Application Of Metabolomics Research Articles

High-Throughput Metabolomics Applications in Pathogenesis and Diagnosis of Valvular Heart Disease.

High-throughput metabolomics techniques are a useful tool to understand many disease conditions including cardiovascular disease such as valvular heart disease(s) (VHD). VHD involves damage to heart valves, mostly presenting as stenosis, regurgitation or prolapse and can be classified into degenerative, rheumatic, congenital, or prosthetic valve disease. Gaps remain in our understanding of the pathogenesis of the common VHD. It is now fitting to place into perspective the contribution of metabolomics in the mechanism of development, diagnosis, and prognosis of VHD. A structured search for metabolomics studies centred on human VHD was undertaken. Biomarkers associated with the pathogenesis of bicuspid aortic valve disease, mitral valve disease, rheumatic heart disease, and degenerative aortic valve stenosis are reviewed and discussed. In addition, metabolic biomarkers reported to prognosticate patient outcomes of post-valve repair or replacement are highlighted. Finally, we also review the pitfalls and limitations to consider when designing metabolomics studies, especially from a clinician's viewpoint. In the future, reliable and simple metabolic biomarker(s) may supplement the existing diagnostic tools in the early diagnosis of VHD.

Advances in gut microbiome in metabonomics perspective: based on bibliometrics methods and visualization analysis.

Gastrointestinal microbial metabolomics is closely related to the state of the organism and has significant interaction with the pathogenesis of many diseases. Based on the publications in Web of Science Core Collection(WoSCC) from 2004 to 2022, this study conducted a bibliometric analysis of this field, aiming to understand its development trend and frontier, and provide basic information and potential points for in-depth exploration of this field. All articles on gastrointestinal flora and metabolism published from 2004 to 2022 were collected and identified in WoCSS. CiteSpace v.6.1 and VOSviewer v.1.6.15.0 were used to calculate bibliometric indicators, including number of publications and citations, study categories, countries/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords. A map was drawn to visualize the data based on the analysis results for a more intuitive view. There were 3811 articles in WoSCC that met our criteria. Analysis results show that the number of publications and citations in this field are increasing year by year. China is the country with the highest number of publications and USA owns the highest total link strength and citations. Chinese Acad Sci rank first for the number of institutional publications and total link strength. Journal of Proteome Research has the most publications. Nicholson, Jeremy K. is one of the most important scholars in this field. The most cited reference is "Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease". Burst detection indicates that Urine, spectroscopy, metabonomic and gut microflora are long-standing hot topics in this field, while autism spectrum disorder and omics are likely to be at the forefront of research. The study of related metabolic small molecules and the application of gastrointestinal microbiome metabolomics in various diseases are currently emerging research directions and frontier in this field. This study is the first to make a bibliometric analysis of the studies related to gastrointestinal microbial metabolomics and reveal the development trends and current research hotspots in this field. This can contribute to the development of the field by providing relevant scholars with valuable and effective information about the current state of the field.

The application of metabolomics toward idiopathic pulmonary fibrosis and potential metabolomic value of diverse samples in interstitial lung diseases

AbstractInterstitial lung diseases (ILDs), also known as diffuse lung diseases, pose a significant challenge to the respiratory health of individuals worldwide. Among these conditions, idiopathic pulmonary fibrosis (IPF) is one of the most prevalent, contributing substantially to morbidity and mortality. However, IPF is characterized by high heterogeneity, presenting a substantial obstacle to clinical program development and scientific research due to significant variations in disease progression, treatment response, and prognosis. Recent advances in metabolomics have enabled the identification of specific biochemical pathways and disease biomarkers, offering a beacon of hope for patients afflicted with various diseases. Remarkably, metabolomics has made significant strides in ILDs, particularly in IPF. Metabonomics, a branch of life science, provides an in‐depth analysis of metabolic pathways and the specific biological molecular composition of omics, obtained primarily from biological samples such as serum, plasma, pleural effusion, bronchoalveolar lavage fluid, lung surgical biopsy samples, urine, feces, sputum, and cerebrospinal fluid. In this review, we aim to provide a comprehensive overview of the application of metabolomics in ILDs, with a particular focus on IPF. By summarizing the current state of research in this field, we hope to shed light on the latest advances, challenges, and opportunities that metabolomics can provide in managing ILDs.

