On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles

Dysregulated microRNA (miRNA) expression is a well-established feature of human cancer. However, the role of specific miRNAs in determining cancer outcomes remains unclear. Using Level 3 expression data from the Cancer Genome Atlas (TCGA), we identified 61 miRNAs that are associated with overall survival in 469 ovarian cancers profiled by microarray (p<0.01). We also identified 12 miRNAs that are associated with survival when miRNAs were profiled in the same specimens using Next Generation Sequencing (miRNA-Seq) (p<0.01). Surprisingly, only 1 miRNA transcript is associated with ovarian cancer survival in both datasets. Our analyses indicate that this discrepancy is due to the fact that miRNA levels reported by the two platforms correlate poorly, even after correcting for potential issues inherent to signal detection algorithms. Further investigation is warranted

Stochastic neural network models for gene regulatory networks

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models

Pathogen Response Genes Mediate Caenorhabditis elegans Innate Immunity

Innate immunity is crucial in the response and defense against pathogens for invertebrates and vertebrates alike. The soil nematode Caenorhabditis elegans is a useful model to study the eukaryotic innate immune response to microbial pathogenesis. Prior research indicates that the protein receptor FSHR-1 plays an important role in the innate recognition of intestinal infection due to pathogen consumption. Determining what genes are controlled by FSHR-1 may uncover an unknown pathway that could increase not only the comprehension of the C. elegans immune system but also innate immunity generally. To characterize the function of FSHR-1, four candidate pathogen response genes that appear to be regulated by FSHR-1 were evaluated in worms infected with Pseudomonas aeruginosa. Although intestine specific RNA interference of these four genes did not show immunity phenotypes, quantitative PCR suggests that FSHR-1 regulates the basal and/or infection-induced expression of three of the four genes. To explore this FSHR-1-dependent transcriptional induction, fluorescent transgenic reporters were constructed for the three candidate FSHR-1 target genes. The spatial expression of one putative pathogen response gene was characterized in transgenic worms under both control and pathogenic conditions. RNA interference was performed to assess the FSHR-1 dependency of this expression pattern

Temporal Control of the TGF-β Signaling Network by Mouse ESC MicroRNA Targets of Different Affinities.

Although microRNAs (miRNAs) function in the control of embryonic stem cell (ESC) pluripotency, a systems-level understanding is still being developed. Through the analysis of progressive Argonaute (Ago)-miRNA depletion and rescue, including stable Ago knockout mouse ESCs, we uncover transforming growth factor beta (TGF-β) pathway activation as a direct and early response to ESC miRNA reduction. Mechanistically, we link the derepression of weaker miRNA targets, including TGF-β receptor 1 (Tgfbr1), to the sensitive TGF-β pathway activation. In contrast, stronger miRNA targets impart a more robust repression, which dampens concurrent transcriptional activation. We verify such dampened induction for TGF-β antagonist Lefty. We find that TGF-β pathway activation contributes to the G1 cell-cycle accumulation of miRNA-deficient ESCs. We propose that miRNA target affinity is a determinant of the temporal response to miRNA changes, which enables the coordination of gene network responses

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Effects of Flight on Gene Expression and Aging in the Honey Bee Brain and Flight Muscle

Honey bees move through a series of in-hive tasks (e.g., “nursing”) to outside tasks (e.g., “foraging”) that are coincident with physiological changes and higher levels of metabolic activity. Social context can cause worker bees to speed up or slow down this process, and foragers may revert back to their earlier in-hive tasks accompanied by reversion to earlier physiological states. To investigate the effects of flight, behavioral state and age on gene expression, we used whole-genome microarrays and real-time PCR. Brain tissue and flight muscle exhibited different patterns of expression during behavioral transitions, with expression patterns in the brain reflecting both age and behavior, and expression patterns in flight muscle being primarily determined by age. Our data suggest that the transition from behaviors requiring little to no flight (nursing) to those requiring prolonged flight bouts (foraging), rather than the amount of previous flight per se, has a major effect on gene expression. Following behavioral reversion there was a partial reversion in gene expression but some aspects of forager expression patterns, such as those for genes involved in immune function, remained. Combined with our real-time PCR data, these data suggest an epigenetic control and energy balance role in honey bee functional senescence