- Short Report
- Open Access
Relating perturbation magnitude to temporal gene expression in biological systems
- Stephen J Callister†3,
- J Jacob Parnell†4, 5,
- Michael E Pfrender4 and
- Syed A Hashsham1, 2Email author
© Hashsham et al; licensee BioMed Central Ltd. 2009
- Received: 29 January 2009
- Accepted: 19 March 2009
- Published: 19 March 2009
Most transcriptional activity is a result of environmental variability. This cause (environment) and effect (gene expression) relationship is essential to survival in any changing environment. The specific relationship between environmental perturbation and gene expression – and stability of the response – has yet to be measured in detail. We describe a method to quantitatively relate perturbation magnitude to response at the level of gene expression. We test our method using Saccharomyces cerevisiae as a model organism and osmotic stress as an environmental stress.
Patterns of gene expression were measured in response to increasing sodium chloride concentrations (0, 0.5, 0.7, 1.0, and 1.2 M) for sixty genes impacted by osmotic shock. Expression of these genes was quantified over five time points using reverse transcriptase real-time polymerase chain reaction. Magnitudes of cumulative response for specific pathways, and the set of all genes, were obtained by combining the temporal response envelopes for genes exhibiting significant changes in expression with time. A linear relationship between perturbation magnitude and response was observed for the range of concentrations studied.
This study develops a quantitative approach to describe the stability of gene response and pathways to environmental perturbation and illustrates the utility of this approach. The approach should be applicable to quantitatively evaluate the response of organisms via the magnitude of response and stability of the transcriptome to environmental change.
- Osmotic Stress
- Osmotic Shock
- Dihydroxyacetone Phosphate
- Cumulative Response
- Perturbation Magnitude
The cause and effect relationship between perturbation and response is routinely used to study and characterize ecosystems in terms of stability. To extend this concept, we investigated the relationship between the affect of different perturbation magnitudes on the dynamic level of transcriptional response. The establishment of a quantitative relationship between stress and response has implications for predictive capabilities related to the behavior of organisms in natural and engineered systems and can be established on many levels including community, population, proteome, and transcriptome.
Transcription is often compared by measuring the fold-change in relative expression [1, 2]; however, a simple fold-change approach only accounts for one aspect of the response. Adjusting to perturbation spans a continuum of short-term stress responses, long-term acclimation, and genetic adaptation. Improved analytical tools for understanding the transcriptional mechanisms underlying variation in environmental tolerance will help address key issues such as what distinguishes generalists from specialists, and how do organisms increase tolerance [3, 4].
We evaluate a standard area of displacement to determine the quantitative relationship between perturbation and transcriptional response. We selected Saccharomyces cerevisiae as a model organism because of its existing knowledgebase related to gene expression, particularly in high salt concentration (model stress). Our method demonstrates a linear relationship resulting in a proportional response at very high perturbation magnitudes.
Saccharomyces cerevisiae W303 (ATCC 200060) was grown in batch culture at 30°C in YDP medium containing 2% bacto-peptone, 1% yeast extract, and 2% glucose. Cells were harvested by centrifugation, washed, and suspended in YDP to a final OD600 of approximately 0.16. 5 M NaCl was added in triplicate to early log phase (OD600 of ~1.0) cells resulting in a final NaCl concentration of 0.5 M, 0.7 M, 1.0 M, or 1.2 M. Prior to salt addition, a 15 ml sample was collected to characterize the base-line gene expression level. During perturbation, cells were harvested at regular intervals  until growth resumed as indicated by three consecutive increases in OD600.
RNA extraction and purification
Total RNA was extracted using the modified hot acid phenol extraction method . A 5 μ l sub-sample was removed and reserved for RNA quantification and to check for genomic DNA contamination. All RNA extractions were performed in a dedicated hood, cleaned to reduce or destroy residual RNase activity using either a 10% bleach solution or RNase wipes (Ambion, Austin TX).
Extracted RNA was evaluated for DNA contamination using a no-RT control as described previously  with the following PCR conditions as follows: 94°C 2 min, 40 cycles of 94°C 0.5 min, 55°C 0.5 min, 72°C 1 min followed by a final elongation step 72°C 1 min. Amplified PCR products were detected on a 3% agarose gel. Samples containing DNA contamination digested with Turbo™ DNase (Ambion, Austin, TX) at 37°C for 30 minutes and reevaluated for DNA contamination.
Gene selection and primer design
Genes used in this study belonging to one of three osmotolerance biochemical pathways.
Phosphatidate cytidylyltransferase (CDP-diglyceride synthetase)
Glycolytic enzyme phosphoglucose isomerase
Homolog of Gpm1p phosphoglycerate mutase
NAD-dependent glycerol-3-phosphate dehydrogenase
Bifunctional enzyme containing both alcohol dehydrogenase and glutathione-dependent formaldehyde dehydrogenase activities
Phosphorelay intermediate protein
Putative aryl-alcohol dehydrogenase
Induced under stress conditions; triosephosphate isomerase
Repressible alkaline phosphatase
Mitochondrial aldehyde dehydrogenase
E1 alpha subunit of the pyruvate dehydrogenase (PDH) complex
Putative aryl-alcohol dehydrogenase
Hexokinase isoenzyme 1
Choline phosphate cytidylyltransferase
Cytosolic catalase T
Alpha subunit of heterooctameric phosphofructokinase
Signal transducing kinase of the PAK (p21-activated kinase) family
sn-1,2-diacylglycerol ethanolamine- and cholinephosphotranferase
Histidine kinase osmosensor that regulates a MAP kinase cascade
Mitochondrial glycerol-3-phosphate dehydrogenase
MAP kinase kinase
Fructose 1,6-bisphosphate aldolase
Cytoplasmic response regulator
Mitogen-activated protein kinase
Signal transducing MEK kinase involved
Monomeric glyoxalase I
Transcriptional activator related to Msn4p
Aldehyde dehydrogenase that uses NAD+ as the preferred coenzyme
Glucose-repressible alcohol dehydrogenase II
Suppressor of Sensor Kinase (SLN1)
Homolog of Gpm1p phosphoglycerate mutase
NAD-dependent glycerol 3-phosphate dehydrogenase
Putative NADP(+) coupled glycerol dehydrogenase
Mitochondrial aldehyde dehydrogenase
Putative S-adenosylmethionine-dependent methyltransferase
Endoplasmic reticulum protein of unknown function
Primers for each gene were designed using Primer Express™ 2.0 (Perkin Elmer Applied Biosystems, Foster City, CA). Design parameters included a Tm from 58°C to 62°C, GC content from 45% to 55%, length from 20 bp to 22 bp, and amplicon length from 100 bp to 110 bp. Additional design parameters were set according to the manufacturer's suggestions. Specificity of primers was evaluated by gel electrophoresis of the amplified products, and analyzing dissociation curves for amplicons following quantitative real-time PCR.
Quantitative real-time PCR
RNA samples were reverse transcribed using a High Capacity cDNA Conversion Kit™ (Applied Biosystems, Foster City, CA), purified using Qiagen PCR purification columns (Qiagen, Valencia, CA) and quantified at 260 nm. 10–20 ng of purified cDNA was added to a reaction cocktail containing the PCR buffer described earlier with 2.5% (vol/vol) SYBR™ Green I (Molecular Probes, Eugene, OR; 1000×), and 2.3 μ M 6-ROX (6-carboxy-x-rhodamine, Molecular Probes, Eugene, OR). PCR was performed on an ABI 7900 HT (Applied Biosystems, Foster City, CA) using the reaction conditions, minus the final elongation step, described earlier.
Calibration curves of template dilution series of a 101 bp fragment of inorganic phosphatase and a 106 bp fragment of glycerol-3-phosphate were run in triplicate for each PCR run to normalize differences in amplicon sizes. The 101 bp and 106 bp fragments corresponded to the range of amplicons bounded by the primers designed for selected genes. PCR products were purified using 10,000 MWCO spin columns (Millipore, Bedford, MA), and quantified by absorbance at 260 nm. Purified products were adjusted to the same absorbance and a 1:10 dilution series was prepared as the starting template for the standard curves. Four dilutions from each series (100, 10-2, 10-4, and 10-6) were evaluated for linearity and agreement in cycle threshold (Ct).
To evaluate gene expression patterns over time to the applied perturbations, the relative response of an individual gene was calculated as:
r(t) i = (x i (t) - x i (0))/x i (0)
This calculation for a given time period represents the relative response envelope of the aggregate. For the response of a gene to be included in the aggregate relative response, the gene must meet the following criteria: 1) a measured response in two of three replicates at each sampling event (five total), 2) the measured response was within the dynamic range of the standard curve used to determine its expression value, and 3) the fractional coefficient of variation among replicate measurements was less than 1. This latter criterion was included because the cumulative response is highly subject to uncertainty from propagation of random error. To quantify the total amount of displacement, the area inscribed by the response envelope was determined by curve fitting using a polynomial regression model:
y(t) = β0 + β1 t1 + β2 t2 + ... β k t k .
Where, β k are the coefficients describing the shape of the relative response and y(t) is the specific model for each relative response envelope from the onset of the perturbation (t = 0) to the last measurement.
One of the most dominant genes in all four perturbations is GPD1 (glycerol-3-phosphate dehydrogenase). However, DAK1 (dihydroxyacetone kinase), GLO1 (oxidoreductase), PGM2 (phosphoglucomutase), and GCY1, (phosphatidylserine synthase) follow similar patterns. These genes, in part, produce and regulate glycerol; essential for growth under osmotic stress [18–20]. GPD1, DAK1 and GCY1 are thought to be co-transcribed . Glycerol is synthesized from glucose, and a rate-limiting enzyme in glycerol biosynthesis is the GPD1 gene product, which catalyzes reduction of dihydroxyacetone phosphate to glycerol 3-phosphate [14, 21, 22]. Although the halometric increase of GPD1 and DAK1 has been noted , the increase of GPD1 is proportional to the perturbation up to 1.2 M NaCl, while the abundance of DAK1 plateaus between 0.5 and 0.7 M NaCl (see Figs. 2C &2D). GLO1 detoxifies a cytotoxic metabolite derived from dihydroxyacetone phosphate which increases during glycerol production . PGM2 converts glucose-1 phosphate and glucose-6 phosphate  and is an important factor in glycolysis (eventually leading to glycerol production) as well as the production of other compatible solutes such as trehalose during salt stress.
The ability of a system, in this case the set of selected genes and pathways, to adjust to environmental changes has been described as resilience [26–29], and the ability to withstand the effects imposed by the changes – similar to the concept of buffering capacity – has been described as resistance [25, 28]. Resilience at each perturbation magnitude (0.5, 0.7, 1.0, and 1.2 M NaCl) was determined by the time required for either the aggregate set of genes or biochemical pathway to approach pre-perturbed displacement  or the new steady state of S. cerevisiae gene expression following exposure to high salt concentrations . Resistance was estimated as the maximum displacement of the response envelope . In terms of the aggregate set of genes and pathways, a larger stress resulted in decreased resilience (see Figs. 2 &3) and less resistance.
Although our model determines the resilience and resistance of S. cerevisiae genes and pathways to elevated NaCl concentrations, this approach can be applied to measure the transcriptional response (positive or negative) to a range of environmental changes. This model is designed to quantify an overall response by determining the change in expression. In the terms of strain, or transcriptome response, down-regulated and up-regulated genes can be represented as a positive response value, for instance one gene that is up-regulated and another equally down-regulated will not cancel each other out, but the response value will be amplified.
Examining the response over a prolonged exposure to stress, rather than a snapshot approach, allows the quantification of the response to increased stress. This approach allows a direct comparison of the contribution of each gene or pathway on the aggregate transcriptome. The relationship between fitness and environmental conditions is inherently a multigenic phenomenon requiring this type of an approach. Understanding the relationship between stress, response, and resulting stability at the level of the transcriptome could provide predictive capabilities for other areas of biological research, such as engineering biostimulation for de-toxification of pollutants, and as a predictive indicator for adaptation to changing environments.
This work was funded in part by the Center for Microbial Ecology and Intramural Research Grant Program at Michigan State University. Funding for this project was also provided by NSF grant DEB-021212487 to MEP and a USU Center for Integrated BioSystems research grant.
We are thankful to Mr. Sean Spellman for help in sample collection. We are also thankful to Dr. Annette P. Thelen, Research Technology Support Facility at Michigan State University for help with sample preparation and quantitative real-time PCR.
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