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1
Zhou, Jinna, Donghai Hou, Weiqiu Zou, Jinhu Wang, Run Luo, Mu Wang, and Hong Yu. "Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps Sinensis." Molecules 27, no. 11 (June 6, 2022): 3645. http://dx.doi.org/10.3390/molecules27113645.
Анотація:
The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.
2
CREEK, DARREN J., and MICHAEL P. BARRETT. "Determination of antiprotozoal drug mechanisms by metabolomics approaches." Parasitology 141, no. 1 (June 5, 2013): 83–92. http://dx.doi.org/10.1017/s0031182013000814.
Анотація:
SUMMARYThe discovery, development and optimal utilization of pharmaceuticals can be greatly enhanced by knowledge of their modes of action. However, many drugs currently on the market act by unknown mechanisms. Untargeted metabolomics offers the potential to discover modes of action for drugs that perturb cellular metabolism. Development of high resolution LC-MS methods and improved data analysis software now allows rapid detection of drug-induced changes to cellular metabolism in an untargeted manner. Several studies have demonstrated the ability of untargeted metabolomics to provide unbiased target discovery for antimicrobial drugs, in particular for antiprotozoal agents. Furthermore, the utilization of targeted metabolomics techniques has enabled validation of existing hypotheses regarding antiprotozoal drug mechanisms. Metabolomics approaches are likely to assist with optimization of new drug candidates by identification of drug targets, and by allowing detailed characterization of modes of action and resistance of existing and novel antiprotozoal drugs.
3
Chen, Li, Fanyi Zhong, and Jiangjiang Zhu. "Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches." Metabolites 10, no. 9 (August 27, 2020): 348. http://dx.doi.org/10.3390/metabo10090348.
Анотація:
This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.
4
Rosen Vollmar, Ana K., Nicholas J. W. Rattray, Yuping Cai, Álvaro J. Santos-Neto, Nicole C. Deziel, Anne Marie Z. Jukic, and Caroline H. Johnson. "Normalizing Untargeted Periconceptional Urinary Metabolomics Data: A Comparison of Approaches." Metabolites 9, no. 10 (September 21, 2019): 198. http://dx.doi.org/10.3390/metabo9100198.
Анотація:
Metabolomics studies of the early-life exposome often use maternal urine specimens to investigate critical developmental windows, including the periconceptional period and early pregnancy. During these windows changes in kidney function can impact urine concentration. This makes accounting for differential urinary dilution across samples challenging. Because there is no consensus on the ideal normalization approach for urinary metabolomics data, this study’s objective was to determine the optimal post-analytical normalization approach for untargeted metabolomics analysis from a periconceptional cohort of 45 women. Urine samples consisted of 90 paired pre- and post-implantation samples. After untargeted mass spectrometry-based metabolomics analysis, we systematically compared the performance of three common approaches to adjust for urinary dilution—creatinine adjustment, specific gravity adjustment, and probabilistic quotient normalization (PQN)—using unsupervised principal components analysis, relative standard deviation (RSD) of pooled quality control samples, and orthogonal partial least-squares discriminant analysis (OPLS-DA). Results showed that creatinine adjustment is not a reliable approach to normalize urinary periconceptional metabolomics data. Either specific gravity or PQN are more reliable methods to adjust for urinary concentration, with tighter quality control sample clustering, lower RSD, and better OPLS-DA performance compared to creatinine adjustment. These findings have implications for metabolomics analyses on urine samples taken around the time of conception and in contexts where kidney function may be altered.
5
Turck, Christoph W., Tytus D. Mak, Maryam Goudarzi, Reza M. Salek, and Amrita K. Cheema. "The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics." Metabolites 10, no. 4 (March 27, 2020): 128. http://dx.doi.org/10.3390/metabo10040128.
Анотація:
Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.
6
Alvarez, Sophie, and Michael J. Naldrett. "Mass spectrometry based untargeted metabolomics for plant systems biology." Emerging Topics in Life Sciences 5, no. 2 (March 11, 2021): 189–201. http://dx.doi.org/10.1042/etls20200271.
Анотація:
Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.
7
Szeremeta, Michal, Karolina Pietrowska, Anna Niemcunowicz-Janica, Adam Kretowski, and Michal Ciborowski. "Applications of Metabolomics in Forensic Toxicology and Forensic Medicine." International Journal of Molecular Sciences 22, no. 6 (March 16, 2021): 3010. http://dx.doi.org/10.3390/ijms22063010.
Анотація:
Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.
8
Balashova, E. E., O. P. Trifonova, D. L. Maslov, S. R. Lichtenberg, P. G. Lokhov, and A. I. Archakov. "Metabolome profiling in the study of aging processes." Biomeditsinskaya Khimiya 68, no. 5 (2022): 321–38. http://dx.doi.org/10.18097/pbmc20226805321.
Анотація:
Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.
9
Chanukuppa, Venkatesh, Tushar H. More, Khushman Taunk, Ravindra Taware, Tathagata Chatterjee, Sanjeevan Sharma, and Srikanth Rapole. "Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study." RSC Advances 9, no. 51 (2019): 29522–32. http://dx.doi.org/10.1039/c9ra04458b.
10
Madrid-Gambin, Francisco, Sergio Oller-Moreno, Luis Fernandez, Simona Bartova, Maria Pilar Giner, Christopher Joyce, Francesco Ferraro, Ivan Montoliu, Sofia Moco, and Santiago Marco. "AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics." Bioinformatics 36, no. 9 (January 13, 2020): 2943–45. http://dx.doi.org/10.1093/bioinformatics/btaa022.
Анотація:
Abstract Summary Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. Availability and implementation The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.
11
Pinu, Farhana. "Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches." Fermentation 4, no. 4 (November 7, 2018): 92. http://dx.doi.org/10.3390/fermentation4040092.
Анотація:
Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.
12
Allwood, James William, Alex Williams, Henriette Uthe, Nicole M. van Dam, Luis A. J. Mur, Murray R. Grant, and Pierre Pétriacq. "Unravelling Plant Responses to Stress—The Importance of Targeted and Untargeted Metabolomics." Metabolites 11, no. 8 (August 22, 2021): 558. http://dx.doi.org/10.3390/metabo11080558.
