Welcome to FUNKy Town!

This summer, Auden, Bryn, Danielle, and Emily have taken over the Organic Chemistry Lab, working hard and having fun doing research with Dr. Funk. We are working on two main projects: Sustainable Oxidations and Reductions using Iron-based Catalysts and Lipid Synthesis. Keep reading to find out what each Funk Lab member does!


Project 1: Sustainable Alcohol Oxidation

Danielle Kleinberg ’23

Danielle is a rising junior double-majoring in Chemistry and Environmental Studies. With an interest in green chemistry, working in Dr. Funk’s lab is a perfect fit for her! This summer, Danielle is oxidizing alcohols using a sustainable iron catalyst.

What’s so great about oxidizing alcohols? Alcohol oxidation is a very common step in the synthesis of important molecules. Making oxidations more sustainable is an important stride towards greener chemistry. What makes iron so sustainable? Historically, alcohols have been oxidized using heavy transition metals such as ruthenium, palladium, or rhodium. These precious metals are rare, making them not only unsustainable but also quite expensive. Iron is a much cheaper and more sustainable option for a catalyst in these oxidation reactions. Making up 5% of the Earth’s crust, iron is the 2nd most abundant metal in it. In addition, iron is typically viewed as environmentally friendly due to its generally low toxicity. If iron-catalyzed oxidations can be performed just as effectively as oxidations catalyzed by rare metals, we can reduce both the financial and environmental cost of this chemistry.

Now that you have some background on these reactions, let’s go into some specifics. Below is the general mechanism for reactions Danielle is running:

Danielle oxidizes alcohols (I) with varying functional groups, sterics, and electronics in order to explore the substrate scope of this iron catalyst. Compound II, our iron catalyst, becomes activated once trimethylamine N-oxide (III) removes one of its carbonyl ligands, creating an active site where the alcohol can react. After II gets reduced, it must be reoxidized in order to keep working as a catalyst – this is where furfural (IV) comes in handy! The use of furfural as a terminal oxidant increases the sustainability of this reaction, since it is produced on an industrial scale using cellulosic biomass waste. After these reactions run overnight, Danielle isolates the carbonyl product (A) in order to see how well the reaction worked. In order to isolate A, she rinses away any water-soluble products and then performs column chromatography in order to separate the remaining products. After isolating A, Danielle calculates an isolated yield, which is the percent of alcohol (I) that is converted to ketone or aldehyde (A). Based on this calculation, she can see how well the reaction worked and identify the reactivity trends of this catalyst.

Along with using the iron catalyst, Danielle is working to further improve the sustainability of this process by testing it in different solvents. As seen in the reaction scheme, Danielle runs these reactions in toluene or water with 2% TPGS-750-M. Toluene is an organic solvent – organic solvents are convenient for these oxidations because our reagents dissolve in them and can therefore readily interact and react with each other. Although convenient, organic solvents derived from fossil fuels are not sustainable. Danielle is working to make these oxidations more sustainable by performing them in water mixed with a vitamin E-based lipid, TPGS-750-M, which creates tiny spheres of grease, known as “micelles”, which act as nanoreactors where the reagents can dissolve and interact. This process is known as micellar catalysis. TPGS-750-M is also non-toxic, making it even more environmentally friendly.


Project 2: Steric Bulk and Chemoselectivity in Alcohol Oxidations

Auden Cameron Lampariello ’22

Auden is a rising senior majoring in Chemistry. For Auden’s project, he synthesizes catalysts whose ligands have differences in steric bulk near the active site of the catalyst.

The list of catalysts Auden has synthesized and will be synthesizing may be seen below. It is expected that the increase in steric bulk near the active site of the catalyst will increase the catalyst’s selectivity towards oxidizing primary alcohols over secondary alcohols.

This study is based on a paper previously published by Professor Funk that showed that a catalyst with a trimethylsilyl ligand was able to selectively oxidize primary alcohols over secondary alcohols in diols to form lactones. This is contrary to the catalysts usual behavior as other forms of this catalyst normally favor the oxidation of secondary alcohols since this forms more stable products. It is the hope of this project to make a catalyst that can reliably oxidize primary alcohols over secondary alcohols, such that the catalyst may be used in more complex molecules for oxidations. 

As each catalyst is obtained, it is used in a conversion experiment to determine how well it can oxidize primary alcohols over secondary alcohols when both are present. The conversions for each catalyst are monitored by extracting samples for gas chromatography analysis before the reaction has started and 24 hours after the reaction has started. The specific conditions may be seen below. The conversions for each catalyst are compared to one another to determine which catalyst converts more primary alcohols for every secondary alcohol converted. 

Based on current findings, the increased steric bulk in the catalyst had minimal impact on the conversions for the primary alcohol but greatly decreased the conversions for the secondary alcohol. While the bulkiest ligand for this project has yet to be synthesized, Auden is optimistic that it will have the highest selectivity towards primary alcohols. This is based on the trends we have seen for three of the catalysts synthesized.


Project 3: Reactivity of a Novel Trimethylamine Adduct

Bryn Werley ’23

Bryn is a rising junior double majoring in Chemistry and Music who joined the Funk lab to pursue her interests in chemical synthesis. This summer, Bryn is working with both oxidations and reductions catalyzed by (cyclopentadienone)iron carbonyl catalysts. 

Bryn’s work centers on our catalysts’ performance in oxidations and reductions. By tuning the electronic properties of our cyclopentadienone ligands, we are able to improve the reactivity of our catalysts. In general, increases in electron density increase catalytic activity. 

Bryn is also investigating the catalytic activity of different versions of our tricarbonyl catalysts. Our tricarbonyl catalysts (Structure A) must be activated using trimethylamine N-oxide through a process that produces carbon dioxide and trimethylamine (two gases). Because trimethylamine is a gas, we assumed that it would just float away after the catalyst was activated (i.e., go from A to C directly, skipping Structure B), but this does not seem to be the case! Bryn recently isolated crystals in which trimethylamine had stuck to the iron atom of the catalyst (Structure B)!

She is currently using this trimethylamine-bound species (B) as a catalyst and has found that it 1) does not need to be activated in order to catalyze oxidations or reductions and 2) has faster initial reaction rates than our tricarbonyl compounds in both oxidations and reductions. Overall, the trimethylamine-bound species is much more effective for reductions than it is for oxidations. Bryn is currently working to explain these differences in reactivity and also hopes to compare the reactivity of similar catalysts later this summer! She is also planning experiments that may allow her to better understand the mechanisms behind these reactions (i.e., how the molecules and their electrons physically interact during the course of each reaction).


Project 4: Lipid Synthesis

Emily Howe ’23

Emily is a rising junior double major in BMB and Spanish, and is a lab TA for the Chem department. She’s working on creating a library of synthetic lipids based on the compound cyanuric chloride.

Lipids are characterized by having a hydrophobic region (the “tail”) and a hydrophilic region (the “head). Synthetic lipids can be created by attaching a fatty acyl tail (which are generally long, greasy, and hydrophobic) and some kind of hydrophilic group to a “linker” molecule which holds the two groups together. The linker my research focuses on is cyanuric chloride, a molecule with three potential positions that can be functionalized. A tri-substituted ring could lend itself to synthetic lipids of diverse geometries, one of which is shown below. This research is partially collaborative with Dr. Vince Venditto of the University of Kentucky College of Pharmacy, whose lab focuses on creating liposomes to use in vaccine delivery.

Based on the data that Dr. Venditto’s lab has obtained regarding the types of lipids that spontaneously self-assemble into liposomes, we are tailoring the types of lipids that we make to give us the greatest chance of producing lipids applicable to this branch of drug/vaccine delivery research. 


Now That You Know Us, Let’s Get to Know You!

To conduct our work, we use many different analytical instruments. Each instrument has its own important purpose in our work. In fact, our instruments are so central to our work that we consider them honorary lab members, each with their own personality and quirks! So, let’s find out…

Which Funk Lab Instrument Are You?


Congratulations on making it through the quiz! As your reward, we have created an alignment chart for each of our instruments, as well one for our most used solvents.

Funk Lab Instruments Alignment Chart

Lawful Good: Detail-oriented and reliable, the Gas Chromatograph has never led us astray. Bryn and Auden use this instrument to quantitatively monitor the progress of their reactions over time. The instrument uses mere microliters of their reaction mixtures to operate and allows for automated analysis of their samples.


Neutral Good: Running iron-catalyzed reactions means that we have some unique cleaning to do in the Funk Lab. In order to clean any oxidized iron from our reaction flasks, only hydrochloric acid can do the trick! But, we can’t dispose of HCl without first making it neutral. Meeting our needs of acid neutralization, Sodium Sesquicarbonate has surely gained our respect in the lab.

Chaotic Good: Though he appears intimidating at first, the NMR Spectrometer is a favorite instrument in the Funk Lab! It can take some time to understand the complicated spectra produced by this instrument, but the data provided by this instrument is integral to elucidating the structures of numerous compounds.

Lawful Neutral: Thin Layer Chromatography is a technique that helps us to determine the best method to separate compounds based on their polarities and identify the compounds present in the fractions we obtain during column chromatography. A simple method that produces consistent results, this is one of the first techniques that organic chemistry students learn in the lab!

True Neutral: Keeping us safe from any dangerous chemicals in the air, the Fume Hood keeps the atmosphere truly neutral.

Chaotic Neutral: The Rotavap (or Rotary Evaporator) combines the power of a vacuum pump with a heated water bath and rotating flask to evaporate solvents quickly. By lowering the pressure in a flask, heating a solution, and rotating the flask to form a thin layer of solvent, we remove solvents from solutions in order to isolate our products. While the rotation of the flask can certainly be hypnotizing, we must be careful not to get distracted and “bump” the solution, which refers to violent, rapid boiling of solvents under low pressure. Bumping can occur if we create too strong of a vacuum too quickly.

Lawful Evil: Many of our reactions are sensitive and do not run well when exposed to air. Our Gas Lines help to ensure that our reactions take place under nitrogen, an inert atmosphere. 

Neutral Evil: A grown-up version of TLC, Column Chromatography is a technique that we use to isolate and purify the products of our reactions. Arguably the prettiest lab technique, column chromatography requires us to collect a series of (often colorful) fractions. Fractions are small amounts of a solution that drips out of the bottom of the column. We then use TLC to identify the contents of each fraction to see which fractions contain our target product.

Chaotic Evil: Kugelrohr… the name says it all. “Hot N Cold” by Katy Perry is this instrument’s favorite song. This instrument uses extreme temperature differences in order to distill small samples.


Funk Lab Solvents Alignment Chart

Lawful Good: This was an easy choice! Ethyl Acetate is one of our favorite solvents in the Funk Lab. It is super useful for cleaning grease and oil, and comes in a pretty green squirt bottle! In a pinch, Danielle has been known to use this solvent to remove old nail polish that she is tired of.

Neutral Good: Another useful solvent, Acetone is used for a majority of our glassware cleaning in the Funk lab. It is dependable and pretty harmless if accidentally gotten on the skin. It smells like nail polish remover, too – mmmm, tasty!

Chaotic Good: Auden has used Tetrahydrofuran (THF) as a solvent in some pretty intense reactions, but it has never let him down.

Lawful Neutral: Deuterated Chloroform gets the job done as a solvent when using the NMR. With an added bonus of tetramethylsilane, you can be sure that your chemical shifts will be on lock!

True Neutral: Deionized Water… with a pH of 7, you can’t get any more neutral than this solvent.

Chaotic Neutral: Sometimes, Bryn just doesn’t feel like using deuterated chloroform for her NMR experiments. To better mirror the experiments that her NMR studies model, she adds Deuterated Benzene (C6D6) to isopropanol. A bit hard to come by, C6D6 comes in glass ampules, which Bryn has to break open in order to access this helpful solvent.

Lawful Evil: Toluene works well in our alcohol oxidation reactions, but is a bit of an attention-seeker. Every morning, Auden or Danielle takes around 10 minutes to “degas” the toluene, which is the process of flushing out any air bubbles in the toluene through bubbling it with nitrogen. Each morning, one of the most common questions on Auden and Danielle’s side of the lab is: “Has the toluene been degassed yet?”

Neutral Evil: When you need to quench an oxidation or reduction, look no further than Hexanes! Bryn uses this nonpolar solvent to ensure the accuracy of her kinetic experiments, but it comes in the same color bottle as her dichloromethane! This devious mimicry can get quite confusing if she isn’t careful!

Chaotic Evil: Dichloromethane (DCM) introduces herself as the solvent that’s able to rinse permanent marker off of glassware – how handy! Unfortunately, the more you get to know her, the more she tries to give you chemical burns. You know how you wear gloves in chem lab to protect yourself from dangerous substances? Well, DCM has a special trick up her sleeve! She likes to soak right through your gloves to begin burning your skin; while it initially feels similar to the cool mist of acetone evaporating from your hand, it simply gets worse and worse even after you run to the sink to wash your hands! These days, Emily finds that wearing gloves while dispensing DCM actually works against her, forcing her to first rip off her gloves before dashing to get soap and running water while yelling, “Oh god, make it stop!”

XSIG Trek: The Search for SPP1 — The Musical

We, the Delesalle Lab, work with bacteriophages (phages for short!). Phages are viruses that “eat” bacteria and are the most numerous organisms in the world. There are an estimated 1031 phages on Earth, more than any other domain combined. They “eat” bacteria by injecting their DNA and hijacking the bacterial DNA replication processes to reproduce more phage particles. The new phages can either be immediately made and explode out of the bacteria (lytic phages) or rest dormant in a bacterial cell with phage DNA integrated into the genome (prophages). Since phages cannot reproduce by themselves, they can be considered to be non-living. Phages and bacteria are in a continual “arms race” as the phages evolve to better eat the bacteria while the bacteria evolve to better defend themselves. This summer, we are continuing work from Summer 2019 to explore these evolutionary dynamics. But first, let’s meet the lab!

In Summer 2019, we performed a passaging experiment with an end goal of comparing the final phage genomes against their initial genomes to see what mutations appeared. The passaging involved filtering phages from old flasks into new flasks with fresh broth and bacteria. This allowed the phages to continuously evolve against fresh bacteria, so we could observe just the phages’ evolution in 8 combinations of phages and bacteria, six replicates each. Every four passages we saved the extra filtered phage and the remaining phage/broth combination. Unfortunately (but still excitingly!) the data we got back wasn’t what we expected. Only two flasks had the original phage in it; once sequenced, we found it had many more mutations than we had anticipated as well.

This summer, we are membrane probing the stored phage samples from two summers ago to check for when the SPP1 phages and SPP1-like phages were lost. To do this, we designed a DNA probe that attaches to the SPP1 gene sequences, allowing us to identify SPP1 and SPP1-like phages. The rest of the process involves plating the phages, lifting the plaques onto paper-like membranes, attaching the probe, performing multiple washes in different solutions to develop the membrane, and finally imaging the results.

To make sure our probe was properly designed and working, we probed plates of known SPP1 as a control. The opaque part of the plate is the bacterial lawn. Each of the little holes is called a “plaque” and is where the phages have eaten through the bacteria. You can see the probe is attached to the majority of the plaques, showing that SPP1 is present on the plate! 

The rest of the summer, we intend to continue probing the rest of the Summer 2019 saved phage samples to determine when the SPP1 phages were lost from each flask. From there, we can amplify each of the identified SPP1 samples, extract and sequence the DNA, and examine when mutations appeared in the genome. We also are planning to do a mini-passaging experiment to retest phage/bacteria combinations and to begin testing co-evolution of phages and bacteria.

We also have daily housekeeping tasks, summarized in musical form!

Lung Rudiments & Regeneration

Greetings from the Kerney lab!
From left to right:
Matt Cherubino, Cristina Sanchez, Rose Redback, Ryan Kerney

Lung Rudiments

This is Cristina’s first summer working in the Kerney lab. Throughout the summer she has worked to practice a couple of skills that will eventually help her advance her project. Some of these skills have been: DNA extraction, PCR, gel electrophoresis, histological sectioning, and injecting salamanders. Cristina’s main focus has been going out to collect red-backed salamanders (Plethodon cinereus) and obtain some embryos from them (along with Matt and Dr. Kerney). After obtaining embryos, they are fixed. embedded in wax, and sectioned to study their tissues. Cristina is specifically looking for lung rudiments originating at earlier stages in their development. The adult red back salamander lack lungs, they primarily use their skin to breathe. So, during the search logs and rocks had to be turned over to find them. Once collected, they were individually placed in plastic Tupperware with wet paper towels.

Back in the lab, Cristina was in charge of feeding and injecting the red back salamanders. The salamanders were injected with a special kind of hormone, gonadotropin releasing hormone (GnRH) that induced oviposition. Over a couple of days some eggs were obtained!

Come look for Salamanders with us!

Cristina has been practicing her histological sectioning on embryos from a frog native to Puerto Rico (Eleutherodactylus coqui).

Regeneration

These 119 axolotl eggs were grown with or without a green, symbiotic algae. Earlier this week, Matt amputated the tail tips from his axolotl friends with a lovely razor blade.

Today, he injected them with a friendly needle…

The injected fluid will stain dividing cells as his friends grow (not shown). After compassionately euthanizing his friends, they will be stained twice more to color cartilage (red) and the overall handsome carcass (blue).

Will the algae help his friends regenerate better? If so, is it caused by the algal proteome? Or oxygen exchange?

To answer this question, Matt built a deoxygenation column.

Constructing an apparatus around the column, he measured regeneration in low-oxygen and normal-oxygen water.

Matt has big plans for his regeneration research, including one day studying a mammalian model…

BK Lab: Analyzing Scientific Innovation and the Science Workforce

Sarah Kummer ’22, Amila Zigic ’22, and Ben Durham ’24

Hi everyone, welcome to the BK Economics lab for the Summer of 2021! We are all working on different projects, but they do have a common theme around scientific discovery, productivity, and the workforce. Two members of the lab, Sarah and Amila, are on campus while our third member, Ben, is working remotely. We meet as a group on Monday every week during which we describe our progress and plans for the week to each other. We also meet with Prof. Blume-Kohout (Prof. BK) towards the end of every week and/or we have questions or want to discuss our project. Other than that, we each work on our own project and therefore will discuss these projects below.

Women in STEM Occupations

Hi everyone! My name is Sarah Kummer and I am a rising senior majoring in economics with minors in business and data science. Outside of research, I enjoy hiking, listening to music, and hanging out with my friends.

This summer, I have been researching women and minorities in STEM occupations. Both of these groups are underrepresented in STEM and females are more likely to leave STEM occupations due to bad working conditions compared to women in non-STEM occupations as found by a previous student’s thesis work (Jack 2021). I have been expanding on this work using additional years of data from the National Survey of College Graduates. Following over 6,000 individuals across four biennial surveys, we can observe any job changes, changes in job-related preferences, etc., that occur over time. The first step in my research project was to create variables of interest from the survey data so that they can be tested to answer questions related to my research project. In order to do this, I have been using the statistical program Stata to generate new variables based on specific criteria. For example, I created binary variables based on questions regarding an individual’s level of satisfaction with certain aspects of their job as I am interested in why women leave STEM occupations. I coded the variable so that it would equal 1 if an individual answered that they are either “very dissatisfied” or “somewhat dissatisfied” with the opportunities for advancement within their principal job and 0 otherwise. Therefore, this variable represents whether or not an individual is dissatisfied with opportunities for advancement within their job, and I continued this process for several other job characteristics. Generating variables is a long process, so I have just recently moved on to the next step of my project. I am currently in the process of trying to answer the question of why females in STEM changed jobs and if it is due to working conditions within STEM or changes in job preferences overall. I have been using Stata to test the above questions with regression analysis.


This figure shows that more female STEM degree holders change jobs than male STEM degree holders.

This is important in figuring out why females in STEM leave these occupations more than females in non-STEM occupations as females are already underrepresented in STEM as it is. This can help understand the cause of the problem and suggest solutions to fix it.

Outcomes of Women in STEM

Hello everyone! My name is Amila Zigic, and I am a rising senior from Gettysburg, PA. I’m majoring in mathematical economics and minoring in math and business.  On campus, I’ve been a PLA for the Economics department and I am excited to continue doing so next semester! My love for economics come from the math side of it all and I’m specifically interested in health economics and macroeconomics. This summer I’m working in BK’s lab and doing research on women in STEM (Science, Technology, Engineering and Mathematics). It is no surprise that STEM fields are largely male dominated. This underrepresentation of females in STEM in academics and the job market, leads to an earnings gap because of there being a larger average wage in STEM jobs. A recent Gettysburg College alum, Sophie Howie ‘21, did her senior capstone on why female students are not going into STEM fields and the impact that female teachers had on this, as well as identity. Howie found that having a positive STEM identity, specifically “I am a math person” identity, has a positive impact on whether a student is considering majoring in a STEM field during their senior year of high school. She also found that female students with female math teachers are not more likely to identify as a “math person“ or consider going into a STEM major their senior year of high school. Going off her work, I am looking at what these female students actually ended up majoring in in college and why. Even though they had identified as a person with a STEM identity in high school, did they end up majoring in a STEM field in college and if not, why not? This will help us get a better understanding of why female students decide to major in STEM. Currently, I am working on manipulating my dataset to focus on the key variables that I need for this study. Once that is complete, we can start running regressions and getting answers to our questions!

Medicare Part D and Pharmaceutical Innovation

Hello everyone, my name is Ben Durham and I am a mathematics and mathematical economics double major, with a minor in computer science.

We are preparing to analyze the effect of the introduction of Medicare Part D–which provided federal prescription drug insurance for seniors–on pharmaceutical innovation. Specifically, we hope to identify if Medicare Part D encouraged pharmaceutical companies to push drugs into clinical trials that would have otherwise remained “on the shelf” because they may have been considered too risky. This project is an extension of Prof. BK’s research done shortly after the introduction of Medicare Part D, so we are looking to see the outcomes of the trials that were in progress during her prior research.

To find out whether this is true, we are combining data that we extracted from ClinicalTrials.gov, PharmaProjects (a licensed database of pharmaceutical R&D projects), and from the FDA about new drug approvals. Eventually, we want to combine the data from these sources to have a timeline for each drug (or perhaps multiple timelines for different therapeutic classes for the same drug, if applicable), tracking the drug through Phase I, Phase II, and Phase III clinical trials and FDA approval. Not every drug that is put through a Phase I clinical trial makes it all the way to FDA approval, but it is still valuable to us to know where each drug failed in the process, as this could indicate whether the specific drug R&D project was expected to be less likely to succeed (and so may not have been put through human clinical trials, had it not been for the introduction of Medicare Part D).

Unfortunately, there is no unique identifier with which we can easily link all of the clinical trials from PharmaProjects or ClinicalTrials.gov to a given FDA approval. Much of our work has been matching on the names of interventions, which sometimes can appear in different variations across different data sources. We use the Python library Pandas and a variety of tools such as Python’s built-in string manipulation capabilities and regular expressions for our data manipulation, matching, and merging.

Building an Interactive Tool for Completing Data Structure Assignments

The central course of Gettysburg College’s computer science program is CS216 – Data Structures. This class introduces students to the primary data structures used in advanced programming and the algorithms most commonly associated with them. One of the main types of assignments in this class is drawing assignments, where students visually depict these structures and algorithms using pen and paper. When the COVID-19 pandemic hit, however, this type of assignment became harder to submit and grade because of complications due to scanning/image-taking software and Moodle dropbox issues. For this X-SIG project, our goal is to create a web-based tool which will allow students to easily draw and submit this kind of assignment on a browser. This will eliminate complications due to fuzzy images or dropbox issues.

The first step in developing this site was to select the programming language and libraries that would be used for this project. To expand my own programming experiences, Dr. Kim recommended that our project be JavaScript based, including libraries such as Node.js, React.js, Express.js, and MongoDB. Node is a JavaScript runtime environment used for the backend of a web application. React is a frontend library for fast and dynamic user-interface controls and design. Express is a server-side library that handles communication between the browser and the server’s files. MongoDB is a NO-SQL (not only SQL) database that allows more dynamic relationships between documents in a collection than a typical relational database would.

After completing some tutorials on these libraries, I came up with a development plan, detailing the steps I would take in the project. These steps are shown in the animation below. The first step was to create the initial interface with an interactive canvas. This interface includes working tools for drawing, exporting, and importing. The second step was to add a timeline to the interface to allow users to track changes through time, such as tracing the execution of an algorithm. The final step is to connect this frontend design to a backend database by adding a login system, where a user’s files can be stored online, and eventually submitted from the server itself. As my change in tense suggests, I am currently working on the third step of this project.

Planned UI design progression
Current Design

Once these steps are completed, we would like to set up the backend database so that the files stored on a student’s account can be sent to the professor for grading. This will require some simple restructuring of the user collection in our database, as well as the possibility of adding a new collection, but should be achievable. Before launching this site to our students, we will make sure to add cheating safeguards such as hidden metadata that could indicate if a submission seems suspicious. This part of the project is still in a brainstorming state, but once the main program is completed, we will likely focus most of our attention on this issue. In the future, this project could be extended to support automatic grading by comparing the structure of a student’s solution with a professor’s answer key.

Andresen Lab…and a Crab

Greetings from the Andresen lab! We all have very different projects both in terms of what we’re physically doing and what we’re studying, but all of our projects are based on DNA electrostatics. While the entire human genome has been mapped, the underlying physical mechanisms behind DNA storage aren’t well understood. By learning more about the way DNA interacts with other particles, hopefully we can learn more about how DNA behaves in the human body.

Keeping an ion things: DNA Simulation and Ion Competitions

Hello, I’m Matt. My research into DNA electrostatics involves looking into and simulating an ion competition between +1 and +3 ions. To give some background, there are many ions present during the process of DNA condensation. This is because DNA is negatively charged; the ions rush to bind to the DNA in order to make it neutral. The “competition” in ion competition is based on the fact that if an ion is occupying a location, another ion cannot occupy the same location. So for my case, what I’m trying to see is how the number of excess ions bind to the DNA changes as we add more of the ions. To be more specific, my job isn’t actual data collection per se, I’m not in the lab with a pipette measuring out the concentrations, instead I am writing the code that is supposed to simulate DNA according to something called the Poisson-Boltzmann equation. Essentially, this equation tells us all about the electrostatic potential at every location in space that we input into the equation, based on the potentials at all the points around it. After this program is complete, we will be able to compare our expected simulation results with actual experimental data. Currently, I have worked out about 90% of what needs to be done in this program, including setting up the DNA pdb file (pdb files are just a fancy csv file: they contain all the information about the DNA for programs to use such as their position in space, what the atoms makeup the strands, their chain (how it knows what to bind to, all As are bound together, all Bs together, etc.), etc.) in a usable way, including making all the necessary adjustments to the DNA in terms of relocation, generating my own pdb file with everything worked out, running that pdb file through a wonderful set of programs called PDB2PQR and APBS. These programs first convert the pdb file into a pqr file so that APBS can read it, then create an output file with all the potentials in them. In short, they do all the math for me using the Poisson-Boltzmann eq; always a wonderful thing.

This is what the DNA looks like when viewed in .pdb file viewing program VMD.

In the past, Dr. Andresen did an experiment very similar to this one involving +2 and +3 ions. The results of that experiment showed that at low concentrations, the equations and reality agreed, but as the concentrations increased far more +3 ions bound to the DNA than expected. Without being too speculative, I would expect a similar result with the +1 and +3 ions purely because of entropy as, while it may lower the energy of the system more to have all +1 ions, it is far more difficult to find three particles to fit in a small area than it is to just have one particle do the job (by “more difficult” I mean less likely – entropy is all statistics). Hopefully this program will be finished soon, it’s been a real pain so far but in a fun, hardworking kind of way, so that we can work on analysis.

Sailing the High C’s: Coding and Condensation

Hey there, I’m Dani! I’ve been working for the past few years on Dr. Andresen’s DNA binding and condensation project. Specifically, I’m looking into the thermodynamic properties of these processes by modeling data collected with isothermal titration calorimetry (ITC – this also stands for isothermal titration calorimeter, the machine used to collect data). Let’s break this down a little. The ITC is a very useful device for collecting thermodynamic data. In our case, we load DNA into the machine, as well as particles, known as ligands, to bind to it (we use cobalt hexammine as our ligand). The machine then adds the ligand to the DNA and measures the amount of heat released, giving us a curve we can fit.

Two-Peak binding curve from Kim et al. (2006)

The way we can tell DNA is condensing is that these ITC curves have two peaks, the first corresponding to DNA-ligand binding, and the second corresponding to DNA condensation. However, this second peak also makes fitting the curve much more complex.

Fitting function developed by Kim et al. (2006)

As you can see, the equations we use to fit this data are very long. The best part is that this has to be used multiple times to give us our final fit. First, we use this to fit the first peak (NDH1), then by using different parameters we fit the decreasing part of the second peak (NDH2′). After that we fit the increasing part of the second peak (abs((dH1 – NDH3)/dH1) and take a combination of parameters from both of those fits to describe the second peak as a whole (NDH2). Then the models for the first and second peaks are added together for the final fit (NDH1 + NDH2). When the computer runs this fit, it only fits to the final sum, so it ends up fitting 7 variables at once.

Writing the fitting program for this model took a long time, but it works now! The rest of my summer will involve collecting data using linear sheared DNA, short 25 base pair DNA, and circular plasmid DNA. Once we find DNA that works well for us, we’ll start varying some experimental conditions such as adding solutions that will compress the DNA in different ways. The other goal for the summer is to continue improving the code for fitting two-peak ITC data, hopefully to the point where we can make it available on GitHub. This project has been a lot of hard work, so it’s exciting to finally be able to see an endpoint.

Fit of our actual data.

Wrapping DNA on Gold Nanoparticles: Under the Chemis-Tree

Hello! My name is Thomas Gilman. I am working on the gold nanoparticle coating lab under Dr. Andresen and Dr. Thompson’s collaborative project. In this project we are aiming to wrap DNA around gold nanoparticles and be able to characterize them to see the treatment potential they have on diseases in the human body. In wrapping DNA, the nanoparticles must first be wrapped in a positive charged polymer, PAH (polycyclic aromatic hydrocarbon). Once this has successfully been completed, the DNA is then coated onto these nanoparticles with PAH already wrapped onto the gold nanoparticles. These particles are characterized through UV-Visibility spectra, which measures the absorbance of the sample; DLS (Dynamic Light Scattering), which measures the size of the particles in the sample; and Zeta, which measures the Zeta potential of the sample. Through characterization, we will be able to see the success of the coating and determine the possible further uses of such particles.

In the first week, we completed trial runs of the first step of the coating process which was the initial coating of PAH on the gold nanoparticles. Our initial runs were conducted including a 10:2:1 ratio of the nanoparticles, PAH, and NaCl, respectively. With these samples, we allowed for a coating time of 30 minutes. After the 30 minutes concluded, the samples were run through the centrifuge in order to create a pellet of the coated particles. The samples were then run through a cleaning process to remove excess non-coated particles and solution components, and then the remaining pellet was resuspended in water. These resuspended samples were then run through the characterization tests described before. Our data, depicted below (Figure 1), shows that there is slight aggregation with the 30 minute coating samples. With this, I then conducted the same test again, as well as comparable 24 hour coating time samples. For these samples I also tested a 10:4:1 and 10:6:1 ratio of the components. After concluding these tests then, it was found that the longer the coating time, the less the aggregation present among the nanoparticles. This lesser aggregation can be seen in the normalization graphs presented (Figure 2) as well, with the curves being more matched together for the 24 hour sample, compared to the 30 minute sample. 

With this data, and the overall success of the initial coating with PAH, the next steps are to look into coating these wrapped nanoparticles once more, now with DNA. In order to do so, the DNA must be sheared and prepared first to be added to the existing solution in order for a more successful wrapping. For week two, this was our focus, and being able to do the first test coating on a smaller sample on Thursday, and letting it coat for 16 hours overnight into Friday. In the meantime I made larger sample sizes to be ready, in the best case scenario where the DNA successfully wrapped around the nanoparticles. Based on our first sample characterizations, we made the general assumption that a DNA coating was successful, due to their being DNA activity in the sample, as seen through the UV-Vis between the wavelength of 220 and 320 nm (Figure 3). An increase in size of material within the resuspended pellet was also observed, with a range in distribution for the amount of certain sized particles. This led to us seeing a low count of the estimated size of the DNA wrapped particles, but still a decent amount to assume the wrapping was successful. The Zeta potential was also flipped to a negative charge, indicating the presence of DNA, due to it being the only negatively charged reactant in the solution. Through this, it was decided to then go ahead in making larger samples – in which we tested for different wrapping methods.

To make the next batch of samples, I tested the addition of salt to the coating process rather than just allowing the DNA to wrap on it’s own, without a catalyst. I believed this to be more beneficial due to the use of salt in the first coating, regarding PAH. With the three samples, two were run with added salt, and one without. After centrifuged, cleaned, and resuspended, heavy aggregation was seen. Therefore, in order to isolate the supernatant from the aggregated pellet to ensure uncontaminated tests, I used a pipette to transfer the supernatant to a clean tube to then be used for testing. Based on the results, the salt was proven to give some benefit, but due to the high aggregation, the solutions provided a low concentration – making the values obtained through measurements harder to analyze and understand. With these samples though, two new tests were complete: gel electrophoresis and an Atomic Force Microscope (AFM).

For further analysis of our particles, we used an Atomic Force Microscope to try to image our particles. We did this through drying our sample onto a disk of mica, after removing a clean film layer with scotch tape. The AFM allowed for a taping measure of particles that dried onto the surface of the mica, in order to see particles’ shape and size. This was challenging for the particles seemed to be constantly on the move, and not being able to completely track particles that looked more promising as a DNA wrapped nanoparticle. Though it also seemed visible that there were independent nanoparticles that did not become wrapped, which are seen as the smallest particles in the AFM image provided below. The larger white strips seem to be the most likely DNA wrapped nanoparticles, but still we are not completely sure (Figure 4).

Another test we conducted was gel electrophoresis to observe the length of our DNA on the particles, or in the supernatant, and to view DNA digestion. In the images below, we can see light bands presented across all samples which is indicative of the DNA being present in each sample exactly the same. We then did gel electrophoresis for the DNA digestion comparing it to an undigested sample to see the ultimate effect of the digestion from micrococcal nuclease. Seen in our results (Figure 5), the digestion did work, but was overly successful and the nanoparticles seem to have offered no protection to the DNA from the micrococcal nuclease. We had hoped that the correct enzyme, DNase I (as found in previous studies), was not used – but after we obtained DNase I and ran the same test, the same results were still produced, indicating no protection from the gold nanoparticles still.

With this, we are now putting a pause on testing for digestion, and we are going to run our three samples through the AFM machine to see if we can better image these more concentrated samples and see more prominent, wrapped, particles. In preparing for this test, we coated pieces of mica using two different techniques to see if there is a benefit with one over the other. One is just pipetting and drying, and the other is spin coating. In the remainder of the week, results for this imaging on the AFM will be obtained, and more samples of DNA coated gold nanoparticles will be obtained and generated, as we continue to move forward and reach the end of the internship duration.

And now…

…Say hello to Reginald!

The Power of NAD+

With few exceptions, genetic information in the human body is stored in 23 pairs of chromosomes. One such exception is observed in Down Syndrome (DS) which is typically the result of the triplication of the 21st chromosome. DS causes widespread developmental and physical changes to the body and mind. One commonly observed physical change of DS is impaired skeletal muscle function such as weak muscle tone (hypotonia). This creates difficulty in executing activities of daily living. However, there is presently a deficit in scientific knowledge of how this extra chromosome leads to muscle weakness.

Recent research suggests that a molecule called nicotinamide adenine dinucleotide (NAD) is an important determinant of skeletal muscle function. In human cells, NAD plays a critical role in the production of adenosine triphosphate (ATP) in the mitochondria. Mitochondrial ATP is the major fuel source for many biological processes, and virtually all biological systems require active mitochondria to function. NAD is also an important factor in regulating mitochondrial and cellular activity by supporting post-translational protein modifications. This means that NAD contributes to modifying the protein after it’s been made in order to modulate its function. Currently, we do not know whether muscle weakness in those with DS is at least partially caused by an imbalance of NAD.

NAD can be produced via two separate processes (de novo pathway and salvage synthesis). Whilst the de novo synthesis is a linear pathway beginning with the amino acid tryptophan, the salvage pathway is a circular process which recycles ‘spent’ NAD. Most cellular NAD is ‘recycled’ NAD, and the protein Nicotinamide phosphoribosyl transferase (Nampt) is the rate-limiting enzyme in this salvage pathway. Essentially, this means that Nampt determines the rate of production of NAD in the body.

Research studies have shown that NAD is required in maintaining skeletal and cardiac muscle function. There is a documented decrease in available NAD as we age which likely leads to decreased system function, and low NAD has been suggested as a contributing factor in several age-related diseases. The connection of reduced Nampt and loss of muscle function in other conditions illustrates why there is a benefit into investigating a possible connection between skeletal muscle NAD biology and Down Syndrome, and this is our objective this summer.   

The Ts65Dn mouse is a commonly used DS rodent research model that has been studied extensively by Dr Lara DeRuisseau, our ongoing collaborator at Le Moyne College. Our first set of experiments aims to quantify Nampt protein using a technique called a Western blot. A Western Blot is an assay that detects specific proteins (in our case, Nampt) within a tissue sample (in our case, muscle tissue). We support our Nampt concentration data with measurements of mitochondrial activity to evaluate the overall health in the tissues we study.

Running the gels with the protein samples
Gel transferred onto nitrocellulose
Western Blot made from this protein sample

Recently, Thia Anyaoku, the summer research student working with Dr Brandauer on this project, has learnt and completed both of these complex quantitative analyses in preparation for the Ts65Dn samples that arrived at the lab last week. The research is ongoing and should result in a clear research poster by the end of the summer. Check this space for more updates and let us know if you have any questions.

Thia Anyaoku making gels for the western blot

A Day In Dr. Labonte’s Virtual Lab

Currently, there is no computational tool that can design and predict new structures with modified saccharide residues that change the function of the structure to desired effects. By making changes to biological structures’ glycosylation patterns there are respective changes in the system’s enzyme activity, cell-communication, and disease pathways. Studying the changes of the glycosylation patterns can lead to a fuller understanding of these systems, leading to disease treatments. Dr. Labonte’s research project addresses this issue. For this summer, I am working on a glycoengineering project that expands the Rosetta design algorithm to use new functional groups previously added to the database. Rosetta is a tool that helps model and design structures. It specifically searches and builds structures when at their thermodynamic energy minima. To expand the program, I will be developing software in the C++ and python language. The new algorithm will replace saccharide residues with modified versions, using the functional groups in the database. Then, the program will evaluate the energy of the system with the new residues. By expanding the code to use new functional groups, predictions of more stable antibody constant regions will be made.

