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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. cerevisiae gene                            expression datasets.Our model of the growth rate transcriptional response can be used to                            predict the growth rate of a cellular culture from gene expression data,                            robust to the originating biological conditions, growth regime, and                            experimental platform. Here, we apply the model to three selected data                            sets to infer relative and absolute growth rates. (A) A brief                            (<30 s) heat pulse was administered to a steady state chemostat                            culture immediately before time zero, and gene expression was assayed                            with an expression time course (see Figure                            S1 and Table S1). The relative growth rates                            inferred from this data show an abrupt departure from steady state                            growth, followed by a return to steady state (including a brief                            regulatory overshoot). Our predictions monitor these changes in growth                            rate at an instantaneous time scale (<5 m) inaccessible by                            standard experimental assays for growth rate. (B) Predicted growth rates                            for a portion of the environmental stress response data [6], assaying the response to a                            30–37°C heat shock. Our model captures the cessation                            and resumption of growth induced by the stress, even for a batch culture                            in which the growth rate is not fixed a priori. (C) A collection of 24                            chemostats were run at four growth rates (0.05 hr−1                            through 0.2 hr−1) and limited on six different                            nitrogen sources. Using only expression data from each condition, our                            model predicts accurate relative growth rates. However, when provided                            with the known growth rate for a single condition, the model is                            additionally able to infer absolute growth rates for all other data sets                            sharing that condition's mRNA reference channel. Note that the                            actual growth rate is measured empirically and thus deviates slightly                            from an ideal straight line due to technical variation in the growth                            equipment.
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pcbi-1000257-g004: Predicted growth rates for S. cerevisiae gene expression datasets.Our model of the growth rate transcriptional response can be used to predict the growth rate of a cellular culture from gene expression data, robust to the originating biological conditions, growth regime, and experimental platform. Here, we apply the model to three selected data sets to infer relative and absolute growth rates. (A) A brief (<30 s) heat pulse was administered to a steady state chemostat culture immediately before time zero, and gene expression was assayed with an expression time course (see Figure S1 and Table S1). The relative growth rates inferred from this data show an abrupt departure from steady state growth, followed by a return to steady state (including a brief regulatory overshoot). Our predictions monitor these changes in growth rate at an instantaneous time scale (<5 m) inaccessible by standard experimental assays for growth rate. (B) Predicted growth rates for a portion of the environmental stress response data [6], assaying the response to a 30–37°C heat shock. Our model captures the cessation and resumption of growth induced by the stress, even for a batch culture in which the growth rate is not fixed a priori. (C) A collection of 24 chemostats were run at four growth rates (0.05 hr−1 through 0.2 hr−1) and limited on six different nitrogen sources. Using only expression data from each condition, our model predicts accurate relative growth rates. However, when provided with the known growth rate for a single condition, the model is additionally able to infer absolute growth rates for all other data sets sharing that condition's mRNA reference channel. Note that the actual growth rate is measured empirically and thus deviates slightly from an ideal straight line due to technical variation in the growth equipment.

Mentions: Our model of the growth rate transcriptional response can be used to predict relative instantaneous growth rates from any S. cerevisiae gene expression data. For example, Figure 4A shows our predicted growth rates for a gene expression time course sampled from a steady state culture exposed to a brief (<30 s) heat pulse (Table S1). The predictions clearly show a departure from steady state within five minutes of the heat pulse, followed by recovery within 15 minutes. Similar predictions over a range of chemostat flow rates (Figure S1) reveal that this cellular behavior is consistent, although there is some variation in the degree of growth cessation during stress, in agreement with tolerance and sensitization models of the yeast stress response [21]. Notably, standard experimental assays for growth rate (e.g. optical density) would be incapable of monitoring such a response, while our model is able to observe these growth changes on an instantaneous time scale.


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. cerevisiae gene                            expression datasets.Our model of the growth rate transcriptional response can be used to                            predict the growth rate of a cellular culture from gene expression data,                            robust to the originating biological conditions, growth regime, and                            experimental platform. Here, we apply the model to three selected data                            sets to infer relative and absolute growth rates. (A) A brief                            (<30 s) heat pulse was administered to a steady state chemostat                            culture immediately before time zero, and gene expression was assayed                            with an expression time course (see Figure                            S1 and Table S1). The relative growth rates                            inferred from this data show an abrupt departure from steady state                            growth, followed by a return to steady state (including a brief                            regulatory overshoot). Our predictions monitor these changes in growth                            rate at an instantaneous time scale (<5 m) inaccessible by                            standard experimental assays for growth rate. (B) Predicted growth rates                            for a portion of the environmental stress response data [6], assaying the response to a                            30–37°C heat shock. Our model captures the cessation                            and resumption of growth induced by the stress, even for a batch culture                            in which the growth rate is not fixed a priori. (C) A collection of 24                            chemostats were run at four growth rates (0.05 hr−1                            through 0.2 hr−1) and limited on six different                            nitrogen sources. Using only expression data from each condition, our                            model predicts accurate relative growth rates. However, when provided                            with the known growth rate for a single condition, the model is                            additionally able to infer absolute growth rates for all other data sets                            sharing that condition's mRNA reference channel. Note that the                            actual growth rate is measured empirically and thus deviates slightly                            from an ideal straight line due to technical variation in the growth                            equipment.
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getmorefigures.php?uid=PMC2599889&req=5

pcbi-1000257-g004: Predicted growth rates for S. cerevisiae gene expression datasets.Our model of the growth rate transcriptional response can be used to predict the growth rate of a cellular culture from gene expression data, robust to the originating biological conditions, growth regime, and experimental platform. Here, we apply the model to three selected data sets to infer relative and absolute growth rates. (A) A brief (<30 s) heat pulse was administered to a steady state chemostat culture immediately before time zero, and gene expression was assayed with an expression time course (see Figure S1 and Table S1). The relative growth rates inferred from this data show an abrupt departure from steady state growth, followed by a return to steady state (including a brief regulatory overshoot). Our predictions monitor these changes in growth rate at an instantaneous time scale (<5 m) inaccessible by standard experimental assays for growth rate. (B) Predicted growth rates for a portion of the environmental stress response data [6], assaying the response to a 30–37°C heat shock. Our model captures the cessation and resumption of growth induced by the stress, even for a batch culture in which the growth rate is not fixed a priori. (C) A collection of 24 chemostats were run at four growth rates (0.05 hr−1 through 0.2 hr−1) and limited on six different nitrogen sources. Using only expression data from each condition, our model predicts accurate relative growth rates. However, when provided with the known growth rate for a single condition, the model is additionally able to infer absolute growth rates for all other data sets sharing that condition's mRNA reference channel. Note that the actual growth rate is measured empirically and thus deviates slightly from an ideal straight line due to technical variation in the growth equipment.
Mentions: Our model of the growth rate transcriptional response can be used to predict relative instantaneous growth rates from any S. cerevisiae gene expression data. For example, Figure 4A shows our predicted growth rates for a gene expression time course sampled from a steady state culture exposed to a brief (<30 s) heat pulse (Table S1). The predictions clearly show a departure from steady state within five minutes of the heat pulse, followed by recovery within 15 minutes. Similar predictions over a range of chemostat flow rates (Figure S1) reveal that this cellular behavior is consistent, although there is some variation in the degree of growth cessation during stress, in agreement with tolerance and sensitization models of the yeast stress response [21]. Notably, standard experimental assays for growth rate (e.g. optical density) would be incapable of monitoring such a response, while our model is able to observe these growth changes on an instantaneous time scale.

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