A concentrated YP solution is autoclaved, while the concentrated sugar solutions are filter-sterilized. After cooling, they are mixed in the right proportions. In specific experiments, the respiratory inhibitor antimycin A, or the chitin-staining dye Calcofluor White were added to the medium. To measure the effect of the time grown on glucose on the lag phase length, we pre-grow cultures in the following way. Incubation lasts maximally 24 hr, after which cultures are transferred to fresh medium.
The cell density is controlled in this way throughout the experiment until the final shift from glucose to the alternative sugar, where the cultures experience a lag phase. Only during the specific experiments that were designed to measure the effect of OD on the lag time, cultures were allowed to reaches higher densities during the pre-growth Figure 1—figure supplements 5 and 6.
The 2, 4, 6, 8, 10 and 12 hr glucose cultures are grown for two overnights in the pre-glucose sugar usually maltose or galactose. Next, at different times during the day, cultures are washed twice and transferred to glucose.
The 0 hr glucose condition is grown in the same way, but is never transferred to glucose. For the 24 hr glucose condition, cultures are grown for one overnight in the pre-glucose sugar, after which they are washed twice and transferred to glucose for another overnight.
The next morning, another dilution is made into fresh glucose to avoid overgrowth. To measure the lag time of these cultures, they are transferred to the Bioscreen C plate reader population-level measurement or the microscope for time-lapse imaging single-cell level measurement.
The lag phase under each condition is measured in duplicate or more. Given the 90 min doubling time in glucose, the 2, 4, 6, 8, 10 and 12 hr of glucose growth corresponds to 1.
To measure the growth dynamics during the lag phase, cultures pre-grown as described above are transferred to the Bioscreen C plate reader. Per honeycomb plate, only 20 — 25 wells are filled with culture in such a way that no culture is directly neighboring another. The remaining 75 — 80 well are filled with blank medium. Once all cultures have reached stationary phase, the experiment is stopped. Population lag times are calculated from the acquired growth curves using R and MS Excel.
First, the background OD -signal as measured in the wells containing blank medium is subtracted from all OD -measurements. Second, these OD -values are corrected for the non-linear relation between cell density and optical density using the formula:.
Fourth, the maximum growth rate m a x R is calculated by taking the mean of the five largest growth rate values. The time and OD at which this maximum growth rate occurs respectively m a x R t i m e and m a x R O D is calculated by taking the mean of the time- and OD -values corresponding to the five largest growth rate values.
Fifth, the initial OD O D , i n i t i a l is calculated by taking the mean of the first two OD -measurements of each growth curve. Finally, the lag time is calculated using the following formula:. This calculates the time corresponding to the intersection point between two lines on an OD versus time graph: 1 a horizontal line crossing the y-axis at OD ,initial , and 2 an exponential line tangential to the point of maximum growth, and an exponential parameter equal to the maximum growth rate.
These calculations agree with the most common definition of the population lag time after a sudden transition between media Swinnen et al. The fold increase in population density, as shown in Figures 1B and 3I , is calculated by dividing the O D , c o r r e c t e d -values by the O D , i n i t i a l.
For the measurements during the lag phase, respiratory deficient mutant coxnatR , over-active respiratory mutant pGPD-HAP4 and wild-type strains were pregrown in maltose for two overnights, washed and grown in glucose for 6 hr. The detailed setup of such pre-growth conditions is described above. The cells were washed and transferred to maltose media after this pre-growth. The cultures are then loaded into a well plate with fluorescent oxygen sensor embedded at the bottom of the wells OxoPlates, PreSens Precison Sensing.
Wells with only growth media and no inoculation were used as blank controls. The measurements during glucose growth condition are performed similarly except that the cells are pregrown for two overnights in glucose, washed in fresh glucose medium and then transferred to the OxoPlate.
To measure the growth dynamics during the lag phase at the single-cell-level, cultures pre-grown as described above are sandwiched between an agar pad containing the same medium as used for the final resuspension, and a coverslip Cerulus et al. The agar pad is wrapped in plastic foil so it does not dry through the experiment. This allows tracking growth and gene expression of hundreds of single cells during the lag phase by periodically every 15—30 min taking differential interference contrast DIC and fluorescence pictures.
The time-lapse movies are acquired automatically by the Metamorph software version 7. Single-cell lag times are analyzed manually by scoring the time of growth resumption. For cells with a bud, the time at which the bud increased in size was scored. For cells without a bud, the time of the first morphological change leading to a new bud was scored. Under our experimental set-up, individual cells form microcolonies as they resume growth. For all the single-cell microscopy experiments, except for the heterokaryon experiment, the microcolony area and gene expression changes as measured using fluorescent protein fusions are scored semi-automatically using a pipeline based on MATLAB and CellProfiler scripts Carpenter et al.
Only microcolonies originating from single cells with or without a bud are scored. First, the DIC and fluorescence pictures are aligned correctly. Second, a mask is created using CellProfiler to indicate pixels corresponding to cells mask value become 1 , and pixels which are background mask value becomes 0. In this way, adjacent pixels corresponding to microcolonies form individual objects. In our pipeline, each image is subjected to a Sobel edge detection algorithm, followed by a thresholding step, and a series of consecutive dilation and erosion steps to generate continuous objects.
This procedure is based on a series of image processing steps inspired by Levy et al. Third, microcolony tracking through time is done by 1 a manual selection of objects corresponding to single cells in the initial images, followed by 2 an automatic detection of related objects in the images from the following time points.
Tracking of individual colonies is prematurely aborted when the microcolony grows outside of the field of view, or when it collides with another microcolony. Occasional out-of-focus images were excluded from the analysis. Fourth, the microcolony area number of pixels and the background-corrected mean fluorescence within each tracked object are calculated.
Finally, other qualitative parameters such as the presence of a bud, or the amount of bud scars detected after Calcofluor White staining are scored manually. The induction time of MAL11 and MAL12 fluorescent protein fusions was scored by calculating the time at which the signal increased distinguishably above its initial level.
Visualization of the microcolony area and fluorescence changes is done using kymographs, where horizontal lines represent individual microcolonies tracked versus time. The color scale represents either the mean fluorescence or the relative area increase.
The individual tracks are sorted based on the pattern of area increase from an early to a late area increase. Cross-correlation of the growth rate time course with that of the mean fluorescence was performed for each microcolony and the population average is shown in the cross-correlation plots.
After this pre-growth, the cultures were washed into glucose media. Culture pairs of opposite mating type pre-grown in glucose or maltose media were mixed together in combinations as outlined in Figure 3—figure supplement 6. When mixing cultures of opposite mating type, equal number of cells from each culture were mixed together.
Mixed cultures of different dilution levels were grown in glucose media for 4 hr. During this period, some cells from the opposite mating types would mate. At the end of glucose growth, cultures were washed into maltose and prepared for time-lapse microscopy on an agar pad with maltose media. The lag time and fluorescence signal were measured for each cell. Cumulative fraction of cells escaping lag phase through time was calculated for each of these groups.
We utilized a karyogamy mutant kar strain to investigate whether glucose pre-growth time affects the time of MAL gene induction due to a cytoplasmic factor or a nuclear factor. After mating of two cells with these genotypes, the resulting diploid cell will have two separate nuclei. As outlined in Figure 3—figure supplement 7 the two strains were pre-grown in both glucose and maltose separately.
Cells from the two strains were not mixed during this pre-growth. At the end of the pre-growth, the cultures were washed separately to glucose media. After making a serial dilution, these mixed cultures along control unmixed cultures and also control mixed cultures with no kar mutation were grown in glucose for 4 hr. The controls are not shown here. During this 4 hr of glucose growth, some cells within the mixed cultures of opposite mating type mate. At the end of this glucose growth period, the cultures were washed into maltose and prepared for time-lapse microscopy on an agar pad with maltose media.
For this experiment microscopy images were segmented so that single cells are separated and tracked through time. The fluorescence intensity for mCherry and yECitrine was measured for each cell. Segmentation and tracking of single cells was done using a pipeline based on CellProfiler, Ilastik Sommer et al. Only the 8-shaped cells which are the diploids resulted from mating are considered for the analysis.
The induction time of the MalmCherry or MALyECitrine was quantified for each single cell by comparing the average signal within a window of three time points, centered at a given time point to the basal level. The basal level was calculated as the mean signal from time zero to the given time frame.
If the difference between these two values was larger than a given threshold, induction time was scored at that given time point. The threshold for this comparison was chosen by inspecting the intensity distribution at the very first time point. For both of the yellow and red channels, each diploid cell initially has one channel at an undetectable level and the other channel at a detectable level.
This is because each diploid has risen from two haploids: one haploid with no recent pre-growth in maltose and hence no detectable Mal protein while the other haploid had recent exposure to maltose 4 hr ago so it has a detectable level of Mal protein. For the yellow channel the mean of this detectable signal level at time point zero is set as the threshold for induction detection. For the red channel due to the lower brightness of mCherry compared to yECitrine, twice the mean of the detectable signal at time point zero is set as the threshold.
Cultures were pre-grown as described above. For each sample, a total of 50, single-cell events were acquired. The analysis was done in R using the flowCore package Hahne et al.
After filtering, the mean fluorescence of each sample was calculated using the arithmetic mean. Changes in gene expression are measured using biological duplicates.
The barcode sequencing experiment was done using the haploid MATa yeast deletion collection Winzeler et al. However, this collection is made using a parental strain that has growth defects on maltose due to the absence of a maltose-responsive MAL-activator Brown et al.
Therefore, we first constructed a maltose-prototrophic yeast deletion collection, by incorporating a functional MAL -regulator into each mutant using the synthetic genetic array SGA technology Hin Yan Tong and Boone, ; Xiao, All these modifications occur within a 17 kb region on the subtelomere of chromosome VII. Since this region is flanked by a NAT R on the centromeric side, and LEU2 on the telomeric side, this whole region could be selected during SGA by simultaneous selection on natamycin resistance and leucine prototrophy.
In our hands, initially deletion mutants could be retrieved from our original frozen stock. A total of mutants were lost during the SGA procedure, leading to a maltose-prototrophic collection containing mutants.
All of these cultures were then pooled without accounting for potential differences in cell density between cultures, and aliquots were made of 1 mL. For the Bar-sequencing experiment, cultures were grown in the following way. The first day, one aliquot containing the maltose-adapted gene deletion pool see above was inoculated in a 1 L flask containing mL of maltose medium, and incubated overnight.
Different aliquots of this resuspension were used to inoculate different 1 L flasks containing mL of glucose medium, and these flasks were incubated for 2,4,6,8,10 and 12 hr. At the end of glucose growth, the cultures were transferred to maltose medium where they experienced a lag phase. Again, transferring involved two washes and a final resuspension in maltose medium.
Cultures were grown in biological duplicates. At the end of each growth phase pre-growth maltose, glucose, maltose a sample was taken. Chloramphenicol was added to the medium to avoid bacterial contamination during the experiment. DNA was extracted using a Zymolyase-based protocol, and the genomic DNA extracts were equalized based on the cell density of the samples. A limited number of amplification cycles 16 was done to avoid issues associated with over-amplification.
Analysis of the sequencing reads was done by blasting them to in-silico generated sequences corresponding to the different gene deletion mutants, allowing for 2 mismatches and three gaps.
The frequency of each mutant in a sample was calculated by dividing its count by the total count of matched barcodes. In order to determine growth rates and the lag time, the frequencies were multiplied by the total population growth of the culture during the experiment. This transforms relative growth change in frequency to absolute growth change in absolute numbers , and allow calculation of growth rate and lag, when the following assumptions are made.
This growth rate was calculated using the formula:. To calculate the adapted glucose growth rate, we assume exponential growth between 4 and 12 h after the transition to glucose.
To calculate the lag time, we assume that cells experience a period of no growth, after which they immediately resume growth at the adapted maltose growth rate. The formula to calculate the lag time is:. All calculations were performed separately on the data from the two biological replicates A and B, and their technical replicates UP and DN tags.
When both technical replicates UP and DN were valid, the lag time and growth rates were averaged per biological replicate. Otherwise, only the valid technical replicate was used.
Finally, the mean and SD of the biological replicates was calculated, and are used in Figure 4A , Figure 4—figure supplement 4 and Figure 5—figure supplement 3. Different classes of deletion mutants with altered HDB dynamics were obtained: 1 consistently shorter lag, 2 consistently longer lag, 3 long-then-short lag, and 4 short-then-long lag. This was based on their lag times in the 2—12 hr glucose conditions relative to the other mutants:. These classes contain respectively , , 75 and 72 mutants.
The interactions between these genes were investigated using the PheNetic interaction network De Maeyer et al. This network is based on protein-protein, protein-DNA and phosphorylation interactions.
The subnetworks created by selecting the different classes of deletion mutants are visualized using Cytoscape Shannon et al. These glucose cultures were then washed into maltose where they experience a lag phase. Two biological replicates were used in parallel. This mix was centrifuged, washed with in 1 mL of ice-cold DEPC-treated water and transferred to an Eppendorf tube, centrifuged again and frozen after removal of the supernatant.
Sequencing reads were mapped to the reference genome using tophat2 Kim et al. The resulting bam files were converted to sam format using samtools Genome Project Data Processing Subgroup et al. The number of mapped reads covering each gene were counted using HTSeq Anders et al.
The R package DESeq2 was used to detect differentially expressed genes across the different samples Love et al. Genes that were detected in less than 15 out of 29 conditions were not included in the analysis.
