• International Journal of Advanced Culture Technology
• /
• v.6 no.4
• /
• pp.275-283
• /
• 2018

Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell's gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell's gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain's feature and at the same time adding the new domain's feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input.

• Journal of Life Science
• /
• v.30 no.10
• /
• pp.886-895
• /
• 2020

The development of novel peptide antibiotics with potent antimicrobial activity and anti-inflammatory activity is urgently needed. In a previous work, we performed an in-silico analysis of the Papilio xuthus transcriptome to identify putative antimicrobial peptides and identified several candidates. In this study, we investigated the antibacterial and anti-inflammatory activities of papiliocin 3, which was selected bioinformatically based on its physicochemical properties against bacteria and mouse macrophage Raw264.7 cells. Papiliocin 3 showed antibacterial activities against E. coli and S. aureus without inducing hemolysis and decreased the nitric oxide production of the lipopolysaccharide-induced Raw264.7 cells. Moreover, ELISA and Western blot analysis revealed that papiliocin 3 reduced the expression levels of pro-inflammatory enzymes, such as inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and prostaglandin E₂ (PGE₂). In addition, we examined whether papiliocin 3 could inhibit the expression of pro-inflammatory cytokines (interleukin-6 and interleukin-1β) in LPS-induced Raw264.7 cells. We found that papiliocin 3 markedly reduced the expression level of cytokines through the regulation of mitogen-activated protein kinases (MAPK) and nuclear factor kappa B (NF-κB) signaling. We also confirmed that papiliocin 3 binds to bacterial cell membranes via a specific interaction with lipopolysaccharides. Collectively, these findings suggest that papiliocin 3 could be a promising molecule for development as a novel peptide antibiotic.

• Clinical and Experimental Reproductive Medicine
• /
• v.33 no.2
• /
• pp.125-132
• /
• 2006

Objective: In the previous study, we complied the differentially expressed genes during early folliculogenesis. Objective of the present study was to identify downstream target genes of transcription factors (TFs) using bioinformatics for selecting the target TFs among the gene lists for further functional analysis. Materials & Methods: By using bioinformatics tools, constituent domains were identified from database searches using Gene Ontology, MGI, and Entrez Gene. Downstream target proteins/genes of each TF were identified from database searches using TF database ($TRANSFAC^{(R)}$ 6.0) and eukaryotic promoter database (EPD). Results: DNA binding and trans-activation domains of all TFs listed previously were identified, and the list of downstream target proteins/genes was obtained from searches of TF database and promoter database. Based on the known function of identified downstream genes and the domains, 3 (HNF4, PPARg, and TBX2) out of 26 TFs were selected for further functional analysis. The genes of wee1-like protein kinase and p21WAF1 (cdk inhibitor) were identified as potential downstream target genes of HNF4 and TBX2, respectively. PPARg, through protein-protein interaction with other protein partners, acts as a transcription regulator of genes of EGFR, p21WAF1, cycD1, p53, and VEGF. Among the selected 3 TFs, further study is in progress for HNF4 and TBX2, since wee1-like protein kinase and cdk inhibitor may involved in regulating maturation promoting factor (MPF) activity during early folliculogenesis. Conclusions: Approach used in the present study, in silico analysis of downstream target genes, was useful for analyzing list of TFs obtained from high-throughput cDNA microarray study. To verify its binding and functions of the selected TFs in early folliculogenesis, EMSA and further relevant characterizations are under investigation.

