A regulatory role for CD72 expression on B cells and increased soluble CD72 in primary Sjogren's syndrome

Differential Gene Expression Involved in Bone Turnover of Mice Expressing Constitutively Active TGFβ Receptor Type I

Transforming growth factor beta (TGF-β) is ubiquitously found in bone and plays a key role in bone turnover. Mice expressing constitutively active TGF-β receptor type I (Mx1;TβRICA mice) are osteopenic. Here, we identified the candidate genes involved in bone turnover in Mx1;TβRICA mice using RNA sequencing analysis. A total of 285 genes, including 87 upregulated and 198 downregulated genes, were differentially expressed. According to the KEGG analysis, some genes were involved in osteoclast differentiation (Fcgr4, Lilrb4a), B cell receptor signaling (Cd72, Lilrb4a), and neutrophil extracellular trap formation (Hdac7, Padi4). Lilrb4 is related to osteoclast inhibition protein, whereas Hdac7 is a Runx2 corepressor that regulates osteoblast differentiation. Silencing Lilrb4 increased the number of osteoclasts and osteoclast marker genes. The knocking down of Hdac7 increased alkaline phosphatase activity, mineralization, and osteoblast marker genes. Therefore, our present study may provide an innovative idea for potential therapeutic targets and pathways in TβRI-associated bone loss.

Sexual dimorphism in atherosclerotic plaques of aged Ldlr-/- mice

BACKGROUND

Atherosclerosis, the main underlying pathology of cardiovascular disease, is a chronic inflammatory disease characterized by lipid accumulation and immune cell responses in the vascular wall, resulting in plaque formation. It is well-known that atherosclerosis prevalence and manifestation vary by sex. However, sexual dimorphism in the immune landscape of atherosclerotic plaques has up to date not been studied at high-resolution. In this study, we investigated sex-specific differences in atherosclerosis development and the immunological landscape of aortas at single-cell level in aged Ldlr-/- mice.

METHODS

We compared plaque morphology between aged male and female chow diet-fed Ldlr-/- mice (22 months old) with histological analysis. Using single-cell RNA-sequencing and flow cytometry on CD45+ immune cells from aortas of aged Ldlr-/- mice, we explored the immune landscape in the atherosclerotic environment in males and females.

RESULTS

We show that plaque volume is comparable in aged male and female mice, and that plaques in aged female mice contain more collagen and cholesterol crystals, but less necrotic core and macrophage content compared to males. We reveal increased immune cell infiltration in female aortas and found that expression of pro-atherogenic markers and inflammatory signaling pathways was enriched in plaque immune cells of female mice. Particularly, female aortas show enhanced activation of B cells (Egr1, Cd83, Cd180), including age-associated B cells, in addition to an increased M1/M2 macrophage ratio, where Il1b+ M1-like macrophages display a more pro-inflammatory phenotype (Nlrp3, Cxcl2, Mmp9) compared to males. In contrast, increased numbers of age-associated Gzmk+CD8+ T cells, dendritic cells, and Trem2+ macrophages were observed in male aortas.

CONCLUSIONS

Altogether, our findings highlight that sex is a variable that contributes to immunological differences in the atherosclerotic plaque environment in mice and provide valuable insights for further preclinical studies into the impact of sex on the pathophysiology of atherosclerosis.

A novel lncRNA-mediated epigenetic regulatory mechanism in periodontitis

Periodontitis is a highly prevalent chronic inflammatory disease with an exaggerated host immune response, resulting in periodontal tissue destruction and potential tooth loss. The long non-coding RNA, LncR-ANRIL, located on human chromosome 9p21, is recognized as a genetic risk factor for various conditions, including atherosclerosis, periodontitis, diabetes, and cancer. LncR-APDC is an ortholog of ANRIL located on mouse genome chr4. This study aims to comprehend the regulatory role of lncR-APDC in periodontitis progression. Our experimental findings, obtained from lncR-APDC gene knockout (KO) mice with induced experimental periodontitis (EP), revealed exacerbated bone loss and disrupted pro-inflammatory cytokine regulation. Downregulation of osteogenic differentiation occurred in bone marrow stem cells harvested from lncR-APDC-KO mice. Furthermore, single-cell RNA sequencing of periodontitis gingival tissue revealed alterations in the proportion and function of immune cells, including T and B cells, macrophages, and neutrophils, due to lncR-APDC silencing. Our findings also unveiled a previously unidentified epithelial cell subset that is distinctively presenting in the lncR-APDC-KO group. This epithelial subset, characterized by the positive expression of Krt8 and Krt18, engages in interactions with immune cells through a variety of ligand-receptor pairs. The expression of Tff2, now recognized for its role in chronic inflammatory conditions, exhibited a notable increase across various tissue and cell types in lncR-APDC deficient mice. Additionally, our investigation revealed the potential for a direct binding interaction between lncR-APDC and Tff2. Intra-gingival administration of AAV9-lncR-APDC was shown to have therapeutic effects in the EP model. In conclusion, our results suggest that lncR-APDC plays a critical role in the progression of periodontal disease and holds therapeutic potential for periodontitis. Furthermore, the presence of the distinctive epithelial subpopulation and significantly elevated Tff2 levels in the lncR-APDC-silenced EP model offer new perspectives on the epigenetic regulation of periodontitis pathogenesis.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

Construction of lncRNA and mRNA co-expression network associated with nasopharyngeal carcinoma progression

Nasopharyngeal carcinoma is a type of head and neck cancer with a high incidence in men. In the past decades, the survival rate of NPC has remained around 70%, but it often leads to treatment failure due to its distant metastasis or recurrence. The lncRNA-mRNA regulatory network has not been fully elucidated. We downloaded the NPC-related gene expression datasets GSE53819 and GSE12452 from the Gene Expression Omnibus database; GSE53819 included 18 NPC tissues and 18 normal tissues, and GSE12452 included 31 NPC tissues and 10 normal tissues. Weighted gene co-expression network analysis was performed on mRNA and lncRNA to screen out modules that were highly correlated with tumor progression. The two datasets were subjected to differential analysis after removing batch effects, and then Venn diagrams were used to screen for overlapping genes in the module genes and differential genes. The lncRNA-mRNA co-expression network was then constructed, and key mRNAs were identified by MCODE analysis and expression analysis. GSEA analysis and qRT-PCR were performed on key mRNAs. Through a series of analyses, we speculated that BTK, CD72, PTPN6, and VAV1 may be independent predictors of the prognosis of NPC patients.Taken together, our study provides potential candidate biomarkers for NPC diagnosis, prognosis, or precise treatment.