Анотація:
Climate change and an increasing population, present a massive global challenge with respect to environmentally sustainable nutritious food production. Crop yield enhancements, through breeding, are decreasing, whilst agricultural intensification is constrained by emerging, re-emerging, and endemic pests and pathogens, accounting for ~30% of global crop losses, as well as mounting abiotic stress pressures, due to climate change. Metabolomics approaches have previously contributed to our knowledge within the fields of molecular plant pathology and plant–insect interactions. However, these remain incredibly challenging targets, due to the vast diversity in metabolite volatility and polarity, heterogeneous mixtures of pathogen and plant cells, as well as rapid rates of metabolite turn-over. Unravelling the systematic biochemical responses of plants to various individual and combined stresses, involves monitoring signaling compounds, secondary messengers, phytohormones, and defensive and protective chemicals. This demands both targeted and untargeted metabolomics approaches, as well as a range of enzymatic assays, protein assays, and proteomic and transcriptomic technologies. In this review, we focus upon the technical and biological challenges of measuring the metabolome associated with plant stress. We illustrate the challenges, with relevant examples from bacterial and fungal molecular pathologies, plant–insect interactions, and abiotic and combined stress in the environment. We also discuss future prospects from both the perspective of key innovative metabolomic technologies and their deployment in breeding for stress resistance.
13
Chávez-Márquez, Alejandra, Alfonso A. Gardea, Humberto González-Rios, and Luz Vazquez-Moreno. "Characterization of Cabernet Sauvignon Wines by Untargeted HS-SPME GC-QTOF-MS." Molecules 27, no. 5 (March 7, 2022): 1726. http://dx.doi.org/10.3390/molecules27051726.
Анотація:
Untargeted metabolomics approaches are emerging as powerful tools for the quality evaluation and authenticity of food and beverages and have been applied to wine science. However, most fail to report the method validation, quality assurance and/or quality control applied, as well as the assessment through the metabolomics-methodology pipeline. Knowledge of Mexican viticulture, enology and wine science remains scarce, thus untargeted metabolomics approaches arise as a suitable tool. The aim of this study is to validate an untargeted HS-SPME-GC-qTOF/MS method, with attention to data processing to characterize Cabernet Sauvignon wines from two vineyards and two vintages. Validation parameters for targeted methods are applied in conjunction with the development of a recursive analysis of data. The combination of some parameters for targeted studies (repeatability and reproducibility < 20% RSD; linearity > 0.99; retention-time reproducibility < 0.5% RSD; match-identification factor < 2.0% RSD) with recursive analysis of data (101 entities detected) warrants that both chromatographic and spectrometry-processing data were under control and provided high-quality results, which in turn differentiate wine samples according to site and vintage. It also shows potential biomarkers that can be identified. This is a step forward in the pursuit of Mexican wine characterization that could be used as an authentication tool.
14
García, Carlos J., Verónica Alacid, Francisco A. Tomás-Barberán, Carlos García, and Pedro Palazón. "Untargeted Metabolomics to Explore the Bacteria Exo-Metabolome Related to Plant Biostimulants." Agronomy 12, no. 8 (August 16, 2022): 1926. http://dx.doi.org/10.3390/agronomy12081926.
Анотація:
The control and development of plant growth promoters is a key factor for the agro-nomy industry in its economic performance. Different genera of bacteria are widely used as natural biostimulants with the aim of enhancing nutrition efficiency, abiotic stress tolerance and/or crop quality traits, regardless of their nutrients content. However, the complete exo-metabolome of the bacteria responsible for the biostimulant effect is still unknown and needs to be investigated. Three bacteria with different biostimulant effects were studied by untargeted metabolomics in order to describe the metabolites responsible for this effect. The pentose phosphate pathway, tryptophan metabolism, zeatin biosynthesis, vitamin B6 metabolism and amino acid metabolism were the highlighted pathways related to bacteria biostimulant activity. These results are related to the plant hormones biosynthesis pathway for auxins and zeatins biosynthesis. Fourteen metabolites were identified as biomarkers of the biostimulant activity. The results suggest a greater relevance of auxins than cytokinin pathways due the importance of the precursors identified. The results show a clear trend of using indole-3-pyruvate and 3-Indoleglycolaldehyde pathways to produce auxins by bacteria. The results demonstrate for the first time that 4-Pyridoxic acid, the fructosamines N-(1-Deoxy-1-fructosyl)phenylalanine and N-(1-Deoxy-1-fructosyl)isoleucine and the tripeptides diprotin A and B are metabolites related to biostimulant capabilities. This study shows how untargeted metabolomic approaches can be useful tools to investigate the bacteria exo-metabolomes related to biostimulant effects.
15
Katsila, Theodora, Styliani A. Chasapi, Jose Carlos Gomez Tamayo, Constantina Chalikiopoulou, Eleni Siapi, Giorgos Moros, Panagiotis Zoumpoulakis, Georgios A. Spyroulias, and Dimitrios Kardamakis. "Three-Dimensional Cell Metabolomics Deciphers the Anti-Angiogenic Properties of the Radioprotectant Amifostine." Cancers 13, no. 12 (June 9, 2021): 2877. http://dx.doi.org/10.3390/cancers13122877.
Анотація:
Aberrant angiogenesis is a hallmark for cancer and inflammation, a key notion in drug repurposing efforts. To delineate the anti-angiogenic properties of amifostine in a human adult angiogenesis model via 3D cell metabolomics and upon a stimulant-specific manner, a 3D cellular angiogenesis assay that recapitulates cell physiology and drug action was coupled to untargeted metabolomics by liquid chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy. The early events of angiogenesis upon its most prominent stimulants (vascular endothelial growth factor-A or deferoxamine) were addressed by cell sprouting measurements. Data analyses consisted of a series of supervised and unsupervised methods as well as univariate and multivariate approaches to shed light on mechanism-specific inhibitory profiles. The 3D untargeted cell metabolomes were found to grasp the early events of angiogenesis. Evident of an initial and sharp response, the metabolites identified primarily span amino acids, sphingolipids, and nucleotides. Profiles were pathway or stimulant specific. The amifostine inhibition profile was rather similar to that of sunitinib, yet distinct, considering that the latter is a kinase inhibitor. Amifostine inhibited both. The 3D cell metabolomics shed light on the anti-angiogenic effects of amifostine against VEGF-A- and deferoxamine-induced angiogenesis. Amifostine may serve as a dual radioprotective and anti-angiogenic agent in radiotherapy patients.
