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Identification of thyroid hormone receptor binding sites in developing mouse cerebellum.

Gagne R, Green JR, Dong H, Wade MG, Yauk CL - BMC Genomics (2013)

Bottom Line: We found that while the occurrence of the TRE motif is strongly correlated with gene regulation by TH for some genes, other TH-regulated genes do not exhibit an increased density of TRE half-site motifs.Furthermore, we demonstrate that an increase in the rate of occurrence of the half-site motifs does not always indicate the specific location of the TRE within the promoter region.While we have identified 85 putative TREs within these regions, future work will study other mechanisms of action that may mediate the remaining observed TR-binding activity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0L2, Canada.

ABSTRACT

Background: Thyroid hormones play an essential role in early vertebrate development as well as other key processes. One of its modes of action is to bind to the thyroid hormone receptor (TR) which, in turn, binds to thyroid response elements (TREs) in promoter regions of target genes. The sequence motif for TREs remains largely undefined as does the precise chromosomal location of the TR binding sites. A chromatin immunoprecipitation on microarray (ChIP-chip) experiment was conducted using mouse cerebellum post natal day (PND) 4 and PND15 for the thyroid hormone receptor (TR) beta 1 to map its binding sites on over 5000 gene promoter regions. We have performed a detailed computational analysis of these data.

Results: By analysing a recent spike-in study, the optimal normalization and peak identification approaches were determined for our dataset. Application of these techniques led to the identification of 211 ChIP-chip peaks enriched for TR binding in cerebellum samples. ChIP-PCR validation of 25 peaks led to the identification of 16 true positive TREs. Following a detailed literature review to identify all known mouse TREs, a position weight matrix (PWM) was created representing the classic TRE sequence motif. Various classes of promoter regions were investigated for the presence of this PWM, including permuted sequences, randomly selected promoter sequences, and genes known to be regulated by TH. We found that while the occurrence of the TRE motif is strongly correlated with gene regulation by TH for some genes, other TH-regulated genes do not exhibit an increased density of TRE half-site motifs. Furthermore, we demonstrate that an increase in the rate of occurrence of the half-site motifs does not always indicate the specific location of the TRE within the promoter region. To account for the fact that TR often operates as a dimer, we introduce a novel dual-threshold PWM scanning approach for identifying TREs with a true positive rate of 0.73 and a false positive rate of 0.2. Application of this approach to ChIP-chip peak regions revealed the presence of 85 putative TREs suitable for further in vitro validation.

Conclusions: This study further elucidates TRβ gene regulation in mouse cerebellum, with 211 promoter regions identified to bind to TR. While we have identified 85 putative TREs within these regions, future work will study other mechanisms of action that may mediate the remaining observed TR-binding activity.

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Microarray signal intensity biases and post normalization plots with respect to nucleotide composition and count. Both biases follow a polynomial trend that is unrelated to ChIP-chip signal. Since there is no relation between probe characteristics and signal, one should expect no trend in the data. a. The effect of base position along the probe on the signal intensity for the TI channel. A quadratic effect was observed for base position along the probe for A, C and G. b. Median log2 signal intensity of TI probes on the microarray versus the number of individual nucleotides in each probe.
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Figure 1: Microarray signal intensity biases and post normalization plots with respect to nucleotide composition and count. Both biases follow a polynomial trend that is unrelated to ChIP-chip signal. Since there is no relation between probe characteristics and signal, one should expect no trend in the data. a. The effect of base position along the probe on the signal intensity for the TI channel. A quadratic effect was observed for base position along the probe for A, C and G. b. Median log2 signal intensity of TI probes on the microarray versus the number of individual nucleotides in each probe.

Mentions: A linear-based model was developed by Potter et al. [18] to correct multiple biases for two –colour chIP-methylation microarrays, which also uses immunoprecipitation. This model was used to determine the influence of probe characteristics as a source of bias in our data. Relative signal intensity in relation to nucleotide position and number was plotted as described in Potter et al.[18] (Figures two A and B). Figure 1a illustrates the median log2 signal intensities for one typical microarray for the total input (TI) sample. For each nucleotide, we plotted the median intensity of all probes that contain that nucleotide at each position within the probe. Quadratic regression was performed on the plot, and clearly showed that the data fit the quadratic model, particularly for the first 45 nucleotides of each probe, with the trend escaping correlation after the 45th position. Likewise, Figure 1b illustrates the relationship between nucleotide composition within the probe and signal intensity. Again, it is apparent that the relationship follows a quadratic curve. Therefore, a slightly modified version of Potter’s method of normalization (in which an additional quadratic term relating nucleotide composition and nucleotide position was used) was examined further below, in the context of peak identification.


