MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction

Background: MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes). Results: We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anticorrelation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions. Conclusions: The MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses

Representing and analysing molecular and cellular function in the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.

We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu

WormBase 2012: more genomes, more data, new website

Since its release in 2000, WormBase (http://www.wormbase.org) has grown from a small resource focusing on a single species and serving a dedicated research community, to one now spanning 15 species essential to the broader biomedical and agricultural research fields. To enhance the rate of curation, we have automated the identification of key data in the scientific literature and use similar methodology for data extraction. To ease access to the data, we are collaborating with journals to link entities in research publications to their report pages at WormBase. To facilitate discovery, we have added new views of the data, integrated large-scale datasets and expanded descriptions of models for human disease. Finally, we have introduced a dramatic overhaul of the WormBase website for public beta testing. Designed to balance complexity and usability, the new site is species-agnostic, highly customizable, and interactive. Casual users and developers alike will be able to leverage the public RESTful application programming interface (API) to generate custom data mining solutions and extensions to the site. We report on the growth of our database and on our work in keeping pace with the growing demand for data, efforts to anticipate the requirements of users and new collaborations with the larger science community

Transcription analysis of apple fruit development using cDNA microarrays

The knowledge of the molecular mechanisms underlying fruit quality traits is fundamental to devise efficient marker-assisted selection strategies and to improve apple breeding. In this study, cDNA microarray technology was used to identify genes whose expression changes during fruit development and maturation thus potentially involved in fruit quality traits. The expression profile of 1,536 transcripts was analysed by microarray hybridisation. A total of 177 genes resulted to be differentially expressed in at least one of the developmental stages considered. Gene ontology annotation was employed to univocally describe gene function, while cluster analysis allowed grouping genes according to their expression profile. An overview of the transcriptional changes and of the metabolic pathways involved in fruit development was obtained. As expected, August and September are the two months where the largest number of differentially expressed genes was observed. In particular, 85 genes resulted to be up-regulated in September. Even though most of the differentially expressed genes are involved in primary metabolism, several other interesting functions were detected and will be presented

Gene expression profiling in slow-Type calf soleus muscle of 30 days space-flown mice

Microgravity exposure as well as chronic disuse are two main causes of skeletal muscle atrophy in animals and humans. The antigravity calf soleus is a reference postural muscle to investigate the mechanism of disuse-induced maladaptation and plasticity of human and rodent (rats or mice) skeletal musculature. Here, we report microgravity-induced global gene expression changes in space-flown mouse skeletal muscle and the identification of yet unknown disuse susceptible transcripts found in soleus (a mainly slow phenotype) but not in extensor digitorum longus (a mainly fast phenotype dorsiflexor as functional counterpart to soleus). Adult C57Bl/N6 male mice (n = 5) flew aboard a biosatellite for 30 days on orbit (BION-M1 mission, 2013), a sex and age-matched cohort were housed in standard vivarium cages (n = 5), or in a replicate flight habitat as ground control (n = 5). Next to disuse atrophy signs (reduced size and myofiber phenotype I to II type shift) as much as 680 differentially expressed genes were found in the space-flown soleus, and only 72 in extensor digitorum longus (only 24 genes in common) compared to ground controls. Altered expression of gene transcripts matched key biological processes (contractile machinery, calcium homeostasis, muscle development, cell metabolism, inflammatory and oxidative stress response). Some transcripts (Fzd9, Casq2, Kcnma1, Ppara, Myf6) were further validated by quantitative real-time PCR (qRT-PCR). Besides previous reports on other leg muscle types we put forth for the first time a complete set of microgravity susceptible gene transcripts in soleus of mice as promising new biomarkers or targets for optimization of physical countermeasures and rehabilitation protocols to overcome disuse atrophy conditions in different clinical settings, rehabilitation and spaceflight

Partial mixture model for tight clustering of gene expression time-course

Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored. Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms. Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion

Transcriptome analysis of porcine M. semimembranosus divergent in intramuscular fat as a consequence of dietary protein restriction