16
Mohd Kamal, Khairunnisa, Mohd Hafidz Mahamad Maifiah, Nusaibah Abdul Rahim, Yumi Zuhanis Has-Yun Hashim, Muhamad Shirwan Abdullah Sani, and Kamalrul Azlan Azizan. "Bacterial Metabolomics: Sample Preparation Methods." Biochemistry Research International 2022 (April 12, 2022): 1–14. http://dx.doi.org/10.1155/2022/9186536.
Анотація:
Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important “omics” tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.
17
Misra, Biswapriya B. "Data normalization strategies in metabolomics: Current challenges, approaches, and tools." European Journal of Mass Spectrometry 26, no. 3 (April 10, 2020): 165–74. http://dx.doi.org/10.1177/1469066720918446.
Анотація:
Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from – either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.
18
Chaby, Lauren E., Heather C. Lasseter, Kévin Contrepois, Reza M. Salek, Christoph W. Turck, Andrew Thompson, Timothy Vaughan, Magali Haas, and Andreas Jeromin. "Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease." Metabolites 11, no. 9 (September 8, 2021): 609. http://dx.doi.org/10.3390/metabo11090609.
Анотація:
Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9–63.2% (coefficient of variation) and 0.6–99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16–70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.
19
Pezzatti, Bergé, Boccard, Codesido, Gagnebin, Viollier, González-Ruiz, and Rudaz. "Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches." Metabolites 9, no. 10 (September 20, 2019): 193. http://dx.doi.org/10.3390/metabo9100193.
Анотація:
Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H2O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.
20
Haslauer, Kristina E., Philippe Schmitt-Kopplin, and Silke S. Heinzmann. "Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis." Metabolites 11, no. 5 (April 29, 2021): 285. http://dx.doi.org/10.3390/metabo11050285.
Анотація:
Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
21
Rubić, Ivana, Richard Burchmore, Stefan Weidt, Clement Regnault, Josipa Kuleš, Renata Barić Rafaj, Tomislav Mašek, et al. "Multi Platforms Strategies and Metabolomics Approaches for the Investigation of Comprehensive Metabolite Profile in Dogs with Babesia canis Infection." International Journal of Molecular Sciences 23, no. 3 (January 29, 2022): 1575. http://dx.doi.org/10.3390/ijms23031575.
Анотація:
Canine babesiosis is an important tick-borne disease worldwide, caused by parasites of the Babesia genus. Although the disease process primarily affects erythrocytes, it may also have multisystemic consequences. The goal of this study was to explore and characterize the serum metabolome, by identifying potential metabolites and metabolic pathways in dogs naturally infected with Babesia canis using liquid and gas chromatography coupled to mass spectrometry. The study included 12 dogs naturally infected with B. canis and 12 healthy dogs. By combining three different analytical platforms using untargeted and targeted approaches, 295 metabolites were detected. The untargeted ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) metabolomics approach identified 64 metabolites, the targeted UHPLC-MS/MS metabolomics approach identified 205 metabolites, and the GC-MS metabolomics approach identified 26 metabolites. Biological functions of differentially abundant metabolites indicate the involvement of various pathways in canine babesiosis including the following: glutathione metabolism; alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; cysteine and methionine metabolism; and phenylalanine, tyrosine, and tryptophan biosynthesis. This study confirmed that host–pathogen interactions could be studied by metabolomics to assess chemical changes in the host, such that the differences in serum metabolome between dogs with B. canis infection and healthy dogs can be detected with liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) methods. Our study provides novel insight into pathophysiological mechanisms of B. canis infection.
22
Züllig, Thomas, Martina Zandl-Lang, Martin Trötzmüller, Jürgen Hartler, Barbara Plecko, and Harald C. Köfeler. "A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches." Metabolites 10, no. 9 (August 25, 2020): 342. http://dx.doi.org/10.3390/metabo10090342.
Анотація:
In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.
23
Brini, Alberto, Vahe Avagyan, Ric C. H. de Vos, Jack H. Vossen, Edwin R. van den Heuvel, and Jasper Engel. "Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage." Metabolites 11, no. 4 (April 13, 2021): 237. http://dx.doi.org/10.3390/metabo11040237.
Анотація:
One-class modelling is a useful approach in metabolomics for the untargeted detection of abnormal metabolite profiles, when information from a set of reference observations is available to model “normal” or baseline metabolite profiles. Such outlying profiles are typically identified by comparing the distance between an observation and the reference class to a critical limit. Often, multivariate distance measures such as the Mahalanobis distance (MD) or principal component-based measures are used. These approaches, however, are either not applicable to untargeted metabolomics data, or their results are unreliable. In this paper, five distance measures for one-class modeling in untargeted metabolites are proposed. They are based on a combination of the MD and five so-called eigenvalue-shrinkage estimators of the covariance matrix of the reference class. A simple cross-validation procedure is proposed to set the critical limit for outlier detection. Simulation studies are used to identify which distance measure provides the best performance for one-class modeling, in terms of type I error and power to identify abnormal metabolite profiles. Empirical evidence demonstrates that this method has better type I error (false positive rate) and improved outlier detection power than the standard (principal component-based) one-class models. The method is illustrated by its application to liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic response spectroscopy (NMR) untargeted metabolomics data from two studies on food safety assessment and diagnosis of rare diseases, respectively.
24
Han, Ting-Li, Yang Yang, Hua Zhang, and Kai P. Law. "Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy." F1000Research 6 (June 22, 2017): 967. http://dx.doi.org/10.12688/f1000research.11823.1.
Анотація:
Background: A challenge of metabolomics is data processing the enormous amount of information generated by sophisticated analytical techniques. The raw data of an untargeted metabolomic experiment are composited with unwanted biological and technical variations that confound the biological variations of interest. The art of data normalisation to offset these variations and/or eliminate experimental or biological biases has made significant progress recently. However, published comparative studies are often biased or have omissions. Methods: We investigated the issues with our own data set, using five different representative methods of internal standard-based, model-based, and pooled quality control-based approaches, and examined the performance of these methods against each other in an epidemiological study of gestational diabetes using plasma. Results: Our results demonstrated that the quality control-based approaches gave the highest data precision in all methods tested, and would be the method of choice for controlled experimental conditions. But for our epidemiological study, the model-based approaches were able to classify the clinical groups more effectively than the quality control-based approaches because of their ability to minimise not only technical variations, but also biological biases from the raw data. Conclusions: We suggest that metabolomic researchers should optimise and justify the method they have chosen for their experimental condition in order to obtain an optimal biological outcome.
