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Computational identification of transcription frameworks of early committed spermatogenic cells.

ABSTRACT: It is known that transcription factors (TFs) work in cooperation with each other to govern gene expression and thus single TF studies may not always reflect the underlying biology. Using microarray data obtained from two independent studies of the first wave of spermatogenesis, we tested the hypothesis that co-expressed spermatogenic genes in cells committed to differentiation are regulated by a set of distinct combinations of TF modules. A computational approach was designed to identify over-represented module combinations in the promoter regions of genes associated with transcripts that either increase or decrease in abundance between the first two major spermatogenic cell types: spermatogonia and spermatocytes. We identified five TFs constituting four module combinations that were correlated with expression and repression of similarly regulated genes. These modules were biologically assessed in the context that they represent the key transcriptional mediators in the developmental transition from the spermatogonia to spermatocyte.

SUBMITTER: Lalancette C

PROVIDER: S-EPMC3837529 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Computational identification of transcription frameworks of early committed spermatogenic cells.

Lalancette Claudia C Platts Adrian E AE Lu Yi Y Lu Shiyong S Krawetz Stephen A SA

Molecular genetics and genomics : MGG 20080710 3

It is known that transcription factors (TFs) work in cooperation with each other to govern gene expression and thus single TF studies may not always reflect the underlying biology. Using microarray data obtained from two independent studies of the first wave of spermatogenesis, we tested the hypothesis that co-expressed spermatogenic genes in cells committed to differentiation are regulated by a set of distinct combinations of TF modules. A computational approach was designed to identify over-re ...[more]

PMID: 18615256

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Project description:ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called "STAP," to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed ("primary") TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ?150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA.

Dataset Information

Computational identification of transcription frameworks of early committed spermatogenic cells.

Publications

Computational identification of transcription frameworks of early committed spermatogenic cells.

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