peer-reviewedBackground: Intramuscular fat (IMF) content is positively correlated with aspects of pork palatability, including flavour, juiciness and overall acceptability. The ratio of energy to protein in the finishing diet of growing pigs can impact on IMF content with consequences for pork quality. The objective of this study was to compare gene expression profiles of Musculus semimembranosus (SM) of animals divergent for IMF as a consequence of protein dietary restriction in an isocaloric diet. The animal model was derived through the imposition of low or high protein diets during the finisher stage in Duroc gilts. RNA was extracted from post mortem SM tissue, processed and hybridised to Affymetrix porcine GeneChip® arrays. Results: IMF content of SM muscle was increased on the low protein diet (3.60 ± 0.38% versus 1.92 ± 0.35%). Backfat depth was also greater in animals on the low protein diet, and average daily gain and feed conversion ratio were lower, but muscle depth, protein content and moisture content were not affected. A total of 542 annotated genes were differentially expressed (DE) between animals on low and high protein diets, with 351 down-regulated and 191 up-regulated on the low protein diet. Transcript differences were validated for a subset of DE genes by qPCR. Alterations in functions related to cell cycle, muscle growth, extracellular matrix organisation, collagen development, lipogenesis and lipolysis, were observed. Expression of adipokines including LEP, TNFα and HIF1α were increased and the hypoxic stress response was induced. Many of the identified transcriptomic responses have also been observed in genetic and fetal programming models of differential IMF accumulation, indicating they may be robust biological indicators of IMF content. Conclusion: An extensive perturbation of overall energy metabolism in muscle occurs in response to protein restriction. A low protein diet can modulate IMF content of the SM by altering gene pathways involved in lipid biosynthesis and degradation; however this nutritional challenge negatively impacts protein synthesis pathways, with potential consequences for growth.Department of Agriculture, Food and the Marine, Ireland - Food Institutional Research Measur

Gene Expression Profile Changes After Short-activating RNA-mediated Induction of Endogenous Pluripotency Factors in Human Mesenchymal Stem Cells

It is now recognized that small noncoding RNA sequences have the ability to mediate transcriptional activation of specific target genes in human cells. Using bioinformatics analysis and functional screening, we screened short-activating RNA (saRNA) oligonucleotides designed to target the promoter regions of the pluripotency reprogramming factors, Kruppel-like factor 4 (KLF4) and c-MYC. We identified KLF4 and c-MYC promoter-targeted saRNA sequences that consistently induced increases in their respective levels of nascent mRNA and protein expression in a time- and dose-dependent manner, as compared with scrambled sequence control oligonucleotides. The functional consequences of saRNA-induced activation of each targeted reprogramming factor were then characterized by comprehensively profiling changes in gene expression by microarray analysis, which revealed significant increases in mRNA levels of their respective downstream pathway genes. Notably, the microarray profile after saRNA-mediated induction of endogenous KLF4 and c-MYC showed similar gene expression patterns for stem cell- and cell cycle-related genes as compared with lentiviral vector-mediated overexpression of exogenous KLF4 and c-MYC transgenes, while divergent gene expression patterns common to viral vector-mediated transgene delivery were also noted. The use of promoter-targeted saRNAs for the activation of pluripotency reprogramming factors could have broad implications for stem cell research

Genome-wide gene expression profiling of stress response in a spinal cord clip compression injury model.

BackgroundThe aneurysm clip impact-compression model of spinal cord injury (SCI) is a standard injury model in animals that closely mimics the primary mechanism of most human injuries: acute impact and persisting compression. Its histo-pathological and behavioural outcomes are extensively similar to human SCI. To understand the distinct molecular events underlying this injury model we analyzed global mRNA abundance changes during the acute, subacute and chronic stages of a moderate to severe injury to the rat spinal cord.ResultsTime-series expression analyses resulted in clustering of the majority of deregulated transcripts into eight statistically significant expression profiles. Systematic application of Gene Ontology (GO) enrichment pathway analysis allowed inference of biological processes participating in SCI pathology. Temporal analysis identified events specific to and common between acute, subacute and chronic time-points. Processes common to all phases of injury include blood coagulation, cellular extravasation, leukocyte cell-cell adhesion, the integrin-mediated signaling pathway, cytokine production and secretion, neutrophil chemotaxis, phagocytosis, response to hypoxia and reactive oxygen species, angiogenesis, apoptosis, inflammatory processes and ossification. Importantly, various elements of adaptive and induced innate immune responses span, not only the acute and subacute phases, but also persist throughout the chronic phase of SCI. Induced innate responses, such as Toll-like receptor signaling, are more active during the acute phase but persist throughout the chronic phase. However, adaptive immune response processes such as B and T cell activation, proliferation, and migration, T cell differentiation, B and T cell receptor-mediated signaling, and B cell- and immunoglobulin-mediated immune response become more significant during the chronic phase.ConclusionsThis analysis showed that, surprisingly, the diverse series of molecular events that occur in the acute and subacute stages persist into the chronic stage of SCI. The strong agreement between our results and previous findings suggest that our analytical approach will be useful in revealing other biological processes and genes contributing to SCI pathology