• Journal of Life Science
• /
• v.27 no.10
• /
• pp.1215-1224
• /
• 2017

Until now, various oncogenic pathways were idenfied. The accumulation of DNA mutation induces genomic instability in the cell, and it makes cancer. The development of bioinformatics and genomics, to find the precise and reliable biomarker is available. This biomarker could be applied the early-dignosis, prediction and convalescence of cancer. Recently, Transposable elements (TEs) have been attracted as the regulator of genes, because they occupy a half of human genome, and the cause of various diseases. TEs induce DNA mutation, as well as the regulation of gene expression, that makes to cancer development. So, we confirmed the relationship between TEs and colon cancer, and provided the clue for colon cancer biomarker. First, we confirmed long interspersed nuclear element-1 (LINE-1), Alu, and long terminal repeats (LTRs) and their relationship to colon cancer. Because these elements have large composition and enormous effect to the human genome. Interestingly, colon cancer specific patterns were detected, such as the hypomethylation of LINE-1, LINE-1 insertion in the APC gene, hypo- or hypermethylation of Alu, and isoform derived from LTR insertion. Moreover, hypomethylation of LINE-1 in proto-oncogene is used as the biomarker of colon cancer metastasis, and MLH1 mutation induced by Alu is detected in familial or hereditary colon cancer. The genes, effected by TEs, were analyzed their expression patterns by in silico analysis. Then, we provided tissue- and gender-specific expression patterns. This information can provide reliable cancer biomarker, and apply to prediction and diagnosis of colon cancer.

• Korean Chemical Engineering Research
• /
• v.61 no.4
• /
• pp.523-541
• /
• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• BMB Reports
• /
• v.53 no.3
• /
• pp.154-159
• /
• 2020

We investigated the effects of physalin A, B, D, and F on osteoclastogenesis induced by receptor activator of nuclear factor κB ligand (RANKL). The biological functions of different physalins were first predicted using an in silico bioinformatic tool (BATMAN-TCM). Afterwards, we tested cell viability and cell apoptosis rate to analyze the cytotoxicity of different physalins. We analyzed the inhibitory effects of physalins on RANKL-induced osteoclastogenesis from mouse bone-marrow macrophages (BMMs) using a tartrate-resistant acid phosphatase (TRAP) stain. We found that physalin D has the best selectivity index (SI) among all analyzed physalins. We then confirmed the inhibitory effects of physalin D on osteoclast maturation and function by immunostaining of F-actin and a pit-formation assay. On the molecular level, physalin D attenuated RANKL-evoked intracellular calcium ([Ca(2+)](i)) oscillation by inhibiting phosphorylation of phospholipase Cγ2 (PLCγ2) and thus blocked the downstream activation of Ca2+/calmodulin-dependent protein kinases (CaMK)IV and cAMP-responsive element-binding protein (CREB). An animal study showed that physalin D treatment rescues bone microarchitecture, prevents bone loss, and restores bone strength in a model of rapid bone loss induced by soluble RANKL. Taken together, these results suggest that physalin D inhibits RANKL-induced osteoclastogenesis and bone loss via suppressing the PLCγ2-CaMK-CREB pathway.

• Genomics & Informatics
• /
• v.18 no.4
• /
• pp.43.1-43.13
• /
• 2020

The coronavirus disease 2019 is a contagious disease and had caused havoc throughout the world by creating widespread mortality and morbidity. The unavailability of vaccines and proper antiviral drugs encourages the researchers to identify potential antiviral drugs to be used against the virus. The presence of RNA binding domain in the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be a potential drug target, which serves multiple critical functions during the viral life cycle, especially the viral replication. Since vaccine development might take some time, the identification of a drug compound targeting viral replication might offer a solution for treatment. The study analyzed the phylogenetic relationship of N protein sequence divergence with other 49 coronavirus species and also identified the conserved regions according to protein families through conserved domain search. Good structural binding affinities of a few natural and/or synthetic phytocompounds or drugs against N protein were determined using the molecular docking approaches. The analyzed compounds presented the higher numbers of hydrogen bonds of selected chemicals supporting the drug-ability of these compounds. Among them, the established antiviral drug glycyrrhizic acid and the phytochemical theaflavin can be considered as possible drug compounds against target N protein of SARS-CoV-2 as they showed lower binding affinities. The findings of this study might lead to the development of a drug for the SARS-CoV-2 mediated disease and offer solution to treatment of SARS-CoV-2 infection.

