Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies

Single-Cell Meta-Analysis of Neutrophil Activation in Kawasaki Disease and Multisystem Inflammatory Syndrome in Children Reveals Potential Shared Immunological Drivers

BACKGROUND

Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) share similar clinical manifestations, including cardiovascular complications, suggesting similar underlying immunopathogenic processes. Aberrant neutrophil activation may play a crucial role in the shared pathologies of KD and MIS-C; however, the associated pathogenic mechanisms and molecular drivers remain unknown.

METHODS

We performed a single-cell meta-analysis of neutrophil activation with 103 pediatric single-cell transcriptomic peripheral blood mononuclear cell data across 9 cohorts, including healthy controls, KD, MIS-C, compared with dengue virus infection, juvenile idiopathic arthritis, and pediatric celiac disease. We used a series of computational analyses to investigate the shared neutrophil transcriptional programs of KD and MIS-C that are linked to systemic damage and cardiac pathologies, and suggested Food and Drug Administration-approved drugs to consider as KD and MIS-C treatment.

RESULTS

We meta-analyzed 521 950 high-quality cells. We found that blood signatures associated with risks of cardiovascular events are enriched in neutrophils of KD and MIS-C. We revealed the expansion of CD177+ neutrophils harboring hyperactivated effector functions in both KD and MIS-C, but not in healthy controls or in other viral-, inflammatory-, or immune-related pediatric diseases. KD and MIS-C CD177+ neutrophils had highly similar transcriptomes, marked by conserved signatures and pathways related to molecular damage. We found the induction of a shared neutrophil expression program, potentially regulated by SPI1 (Spi-1 proto-oncogene), which confers enhanced effector functions, especially neutrophil degranulation. CD177 and shared neutrophil expression program expressions were associated with acute stages and attenuated during KD intravenous immunoglobulin treatment and MIS-C recovery. Network analysis identified hub genes that correlated with the high activation of CD177+ neutrophils. Disease-gene association analysis revealed that the KD and MIS-C CD177+ neutrophils' shared expression program was associated with the development of coronary and myocardial disorders. Last, we identified and validated TSPO (translocator protein) and S100A12 (S100 calcium-binding protein A12) as main molecular targets, for which the Food and Drug Administration-approved drugs methotrexate, zaleplon, metronidazole, lorazepam, clonazepam, temazepam, and zolpidem, among others, are primary candidates for drug repurposing.

CONCLUSIONS

Our findings indicate that CD177+ neutrophils may exert systemic pathological damage contributing to the shared morbidities in KD and MIS-C. We uncovered potential regulatory drivers of CD177+ neutrophil hyperactivation and pathogenicity that may be targeted as a single therapeutic strategy for either KD or MIS-C.

LncRNA HIF1A-AS2: a potential biomarker for early diagnosis of acute myocardial infarction and predictor of left ventricular dysfunction

BACKGROUND

Rapid diagnosis of acute myocardial infarction (AMI) is the subject of many clinical studies as it enables an effective therapy, preventing adverse progression of AMI and increasing survival rates. Recent studies have revealed that specific blood-based long non-coding RNAs (lncRNAs) are deregulated in patients with AMI and serve as promising diagnostic and prognostic tools. The current study aimed to determine the potential role of a hypoxia-responsive lncRNA, hypoxia-inducible factor 1A antisense RNA 2 (HIF1A-AS2), as a biomarker for early diagnosis and predictor of left ventricular dysfunction (LVD).

METHODS

This study was carried out on 48 patients with AMI and 50 age-and sex-matched controls. The relative quantification of HIF1A-AS2 expression was done using reverse transcription real-time polymerase chain reaction.

RESULTS

Compared to the control group, HIF1A-AS2 were significantly higher in MI patients (P < 0.001). Interestingly, patients presenting within 3 h of chest pain onset had elevated levels of HIF1A-AS2 as compared to patients with late presentation. The ROC curve was constructed to assess HIF1A-AS2 as an early marker. It demonstrated higher sensitivity (94%) and specificity (86%). Moreover, the multivariate regression analysis revealed that HIF1A-AS2 was significantly associated with LVD in the patient group after 6 months follow up (p = 0.018).

CONCLUSION

Our study suggests that HIF1A-AS2 may be a potential early diagnostic biomarker of AMI with high sensitivity. In addition, it might have a promising role as a predictor of left ventricular dysfunction.

Potential therapeutic strategies for myocardial infarction: the role of Toll-like receptors

Myocardial infarction (MI) is a life-threatening condition among patients with cardiovascular diseases. MI increases the risk of stroke and heart failure and is a leading cause of morbidity and mortality worldwide. Several genetic and epigenetic factors contribute to the development of MI, suggesting that further understanding of the pathomechanism of MI might help in the early management and treatment of this disease. Toll-like receptors (TLRs) are well-known members of the pattern recognition receptor (PRR) family and contribute to both adaptive and innate immunity. Collectively, studies suggest that TLRs have a cardioprotective effect. However, prolonged TLR activation in the response to signals generated by damage-associated molecular patterns (DAMPs) results in the release of inflammatory cytokines and contributes to the development and exacerbation of myocardial inflammation, MI, ischemia-reperfusion injury, myocarditis, and heart failure. The objective of this review is to discuss and summarize the association of TLRs with MI, highlighting their therapeutic potential for the development of advanced TLR-targeted therapies for MI.

