Role of Human Antigen R (HuR) in the Regulation of Pulmonary ACE2 Expression

An association study of m6A methylation with major depressive disorder

OBJECTIVE

To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD).

METHODS

Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.

RESULTS

Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.

CONCLUSION

Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Background

Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses.

Methods

Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes.

Results

The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified.

Conclusion

This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

Endotyping Eosinophilic Inflammation in COPD with ELAVL1, ZfP36 and HNRNPD mRNA Genes

Background: Chronic obstructive pulmonary disease (COPD) is a common disease characterized by progressive airflow obstruction, influenced by genetic and environmental factors. Eosinophils have been implicated in COPD pathogenesis, prompting the categorization into eosinophilic and non-eosinophilic endotypes. This study explores the association between eosinophilic inflammation and mRNA expression of ELAVL1, ZfP36, and HNRNPD genes, which encode HuR, TTP and AUF-1 proteins, respectively. Additionally, it investigates the expression of IL-9 and IL-33 in COPD patients with distinct eosinophilic profiles. Understanding these molecular associations could offer insights into COPD heterogeneity and provide potential therapeutic targets. Methods: We investigated 50 COPD patients, of whom 21 had eosinophilic inflammation and 29 had non-eosinophilic inflammation. Epidemiological data, comorbidities, and pulmonary function tests were recorded. Peripheral blood mononuclear cells were isolated for mRNA analysis of ELAVL1, ZfP36, and HNRNPD genes and serum cytokines (IL-9, IL-33) were measured using ELISA kits. Results: The study comprised 50 participants, with 66% being male and a mean age of 68 years (SD: 8.9 years). Analysis of ELAVL1 gene expression revealed a 0.45-fold increase in non-eosinophilic and a 3.93-fold increase in eosinophilic inflammation (p = 0.11). For the ZfP36 gene, expression was 6.19-fold higher in non-eosinophilic and 119.4-fold higher in eosinophilic groups (p = 0.07). Similarly, HNRNPD gene expression was 0.23-fold higher in non-eosinophilic and 0.72-fold higher in eosinophilic inflammation (p = 0.06). Furthermore, serum levels of IL-9 showed no statistically significant difference between the eosinophilic and non-eosinophilic group (58.03 pg/mL vs. 52.55 pg/mL, p = 0.98). Additionally, there was no significant difference in IL-33 serum levels between COPD patients with eosinophilic inflammation and those with non-eosinophilic inflammation (39.61 pg/mL vs. 37.94 pg/mL, p = 0.72). Conclusions: The data suggest a notable trend, lacking statistical significance, towards higher mRNA expression for the ZfP36 and HNRNPD genes for COPD patients with eosinophilic inflammation compared to those with non-eosinophilic inflammation.

Catch your breath: The protective role of the angiotensin AT2 receptor for the treatment of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease whereby excessive deposition of extracellular matrix proteins (ECM) ultimately leads to respiratory failure. While there have been advances in pharmacotherapies for pulmonary fibrosis, IPF remains an incurable and irreversible disease. There remains an unmet clinical need for treatments that reverse fibrosis, or at the very least have a more tolerable side effect profile than currently available treatments. Transforming growth factor β1(TGFβ1) is considered the main driver of fibrosis in IPF. However, as our understanding of the role of the pulmonary renin-angiotensin system (PRAS) in the pathogenesis of IPF increases, it is becoming clear that targeting angiotensin receptors represents a potential novel treatment strategy for IPF - in particular, via activation of the anti-fibrotic angiotensin type 2 receptor (AT2R). This review describes the current understanding of the pathophysiology of IPF and the mediators implicated in its pathogenesis; focusing on TGFβ1, angiotensin II and related peptides in the PRAS and their contribution to fibrotic processes in the lung. Preclinical and clinical assessment of currently available AT2R agonists and the development of novel, highly selective ligands for this receptor will also be described, with a focus on compound 21, currently in clinical trials for IPF. Collectively, this review provides evidence of the potential of AT2R as a novel therapeutic target for IPF.

Targeting HuR-Vav3 mRNA interaction prevents Pseudomonas aeruginosa adhesion to the cystic fibrosis airway epithelium

Cystic fibrosis (CF) is characterized by chronic bacterial infections leading to progressive bronchiectasis and respiratory failure. Pseudomonas aeruginosa (Pa) is the predominant opportunistic pathogen infecting the CF airways. The guanine nucleotide exchange factor Vav3 plays a critical role in Pa adhesion to the CF airways by inducing luminal fibronectin deposition that favors bacteria trapping. Here we report that Vav3 overexpression in CF is caused by upregulation of the mRNA-stabilizing protein HuR. We found that HuR accumulates in the cytoplasm of CF airway epithelial cells and that it binds to and stabilizes Vav3 mRNA. Interestingly, disruption of the HuR-Vav3 mRNA interaction improved the CF epithelial integrity, inhibited the formation of the fibronectin-made bacterial docking platforms, and prevented Pa adhesion to the CF airway epithelium. These findings indicate that targeting HuR represents a promising antiadhesive approach in CF that can prevent initial stages of Pa infection in a context of emergence of multidrug-resistant pathogens.

m6A Regulator-Mediated Methylation Modification Patterns and Characteristics in COVID-19 Patients

Background

RNA N6-methyladenosine (m6A) regulators may be necessary for diverse viral infectious diseases, and serve pivotal roles in various physiological functions. However, the potential roles of m6A regulators in coronavirus disease 2019 (COVID-19) remain unclear.

