Screening for Regulatory Network of miRNA–Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study

Profiles and interactions of gut microbiome and intestinal microRNAs in pediatric Crohn's disease

Gut dysbiosis is closely related to dysregulated microRNAs (miRNAs) in the intestinal epithelial cells, which plays an important role in the pathogenesis of Crohn's disease (CD). We investigated the relationship between fecal gut microbiome (GM) and intestinal tissue miRNAs in different stages of pediatric CD. Metagenomic analysis and miRNA sequencing were conducted to examine the GM and intestinal miRNA profiles of CD patients before and after clinical induction therapy and the controls. Twenty-seven newly diagnosed, therapy-naïve pediatric patients with active CD and 11 non-inflammatory bowel disease (IBD) controls were recruited in this study. Among CD patients, 11 patients completed induction treatment and reached clinical remission. Both GM and miRNA profiles were significantly changed between CD patients and controls. Seven key bacteria were identified at species level including Defluviitalea raffinosedens, Thermotalea metallivorans, Roseburia intestinalis, Dorea sp. AGR2135, Escherichia coli, Shigella sonnei, and Salmonella enterica, the exact proportions of which were further validated by real-time quantitative PCR analysis. Eight key miRNAs were also identified including hsa-miR-215-5p, hsa-miR-194-5p, hsa-miR-12135, hsa-miR-509-3-5p, hsa-miR-212-5p, hsa-miR-4448, hsa-miR-501-3p, and hsa-miR-503-5p. The functional enrichment analysis of differential miRNAs indicated the significantly altered cyclin protein, cyclin-dependent protein, and cell cycle pathway. The close interactions between seven key bacteria and eight key miRNAs were further investigated by miRNA target prediction. The association between specific miRNA expressions and key gut bacteria at different stages of CD supported their important roles as potential molecular biomarkers. Understanding the relationship between them will help us to explore the molecular mechanisms of CD.

IMPORTANCE

Since previous studies have focused on the change of the fecal gut microbiome and intestinal tissue miRNA in pediatric Crohn's disease (CD), the relationship between them in different stages is still not clear. This is the first study to explore the gut microbiota and miRNA and their correlations with the Pediatric Crohn's Disease Activity Index (PCDAI). Crohn's Disease Endoscopic Index of Severity (CDEIS), and calprotectin, by applying two omics approach in three different groups (active CD, CD in remission with exclusive enteral nutrition or infliximab induction therapy, and the healthy controls). Both gut microbiome structure and the miRNA profiles were significantly changed in the different stage of CD. Seven key gut microbiome at species and eight key miRNAs were found, and their close interactions were further fully investigated by miRNA target prediction.

The promotion of sestrin2/AMPK signaling by HIF-1α overexpression enhances the damage caused by acute myocardial infarction

OBJECTIVE

Acute myocardial infarction (AMI), is a serious form of coronary heart disease. The present study sought to investigate the impact of HIF-1α on AMI, along with its fundamental mechanism.

METHODS

Sprague-Dawley (SD) rats were used to conduct an AMI model. 2,3,5-triphenyl-2H-tetrazolium chloride (TTC) staining was used examine the region of myocardial infract area at various time intervals. Protein expression levels were detected using western blotting. The rats were randomly divided into sham, model, negative control (NC), HIF-1α overexpression (HIF-1α-OE), and HIF-1α-OE+ si-sestrin2 groups. We examined the impact of HIF-1α overexpression on AMI rats using Haematoxylin-Eosin (H&E) staining, TTC staining, enzyme-linked immunosorbent assay (ELISA), TdT-mediated dUTP Nick-End Labeling (TUNEL) assay, and immunohistochemistry (IHC) staining.

RESULTS

According to the TTC findings, the region affected by myocardial infarction reached its peak at day 14. Based on the results from the western blot analysis, the levels of HIF-1α and sestrin2 were found the minimum on day 28. Subsequently, we discovered that the overexpression of HIF-1α rescued the cardiac function parameters, improved the morphology of myocardial tissue, and mitigated inflammation. Furthermore, the overexpression of HIF-1α led to a reduction in the levels of MDA and an increase in the levels of SOD. Moreover, the overexpression of HIF-1α resulted in a decrease in cellular apoptosis. This result was confirmed by the expression levels of Bcl-2 and Bax. Nevertheless, the defensive impact of elevated HIF-1α expression was somewhat counteracted by the suppression of sestrin2. In terms of mechanism, the overexpression of HIF-1α enhanced the levels of sestrin2 and the protein adenosine monophosphate activated kinase (AMPK).

CONCLUSION

Our research suggests that the overexpression of HIF-1α may rescue the damage to myocardial tissue, and this effect is associated with the sestrin2/AMPK signaling pathway. Our study provides a novel comprehension of the protective effects of HIF-1α overexpression on AMI.

Identification of immune-related biomarkers co-occurring in acute ischemic stroke and acute myocardial infarction

Background

Acute ischemic stroke (AIS) and acute myocardial infarction (AMI) share several features on multiple levels. These two events may occur in conjunction or in rapid succession, and the occurrence of one event may increase the risk of the other. Owing to their similar pathophysiologies, we aimed to identify immune-related biomarkers common to AIS and AMI as potential therapeutic targets.

Methods

We identified differentially expressed genes (DEGs) between the AIS and control groups, as well as AMI and control groups using microarray data (GSE16561 and GSE123342). A weighted gene co-expression network analysis (WGCNA) approach was used to identify hub genes associated with AIS and/or AMI progression. The intersection of the four gene sets identified key genes, which were subjected to functional enrichment and protein-protein interaction (PPI) network analyses. We confirmed the expression levels of hub genes using two sets of gene expression profiles (GSE58294 and GSE66360), and the ability of the genes to distinguish patients with AIS and/or AMI from control patients was assessed by calculating the receiver operating characteristic values. Finally, the investigation of transcription factor (TF)-, miRNA-, and drug-gene interactions led to the discovery of therapeutic candidates.