25
Carey, Maureen A., Vincent Covelli, Audrey Brown, Gregory L. Medlock, Mareike Haaren, Jessica G. Cooper, Jason A. Papin, and Jennifer L. Guler. "Influential Parameters for the Analysis of Intracellular Parasite Metabolomics." mSphere 3, no. 2 (April 18, 2018): e00097-18. http://dx.doi.org/10.1128/msphere.00097-18.
Анотація:
ABSTRACT Metabolomics is increasingly popular for the study of pathogens. For the malaria parasite Plasmodium falciparum, both targeted and untargeted metabolomics have improved our understanding of pathogenesis, host-parasite interactions, and antimalarial drug treatment and resistance. However, purification and analysis procedures for performing metabolomics on intracellular pathogens have not been explored. Here, we purified in vitro-grown ring-stage intraerythrocytic P. falciparum parasites for untargeted metabolomics studies; the small size of this developmental stage amplifies the challenges associated with metabolomics studies as the ratio between host and parasite biomass is maximized. Following metabolite identification and data preprocessing, we explored multiple confounding factors that influence data interpretation, including host contamination and normalization approaches (including double-stranded DNA, total protein, and parasite numbers). We conclude that normalization parameters have large effects on differential abundance analysis and recommend the thoughtful selection of these parameters. However, normalization does not remove the contribution from the parasite’s extracellular environment (culture media and host erythrocyte). In fact, we found that extraparasite material is as influential on the metabolome as treatment with a potent antimalarial drug with known metabolic effects (artemisinin). Because of this influence, we could not detect significant changes associated with drug treatment. Instead, we identified metabolites predictive of host and medium contamination that could be used to assess sample purification. Our analysis provides the first quantitative exploration of the effects of these factors on metabolomics data analysis; these findings provide a basis for development of improved experimental and analytical methods for future metabolomics studies of intracellular organisms. IMPORTANCE Molecular characterization of pathogens such as the malaria parasite can lead to improved biological understanding and novel treatment strategies. However, the distinctive biology of the Plasmodium parasite, including its repetitive genome and the requirement for growth within a host cell, hinders progress toward these goals. Untargeted metabolomics is a promising approach to learn about pathogen biology. By measuring many small molecules in the parasite at once, we gain a better understanding of important pathways that contribute to the parasite’s response to perturbations such as drug treatment. Although increasingly popular, approaches for intracellular parasite metabolomics and subsequent analysis are not well explored. The findings presented in this report emphasize the critical need for improvements in these areas to limit misinterpretation due to host metabolites and to standardize biological interpretation. Such improvements will aid both basic biological investigations and clinical efforts to understand important pathogens.
26
Bhargava, Pavan, and Peter A. Calabresi. "Metabolomics in multiple sclerosis." Multiple Sclerosis Journal 22, no. 4 (January 11, 2016): 451–60. http://dx.doi.org/10.1177/1352458515622827.
Анотація:
Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system with inflammatory and degenerative components. The cause of MS remains unknown although genetic and environmental factors appear to play a role in its etiopathogenesis. Metabolomics is a new “omics” technology that aims at measuring small molecules in various biological matrices and can provide information that is not readily obtained from genomics, transcriptomics, or proteomics. Currently, several different analytical platforms exist for metabolomics, and both untargeted and targeted approaches are being employed. Methods of analysis of metabolomics data are also being developed and no consensus currently exists on the optimal approach to analysis and interpretation of these data. Metabolomics has the potential to provide putative biomarkers, insights into the pathophysiology of the disease, and to aid in precision medicine for patients with MS.
27
Ismail, Showalter, and Fiehn. "Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics." Metabolites 9, no. 10 (October 21, 2019): 242. http://dx.doi.org/10.3390/metabo9100242.
Анотація:
Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.
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Kelly, Patricia E., H. Jene Ng, Gillian Farrell, Shona McKirdy, Richard K. Russell, Richard Hansen, Zahra Rattray, Konstantinos Gerasimidis, and Nicholas J. W. Rattray. "An Optimised Monophasic Faecal Extraction Method for LC-MS Analysis and Its Application in Gastrointestinal Disease." Metabolites 12, no. 11 (November 14, 2022): 1110. http://dx.doi.org/10.3390/metabo12111110.
Анотація:
Liquid chromatography coupled with mass spectrometry (LC-MS) metabolomic approaches are widely used to investigate underlying pathogenesis of gastrointestinal disease and mechanism of action of treatments. However, there is an unmet requirement to assess faecal metabolite extraction methods for large-scale metabolomics studies. Current methods often rely on biphasic extractions using harmful halogenated solvents, making automation and large-scale studies challenging. The present study reports an optimised monophasic faecal extraction protocol that is suitable for untargeted and targeted LC-MS analyses. The impact of several experimental parameters, including sample weight, extraction solvent, cellular disruption method, and sample-to-solvent ratio, were investigated. It is suggested that a 50 mg freeze-dried faecal sample should be used in a methanol extraction (1:20) using bead beating as the means of cell disruption. This is revealed by a significant increase in number of metabolites detected, improved signal intensity, and wide metabolic coverage given by each of the above extraction parameters. Finally, we addressed the applicability of the method on faecal samples from patients with Crohn’s disease (CD) and coeliac disease (CoD), two distinct chronic gastrointestinal diseases involving metabolic perturbations. Untargeted and targeted metabolomic analysis demonstrated the ability of the developed method to detect and stratify metabolites extracted from patient groups and healthy controls (HC), highlighting characteristic changes in the faecal metabolome according to disease. The method developed is, therefore, suitable for the analysis of patients with gastrointestinal disease and can be used to detect and distinguish differences in the metabolomes of CD, CoD, and HC.
29
Tsugawa, Hiroshi, Aya Satoh, Haruki Uchino, Tomas Cajka, Makoto Arita, and Masanori Arita. "Mass Spectrometry Data Repository Enhances Novel Metabolite Discoveries with Advances in Computational Metabolomics." Metabolites 9, no. 6 (June 24, 2019): 119. http://dx.doi.org/10.3390/metabo9060119.
