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

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 expression                            data collected from 36 chemostats at six growth rates (0.05                                hr−1 through 0.3 hr−1)                            under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur,                            Leucine, and Uracil) as described in [4]. By                            employing the genes responding strongly, consistently, and only to                            changes in growth rate (and not specific nutrients) as growth-specific                            genes, we can apply our model to predict relative growth rates in new                            expression data. Gene expression in our original 36 conditions fell into                            three main categories as shown here. (A) Genes strongly up- or                            down-regulated in response to changes in growth rate, independent of                            limiting nutrient. The most statistically significant members of this                            set became our growth-specific calibration genes for application of the                            linear model to other expression data. (B) A subset of conditions                            highlighting genes with expression levels showing some correlation with                            growth rate, but with a strong nutrient-specific component. This                            represents a sizeable portion of the genome (∼25%),                            with positively growth-correlated genes enriched mainly for ribosomal                            function and negatively correlated genes enriched for oxidative                            metabolism. (C) A subset of conditions highlighting genes showing a                            non-systematic or negligible change in gene expression. Unresponsive                            genes were enriched for a variety of cellular processes not expected to                            show a strong relationship with growth, e.g. transcription, DNA                            metabolism and packaging, secretion, and many others.
© Copyright Policy
Related In: Results  -  Collection


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 expression data collected from 36 chemostats at six growth rates (0.05 hr−1 through 0.3 hr−1) under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur, Leucine, and Uracil) as described in [4]. By employing the genes responding strongly, consistently, and only to changes in growth rate (and not specific nutrients) as growth-specific genes, we can apply our model to predict relative growth rates in new expression data. Gene expression in our original 36 conditions fell into three main categories as shown here. (A) Genes strongly up- or down-regulated in response to changes in growth rate, independent of limiting nutrient. The most statistically significant members of this set became our growth-specific calibration genes for application of the linear model to other expression data. (B) A subset of conditions highlighting genes with expression levels showing some correlation with growth rate, but with a strong nutrient-specific component. This represents a sizeable portion of the genome (∼25%), with positively growth-correlated genes enriched mainly for ribosomal function and negatively correlated genes enriched for oxidative metabolism. (C) A subset of conditions highlighting genes showing a non-systematic or negligible change in gene expression. Unresponsive genes were enriched for a variety of cellular processes not expected to show a strong relationship with growth, e.g. transcription, DNA metabolism and packaging, secretion, and many others.

Mentions: Figure 3 highlights the sources of variability in the gene expression profiles that the experimental design aims at capturing. The resulting data contain a number of characteristic gene expression patterns, including genes with strong growth-specific transcriptional regulation and negligible nutrient-specific response (Figure 3A). Other genes include a growth-specific expression component but are also strongly up- or down-regulated under specific nutrient limitations (Figure 3B). Finally, Figure 3C displays expression profiles that show unsystematic or negligible responses under these conditions. The linear model described below summarizes the variability in the expression profiles of individual genes specifically due to changes in growth rate, which leads to a characterization of growth-specific calibration genes such as those shown in Figure 3A. This growth-specific signature enables predictions of the instantaneous growth rate of any cellular culture based on the relative expression values these growth-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 expression                            data collected from 36 chemostats at six growth rates (0.05                                hr−1 through 0.3 hr−1)                            under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur,                            Leucine, and Uracil) as described in [4]. By                            employing the genes responding strongly, consistently, and only to                            changes in growth rate (and not specific nutrients) as growth-specific                            genes, we can apply our model to predict relative growth rates in new                            expression data. Gene expression in our original 36 conditions fell into                            three main categories as shown here. (A) Genes strongly up- or                            down-regulated in response to changes in growth rate, independent of                            limiting nutrient. The most statistically significant members of this                            set became our growth-specific calibration genes for application of the                            linear model to other expression data. (B) A subset of conditions                            highlighting genes with expression levels showing some correlation with                            growth rate, but with a strong nutrient-specific component. This                            represents a sizeable portion of the genome (∼25%),                            with positively growth-correlated genes enriched mainly for ribosomal                            function and negatively correlated genes enriched for oxidative                            metabolism. (C) A subset of conditions highlighting genes showing a                            non-systematic or negligible change in gene expression. Unresponsive                            genes were enriched for a variety of cellular processes not expected to                            show a strong relationship with growth, e.g. transcription, DNA                            metabolism and packaging, secretion, and many others.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
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 expression data collected from 36 chemostats at six growth rates (0.05 hr−1 through 0.3 hr−1) under six nutrient limitations (Glucose, Nitrogen, Phosphate, Sulfur, Leucine, and Uracil) as described in [4]. By employing the genes responding strongly, consistently, and only to changes in growth rate (and not specific nutrients) as growth-specific genes, we can apply our model to predict relative growth rates in new expression data. Gene expression in our original 36 conditions fell into three main categories as shown here. (A) Genes strongly up- or down-regulated in response to changes in growth rate, independent of limiting nutrient. The most statistically significant members of this set became our growth-specific calibration genes for application of the linear model to other expression data. (B) A subset of conditions highlighting genes with expression levels showing some correlation with growth rate, but with a strong nutrient-specific component. This represents a sizeable portion of the genome (∼25%), with positively growth-correlated genes enriched mainly for ribosomal function and negatively correlated genes enriched for oxidative metabolism. (C) A subset of conditions highlighting genes showing a non-systematic or negligible change in gene expression. Unresponsive genes were enriched for a variety of cellular processes not expected to show a strong relationship with growth, e.g. transcription, DNA metabolism and packaging, secretion, and many others.
Mentions: Figure 3 highlights the sources of variability in the gene expression profiles that the experimental design aims at capturing. The resulting data contain a number of characteristic gene expression patterns, including genes with strong growth-specific transcriptional regulation and negligible nutrient-specific response (Figure 3A). Other genes include a growth-specific expression component but are also strongly up- or down-regulated under specific nutrient limitations (Figure 3B). Finally, Figure 3C displays expression profiles that show unsystematic or negligible responses under these conditions. The linear model described below summarizes the variability in the expression profiles of individual genes specifically due to changes in growth rate, which leads to a characterization of growth-specific calibration genes such as those shown in Figure 3A. This growth-specific signature enables predictions of the instantaneous growth rate of any cellular culture based on the relative expression values these growth-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