Methods

The gene expression profile of patients with or without COVID-19 was acquired from Gene Expression Omnibus (GEO) database, and bioinformatics analysis of differentially expressed genes was conducted. Random forest modal and nomogram were established to predict the occurrence of COVID-19. Afterward, the consensus clustering method was utilized to establish two different m6A subtypes, and associations between subtypes and immunity were explored.

Results

Based on the transcriptional data from GSE157103, we observed that the m6A modification level was markedly enriched in the COVID-19 patients than those in the non-COVID-19 patients. And 18 essential m6A regulators were identified with differential analysis between patients with or without COVID-19. The random forest model was utilized to determine 8 optimal m6A regulators for predicting the emergence of COVID-19. We then established a nomogram based on these regulators, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm was conducted to categorize COVID-19 patients into two m6A subtypes from the identified m6A regulators. The patients in cluster A were correlated with activated T-cell functions and may have a superior prognosis.

Conclusions

Collectively, m6A regulators may be involved in the prevalence of COVID-19 patients. Our exploration of m6A subtypes may benefit the development of subsequent treatment modalities for COVID-19.

Thrombosis-related circulating miR-16-5p is associated with disease severity in patients hospitalised for COVID-19

SARS-CoV-2 tropism for the ACE2 receptor, along with the multifaceted inflammatory reaction, is likely to drive the generalized hypercoagulable and thrombotic state seen in patients with COVID-19. Using the original bioinformatic workflow and network medicine approaches we reanalysed four coronavirus-related expression datasets and performed co-expression analysis focused on thrombosis and ACE2 related genes. We identified microRNAs (miRNAs) which play role in ACE2-related thrombosis in coronavirus infection and further, we validated the expressions of precisely selected miRNAs-related to thrombosis (miR-16-5p, miR-27a-3p, let-7b-5p and miR-155-5p) in 79 hospitalized COVID-19 patients and 32 healthy volunteers by qRT-PCR. Consequently, we aimed to unravel whether bioinformatic prioritization could guide selection of miRNAs with a potential of diagnostic and prognostic biomarkers associated with disease severity in patients hospitalized for COVID-19. In bioinformatic analysis, we identified EGFR, HSP90AA1, APP, TP53, PTEN, UBC, FN1, ELAVL1 and CALM1 as regulatory genes which could play a pivotal role in COVID-19 related thrombosis. We also found miR-16-5p, miR-27a-3p, let-7b-5p and miR-155-5p as regulators in the coagulation and thrombosis process. In silico predictions were further confirmed in patients hospitalized for COVID-19. The expression levels of miR-16-5p and let-7b in COVID-19 patients were lower at baseline, 7-days and 21-day after admission compared to the healthy controls (p < 0.0001 for all time points for both miRNAs). The expression levels of miR-27a-3p and miR-155-5p in COVID-19 patients were higher at day 21 compared to the healthy controls (p = 0.007 and p < 0.001, respectively). A low baseline miR-16-5p expression presents predictive utility in assessment of the hospital length of stay or death in follow-up as a composite endpoint (AUC:0.810, 95% CI, 0.71-0.91, p < 0.0001) and low baseline expression of miR-16-5p and diabetes mellitus are independent predictors of increased length of stay or death according to a multivariate analysis (OR: 9.417; 95% CI, 2.647-33.506; p = 0.0005 and OR: 6.257; 95% CI, 1.049-37.316; p = 0.044, respectively). This study enabled us to better characterize changes in gene expression and signalling pathways related to hypercoagulable and thrombotic conditions in COVID-19. In this study we identified and validated miRNAs which could serve as novel, thrombosis-related predictive biomarkers of the COVID-19 complications, and can be used for early stratification of patients and prediction of severity of infection development in an individual.Abbreviations: ACE2, angiotensin-converting enzyme 2AF, atrial fibrillationAPP, Amyloid Beta Precursor ProteinaPTT, activated partial thromboplastin timeAUC, Area under the curveAβ, amyloid betaBMI, body mass indexCAD, coronary artery diseaseCALM1, Calmodulin 1 geneCaM, calmodulinCCND1, Cyclin D1CI, confidence intervalCOPD, chronic obstructive pulmonary diseaseCOVID-19, Coronavirus disease 2019CRP, C-reactive proteinCV, CardiovascularCVDs, cardiovascular diseasesDE, differentially expressedDM, diabetes mellitusEGFR, Epithelial growth factor receptorELAVL1, ELAV Like RNA Binding Protein 1FLNA, Filamin AFN1, Fibronectin 1GEO, Gene Expression OmnibushiPSC-CMs, Human induced pluripotent stem cell-derived cardiomyocytesHSP90AA1, Heat Shock Protein 90 Alpha Family Class A Member 1Hsp90α, heat shock protein 90αICU, intensive care unitIL, interleukinIQR, interquartile rangelncRNAs, long non-coding RNAsMI, myocardial infarctionMiRNA, MiR, microRNAmRNA, messenger RNAncRNA, non-coding RNANERI, network-medicine based integrative approachNF-kB, nuclear factor kappa-light-chain-enhancer of activated B cellsNPV, negative predictive valueNXF, nuclear export factorPBMCs, Peripheral blood mononuclear cellsPCT, procalcitoninPPI, Protein-protein interactionsPPV, positive predictive valuePTEN, phosphatase and tensin homologqPCR, quantitative polymerase chain reactionROC, receiver operating characteristicSARS-CoV-2, severe acute respiratory syndrome coronavirus 2SD, standard deviationTLR4, Toll-like receptor 4TM, thrombomodulinTP53, Tumour protein P53UBC, Ubiquitin CWBC, white blood cells.