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Stochastic resonance reveals "pilot light" expression in mammalian genes.

Ptitsyn A - PLoS ONE (2008)

Bottom Line: This effect is corroborated by the analysis of oscillating gene expression in mouse (M. musculus) and yeast (S. cerevisae).Most genes usually considered silent are in fact expressed at a very low level.Stochastic resonance can be applied to detect changes in expression pattern of low-expressed genes as well as for the validation of the probe performance in microarrays.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, College of Veterinary and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, United States of America. Andrey.Ptitsyn@colostate.edu

ABSTRACT

Background: Microarrays are widely used for estimation of expression of thousands of genes in a biological sample. The resolution ability of this method is limited by the background noise. Low expressed genes are detected with insufficient reliability and expression of many genes is never detected at all.

Methodology/principal findings: We have applied the principles of stochastic resonance to detect expression of genes from microarray signals below the background noise level. We report the periodic pattern detected in genes called "Absent" by traditional analysis. The pattern is consistent with expression of the conventionally detected genes and specific to the tissue of origin. This effect is corroborated by the analysis of oscillating gene expression in mouse (M. musculus) and yeast (S. cerevisae).

Conclusion/significance: Most genes usually considered silent are in fact expressed at a very low level. Stochastic resonance can be applied to detect changes in expression pattern of low-expressed genes as well as for the validation of the probe performance in microarrays.

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A scatter plot of mean intensity (axis X) and likeliness of periodicity estimated by Pt-test p-value (axis Y).
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pone-0001842-g002: A scatter plot of mean intensity (axis X) and likeliness of periodicity estimated by Pt-test p-value (axis Y).

Mentions: In most studies such “silent” genes are excluded from further analysis on the early stages. The filtration criteria are usually more stringent, selecting only genes called “Present” in at least half of all time points [15]. In the previous publications we have reported circadian oscillation in nearly 100% of all genes [13]. But the oscillating pattern does not show a strong dependence on the absolute level of expression or any regard to the signal/background noise ratio of the Affymetrix GeneChip. Figure 2 shows relations between likeliness of circadian oscillation (estimated by a periodicity test p-value) and the overall median of expression signal in time series. There is no indication of a threshold associated with presence or absence call. Genes expressed below the noise level (typically with signal reading under 150) generate the same pattern as highly expressed genes. This finding is corroborated by the results of periodicity tests performed on the subset of non-present genes (see Table 1). As expected, the number of “absent” genes for which a periodic pattern is observed with the confidence level of p<0.1 is lower and the expression profiles are generally noisier compared to analysis of entire set of transcripts [13]. However, in spite of the lower signal to noise ratio the underlying baseline circadian, oscillation is detectable in majority of the profiles. This pattern and the proportion between phase groups are consistent with that of the “present” genes or the mixture of “present” and “absent” genes (transcripts). These observations lead to the conclusion that the criteria separating “present” from “absent” genes is arbitrary. The low signal emitted from the microarray probes can be below the noise level for the chip at each particular time point. However, it reflects the pattern of gene expression rather than an ambient noise. This point can be further illustrated by Figure 3 which shows expression pattern of transcripts called “Absent” and “Present” in the contexts of a biological pathway (a fragment of insulin signaling pathway, data from Metacore database, GeneGo Inc.). In spite of the absence call for the insulin receptor the white adipose tissue is known to respond to the insulin signal [16]. The profile for the “Absent” insulin receptor is pronouncedly circadian and perfectly synchronized with production rate of the insulin receptor substrate, which is called present.


Stochastic resonance reveals "pilot light" expression in mammalian genes.

Ptitsyn A - PLoS ONE (2008)

A scatter plot of mean intensity (axis X) and likeliness of periodicity estimated by Pt-test p-value (axis Y).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2266998&req=5

pone-0001842-g002: A scatter plot of mean intensity (axis X) and likeliness of periodicity estimated by Pt-test p-value (axis Y).
Mentions: In most studies such “silent” genes are excluded from further analysis on the early stages. The filtration criteria are usually more stringent, selecting only genes called “Present” in at least half of all time points [15]. In the previous publications we have reported circadian oscillation in nearly 100% of all genes [13]. But the oscillating pattern does not show a strong dependence on the absolute level of expression or any regard to the signal/background noise ratio of the Affymetrix GeneChip. Figure 2 shows relations between likeliness of circadian oscillation (estimated by a periodicity test p-value) and the overall median of expression signal in time series. There is no indication of a threshold associated with presence or absence call. Genes expressed below the noise level (typically with signal reading under 150) generate the same pattern as highly expressed genes. This finding is corroborated by the results of periodicity tests performed on the subset of non-present genes (see Table 1). As expected, the number of “absent” genes for which a periodic pattern is observed with the confidence level of p<0.1 is lower and the expression profiles are generally noisier compared to analysis of entire set of transcripts [13]. However, in spite of the lower signal to noise ratio the underlying baseline circadian, oscillation is detectable in majority of the profiles. This pattern and the proportion between phase groups are consistent with that of the “present” genes or the mixture of “present” and “absent” genes (transcripts). These observations lead to the conclusion that the criteria separating “present” from “absent” genes is arbitrary. The low signal emitted from the microarray probes can be below the noise level for the chip at each particular time point. However, it reflects the pattern of gene expression rather than an ambient noise. This point can be further illustrated by Figure 3 which shows expression pattern of transcripts called “Absent” and “Present” in the contexts of a biological pathway (a fragment of insulin signaling pathway, data from Metacore database, GeneGo Inc.). In spite of the absence call for the insulin receptor the white adipose tissue is known to respond to the insulin signal [16]. The profile for the “Absent” insulin receptor is pronouncedly circadian and perfectly synchronized with production rate of the insulin receptor substrate, which is called present.

Bottom Line: This effect is corroborated by the analysis of oscillating gene expression in mouse (M. musculus) and yeast (S. cerevisae).Most genes usually considered silent are in fact expressed at a very low level.Stochastic resonance can be applied to detect changes in expression pattern of low-expressed genes as well as for the validation of the probe performance in microarrays.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, College of Veterinary and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, United States of America. Andrey.Ptitsyn@colostate.edu

ABSTRACT

Background: Microarrays are widely used for estimation of expression of thousands of genes in a biological sample. The resolution ability of this method is limited by the background noise. Low expressed genes are detected with insufficient reliability and expression of many genes is never detected at all.

Methodology/principal findings: We have applied the principles of stochastic resonance to detect expression of genes from microarray signals below the background noise level. We report the periodic pattern detected in genes called "Absent" by traditional analysis. The pattern is consistent with expression of the conventionally detected genes and specific to the tissue of origin. This effect is corroborated by the analysis of oscillating gene expression in mouse (M. musculus) and yeast (S. cerevisae).

Conclusion/significance: Most genes usually considered silent are in fact expressed at a very low level. Stochastic resonance can be applied to detect changes in expression pattern of low-expressed genes as well as for the validation of the probe performance in microarrays.

Show MeSH