Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response


Your institution must subscribe to JoVE's Genetics section to access this content.

Fill out the form below to receive a free trial or learn more about access:


Enter your email below to get your free 1 hour trial to JoVE!

We use/store this info to ensure you have proper access and that your account is secure. We may use this info to send you notifications about your account, your institutional access, and/or other related products. To learn more about our GDPR policies click here.

If you want more info regarding data storage, please contact



Here, we present a protocol using RNA-seq to monitor mRNA levels over time during the hypoxic response of S. cerevisiae cells. This method can be adapted to analyze gene expression during any cellular response.

Cite this Article

Copy Citation | Download Citations

Willis, S. D., Hossian, A. K., Evans, N., Hickman, M. J. Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response. J. Vis. Exp. (126), e56226, doi:10.3791/56226 (2017).


Complex changes in gene expression typically mediate a large portion of a cellular response. Each gene may change expression with unique kinetics as the gene is regulated by the particular timing of one of many stimuli, signaling pathways or secondary effects. In order to capture the entire gene expression response to hypoxia in the yeast S. cerevisiae, RNA-seq analysis was used to monitor the mRNA levels of all genes at specific times after exposure to hypoxia. Hypoxia was established by growing cells in ~100% N2 gas. Importantly, unlike other hypoxic studies, ergosterol and unsaturated fatty acids were not added to the media because these metabolites affect gene expression. Time points were chosen in the range of 0 - 4 h after hypoxia because that period captures the major changes in gene expression. At each time point, mid-log hypoxic cells were quickly filtered and frozen, limiting exposure to O2 and concomitant changes in gene expression. Total RNA was extracted from cells and used to enrich for mRNA, which was then converted to cDNA. From this cDNA, multiplex libraries were created and eight or more samples were sequenced in one lane of a next-generation sequencer. A post-sequencing pipeline is described, which includes quality base trimming, read mapping and determining the number of reads per gene. DESeq2 within the R statistical environment was used to identify genes that change significantly at any one of the hypoxic time points. Analysis of three biological replicates revealed high reproducibility, genes of differing kinetics and a large number of expected O2-regulated genes. These methods can be used to study how the cells of various organisms respond to hypoxia over time and adapted to study gene expression during other cellular responses.


Many organisms respond to hypoxia, or low O2, by altering gene expression 1,2,3. This response helps cells cope with the lack of a substrate critical for aerobic respiration and for several biosynthetic reactions, but also with a changing redox state 4. Several microarray studies performed in S. cerevisiae show that the mRNA levels of hundreds of genes change in response to hypoxia 5,6,7,8,9,10,11,12. Recently, RNA-seq was used to characterize gene expression changes over time during hypoxia 13. Here, the experimental details are presented and discussed.

Hypoxia can be achieved in various ways, each producing a different level of O2. Here, hypoxia was established by continuously flowing ultra-high-purity N2 into flasks, which lowers [O2] dissolved immediately with reproducible kinetics 10. It is possible that there are some O2 molecules present that contribute to metabolism and gene expression but this environment is considered very close to anaerobic. In the absence of O2, yeast cells cannot biosynthesize heme, ergosterol and unsaturated fatty acids 4,12,14. Thus, previous studies have included these metabolites when growing yeast without oxygen 5,10,15. However, many hypoxic responses are mediated by depletion of these metabolites and thus replenishing them reverses the hypoxic gene expression responses 12,16. In order to mimic natural hypoxia, these metabolites were not added to the media. In the short time that cells were exposed to hypoxia without the presence of these essential metabolites, there was no noticeable increase in cell death (data not shown) nor a prolonged stress response 13.

The response is also dependent upon the strain and its genotype. Especially important are the alleles of the known regulators of hypoxic responses 2. The S288C strain background is highly desired so that results can be compared to the other genomic studies performed with this strain. However, S288C contains a partial loss-of-function allele of the HAP1 gene 17, a transcriptional regulator critical for the hypoxic response. This allele was repaired in S288C using a wildtype copy from the Σ1278b strain background 11.

Gene expression is highly dependent upon the cellular environment. Thus, when performing genome-wide mRNA analysis, it is important to maintain a constant environment while varying another parameter such as time, stimulus, or genotype. To achieve highly reproducible results, consider these three practices for the study and all of its biological or technical replicates. First, the same experimenter(s) should carry out the study, since technical practices may vary across experimenters. Second, the same batch of ingredients should be used in the growth media as each batch has a slightly different composition that can affect gene expression. Third, to minimize cell cycle effects, each time point should consist of asynchronous cells in the mid-log phase of growth (1 - 2 x 107 cells/mL).

When characterizing a complex response like the gene expression response to hypoxia, a time course is advantageous for determining the kinetics of various events. Specific time points should be chosen that will capture the major changes of the response. In this study, time points between 0 and 4 h were observed, because past experiments revealed widespread changes in gene expression during this period 13.

To measure global gene expression, RNA-seq was used 18,19. This method uses next-generation sequencing to determine the relative abundance of each gene's transcript. Compared to DNA microarray analysis, RNA-seq exhibits higher sensitivity (to detect less abundant transcripts), a greater dynamic range (to measure greater fold changes) and superior reproducibility (to accurately follow gene expression over time). Typically, most cellular RNA is ribosomal RNA so many methods have been developed to enrich for specific RNA species 20. Here, poly-T beads were used to purify poly-A-containing mRNA transcripts, though the various commercially- available rRNA depletion kits could also be effective in mRNA enrichment.

