A circular network of purine metabolism as coregulators of dilated cardiomyopathy

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes

Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification

Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from complex data. Bringing together domain-specific knowledge from metabolomics with explainable ML methods can refine the predictive performance and interpretability of models used in atherosclerosis research. In this work, we aimed to identify the most impactful metabolites associated with the presence of atherosclerotic cardiovascular disease (ASCVD) in cross-sectional case-control studies using explainable ML methods integrated with metabolomics domain knowledge. For this, a subset from the FLEMENGHO cohort with metabolomic data available was used as the training cohort, including 63 patients with a history of ASCVD and 52 non-smoking controls matched by age, sex, and body mass index from the same population. First, Partial Least Squares Discriminant Analysis (PLS-DA) was applied for dimensionality reduction. The selected metabolites' correlations were analyzed by considering their chemical categorization. Then, eXtreme Gradient Boosting (XGBoost) was used to identify metabolites that characterize ASCVD. Next, the selected metabolites were evaluated in an external cohort to determine their effectiveness in distinguishing between cases and controls. A total of 56 metabolites were selected for ASCVD discrimination using PLS-DA. The primary identified metabolites' superclasses included lipids, organic acids, and organic oxygen compounds. Upon integrating these metabolites with the XGBoost model, the classification yielded a test area under the curve (AUC) of 0.75. SHAP analyses ranked cholesterol, 3-methylhistidine, and glucuronic acid among the most impactful features and showed the diversity of metabolites considered for building the ASCVD discriminator. Also using XGBoost, the selected metabolites achieved an AUC of 0.93 in an independent external validation cohort. In conclusion, the combination of different metabolites has the potential to build classifiers for ASCVD. Integrating metabolite categorization within the SHAP analysis further enhanced the interpretability of the model, offering insights into metabolite-specific contributions to ASCVD risk.

Shenfu injection improves isoproterenol-induced heart failure in rats by modulating co-metabolism and regulating the trimethylamine-N-oxide - inflammation axis

Heart failure (HF) is a chronic condition that progressively worsens and continues to be a major financial burden and public health concern. The "gut-heart" axis provides an innovative perspective and therapeutic strategy for preventing and treating heart failure. Shenfu injection (SFI) is a Traditional Chinese Medicine-based treatment demonstrating potential as a therapeutic strategy for heart failure. However, the precise therapeutic mechanisms of SFI in heart failure are not completely characterized. In this study, HF models were established utilizing subcutaneous multipoint injection of isoproterenol (ISO) at a dosage of 5 mg kg-1·d-1 for 7 days. Serum levels of inflammatory biomarkers were quantified using protein microarrays. Rat feces were analyzed using untargeted metabolomics research and 16S rRNA sequencing. The link between gut microbiota and metabolites was examined using a MetOrigin and Spearman correlation analysis. Our results show that Shenfu injection effectively enhances cardiac function in rats with ISO-induced heart failure by potentially modulating pro-/anti-inflammatory imbalance and reducing serum and urine Trimethylamine-N-oxide (TMAO) levels. Moreover, SFI significantly increases the abundance of Bacteroidota at the phylum level, thereby improving disrupted gut microbiota composition. Additionally, SFI supplementation enriches specific genera known for their capacity to produce short-chain fatty acids. SFI was found to be associated with three key metabolic pathways, as revealed by fecal metabonomics analysis, including the pentose phosphate pathway, pyrimidine metabolism, and purine metabolism. Metabolite tracing analysis revealed that Taurine and hypotaurine metabolism was found to be specific to the microbial community. The biosynthesis of Pyrimidine metabolism, Purine metabolism, beta-alanine metabolism, Naphthalene degradation, Pantothenate, and CoA biosynthesis were identified as co-metabolic pathways between microbes and host. The Spearman correlation analysis was also significantly correlated to differentially expressed metabolites regulated by SFI and the gut microbiota. These results suggest that SFI improves ISO-induced heart failure by modulating co-metabolism and regulating the TMAO-inflammation axis.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Serum multi-omics analysis in hindlimb unloading mice model: Insights into systemic molecular changes and potential diagnostic and therapeutic biomarkers

Microgravity, in space travel and prolonged bed rest conditions, induces cardiovascular deconditioning along with skeletal muscle mass loss and weakness. The findings of microgravity research may also aid in the understanding and treatment of human health conditions on Earth such as muscle atrophy, and cardiovascular diseases. Due to the paucity of biomarkers and the unknown underlying mechanisms of cardiovascular and skeletal muscle deconditioning in these environments, there are insufficient diagnostic and preventative measures. In this study, we employed hindlimb unloading (HU) mouse model, which mimics astronauts in space and bedridden patients, to first evaluate cardiovascular and skeletal muscle function, followed by proteomics and metabolomics LC-MS/MS-based analysis using serum samples. Three weeks of unloading caused changes in the function of the cardiovascular system in c57/Bl6 mice, as seen by a decrease in mean arterial pressure and heart weight. Unloading for three weeks also changed skeletal muscle function, causing a loss in grip strength in HU mice and atrophy of skeletal muscle indicated by a reduction in muscle mass. These modifications were partially reversed by a two-week recovery period of reloading condition, emphasizing the significance of the recovery process. Proteomics analysis revealed 12 dysregulated proteins among the groups, such as phospholipid transfer protein, Carbonic anhydrase 3, Parvalbumin alpha, Major urinary protein 20 (Mup20), Thrombospondin-1, and Apolipoprotein C-IV. On the other hand, metabolomics analysis showed altered metabolites among the groups such as inosine, hypoxanthine, xanthosine, sphinganine, l-valine, 3,4-Dihydroxyphenylglycol, and l-Glutamic acid. The joint data analysis revealed that HU conditions mainly impacted pathways such as ABC transporters, complement and coagulation cascades, nitrogen metabolism, and purine metabolism. Overall, our results indicate that microgravity environment induces significant alterations in the function, proteins, and metabolites of these mice. These observations suggest the potential utilization of these proteins and metabolites as novel biomarkers for assessing and mitigating cardiovascular and skeletal muscle deconditioning associated with such conditions.

High Stretch Modulates cAMP/ATP Level in Association with Purine Metabolism via miRNA-mRNA Interactions in Cultured Human Airway Smooth Muscle Cells

High stretch (>10% strain) of airway smooth muscle cells (ASMCs) due to mechanical ventilation (MV) is postulated to contribute to ventilator-induced lung injury (VILI), but the underlying mechanisms remain largely unknown. We hypothesized that ASMCs may respond to high stretch via regulatory miRNA-mRNA interactions, and thus we aimed to identify high stretch-responsive cellular events and related regulating miRNA-mRNA interactions in cultured human ASMCs with/without high stretch. RNA-Seq analysis of whole genome-wide miRNAs revealed 12 miRNAs differentially expressed (DE) in response to high stretch (7 up and 5 down, fold change >2), which target 283 DE-mRNAs as identified by a parallel mRNA sequencing and bioinformatics analysis. The KEGG and GO analysis further indicated that purine metabolism was the first enriched event in the cells during high stretch, which was linked to miR-370-5p-PDE4D/AK7. Since PDE4D/AK7 have been previously linked to cAMP/ATP metabolism in lung diseases and now to miR-370-5p in ASMCs, we thus evaluated the effect of high stretch on the cAMP/ATP level inside ASMCs. The results demonstrated that high stretch modulated the cAMP/ATP levels inside ASMCs, which could be largely abolished by miR-370-5p mimics. Together, these findings indicate that miR-370-5p-PDE4D/AK7 mediated high stretch-induced modulation of cAMP and ATP synthesis inside ASMCs. Furthermore, such interactive miRNA-mRNA pairs may provide new insights for the discovery of effective biomarkers/therapeutic targets for the diagnosis and treatment of VILI and other MV-associated respiratory diseases.

Integrated Functions of Cardiac Energetics, Mechanics, and Purine Nucleotide Metabolism

Purine nucleotides play central roles in energy metabolism in the heart. Most fundamentally, the free energy of hydrolysis of the adenine nucleotide adenosine triphosphate (ATP) provides the thermodynamic driving force for numerous cellular processes including the actin-myosin crossbridge cycle. Perturbations to ATP supply and/or demand in the myocardium lead to changes in the homeostatic balance between purine nucleotide synthesis, degradation, and salvage, potentially affecting myocardial energetics and, consequently, myocardial mechanics. Indeed, both acute myocardial ischemia and decompensatory remodeling of the myocardium in heart failure are associated with depletion of myocardial adenine nucleotides and with impaired myocardial mechanical function. Yet there remain gaps in the understanding of mechanistic links between adenine nucleotide degradation and contractile dysfunction in heart disease. The scope of this article is to: (i) review current knowledge of the pathways of purine nucleotide depletion and salvage in acute ischemia and in chronic heart disease; (ii) review hypothesized mechanisms linking myocardial mechanics and energetics with myocardial adenine nucleotide regulation; and (iii) highlight potential targets for treating myocardial metabolic and mechanical dysfunction associated with these pathways. It is hypothesized that an imbalance in the degradation, salvage, and synthesis of adenine nucleotides leads to a net loss of adenine nucleotides in both acute ischemia and under chronic high-demand conditions associated with the development of heart failure. This reduction in adenine nucleotide levels results in reduced myocardial ATP and increased myocardial inorganic phosphate. Both of these changes have the potential to directly impact tension development and mechanical work at the cellular level. © 2024 American Physiological Society. Compr Physiol 14:5345-5369, 2024.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates such as fatty acids, carbohydrates, lipids, and amino acids to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic extractions) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection - direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF MS/MS). Using DI-FTICR MS, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.