Mapping Antibiotic Resistance in Pseudomonas aeruginosa Biofilms to Develop Better Therapies for Cystic Fibrosis

Posted on July 23, 2018 by Danielle Whittaker

This blog post is by MSU graduate student Michael Maiden.

MSU researchers, Chris Waters, Michael Maiden and Alessandra Hunt, BPS, 04.30.18

Currently, I am a 7th year DO-PhD student in the physician scientist training program in Dr. Christopher Water’s drug development and biofilm laboratory in the department of Microbiology & Molecular Genetics. I was attracted to the Michigan State University and the College of Osteopathic Medicine and, specifically the DO-PhD program, because it offered the opportunity to work on clinically relevant projects that may lead to better therapies for patients in the future.

In the Waters’ lab my research is focused on developing new therapies for chronic infections caused by bacteria in the form of biofilms. Biofilms are a community of cells enmeshed in a self-made gel that renders the community up to 1,000x more resistant to antimicrobial therapies. For this reason, bacteria growing in biofilm communities are a major contributor to chronic infections and death.

One bacterial pathogen, that often infects and forms biofilms in patients, is Pseudomonas aeruginosa. In fact, P. aeruginosa is the leading cause of death in patients with cystic fibrosis (CF). CF is a debilitating genetic disease that results in dry and clogged airways, which trap bacteria and leads to life-long chronic infections, resulting in premature death between the ages of 30 and 40 YO.

A biofilm colony formed by P. aeruginosa surrounded by a secreted self-made mucus that makes the bacteria very difficult to treat.

By early adulthood, nearly 50% of CF patients are chronically infected with P. aeruginosa. To extend the lives of CF patients, it is essential to develop therapeutic interventions that eradicate P. aeruginosa before it is able to form a chronic infection in these patients.

We found that by treating with two specific antimicrobials, tobramycin and triclosan, we could kill up to 99% of P. aeruginosa cells growing in biofilm communities. Further, this combination was effective in as-little-as 2-hrs. These exciting results raised one very difficult question, how?

One way to determine how antimicrobials work is to go after well-known targets and pathways. By either turning them on or off, using various molecular techniques, you can test to see if your particular drug is working through that pathway. We tried this approach with little success. So, we turned to an un-biased evolutionary approach.

Using this method, we took advantage of the natural tendency for bacteria to evolve resistance to any antimicrobial given enough time and small enough doses so that some bacteria may survive and thus mutate their genome. We evolved P. aeruginosa cells growing in a biofilm and rendered them resistant to the combination, by slowing raising the dose with time. Next, we performed whole-genome sequencing to identify the genetic mutation(s) that could help to explain how they became resistant.

We found a novel mutation in P. aeruginosa renders the bacteria resistant to the combination. This mutation is located within an enzyme essential for protein synthesis. This gave us a valuable clue for how triclosan may be enhancing tobramycin activity, allowing us to formulate a model for how the two work synergistically. Subsequent experiments have supported this model.

Further, the mutation we identified in our evolution mutants has been identified independently in clinical CF isolates of P. aeruginosa, which renders them resistant to tobramycin. Thus, our artificial evolution work in the lab has been validated by the natural evolution taking place in the clinics, specifically in the lungs of CF patients.

We now have a possible lead for how our combination may be working synergistically against P. aeruginosa cells growing in biofilm communities. This new resistance mechanism could be targeted in the future to develop compounds that inhibit this resistance mechanism. Further, knowledge of this mechanism could pave the way for the future development of compounds that work in a similar fashion to our combination, thus, yielding much needed new antimicrobial therapies. Currently, we are exploring how this mechanism renders bacterial cells in a biofilm resistant to our combination.

As antimicrobial resistance continues to be a major threat to human health, it is important to develop better strategies that more effectively use our current antimicrobial arsenal. This combination may by an example of one such strategy. As a future clinician, I am grateful to be a part of a project with strong clinical implications. And as a scientist, I have always been interested in how organisms evolve. The opportunity to perform evolution studies in the lab is both exciting and rewarding, providing little hints into what sustains life and what great trials and tribulations all living organisms have gone through to maintain it.

