Talk Birdy To Me

Talk Birdy To Me

Monitoring bird populations is extremely important to gain an understanding of the overall ecosystem. Additionally, many species distributions are currently moving north, which is a sign of climate change. In order to measure the bird populations, trained ornithologists use the “point count” methodology, in which the observer stands in an area for a given amount of time and tallies the birds that can be heard or seen. The “point count” methodology has a few limitations. Since bird identification is an imprecise science, two very experienced ornithologists can survey the same area yet identify different birds. To account for this, most surveys increase their sample size. In order to reach the high number of survey areas in the given amount of time, ornithologists must start to cut corners, such as finding survey areas that are close to roads. However, birds are very sensitive to noise pollution, so the roadside surveys don’t accurately represent a population within the inner forest.


A new methodology to study bird populations and reduce the limitations associated with “point count” includes the usage of drones. Many ecologists use drones in their research, since they provide a unique and developing technique for studying wildlife. By using drones, we can save and refer back to an audio recording, thereby allowing ornithologists to agree on the birds present. Additionally, drones are fast and easy to maneuver, and will be able to travel to more remote survey areas.


Dr. Andy Wilson recently determined that counting birds from audio recorders on drones yields similar results to the “point count” method (Wilson 2017). Due to these benefits, drones may have a future in bird population monitoring. However, this methodology is still very new, and Dr. Wilson is skeptical about the impacts of drone usage on bird behavior. Because birds are sensitive to noise pollution, could noisy drones affect bird singing behavior? If so, there are ethical issues with using this methodology. Therefore, Dr. Wilson’s lab is currently testing the effects of drones on bird song. Check out video footage from our drone!


We travel to State Game Lands 249, where we have a permit to conduct research in the surrounding fields. We placed a 200m grid on the land, so that all survey points are equally spaced out (Figure 1). At each survey point, we create a 50m square array of SM3 wildlife audio recorders (Figure 2). After placing and turning on the SM3 recorders, we leave the area at least 10 minutes before the experimental data collection starts. This allows the birds to settle and resume normal activity. Next, we allow the recorders to continue gathering audio data for 4 minutes. This period is used as the “before drone” data. After this period, we fly the DJI Mavic Pro drone to the middle of the survey point at 48m high. The drone hovers at this location for 3 minutes. A portable audio recorder is hung from the drone by 8m of fishing line, placing the recorder at 40m from the ground. After the drone returns from the hovering point, we allow the recorders to continue for four minutes, as the “after” period. In this way, we have data from four SM3 recorders before, during, and after the drone flight, as well as data from the recorder suspended from the drone. The recordings are taken back to the lab for further analysis.

Figure 2. Google Imagery for survey point 17. The drone hovers at the yellow point and the four recorders are placed at the surrounding black points.

Figure 1. Thirty-six survey points in the State Game Lands 249.









Each individual bird has its own distinct call pattern that can be used by a skilled birder or new lab workers to identify the species and location of the bird. After clipping our audio files to the 11-minute segment of interest, we use two high pass filters to eliminate as much of the drone’s noise without sacrificing the birdcalls. The final step in the editing process includes clipping every other minute for analysis. This allows us to identify which birds are singing throughout the experiment. In this way, we can compare the number of birds singing in the before (1st and 3rd minute), during (5th and 7th minute), and after (9th and 11th minute) segments. Raven Pro version 1.5 allows us to simultaneously look at the four SM3 recording spectrograms for a single minute and identify the birds calling (Figure 3). However, there can be many individuals of the same species singing at the same time. Plus, birds may move further away from the drone throughout the experiment, which would be an important result. Therefore, we seek to determine each bird’s exact location. In order for a bird to be localized, the song must be identified on at least three of the four recordings. By using the correlator tool in Raven, we can determine the time of arrival differences to millisecond, in order to determine the location of the bird (Figure 4).

Figure 4. A correlation completed in Raven, comparing Field Sparrow song at recorder A and B. The correlation coefficient is 0.03 (the blue number in the middle). That number is entered into Soundfinder.

Figure 3. Spectrograms from the four recorders in Raven. For example, this is Point 10 Minute 0. There is a Field Sparrow at 2.5-5 seconds.








Now that we have three separate correlations, we can enter the data into a downloadable program called Soundfinder. The only information that we need to supply is the temperature of the air when we took the recordings, the three correlation numbers from Raven, and the GPS coordinates of the ground-based recording array. Soundfinder is then able to localize the bird to within 3 meters of the birds’ approximate locations. The UTM (universal transverse Mercator) coordinates provided by Soundfinder can be plugged into Google imagery to double-check the birds’ locations. For example, you would know that you had incorrectly correlated a bird’s call if a woodland bird, such as the White-eyed vireo, was localized in the middle of a field. Therefore, there is some trial by error involved and so it takes a practiced eye to properly correlate a single birdcall.


Raven and Soundfinder allow us to track individual birds throughout the 11-minute long experiment, and determine if the birds are moving away from the drone or if they stop singing. We hypothesize that the drone will not have an effect of the bird behavior, but further analysis of our data are needed to determine our results. If there is no effect, then this methodology can be used in the future. An ornithologist would only need to use the drone and the hanging recorder to gather audio data, thereby quickening and simplifying the bird population monitoring process.


Works Cited

Wilson, A.M., Barr, J. and M. Zagorski (2017). The feasibility of counting songbirds using unmanned aerial vehicles. American Ornithological Society, 134, 350-362.


Out with the Mold, in with the New

In a fungus, cell division meets mRNA transport

Why would anyone choose a common bread mold fungus to study the way cells work? –  in particular, to unravel the ways that living cells beget new cells by the process known as cell division?  In the James laboratory, we seek to understand “why normal cells get it right every time they divide, and why cancer cells get it wrong every time”.  A worst-case scenario, played out again and again in cancer, occurs when a normal cell “forgets” how to stop dividing, and begins to proliferate incessantly and relentlessly.  We address this fundamental problem in the fungus Aspergillus nidulans because basic features of cell division evolved early on, and proved so effective in promoting survival that they’ve been conserved with little or no change in increasingly complex creatures, including us.  In fact, fungi and humans are similar enough that we could literally swap many genes without harm to either organism, like trading sparkplugs between a Volkswagen and a Ferrari.

