This project was supported by grants of the Consejo Nacional de Ciencia y Tecnolog&#237;a (grant number 293940) and Fundaci&#243;n TELMEX-TELCEL (grant number 162005585), both to IKOM.

1. Culture Media and Inoculum Preparation
<ol>
	<li>Prepare 100 mL of 2% yeast extract-peptone-dextrose (YPD) liquid medium (add 1 g of yeast extract, 2 g of casein peptone, and 2 g of glucose to 100 mL of distilled water). Dispense 3 mL of the media into 15 mL sterilizable conical tubes. Autoclave the media for 15 min at 121 &#176;C and 1.5 psi. 
	NOTE: The media can be stored for up to one month at 4&#8211;8 &#176;C.</li>
	<li>Inoculate a conical tube filled with 3 mL of cool sterile 2% YPD broth with 250 &#956;L of S. cerevisiae cells preserved in glycerol at -20 &#176;C. Incubate overnight in an orbital shaker at 30 &#176;C with agitation at 200 rpm.</li>
	<li>Streak a Petri dish (60 x 15 mm2) filled with sterile 2% YPD agar (add 10 g of yeast extract, 20 g of casein peptone, 20 g of glucose, and 20 g of bacteriological agar to 1,000 mL of distilled water) with the cells grown in the conical tube using a sterile loop. Incubate the Petri dish at 30 &#176;C until the isolated colonies show growth.</li>
	<li>Prepare the pre-inoculum by inoculating a single isolated colony into a conical tube filled with 10 mL of cool sterile 2% YPD broth and incubate it overnight at 30 &#176;C with continuous agitation at 200 rpm.</li>
</ol>
2. Culture Media and Growth Curves in Microplate
<ol>
	<li>Prepare 10 mL of 1% YPD broth (add 0.1 g of yeast extract, 0.2 g of peptone, and 0.1 g of glucose to 10 mL of distilled water), 10 mL of 2% YPD broth (add 0.1 g of yeast extract, 0.2 g of peptone, and 0.2 g of glucose to 10 mL of distilled water), and 10 mL of 10% YPD broth (add 0.1 g of yeast extract, 0.2 g of peptone, and 1 g of glucose to 10 mL of distilled water). Autoclave these growth mediums for 15 min at 121 &#176;C and 1.5 psi. 
	NOTE: The culture media serves as a control of fermentative growth.</li>
	<li>Prepare 10 mL of 2% yeast extract-peptone-ethanol (YPE) broth and 10 mL of 2% yeast extract-peptone-glycerol (YPG) broth. For 2% YPE: add 0.1 g of yeast extract, 0.2 g of peptone, and 0.2 mL of ethanol to 10 mL of distilled water. For 2% YPG: add 0.1 g of yeast extract, 0.2 g of peptone, and 0.2 mL of glycerol to 10 mL of distilled water. Autoclave these growth mediums for 15 min at 121 &#176;C and 1.5 psi. 
	NOTE: Glycerol and ethanol are non-fermentable carbon sources that are exclusively metabolized by mitochondrial respiration. For this reason, they are used as a respiratory growth controls. In this step, the type and concentration of carbon source in the culture media may be changed to test the effects on growth phenotype using yeast extract-peptone (YP) broth (10 g of yeast extract and 20 g of casein peptone in 1,000 mL of distilled water) as a base. To test the effects of other nutrients besides carbon, synthetic complete (SC) medium is supplemented according to the aim of the experiment. The base for SC medium is 1.8 g of yeast nitrogen base without amino acids, 5 g of ammonium sulfate [(NH4)2SO4], 1.4 g of monosodium phosphate (NaH2PO4), and 2 g of drop-out mix in 1,000 mL of distilled water. Supplement the media with a carbon source and additional nitrogen source in the needed concentrations.</li>
	<li>Add 145 &#956;L of suitable culture media for the experimental design to each well of a sterile microplate (10 x 10-well) with a lid and inoculate them with 5 &#956;L of the pre-inoculum. 
	NOTE: If the aim is to screen the influence of substances/compounds on the energetic metabolism of the yeast, that substance/compound should be added during this step. Also, any type of microplate with a lid should be useful.</li>
	<li>Incubate the multi-well plate in a microplate reader at 30 &#176;C with constant agitation at 200 rpm for 48 h. Measure the optical density (OD) at 600 nm every 30 or 60 min. 
	NOTE: If the microplate reader does not have the option to incubate the microplate, it may be incubated in an orbital shaker at the same conditions between every measurement of the OD.</li>
</ol>
3. Growth Curves in Shaken Conical Flasks
<ol>
	<li>Add 4.8 mL of the suitable culture media to 20 mL conical flasks and autoclave. Inoculate each flask with 200 &#956;L of the pre-inoculum culture at OD600nm ~2 in cool, sterile 2% YPD broth. Incubate them at 30 &#176;C with constant agitation at 250 rpm for 24 h. 
	NOTE: If the incubation period is higher than 24 h, add 12 mL of the suitable culture media to 50 mL conical flasks and autoclave. Inoculate them with 500 &#956;L of the pre-inoculum. It is also important to consider the fermentative growth controls (YP media with 1%, 2%, or 10% glucose) and respiratory growth controls (YP media with 2% glycerol or ethanol).</li>
	<li>Take 100 &#956;L of the shaken conical flask culture and dilute it in 900 &#956;L of distilled water in a 1 mL spectrophotometer cuvette, and gently mix by pipetting. Measure the OD at 600 nm using a spectrophotometer every 2 h. To obtain the real OD, multiply the result by ten.</li>
</ol>
4. Data Processing and Kinetic Parameters Calculation
<ol>
	<li>Create a new project file in the statistical package software. In the section of &#34;New table and graph&#34;, choose the &#34;XY&#34; option.
	<ol>
		<li>Select the option: &#34;Start with an empty data table&#34; in the &#34;Sample data&#34; section.</li>
		<li>Select the &#34;Points and connecting line&#34; graph type. Leave the &#34;X&#34; section unselected in the segment of &#34;Subcolumns for replicates or error values&#34;, and in the &#34;Y&#34; section choose the option: &#34;Enter (10) replicate values in side by side subcolumns and plot Mean and Error SD&#34;. Click the create button. 
		NOTE: The table format has one X column and several Y columns, each with the 10 subcolumns chosen previously.</li>
	</ol>
	</li>
	<li>Write the time at which OD measurements were taken in the X column (e.g., 0, 30, 60, 90 min or 0, 1, 1.5, 2 h). In the Y columns, write the OD values obtained from the cultures.</li>
	<li>Identify the exponential growth phase transforming the Y column data into logarithms. Click the &#34;Analyze&#34; button, choose the &#34;Transform&#34;&#160;analysis, select Y values using Y = Log(Y). Go to the &#34;Gallery of results&#34;, select &#34;Transform&#34;&#160;data table, click the &#34;Analyze&#34; button, and choose the &#34;Linear regression&#34;&#160;analysis. Exclude the OD values that do not follow a linear behavior. To exclude values, select them in the data table and type &#34;Ctrl + E&#34;. 
	NOTE: It is important to exclude values that not follow the exponential phase since the exponential growth equation fits well with the exponential phase.</li>
	<li>In the &#34;Data Tables gallery&#34;, select the data table &#34;Data 1&#34;. Click the &#34;Analyze&#34; button and choose the &#34;Nonlinear regression (curve fit)&#34;&#160;analysis among the &#34;XY analyses&#34;.</li>
	<li>Select the data sets to analyze and click the &#34;OK&#34; button. In the &#34;Fit&#34;&#160;tab choose the &#34;Exponential equation&#34; section and select the &#34;Exponential growth equation&#34;&#160;( , where X is cell concentration, X0 is cell concentration at zero time, &#956; is specific growth rate, and t is time). Use the &#34;Least squares fit&#34; as a fitting method and leave unselected the interpolate section. Click the &#34;OK&#34; button. 
	NOTE: The tab with the nonlinear fit results is in the &#34;Gallery of results&#34;. The software calculates doubling time (Dt) and specific growth rate (&#956;). The software shows &#956; as K in the results tab.</li>
	<li>Open a new project file, and in the &#34;New table and graph&#34;&#160;section, select the column choice. Choose the option &#34;Start with an empty data table&#34; and select the desired graph. Choose to plot the mean with the SD. Select the &#34;Create&#34; button.</li>
	<li>Copy the &#956; or Dt values from the nonlinear fit results tab that is in the &#34;Gallery of results&#34;&#160;and paste them in a column. Finally, select the &#34;Analyze&#34; button and choose the desired analysis in the&#160;&#34;Column analyses&#34;&#160;section to compare the kinetic parameters of the different nutrimental conditions. 
	NOTE: The kinetic parameters obtained from the fermentative growth controls (YP media with 1%, 2%, or 10% glucose) and respiratory growth controls (YP media with 2% glycerol or ethanol) helps establish the threshold of fermentative and mitochondrial respiration, respectively. Comparing the kinetic parameters from the nutritional condition and substance/compound assayed with the respiratory and fermentative growth help identify possible effect on respiratory or fermentative metabolism.</li>
</ol>

