Reverse engieering gene regulatory networks: Elucidation of trancriptome organization, gene function and gene regulation in mammalian systems

The main aim of this thesis was to infer mammalian gene regulatory networks from tens of thousands gene expression profiles via a new "reverse-engineering" approach. Human arid Mouse gene regulatory networks were both inferred and the results collected in a database that represents part of the non-written material that will support. the thesis (http: //rretview. tigem. it). Each gene regulatory network consists of a set of gene pairs (connections) and a score based on Mutual Information, which states their statistical dependence. The inferred connections are organized into a network that allows exploration of the global features of gene regulation in a mammalian cell. We collected a massive and heterogeneous dataset of 22,255 gene expression profiles from a variety of human samples and experimental conditions. We developed a new mutual-information (MI) reverse-engineering approach able to quantify the extent to which the mRNA levels of two genes are related to each other across such a complex dataset. The resulting network consists of 4,817,629 connections among 22,255 transcripts. The inferred connections were compared against known protein-protein and other regulatory interactions to assess their biological significance. We experimentally identified a subset of predicted protein-protein interactions not previously described in the literature. We discovered regulatory modules within the network, consisting of genes strongly connected to each other, which carry out specific biological functions. We found that these connected genes tend to be in physical proximity at the chromatin level in the nucleus. We show that the network can be used to predict the biological function and subcellular localization of a protein, and to elucidate the function of a disease-gene. Specifically we discovered that the gene granulin precursor GRN), whose mutations cause frontotemporal lobar degeneration, is involved in lysosome function. We have developed an online tool (http://netview.tigem.it) for querying and exploring the gene regulatory network

Ictal epileptic headache. an old story with courses and appeals

The term "ictal epileptic headache" has been recently proposed to classify the clinical picture in which headache is the isolated ictal symptom of a seizure. There is emerging evidence from both basic and clinical neurosciences that cortical spreading depression and an epileptic focus may facilitate each other, although with a different degree of efficiency. This review address the long history which lead to the 'migralepsy' concept to the new emerging pathophysiological aspects, and clinical and electroencephalography evidences of ictal epileptic headache. Here, we review and discuss the common physiopathology mechanisms and the historical aspects underlying the link between headache and epilepsy. Either experimental or clinical measures are required to better understand this latter relationship: the development of animal models, molecular studies defining more precise genotype/phenotype correlations as well as multicenter clinical studies with revision of clinical criteria for headache-/epilepsy-related disorders represent the start of future research. Therefore, the definition of ictal epileptic headache should be used to classify the rare events in which headache is the only manifestation of a seizure. Finally, using our recently published criteria, we will be able to clarify if ictal epileptic headache represents an underestimated phenomenon or not

A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes - as is the case in biological networks - due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications

Hot water epilepsy and Mccune–Albright syndrome: A case report

AbstractWe observed a 15-year-old Caucasian boy with a rare form of reflex epilepsy, known as hot water epilepsy (HWE), associated to McCune–Albright syndrome (MAS). This is a rare disease due to post-zygotic and somatic mutations of the Gs-alpha gene, that results in cellular mosaicism. Predominant features of MAS occur in the bony skeleton, the skin, the endocrine system, and, in atypical presentations, in other non-endocrine tissues. It is unknown whether or not an expression of the GNAS1 product in the brain does exist. Although the association of MAS with HWE in our patient may be merely casual, it brings up the possibility that the striking phenotypic variability of MAS might also include epilepsy

Electrophysiological actions of zonisamide on striatal neurons: Selective neuroprotection against complex I mitochondrial dysfunction

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How to infer gene networks from expression profiles

Inferring, or ‘reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful

A2A adenosine receptor antagonists protect the striatum against rotenone-induced neurotoxicity.

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Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

A crowd-sourcing approach for the construction of species-specific cell signaling networks

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin