Showing papers by "Jens Allmer published in 2014"

Computational methods for microRNA target prediction.

Abstract: MicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.

EPO Mediates Neurotrophic, Neuroprotective, Anti-Oxidant, and Anti-Apoptotic Effects via Downregulation of miR-451 and miR-885-5p in SH-SY5Y Neuron-Like Cells

Abstract: Erythropoietin (EPO) is a neuroprotective cytokine which has been applied in several animal models presenting neurological disorders. One of the proposed modes of action resulting in neuroprotection is post-transcriptional gene expression regulation. This directly brings to mind microRNAs (miRNAs) which are small non-coding RNAs that regulate gene expression at the post-transcriptional level. It has not yet been evaluated whether miRNAs participate in the biological effects of EPO or whether it, inversely, modulates specific miRNAs in neuronal cells. In this study, we employed miRNA and mRNA arrays to identify how EPO exerts its biological function. Notably, miR-451 and miR-885-5p are downregulated in EPO treated SH-SY5Y neuronal-like cells. Accordingly, Target prediction and transcriptome analysis of cells treated with EPO revealed an alteration of the expression of genes involved in apoptosis, cell survival, proliferation, and migration. Low expression of miRNAs in SH-SY5Y was correlated with high expression of their target genes, vascular endothelial growth factor A (VEGFA), matrix metallo peptidase 9 (MMP9), cyclin-dependent kinase 2 (CDK2), erythropoietin receptor (EPOR), Mini chromosome maintenance complex 5 (MCM5), B-cell lymphoma 2 (BCL2) and Galanin (GAL). Cell viability, apoptosis, proliferation, and migration assays were carried out for functional analysis after transfection with miRNA mimics which inhibited some biological actions of EPO such as neuroprotection, anti-oxidation, anti-apoptosis, and migratory effects. In this study, we report for the first time that EPO downregulates the expression of miRNAs (miR-451 and miR-885-5p) in SH-SY5Y neuronal- like cells. The correlation between the overexpression of miRNAs and the decrease in EPO-mediated biological effects suggests that miR-451 and miR-885-5p may play a key role in the mediation of biological function.

Machine learning methods for microRNA gene prediction.

Abstract: MicroRNAs (miRNAs) are single-stranded, small, noncoding RNAs of about 22 nucleotides in length, which control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, or destabilization of their target mRNAs. Although hundreds of miRNAs have been identified in various species, many more may still remain unknown. Therefore, discovery of new miRNA genes is an important step for understanding miRNA-mediated posttranscriptional regulation mechanisms. It seems that biological approaches to identify miRNA genes might be limited in their ability to detect rare miRNAs and are further limited to the tissues examined and the developmental stage of the organism under examination. These limitations have led to the development of sophisticated computational approaches attempting to identify possible miRNAs in silico. In this chapter, we discuss computational problems in miRNA prediction studies and review some of the many machine learning methods that have been tried to address the issues.

Development of genomic simple sequence repeat markers in opium poppy by next-generation sequencing

Abstract: Opium poppy (Papaver somniferum L.) is an important pharmaceutical crop with very few genetic marker resources. To expand these resources, we sequenced genomic DNA using pyrosequencing technology and examined the DNA sequences for simple sequence repeats (SSRs). A total of 1,244,412 sequence reads were obtained covering 474 Mb. Approximately half of the reads (52 %) were assembled into 166,724 contigs representing 105 Mb of the opium poppy genome. A total of 23,283 non-redundant SSRs were identified in 18,944 contigs (11.3 % of total contigs). Trinucleotide and tetranucleotide repeats were the most abundant SSR repeats, accounting for 49.0 and 27.9 % of all SSRs, respectively. The AAG/TTC repeat was the most abundant trinucleotide repeat, representing 19.7 % of trinucleotide repeats. Other SSR repeat types were AT-rich. A total of 23,126 primer pairs (98.7 % of total SSRs) were designed to amplify SSRs. Fifty-three genomic SSR markers were tested in 37 opium poppy accessions and seven Papaver species for determination of polymorphism and transferability. Intraspecific polymorphism information content (PIC) values of the genomic SSR markers were intermediate, with an average 0.17, while the interspecific average PIC value was slightly higher, 0.19. All markers showed at least 88 % transferability among related species. This study increases sequence coverage of the opium poppy genome by sevenfold and the number of opium poppy-specific SSR markers by sixfold. This is the first report of the development of genomic SSR markers in opium poppy, and the genomic SSR markers developed in this study will be useful in diversity, identification, mapping and breeding studies in opium poppy.

miRNomics MicroRNA Biology and Computational Analysis

Abstract: MicroRNAs are 20–24-nucleotide-long noncoding RNAs that bind to the 3′ UTR (untranslated region) of target mRNAs. Since their discovery, microRNAs have been gaining attention for their ability to contribute to gene expression regulation under various physiological conditions. Consequently, deregulated expression of microRNAs has been linked to different disease states. Here, a brief overview of the canonical and alternative microRNA biogenesis pathways and microRNA functions in biological systems is given based on recent developments. In addition, newly emerging regulatory mechanisms, such as alternative polyadenylation, in connection with microRNA-dependent gene expression regulation are discussed.

Computational and Bioinformatics Methods for MicroRNA Gene Prediction

Abstract: MicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.

Intersection of MicroRNA and Gene Regulatory Networks and their Implication in Cancer

Abstract: MicroRNAs (miRNAs) have attracted heightened attention for their role as post-transcriptional regulators of gene expression. It has become clear that miRNAs can both up- and downregulate protein expression. According to current estimates, most human genes are harboring miRNAs and/or are regulated by them. Thus miRNAs form a complex network of expression regulation which tightly interacts with known gene regulatory networks. Similar to some transcription factors, some miRNAs can have hundreds of target transcripts whose expression they modulate. Thus miRNAs can form complex regulatory networks by themselves, but because their expression is often tightly coordinated with gene expression, they form an intertwined regulatory network with many possible interactions among gene and miRNA regulatory pathways. In this review we first consider gene regulatory networks. Then we discuss microRNAs and their implication in cancer and how they may form regulatory networks. Finally, we give our perspective and provide an outlook including the aspect of personalized medicine.