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The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

ABSTRACT: The regulation of gene expression in a cell relies to a major extent on transcription factors, proteins which recognize and bind the DNA at specific binding sites (response elements) within promoter regions associated with each gene. We present an information theoretic approach to modeling transcriptional regulatory networks, in terms of a simple "sequence-matching" rule and the statistics of the occurrence of binding sequences of given specificity in random promoter regions. The crucial biological input is the distribution of the amount of information coded in these cognate response elements and the length distribution of the promoter regions. We provide an analysis of the transcriptional regulatory network of yeast Saccharomyces cerevisiae, which we extract from the available databases, with respect to the degree distributions, clustering coefficient, degree correlations, rich-club coefficient and the k-core structure. We find that these topological features are in remarkable agreement with those predicted by our model, on the basis of the amount of information coded in the interaction between the transcription factors and response elements.

SUBMITTER: Balcan D

PROVIDER: S-EPMC1876808 | biostudies-literature | 2007 Jun

REPOSITORIES: biostudies-literature

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The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

Balcan Duygu D Kabakçioğlu Alkan A Mungan Muhittin M Erzan Ayşe A

PloS one 20070606 6

The regulation of gene expression in a cell relies to a major extent on transcription factors, proteins which recognize and bind the DNA at specific binding sites (response elements) within promoter regions associated with each gene. We present an information theoretic approach to modeling transcriptional regulatory networks, in terms of a simple "sequence-matching" rule and the statistics of the occurrence of binding sequences of given specificity in random promoter regions. The crucial biologi ...[more]

PMID: 17551581

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Project description:The control of gene expression in eukaryotic cells occurs both transcriptionally and post-transcriptionally. Although many genes are now known to be regulated at the translational level, in general, the mechanisms are poorly understood. We have previously presented polysomal gradient and array-based evidence that translational control is widespread in a significant number of genes when yeast cells are exposed to a range of stresses. Here we have re-examined these gene sets, considering the role of UTR sequences in the translational responses of these genes using recent large-scale datasets which define 5' and 3' transcriptional ends for many yeast genes. In particular, we highlight the potential role of 5' UTRs and upstream open reading frames (uORFs).We show a highly significant enrichment in specific GO functional classes for genes that are translationally up- and down-regulated under given stresses (e.g. carbohydrate metabolism is up-regulated under amino acid starvation). Cross-referencing these data with the stress response data we show that translationally upregulated genes have longer 5' UTRs, consistent with their role in translational regulation. In the first genome-wide study of uORFs in a set of mapped 5' UTRs, we show that uORFs are rare, being statistically under-represented in UTR sequences. However, they have distinct compositional biases consistent with their putative role in translational control and are more common in genes which are apparently translationally up-regulated.These results demonstrate a central regulatory role for UTR sequences, and 5' UTRs in particular, highlighting the significant role of uORFs in post-transcriptional control in yeast. Yeast uORFs are more highly conserved than has been suggested, lending further weight to their significance as functional elements involved in gene regulation. It also suggests a more complex and novel mechanism of control, whereby uORFs permit genes to escape from a more general attenuation of translation under conditions of stress. However, since uORFs are relatively rare (only ~13% of yeast genes have them) there remain many unanswered questions as to how UTR elements can direct translational control of many hundreds of genes under stress.

Project description:BackgroundHigh throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome.ResultsWe constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity.ConclusionTranscription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.

Dataset Information

The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

Publications

The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

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