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Mimulus guttatus Lab Report

December 6, 2019


We conducted a Restriction Double Digest Restriction DNA study on Mimulus guttatus. The first step was collecting samples which we did on 2 field trips. Details of the protocol to complete these steps can be found here: https://usfblogs.usfca.edu/evangelinab/ . Next, we extracted DNA from the samples we collected as well as sampled previously collected by our golden boy, Alec. Details of the protocol following steps of extraction can be found here: https://usfblogs.usfca.edu/evangelinab/ . Next, we double digested our DNA using two restriction enzymes. These enzymes cut up the genome into many smaller pieces. Details for how the double digest was completed can be found here: https://usfblogs.usfca.edu/evangelinab/ . Next, we ligated unique DNA barcodes onto each of our individuals. Our next step was to use PCR for two purposes which are; 1. to add a second unique barcode and 2. to test if our library construction was successful. Our PCR was successful by evidence of a photo that Professor Paul was supposed to upload but did not because he has better things to do. After the test PCR, we did a larger reaction of 25 micrometers that is identical. this is the last step that we were able to do as a class. Details of the protocol for how the two PCR tests were run can be found here: https://usfblogs.usfca.edu/evangelinab/ . In a perfect world, we would do the following other steps. However, Professor Paul will try to do these steps over the break while I sleep. Hypothetically, the next step would be size selection. Size selection selects DNA of specific sizes. Specifically, we would target approximately 400-600 base pairs which is typically a good size to sequence with Illumina. Size selection can be done in 3 different ways using an automated system called Pippen Prep of which we have housed in the Suni lab #Suni #doc #bees #pollen. A second way is to use gel extraction. Lastly, we could also use magnetic beads to isolate the DNA. After size selection, we would then normalize our DNA samples which means we would bring all of our DNA sample to approximately the same concentration. Having equal concentrations makes more likely equal number of DNA to be sequenced. The final step would be to combine all of our size selected normalized PCR products into one vessel. Then, we would run these samples on any Illumina sequencer. For our class, we would run them on our in house I-seq1000 a.k.a. Wall-e. Sequencing would take approximately 16 hours and if successful, the sequencing would generate tens of millions of reads. These data would be run through a bioinformatics pipeline (sorry Dr. Z). Ultimately, we would align these sequence data with the Mimulus guttatus genome and call SNPs. Finally, we would use these SNPs to infer population differentiation using a metric like FST and assess population genetic diversity looking at things like the number of alleles, allelic diversity, etc.. Based on what I know about Mimulus guttatus, I might expect populations to be geographically divergent.

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Lab 12: DDRadseq

November 19, 2019


Test PCR 1

To begin our lab, we tested for successful library construction of our Mimulus guttatus samples (restriction digest and ligation of barcodes) using a test PCR. We first labeled 8 PCR tubes with the sample ID# 17-24. I then prepared a master mix for RADseq which contained; 88μL of NEB one-taq 2x Master Mix, 4.4μL ofForward Primer (PCR1 X), 4.4μL of Reverse Primer (PCR2 6), 68.2μL of pure H2O, and 1μL of the library DNA template. I added the 1μL of each library DNA template to their corresponding labeled tubes and added the 15μL of Master mix as well. The PCR was run using the “PCR1” on BIORAD #1/2. Next, the products of PCR 1 were ran for each sample on a 1.5% agarose gel with a 100 bp ladder at 130V for 40 minutes.


Test PCR 2

In the second part of our lab, we completed a second PCR run. First, we had to ass the special second barcode sequences and the illumina primers to our Mimulus guttatus libraries. This allowed us to identify which specific individuals a given sequence comes from. I started off by labelling 8 tubes with the sample ID# 17-24. We then moved on to prepare the master mix which contained; 3.4μL of Phusion DNA polymerase, 68.8μL of %x Phusion HF buffer, 17.2μL of 10μM PCR 2-5 forward primer, 17.2μL of 10μM PCR 2-5 reverse primer, 6.9μL of 10mm dNTPs, 10.3μL of DMSO and, 118.30μL pure H2O. After the master mix was made, I added 3μL of each template DNA to its corresponding labeled tubes. In addition, I added 22μL of the master mix. The tubes were capped, vortexed, and microcentrifuged. The PCR was run using the “PCR2” on BIORAD #2. Lastly, 2μL of the products from PCR2  were ran on a 1% agarose gel with a 100bp ladder.

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Lab 11: Double Digest and Ligation

November 12, 2019


Double Digest 

In our lab, we set out to digest our Mimulus guttatus samples by beginning our double-digest restriction associated DNA sequencing. I labeled 8 PCR tubes (1-8) that contained 6μL of sample each and stored the tubes on ice. We then prepared a master mix that contained; 9.90μL of CutSmart buffer 10X, 3.1μL of EcoRI-HF enzyme, 1.3μL of MSPI enzyme and 18.7μL of pure H2O for a total of roughly 33.0μL. The master mix was capped in the tube, mixed well, centrifuged, and stored on ice. I then added 3μL of master mix to all 8 PCR tubes with the DNA samples. The tubes were sealed, vortexed, placed in the centrifuge and incubated at 37° C for 8 hours on a thermocycler with a heated lid set to 50°C.


Ligation

Next, we were given the task of completing an adapter ligation for RADseq. Before making the Master Mix, I added 1μL of the working stock EcoRI adapter, EcoRI 8 and EcoRI 9, directly to my digested DNA samples which were sample ID# 23 and 24. I was then able to create the adapter ligation master mix for RADseq. The master mix contained; 4.4μL of CutSmart buffer 10X, 14.3μL of ATP, 2.2μL of T4 Ligase, 1.1μL of pure H2O and 11.0μL of Universal P2 MSPI adapter for a total volume of 33.0μL. After it was complete, I added 3μL of the master mix to the digested DNA. The tubes were sealed, vortexed, placed in the centrifuge, and incubated at 16°C for 6 hours on a thermocycler with a heated lid set to 50°C.

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Lab 10: PCR Reactions

October 5, 2019


We started our lab session by first collecting our tubes of Mimulus guttatus DNA. We obtained 12 1μL tubes and labeled them ‘QS 1-12.’ QS stands for our group name ‘Queen Salmon.’ We were sure to write down the corresponding specimen ID number to the tube number assigned to each group member.

We then proceeded to make the master mix that was a key part of this PCR reaction. We were given the amount of each ingredient that was made for one reaction. Since we had 12 PCR reactions to complete, we decided to multiply these values by 18 to account for each reaction as well as any error that could occur. The ingredients consisted of; 240.48μL ddH2O, 36μL 10x buffer, 36μL MgCl2, 18μL BSA, 3.6μL dNTPs, 3.6μL F-primer, 3.6μL R-primer, 0.8μL Taq. This mixture was then vortexed to make sure that the ingredients were properly mixed together.

We then took the new labeled tubes and added 19μL of the master mix as well as the 1μL template from our individual samples. The tubes were then vortexed for a few seconds before being stored on ice.

 

 

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Lab 9: Gel Electrophoresis

October 29, 2019


We began our electrophoresis lab by gathering our Mimulus guttatus DNA samples from our last lab. I first started by dotting out 16 loading dye dots on a sheet of parafilm. I loaded 1 µl dye dots using a 20 µl micropipette. We then pipetted 3 µl of each PCR product into its own dot. After all the product was pipetted into the loading dyes, we then set the pipette to 5 µl and loaded the dots into each well. We then ran the gel at 130 volts for 30 minutes.

 

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Lab 8: Modified Alexander et al. tube protocol for DNA extraction

October 22, 2019


In this lab, we were given the sample of the Mimulus guttatus leaf that we had picked in the previous field trip that we had taken. We also were given two more samples of leaves that Alec had stored for us. When I was given the leaves, they were dried up in the tubes full of silica. Before we began using the leaf samples, we labeled 3 2.0 mL tubes with our samples codes. The first sample was labeled ‘RDRK 001’ the second was ‘EB’ and the third sample was labeled ‘SHOR 005.’  In each tube, I added three sterile 3.2-mm stainless steel beads to each tube. I added a small amount of leaf tissue to each tube using clean tweezers between each sample to avoid cross contamination.


