Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning

Dual roles of α1,4-galactosyltransferase 1 in spermatogenesis of Drosophila melanogaster

Spermatogenesis is critical for insect reproduction and the process is regulated by multiple genes. Glycosyltransferases have been shown to participate in the development of Drosophila melanogaster; however, their role in spermatogenesis is still unclear. In this study, we found that α1,4-galactosyltransferase 1 (α4GT1) was expressed at a significantly higher level in the testis than in the ovary of Drosophila. Importantly, the hatching rate was significantly decreased when α4GT1 RNA interference (RNAi) males were crossed with w1118 females, with only a few mature sperm being present in the seminal vesicle of α4GT1 RNAi flies. Immunofluorescence staining further revealed that the individualization complex (IC) in the testes from α4GT1 RNAi flies was scattered and did not move synchronically, compared with the clustered IC observed in the control flies. Terminal deoxyribonucleotide transferase (TdT)-mediated dUTP nick end labeling (TUNEL) assay showed that apoptosis signals in the sperm bundles of α4GT1 RNAi flies were significantly increased. Moreover, the expression of several individualization-related genes, such as Shrub, Obp44a and Hanabi, was significantly decreased, whereas the expression of several apoptosis-related genes, including Dronc and Drice, was significantly increased in the testes of α4GT1 RNAi flies. Together, these results suggest that α4GT1 may play dual roles in Drosophila spermatogenesis by regulating the sperm individualization process and maintaining the survival of sperm bundles.

Effects of exosomes from human dental pulp stem cells on the biological behavior of human fibroblasts

The aim of this study was to investigate the effect of dental pulp stem cell-derived exosomes (DPSCs-Exos) on the biological behaviour of fibroblasts, particularly on keloid fibroblasts (KFs) and normal skin fibroblasts (NFs), with a view to providing new insights into cellular regenerative medicine. We obtained DPSCs-Exos by ultracentrifugation and co-cultured it with KFs and NFs. We detected its effect on cell proliferation using the CCK-8 assay; cell migration ability by cell scratch and Transwell assays; extracellular matrix synthesis using the hydroxyproline content assay; the expression levels of genes associated with fibrosis by PCR assay; and the expression levels of proteins related to fibrosis in the cells using the Western Blot method. DPSCs-Exos was able to be taken up by fibroblasts after addition to the culture medium and affected the biological behavior of NFs and KFs. DPSCs-Exos promoted the proliferation of NFs, inhibited the migration and extracellular matrix synthesis of KFs. In addition, DPSCs-Exos was able to inhibit the expression of fibrosis-related genes and proteins in KFs. This study highlights the role of DPSCs-Exos in regulating the biological behaviour of fibroblasts, providing new insights for future applications in the field of cell-free regenerative medicine.

Glycosphingolipids in Cardiovascular Disease: Insights from Molecular Mechanisms and Heart Failure Models

This review explores the crucial role of glycosphingolipids (GSLs) in the context of cardiovascular diseases (CVDs), focusing on their biosynthesis, metabolic pathways, and implications for clinical outcomes. GSLs are pivotal in regulating a myriad of cellular functions that are essential for heart health and disease progression. Highlighting findings from both human cohorts and animal models, this review emphasizes the potential of GSLs as biomarkers and therapeutic targets. We advocate for more detailed mechanistic studies to deepen our understanding of GSL functions in cardiovascular health, which could lead to innovative strategies for diagnosis, treatment, and personalized medicine in cardiovascular care.

Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer

BACKGROUND

Parthanatos is a novel programmatic form of cell death based on DNA damage and PARP-1 dependency. Nevertheless, its specific role in the context of gastric cancer (GC) remains uncertain.

METHODS

In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes.

RESULTS

Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients' survival times and statuses.

CONCLUSIONS

In this study, the molecular characteristics of parthanatos' unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.

Myofibroblasts reside in the middle dermis of the keloids but do not predict the response to injection therapies: a double-blinded, randomized, controlled trial

Introduction

Keloids form as a pathological response to skin wound healing, and their etiopathology is poorly understood. Myofibroblasts, which are cells transformed from normal fibroblasts, are believed to contribute to pathological scar formation in wounds.

Methods

We carried out a double-blinded randomized controlled trial (RCT) comparing the efficacy of intralesional 5-fluorouracil (5-FU) and triamcinolone (TAC) injections in treating keloids. A total of 43 patients with 50 keloids were treated with either intralesional TAC or 5-FU injections, and their clinical response was evaluated. Biopsies were collected before, during, and after injection therapy from the active border of a keloid. To understand the role of myofibroblasts in keloids, we conducted an immunohistochemical analysis to identify myofibroblasts [α-smooth muscle actin (αSMA)] from the biopsies. We first defined the three histologically distinct regions-superficial, middle, and deep dermis-in each keloid.

Results

We then demonstrated that myofibroblasts almost exclusively exist in the middle dermis of the keloids as 80% of the cells in the middle dermis were αSMA positive. However, both the percentage of myofibroblasts as well as the area covered by them was substantially lower in the superficial and deep dermis than in the middle dermis of the keloids. Myofibroblasts do not predict the clinical response to intralesional injection therapies. There is no difference in the myofibroblast numbers in keloids or in the induced change in myofibroblasts between the responders and non-responders after treatment.

Discussion

This study demonstrates that myofibroblasts reside almost exclusively in the middle dermis layer of the keloids, but their numbers do not predict the clinical response to intralesional injection therapies in the RCT.

Machine learning and single-cell transcriptome profiling reveal regulation of fibroblast activation through THBS2/TGFβ1/P-Smad2/3 signalling pathway in hypertrophic scar

Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques

Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment.