BIOD21 LABS – Fall 2024 Weeks 3-5 PDF

© Jasmin Patel and Sonia Gazzarrini, 2024 BIOD21 LABS – Fall 2024 WEEKS 3 - 5: Gene expression analysis by RT-qPCR ______________________________________________________________________ WEEK 3 (Sep 18 -19, 2024) BIOINFORMATICS lab 2: Gene Expression Analysis and qPCR **TA office hours for oral...

© Jasmin Patel and Sonia Gazzarrini, 2024 BIOD21 LABS – Fall 2024 WEEKS 3 - 5: Gene expression analysis by RT-qPCR ______________________________________________________________________ WEEK 3 (Sep 18 -19, 2024) BIOINFORMATICS lab 2: Gene Expression Analysis and qPCR **TA office hours for oral presentations: students prepare drafts of weeks 1-2 Figures in PowerPoint, Google slides, or similar programs - in the labs** Include: CBF4, ACT7 and PP2A-A3 gene structures, primer annealing sites, gDNA isolation and gel electrophoresis gels, and PCR gels. Show them to the TA WEEK 4 (Sep 25 – 26, 2024) Exercise 6: RNA isolation Exercise 7: RNA analysis agarose gel electrophoresis spectrophotometer Exercise 8: One-step RT-qPCR (cDNA synthesis and PCR) Þ QUIZ2 (RNA and RT-qPCR). In class, Sep 26, 2-3 pm. Þ Bioinformatics Assignment 2 due Sep 27, 11:59 pm. WEEK 5 (Oct 2 – 3, 2024) Exercise 9: Gene expression analysis (∆∆CT method) Gel loading of qPCR products qPCR graph analysis (Melt curves; Ct values; gene expression level, statistical analysis) Data discussion, Q&A for oral presentations **TA office hours for oral presentations Students prepare drafts of weeks3-5 Figures in PowerPoint/Google - in the labs** Include: schematics of RNA isolation, RNA gel, spectrophotometer readings, RT- qPCR data (PCR curves, melting curves) qPCR gels, graph showing normalized gene expression levels in control vs treatment with statistical analysis Exercise 10: Cloning CBF4 sgRNA1 and sgRNA11 into CRISPR/Cas9 vector: PCR-amplification of the insert fragment containing the sgRNA sequences à needed for WEEK 7 LAB exercises Þ ORAL PRESENTATIONS. Oct 9, 2024; 2-5 pm __________________________________________________________________ 1 Outline of Weeks 3 to 5 Labs In weeks 3 to 5, you will characterize CBF4 expression pattern in silico (microarrays and RNA sequencing) and then CBF4 transcriptional regulation by abiotic stress and by the stress hormone, ABA. First, you will conduct in silico analysis of microarray and RNAseq data; design primers for qPCR; quantify gene expression level/pattern and conduct statistical analysis. Then, you will isolate RNA using the Qiagen RNeasy kit. You will then remove genomic DNA contamination by treating samples with DNase I. Next, you will analyze the integrity, purity, and amount of RNA isolated, by gel electrophoresis and UV spectroscopy (spectrophotometer). Next, you will proceed to synthesize cDNA using iScript Reverse Transcriptase and amplify your gene of interest (CBF4) and reference gene (ACT7) by qPCR using iTaq DNA pol. cDNA synthesis and PCR will be done in one step. Last, you will compare CBF4 expression level in control versus treatment and characterize the transcriptional regulation of CBF4 by abiotic stress or ABA. 2 WEEK 3 BIOINFORMATICS LAB 2: In Silico Gene Expression Analysis and qPCR Primer Design Started in class, finished on your own. Today you will learn how to navigate various websites to 1) retrieve in-silico expression data from microarray and RNASeq databases, 2) analyze the primers you will use in qPCR in the lab, 3) design your own qPCR primers, 4) set-up your own qPCR reaction and analyze qPCR results including running statistical analyses. This analysis will be included in the Bioinformatics Report (download from Quercus “Bioinformatics Assignment 2” before joining the lab). 1) Determining in-silico CBF4 expression pattern and regulation using microarrays and RNAseq data. The eFP browser (http://bar.utoronto.ca/efp/cgi-bin/efpWeb.cgi) allows visualization of gene expression patterns in plants using publicly available microarrays and RNAseq data. To find CBF4 expression pattern, type CBF4 AGI (AT5G51990) in the ‘Primary Gene ID’ box and select ‘Developmental map’, under ‘Data source’. Then hit Go! This page shows you the relative expression level of CBF4 in all plant organs from microarrays. See the heat map (bottom left) to get an idea of the expression level in various organs. You can also hover your mouse cursor over the various plant organs to get the absolute expression values. Take a screen shot. Now select ‘Klepikova atlas’ under ‘Data source’, and then hit go! This page shows the expression pattern of CBF4 in various organs of Arabidopsis obtained from RNAseq data. Take a screen shot. To find the transcriptional regulation of CBF4, select other Data sources, such as “hormones” or “abiotic stress”. To find whether CBF4 is regulated by hormones or abiotic stress, and the fold of up or downregulation, under “Mode”, select “Relative” to compare expression level of treatment versus control. As a cut off for upregulation we will select at least a 2-fold change in expression level, and at least 0.5-fold for downregulated. However, this cut off is relative and one can be more of less stringent depending on the dataset and experiment. Take screenshots, including the heat map at the top, right corner (NB: it is in Log2 scale). Organize the screenshots in Figure 1, with legend. Þ Question 1: In which plant organs is CBF4 expressed? Is CBF4 regulated by abiotic stress and hormones? Which one? Þ Question 2: In week 3-5 of BIOD21 labs, RNA is isolated from seedlings exposed to ABA and various abiotic stresses. Do your qPCR results agree with CBF4 expression patterns found in these databases? If not, what could be the reason? (Check also the conditions of the experiments shown in the eFP browser). 3  FYI: The Gene Expression Omnibus (GEO) from NCBI (https://www.ncbi.nlm.nih.gov/geoprofiles/) is a public repository of microarray, next- generation sequencing, and other high-throughput functional genomic data submitted by the scientific community. 2) Determining ACT7 (AT5G0981) and PP2A (AT1G13320) expression patterns in- silico and their transcriptional regulation using microarrays and RNAseq data. Repeat the analyses done in Exercise 1 and make Figure 2 and Figure 3 (in the lab). Þ Question 3: ACT7 and PP2AA3 have been used as the reference genes in different experiments. Are they transcriptionally regulated by abiotic stress and ABA? Why is it important to know this? Þ Question 4: which reference gene would work best for your experiment, ACT7 or PP2A-A3? Explain your answer. Þ Question 5: Do your qPCR results agree with these expression patterns? If not, what could be the reason? 3. Designing your own qPCR primers. In the lab you will use primers that were designed for you. Taking into consideration what you have learned so far about primer design, design your own qPCR primers. Design optimal ACT7 For and Rev intron-flanking AND intron-spanning primers (review the lectures). Write the sequence 5’à3’ Determine the GC content, Tm, Ta, and amplicon size. You can use Primer Blast. o Keep in mind that we usually run everything in one qPCR machine, therefore the PCR cycle and Ta for your ACT7 primers should work well those used for the CBF4 primers (if possible). Show the annealing sites of these primers on the ACT7 cDNA and gDNA sequences (See Bioinformatics Lab1). Include your results in Figure 4, with legend. Þ Question 6: Which ACT7 primers would you use in the qPCR reactions to determine the transcriptional regulation of CBF4, the intron spanning or intron flanking ACT7 primers? Why? 4. Setting up a qPCR reaction. In preparation for the lab exercises, you will do the following practice exercises. The CBF4 gene is repressed by the plant growth hormone, gibberellin (GA). To confirm this, RNA was isolated from seedlings (SDL) treated with or without GA, see table: Spectrophotometer (Nanodrop) Readings [RNA] Genotype Tissue (ng/uL) 260/280 230/260 WT (Control) seedling 199 2.14 1.92 WT (+GA) seedling 373 2.15 2.04 4  Was the RNA of good quality? Why? See lecture and/or description in Exercise 7. Calculate the volume of RNA stock needed to add 50 ng RNA in the qPCR reaction (see Exercises 7 and 8). 5. Calculate gene expression level After running the qPCR, you obtain the following Ct values: Ct values Genotype Tissue Gene CT Average ∆CT ∆∆CT RQ Type CT WT (Control) SDL ACT7 14.76 SDL ACT7 14.67 SDL ACT7 14.78 SDL CBF4 19.96 SDL CBF4 20.15 SDL CBF4 20.35 WT (+GA) SDL ACT7 15.05 SDL ACT7 15.32 SDL ACT7 15.12 SDL CBF4 22.16 SDL CBF4 21.85 SDL CBF4 22.25 Calculate gene expression level using the -delta delta Ct method. Show your calculations. Þ Include your results in Table 1. 6. Calculate statistical significance of gene expression level For this exercise you will be using Microsoft Excel or an equivalent program to calculate the statistical significance of your relative expression level values. In qPCR, you would calculate the values using the averages of three technical repeats (made with the same RNA/cDNA) of three separate biological repeats (RNA isolated from three different samples) of control versus treatment. This is to ensure that there aren’t pipetting mistakes. For simplicity, we will calculate the averages of three biological repeats (no technical repeats). Þ Question 7: Why can’t we run the statistical analysis on the single value (i.e. one biological repeat) for control and treatment? Now open Excel or an Excel equivalent program and make the following chart. Input the values from the chart above in the correct location. 5  Calculate the - DCT o You can do this easily on Excel by clicking the cell to do the calculation in (D3) and type “=C3-B3” and this will do the calculation for you. To do the rest of the cells, hover to the bottom right corner of D3 and a black coloured “+” should appear, click that and drag down to D8 and all the calculations should be done. Calculate the DDCT o You will first take the average of the DCT values for your control and calculate that in cell E2, this will be the value you use to calculate the DDCT. o Next, in cell E3, type “=D3-$E$2” and hit enter. Hover over the bottom right corner of cell E3 and click the black “+” and drag it down to E8. o This will calculate based on that average DCT value from your control. Calculate the RQ o In cell F3 type “POWER(2,-E3)” and hit enter. Hover over the bottom right of cell F3 and click the black “+” and drag it down to F8. o You should now have three RQ values Calculate the P value using a T-test o We will now use the t-test formula which require four values § Array1: is the first data set § Array2: is the second data set § Tails: specifies the number of distributions tails to return: one-tailed distribution = 1; two-tailed distribution = 2 § Type: is the kind of t-test: paired=1, two-sample equal variance = 2, two sample unequal variance = 3 o In an empty cell type “=T.TEST(select the control RQ values, select the treatment RQ values, 2, 2)” and hit enter and this is your P value Þ Question 8: What is a significant P value? What is your P value? What does the P value mean and what conclusion can you make about your relative expression value? 6 WEEK 4 Before coming to the lab, read the protocol, complete the Laboratory Notebook –Prelab. Exercise 6: RNA isolation PRECAUTIONS FOR HANDLING OF RNA (from Roche). Working with RNA is more demanding than working with DNA, because of the chemical instability of the RNA and the ubiquitous presence of RNases. Unlike DNases, RNases do not need metal ion co-factors and can maintain activity even after prolonged boiling or autoclaving. Therefore, special precautions should be taken when working with RNA: Always wear gloves when working with RNA. Clean benches with 100% ethanol. Electrophoresis tanks for RNA analysis can be cleaned with 1% SDS, rinsed with H2O, rinsed with absolute ethanol, and finally soaked in 3% H2O2 for 10 minutes. Rinse tanks with DEPC-treated H2O before use. All solutions should be made with DEPC-treated H2O. DEPC (Diethyl pyrocarbonate) is a strong, but not absolute, inhibitor of RNases. It is commonly used at a concentration of 0.1% to inactivate RNases on glass or plasticware or to create RNase- free solutions and water. DEPC inactivates RNases by covalent modification of histidine, lysine, cysteine, and tyrosine residues. Extract RNA as quickly as possible after obtaining samples. For best results, use either fresh samples or samples that have been quickly frozen in liquid nitrogen and stored at –70°C. This procedure minimizes degradation of crude RNA by limiting the activity of endogenous RNases. See https://lifescience.roche.com/en_ca/articles/precautions-for-handling-of-rna.html RNA PURIFICATION USING RNEASY TECHNOLOGY (from Qiagen). “The RNeasy procedure combines the selective binding properties of a silica-based membrane with the speed of microspin technology. A specialized high-salt buffer system allows RNA longer than 200 bases to bind to the RNeasy silica membrane. Samples are first lysed and homogenized in the presence of a highly denaturing guanidine-thiocyanate– containing buffer, which immediately inactivates RNases to ensure purification of intact RNA. Ethanol is added to provide appropriate binding conditions, and the sample is then applied to the RNeasy Mini spin column, where the total RNA binds to the membrane and contaminants are efficiently washed away. High-quality RNA is then eluted in water. With the RNeasy procedure, all RNA molecules longer than 200 nucleotides are purified. The procedure provides an enrichment for mRNA since most RNAs