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The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of to unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%–33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes. [ABSTRACT FROM AUTHOR]

Retroviral insertional mutagenesis (RIM) is a powerful tool for cancer genomics that was combined in this study with deep sequencing (RIM/DS) to facilitate a comprehensive analysis of lymphoma progression. Transgenic mice expressing two potent collaborating oncogenes in the germ line (CD2-MYC, -Runx2) develop rapid onset tumours that can be accelerated and rendered polyclonal by neonatal Moloney murine leukaemia virus (MoMLV) infection. RIM/DS analysis of 28 polyclonal lymphomas identified 771 common insertion sites (CISs) defining a ‘progression network’ that encompassed a remarkably large fraction of known MoMLV target genes, with further strong indications of oncogenic selection above the background of MoMLV integration preference. Progression driven by RIM was characterised as a Darwinian process of clonal competition engaging proliferation control networks downstream of cytokine and T-cell receptor signalling. Enhancer mode activation accounted for the most efficiently selected CIS target genes, including Ccr7 as the most prominent of a set of chemokine receptors driving paracrine growth stimulation and lymphoma dissemination. Another large target gene subset including candidate tumour suppressors was disrupted by intragenic insertions. A second RIM/DS screen comparing lymphomas of wild-type and parental transgenics showed that CD2-MYC tumours are virtually dependent on activation of Runx family genes in strong preference to other potent Myc collaborating genes (Gfi1, Notch1). Ikzf1 was identified as a novel collaborating gene for Runx2 and illustrated the interface between integration preference and oncogenic selection. Lymphoma target genes for MoMLV can be classified into (a) a small set of master regulators that confer self-renewal; overcoming p53 and other failsafe pathways and (b) a large group of progression genes that control autonomous proliferation in transformed cells. These findings provide insights into retroviral biology, human cancer genetics and the safety of vector-mediated gene therapy. [ABSTRACT FROM AUTHOR]

An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple data sources. Test data set, a web tool, source codes and supplementary data are available at: http://www.probtf.org. [ABSTRACT FROM AUTHOR]

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation. [ABSTRACT FROM AUTHOR]

Retroviral insertional mutagenesis (RIM) is a powerful tool for cancer genomics that was combined in this study with deep sequencing (RIM/DS) to facilitate a comprehensive analysis of lymphoma progression. Transgenic mice expressing two potent collaborating oncogenes in the germ line (CD2-MYC, -Runx2) develop rapid onset tumours that can be accelerated and rendered polyclonal by neonatal Moloney murine leukaemia virus (MoMLV) infection. RIM/DS analysis of 28 polyclonal lymphomas identified 771 common insertion sites (CISs) defining a 'progression network' that encompassed a remarkably large fraction of known MoMLV target genes, with further strong indications of oncogenic selection above the background of MoMLV integration preference. Progression driven by RIM was characterised as a Darwinian process of clonal competition engaging proliferation control networks downstream of cytokine and T-cell receptor signalling. Enhancer mode activation accounted for the most efficiently selected CIS target genes, including Ccr7 as the most prominent of a set of chemokine receptors driving paracrine growth stimulation and lymphoma dissemination. Another large target gene subset including candidate tumour suppressors was disrupted by intragenic insertions. A second RIM/DS screen comparing lymphomas of wild-type and parental transgenics showed that CD2-MYC tumours are virtually dependent on activation of Runx family genes in strong preference to other potent Myc collaborating genes (Gfi1, Notch1). Ikzf1 was identified as a novel collaborating gene for Runx2 and illustrated the interface between integration preference and oncogenic selection. Lymphoma target genes for MoMLV can be classified into (a) a small set of master regulators that confer self-renewal; overcoming p53 and other failsafe pathways and (b) a large group of progression genes that control autonomous proliferation in transformed cells. These findings provide insights into retroviral biology, human cancer genetics and the safety of vector-mediated gene therapy.