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Distinct modes of regulation by chromatin encoded through nucleosome positioning signals.

Field Y, Kaplan N, Fondufe-Mittendorf Y, Moore IK, Sharon E, Lubling Y, Widom J, Segal E - PLoS Comput. Biol. (2008)

Bottom Line: The detailed positions of nucleosomes profoundly impact gene regulation and are partly encoded by the genomic DNA sequence.We find that Poly(dA:dT) tracts are an important component of these nucleosome positioning signals and that their nucleosome-disfavoring action results in large nucleosome depletion over them and over their flanking regions and enhances the accessibility of transcription factors to their cognate sites.Our results suggest that the yeast genome may utilize these nucleosome positioning signals to regulate gene expression with different transcriptional noise and activation kinetics and DNA replication with different origin efficiency.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.

ABSTRACT
The detailed positions of nucleosomes profoundly impact gene regulation and are partly encoded by the genomic DNA sequence. However, less is known about the functional consequences of this encoding. Here, we address this question using a genome-wide map of approximately 380,000 yeast nucleosomes that we sequenced in their entirety. Utilizing the high resolution of our map, we refine our understanding of how nucleosome organizations are encoded by the DNA sequence and demonstrate that the genomic sequence is highly predictive of the in vivo nucleosome organization, even across new nucleosome-bound sequences that we isolated from fly and human. We find that Poly(dA:dT) tracts are an important component of these nucleosome positioning signals and that their nucleosome-disfavoring action results in large nucleosome depletion over them and over their flanking regions and enhances the accessibility of transcription factors to their cognate sites. Our results suggest that the yeast genome may utilize these nucleosome positioning signals to regulate gene expression with different transcriptional noise and activation kinetics and DNA replication with different origin efficiency. These distinct functions may be achieved by encoding both relatively closed (nucleosome-covered) chromatin organizations over some factor binding sites, where factors must compete with nucleosomes for DNA access, and relatively open (nucleosome-depleted) organizations over other factor sites, where factors bind without competition.

Show MeSH
Testing the universality of nucleosome positioning signals across eukaryotes.Our nucleosome model trained from yeast predicts nucleosome locations across several eukaryotes. For various nucleosome collections, including five new ones in fly and human that we isolated here, shown are scores assigned by our full model (“1”; score(S) from Equation 1 of the Methods section), by only the (position-independent) individual 5-mer component of the nucleosome-disfavoring component (“2”; Pl from Equation 1 above), by the entire nucleosome-disfavoring component of our model (“3”; PL from Equation 1 above), and by the (position-dependent) periodic component of our model (column “4”; PN from Equation 1 above). The sequences in each collection were mapped to their respective genome, and the score shown in each column at x-axis position i is the average score across all sequences in the collection, of the 147 bp (5 bp for column “2”) sequence whose center is i basepairs away from the center of the mapped sequence. For the full model (“1”) and nucleosome-disfavoring component (“3”), scores are shown in a window that extends up to 73 bp (half a nucleosome) around the center of the mapped nucleosome. Successful predictions assign their highest (“1”) or lowest (“3”) score at x-axis position zero. The p-value represents a student t-test that tests whether the distribution of scores in the 40 bp region centered on the mapped nucleosome is significantly higher (“1”) or lower (“3”) than that in the outer 40 bp (20 bp on each end of the mapped nucleosome). For the periodic component (“4”) scores are shown in a 10 bp window around the center of the mapped nucleosome, such that successful predictions assign the highest score at x-axis position zero; the P-value tests whether the distribution of scores in the 5 bp centered on the mapped nucleosome is significantly higher than that in the outer 6 bp (3 bp on each side, i.e., bp −5,−4,−3 and bp +3,+4,+5 from the center of the mapped nucleosome). Note that in several collections (e.g., worm), the 5-mer component itself (“2”) precisely demarcates the nucleosome positions, by assigning higher scores at the linker regions (more than 73 bp away from the center) compared to the nucleosomal regions (central 147 bp). For all four columns, the y-axis is scaled between the minimum and maximum score of the entire 293 bp region centered around the mapped nucleosome.
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pcbi-1000216-g005: Testing the universality of nucleosome positioning signals across eukaryotes.Our nucleosome model trained from yeast predicts nucleosome locations across several eukaryotes. For various nucleosome collections, including five new ones in fly and human that we isolated here, shown are scores assigned by our full model (“1”; score(S) from Equation 1 of the Methods section), by only the (position-independent) individual 5-mer component of the nucleosome-disfavoring component (“2”; Pl from Equation 1 above), by the entire nucleosome-disfavoring component of our model (“3”; PL from Equation 1 above), and by the (position-dependent) periodic component of our model (column “4”; PN from Equation 1 above). The sequences in each collection were mapped to their respective genome, and the score shown in each column at x-axis position i is the average score across all sequences in the collection, of the 147 bp (5 bp for column “2”) sequence whose center is i basepairs away from the center of the mapped sequence. For the full model (“1”) and nucleosome-disfavoring component (“3”), scores are shown in a window that extends up to 73 bp (half a nucleosome) around the center of the mapped nucleosome. Successful predictions assign their highest (“1”) or lowest (“3”) score at x-axis position zero. The p-value represents a student t-test that tests whether the distribution of scores in the 40 bp region centered on the mapped nucleosome is significantly higher (“1”) or lower (“3”) than that in the outer 40 bp (20 bp on each end of the mapped nucleosome). For the periodic component (“4”) scores are shown in a 10 bp window around the center of the mapped nucleosome, such that successful predictions assign the highest score at x-axis position zero; the P-value tests whether the distribution of scores in the 5 bp centered on the mapped nucleosome is significantly higher than that in the outer 6 bp (3 bp on each side, i.e., bp −5,−4,−3 and bp +3,+4,+5 from the center of the mapped nucleosome). Note that in several collections (e.g., worm), the 5-mer component itself (“2”) precisely demarcates the nucleosome positions, by assigning higher scores at the linker regions (more than 73 bp away from the center) compared to the nucleosomal regions (central 147 bp). For all four columns, the y-axis is scaled between the minimum and maximum score of the entire 293 bp region centered around the mapped nucleosome.

Mentions: Notably, in all of the above 12 nucleosome collections, our model assigns, on average, significantly higher scores around the center of the mapped nucleosome locations compared to scores that it assigns to nearby regions, suggesting that the nucleosome positioning signals of yeast are indeed predictive of nucleosome organizations in other eukaryotes (Figure 5). We also separately evaluated each of the two components of our model. We find that in all 10 collections obtained by direct sequencing, the periodic dinucleotide component alone predicts the correct rotational setting to within a 5 bp resolution, since on average, it assigns a higher score to the center of the nucleosome bound sequences in each collection compared to the score that it assigns to positions that are 5 bp away from that center (Figure 5). Similarly, in all 12 collections, the nucleosome disfavoring component of our model alone predicts the correct translational settings of the nucleosomes in each collection, since on average, it assigns a lower score to the center of the nucleosome bound sequences in each collection, compared to scores that it assigns in nearby regions (Figure 5). We also note that the 4th order Markov model alone (this component is the constituent repeating component of the 147 bp nucleosome disfavoring component), readily reveals that its preferred and disfavored 5-mers, learned only from yeast, show similar preferences in these nucleosome collections from higher eukaryotes, such that linkers contain more nucleosome-disfavoring sequences (Figure 5).


Distinct modes of regulation by chromatin encoded through nucleosome positioning signals.

Field Y, Kaplan N, Fondufe-Mittendorf Y, Moore IK, Sharon E, Lubling Y, Widom J, Segal E - PLoS Comput. Biol. (2008)

Testing the universality of nucleosome positioning signals across eukaryotes.Our nucleosome model trained from yeast predicts nucleosome locations across several eukaryotes. For various nucleosome collections, including five new ones in fly and human that we isolated here, shown are scores assigned by our full model (“1”; score(S) from Equation 1 of the Methods section), by only the (position-independent) individual 5-mer component of the nucleosome-disfavoring component (“2”; Pl from Equation 1 above), by the entire nucleosome-disfavoring component of our model (“3”; PL from Equation 1 above), and by the (position-dependent) periodic component of our model (column “4”; PN from Equation 1 above). The sequences in each collection were mapped to their respective genome, and the score shown in each column at x-axis position i is the average score across all sequences in the collection, of the 147 bp (5 bp for column “2”) sequence whose center is i basepairs away from the center of the mapped sequence. For the full model (“1”) and nucleosome-disfavoring component (“3”), scores are shown in a window that extends up to 73 bp (half a nucleosome) around the center of the mapped nucleosome. Successful predictions assign their highest (“1”) or lowest (“3”) score at x-axis position zero. The p-value represents a student t-test that tests whether the distribution of scores in the 40 bp region centered on the mapped nucleosome is significantly higher (“1”) or lower (“3”) than that in the outer 40 bp (20 bp on each end of the mapped nucleosome). For the periodic component (“4”) scores are shown in a 10 bp window around the center of the mapped nucleosome, such that successful predictions assign the highest score at x-axis position zero; the P-value tests whether the distribution of scores in the 5 bp centered on the mapped nucleosome is significantly higher than that in the outer 6 bp (3 bp on each side, i.e., bp −5,−4,−3 and bp +3,+4,+5 from the center of the mapped nucleosome). Note that in several collections (e.g., worm), the 5-mer component itself (“2”) precisely demarcates the nucleosome positions, by assigning higher scores at the linker regions (more than 73 bp away from the center) compared to the nucleosomal regions (central 147 bp). For all four columns, the y-axis is scaled between the minimum and maximum score of the entire 293 bp region centered around the mapped nucleosome.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC2570626&req=5

pcbi-1000216-g005: Testing the universality of nucleosome positioning signals across eukaryotes.Our nucleosome model trained from yeast predicts nucleosome locations across several eukaryotes. For various nucleosome collections, including five new ones in fly and human that we isolated here, shown are scores assigned by our full model (“1”; score(S) from Equation 1 of the Methods section), by only the (position-independent) individual 5-mer component of the nucleosome-disfavoring component (“2”; Pl from Equation 1 above), by the entire nucleosome-disfavoring component of our model (“3”; PL from Equation 1 above), and by the (position-dependent) periodic component of our model (column “4”; PN from Equation 1 above). The sequences in each collection were mapped to their respective genome, and the score shown in each column at x-axis position i is the average score across all sequences in the collection, of the 147 bp (5 bp for column “2”) sequence whose center is i basepairs away from the center of the mapped sequence. For the full model (“1”) and nucleosome-disfavoring component (“3”), scores are shown in a window that extends up to 73 bp (half a nucleosome) around the center of the mapped nucleosome. Successful predictions assign their highest (“1”) or lowest (“3”) score at x-axis position zero. The p-value represents a student t-test that tests whether the distribution of scores in the 40 bp region centered on the mapped nucleosome is significantly higher (“1”) or lower (“3”) than that in the outer 40 bp (20 bp on each end of the mapped nucleosome). For the periodic component (“4”) scores are shown in a 10 bp window around the center of the mapped nucleosome, such that successful predictions assign the highest score at x-axis position zero; the P-value tests whether the distribution of scores in the 5 bp centered on the mapped nucleosome is significantly higher than that in the outer 6 bp (3 bp on each side, i.e., bp −5,−4,−3 and bp +3,+4,+5 from the center of the mapped nucleosome). Note that in several collections (e.g., worm), the 5-mer component itself (“2”) precisely demarcates the nucleosome positions, by assigning higher scores at the linker regions (more than 73 bp away from the center) compared to the nucleosomal regions (central 147 bp). For all four columns, the y-axis is scaled between the minimum and maximum score of the entire 293 bp region centered around the mapped nucleosome.
Mentions: Notably, in all of the above 12 nucleosome collections, our model assigns, on average, significantly higher scores around the center of the mapped nucleosome locations compared to scores that it assigns to nearby regions, suggesting that the nucleosome positioning signals of yeast are indeed predictive of nucleosome organizations in other eukaryotes (Figure 5). We also separately evaluated each of the two components of our model. We find that in all 10 collections obtained by direct sequencing, the periodic dinucleotide component alone predicts the correct rotational setting to within a 5 bp resolution, since on average, it assigns a higher score to the center of the nucleosome bound sequences in each collection compared to the score that it assigns to positions that are 5 bp away from that center (Figure 5). Similarly, in all 12 collections, the nucleosome disfavoring component of our model alone predicts the correct translational settings of the nucleosomes in each collection, since on average, it assigns a lower score to the center of the nucleosome bound sequences in each collection, compared to scores that it assigns in nearby regions (Figure 5). We also note that the 4th order Markov model alone (this component is the constituent repeating component of the 147 bp nucleosome disfavoring component), readily reveals that its preferred and disfavored 5-mers, learned only from yeast, show similar preferences in these nucleosome collections from higher eukaryotes, such that linkers contain more nucleosome-disfavoring sequences (Figure 5).

Bottom Line: The detailed positions of nucleosomes profoundly impact gene regulation and are partly encoded by the genomic DNA sequence.We find that Poly(dA:dT) tracts are an important component of these nucleosome positioning signals and that their nucleosome-disfavoring action results in large nucleosome depletion over them and over their flanking regions and enhances the accessibility of transcription factors to their cognate sites.Our results suggest that the yeast genome may utilize these nucleosome positioning signals to regulate gene expression with different transcriptional noise and activation kinetics and DNA replication with different origin efficiency.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.

ABSTRACT
The detailed positions of nucleosomes profoundly impact gene regulation and are partly encoded by the genomic DNA sequence. However, less is known about the functional consequences of this encoding. Here, we address this question using a genome-wide map of approximately 380,000 yeast nucleosomes that we sequenced in their entirety. Utilizing the high resolution of our map, we refine our understanding of how nucleosome organizations are encoded by the DNA sequence and demonstrate that the genomic sequence is highly predictive of the in vivo nucleosome organization, even across new nucleosome-bound sequences that we isolated from fly and human. We find that Poly(dA:dT) tracts are an important component of these nucleosome positioning signals and that their nucleosome-disfavoring action results in large nucleosome depletion over them and over their flanking regions and enhances the accessibility of transcription factors to their cognate sites. Our results suggest that the yeast genome may utilize these nucleosome positioning signals to regulate gene expression with different transcriptional noise and activation kinetics and DNA replication with different origin efficiency. These distinct functions may be achieved by encoding both relatively closed (nucleosome-covered) chromatin organizations over some factor binding sites, where factors must compete with nucleosomes for DNA access, and relatively open (nucleosome-depleted) organizations over other factor sites, where factors bind without competition.

Show MeSH