Identification of thyroid hormone receptor binding sites in developing mouse cerebellum.

Gagne R, Green JR, Dong H, Wade MG, Yauk CL - BMC Genomics (2013)

Microarray signal intensity biases and post normalization plots with respect to nucleotide composition and count. Both biases follow a polynomial trend that is unrelated to ChIP-chip signal. Since there is no relation between probe characteristics and signal, one should expect no trend in the data. a. The effect of base position along the probe on the signal intensity for the TI channel. A quadratic effect was observed for base position along the probe for A, C and G. b. Median log2 signal intensity of TI probes on the microarray versus the number of individual nucleotides in each probe.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3716714&req=5

Figure 1: Microarray signal intensity biases and post normalization plots with respect to nucleotide composition and count. Both biases follow a polynomial trend that is unrelated to ChIP-chip signal. Since there is no relation between probe characteristics and signal, one should expect no trend in the data. a. The effect of base position along the probe on the signal intensity for the TI channel. A quadratic effect was observed for base position along the probe for A, C and G. b. Median log2 signal intensity of TI probes on the microarray versus the number of individual nucleotides in each probe.
Mentions: A linear-based model was developed by Potter et al. [18] to correct multiple biases for two –colour chIP-methylation microarrays, which also uses immunoprecipitation. This model was used to determine the influence of probe characteristics as a source of bias in our data. Relative signal intensity in relation to nucleotide position and number was plotted as described in Potter et al.[18] (Figures two A and B). Figure 1a illustrates the median log2 signal intensities for one typical microarray for the total input (TI) sample. For each nucleotide, we plotted the median intensity of all probes that contain that nucleotide at each position within the probe. Quadratic regression was performed on the plot, and clearly showed that the data fit the quadratic model, particularly for the first 45 nucleotides of each probe, with the trend escaping correlation after the 45th position. Likewise, Figure 1b illustrates the relationship between nucleotide composition within the probe and signal intensity. Again, it is apparent that the relationship follows a quadratic curve. Therefore, a slightly modified version of Potter’s method of normalization (in which an additional quadratic term relating nucleotide composition and nucleotide position was used) was examined further below, in the context of peak identification.

Bottom Line: We found that while the occurrence of the TRE motif is strongly correlated with gene regulation by TH for some genes, other TH-regulated genes do not exhibit an increased density of TRE half-site motifs.Furthermore, we demonstrate that an increase in the rate of occurrence of the half-site motifs does not always indicate the specific location of the TRE within the promoter region.While we have identified 85 putative TREs within these regions, future work will study other mechanisms of action that may mediate the remaining observed TR-binding activity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0L2, Canada.

ABSTRACT

Background: Thyroid hormones play an essential role in early vertebrate development as well as other key processes. One of its modes of action is to bind to the thyroid hormone receptor (TR) which, in turn, binds to thyroid response elements (TREs) in promoter regions of target genes. The sequence motif for TREs remains largely undefined as does the precise chromosomal location of the TR binding sites. A chromatin immunoprecipitation on microarray (ChIP-chip) experiment was conducted using mouse cerebellum post natal day (PND) 4 and PND15 for the thyroid hormone receptor (TR) beta 1 to map its binding sites on over 5000 gene promoter regions. We have performed a detailed computational analysis of these data.

Results: By analysing a recent spike-in study, the optimal normalization and peak identification approaches were determined for our dataset. Application of these techniques led to the identification of 211 ChIP-chip peaks enriched for TR binding in cerebellum samples. ChIP-PCR validation of 25 peaks led to the identification of 16 true positive TREs. Following a detailed literature review to identify all known mouse TREs, a position weight matrix (PWM) was created representing the classic TRE sequence motif. Various classes of promoter regions were investigated for the presence of this PWM, including permuted sequences, randomly selected promoter sequences, and genes known to be regulated by TH. We found that while the occurrence of the TRE motif is strongly correlated with gene regulation by TH for some genes, other TH-regulated genes do not exhibit an increased density of TRE half-site motifs. Furthermore, we demonstrate that an increase in the rate of occurrence of the half-site motifs does not always indicate the specific location of the TRE within the promoter region. To account for the fact that TR often operates as a dimer, we introduce a novel dual-threshold PWM scanning approach for identifying TREs with a true positive rate of 0.73 and a false positive rate of 0.2. Application of this approach to ChIP-chip peak regions revealed the presence of 85 putative TREs suitable for further in vitro validation.

Conclusions: This study further elucidates TRβ gene regulation in mouse cerebellum, with 211 promoter regions identified to bind to TR. While we have identified 85 putative TREs within these regions, future work will study other mechanisms of action that may mediate the remaining observed TR-binding activity.

Show MeSH