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Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

Background Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator. Results We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above. Conclusions In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders

Delta/Notch-like Epidermal Growth Factor-Related Receptor (DNER), a Potential Prognostic Marker of Gastric Cancer Regulates Cell Survival and Cell Cycle Progression

Upregulation of the expression of Delta/notch-like epidermal growth factor-related receptor (DNER) and its oncogenic role have been reported in several cancers, including gastric, breast, and prostate cancers. This study aimed to investigate the oncogenic role of DNER and the mechanisms behind its oncogenic role in gastric cancer. Analysis of the RNASeq data of gastric cancer tissues obtained from the TCGA database revealed that the expression of DNER was associated with the pathology of advanced gastric cancer and the prognosis of patients. DNER expression was increased upon stem cell-enriching cancer spheroid culture. Knockdown of DNER expression inhibited cell proliferation and invasion, induced apoptosis, enhanced chemosensitivity, and decreased spheroid formation of SNU-638 gastric cancer cells. DNER silencing elevated the expression of p53, p21cip/waf, and p27, and increased G1 phase cells at the expense of S phase cells. Knockdown of p21cip/waf expression in the DNER-silenced cells partially restored cell viability and S phase progression. DNER silencing also induced the apoptosis of SNU-638 cells. While both cleaved caspases-8 and 9 were detected in adherent cells, only cleaved caspase-8 was found to have increased in spheroid-cultured cells, suggesting a distinct activation pattern of caspase activation depending on the growth condition. Knockdown of p53 expression rescued the DNER-silenced cells from apoptosis and partially restored cell viability. In contrast, overexpression of the Notch intracellular domain (NICD) decreased the expression of p53, p21cip/waf, and cleaved caspase-3 in DNER-silenced cells. Moreover, NICD expression fully reverted the cell viability reduction, arrest in the G1 phase, and elevated apoptosis caused by DNER silencing, thereby suggesting activation of Notch signaling by DNER. Expression of a membrane-unbound mutant of mDNER also decreased cell viability and induced apoptosis. On the other hand, TGF-β signals were found to be involved in DNER expression in both adherent and spheroid-cultured cells. DNER could therefore be a link connecting TGF-β signaling to Notch signaling. Taken together, DNER regulates cell proliferation, survival, and invasive capacity of the gastric cancer cells through the activation of Notch signaling, which may facilitate tumor progression into an advanced stage. This study provides evidences suggesting that DNER could be a potential prognostic marker, a therapeutic target, and a drug candidate in the form of a cell-free mutant

Stable solution to l 2,1-based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases

Abstract Backgrounds A large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities through biological experiments are expensive. However, a plethora lincRNA-data are available now, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc, which opens the opportunity for cutting-edge machine learning and data mining approaches to extract meaningful relationships among lincRNAs and diseases. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of them utilizes side information of both the entities simultaneously in a single framework. Methods The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information about them. However, the formulation of IMC is incapable of handling noise and outliers that may be present in the datasets, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve the two issues. As a remedy, in this paper, we propose Stable Robust Inductive Matrix Completion (SRIMC) that utilizes the l 2,1 norm based regularization to optimize the objective function with a unique 2-step stable solution approach. Results We applied SRIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. The method performs better than the state-of-the-art methods in terms of p r e c i s i o n @ k and r e c a l l @ k at the top-k disease prioritization to the subject lincRNAs. We also demonstrate that SRIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs. Conclusions With the experimental results and computational evaluation, we show that SRIMC is robust in handling datasets with noise and outliers as well as dealing with novel lincRNAs and disease phenotypes

Competitive Hybridization of a Microarray Identifies CMKLR1 as an Up-Regulated Gene in Human Bone Marrow-Derived Mesenchymal Stem Cells Compared to Human Embryonic Fibroblasts

Mesenchymal stem cells (MSCs) have been widely applied to the regeneration of damaged tissue and the modulation of immune response. The purity of MSC preparation and the delivery of MSCs to a target region are critical factors for success in therapeutic application. In order to define the molecular identity of an MSC, the gene expression pattern of a human bone marrow-derived mesenchymal stem cell (hBMSC) was compared with that of a human embryonic fibroblast (hEF) by competitive hybridization of a microarray. A total of 270 and 173 genes were two-fold up- and down-regulated with FDR < 0.05 in the hBMSC compared to the hEF, respectively. The overexpressed genes in the hBMSC over the hEF, including transcription factors, were enriched for biological processes such as axial pattern formation, face morphogenesis and skeletal system development, which could be expected from the differentiation potential of MSCs. CD70 and CD339 were identified as additional CD markers that were up-regulated in the hBMSC over the hEF. The differential expression of CD70 and CD339 might be exploited to distinguish hEF and hBMSC. CMKLR1, a chemokine receptor, was up-regulated in the hBMSC compared to the hEF. RARRES2, a CMKLR1 ligand, stimulated specific migration of the hBMSC, but not of the hEF. RARRES2 manifested as ~two-fold less effective than SDF-1α in the directional migration of the hBMSC. The expression of CMKLR1 was decreased upon the osteoblastic differentiation of the hBMSC. However, the RARRES2-loaded 10% HA-silk scaffold did not recruit endogenous cells to the scaffold in vivo. The RARRES2–CMKLR1 axis could be employed in recruiting systemically delivered or endogenous MSCs to a specific target lesion

Modulation of Spheroid Forming Capacity and TRAIL Sensitivity by KLF4 and Nanog in Gastric Cancer Cells

The expression of pluripotency factors, and their associations with clinicopathological parameters and drug response have been described in various cancers, including gastric cancer. This study investigated the association of pluripotency factor expression with the clinicopathological characteristics of gastric cancer patients, as well as changes in the expression of these factors upon the stem cell-enriching spheroid culture of gastric cancer cells, regulation of sphere-forming capacity, and response to cisplatin and TRAIL treatments by Nanog and KLF4. Nanog expression was significantly associated with the emergence of a new tumor and a worse prognosis in gastric cancer patients. The expression of the pluripotency factors varied among six gastric cancer cells. KLF4 and Nanog were expressed high in SNU-601, whereas SOX2 was expressed high in SNU-484. The expression of KLF4 and SOX2 was increased upon the spheroid culture of SNU-601 (KLF4/Nanog-high) and SNU-638 (KLF4/Nanog-low). The spheroid culture of them enhanced TRAIL-induced viability reduction, which was accompanied by the upregulation of death receptors, DR4 and DR5. Knockdown and overexpression of Nanog in SNU-601 and SNU-638, respectively, did not affect spheroid-forming capacity, however, its expression was inversely correlated with DR4/DR5 expression and TRAIL sensitivity. In contrast, KLF4 overexpression in SNU-638 increased spheroid formation, susceptibility to cisplatin and TRAIL treatments, and DR4/DR5 expression, while the opposite was found in KLF4-silenced SNU-601. KLF4 is supposed to play a critical role in DR4/DR5 expression and responses to TRAIL and cisplatin, whereas Nanog is only implicated in the former events only. Direct regulation of death receptor expression and TRAIL response by KLF4 and Nanog have not been well documented previously, and the regulatory mechanism behind the process remains to be elucidated