Abstract time: Structures for motility are non-existent in vegetative-stage centric diatoms yet they exhibit a sinking property and have been found to be positively buoyant. The sinking and ascent rates of diatoms can vary for different reasons within species as well as between species. This could allow them to migrate to areas abundant in nutrients and light which is key to their survival. These rates can have a large impact by affecting the transport of carbon to the ocean floor. To obtain these rates, the following methods were used: the SETCOL method, where the rates are a measure of a homogenous population, and particle tracking via video recording, where the rate of each individual particle is measured. To study the differences in mean sinking and ascent rate outputs of both methods, a model incorporating the basic principles of each was created using Python 2.7. The model will prove useful in determining combinations of sinking and ascent rates that lead to a positively buoyant SETCOL measurement. We will also be able to assess how individual sinking rates modify the population’s mean sinking rate and the percent of buoyant particles that must be present in a population for there to be a significant mean ascent rate measurement.
So this is the last blog post. It’s been an amazing experience with amazing people. I learned so much from my mentors and I really appreciate the time they spent teaching me. I also want to thank them for turning me into a diatom believer. This summer would not have been complete without my REU family. They’re the best bunch of people. I’ll definitely miss it all and look forward to our REUnion.
Well everyone, this is it, my final blog post for the summer. I’ve learned a lot and spent some great moments here at UTMSI and I’d like to thank everyone that made it possible. Our mentors provided us with useful advice and lessons that will definitely help us in our future years. And finally my fellow REUs made the entire experience that much greater, we had many laughs and moments we’ll never forget. It was definitely an unforgetable experience from which we all learned a lot, and I’m sure going to miss everyone. But as is the custom, here is the abstract for my seminar presentation.
In recent years there has been a large expansion of the tree of life from the massive number of novel genomes that have been obtained using metagenomics in recent years. Examination of commonly used amplification primers for 16S rRNA gene diversity surveys revealed that they overlook many newly discovered archaeal groups. Thus, we redesigned universal PCR primers 530F and 907R that target novel archaeal groups. The community structure of the Mission Aransas NERR Research Reserve (near Port Aransas, TX) shallow estuary sediments has not been previously explored. To investigate the diversity of these communities we sectioned (3 cm intervals, down to 15 cm) sediment cores from 3 different sites (east and west Copano Bay and Mesquite Bay) in the reserve. Dissolved oxygen, nitrate, and nitrite porewater concentrations were measured in the sediments and the cores were subdivided to extract total DNA and RNA. Phylogenetic analyses revealed the presence of several novel lineages including 4 archaeal phyla and 9 different bacterial phyla in Copano Bay. The archaea belong to phyla that have been previously associated with marine and estuary sediments around the world including 2 clades (Altiarchaeales and a novel lineage) within the DPANN superphylum, Euryarchaeota (Marine Benthic Group D), and Bathyarcheota. The bacteria include Planctomycetes, Betaproteobacteria, Deltaproteobacteria, and candidate phyla WS3 and OP8, and what appears to be a novel phylum. This novel phylum is related to sequences previously recovered from South Pacific deep abyssal sediments. While these new primers revealed there is substantial novel diversity present in the NERR reserve, much work remains to be done involving the geochemical properties of the sediments and the ecological roles of these organisms.
Wow! It’s hard to believe that it’s already the last day of the REU! At first I was nervous, but I am more than glad I accepted this adventure. During the summer I had the amazing opportunity to work under the mentoring of Deana Erdner and wow! I learned a lot! When starting with my experiment I was a bit worried, to be honest, qPCR was something that I had never heard before. Thankfully Deana, and her lab (Tatiana, Ingrid, Yida), where always there for me. In terms of my project, thinks turned out really good! For my project I wanted to see if there was a change in the rRNA gene content of the A. tamarense during different growth phases, and after dealing with the qPCR and the standards, my results demonstrated a variability in the gene content. My experiment changed a lot from what I started with on June, so to keep you updated, here is the abstract for my REU-experiment during this summer 2016.
Ribosomal RNA gene content of the dinoflagellate Alexandrium tamarense
Phytoplankton are planktonic phototrophic organisms that play an important role in the marine ecosystem. They grow according to the availability of carbon dioxide, sunlight and nutrients. It is often challenging to understand them in their natural environment, for why scientists have been studying the ribosomal RNA (rRNA) to understand more about their physiology and way of living. The rRNA is essential to the cell because it provides structure for ribosomal protein. There have been published and unpublished studies demonstrating that there have been cases when the rRNA gene content from phytoplankton changes. For this project, quantitative PCR (qPCR) was used to analyze the rRNA gene content of the phytoplankton dinoflagellate Alexandrium tamarense. For the qPCR, the fluorescence DNA-binding dye SYBR green assays was used. The gene content was analyzed from two different perspectives: 1. taking samples in different growth stages; 2. exposing cultures to different amount of light to see if there is a relation between growth rate and rRNA gene copy number. The growth stages used for the first experiment were early and late exponential. For the second experiment, cultures were grown in different amount of light and synchronized to receive an accurate copy per cell number. The results show an unexpected change in rRNA gene content during the two phases of the early and late exponential stage. The numbers increased from early exponential to late exponential. The results were contradict for what has been seen in previous published studies. Additionally, a variability in rRNA gene content was observed from the different levels of light. This bring us to the possibility that the change in gene copy number is related to physiological adaptations.
I’m leaving Port Aransas Tx. with many good memories. Besides learning new things everyday, and having an amazing mentor, I also met a lot of amazing people it the UT-MSI. Specially the other REU students (fam). If it wasn’t because of them, their support, and their craziness, this summer experience would have not been the same! I am really grateful for this amazing opportunity, and I know this is the beginning to a new journey.
A lot of data has been gathered about lentic ecosystems, such as lakes and ponds, and lotic ecosystems, such as rivers. However, there is a gap in the literature that doesn’t address ecosystems that fall somewhere in between. One example of this is in the tidal freshwater zone. Much of the time the tidal freshwater zone flows like a river, however, when the tide rises it can cause the flow of the river to stop completely and in some cases can even cause the flow to reverse. For my project, I specifically focused on sediment respiration. Most reactions happen where the sediment meets the water so the tidal freshwater zone cannot be fully understood without a full understanding of the processes occurring along this boundary.
My goal was to determine how respiration rates differ in various tidal freshwater zones of each river. In addition, I compared respiration rates in winter months to summer months. To estimate respiration rates, I measured oxygen consumption rates and porewater nutrient concentrations at these locations. I used an oxygen microsensor to create a profile of the sediment because oxygen is required for aerobic respiration and, therefore, it is a direct indicator of cellular respiration. I also analyzed the porewater to determine fluctuations in porewater nutrient concentration since these nutrients are a product of decomposition. My findings show that respiration rates increase linearly as you move downstream. In addition, I found that respiration rates were higher in summer months. When analyzing my porewater, I found a similar bell shaped curve showing ammonium concentrations to be highest in the center of the tidal freshwater zone on both rivers. The biggest factor I found that effected respiration rate was the sediment size, the smaller the sediment size, the higher the rate of respiration.
My findings were significant because they insinuate that the tidal freshwater zone is effective and efficient at removing organic matter from the water column. This is important because if the organic matter is not removed it will flow into the estuaries and tidal waters where it will promote the growth of phytoplankton and algae and eventually alter the ecosystems in these areas.
I learned more this summer than I ever could have imagined. All of the members of my lab were so patient and easy to work for. I was also very lucky to have such a fun group of people to go through my REU with. I look forward to watching my project progress with the next sampling series. A huge thanks to everyone, hasta luego!
Hi everyone, this is the final blog post that I’ll be posting for this REU. All in all, I had a great summer of research and learned a lot, and met a lot of great people (especially my REU fam that I became so close with). Below is the abstract for my summer research symposium that I created.
Freshwater mussel species Quadrula. aurea and Quadrula. petrina have shown a strong decline in population density in Texas during recent years for unknown reasons (Burlakova et al., 2011). Sclerochronology, the study of growth patterns in hard tissues of animals, could help to identify the underlying cause of the decline by reconstructing historical population-wide growth patterns. Such growth patterns can be integrated with climate data to establish the drivers of growth. When using this approach, it is crucial to correctly identify annual increments in the prismatic layer of the mussel shells. Stable isotopes of oxygen (δ 18 O) and carbon (δ 13 C) were used to assess if visually identified increment boundaries on the mussel shells represented annual growth rings. This was done by checking for consistent oscillations of isotope values between growth boundaries. Oscillations in the isotope values likely reflect seasonal climate variation, with previous studies showing higher δ 18 O and δ 13 C values during winter (Versteegh et al., 2011). In this experiment, a computer operated micromill was used to extract carbonate samples along the prismatic growth axis in the shells of five mussel specimens for each species in order to be to be analyzed for their δ 18 O and δ 13 C values. Optimal sample weights were within 170-210µg. A significant correlation was found between δ 18 O and δ 13 C with Q. aurea(r=.69, p=7.85×10 -9 ) suggesting that climate drivers affect δ 18 O and δ 13 C variability alike. However, Q. petrina did not exhibit this correlation. The results showed that the seasonality of the isotopes did not match well with visually identified growth increments on the shell, indicating these might not represent annual growth increments. However, when averaging isotope values within visually identified ring boundaries (and in doing so, assigning a specific calendar year to these isotope values), weak trends were found between temperature and carbon and oxygen isotope values, which is consistent with previously published results (Dettman et al., 1998). The weakness of the trends is possibly due to a small sample size, unclear growth boundaries, boundaries, and the use of air instead of water temperature. From these results, it seems that these species seem to have low potential for use in sclerochronology; however, this shortcoming may be overcome with older specimens and a larger sample size.