Here, the S. cerevisiae gene expression response to hypoxia was characterized. Cells were exposed to hypoxia and then sampled at eight time points (0, 5, 10, 30, 60, 120, 180 and 240 min). To confirm reproducibility and to identify statistically changed transcripts, three biological replicates were performed. RNA was extracted by mechanical disruption and column purification, and then processed for RNA-seq analysis. The post-sequencing pipeline is described and programming scripts are provided that allow exact replication of the analyses performed. Specifically, Trimmomatic 21, TopHat2 22, HTseq 23, the R statistical environment 24, and the DESeq2 package 25 were used to process the RNA-seq data and to identify 607 genes that change significantly during hypoxia. Principal component analysis (PCA) and gene expression of the replicates indicated the reproducibility of the technique. Clustering and heatmaps revealed wide-ranging expression kinetics, while gene ontology (GO) analysis showed that many cellular processes, like aerobic respiration, are enriched in the set of oxygen-regulated genes.

Subscription Required. Please recommend JoVE to your librarian.


1. Inducing Hypoxia

  1. One day or more before the hypoxia time course: Prepare the incubator, cell filtering system, vacuum, gas tank, flasks, stoppers, glass tubing, and tubing, as in the Materials Table.
  2. Place the N2 tank, incubator, vacuum and filtering system in close proximity, to enable quick processing of cells.
  3. Prepare sterile liquid YPD media (1% Yeast Extract, 2% peptone, 2% glucose) by mixing the components in a glass bottle and autoclaving.
  4. Plan layout of flasks in the incubator.
    NOTE: From the N2 tank, the first flask will be a water trap, the second flask will be the last time point, the third flask will be the second-to-last time point and so on until the last flask which is a final water trap. Figure 1 shows the order and connections between the flasks.
  5. The day before the time course, inoculate a yeast colony into a culture of 5 mL liquid YPD within a sterile test tube. Specifically, pick up a full colony from a plate using a sterile applicator stick. Place the stick into the liquid YPD and shake the stick until most of the cells are in suspension.
  6. Rotate the tubes at 30 °C overnight (~16 h).
    NOTE: After 16 h, wildtype cells will achieve saturation (~2 x 108 cells/mL), indicated by a very cloudy culture. Other strains may take longer to reach saturation and should be tested by measuring cell concentration with a spectrophotometer, as indicated in Step 1.9.4.
  7. On the day of the time course, after 16 h of incubation, dilute the saturated culture 1:50 by using a 5 mL pipet to place 4 mL of overnight culture into 196 mL of liquid YPD within a sterile 500 mL flask. Grow with shaking at ~200 rpm for 4 h at 30 °C.
    NOTE: After 4 h, a wildtype culture will reach a mid-log concentration of ~1-2 x 107 cells/mL.
  8. During the 4 h incubation, label the flasks (for hypoxia and water traps) and the 50 mL collection tubes. If the incubator in step 1.7 above is not used for the hypoxia time course, turn on the other incubator and set to 30 °C.
  9. Just before subjecting cells to hypoxia:
    1. Add ~50 mL water to two of the sterile 250 mL flasks which will serve as the water traps.
    2. Fill 1/3 of an ice bucket with liquid nitrogen and submerge a polystyrene foam rack to hold 50 mL centrifuge tubes.
    3. Set up the filter system for collecting cells. Add a sterile filter disc onto the bottom unit of the filtration system using sterile tweezers, being careful to only touch the edges of the filter disc. Place the filter top unit onto the filter bottom unit and secure with the clamps provided with the system. Ensure that the bottom and top units are aligned so that there is a tight seal and no leakage.
    4. Measure the cell concentration with a spectrophotometer. Dilute cells so that they can be measured in the linear range of the spectrophotometer – OD600 between ~0.2 - 0.6, depending on the spectrophotometer.
      NOTE: One OD600 unit is ~3 x 107 cells/mL, depending on cell morphology and spectrophotometer. A concentration of ~1-2 x 107 cells/mL is expected, corresponding to OD600 of ~0.33 - 0.66.
    5. Quickly obtain the time 0 sample.
      1. Connect the filter system to a strong (~10 mbar) vacuum via vacuum tubing and a 1,000 mL flask trap. Turn on the vacuum. Pour the culture (usually ~20 mL) into the top unit and wait for liquid to be pulled through the filter (~10 s, depending on the strength of the vacuum).
      2. Carefully remove the filter disc with clean tweezers and place into a 50 mL centrifuge tube. Immediately place the centrifuge tube into the rack submerged in liquid nitrogen. Importantly, do not pre-chill the tubes before inserting the filter as the tubes will explode.
      3. After >30 s, place the tube into a -80 °C freezer for later RNA extraction. After each filtration, clean and reassemble the filter system. Rinse the system by pulling water through for 10 s without a filter disc. Finally, wipe dry the system with a paper towel.
    6. Dilute the 4 h culture into different flasks for the various time points, so that each flask reaches mid-log concentration (~1-2 x 107 cells/mL) at the indicated time point of hypoxia.
      NOTE: Table 1 shows dilutions that were used for the wild-type S288C HAP1+ haploid strain. It is recommended to test each strain in these hypoxic culture conditions and adjust the dilutions accordingly.
    7. Once cells and media are added to flasks, replace the aluminum foil covering the flask opening with a stopper containing two glass tubes inserted.
    8. Place the flasks in the incubator in the layout determined earlier.
    9. Securely connect the tubing as shown in Figure 1.
    10. Check that all the stoppers are tightly pushed into the flask openings.
  10. At time 0, open the regulator valve. Then set the flow meter to 3 L/min.
    NOTE: Bubbling will be observed in both water flasks, indicating proper gas flow. If this is not the case, check that all stoppers are tight and tubing is properly attached.
  11. Close the incubator and set the shaking speed to ~200 rpm. Start a timer.
  12. Before each time point, set up the filter system as describe above in step 1.9.3.
  13. At each time point (e.g., 5 min, 10 min, etc.), have two people quickly process the cells as follows:
    1. First person: remove the appropriate flask from the holder and take out the stopper (with glass tubing attached).
      NOTE: This will not break the flow of N2 to any of the remaining culture flasks but will temporarily break the flow to the last water trap.
    2. Second person: Pipet 1 mL of culture and dispense into a cuvette. Reconnect the tubing from the culture flask that is now at the end of the line to the last water trap (One tube and one stopper will be removed in the process.). Then, measure cell concentration in the cuvette, as described above in step 1.9.4.
    3. First person: Pour the remainder of the culture into the vacuum filtration system, and then perform filtering and freezing as described above in step 1.9.5.
  14. When finished with all the time points, turn off the N2 gas. First close the regulator and wait for the pressure to release, and then turn off the flow meter. Finally, disassemble the system and clean all materials with water and then 70% ethanol.

2. RNA Extraction

  1. Prepare RLT buffer for RNA column purification, as described in the Materials Table.
  2. Prepare a working solution of DNase by adding 10 µL of the DNase stock solution to 70 µL of buffer RDD per sample (see Materials Table).
  3. Remove the 50 mL tubes containing filters and cells from the -80 °C freezer and place on ice to thaw (~15 min). Perform the following steps with tubes on ice.
  4. Label 2 mL screw-cap tubes and place them on ice, one tube for each sample. Add ~0.6 mL of acid-washed beads to each tube, using a 1.5 mL microcentrifuge tube to scoop and measure the beads.
  5. Add 0.6 mL of cold RLT buffer to the 50 mL tubes.
  6. Pipet up and down to remove cells from filter and suspend into solution. Avoid letting the filter remain in the solution as the filter will soak up the solution. Remove all solution absorbed into the filter by using the pipet tip to squeeze the filter against the wall of the tube.
  7. Transfer all of the liquid from the 50 mL tube to the screw-cap tubes containing beads.
  8. Place the screw-cap tubes into a bead mill homogenizer and run for one min.
    Immediately place the tubes on ice for three min. Again, place the tubes into the homogenizer and run for one min.
  9. Remove the tubes from the homogenizer and place on ice for 5 min. The beads will settle to the bottom of the tubes.
  10. Perform the remainder of the steps at room temperature.
  11. With a pipet, transfer only the lysate (~350 µL), avoiding the beads, to a new 1.5 mL microcentrifuge tube.
  12. Microcentrifuge for 2 min at max speed and then transfer the supernatant to a new 1.5 mL microcentrifuge tube.
  13. Add 1 volume of 70% ethanol (made from 200 proof molecular biology-grade ethanol). Mix well by pipetting.
  14. Transfer the solution and any precipitate to an RNA column that has been placed inside a 2 mL collection tube.
  15. Centrifuge for 15 s at ≥12,000 x g and discard the flow-through (the liquid in the tube).
  16. Add 350 µL of buffer RW1 to the column. Centrifuge for 15 s at ≥12,000 x g and discard the flow-through.
  17. Add 80 µL DNase I incubation mix to the column membrane (do not get on sides of tube) and allow to sit for 15 min.
  18. Add 350 µL of buffer RW1 to column. Microcentrifuge for 15 s at ≥12,000 x g and discard the flow-through.
  19. Add 500 µL of buffer RPE to the column. Microcentrifuge for 15 s at ≥12,000 x g and discard flow-through.
  20. Add 500 µL of buffer RPE to the column. Microcentrifuge for 2 min at ≥12,000 x g.
  21. Carefully remove the column and place into a new 2 mL collection tube. Microcentrifuge at full speed for 1 min (to completely dry the membrane).
  22. Discard the collection tube and place the column into a 1.5 mL microcentrifuge tube. Add 30 µL of RNase-free water to the column membrane.
  23. Elute the RNA from the column by microcentrifuging for 1 min at ≥12,000 x g. When loading the columns into the microcentrifuge, make sure the lids of the collection tube are facing in the direction the centrifuge spins, to prevent the lids breaking off.
  24. Add another 30 µL of RNase-free water to the column membrane, while keeping the column in the same microcentrifuge tube.
  25. Microcentrifuge for 1 min at ≥12,000 x g, so that the final eluate volume is 60 µL.
  26. Store each 1.5 mL tube containing 60 µL of RNA in the -80 °C freezer.

3. Determining RNA Concentration and Quality

  1. Measure the concentration of RNA and DNA in each RNA sample, using (a) a fluorometer, (b) DNA- and RNA-specific fluorescent dyes and (c) assay tubes, as listed in the Materials Table. Follow the instructions of the fluorometer and the dyes.
  2. Alternatively, measure the nucleic acid concentration with a UV spectrophotometer set at 260 nm.
    NOTE: An A260 reading of 1.0 is equivalent to ~40 µg/mL single-stranded RNA.
  3. Test RNA quality by running the RNA on a commercial nucleic acid analyzer (see Materials Table) or on a standard formaldehyde/agarose gel 26.

