Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

A network-based method for the identification of putative genes related to infertility

BACKGROUND

Infertility has become one of the major health problems worldwide, with its incidence having risen markedly in recent decades. There is an urgent need to investigate the pathological mechanisms behind infertility and to design effective treatments. However, this is made difficult by the fact that various biological factors have been identified to be related to infertility, including genetic factors.

METHODS

A network-based method was established to identify new genes potentially related to infertility. A network constructed using human protein-protein interactions based on previously validated infertility-related genes enabled the identification of some novel candidate genes. These genes were then filtered by a permutation test and their functional and structural associations with infertility-related genes.

RESULTS

Our method identified 23 novel genes, which have strong functional and structural associations with previously validated infertility-related genes.

CONCLUSIONS

Substantial evidence indicates that the identified genes are strongly related to dysfunction of the four main biological processes of fertility: reproductive development and physiology, gametogenesis, meiosis and recombination, and hormone regulation.

GENERAL SIGNIFICANCE

The newly discovered genes may provide new directions for investigating infertility. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Overlapping Community Detection based on Network Decomposition

Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

Comparative Screening of Digestion Tract Toxic Genes in Proteus mirabilis

Proteus mirabilis is a common urinary tract pathogen, and may induce various inflammation symptoms. Its notorious ability to resist multiple antibiotics and to form urinary tract stones makes its treatment a long and painful process, which is further challenged by the frequent horizontal gene transferring events in P. mirabilis genomes. Three strains of P. mirabilis C02011/C04010/C04013 were isolated from a local outbreak of a food poisoning event in Shenzhen, China. Our hypothesis is that new genes may have been acquired horizontally to exert the digestion tract infection and toxicity. The functional characterization of these three genomes shows that each of them independently acquired dozens of virulent genes horizontally from the other microbial genomes. The representative strain C02011 induces the symptoms of both vomit and diarrhea, and has recently acquired a complete type IV secretion system and digestion tract toxic genes from the other bacteria.

4C-seq revealed long-range interactions of a functional enhancer at the 8q24 prostate cancer risk locus

Genome-wide association studies (GWAS) have identified >100 independent susceptibility loci for prostate cancer, including the hot spot at 8q24. However, how genetic variants at this locus confer disease risk hasn’t been fully characterized. Using circularized chromosome conformation capture (4C) coupled with next-generation sequencing and an enhancer at 8q24 as “bait”, we identified genome-wide partners interacting with this enhancer in cell lines LNCaP and C4-2B. These 4C-identified regions are distributed in open nuclear compartments, featuring active histone marks (H3K4me1, H3K4me2 and H3K27Ac). Transcription factors NKX3-1, FOXA1 and AR (androgen receptor) tend to occupy these 4C regions. We identified genes located at the interacting regions, and found them linked to positive regulation of mesenchymal cell proliferation in LNCaP and C4-2B, and several pathways (TGF beta signaling pathway in LNCaP and p53 pathway in C4-2B). Common genes (e.g. MYC and POU5F1B) were identified in both prostate cancer cell lines. However, each cell line also had exclusive genes (e.g. ELAC2 and PTEN in LNCaP and BRCA2 and ZFHX3 in C4-2B). In addition, BCL-2 identified in C4-2B might contribute to the progression of androgen-refractory prostate cancer. Overall, our work reveals key genes and pathways involved in prostate cancer onset and progression.

miRepress: modelling gene expression regulation by microRNA with non-conventional binding sites

Some earlier studies have reported an alternative mode of microRNA-target interaction. We detected target regions within mRNA transcripts from AGO PAR-CLIP that did not contain any conventional microRNA seed pairing but only had non-conventional binding sites with microRNA 3' end. Our study from 7 set of data that measured global protein fold change after microRNA transfection pointed towards the association of target protein fold change with 6-mer and 7-mer target sites involving microRNA 3' end. We developed a model to predict the degree of microRNA target regulation in terms of protein fold changes from the number of different conventional and non-conventional target sites present in the target, and found significant correlation of its output with protein expression changes. We validated the effect of non-conventional interactions with target by modulating the abundance of microRNA in a human breast cancer cell line MCF-7. The validation was done using luciferase assay and immunoblot analysis for our predicted non-conventional microRNA-target pair WNT1 (3' UTR) and miR-367-5p and immunoblot analysis for another predicted non-conventional microRNA-target pair MYH10 (coding region) and miR-181a-5p. Both experiments showed inhibition of targets by transfection of microRNA mimics that were predicted to have only non-conventional sites.

Epigallocatechin-3-O-gallate up-regulates microRNA-let-7b expression by activating 67-kDa laminin receptor signaling in melanoma cells

MicroRNAs (miRNAs) are non-coding RNAs involved in various biological processes by regulating their target genes. Green tea polyphenol (−)-epigallocatechin-3-O-gallate (EGCG) inhibits melanoma tumor growth by activating 67-kDa laminin receptor (67LR) signaling. To examine the effect of EGCG on miRNA expression in melanoma cells, we performed miRNA microarray analysis. We showed that EGCG up-regulated miRNA-let-7b expression through 67LR in melanoma cells. The EGCG-induced up-regulation of let-7b led to down-regulation of high mobility group A2 (HMGA2), a target gene related to tumor progression. 67LR-dependent cAMP/protein kinase A (PKA)/protein phosphatase 2A (PP2A) signaling pathway activation was involved in the up-regulation of let-7b expression induced by EGCG. These findings provide a basis for understanding the mechanism of miRNA regulation by EGCG.

iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions

The identification of microRNA precursors (pre-miRNAs) helps in understanding regulator in biological processes. The performance of computational predictors depends on their training sets, in which the negative sets play an important role. In this regard, we investigated the influence of benchmark datasets on the predictive performance of computational predictors in the field of miRNA identification, and found that the negative samples have significant impact on the predictive results of various methods. We constructed a new benchmark set with different data distributions of negative samples. Trained with this high quality benchmark dataset, a new computational predictor called iMiRNA-SSF was proposed, which employed various features extracted from RNA sequences. Experimental results showed that iMiRNA-SSF outperforms three state-of-the-art computational methods. For practical applications, a web-server of iMiRNA-SSF was established at the website http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/.

Predicting cancerlectins by the optimal g-gap dipeptides

The cancerlectin plays a key role in the process of tumor cell differentiation. Thus, to fully understand the function of cancerlectin is significant because it sheds light on the future direction for the cancer therapy. However, the traditional wet-experimental methods were money- and time-consuming. It is highly desirable to develop an effective and efficient computational tool to identify cancerlectins. In this study, we developed a sequence-based method to discriminate between cancerlectins and non-cancerlectins. The analysis of variance (ANOVA) was used to choose the optimal feature set derived from the g-gap dipeptide composition. The jackknife cross-validated results showed that the proposed method achieved the accuracy of 75.19%, which is superior to other published methods. For the convenience of other researchers, an online web-server CaLecPred was established and can be freely accessed from the website http://lin.uestc.edu.cn/server/CalecPred. We believe that the CaLecPred is a powerful tool to study cancerlectins and to guide the related experimental validations.

Genome-wide identification and characterization of microRNA genes and their targets in large yellow croaker (Larimichthys crocea)

MicroRNAs (miRNAs or miRs) are a class of non-coding RNAs of 20-25 nucleotides (nt) in length, which regulates the expression of gene in eukaryotic organism. Studies has been confirmed that miRNA plays an important role in various biological and metabolic processes in both animals and plants. Predicting new miRNAs by computer based homology search analysis is an effective way to discover novel miRNAs. Though a large number of miRNAs have been reported in many fish species, reports of miRNAs in large yellow croaker (L. crocea) are limited especially via the computational-based approaches. In this paper, a method of comparative genomic approach by computational genomic homology based on the conservation of miRNA sequences and the stem-loop hairpin secondary structures of miRNAs was adopted. A total of 199 potential miRNAs were predicted representing 81 families. 12 of them were chose to be validated by real time RT-PCR, apart from miR-7132b-5p which was not detected. Results indicated that the prediction method that we used to identify the miRNAs was effective. Furthermore, 948 potential target genes were predicted. Gene ontology (GO) analysis revealed that 175, 287, and 486 target genes were involved in cellular components, biological processes and molecular functions, respectively. Overall, our findings provide a first computational identification and characterization of L. crocea miRNAs and their potential targets in functional analysis, and will be useful in laying the foundation for further characterization of their role in the regulation of diversity of physiological processes.

Computational Identification of MicroRNAs and Their Targets from Finger Millet (Eleusine coracana)

MicroRNAs are endogenous small RNAs regulating intrinsic normal growth and development of plant. Discovering miRNAs, their targets and further inferring their functions had become routine process to comprehend the normal biological processes of miRNAs and their roles in plant development. In this study, we used homology-based analysis with available expressed sequence tag of finger millet (Eleusine coracana) to predict conserved miRNAs. Three potent miRNAs targeting 88 genes were identified. The newly identified miRNAs were found to be homologous with miR166 and miR1310. The targets recognized were transcription factors and enzymes, and GO analysis showed these miRNAs played varied roles in gene regulation. The identification of miRNAs and their targets is anticipated to hasten the pace of key epigenetic regulators in plant development.

DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation

DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification, and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.

Survey of Natural Language Processing Techniques in Bioinformatics

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Similarity computation strategies in the microRNA-disease network: a survey

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.