I capped my tubes and loaded them within a tube rack into the modified reciprocating saw rack and mounted the rack to the saw. Professor Paul made sure that the blade was completely secured and locked into the saw before plugging in the saw. Professor Paul put safety goggles on and turned on the reciprocating saw on speed 3 for 40 seconds.


We briefly spun down the tubes in the centrifuge for 15-20 seconds at fast speed to pull plant dust down from the lids. After the plant dust was pulled down, I added 434 μL preheated grind buffer to each tube. We incubated the buffer grindate at 65°C for 10 minutes in a hot water bath. We made sure to mix the tubes by inverting them every 3 minutes. After incubation, we took the tubes out and added 130 μL of 3M pH 4.7 potassium acetate into each tube. The tubes were inverted several times and incubated on ice for 5 minutes. After ice incubation, we took the tubes out and placed them in a centrifuge at maximum force for 20 minutes making sure that the centrifuge was balanced.


We labeled new 1.5 mL tubes with the sample ID. I transferred the supernatant to these sterile 1.5 mL microcentrifuge tubes avoiding the transfer of any precipitate. I added 1.5 volumes of binding buffer.


We applied 650 μL of the mixture from the previous step to the Epoch spin column tubes and centrifuged the tubes for 10 minutes until all the liquid has passed through. The centrifuge was placed at 15,000 rpm and the flow-through was placed in a hazardous waste container once the centrifuge was complete. This step was repeated once more using the remaining volume from the previous step.


We washed the DNA bound to silica membrane by adding 500 μL of 70% EtOH to the column and centrifuging it at 15,000 rpm until all the liquid passed through to the collection tube. This process was done in 8 minutes and the flow-through was discarded. Repeat this step once more.


After discarding the flow-through from the last step, we centrifuged the columns at 15,000 rpm for an additional 5 minutes to make sure that any residual ethanol was removed. We were instructed to discard the collection tubes and place the columns in sterile 1.5 mL microcentrifuge tubes and add 100 μL of pure H2O. However, I made the error of placing the centrifuged columns into the tubes that contained the binding buffer and place 100 μL of H2O. When I caught my mistake, Professor Paul advised me to transfer the columns into clean 1.5 mL tubes and add an additional 100 μL of 65°C H2O to each tube and let it sit for 5 minutes. After this was done, I put the tubes in the centrifuge for 2 minutes at 15,000 rpm to elute the DNA. The new tubes were labeled ‘EB 1’ ‘EB 2’ and ‘EB 3’. I drew a star on the tubes that the columns were initially put into so that we may use them in the future if we find that no DNA was extracted into the new tubes.


 

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Lab 7: Phylogenetic Inference

October 8, 2019


In Lab Portion:

To prepare for this lab, we were assigned the task of assembling an alignment of COI sequences that includes our fish DNA barcode sequences and sequences downloaded from NCBI through Geneious. Once we had this alignment, we were told to use the ‘allow editing’ tool to ‘clean-up’ our alignment so that all the sequences begin and end at the same point. When I looked through the first 20 columns of my alignment, I found only one column that showed a polymorphism.


Choosing the best model of molecular evolution

Next, we used a program called jmodelTest2 to establish the best model of molecular evolution for my sequences. Within the folder called “jmodeltest-2.1.10.tar.gz” there was a file named ‘jModelTest.jar’ that opened a window. Before using the jmodelTest2, we had to return back to the Geneious program and export the alignment in Phylip format (relaxed). Once that was completed, I could go back to the jModelTest2 and open the exported alignment. We then were able to compute the likelihood scores’ of each model of molecular evolution as the program goes through a set of hierarchal set of 88 models based on an initial tree that it uses for all tests.

Once the likelihood calculations were completed, we used two methods to choose the best model based on some optimality criterion. The two methods were the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The best model based on AIC was model 79. Similarly, the best model based on BIC was model 79.