<ol><li>Parrella, E., Longo, V. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+chronological+life+span+of+Saccharomyces+cerevisiae+to+study+mitochondrial+dysfunction+and+disease%5BTitle%5D)+AND+%22Methods%22%5BJournal%5D)">The chronological life span of Saccharomyces cerevisiae to study mitochondrial dysfunction and disease.</a> Methods. 46 (4), 256-262 (2008).</li>
<li>Rosas Lemus, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+glycolysis-derived+hexose+phosphates+in+the+induction+of+the+Crabtree+effect%5BTitle%5D)+AND+%22Journal+of+Biological+Chemistry%22%5BJournal%5D)">The role of glycolysis-derived hexose phosphates in the induction of the Crabtree effect.</a> Journal of Biological Chemistry. , (2018).</li>
<li>Xu, X. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Warburg+effect+or+reverse+Warburg+effect%3F+A+review+of+cancer+metabolism%5BTitle%5D)+AND+%22Oncology+Research+and+Treatment%22%5BJournal%5D)">Warburg effect or reverse Warburg effect? A review of cancer metabolism.</a> Oncology Research and Treatment. 38 (3), 117-122 (2015).</li>
<li>De Deken, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Crabtree+effect%3A+a+regulatory+system+in+yeast%5BTitle%5D)+AND+%22Journal+of+General+Microbiology%22%5BJournal%5D)">The Crabtree effect: a regulatory system in yeast.</a> Journal of General Microbiology. 44 (2), 149-156 (1966).</li>
<li>Hagman, A., Sall, T., Piskur, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Analysis+of+the+yeast+short-term+Crabtree+effect+and+its+origin%5BTitle%5D)+AND+%22The+FEBS+Journal%22%5BJournal%5D)">Analysis of the yeast short-term Crabtree effect and its origin.</a> The FEBS Journal. 281 (21), 4805-4814 (2014).</li>
<li>Hammad, N., Rosas-Lemus, M., Uribe-Carvajal, S., Rigoulet, M., Devin, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Crabtree+and+Warburg+effects%3A+Do+metabolite-induced+regulations+participate+in+their+induction%3F%5BTitle%5D)+AND+%22Biochim+Biophys+Acta%22%5BJournal%5D)">The Crabtree and Warburg effects: Do metabolite-induced regulations participate in their induction?</a> Biochim Biophys Acta. 1857 (8), 1139-1146 (2016).</li>
<li>Keating, E., Martel, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Antimetabolic+Effects+of+Polyphenols+in+Breast+Cancer+Cells%3A+Focus+on+Glucose+Uptake+and+Metabolism%5BTitle%5D)+AND+%22Frontiers+in+Nutrition%22%5BJournal%5D)">Antimetabolic Effects of Polyphenols in Breast Cancer Cells: Focus on Glucose Uptake and Metabolism.</a> Frontiers in Nutrition. 5, 25(2018).</li>
<li>Pfeiffer, T., Morley, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+evolutionary+perspective+on+the+Crabtree+effect%5BTitle%5D)+AND+%22Frontiers+in+Molecular+Biosciences%22%5BJournal%5D)">An evolutionary perspective on the Crabtree effect.</a> Frontiers in Molecular Biosciences. 1, 17(2014).</li>
<li>Olivares-Marin, I. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Interactions+between+carbon+and+nitrogen+sources+depend+on+RIM15+and+determine+fermentative+or+respiratory+growth+in+Saccharomyces+cerevisiae%5BTitle%5D)+AND+%22Applied+Microbiology+and+Biotechnology%22%5BJournal%5D)">Interactions between carbon and nitrogen sources depend on RIM15 and determine fermentative or respiratory growth in Saccharomyces cerevisiae.</a> Applied Microbiology and Biotechnology. 102 (10), 4535-4548 (2018).</li>
<li>Monod, J. <a target="_blank" href="https://www.annualreviews.org/doi/10.1146/annurev.mi.03.100149.002103">The growth of bacterial cultures.</a> Annual Review of Microbiology. 3 (1), 371-394 (1949).</li>
<li>Cui, S., Xu, S. <a target="_blank" href="https://www-sciencedirect-com.remotexs.ntu.edu.sg/science/article/pii/S0022247X07002260">Analysis of mathematical models for the growth of tumors with time delays in cell proliferation.</a> Journal of Mathematical Analysis and Applications. 336 (1), 523-541 (2007).</li>
<li>Benzekry, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Classical+mathematical+models+for+description+and+prediction+of+experimental+tumor+growth%5BTitle%5D)+AND+%22Public+Library+of+Science+Computational+Biology%22%5BJournal%5D)">Classical mathematical models for description and prediction of experimental tumor growth.</a> Public Library of Science Computational Biology. 10 (8), e1003800(2014).</li>
<li>Ramos-Gomez, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Resveratrol+induces+mitochondrial+dysfunction+and+decreases+chronological+life+span+of+Saccharomyces+cerevisiae+in+a+glucose-dependent+manner%5BTitle%5D)+AND+%22Journal+of+Bioenergetics+and+Biomembranes%22%5BJournal%5D)">Resveratrol induces mitochondrial dysfunction and decreases chronological life span of Saccharomyces cerevisiae in a glucose-dependent manner.</a> Journal of Bioenergetics and Biomembranes. 49 (3), 241-251 (2017).</li>
<li>Madrigal-Perez, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Energy-dependent+effects+of+resveratrol+in+Saccharomyces+cerevisiae%5BTitle%5D)+AND+%22Yeast%22%5BJournal%5D)">Energy-dependent effects of resveratrol in Saccharomyces cerevisiae.</a> Yeast. 33 (6), 227-234 (2016).</li>
<li>Peleg, M., Corradini, M. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Microbial+growth+curves%3A+what+the+models+tell+us+and+what+they+cannot%5BTitle%5D)+AND+%22Critical+Reviews+in+Food+Science+and+Nutrition%22%5BJournal%5D)">Microbial growth curves: what the models tell us and what they cannot.</a> Critical Reviews in Food Science and Nutrition. 51 (10), 917-945 (2011).</li>
</ol>

