Current Genomics - Volume 20, Issue 4, 2019

Background: Long non-coding RNAs (lncRNAs) are emerging as important regulators in the modulation of virus infection by targeting mRNA transcription. However, their roles in chronic hepatitis B (CHB) remain to be elucidated. Objective: The study aimed to explore the lncRNAs and mRNA expression profiles in CHB and asymptomatic HBsAg carriers (ASC) and construct mRNA-lncRNA co-expression profile and ceRNA networks to identify the potential targets of diagnosis and treatment in CHB. Methods: We determined the expression profiles of lncRNAs and mRNAs in CHB and ASC using microarray analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore their function. We also constructed coexpression, cis-regulatory, and competing endogenous RNA (ceRNA) networks with bioinformatics methods. Results: We identified 1634 mRNAs and 5550 lncRNAs that were differentially expressed between CHB and ASC. Significantly enriched GO terms and pathways were identified, many of which were linked to immune processes and inflammatory responses. Co-expression analysis showed 1196 relationships between the top 20 up/downregulated lncRNAs and mRNA, especially 213 lncRNAs interacted with ZFP57. The ZFP57-specific ceRNA network covered 3 lncRNAs, 5 miRNAs, and 17 edges. Cis-correlation analysis showed that lncRNA T039096 was paired with the most differentially expressed gene, ZFP57. Moreover, by expending the clinical samples size, the qRT-PCR results showed that the expression of ZFP57 and T039096 increased in CHB compared to ASC. Conclusion: Our study provides insights into the roles of mRNA and lncRNA networks in CHB, highlighting potential applications of lncRNA-T039096 and mRNA-ZFP57 for diagnosis and treatment.

Background: Previous GWASs have revealed several susceptible variants associated with adolescent idiopathic scoliosis (AIS). Risk prediction based on these variants can potentially improve disease prognosis. We aimed to evaluate the combined effects of genetic factors on the development of AIS and to further develop a genetic predictive model. Methods: A total of 914 AIS patients and 1441 normal controls were included in the discovery stage, which was followed by the replication stage composed of 871 patients and 1239 controls. Genotyping assay was performed to analyze 10 previously reported susceptible variants, including rs678741 of LBX1, rs241215 of AJAP1, rs13398147 of PAX3, rs16934784 of BNC2, rs2050157 of GPR126, rs2180439 of PAX1, rs4940576 of BCL2, rs7593846 of MEIS1, rs7633294 of MAGI1 and rs9810566 of TNIK. Logistic regression analysis was performed to generate a risk predictive model. The predicted risk score was calculated for each participant in the replication stage. Results: The association of the 10 variants with AIS was successfully validated. The established model could explain approximately 7.9% of the overall variance. In the replication stage, patients were found to have a remarkably higher risk score as compared to the controls (44.2 ± 14.4 vs. 33.9 ± 12.5, p <0.001). There was a remarkably higher proportion of the risk score i.e. >40 in the patients than in the controls (59% vs. 28.9%, p <0.001). Conclusion: Risk predictive model based on the previously reported genetic variants has a remarkable discriminative power. More clinical and genetic factors need to be studied, to further improve the probability to predict the onset of AIS.

Background: Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed. Methods and Results: We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods. Conclusion: Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

Endocrine disrupting compounds (EDCs) have the potential to cause adverse effects on wildlife and human health. Two important EDCs are the synthetic estrogen 17α-ethynylestradiol (EE2) and bisphenol-A (BPA) both of which are xenoestrogens (XEs) as they bind the estrogen receptor and disrupt estrogen physiology in mammals and other vertebrates. In the recent years the influence of XEs on oncogenes, specifically in relation to breast and prostate cancer has been the subject of considerable study. Methodology: In this study, healthy primary human prostate epithelial cells (PrECs) were exposed to environmentally relevant concentrations of BPA (5nM and 25nM BPA) and interrogated using a whole genome microarray. Results: Exposure to 5 and 25nM BPA resulted in 7,182 and 7,650 differentially expressed (DE) genes, respectively in treated PrECs. Exposure to EE2 had the greatest effect on the PrEC transcriptome (8,891 DE genes). Conclusion: We dissected and investigated the nature of the non-estrogenic gene signature associated with BPA with a focus on transcripts relevant to epigenetic modifications. The expression of transcripts encoding nuclear hormone receptors as well as histone and DNA methylation, modifying enzymes were significantly perturbed by exposure to BPA.

Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming. Objective: Herein, we propose a computational predictor named iMethylK-PseAAC to identify lysine methylation sites. Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition relative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing. Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing. Conclusion: It is concluded that iMethylK-PseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl-PseACC, BPB-PPMS and PMeS.

Background: Cattleyak are the Fl hybrids between (153;) yak (Bos grunniens) and (153;‘) cattle (Bos taurus). Cattleyak exhibit higher capability in adaptability to a harsh environment and display much higher performances in production than the yak and cattle. The cattleyak, however, are females fertile but males sterile. All previous studies greatly focused on testes tissues to study the mechanism of male infertility in cattleyak. However, so far, no transcriptomic study has been conducted on the epididymides of yak and cattleyak. Objective: Our objective was to perform comparative transcriptome analysis between the epididymides of yak and cattleyak and predict the etiology of male infertility in cattleyak. Methods: We performed comparative transcriptome profiles analysis by mRNA sequencing in the epididymides of yak and cattleyak. Results: In total 3008 differentially expressed genes (DEGs) were identified in cattleyak, out of which 1645 DEGs were up-regulated and 1363 DEGs were down-regulated. Thirteen DEGs were validated by quantitative real-time PCR. DEGs included certain genes that were associated with spermatozoal maturation, motility, male fertility, water and ion channels, and beta-defensins. LCN9, SPINT4, CES5A, CD52, CST11, SERPINA1, CTSK, FABP4, CCR5, GRIA2, ENTPD3, LOC523530 and DEFB129, DEFB128, DEFB127, DEFB126, DEFB124, DEFB122A, DEFB122, DEFB119 were all downregulated, whereas NRIP1 and TMEM212 among top 30 DEGs were upregulated. Furthermore, protein processing in endoplasmic reticulum pathway was ranked at top-listed three significantly enriched KEGG pathways that as a consequence of abnormal expression of ER-associated genes in the entire ER protein processing pathway might have been disrupted in male cattleyak which resulted in the downregulation of several important genes. All the DEGs enriched in this pathway were downregulated except NEF. Conclusion: Taken together, our findings revealed that there were marked differences in the epididymal transcriptomic profiles of yak and cattleyak. The DEGs were involved in spermatozoal maturation, motility, male fertility, water and ion channels, and beta-defensins. Abnormal expression of ERassociated genes in the entire ER protein processing pathway may have disrupted protein processing pathway in male cattleyak resulting in the downregulation of several important genes involved in sperm maturation, motility and defense.

Background: The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological processes. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites. Methodology: In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are incorporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and independent testing. Results: Accuracy determined through validation was 93.93% for jackknife test, 95.16% for crossvalidation, 94.3% for self-consistency and 94.3% for independent testing. Conclusion: The proposed model has better performance as compared to the existing predictors, however, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.