Bayesian Inference

Back in Geneious, we were able to install the ‘Mr.Bayes’ tab by using ‘Tools’ > ‘plugins’ in the menu bar. We selected our alignment and chose Mr.Bayes as our ‘tree’ of choice. In the settings, I chose to use the GTR (complicated) substitution model as my model of molecular evolution that was closest to my jModelTest complexity. Next, I chose to set my ‘rate variation’ to ‘gamma’ because my jModelTest best model contained a ‘+G’ in it. The outgroup that I chose for this alignment was a shark species under the consensus identity #HM991188. Under the MCMC settings, I chose a ‘chain length’ of 10,100, a ‘subsample frequency’ of 200, and a ‘burn-in length’ of 100,000. Under the ‘Priors’ settings, we used ‘Unconstrained Branch Length: Exponential (10)’ and ‘Shape Parameter (10)’ which affects the shape of the prior statistical distribution. The Posterior distribution showed that this analysis was not run for long enough. The Trace figures showed an upward sloping line that is representative of an increase in the posterior probability and is part of what you want to remove with the burn-in length. We ran our analysis one more time but used a ‘chain length’ of 110,000 and 10,000 for the ‘burn-in length’ this time. With this second run, we found that the Posterior distribution was normal and the Trace figures looked like the ‘fuzzy caterpillar’. I recovered the same clades in my tree and they both had similar support values.


Maximum Likelihood 

In this last section, I used maximum likelihood to infer a phylogenetic tree of my aligned data set. First, I went the menu bar and through ‘Tools’>’Plugin’, I was able to install RAxML which does a very fast maximum likelihood inference. For the evolutionary model, I chose ‘Rapid bootstrapping’ and run the it. Once the process was finished, I built a consensus tree by selecting the ‘RAxML bootstrapping trees.’  I found that the clades with high support do not match those from your bayesian analysis.

Next, we used a different program that uses maximum likelihood called ‘PHYML.’ We ran this analysis with bootstrapping and we used the HKY85 model of molecular evolution for this and our final Bayesian Tree.


Results from our ‘In Lab’ portion:

 


At Home portion:

Our Assignment for the second portion of this lab was to use the same alignment and the HKY85 model of molecular evolution to infer my best tree using Mr.Bayes. For this run the only modified setting were the ‘chain-length’ was set to 3,000,000, the ‘burn-in length’ at 300,000, the ‘subsample frequency’ at 500. This run lasted for about 5 hours long!


Result from our ‘At Home’ portion:

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Lab 6: An Introduction to Geneious

October 1, 2019


We began our lab session by downloading a software called Geneious. Geneious is a commercial program that performs many functions on DNA and protein sequence data. I first created a folder where I was able to download and store Fish DNA barcoding sequences from canvas. The folder contained both the reverse and forward reads and contained the chromatograms of our sequences. However, when I clicked on the forward read of my sequence it looks like I did not have a very successful PCR run at all. This may mean that bacteria or fungi could have contaminated my sample or that my sequencing run was a complete fail. Because of this, I used the sample sequences from other students which were much more successful in their PCR runs. I used the files “ARA01” and “BP01” as my new sequences. After I dropped these files into my “Fish Barcode” folder, I was able to begin assembling the forward and reverse sequences of the sample “BP01”. In this step, I was able to examine my sequences and delete any bases on the two ends that were unreadable or trim off any ambiguities. Once that was completed, I generated a consensus sequence and put it into a new file where it was ready to undergo BLAST. BLAST stands for Basic Local Alignment Search Tool and is used to search the database for highly similar sequences. The top hit for my sequence BP01 was Thunnus obesus which is yellowfin tuna. This is exactly the name of the fish that the student was told when they purchased it. For the sequence ARA03, I found that the top hit was Seriola quinqueradiata which is Yellowtail or Japanese amberjack. Once again, this is exactly the name of the fish that the student was told when they purchased it. There were polymorphisms present in both of these sequences. In the ARA03 sequence, I found 599 polymorphisms which were all SNPs. The first 10 polymorphisms were at sites 63, 68, 70, 89, 92, 95,113,128, 131, and 146. In the BP01 sequence, I found 8 polymorphisms which were also all SNPs. The polymorphisms were at sites 9, 275, 291, 363, 384, 405, and 418.