Relevant MSU Today Articles:

https://msutoday.msu.edu/news/2018/ingredient-in-your-toothpaste-may-combat-severe-lung-disease

https://msutoday.msu.edu/news/2017/fighting-an-old-enemy-in-the-battle-against-cystic-fibrosis/

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Kalyanmoy Deb honored with IEEE Computational Intelligence Pioneer Award

Posted on July 13, 2018 by Danielle Whittaker

BEACON’s own Professor Kalyanmoy Deb, the Koenig Endowed Chair in Electrical and Computer Engineering at Michigan State University, was honored today by the IEEE Computational Intelligence Society. At the World Congress of Computational Intelligence meeting in Rio de Janeiro, Brazil, he was given the IEEE Computational Intelligence Pioneer Award, which is given to at most one person each year who has made major contributions to the field. It recognizes contributions across one’s entire career. Prof. Deb was honored for his pioneering contributions to the field of evolutionary multi-objective optimization. Among those contributions was the algorithm NSGA-II, which has been more widely used than any other evolutionary multi-objective optimization tool. He has led the community in development of this new field that has spurred both widespread academic research and worldwide industrial application.

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Learning an Evolvable Genotype-Phenotype Map

Posted on June 25, 2018 by hageyt

me!

This post is by MSU graduate student Matthew Andres Moreno

Hi! My name is Matthew Andres Moreno. I’m a graduate student finishing up my first year studying digital evolution with my advisor Dr. Charles Ofria.

Today, I’m going to talk to you about police detective work. Eventually, we’ll talk about evolvability and genotype-phenotype maps, but first let’s talk CSI.

Police Composite Sketches

Specifically, let’s think about how a police composite sketch works. First, someone sees a criminal and describes the face’s physical features with words. This description is the compact representation. Then, the police artist reconstructs the criminal’s face from the description.

Schematic of hypothetical police composite process. Mug shot and composite reconstruction were taken from the Crime Scene Training Blog.

Why does this work? It works because the witness has seen lots of faces and knows what the important bits to describe are. It works because the police artist understands the witness’ words and has also seen lots of faces — from experience, she knows that the mouth goes under the nose, the nose goes between the eyes, etc. and doesn’t need the witness to tell her absolutely everything about the face in order to draw it.

Well, autoencoders can also be used to reconstruct a corrupted input. This works something like a police sketch, too. Suppose that the criminal was wearing pantyhose that partially obscured his face. The witness can still describe the suspect’s face and the police artist can still draw it. Under the right conditions the missing part of the face can be reconstructed reasonably well.

Schematic of hypothetical police composite process with suspect in disguise (incomplete input). Mug shot and composite reconstruction were taken from the Crime Scene Training Blog.

Why does this work? It works because the witness can still see and describe part of the face. It works because the police artist understands the witness’ words and has also seen lots of faces — from experience, she can make a pretty good guess by cluing off the fact, for example, that faces have left-right symmetry or maybe that the criminal probably had a cheekbone and ear on the part of the face that was obscured. Again, because she’s seen lots of faces the police artist doesn’t need the witness to tell her absolutely everything about the face in order to draw it.

Deep-Learning and Autoencoders

The jig’s up.. it was all a set-up! A set-up, that is, to help you understand what autoencoders do. Unless you’re technically inclined, understanding exactly what autoencoders areisn’t particularly important for our discussion. Suffice it to say that what autoencoders are is a type of clever deep learning algorithm.

What autoencoders do is directly analogous to what the witness and police artist do. By looking at lots of examples of complex objects like faces, autoencoders learn to

  1. compactly describe the important features of a complex object (“encoding”, just like the witness) and
  2. reconstruct a complex object from that description (“decoding”, just like the police artist).

I’ll refer to these as the two powers of autoencoders.

The following graphic, a “latent space interpolation” between three faces, gives a neat glimpse of how autoencoders work and how powerful they are. The latent space refers to the set of all compact descriptions an autoencoder can read. To understand what’s going on here, let’s just look at the top row of images.

Autoencoder latent space interpolation with faces! Graphic from [White, 2016].

At the top-left, we see an image of a woman with curly hair. To get to the image immediately adjacent on the right, we use power 1 of autoencoders to generate a compact description and then use power 2 of autoencoders go reconstitute a face image.

Then, going left to right across the top row, things start to get interesting. We gradually change the compact description of the curly-haired woman until it matches the compact description of the red-haired woman on the far right. Each image shows an intermediate compact description that was reconstituted using power 2 of autoencoders. This visualization shows a very natural-looking transition between the two faces!

I won’t walk you through it, but the rest of the grid of images shown above was generated analogously.

Genotype-Phenotype Maps

What does any of this have to do with evolution? This year, I’ve been investigating how  autoencoders can be useful as genotype-phenotype maps in digital evolution. One idea of how this can work: use power 2 of autoencoders (the “decoder”) as the genotype-phenotype map. In this scheme, the genotype lives in the latent space.

In order to drive home the implications autoencoder genotype-phenotype maps on evolvability let’s talk through a little thought experiment. Think back the problem of police face reconstruction we’ve been thinking about. Suppose we’re trying to evolve a face that, as judged by the witness of a crime, maximally resembles the perpetrator. (Yes, this is a real thing people do [Frowd et al., 2004]). To accomplish this, we start out with a set of random genotypes that map to different phenotypes (images). The witness selects the images that most closely resemble the suspect’s face. Then, we mutate and recombine the best matches to make a new batch of images for the witness to consider As we iterate through this process, hopefully we generate images that more and more closely resemble the suspect’s face.

Consider trying to evolve a facial composite using the direct genotype-phenotype map. Under this map, the intensity of each pixel of the image is directly encoded in the genotype. First of all, the randomly generated images wouldn’t look very much like faces at all — they’d look more like static. Supposing that we were actually able to eventually get to an image that vaguely resembles a face at all, then what? Is there a path of pixel-by-pixel changes that leads to the suspect’s face where every pixel-by-pixel change more closely resembles the perpetrator’s face? I’d argue we’d be likely to sooner or later get stuck at a dead end where the image doesn’t resemble the perpetrator’s face but pixel-by-pixel changes to the image make it look less like the perpetrator’s face (or a face at all).

Evolving the composite using the direct genotype-phenotype map probably won’t work well.

What if instead of having the genotype directly represent the image at the pixel level, encode genotypes analogously to a verbal description then use a police artist who can draw a suspect from verbal descriptions to generate phenotypes. This is analogous to what the our “decoding” genotype-phenotype map, accomplishes.

(For those who are curious, software to evolve police composites use an indirect genotype-phenotype map based on eigenfaces [Frowd et al., 2004].)

Wrap-Up

This work — which we call AutoMap — was, in part, inspired by recent efforts efforts to understand evolvability in terms of learning theory [Kouvaris et al., 2017]. We hope that this work helps to strengthen an explicit connection between applied learning theory (i.e., machine learning) and evolvability. We’re also looking forward to expanding on the exploratory AutoMap experimental work that we’re taking to GECCO this summer.

If you’re interested in more detail, This blog is based on a more in-depth (but still non-technical and fun!) introduction to our work with AutoMap, which you can find here. If you want to check out our technical write-up on AutoMap, you can find the PDF here and the paper’s supporting materials here.

Finally, thanks also to my AutoMap coauthors Charles Ofria andWolfgang Banzhaf.

References

Frowd, Charlie D., et al. “EvoFIT: A holistic, evolutionary facial imaging technique for creating composites.” ACM Transactions on applied perception (TAP)1.1 (2004): 19-39.

Kouvaris, Kostas, et al. “How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation.” PLoS computational biology13.4 (2017): e1005358.

