Although multiple gene and protein expression have been extensively profiled in human pulmonary arterial hypertension (PAH), the mechanism for the development and progression of pulmonary hypertension remains elusive. Analysis of the global metabolomic heterogeneity within the pulmonary vascular system leads to a better understanding of disease progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we showed unbiased metabolomic profiles of disrupted glycolysis, increased TCA cycle, and fatty acid metabolites with altered oxidation pathways in the human PAH lung. The results suggest that PAH has specific metabolic pathways contributing to increased ATP synthesis for the vascular remodeling process in severe pulmonary hypertension. These identified metabolites may serve as potential biomarkers for the diagnosis of PAH. By profiling metabolomic alterations of the PAH lung, we reveal new pathogenic mechanisms of PAH, opening an avenue of exploration for therapeutics that target metabolic pathway alterations in the progression of PAH.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3923046 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088727 | PLOS |
Plant Genome
March 2025
Department of Fundamental Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
The plant Polygonum capitatum (P. capitatum) contains a variety of flavonoids that are distributed differently among different parts. Nevertheless, differentially expressed genes (DEGs) associated with this heterogeneous distribution have not been identified.
View Article and Find Full Text PDFBackground: The rupture of intracranial aneurysms (IAs) leads to aneurysmal subarachnoid hemorrhage (aSAH), which is associated with significant disability and mortality rates. This study aims to identify metabolic markers causally linked to the occurrence of IAs and aSAH through Mendelian randomization (MR), thereby offering novel predictive and therapeutic targets.
Methods: We conducted a genome-wide association study (GWAS) on IAs and aSAH, analyzing 1,400 metabolomic indices from the Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8,299).
Comput Biol Med
January 2025
Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6DZ, UK; Institute for Food, Nutrition and Health (IFNH), University of Reading, Reading, RG6 6AH, UK. Electronic address:
Background: Machine learning (ML) integration of clinical, metabolite, and genetic data reveals variable results in predicting cardiometabolic health (CMH) outcomes. Therefore, we aim to (1) evaluate whether a multi-modal approach incorporating all three data types using ML algorithms can improve CMH outcome prediction compared to single-modal or paired-modal models, and (2) compare the methodologies used in existing prediction models.
Methods: We systematically searched five databases from 1998 to 2024 for ML predictive modelling studies using the multi-modal approach for CMH outcomes.
Int J Mol Sci
December 2024
School of Medicine and Surgery, University of Milan-Bicocca, 20126 Milan, Italy.
Genetic studies of haematological cancers have pointed out the heterogeneity of leukaemia in its different subpopulations, with distinct mutations and characteristics, impacting the treatment response. Next-generation sequencing (NGS) and genome-wide analyses, as well as single-cell technologies, have offered unprecedented insights into the clonal heterogeneity within the same tumour. A key component of this heterogeneity that remains unexplored is the intracellular metabolome, a dynamic network that determines cell functions, signalling, epigenome regulation, immunity and inflammation.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang 110004, China. Electronic address:
Background: Metabolomics research is a promising orientation for the diagnosis and intervention of several diseases, and observational studies have found many metabolic profiles to be associated with mental disorders. However, the causal relationship between plasma and cerebrospinal fluid (CSF) metabolites and mental disorders has not been established.
Methods: We identified independent genetic variants associated with plasma, CSF metabolites, and mental disorders from pooled data in the published Genome-wide association studies (GWASs) and performed Mendelian randomization (MR) to investigate causal relationships.
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