Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration

Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

<p>Abstract</p> <p>Background</p> <p>Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.</p> <p>Results</p> <p>The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.</p> <p>Conclusion</p> <p>The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (<it>e.g</it>., LOOCV) and biologically (<it>e.g</it>., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.</p

New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

Optimization is a process to discover the most effective element or solution from a set of all possible resources or solutions. Currently, there are various biological problems such as extending from biomolecule structure prediction to drug discovery that can be elevated by opting standard protocol for optimization. Particle swarm optimization (PSO) process, purposed by Dr. Eberhart and Dr. Kennedy in 1995, is solely based on population stochastic optimization technique. This method was designed by the researchers after inspired by social behavior of flocking bird or schooling fishes. This method shares numerous resemblances with the evolutionary computation procedures such as genetic algorithms (GA). Since, PSO algorithms is easy process to subject with minor adjustment of a few restrictions, it has gained more attention or advantages over other population based algorithms. Hence, PSO algorithms is widely used in various research fields like ranging from artificial neural network training to other areas where GA can be used in the system

On the use of algorithms to discover motifs in DNA sequences

Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging for specialists nowadays due to the difficulties they find to deeply understand gene regulatory mechanisms, especially when analyzing binding sites in DNA. These sites or specific nucleotide sequences are known to be responsible for transcription processes. Thus, this work aims at providing an updated overview on strategies developed to discover meaningful motifs in DNA-related sequences, and, in particular, their attempts to find out relevant binding sites. From all existing approaches, this work is focused on dictionary, ensemble, and artificial intelligence-based algorithms since they represent the classical and the leading ones, respectively.Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00Junta de Andalucia P07-TIC- 02611

Regulation of nuclear factor-κ B and activator protein-1 activities after stimulation of T cells via glycosylphosphatidylinositol-anchored Ly-6A/E.

Cross-linking of glycosylphosphatidylinositol-anchored proteins, including mouse Ly-6A/E, leads to IL-2 secretion and T cell activation, whereas engagement of Ly-6A/E uniquely inhibits IL-2 production induced via TCR. However, little is known concerning the molecular mechanism by which glycosylphosphatidylinositol-anchored proteins regulate IL-2 expression. In this study, we have examined the ability of an anti-Ly-6A/E mAb to regulate transcription factors controlling IL-2 expression. Stimulation of EL4J(Ly-6E).A4 cells with anti-CD3 epsilon or anti-Ly6A/E mAbs induced nuclear factor (NF)-κ B p65-p50 (RelA/p50) and AP-1 (Fos/Jun) binding activities and increased nuclear factor of activated T cells (NF-AT) activity, whereas octamer-binding factor and NF-Y levels were stable. Cyclic AMP response element binding protein and T cell-specific factor-1 (α) activities were selectively enhanced by anti-CD3 epsilon, but not by anti-Ly6A/E, which suggests that signaling via the TCR and Ly-6 were not identical. Costimulation of these cells with both mAbs produced substantially reduced levels of AP-1, NF-AT, and, especially, NF-κ B p65-p50 whereas cyclic AMP response element binding protein and T cell-specific factor-1(α) were induced to a level seen after stimulation by anti-CD3 epsilon. The inducibility of the IL-2 enhancer in vivo and the contribution of individual transcription factors for this induction were assessed with use of reporter chloramphenicol acetyltransferase constructs containing the IL-2 enhancer or oligomerized binding sites for transcription factors. These experiments also demonstrated a key role for NF-κ B and AP-1 in the transcriptional regulation of the IL-2 gene by TCR- and Ly6A/E-mediated signaling. By using the 2B4.11 T cell hybridoma and a mutated variant, were revealed a crucial role for the zeta-chain in Ly6A/E-mediated activation of NF-κ B

A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology

We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following massively parallel sequencing technology). The statistical model assumes that the number of Pol II-targeted sequences contained within each genomic region follows a Poisson distribution. A Poisson mixture model was then developed to distinguish Pol II binding changes in transcribed region using an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and inference. In order to achieve a global maximum in the M-step, a particle swarm optimization (PSO) was implemented. We applied this model to Pol II binding data generated from hormone-dependent MCF7 breast cancer cells and antiestrogen-resistant MCF7 breast cancer cells before and after treatment with 17β-estradiol (E2). We determined that in the hormone-dependent cells, ~9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only ~0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a Poisson mixture model can be used to analyze ChIP-seq data

Transcriptomic analysis of the temporal host response to skin infestation with the ectoparasitic mite Psoroptes ovis

<p>Abstract</p> <p>Background</p> <p>Infestation of ovine skin with the ectoparasitic mite <it>Psoroptes ovis </it>results in a rapid cutaneous immune response, leading to the crusted skin lesions characteristic of sheep scab. Little is known regarding the mechanisms by which such a profound inflammatory response is instigated and to identify novel vaccine and drug targets a better understanding of the host-parasite relationship is essential. The main objective of this study was to perform a combined network and pathway analysis of the <it>in vivo </it>skin response to infestation with <it>P. ovis </it>to gain a clearer understanding of the mechanisms and signalling pathways involved.</p> <p>Results</p> <p>Infestation with <it>P. </it>ovis resulted in differential expression of 1,552 genes over a 24 hour time course. Clustering by peak gene expression enabled classification of genes into temporally related groupings. Network and pathway analysis of clusters identified key signalling pathways involved in the host response to infestation. The analysis implicated a number of genes with roles in allergy and inflammation, including pro-inflammatory cytokines (<it>IL1A, IL1B, IL6, IL8 </it>and <it>TNF</it>) and factors involved in immune cell activation and recruitment (<it>SELE, SELL, SELP, ICAM1, CSF2, CSF3, CCL2 </it>and <it>CXCL2</it>). The analysis also highlighted the influence of the transcription factors NF-kB and AP-1 in the early pro-inflammatory response, and demonstrated a bias towards a Th2 type immune response.</p> <p>Conclusions</p> <p>This study has provided novel insights into the signalling mechanisms leading to the development of a pro-inflammatory response in sheep scab, whilst providing crucial information regarding the nature of mite factors that may trigger this response. It has enabled the elucidation of the temporal patterns by which the immune system is regulated following exposure to <it>P. ovis</it>, providing novel insights into the mechanisms underlying lesion development. This study has improved our existing knowledge of the host response to <it>P. ovis</it>, including the identification of key parallels between sheep scab and other inflammatory skin disorders and the identification of potential targets for disease control.</p

Inference of Genetic Regulatory Networks with Recurrent Neural Network Models using Particle Swarm Optimization

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes