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Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors.

Andersson CR, Hvidsten TR, Isaksson A, Gustafsson MG, Komorowski J - BMC Syst Biol (2007)

Bottom Line: We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions.The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms.Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Linnaeus Centre for Bioinformatics, Uppsala University and Swedish University of Agricultural Sciences, Uppsala, Sweden. claes.andersson@lcb.uu.se

ABSTRACT

Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.

Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.

Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

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Examples of cell cycle control. Transcription factors (ellipses) are linked by (red) edges if they appear in the same combination at p-value threshold 0.000195. The corresponding sequence motifs (rectangles) occurring in cis-regulatory descriptors with these transcription factors are also shown, and black edges indicate with which factors they belong. Green indicate known phase-specific factors, while blue indicate known phase-specific motifs. Motifs named P# are putative motifs (see Additional file 4). Thus, for instance, one sees that the forkhead proteins FKH1 and FKH2 bind to genes that have the SFF and SFF' motifs and tend to bind simultaneously.
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Figure 5: Examples of cell cycle control. Transcription factors (ellipses) are linked by (red) edges if they appear in the same combination at p-value threshold 0.000195. The corresponding sequence motifs (rectangles) occurring in cis-regulatory descriptors with these transcription factors are also shown, and black edges indicate with which factors they belong. Green indicate known phase-specific factors, while blue indicate known phase-specific motifs. Motifs named P# are putative motifs (see Additional file 4). Thus, for instance, one sees that the forkhead proteins FKH1 and FKH2 bind to genes that have the SFF and SFF' motifs and tend to bind simultaneously.

Mentions: Given the system-wide evaluation presented so far, we have reason to believe that many of the combinations retrieved may offer new insight into the specific regulation of cell cycle-related genes. It might be particularly interesting to look at cis-regulatory descriptor combinations at a p-value threshold that we know will include mostly known phase-specific regulators. Combinations of these known phase-specific regulators as well as of known phase-specific regulators and other regulators may provide testable hypotheses explaining the selective regulation of periodic expression. The point (0.034, 0.73) on the curve representing our method in Figure 4 (accidentally the same as the Bonferroni point) is a good example. This point is associated with 145 rules with p-value lower than 0.000195. These rules include 19 of the 26 known phase specific regulators (73%) and 18 other regulators (3.4%). Furthermore, they describe 24% of the genes in the periodic classes. Figure 5 shows the co-occuring transcription factors in these rules. The figure mostly explains the regulation of genes in classes 011 and 111 since these classes are associated with the most significant combinations of cis-regulatory descriptors. Consequently, lower thresholds need to be chosen if one wants to study the specific regulatory mechanisms of other classes. Thresholds that balance the number of known phase-specific regulators against the number of other regulators may be obtained from the class-specific curves similar to that of Figure 4 (for motive numbers see additional file 4).


Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors.

Andersson CR, Hvidsten TR, Isaksson A, Gustafsson MG, Komorowski J - BMC Syst Biol (2007)

Examples of cell cycle control. Transcription factors (ellipses) are linked by (red) edges if they appear in the same combination at p-value threshold 0.000195. The corresponding sequence motifs (rectangles) occurring in cis-regulatory descriptors with these transcription factors are also shown, and black edges indicate with which factors they belong. Green indicate known phase-specific factors, while blue indicate known phase-specific motifs. Motifs named P# are putative motifs (see Additional file 4). Thus, for instance, one sees that the forkhead proteins FKH1 and FKH2 bind to genes that have the SFF and SFF' motifs and tend to bind simultaneously.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2200664&req=5

Figure 5: Examples of cell cycle control. Transcription factors (ellipses) are linked by (red) edges if they appear in the same combination at p-value threshold 0.000195. The corresponding sequence motifs (rectangles) occurring in cis-regulatory descriptors with these transcription factors are also shown, and black edges indicate with which factors they belong. Green indicate known phase-specific factors, while blue indicate known phase-specific motifs. Motifs named P# are putative motifs (see Additional file 4). Thus, for instance, one sees that the forkhead proteins FKH1 and FKH2 bind to genes that have the SFF and SFF' motifs and tend to bind simultaneously.
Mentions: Given the system-wide evaluation presented so far, we have reason to believe that many of the combinations retrieved may offer new insight into the specific regulation of cell cycle-related genes. It might be particularly interesting to look at cis-regulatory descriptor combinations at a p-value threshold that we know will include mostly known phase-specific regulators. Combinations of these known phase-specific regulators as well as of known phase-specific regulators and other regulators may provide testable hypotheses explaining the selective regulation of periodic expression. The point (0.034, 0.73) on the curve representing our method in Figure 4 (accidentally the same as the Bonferroni point) is a good example. This point is associated with 145 rules with p-value lower than 0.000195. These rules include 19 of the 26 known phase specific regulators (73%) and 18 other regulators (3.4%). Furthermore, they describe 24% of the genes in the periodic classes. Figure 5 shows the co-occuring transcription factors in these rules. The figure mostly explains the regulation of genes in classes 011 and 111 since these classes are associated with the most significant combinations of cis-regulatory descriptors. Consequently, lower thresholds need to be chosen if one wants to study the specific regulatory mechanisms of other classes. Thresholds that balance the number of known phase-specific regulators against the number of other regulators may be obtained from the class-specific curves similar to that of Figure 4 (for motive numbers see additional file 4).

Bottom Line: We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions.The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms.Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Linnaeus Centre for Bioinformatics, Uppsala University and Swedish University of Agricultural Sciences, Uppsala, Sweden. claes.andersson@lcb.uu.se

ABSTRACT

Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.

Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.

Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

Show MeSH
Related in: MedlinePlus