Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis

Integrated Omics Approach: Revealing the Mechanism of Auxenochlorella pyrenoidosa Protein Extract Replacing Fetal Bovine Serum for Fish Muscle Cell Culture

The process of producing cell-cultured meat involves utilizing a significant amount of culture medium, including fetal bovine serum (FBS), which represents a considerable portion of production expense while also raising environmental and safety concerns. This study demonstrated that supplementation with Auxenochlorella pyrenoidosa protein extract (APE) under low-serum conditions substantially increased Carassius auratus muscle (CAM) cell proliferation and heightened the expression of Myf5 compared to the absence of APE. An integrated intracellular metabolomics and proteomics analysis revealed a total of 13 and 67 differentially expressed metabolites and proteins, respectively, after supplementation with APE in the medium containing 5%FBS, modulating specific metabolism and signaling pathways, which explained the application of APE for passage cell culture under low-serum conditions. Further analysis revealed that the bioactive factors in the APE were protein components. Moreover, CAM cells cultured in reconstructed serum-free media containing APE, l-ascorbic acid, insulin, transferrin, selenium, and ethanolamine exhibited significantly accelerated growth in a scale-up culture. These findings suggest a promising alternative to FBS for fish muscle cell culture that can help reduce production costs and environmental impact in the production of cultured meat.

The Functional Role of microRNAs and mRNAs in Diabetic Kidney Disease: A Review

Diabetes is a group of diseases marked by poor control of blood glucose levels. Diabetes mellitus (DM) occurs when pancreatic cells fail to make insulin, which is required to keep blood glucose levels stable, disorders, and so on. High glucose levels in the blood induce diabetic effects, which can cause catastrophic damage to bodily organs such as the eyes and lower extremities. Diabetes is classified into many forms, one of which is controlled by hyperglycemia or Diabetic Kidney Disease (DKD), and another that is not controlled by hyperglycemia (nondiabetic kidney disease or NDKD) and is caused by other factors such as hypertension, hereditary. DKD is associated with diabetic nephropathy (DN), a leading cause of chronic kidney disease (CKD) and end-stage renal failure. The disease is characterized by glomerular basement membrane thickening, glomerular sclerosis, and mesangial expansion, resulting in a progressive decrease in glomerular filtration rate, glomerular hypertension, and renal failure or nephrotic syndrome. It is also represented by some microvascular complications such as nerve ischemia produced by intracellular metabolic changes, microvascular illness, and the direct impact of excessive blood glucose on neuronal activity. Therefore, DKD-induced nephrotic failure is worse than NDKD. MicroRNAs (miRNAs) are important in the development and progression of several diseases, including diabetic kidney disease (DKD). These dysregulated miRNAs can impact various cellular processes, including inflammation, fibrosis, oxidative stress, and apoptosis, all of which are implicated during DKD. MiRNAs can alter the course of DKD by targeting several essential mechanisms. Understanding the miRNAs implicated in DKD and their involvement in disease development might lead to identifying possible therapeutic targets for DKD prevention and therapy. Therefore, this review focuses specifically on DKD-associated DN, as well as how in-silico approaches may aid in improving the management of the disease.

An integrated bioinformatics approach to identify key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and construction of mRNA-miRNA-lncRNA/circRNA networks

OBJECTIVE

The purpose of this study was to identify potential biomarkers in the tubulointerstitium of focal segmental glomerulosclerosis (FSGS) and comprehensively analyze its mRNA-miRNA-lncRNA/circRNA network.

METHODS

The expression data (GSE108112 and GSE200818) were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Identification and enrichment analysis of differentially expressed genes (DEGs) were performed. the PPI networks of the DEGs were constructed and classified using the Cytoscape molecular complex detection (MCODE) plugin. Weighted gene coexpression network analysis (WGCNA) was used to identify critical gene modules. Least absolute shrinkage and selection operator regression analysis were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The screening results were verified by quantitative real-time-PCR (qRT-PCR) and Western blot. The transcription factors (TFs) affecting the hub genes were identified by Cytoscape iRegulon. The mRNA-miRNA-lncRNA/circRNA network for identifying potential biomarkers was based on the starBase database.

RESULTS

A total of 535 DEGs were identified. MCODE obtained eight modules. The green module of WGCNA had the greatest association with the tubulointerstitium in FSGS. PPARG coactivator 1 alpha (PPARGC1A) was screened as a potential tubulointerstitial biomarker for FSGS and verified by qRT-PCR and Western blot. The TFs FOXO4 and FOXO1 had a regulatory effect on PPARGC1A. The ceRNA network yielded 17 miRNAs, 32 lncRNAs, and 50 circRNAs.

CONCLUSIONS

PPARGC1A may be a potential biomarker in the tubulointerstitium of FSGS. The ceRNA network contributes to the comprehensive elucidation of the mechanisms of tubulointerstitial lesions in FSGS.

Analysis of the alleviating effect of modified Huangqi Chifeng decoction on rats with focal segmental glomerulosclerosis based on gut microbiota and fecal metabolomics

AIMS

To investigate the reno-protective effects of modified Huangqi Chifeng decoction (MHCD) on focal segmental glomerulosclerosis (FSGS) rats, and the underlying mechanisms of systemic regulation of gut microbiota and metabolite profiles.

METHODS AND RESULTS

A rat FSGS model was established via unilateral nephrectomy plus doxorubicin injections. Rats were divided into sham, FSGS, and MHCD groups from which urine, blood, and histological tests were conducted. Fecal microbiotas were identified via 16S rRNA gene sequencing. Fecal metabolomics allowed for metabolic pathways analysis. Biochemical indices and pathological examination revealed that MHCD treatment improved the symptoms of FSGS, and corrected dysbiosis of gut microbiota, enriched the abundance of Bifidobacterium, Odoribacter, Christensella, Oscillospira, and reduced that of harmful bacteria such as Collinsella and Coprobacterilus at the genus level. Fecal metabolomic profiles revealed 152 different metabolites between the FSGS and sham groups, which are mainly enriched in signaling pathways like arachidonic acid, serotonergic synapse, and oxytocin. Besides, 93 differential metabolites between MHCD and FSGS groups were identified, which are mainly enriched in signaling pathways like steroid hormone biosynthesis, prostate cancer, and linoleic acid metabolism. Spearman's correlation analysis showed a correlation between differential fecal metabolites and enriched gut microbiota or serum biochemical parameters.

CONCLUSIONS

MHCD may exert a reno-protective effect by regulating the gut microbiome and metabolite profiles in FSGS rats.

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

Methods

Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes.

Results

A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis.

Conclusion

15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.