Results

We identified 477 and 440 DEGs between the AIS and control groups and between the AMI and control groups, respectively. Using WGCNA, 2,776 and 2,811 genes in the key modules were identified for AIS and AMI, respectively. Sixty key genes were obtained from the intersection of the four gene sets, which were used to identify the 10 hub genes with the highest connection scores through PPI network analysis. Functional enrichment analysis revealed that the key genes were primarily involved in immunity-related processes. Finally, the upregulation of five hub genes was confirmed using two other datasets, and immune infiltration analysis revealed their correlation with certain immune cells. Regulatory network analyses indicated that GATA2 and hsa-mir-27a-3p might be important regulators of these genes.

Conclusion

Using comprehensive bioinformatics analyses, we identified five immune-related biomarkers that significantly contributed to the pathophysiological mechanisms of both AIS and AMI. These biomarkers can be used to monitor and prevent AIS after AMI, or vice versa.

Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics

Purpose

Previous studies have confirmed that inflammation and immunity are involved in the pathogenesis of acute myocardial infarction (AMI). However, only few related genes are identified as biomarkers for the diagnosis and treatment of AMI.

Patients and Methods

GSE48060 and GSE60993 datasets were retrieved from Gene Expression Omnibus. The differentially expressed immuno-inflammation-related genes (DEIIRGs) were obtained from GSE48060, and the biomarkers for AMI were screened and validated using the "Neuralnet" package and GSE60993 dataset. Further, the biomarker-based nomogram was constructed, and miRNAs, transcription factors (TFs), and potential drugs targeting the biomarkers were explored. Furthermore, immune infiltration analysis was analyzed in AMI. Finally, the biomarkers were verified by assessing their mRNA levels using real-time quantitative PCR (RT-qPCR).

Results

First, eight biomarkers were screened via bioinformatics, and the artificial neural network model indicated a higher prediction accuracy for AMI even in the validation dataset. Nomogram had accurate forecasting ability for AMI as well. The TFs GTF2I, PHOX2B, RUNX1, and FOS targeting hsa-miR-1297 could regulate the expressions of ADM and CBLB, and RORA could effectively interact with melatonin and citalopram. RT-qPCR results for ADM, PI3, MMP9, NRG1 and CBLB were consistent with those of bioinformatic analysis.

Conclusion

In conclusion, eight key immuno-inflammation-related genes, namely, SH2D1B, ADM, PI3, MMP9, NRG1, CBLB, RORA, and FASLG, may serve as the potential biomarkers for AMI, in which the downregulation of CBLB and upregulation of ADM, PI3, and NRG1 in AMI was detected for the first time, providing a new strategy for the diagnosis and treatment of AMI.

Identification of oxidative stress-related genes and potential mechanisms in atherosclerosis

Atherosclerosis (AS) is the main cause of death in individuals with cardiovascular and cerebrovascular diseases. A growing body of evidence suggests that oxidative stress plays an essential role in Atherosclerosis pathology. The aim of this study was to determine genetic mechanisms associated with Atherosclerosis and oxidative stress, as well as to construct a diagnostic model and to investigate its immune microenvironment. Seventeen oxidative stress-related genes were identified. A four-gene diagnostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm based on these 17 genes. The area under the Receiver Operating Characteristic (ROC) curve (AUC) was 0.967. Based on the GO analysis, cell-substrate adherens junction and focal adhesion were the most enriched terms. KEGG analysis revealed that these overlapping genes were enriched in pathways associated with Alzheimer's disease and Parkinson's disease, as well as with prion disease pathways and ribosomes. Immune cell infiltration correlation analysis showed that the immune cells with significant differences were CD4 memory activated T cells and follicular helper T cells in the GSE43292 dataset and CD4 naïve T cells and CD4 memory resting T cells in the GSE57691 dataset. We identified 17 hub genes that were closely associated with oxidative stress in AS and constructed a four-gene (aldehyde dehydrogenase six family member A1 (ALDH6A1), eukaryotic elongation factor 2 kinase (EEF2K), glutaredoxin (GLRX) and l-lactate dehydrogenase B (LDHB)) diagnostic model with good accuracy. The four-gene diagnostic model was also found to have good discriminatory efficacy for the immune cell infiltration microenvironment of AS. Overall, these findings provide valuable information and directions for future research into Atherosclerosis diagnosis and aid in the discovery of biological mechanisms underlying AS with oxidative stress.

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Heart failure (HF) is the main manifestation of cardiovascular disease. Recent studies have shown that various RNA molecules and their complex connections play an essential role in HF’s pathogenesis and pathological progression. This paper aims to mine key RNA molecules associated with HF. We proposed a Prior-knowledge Driven Joint Deep Semi-Negative Matrix Factorization (PD-JDSNMF) model that uses a hierarchical nonlinear feature extraction method that integrates three types of data: mRNA, lncRNA, and miRNA. The PPI information is added to the model as prior knowledge, and the Laplacian constraint is used to help the model resist the noise in the genetic data. We used the PD-JDSNMF algorithm to identify significant co-expression modules. The elements in the module are then subjected to bioinformatics analysis and algorithm performance analysis. The results show that the PD-JDSNMF algorithm can robustly select biomarkers associated with HF. Finally, we built a heart failure diagnostic model based on multiple classifiers and using the Top 13 genes in the significant module, the AUC of the internal test set was up to 0.8714, and the AUC of the external validation set was up to 0.8329, which further confirmed the effectiveness of the PD-JDSNMF algorithm.