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Lab 5: Finding Mimulus

September 24, 2019


The first stop on our field trip was Mount Tamalpais. When we first pulled into this area, we saw that there was a natural stream of water coming from the mountain. Here we were able to observe a large costal parenial Mimulus. It was important to note that, we found these mats or patches of Mimulus directly next to the water source. Mimulus loves to keep its feet wet so it is not surprising that we found Mimulus near a spring. In a location like Mount Tamalpais, we were able to see how important the bee population is. Usually, the bees are the ones to pollinate the flowers along this mountain side, including Mimulus, which promotes intrabreeding and interbreeding amongst plant species. We saw that the baby Mimulus did not have much space to “move” or pollinate on its own so, it is vital that bees are able to pollinate around the mountain.

Professor Paul brought it to our attention that the Mimulus population we saw could be a sink population. This means that the population would thrive near a body of water but may get flooded out by rainfall before they are able to flower. Additionally, Alec shared that in drought years, Mimulus may thrive better because they have a longer time to flower and germinate their seeds. This would be beneficial because these Mimulus would be more likely to contribute to the next generation. I was able to learn that Mimulus responds to long day periods but this time of year, there are shorter day periods so, it may not be long enough for these Mimulus to get the proper environmental cues to grow and persist.


We then drove a few miles away from the mountain to a location called Redwood Creek. The creek was well shaded and made for a cool and moist environment with patches of sunlight for Mimulus. Our task in this location was to find a Mimulus population that was growing along this creek. The class as a whole was not able to locate these populations but, we managed to find a few small Mimulus with the help of a few careful eyes. At Redwood creek, it seemed that the populations of Mimulus that we found were much smaller and hidden than the past populations that we had seen. It was explained to us that plant species that live along Redwood creek are susceptible to flooding during the winter time. This, again, is detrimental to Mimulus because they are not able to properly germinate and contribute to future generations. Since the creek stretches for miles, it is apparent that the separated populations of Mimulus have been limited on insect pollinators that are able to travel long distances to pollinate.

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Lab 4: Gel Electrophoresis/ PCR Cleanup

Date: September 17, 2019


Electrophoresis of PCR products

We first obtained our four PCR tubes and allowed them to thaw while we prepared the gel. We used a 10 µl micropipette to load sixteen 1 µl dye dots across a sheet of parafilm. Then, we pipetted 3-5 µl of PCR product into each dye dot. We used one additional dye dot as a negative control and two additional dye dots as a ladder. As we added our PCR product, we were recording the precise location of where each PCR sample went and who each sample belonged to. We set our micropipette to 5µl and used a filtered tip. We used the micropipette to suck up the dye dot and PCR mixture from the parafilm and transfer them into the wells of the gel. After all the wells were filled, the gel was run at 130 voltz for 30 minutes.


Clean-up of PCR products for sequencing – ExoSAP

I first obtained a strip of 8 new 0.2µl PCR tubes that I shared with my lab partner. We labeled each one with our individual sample codes without dividing the individual tubes. My sample codes were; EB01, EB02, EB03, EB04.  After we labeled the tubes, we proceeded to make the ExoSAP Master Mix for our table. We decided to run 18 PCR clean-ups to account for pipette errors. We first had to multiply each ingredient in our master mix by 18 to get the correct measurements to begin making the master mix. Our masters mix consisted of; 190.6 µl of H2O, 22.5µl of 10x buffer (SAP 10x), 7.92µl of SAP, and 3.96µl of Exo. All of these reagents were kept on ice throughout the process of making the master mix. The total volume of the master mix was 225µl. We then added 7.5µl of each of our PCR products into the new clean PCR tubes we had just labeled. Next, we used a 20µl micropipette to place 12.5µl of the mix into each individual PCR tube. The tubes were all sealed with their caps and placed in the thermocycler to start the EXOSAP program. Professor Paul removed the PCR tubes after 45 minutes of running the program, and placed them in a labeled tube rack and placed in the freezer.