Анотація:
Mass spectrometry raw data repositories, including Metabolomics Workbench and MetaboLights, have contributed to increased transparency in metabolomics studies and the discovery of novel insights in biology by reanalysis with updated computational metabolomics tools. Herein, we reanalyzed the previously published lipidomics data from nine algal species, resulting in the annotation of 1437 lipids achieving a 40% increase in annotation compared to the previous results. Specifically, diacylglyceryl-carboxyhydroxy-methylcholine (DGCC) in Pavlova lutheri and Pleurochrysis carterae, glucuronosyldiacylglycerol (GlcADG) in Euglena gracilis, and P. carterae, phosphatidylmethanol (PMeOH) in E. gracilis, and several oxidized phospholipids (oxidized phosphatidylcholine, OxPC; phosphatidylethanolamine, OxPE; phosphatidylglycerol, OxPG; phosphatidylinositol, OxPI) in Chlorella variabilis were newly characterized with the enriched lipid spectral databases. Moreover, we integrated the data from untargeted and targeted analyses from data independent tandem mass spectrometry (DIA-MS/MS) acquisition, specifically the sequential window acquisition of all theoretical fragment-ion MS/MS (SWATH-MS/MS) spectra, to increase the lipidomic annotation coverage. After the creation of a global library of precursor and diagnostic ions of lipids by the MS-DIAL untargeted analysis, the co-eluted DIA-MS/MS spectra were resolved in MRMPROBS targeted analysis by tracing the specific product ions involved in acyl chain compositions. Our results indicated that the metabolite quantifications based on DIA-MS/MS chromatograms were somewhat inferior to the MS1-centric quantifications, while the annotation coverage outperformed those of the untargeted analysis of the data dependent and DIA-MS/MS data. Consequently, integrated analyses of untargeted and targeted approaches are necessary to extract the maximum amount of metabolome information, and our results showcase the value of data repositories for the discovery of novel insights in lipid biology.
30
Rebholz, Casey M., Alice H. Lichtenstein, Zihe Zheng, Lawrence J. Appel, and Josef Coresh. "Serum untargeted metabolomic profile of the Dietary Approaches to Stop Hypertension (DASH) dietary pattern." American Journal of Clinical Nutrition 108, no. 2 (June 18, 2018): 243–55. http://dx.doi.org/10.1093/ajcn/nqy099.
Анотація:
ABSTRACT Background The Dietary Approaches to Stop Hypertension (DASH) dietary pattern is recommended for cardiovascular disease risk reduction. Assessment of dietary intake has been limited to subjective measures and a few biomarkers from 24-h urine collections. Objective The aim of the study was to use metabolomics to identify serum compounds that are associated with adherence to the DASH dietary pattern. Design We conducted untargeted metabolomic profiling in serum specimens collected at the end of 8 wk following the DASH diet (n = 110), the fruit and vegetables diet (n = 111), or a control diet (n = 108) in a multicenter, randomized clinical feeding study (n = 329). Multivariable linear regression was used to determine the associations between the randomized diets and individual log-transformed metabolites after adjustment for age, sex, race, education, body mass index, and hypertension. Partial least-squares discriminant analysis (PLS-DA) was used to identify a panel of compounds that discriminated between the dietary patterns. The area under the curve (C statistic) was calculated as the cumulative ability to distinguish between dietary patterns. We accounted for multiple comparisons with the use of the Bonferroni method (0.05 of 818 metabolites = 6.11 × 10−5). Results Serum concentrations of 44 known metabolites differed significantly between participants randomly assigned to the DASH diet compared with both the control diet and the fruit and vegetables diet, which included an amino acid, 2 cofactors and vitamins (n = 2), and lipids (n = 41). With the use of PLS-DA, component 1 explained 29.4% of the variance and component 2 explained 12.6% of the variance. The 10 most influential metabolites for discriminating between the DASH and control dietary patterns were N-methylproline, stachydrine, tryptophan betaine, theobromine, 7-methylurate, chiro-inositol, 3-methylxanthine, methyl glucopyranoside, β-cryptoxanthin, and 7-methylxanthine (C statistic = 0.986). Conclusions An untargeted metabolomic platform identified a broad array of serum metabolites that differed between the DASH diet and 2 other dietary patterns. This newly identified metabolite panel may be used to assess adherence to the DASH dietary pattern. This trial was registered at http://www.clinicaltrials.gov as NCT03403166.
31
Bliziotis, Nikolaos G., Leo A. J. Kluijtmans, Gerjen H. Tinnevelt, Parminder Reel, Smarti Reel, Katharina Langton, Mercedes Robledo, et al. "Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension." Metabolites 12, no. 8 (July 24, 2022): 679. http://dx.doi.org/10.3390/metabo12080679.
Анотація:
Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.
32
Bögl, Thomas, Franz Mlynek, Markus Himmelsbach, Norbert Sepp, Wolfgang Buchberger, and Marija Geroldinger-Simić. "Plasma Metabolomic Profiling Reveals Four Possibly Disrupted Mechanisms in Systemic Sclerosis." Biomedicines 10, no. 3 (March 4, 2022): 607. http://dx.doi.org/10.3390/biomedicines10030607.
Анотація:
Systemic sclerosis (SSc) is a rare systemic autoimmune disorder marked by high morbidity and increased risk of mortality. Our study aimed to analyze metabolomic profiles of plasma from SSc patients by using targeted and untargeted metabolomics approaches. Furthermore, we aimed to detect biochemical mechanisms relevant to the pathophysiology of SSc. Experiments were performed using high-performance liquid chromatography coupled to mass spectrometry technology. The investigation of plasma samples from SSc patients (n = 52) compared to a control group (n = 48) allowed us to identify four different dysfunctional metabolic mechanisms, which can be assigned to the kynurenine pathway, the urea cycle, lipid metabolism, and the gut microbiome. These significantly altered metabolic pathways are associated with inflammation, vascular damage, fibrosis, and gut dysbiosis and might be relevant for the pathophysiology of SSc. Further studies are needed to explore the role of these metabolomic networks as possible therapeutic targets of SSc.
33
Balashova, Elena E., Dmitry L. Maslov, Oxana P. Trifonova, Petr G. Lokhov, and Alexander I. Archakov. "Metabolome Profiling in Aging Studies." Biology 11, no. 11 (October 26, 2022): 1570. http://dx.doi.org/10.3390/biology11111570.
Анотація:
Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics—untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development.
34
Tang, Fenfen, and Emmanuel Hatzakis. "NMR-Based Analysis of Pomegranate Juice Using Untargeted Metabolomics Coupled with Nested and Quantitative Approaches." Analytical Chemistry 92, no. 16 (July 13, 2020): 11177–85. http://dx.doi.org/10.1021/acs.analchem.0c01553.
35
Oliveira, Regina V., Ana Valéria C. Simionato, and Quezia B. Cass. "Enantioselectivity Effects in Clinical Metabolomics and Lipidomics." Molecules 26, no. 17 (August 28, 2021): 5231. http://dx.doi.org/10.3390/molecules26175231.
Анотація:
Metabolomics and lipidomics have demonstrated increasing importance in underlying biochemical mechanisms involved in the pathogenesis of diseases to identify novel drug targets and/or biomarkers for establishing therapeutic approaches for human health. Particularly, bioactive metabolites and lipids have biological activity and have been implicated in various biological processes in physiological conditions. Thus, comprehensive metabolites, and lipids profiling are required to obtain further advances in understanding pathophysiological changes that occur in cells and tissues. Chirality is one of the most important phenomena in living organisms and has attracted long-term interest in medical and natural science. Enantioselective separation plays a pivotal role in understanding the distribution and physiological function of a diversity of chiral bioactive molecules. In this context, it has been the goal of method development for targeted and untargeted metabolomics and lipidomic assays. Herein we will highlight the benefits and challenges involved in these stereoselective analyses for clinical samples.
36
Santos, Adalberto, Helena Pité, Cláudia Chaves-Loureiro, Sílvia M. Rocha, and Luís Taborda-Barata. "Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications." Metabolites 11, no. 8 (August 11, 2021): 534. http://dx.doi.org/10.3390/metabo11080534.
Анотація:
Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.
37
Burton, Casey, and Yinfa Ma. "Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges." Current Medicinal Chemistry 26, no. 1 (March 14, 2019): 5–28. http://dx.doi.org/10.2174/0929867324666170914102236.
Анотація:
Background:The development of effective screening methods for early cancer detection is one of the foremost challenges facing modern cancer research. Urinary metabolomics has recently emerged as a potentially transformative approach to cancer biomarker discovery owing to its noninvasive sampling characteristics and robust analytical feasibility.Objective:To provide an overview of new developments in urinary metabolomics, cover the most promising aspects of hyphenated techniques in untargeted and targeted metabolomics, and to discuss technical and clinical limitations in addition to the emerging challenges in the field of urinary metabolomics and its application to cancer biomarker discovery.Methods:A systematic review of research conducted in the past five years on the application of urinary metabolomics to cancer biomarker discovery was performed. Given the breadth of this topic, our review focused on the five most widely studied cancers employing urinary metabolomics approaches, including lung, breast, bladder, prostate, and ovarian cancers.Results:As an extension of conventional metabolomics, urinary metabolomics has benefitted from recent technological developments in nuclear magnetic resonance, mass spectrometry, gas and liquid chromatography, and capillary electrophoresis that have improved urine metabolome coverage and analytical reproducibility. Extensive metabolic profiling in urine has revealed a significant number of altered metabolic pathways and putative biomarkers, including pteridines, modified nucleosides, and acylcarnitines, that have been associated with cancer development and progression.Conclusion:Urinary metabolomics presents a transformative new approach toward cancer biomarker discovery with high translational capacity to early cancer screening.
38
Hosseinkhani, Farideh, Luojiao Huang, Anne-Charlotte Dubbelman, Faisa Guled, Amy C. Harms, and Thomas Hankemeier. "Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma." Metabolites 12, no. 2 (February 9, 2022): 165. http://dx.doi.org/10.3390/metabo12020165.
Анотація:
Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.
39
Wenck, Soeren, Marina Creydt, Jule Hansen, Florian Gärber, Markus Fischer, and Stephan Seifert. "Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth." Metabolites 12, no. 1 (December 21, 2021): 5. http://dx.doi.org/10.3390/metabo12010005.
Анотація:
For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.
40
Xu, Mengda, Hao Cui, Xiao Chen, Xiumeng Hua, Jiangping Song, and Shengshou Hu. "Changes of Plasma Tris(hydroxymethyl)aminomethane and 5-Guanidino-3-methyl-2-oxopentanoic Acid as Biomarkers of Heart Remodeling after Left Ventricular Assist Device Support." Metabolites 12, no. 11 (November 4, 2022): 1068. http://dx.doi.org/10.3390/metabo12111068.
Анотація:
Cardiac function is closely related to heart metabolism. Heart failure patients undergoing LVAD support have shown varying degrees of remodeling of both cardiac function and morphology. However, the metabolic changes in patients with different outcomes are unclear. This study aimed to identify metabolic differences and evaluate metabolomics-based biomarkers in patients with non-improved/improved cardiac function after LVAD support. Sixteen patients were enrolled in this study. Plasma samples were analyzed by using untargeted metabolomic approaches. Multivariate statistical analysis and a Mann–Whitney U-test was performed to clarify the separation in metabolites and to identify changes in plasma metabolites between the two groups, respectively. The efficacy of candidate biomarkers was tested by the area under the curve receiver operating characteristic curve. Using the Metabolomics Standards Initiative level 2, a total of 1542 and 619 metabolites were detected in the positive and negative ion modes, respectively. Enrichment analysis showed that metabolites in improved cardiac function patients were mainly involved in carbohydrate metabolism and amino acid metabolism. Metabolites from non-improved cardiac function patients were mainly involved in hormone metabolism. Furthermore, we found tris(hydroxymethyl)aminomethane and 5-guanidino-3-methyl-2-oxopentanoic acid could serve as biomarkers to predict whether a patient’s cardiac function would improve after LVAD support.
41
Katam, Ramesh, Chuwei Lin, Kirstie Grant, Chaquayla S. Katam, and Sixue Chen. "Advances in Plant Metabolomics and Its Applications in Stress and Single-Cell Biology." International Journal of Molecular Sciences 23, no. 13 (June 23, 2022): 6985. http://dx.doi.org/10.3390/ijms23136985.