The authors have nothing to disclose.

A long time has passed since J. Monod10 expressed that the study of the growth of bacterial cultures is the basic method of microbiology. The advent of the molecular tools delays the usage and study of the growth as a technique. Despite the complexity of growth which involves numerous interrelated processes, its underlying mechanisms can be described by using mathematical models11. This is a robust approach that can be used as a complementary tool to elucidate the most intricate molecular mechanisms12.
To obtain reliable results from this method, it is important to consider the following critical steps. Agitation at 250 rpm is critical to achieving good growth in low carbon sources that exert respiratory growth, since when using lower agitation speeds (150&#8211;180 rpm) we observed reduced growth in respiratory conditions. It is important to initiate the protocol employing a fresh stock, and it is also essential to maintain a uniform phenotype in the assays. Growth kinetics threshold values vary among strains and must be validated according to the condition to be used in the assays. The GraphPad Prism software is proposed because the exponential growth equation is already preloaded; however, any other software is suitable, provided it allows programming of the exponential growth equation. In the microplate, it is recommended to include three wells with 150 &#956;L of growth media without inoculum &#8212; this is a control that indicates if the microplate wells have suffered contamination.
It is important to indicate that plotting the OD data on a linear scale may make it hard to determine the logarithmic growth phase. This problem can be overcome by plotting the OD data in a log scale so that lag phase and logarithmic growth phase are clearly visible.
This protocol is only used to screen a phenotype, so the specific effects on fermentation and mitochondrial respiration must be confirmed. We recommend the quantification of mitochondrial respiration by oxygen consumption assays13,14, while fermentation can be determined by the accumulation of byproducts such as acetate, succinate, glycerol, and ethanol9.
S. cerevisiae growth has served as a means for understanding molecular mechanisms and phenotypes, with methods from screening to testing assays. Nonetheless, numerous experimental designs in the literature use yeast growth only as a qualitative parameter (e.g., plate dilutions or growth curves), so the employment of these techniques neglects valuable evidence. For example, the oxygen available in agar plates used for counting microbial population or making serial-dilutions is limited, complicating growth measurement under respiratory conditions. Besides, when two or more cells remain attached to each other, only one colony is observed. Another limitation is the use of growth curves as a visual result to highlight differences in growth, which may leave aside facts about subtle differences. Therefore, the translation of growth information to quantitative parameters permits the achievement of precise data about the exerted phenotype. Nevertheless, the mathematical representation of growth is a complicated scientific enterprise. Most studies examine full growth (lag, log, and stationary phases) of the microorganisms, obtaining high-order polynomial equations that only serve one environmental condition. Despite the relative simplicity when calculating coefficients of these equations by linear regression, it is difficult to assign them to an inherent biological meaning15. The analysis of growth by separating growth phases is mathematically reliable and straightforward. An example of this is the exponential growth equation, which is based on first-order kinetics and fits well with a short lag phase and exponential phase.
Finally, studying the log phase using the exponential growth equation is a consistent method to understand the influence of mitochondrial respiration or fermentation on a specific compound or environment. As a proof of concept, quantification of growth in the exponential phase demonstrated that RSV specifically affects respiratory growth13,14. Here, growth served as a valuable tool to identify that RIM15 deletion likely improves fermentative metabolism by partial inhibition of the respiration capacity in S. cerevisiae9. These data corroborate that observing growth allows for the study of fermentative and respiratory metabolisms and is a simple and reliable method. Hence, this approach allows for screening the effects of a specific substance on mitochondrial respiration or fermentation.

Saccharomyces cerevisiae growth has served as a valuable tool to identify dozens of physiological and molecular mechanisms. Growth is measured primarily by three methods: serial dilutions for spot testing, colony-forming unit counting, and growth curves. These techniques can be used alone or in combination with a variety of substrates, environmental conditions, mutants, and chemicals to investigate specific responses or phenotypes.
Mitochondrial respiration is a biological process in which growth kinetics has been successfully applied for discovering unknown mechanisms. In this case, the supplementation of growth media with non-fermentable carbon sources such as glycerol, lactate, or ethanol (which are exclusively metabolized by mitochondrial respiration), as the sole carbon and energy source allows for evaluating the respiratory growth, which is important to detect perturbations in oxidative phosphorylation activity1. On the other hand, it is complicated to use growth kinetic models as a method for deciphering the mechanisms behind fermentation.
The study of fermentation and mitochondrial respiration is essential to elucidate the molecular mechanisms behind certain phenotypes such as the Crabtree and Warburg effects2,3. The Crabtree effect is characterized by an increase of glycolytic flux, repression of mitochondrial respiration, and establishment of fermentation as the primary pathway to generate ATP in the presence of high concentrations of fermentable carbohydrates (&#62;0.8 mM)4,5. The Warburg effect is metabolically analog to the Crabtree effect, with the difference being that in mammalian cells, the main product of fermentation is lactate6. Indeed, the Warburg effect is exhibited by a variety of cancer cells, triggering glucose uptake and consumption even in the presence of oxygen7. Thereby, studying the molecular basis of the switch from respiration to fermentation in the Crabtree effect has both biotechnological repercussions (for ethanol production) and potential impacts in cancer research.
S. cerevisiae growth may be a suitable tool to study the Crabtree and Warburg effects. This idea is based on the fact that in the yeast exponential phase, the central pathways used to produce ATP are mitochondrial respiration and fermentation, which are essential to sustain growth. For instance, the growth of S. cerevisiae is intimately related to the function of ATP-generating pathways. In S. cerevisiae, the mitochondrial respiration produces approximately 18 ATP molecules per glucose molecule, whereas fermentation only generates 2 ATP molecules, hence it is expected that the growth rate has tight links with the metabolic pathways producing ATP8. In this regard, when fermentation is the principal route to generate ATP, the yeast compensates for the low ATP production by increasing the rate of glucose uptake. On the contrary, the glucose consumption by yeast cells that use mitochondrial respiration as the main ATP source is low. This indicates that it is important for the yeast to sense carbohydrate availability before determining how ATP will be generated. Therefore, glucose availability plays an important role in the switch between fermentation and mitochondrial respiration in S. cerevisiae. In the presence of high quantities of glucose, the yeast prefers fermentation as the central route to generate ATP. Interestingly, when the yeast is fermenting, the specific growth rate is maintained at its maximum. On the other hand, under low levels of glucose, S. cerevisiae produces ATP using mitochondrial respiration, maintaining lower growth rates. Thereby, variation in the concentration of glucose and the use of other carbon sources induce changes in the yeast&#39;s preference between fermentative and respiratory growth. By taking into account this fact with the exponential growth equation, one can obtain the biological meaning of kinetic parameters such as doubling time (Dt) and specific growth rate (&#181;). For example, lower &#181; values were found when the yeast uses mitochondrial respiration as the primary pathway. On the contrary, under conditions that favor fermentation, higher &#181; values were found. This methodology may be used to measure the probable mechanisms of any chemicals affecting fermentation and mitochondrial respiration in S. cerevisiae.
The objective of this paper is to propose a method based on growth kinetics for screening the effects of a given substance/compound on mitochondrial respiration or fermentation.

<table><tbody><tr><td>Orbital Shaker</td><td>Thermo Scientific</td><td>4353</td><td>For inoculum incubation or conical fask cultures</td></tr><tr><td>Bioscreen&amp;nbsp;</td><td>Growth curves</td><td>C MBR</td><td>For batch cultures in microplates</td></tr><tr><td>Glucose</td><td>Sigma&amp;nbsp;</td><td>G7021</td><td>For YPD broth preparation</td></tr><tr><td>Peptone from casein, enzymatic digest</td><td>Sigma&amp;nbsp;</td><td>82303</td><td>For YPD broth preparation</td></tr><tr><td>Yeast extract</td><td>Sigma&amp;nbsp;</td><td>09182-1KG-F</td><td>For YPD broth preparation</td></tr><tr><td>Bacteriological Agar</td><td>Sigma&amp;nbsp;</td><td>A5306</td><td>For YPD agar preparation</td></tr><tr><td>NaH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma&amp;nbsp;</td><td>S8282</td><td>For SC broth preparation</td></tr><tr><td>(NH&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma&amp;nbsp;</td><td>A4418</td><td>For SC broth preparation</td></tr><tr><td>Yeast nitrogen base without amino acids and ammonium sulfate</td><td>Sigma&amp;nbsp;</td><td>Y1251</td><td>For SC broth preparation</td></tr><tr><td>Yeast synthetic drop-Out medium supplements</td><td>Sigma&amp;nbsp;</td><td>Y1501</td><td>For SC broth preparation</td></tr><tr><td>Ammonium sulfate granular</td><td>J.T. Baker</td><td>0792-R</td><td>For medium supplementation example</td></tr><tr><td>Resveratrol</td><td>Sigma&amp;nbsp;</td><td>R5010</td><td>For medium supplementation example</td></tr><tr><td>Galactose</td><td>Sigma&amp;nbsp;</td><td>G8270</td><td>For medium supplementation example</td></tr><tr><td>Sucrose</td><td>Sigma&amp;nbsp;</td><td>S7903</td><td>For medium supplementation example</td></tr><tr><td>Absolut ethanol</td><td>Merck</td><td>107017</td><td>For medium supplementation example</td></tr><tr><td>Glycerol</td><td>J.T. Baker</td><td>2136-01</td><td>For medium supplementation example</td></tr><tr><td>GraphPad Prism</td><td>GraphPad Software</td><td></td><td>For data analysis</td></tr><tr><td>Honeycomb microplates</td><td>Thermo Scientific</td><td>9502550</td><td>For microplate cultures</td></tr></tbody></table>

saccharomyces cerevisiae exponential growth kinetics in batch culture to analyze respiratory and fermentative metabolism

Saccharomyces cerevisiae cells in the exponential phase sustain their growth by producing ATP through fermentation and/or mitochondrial respiration. The fermentable carbon concentration mainly governs how the yeast cells generate ATP; thus, the variation in fermentable carbohydrate levels drives the energetic metabolism of S. cerevisiae. This paper describes a high-throughput method based on exponential yeast growth to estimate the effects of concentration changes and nature of the carbon source on respiratory and fermentative metabolism. The growth of S. cerevisiae is measured in a microplate or shaken conical flask by determining the optical density (OD) at 600 nm. Then, a growth curve is built by plotting OD versus time, which allows identification and selection of the exponential phase, and is fitted with the exponential growth equation to obtain kinetic parameters. Low specific growth rates with higher doubling times generally represent a respiratory growth. Conversely, higher specific growth rates with lower doubling times indicate fermentative growth. Threshold values of doubling time and specific growth rate are estimated using well-known respiratory or fermentative conditions, such as non-fermentable carbon sources or higher concentrations of fermentable sugars. This is obtained for each specific strain. Finally, the calculated kinetic parameters are compared with the threshold values to establish whether the yeast shows fermentative and/or respiratory growth. The advantage of this method is its relative simplicity for understanding the effects of a substance/compound on fermentative or respiratory metabolism. It is important to highlight that growth is an intricate and complex biological process; therefore, preliminary data from this method must be corroborated by the quantification of oxygen consumption and accumulation of fermentation byproducts. Thereby, this technique can be used as a preliminary screening of compounds/substances that may disturb or enhance fermentative or respiratory metabolism.

Growth curves can be used to preliminarily discriminate between respiratory and fermentative phenotypes in the S. cerevisiae yeast. Therefore, we performed batch cultures of S. cerevisiae (BY4742) with different glucose concentrations that have been reported to induce fermentative growth: 1%, 2%, and 10% (w/v)9. Cultures showing a fermentative phenotype have a small lag phase and an exponential phase with a high growth rate (Figure 1). Ethanol, glycerol, and lactate are the carbon sources that can be metabolized only through respiration; thus, we performed cultures of the yeast using those carbon sources. Cultures that obtain energy mainly by oxidative phosphorylation showed a longer lag phase and slow rate of growth during the exponential phase (Figure 2).
Analysis of the growth curves provides only qualitative information; hence, it is important to calculate the kinetic parameters to obtain quantitative information. We calculated the Dt and &#956; using the exponential growth equation (Figure 3). We set a threshold for respiratory growth values at Dt &#8805;11.5 h and &#956; &#8804;0.059/h. The threshold for fermentative growth was set at Dt &#8804;6.5 h and &#956; &#8805;0.149/h (Figure 3).
To prove the usefulness of this screening tool we designed three experiments, with the first for evaluating the effects of different resveratrol (RSV) concentrations on S. cerevisiae BY4742 cells with different energetic status (Figure 4) in shaken flasks. The second experiment aimed to detect the effects of different ammonium sulfate (NH4+) concentrations on the energy metabolism of S. cerevisiae BY4742 (Figure 5) in a microplate. Finally, the third aimed to illustrate how changes in carbon source affect fermentative growth in two different S. cerevisiae strains (W303 and the industrial WLP530 used for beer fermentation) (Figure 6). In the experiment using RSV, the cell energetic status was modified by varying glucose concentration. At 10% glucose, all RSV concentrations tested did not change the respiro-fermentative phase of the yeast but did decrease the respiratory phase (during the diauxic shift, S. cerevisiae metabolized the ethanol produced during the exponential phase by oxidative phosphorylation). The &#956; values confirmed that under all conditions tested, S. cerevisiae showed fermentative behavior, contrary to its phenotype when grown in 2% glycerol (condition used as a respiratory control) (Figure 4a). When cells were energized with 1% glucose only, the &#956; values confirmed that at 0.1, 1, 10, and 100 &#181;M RSV, fermentative behavior in the respiro-fermentative phase was not affected. However, a decrease in the respiratory phase during the diauxic shift was observed. Moreover, at a concentration of 1,000 &#181;M, RSV completely inhibited cell growth.
In the second experiment, cells were supplemented with 10% glucose, a concentration considered sufficient to induce the Crabtree effect in S. cerevisiae. Hence, we could observe the effect promoted by the supplementation of different concentrations of NH4+ in fermentative metabolism. Dt values suggested that 0.13, 0.66, and 1.99% NH4+ favor fermentative metabolism, and it can be observed in the growth curves that these concentrations extended the culture&#39;s exponential phase. Nonetheless, at 3.31% mM NH4+, an increment in the Dt value showed that this concentration induces a respiro-fermentative metabolism. This phenotype showed an extended lag phase in the growth curves and slower growth rate in the exponential phase (Figure 5).
In the third experiment, cells of two different S. cerevisiae strains (W303 and WLP530) were grown in different concentrations of sucrose or galactose to test if the effects of the carbon source on Dt values were strain-dependent. As a fermentative control, both strains were grown in 2% glucose, and Dt values were calculated to set a threshold for fermentative growth. The fermentative Dt values for the strains were different, with &#8804;3.25 h for the W303 strain and &#8804;6.84 h for the WLP530 strain. Therefore, it is important to highlight the necessity for validating the Dt threshold for the different strains used. Moreover, when sucrose was used as the carbon source, a fermentative phenotype was observed at 2% and 10% in both strains. However, when galactose was used, the strain W303 did not show fermentative behavior in any of the concentrations tested, while the WLP530 strain showed a fermentative phenotype at 2% galactose. Interestingly, the W303 strain grew in all concentrations of both carbon sources tested. Though, the WLP530 strain did not show growth at the lowest concentration of carbon used (0.01%). This may be because WLP530 is used in the beer industry and the carbon concentrations to which it is exposed are generally much higher (Figure 6).
Altogether, the data from these three experiments prove that growth curves and kinetic parameters are useful for the preliminary discrimination between respiratory and fermentative growth in S. cerevisiae. Also, it is important to highlight that this is a versatile tool that may be used in different aspects of energy metabolism research.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58192/58192fig1.jpg" /> 
Figure 1: Effect of different glucose concentrations on S. cerevisiae growth phenotype. Growth curves were constructed by measuring the optical density at 600 nm every 30 min for 48 h. When S. cerevisiae cells ferment, cultures showed a short lag phase and fast growth rate during the exponential phase. The respiro-fermentative phase could be followed by a growth deceleration phase (respiratory phase), where the ethanol produced by fermentation is metabolized using the oxidative phosphorylation pathway, then the stationary phase is reached. Data are presented as the mean &#177; standard error. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58192/58192fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58192/58192fig2.jpg" /> 
Figure 2: Effect of respirable carbon sources on S. cerevisiae growth phenotype. Growth curves were constructed by measuring the optical density at 600 nm every 30 min for 48 h. Cells of S. cerevisiae showed a prolonged lag phase and slow growth rate during the exponential phase and generally did not show a diauxic shift. Data are presented as the mean &#177; standard error. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58192/58192fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58192/58192fig3.jpg" /> 
Figure 3: Effect of carbon source and its concentration on S. cerevisiae kinetic parameters. (a) Dt values of S. cerevisiae BY4742 growth under different carbon sources; (b) &#956; values of S. cerevisiae BY4742 growth under different concentrations of diverse carbon sources. Data are presented as the mean &#177; standard deviation. Statistical analyses were performed using a one-way ANOVA followed by a Dunnett&#39;s test (*p &#60;0.01 vs. 2% glucose). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58192/58192fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58192/58192fig4.jpg" /> 
Figure 4: Effect of resveratrol supplementation on different cell energy statuses. (a) Growth phenotype and &#956; values using 10% glucose; (b) growth phenotype and &#956; values using 1% glucose. Data from growth curves are presented as the mean &#177; standard error, and &#956; values are presented as the mean &#177; standard deviation. Statistical analyses for &#956; values were performed using a one-way ANOVA followed by a Dunnett&#39;s test [*p &#60;0.01 vs. respiratory control with 2% glycerol (RC)]. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58192/58192fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58192/58192fig5.jpg" /> 
Figure 5: Effect of ammonium sulfate supplementation on S. cerevisiae energy metabolism. Growth curves were constructed by measuring the optical density at 600 nm every 30 min for 48 h. Data from growth curves are presented as the mean &#177; standard error, and &#956; values are presented as the mean &#177; standard deviation. Statistical analyses for Dt values were performed using a one-way ANOVA followed by a Dunnett&#39;s test [*p &#60;0.01 vs. respiratory control with 2% glycerol (RC)]. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58192/58192fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58192/58192fig6.jpg" /> 
Figure 6: Effect of galactose and sucrose concentration in the fermentative metabolism of Saccharomyces cerevisiae. (a) Dt values for the W303 lab strain and (b) Dt values for the WLP530 industrial strain. Data are presented as the mean &#177; standard deviation. Statistical analyses for Dt values were performed using a one-way ANOVA followed by a Dunnett&#39;s test [*p &#60;0.01 vs. 2% glucose (fermentative control)]. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58192/58192fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism at JoVE.com

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

Here we present a protocol to estimate the respiratory and fermentative metabolism by fitting the exponential growth of Saccharomyces cerevisiae to the exponential growth equation. Calculation of the kinetic parameters allows for the screening of influences of substances/compounds on fermentation or mitochondrial respiration.

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

Saccharomyces cerevisiae cells in the exponential phase sustain their growth by producing ATP through fermentation and/or mitochondrial respiration. The ...

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Nanyang Technological University

Research

JoVE Journal

Biology

41.9K Views.  Universidad Autónoma de Querétaro. Deze methode kan helpen bij het beantwoorden van belangrijke vragen op biotechnologische en biomedische gebieden, zoals wat de moleculaire basis zijn achter de Warburg en de Crabtree-effecten. Het belangrijkste voordeel van deze techniek is dat het de hoge doorvoer studie van de effecten van bepaalde stoffen in de luchtwegen en fermentatieve metabolisme mogelijk maakt. Begin met het inenten van een 15-milliliter conische buis met drie milliliter koele, steriele 2%gist extract peptone dextrose...

Video: Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and chronological life spans are two established paradigms used to study aging in yeast. Replicative aging is defined as the number of daughter cells a single yeast mother cell produces before senescence; chronological aging is defined by the length of time cells can survive in a non-dividing, quiescence-like state 2. We have developed a high-throughput method for quantitative measurement of chronological life span. This method involves aging the cells in a defined medium under agitation and at constant temperature. At each age-point, a sub-population of cells is removed from the aging culture and inoculated into rich growth medium. A high-resolution growth curve is then obtained for this sub-population of aged cells using a Bioscreen C MBR machine. An algorithm is then applied to determine the relative proportion of viable cells in each sub-population based on the growth kinetics at each age-point. This method requires substantially less time and resources compared to other chronological lifespan assays while maintaining reproducibility and precision. The high-throughput nature of this assay should allow for large-scale genetic and chemical screens to identify novel longevity modifiers for further testing in more complex organisms.

Chronological aging in yeast refers to the loss of cell viability associated with time in stationary phase. Here we describe a high-throughput method for quantitatively determining yeast chronological life span.

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and ...

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within a cell, tissue, organ, biological fluid, or organism. Metabolomics traditionally focuses on water-soluble metabolites. The water-soluble metabolome is the final product of a complex cellular network that integrates various genomic, epigenomic, transcriptomic, proteomic, and environmental factors. Hence, the metabolomic analysis directly assesses the outcome of the action for all these factors in a plethora of biological processes within various organisms. One of these organisms is the budding yeast Saccharomyces cerevisiae, a unicellular eukaryote with the fully sequenced genome. Because S. cerevisiae is amenable to comprehensive molecular analyses, it is used as a model for dissecting mechanisms underlying many biological processes within the eukaryotic cell. A versatile analytical method for the robust, sensitive, and accurate quantitative assessment of the water-soluble metabolome would provide the essential methodology for dissecting these mechanisms. Here we present a protocol for the optimized conditions of metabolic activity quenching in and water-soluble metabolite extraction from S. cerevisiae cells. The protocol also describes the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of the extracted water-soluble metabolites. The LC-MS/MS method of non-targeted metabolomics described here is versatile and robust. It enables the identification and quantification of more than 370 water-soluble metabolites with diverse structural, physical, and chemical properties, including different structural isomers and stereoisomeric forms of these metabolites. These metabolites include various energy carrier molecules, nucleotides, amino acids, monosaccharides, intermediates of glycolysis, and tricarboxylic cycle intermediates. The LC-MS/MS method of non-targeted metabolomics is sensitive and allows the identification and quantitation of some water-soluble metabolites at concentrations as low as 0.05 pmol/&#181;L. The method has been successfully used for assessing water-soluble metabolomes of wild-type and mutant yeast cells cultured under different conditions.

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol for the identification and quantitation of major classes of water-soluble metabolites in the yeast Saccharomyces cerevisiae. The described method is versatile, robust, and sensitive. It allows the separation of structural isomers and stereoisomeric forms of water-soluble metabolites from each other.

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within ...

Biochemistry

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

Bioengineering

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Studies using the Saccharomyces cerevisiae aging model have uncovered life span regulatory pathways that are partially conserved in higher eukaryotes1-2. The simplicity and power of the yeast aging model can also be explored to study DNA damage and genome maintenance as well as their contributions to diseases during aging. Here, we describe a system to study age-dependent DNA mutations, including base substitutions, frame-shift mutations, gross chromosomal rearrangements, and homologous/homeologous recombination, as well as nuclear DNA repair activity by combining the yeast chronological life span with simple DNA damage and mutation assays. The methods described here should facilitate the identification of genes/pathways that regulate genomic instability and the mechanisms that underlie age-dependent DNA mutations and cancer in mammals.

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Here we describe a set of DNA mutation assays that can be combined with the yeast chronological life span model to study the genes/pathways that regulate or contribute to genomic DNA instability during aging.

Studies using the Saccharomyces cerevisiae aging model have uncovered life span regulatory pathways that are partially conserved in higher eukaryotes1-2. ...

Measuring Yeast Mitochondrial Respiration While Maintaining Cellular Context

Mitochondrial Respiration Quantification in Yeast Whole Cells

Metabolism is mainly coordinated by cellular energy availability and environmental conditions. Assays for knowing how cells adapt energetic metabolism to different nutritional and environmental conditions give valuable information to elucidate molecular mechanisms. Oxidative phosphorylation is the primary source of ATP in most cells, and mitochondrial respiration activity is a key component of oxidative phosphorylation, maintaining mitochondrial membrane potential for ATP synthesis. Mitochondrial respiration is often studied in isolated mitochondria that are missing the cellular context. Here, we present a method for quantifying mitochondrial respiration in yeast-intact cells. This method applies to any yeast species, although it has been generally used for Saccharomyces cerevisiae cells. First, the yeast growth in specific conditions is tested. Then, cells are washed and resuspended in deionized water with a 1:1 ratio (w/v). Cells are then placed in an oximeter chamber with constant stirring, and a Clark electrode is used to quantify oxygen consumption. Some molecules are sequentially placed into the chamber and selected according to this effect on the electron transport chain or ATP synthesis. ATPase inhibitor oligomycin is first added to measure respiration coupled to ATP synthesis. Afterward, an uncoupler is used to measure the maximal respiratory capacity. Finally, a mix of electron transport chain inhibitors is added to discard oxygen consumption unrelated to mitochondrial respiration. Data are analyzed using a linear regression to obtain the slope, representing the oxygen consumption rate. The advantage of this method is that it is specific for yeast mitochondrial respiration, maintaining the cellular context. It is essential to highlight that inhibitors used in mitochondrial respiration quantification could vary between yeast species.

Mitochondrial respiration in yeast whole cells is a valuable indicator of cell bioenergetics. Here, we present a protocol to quantify this phenotype applicable to different yeast species.

Metabolism is mainly coordinated by cellular energy availability and environmental conditions. Assays for knowing how cells adapt energetic metabolism to ...

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are used as models in lipid metabolic research or for industrial lipid production. Lipid droplets are highly dynamic storage bodies and their cellular content represents a convenient readout of the lipid metabolic state. Fluorescence microscopy is a method of choice for quantitative analysis of cellular lipid droplets, as it relies on widely available equipment and allows analysis of individual lipid droplets. Furthermore, microscopic image analysis can be automated, greatly increasing overall analysis throughput. Here, we describe an experimental and analytical workflow for automated detection and quantitative description of individual lipid droplets in three different model yeast species: the fission yeasts Schizosaccharomyces pombe and Schizosaccharomyces japonicus, and the budding yeast Saccharomyces cerevisiae. Lipid droplets are visualized with BODIPY 493/503, and cell-impermeable fluorescent dextran is added to the culture media to help identify cell boundaries. Cells are subjected to 3D epifluorescence microscopy in green and blue channels and the resulting z-stack images are processed automatically by a MATLAB pipeline. The procedure outputs rich quantitative data on cellular lipid droplet content and individual lipid droplet characteristics in a tabular format suitable for downstream analyses in major spreadsheet or statistical packages. We provide example analyses of lipid droplet content under various conditions that affect cellular lipid metabolism.

