Gene network exploration of crosstalk between apoptosis and autophagy in chronic myelogenous leukemia

Genistein alleviates doxorubicin-induced cardiomyocyte autophagy and apoptosis via ERK/STAT3/c-Myc signaling pathway in rat model

Doxorubicin (Dox) is a highly effective anti-neoplastic agent. Still, its utility in the clinic has been hindered by toxicities, including vomiting, hematopoietic suppression and nausea, with cardiotoxicity being the most serious side effect. Genistein (Gen) is a natural product with extensive biological effects, including anti-oxidation, anti-tumor, and cardiovascular protection. This study evaluated whether Gen protected the heart from Dox-induced cardiotoxicity and explored the underlying mechanisms. Male Sprague-Dawley (SD) rats were categorized into control (Ctrl), genistein (Gen), doxorubicin (Dox), genistein 20 mg/kg/day + doxorubicin (Gen20 + Dox) and genistein 40 mg/kg/day + doxorubicin (Gen40 + Dox) groups. Six weeks after injection, immunohistochemistry (IHC), transmission electron microscopy (TEM), and clinical cardiac function analyses were performed to evaluate the effects of Dox on cardiac function and structural alterations. Furthermore, each heart histopathological lesions were given a score of 0-3 in compliance with the articles for statistical analysis. In addition, molecular and cellular response of H9c2 cells toward Dox were evaluated through western blotting, Cell Counting Kit-8 (CCK8), AO staining and calcein AM/PI assay. Dox (5 μM in vitro and 18 mg/kg in vivo) was used in this study. In vivo, low-dose Gen pretreatment protected the rat against Dox-induced cardiac dysfunction and pathological remodeling. Gen inhibited extracellular signal-regulated kinase1/2 (ERK1/2)'s phosphorylation, increased the protein levels of STAT3 and c-Myc, and decreased the autophagy and apoptosis of cardiomyocytes. U0126, a MEK1/2 inhibitor, can mimic the effect of Gen in protecting against Dox-induced cytotoxicity both in vivo and in vitro. Molecular docking analysis showed that Gen forms a stable complex with ERK1/2. Gen protected the heart against Dox-induced cardiomyocyte autophagy and apoptosis through the ERK/STAT3/c-Myc signaling pathway.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

Deciphering the Rules of in Silico Autophagy Methods for Expediting Medicinal Research

Autophagy is a highly evolutionarily conserved cellular catabolic process responsible for degradation of damaged organelles and long-lived proteins. It is not surprising that scientific interest in the connection of autophagy and diseases has been growing. Over the past 2 decades, many new experimental approaches have been emerging in the field of targeting autophagy for medicinal research. Interestingly, in addition to experimental methods, a number of databases, Web servers, mathematics models, omics approaches, and systems biology network approaches related to autophagy have become available online, which may be collectively considered to be "in silico autophagy methods" for expediting current medicinal research. Thus, we have summarized a series of relevant in silico autophagy approaches for promoting the most appropriate usage of these resources for potential therapeutic purposes.

Co-expression Pattern Analysis of miR-17-92 Target Genes in Chronic Myelogenous Leukemia

MicroRNAs (miRNAs) are post-transcriptional regulators that regulate gene expression by binding to the 3' untranslated region of target mRNAs. Mature miRNAs transcribed from the miR-17-92 cluster have an oncogenic activity, which are overexpressed in chronic-phase chronic myelogenous leukemia (CML) patients compared with normal individuals. Besides, the tyrosine kinase activity of BCR-ABL oncoprotein from the Philadelphia chromosome in CML can affect this miRNA cluster. Genes with similar mRNA expression profiles are likely to be regulated by the same regulators. We hypothesize that target genes regulated by the same miRNA are co-expressed. In this study, we aim to explore the difference in the co-expression patterns of those genes potentially regulated by miR-17-92 cluster between the normal and the CML groups. We applied a statistical method for gene pair classification by identifying a disease-specific cutoff point that classified the co-expressed gene pairs into strong and weak co-expression classes. The method effectively identified the differences in the co-expression patterns from the overall structure. Functional annotation for co-expressed gene pairs showed that genes involved in the metabolism processes were more likely to be co-expressed in the normal group compared to the CML group. Our method can identify the co-expression pattern difference from the overall structure between two different distributions using the distribution-based statistical method. Functional annotation further provides the biological support. The co-expression pattern in the normal group is regarded as the inter-gene linkages, which represents the healthy pathological balance. Dysregulation of metabolism may be related to CML pathology. Our findings will provide useful information for investigating the novel CML mechanism and treatment.

