Transcriptome analysis

Comprehensive analysis of the relationship between xanthine oxidoreductase activity and chronic kidney disease

Chronic kidney disease (CKD) is a common disease that seriously endangers human health. However, the potential relationship between xanthine oxidoreductase (XOR) activity and CKD remains unclear. In this study, we used clinical data, CKD datasets from the Gene Expression Omnibus database, and untargeted metabolomics to explain the relationship between XOR activity and CKD. First, XOR activity showed high correlation with the biomarkers of CKD, such as serum creatinine, blood urea nitrogen, uric acid, and estimated glomerular filtration rate. Then, we used least absolute shrinkage and selection operator logical regression algorithm and random forest algorithm to screen CKD molecular markers from differentially expressed genes, and the results of qRT-PCR of XDH, KOX-1, and ROMO1 were in accordance with the results of bioinformatics analyses. In addition, untargeted metabolomics analysis revealed that the purine metabolism pathway was significantly enriched in CKD patients in the simulated models of kidney fibrosis.

Construction of a Novel Diagnostic Model Based on Ferroptosis-Related Genes for Hepatocellular Carcinoma Using Machine and Deep Learning Methods

Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Ferroptosis, a type of necrotic cell death that is oxidative and iron-dependent, has a strong correlation with the development of tumors and the progression of cancer. The present study was designed to identify potential diagnostic Ferroptosis-related genes (FRGs) using machine learning. From GEO datasets, two publicly available gene expression profiles (GSE65372 and GSE84402) from HCC and nontumor tissues were retrieved. The GSE65372 database was used to screen for FRGs with differential expression between HCC cases and nontumor specimens. Following this, a pathway enrichment analysis of FRGs was carried out. In order to locate potential biomarkers, an analysis using the support vector machine recursive feature elimination (SVM-RFE) model and the LASSO regression model were carried out. The levels of the novel biomarkers were validated further using data from the GSE84402 dataset and the TCGA datasets. In this study, 40 of 237 FRGs exhibited a dysregulated level between HCC specimens and nontumor specimens from GSE65372, including 27 increased and 13 decreased genes. The results of KEGG assays indicated that the 40 differential expressed FRGs were mainly enriched in the longevity regulating pathway, AMPK signaling pathway, the mTOR signaling pathway, and hepatocellular carcinoma. Subsequently, HSPB1, CDKN2A, LPIN1, MTDH, DCAF7, TRIM26, PIR, BCAT2, EZH2, and ADAMTS13 were identified as potential diagnostic biomarkers. ROC assays confirmed the diagnostic value of the new model. The expression of some FRGs among 11 FRGs was further confirmed by the GSE84402 dataset and TCGA datasets. Overall, our findings provided a novel diagnostic model using FRGs. Prior to its application in a clinical context, there is a need for additional research to evaluate the diagnostic value for HCC.

Transcriptome analysis revealed gene expression feminization of testis after exogenous tetrodotoxin administration in pufferfish Takifugu flavidus

Tetrodotoxin (TTX) is a deadly neurotoxin and usually accumulates in large amounts in the ovaries but is non-toxic or low toxic in the testis of pufferfish. The molecular mechanism underlying sexual dimorphism accumulation of TTX in ovary and testis, and the relationship between TTX accumulation with sex related genes expression remain largely unknown. The present study investigated the effects of exogenous TTX treatment on Takifugu flavidus. The results demonstrated that exogenous TTX administration significantly incresed level of TTX concentration in kidney, cholecyst, skin, liver, heart, muscle, ovary and testis of the treatment group (TG) than that of the control group (CG). Transcriptome sequencing and analysis were performed to study differential expression profiles of mRNA and piRNA after TTX administration of the ovary and testis. The results showed that compared with female control group (FCG) and male control group (MCG), TTX administration resulted in 80 and 23 piRNAs, 126 and 223 genes up and down regulated expression in female TTX-treated group (FTG), meanwhile, 286 and 223 piRNAs, 2 and 443 genes up and down regulated expression in male TTX-treated group (MTG). The female dominant genes cyp19a1, gdf9 and foxl2 were found to be up-regulated in MTG. The cyp19a1, whose corresponding target piRNA uniq_554482 was identified as down-regulated in the MTG, indicating the gene expression feminization in testis after exogenous TTX administration. The KEGG enrichment analysis revealed that differentially expressed genes (DEGs) and piRNAs (DEpiRNAs) in MTG vs MCG group were more enriched in metabolism pathways, indicating that the testis produced more metabolic pathways in response to exogenous TTX, which might be a reason for the sexual dimorphism of TTX distribution in gonads. In addition, TdT-mediated dUTP-biotin nick end labeling staining showed that significant apoptosis was detected in the MTG testis, and the role of the cell apoptotic pathways was further confirmed. Overall, our research revealed that the response of the ovary and testis to TTX administration was largely different, the ovary is more tolerant whereas the testis is more sensitive to TTX. These data will deepen our understanding on the accumulation of TTX sexual dimorphism in Takifugu.