White, Tom. “Sampling generative networks: Notes on a few effective techniques.” arXiv preprintarXiv:1609.04468 (2016).

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On the Hunt: How Bacteria Find Food

Posted on June 20, 2018 by hageyt

This post is by MSU graduate student Joshua Franklin

Imagine you are half-starved, blindfolded, then placed into a large gymnasium with a plate full of freshly-baked cookies. How do you find the cookies? You could try to randomly walk around until you step on them. Interestingly, you are mathematically guaranteed to reach the cookies this way, even if the gym is infinitely large[1], but that could take a very long time, and you’re hungry now. Luckily, you can use your senses to speed things up.

It turns out that smell is the only useful sense here, as your vision is blocked and your other senses are either too short-ranged (taste, touch) or functionally unable (hearing) to help you find the cookies. But, generally, you cannot use smell to turn towards distant objects, because the change in odor intensity is too weak to detect when standing in place and turning around. So how can you find the cookies?

You could start by sniffing, walking for a while, then sniffing again. If the odor got stronger, then you must be moving towards the cookies. Great! You should keep moving in that direction. If the smell got weaker, that’s okay; just spin around and try again in a new direction. By following this procedure you will reach the cookies much more quickly than before (Figure 1).

Figure 1. A computer simulation of the paths taken by a person walking randomly (red) and a person using the cookie-finding procedure (blue). The black spot is the plate of cookies. Both strategies were allowed to walk for the same amount of time and both walkers move at the same speed. Even though the random walker starts closer to the cookies, they make far less progress than the cookie-finder.

It turns out that many bacteria use this cookie-finding procedure to help them move towards food, and in this context the procedure is called chemotaxis. Why do bacteria need to do this? Well, many kinds of bacteria can move using a flagella, which is a long filament that sticks off of the cell surface and rotates, working like a propeller to push the cell forward. But just like our noses are too weak to directly help us turn toward the cookies, bacteria are too small to directly sense the direction food is in. So bacteria use chemotaxis to move towards their food by swimming, sensing whether it has moved towards or away from the food, then deciding whether to keep swimming in the same direction or to change directions.

But there’s a hidden assumption here. Think back to the cookie-finding procedure. You smelled, walked, then smelled again and compared the current smell to the previous smell. That means that you need to remember what the smell was previously. Can bacteria actually do this? It turns out the answer is yes. A series of chemical reactions inside of the cell stores information about how strong the “smell” of the food was previously, so that the bacteria can tell if they have moved toward or away from the food source.

The reason chemotaxis works so well for bacteria is that, at the size of a bacterium, diffusion spreads chemicals into gradients very quickly. In fact, the bacterial world is dominated by diffusion-generated chemical gradients. This is hugely different from the world we normally see, where diffusion plays a only minor role[2]. From a bacterial point of view, the world is a series of chemical gradients that can lead them toward food or away from predators, and chemotaxis enables the cells to navigate these gradients effectively.

Figure 2. A diagram of the chemotaxis system in the E. coli. The network that controls the cell decision-making process is composed of only a handful of different proteins. Stars are molecules cells can “smell”, and the rectangular white bars are the sensory proteins. Letters in shapes are chemotaxis-associated proteins. Filaments coming off of the cell surface are flagella, which rotate to push the cell forward.

Our lab seeks to identify traits that affect how cells perform chemotaxis. Bacteria carry out chemotaxis using a set of proteins that detect food molecules outside of the cell and control the cell’s movement (Figure 2). Biochemical processes in this system control how well the cell performs chemotaxis in different environments. Cells which swim towards food more effectively will reproduce more often than cells which can’t, so natural selection will tend to optimize the chemotaxis system for a given environment.

While we have a pretty good understanding of how the chemotaxis system works, it is still difficult to predict how the strategy should be optimized to suit different environments. For example, how long should a cell swim before it is confident that it is going in the right or wrong direction? What if there are obstacles along the way? Which of the many chemicals that can be sensed should be followed? There are still many questions with unintuitive answers that need to be explored to understand why we see so much diversity in motile behavior and morphology in the microbial world.

Our lab has created a web app to explore the evolution of chemotaxis. The virtual environment consists of a rectangular world with a food gradient that increases from left to right. The simulation places cells into the world where they can perform chemotaxis to move towards the food. Each cell is given a number of traits, which control the chemotaxis system as described above and their fitness in the virtual world.

In this simulation cells that are better at chemotaxis reproduce more often than cells that are not as good. Eventually, cells with traits that are optimal for the specified environment will dominate the population. There are a number of environmental details you can change, such as the strength of the chemoattractant, the shape of the chemical gradient, and the presence of obstacles that the cell must navigate to reach the target. Different environments will select for different chemotaxis traits. For more details, see the guide on our website.

While the simulation is a simplified version of the E. coli chemotaxis system, it reproduces the behavior of real bacterial cells really well. Modeling and simulations allow us to explore the behavior of bacteria to generate hypotheses that can be tested in the laboratory. Part of my research involves using models and simulations to understand the performance tradeoffs that are imposed on bacterial motility.

The combination of biological and computational work in my research is a fantastic opportunity for me, as I have enjoyed programming since my early teens, and basically living in a forest as I was growing up nurtured my interests in biology. Being able to combine both of my main interests helps keep me engaged in my research despite the challenges it poses. When experiments are testing my patience I can generate news ideas by doing computer work, and when computer work wears thin I can refresh by returning to the bench.

Figure 3. Me in action at MSU Science Fest 2018.

One of my favorite features of my research is that, as this blog post shows, it provides an opportunity to design and write programs that allow students to study non-intuitive aspects of biology using interactive tools, without the constraints of setting up experiments. I think it can also help students appreciate the power of modeling and simulations in exploring the complexity of biological systems. Using educational programs for public outreach and education is something that I feel strongly about (Figure 3), and hope to expand upon as my graduate career continues.

[1]Technically, this assumes the floor is two-dimensional and space is discrete at some level. Perhaps even more interestingly, a bird flying around randomly in an infinite gymnasium would not be guaranteed to ever reach the cookies, even if given an infinite amount of time. For an approachable explanation of both phenomena, see: https://www.youtube.com/watch?v=stgYW6M5o4k

[2]When in school, I remember our teacher explaining diffusion by asking us what happens when when someone breaks a bottle of perfume in a store. We said “Everyone starts to smell it”. The teacher explained this as diffusion, but in reality it’s due to air currents. For example, in perfectly still air it would take about three days for oxygen (D = 0.176cm2/s, according to Wikipedia) to diffuse a distance of 10 feet. See: http://www.physiologyweb.com/calculators/diffusion_time_calculator.html

 

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Gallium cannot be used as a Trojan horse to fool Iron-selected bacteria

Posted on June 14, 2018 by hageyt

This post is by NCAT postdoc Akamu Jude Ewunkem, faculty Misty Thomas, grad student Sada Boyd, and faculty Joseph Graves Jr.

Antibiotics have heretofore been used as therapeutic agents (Butler et al., 2017). However, bacteria are increasingly developing resistance to these therapeutic agents. Due to this continuum of resistance evolution there has been a stagnation in the development of novel antimicrobial agents to treat multidrug-resistance organisms. However, alternative therapeutic options are currently being exploited for treatments. Iron acquisition is an alternate target of antimicrobial agents.

Iron is an important micronutrient for virtually all living organisms. Iron is involved in a wide variety of important metabolic processessuch as photosynthesis, respiration, the tricarboxylic acid cycle, oxygen transport, gene regulation and DNA biosynthesis (Weinberg, 2009). In bacteria, iron influences cell wall composition, intermediary metabolism, secondary metabolism, enzyme activity, and host cell interactions. Bacteria use surface proteins, heme group and siderophores for acquisition of iron. The competition for iron between host and bacteria is so important that many multicellular organisms have evolved defense mechanisms that sequester iron away from pathogenic microbes.

Gallium is a transition metal element, and has a similar ionic radius to that of iron. Thus, gallium can efficiently compete with iron for binding to iron-containing enzymes, transferrin, lactoferrin and siderophores. Gallium is used as a trojan horse to iron-seeking bacteria. Invading bacteria are tricked, in a way, into taking it up. However, while the binding of iron to a protein promotes protein function, the substitution of gallium for iron in a protein usually disrupts its function and may lead to adverse downstream effects in cells (Choi et al., 2017).

Our laboratory is evaluating the fitness of iron resistant bacteria in gallium. We utilized experimental evolution to create 12 iron (II) and 12 iron (III) selected replicates in Escherichia coli.  These cells had been selected in excess iron for 28 days. Within each selection regime, 5 had no history of silver resistance and 7 were derived from Ag+ resistant replicates (Tajkarimi et al. 2017).  The control cells had no history of either iron or silver resistance. Fitness of these replicates was evaluated in the presence of increasing concentrations of Gallium (III) nitrate.  Our results indicated that bacteria selected in Iron (II) as well as Iron (III) showed a significantly superior 24 hour growth in Gallium nitrate compared to the controls (Fig 1 and 2).

Fig 1: Twenty-four-hour growth curve of Iron (II)-Selected E. coli in the presence of increasing concentrations of gallium (III) nitrate. Fe2+=Iron(II)-selected bacteria. Fe2+Ag= Iron (II)-selected bacteria with silver background.

Fig 2: Twenty-four-hour growth curve of Iron (III)-Selected E. coli in the presence of increasing concentrations of gallium (III) nitrate. Fe3+=Iron(III)-selected bacteria. Fe3+Ag= Iron (III)-selected bacteria with silver background.

For example, for the iron (III) resistant populations there was virtually no reduction in growth from 60—1000 mg/L of gallium, while for the controls growth was completely eliminated at 1000 mg/L. Interestingly, gallium was also very toxic to the ancestors of iron-selected cells with silver background (i.e. silver selected bacteria) (data not shown). Whole genome sequencing of our iron-selected bacterial cells demonstrated that mutations occurred in genes that confer anti-transition metal stress resistance. Examples of these genes include fecA (ferric citrate outer membrane transporer), rho (transcription termination factor), fur (ferric iron uptake regulon transcriptional repressor), murC (UDP-N-acetylmuramate: L-alanine ligase), dnaK (chaperone HSP70), tolC (transport channel), and nusA (transcription termination/antitermination factor).

In addition to whole genome sequencing, we utilized Nanostring technology to examine gene expression profiles of 50 genes we determined to be involved in iron metabolism or general metal resistance. We found striking patterns of expression difference in the presence of excess iron for genes: regulated by fur (ferric iron uptake regulon transcriptional repressor), involved in cell wall synthesis, general metabolism, transcription, transport, and transcription regulation.  Generally, genes in these categories in the iron resistant bacteria were significantly up-regulated, while these same genes were significantly down-regulated in the controls.

Thus, we hypothesize that the genomic profile and altered gene expression patterns of our excess iron resistant E. coli has also changed the way they interact with the iron analog gallium. Either these mutations reduce the rate that gallium enters the cell, increases the rate in which it is effluxed from the cell, or alter the targets of gallium toxicity once inside the cell.  These results suggest that gallium cannot be used as a Trojan horse to fool iron-selected bacteria, as there survivorship in the presence of increasing gallium suggests the capacity to rapidly evolve resistance to it. We will test this idea in subsequent experiments.

References

Butler, M. S., Blaskovich, M. A., & Cooper, M. A. (2017). Antibiotics in the clinical pipeline at the end of 2015. The Journal of antibiotics, 70(1), 3.

Choi, S. R., Britigan, B. E., Moran, D. M., & Narayanasamy, P. (2017). Gallium nanoparticles facilitate phagosome maturation and inhibit growth of virulent Mycobacterium tuberculosis in macrophages. PloS one, 12(5), e0177987.

