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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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Representative genes responding to growth rate, specific nutrients,or unsystematically in our chemostat-derived training data.Our statistical model of growth rate regulation is based on expressiondata collected from 36 chemostats at six growth rates (0.05hr−1 through 0.3 hr−1)under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur,Leucine, and Uracil) as described in [4]. Byemploying the genes responding strongly, consistently, and only tochanges in growth rate (and not specific nutrients) as growth-specificgenes, we can apply our model to predict relative growth rates in newexpression data. Gene expression in our original 36 conditions fell intothree main categories as shown here. (A) Genes strongly up- ordown-regulated in response to changes in growth rate, independent oflimiting nutrient. The most statistically significant members of thisset became our growth-specific calibration genes for application of thelinear model to other expression data. (B) A subset of conditionshighlighting genes with expression levels showing some correlation withgrowth rate, but with a strong nutrient-specific component. Thisrepresents a sizeable portion of the genome (∼25%),with positively growth-correlated genes enriched mainly for ribosomalfunction and negatively correlated genes enriched for oxidativemetabolism. (C) A subset of conditions highlighting genes showing anon-systematic or negligible change in gene expression. Unresponsivegenes were enriched for a variety of cellular processes not expected toshow a strong relationship with growth, e.g. transcription, DNAmetabolism and packaging, secretion, and many others.
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pcbi-1000257-g003: Representative genes responding to growth rate, specific nutrients,or unsystematically in our chemostat-derived training data.Our statistical model of growth rate regulation is based on expressiondata collected from 36 chemostats at six growth rates (0.05hr−1 through 0.3 hr−1)under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur,Leucine, and Uracil) as described in [4]. Byemploying the genes responding strongly, consistently, and only tochanges in growth rate (and not specific nutrients) as growth-specificgenes, we can apply our model to predict relative growth rates in newexpression data. Gene expression in our original 36 conditions fell intothree main categories as shown here. (A) Genes strongly up- ordown-regulated in response to changes in growth rate, independent oflimiting nutrient. The most statistically significant members of thisset became our growth-specific calibration genes for application of thelinear model to other expression data. (B) A subset of conditionshighlighting genes with expression levels showing some correlation withgrowth rate, but with a strong nutrient-specific component. Thisrepresents a sizeable portion of the genome (∼25%),with positively growth-correlated genes enriched mainly for ribosomalfunction and negatively correlated genes enriched for oxidativemetabolism. (C) A subset of conditions highlighting genes showing anon-systematic or negligible change in gene expression. Unresponsivegenes were enriched for a variety of cellular processes not expected toshow a strong relationship with growth, e.g. transcription, DNAmetabolism and packaging, secretion, and many others.

Mentions: Figure 3 highlights thesources of variability in the gene expression profiles that the experimentaldesign aims at capturing. The resulting data contain a number of characteristicgene expression patterns, including genes with strong growth-specifictranscriptional regulation and negligible nutrient-specific response (Figure 3A). Other genesinclude a growth-specific expression component but are also strongly up- ordown-regulated under specific nutrient limitations (Figure 3B). Finally, Figure 3C displays expression profiles thatshow unsystematic or negligible responses under these conditions. The linearmodel described below summarizes the variability in the expression profiles ofindividual genes specifically due to changes in growth rate, which leads to acharacterization of growth-specific calibration genes such asthose shown in Figure 3A.This growth-specific signature enables predictions of the instantaneous growthrate of any cellular culture based on the relative expression values thesegrowth-specific genes.


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)

Representative genes responding to growth rate, specific nutrients,or unsystematically in our chemostat-derived training data.Our statistical model of growth rate regulation is based on expressiondata collected from 36 chemostats at six growth rates (0.05hr−1 through 0.3 hr−1)under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur,Leucine, and Uracil) as described in [4]. Byemploying the genes responding strongly, consistently, and only tochanges in growth rate (and not specific nutrients) as growth-specificgenes, we can apply our model to predict relative growth rates in newexpression data. Gene expression in our original 36 conditions fell intothree main categories as shown here. (A) Genes strongly up- ordown-regulated in response to changes in growth rate, independent oflimiting nutrient. The most statistically significant members of thisset became our growth-specific calibration genes for application of thelinear model to other expression data. (B) A subset of conditionshighlighting genes with expression levels showing some correlation withgrowth rate, but with a strong nutrient-specific component. Thisrepresents a sizeable portion of the genome (∼25%),with positively growth-correlated genes enriched mainly for ribosomalfunction and negatively correlated genes enriched for oxidativemetabolism. (C) A subset of conditions highlighting genes showing anon-systematic or negligible change in gene expression. Unresponsivegenes were enriched for a variety of cellular processes not expected toshow a strong relationship with growth, e.g. transcription, DNAmetabolism and packaging, secretion, and many others.
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getmorefigures.php?uid=PMC2599889&req=5

pcbi-1000257-g003: Representative genes responding to growth rate, specific nutrients,or unsystematically in our chemostat-derived training data.Our statistical model of growth rate regulation is based on expressiondata collected from 36 chemostats at six growth rates (0.05hr−1 through 0.3 hr−1)under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur,Leucine, and Uracil) as described in [4]. Byemploying the genes responding strongly, consistently, and only tochanges in growth rate (and not specific nutrients) as growth-specificgenes, we can apply our model to predict relative growth rates in newexpression data. Gene expression in our original 36 conditions fell intothree main categories as shown here. (A) Genes strongly up- ordown-regulated in response to changes in growth rate, independent oflimiting nutrient. The most statistically significant members of thisset became our growth-specific calibration genes for application of thelinear model to other expression data. (B) A subset of conditionshighlighting genes with expression levels showing some correlation withgrowth rate, but with a strong nutrient-specific component. Thisrepresents a sizeable portion of the genome (∼25%),with positively growth-correlated genes enriched mainly for ribosomalfunction and negatively correlated genes enriched for oxidativemetabolism. (C) A subset of conditions highlighting genes showing anon-systematic or negligible change in gene expression. Unresponsivegenes were enriched for a variety of cellular processes not expected toshow a strong relationship with growth, e.g. transcription, DNAmetabolism and packaging, secretion, and many others.
Mentions: Figure 3 highlights thesources of variability in the gene expression profiles that the experimentaldesign aims at capturing. The resulting data contain a number of characteristicgene expression patterns, including genes with strong growth-specifictranscriptional regulation and negligible nutrient-specific response (Figure 3A). Other genesinclude a growth-specific expression component but are also strongly up- ordown-regulated under specific nutrient limitations (Figure 3B). Finally, Figure 3C displays expression profiles thatshow unsystematic or negligible responses under these conditions. The linearmodel described below summarizes the variability in the expression profiles ofindividual genes specifically due to changes in growth rate, which leads to acharacterization of growth-specific calibration genes such asthose shown in Figure 3A.This growth-specific signature enables predictions of the instantaneous growthrate of any cellular culture based on the relative expression values thesegrowth-specific genes.

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