Identification and Validation of Candidate Gene Module Along With Immune Cells Infiltration Patterns in Atherosclerosis Progression to Plaque Rupture via Transcriptome Analysis

Objective

To explore the differentially expressed genes (DEGs) along with infiltrating immune cells landscape and their potential mechanisms in the progression of atherosclerosis from onset to plaque rupture.

Methods

In this study, three atherosclerosis-related microarray datasets were downloaded from the NCBI-GEO database. The gene set enrichment analysis (GSEA) was performed for interpreting the biological insights of gene expression data. The CIBERSORTx algorithm was applied to infer the relative proportions of infiltrating immune cells of the atherosclerotic samples. DEGs of the datasets were screened using R. The protein interaction network was constructed via STRING. The cluster genes were analyzed by the Cytoscape software. Gene ontology (GO) enrichment was performed via geneontology.org. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics (ROC) analyses were performed to build machine learning models for differentiating atherosclerosis status. The Pearson correlation analysis was carried out to illustrate the relationship between cluster genes and immune cells. The expression levels of the cluster genes were validated in two external cohorts. Transcriptional factors and drug-gene interaction analysis were performed to investigate the promising targets for atherosclerosis intervention.

Results

Pathways related to immunoinflammatory responses were identified according to GSEA analysis, and the detailed fractions infiltrating immune cells were compared between the early and advanced atherosclerosis. Additionally, we identified 170 DEGs in atherosclerosis progression (|log2FC|≥1 and adjusted p < 0.05). They were mainly enriched in GO terms relating to inflammatory response and innate immune response. A cluster of nine genes, such as ITGB2, C1QC, LY86, CTSS, C1QA, CSF1R, LAPTM5, VSIG4, and CD163, were found to be significant, and their correlations with infiltrating immune cells were calculated. The cluster genes were also validated to be upregulated in two external cohorts. Moreover, C1QA and ITGB2 may exert pathogenic functions in the entire process of atherogenesis.

Conclusions

We reanalyzed the transcriptomic signature of atherosclerosis development from onset to plaque rupture along with the landscape of the immune cell, as well as revealed new insights and specific prospective DEGs for the investigation of disease-associated dynamic molecular processes and their regulations with immune cells.

Lung Surfactant Accelerates Skin Wound Healing: A Translational Study with a Randomized Clinical Phase I Study

Lung surfactants are used for reducing alveolar surface tension in preterm infants to ease breathing. Phospholipid films with surfactant proteins regulate the activity of alveolar macrophages and reduce inflammation. Aberrant skin wound healing is characterized by persistent inflammation. The aim of the study was to investigate if lung surfactant can promote wound healing. Preclinical wound models, e.g. cell scratch assays and full-thickness excisional wounds in mice, and a randomized, phase I clinical trial in healthy human volunteers using a suction blister model were used to study the effect of the commercially available bovine lung surfactant on skin wound repair. Lung surfactant increased migration of keratinocytes in a concentration-dependent manner with no effect on fibroblasts. Significantly reduced expression levels were found for pro-inflammatory and pro-fibrotic genes in murine wounds. Because of these beneficial effects in preclinical experiments, a clinical phase I study was initiated to monitor safety and tolerability of surfactant when applied topically onto human wounds and normal skin. No adverse effects were observed. Subepidermal wounds healed significantly faster with surfactant compared to control. Our study provides lung surfactant as a strong candidate for innovative treatment of chronic skin wounds and as additive for treatment of burn wounds to reduce inflammation and prevent excessive scarring.