Current Bioinformatics - Volume 14, Issue 5, 2019

Objective: The purpose of this study was to establish whether Triglycerides (TGs) are related to Blood Pressure (BP) variability and whether controlling TG levels leads to better BP variability management and prevents Cardiovascular Disease (CVD). Methods: In this study, we enrolled 106 hypertensive patients and 80 non-hypertensive patients. Pearson correlation and partial correlation analyses were used to define the relationships between TG levels and BP variability in all subjects. Patients with hypertension were divided into two subgroups according to TG level: Group A (TG<1.7 mmol/L) and Group B (TG>=1.7 mmol/L). The heterogeneity between the two subgroups was compared using t tests and covariance analysis. Results: TG levels and BP variability were significantly different between the hypertensive and non-hypertensive patients. Two-tailed Pearson correlation tests showed that TG levels are positively associated with many BP variability measures in all subjects. After reducing other confounding factors, the partial correlation analysis revealed that TG levels are still related to the Standard Deviation (SD), Coefficient of Variation (CV) of nighttime systolic blood pressure and CV of nighttime diastolic blood pressure, respectively (each p<0.05). In the subgroups, group A had a lower SD of nighttime Systolic Blood Pressure (SBP_night_SD; 11.39±3.80 and 13.39±4.16, p=0.011), CV of nighttime systolic blood pressure (SBP_night_CV; 0.09±0.03 and 0.11±0.03, p=0.014) and average real variability of nighttime systolic blood pressure (SBP_night_ARV; 10.99±3.98 and 12.6±3.95, p=0.024) compared with group B, even after adjusting for age and other lipid indicators. Conclusion: TG levels are significantly associated with BP variability and hypertriglyceridemia, which affects blood pressure variability before causing target organ damage.

Background: HIF-1α can affect the apelin expression and participates in the developments in cancers but the mechanism need to be explored further. Objective: This paper investigates apelin expression in nasopharyngeal carcinoma CNE-2 cells and its regulation by hypoxia inducible factor-1α (HIF-1α) under hypoxic conditions. Methods: CoCl2 was used to induce hypoxia in CNE-2 cells for 12h, 24h and 48h. HIF-1α small interference RNA (siRNA) was transfected into CNE-2 cells using a transient transfection method. HIF-1α and apelin mRNA levels were detected by real time PCR. Western blot was used to measure HIF-1α protein expression. The concentration of apelin in cell culture supernatant was determined by enzyme linked immunosorbent assay (ELISA). Results: HIF-1α and apelin mRNA levels and protein expression in CNE-2 cells increased gradually with increased duration of hypoxic exposure and were significantly reduced in HIF-1α siRNA transfected cells exposed to the same hypoxic conditions. Conclusion: Apelin expression is induced by hypoxia and regulated by HIF-1α in CNE-2 cells.

Background: CYP2C19 is an important genetic factor modulating clopidogrel dose requirement. Objective: Therefore, a simple and economic genotyping method for predicting the clopidogrel dose of patients would be useful in clinical applications. Methods: In this study, the MGB blocker ARMS real-time PCR contained two forward primers, two MGB blockers and a common reverse primer have been used for CYP2C19*2, *3 and *17 substitutions. Results: Results showed that heterozygotes and homozygotes of CYP2C19*2, *3 and *17 could be distinguished by the MGB blocker ARMS real-time PCR successfully. In the Chinese population, patients with allele frequencies of CYP2C19*2, *3, and *17 are 18.43%, 3.03% and 0.76%, respectively. Conclusion: This study indicates that the MGB blocker ARMS real-time PCR will be a simple, economical method for the rapid detection of SNPs in CYP2C19.

Objective: This research aimed to make comparisons of sensitivity and specificity between Quantitative real Time Polymerase Chain Reaction (Q-PCR) and Reverse Transcription PCR (RT-PCR) in detecting the ribonucleic acid (RNA) expression levels of Hepatitis C Virus (HCV). Methods: 121 patients suffering from hepatitis C and 98 healthy participants with normal liver functions were identified. The venous blood collections were carried out, were subjected to detect the expression levels of HCV RNA via Q-PCR and RT-PCR. And then, the data obtained from these above two detection methods were compared, including the sensitivity and specificity. Results: In terms of Q-PCR, the positive rate of HCV RNA was 72.16%, which was significantly higher when compared with 55.26% of RT-PCR. After statistical analysis, the difference between them was statistically significant (P#156;0.05). Among the healthy participants, 4 cases were false positive by means of RT-PCR, there was the possibility of missed diagnosis when the samples were evaluated by Q-PCR. Conclusion: The Q-PCR detection technology performed well in testing HCV, with pretty high sensitivity and specificity. Nevertheless, the false negative results obtained from Q-PCR could not be avoided. In clinical practice, these above two detection methods should be referred to, in order to avoid missed diagnosis.