The Flow Cytometer
One of the normal things people say about science, is that things almost never goes by the way you expect them to, that behind every project there are many backup plans, and I’ve proven this to be right. For the last few weeks I have been growing cultures of six different phytoplankton, and running fluorescence test on each one of them to keep track of their growth phase: exponential or stationary. With the intention of isolating single cells from each culture and do the qPCR and cell cycle analysis. This way I would be able to know the rRNA gene content number. Plan A was to isolate single cells using the flow cytometer: a machine that analyzes single cells, making them pass through a laser and using fluorescence signals to sort the cells and separate them into different properties using light.
So many Alexandrium cells moving around!
Still, the flow cytometer wouldn’t work when I tried to use it. Therefore I had to go with plan B: isolate singles cells from one of my phytoplankton cultures with a pipette. Meaning, I spent a day, in front of a microscope, isolating 78 single cells from Alexandrium (the phytoplankton I ended up choosing). After isolating the cells, I ran qPCR analysis on them. This was made today, and it appears to be successful, still the data needs yet to be analyzed to see how it turned out. Hopefully it will turn out the way I expect it to, and even better, we manage to fix the flow cytometer.
There is ONE cell in each of this PCR’s wells!
Hello everybody! It has now been about a month living in Port Aransas and working in Dr. Zhanfei Liu’s lab. I must say that I am having the greatest summer of my life here. I’ve met and became great friends with the other interns, faculty, and graduate advisers. The atmosphere here is very positive and I feel like I can accomplish any goals and milestones on my project and potential career.
I have now developed a protocol for what I want to do with my research. My overarching question is, “Is there a definite spatial and temporal distribution of phytoplankton and nutrients between the San Antonio, Lavaca, Mission-Aransas, and Nueces rivers?”. My hypothesis is that distribution of phytoplankton and nutrients increases at lower latitudes. There have already been major weather events such as flooding earlier this year. Each river may vary in nutrient and pigment concentrations after this weather event due to the size and spread of their corresponding watersheds. Furthermore, I predict that the San Antonio river will have the greatest amount of diversity and nutrient concentration within its community structure due to its enormous length and watershed size encompassing many towns and cities. I also predict that pigment composition will be its highest in all rivers during the summer season.
Extraction of pigments from GF/F filters using acetone
Extracts are loaded in this sample holder.
Analysis of pigment types and concentrations are achieved through instrumental analysis via HPLC
Peaks on the data spreadsheet indicate a certain pigment and its relative concentration
Summer 2016 Phytoplankton community profiles will be created via HPLC analyzed pigment composition of the river samples. These will then be graphed and coupled with analyzed data from fall 2015, winter 2016, and spring 2016 to determine a pattern, if any, between seasons, latitude, and nutrient concentrations of the five rivers. Diversity and dominant species of phytoplankton communities will be calculated using Shannon-Weiner Diversity Index and Simpson’s Dominance Index. Similarity between the five rivers will be analyzed using Bray-Curtis Index.
I have already analyzed pigment samples from fall 2015 to spring 2016. My next task is to go and sample the five rivers within the first week of July. After all nutrient and pigment samples have been ran, I will then process and integrate the metadata! I’m so excited about the this research and I think I will find something pretty interesting about these rivers! Thanks for reading and stay tuned for future blog posts! 🙂
Hey guys, this summer I am studying two species of freshwater mussels native to Texas, Q. petrina and Q. aurea. The significance of studying these species is that both are considered threatened species in Texas, and little is known about the environments that they live in throughout the course of their lives. I have made significant headway on my project since my last blog post. I have micromilled 130 samples along the prismatic growth layer of the mussel species Q. petrina. Approximately half of these samples have already been analyzed in the mass spectrometer, which will provide us with the oxygen isotope ratios (oxygen 18, in particular) and carbon isotope ratios (carbon 13) for each sample.
