Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Microglial cells are involved in the susceptibility of NADPH oxidase knockout mice to 6-hydroxy-dopamine-induced neurodegeneration

We explored the impact of Nox-2 in modulating inflammatory-mediated microglial responses in the 6-hydroxydopamine (6-OHDA)-induced Parkinson’s disease (PD) model. Nox1 and Nox2 gene expression were found to increase in striatum, whereas a marked increase of Nox2 expression was observed in substantia nigra (SN) of wild-type (wt) mice after PD induction. Gp91phox-/- 6-OHDA-lesioned mice exhibited a significant reduction in the apomorphine-induced rotational behavior, when compared to wt mice. Immunolabeling assays indicated that striatal 6-OHDA injections reduced the number of dopaminergic (DA) neurons in the SN of wt mice. In gp91phox-/- 6-OHDA-lesioned mice the DA degeneration was negligible, suggesting an involvement of Nox in 6-OHDA-mediated SN degeneration. Gp91phox-/- 6-OHDA-lesioned mice treated with minocycline, a tetracycline derivative that exerts multiple anti-inflammatory effects, including microglial inhibition, exhibited increased apomorphine-induced rotational behavior and degeneration of DA neurons after 6-OHDA injections. The same treatment also increased TNF-α release and potentiated NF-κB activation in the SN of gp91phox-/--lesioned mice. Our results demonstrate for the first time that inhibition of microglial cells increases the susceptibility of gp91phox-/- 6-OHDA lesioned mice to develop PD. Blockade of microglia leads to NF-κB activation and TNF-α release into the SN of gp91phox-/- 6-OHDA lesioned mice, a likely mechanism whereby gp91phox-/- 6-OHDA lesioned mice may be more susceptible to develop PD after microglial cell inhibition. Nox2 adds an essential level of regulation to signaling pathways underlying the inflammatory response after PD induction