4. RNA-seq Analysis

  1. From the total RNA stock, make up 21 µL of 100 ng/µL RNA in RNase-free water. Use 1 µL of this 21 µL to test the RNA concentration as above. Submit the remaining 20 µL (2 µg) total RNA to an external sequencing center for mRNA enrichment, strand-specific library preparation (with eight or more barcodes for multiplexing) and sequencing (see Materials Table). Alternatively, locally perform mRNA enrichment and library preparation, and then submit the library for sequencing.
  2. Pool eight or more multiplexed samples and sequence in one lane of a next-generation sequencer.
  3. Download the FASTQ file containing all of the sequence reads and the corresponding index reads. Also, download the text file containing the multiplex barcodes.
    NOTE: The index reads may be present in a separate FASTQ file. Place all of these files into one directory.
  4. Place the shell script provided here, "", in the same directory. Edit this script as appropriate for the experiment and the computer directories.
    NOTE: this script contains extensive annotations explaining the steps. In brief, the script quality trims the reads, maps the reads to the genome, and generates a tab-delimited file containing the number of reads per gene for each sample.
  5. Deposit the FASTQ files and raw read count data into NCBI's Gene Expression Omnibus before publication 27. Follow the instructions for "Submitting high-throughput sequence data to GEO":
  6. Import the read count data into the R statistical environment and perform statistical analysis, using the R script provided here, "time_course_script.R". Edit this script as appropriate for the experiment and the computer directories.
    NOTE: This script contains extensive annotations explaining the steps. In brief, this script imports the read data, normalizes the reads, identifies genes that change significantly during the time course, performs PCA analysis, and graphs the expression of selected genes.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

The hypoxia time course and RNA-seq analysis were performed independently three times. To examine the reproducibility of the three replicates, gene expression data for all genes was analyzed using Principal Component Analysis (PCA). Figure 2 shows how the samples change over the first two principal components, which together represent 58.9% of the variability. This analysis indicated that each time course exhibits similar changes (as depicted by the similar shape of each curve). In addition, the last two replicates were more similar to each other than to the first replicate. This is consistent with the fact that the last two replicates were performed by the same person using the same batches of media components, while the first replicate was performed by a different person and batches. This finding highlights the fact that consistency of technique and chemicals is an important factor in obtaining high reproducibility. Lastly, PCA analysis is useful in assessing whether the chosen time points capture the major changes that occur during the time course. Focusing on one replicate curve, there are larger distances between the earlier samples, indicating large changes in gene expression, and smaller distances between the later samples, indicating smaller changes and possibly the end of the switch from aerobic to hypoxic gene expression.

Next, the DESeq2 package in R25 was used to identify genes showing a significant change compared to time 0 (p-values in Supplementary Table 1). Of these significant genes, 607 changed more than 4-fold (fold changes in Supplementary Table 1). The expression patterns of these genes were clustered, so that similarly-regulated genes were grouped together, and then displayed in a heatmap (Figure 3). This figure shows that expression changes are highly reproducible across replicates. Also, genes exhibit variable kinetics with both increases and decreases in expression. Many genes exhibit a peak increase or decrease at 30 min, likely due to a transient stress response 13.

Figure 4 depicts the expression of two downregulated and two upregulated genes previously found to be O2-regulated. The genes change expression reproducibly, with similar curves between the biological replicates. The gene with the least variability between replicates is DAN1, with overlapping curves. The gene with the most variability is CYC1, perhaps because expression of this gene is naturally variable. This figure also illustrates that large fold changes (up to 1,024-fold) were detected.

To identify the cellular processes enriched in the set of 607 O2-regulated genes, GO Term enrichment was performed (Supplementary Table 2). As expected, many O2-regulated genes play a role in cellular respiration, other aspects of metabolism, and in ribosomal processes 13.

Sample# Time in Hypoxia mL culture + mL YPD
2 5 min 20 mL + 0 mL
3 10 min 20 mL + 0 mL
4 30 min 20 mL + 0 mL
5 60 min 10 mL + 10 mL
6 120 min 10 mL + 10 mL
7 180 min 5 mL + 15 mL
8 240 min 4 mL + 16 mL

Table 1. Cell Dilutions Performed at Time 0.
These dilutions have been experimentally determined to result in mid-log concentration (1 - 2 × 107 cells/mL) after the indicated time in N2.

Supplementary Table 1. Expression and Statistical Data for all Genes.
This table contains the systematic name, the adjusted p-values for seven DESeq2 tests (i.e., 5 min vs. 0 min, 10 min vs. 0 min, etc.), the minimum adjusted p-value, and the log2 maximum fold-change. Please click here to download this file.

Supplementary Table 2. GO Term Enrichment Results.
This table shows GO Processes, their representation in the set of 607 O2-regulated genes and their representation in the genome. Then, the chi-square test was used to identify GO Terms that were significantly over- or under-represented in the set of 607 genes. GO enrichment analysis was performed using GO Slim Mapper at SGD 28. Please click here to download this file.

Figure 1
Figure 1. Hypoxia Set-up for Taking Time Points without Disrupting Gas Flow to Later Time Points.
It is important that all of the connections remain secure, as described in the protocol. Note the location of the short (9 cm) and long (17 cm) glass tubes. Please click here to view a larger version of this figure.

Figure 2
Figure 2. Principal Component Analysis (PCA) Shows the Relationship Between the Three Biological Replicates.
Each curve has the 0 min to 240 min samples in order. The log2 normalized gene expression for all genes was used in the PCA. PC1 accounts for 34.7% of the total variance and PC2 accounts for 24.2%. The black line is the first replicate, the red line is the second, and the blue line is the third. Note that the arrows are being used to point out the 240 min time-point in different samples. Principal component analysis (PCA) was carried out in R by using the prcomp function (Q-mode, or singular value decomposition) on the log2-transformed matrix containing genes in columns and time points in rows 29. Please click here to view a larger version of this figure.

Figure 3
Figure 3. Heatmap Showing Expression of the 607 Genes Identified as O2-regulated.
These genes were significant in at least one of the DESeq2 tests and changed expression by more than 4-fold. Shown is log2(relative expression). Red = increased expression. Green = decreased expression. Intensity of color is proportional to degree of change. Before creating the heatmap in TreeView 30, gene expression was k-means clustered with k=10 in Cluster 3.0 31. The key below the heatmap shows the scale. Please click here to view a larger version of this figure.

Figure 4
Figure 4. Relative mRNA Levels of Four Genes Over Time in the Three Replicates.
The black line is the first replicate, the red line is the second, and the blue line is the third. Common/systematic gene names are CYC1/YJR048W, ERG5/YMR015C, DAN1/YJR150C, and NCE103/YNL036W. Please click here to view a larger version of this figure.

Supplemental File 1.
Please click here to download this file.

Supplemental File 2. time_course_script.R
Please click here to download this file.

Subscription Required. Please recommend JoVE to your librarian.


In this study, the mRNA levels for all genes was measured during hypoxia in the yeast S. cerevisiae. The goal was to analyze how global gene expression changes due to growth in a controlled near-anoxic environment. Several steps were taken to ensure that the method described here was carefully controlled and reproducible. First, cells were exposed to a precisely defined hypoxic environment: 99.999% N2 in rich media (YPD). Other studies of hypoxia have closed off the flask or tube to air 32, used the hypoxia-mimetic cobalt chloride 33, or employed hypoxia-inducing anaerobic chambers or bags 34. Each of these methods may result in varying levels of hypoxia or other unintended environmental changes. It would be useful to study gene expression while systematically varying the gas mixture (e.g., 90% N2 and 10% O2), as yeast in the wild are likely to experience less-severe hypoxic conditions. Second, ergosterol and unsaturated fatty acids (e.g., Tween 80) were not added to the culture media, since they influence gene expression as discussed in the Introduction. Third, to minimize gene expression changes caused by different phases of growth, empirically-determined dilutions were performed so that when a culture reached a time point, it was in the mid-log phase of growth (~1 - 2 x 107 cells/mL). Fourth, the quick filtering and freezing (~15 s) of cells that have been removed from hypoxia is critical, as gene expression changes can occur in <5 min of exposure to O2, as occurs when cells are collected by centrifugation (data not shown). The time that cells are exposed to O2 could be reduced by performing the growth and harvesting of cells in an anoxic hood or chamber. Fifth, an appropriate strain was chosen for this study; to be consistent with other genomic studies, the common S288C lab strain background was employed. However, the S288C strains contains a mutant hap1 allele and thus was repaired 11. This allowed the study of genes like CYC1 that respond to O2 levels via the Hap1 transcription factor 11.

Time points were chosen because they exhibited large gene expression changes in previous hypoxia experiments. The results shown here can inform future experiments. For example, it would be helpful to use a higher resolution of time points during intervals that show large changes (e.g., 0 to 30 min hypoxia) to more finely capture the diverse kinetics. Additionally, later time points, beyond the time period assayed here (i.e., 4 h of hypoxia) may reveal further expression changes.

RNA-seq was used to measure mRNA levels because its reproducibility and wide dynamic range allow measurement of large fold changes 18,35. RNA was extracted by mechanical disruption of the cells using vigorously shaken beads and purified on a column. Other RNA purification methods could be used, such as hot phenol 36. Ideally, the RNA concentration will be in the range of 200 ng/µL - 1000 ng/µL since the RNA concentration will be diluted to 100 ng/µL for reverse transcription and library preparation. The RNA concentration should be measured with a fluorometer and RNA-specific dye; a UV spectrophotometer is limited because it measures the total concentration of nucleic acids, consisting of both RNA and DNA. The quality of the RNA is determined by gel electrophoresis; ribosomal RNA bands should be well-defined on the gel and smearing indicates RNA degradation. Here, mRNA transcripts were enriched, but there are diverse methods for isolating different RNA types 20 that may also be important in the response.

To save resources, between 8 and 12 samples were multiplexed and sequenced together in one lane, resulting in an average of at least 693 reads per gene for each sample, sufficient to reproducibly determine the number of reads per gene. In fact, more than 12 samples could be multiplexed in one lane, likely resulting in adequate sampling of most genes. In order to calculate the average reads per gene, the total number of reads that mapped to genes (in the HTSeq output file) was divided by the number of genes provided to HTSeq (in this study, 7140). When estimating the maximum number of samples that can be multiplexed in one lane, consider genes of interest that exhibit the lowest expression (i.e., lowest normalized counts), using data from this and other RNA-seq studies. If the normalized read counts for a gene vary significantly across replicates, then the number of reads may be too low, suggesting that less samples should be multiplexed per sequencing lane.

