Metformin as a protective agent against natural or chemical toxicities: a comprehensive review on drug repositioning

Exploring the bone marrow micro environment in thalassemia patients: potential therapeutic alternatives

Genetic mutations in the β-globin gene lead to a decrease or removal of the β-globin chain, causing the build-up of unstable alpha-hemoglobin. This condition is referred to as beta-thalassemia (BT). The present treatment strategies primarily target the correction of defective erythropoiesis, with a particular emphasis on gene therapy and hematopoietic stem cell transplantation. However, the presence of inefficient erythropoiesis in BT bone marrow (BM) is likely to disturb the previously functioning BM microenvironment. This includes accumulation of various macromolecules, damage to hematopoietic function, destruction of bone cell production and damage to osteoblast(OBs), and so on. In addition, the changes of BT BM microenvironment may have a certain correlation with the occurrence of hematological malignancies. Correction of the microenvironment can be achieved through treatments such as iron chelation, antioxidants, hypoglycemia, and biologics. Hence, This review describes damage in the BT BM microenvironment and some potential remedies.

GCNGAT: Drug-disease association prediction based on graph convolution neural network and graph attention network

Predicting drug-disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug-disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug-disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug-disease information, a drug-disease heterogeneous graph structure is constructed based on all known drug-disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug-disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug-disease associations by learning drug-disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug-disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.

Protection by metformin against severe Covid-19: an in-depth mechanistic analysis

Since the outbreak of Covid-19, several observational studies on diabetes and Covid-19 have reported a favourable association between metformin and Covid-19-related outcomes in patients with type 2 diabetes mellitus (T2DM). This is not surprising since metformin affects many of the pathophysiological mechanisms implicated in SARS-CoV-2 immune response, systemic spread and sequelae. A comparison of the multifactorial pathophysiological mechanisms of Covid-19 progression with metformin's well-known pleiotropic properties suggests that the treatment of patients with this drug might be particularly beneficial. Indeed, metformin could alleviate the cytokine storm, diminish virus entry into cells, protect against microvascular damage as well as prevent secondary fibrosis. Although our in-depth analysis covers many potential metformin mechanisms of action, we want to highlight more particularly its unique microcirculatory protective effects since worsening of Covid-19 disease clearly appears as largely due to severe defects in the structure and functioning of microvessels. Overall, these observations confirm that metformin is a unique, pleiotropic drug that targets many of Covid-19′s pathophysiology processes in a diabetes-independent manner.

Metformin activated AMPK signaling contributes to the alleviation of LPS-induced inflammatory responses in bovine mammary epithelial cells

Background

Lipopolysaccharides (LPS) derived from gram-negative bacterial are often regarded as primary inducer of bovine mammary inflammation. This study evaluated the biological response of metformin activated AMPK signaling on LPS-induced inflammatory responses and metabolic changes in primary bovine mammary epithelial cells (pbMEC). The pbMEC were exposed to either 3 mmol/L Metf. for 12 h as Metf. group (Metf.) or 2 μg/mL LPS for 6 h as LPS group (LPS). Cells pretreated with 3 mmol/L metformin for 12 h followed by washing and 2 μg/mL LPS exposure for 6 h were served as ML group (ML). PBS was added to cells as the control group (Con.).

Results

Pre-incubation with Metf. inhibited LPS-induced expression of pro-inflammatory genes (TNF, IL1B, IL6, CXCL8, MYD88 and TLR4) and proteins (IL-1β, TNF-α, NLRP3, Caspase1, ASC) and was accompanied by increased activation of AMPK signaling. Compared with the LPS group, phosphorylation of p65 and IκBα in the ML group were decreased and accumulation of NF-κB in the nucleus was significantly reduced by pretreatment with metformin. Metformin protects the cells from the increase of LPS-induced binding activity of NF-κB on both TNFA and IL1B promoters. Compared with the LPS group, genes (G6PC, PCK2) and proteins (SREBP1, SCD1) related to lipogenesis and carbohydrate metabolism were downregulated while catabolic ones (PPARA, ACSL1, Glut1, HK1) were upregulated in the ML group. Furthermore, increased acetylation of H3K14 by LPS challenge was reversed by pretreatment with metformin.

Conclusion

Altogether, our results indicated that pretreatment with metformin dampens LPS-induced inflammatory responses mediated in part by AMPK/NF-κB/NLRP3 signaling and modification of histone H3K14 deacetylation and metabolic changes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-02797-x.