Анотація:
In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics approaches have evolved to integrate cellular processes at various levels into a systems biology knowledge base. Metabolomics plays a crucial role in molecular networking to bridge the gaps between genotypes and phenotypes. However, the greater complexity of metabolites with diverse chemical and physical properties has limited the advances in plant metabolomics. For several years, applications of liquid/gas chromatography (LC/GC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been constantly developed. Recently, ion mobility spectrometry (IMS)-MS has shown utility in resolving isomeric and isobaric metabolites. Both MS and NMR combined metabolomics significantly increased the identification and quantification of metabolites in an untargeted and targeted manner. Thus, hyphenated metabolomics tools will narrow the gap between the number of metabolite features and the identified metabolites. Metabolites change in response to environmental conditions, including biotic and abiotic stress factors. The spatial distribution of metabolites across different organs, tissues, cells and cellular compartments is a trending research area in metabolomics. Herein, we review recent technological advancements in metabolomics and their applications in understanding plant stress biology and different levels of spatial organization. In addition, we discuss the opportunities and challenges in multiple stress interactions, multi-omics, and single-cell metabolomics.
42
Rocchetti, Gabriele, Francesca Ghilardelli, Paolo Bonini, Luigi Lucini, Francesco Masoero, and Antonio Gallo. "Changes of Milk Metabolomic Profiles Resulting from a Mycotoxins-Contaminated Corn Silage Intake by Dairy Cows." Metabolites 11, no. 8 (July 23, 2021): 475. http://dx.doi.org/10.3390/metabo11080475.
Анотація:
In this study, an untargeted metabolomics approach based on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) was used for investigating changes in chemical profiles of cow milk considering diets based on mycotoxins-contaminated corn silages. For this purpose, 45 milk samples were classified into five clusters according to the corn silage contamination profile, namely (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites, and subsequently analyzed by UHPLC-HRMS followed by a multivariate statistical analysis (both unsupervised and supervised statistical approaches). Overall, the milk metabolomic profile highlighted potential correlations between the quality of contaminated corn silages (as part of the total mixed ration) and milk composition. Metabolomics allowed to identify 628 significant milk metabolites as affected by the five levels of corn silage contamination considered, with amino acids and peptides showing the highest metabolite set enrichment (134 compounds). Additionally, 78 metabolites were selected as the best discriminant of the prediction model built, possessing a variable importance in projection score >1.2. The average Log Fold-Change variations of the discriminant metabolites provided evidence that sphingolipids, together with purine and pyrimidine-derived metabolites were the most affected chemical classes. Also, metabolomics revealed a significant accumulation of oxidized glutathione in milk samples belonging to the silage cluster contaminated by emerging Aspergillus toxins, likely involved in the oxidative imbalance. These preliminary findings provide new insights into the potential role of milk metabolomics to provide chemical indicators of mycotoxins-contaminated corn silage feeding systems.
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Păucean, Adriana, Vlad Mureșan, Simona Maria-Man, Maria Simona Chiș, Andruța Elena Mureșan, Larisa Rebeca Șerban, Anamaria Pop, and Sevastița Muste. "Metabolomics as a Tool to Elucidate the Sensory, Nutritional and Safety Quality of Wheat Bread—A Review." International Journal of Molecular Sciences 22, no. 16 (August 19, 2021): 8945. http://dx.doi.org/10.3390/ijms22168945.
Анотація:
Wheat (Triticum aestivum) is one of the most extensively cultivated and used staple crops in human nutrition, while wheat bread is annually consumed in more than nine billion kilograms over the world. Consumers’ purchase decisions on wheat bread are largely influenced by its nutritional and sensorial characteristics. In the last decades, metabolomics is considered an effective tool for elucidating the information on metabolites; however, the deep investigations on metabolites still remain a difficult and longtime action. This review gives emphasis on the achievements in wheat bread metabolomics by highlighting targeted and untargeted analyses used in this field. The metabolomics approaches are discussed in terms of quality, processing and safety of wheat and bread, while the molecular mechanisms involved in the sensorial and nutritional characteristics of wheat bread are pointed out. These aspects are of crucial importance in the context of new consumers’ demands on healthy bakery products rich in bioactive compounds but, equally, with good sensorial acceptance. Moreover, metabolomics is a potential tool for assessing the changes in nutrient composition from breeding to processing, while monitoring and understanding the transformations of metabolites with bioactive properties, as well as the formation of compounds like toxins during wheat storage.
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Estrada-Pérez, Alan Rubén, Martha Cecilia Rosales-Hernández, Juan Benjamín García-Vázquez, Norbert Bakalara, Benedicte Fromager, and José Correa-Basurto. "Untargeted LC-MS/MS Metabolomics Study on the MCF-7 Cell Line in the Presence of Valproic Acid." International Journal of Molecular Sciences 23, no. 5 (February 28, 2022): 2645. http://dx.doi.org/10.3390/ijms23052645.
Анотація:
To target breast cancer (BC), epigenetic modulation could be a promising therapy strategy due to its role in the genesis, growth, and metastases of BC. Valproic acid (VPA) is a well-known histone deacetylase inhibitor (HDACi), which due to its epigenetic focus needs to be studied in depth to understand the effects it might elicit in BC cells. The aim of this work is to contribute to exploring the complete pharmacological mechanism of VPA in killing cancer cells using MCF-7. LC-MS/MS metabolomics studies were applied to MCF-7 treated with VPA. The results show that VPA promote cell death by altering metabolic pathways principally pentose phosphate pathway (PPP) and 2′deoxy-α-D-ribose-1-phosphate degradation related with metabolites that decrease cell proliferation and cell growth, interfere with energy sources and enhance reactive oxygen species (ROS) levels. We even suggest that mechanisms such as ferropoptosis could be involved due to deregulation of L-cysteine. These results suggest that VPA has different pharmacological mechanisms in killing cancer cells including apoptotic and nonapoptotic mechanisms, and due to the broad impact that HDACis have in cells, metabolomic approaches are a great source of information to generate new insights for this type of molecule.
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Kimble, Laura P., Sharon Leslie, and Nicole Carlson. "Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review." Biological Research For Nursing 22, no. 4 (July 10, 2020): 436–48. http://dx.doi.org/10.1177/1099800420940041.
Анотація:
Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists’ use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review ( N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics ( n = 16, 53.3%) used to generate hypotheses ( n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.
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Wang, Xinran, Yi Li, Lanzhen Chen, and Jinhui Zhou. "Analytical Strategies for LC–MS-Based Untargeted and Targeted Metabolomics Approaches Reveal the Entomological Origins of Honey." Journal of Agricultural and Food Chemistry 70, no. 4 (January 13, 2022): 1358–66. http://dx.doi.org/10.1021/acs.jafc.1c07153.
