Name: measuring mrna levels over time during the yeast s. cerevisiae hypoxic response
Uploaded: 2017-08-10T15:00:00
Description: Watch this Scientific Journal Video about Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response at JoVE.com

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.

1. Inducing Hypoxia
<ol>
	<li>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.</li>
	<li>Place the N2 tank, incubator, vacuum and filtering system in close proximity, to enable quick processing of cells.</li>
	<li>Prepare sterile liquid YPD media (1% Yeast Extract, 2% peptone, 2% glucose) by mixing the components in a glass bottle and autoclaving.</li>
	<li>Pl...

<ol>
	<li>Semenza, G. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxygen+sensing,+homeostasis,+and+disease.">Oxygen sensing, homeostasis, and disease.</a> New Eng. J Med. 365 (6), 537-547 (2011).</li><li>Butler, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hypoxia+and+gene+expression+in+eukaryotic+microbes.">Hypoxia and gene expression in eukaryotic microbes.</a> Annu. Rev. Micro. 67, 291-312 (2013).</li><li>Ratcliffe, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxygen+sensing+and+hypoxia+signalling+pathways+in+animals:+the+implications+of+physiology+for+cancer.">Oxygen sensing and hypoxia signalling pathways in animals: the implications of physiology for cancer.</a> J. Physiol. 591 (Pt 8), 2027-2042 (2013).</li><li>Rosenfeld, E., Beauvoit, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+the+non-respiratory+pathways+in+the+utilization+of+molecular+oxygen+bySaccharomyces+cerevisiae.">Role of the non-respiratory pathways in the utilization of molecular oxygen bySaccharomyces cerevisiae.</a> Yeast. 20 (13), 1115-1144 (2003).</li><li>Ter Linde, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+transcriptional+analysis+of+aerobic+and+anaerobic+chemostat+cultures+of+Saccharomyces+cerevisiae.">Genome-wide transcriptional analysis of aerobic and anaerobic chemostat cultures of Saccharomyces cerevisiae.</a> J. Bacteriol. 181 (24), 7409-7413 (1999).</li><li>Ter Linde, J. J., Steensma, H. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+microarray-assisted+screen+for+potential+Hap1+and+Rox1+target+genes+in+Saccharomyces+cerevisiae.">A microarray-assisted screen for potential Hap1 and Rox1 target genes in Saccharomyces cerevisiae.</a> Yeast. 19 (10), 825-840 (2002).</li><li>Kwast, K. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+analyses+of+anaerobically+induced+genes+in+Saccharomyces+cerevisiae:+functional+roles+of+Rox1+and+other+factors+in+mediating+the+anoxic+response.">Genomic analyses of anaerobically induced genes in Saccharomyces cerevisiae: functional roles of Rox1 and other factors in mediating the anoxic response.</a> J. Bacteriol. 184 (1), 250-265 (2002).</li><li>Becerra, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+yeast+transcriptome+in+aerobic+and+hypoxic+conditions:+effects+of+hap1,+rox1,+rox3+and+srb10+deletions.">The yeast transcriptome in aerobic and hypoxic conditions: effects of hap1, rox1, rox3 and srb10 deletions.</a> Mol. Microbiol. 43 (3), 545-555 (2002).</li><li>Lai, L. -. C., Kosorukoff, A. L., Burke, P. V., Kwast, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=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.">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.</a> Mol. Cell. Biol. 25 (10), 4075-4091 (2005).</li><li>Lai, L. C., Kosorukoff, A. L., Burke, P. V., Kwast, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolic-State-Dependent+Remodeling+of+the+Transcriptome+in+Response+to+Anoxia+and+Subsequent+Reoxygenation+in+Saccharomyces+cerevisiae.">Metabolic-State-Dependent Remodeling of the Transcriptome in Response to Anoxia and Subsequent Reoxygenation in Saccharomyces cerevisiae.</a> Euk. Cell. 5 (9), 1468-1489 (2006).</li><li>Hickman, M. J., Winston, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heme+levels+switch+the+function+of+Hap1+of+Saccharomyces+cerevisiae+between+transcriptional+activator+and+transcriptional+repressor.">Heme levels switch the function of Hap1 of Saccharomyces cerevisiae between transcriptional activator and transcriptional repressor.</a> Mol. Cell. Biol. 27 (21), 7414-7424 (2007).</li><li>Hickman, M. J., Spatt, D., Winston, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Hog1+mitogen-activated+protein+kinase+mediates+a+hypoxic+response+in+Saccharomyces+cerevisiae.">The Hog1 mitogen-activated protein kinase mediates a hypoxic response in Saccharomyces cerevisiae.</a> Genetics. 188 (2), 325-338 (2011).</li><li>Bendjilali, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time-Course+Analysis+of+Gene+Expression+During+the+Saccharomyces+cerevisiae+Hypoxic+Response.">Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response.</a> G3. 7 (1), 221-231 (2017).</li><li>Chellappa, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=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.">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.</a> J. Biol. Chem. 276 (47), 43548-43556 (2001).</li><li>Abramova, N. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulatory+mechanisms+controlling+expression+of+the+DAN/TIR+mannoprotein+genes+during+anaerobic+remodeling+of+the+cell+wall+in+Saccharomyces+cerevisiae.">Regulatory mechanisms controlling expression of the DAN/TIR mannoprotein genes during anaerobic remodeling of the cell wall in Saccharomyces cerevisiae.</a> Genetics. 157 (3), 1169-1177 (2001).</li><li>Hughes, A. L., Todd, B. L., Espenshade, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SREBP+Pathway+Responds+to+Sterols+and+Functions+as+an+Oxygen+Sensor+in+Fission+Yeast.">SREBP Pathway Responds to Sterols and Functions as an Oxygen Sensor in Fission Yeast.</a> Cell. 120 (6), 831-842 (2005).</li><li>Gaisne, M., B&#233;cam, A. M., Verdi&#232;re, J., Herbert, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+&amp;#34;natural&amp;#34;+mutation+in+Saccharomyces+cerevisiae+strains+derived+from+S288c+affects+the+complex+regulatory+gene+HAP1+(CYP1).">A &amp;#34;natural&amp;#34; mutation in Saccharomyces cerevisiae strains derived from S288c affects the complex regulatory gene HAP1 (CYP1).</a> Curr. Gen. 36 (4), 195-200 (1999).</li><li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nat. Rev. Gen. 10 (1), 57-63 (2009).</li><li>Anders, S., Huber, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+expression+analysis+for+sequence+count+data.">Differential expression analysis for sequence count data.</a> Genome Biol. 11 (10), R106 (2010).</li><li>Conesa, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+survey+of+best+practices+for+RNA-seq+data+analysis.">A survey of best practices for RNA-seq data analysis.</a> Genome Biol. 17 (1), 13 (2016).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinf. 30 (15), 2114-2120 (2014).</li><li>Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TopHat2:+accurate+alignment+of+transcriptomes+in+the+presence+of+insertions,+deletions+and+gene+fusions.">TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.</a> Genome Biol. 14 (4), R36 (2013).</li><li>Anders, S., Pyl, P. T., Huber, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HTSeq--A+Python+framework+to+work+with+high-throughput+sequencing+data.">HTSeq--A Python framework to work with high-throughput sequencing data.</a> Bioinf. 31 (2), 166-169 (2015).</li><li>. R: A Language and Environment for Statistical Computing Available from: <a target="_blank" href="https://www.R-project.org">https://www.R-project.org</a> (2015)</li><li>Love, M. I., Huber, W., Anders, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moderated+estimation+of+fold+change+and+dispersion+for+RNA-seq+data+with+DESeq2.">Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.</a> Genome Biol. 15 (12), 550 (2014).</li><li>Brown, T., Mackey, K., Du, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+RNA+by+Northern+and+Slot+Blot+Hybridization.">Analysis of RNA by Northern and Slot Blot Hybridization.</a> Curr. Prot. Mol. Biol. 74, 4.9.1-4.9.19 (2004).</li><li>Edgar, R., Domrachev, M., Lash, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+Expression+Omnibus:+NCBI+gene+expression+and+hybridization+array+data+repository.">Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.</a> Nuc. Acids Res. 30 (1), 207-210 (2002).</li><li>Cherry, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Saccharomyces+Genome+Database:+the+genomics+resource+of+budding+yeast.">Saccharomyces Genome Database: the genomics resource of budding yeast.</a> Nuc. Acids Res. 40, D700-D705 (2012).</li><li>Hothorn, T., Everitt, B. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A Handbook of Statistical Analyses using R. , (2014).</li><li>Saldanha, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Java+Treeview--extensible+visualization+of+microarray+data.">Java Treeview--extensible visualization of microarray data.</a> Bioinformatics. 20 (17), 3246-3248 (2004).</li><li>Eisen, M. B., Spellman, P. T., Brown, P. O., Botstein, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cluster+analysis+and+display+of+genome-wide+expression+patterns.">Cluster analysis and display of genome-wide expression patterns.</a> Proc. Nat. Acad. Sci. 95 (25), 14863-14868 (1998).</li><li>Davies, B. S. J., Rine, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Role+for+Sterol+Levels+in+Oxygen+Sensing+in+Saccharomyces+cerevisiae.">A Role for Sterol Levels in Oxygen Sensing in Saccharomyces cerevisiae.</a> Genetics. 174 (1), 191-201 (2006).</li><li>Meena, R. C., Kumar, N., Nath, S., Chakrabarti, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Homologous+Recombination+is+Activated+at+Early+Time+Points+Following+Exposure+to+Cobalt+Chloride+Induced+Hypoxic+Conditions+in+Saccharomyces+cerevisiae.">Homologous Recombination is Activated at Early Time Points Following Exposure to Cobalt Chloride Induced Hypoxic Conditions in Saccharomyces cerevisiae.</a> Ind. J. Microb. 52 (2), 209-214 (2012).</li><li>Snoek, I. S. I., Tai, S. L., Pronk, J. T., Yde Steensma, H., Daran, J. -. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Involvement+of+Snf7p+and+Rim101p+in+the+transcriptional+regulation+of+TIR1+and+other+anaerobically+upregulated+genes+in+Saccharomyces+cerevisiae.">Involvement of Snf7p and Rim101p in the transcriptional regulation of TIR1 and other anaerobically upregulated genes in Saccharomyces cerevisiae.</a> FEMS Yeast Res. 10 (4), 367-384 (2010).</li><li>Ellahi, A., Thurtle, D. M., Rine, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Chromatin+and+Transcriptional+Landscape+of+Native+Saccharomyces+cerevisiae+Telomeres+and+Subtelomeric+Domains.">The Chromatin and Transcriptional Landscape of Native Saccharomyces cerevisiae Telomeres and Subtelomeric Domains.</a> Genetics. 200 (2), 505-521 (2015).</li><li>Collart, M. A., Oliviero, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preparation+of+yeast+RNA.">Preparation of yeast RNA.</a> Curr. Prot. Mol. Biol. , (2001).</li><li>Blankenberg, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manipulation+of+FASTQ+data+with+Galaxy.">Manipulation of FASTQ data with Galaxy.</a> Bioinf. 24 (14), 1783-1785 (2010).</li><li>Martini, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=timeClip:+pathway+analysis+for+time+course+data+without+replicates.">timeClip: pathway analysis for time course data without replicates.</a> BMC Bioinf. 15, (2014).</li><li>Oh, S., Song, S., Grabowski, G., Zhao, H., Noonan, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time+series+expression+analyses+using+RNA-seq:+a+statistical+approach.">Time series expression analyses using RNA-seq: a statistical approach.</a> BioMed Res. Int. 2013, 1-16 (2013).</li></ol>