My next task is to use these shell isotope ratios to reconstruct the environmental conditions that the mussels experienced during their lifetimes. The shells oxygen isotope values can be used to estimate the water temperatures that these mussels experienced using methods in previous studies. Additionally, the isotope ratios can be compared to a time series of the mussel using growth increment chronology in order to show seasonal oscillations in the isotope values, hopefully illustrating temperature fluctuation and other events such as flooding.
The below picture is a cross section of Q. petrina. Each cross section of this species contains three different layers. The periostracum is the very outer layer, contains no growth increments and under the microscope it is the darkest of the three layers. The second is the prismatic layer, which is the colored middle layer, and contains the growth increments that we want for the experiment. The third layer is the nacreous layer, which contains many growth increments, but cannot be used because there are too many growth increments in each area, and therefore cannot serve as proxy for any period of time in particular. One of my jobs is to use the computer operated Micromill to drill out samples in the shape of raster’s (basically rectangles) into the prismatic layer. By lining the raster’s one after another, it allows us to then use the shell isotope values from the samples to compare with their respective raster areas, combined with measuring the distance between the midpoints of each raster to the next, to create a time series with a growth-climate relationship. More to come soon!
P.S. Sorry for picture quality, my original picture file was too big to post, so I had to get creative.
We are halfway through the REU program. My model is complete. It is a series of functions that takes a list of diatom sinking and ascent rates, as well as properties of the SETCOL column, and individually assigns each diatom a position. Inputting time gives the diatoms a new position based on their sinking or ascent rates. We are then left with all the information necessary to analyze it through the SETCOL method, yielding the mean ascent and mean sinking rates. I now have to test it to make sure that it is doing what it is supposed to do. The next step is to obtain frequency distributions of diatom sinking and ascent rates to input into the model. This will be done by tracking individual particles with video technology. For this I have been growing different species of diatoms. There isn’t much else to say so I will post some pretty pictures of diatom chains that I’ve taken.
Pictures of diatoms I’ve seen.
From left to right: Odontella spp., Chaetoceros spp., Hemiaulus spp.
This picture is courtesy of Eduardo Perez. It shows the setup for tracking individual diatoms. (Also we didn’t come up with this setup. See O’Brian et al., 2006. )
These are the key equations that give the mean sinking and mean ascent rate of diatoms. It is the final step in the SETCOL method and therefore the final step in my model.
Bienfang, P. K. “SETCOL – a Technologically Simple and Reliable Method for Measuring Phytoplankton Sinking Rates.” Canadian Journal of Fisheries and Aquatic Sciences 38, no. 10 (1981): 1289-94.
O’Brien, K. R., A. M. Waite, B. L. Alexander, K. A. Perry, and L. E. Neumann. “Particle Tracking in a Salinity Gradient: A Method for Measuring Sinking Rate of Individual Phytoplankton in the Laboratory.” Limnology and Oceanography-Methods 4 (Oct 2006): 329-35.
Also refer to my previous blog post if curious as to what the purpose of my model is.
As I reach the halfway point of the program I am realizing that time is flying by, yet I have learned so much about the research process and I have experienced both the lab and the field aspect of this research endeavor. A couple of weeks ago my lab team and I began our sampling of the freshwater tidal zone for the month of June in the Aransas and Mission rivers. I took two sediment cores from five different sites from each river, and in the lab I took 1cm and 2cm slices from each core and further divided each slice for the measurements of porosity, water content, grain size and organic matter content. For grain size, I took a quarter from each slice and ran the sediment through a series of four sieves and transferred the sieved sediment into foil pans and placed in a furnace at 60°C to dry for a few days, and later weighed. Half of each slice was put in a foil envelope, weighed and placed in the furnace to dry to later be used for organic matter measurements. It took all of my samples about a week to dry, and since the Aransas and Mission river sampling was done a week apart, I am just now getting the final dry weights for the Mission river samples. I am almost done transferring my raw data onto a spreadsheet and currently working to organize everything and calculate my grain sizes, water content and porosity. Today, I began grinding the sediment samples I dried in the foil envelopes, and soon I will began the process of measuring total organic content. Although there is so much to do and it seems like there is not enough time, in a week my team and I will head out to the rivers again for the July sampling and I will repeat all of the above again and compile my data. I am not sure if I will include the data from July in my final presentation since the time frame is small, but I do hope to finish all of my work in time to be able to contribute to the overall research with my data and findings. Stay tuned for more science to come, cheers!