Knockdown of long non‑coding RNA DLEU2 suppresses idiopathic pulmonary fibrosis by regulating the microRNA‑369‑3p/TRIM2 axis

DPSCs regulate epithelial-T cell interactions in oral submucous fibrosis

BACKGROUND

Oral submucous fibrosis (OSF) is a precancerous lesion characterized by fibrous tissue deposition, the incidence of which correlates positively with the frequency of betel nut chewing. Prolonged betel nut chewing can damage the integrity of the oral mucosal epithelium, leading to chronic inflammation and local immunological derangement. However, currently, the underlying cellular events driving fibrogenesis and dysfunction are incompletely understood, such that OSF has few treatment options with limited therapeutic effectiveness. Dental pulp stem cells (DPSCs) have been recognized for their anti-inflammatory and anti-fibrosis capabilities, making them promising candidates to treat a range of immune, inflammatory, and fibrotic diseases. However, the application of DPSCs in OSF is inconclusive. Therefore, this study aimed to explore the pathogenic mechanism of OSF and, based on this, to explore new treatment options.

METHODS

A human cell atlas of oral mucosal tissues was compiled using single-cell RNA sequencing to delve into the underlying mechanisms. Epithelial cells were reclustered to observe the heterogeneity of OSF epithelial cells and their communication with immune cells. The results were validated in vitro, in clinicopathological sections, and in animal models. In vivo, the therapeutic effect and mechanism of DPSCs were characterized by histological staining, immunohistochemical staining, scanning electron microscopy, and atomic force microscopy.

RESULTS

A unique epithelial cell population, Epi1.2, with proinflammatory and profibrotic functions, was predominantly found in OSF. Epi1.2 cells also induced the fibrotic process in fibroblasts by interacting with T cells through receptor-ligand crosstalk between macrophage migration inhibitory factor (MIF)-CD74 and C-X-C motif chemokine receptor 4 (CXCR4). Furthermore, we developed OSF animal models and simulated the clinical local injection process in the rat buccal mucosa using DPSCs to assess their therapeutic impact and mechanism. In the OSF rat model, DPSCs demonstrated superior therapeutic effects compared with the positive control (glucocorticoids), including reducing collagen deposition and promoting blood vessel regeneration. DPSCs mediated immune homeostasis primarily by regulating the numbers of KRT19 + MIF + epithelial cells and via epithelial-stromal crosstalk.

CONCLUSIONS

Given the current ambiguity surrounding the cause of OSF and the limited treatment options available, our study reveals that epithelial cells and their crosstalk with T cells play an important role in the mechanism of OSF and suggests the therapeutic promise of DPSCs.

LncRNA SNHG8 upregulates MUC5B to induce idiopathic pulmonary fibrosis progression by targeting miR-4701-5p

Long noncoding RNAs (lncRNAs) play a critical role in idiopathic pulmonary fibrosis (IPF); however, the underlying molecular mechanisms are unclear. Our study demonstrated that lncRNA small nucleolar RNA host gene 8 (SNHG8) was increased in bleomycin (BLM)-induced A549 cells. LncRNA SNHG8 overexpression further elevated fibrosis-related factors monocyte chemotactic protein 1 (MCP1), CC motif chemokine ligand 18 (CCL18), and α-smooth muscle actin (α-SMA), as well as increased collagen type I alpha-1 chain (COL1A1) and collagen type III alpha-1 chain (COL3A1). Meanwhile, lncRNA SNHG8 knockdown exhibited an opposite role in reducing BLM-induced pulmonary fibrosis. With regard to the mechanism, SNHG8 was then revealed to act as a competing endogenous RNA (ceRNA) for microRNA (miR)-4701-5p in regulating Mucin 5B (MUC5B) expression. Furthermore, the interactions between SNHG8 and miR-4701-5p, between miR-4701-5p and MUC5B, and between SNHG8 and MUC5B on the influence of fibrosis-related indicators were confirmed, respectively. In addition, SNHG8 overexpression enhanced the levels of transforming growth factor (TGF)-β1 and phosphorylation Smad2/3 (p-Smad2/3), which was suppressed by SNHG8 knockdown in BLM-induced A549 cells. Moreover, miR-4701-5p inhibitor-induced elevation of TGF-β1 and p-Smad2/3 was significantly suppressed by SNHG8 knockdown. In conclusion, SNHG8 knockdown attenuated pulmonary fibrosis progression by regulating miR-4701-5p/MUC5B axis, which might be associated with the modulation of TGF-β1/Smad2/3 signaling. These findings reveal that lncRNA SNHG8 may become a potential target for the treatment of IPF.

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents

Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.

Epigenetic hallmarks in pulmonary fibrosis: New advances and perspectives

Epigenetics indicates that certain phenotypes of an organism can undergo heritable changes in the absence of changes in the genetic DNA sequence. Many studies have shown that epigenetic patterns play an important role in the lung and lung diseases. Pulmonary fibrosis (PF) is also a type of lung disease. PF is an end-stage change of a large group of lung diseases, characterized by fibroblast proliferation and massive accumulation of extracellular matrix, accompanied by inflammatory injury and histological destruction, that is, structural abnormalities caused by abnormal repair of normal alveolar tissue. It causes loss of lung function in patients with multiple complex diseases, leading to respiratory failure and subsequent death. However, current treatment options for IPF are very limited and no drugs have been shown to significantly prolong the survival of patients. Therefore, based on a systematic understanding of the disease mechanisms of PF, this review integrates the role of epigenetics in the development and course of PF, describes preventive and potential therapeutic targets for PF, and provides a theoretical basis for further exploration of the mechanisms of PF.

Construction of a TFs-miRNA-mRNA network related to idiopathic pulmonary fibrosis

Background

The transcription factors (TFs)-microRNA (miRNA)-messenger RNA (mRNA) network plays an important role in a variety of diseases. However, the relationship between the TFs-miRNA-mRNA network and idiopathic pulmonary fibrosis (IPF) remains unclear.

Methods

The GSE110147 and GSE53845 datasets from the Gene Expression Omnibus (GEO) database were used to process differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), as well as Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The GSE13316 dataset was used to perform differentially expressed miRNAs (DEMs) analysis and TFs prediction. Finally, a TFs-miRNA-mRNA network related to IPF was constructed, and its function was evaluated by Gene Ontology (GO) and KEGG analyses. Also, 19 TFs in the network were verified by quantitative real time polymerase chain reaction (qRT-PCR).

Results

Through our analysis, 53 DEMs and 2,630 DEGs were screened. The GSEA results suggested these genes were mainly related to protein digestion and absorption. The WGCNA results showed that these DEGs were divided into eight modules, and the GO and KEGG analyses results of blue module genes showed that these 86 blue module genes were mainly enriched in cilium assembly and cilium organization. Moreover, a TFs-miRNA-mRNA network comprising 25 TFs, 11 miRNAs, and 60 mRNAs was constructed. Ultimately, the functional enrichment analysis showed that the TFs-miRNA-mRNA network was mainly related to the cell cycle and the phosphatidylinositol 3 kinase-protein kinase B (PI3K-Akt) signaling pathway. Furthermore, experimental verification of the TFs showed that ARNTL, TRIM28, EZH2, BCOR, and ASXL1 were sufficiently up-regulated in the transforming growth factor (TGF)-β1 treatment groups, while BCL6, BHLHE40, FOXA1, and EGR1 were significantly down-regulated.

Conclusions

The novel TFs-miRNA-mRNA network that we constructed could provide new insights into the underlying molecular mechanisms of IPF. ARNTL, TRIM28, EZH2, BCOR, ASXL1, BCL6, BHLHE40, FOXA1, and EGR1 may play important roles in IPF and become effective biomarkers for diagnosis and treatment.

Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.

