Limits...
Coding limits on the number of transcription factors.

Itzkovitz S, Tlusty T, Alon U - BMC Genomics (2006)

Bottom Line: For example, the number of winged helix factors does not generally exceed 300, even in very large genomes.The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family.This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel. shalev.itzkovitz@weizmann.ac.il

ABSTRACT

Background: Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of transcription factors encoded in a genome increases with the number of genes in the genome. Here, we examined the number of transcription factors from each super-family in diverse organisms.

Results: We find that the number of transcription factors from most super-families appears to be bounded. For example, the number of winged helix factors does not generally exceed 300, even in very large genomes. The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family. Coding theory predicts that such upper bounds on the number of transcription factors should exist, in order to minimize cross-binding errors between transcription factors. This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal. We present evidence that transcription factors with similar binding sequences tend to regulate genes with similar biological functions, supporting this prediction.

Conclusion: The present study suggests limits on the transcription factor repertoire of cells, and suggests coding constraints that might apply more generally to the mapping between binding sites and biological function.

Show MeSH
Transcription factors with overlapping binding sequences in S. cerevisae. Nodes represent TFs, edges connect pairs of TFs if their corresponding sets of binding sequences have significant overlap according to the present measure. Bold edges connect TFs which also have biological similarity according to the functional annotation and transcription network (gene co-regulation) measures. Shown are the TF logos [11]. Logo length was limited to the highly conserved base pairs for clarity.
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Figure 4: Transcription factors with overlapping binding sequences in S. cerevisae. Nodes represent TFs, edges connect pairs of TFs if their corresponding sets of binding sequences have significant overlap according to the present measure. Bold edges connect TFs which also have biological similarity according to the functional annotation and transcription network (gene co-regulation) measures. Shown are the TF logos [11]. Logo length was limited to the highly conserved base pairs for clarity.

Mentions: To test the prediction that TFs with similar sequences should tend to have similar function, we examined TFs in E. coli, yeast and human, and compared their sequence similarity by means of several distance metrics. In these organisms there exists a significant sequence similarity between the binding sites of some TF pairs (Fig 4, 5, 6). The yeast set of 94 well characterized TFs contained 18 pairs with highly similar sequences (Fig 4). The E. coli set of 46 well characterized TFs contained 6 pairs with highly similar sequences (Fig 5). The human set of 49 TFs contained 9 pairs with highly similar sequences (Fig 6). In other words, the TF "spheres" often overlap significantly (Fig 3B).


Coding limits on the number of transcription factors.

Itzkovitz S, Tlusty T, Alon U - BMC Genomics (2006)

Transcription factors with overlapping binding sequences in S. cerevisae. Nodes represent TFs, edges connect pairs of TFs if their corresponding sets of binding sequences have significant overlap according to the present measure. Bold edges connect TFs which also have biological similarity according to the functional annotation and transcription network (gene co-regulation) measures. Shown are the TF logos [11]. Logo length was limited to the highly conserved base pairs for clarity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1590034&req=5

Figure 4: Transcription factors with overlapping binding sequences in S. cerevisae. Nodes represent TFs, edges connect pairs of TFs if their corresponding sets of binding sequences have significant overlap according to the present measure. Bold edges connect TFs which also have biological similarity according to the functional annotation and transcription network (gene co-regulation) measures. Shown are the TF logos [11]. Logo length was limited to the highly conserved base pairs for clarity.
Mentions: To test the prediction that TFs with similar sequences should tend to have similar function, we examined TFs in E. coli, yeast and human, and compared their sequence similarity by means of several distance metrics. In these organisms there exists a significant sequence similarity between the binding sites of some TF pairs (Fig 4, 5, 6). The yeast set of 94 well characterized TFs contained 18 pairs with highly similar sequences (Fig 4). The E. coli set of 46 well characterized TFs contained 6 pairs with highly similar sequences (Fig 5). The human set of 49 TFs contained 9 pairs with highly similar sequences (Fig 6). In other words, the TF "spheres" often overlap significantly (Fig 3B).

Bottom Line: For example, the number of winged helix factors does not generally exceed 300, even in very large genomes.The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family.This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel. shalev.itzkovitz@weizmann.ac.il

ABSTRACT

Background: Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of transcription factors encoded in a genome increases with the number of genes in the genome. Here, we examined the number of transcription factors from each super-family in diverse organisms.

Results: We find that the number of transcription factors from most super-families appears to be bounded. For example, the number of winged helix factors does not generally exceed 300, even in very large genomes. The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family. Coding theory predicts that such upper bounds on the number of transcription factors should exist, in order to minimize cross-binding errors between transcription factors. This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal. We present evidence that transcription factors with similar binding sequences tend to regulate genes with similar biological functions, supporting this prediction.

Conclusion: The present study suggests limits on the transcription factor repertoire of cells, and suggests coding constraints that might apply more generally to the mapping between binding sites and biological function.

Show MeSH