Induction of DNMT1-dependent demethylation of SHP-1 by the natural flavonoid compound Baicalein overcame Imatinib-resistance in CML CD34+ cells.

BACKGROUND: The most significant cause of treatment failure in chronic myeloid leukemia (CML) is a persistent population of minimal residual cells. Emerging evidences showed that methylation of SHP-1 contributed to Imatinib (IM) resistance. Baicalein was reported to have an effect on reversal of chemotherapeutic agents resistance. However, the molecular mechanism of Baicalein on JAK2/STAT5 signaling inhibition against drug resistance in bone marrow (BM) microenvironment that had not been clearly revealed. METHODS: We co-cultured hBMSCs and CML CD34+ cells as a model of SFM-DR. Further researches were performed to clarify the reverse mechanisms of Baicalein on SFM-DR model and engraftment model. The apoptosis, cytotoxicity, proliferation, GM-CSF secretion, JAK2/STAT5 activity, the expression of SHP-1 and DNMT1 were analyzed. To validate the role of SHP-1 on the reversal effect of Baicalein, the SHP-1 gene was over-expressed by pCMV6-entry shp-1 and silenced by SHP-1 shRNA, respectively. Meanwhile, the DNMT1 inhibitor decitabine was used. The methylation extent of SHP-1 was evaluated using MSP and BSP. The molecular docking was replenished to further explore the binding possibility of Baicalein and DNMT1. RESULTS: BCR/ABL-independent activation of JAK2/STAT5 signaling was involved in IM resistance in CML CD34+ subpopulation. Baicalein significantly reversed BM microenvironment-induced IM resistance not through reducing GM-CSF secretion, but interfering DNMT1 expression and activity. Baicalein induced DNMT1-mediated demethylation of the SHP-1 promoter region, and subsequently activated SHP-1 re-expression, which resulted in an inhibition of JAK2/STAT5 signaling in resistant CML CD34+ cells. Molecular docking model indicated that DNMT1 and Baicalein had binding pockets in 3D structures, which further supported Baicalein might be a small-molecule inhibitor targeting DNMT1. CONCLUSIONS: The mechanism of Baicalein on improving the sensitivity of CD34+ cells to IM might be correlated with SHP-1 demethylation by inhibition of DNMT1 expression. These findings suggested that Baicalein could be a promising candidate by targeting DNMT1 to eradicate minimal residual disease in CML patients. Video Abstract.

Modification of lysine deacetylation regulates curcumol-induced necroptosis through autophagy in hepatic stellate cells.

The excessive deposition of extracellular matrix (ECM) is the main characteristic of liver fibrosis, and hepatic stellate cells (HSCs) are the main source of ECM. The removal of activated HSCs has a reversal effect on liver fibrosis. Western blot and MTT analysis indicated that curcumol could relieve hepatic fibrosis by promoting HSCs receptor-interacting protein kinase 1/3 (RIP1/RIP3)-dependent necroptosis. Importantly, autophagy flow was monitored by constructing the mRFP-GFP-LC3 plasmid, and it was found that curcumol cleared activated HSCs in a necroptosis manner that was dependent on autophagy. Our study suggested that the activation of necrosome formed by RIP1 and RIP3 depended on Atg5, and that autophagosomes were also necessary for curcumol-induced necroptosis. Furthermore, microscale thermophoresis and co-immunoprecipitation assay results proved that curcumol could target Sirt1 to regulate autophagy by reducing the acetylation level of Atg5. The HSCs-specific silencing of Sirt1 exacerbated CCl4 -induced liver fibrosis in mice. The deacetylation of Atg5 not only accelerated the accumulation of autophagosomes but also enhanced the interaction between Atg5 and RIP1/RIP3 to induce necroptosis. Overall, our study indicated that curcumol could activate Sirt1 to promote Atg5 deacetylation and enhanced its protein-protein interaction function, thereby inducing autophagy and promoting the necroptosis of HSCs to reduce liver fibrosis.