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 <sup class="xr...

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 &#931;1278b 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&#39;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.

<table border="0" cellpadding="0" cellspacing="0" width="973">
	<tbody>
		<tr height="126">
			<td height="126" style="height:126px;width:292px;">Enclosed dry incubator</td>
			<td style="width:183px;">Thermo Scientific</td>
			<td style="width:119px;">MaxQ 4000</td>
			<td style="width:380px;">Set at 30&#176;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.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:292px;">Micro-analysis Filter Holder</td>
			<td style="width:183px;">Millipore</td>
			<td style="width:119px;">XX1002530</td>
			<td style="width:380px;">25mm diameter, stainless steel support, no. 5 perforated silicone stopper mounts in standard 125mL filtering flask</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:292px;">Strong vacuum</td>
			<td style="width:183px;">Edwards</td>
			<td style="width:119px;">E-LAB 2</td>
			<td style="width:380px;">The &#8220;house&#8221; vacuum may be too weak. Alternatively, use an electric-power portable vacuum pump like the one listed here.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">1000-mL flask</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">To act as vacuum trap.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:292px;">~2-foot lengths of Heavy Wall Vacuum Tubing, inner diameter 3/8 in, outer diameter 7/8 in</td>
			<td style="width:183px;">Tygon</td>
			<td style="width:119px;">38TT</td>
			<td style="width:380px;">Two pieces: the first connects vacuum to trap, and the second connects trap to filter system.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">High-pressure N2 gas tank</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">99.999% purity, &#62;1000psi, with a regulator and gas flow controller</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Autoclaved 500mL flask</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">Opening covered with aluminum foil. One for each yeast strain.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Autoclaved 250mL flasks</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">Openings covered with aluminum foil. One for each time point plus two for water traps.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Flask stoppers (size 6, two holes with 5mm diameter)</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">Sterilized with 70% ethanol. One for each flask.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:292px;">Glass tubing, length 9 cm or 17 cm, inner diameter 2 mm, outer diameter 5 mm</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">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.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">~25-cm lengths of plastic tubing, inner diameter 5 mm</td>
			<td style="width:183px;">Tygon</td>
			<td style="width:119px;">E-3603</td>
			<td style="width:380px;">One piece for each flask. Sterilized with 70% ethanol.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Sterile filter discs</td>
			<td style="width:183px;">Millipore</td>
			<td style="width:119px;">HAWP02500</td>
			<td style="width:380px;">25mm diameter, 0.45 &#181;m pore size, one for each time point</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;width:292px;">Sterile dH2O (~100mL)</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">1-mL cuvettes</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">For measuring OD600 (i.e., cell concentration)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">50-mL sterile centrifuge tubes</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">One for each time point</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Clean and sterile tweezers</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">liquid nitrogen</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">For freezing cells</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">acid-washed beads</td>
			<td style="width:183px;">Sigma</td>
			<td style="width:119px;">G8772</td>
			<td style="width:380px;">Keep at 4&#176;C for lysing cells</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Qiagen RNeasy Mini Kit</td>
			<td style="width:183px;">Qiagen</td>
			<td align="right" style="width:119px;">74104</td>
			<td style="width:380px;">For RNA column purification</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Qiagen RLT buffer</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">Prepare by adding 10&#181;L of &#946;-Mercaptoethanol per 1mL of RLT buffer, keep at 4C.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">2-mL collection tubes</td>
			<td style="width:183px;">Qiagen</td>
			<td style="width:119px;"></td>
			<td style="width:380px;">included in the Qiagen Rneasy Mini Kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Buffer RPE</td>
			<td style="width:183px;">Qiagen</td>
			<td style="width:119px;"></td>
			<td style="width:380px;">included in the Qiagen Rneasy Mini Kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Buffer RW1</td>
			<td style="width:183px;">Qiagen</td>
			<td style="width:119px;"></td>
			<td style="width:380px;">included in the Qiagen Rneasy Mini Kit</td>
		</tr>
		<tr height="168">
			<td height="168" style="height:168px;width:292px;">DNase I stock and working solutions</td>
			<td style="width:183px;">Qiagen</td>
			<td align="right" style="width:119px;">79254</td>
			<td style="width:380px;">The DNase I enzyme comes as lyophilized powder in a glass vial. Using a sterile needle and syringe, inject 550&#181;L 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&#176;C) in single-use aliquots (80&#181;L each). The stock solution should not be thawed and refrozen.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Buffer RDD</td>
			<td style="width:183px;">Qiagen</td>
			<td style="width:119px;"></td>
			<td style="width:380px;">included in the Qiagen DNase Kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Ice cold 2-mL screw-cap tubes</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">For lysing cells during RNA extraction</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">bead mill homogenizer</td>
			<td style="width:183px;">Biospec Mini-Beadbeater-24</td>
			<td align="right" style="width:119px;">112011</td>
			<td style="width:380px;">Keep in cold room</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Bacto Peptone</td>
			<td style="width:183px;">BD</td>
			<td style="width:119px;">DF0118</td>
			<td style="width:380px;">for liquid YPD media</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Bacto Yeast Extract</td>
			<td style="width:183px;">BD</td>
			<td style="width:119px;">DF0886</td>
			<td style="width:380px;">for liquid YPD media</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">glucose</td>
			<td style="width:183px;">Fisher</td>
			<td style="width:119px;">D16</td>
			<td style="width:380px;">for liquid YPD media</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Qubit assay tubes</td>
			<td style="width:183px;">Thermo Fisher</td>
			<td style="width:119px;">Q32856</td>
			<td style="width:380px;">for measuring nucleic acid concentration</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Quant-iTTM dsDNA BR Assay Kit</td>
			<td style="width:183px;">Thermo Fisher</td>
			<td style="width:119px;">Q32853</td>
			<td style="width:380px;">for measuring nucleic acid concentration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Quant-iTTM RNA Assay Kit</td>
			<td style="width:183px;">Thermo Fisher</td>
			<td style="width:119px;">Q32855</td>
			<td style="width:380px;">for measuring nucleic acid concentration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Qubit Fluorometer</td>
			<td style="width:183px;">Thermo Fisher</td>
			<td style="width:119px;">Q33216</td>
			<td style="width:380px;">for measuring nucleic acid concentration</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Commercial electrophoresis system</td>
			<td style="width:183px;">Agilent</td>
			<td>Bioanalyzer 2100</td>
			<td style="width:380px;">for measuring nucleic acid quality</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Next-generation sequencer</td>
			<td style="width:183px;">Illumina</td>
			<td style="width:119px;">HiSeq 2500</td>
			<td style="width:380px;">for sequencing libraries</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">automated liquid handling system</td>
			<td style="width:183px;">Wafergen</td>
			<td style="width:119px;">Apollo 324</td>
			<td style="width:380px;">for creating sequencing libraries</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">PrepX PolyA mRNA Isolation Kit</td>
			<td style="width:183px;">Wafergen</td>
			<td align="right" style="width:119px;">400047</td>
			<td style="width:380px;">for isolating mRNA from total RNA</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">PrepX RNA SEQ for Illumina Library Kit</td>
			<td style="width:183px;">Wafergen</td>
			<td align="right" style="width:119px;">400039</td>
			<td style="width:380px;">for creating strand-specific sequencing libraries from total RNA</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:292px;">Barcode Splitter</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">https://toolshed.g2.bx.psu.edu/repository?repository_id=7119c4f7a89efa57&#38;changeset_revision=e7b7cdc1834d</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Samtools, which includes the gzip command</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">http://www.htslib.org/download/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">Trimmomatic</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">http://www.usadellab.org/cms/?page=trimmomatic</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:292px;">Bowtie2 (installed before TopHat)</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">TopHat</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">https://ccb.jhu.edu/software/tophat/index.shtml</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">HTSeq</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">http://www-huber.embl.de/HTSeq/doc/overview.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">R (installed before R Studio)</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">https://cran.rstudio.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:292px;">R Studio (free version)</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td style="width:380px;">https://www.rstudio.com/products/rstudio/download/</td>
		</tr>
	</tbody>
</table>

measuring mrna levels over time during the yeast s. cerevisiae hypoxic response

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.

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 s...

Watch this Scientific Journal Video about Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response at JoVE.com

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

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.

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

Complex changes in gene expression typically mediate a large portion of a cellular response. Each gene may change expression with unique kinetics as the ...

The overall goal of this experiment is to monitor the mRNA levels of all genes over time as S.Cerevisiae cells are exposed to hypoxia.This method can help answer key questions in the cell signaling fields such as what it is the kinetics of gene expression changes during a cellular response?The main advantage of this technique is that mRNA levels are measured over time as opposed to being measured at one point during the cellular response.Though this method can provide insight into the yeast hypoxic response, it can also be applied to other microbial species that can be grown in liquid culture.Visual demonstration of this method is critical for conveying the detailed layout of flasks and tubing connections and the rapid collection of cells from hypoxia.One day or more before the hypoxia time course prepare the materials needed.Place the nitrogen tank incubator, vacuum, and filtering system in close proximity to enable quick processing of cells.Prepare sterile liquid YPD media by mixing glucose yeast extract, and peptone in a glass bottle and autoclaving it.The next step is to plan the layout of the flasks in the incubator following this schematic, from the nitrogen 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.For example, here is a possible layout of flasks and connections for seven hypoxic time points.On the day before the time course prepare a pre-culture of one yeast strain.Use a sterile applicator stick to pick up a full colony from a plate, place the stick into 5 mL of liquid YPD in a sterile test tube and shake the stick until most of the cells are in suspension.Rotate the tube at 30

measuring-mrna-levels-over-time-during-yeast-s-cerevisiae-hypoxic

Research

JoVE Journal

Genetics

Determination of the Optimal Chromosomal Location(s) for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In bacteria, the impact of the genetic context on the function of a genetic element can be difficult to assess. Several mechanisms, including topological effects, transcriptional interference from neighboring genes, and/or replication-associated gene dosage, may affect the function of a given genetic element. Here, we describe a method that permits the random integration of a DNA element into the chromosome of Escherichia coli and select the most favorable locations using a simple growth competition experiment. The method takes advantage of a well-described transposon-based system of random insertion, coupled with a selection of the fittest clone(s) by growth advantage, a procedure that is easily adjustable to experimental needs. The nature of the fittest clone(s) can be determined by whole-genome sequencing on a complex multi-clonal population or by easy gene walking for the rapid identification of selected clones. Here, the non-coding DNA region DARS2, which controls the initiation of chromosome replication in E. coli, was used as an example. The function of DARS2 is known to be affected by replication-associated gene dosage; the closer DARS2 gets to the origin of DNA replication, the more active it becomes. DARS2 was randomly inserted into the chromosome of a DARS2-deleted strain. The resultant clones containing individual insertions were pooled and competed against one another for hundreds of generations. Finally, the fittest clones were characterized and found to contain DARS2 inserted in close proximity to the original DARS2 location.

Determination of the Optimal Chromosomal Locations for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

Here, the power of a transposon-mediated random insertion of a non-coding DNA element was used to resolve its optimal chromosomal position.

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In ...

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Sensory systems allow animals to detect, process, and respond to their environment. Food abundance is an environmental cue that has profound effects on animal physiology and behavior. Recently, we showed that modulation of longevity in the nematode Caenorhabditis elegans by food abundance is more complex than previously recognized. The responsiveness of the lifespan to changes in food level is determined by specific genes that act by controlling information processing within a neural circuit. Our framework combines genetic analysis, high-throughput quantitative imaging and information theory. Here, we describe how these techniques can be used to characterize any gene that has a physiological relevance to broad-range dietary restriction. Specifically, this workflow is designed to reveal how a gene of interest regulates lifespan under broad-range dietary restriction; then to establish how the expression of the gene varies with food level; and finally, to provide an unbiased quantification of the amount of information conveyed by gene expression about food abundance in the environment. When several genes are examined simultaneously under the context of a neural circuit, this workflow can uncover the coding strategy employed by the circuit.

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Here, we present a framework to relate broad-range dietary restriction to gene expression and lifespan. We describe protocols for broad-range dietary restriction and for quantitative imaging of gene expression under this paradigm. We further outline computational analyses to reveal underlying information processing features of the genetic circuits involved in food-sensing.

Sensory systems allow animals to detect, process, and respond to their environment. Food abundance is an environmental cue that has profound effects on ...

Measuring Gene Expression in Bombarded Barley Aleurone Layers with Increased Throughput

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for germination or early seedling development are activated by gibberellin (GA), while genes associated with stress responses are activated by abscisic acid (ABA). The mechanisms of GA and ABA signaling can be interrogated by introducing reporter gene constructs into aleurone cells via particle bombardment, with the resulting transient expression measured using enzyme assays. An improved protocol is reported that partially automates and streamlines the grain homogenization step and the enzyme assays, allowing significantly more throughput than existing methods. Homogenization of the grain samples is carried out using an automated tissue homogenizer, and GUS (&#946;-glucuronidase) assays are carried out using a 96-well plate system. Representative results using the protocol suggest that phospholipase D activity may play an important role in the activation of HVA1 gene expression by ABA, through the transcription factor TaABF1.

An improved protocol is presented for the measurement of transient gene expression from reporter constructs in barley aleurone cells after particle bombardment. The combination of automated grain grinding with 96-well plate enzyme assays provides high throughput for the procedure.

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for ...

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Measuring Exercise Levels in Drosophila melanogaster Using the Rotating Exercise Quantification System (REQS)

Drosophila melanogaster is a new model organism for studies in exercise biology. To date, two main exercise systems, the Power Tower and the Treadwheel have been described. However, a method to measure the amount of additional animal activity induced through the exercise treatment has been lacking. The Rotating Exercise Quantification System (REQS) fills this need, providing a measure of animal activity for animals experiencing rotational exercise. This protocol details how to use the REQS to assess animal activity during rotational exercise and illustrates the type of data that can be generated. Here, we demonstrate how the REQS is used to measure sex- and strain-specific differences in exercise induced activity. The REQS can also be used to evaluate the impact of various other experimental parameters such as age, diet, or population size on exercise induced activity. In addition, it can be used to compare the efficacy of different exercise training protocols. Importantly, it provides an opportunity to standardize exercise treatments between strains, allowing the researcher to achieve equal amounts of activity between groups if needed. Thus, the REQS is a notable new resource for exercise biologists working with the Drosophila model system and complements existing exercise systems.

Measuring Exercise Levels in Drosophila melanogaster Using the Rotating Exercise Quantification System REQS

The Rotating Exercise Quantification System (REQS) can induce exercise in Drosophila melanogaster through rotation while simultaneously measuring the amount of activity performed by the animals. Here, we present a point-by-point protocol detailing how to measure activity levels of animals experiencing rotational exercise treatments using the REQS.

Drosophila melanogaster is a new model organism for studies in exercise biology. To date, two main exercise systems, the Power Tower and the Treadwheel ...

Mating-based Overexpression Library Screening in Yeast

Budding yeast has been widely used as a model in studying proteins associated with human diseases. Genome-wide genetic screening is a powerful tool commonly used in yeast studies. The expression of a number of neurodegenerative disease-associated proteins in yeast causes cytotoxicity and aggregate formation, recapitulating findings seen in patients with these disorders. Here, we describe a method for screening a yeast model of the Amyotrophic Lateral Sclerosis-associated protein FUS for modifiers of its toxicity. Instead of using transformation, this new screening platform relies on the mating of yeast to introduce an arrayed library of plasmids into the yeast model. The mating method has two clear advantages: first, it is highly efficient; second, the pre-transformed arrayed library of plasmids can be stored for long-term as a glycerol stock, and quickly applied to other screens without the labor-intensive step of transformation into the yeast model each time. We demonstrate how this method can successfully be used to screen for genes that modify the toxicity of FUS.

This article presents a mating-based method to facilitate overexpression screening in budding yeast using an arrayed plasmid library.

Budding yeast has been widely used as a model in studying proteins associated with human diseases. Genome-wide genetic screening is a powerful tool ...

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Global defects in RNA polymerase II transcription might be overlooked by transcriptomic studies analyzing steady-state RNA. Indeed, the global decrease in mRNA synthesis has been shown to be compensated by a simultaneous decrease in mRNA degradation to restore normal steady-state levels. Hence, the genome-wide quantification of mRNA synthesis, independently from mRNA decay, is the best direct reflection of RNA polymerase II transcriptional activity. Here, we discuss a method using non-perturbing metabolic labeling of nascent RNAs in Saccharomyces cerevisiae (S. cerevisiae). Specifically, the cells are cultured for 6 min with a uracil analog, 4-thiouracil, and the labeled newly transcribed RNAs are purified and quantified to determine the synthesis rates of all individual mRNA. Moreover, using labeled Schizosaccharomyces pombe cells as internal standard allows comparing mRNA synthesis in different S. cerevisiae strains. Using this protocol and fitting the data with a dynamic kinetic model, the corresponding mRNA decay rates can be determined.

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

The protocol described here is based on the genome-wide quantification of newly synthesized mRNA purified from yeast cells labeled with 4-thiouracil. This method allows to measure mRNA synthesis uncoupled from mRNA decay and, thus, provides an accurate measurement of RNA polymerase II transcription.