This summer the excitement is palpable as we close in on two genes, discovered by us, that harbor previously unreported roles in cell division.  Mutant cells lacking these genes let slip the brakes on cell division, allowing cells to divide more readily than normal.  By extension, this means that the normal versions of these two genes must act as restraints to keep cell division from getting out of control.

One of the two new cell division genes, called snxA, is a conserved gene in many other species, where it is best known for its role in escorting messenger RNAs (mRNAs) out of the confines of the nucleus, where mRNAs are made, and into the cytoplasm where these RNA messages are translated into proteins.  In this process, snxAparticipates with several other proteins that together govern the export of mRNAs out of the nucleus.  To better understand snxA activity, it is important to know how snxA works with these other proteins to influence mRNA export and cell division.  To this end, Julia Palmucci (’18) and Elliot Rodriguez (’18) are studying some of these other proteins, as they describe below.

The other new cell division gene, called GYF, is also conserved across the evolutionary spectrum from fungi to humans, but comparatively little is known about its function.  This summer, Morgan Brown (’19) is completing an initial study of this gene, as she explains below.


Elliot Rodriguez (’18)


As mentioned above, our lab studies cell cycle control, specifically proteins involved in the shuttling of messenger RNA (mRNA) from the nucleus into the cytoplasm. Along with the star protein of our lab, snxA, we also study the THO/TREX protein complex. THO/TREX is an 8-protein complex responsible for binding nascent mRNA and escorting it through nuclear pores with the assistance of snxA. The THO/TREX complex is the focus of my research. Our lab has worked to characterize some of the subunits that comprise the THO/TREX complex. By characterizing each subunit of the THO/TREX complex, it will provide us with a better understanding of the role each subunit has in the complex as well as how the complex interacts with other proteins such as snxA. My research in particular involves characterizing THO complex subunit 5, or thoc5.

Screen Shot 2017-06-22 at 2.09.49 PMThoc5 is evolutionarily conserved in higher eukaryotes, however the exact roles of Thoc5 in transcription and mRNA export are still unclear. In adult mice, thoc5 is essential in the maintenance of hematopoietic stem cells and cytokine-mediated hematopoiesis. In Drosophila, thoc5 mutants are viable but have spermatogenesis defects. Thoc5 is present in fungi and mammals but is absent in the popular model organism budding yeast, Saccharomyces cerevisiae. Studies of thoc5 in fungal model systems are limited, which makes Aspergillus an especially relevant model system for studying thoc5. The main goals of my research are to complete the initial characterization of thoc5 mutants as well as to investigate potential thoc5 interactions with the snxA shuttling mRNA-binding protein that has been the long-term focus of our lab’s efforts.

I am also assisting Julia with her work with another THO/TREX subunit, thoc6. Our lab has shown that the absence of thoc6 causes a mislocalization of snxA. Normally snxA remains in the nucleus, but the absence of thoc6 delocalizes snxA to the cytoplasm through an unknown mechanism. I am adding a green fluorescent protein tag to thoc6. This will allow us to visualize thoc6 in Aspergillus using fluorescence microscopy. By tagging thoc6 and other proteins such as snxA, we hope to better understand the interactions between these proteins as well as the unknown mechanism responsible for the mutant phenotype.


Julia Palmucci (’18)IMG_1197-1

This has been my first week as a summer research student in the James Lab.  The earlier part of my summer was spent doing research of a much different sort in the Peruvian Amazon with Dr. Trillo’s Tropical Terrestrial Biology course.  The class brought us to the one of the most biodiverse areas in the world where we identified over 100 species of birds, observed numerous mammals—including nine species of monkeys, and learned how to characterize insects and amphibians. We were also given the opportunity to use the pristine natural resources of Cocha Cashu Biological Station in Manú National Park for independently designed research studies.  Utilizing the area’s untouched mature forest (500-700 years old) and the more successional forest (about 150 years old) formed as a result of the meanderings of the nearby Rio Manú. I compared soil composition and insect diversity across differently aged forests.

The research I am undertaking for the remainder of this summer involves the interaction between snxA and the THO/TREX complex.  Specifically, I am studying a subunit of the THO/TREX complex—THOC6. This subunit is one of seven THO components in animals, and we believe Aspergillus nidulans may be the least complex organism that contains THOC6. Other common model eukaryotes used in molecular genetics research such as the budding yeast Saccharomyces cervisiae lacks thoc6.  Thoc6 is a fascinating protein, composed of multiple WD40-repeats that confer a β-propeller structure important in mediating its interactions with other proteins. A β-propeller protein (seen below) is composed of 5 to 8 “propeller blades” each with 4 to 8 β-pleated sheets.  A common misconception is

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Basic structure of the aptly named β-propeller protein (Fulhop and Jones, 1999)

that each WD40 repeat translates directly to one blade, but this is not the case. Instead, each repeat results in a majority of one blade and some fraction of the next, allowing for a gene with numerous WD40-repeats to have fewer blades. Additionally, the approximately 40 amino acid WD40 repeats are notoriously diverse in sequence between their starting ‘GH’ and ending ‘WD,’ and even these namesake characteristics can vary as well.

Previously, it was unclear if the gene we were studying, AN1056, is actually the ortholog of thoc6 in higher organisms. Much of the research on THOC6 focuses on the pathology of thoc6 mutations in humans. For example, several thoc6 mutations cause intellectual disability. In addition, thoc6 defects confer stress sensitivity in the fruit fly Drosophila melanogaster. This past year, however, our lab found that a deletion of AN1056 results in a cold-sensitive phenotype and delocalization of snxA into the cytoplasm, indicating that this protein may be associated with snxA and may be involved in cell cycle regulation. This summer we have begun accumulating answers to this puzzle in hopes to determine more conclusively if AN1056 is indeed THOC6, and how this protein interacts with snxA.  Using a new program that more accurately predicts WD40, we have discovered tantalizing commonalities between AN1056 and the sequences of thoc6 in flies and humans, including the same number of similarly arranged WD40-repeats.

My goal for the remainder of the summer is to determine the function of THOC6 and its interaction with snxA using fluorescence microscopy.  I will use strains carrying several fluorescent tags, including green-fluorescent snxA and red-fluorescent thoc6 to monitor defects in snxA localization during the cell cycle in wild type and Δthoc6 mutants using the Nikon Ti-U inverted epifluorescence microscope.  These tagged strains will be helpful for determining the role of THOC6 and clarify the reason for the defective phenotype exhibited by the Δthoc6 mutants.


Morgan Brown (’19)


Morgan (left) and Dr. James (right)

While my labmates have been focusing on proteins that function alongside snxA, my research looks to characterize snxA itself. Much of our previous work has involved mutated strains of A. nidulans that suppress defects in regulators of the CDK1 mitotic induction pathway, which controls whether or not a cell can enter mitosis. Other phenotypes of these strains include extreme cold-sensitivity and decreased levels of mRNA, and we’ve been referring to these as snxA1 and snxA2, as though there were mutations within the snxA gene of these strains that caused the defects. However, we were stunned and flabbergasted to discover that, rather than a simple point mutation, the disruption of the snxA locus actually resulted from a reciprocal translocation! This means that the arm of Chromosome II where snxA is located, swapped with an arm of Chromosome I. Consequently, the snxA locus was broken within the first intron, separating the promoter region and first exon from most of the coding region. At the same time, the Chromosome I breakpoint occurred within the fourth exon (out of five) in a novel unstudied gene, AN6228. We know only that this gene contains a GYF domain, which is known to mediate binding to proline-rich motifs in certain other proteins. Now that we find  that the “snxA mutant” is actually two mutations in unrelated genes, I am recreating the GYF gene truncation and comparing it to a deletion of snxA to determine how each affects cell cycle control. This will allow us to tease out the contribution of each to the overall mutant phenotype.

Screen Shot 2017-06-22 at 2.10.21 PM

A diagram of the reciprocal translocation occurring between snxA and GYF.

To determine the contribution of each disrupted gene, snxA and GYF, to cell cycle regulation, I first deleted GYF from a wild-type background. It grew perfectly fine at cold temperatures, while strains in which snxA was deleted couldn’t grow at all, so our cold-sensitive phenotype was clearly a result of the snxA truncation. A deletion of snxA was shown to rescue cell cycle defects, leading us to believe that snxA (and not GYF) was responsible for cell cycle rescue. To our lasting surprise, a deletion of GYF also suppressed these defects. This suggests that the reciprocal translocation serendipitously occurred within two unrelated genes that both happen to play a role in regulating the G2-M transition of the cell cycle. It is important to note that a gene deletion completely eliminates its function, but since the translocation breaks GYF in the fourth exon, this truncated allele could retain all of its function, partial function, or no function. So, in order to resolve this question, I have engineered the truncated version of GYF into a wild-type strain, and am now testing the phenotype.

Screen Shot 2017-06-22 at 2.10.53 PM.png

Enter a caption

Currently I am performing genetic crosses to generate strains carrying both the GYF deletion or truncation with mutations in G2-M cell cycle regulators. Furthermore, today I am tagging GYF with Green Fluorescent Protein (GFP) and other fluorescent tags in order to characterize the location and function of this novel gene. It’s been busy over here in the mold lab, but it’s been exciting as well!

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An image of the lovely protoplasts that we will be injecting our mutations into. Aren’t they adorable??

Music, Media, and Aggression

Media exposure is ever-present in our society – its effects have been studied in relation to a number of human behaviors. In most research, media exposure is often operationalized as how often individuals view/partake in TV Shows, Movies, and Video Games. While this is not a bad measure of media exposure, I can’t help but think that there is a gaping hole in it – namely exposure to auditory media. That’s right, music. Music is a generally understudied aspect of media exposure in the realm of psychology. This summer, music is being studied (by Dr. Chris Barlett and Doug Kowalewski) in regards to its relationship with aggressive behavior. In addition, several other studies are being undertaken to further enrich the media exposure literature – in terms of both adding music and the entire paradigm itself.

Music has been studied in psychology rather minimally – especially in comparison to other types of media. In the past, social psychologists have primarily studied music lyrics and their effect on pro-social or aggressive behaviors/attitudes. Knowing this, the first project being undertaken this summer is a meta-analysis of all past studies that have looked at manipulating lyrical content and its effect on aggressive behavior. To do this, I am searching the library’s online databases – as well as Google Scholar – to find all semi-relevant studies. Then I’m going through each individual one, coding the songs that were used, and locating the results. Coding the songs usually takes the longest – I’m essentially inserting standardized musical information for each song (i.e. – the song “It’s My Life” by Bon Jovi is in English, has 234 words in its lyrics, 1 aggressive word, 3 aggressive phrases, is at 120 beats per minute (tempo), is 3:44 long, has 4 (out of a scale of 5) bass, has traditional instrumentation (not electronic), has a simple harmony, is sung by a male, is performed by a band of 5 members, and is Rock). I’m coding each song similarly (and with even more variables) so that at the end of the study we can see what other aspects of the music, other than lyrical content, have an effect on aggressive behavior. We’re just at the beginning of this project (it’s our big, overarching one), so we’re not sure how it’s going to work out!

In addition to the meta-analysis, I’m also combing through correlational data that we collected on Amazon Mechanical Turk (an international survey site that provides data cheaply and quickly) that has implications for both media exposure and, more specifically, music exposure. We’ve already submitted for publication a study, using this data, looking at the relationship between violent media exposure and cyberbullying behavior. In addition, I’m also coding the songs that participants listed as their three favorites in a manner similar to the way I’m doing the meta-analysis songs. The goal is to see if there is any relationship between particular elements of the song and the participants’ self-reported aggressive attitudes and behaviors. We’re still going through this project too, but the data looks pretty good and we’re hopeful to find results that we can look at along with the meta-analysis data.

Speaking of cyberbullying, we’re also very close to submitting for publication a paper regarding the Barlett-Gentile Cyberbullying Model (BGCM). The longitudinal research, taken over ten waves, provides insights into the learning mechanisms that underlay the BGCM. Specifically, our results show that as individuals cyberbully, they learn that they are relatively anonymous when they do so, and that their physical strength doesn’t matter. This learning leads to positive attitudes regarding cyberbullying behavior, which in turn leads to increased cyberbullying behavior. Our results also show that this increased cyberbullying behavior predicts even higher feelings of anonymity and the belief that physical strength doesn’t matter online. This final finding is a very important addition to the BGCM, as it shows that the model continues on over time.

Overall, psychology research involves a lot of looking at past work, finding how you can contribute to the literature, and designing studies that will allow you to do so. It also helps to be interested in the topic you’re researching (I for one really enjoy the music and media aspects of our work). So far, the experience of researching music, media, and aggression has been thoroughly enjoyable and rewarding.

I better get back to it – there’s plenty of more music to listen to!

The Wrath of Con(ceicao)

Research in the field of mathematics does not have fancy labs or fancy procedures like other fields. Unlike how the name of this post suggests, math research is neither stressful nor aggravating. While mathematics research does sometimes involve staring at equations erratically scrawled on a chalkboard, our tools are not limited to mundane methods like chalkboards and paper. Research in mathematics is a puzzle. It is finding trends and proving them. It is making relations between an unknown solution and known theorems to further knowledge of both the known and unknown.

This summer, Dr. Conceicao is working with two math majors: Sam VanFossen and Rachael Kelly, whom you can see busy at work in the pictures at the bottom of the page. Our research is based off of the Markov equation x^2+y^2+z^2 = xyz. The solution (x, y, z) to the equation is known as a triple. All integer solutions to Markov’s equation have been known for over 100 years using a simple recursive formula that allows a single solution to “branch” to other solutions. We, however, took a different approach to this equation. What if x, y, and z were polynomials, not integers? More specifically, what if they were monic polynomials? A monic polynomial is a polynomial whose leading coefficient is 1. For example, 5t^2 + 3t + 2 and t^2 + 3t + 2 are both polynomials but only the latter is monic.

Now, in order for polynomials to satisfy Markov’s equation, we must work in what is called a finite field. The “standard” field has infinite numbers. 1, 2, 3, 4… and so on. We can count upwards indefinitely. In finite fields, however, there are a set, or finite, number of elements. In our research, we work with primes of 1 mod 4. The “mod” of a number is the remainder of that number. Thus, 5 mod 4 is equivalent to 1 mod 4. We require our primes to be 1 mod 4 because -1 is a quadratic residue of numbers 1 mod 4. That is to say that i exists in the finite fields of primes that are 1 mod 4. i is the number such that i^2 = -1. In the standard field, i is known as an imaginary number because it does not exist; however, it is possible for i to exist in finite fields. In mod 5, -1 is equivalent to 4, since both -1 and 4 are divisible by 5 when 1 is added to them, and, as we know, 4 = 2^2. Thus, in mod 5, 2 = i. Further, 3^2 = 9 = 4 in mod 5. Therefore, both 3 and 2 are i in mod 5. exists in all finite fields created by numbers that are equivalent to 1 mod 4 . For example, 5, 13, 29, and 37 are primes that are equivalent to 1 mod 4.

Using this information and returning to Markov’s equation, we can prove that all monic solutions stem from a triple in the form (f, f + \beta, f^2 + \beta f - 2) where \beta = 2i and f is some monic polynomial. Thus, we have an infinite number of primes with an infinite number of trees with an infinite number of entries on each tree. And we aren’t even considering nonmonic solutions. Now that we have effectively found all solutions, what can we say about them? What other relationships do they have? A generating function is able to generate polynomials down a set “branch” of the tree. Using this function, we are able to have an explicit formula for the polynomials.

A classic conjecture that we would like to investigate is about the uniqueness of Markov polynomials. Does every polynomial appear on each tree as the maximum in a triple exactly once? Another question that we would like to investigate is if all Markov polynomials are reducible (or factorable). We are trying to understand the subfamily of Markov polynomials, F_n, defined by the generating function. Further, we can use the generating function to determine the values of n for which F_n has roots modulo a prime. For example, i will always be a root of F_3 and F_n if n=ko+3, for some integer o and all integers k. The value of o is known as its order. Rewriting the generating function as a Pell equation is useful in finding the order of certain roots. As such, we can create parallels between the Pell numbers, divisibility and Markov polynomials.  For example, in the finite field with p elements the order of β, or 2i, is the index of the first Pell number divisible by p. Similarly, the order of i for a specific prime p is found using Fibonacci numbers.

To further our research and save hundreds of hours, we use a program called Sage (when/if it works) to perform lengthy (to say the least) computations for us. We then enter the data on Excel spreadsheets, where we look for patterns in the data. When we find patterns, we then look for ways to prove that these patterns occur in every possible case or ways to generalize these results. Researching this topic has been a rewarding and illuminating experience, and we look forward to discovering more about these fascinating polynomials!

NanoParticle Lab




Despite their incredibly small size, nanoparticles have already been identified to serve in a diverse range of applications, with further uses continually being investigated.  One of the most heavily researched applications of nanoparticles is their potential use in targeted pharmaceuticals. For example, the small size of gold nanoparticles allows them to penetrate through porous vasculature at specific sites within the body, such that they can selectively accumulate within that site. If we coat nanoparticles with certain drug molecules, we can potentially target that drug to the area of interest rather than allowing it to concentrate in other locations throughout the body where it can produce harmful side effects.





New cancer research is exploring the attachment of chemotherapeutic drugs to the surface of gold nanoparticles in order to apply EPR to selectively target the drugs to the tumor site.

Despite being very small and sounding very complicated, [some types of] gold nanoparticles are relatively simple to synthesize in large volumes. This is important when producing large quantities of nanoparticles for industrial applications.




A little bit of citrate into a little bit of gold gives us a big batch of bright red gold nanoparticles! (Yes…gold nanoparticles are red, not gold)

This brings us to my current work. We are currently exploring the potential of using gold nanoparticles as carriers for antidepressants, specifically the Selective Serotonin Reuptake Inhibitor (SSRI) fluoxetine (Prozac ®).


We have been examining two methods by which we can prepare a fluoxetine-loaded polymer that can attach to the surface of our gold nanoparticles. Most nanoparticles cannot exist on their own; they need some type of molecular coating – a surfactant – to provide stability. In the nanoparticles with which I am working, negatively charged citrate provides charge screening against other negatively charged particles. While it is possible to attach molecules individually to the nanoparticle, it would involve replacing the citrate stabilizer with the drug, thus reducing the charge stability of these particles, causing them to aggregate (which is BAD!) Therefore, we are exploring methods to incorporate fluoxetine into a polymer that we attach to the gold nanoparticle. The polymer acts as a coating to prevent the particles from colliding with each other (which is GOOD!)





Just because we work in an analytical chemistry lab, it doesn’t mean we can’t do some synthetic organic chemistry too!






Mix some polymer with some nanoparticles à get some polymer coated nanoparticles.

As nanotechnology is a relatively new concept in the context of pharmaceutical delivery, a substantial amount of work is being performed in order to quantify the amount of drug molecules attached to each nanoparticle. Unfortunately, we can’t simply grab a nanoparticle and count the number of drug molecules, so we have to employ some other methods. Now that we have synthesized our fluoxetine-linked gold nanoparticles, the bulk of our work involves quantifying the amount of fluoxetine per nanoparticle. One of these methods involves the bromination of fluoxetine and methyl orange dye to perform spectrophotometric measurements.





When there is more fluoxetine, more Br2 is consumed; therefore there is less Br2 available to inactivate methyl orange, such that the reaction will have a greater absorption of light.





One of the concerns about nanomaterials and many pharmaceuticals is their persistence in the aquatic environment once released via wastewater. For this reason, we plan to assess our fluoxetine-nanoparticle conjugates on a variety of aquatic model organisms, such as frog tadpoles.






I am new to the lab this summer, so I’m looking to build off a previous lab member’s project. Celina’s work looked to quantify the number of polymers, specifically polystyrene sulfonate (PSS) that were wrapping around each gold nanoparticle. The work that she did involved developing and optimizing a dialysis stage to “clean” the nanoparticles in order to remove any excess polymers in solution. Over the course of these eight weeks, my project, while still in it’s initial stages, will look to explore the effects of changing the identity of the salt during the coating stage.

I have looked at how changing between the salts lithium, potassium and sodium chloride respectively changes either the “thickness” of the polymer coating or even the number of polymers associated with the nanoparticles. The first trial has just been completed and the data is currently being processed! The results suggest that there is a difference in the number of polymers per nanoparticle, but more trials will have to be conducted in order to determine whether this is statistically significant.

Some of the difficulties that I have run in to are both the pace of the dialysis and the difficulties associated with making large batches of monodisperse particles. The dialysis process, while efficient, lasts up to a full day meaning that it’s roughly a week-long window in order for one trial of results to be collected. This means that any alterations to the procedure take a while to be observed. The monodisperse particles, however, are more important. Given that analytical techniques are being used to calculate the number of polymers per gold nanoparticle, the surface area and thus the size of the nanoparticles are important to control. During the first couple of weeks in this lab therefore, I worked on refining the production of 250 mL batches of spherical nanoparticles.

Two weeks ago, the lab also attended the regional ACS Conference in Hershey, PA. Even though the day started at 6.30 (yes A.M) I thoroughly enjoyed my first Chemistry conference. The days were divided into several different larger topics, but some of my favourite talks that I attended were a guide to a chemical history walking tour in Paris and another talk about the future of 3D printed personalized medication!





This summer I am continuing my work on polyelectrolyte quantification on the surface of gold nanoparticles. I am additionally studying the thermodynamics of the binding interactions between polyelectrolytes and gold nanoparticles using an instrument called the Isothermal Titration Calorimeter. The isothermal titration calorimeter, or ITC for short, is a way of carrying out small-scale titrations to determine key thermodynamic properties such as binding constants, enthalpy, and the stoichiometry of a reaction. In other words, it is basically a very expensive thermometer. The ITC works by injecting a very small amount of a specific polyelectrolyte over time into the sample cell, which contains the gold nanoparticles. The instrument measures the heat that is absorbed or released for each injection. At the moment, I am working on testing different concentrations of polymer to see which concentrations cause the nanoparticles to aggregate, or deform, before the solutions are used in the ITC. Once the optimal concentrations are found, I will be able to use those solutions to measure the binding interactions.

My other project looks at the quantification of polyelectrolytes on the surface of gold nanoparticles. We begin by synthesizing the nanoparticles via a seed-mediated growth method. The nanoparticles are then coated with a polymer, polyanetholesulfonic acid, also known as PAS. Last summer we did the same procedure with a different polymer, polystyrene sulfonate (PSS). We decided to use PAS due to its similarities in molecular structure to PAS. Ultimately we are testing to see if a polyelectrolyte with a smaller molecular weight, such as PAS, will vary in the quantification of polyelectrolyte on the surface of the nanoparticles compared to polyelectrolytes with a larger molecular weight, such as PSS. The coated nanoparticles must undergo a vigorous purification process to make sure the excess polymer is removed and we can quantify the polymer that is tightly bound to the surface of the nanoparticles. I am currently working on optimizing the purification process for PAS coated nanoparticles, as the particles are more prone to aggregation due to the smaller mass of the polymer.

After undergoing the purification, the particles are analyzed using Inductively-Coupled Plasmon Optical Emissions Spectroscopy, or ICP-OES. The particles are passed through the instrument where they are exposed to a plasma flame that is about the temperature of the surface of the sun, or around 15,000 degrees Fahrenheit! The hot plasma breaks all of the bonds in the solution, exciting the electrons. The ICP-OES measures the intensity of the light that is given off as the electrons return to their respective ground state. We are ultimately able to determine the number of polymer units per gold nanoparticle by the concentrations that the instrument works out from the intensities. This involves many Excel spreadsheets and a lot of patience!

When I’m not in the lab, I have spent most of my time supporting the best hockey team on the planet, the Pittsburgh Penguins, as they won the Stanley Cup for the second year in a row! LGP!



Lookin’ Fly

gel extraction irl

We don’t always look this cool, but when we do, we’re doing a gel extraction.

Hey everyone, this is Connor McLoughlin (’17) and Rachel Wigmore (’18) and we’re working in Dr. Hiraizumi’s lab this summer. The main focus of our research is the genetic basis for the expression of a class of enzymes called dipeptidases which breaks down small peptides into amino acids. Why is this research relevant? In diseases such as Alzheimer’s, Crohn’s, and Celiacs, symptoms are correlated with low dipeptidase activity. By better understanding the genetics of dipeptidase expression, we may learn more about their connection to human diseases.

To accomplish this, we study the dipeptidase B gene (Dip-B) in Drosophila melanogaster, or the fruit fly, as a model system. Specifically we use the strains NC25 III and CL55; NC25III  has DIP-B enzymatic activity that is only one-tenth that of CL55.

We’re focusing on two possibilities for the difference in enzymatic activity between the strains. First, NC25 III produces fewer messenger RNA (mRNA) that is translated into protein. Second, the two strains produce the same number of enzyme molecules but DIP-B of NC25 III is catalytically less active than that of CL55. How do we address these possibilities?

For the first possibility, an efficient and practical method to quantify Dip-B mRNA transcripts was needed. To do this, a process called Reverse Transcription- Polymerase Chain Reaction was used to make many copies of cDNA (copy DNA) from mRNA. The quantity of cDNA produced is a function of the quantity of mRNA in the samples. To compare the samples, a quantitative densitometric assay was developed. The cDNA was subjected to agarose gel electrophoresis, which separates DNA fragments by size in a matrix of electrical gradient, with a stain specific for DNA. The separated cDNA is imaged using a ChemiDoc machine, which detects the stain intensity by a densitometric scan. Densitometric intensity is a function of DNA quantity. The protocol for the densitometric quantitative assay using an external standard is under refinement but results are promising, as shown below.

goodjel         Screen Shot 2017-06-09 at 2.12.47 PM

The second possibility for the difference in activity levels is that NC25III might have a less active DIP-B enzyme in comparison to CL55. Will Ueckermann, a former member of the research lab, obtained the coding sequence for Dip-B in CL55 and NC25III.  Compared to the gene sequence in the NCBI database, both strains contained SNPs or single base mutations. Will’s findings suggest that NC25III strain has a non-conservative missense mutation which changes the hydrophilic amino acid serine into a hydrophobic amino acid isoleucine. This change can very well affect the structure of the DIP-B enzyme molecule. We are sequencing more independent samples to confirm the sequence data, so stay tuned until the end of summer for further updates!

In eukaryotes (such as plants, fungi, Drosophila melanogaster, and humans), non-coding sequences (introns) are spliced out during transcription of DNA into mRNA. However, sometimes some introns are retained to generate different mRNA molecules (mRNA isoforms). This process, referred to as alternative splicing, is not unusual and Drosophila melanogaster is not an exception.

Dip-B gene has been reported to produce several mRNA isoforms. Four of the isoforms (A, B, C, and D) are documented in the NCBI database; our lab found evidence for two more isoforms called E, and A/C that are diagrammed below. What makes Dip-B different from conventional alternative splicing is that the coding region for all mRNA isoforms are identical, but the mRNA isoforms vary in the sequence that precedes the coding region called the 5’ Untranslated Region (5’-UTR).

isforms UTRs

Isoforms A and C contain identical transcription initiation site, but what makes them interesting is the presence of introns in their 5’-UTR. Presence of introns in the 5’UTR in eukaryotic genes is not  uncommon, with 4000/14000 Drosophila genes and 35% of human genes having this feature. The function of introns in 5′ UTR is unknown, but it sure would be interesting to learn more about them during research here!

We have performed many experiments involving PCR amplification of isoforms A and C and gel imaging of PCR products. There have been good days…

6-2 Actin Gelgreen invert 2

Actin is the external standard of Connor’s Quantitative Densitometric Assay, so we need a lot of pure actin, shown in this gel.

…and not so good days.


An attempt to image Isoforms A and C.

Our main goals for the rest of the summer include sequencing non-coding regions, specifically the 5′ UTR of the Dip-B gene, as well as to refine the protocol for a densitometric quantitative assay. We hope to isolate more Dip-B DNA to be sent out for sequencing to confirm the existence of the SNPs Will found.


Phage: The Phinal Phrontier

What phages are:

Our lab, the Phages Rock lab, works with bacteriophages (phages), and we work to isolate different types of phage as well as to analyze their genomes using computer programs. Phages infect bacteria in a similar way that viruses infect us, and once a phage is inside of a bacterial cell, it uses that bacterial cell to replicate itself. Depending on the phage’s genome, it can either integrate its DNA into the bacteria’s DNA, thus being replicated when the bacteria goes through DNA replication, or the phage can use the machinery within the bacteria to make clones before simply killing the bacteria. Some phages are able to infect many types of bacteria while some can only infect a few, and one type of bacteria can be infected by many different phages. This field of study isn’t new, but there is still much to learn, and there are thousands of phages yet to be discovered.

We work with phages that infect the bacteria Bacillus subtilis, which lives in the soil. In previous years, our lab has collected soil samples and extracted phage from them. In the past few weeks, we have been working with the phages that come from these soil samples. Much of our work deals with isolating different strains of phage, and our goal is to isolate new species of phage. Right now, we’re aiming to isolate 50-100 novel phages (but that number increases with every lab meeting). But, we don’t stop at isolating phages! We also do a lot of bioinformatics work to analyze different clusters of phage. Using several different computer programs such as DNAMaster, HHPred, and GeneMark, we look at phage DNA sequences to determine where genes are located in the DNA and what the functions of those genes are. But more on that later.

Wet Lab Work:

What we do at the beginning of our attempt to isolate phage is plate small portions of phage samples on a petri dish containing agar (a solidified substance for the bacteria and phage to interact in the petri dish) with small portions of various bacteria.  What we end up with are multiple petri dishes where the bacteria changes but the phage sample does not. After 18 to 24 hours of keeping these dishes in the incubator, we check to see if there are plaques, or clearings in the bacterial lawn, present on the different plates.  If there are plaques, then we know that the phage present in our sample infect that type of bacteria and we can attempt to isolate that phage on that strain of bacteria.  We will attempt isolation for each different morphology of plaque that is formed in a plaque search, assuming that different plaque formations will yield different phages.


Figure 1. Plaque search from soil sample 280-A-7-8 by Marana Tso.


Figure 2. Streak plate from soil sample 279-1-0-8 by Holly Wentworth.


Once we have identified the different morphologies of phages on our plaque search plates, we take isolated plaques, streak them out to ensure that each plaque represents one phage and not several phages, and then allow that specific morphology of phage to multiply. In the future, after we have built up our titers for the different phages, we will extract the DNA and send it off to be sequenced.  After the DNA has been sequenced, we will annotate the genome and call functions for specific genes through protein prediction software and by comparison to known phage genes. Isolated phages are also imaged using the transmission electron microscope.


Figure 3. A transmission electron microscope of phage 015DV002.


Once we have a complete DNA sequence for a phage, we use computer tools to annotate the genome. We use a program called DNAMaster to determine where all of the genes in the genome are, and we look at information from other sources to determine which start codons actually start the genes. To determine the starts of the genes, we typically use information about what parts of the genome have the potential for protein coding, which sequence a ribosome is most likely to attach to, and which start similar phages use. Once we have determined where a gene starts and stops, we work to find the function. In many cases, genes have no known function. We know the functions of genes when someone else has done extensive lab work to determine the function. Even if there is no lab work, we can determine a putative function based on how close enough the gene is to others already determined. To determine the functions of genes, we often use programs such as BLASTP, HHPred, and Phamerator which compare the amino acid sequence of our gene to genes in the database to find a function based on similarity. We also use a program called Phyre2 which determines function based on the predicted  structure of the protein that the gene forms. Once the genomes have been annotated, we can begin performing comparative genomics.


Figure 4. Screenshot of DNAMaster with the file for phage 015DV002 open.

Comparative Genomics:

The comparison and analysis of annotated genomes is known as comparative genomics. Phages are often compared to those that are similar in order to identify differences and similarities. This allows the phages to be classified as different phages or to be identified as a duplicate of another. An example of a possible difference between phages is in transfer RNAs (tRNAs). tRNAs are used to assemble proteins. Some phages code for tRNAs and between two similar phages there could be a difference in the number of tRNAs or the types of tRNAs that are called. The genomes can be compared in a multitude of ways, such as by sequence, gene order, gene function, and gene inclusion. Comparing the genomes of phages in different ways helps discern differences and similarities quicker than if the phages were compared simply between sequences or gene order. Computer programs like MEGA, which can align sequences and create phylogenetic trees, are often used in comparing genomes.

Comparative genomics is also useful when paired with wet lab work. For example, in evolutionary studies, a phage from the beginning of an evolutionary lineage and at the end of the lineage can be compared and differences can be identified. Experiments can also force phages to mutate and change the host range. That application is particularly pertinent if the ancestral phage has been fully annotated. Paired with comparative genomics, it is possible to compare phenotypic changes with genotypic changes. Similarly, if there are differences between the bacteria that can be infected by similar phages, comparing the genomes can shed some light on the differences in infection ability based upon the genes that may confer such capability. Furthermore, annotations and genomic analysis can be confirmed or denied through wet lab work.

*Title attributed to a tweet by Jason W. Shapiro.

Andresen Lab

There are currently three students working in Professor Andresen’s lab, Jose, Dylan and Amlan. Each of us are working on different projects and therefore we all pretty much do our own stuff with very little overlap between what we do. When we first got into the lab, things were a little chaotic so one of the first things we did was clean and reorganize the lab. This included alphabetizing not only the sample cabinet but also the bookshelf. We also freed up three drawers worth of space that new things can now be stored in, freeing up more lab table space. This cleaning day, we worked together like a unit, it was a very productive day. Although the photo quality is not high enough to allow you to read the labels in the image below, I assure you that they are alphabetized.


The machine pictured below is called the UV-Vis machine. Dylan runs this machine almost every day to look at both nano-particle concentrations and DNA concentrations in various samples but other lab members will also use the machine when needed. The object in his hand is called a Quartz cuvette. When looking at the wavelengths over 400nm, a normal cuvette may be used. However, a normal cuvette interferes with the wavelengths that the machine must run at to view DNA, between 320nm and 220nm, so it must be used instead. This machine essentially just shoots light through the cuvette and records the intensity at each wavelength, this can allow us to identify what exactly is in the sample.


Below is an example of what the data looks like from the UV-Vis. The top graph was run from 320nm to 220nm, its peak wavelength is right around 250 and its absorbance is right around 1. This means that there is a large concentration of DNA in the sample. The second graph was run from 800nm to 400nm, its peak is around 520nm and its absorbance was around .1 at peak. This means that there are ideal gold Nano-particles in the solution but at a low concentration. It’s important to know why we used 220nm and 320nm wavelength, this is because nucleic acids absorb more strongly at 260nm while proteins absorb at 280nm which is not what we want on our results. Jose uses Beer’s Law in order to find the concentration of DNA in his samples.


