinical pregnancy. The need for a noninvasive, reliable, and rapid embryo assessment strategy promoted metabolomics studies in vitro fertilization (IVF) in an effort to increase success rates of single embryo transfers. With the advance of analytical techniques and bioinformatics, commercial instruments were developed to predict embryo viability using spectroscopic analysis of surplus culture media. However, despite the initial promising results from proof-of-principal studies, recent randomized controlled trials using commercial instruments failed to show a consistent benefit in improving pregnancy rates when metabolomics is used as an adjunct to morphology. At present, the application of metabolomics technology in clinical IVF laboratory requires the elimination of factors underlying inconsistent findings, when possible, and development of reliable predictive models accounting for all possible sources of bias throughout the embryo selection process.
Topics: Blastocyst; Cell Survival; Embryo Implantation; Female; Humans; Metabolome; Metabolomics; Pregnancy; Preimplantation Diagnosis
PubMed: 24515909
DOI: 10.1055/s-0033-1363556 -
Biomedicine & Pharmacotherapy =... Dec 2023Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of... (Review)
Review
Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of which were derived with the aid of artificial intelligence, were brought up in a strive for perfection in predicting the status of sepsis-related survival or exacerbation. This review aims to provide key insights on the contextual utilization of metabolomics. The aim of this the metabolomic approach set of methods can be used to investigate both bacterial and host metabolite sets from both the host and its microbes in several types of specimens - even in one's breath, mainly with use of two methods - Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR). Metabolomics, and has been used to elucidate the mechanisms underlying disease development and metabolic identification changes in a wide range of metabolite contents, leading to improved methods of diagnosis, treatment, and prognosis of meningitis. Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) are the main analytical platforms used in metabolomics. Its high sensitivity accounts for the usefulness of metabolomics in studies into meningitis, its sequelae, and concomitant comorbidities. Metabolomics approaches are a double-edged sword, due to not only their flexibility, but also - high complexity, as even minor changes in the multi-step methods can have a massive impact on the results. Information on the differential diagnosis of meningitis act as a background in presenting the merits and drawbacks of the use of metabolomics in context of meningeal infections.
Topics: Humans; Artificial Intelligence; Metabolomics; Meningitis; Metabolome; Mass Spectrometry
PubMed: 37837878
DOI: 10.1016/j.biopha.2023.115685 -
Methods in Molecular Biology (Clifton,... 2019Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is...
Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is "fragile" and may be negatively impacted by improper sample collection, storage, and extraction procedures. Similarly, biologically irrelevant conclusions may result from incorrect data collection, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.
Topics: Animals; Bacteria; Biomarkers; Drug Discovery; Drug Evaluation, Preclinical; Humans; Magnetic Resonance Spectroscopy; Metabolome; Metabolomics; Mice; Models, Biological; Pharmaceutical Preparations
PubMed: 31463851
DOI: 10.1007/978-1-4939-9690-2_16 -
Analytical and Bioanalytical Chemistry Nov 2023Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput,... (Review)
Review
Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.
Topics: Mass Spectrometry; Metabolomics; Metabolome; Proteomics; Lipidomics
PubMed: 37466681
DOI: 10.1007/s00216-023-04850-0 -
International Journal of Molecular... May 2022Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical... (Review)
Review
Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for -based precision medicine approaches. -based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.
Topics: Biomarkers; Humans; Metabolome; Metabolomics; Precision Medicine; Prospective Studies
PubMed: 35563604
DOI: 10.3390/ijms23095213 -
Journal of Chromatography. B,... Dec 2022For large-scale and long-term metabolomics studies that involve a large batch or multiple batches of analyses, batch effects cause nonbiological systematic biases that...
For large-scale and long-term metabolomics studies that involve a large batch or multiple batches of analyses, batch effects cause nonbiological systematic biases that may lead to false positive or false negative findings. Quantitative monitoring and correction of batch effects is critical to the development of reproducible and robust metabolomics platforms either for untargeted or targeted analyses. To achieve sufficient retention and separation of a broad range of metabolites with diverse chemical structures and physicochemical properties, LC-MS/MS based targeted metabolomics often involves 3 complemented chromatographic separation methods, including reversed-phase liquid chromatography (RP-LC), hydrophilic interaction liquid chromatography (HILIC), and ion-pair liquid chromatography (IP-LC). The purpose of this study is to quantitatively evaluate intra-batch variations or injection order effects of the RP-LC, HILIC, and IP-LC methods for targeted metabolomics analyses, and develop strategies to minimize intra-batch variations and correct injection order effects for problematic metabolites. Both RP-LC and HILIC methods exhibit robust intra-batch reproducibility in 0.2 µM standard mix QC, with ∼96 % of the measured metabolites showing acceptable intra-batch variations (<20 %); whereas, the intra-batch reproducibility for some metabolites in cell matrix QC may be compromised due to stability issue, suboptimal chromatographic retention, and/or matrix effects causing ionization suppression and/or retention instability. The IP-LC method exhibits significant injection order effects, which could be effectively corrected by the developed exponential models of signal drift trends as a function of injection order for individual targeted metabolites.
Topics: Chromatography, Liquid; Reproducibility of Results; Metabolome; Tandem Mass Spectrometry; Metabolomics; Hydrophobic and Hydrophilic Interactions
PubMed: 36283260
DOI: 10.1016/j.jchromb.2022.123513 -
Molecules (Basel, Switzerland) Dec 2022Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics...
Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics allows the identification of several metabolites and may contribute to clarifying the aging-regulated metabolic pathways. We aimed to investigate aging-related serum metabolic changes using a metabolomics approach. Fasting blood serum samples from 138 apparently healthy individuals (20−70 years old, 56% men) were analyzed by Proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS), and for clinical markers. Associations of the metabolic profile with age were explored via Correlations (r); Metabolite Set Enrichment Analysis; Multiple Linear Regression; and Aging Metabolism Breakpoint. The age increase was positively correlated (0.212 ≤ r ≤ 0.370, p < 0.05) with the clinical markers (total cholesterol, HDL, LDL, VLDL, triacylglyceride, and glucose levels); negatively correlated (−0.285 ≤ r ≤ −0.214, p < 0.05) with tryptophan, 3-hydroxyisobutyrate, asparagine, isoleucine, leucine, and valine levels, but positively (0.237 ≤ r ≤ 0.269, p < 0.05) with aspartate and ornithine levels. These metabolites resulted in three enriched pathways: valine, leucine, and isoleucine degradation, urea cycle, and ammonia recycling. Additionally, serum metabolic levels of 3-hydroxyisobutyrate, isoleucine, aspartate, and ornithine explained 27.3% of the age variation, with the aging metabolism breakpoint occurring after the third decade of life. These results indicate that the aging process is potentially associated with reduced serum branched-chain amino acid levels (especially after the third decade of life) and progressively increased levels of serum metabolites indicative of the urea cycle.
Topics: Male; Humans; Young Adult; Adult; Middle Aged; Aged; Female; Leucine; Isoleucine; Aspartic Acid; Metabolomics; Metabolome; Aging; Biomarkers; Valine; Ornithine; Urea
PubMed: 36557788
DOI: 10.3390/molecules27248656