Global defects in RNA polymerase II transcription might be overlooked by transcriptomic studies analyzing steady-state RNA. Indeed, the global decrease in ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences is desired. We herein provide a detailed in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) protocol to study the RNA structural change in the presence of an experimental drug for spinal muscular atrophy (SMA), survival of motor neuron (SMN)-C2, and in exon 7 of the pre-mRNA of the SMN2 gene. In in vitro SHAPE, an RNA sequence of 140 nucleotides containing SMN2 exon 7 is transcribed by T7 RNA polymerase, folded in the presence of SMN-C2, and subsequently modified by a mild 2&#39;-OH acylation reagent, 2-methylnicotinic acid imidazolide (NAI). This 2&#39;-OH-NAI adduct is further probed by a 32P-labeled primer extension and resolved by polyacrylamide gel electrophoresis (PAGE). Conversely, 2&#39;-OH acylation in in-cell SHAPE takes place in situ with SMN-C2 bound cellular RNA in living cells. The pre-mRNA sequence of exon 7 in the SMN2 gene, along with SHAPE-induced mutations in the primer extension, was then amplified by PCR and subject to next-generation sequencing. Comparing the two methodologies, in vitro SHAPE is a more cost-effective method and does not require computational power to visualize results. However, the in vitro SHAPE-derived RNA model sometimes deviates from the secondary structure in a cellular context, likely due to the loss of all interactions with RNA-binding proteins. In-cell SHAPE does not need a radioactive material workplace and yields a more accurate RNA secondary structure in the cellular context. Furthermore, in-cell SHAPE is usually applicable for a larger range of RNA sequences (~1,000 nucleotides) by utilizing next-generation sequencing, compared to in vitro SHAPE (~200 nucleotides) that usually relies on PAGE analysis. In case of exon 7 in SMN2 pre-mRNA, the in vitro and in-cell SHAPE derived RNA models are similar to each other.

Detailed protocols for both in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) experiments to determine the secondary structure of pre-mRNA sequences of interest in the presence of an RNA-targeting small molecule are presented in this article.

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

Characterizing Histone Post-translational Modification Alterations in Yeast Neurodegenerative Proteinopathy Models

Neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Parkinson&#39;s disease (PD), cause the loss of hundreds of thousands of lives each year. Effective treatment options able to halt disease progression are lacking. Despite the extensive sequencing efforts in large patient populations, the majority of ALS and PD cases remain unexplained by genetic mutations alone. Epigenetics mechanisms, such as the post-translational modification of histone proteins, may be involved in neurodegenerative disease etiology and progression and lead to new targets for pharmaceutical intervention. Mammalian in vivo and in vitro models of ALS and PD are costly and often require prolonged and laborious experimental protocols. Here, we outline a practical, fast, and cost-effective approach to determining genome-wide alterations in histone modification levels using Saccharomyces cerevisiae as a model system. This protocol allows for comprehensive investigations into epigenetic changes connected to neurodegenerative proteinopathies that corroborate previous findings in different model systems while significantly expanding our knowledge of the neurodegenerative disease epigenome.

This protocol outlines experimental procedures to characterize genome-wide changes in the levels of histone post-translational modifications (PTM) occurring in connection with the overexpression of proteins associated with ALS and Parkinson&#39;s disease in Saccharomyces cerevisiae models. After SDS-PAGE separation, individual histone PTM levels are detected with modification-specific antibodies via Western blotting.

Neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Parkinson&#39;s disease (PD), cause the loss of hundreds of thousands of lives ...

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely related biochemical pathways. Previous methods such as the Synthetic Genetic Array (SGA) analysis, and random mutagenesis techniques using ultraviolet (UV) or chemicals like ethyl methanesulfonate (EMS) or N-ethyl-N- nitrosourea (ENU), have been widely used but are often costly and laborious. Also, these mutagen-based screening methods are frequently associated with severe side effects on the organism, inducing multiple mutations that add to the complexity of isolating the suppressors. Here, we present a simple and effective protocol to identify suppressor mutations in mutants which confer a growth defect in Schizosaccharomyces pombe. The fitness of cells with a growth deficiency in standard rich liquid media or synthetic liquid media can be monitored for recovery using an automated 96-well plate reader over an extended period. Once a cell acquires a suppressor mutation in the culture, its descendants outcompete those of the parental cells. The recovered cells that have a competitive growth advantage over the parental cells can then be isolated and backcrossed with the parental cells. The suppressor mutations are then identified using whole-genome sequencing. Using this approach, we have successfully isolated multiple suppressors that alleviate the severe growth defects caused by loss of Elf1, an AAA+ family ATPase that is important in nuclear mRNA transport and maintenance of genomic stability. There are currently over 400 genes in S. pombe with mutants conferring a growth defect. As many of these genes are uncharacterized, we propose that our method will hasten the identification of novel functional interactions with this user-friendly, high-throughput approach.

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

We present a simple suppressor screen protocol in fission yeast. This method is efficient, mutagen-free, and&#160; selective&#160; for&#160; mutations that&#160; often&#160; occur at&#160; &#160;a&#160; single&#160; genomic&#160; locus.&#160; The protocol is suitable for isolating suppressors that alleviate growth defects in liquid culture that are caused by a mutation or a drug.

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely ...