Current Genomics - Volume 20, Issue 5, 2019

This opinion paper highlights strategies for a better understanding of non-Mendelian genetic risk that was revealed by genome-wide association studies (GWAS) of complex diseases. The genetic risk resides predominantly in non-coding regulatory DNA, such as in enhancers. The identification of mechanisms, the causal variants (mainly SNPs), and their target genes are, however, not always apparent but are likely involved in a network of risk determinants; the identification presents a bottle-neck in the full understanding of the genetics of complex phenotypes. Here, we propose strategies to identify functional SNPs and link risk enhancers with their target genes. The strategies are 1) identifying finemapped SNPs that break/form response elements within chromatin bio-features in relevant cell types 2) considering the nearest gene on linear DNA, 3) analyzing eQTLs, 4) mapping differential DNA methylation regions and relating them to gene expression, 5) employing genomic editing with CRISPR/cas9 and 6) identifying topological associated chromatin domains using chromatin conformation capture.

Advances in transcriptomic methods have led to a large number of published Genome- Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

Circadian clocks are intrinsic, time-tracking systems that bestow upon organisms a survival advantage. Under natural conditions, organisms are trained to follow a 24-h cycle under environmental time cues such as light to maximize their physiological efficiency. The exact timing of this rhythm is established via cell-autonomous oscillators called cellular clocks, which are controlled by transcription/ translation-based negative feedback loops. Studies using cell-based systems and genetic techniques have identified the molecular mechanisms that establish and maintain cellular clocks. One such mechanism, known as post-translational modification, regulates several aspects of these cellular clock components, including their stability, subcellular localization, transcriptional activity, and interaction with other proteins and signaling pathways. In addition, these mechanisms contribute to the integration of external signals into the cellular clock machinery. Here, we describe the post-translational modifications of cellular clock regulators that regulate circadian clocks in vertebrates.

Background: In the current study, we aimed to analyze the hypothesis that human myocardial- specific extracellular RNAs expression could be used for acute myocardial injury(AMI) diagnosis. Methodology: We used bioinformatics’ analysis to identify RNAs linked to ubiquitin system and specific to AMI, named, (lncRNA-RP11-175K6.1), (LOC101927740), microRNA-106b-5p (miR-106b- 5p) and Anaphase, promoting complex 11 (ANapc11mRNA). We measured the serum expression of the chosen RNAs in 69 individuals with acute coronary syndromes, 31 individuals with angina pectoris without MI and non-cardiac chest pain and 31 healthy control individuals by real-time reversetranscription PCR. Results: Our study revealed a significant decrease in both lncRNA-RP11-175K6.1 and ANapc11mRNA expression of in the sera samples of AMI patients compared to that of the two control groups alongside with significant upregulation of miR-106b-5p. Conclusion: Of note, the investigated serum RNAs decrease the false discovery rate of AMI to 3.2%.

Background: Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive. Objective: This study aims to mine hub genes associated with HCC using multiple databases. Methods: Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients’ information from TCGA database by survminer R package. Results: From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients. Conclusion: TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

Background: Lysine lipoylation which is a rare and highly conserved post-translational modification of proteins has been considered as one of the most important processes in the biological field. To obtain a comprehensive understanding of regulatory mechanism of lysine lipoylation, the key is to identify lysine lipoylated sites. The experimental methods are expensive and laborious. Due to the high cost and complexity of experimental methods, it is urgent to develop computational ways to predict lipoylation sites. Methodology: In this work, a predictor named LipoSVM is developed to accurately predict lipoylation sites. To overcome the problem of an unbalanced sample, synthetic minority over-sampling technique (SMOTE) is utilized to balance negative and positive samples. Furthermore, different ratios of positive and negative samples are chosen as training sets. Results: By comparing five different encoding schemes and five classification algorithms, LipoSVM is constructed finally by using a training set with positive and negative sample ratio of 1:1, combining with position-specific scoring matrix and support vector machine. The best performance achieves an accuracy of 99.98% and AUC 0.9996 in 10-fold cross-validation. The AUC of independent test set reaches 0.9997, which demonstrates the robustness of LipoSVM. The analysis between lysine lipoylation and non-lipoylation fragments shows significant statistical differences. Conclusion: A good predictor for lysine lipoylation is built based on position-specific scoring matrix and support vector machine. Meanwhile, an online webserver LipoSVM can be freely downloaded from https://github.com/stars20180811/LipoSVM.

Background: The TIFY gene family is a group of plant-specific proteins involved in the jasmonate (JA) metabolic process, which plays a vital role in plant growth and development as well as stress response. Although it has been extensively studied in many species, the significance of this family is not well studied in wheat. Objective: To comprehensively understand the genome organization and evolution of TIFY family in wheat, a genome-wide identification was performed in wheat and its two progenitors using updated genome information provided here. Results: In total, 63, 13 and 17 TIFY proteins were identified in wheat, Triticum urartu and Aegilops tauschii respectively. Phylogenetic analysis clustered them into 18 groups with 14 groups possessing A, B and D copies in wheat, demonstrating the completion of the genome as well as the two rounds of allopolyploidization events. Gene structure, conserved protein motif and cis-regulatory element divergence of A, B, D homoeologous copies were also investigated to gain insight into the evolutionary conservation and divergence of homoeologous genes. Furthermore, the expression profiles of the genes were detected using the available RNA-seq and the expression of 4 drought-responsive candidates was further validated through qRT-PCR analysis. Finally, the co-expression network was constructed and a total of 22 nodes with 121 edges of gene pairs were found. Conclusion: This study systematically reported the characteristics of the wheat TIFY family, which ultimately provided important targets for further functional analysis and also facilitated the elucidation of the evolution mechanism of TIFY genes in wheat and more.

Background: Ubiquitination, as a post-translational modification, is a crucial biological process in cell signaling, apoptosis, and localization. Identification of ubiquitination proteins is of fundamental importance for understanding the molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well-studied model organisms. Objective: To reduce experimental costs, computational methods have been introduced to predict ubiquitination sites, but the accuracy is unsatisfactory. If it can be predicted whether a protein can be ubiquitinated or not, it will help in predicting ubiquitination sites. However, all the computational methods so far can only predict ubiquitination sites. Methods: In this study, the first computational method for predicting ubiquitination proteins without relying on ubiquitination site prediction has been developed. The method extracts features from sequence conservation information through a grey system model, as well as functional domain annotation and subcellular localization. Results: Together with the feature analysis and application of the relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 90.13%, with Matthew’s correlation coefficient of 80.34%. The predicted results on an independent test data achieved 87.71% as accuracy and 75.43% of Matthew’s correlation coefficient, better than the prediction from the best ubiquitination site prediction tool available. Conclusion: Our study may guide experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways. The code is available at: https://github.com/Chunhuixu/UBIPredic_QWRCHX.