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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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Predicted growth rates for S. bayanus and S.pombe expression datasets.By examining genes orthologous to our ∼70 S.cerevisiae growth-specific calibration genes, we successfullyapplied our model to predict growth rates in S. bayanus(∼50 orthologous growth-specific genes, ∼20 M yearsdiverged) and S. pombe (∼75 growth-specificgenes due to one-to-many mappings, ∼1B years diverged). (A)Predicted growth rates for S. bayanus undergoing thediauxic shift from fermentative to respiratory growth (TableS3). As observed for the S. cerevisiae diauxicshift in [4], growth pauses as glucose is exhaustedand resumes as the yeast begins consuming ethanol. (B) Predicted growthrates for S. bayanus exposed to a 25–37 C heat shock (TableS3). In contrast to Figure 4B, in which S. cerevisiae isobserved to recover from a 37 C heat shock, the less-thermotolerant S.bayanus [28] is predicted to halt growth at hightemperatures. (C) Predicted growth rates for S. pombe wild-type andrad3Δ time courses, grown normally and exposed to hydroxyurea(HU, an inhibitor of DNA synthesis and thus growth) [29]. Despite the wide evolutionary divergencebetween S. pombe and our S. cerevisiaetraining data, predicted growth rates are in substantial agreement withexpected biology. Each time course begins with low growth in asynchronized culture. When the synchronization block is released, cellsbegin growing, wild-type more efficiently than the rad3Δ mutant.Exposure to HU decreases growth over time, and this effect isexacerbated by RAD3 deletion. While the S. cerevisiaeRAD3 ortholog MEC1 is essential, knockouts of the MEC1 pathway membersSOD1 and LYS7 have been previously observed to induce HU sensitivity[30].
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pcbi-1000257-g006: Predicted growth rates for S. bayanus and S.pombe expression datasets.By examining genes orthologous to our ∼70 S.cerevisiae growth-specific calibration genes, we successfullyapplied our model to predict growth rates in S. bayanus(∼50 orthologous growth-specific genes, ∼20 M yearsdiverged) and S. pombe (∼75 growth-specificgenes due to one-to-many mappings, ∼1B years diverged). (A)Predicted growth rates for S. bayanus undergoing thediauxic shift from fermentative to respiratory growth (TableS3). As observed for the S. cerevisiae diauxicshift in [4], growth pauses as glucose is exhaustedand resumes as the yeast begins consuming ethanol. (B) Predicted growthrates for S. bayanus exposed to a 25–37 C heat shock (TableS3). In contrast to Figure 4B, in which S. cerevisiae isobserved to recover from a 37 C heat shock, the less-thermotolerant S.bayanus [28] is predicted to halt growth at hightemperatures. (C) Predicted growth rates for S. pombe wild-type andrad3Δ time courses, grown normally and exposed to hydroxyurea(HU, an inhibitor of DNA synthesis and thus growth) [29]. Despite the wide evolutionary divergencebetween S. pombe and our S. cerevisiaetraining data, predicted growth rates are in substantial agreement withexpected biology. Each time course begins with low growth in asynchronized culture. When the synchronization block is released, cellsbegin growing, wild-type more efficiently than the rad3Δ mutant.Exposure to HU decreases growth over time, and this effect isexacerbated by RAD3 deletion. While the S. cerevisiaeRAD3 ortholog MEC1 is essential, knockouts of the MEC1 pathway membersSOD1 and LYS7 have been previously observed to induce HU sensitivity[30].

Mentions: While our growth rate model is based on a transcriptional growth signature inS. cerevisiae, the model can be applied to any organismwith sufficiently orthologous transcriptional activity. This is likely to be thecase within the sensu stricto yeasts, separated by ∼25million years of evolution [27]. By finding the ∼50 S.bayanus genes orthologous to our ∼70 S.cerevisiae growth-specific calibration genes [19], we can apply ourmodel directly to S. bayanus expression data (Table S4).Figure 6 demonstratessuch a result for two S. bayanus time courses assaying thediauxic shift and a response to heat shock. These results have comparableprofile to those from S. cerevisiae and are similarlybiologically compelling. For example, the diauxic shift in S.bayanus results in a very similar growth pattern to the known responsein S. cerevisiae, with a near-cessation of growth during theshift and subsequent rebound. Conversely, S. bayanus is lessresistant to high temperatures than S. cerevisiae [28], and our growth rate inferences show acorresponding failure in its ability to grow following severe heat shock.


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)

Predicted growth rates for S. bayanus and S.pombe expression datasets.By examining genes orthologous to our ∼70 S.cerevisiae growth-specific calibration genes, we successfullyapplied our model to predict growth rates in S. bayanus(∼50 orthologous growth-specific genes, ∼20 M yearsdiverged) and S. pombe (∼75 growth-specificgenes due to one-to-many mappings, ∼1B years diverged). (A)Predicted growth rates for S. bayanus undergoing thediauxic shift from fermentative to respiratory growth (TableS3). As observed for the S. cerevisiae diauxicshift in [4], growth pauses as glucose is exhaustedand resumes as the yeast begins consuming ethanol. (B) Predicted growthrates for S. bayanus exposed to a 25–37 C heat shock (TableS3). In contrast to Figure 4B, in which S. cerevisiae isobserved to recover from a 37 C heat shock, the less-thermotolerant S.bayanus [28] is predicted to halt growth at hightemperatures. (C) Predicted growth rates for S. pombe wild-type andrad3Δ time courses, grown normally and exposed to hydroxyurea(HU, an inhibitor of DNA synthesis and thus growth) [29]. Despite the wide evolutionary divergencebetween S. pombe and our S. cerevisiaetraining data, predicted growth rates are in substantial agreement withexpected biology. Each time course begins with low growth in asynchronized culture. When the synchronization block is released, cellsbegin growing, wild-type more efficiently than the rad3Δ mutant.Exposure to HU decreases growth over time, and this effect isexacerbated by RAD3 deletion. While the S. cerevisiaeRAD3 ortholog MEC1 is essential, knockouts of the MEC1 pathway membersSOD1 and LYS7 have been previously observed to induce HU sensitivity[30].
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2599889&req=5

pcbi-1000257-g006: Predicted growth rates for S. bayanus and S.pombe expression datasets.By examining genes orthologous to our ∼70 S.cerevisiae growth-specific calibration genes, we successfullyapplied our model to predict growth rates in S. bayanus(∼50 orthologous growth-specific genes, ∼20 M yearsdiverged) and S. pombe (∼75 growth-specificgenes due to one-to-many mappings, ∼1B years diverged). (A)Predicted growth rates for S. bayanus undergoing thediauxic shift from fermentative to respiratory growth (TableS3). As observed for the S. cerevisiae diauxicshift in [4], growth pauses as glucose is exhaustedand resumes as the yeast begins consuming ethanol. (B) Predicted growthrates for S. bayanus exposed to a 25–37 C heat shock (TableS3). In contrast to Figure 4B, in which S. cerevisiae isobserved to recover from a 37 C heat shock, the less-thermotolerant S.bayanus [28] is predicted to halt growth at hightemperatures. (C) Predicted growth rates for S. pombe wild-type andrad3Δ time courses, grown normally and exposed to hydroxyurea(HU, an inhibitor of DNA synthesis and thus growth) [29]. Despite the wide evolutionary divergencebetween S. pombe and our S. cerevisiaetraining data, predicted growth rates are in substantial agreement withexpected biology. Each time course begins with low growth in asynchronized culture. When the synchronization block is released, cellsbegin growing, wild-type more efficiently than the rad3Δ mutant.Exposure to HU decreases growth over time, and this effect isexacerbated by RAD3 deletion. While the S. cerevisiaeRAD3 ortholog MEC1 is essential, knockouts of the MEC1 pathway membersSOD1 and LYS7 have been previously observed to induce HU sensitivity[30].
Mentions: While our growth rate model is based on a transcriptional growth signature inS. cerevisiae, the model can be applied to any organismwith sufficiently orthologous transcriptional activity. This is likely to be thecase within the sensu stricto yeasts, separated by ∼25million years of evolution [27]. By finding the ∼50 S.bayanus genes orthologous to our ∼70 S.cerevisiae growth-specific calibration genes [19], we can apply ourmodel directly to S. bayanus expression data (Table S4).Figure 6 demonstratessuch a result for two S. bayanus time courses assaying thediauxic shift and a response to heat shock. These results have comparableprofile to those from S. cerevisiae and are similarlybiologically compelling. For example, the diauxic shift in S.bayanus results in a very similar growth pattern to the known responsein S. cerevisiae, with a near-cessation of growth during theshift and subsequent rebound. Conversely, S. bayanus is lessresistant to high temperatures than S. cerevisiae [28], and our growth rate inferences show acorresponding failure in its ability to grow following severe heat shock.

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