Odanacatib Inhibits Resistin-induced Hypertrophic H9c2 Cardiomyoblast Cells Through LKB1/AMPK Pathway

ABSTRACT Odanacatib (ODN) is a selective inhibitor of cathepsin K. The cysteine protease cathepsin K has been implicated in cardiac hypertrophy. Resistine is an adipokine which is identified to promote cardiac hypertrophy. Here, we hypothesize that ODN mitigates resistin-induced myocyte hypertrophy. Cell surface area and protein synthesis were measured after treatment with resistin and ODN in H9c2 cells. The expression of cardiomyocyte hypertrophy marker BNP and β-MHC was detected by RT-qPCR. The expression and phosphorylation of AMPK and LKB1 were analyzed with Western blot. Resistin could significantly increase cardiomyocyte cell surface area, protein synthesis, and embryonic gene BNP and β-MHC expression, inhibit phosphorylation of AMPK and LKB1. ODN could significantly reverse the effects of resistin. Collectively, our data suggest that ODN can inhibit cardiomyocyte hypertrophy induced by resistin and the underlying mechanism may be involved in LKB1/AMPK pathway

Cucurbitacin B induces inhibitory effects via the CIP2A/PP2A/C-KIT signaling axis in t(8;21) acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common subtype of hematological malignancy in humans, and its incidence increases with age. The treatment of AML still faces challenges. Therefore, there is an urgent need to develop more effective targeted therapies. The receptor tyrosine kinase C-KIT confers critical proliferative signals to AML. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an endogenous inhibitor of protein phosphatase 2A (PP2A), which promotes the growth and transformation of various solid tumors. These actions make CIP2A a promising target for tumor treatment. Here, we reported the effects and underlying mechanisms of a natural compound, cucurbitacin B (CuB), on AML. We reported that CuB suppressed growth and induced apoptosis in AML cells. The inhibition of growth and activation of apoptosis were mediated through CuB-induced downregulation of the CIP2A/PP2A/C-KIT signal pathway. Furthermore, CuB inactivated the JAK2 and STAT3 molecules downstream of C-KIT via the downregulation of CIP2A. These results advance our understanding of CuB-induced growth inhibition and apoptosis and support further investigation of CuB as a CIP2A inhibitor for AML therapies. Keywords: Cucurbitacin B, Acute myeloid leukemia, CIP2A, C-KIT, PP2

Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005–2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models’ predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate

Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005–2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models’ predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate

Putative endothelial progenitor cells do not promote vascular repair but attenuate pericyte–myofibroblast transition in UUO-induced renal fibrosis

Abstract Background Putative endothelial progenitor cells (pEPCs) have been confirmed to participate in alleviation of renal fibrosis in several ischaemic diseases. However, their mechanistic effect on renal fibrosis, which is characterized by vascular regression and further rarefaction-related pathology, remains unknown. Methods To explore the effect and molecular mechanisms by which pEPCs act on unilateral ureteral obstruction (UUO)-induced renal fibrosis, we isolated pEPCs from murine bone marrow. In vivo, pEPCs (2 × 105 cells/day) and pEPC-MVs (microvesicles) were injected into UUO mice via the tail vein. In vitro, pEPCs were co-cultured with renal-derived pericytes. Pericyte-myofibroblast transition was evaluated using the myofibroblast marker α-smooth muscle actin (α-SMA) and pericyte marker platelet-derived growth factor receptor β (PDGFR-β). Results Exogenous supply of bone marrow-derived pEPCs attenuated renal fibrosis by decreasing pericyte-myofibroblast transition without significant vascular repair in the UUO model. Our results indicated that pEPCs regulated pericytes and their transition into myofibroblasts via pEPC-MVs. Co-culture of pericytes with pEPCs in vitro suggested that pEPCs inhibit transforming growth factor-β (TGF-β)-induced pericyte–myofibroblast transition via a paracrine pathway. Conclusion pEPCs effectively attenuated UUO-induced renal fibrosis by inhibiting pericyte–myofibroblast transition via a paracrine pathway, without promoting vascular repair