Insulin, a key hormone in the regulation of glucose homoeostasis, is secreted by pancreatic β‐cells in response to elevated glucose levels. Insulin is released in a biphasic manner in response to glucose metabolism in β‐cells. The first phase of insulin secretion is triggered by an increase in the ATP:ADP ratio; the second phase occurs in response to both a rise in ATP:ADP and other key metabolic signals, including a rise in the NADPH:NADP⁺ ratio. Experimental evidence indicates that pyruvate‐cycling pathways play an important role in the elevation of the NADPH:NADP⁺ ratio in response to glucose. The authors developed a kinetic model for the tricarboxylic acid cycle and pyruvate cycling pathways. The authors successfully validated the model against experimental observations and performed a sensitivity analysis to identify key regulatory interactions in the system. The model predicts that the dicarboxylate carrier and the pyruvate transporter are the most important regulators of pyruvate cycling and NADPH production. In contrast, the analysis showed that variation in the pyruvate carboxylase flux was compensated by a response in the activity of mitochondrial isocitrate dehydrogenase (ICD_m) resulting in minimal effect on overall pyruvate cycling flux. The model predictions suggest starting points for further experimental investigation, as well as potential drug targets for the treatment of type 2 diabetes.

Insulin, a key hormone in the regulation of glucose homoeostasis, is secreted by pancreatic β‐cells in response to elevated glucose levels. Experimental evidence indicates that pyruvate‐cycling pathways play an important role in the elevation of the NADPH:NADP+ ratio in response to glucose. The authors developed a kinetic model for the tricarboxylic acid cycle and pyruvate cycling pathways. The model predictions suggest starting points for further experimental investigation, as well as potential drug targets for treatment of type 2 diabetes.image

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM‐related markers in DKD and explored the immune cell infiltration in this process. The expression profiles of three datasets were downloaded from the Gene Expression Omnibus database. BM‐related differentially expression genes (DEGs) were identified and Kyoto encyclopaedia of genes and genomes pathway enrichment analysis were applied to biological functions. Immune cell infiltration and immune function in the kidneys of patients with DKD and healthy controls were evaluated and compared using the ssGSEA algorithm. The association of hub genes and immune cells and immune function were explored. A total of 30 BM‐related DEGs were identified. The functional analysis showed that BM‐related DEGs were notably associated with basement membrane alterations. Crucially, BM‐related hub genes in DKD were finally identified, which were able to distinguish patients with DKD from controls. Moreover, the authors observed that laminin subunit gamma 1(LAMC1) expression was significantly high in HK2 cells treated with high glucose. Immunohistochemistry results showed that, compared with those in db/m mouse kidneys, the levels of LAMC1 in db/db mouse kidneys were significantly increased. The biomarkers genes may prove crucial for DKD treatment as they could be targeted in future DKD treatment protocols.

The authors combined experimental and bioinformatical analyses to investigate the differentially expressed genes in DKD patients to try and elucidate which genes are involved in the thickening of basement membranes. Crucially four BM‐related hub genes (ITGAV, ITGB3, LAMA5 and LAMC1) were able to distinguish DKD patients from controls.image

Since a 25% mortality rate occurred in critical Coronavirus disease 2019 (COVID‐19) patients, investigating the potential drivers remains to be important. Here, the authors applied Weighted Gene Co‐expression Network Analysis to identify the potential drivers in the blood samples of multiple COVID‐19 expression profiles. The authors found that the darkslateblue module was significantly correlated with critical COVID‐19, and Gene Ontology analysis indicated terms associated with the inflammation pathway and apoptotic process. The authors intersected differentially expressed genes, Maximal Clique Centrality calculated hub genes, and COVID‐19 related genes in the Genecards dataset, and two genes, toll‐like receptor 5 (TLR5) and acyl‐CoA synthetase long chain family member 1 (ACSL1), were screened out. The Gene Set Enrichment Analysis further supports their core role in the inflammatory pathway. Furthermore, the cell‐type identification by estimating relative subsets of RNA transcript demonstrated that TLR5 and ACSL1 were associated with neutrophil enrichment in critical COVID‐19 patients. Collectively, the aurthors identified two hub genes that were strongly correlated with critical COVID‐19. These may help clarify the pathogenesis and assist the immunotherapy development.

TLR5 and ACLS1 might play important roles in critical COVID‐19 patients, and they were associated with neutrophil enrichment. These findings may help clarify the pathogenesis and assist the immunotherapy development.image

The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.

With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.image

With increasing research on idiopathic pulmonary fibrosis (IPF) and gastroesophageal reflux disease (GERD), more and more studies have indicated that GERD is associated with IPF, but the underlying pathological mechanisms remain unclear. The aim of the present study is to identify and analyse the differentially expressed genes (DEGs) between IPF and GERD and explore the relevant molecular mechanisms via bioinformatics analysis. Four GEO datasets (GSE24206, GSE53845, GSE26886, and GSE39491) were downloaded from the GEO database, and DEGs between IPF and GERD were identified with the online tool GEO2R. Subsequently, a series of bioinformatics analyses are conducted, including Kyoto Encyclopaedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses, the PPI network, biological characteristics, TF‐gene interactions, TF‐miRNA coregulatory networks, and the prediction of drug molecules. Totally, 71 genes were identified as DEGs in IPF and GERD. Five KEGG pathways, including Amoebiasis, Protein digestion and absorption, Relaxin signalling pathway, AGE‐RAGE signalling pathway in diabetic complications, and Drug metabolism ‐ cytochrome P450, were significantly enriched. In addition, eight hub genes, including POSTN, MMP1, COL3A1, COL1A2, CXCL12, TIMP3, VCAM1, and COL1A1 were selected from the PPI network by Cytoscape software. Then, five hub genes (MMP1, POSTN, COL3A1, COL1A2, and COL1A1) with high diagnostic values for IPF and GERD were validated by GEO datasets. Finally, TF‐gene and miRNA interaction was identified with hub genes and predicted drug molecules for the IPF and GERD. And the results suggest that cetirizine, luteolin, and pempidine may have great potential therapeutic value in IPF and GERD. This study will provide novel strategies for the identification of potential biomarkers and valuable therapeutic targets for IPF and GERD.

TF‐gene and miRNA interaction was identified with hub genes and predicted drug molecules for the IPF and GERD. And the results suggest that cetirizine, luteolin, and pempidine may have great potential therapeutic value in IPF and GERD.image