Endocannabinoid system as a regulator of tumor cell malignancy - biological pathways and clinical significance

The endocannabinoid system (ECS) comprises cannabinoid receptors (CBs), endogenous cannabinoids, and enzymes responsible for their synthesis, transport, and degradation of (endo)cannabinoids. To date, two CBs, CB1 and CB2, have been characterized; however, orphan G-protein-coupled receptor GPR55 has been suggested to be the third putative CB. Several different types of cancer present abnormal expression of CBs, as well as other components of ECS, and this has been shown to correlate with the clinical outcome. Although most effects of (endo)cannabinoids are mediated through stimulation of classical CBs, they also interact with several molecules, either prosurvival or proapoptotic molecules. It should be noted that the mode of action of exogenous cannabinoids differs significantly from that of endocannabinoid and results from the studies on their activity both in vivo and in vitro could not be easily compared. This review highlights the main signaling pathways involved in the antitumor activity of cannabinoids and the influence of their activation on cancer cell biology. We also discuss changes in the expression pattern of the ECS in various cancer types that have an impact on disease progression and patient survival. A growing amount of experimental data imply possible exploitation of cannabinoids in cancer therapy.

miR-125b regulates cell progression in chronic myeloid leukemia via targeting BAK1

Chronic myeloid leukemia (CML) is a type of malignant tumor characterized by the accumulation of a large number of immature white blood cells in the blood and bone marrow. BAK1 was predicted to be the target gene of microRNA-451 (miR-125b). The present study was designed to illustrate the mechanism of miR-125b in regulating CML via targeting BAK1. In this study, we found that the expression of miR-125b increased strongly, whereas the expression of BAK1 decreased significantly in CML patients and CML cell lines compared with healthy controls. Moreover, the luciferase report assay confirmed the interaction between miR-125b and BAK1 mRNA. After transfection of the miR-125b mimic or miR-125b inhibitor into CML cells, we found that the inhibition of miR-125b decreased the proliferation rates and promoted apoptosis with cell cycle arrest at the G0/G1 phase in both K562 and NB-4 cells, increased the expression of BAK1 and Caspase-3, and decreased the expression of Bcl-2 and c-myc; the miR-125b mimic yielded the opposite results. In addition, siBAK1 offset the suppression effect of the miR-125b inhibitor in K562 cells, indicating that miR-125b promotes these cellular processes by inhibiting the expression of BAK1. Further in vivo experiments supported these findings because miR-125b suppression reduced CML growth in mice. Taken together, our study suggests that the down-regulation of miR-125b affects the expression of BAK1, promotes cell apoptosis and inhibits cell proliferation, leading to up-regulated expression of pro-apoptosis factors, down-regulated expression of anti-apoptosis factors in the mitochondrial apoptotic pathway, and decreased tumor size and weight of CML in vivo. These results provide a potential therapeutic strategy for CML.

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer

In this work an integrated approach was used to identify functional miRNAs regulating gene pathway cross-talk in breast cancer (BC). We first integrated gene expression profiles and biological pathway information to explore the underlying associations between genes differently expressed among normal and BC samples and pathways enriched from these genes. For each pair of pathways, a score was derived from the distribution of gene expression levels by quantifying their pathway cross-talk. Random forest classification allowed the identification of pairs of pathways with high cross-talk. We assessed miRNAs regulating the identified gene pathways by a mutual information analysis. A Fisher test was applied to demonstrate their significance in the regulated pathways. Our results suggest interesting networks of pathways that could be key regulatory of target genes in BC, including stem cell pluripotency, coagulation, and hypoxia pathways and miRNAs that control these networks could be potential biomarkers for diagnostic, prognostic, and therapeutic development in BC. This work shows that standard methods of predicting normal and tumor classes such as differentially expressed miRNAs or transcription factors could lose intrinsic features; instead our approach revealed the responsible molecules of the disease.