Next-generation sequencing will generate FASTQ files containing the reads and other information. If multiplexing was performed, a barcode file and an additional FASTQ file may be generated. These files can be downloaded for local analysis, as in this protocol. Alternatively, there are several online next-gen sequence analysis tools for those not familiar with command-line functions or R. In this regard, we recommend Galaxy ( 37 and GenomeSpace ( In the present protocol, two scripts that have been written by the authors are used for FASTQ processing and expression analysis. The scripts are well-annotated and can be edited for different experimental designs and computer setups. Also, the two scripts and the “Materials” Table provide websites for downloading the tools used in these scripts. The first script, “”, is a shell script to be run at the command line in a UNIX/Linux terminal. This script de-multiplexes the original FASTQ file containing all of the reads obtained from one lane of the sequencer, thus generating one FASTQ file for each sample present in that lane. Next, the reads in each FASTQ file are quality trimmed using Trimmomatic with default settings 21. The trimmed reads are then mapped to the S. cerevisiae S288C reference genome (SGD R64-1-1_20110203) using TopHat2 with default settings 22. The script finally uses HTSeq to determine the number of reads that map to each annotated feature 19.

The second script, "time_course_script.R", has also been written by the authors and is run within the R statistical environment 24. The script initially imports into R the read count data generated by HTSeq. Each sample is assumed to be a time point and thus is organized relative to the other time points. The raw read counts are then imported into the R package, DESeq2 25, in order to identify genes that are differentially expressed at any of the time points relative to the first (i.e., aerobic, time 0). Because of its flexibility, DESeq2 can be used to perform other statistical tests, such as whether expression changes as a function of time 13. Alternatively, other tools employing parametric and non-parametric statistics for RNA-seq read count data could be used 20. The script then normalizes the read counts to control for the degree of sequencing of each sample, and log2 transforms the counts. These processed reads are used to perform PCA analysis, to determine the maximum fold-change for each gene, and to create the expression graphs in Figure 4.

An important issue is how best to employ statistics to identify genes that respond significantly to hypoxia. At least three biological replicates of the time course are necessary to calculate the natural variability of each gene and thus determine genes that respond to hypoxia more significantly than expected by chance. Alternatively, it is possible to successfully identify genes that respond over time, without biological replicates 13,25,38,39. The main drawback to these methods is that they do not take into account the biological variability of each gene. However, if RNA-seq replicates are not performed, individual gene expression changes could be verified by performing RT-qPCR with multiple biological replicates 13.

There are two primary benefits to assessing multiple time points of a response. First, as discussed earlier (especially in Figure 2), a time course identifies the intervals exhibiting large expression changes, informing whether additional time points are required to improve resolution of kinetics or to observe later events of the response. Second, a time course allows the differentiation of each gene's expression kinetics. This helps with identifying unique signaling pathways that act at specific times during the response, and gives insight into the timing of initiation and termination of signaling. Specifically, the clustering of genes by expression pattern that was performed here resulted in sets of co-regulated genes. The promoters of a gene set may share a transcription factor binding site, suggesting that the transcription factor becomes active when the genes change expression.

When studying a cellular response, the observed variability is ideally due to the stimulus (e.g., hypoxia) and not to other experimental variables. However, as seen in Figure 2, the individual researcher and/or the batches of the media components make major contributions to the variability in gene expression. Thus, these parameters should be considered in order to achieve high reproducibility and minimal experimental variability. As little time as possible should separate each replicate, because experimental variables are more likely to change as time passes.

In conclusion, this method can be used to accurately monitor genome-wide alterations in mRNA levels (or other events such as histone modifications or protein levels) during a hypoxia time course. The method can be adapted for other organisms, especially microbial species that can be grown in liquid culture. Such genome-wide time-course analyses will complement studies of cell morphology, protein activity and metabolism. Together, these data will lead to a richer understanding of a cellular response.

Subscription Required. Please recommend JoVE to your librarian.


The authors have nothing to disclose.


We thank the Lewis-Sigler Institute for Integrative Genomics Sequencing Core Facility at Princeton University for technical advice and for RNA library preparation and sequencing. This work was supported by grants from Rowan University and NIH NIGMS R15GM113187 to M.J.H.