Applying Information Gain to Explore Factors Affecting Small-Incision Lenticule Extraction: A Multicenter Retrospective Study.

Purpose: This retrospective study aimed to identify the key factors influencing postoperative refraction after small-incision lenticule extraction (SMILE) using information gain. Methods: This study comprised 2,350 eyes of 1,200 patients who underwent SMILE using a Visumax 500-kHz femtosecond laser (Carl Zeiss Meditec AG) in three ophthalmic centers: Tianjin Eye Hospital (center A), Jinan Mingshui Eye Hospital (center B), and Qingdao Eye Hospital (center C). Anterior segment features, including corneal curvature and central corneal thickness (CCT), were obtained from Pentacam HR (Oculus, Wetzlar, Germany). Information gain was calculated to analyze the importance of features affecting postoperative refraction. Results: Preoperative and postoperative mean spherical equivalent (SE) refraction were -5.00 (-6.13, -3.88) D and 0.00 (-0.25, 0.13) D, respectively. None of the patients lost more than two lines of corrected distance visual acuity. The safety index was 1.32 ± 0.24, 1.03 ± 0.08, and 1.13 ± 0.16 in centers A, B, and C, respectively. The efficacy index was 1.31 ± 0.25, 1.02 ± 0.08, and 1.13 ± 0.17 in centers A, B, and C, respectively. At least 95% of the eyes were within ±1.00 D of the attempted correction. Postoperative refraction was related to preoperative spherical diopter refraction (r = 0.369, p < 0.001), preoperative SE (r = 0.364, p < 0.001), maximum lenticule thickness (r = -0.311, p < 0.001), preoperative uncorrected distance visual acuity (r = 0.164, p < 0.001), residual stromal thickness (r = 0.139, p < 0.001), preoperative mean anterior corneal curvature (r = -0.127, p < 0.001), preoperative flattest anterior corneal curvature (r = -0.122, p < 0.001), nomogram (r = -0.100, p < 0.001) and preoperative CCT (r = -0.058, p = 0.005). Conclusions: SMILE was considered a safe and effective procedure for correcting myopia. Based on information gain, postoperative refraction was influenced by preoperative mean anterior corneal curvature, CCT, refraction, and residual stromal thickness.

Clinical Significance and Underlying Mechanisms of CELSR3 in Metastatic Prostate Cancer Based on Immunohistochemistry, Data Mining, and In Silico Analysis.

Background: The treatment and survival rate of patients with metastatic prostate cancer (MPCa) remain unsatisfactory. Herein, the authors investigated the clinical value and potential mechanisms of cadherin EGF LAG seven-pass G-type receptor 3 (CELSR3) in MPCa to identify novel targets for clinical diagnosis and treatment. Materials and Methods: mRNA microarray and RNA-Seq (n = 1246 samples) data were utilized to estimate CELSR3 expression and to assess its differentiation ability in MPCa. Similar analyses were performed with miRNA-221-3p. Immunohistochemistry performed on clinical samples were used to evaluate the protein expression level of CELSR3 in MPCa. Based on CELSR3 differentially coexpressed genes (DCEGs), enrichment analysis was performed to investigate potential mechanisms of CELSR3 in MPCa. Results: The pooled standard mean difference (SMD) for CELSR3 was 0.80, demonstrating that CELSR3 expression was higher in MPCa than in localized prostate cancer (LPCa). CELSR3 showed moderate potential to distinguish MPCa from LPCa. CELSR3 protein expression was found to be markedly upregulated in MPCa than in LPCa tissues. The authors screened 894 CELSR3 DCEGs, which were notably enriched in the focal adhesion pathway. miRNA-221-3p showed a significantly negative correlation with CELSR3 in MPCa. Besides, miRNA-221-3p expression was downregulated in MPCa than in LPCa (SMD = -1.04), and miRNA-221-3p was moderately capable of distinguishing MPCa from LPCa. Conclusions: CELSR3 seems to play a pivotal role in MPCa by affecting the focal adhesion pathway and/or being targeted by miRNA-221-3p.

Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model.

Magnetic Resonance Imaging (MRI) technology has been widely applied to generate high-resolution images for brain tumor diagnosis. However, manual image reading is very time and labor consuming. Instead, automatic tumor detection based on deep learning models has emerged recently. Although existing models could well detect brain tumors from MR images, they seldom distinguished primary intracranial tumors from secondary ones. Therefore, in this paper, we propose an attention guided deep Convolution Neural Network (CNN) model for brain tumor diagnosis. Experimental results show that our model could effectively detect tumors from brain MR images with 99.18% average accuracy, and distinguish the primary and secondary intracranial tumors with 83.38% average accuracy, both under ten-fold cross-validation. Our model, outperforming existing works, is competitive to medical experts on brain tumor diagnosis.

The biological function and clinical significance of STIL in osteosarcoma.

BACKGROUND: SCL/TAL1 interrupting locus (STIL) is associated with the progression of several tumors; however, the biological role of STIL in osteosarcoma remains poorly understood. METHODS: In this study, the clinical significance of STIL in osteosarcoma was analyzed by gene chip data recorded in public databases. STIL expression was silenced in osteosarcoma cell lines to observe the effects on proliferation, apoptosis, invasion, and migration. Differentially expressed genes (DEGs) in the osteosarcoma chip were analyzed using The Limma package, and STIL co-expressed genes were obtained via the Pearson correlation coefficient. The potential molecular mechanism of STIL in osteosarcoma was further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. RESULTS: Osteosarcoma was associated with higher STIL expression compared to the control samples, and the standardized mean difference (SMD) was 1.52. STIL also had a good ability to distinguish osteosarcoma from non-osteosarcoma samples [area under the curve (AUC) = 0.96]. After silencing STIL, osteosarcoma cell proliferation decreased, apoptosis increased, and the migratory and invasion ability decreased. A total of 294 STIL differentially co-expressed genes were screened, and a bioinformatics analysis found that differentially co-expressed genes were primarily enriched in the cell signaling pathways. The protein-protein interaction (PPI) network indicated that the hub differentially co-expressed genes of STIL were CDK1, CCNB2, CDC20, CCNA2, BUB1, and AURKB. CONCLUSIONS: STIL is associated with osteosarcoma proliferation and invasion, and may be promote the progression of osteosarcoma by regulating the expression of CDK1, CCNB2, CDC20, CCNA2, BUB1 and AURKB.

Clinical significance and potential molecular mechanism of miRNA-222-3p in metastatic prostate cancer.

The clinical significance and underlying molecular mechanism of miRNA-222-3p in metastatic prostate cancer (MPCa) remain unclear. The present study used a large number of cases (n = 1,502) based on miRNA chip and miRNA sequencing datasets to evaluate the expression and diagnostic potential of miRNA-222-3p in MPCa. We applied a variety of meta-analytic methods, including forest maps, sensitivity analysis, subgroup analysis and summary receiver operating characteristic curves, to prove the final results. MiRNA-222-3p was reduced in MPCa and had a moderate diagnostic potential in MPCa. We screened 118 miRNA-222-3p targets using three different methods including miRNA-222-3p transfected MPCa cell lines, online prediction databases and differently upregulated genes in MPCa. Moreover, functional enrichment analysis performed to explore the potential molecular mechanism of miRNA-222-3p showed that the potential target genes of miRNA-222-3p were significantly enriched in the p53 signal pathway. In the protein-protein interaction network analysis, SNAP91 was identified as a hub gene that may be closely related to MPCa. Gene chip and RNA sequencing datasets containing 1,237 samples were used to determine the expression level and diagnostic potential of SNAP91 in MPCa. SNAP91 was found to be overexpressed in MPCa and had a moderate diagnostic potential in MPCa. In addition, miRNA-222-3p expression was negatively correlated with SNAP91 expression in MPCa (r = -0.636, P = 0.006). These results demonstrated that miRNA-222-3p might play an important role in MPCa by negatively regulating SNAP91 expression. Thus, miRNA-222-3p might be a potential biomarker and therapeutic target of MPCa.

Applying Machine Learning Techniques in Nomogram Prediction and Analysis for SMILE Treatment.

PURPOSE: To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram. DESIGN: Prospective, comparative clinical study. METHODS: A comparative study was conducted on the outcomes of SMILE surgery between surgeon group (nomogram set by surgeon) and machine learning group (nomogram predicted by machine learning model). The machine learning model was trained by 865 ideal cases (spherical equivalent [SE] within ±0.5 diopter [D] 3 months postoperatively) from an experienced surgeon. The visual outcomes of both groups were compared for safety, efficacy, predictability, and SE correction. RESULTS: There was no statistically significant difference between the baseline data in both groups. The efficacy index in the machine learning group (1.48 ± 1.08) was significantly higher than in the surgeon group (1.3 ± 0.27) (t = -2.17, P < .05). Eighty-three percent of eyes in the surgeon group and 93% of eyes in the machine learning group were within ±0.50 D, while 98% of eyes in the surgeon group and 96% of eyes in the machine learning group were within ±1.00 D. The error of SE correction was -0.09 ± 0.024 and -0.23 ± 0.021 for machine learning and surgeon groups, respectively. CONCLUSIONS: The machine learning technique performed as well as surgeon in safety, but significantly better than surgeon in efficacy. As for predictability, the machine learning technique was comparable to surgeon, although less predictable for high myopia and astigmatism.

Geographical Topics Learning of Geo-Tagged Social Images.

With the availability of cheap location sensors, geotagging of images in online social media is very popular. With a large amount of geo-tagged social images, it is interesting to study how these images are shared across geographical regions and how the geographical language characteristics and vision patterns are distributed across different regions. Unlike textual document, geo-tagged social image contains multiple types of content, i.e., textual description, visual content, and geographical information. Existing approaches usually mine geographical characteristics using a subset of multiple types of image contents or combining those contents linearly, which ignore correlations between different types of contents, and their geographical distributions. Therefore, in this paper, we propose a novel method to discover geographical characteristics of geo-tagged social images using a geographical topic model called geographical topic model of social images (GTMSIs). GTMSI integrates multiple types of social image contents as well as the geographical distributions, in which image topics are modeled based on both vocabulary and visual features. In GTMSI, each region of the image would have its own topic distribution, and hence have its own language model and vision pattern. Experimental results show that our GTMSI could identify interesting topics and vision patterns, as well as provide location prediction and image tagging.

Identification Exon Skipping Events From High-Throughput RNA Sequencing Data.

The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Identifying exon skipping (ES) events is an essential part in genome-wide alternative splicing event identification. In this paper, we propose a novel method ESFinder, a random forest classifier to identify ES events from RNA-Seq data. ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification. Experimental results on real human skeletal muscle and brain RNA-Seq data show that ESFinder could effectively predict ES events with high predictive accuracy. The codes of ESFinder are available at http://mlg.hit.edu.cn/ybai/ES/ESFinder.html.

IRcall and IRclassifier: two methods for flexible detection of intron retention events from RNA-Seq data.

BACKGROUND: The emergence of next-generation RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate detection of intron retention (IR) events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. RESULTS: We propose two new methods: IRcall and IRclassifier to detect IR events from RNA-Seq data. Our methods combine together gene expression information, read coverage within an intron, and read counts (within introns, within flanking exons, supporting splice junctions, and overlapping with 5' splice site/ 3' splice site), employing ranking strategy and classifiers to detect IR events. We applied our approaches to one published RNA-Seq data on contrasting skip mutant and wild-type in Arabidopsis thaliana. Compared with three state-of-the-art methods, IRcall and IRclassifier could effectively filter out false positives, and predict more accurate IR events. AVAILABILITY: The data and codes of IRcall and IRclassifier are available at http://mlg.hit.edu.cn/ybai/IR/IRcallAndIRclass.html.