Signaling pathways and targeted therapy for myocardial infarction

Although the treatment of myocardial infarction (MI) has improved considerably, it is still a worldwide disease with high morbidity and high mortality. Whilst there is still a long way to go for discovering ideal treatments, therapeutic strategies committed to cardioprotection and cardiac repair following cardiac ischemia are emerging. Evidence of pathological characteristics in MI illustrates cell signaling pathways that participate in the survival, proliferation, apoptosis, autophagy of cardiomyocytes, endothelial cells, fibroblasts, monocytes, and stem cells. These signaling pathways include the key players in inflammation response, e.g., NLRP3/caspase-1 and TLR4/MyD88/NF-κB; the crucial mediators in oxidative stress and apoptosis, for instance, Notch, Hippo/YAP, RhoA/ROCK, Nrf2/HO-1, and Sonic hedgehog; the controller of myocardial fibrosis such as TGF-β/SMADs and Wnt/β-catenin; and the main regulator of angiogenesis, PI3K/Akt, MAPK, JAK/STAT, Sonic hedgehog, etc. Since signaling pathways play an important role in administering the process of MI, aiming at targeting these aberrant signaling pathways and improving the pathological manifestations in MI is indispensable and promising. Hence, drug therapy, gene therapy, protein therapy, cell therapy, and exosome therapy have been emerging and are known as novel therapies. In this review, we summarize the therapeutic strategies for MI by regulating these associated pathways, which contribute to inhibiting cardiomyocytes death, attenuating inflammation, enhancing angiogenesis, etc. so as to repair and re-functionalize damaged hearts.

Cardiac biomarkers and detection methods for myocardial infarction

Background

A significant heart attack known as a myocardial infarction (MI) occurs when the blood supply to the heart is suddenly interrupted, harming the heart muscles due to a lack of oxygen. The incidence of myocardial infarction is increasing worldwide. A relationship between COVID-19 and myocardial infarction due to the recent COVID-19 pandemic has also been revealed.

Objective

We propose a biomarker and a method that can be used for the diagnosis of myocardial infarction, and an aptamer-based approach.

Results

For the diagnosis of myocardial infarction, an algorithm-based diagnosis method was developed using electrocardiogram data. A diagnosis method through biomarker detection was then developed.

Conclusion

Myocardial infarction is a disease that is difficult to diagnose based on the aspect of a single factor. For this reason, it is necessary to use a combination of various methods to diagnose myocardial infarction quickly and accurately. In addition, new materials such as aptamers must be grafted and integrated into new ways.

Purpose of Review

The incidence of myocardial infarction is increasing worldwide, and some studies are being conducted on the association between COVID-19 and myocardial infarction. The key to properly treating myocardial infarction is early detection, thus we aim to do this by offering both tools and techniques as well as the most recent diagnostic techniques.

Recent Findings

Myocardial infarction is diagnosed using an electrocardiogram and echocardiogram, which utilize cardiac signals. It is required to identify biomarkers of myocardial infarction and use biomarker-based ELISA, SPR, gold nanoparticle, and aptamer technologies in order to correctly diagnose myocardial infarction.

Screening for Core Genes Related to Pathogenesis of Alzheimer's Disease

Alzheimer’s disease (AD), a nervous system disease, lacks effective therapies at present. RNA expression is the basic way to regulate life activities, and identifying related characteristics in AD patients may aid the exploration of AD pathogenesis and treatment. This study developed a classifier that could accurately classify AD patients and healthy people, and then obtained 3 core genes that may be related to the pathogenesis of AD. To this end, RNA expression data of the middle temporal gyrus of AD patients were firstly downloaded from GEO database, and the data were then normalized using limma package following a supplementation of missing data by k-Nearest Neighbor (KNN) algorithm. Afterwards, the top 500 genes of the most feature importance were obtained through Max-Relevance and Min-Redundancy (mRMR) analysis, and based on these genes, a series of AD classifiers were constructed through Support Vector Machine (SVM), Random Forest (RF), and KNN algorithms. Then, the KNN classifier with the highest Matthews correlation coefficient (MCC) value composed of 14 genes in incremental feature selection (IFS) analysis was identified as the best AD classifier. As analyzed, the 14 genes played a pivotal role in determination of AD and may be core genes associated with the pathogenesis of AD. Finally, protein-protein interaction (PPI) network and Random Walk with Restart (RWR) analysis were applied to obtain core gene-associated genes, and key pathways related to AD were further analyzed. Overall, this study contributed to a deeper understanding of AD pathogenesis and provided theoretical guidance for related research and experiments.