Name Company Catalog Number Comments
Enclosed dry incubator Thermo Scientific MaxQ 4000 Set at 30 °C. Modify the door to allow entry of one Tygon tube. Alternatively, use the New Brunswick G25 incubator, which contains a tube port. Do not use an open-air water shaker, as condensation will collect in the tubes between flasks, possibly cross-contaminating cultures.
Micro-analysis Filter Holder Millipore XX1002530 25mm diameter, stainless steel support, no. 5 perforated silicone stopper mounts in standard 125 mL filtering flask
Strong vacuum Edwards E-LAB 2 The “house” vacuum may be too weak. Alternatively, use an electric-power portable vacuum pump like the one listed here.
1,000 mL flask To act as vacuum trap.
~2 foot lengths of Heavy Wall Vacuum Tubing, inner diameter 3/8 in, outer diameter 7/8 in Tygon 38TT Two pieces: the first connects vacuum to trap, and the second connects trap to filter system.
High-pressure N2 gas tank 99.999% purity, >1,000 psi, with a regulator and gas flow controller
Autoclaved 500 mL flask Opening covered with aluminum foil. One for each yeast strain.
Autoclaved 250 mL flasks Openings covered with aluminum foil. One for each time point plus two for water traps.
Flask stoppers (size 6, two holes with 5 mm diameter) Sterilized with 70% ethanol. One for each flask.
Glass tubing, length 9 cm or 17 cm, inner diameter 2 mm, outer diameter 5 mm Sterilized with 70% ethanol. Two tubes for each flask. Place into the holes of each stopper. See Figure 1 for placement of 9- vs 17- cm tubes.
~25 cm lengths of plastic tubing, inner diameter 5 mm Tygon E-3603 One piece for each flask. Sterilized with 70% ethanol.
Sterile filter discs Millipore HAWP02500 25 mm diameter, 0.45 µm pore size, one for each time point
Sterile dH2O (~100 mL)
1 mL cuvettes For measuring OD600 (i.e., cell concentration)
50 mL sterile centrifuge tubes One for each time point
Clean and sterile tweezers
liquid nitrogen For freezing cells
acid-washed beads Sigma G8772 Keep at 4 °C for lysing cells
Qiagen RNeasy Mini Kit Qiagen 74104 For RNA column purification
Qiagen RLT buffer Prepare by adding 10 µL of β-Mercaptoethanol per 1 mL of RLT buffer, keep at 4 °C.
2 mL collection tubes Qiagen included in the Qiagen Rneasy Mini Kit
Buffer RPE Qiagen included in the Qiagen Rneasy Mini Kit
Buffer RW1 Qiagen included in the Qiagen Rneasy Mini Kit
DNase I stock and working solutions Qiagen 79254 The DNase I enzyme comes as lyophilized powder in a glass vial. Using a sterile needle and syringe, inject 550 µL of RNase-free water (provided in Qiagen kit) into the vial. Mix by gently inverting the bottle. To avoid denaturing the enzyme, do not vortex. Using a pipet, remove this stock solution from the vial and store in freezer (-20 °C) in single-use aliquots (80 µL each). The stock solution should not be thawed and refrozen.
Buffer RDD Qiagen included in the Qiagen DNase Kit
Ice cold 2 mL screw-cap tubes For lysing cells during RNA extraction
bead mill homogenizer Biospec Mini-Beadbeater-24 112011 Keep in cold room
Bacto Peptone BD DF0118 for liquid YPD media
Bacto Yeast Extract BD DF0886 for liquid YPD media
glucose Fisher D16 for liquid YPD media
Qubit assay tubes Thermo Fisher Q32856 for measuring nucleic acid concentration
Quant-iTTM dsDNA BR Assay Kit Thermo Fisher Q32853 for measuring nucleic acid concentration
Quant-iTTM RNA Assay Kit Thermo Fisher Q32855 for measuring nucleic acid concentration
Qubit Fluorometer Thermo Fisher Q33216 for measuring nucleic acid concentration
Commercial electrophoresis system Agilent Bioanalyzer 2100 for measuring nucleic acid quality
Next-generation sequencer Illumina HiSeq 2500 for sequencing libraries
automated liquid handling system Wafergen Apollo 324 for creating sequencing libraries
PrepX PolyA mRNA Isolation Kit Wafergen 400047 for isolating mRNA from total RNA
PrepX RNA SEQ for Illumina Library Kit Wafergen 400039 for creating strand-specific sequencing libraries from total RNA
Barcode Splitter
Samtools, which includes the gzip command
Bowtie2 (installed before TopHat)
R (installed before R Studio)
R Studio (free version)



