Identification of candidate regulatory sequences in mammalian 3' UTRs by statistical analysis of oligonucleotide distributions

3' untranslated regions (3' UTRs) contain binding sites for many regulatory elements, and in particular for microRNAs (miRNAs). The importance of miRNA-mediated post-transcriptional regulation has become increasingly clear in the last few years. We propose two complementary approaches to the statistical analysis of oligonucleotide frequencies in mammalian 3' UTRs aimed at the identification of candidate binding sites for regulatory elements. The first method is based on the identification of sets of genes characterized by evolutionarily conserved overrepresentation of an oligonucleotide. The second method is based on the identification of oligonucleotides showing statistically significant strand asymmetry in their distribution in 3' UTRs. Both methods are able to identify many previously known binding sites located in 3'UTRs, and in particular seed regions of known miRNAs. Many new candidates are proposed for experimental verification.Comment: Added two reference

Computational identification of transcription factor binding sites by functional analysis of sets of genes sharing overrepresented upstream motifs

BACKGROUND: Transcriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance. RESULTS: To exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set. CONCLUSIONS: The method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcription factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network.Comment: 19 pages, 1 figure. Published version with several improvements. Supplementary material available from the author

p53-sensitive epileptic behavior and inflammation in Ft1 hypomorphic mice

Epilepsy is a complex clinical condition characterized by repeated spontaneous seizures. Seizures have been linked to multiple drivers including DNA damage accumulation. Investigation of epilepsy physiopathology in humans imposes ethical and practical limitations, for this reason model systems are mostly preferred. Among animal models, mouse mutants are particularly valuable since they allow conjoint behavioral, organismal, and genetic analyses. Along with this, since aging has been associated with higher frequency of seizures, prematurely aging mice, simulating human progeroid diseases, offer a further useful modeling element as they recapitulate aging over a short time-window. Here we report on a mouse mutant with progeroid traits that displays repeated spontaneous seizures. Mutant mice were produced by reducing the expression of the gene Ft1 (AKTIP in humans). In vitro, AKTIP/Ft1 depletion causes telomere aberrations, DNA damage, and cell senescence. AKTIP/Ft1 interacts with lamins, which control nuclear architecture and DNA function. Premature aging defects of Ft1 mutant mice include skeletal alterations and lipodystrophy. The epileptic behavior of Ft1 mutant animals was age and sex linked. Seizures were observed in 18 mutant mice (23.6% of aged ≥ 21 weeks), at an average frequency of 2.33 events/mouse. Time distribution of seizures indicated non-random enrichment of seizures over the follow-up period, with 75% of seizures happening in consecutive weeks. The analysis of epileptic brains did not reveal overt brain morphological alterations or severe neurodegeneration, however, Ft1 reduction induced expression of the inflammatory markers IL-6 and TGF-β. Importantly, Ft1 mutant mice with concomitant genetic reduction of the guardian of the genome, p53, showed no seizures or inflammatory marker activation, implicating the DNA damage response into these phenotypes. This work adds insights into the connection among DNA damage, brain function, and aging. In addition, it further underscores the importance of model organisms for studying specific phenotypes, along with permitting the analysis of genetic interactions at the organismal level

Additive Functions in Boolean Models of Gene Regulatory Network Modules

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome’s evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate

Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC

International audienceMotivation: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed. Results: In this study we describe an automatic method for exploring in situ hybridization data and discover neuropil-enriched RNAs in the mouse hippocampus. We called it Hippo-ATESC (Automatic Texture Extraction from the Hippocampal region using Soft Computing). Bioinformatic validation showed that the Hippo-ATESC is very efficient in the recognition of RNAs which are manually identified by expert curators as neuropil-enriched on the same image series. Moreover, we show that our method can also highlight genes revealed by microdissection-based methods but missed by human visual inspection. We experimentally validated our approach by identifying a non-coding transcript enriched in mouse synaptosomes. The code is freely available on the web at http://ibislab.ce.unipr.it/software/hippo/

CLOE: Identification of putative functional relationships among genes by comparison of expression profiles between two species

BACKGROUND: Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation. RESULTS: We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest. CONCLUSIONS: CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques

The impact of TP53 activation and apoptosis in primary hereditary microcephaly

Autosomal recessive primary microcephaly (MCPH) is a constellation of disorders that share significant brain size reduction and mild to moderate intellectual disability, which may be accompanied by a large variety of more invalidating clinical signs. Extensive neural progenitor cells (NPC) proliferation and differentiation are essential to determine brain final size. Accordingly, the 30 MCPH loci mapped so far (MCPH1-MCPH30) encode for proteins involved in microtubule and spindle organization, centriole biogenesis, nuclear envelope, DNA replication and repair, underscoring that a wide variety of cellular processes is required for sustaining NPC expansion during development. Current models propose that altered balance between symmetric and asymmetric division, as well as premature differentiation, are the main mechanisms leading to MCPH. Although studies of cellular alterations in microcephaly models have constantly shown the co-existence of high DNA damage and apoptosis levels, these mechanisms are less considered as primary factors. In this review we highlight how the molecular and cellular events produced by mutation of the majority of MCPH genes may converge on apoptotic death of NPCs and neurons, via TP53 activation. We propose that these mechanisms should be more carefully considered in the alterations of the sophisticated equilibrium between proliferation, differentiation and death produced by MCPH gene mutations. In consideration of the potential druggability of cell apoptotic pathways, a better understanding of their role in MCPH may significantly facilitate the development of translational approaches