Limits...
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.

Show MeSH

Related in: MedlinePlus

Predicted growth rates for S. cerevisiae geneexpression datasets.Our model of the growth rate transcriptional response can be used topredict the growth rate of a cellular culture from gene expression data,robust to the originating biological conditions, growth regime, andexperimental platform. Here, we apply the model to three selected datasets to infer relative and absolute growth rates. (A) A brief(<30 s) heat pulse was administered to a steady state chemostatculture immediately before time zero, and gene expression was assayedwith an expression time course (see FigureS1 and Table S1). The relative growth ratesinferred from this data show an abrupt departure from steady stategrowth, followed by a return to steady state (including a briefregulatory overshoot). Our predictions monitor these changes in growthrate at an instantaneous time scale (<5 m) inaccessible bystandard experimental assays for growth rate. (B) Predicted growth ratesfor a portion of the environmental stress response data [6], assaying the response to a30–37°C heat shock. Our model captures the cessationand resumption of growth induced by the stress, even for a batch culturein which the growth rate is not fixed a priori. (C) A collection of 24chemostats were run at four growth rates (0.05 hr−1through 0.2 hr−1) and limited on six differentnitrogen sources. Using only expression data from each condition, ourmodel predicts accurate relative growth rates. However, when providedwith the known growth rate for a single condition, the model isadditionally able to infer absolute growth rates for all other data setssharing that condition's mRNA reference channel. Note that theactual growth rate is measured empirically and thus deviates slightlyfrom an ideal straight line due to technical variation in the growthequipment.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2599889&req=5

pcbi-1000257-g004: Predicted growth rates for S. cerevisiae geneexpression datasets.Our model of the growth rate transcriptional response can be used topredict the growth rate of a cellular culture from gene expression data,robust to the originating biological conditions, growth regime, andexperimental platform. Here, we apply the model to three selected datasets to infer relative and absolute growth rates. (A) A brief(<30 s) heat pulse was administered to a steady state chemostatculture immediately before time zero, and gene expression was assayedwith an expression time course (see FigureS1 and Table S1). The relative growth ratesinferred from this data show an abrupt departure from steady stategrowth, followed by a return to steady state (including a briefregulatory overshoot). Our predictions monitor these changes in growthrate at an instantaneous time scale (<5 m) inaccessible bystandard experimental assays for growth rate. (B) Predicted growth ratesfor a portion of the environmental stress response data [6], assaying the response to a30–37°C heat shock. Our model captures the cessationand resumption of growth induced by the stress, even for a batch culturein which the growth rate is not fixed a priori. (C) A collection of 24chemostats were run at four growth rates (0.05 hr−1through 0.2 hr−1) and limited on six differentnitrogen sources. Using only expression data from each condition, ourmodel predicts accurate relative growth rates. However, when providedwith the known growth rate for a single condition, the model isadditionally able to infer absolute growth rates for all other data setssharing that condition's mRNA reference channel. Note that theactual growth rate is measured empirically and thus deviates slightlyfrom an ideal straight line due to technical variation in the growthequipment.

Mentions: Our model of the growth rate transcriptional response can be used to predictrelative instantaneous growth rates from any S. cerevisiae geneexpression data. For example, Figure 4A shows our predicted growth rates for a gene expressiontime course sampled from a steady state culture exposed to a brief (<30s) heat pulse (Table S1). The predictions clearly show a departure from steadystate within five minutes of the heat pulse, followed by recovery within 15minutes. Similar predictions over a range of chemostat flow rates (Figure S1)reveal that this cellular behavior is consistent, although there is somevariation in the degree of growth cessation during stress, in agreement withtolerance 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 toobserve 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 geneexpression datasets.Our model of the growth rate transcriptional response can be used topredict the growth rate of a cellular culture from gene expression data,robust to the originating biological conditions, growth regime, andexperimental platform. Here, we apply the model to three selected datasets to infer relative and absolute growth rates. (A) A brief(<30 s) heat pulse was administered to a steady state chemostatculture immediately before time zero, and gene expression was assayedwith an expression time course (see FigureS1 and Table S1). The relative growth ratesinferred from this data show an abrupt departure from steady stategrowth, followed by a return to steady state (including a briefregulatory overshoot). Our predictions monitor these changes in growthrate at an instantaneous time scale (<5 m) inaccessible bystandard experimental assays for growth rate. (B) Predicted growth ratesfor a portion of the environmental stress response data [6], assaying the response to a30–37°C heat shock. Our model captures the cessationand resumption of growth induced by the stress, even for a batch culturein which the growth rate is not fixed a priori. (C) A collection of 24chemostats were run at four growth rates (0.05 hr−1through 0.2 hr−1) and limited on six differentnitrogen sources. Using only expression data from each condition, ourmodel predicts accurate relative growth rates. However, when providedwith the known growth rate for a single condition, the model isadditionally able to infer absolute growth rates for all other data setssharing that condition's mRNA reference channel. Note that theactual growth rate is measured empirically and thus deviates slightlyfrom an ideal straight line due to technical variation in the growthequipment.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000257-g004: Predicted growth rates for S. cerevisiae geneexpression datasets.Our model of the growth rate transcriptional response can be used topredict the growth rate of a cellular culture from gene expression data,robust to the originating biological conditions, growth regime, andexperimental platform. Here, we apply the model to three selected datasets to infer relative and absolute growth rates. (A) A brief(<30 s) heat pulse was administered to a steady state chemostatculture immediately before time zero, and gene expression was assayedwith an expression time course (see FigureS1 and Table S1). The relative growth ratesinferred from this data show an abrupt departure from steady stategrowth, followed by a return to steady state (including a briefregulatory overshoot). Our predictions monitor these changes in growthrate at an instantaneous time scale (<5 m) inaccessible bystandard experimental assays for growth rate. (B) Predicted growth ratesfor a portion of the environmental stress response data [6], assaying the response to a30–37°C heat shock. Our model captures the cessationand resumption of growth induced by the stress, even for a batch culturein which the growth rate is not fixed a priori. (C) A collection of 24chemostats were run at four growth rates (0.05 hr−1through 0.2 hr−1) and limited on six differentnitrogen sources. Using only expression data from each condition, ourmodel predicts accurate relative growth rates. However, when providedwith the known growth rate for a single condition, the model isadditionally able to infer absolute growth rates for all other data setssharing that condition's mRNA reference channel. Note that theactual growth rate is measured empirically and thus deviates slightlyfrom an ideal straight line due to technical variation in the growthequipment.
Mentions: Our model of the growth rate transcriptional response can be used to predictrelative instantaneous growth rates from any S. cerevisiae geneexpression data. For example, Figure 4A shows our predicted growth rates for a gene expressiontime course sampled from a steady state culture exposed to a brief (<30s) heat pulse (Table S1). The predictions clearly show a departure from steadystate within five minutes of the heat pulse, followed by recovery within 15minutes. Similar predictions over a range of chemostat flow rates (Figure S1)reveal that this cellular behavior is consistent, although there is somevariation in the degree of growth cessation during stress, in agreement withtolerance 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 toobserve 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