• Genomics & Informatics
• /
• v.16 no.4
• /
• pp.26.1-26.12
• /
• 2018

Shigella spp. constitutes some of the key pathogens responsible for the global burden of diarrhoeal disease. With over 164 million reported cases per annum, shigellosis accounts for 1.1 million deaths each year. Majority of these cases occur among the children of the developing nations and the emergence of multi-drug resistance Shigella strains in clinical isolates demands the development of better/new drugs against this pathogen. The genome of Shigella flexneri was extensively analyzed and found 4,362 proteins among which the functions of 674 proteins, termed as hypothetical proteins (HPs) had not been previously elucidated. Amino acid sequences of all these 674 HPs were studied and the functions of a total of 39 HPs have been assigned with high level of confidence. Here we have utilized a combination of the latest versions of databases to assign the precise function of HPs for which no experimental information is available. These HPs were found to belong to various classes of proteins such as enzymes, binding proteins, signal transducers, lipoprotein, transporters, virulence and other proteins. Evaluation of the performance of the various computational tools conducted using receiver operating characteristic curve analysis and a resoundingly high average accuracy of 93.6% were obtained. Our comprehensive analysis will help to gain greater understanding for the development of many novel potential therapeutic interventions to defeat Shigella infection.

• Genomics & Informatics
• /
• v.21 no.2
• /
• pp.17.1-17.12
• /
• 2023

Coronavirus has left severe health impacts on the human population, globally. Still a significant number of cases are reported daily as no specific medications are available for its effective treatment. The presence of the CD147 receptor (human basigin) on the host cell facilitates the severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Therefore, the drugs that efficiently alter the formation of CD147 and spike protein complex could be the right drug candidate to inhibit the replication of SARS-CoV-2. Hence, an e-Pharmacophore model was developed based on the receptor-ligand cavity of CD147 protein which was further mapped against pre-existing drugs of coronavirus disease treatment. A total of seven drugs were found to be suited as pharmacophores out of 11 drugs screened which was further docked with CD147 protein using CDOCKER of Biovia discovery studio. The active site sphere of the prepared protein was 101.44, 87.84, and 97.17 along with the radius being 15.33 and the root-mean-square deviation value obtained was 0.73 Å. The protein minimization energy was calculated to be -30,328.81547 kcal/mol. The docking results showed ritonavir as the best fit as it demonstrated a higher CDOCKER energy (-57.30) with correspond to CDOCKER interaction energy (-53.38). However, authors further suggest in vitro studies to understand the potential activity of the ritonavir.

• Journal of Life Science
• /
• v.29 no.11
• /
• pp.1218-1226
• /
• 2019

The white-spotted flower chafer Protaetia brevitarsis seulensis is a medicinally beneficial and important edible insect species. We previously performed an in silico analysis of the Protaetia brevitarsis seulensis transcriptome to identify putative antimicrobial peptides and then tested their antimicrobial and hemolytic activities. These peptides had potent antimicrobial activities against bacteria and yeast without inducing hemolysis. In the present study, the cationic antimicrobial peptide, protaetiamycine 2, was selected for further assessment of its anti-inflammatory properties in mouse macrophage Raw264.7 cells. Protaetiamycine 2 treatment of Raw264.7 cells suppressed LPS-induced nitric oxide production and reduced the expression of inducible nitric oxide synthase and cyclooxygenase-2, as determined by real-time PCR and western blotting. The expression of proinflammatory cytokines ($TNF-{\alpha}$, IL-6, and $IL-1{\beta}$) was also attenuated through the MAPKs and $NF-{\kappa}B$ signaling. We also confirmed that protaetiamycine 2 bound to bacterial cell membranes by a specific interaction with LPS. Collectively, these data obtained from LPS-induced Raw264.7 cells indicated that protaetiamycine 2 could have both antimicrobial and anti-inflammatory properties.