Background: Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods. Methods: In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers. Result & Conclusion: Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

Background: Mining knowledge from microarray data is one of the popular research topics in biomedical informatics. Gene selection is a significant research trend in biomedical data mining, since the accuracy of tumor identification heavily relies on the genes biologically relevant to the identified problems. Objective: In order to select a small subset of informative genes from numerous genes for tumor identification, various computational intelligence methods were presented. However, due to the high data dimensions, small sample size, and the inherent noise available, many computational methods confront challenges in selecting small gene subset. Methods: In our study, we propose a novel algorithm PSONRS_KNN for gene selection based on the particle swarm optimization (PSO) algorithm along with the neighborhood rough set (NRS) reduction model and the K-nearest neighborhood (KNN) classifier. Results: First, the top-ranked candidate genes are obtained by the GainRatioAttributeEval preselection algorithm in WEKA. Then, the minimum possible meaningful set of genes is selected by combining PSO with NRS and KNN classifier. Conclusion: Experimental results on five microarray gene expression datasets demonstrate that the performance of the proposed method is better than existing state-of-the-art methods in terms of classification accuracy and the number of selected genes.

Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets. Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study. Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools. Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives. Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

Background: With the declining trend of new drugs yield each year, more comprehensive knowledge of drug MoAs can help identify new applications of available drugs and discovery novel mechanism of drug action. Objective: Therefore, construction of a specialized drug mode of action (MoA) database is of paramount importance for new drug research & development. Methods: This paper introduces an integrated database for drug mode of action knowledge (MoABank). Results: This database can provide the knowledge about drug MoAs, targets, pathways, side effects and other drug-related information for researchers. Conclusion: We believe MoABank can make it more convenient for users to obtain the drug MoA information in the future.

Background: Leishmaniasis is the major cause of mortality in under-developed countries. One of the main problems in leishmaniasis is the limited number of drug options, resistance and side effects. Such a situation requires to study the new chemical series with anti-leishmanial activity. Objective: To assess the anti-leishmanial activity of antibacterial and antifungal drugs. Methods: We have applied an integrative approach based on computational and in vitro methods to elucidate the efficacy of different antibacterial and antifungal drugs against Leishmania tropica (KWH23). Firstly these compounds were analyzed using in silico molecular docking. This analysis showed that the nystatin and azithromycin interacted with the active site amino acids of the target protein leishmanolysin. The nystatin, followed by azithromycin, produced the lowest binding energies indicating their inhibitive activity against the target. The efficacy of the docked drugs was further validated in vitro which showed that our bioinformatics based predictions completely agreed with experimental results. Stock solutions of drugs, media preparation and parasites cultures were performed according to the standard in-vitro protocol. Results: We found that the half maximal inhibitory concentration (IC50) value of dosage form of nystatin (10,000,00 U) and pure nystatin was 0.05701 μM and 0.00324 μM respectively. The IC50 value of combined azithromycin and nystatin (dosage and pure form) was 0.156 μg/ml and 0.0023 μg /ml (0.00248 μM) respectively. It was observed that IC50 value of nystatin is better than azithromycin and pure form of drugs had significant activity than the dosage form of drugs. Conclusion: From these results, it was also proven that pure drugs combination result is much better than all tested drugs results. The results of both in vitro and in silico studies clearly indicated that comparatively, nystatin is the potential candidate drug in combat against Leishmania tropica.

Background: Neurodevelopmental Disorders (NDDs) are impairment of the growth and development of the brain or central nervous system, which occurs at the developmental stage. This can include developmental brain dysfunction, which can manifest as neuropsychiatric problems or impaired motor function, learning, language or non-verbal communication. These include the array of disorder, including: Autism Spectrum Disorders (ASD), Attention Deficit Hyperactivity Disorders (ADHD) etc. There is no particular diagnosis and cure for NDDs. These disorders seem to be result from a combination of genetic, biological, psychosocial and environmental risk factors. Diverse scientific literature reveals the adverse effect of environmental factors specifically, exposure of pesticides, which leads to growing number of human pathological conditions; among these, neurodevelopmental disorder is an emerging issue nowadays. Objective: The current study focused on in silico identification of potential drug targets for pesticides induced neurodevelopmental disorder including Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) and to design potential drug molecule for the target through drug discovery approaches. Methods: We identified 139 candidate genes for ADHD and 206 candidate genes for ASD from the NCBI database for detailed study. Protein-protein interaction network analysis was performed to identify key genes/proteins in the network by using STRING 10.0 database and Cytoscape 3.3.0 software. The 3D structure of target protein was built and validated. Molecular docking was performed against twenty seven possible phytochemicals i.e. beta amyrin, ajmaline, serpentine, urosolic, huperzine A etc. having neuroprotective activity. The best-docked compound was identified by the lowest Binding Energy (BE). Further, the prediction of drug-likeness and bioactivity analysis of leads were performed by using molinspiration cheminformatics software. Result & Conclusion: Based on betweenness centrality and node degree as a network topological parameter, solute carrier family 6 member 4 (SLC6A4) was identified as a common key protein in both the networks. 3-D structure of SLC6A4 protein was designed and validated respectively. Based on the lowest binding energy, beta amyrin (B.E = -8.54 kcal/mol) was selected as a potential drug candidate against SLC6A4 protein. Prediction of drug-likeness and bioactivity analysis of leads showed drug candidate as a potential inhibitor. Beta amyrin (CID: 73145) was obtained as the most potential therapeutic inhibitor for ASD & ADHD in human.