Below are two pictures of our centrifuge. It has temperature and speed regulation. Dylan’s experiment should be running at room temperature but Jose’s must be run at 4 degrees Celsius. This causes some fighting in the lab, careful planning must be coordinated to avoid such problems. On heavy centrifuging days, we try to politely steal a centrifuge from Professor Thompson’s lab.


The next image is the crown jewel of our lab, the ICP-OES, which stands for Inductively coupled plasma optical emission spectrometry. So, this machine tells you exactly what is in your sample and at what concentrations. First it heats up a green plasma that you can see through a little window on the machine, it gets as hot as the surface of the sun. Don’t worry, we are not in danger around the machine, the plasma stays inside. Then it starts spraying your sample through the plasma, this breaks down all chemical bonds and sends the sample into the next part of the machine where it can determine what is in the sample. The machine takes a long time to warm up and run, so if you must use it you really must plan your day around using the machine. It also comes with two very loud gas canisters, one containing Argon and the other liquid nitrogen. These must be replaced regularly or else we cannot run the machine. Sometimes chemists from across the street come to give us company in our lab while using the machine.


Below is one of our labs most value resources, Milli-Q water. The building we’re in, Masters hall, does not have its own Milli-Q water, so if we want more water we must drag it all the way to professor Frey’s lab in the Science Center. Some of the people in our lab use much more water than others, were talking like 99% or more, then they try to get other people who used much less water to fill it back up. However, other lab members find this practice unacceptable. To settle this dispute, we typically meet right after work to fight and whoever loses must fill it up the next day.


Amlan, on the other hand, works on the enthalpy changes associated with DNA binding and condensation. He uses the Isothermal Titration Calorimetry technique in order to record the heat changes due to DNA binding with certain cationic ligands. You won’t even find him in our lab for most of the day. He spends most of his time working with the Nano-ITC machine in the Physical Chemistry lab in Science Center. In rare instances, he will stop by our lab to pick up a few DNA samples and then off to the P. Chemistry lab again. In case you are wondering what the Nano-ITC machine looks like, here is a picture:


Gettysburg College is going through a lot of construction right now. We are gaining a new building and losing an old one. Luckily, Andresen’s lab has the best view of this construction. Whenever we are curious we can just peep out the window and catch all the construction workers over there on the roof doing their thing. Having this incredible view from our workplace really helps increase morale and general productivity. Below is the picture from the window, wow.


The next picture depicts the tools used to run the agarose gel, which Jose runs. The gel is used to separate DNA based on its length in base pairs, smaller segments move farther then longer segments. The separation of DNA is achieved due to the negative charged of DNA backbone, the gel and the samples are run vertically at a constant voltage with a running buffer containing SDS. SDS is used in the buffer to denature the proteins and bind with them. This process usually takes about 4-5 hours and them the samples are ready to be view in a special UV light.


The next pictures shows a new tradition that was created and inspired by Andresen’s lab and has spread not only throughout the physics department but also to the entire X-SIG program, our goal is to one day be able to make this tradition not only to those working during the summer but to the whole school.


Of Mice and (Wo)Men

Mitochondria are the powerhouse of the cell! Ok…but what do they do? Mitochondria are organelles present within almost every eukaryotic cell type. They are responsible for converting the nutrients that we consume—sugars, proteins, fats, etc.—into a form that can be used by every cell in our bodies to do cellular work. ATP is that universal energy currency of multicellular eukaryotic organisms produced by the mitochondria.

In the Brandauer lab, we are interested in studying metabolism and the effects of different types of interventions, like exercise, on mitochondrial biology.

There are a number of human diseases and disorders associated with metabolic dysfunction, such as diabetes, obesity, neurodegenerative diseases, cancer, and the aging process. Exercise is a known intervention that improves outcomes of individuals with many of these conditions, but the exact cellular details of how it’s done are still unknown. Current research is looking into the specific metabolic effects of exercise to discover the molecular processes responsible for the observed health benefits. From there, drug interventions that duplicate these effects can be created to treat metabolic diseases.

This summer, we are studying the effects of chronic exercise on metabolic function. To do this, we are quantifying nicotinamide adenine dinucleotide (NAD+) concentrations within different tissues using high performance liquid chromatography (HPLC). We will also be comparing the relative concentrations of mitochondrial proteins using Western Blotting.

NAD+ is a small molecule that functions as an electron carrier in metabolic reactions. It is essential in the set of reactions that harvest energy from fuel molecules, like glucose, and result in the production of ATP. NAD+ is also involved in mitochondrial biogenesis, meaning it is necessary in order to create new mitochondria within a cell. NAD+ concentration has been shown to be an effective measure of mitochondrial function. We hypothesize that, because a chronically exercised organism has increased energy needs, the rate of metabolic reactions and mitochondrial production will increase, leading to an increase in NAD+ within the cell.

Proteins of interest include sirtuins, a family of proteins involved in metabolic reactions that regulate gene expression and may help neutralize reactive oxygen species (ROS). ROS are a class of molecules produced through normal metabolism that, left unchecked, are damaging to cells and can contribute to the aging process and the development of cancer and other disorders. In order to perform these functions, sirtuins use NAD+ as a substrate in enzymatic reactions that degrade it into nicotinamide (NAM). This leads to another enzyme of interest called nicotinamide phosphoribosyltransferase (NAMPT), a protein involved in recycling NAM back into NAD+. We hypothesize that chronic exercise will lead to an increase in the proteins within the cell.

In order to test our hypotheses, we must first establish exercised and sedentary experimental groups of mice. While the sedentary mice get to nap all morning, the exercised group is put to work!

Thursday, June 8th marks the start of the third week of training, so we have ramped up the difficulty of their training to keep them working!

mice running

Our first group of 6 mice (out of 12 total exercised mice) getting their morning workout at 17m/min at a 5% incline.

mice napping

Our sedentary mice settling down for their morning nap!

After six weeks of training the mice, we will collect our samples and freeze them so that, when we come back in the fall, we can analyze them. We are honored to be included in this research and thankful to Dr. Brandauer, the XSIG program, and Gettysburg College for this opportunity!

Anna Salmonsen & Ashley Carvajal