Application of surface reconstruction enhanced laser desorption/ionization mass spectrometry in the metabolomics analysis of smokers' saliva

Laser desorption/ionisation mass spectrometry (LDI-MS) can be used as an effective detection method for the analysis of more complex samples. However, the organic matrix of conventional LDI-MS produces large background interference and poor signal results, which hinders its application in metabolomics. Surface reconstruction can improve the performance of LDI-MS. In this study, mesoporous NiO@Pt nanofibre membranes were synthesised by a simple and efficient electrostatic spinning strategy and used as a matrix to significantly increase the intensity of mass spectrometry peaks. Metabolomic analysis of saliva samples from 18 smokers and 15 non-smokers was able to clearly distinguish the differences in salivary metabolism between smokers and non-smokers and showed good results in the qualitative analysis of salivary metabolism in smokers. Our study provides a rapid and convenient method for differentiating differences in salivary metabolism in smokers.

Fundamentals and Analytical Strategies for Metabolomics Workflow: An Overview and Microbial Applications

Metabolomics has become a prominent area within the omics sciences and allows an understanding of complex biological systems. Among several areas of knowledge, the study of microorganisms (microbial metabolomics) has received attention. Due to the many species of microorganisms and their high metabolic complexity, many challenges are involved in metabolomics workflow. Careful experimental design and execution of the experiments will provide reliable results, allowing correct biological interpretation. This review presents the fundamentals of metabolomics and workflow, focusing on the description of the steps and analytical strategies applied to microbial sample preparation, highlighting the current challenges in sample handling. In addition, the state of the art of analytical technologies based on separation techniques hyphenated to mass spectrometry and applications in microbial metabolomics are presented.

The potential of metabolomics in meat science: Current applications, trends, and challenges

Metabolites are the final products of metabolism and provide insights into the biochemical balance of tissue systems. A cascade of reactions involving proteins, carbohydrates, and lipids affects meat color, tenderness, and flavor, specifically, metabolites that are key biomolecules in biochemical reactions associated with attainment of acceptable meat quality. Bioinformatics platforms, such as Kyoto Encyclopedia of Genes and Genomes (KEGG) databases and MetaboAnalyst, are utilized to help understanding the role of differentially abundant metabolites and characterizing their roles in cellular function/metabolism. However, the inability to identify all metabolites using a single platform and limited metabolite libraries specifically for meat/food remains a challenge. Therefore, the advances in metabolite separation, easy-to-use data processing, increased resolution of mass-spectrometry, and data analysis will help to make inferences or develop biomarkers related to meat quality. This review discusses how metabolomics can be exploited to characterize meat quality, the challenges, and current trends. SignificanceMetabolites play an important role in attaining consumer-preferred meat quality traits and nutritive value of foods. Visual appearance of fresh foods, such as muscle foods, are utilized by consumers to assess the quality at the retail market before making purchases. Similarly, tenderness and flavor of meats influence eating satisfaction and re-purchase decisions. Inconsistencies in meat quality lead to enormous economic losses to the food industry. For instance, consumers often associate a bright-cherry red color with freshness, and the US beef industry loses $3.74 billion annually due to discoloration during storage. Both pre-and post-harvest factors influence the extent of meat quality changes. Metabolomics offer robust tools to get a snapshot of small molecules such as acids, amino acids, glycolytic- and tricarboxylic acids, fatty acids, and sugars present in post-mortem muscle tissue and their role in meat quality. Further, using bioinformatics platforms enables characterizing the role of differentially present metabolites in meat quality as well as identifying biomarkers for desirable quality traits such as tender meat or color-stable carcasses. Innovative applications of metabolomics can be exploited to elucidate the underpinnings of meat quality and to develop novel strategies to enhance marketability of retail fresh meats.