A typical day in Dr. Labonte’s virtual lab looks like me coding, staring at my computer and when taking breaks attending the other member in our team, Oso. To the left, you can see the small corner in my room that is our lab area for the summer. Oso is waiting patiently with me for our python file, mentioned below using the 5lwx protein, to finish running.

Typically, early in the morning I wake up and work on the to do list given by Dr. Labonte the day before in our meeting. I work on my own throughout the day with the emotional support of Oso and the help of Dr. Labonte through communication platforms such as slack. Towards the end of the day, I meet with Dr. Labonte to discuss the work that I have done that day, suggestions and feedback, and a to do list for the next day.

So far, I have become familiar with the Rosetta software, converted the starting C++ file into a python file and began to expand the algorithm. To convert the file from C++ to python, the most challenging part was the object-oriented programming and learning to translate between the two. The reason for creating this file was to have a sandbox to edit the file because it’s faster to edit and run in python than in Rosetta. Once I had recreated the “design_glycans” file in python, I expanded the algorithm to be able to pack residues but only design (change) saccharide residues, sugars, attached to the protein. The program currently searches through the modified poses, structures, and returns the one with the lowest score, meaning lowest thermodynamic energy. For instance, if we run the file with the protein, 5lwx from the RCSB protein data bank, below you can see first the original structure that had a score of 3216.3 compared to the modified structure below it that has a score of 919.3. Even though it isn’t a negative value, we know the algorithm is working since the value is much lower and the sugar residues are being modified. Now, I am working on further expanding the code so that the structure’s saccharide residues are substituted with synthetic saccharide residues and for the residues around the substituted sugar residues to change accordingly to obtain a structure with the lowest energy.

Figure 1: 5lwx protein, original protein structure. The sugar residues are the 6 member rings.
Figure 2: The modified 5lwx protein structure with substituted sugar residues. The sugar residues are the 6 member rings.

The vast natural diversity of glycans is multiplied when synthetic saccharides are added; it is not feasible to biochemically test such a large number of conceivable saccharide residue substitutions for desired effects. A computational tool to design and predict favorable candidates for experimentation would lessen costs and speed the advance of science.

Interesting facts about Rosetta:

  • software has been used since 1998
  • open for academic and government laboratory use
  • over 10,000 licenses have been issued
  • provides access to applications that “involve the development of vaccines, new materials, targeted protein binders, and enzyme design”
  • collaborative space for research community wanting to better understand treatments of infectious diseases, cancers, and autoimmune disorders

Works Cited:
Home, new.rosettacommons.org/docs/latest/Home.

Time for Tele-tubB-ies

Starring…

Allison Walsh

Brandon Caban

Fungus – Aspergillus nidulans

Steve James

and

Dr. Steven James

Previously On Tele-tubB-ies…

Dr. James discovered a novel gene – wdA!

This gene was found in Aspergillus nidulans, the fungus species studied in our lab. When deleted, wdA causes cold sensitivity and mitotic catastrophe at around 21°C. Why is this? It all has to do with microtubules (MTs), long, rod-like polymers that comprise the cytoskeleton. The microtubule cytoskeleton governs the shape and movement of cells, it provides the highway for moving materials throughout the cell, and it forms the spindle apparatus for separating chromosomes at mitosis! Microtubules are made up of protein dimer subunits composed of alpha- and beta-tubulin, and are encoded by genes called tubAtubBbenA, and tubC.  The alpha/beta dimers assemble end-to-end to form microtubules.  In cells lacking the wdA gene (we deleted it!), microtubules become unstable and fall apart at low temperatures, leading to fungal death.

Additionally, the deletion of the gene encoding TBCA (tubulin binding cofactor A), a crucial protein in the assembly of MTs, also kills Aspergillus. Strangely, when these two genes are present together, much better growth occurs at most temperatures as the absence of wdA rescues the cell from the lethal effects of the TBCA mutation. 

Furthermore, we recently discovered mutations in two additional genes that rescue the cold sensitivity of the wdAdeletion.  We call these genes wdsA and wdsB (wd suppressor A and B).  These mutations are termed ‘suppressors’.  In other words, when the suppressors are present the cold-sensitivity of the fungus lacking wdA is ‘suppressed’, and it grows similarly to wild type fungus at low temperature.

Why do these interactions occur? How does wdA function, and what is its molecular identity or pathway? These questions will (hopefully) be answered on this summer’s season of Tele-tubB-bies.

Over the hills and far away, fungus come to play….

Project 1: Where is suppressor B (wdsB) hiding?

For one of the projects, Brandon and Allison are attempting to determine the molecular identity of one of the two newly discovered suppressor genes, wdsB. Since this suppressor gene interacts with the wdA deletion, this project will help determine wdA’s function. The wdsB gene was previously mapped to Chromosome 1, one of the 8 Aspergillus nidulans chromosomes. Strains containing a wdsB mutation were crossed with strains containing different mutated genes spread over the length of Chromosome 1 to determine if these other genes are close to, or distant from, wdsB. This is called ‘linkage’. If any of these genes are linked to wdsB, it will help to narrow down the location of wdsB on the chromosome, and eventually its genetic sequence. Some of these genetic markers include mutations in genes that produce leucine, lysine, nicotinamide, para-aminobenzoic acid (paba), proline, pyridoxine-HCl (pyro), and thiamine. So, if a strain contains a mutation for its leucine-producing gene, for example, the Aspergillus nidulans strain can no longer produce its own leucine and requires a plate with that supplement on it in order to grow.

To start the crosses, spores of a fungus containing the wdA deletion were placed in a vial with a strain carrying one or more of these Chromosome 1 genetic ‘markers’.   The crosses were grown over a few days, then the mycelia mats, or resulting growths, were replated on media to allow the two strains to grow together and produce a genetic cross, the result of which are cleistothecia, spherical ‘fruiting bodies’ jam-packed with the sexual progeny of the cross.  These progeny, the products of meiosis, are called ascospores. Six cleistothecia from each cross were harvested for their ascospores.  

Progeny from the crosses were ‘patched’ onto synthetic media plates to test their genotypes and determine if the wdsBsuppressor gene is located near any of the aforementioned genetic markers.  

Project 2: Does suppressing the deleted wdA prevent wdA from rescuing the lethal TBCA deletion? 

As described above, cells lacking both wdA (ΔwdA) and TBCA (ΔTBCA) grow better than cells lacking only TBCA.  In other words, the ΔwdA mutation rescues, or suppresses, the severe growth defect conferred by the loss of TBCA.  However, what happens in fungus that have ΔTBCA + ΔwdA, and ALSO a suppressor mutation? Will suppressing ΔwdA stop it from rescuing the cell from the deadly effects of ΔTBCA?

Plates showing the growth habits of ∆wdA, ∆TBCA, ∆wdA ∆TBCA and wdsA41 at 34°C, 43°C, and 34°C lacking pyro.
— all progeny are ΔTBCA::riboB
-All pyroA4 progeny are wdA+ ΔTBCA::riboB
#1-11:    wdsA41 ΔTBCA +/-ΔwdA
#12-23:   ΔTBCA only
#24-32:  ΔwdA ΔTBCA only

ANSWER:  We discovered that the fungus containing all three mutations survives at the low temperature and, to our great surprise, the suppressor does not prevent ΔwdA from hiding the growth defect of ΔTBCA.

So to our surprise the suppressor did not prevent the rescue of the ΔTBCA by ΔwdA at low temperatures. However, when we tested at the high temperature of 43°C, the suppressor in combination with ΔTBCA led to fungal death. Usually, microtubules have the ability to both form and break down or depolymerize. However, in this case the microtubules become hyperstable at high temperature and were unable to disassemble. If suppressors in combination with ΔTBCA are unable to grow at 43°C, it is most likely due to MT hyper stability. This leads to the possibility that the suppressor mutations may create more stable microtubules, and that the normal function of the suppressors is to prevent hyperstabilization of microtubules.

