Baliga Lab Project

Video slideshow: (in order) Ilinca & Beatrix’s Project Presentation, A Day in the Life of Ilinca & Beatrix

Developing RT-qPCR Transcriptional Biomarkers for Thalassiosira pseudonana, Phaeodactylum tricornutum, Fragilariopsis cylindrus Carbon States

Context

Diatoms are unicellular phytoplankton that are an important part of the ocean ecosystem. They are responsible for over 40% of marine primary productivity and 20% of the world’s oxygen. That means that every fifth breath we breathe, that oxygen was made by diatoms! Diatoms are at the base of the marine ecosystem, so any shift in diatom populations will affect organisms at the top of the food web. Understanding diatoms is vital for understanding overall ocean conditions today as well as the future.

Diatoms have been around for over 250 million years and have experienced both extremely high and low carbon conditions. Currently, ocean acidification predictions estimate that the ocean could be at 1200 PPM (parts per million) of carbon dioxide in 2100. Yet, diatoms have experienced conditions with over six times that amount of carbon dioxide! So our question is not “Will diatoms survive?”, it is “How will diatom populations change?”

Figure 1: (Left to right, top to bottom) Thalassiosira pseudonana, Phaeodactylum tricornutum, Fragilariopsis cornutum, arctic food chain

Goal

Our goal is to optimize a way to predict which diatom species populations will increase and decrease as the ocean acidifies. Previous studies done by Dr. Jacob Valenzuela have identified a specific gene called the Na+(K+)/H+ antiporter that is upregulated in high carbon conditions in the diatom, Thalassiosira pseudonana (Thaps). Additional studies by Dr. Valenzuela have shown that Thaps are more resilient in high carbon conditions. Therefore, the presence of this gene in species of diatoms could be a good biomarker for identifying which diatoms will be resilient in the future. In brief, if a diatom has this gene, its population can handle more stress in higher carbon conditions.

To test whether the antiporter can be used as a biomarker, we must test to see if other species have this gene, and if so, how much is that gene being expressed in high vs. low carbon conditions. This could be done through RNA sequencing, which is expensive. However, it could be measured in a less costly way by optimizing a protocol to measure the expression of the antiporter through quantitative polymerase chain reaction (RT-qPCR). This process makes copies of a selected gene and measures how fast the number of copies is increasing. Since the process is exponential (the number of transcript copies should double every time) – the faster the number of genes increases, the higher the starting expression. 

We aimed to optimize an RT-qPCR protocol to measure the expressions of the Na+(K+)/H+ antiporter in Thaps and other diatoms. 

Process 

The first step was to grow Thaps so we could extract the DNA and RNA. RNA expression reflects the environmental conditions of the organism, so we grew Thaps in high and low carbon conditions. 

In parallel, we began designing primers, which are short single-stranded DNA that specifies which gene to amplify during PCR. We used a database created by the National Institute of Health, called Primer-BLAST, to find primers that had the potential to work. We also wanted to find orthologs (the same gene in another species) to see if the Na+(K+)/H+ antiporter also helps other diatoms be more resilient in high carbon conditions. We did this using the database created by EnsemblProtists and found many other diatoms with the antiporter. We decided to test Phaeodactylum tricornutum (Phaeo) and Fragilariopsis cylindrus (Frag) since we already had cultures of those diatoms and set them up to grow in high and low carbon conditions in mini-bioreactors. 

Figure 2: (Left to right) Large Thaps bioreactors day 1, large Thaps bioreactors day 4, Phaeo and Frag high carbon mini-reactors

Phaeo grew very well and multiplied very quickly, however, we were not able to grow Frag successfully in high and low carbon conditions because it is an arctic diatom and we did not have the proper facilities to grow it at 4°C while controlling the carbon flow. We decided to continue with the experiment using DNA and RNA from the stock cultures we already had that were grown only in ambient carbon conditions in addition to the Thaps DNA and RNA.. 

Figure 3: Frag cold room mini-reactors

Once we had all the cultures we wanted to test primers done, we extracted DNA from them. With that DNA, we did a PCR (not RT-qPCR) to see if our primers had any possibility of working on the RNA without actually having to use RNA. This PCR would only tell us if the primers worked or not. We interpreted the results of the PCR by running a gel electrophoresis. If the bands of PCR product that showed up on the gel electrophoresis were sufficiently bright and in the right location, we would know that our primers worked, and we could move on to testing them on RNA.

Results 

Our gels failed many times for many different reasons, but not because our primers weren’t working, but because of issues with the experiment itself. We had to adapt to the failures, adding more DNA, adding more primers, trying new ladders, redoing our DNA extractions, using fresh reagents, and editing our protocol, and on our sixth gel we got good bands and confirmed our primers worked, allowing us to move on to RT-PCR. 

Figure 4: all DNA gel electrophoresis results

(In order) 7/23/24 PCR #1 (light), 7/23/24 PCR #2 (dark), 7/26/24 PCR #1 (dark), 7/26/24 PCR #2 (dark), 8/2/24 PCR #1 (dark), 8/2/24 PCR #2 (dark), 8/6/24 PCR #1 (dark) (annotated), 8/7/24  PCR #1 (dark) (annotated) 

RT-PCR would allow us to test for the presence of RNA and whether our primers worked on RNA. However, PCR doesn’t work on RNA, so we must reverse transcribe the RNA into cDNA and then perform the PCR. Due to low quality RNA, we had mixed results, but enough confirmation for us to know that RNA was present and that our primers worked. 

Figure 5: all RNA gel electrophoresis results

(In order) 8/9/24 PCR #1 (dark) (annotated), 8/13/24 PCR #1 (dark) (annotated), 8/14/24 PCR #1 (dark) (annotated)