Here, we present a MATLAB implementation of automated detection and quantitative description of lipid droplets in fluorescence microscopy images of fission and budding yeast cells.

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are ...

Immunology and Infection

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for using FISH to quantify the number of mRNAs in single yeast cells. Cells can be grown in any condition of interest and then fixed and made permeable. Subsequently, multiple single-stranded deoxyoligonucleotides conjugated to fluorescent dyes are used to label and visualize mRNAs. Diffraction-limited fluorescence from single mRNA molecules is quantified using a spot-detection algorithm to identify and count the number of mRNAs per cell. While the more standard quantification methods of northern blots, RT-PCR and gene expression microarrays provide information on average mRNAs in the bulk population, FISH facilitates both the counting and localization of these mRNAs in single cells at single-molecule resolution.

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

This protocol describes an experimental procedure for performing Fluorescence in situ Hybridization (FISH) for counting mRNAs in single cells at single-molecule resolution.

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for ...

Measurement of mRNA Decay Rates in Saccharomyces cerevisiae Using rpb1-1 Strains

mRNA steady state levels vary depending on environmental conditions. Regulation of the steady state accumulation levels of an mRNA ensures that the correct amount of protein is synthesized for the cell&#8217;s specific growth conditions. One approach for measuring mRNA decay rates is inhibiting transcription and subsequently monitoring the disappearance of the already present mRNA. The rate of mRNA decay can then be quantified, and an accurate half-life can be determined utilizing several techniques. In S. cerevisiae, protocols that measure mRNA half-lives have been developed and include inhibiting transcription of mRNA using strains that harbor a temperature sensitive allele of RNA polymerase II, rpb1-1. Other techniques for measuring mRNA half-lives include inhibiting transcription with transcriptional inhibitors such as thiolutin or 1,10-phenanthroline, or alternatively, by utilizing mRNAs that are under the control of a regulatable promoter such as the galactose inducible promoter and the TET-off system. Here, we describe measurement of S. cerevisiae mRNA decay rates using the temperature sensitive allele of RNA polymerase II. This technique can be used to measure mRNA decay rates of individual mRNAs or genome-wide.

Measurement of mRNA Decay Rates in Saccharomyces cerevisiae Using rpb1-1 Strains

The steady state level of specific mRNAs is determined by the rate of synthesis and decay of the mRNA. Genome-wide mRNA degradation rates or the decay rates of specific mRNAs can be measured by determining mRNA half-lives. This protocol focuses on measurement of mRNA decay rates in Saccharomyces cerevisiae.

mRNA steady state levels vary depending on environmental conditions. Regulation of the steady state accumulation levels of an mRNA ensures that the ...

Growth-based Determination and Biochemical Confirmation of Genetic Requirements for Protein Degradation in Saccharomyces cerevisiae

Regulated protein degradation is crucial for virtually every cellular function. Much of what is known about the molecular mechanisms and genetic requirements for eukaryotic protein degradation was initially established in Saccharomyces cerevisiae. Classical analyses of protein degradation have relied on biochemical pulse-chase and cycloheximide-chase methodologies. While these techniques provide sensitive means for observing protein degradation, they are laborious, time-consuming, and low-throughput. These approaches are not amenable to rapid or large-scale screening for mutations that prevent protein degradation. Here, a yeast growth-based assay for the facile identification of genetic requirements for protein degradation is described. In this assay, a reporter enzyme required for growth under specific selective conditions is fused to an unstable protein. Cells lacking the endogenous reporter enzyme but expressing the fusion protein can grow under selective conditions only when the fusion protein is stabilized (i.e. when protein degradation is compromised). In the growth assay described here, serial dilutions of wild-type and mutant yeast cells harboring a plasmid encoding a fusion protein are spotted onto selective and non-selective medium. Growth under selective conditions is consistent with degradation impairment by a given mutation. Increased protein abundance should be biochemically confirmed. A method for the rapid extraction of yeast proteins in a form suitable for electrophoresis and western blotting is also demonstrated. A growth-based readout for protein stability, combined with a simple protocol for protein extraction for biochemical analysis, facilitates rapid identification of genetic requirements for protein degradation. These techniques can be adapted to monitor degradation of a variety of short-lived proteins. In the example presented, the His3 enzyme, which is required for histidine biosynthesis, was fused to Deg1-Sec62. Deg1-Sec62 is targeted for degradation after it aberrantly engages the endoplasmic reticulum translocon. Cells harboring Deg1-Sec62-His3 were able to grow under selective conditions when the protein was stabilized.

Growth-based Determination and Biochemical Confirmation of Genetic Requirements for Protein Degradation in Saccharomyces cerevisiae

This article describes a yeast growth-based assay for the determination of genetic requirements for protein degradation. It also demonstrates a method for rapid extraction of yeast proteins, suitable for western blotting to biochemically confirm degradation requirements. These techniques can be adapted to monitor degradation of a variety of proteins.

Regulated protein degradation is crucial for virtually every cellular function. Much of what is known about the molecular mechanisms and genetic ...

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, log-phase, and stationary-phase. Each growth phase can reveal different aspects of fitness that are related to various environmental and genetic conditions. High-resolution and quantitative measurements of all 3 phases of growth are generally difficult to obtain. Here we present a detailed method to characterize all 3 growth phases on solid media using an assay called One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). ODELAY quantifies growth phenotypes of individual cells growing into colonies on solid media using time-lapse microscopy. This method can directly observe population heterogeneity with each growth parameter in genetically identical cells growing into colonies. This population heterogeneity offers a unique perspective for understanding genetic and epigenetic regulation, and responses to genetic and environmental perturbations. While the ODELAY method is demonstrated using yeast, it can be utilized on any colony forming microorganism that is visible by bright field microscopy.

We present a method for quantifying growth phenotypes of individual yeast cells as they grow into colonies on solid media using time-lapse microscopy termed, One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). Population heterogeneity of genetically identical cells growing into colonies can be directly observed and quantified.

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, ...

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

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