We still needed to investigate more deeply:  Since fungi containing all three mutations are still rescued, MAYBE these suppressors of ΔwdA are also able to are also able to rescue the cold-sensitivity of ΔTBCA by itself?

We still needed to investigate more deeply: Since fungi containing all three mutations are still rescued at the low temperature, MAYBE these suppressors of ΔwdA are also able to are also able to rescue the cold sensitivity of ΔTBCA by itself?

In order to test this, fungus containing both ΔTBCA and ΔwdA were crossed with wdsA suppressor mutants to see if the suppressors can save the cold sensitivity.  When we grew Aspergillus nidulans on synthetic media at various temperatures, we discovered that progeny containing ΔTBCA + the wdsA suppressors grew at 21°C, which is uncharacteristic for ΔTBCA alone. It is likely therefore that wdsA suppressors rescued ΔTBCA, restoring its ability to grow at the low temperature.

Plate showing the growth of wdsA41 +/- ∆TBCA and/or ∆wdA, ∆TBCA only and ∆wdA ∆TBCA 21° plate: 1, 4, 5, 6, 7 are –pyro and rescue ∆TBCAwdA+.
Therefore, wdsA41 rescues the cold-sensitivity of ∆TBCA
alone, and confers ts-lethality to any strain carrying ∆TBCA or ∆TBCA ∆wdA.
1-11: wdsA41 +/- ∆TBCA and/or ∆wdA 11-22: ∆TBCA only
23-32: ∆wdA ∆TBCA

Project 3: ∆wdA is not actin up

We know that removing wdA destabilizes MTs, but there is also another part of the cytoskeleton as well: the actin cytoskeleton! What if this cytoskeleton is also affected by this mutation? How can this possibility be ruled out?

To determine if ∆wdA only affects microtubules and not the actin cytoskeleton, Allison and Brandon crossed strains to create progeny containing the ΔwdA mutation in combination with an actin gene (Lifeact) tagged with a fluorescent molecule called mRUBY. Because the Lifeact gene encodes actin, and is tagged with the fluorescent mRUBY protein, the actin can be observed under an epifluorescence microscope to see if the actin cytoskeleton remained intact at the low temperature where microtubules are destabilized. If actin microfilaments remain intact, the cold sensitivity of ∆wdA can be attributed purely to issues with microtubules.

Frey’s Fatty Fries

Answers at bottom of blog post and hints throughout 🙂

Professor Frey – Haverford ‘01

Prof. Frey when she was our age in her Haverford days!

Prof. Frey, our fearless leader, is back for the 9th summer of X-SIG! She fields all our questions and helps us out with our experiments. Plus, she is the baked goods supplier of the chemistry department! Prof. Frey is the full package and loaded with knowledge. We don’t know where we’d be without her. 

Kacie Herr – BMB ‘22

A picture of me with the fluorimeter, which I use very frequently to measure changes in membrane fluidity.

Hey everyone! I am a rising senior majoring in biochemistry and molecular biology with a concentration in neuroscience. Around campus in the summer I enjoy playing soccer with my friends, going on walks around town, and hiking! I also love that there are so many lakes nearby (Codorus State Park is awesome!!) so that I can swim and kayak, too! I love hanging out with my dogs, reading, and going to the beach as well – things I will definitely be doing after my 8 weeks of research here at Gettysburg. I am a permanent resident of the Frey lab where I study how nanoparticles interact with model cell membranes and analyze any effects that they have. Last year I also did X-SIG summer research with Prof. Frey, but it was online. Let’s just say being in person and participating in hands-on research is something I will not take for granted again. After having worked with this material remotely last summer and then in my Salty & Fatty X-lab class, along with lab work during the Spring semester, I was prepared to conduct my own experiments this summer with little introduction to new material.

Cell Membrane and Model Systems:

The cell membrane is a complex structure made up of a bilayer of lipids with hydrophilic head groups and hydrophobic tails. They can be modeled using vesicles, which are structures consisting of a liquid enclosed by a lipid bilayer. A variety of vesicle sizes, including GUVs and SUVs which stand for giant and small unilamellar vesicles, respectively, can be employed as model systems. In the Frey lab, we use these systems to understand preliminary information about biological cells and how they might interact with, affect, and be affected by exogenous materials such as peptides and nanoparticles. 

https://doi.org/10.1016/j.cis.2017.05.009

Nanoparticles and Lipid Membranes:

Nanoparticles are small particles between 1-100 nm in size. A wide variety of nanoparticles exist, each with different material properties and surface modifications. These variations allow nanoparticles to be used extensively in modern medicine with applications ranging from imaging contrast agents to targeted drug and gene delivery. It is important to understand how they affect cell membranes as this is the first part of a cell they come into contact with. By understanding these interactions, any toxic effects can be identified and actions can be taken to avoid them. Additionally, gaining insight into membrane/nanoparticle interactions is essential for further developing therapeutic applications. 

Fluorescence Assays:

Over the past few weeks, I have primarily been working with the fluorimeter which measures the fluorescence emitted by various fluorescent solutions. This instrument is valuable as the emission peak shape of Laurdan dye, a specific fluorescent dye that I use, can indicate the fluidity of membranes. By analyzing changes after introduction of nanoparticles to the vesicle solution (either SUVs or GUVs), I can determine if nanoparticles have an effect on membrane fluidity and what that effect might be. This is helpful when it comes to establishing the manner in which these nanoparticles interact with the vesicles. I use both carboxyl modified (negatively charged) and amine modified (positively charged) nanoparticles which allows me to see the effect of charge within the interactions. Additionally, by using different model systems, I can conclude how membrane curvature plays a role in membrane/nanoparticle interactions. 

Looking Forward:

Hopefully in the coming weeks, in addition to more fluorescence assays, I will be able to perform other techniques, such as isothermal titration calorimetry and fluorescence microscopy, to further elucidate how nanoparticles impact model cell membranes. I am excited to see where this research takes me and what I can accomplish with the rest of the summer and during my senior year!

Pet break, compliments of Kacie! Left to right: Easton (Eastie Beastie), Pilot (Miss P), Drumore (Druey Luey)

Abby Reitz – Chemistry ‘22

Pictured above is me with the ITC, which allows me to quantitatively study the thermodynamics of biomolecular interactions. 

Hi everyone!! I am a rising senior and the resident chemistry major of the Frey Lab. Being on campus this summer, I have been enjoying sunset battlefield walks, trying new restaurants around Gettysburg, and having board game/movie nights with friends! I joined the Frey lab in the spring of 2020 (right when the world collapsed due to the Covid-19 pandemic), and while the universe spoiled my plans to start doing in-person research as a sophomore/junior, I was fortunate to get to participate in remote X-SIG last summer. We read paper after paper and analyzed previous data to familiarize ourselves with everything going on in the realm of lipids and membranes, and were prepared to hit the ground running at the start of this summer!

Huntingtin Protein and Lipid Membranes:

My project focuses on some of the biochemistry behind Huntington’s Disease (HD), which is a genetic, neurodegenerative disease with no cure. HD is caused by a polyglutamine repeat (polyQ) near the N-terminus of the huntingtin (htt) protein. The expanded glutamine region causes the protein to unfold and subsequently misfold into insoluble aggregates that accumulate in neurons, destroying them and leading to overall tissue atrophy. While the physiological role of htt is not well-defined, it associates with lipid membranes through a specific insertion sequence known as Nt17, the first 17 amino acids of the protein. As this protein-membrane interaction may mediate the pathogenesis of HD, an understanding of the mechanism of this interaction is essential in studying the disease. 

Abby + ITC: A Love-Hate Relationship:

This summer I have primarily been using isothermal titration calorimetry (ITC) to investigate the thermodynamic properties of the binding interaction between Nt17 and the cell membrane. An ITC experiment involves the addition of small aliquots of ligand into a compound of interest in a sample cell, and the job of the instrument is to keep the temperature of the sample cell the same as the nearby reference cell (in my case, I am injecting SUVs into Nt17 in the sample cell). Depending on whether heat is absorbed or released with each injection, the instrument either provides or removes heat to maintain a constant temperature between the two cells. The data collected allows you to elucidate the full thermodynamic profile of the binding interaction, including the entropy, enthalpy, Gibbs free energy, stoichiometry, and binding/dissociation constant.

Unfortunately, the ITC instrument can be pretty high maintenance – it’s finicky when it comes to cleaning, it takes 2.5 hours to run a single experiment, and it apparently needs the perfect experimental parameters to produce good data. As this technique is relatively new to the lab, the initial experiments of this project were somewhat of a “shot in the dark.” Since then, I have spent the past few weeks trying to find the optimal buffers, concentrations, etc. to employ to extract useful information about the binding interaction. Despite some hiccups, I always try to keep smiling and pressing on towards elucidating the thermodynamics of this interaction enroute to elucidating a mechanism. I am excited to see how much farther we can get this summer! 🙂

Pet Break II, compliments of Abby. My two cats, Pepper and Winnie!

Jordyn Markle – BMB ‘22

Me with the CD, the instrument I use the most!

Hi everyone! I am a rising senior majoring in Biochemistry & Molecular Biology and minoring in Studio Art. While I’m not in the lab, I really enjoy doing arts and crafts like crochet and painting. I also love going to Ragged Edge and Ugly Mug over the weekends to get fancy coffee and petting every dog that I encounter! This is my third (!!) summer of X-SIG research in the Frey lab, and my second summer of in-person X-SIG research. X-SIG has really helped me grow as a student and as a researcher, and I can’t believe I will soon be a senior writing my thesis! I have gotten much better at the research process and keeping all my data straight and organized to help me plan future experiments (my lab notebook from two summers ago leaves much to be desired….).  

Currently, I am studying the interaction of huntingtin protein with model lipid membranes, but I worked with prion protein two summers ago (which similarly causes neurodegenerative disease when it misfolds). By studying both, we may be able to find mechanistic similarities behind these neurodegenerative proteins. This summer, I’ve been focusing on two main techniques, circular dichroism (CD) and fluorescent imaging, to determine how the properties of the cell membrane affect its interaction with huntingtin. 

Circular dichroism (CD):

CD involves measuring the different absorption between left and right circularly polarized light caused by peptide bonds to give a spectrum that indicates protein secondary structure. Since Nt17 (the segment of huntingtin that I’m working with) converts from random coil to alpha-helical upon binding to an interface, we can use the secondary structure to understand what properties of the cell membrane preferentially cause binding. Right now, I’m focused on the variable of lipid charge! I’ve tested varying ratios of neutral to negatively-charged lipids, and I have found that Nt17 binds more to vesicles that have a higher proportion of negative charge. I’ve also tested two different kinds of negatively-charged lipids, and I will compare the secondary structure to see if there is any difference based on lipid chemistry.

Fluorescence Microscopy:

GUVs imaged under the microscope!

I have also been working with GUVs to determine how Nt17 impacts their structure and stability. Since GUVs are, as the name suggests, giant (on the order of tens of microns), we are able to see them under a microscope when we add a dye molecule to them! So far, our data suggests that NT17 causes the vesicles to burst or break down into many smaller vesicles. It is awesome to actually be able to see the vesicles, since most of my work is done with clear solutions with nanoscopic components! I’m really happy with all the work I’ve done so far, and I’m very excited to see where it leads me for the rest of the summer and the rest of my Gettysburg career!

Pet break III. One of my favorite Gettysburg dogs to pet is Weezie, who lives at a house that I pass during my walk to the Science Center. 

Pet break IV, compliments of Jordyn. These are my dogs from home! From left to right: Tobin, Katie, and Carlie. 

Tristian Kucera – BMB ‘23

Heyo!

I’m Tristian, a nonbinary biochemist. My work in the Lab began in Spring 2021, through the Chem 290 internship class. So far in the summer I have enjoyed singing a whole lot (see, when the campus is empty, one must use their voice to fill it up again), making flawless hacks out in the summer sun, and playing games with my boyfriend. Carcassonne is a great date idea because if your date doesn’t start screaming at the first hint of victory or defeat, then they are not the one, friend. 

My cats, Mr. Kitty depicted above, enrich my life by a factor of forty-six and a half.

Zincosomeeeeesssssss

My project is all about characterizing a mysterious organelle called the zincosome, which is thought to store zinc to help keep the cell in zinc equilibrium. Scifinder, Scopus, Google, all databases I hold with varying levels of regard, don’t have much to say about zinc and lipids, but I intend to give the ideas a good swing or two. We will inspect two experiments down below. The first experiment aims to figure out a protocol for adding proteins to our model membranes. The second experiment simply measures the effect zinc has on lipid behavior.

A Five Thousand Dollar Blender

Watch as Tristian invents the world’s most expensive javelin in a single frame.

Literature suggests zinc ions are moved into storage vesicles by the protein ZnT3, so in the future I want to combine the ZNT3 with vesicles for a more biologically representative system. However, our standard protocol to create vesicles involves freezing the sample, which would denature the protein. Thus, I have to substitute the freezing step with a tip probe sonicator, essentially a titanium shaft, which uses sound waves to turn disordered, multilayered vesicles into unilamellar ones. Sounds great, right? Almost, except that the sonicator’s sound waves are so intense that titanium particles contaminate the sample. These particles can be anywhere from 10 nm in diameter to well over 10 μm. The ongoing adventure has been to separate these titanium shards from the vesicles. And, well, the sound of the mega-blender makes my toes curl.

Langmuir raised a trough!

What if zinc affects the way in which lipids organize to form vesicles? Perhaps the cations take up space in the bilayer membrane, making it more difficult for a full vesicle to form. Langmuir trough experiments will determine if zinc interrupts membrane formation. These experiments involve convincing 2 sextillion lipid molecules to stand their heads on a puddle’s surface. This step is actually quite easy, as the heads love water, while the rest of the molecule does not. Now I can compress the surface to generate a force response curve, as everytime I push on the lipids, they will push back. Eventually I will push too hard, and collapse my monolayer into a disorganized mess of lipids. We can generate a surface pressure vs. area per lipid graph, depicted below, that will indicate how a given lipid system behaves when compressed.

The pressure increases as each molecule of lipid has less space to vibe and dance in, until the monolayer collapses. In the graph, the collapse is around 45 Å2 / molecule because the pressure begins decreasing.

What about zinc? Well, I can dissolve a zinc acetate salt into the puddle of water. Then I can rerun the compression experiment, generating a new surface pressure vs. area per lipid graph. Finally, I can overlay these two graphs and inspect differences in the two curves. If the curves are not identical in their liftoffs from the x-axis, their slopes over different areas per lipid, or the pressures at which their monolayers collapse, then we know that zinc and/or acetate affect the monolayer. Further experiments using different salts, such as sodium chloride and sodium acetate, will help determine if it was the zinc cation or the acetate anion that modified lipid organization. At the very end of my trough experiments, I will have a model for how zinc affects lipid organization.

Thank you for joining the zincosome alliance and good luck, my friend!

“Prof. Frey is the whole package, with loads of knowledge about everything lipids!”

“I love the beach and the boardwalk and whenever I get frustrated in the lab, I can always think about the beach to cheer me up :).”

“And, well, the sound of the mega-blender makes my toes curl.”

“When things don’t go well in lab for me or others (shoutout to the ITC), I always try to keep smiling & remind everyone else to do so as well!”

“I have gotten much better at the research process and keeping all my data straight and organized to help me plan future experiments.” (Jordyn is also the unofficial queen of our lipid lab since she has been here the longest)

Thanks for reading! Left to right: Abby, Prof. Frey, Jordyn, Kacie, Tristian.