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Predicting cellular growth from gene expression signatures.

Airoldi EM, Huttenhower C, Gresham D, Lu C, Caudy AA, Dunham MJ, Broach JR, Botstein D, Troyanskaya OG - PLoS Comput. Biol. (2009)

Bottom Line: The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution.We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes.More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods.

View Article: PubMed Central - PubMed

Affiliation: Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

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Perturbations and potential transcriptional regulators of the growthrate response.(A) Predicted growth rates for gal1Δ cells shifted to glucose, togalactose, and to galactose with a constitutively active RAS2G19Vallele. On glucose, rapid growth is induced within ∼40 m; growthon galactose falls to low levels within ∼40 m, as it cannot bemetabolized by this mutant. However, when glucose sensing is emulated byartificial activation of the Ras/PKA pathway, the transcriptionalregulatory network attempts to induce rapid growth within∼60–80 m despite the unavailability of appropriatenutrients. This disconnect between actual and perceived cellular stateleads to cell death within 4–6 hours and suggests thatnutrient sensing (as opposed to metabolic activity or internal cellularstate) is responsible for a large portion of the transcriptional growthrate response. (B) Regulatory binding sites enriched in growth up- anddown-regulated genes. We clustered the yeast genome by degree of growthrate response, yielding ten clusters with average responses ranging from−12.0 (strongly downregulated with increasing growth rate) to8.6 (strongly upregulated). The FIRE program [34] predicted10 regulatory motifs in the upstream flanks and 3′ UTRs of themost up- and down-regulated clusters. These included the knownstress-responsive MSN2/4 binding sites in downregulated genes, theribosomal regulators RAP1 and PUF4 in upregulated genes, and INO4 sitesin upregulated genes (possibly corresponding to its role in the stressresponse and fatty acid biosynthesis [35]. We alsoidentified five additional putative growth regulatory sites for whichthe binding factor is not yet known.
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pcbi-1000257-g008: Perturbations and potential transcriptional regulators of the growthrate response.(A) Predicted growth rates for gal1Δ cells shifted to glucose, togalactose, and to galactose with a constitutively active RAS2G19Vallele. On glucose, rapid growth is induced within ∼40 m; growthon galactose falls to low levels within ∼40 m, as it cannot bemetabolized by this mutant. However, when glucose sensing is emulated byartificial activation of the Ras/PKA pathway, the transcriptionalregulatory network attempts to induce rapid growth within∼60–80 m despite the unavailability of appropriatenutrients. This disconnect between actual and perceived cellular stateleads to cell death within 4–6 hours and suggests thatnutrient sensing (as opposed to metabolic activity or internal cellularstate) is responsible for a large portion of the transcriptional growthrate response. (B) Regulatory binding sites enriched in growth up- anddown-regulated genes. We clustered the yeast genome by degree of growthrate response, yielding ten clusters with average responses ranging from−12.0 (strongly downregulated with increasing growth rate) to8.6 (strongly upregulated). The FIRE program [34] predicted10 regulatory motifs in the upstream flanks and 3′ UTRs of themost up- and down-regulated clusters. These included the knownstress-responsive MSN2/4 binding sites in downregulated genes, theribosomal regulators RAP1 and PUF4 in upregulated genes, and INO4 sitesin upregulated genes (possibly corresponding to its role in the stressresponse and fatty acid biosynthesis [35]. We alsoidentified five additional putative growth regulatory sites for whichthe binding factor is not yet known.

Mentions: We used a gal1Δ strain carrying the activated alleleRAS2G19V under control of the galactoseinducible GAL10 promoter. Addition of galactose activates theRas/PKA pathway, but since galactose cannot be metabolized by this strain, themetabolic state of the cell remains unaltered [9]. When grown onglycerol our model predicts a relative growth rate of ∼0.2 for thisstrain (Figure 8A), whichchanges to ∼0.6 within twenty minutes following glucose addition,consistent with the change in doubling time from 5.8 hr to 2.6 hr. When weperformed the same experiment on glycerol media and induced theRAS2G19V by means of galactose addition, wedetected a transcriptional response within sixty minutes. The predicted growthrate of the RAS2G19V mutant strain was comparable tothe addition of glucose despite the fact that galactose addition does not yieldan increase in growth, as measured by optical density, since the cells areunable to metabolize galactose. In fact, while the model'ssummarization of gene expression state indicates that the culture is attemptingto increase growth, induction of the RAS2G19V alleleresults in an immediate decrease in growth rate and complete cessation of growthwithin four hours [33]. These results are consistent with the cellsetting its growth-specific transcription program on the basis of itsperception of nutrients present in the environment, ratherthan on the direct availability of energy or metabolites produced from suchnutrients. The mechanism by which the cell integrates this external state inorder to set the appropriate growth rate expression state must be mediated, atleast in part, through the Ras/cAMP/PKA pathway.


Predicting cellular growth from gene expression signatures.

Airoldi EM, Huttenhower C, Gresham D, Lu C, Caudy AA, Dunham MJ, Broach JR, Botstein D, Troyanskaya OG - PLoS Comput. Biol. (2009)

Perturbations and potential transcriptional regulators of the growthrate response.(A) Predicted growth rates for gal1Δ cells shifted to glucose, togalactose, and to galactose with a constitutively active RAS2G19Vallele. On glucose, rapid growth is induced within ∼40 m; growthon galactose falls to low levels within ∼40 m, as it cannot bemetabolized by this mutant. However, when glucose sensing is emulated byartificial activation of the Ras/PKA pathway, the transcriptionalregulatory network attempts to induce rapid growth within∼60–80 m despite the unavailability of appropriatenutrients. This disconnect between actual and perceived cellular stateleads to cell death within 4–6 hours and suggests thatnutrient sensing (as opposed to metabolic activity or internal cellularstate) is responsible for a large portion of the transcriptional growthrate response. (B) Regulatory binding sites enriched in growth up- anddown-regulated genes. We clustered the yeast genome by degree of growthrate response, yielding ten clusters with average responses ranging from−12.0 (strongly downregulated with increasing growth rate) to8.6 (strongly upregulated). The FIRE program [34] predicted10 regulatory motifs in the upstream flanks and 3′ UTRs of themost up- and down-regulated clusters. These included the knownstress-responsive MSN2/4 binding sites in downregulated genes, theribosomal regulators RAP1 and PUF4 in upregulated genes, and INO4 sitesin upregulated genes (possibly corresponding to its role in the stressresponse and fatty acid biosynthesis [35]. We alsoidentified five additional putative growth regulatory sites for whichthe binding factor is not yet known.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC2599889&req=5

pcbi-1000257-g008: Perturbations and potential transcriptional regulators of the growthrate response.(A) Predicted growth rates for gal1Δ cells shifted to glucose, togalactose, and to galactose with a constitutively active RAS2G19Vallele. On glucose, rapid growth is induced within ∼40 m; growthon galactose falls to low levels within ∼40 m, as it cannot bemetabolized by this mutant. However, when glucose sensing is emulated byartificial activation of the Ras/PKA pathway, the transcriptionalregulatory network attempts to induce rapid growth within∼60–80 m despite the unavailability of appropriatenutrients. This disconnect between actual and perceived cellular stateleads to cell death within 4–6 hours and suggests thatnutrient sensing (as opposed to metabolic activity or internal cellularstate) is responsible for a large portion of the transcriptional growthrate response. (B) Regulatory binding sites enriched in growth up- anddown-regulated genes. We clustered the yeast genome by degree of growthrate response, yielding ten clusters with average responses ranging from−12.0 (strongly downregulated with increasing growth rate) to8.6 (strongly upregulated). The FIRE program [34] predicted10 regulatory motifs in the upstream flanks and 3′ UTRs of themost up- and down-regulated clusters. These included the knownstress-responsive MSN2/4 binding sites in downregulated genes, theribosomal regulators RAP1 and PUF4 in upregulated genes, and INO4 sitesin upregulated genes (possibly corresponding to its role in the stressresponse and fatty acid biosynthesis [35]. We alsoidentified five additional putative growth regulatory sites for whichthe binding factor is not yet known.
Mentions: We used a gal1Δ strain carrying the activated alleleRAS2G19V under control of the galactoseinducible GAL10 promoter. Addition of galactose activates theRas/PKA pathway, but since galactose cannot be metabolized by this strain, themetabolic state of the cell remains unaltered [9]. When grown onglycerol our model predicts a relative growth rate of ∼0.2 for thisstrain (Figure 8A), whichchanges to ∼0.6 within twenty minutes following glucose addition,consistent with the change in doubling time from 5.8 hr to 2.6 hr. When weperformed the same experiment on glycerol media and induced theRAS2G19V by means of galactose addition, wedetected a transcriptional response within sixty minutes. The predicted growthrate of the RAS2G19V mutant strain was comparable tothe addition of glucose despite the fact that galactose addition does not yieldan increase in growth, as measured by optical density, since the cells areunable to metabolize galactose. In fact, while the model'ssummarization of gene expression state indicates that the culture is attemptingto increase growth, induction of the RAS2G19V alleleresults in an immediate decrease in growth rate and complete cessation of growthwithin four hours [33]. These results are consistent with the cellsetting its growth-specific transcription program on the basis of itsperception of nutrients present in the environment, ratherthan on the direct availability of energy or metabolites produced from suchnutrients. The mechanism by which the cell integrates this external state inorder to set the appropriate growth rate expression state must be mediated, atleast in part, through the Ras/cAMP/PKA pathway.

Bottom Line: The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution.We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes.More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods.

View Article: PubMed Central - PubMed

Affiliation: Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

Show MeSH
Related in: MedlinePlus