This analysis generated a list of significantly up- and down-regulated genes per sample, and their associated log 2 expression change relative to the reference condition. Since this returned many GO categories, we focused on a smaller number of relevant categories using two selection steps. First, GO categories that were significantly enriched in either up- and down-regulated genes in less than 4 out of the 29 samples were discarded, leading to a reduction from to GO terms.
The mean and slope of the expression change during glucose relative to the maltose pre-growth condition were calculated based on the samples taken in glucose, and excluding the initial reference conditions. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses.
A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included. Thank you for submitting your article "Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources" for consideration by eLife. The following individual involved in review of your submission has agreed to reveal his identity: Florian Bauer Reviewer 3.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. As you will see, the reviewers all recommended publications, but have raised some important points, please address them in full. Of particular note are the controls requested by reviewer 3, and the need to examine additional strains, as requested by reviewer 2.
The ability of Saccharomyces cerevisiae to respond more quickly to galactose given a previous exposure to galactose was an interesting and 'tractable' example of history dependent behavior HDB.
Before Cerulus et al. This method gives the most ATP per energy input. However, if oxygen isn't available, the organism must still convert the energy using other means. Such processes that happen without oxygen are called anaerobic. Fermentation is a common way for living things to make ATP without oxygen.
Does this make fermentation the same thing as anaerobic respiration? The short answer is no. Even though they have similar parts and neither uses oxygen, there are differences between fermentation and anaerobic respiration. In fact, anaerobic respiration is much more like aerobic respiration than it is like fermentation.
Most science classes discuss fermentation only as an alternative to aerobic respiration. Aerobic respiration begins with a process called glycolysis , in which a carbohydrate such as glucose is broken down and, after losing some electrons, forms a molecule called pyruvate. If there's a sufficient supply of oxygen, or sometimes other types of electron acceptors, the pyruvate moves to the next part of aerobic respiration.
The process of glycolysis makes a net gain of 2 ATP. Fermentation is essentially the same process. The carbohydrate is broken down, but instead of making pyruvate, the final product is a different molecule depending on the type of fermentation.
Fermentation is most often triggered by a lack of sufficient amounts of oxygen to continue running the aerobic respiration chain. Humans undergo lactic acid fermentation. Instead of finishing with pyruvate, lactic acid is created. Other organisms can undergo alcoholic fermentation, where the result is neither pyruvate nor lactic acid. Thus, the final product is a yogurt with a sour taste. In other products such as beer, the process can be overhauled by sealing the alcoholic beverage.
In this manner, at the latter part of fermentation, it will produce beer as its final product. It is also the same with root beer. Due to the process of respiration, all of the living things in this planet are able to breathe. We humans breathe because of the exchange of oxygen and carbon dioxide inside our lungs. For fermentation, food and other beverages will not exist without this important process.
In our body, fermentation acts via pepsin and rennin which acts to ferment and soften the food we eat to aid in the digestion and absorption of nutrients. Respiration or anabolic respiration involves the use of oxygen while fermentation involves the use of enzymes such as glucose. Difference Between Fermentation and Respiration.
Respiration: Respiration is found in higher organisms. Fermentation: Fermentation has a less contribution in the production of energy for the cellular processes on earth. Respiration: Respiration has the highest contribution in the production of energy for the cellular processes on earth. Fermentation and respiration are two processes involved in the catabolism of organic substrates which are used as food during the production of energy required by the cellular processes.
During fermentation and respiration, the potential energy stored in organic molecules are converted into kinetic chemical energy in the form of ATP. Both processes begin with glycolysis, resulting in two pyruvate molecules. Glycolysis occurs in the cytoplasm of all cells on earth. Oxygen is not involved in the glycolysis. But in the presence of oxygen, pyruvate in the cytoplasm enters into the mitochondrial matrix in order to undergo citric acid cycle, which completely oxidizes pyruvate.
This complete oxidization only occurs in respiration. They are reduced by oxidative phosphorylation in the inner membrane of the mitochondria. In contrast, fermentation occurs in the absence of oxygen, incompletely oxidizing pyruvate either into ethanol or lactate.
During ethanol fermentation, pyruvate is converted into acetaldehyde, which is then converted into ethanol. The NADH produced in the glycolysis of fermentation, donates its electrons to acetaldehyde while regeneration. Reference: 1. Cooper, Geoffrey M. National Library of Medicine, 01 Jan.
Jurtshuk, Peter, and Jr. Image Courtesy: 1. Figure 2: Respiration.
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