  1. Semenza, G. L. Oxygen sensing, homeostasis, and disease. New Eng. J Med. 365, (6), 537-547 (2011).
  2. Butler, G. Hypoxia and gene expression in eukaryotic microbes. Annu. Rev. Micro. 67, 291-312 (2013).
  3. Ratcliffe, P. J. Oxygen sensing and hypoxia signalling pathways in animals: the implications of physiology for cancer. J. Physiol. 591, (Pt 8), 2027-2042 (2013).
  4. Rosenfeld, E., Beauvoit, B. Role of the non-respiratory pathways in the utilization of molecular oxygen bySaccharomyces cerevisiae. Yeast. 20, (13), 1115-1144 (2003).
  5. Ter Linde, J. J., et al. Genome-wide transcriptional analysis of aerobic and anaerobic chemostat cultures of Saccharomyces cerevisiae. J. Bacteriol. 181, (24), 7409-7413 (1999).
  6. Ter Linde, J. J., Steensma, H. Y. A microarray-assisted screen for potential Hap1 and Rox1 target genes in Saccharomyces cerevisiae. Yeast. 19, (10), 825-840 (2002).
  7. Kwast, K. E., et al. Genomic analyses of anaerobically induced genes in Saccharomyces cerevisiae: functional roles of Rox1 and other factors in mediating the anoxic response. J. Bacteriol. 184, (1), 250-265 (2002).
  8. Becerra, M., et al. The yeast transcriptome in aerobic and hypoxic conditions: effects of hap1, rox1, rox3 and srb10 deletions. Mol. Microbiol. 43, (3), 545-555 (2002).
  9. Lai, L. -C., Kosorukoff, A. L., Burke, P. V., Kwast, K. E. Dynamical remodeling of the transcriptome during short-term anaerobiosis in Saccharomyces cerevisiae: differential response and role of Msn2 and/or Msn4 and other factors in galactose and glucose media. Mol. Cell. Biol. 25, (10), 4075-4091 (2005).
  10. Lai, L. C., Kosorukoff, A. L., Burke, P. V., Kwast, K. E. Metabolic-State-Dependent Remodeling of the Transcriptome in Response to Anoxia and Subsequent Reoxygenation in Saccharomyces cerevisiae. Euk. Cell. 5, (9), 1468-1489 (2006).
  11. Hickman, M. J., Winston, F. Heme levels switch the function of Hap1 of Saccharomyces cerevisiae between transcriptional activator and transcriptional repressor. Mol. Cell. Biol. 27, (21), 7414-7424 (2007).
  12. Hickman, M. J., Spatt, D., Winston, F. The Hog1 mitogen-activated protein kinase mediates a hypoxic response in Saccharomyces cerevisiae. Genetics. 188, (2), 325-338 (2011).
  13. Bendjilali, N., et al. Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response. G3. 7, (1), 221-231 (2017).
  14. Chellappa, R., et al. The membrane proteins, Spt23p and Mga2p, play distinct roles in the activation of Saccharomyces cerevisiae OLE1 gene expression. Fatty acid-mediated regulation of Mga2p activity is independent of its proteolytic processing into a soluble transcription activator. J. Biol. Chem. 276, (47), 43548-43556 (2001).
  15. Abramova, N. E., et al. Regulatory mechanisms controlling expression of the DAN/TIR mannoprotein genes during anaerobic remodeling of the cell wall in Saccharomyces cerevisiae. Genetics. 157, (3), 1169-1177 (2001).
  16. Hughes, A. L., Todd, B. L., Espenshade, P. J. SREBP Pathway Responds to Sterols and Functions as an Oxygen Sensor in Fission Yeast. Cell. 120, (6), 831-842 (2005).
  17. Gaisne, M., Bécam, A. M., Verdière, J., Herbert, C. J. A "natural" mutation in Saccharomyces cerevisiae strains derived from S288c affects the complex regulatory gene HAP1 (CYP1). Curr. Gen. 36, (4), 195-200 (1999).
  18. Wang, Z., Gerstein, M., Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Gen. 10, (1), 57-63 (2009).
  19. Anders, S., Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, (10), R106 (2010).
  20. Conesa, A., et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, (1), 13 (2016).
  21. Bolger, A. M., Lohse, M., Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinf. 30, (15), 2114-2120 (2014).
  22. Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., Salzberg, S. L. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, (4), R36 (2013).
  23. Anders, S., Pyl, P. T., Huber, W. HTSeq--A Python framework to work with high-throughput sequencing data. Bioinf. 31, (2), 166-169 (2015).
  24. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna. Available from: (2015).
  25. Love, M. I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, (12), 550 (2014).
  26. Brown, T., Mackey, K., Du, T. Analysis of RNA by Northern and Slot Blot Hybridization. Curr. Prot. Mol. Biol. 74, 4.9.1-4.9.19 (2004).
  27. Edgar, R., Domrachev, M., Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nuc. Acids Res. 30, (1), 207-210 (2002).
  28. Cherry, J. M., et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nuc. Acids Res. 40, D700-D705 (2012).
  29. Hothorn, T., Everitt, B. S. A Handbook of Statistical Analyses using R. 3rd ed, CRC Press: Taylor & Francis Group. Boca Raton, FL. (2014).
  30. Saldanha, A. J. Java Treeview--extensible visualization of microarray data. Bioinformatics. 20, (17), 3246-3248 (2004).
  31. Eisen, M. B., Spellman, P. T., Brown, P. O., Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Nat. Acad. Sci. 95, (25), 14863-14868 (1998).
  32. Davies, B. S. J., Rine, J. A Role for Sterol Levels in Oxygen Sensing in Saccharomyces cerevisiae. Genetics. 174, (1), 191-201 (2006).
  33. Meena, R. C., Kumar, N., Nath, S., Chakrabarti, A. Homologous Recombination is Activated at Early Time Points Following Exposure to Cobalt Chloride Induced Hypoxic Conditions in Saccharomyces cerevisiae. Ind. J. Microb. 52, (2), 209-214 (2012).
  34. Snoek, I. S. I., Tai, S. L., Pronk, J. T., Yde Steensma, H., Daran, J. -M. Involvement of Snf7p and Rim101p in the transcriptional regulation of TIR1 and other anaerobically upregulated genes in Saccharomyces cerevisiae. FEMS Yeast Res. 10, (4), 367-384 (2010).
  35. Ellahi, A., Thurtle, D. M., Rine, J. The Chromatin and Transcriptional Landscape of Native Saccharomyces cerevisiae Telomeres and Subtelomeric Domains. Genetics. 200, (2), 505-521 (2015).
  36. Collart, M. A., Oliviero, S., et al. Preparation of yeast RNA. Curr. Prot. Mol. Biol. (2001).
  37. Blankenberg, D., et al. Manipulation of FASTQ data with Galaxy. Bioinf. 24, (14), 1783-1785 (2010).
  38. Martini, P., et al. timeClip: pathway analysis for time course data without replicates. BMC Bioinf. 15, Suppl 5. (2014).
  39. Oh, S., Song, S., Grabowski, G., Zhao, H., Noonan, J. P. Time series expression analyses using RNA-seq: a statistical approach. BioMed Res. Int. 2013, 1-16 (2013).



    Post a Question / Comment / Request

    You must be signed in to post a comment. Please or create an account.

    Usage Statistics