Gene Identification and Marker Assisted Selection forIntrogression of important traits in barley

Gene identification is the process of identifying genomic DNA regions that encode proteins. It has a wide range of applications in structural genomics, functional genomics, metabolomics, transcriptomics, proteomics, and other genetic-related studies such as genetic disorder detection, treatment and prevention. There are several methods for finding genes, including the ab-initio method (intrinsic) and the sequence similarity search. Crops are typically improved by crossing plants with desired traits, such as high yield or drought tolerance, and selecting the best offspring after multiple generations of testing. It could take 8 to 10 years to develop a new variety. Breeders are very interested in new technologies that can speed up or improve the efficiency of this process. The term "marker-assisted selection" has been used in plant breeding and genetics science since the mid-1990s. The term MAS broadly refers to all forms of selection based on genetic data. MAS is becoming increasingly important in today's world because it aids and improves plant breeding efficiency by monitoring the presence or absence of desirable genes in breeding populations. This is achieved by precisely transferring genomic regions of interest (Foreground Selection) and hastening the recovery of the recurrent parent genome (Background Selection).

True One Cell Chemical Analysis in Cancer Research: A Review

True One Cell (TOC) analysis Is becoming highly critical for functional studies of cancer cells. This is partially because it is the only form of analysis that provides an avenue for studying the heterogeneity and cell-to-cell variations of individual cancer cells, thus providing unique insight into complex regulatory processes that govern TOC functions within a tumor. Additionally, true one cell techniques are playing an increasingly important role in current attempts to implement TOC metabolomic and proteomic studies, as well as emerging attempts to spatially resolve TOC information. In this review we provide a brief overview of the basis of the field and discuss its applications in TOC metabolomics and proteomics.

Evaluation of extraction methods for untargeted metabolomic studies for future applications in zebrafish larvae infection models

Sample preparation in untargeted metabolomics should allow reproducible extractions of as many molecules as possible. Thus, optimizing sample preparation is crucial. This study compared six different extraction procedures to find the most suitable for extracting zebrafish larvae in the context of an infection model. Two one-phase extractions employing methanol (I) and a single miscible phase of methanol/acetonitrile/water (II) and two two-phase methods using phase separation between chloroform and methanol/water combinations (III and IV) were tested. Additional bead homogenization was used for methods III and IV (III_B and IV_B). Nine internal standards and 59 molecules of interest (MoInt) related to mycobacterial infection were used for method evaluation. Two-phase methods (III and IV) led to a lower feature count, higher peak areas of MoInt, especially amino acids, and higher coefficients of variation in comparison to one-phase extractions. Adding bead homogenization increased feature count, peak areas, and CVs. Extraction I showed higher peak areas and lower CVs than extraction II, thus being the most suited one-phase method. Extraction III and IV showed similar results, with III being easier to execute and less prone to imprecisions. Thus, for future applications in zebrafish larvae metabolomics and infection models, extractions I and III might be chosen.

Fingerprinting Chemical Markers in the Mediterranean Orange Blossom Honey: UHPLC-HRMS Metabolomics Study Integrating Melissopalynological Analysis, GC-MS and HPLC-PDA-ESI/MS.

(1) Background: Citrus honey constitutes a unique monofloral honey characterized by a distinctive aroma and unique taste. The non-targeted chemical analysis can provide pivotal information on chemical markers that differentiate honey based on its geographical and botanical origin. (2) Methods: Within the PRIMA project "PLANT-B", a metabolomics workflow was established to unveil potential chemical markers of orange blossom honey produced in case study areas of Egypt, Italy, and Greece. In some of these areas, aromatic medicinal plants were cultivated to enhance biodiversity and attract pollinators. The non-targeted chemical analysis and metabolomics were conducted using ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). (3) Results: Forty compounds were disclosed as potential chemical markers, enabling the differentiation of the three orange blossom honeys according to geographical origin. Italian honey showed a preponderance of flavonoids, while in Greek honey, terpenoids and iridoids were more abundant than flavonoids, except for hesperidin. In Egyptian honey, suberic acid and a fatty acid ester derivative emerged as chemical markers. New, for honey, furan derivatives were identified using GC-MS in Greek samples. (4) Conclusions: The application of UHPLC-HRMS metabolomics combined with an elaborate melissopalynological analysis managed to unveil several potential markers of Mediterranean citrus honey potentially associated with citrus crop varieties and the local indigenous flora.

