Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice

Dietary forage to concentrate ratios impact on yak ruminal microbiota and metabolites

To improve the rumen fermentation function and growth performance of yaks (Bos grunniens), better understanding of the effect of different dietary forage to concentrate ratios on rumen microbiota and metabolites is needed. In the present study, three diets with different dietary forage to concentrate ratios (50:50, 65:35, and 80:20) were fed to 36 housed male yaks. The changes in the distribution of rumen microorganisms and metabolites and the interactions between them were studied by 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC–MS). The diversity and richness of microorganisms in the rumen varied according to diet. The most abundant phyla were Firmicutes and Bacteroidetes. Firmicutes was the most abundant in the C50 group, and the relative abundance of Bacteroidetes was significantly lower in the C65 group than in the C80 group (p < 0.05). The Christensenellaceae_R-7_group, Rikenellaceae_RC9_gut_group, and Methanobrevibacter had the highest relative abundances at the genus level. Among them, Christensenellace_R-7_group had the highest relative abundance in the C50 group. The Rikenellaceae_RC9_gut_group was significantly abundant in the C80 group compared with the C50 group. The Methanobrevibacter content was higher in the C65 group than in the other two groups. Both the concentration and metabolic pathways of rumen metabolites were influenced by the dietary concentrate ratio; lipids, lipid-like molecules, organic acid metabolites, and organic oxide-related metabolites differed between the groups. Significant changes were found for six metabolic pathways, including arginine and proline metabolism; glycine, serine, and threonine metabolism; glyoxylate and dicarboxylate metabolism; arginine biosynthesis; glycerophospholipid metabolism; glycerolipid metabolism; and nitrogen metabolism.

Narrative review of metabolomics in cardiovascular disease

Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.

Global metabolomics profiling of colorectal cancer in Malaysian patients

Introduction: The serum metabolomics approach has been used to identify metabolite biomarkers that can diagnose colorectal cancer (CRC) accurately and specifically. However, the biomarkers identified differ between studies suggesting that more studies need to be performed to understand the influence of genetic and environmental factors. Therefore, this study aimed to identify biomarkers and affected metabolic pathways in Malaysian CRC patients. Methods: Serum from 50 healthy controls and 50 CRC patients were collected at UKM Medical Centre. The samples were deproteinized with acetonitrile and untargeted metabolomics profile determined using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOFMS, Agilent USA). The data were analysed using Mass Profiler Professional (Agilent, USA) software. The panel of biomarkers determined were then used to identify CRC from a new set of 20 matched samples. Results: Eleven differential metabolites were identified whose levels were significantly different between CRC patients compared to normal controls. Based on the analysis of the area under the curve, 7 of these metabolites showed high sensitivity and specificity as biomarkers. The use of the 11 metabolites on a new set of samples was able to differentiate CRC from normal samples with 80% accuracy. These metabolites were hypoxanthine, acetylcarnitine, xanthine, uric acid, tyrosine, methionine, lysoPC, lysoPE, citric acid, 5-oxoproline, and pipercolic acid. The data also showed that the most perturbed pathways in CRC were purine, catecholamine, and amino acid metabolisms. Conclusion: Serum metabolomics profiling can be used to identify distinguishing biomarkers for CRC as well as to further our knowledge of its pathophysiological mechanisms.

Sputum metabolomic profiling of bronchial asthma based on quadruple time-of-flight mass spectrometry

To improve diagnosis of asthma, we tend to confirm potential biomarkers by comparing sputum metabolome profiles between asthma patients and healthy controls, using ultra-high-performance liquid chromatography coupled to quadruple time-of-flight mass spectrometry (UHPLC-QTOF/MS). Thirty endogenous metabolites contributing to the separation of asthma patients and healthy controls were tentatively identified in positive mode, such as 1-hexadecanoyl-sn-glycerol, glycerol 1-stearate, sphingosine, Phe-Ser, Tyr-Ala and Phe-Gln, and 12 endogenous metabolites were identified in negative mode, such as cytidine 2',3'-cyclic phosphate, 1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-phospho-(1'-rac-glycerol), 1-octadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphoserine, thymidine, gamma-L-glutamyl-L-valine and adenine. Those differential metabolites were mainly participatedin glycerophospholipid metabolism, retrograde endocannabinoid signaling and metabolic pathways in positive mode and 2-oxocarboxylic acid metabolism, biosynthesis of amino acids, phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine degradation and metabolic pathways in negative mode. Importantly, several metabolic pathways including glycerophospholipid metabolism, inositol phosphate metabolism, and glycolysis or gluconeogenesis were found most important. These findings suggest sputum metabolomics can be used for the early diagnosis and risk prediction of asthma.

Metabolic Characterization of Myocardial Infarction Using GC-MS-Based Tissue Metabolomics

Understanding the metabolic features of myocardial infarction (MI) is critical to its prevention and treatment. Here, we aimed to characterize the metabolic features of early MI using a tissue metabolomics method based on gas chromatography-mass spectrometry (GC-MS). Thirty-four pairs of infarcted myocardia and their matched non-infarcted myocardia were collected from 34 rats that underwent coronary artery ligation (CAL); their metabolic profiles were compared by GC-MS-based tissue metabolomics to characterize the metabolic features of MI. On the basis of differential metabolites, their diagnostic potential for MI was analyzed, and MI-related metabolic pathways were investigated. Serum samples before and post MI were used to validate the results obtained in myocardia. The metabolic profile of the infarcted myocardia was obviously different from that of the non-infarcted myocardia, as indicated by partial least squares discriminate analysis (PLS-DA) plots. Twenty-two metabolites were identified to be different between the infarcted myocardia and non-infarcted myocardia. These metabolic alterations reflect energy deficit, acidosis, oxidative stress, ionic imbalance, and cardiac injury post MI. Glutamine, glutamate, and lactate were confirmed to jointly confer a favorable potential for diagnosing MI, which can be well validated in serum.

Livestock metabolomics and the livestock metabolome: A systematic review

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.