The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis

Comparative proteomic profiling of the ovine and human PBMC inflammatory response

Understanding the cellular and molecular mechanisms of inflammation requires robust animal models. Sheep are commonly used in immune-related studies, yet the validity of sheep as animal models for immune and inflammatory diseases remains to be established. This cross-species comparative study analyzed the in vitro inflammatory response of ovine (oPBMCs) and human PBMCs (hPBMCs) using mass spectrometry, profiling the proteome of the secretome and whole cell lysate. Of the entire cell lysate proteome (oPBMCs: 4217, hPBMCs: 4574 proteins) 47.8% and in the secretome proteome (oPBMCs: 1913, hPBMCs: 1375 proteins) 32.8% were orthologous between species, among them 32 orthologous CD antigens, indicating the presence of six immune cell subsets. Following inflammatory stimulation, 71 proteins in oPBMCs and 176 in hPBMCs showed differential abundance, with only 7 overlapping. Network and Gene Ontology analyses identified 16 shared inflammatory-related terms and 17 canonical pathways with similar activation/inhibition patterns in both species, demonstrating significant conservation in specific immune and inflammatory responses. However, ovine PMBCs also contained a unique WC1+γδ T-cell subset, not detected in hPBMCs. Furthermore, differences in the activation/inhibition trends of seven canonical pathways and the sets of DAPs between sheep and humans, emphasize the need to consider interspecies differences in translational studies and inflammation research.

The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis

BACKGROUND

Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.

METHODS

To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls.

RESULTS

A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls.

CONCLUSIONS

To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

Identification and subtype analysis of biomarkers associated with the solute carrier family in acute myocardial infarction

The dysregulation of some solute carrier (SLC) proteins has been linked to a variety of diseases, including diabetes and chronic kidney disease. However, SLC-related genes (SLCs) has not been extensively studied in acute myocardial infarction (AMI). The GSE66360 and GSE60993 datasets, and SLCs geneset were enrolled in this study. Differentially expressed SLCs (DE-SLCs) were screened by overlapping DEGs between the AMI and control groups and SLCs. Next, functional enrichment analysis was carried out to research the function of DE-SLCs. Consistent clustering of samples from the GSE66360 dataset was accomplished based on DE-SLCs selected. Next, the gene set enrichment analysis (GSEA) was performed on the DEGs-cluster (cluster 1 vs cluster 2). Three machine learning models were performed to obtain key genes. Subsequently, biomarkers were obtained through receiver operating characteristic (ROC) curves and expression analysis. Then, the immune infiltration analysis was performed. Afterwards, single-gene GSEA was carried out, and the biomarker-drug network was established. Finally, quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of biomarkers. In this study, 13 DE-SLCs were filtered by overlapping 366 SLCs and 448 DEGs. The functional enrichment results indicated that the genes were implicated with amino acid transport and TNF signaling pathway. After the consistency clustering analysis, the samples were classified into cluster 1 and cluster 2 subtypes. The functional enrichment results showed that DEGs-cluster were implicated with chemokine signaling pathway and so on. Further, SLC11A1 and SLC2A3 were identified as SLC-related biomarkers, which had the strongest negative relationship with resting memory CD4 T cells and the strongest positive association with activated mast cells. In addition, the single-gene GSEA results showed that cytosolic ribosome was enriched by the biomarkers. Five drugs targeting SLC2A3 were predicted as well. Lastly, the experimental results showed that the biomarkers expression trends were consistent with public database. In this study, 2 SLC-related biomarkers (SLC11A1 and SLC2A3) were screened and drug predictions were carried out to explore the prediction and treatment of AMI.

Panax quinquefolium L. and Salvia miltiorrhiza Bunge. Enhances Angiogenesis by Regulating the miR-155-5p/HIF-1α/VEGF Axis in Acute Myocardial Infarction

Background

Combination of Panax quinquefolium L and Salvia miltiorrhiza Bunge. (PS) has been widely used in the clinical treatment of ischemic heart disease. The purpose of this study was to explore the therapeutic effect and mechanism of PS on angiogenesis in rats after acute myocardial infarction (AMI).

Methods

A rat model of AMI was established by ligating the left anterior descending (LAD) artery. The grouping and administration scheme were as follows: sham group, model group, PS low-dose (PS-L) group, PS high-dose (PS-H) group, PX-478 group and angiotensin converting enzyme inhibitor (ACEI) group. After 28 days of treatment, echocardiography, myocardial infarct size, some angiogenesis markers and the miR-155-5p/HIF-1α/VEGF axis were measured.

Results

PS improved cardiac structure and function, reduced infarct size, and alleviated myocardial fibrosis and inflammatory cell infiltration in AMI rats. Mechanistically, PS enhanced the expression of HGF and bFGF in serum, increased the levels of MVD and CD31 in myocardial tissues, and inhibited the activation of the miR-155-5p/HIF-1α/VEGF pathway, which ultimately promoted angiogenesis. In addition, the regulatory effect of PS on angiogenesis was partly abolished by PX-478.

Conclusion

PS increased the expression of MVD and CD31 in the myocardium and stimulated angiogenesis. The above effects of PS may be associated with the inhibition of the miR-155-5p/HIF-1α/VEGF axis.

A plasma proteomic approach in patients with heart failure after acute myocardial infarction: insights into the pathogenesis and progression of the disease

Aims

The pathogenesis of disease progression targets for patients with heart failure after acute myocardial infarction was investigated by using plasma proteomics.

Methods

The plasma proteomes of acute myocardial infarction patients with (MI-HF) and without (MI-WHF) heart failure were compared. Each group consisted of 10 patients who were matched for age and sex. The peptides were analyzed by 2-dimensional liquid chromatography coupled to tandem mass spectrometry in a high definition mode. Parallel reaction monitoring (PRM) verified the selected target proteins.

Results

We identified and quantified 2,589 and 2,222 proteins, respectively, and found 117 differentially expressed proteins (DEPs) (≥1.5-fold), when the MI-HF and MI-WHF groups were compared. Of these 51 and 66 were significantly up-regulated and down-regulated, respectively. The significant DEPs was subjected to protein-protein interaction network analysis which revealed a central role of the NF-κB signaling pathway in the MI-HF patients. PRM verified that MB, DIAPH1, VNN1, GOT2, SLC4A1, CRP, CKM, SOD3, F7, DLD, PGAM2, GOT1, UBA7 and HYOU1 were 14 proteins which were highly expressed in MI-HF patients.

Conclusions

These findings showed a group of proteins related to the NF-κB signaling pathway in the pathogenesis of patients with poor outcomes after experiencing MI-HF. These proteins may be useful candidate markers for the diagnosis of MI-HF as well as help to elucidate the pathophysiology of this major cause of mortality in older patients.

Ribosome biogenesis in disease: new players and therapeutic targets

The ribosome is a multi-unit complex that translates mRNA into protein. Ribosome biogenesis is the process that generates ribosomes and plays an essential role in cell proliferation, differentiation, apoptosis, development, and transformation. The mTORC1, Myc, and noncoding RNA signaling pathways are the primary mediators that work jointly with RNA polymerases and ribosome proteins to control ribosome biogenesis and protein synthesis. Activation of mTORC1 is required for normal fetal growth and development and tissue regeneration after birth. Myc is implicated in cancer development by enhancing RNA Pol II activity, leading to uncontrolled cancer cell growth. The deregulation of noncoding RNAs such as microRNAs, long noncoding RNAs, and circular RNAs is involved in developing blood, neurodegenerative diseases, and atherosclerosis. We review the similarities and differences between eukaryotic and bacterial ribosomes and the molecular mechanism of ribosome-targeting antibiotics and bacterial resistance. We also review the most recent findings of ribosome dysfunction in COVID-19 and other conditions and discuss the consequences of ribosome frameshifting, ribosome-stalling, and ribosome-collision. We summarize the role of ribosome biogenesis in the development of various diseases. Furthermore, we review the current clinical trials, prospective vaccines for COVID-19, and therapies targeting ribosome biogenesis in cancer, cardiovascular disease, aging, and neurodegenerative disease.

Identification and validation of senescence-related genes in circulating endothelial cells of patients with acute myocardial infarction

Background

Acute myocardial infarction (AMI) is the main clinical cause of death and cardiovascular disease and thus has high rates of morbidity and mortality. The increase in cardiovascular disease with aging is partly the result of vascular endothelial cell senescence and associated vascular dysfunction. This study was performed to identify potential key cellular senescence-related genes (SRGs) as biomarkers for the diagnosis of AMI using bioinformatics.

Methods

Using the CellAge database, we identified cellular SRGs. GSE66360 and GSE48060 for AMI patients and healthy controls and GSE19322 for mice were downloaded from the Gene Expression Omnibus (GEO) database. The GSE66360 dataset was divided into a training set and a validation set. The GSE48060 dataset was used as another validation set. The GSE19322 dataset was used to explore the evolution of the screened diagnostic markers in the dynamic process of AMI. Differentially expressed genes (DEGs) of AMI were identified from the GSE66360 training set. Differentially expressed senescence-related genes (DESRGs) selected from SRGs and DEGs were analyzed using Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in DESRGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed and targeted drug prediction was performed.

Results

A total of 520 DEGs were screened from the GSE66360 training set, and 279 SRGs were identified from the CellAge database. The overlapping DEGs and SRGs constituted 14 DESRGs, including 4 senescence suppressor genes and 10 senescence inducible genes. The top 10 hub genes, including FOS, MMP9, CEBPB, CDKN1A, CXCL1, ETS2, BCL6, SGK1, ZFP36, and IGFBP3, were screened. Furthermore, three diagnostic genes were identified: MMP9, ETS2, and BCL6. The ROC analysis showed that the respective area under the curves (AUCs) of MMP9, ETS2, and BCL6 were 0.786, 0.848, and 0.852 in the GSE66360 validation set and 0.708, 0.791, and 0.727 in the GSE48060 dataset. In the GSE19322 dataset, MMP9 (AUC, 0.888) and ETS2 (AUC, 0.929) had very high diagnostic values in the early stage of AMI. Finally, based on these three diagnostic genes, we found that drugs such as acetylcysteine and genistein may be targeted for the treatment of age-related AMI.

Conclusion

The results of this study suggest that cellular SRGs might play an important role in AMI. MMP9, ETS2, and BCL6 have potential as specific biomarkers for the early diagnosis of AMI.