Application of untargeted metabolomics in primary hyperparathyroidism

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction.

Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms. We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research. We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer. We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings. Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.

Research progress of metabolomics in psoriasis.

Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is thought to contribute to the pathogenesis; however, the details of the pathogenesis remain unclear. In addition, reliable biomarkers for diagnosis, assessment of disease activity, and monitoring of therapeutic response are limited. Metabolomics is an emerging science that can be used to identify and analyze low molecular weight molecules in biological systems. During the past decade, metabolomics has been widely used in psoriasis research, and substantial progress has been made. This review summarizes and discusses studies that applied metabolomics to psoriatic disease. These studies have identified dysregulation of amino acids, carnitines, fatty acids, lipids, and carbohydrates in psoriasis. The results from these studies have advanced our understanding of: (1) the molecular mechanisms of psoriasis pathogenesis; (2) diagnosis of psoriasis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. We discuss common research strategies and progress in the application of metabolomics to psoriasis, as well as emerging trends and future directions.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications.

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Simultaneous Quantification of Trehalose and Trehalose 6-Phosphate by Hydrophilic Interaction Chromatography/Electrospray Accurate Mass Spectrometry with Application in Non-Targeted Metabolomics.

High-resolution mass spectrometry (HRMS) was coupled with ultra-high-performance liquid chromatography (UHPLC) to simultaneously quantify trehalose and trehalose 6-phosphate without derivatization or sample preparation. The use of full scan mode and exact mass analysis also makes it possible to carry out metabolomic analyses as well as semi-quantification. In addition, the use of different clusters in negative mode makes it possible to compensate for deficiencies in linearity and inerrant saturation at time-of-flight detectors. The method has been approved and validated for different matrices, yeasts, and bacteria, and has shown differentiation between bacteria as a function of growth temperatures.

Quality assessment of Astragali Radix based on pseudo-targeted metabolomics and chemometric approach.

Astragali Radix is widely used because of its dual use in medicine and food, and its quality evaluation is of great importance. In this study, a pseudo-targeted metabolomics approach based on scheduled multiple reaction monitoring was developed, and a total of 114 compounds with good linearity, sensitivity and reproducibility were selected for relative quantification, and the chemical differences between Astragali Radix of different growth patterns were further compared by chemometric analysis. With the help of multivariate and univariate analysis, 26 differential compounds between wild/semi-wild Astragali Radix and cultivated Astragali Radix were determined. Then 5 marker compounds were screened out by lasso regression, and further verified by systematic clustering, random forest, support vector machine, and logistic regression. In addition, malonyl-substituted flavonoids showed relatively higher content in wild/semi-wild Astragali Radix. Thus, the malonyl-substitution was the characteristic for flavonoids in wild/semi-wild Astragali Radix. In conclusion, the application of pseudo-targeted metabolomics and various statistical methods could offer multi-dimensional information for the holistic quality evaluation of Astragali Radix. This article is protected by copyright. All rights reserved.

Review of the applications of metabolomics approaches in dairy science: From factory to human

Dairy products are nutritious and are increasingly consumed as an important dietary component in China. Exploring the factors that affect the nutritional quality of dairy products and ensuring their safety have become the main focus of dairy research. The composition of metabolites in dairy products is large and complex. The levels and types of metabolites vary according to various factors in the process from factory to human dining table. Therefore, metabolites might be used to assess the nutritional value, traceability and authenticity, and physiological function of dairy products. This review's main goal is to introduce the most recent developments and applications of metabolomics as an efficient tool for comprehensively characterising the composition and dynamic changes of metabolites in the area of food science and nutrition research in the process of getting dairy products from factory to human. The examples are taken from the most relevant metabolomics work published from 2018 to 2022, focusing on potential marker metabolites and metabolic mechanisms related to dairy product quality, authenticity/traceability and dairy intake monitoring. The future direction of metabolomics in the field of dairy science was also discussed. This information will provide a reference for the further application of metabolomics technology to Chinese dairy products to develop their quality, safety and nutritional value.

Beyond cannabinoids: Application of NMR-based metabolomics for the assessment of Cannabis sativa L. crop health.