The UIP/IPF fibroblastic focus is a collagen biosynthesis factory embedded in a distinct extracellular matrix

Usual interstitial pneumonia (UIP) is a histological pattern characteristic of idiopathic pulmonary fibrosis (IPF). The UIP pattern is patchy with histologically normal lung adjacent to dense fibrotic tissue. At this interface, fibroblastic foci (FF) are present and are sites where myofibroblasts and extracellular matrix (ECM) accumulate. Utilizing laser capture microdissection-coupled mass spectrometry, we interrogated the FF, adjacent mature scar, and adjacent alveoli in 6 fibrotic (UIP/IPF) specimens plus 6 nonfibrotic alveolar specimens as controls. The data were subjected to qualitative and quantitative analysis and histologically validated. We found that the fibrotic alveoli protein signature is defined by immune deregulation as the strongest category. The fibrotic mature scar classified as end-stage fibrosis whereas the FF contained an overabundance of a distinctive ECM compared with the nonfibrotic control. Furthermore, FF were positive for both TGFB1 and TGFB3, whereas the aberrant basaloid cell lining of FF was predominantly positive for TGFB2. In conclusion, spatial proteomics demonstrated distinct protein compositions in the histologically defined regions of UIP/IPF tissue. These data revealed that FF are the main site of collagen biosynthesis and that the adjacent alveoli are abnormal. This essential information will inform future mechanistic studies on fibrosis progression.

Circulating microRNA profiling is altered in the acute respiratory distress syndrome related to SARS-CoV-2 infection

One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome and acute respiratory distress syndrome (ARDS). Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs (miRNAs) might reflect this poor condition since they play a significant role in immune cellular performance including inflammatory responses. Circulating miRNAs in patients who underwent ARDS and needed mechanical ventilation (MV+; n = 15) were analyzed by next generation sequencing in comparison with patients who had COVID-19 poor symptoms but without intensive care unit requirement (MV−; n = 13). A comprehensive in silico analysis by integration with public gene expression dataset and pathway enrichment was performed. Whole miRNA sequencing identified 170 differentially expressed miRNAs between patient groups. After the validation step by qPCR in an independent sample set (MV+  = 10 vs. MV− = 10), the miR-369-3p was found significantly decreased in MV+ patients (Fold change − 2.7). After integrating with gene expression results from COVID-19 patients, the most significant GO enriched pathways were acute inflammatory response, regulation of transmembrane receptor protein Ser/Thr, fat cell differentiation, and regulation of biomineralization and ossification. In conclusion, miR-369-3p was altered in patients with mechanical ventilation requirement in comparison with COVID-19 patients without this requirement. This miRNA is involved in inflammatory response which it can be considered as a prognosis factor for ARDS in COVID-19 patients.

Comprehensive analysis of the long noncoding RNA-associated competitive endogenous RNA network in the osteogenic differentiation of periodontal ligament stem cells

BACKGROUD

The mechanism implicated in the osteogenesis of human periodontal ligament stem cells (PDLSCs) has been investigated for years. Previous genomics data analyses showed that long noncoding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) have significant expression differences between induced and control human PDLSCs. Competing for endogenous RNAs (ceRNA), as a widely studied mechanism in regenerative medicine, while rarely reported in periodontal regeneration. The key lncRNAs and their ceRNA network might provide new insights into molecular therapies of periodontal regeneration based on PDLSCs.

RESULTS

Two networks reflecting the relationships among differentially expressed RNAs were constructed. One ceRNA network was composed of 6 upregulated lncRNAs, 280 upregulated mRNAs, and 18 downregulated miRNAs. The other network contained 33 downregulated lncRNAs, 73 downregulated mRNAs, and 5 upregulated miRNAs. Functional analysis revealed that 38 GO terms and 8 pathways related with osteogenesis were enriched. Twenty-four osteogenesis-related gene-centred lncRNA-associated ceRNA networks were successfully constructed. Among these pathways, we highlighted MAPK and TGF-beta pathways that are closely related to osteogenesis. Subsequently, subnetworks potentially linking the GO:0001649 (osteoblast differentiation), MAPK and TGF-beta pathways were constructed. The qRT-PCR validation results were consistent with the microarray analysis.

CONCLUSION

We construct a comprehensively identified lncRNA-associated ceRNA network might be involved in the osteogenesis of PDLSCs, which could provide insights into the regulatory mechanisms and treatment targets of periodontal regeneration.