Exosomes from mesenchymal stromal cells enhance imatinib-induced apoptosis in human leukemia cells via activation of caspase signaling pathway.

BACKGROUND AIMS: Imatinib (IM), a tyrosine kinase inhibitor targeting the BCR-ABL oncoprotein, remains a major therapeutic strategy for patients with chronic myelogenous leukemia (CML). However, IM resistance is still a challenge in the treatment of CML. Recently, it was reported that exosomes (Exo) were involved in drug resistance. Therefore, the present study investigated whether Exo secreted by human umbilical cord mesenchymal stromal cells (hUC-MSC-Exo) affected the sensitivity of K562 cells to IM. METHODS: hUC-MSC-Exo were isolated and identified. K562 cells were then treated or not with IM (1 µmol/L) in combination with hUC-MSC-Exo (50 µg/mL). Cell viability and apoptosis were determined by cell counting kit 8 (CCK-8) and annexin V/propidium iodide (PI) double staining, respectively. Apoptotic proteins, caspase and their cleaved forms were detected by Western blot. RESULTS: It was shown that hUC-MSC-Exo alone had no effect on cell viability and apoptosis of K562 cells. However, hUC-MSC-Exo promoted IM-induced cell viability inhibition and apoptosis. Moreover, hUC-MSC-Exo enhanced the increased Bax expression and the decreased Bcl-2 expression that were induced by IM. Compared with IM alone, caspase-9 and caspase-3 were further activated by combination of hUC-MSC-Exo with IM. Finally, the effects of hUC-MSC-Exo on K562 cells could be reversed by pretreatment of K562 cells with caspase inhibitor Z-VAD-FMK (30 µmol/L) DISCUSSION: These results indicate that hUC-MSC-Exo enhanced the sensitivity of K562 cells to IM via activation of caspase signaling pathway. Therefore, combining IM with hUC-MSC-Exo could be a promising approach to improve the efficacy of CML treatment.

Exploration of the anti-hyperuricemia effect of TongFengTangSan (TFTS) by UPLC-Q-TOF/MS-based non-targeted metabonomics.

BACKGROUND: TongFengTangSan (TFTS) is a commonly used Tibetan prescription for gout treatment. Previously, TFTS (CF) was confirmed to have a significant uric acid-lowering effect. However, the anti-hyperuricemia mechanisms and the main active fractions remain unclear. The current study aimed to investigate the anti-hyperuricemia mechanism using metabolomics and confirm the active CF fraction. METHODS: The hyperuricemia model was established through intraperitoneal injection containing 100 mg/kg potassium oxonate and 150 mg/kg hypoxanthine by gavage. We used serum uric acid (sUA), creatinine (CRE), blood urea nitrogen (BUN), xanthine oxidase (XOD) activity, interleukin-6 (IL-6) and interleukin-1ß (IL-1ß) as indicators to evaluate the efficacy of CF and the four fractions (SX, CF30, CF60, and CF90). The anti-hyperuricemia mechanism of CF was considered through non-targeted metabolomics depending on the UPLC-Q-TOF-MS technology. Principle component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) helped explore the potential biomarkers in hyperuricemia. Moreover, the differential metabolites and metabolic pathways regulated by CF and four fractions were also assessed. RESULTS: CF revealed a significant anti-hyperuricemia effect by down-regulating the level of sUA, sCRE, sIL-1ß, and XOD. SX, CF30, CF60, and CF90 differed in the anti-hyperuricemia effect. Only CF60 significantly lowered the sUA level among the four fractions, and it could be the main efficacy fraction of TFTS. Forty-three differential metabolites were identified in hyperuricemia rats from plasma and kidney. Pathway analysis demonstrated that seven pathways were disrupted among hyperuricemia rats. CF reversed 19 metabolites in hyperuricemia rats and exerted an anti-hyperuricemia effect by regulating purine metabolism. CF60 was the main active fraction of TFTS and exerted a similar effect of CF by regulating purine metabolism. CONCLUSIONS: CF and CF60 could exert an anti-hyperuricemia effect by regulating the abnormal purine metabolism because of hyperuricemia while improving intestinal and renal function. CF60 could be the main active fraction of TFTS.

An investigation into the allosteric mechanism of GPCR A2A adenosine receptor with trajectory-based information theory and complex network model.

G protein-coupled receptors (GPCRs), a large superfamily of transmembrane (TM) proteins, allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to effector proteins in the intracellular (IC) domain, therefore forming the largest class of drug targets. The A2A adenosine receptor (A2AAR), a class-A GPCR, has been extensively studied as it offers numerous possibilities for therapeutic applications. However, the mechanism of allosteric communication between EC and IC domains is not completely clear. In this work, we utilize torsional mutual information to quantify the correlated motions of residue pairs from its molecular dynamics (MD) simulation trajectories, and further use the complex network model to obtain allosteric pipelines and hubs. The identified allosteric communication pipelines mainly transmit the signal from EC domain to the cytoplasmic ends of TM helix 5 (TM5), TM6 and TM7. The allosteric hubs, mostly located at TM5, TM6 and TM7, play an important role in mediating allosteric signal transmission to keep the receptor rigid and prevent G protein from binding to IC domain, which can explain the reason why their mutations distant from ligand-binding site do not affect the ligand binding affinity but affect the ligand efficacy. Additionally, we identify the key residues located in antagonist ZM241385 binding pocket which mediate multiple allosteric pathways and have been experimentally proven to play a critical role in affecting the ligand potency. This study is helpful for understanding the allosteric communication mechanism of A2AAR, and can provide valuable information for the structure-based drug design of GPCRs.Communicated by Ramaswamy H. Sarma.

Genome-wide pathway-based quantitative multiple phenotypes analysis.

For complex diseases, genome-wide pathway association studies have become increasingly promising. Currently, however, pathway-based association analysis mainly focus on a single phenotype, which may insufficient to describe the complex diseases and physiological processes. This work proposes a combination model to evaluate the association between a pathway and multiple phenotypes and to reduce the run time based on asymptotic results. For a single phenotype, we propose a semi-supervised maximum kernel-based U-statistics (mSKU) method to assess the pathway-based association analysis. For multiple phenotypes, we propose the fisher combination function with dependent phenotypes (FC) to transform the p-values between the pathway and each marginal phenotype individually to achieve pathway-based multiple phenotypes analysis. With real data from the Alzheimer Disease Neuroimaging Initiative (ADNI) study and Human Liver Cohort (HLC) study, the FC-mSKU method allows us to specify which pathways are specific to a single phenotype or contribute to common genetic constructions of multiple phenotypes. If we only focus on single-phenotype tests, we may miss some findings for etiology studies. Through extensive simulation studies, the FC-mSKU method demonstrates its advantages compared with its counterparts.