While Cannabis sativa L. varieties have been traditionally characterized by their major cannabinoid profile, it is now well established that other plant metabolites can also have physiological effects, including minor cannabinoids, terpenes, and flavonoids. Given the multiple applications of cannabis in the medical field, it is therefore critical to characterize it according to its chemical composition (i.e., its metabolome) and not only its botanical traits. With this in mind, the cannabinoid and metabolomic profiles from inflorescences of two C. sativa varieties with either high Δ9-tetrahydrocannabinolic acid (THCA) or high cannabidiolic acid (CBDA) contents harvested at different times were studied. According to results from HPLC and NMR-based untargeted metabolomic analyses of organic and aqueous plant material extracts, we show that in addition to expected variations according to cannabinoid profiles, it is possible to distinguish between harvests of the same variety. In particular, it was possible to correlate variations in the metabolome with presence of powdery mildew, leading to the identification of molecular markers associated with this fungal infection in C. sativa.

Kidney-specific metabolomic profiling in machine perfusate

Given their accessibility and relevance to established clinical workflows, blood and urine have been the major focus of investigation in metabolomics studies of human kidney disease. In this issue, Liu etal. describe the application of metabolomics to perfusate from donor kidneys subjected to hypothermic machine perfusion. In addition to providing an elegant model for investigating kidney metabolism, this study highlights the limitations of allograft quality assessment and identifies metabolites of interest in kidney ischemia.

A164 IDENTIFYING THE MOST IMPORTANT PREDICTORS TO CORRELATE SERUM METABOLITES WITH MRE CHANGES IN PATIENTS WITH PEDIATRIC CROHN DISEASE

Abstract Background Endoscopy has been the gold standard for assessing activity in Pediatric Crohn disease (pCD); however, it is limited by its invasiveness and partial assessment of small intestine and transmural inflammation. To that end, the Pediatric Inflammatory Crohn's MRE Index (PICMI) is a valid, reliable, non-invasive, and responsive index that includes transmural inflammation when assessing disease activity. The pathogenesis of pCD remains poorly understood, but evidence suggests that endogenous metabolites produced in the intestinal tract might mediate pathogenesis. Despite the important applicability of metabolomics in increasing the understanding of pCD, there has been limited research on this topic. Purpose Serum metabolomic profiles are linked to disease activity in pediatric Crohn disease. Method ImageKids is a multicenter, prospective, observational cohort study, designed to develop PICMI for pCD. The study was conducted over 18 months with paired serum specimens collected at study initiation and completion for 56 pCD patients. Due to the long time between the visits and the fact that during the study variables that highly affect serum metabolites were not controlled, we considered each patient visit as an individual measure point. Metabolites were identified using a quantitative metabolomics approach through The Metabolomics Innovation Centre (TMIC; University of Alberta). Disease activity was determined by the cutoff values in the total PICMI score of each patient. The most relevant serum metabolites were identified by medium-level and high-level variable selection analysis. Pearson correlation and hypothesis testing were used to select important metabolites. Decision trees, regularization techniques, and support vector machines were used to assess explicit importance of metabolites in disease activity. Result(s) This work provides a strategy to reduce a dimensional dataset from a metabolomic experiment. By medium-level selection analysis we were able to identify 117 statistical important metabolites for disease activity. The high-level selection analysis allowed to indicate the importance of the top 10 metabolites trough disease activity (defined by PICMI index). Results, also show that the evaluation of importance of metabolites through multivariate statistical models is dependent of the intrinsic variable selection model. Figure 1 reveals that Tryptophan ranked highest in the feature importance scoring. Histidine, Methylhistidine, Citric acid, Isoleucine, and Decanoylcarnitine also correlated well with disease severity. Image Conclusion(s) This work uses a unique approach of multivariate statistical analyses, to identify metabolites associated with pCD disease activity. Tryptophan has been previously identified as significantly altered in the blood of IBD patients compared to controls. Histidine is known to be involved in the mediation of oxidative stress, potentially influencing intestinal inflammation. These metabolites could serve as biomarkers and help define pCD pathogenesis. Disclosure of Interest None Declared