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Brunelli, Laura, Giuseppe Ristagno, Renzo Bagnati, Francesca Fumagalli, Roberto Latini, Roberto Fanelli, and Roberta Pastorelli. "A combination of untargeted and targeted metabolomics approaches unveils changes in the kynurenine pathway following cardiopulmonary resuscitation." Metabolomics 9, no. 4 (February 20, 2013): 839–52. http://dx.doi.org/10.1007/s11306-013-0506-0.
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Wahman, Rofida, Stefan Moser, Stefan Bieber, Catarina Cruzeiro, Peter Schröder, August Gilg, Frank Lesske, and Thomas Letzel. "Untargeted Analysis of Lemna minor Metabolites: Workflow and Prioritization Strategy Comparing Highly Confident Features between Different Mass Spectrometers." Metabolites 11, no. 12 (December 2, 2021): 832. http://dx.doi.org/10.3390/metabo11120832.
Анотація:
Metabolomics approaches provide a vast array of analytical datasets, which require a comprehensive analytical, statistical, and biochemical workflow to reveal changes in metabolic profiles. The biological interpretation of mass spectrometric metabolomics results is still obstructed by the reliable identification of the metabolites as well as annotation and/or classification. In this work, the whole Lemna minor (common duckweed) was extracted using various solvents and analyzed utilizing polarity-extended liquid chromatography (reversed-phase liquid chromatography (RPLC)-hydrophilic interaction liquid chromatography (HILIC)) connected to two time-of-flight (TOF) mass spectrometer types, individually. This study (introduces and) discusses three relevant topics for the untargeted workflow: (1) A comparison study of metabolome samples was performed with an untargeted data handling workflow in two different labs with two different mass spectrometers using the same plant material type. (2) A statistical procedure was observed prioritizing significant detected features (dependent and independent of the mass spectrometer using the predictive methodology Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). (3) Relevant features were transferred to a prioritization tool (the FOR-IDENT platform (FI)) and were compared with the implemented compound database PLANT-IDENT (PI). This compound database is filled with relevant compounds of the Lemnaceae, Poaceae, Brassicaceae, and Nymphaceae families according to analytical criteria such as retention time (polarity and LogD (pH 7)) and accurate mass (empirical formula). Thus, an untargeted analysis was performed using the new tool as a prioritization and identification source for a hidden-target screening strategy. Consequently, forty-two compounds (amino acids, vitamins, flavonoids) could be recognized and subsequently validated in Lemna metabolic profile using reference standards. The class of flavonoids includes free aglycons and their glycosides. Further, according to our knowledge, the validated flavonoids robinetin and norwogonin were for the first time identified in the Lemna minor extracts.
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BERG, MAYA, AN MANNAERT, MANU VANAERSCHOT, GERT VAN DER AUWERA, and JEAN-CLAUDE DUJARDIN. "(Post-) Genomic approaches to tackle drug resistance in Leishmania." Parasitology 140, no. 12 (March 12, 2013): 1492–505. http://dx.doi.org/10.1017/s0031182013000140.
Анотація:
SUMMARYLeishmaniasis, like other neglected diseases is characterized by a small arsenal of drugs for its control. To safeguard the efficacy of current drugs and guide the development of new ones it is thus of utmost importance to acquire a deep understanding of the phenomenon of drug resistance and its link with treatment outcome. We discuss here how (post-)genomic approaches may contribute to this purpose. We highlight the need for a clear definition of the phenotypes under consideration: innate and acquired resistance versus treatment failure. We provide a recent update of our knowledge on the Leishmania genome structure and dynamics, and compare the contribution of targeted and untargeted methods for the understanding of drug resistance and show their limits. We also present the main assays allowing the experimental validation of the genes putatively involved in drug resistance. The importance of analysing information downstream of the genome is stressed and further illustrated by recent metabolomics findings. Finally, the attention is called onto the challenges for implementing the acquired knowledge to the benefit of the patients and the population at risk.
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Kim, Hyunju, Emily Hu, Bing Yu, Lyn Steffen, Sara Seidelmann, Eric Boerwinkle, Josef Coresh, and Casey Rebholz. "Serum Metabolites Associated with Healthy Dietary Patterns in Middle-Aged US Adults." Current Developments in Nutrition 4, Supplement_2 (May 29, 2020): 535. http://dx.doi.org/10.1093/cdn/nzaa046_035.
Анотація:
Abstract Objectives Healthy dietary patterns are recommended for health promotion. Metabolomics can be used to identify objective biomarkers of healthy dietary patterns, which has the potential to improve dietary assessment. We used metabolomics to identify serum metabolites associated with healthy dietary patterns and the components within these dietary patterns in middle-aged US adults. Methods We evaluated known metabolites associated with 4 dietary patterns [Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, Mediterranean diet (aMED)] and their components using untargeted metabolomics in two subsamples (N1 = 1864; N2 = 2091) of the Atherosclerosis Risk in Communities Study. Dietary intakes were assessed using a food frequency questionnaire. We used multivariable linear regression models to examine associations between dietary patterns and individual serum metabolites in each sample, adjusting for sociodemographic factors, health behaviors, and clinical factors. Results 21 out of 373 metabolites (HEI = 10; AHEI = 9; DASH = 15; aMED = 2) in sample 1 and 57 out of 758 metabolites (HEI = 32; AHEI = 22; DASH = 44; aMED = 22) in sample 2 were significantly associated with healthy dietary patterns after Bonferroni correction. More than half of the significant metabolites (n1 = 10; n2 = 35) were associated with more than one dietary pattern. The DASH diet had the highest number of unique metabolites (n1 = 7; n2 = 17), a majority of which were amino acids. Other diets had similar number of unique metabolites (range: 0–3), which were mostly lipids. Some of the unique metabolites were positively associated with components of every diet. For example, N-methylproline was associated with fruit and dairy intake in the DASH diet; docosahexaenoate (22:6n3) was associated with omega-3 fatty acid intake in AHEI, and 1-docosahexaenoylglycerophosphoethanolamine was associated with plant protein and saturated fat intake in HEI. Conclusions An untargeted metabolomics approach identified many metabolites associated with healthy dietary patterns. A considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components within healthy diets. Funding Sources NIDDK, NHLBI.