Weinberg, E. D. (2009). Iron availability and infection. Biochimica et Biophysica Acta (BBA)- General Subjects, 1790(7), 600-605.

Tajkarimi, M., Rhinehardt, K., Thomas, M., Ewunkem, J. A., Campbell, A., Boyd, S., … & Graves, J. L. (2017). Selection for ionic-confers silver nanoparticle resistance in Escherichia coli. JSM Nanotechnol. Nanomed, 5, 1047.

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High school phage hunters: an experiment to encourage young scientists

Posted on June 9, 2018 by hageyt

This post is by MSU postdoc Sarah Doore, with contributions from Dr. Kristin Parent and Mr. Kevin Schrad

For the last couple years, our lab at MSU has been advocating “phage hunting” as part of the biology classroom experience. Bacteriophages—”phages” for short—are viruses that infect bacteria. To hunt phages, all you have to do is think about where a phage might be (usually anywhere you can find bacteria), scoop up a handful of water or dirt, then test your sample to see whether it infects certain strains of bacteria. Phage hunting is a great way to get students thinking about the microbial world and to help them relate to science and the scientific process.

I’ve written about a few of our adventures here and here, but thanks to funding from the National Science Foundation, we provided equipment and expertise to a Nebraska high school, Lincoln Southwest (LSW).

Jason Schrad explaining some of the lab techniques to the high school class

Students getting ready to check their samples for phage. Can you spot the class mascot?

This past April, my labmate Jason Schrad and I visited LSW in person. We helped our teacher-partner, Mr. Kevin Schrad, set up the equipment and walked him through the procedure. We explained bacteriophages to the students, discussed where they might be found, then went out and collected some samples from the local environment. We then worked with them to plate their samples and analyze their results. We did this for two classes for a full week, moving through each step of the scientific process along with them.

Spoiler alert: we didn’t find many phages.

This was a huge surprise to everyone, considering how many we found last semester. But we all thought the experience was awesome—phage or no phage! And hey, that’s how science goes sometimes. You always learn something from your experiments even if your starting hypothesis didn’t turn out to be 100% accurate.

Mr. Schrad said, “The Phage Hunting Experiment is a huge success for my classes. My students enjoyed doing what they called ‘real science.’ I know it sounds funny but they liked using the ‘real science’ equipment, especially the vortexers. [side note: evidence for this claim is here] The students enjoyed the hands-on experience and working with real scientists. They enjoyed being able to see the outcome of their work even if they didn’t find any phages.”

Another teacher at the school, Mr. Charley Bittle, was enthusiastic to meet us and see the process. Now he’s going to incorporate phage hunting into some of the other biology classes at LSW.

Mr. Schrad getting a sample from Iggy, the classroom iguana.

Partway through the week our lab leader, Dr. Parent, did a Skype session with both classrooms. This gave the students a chance to explain what they’d found and to ask more questions about a career in science.

Of the experience, Dr. Parent said, “One of the most profound things I experienced was when the girl in the second section who asked me: ‘what happens if scientists mess up?’ I started thinking that a student’s entire focus up until graduate school is to get the answer correct (at least in most STEM courses). So the idea that a hypothesis could be wrong, or that sometimes we don’t really know how to do it right the first time, is something we should prepare students for.

“Fear of not being 100% perfect is the killer for successful science. It’s good to teach the students that not everything works like the positive control every time and that’s ok (actually, troubleshooting can even be part of the fun). Negative results are sometimes very interesting, and we often need to re-evaluate our models/predictions, which means redesign and retesting.”

But you don’t have to take our word for how great this experience is. At the end of the semester, the students filled out an exit survey, which pinpointed what they found most interesting and valuable in the class. Here are three of the questions from the survey and a sampling of the responses:

Describe how the phage hunting experience made a difference in your understanding of biology and science in general:

“It helped me understand what it truly means to ask questions and test things in science.”

“It made it more exciting and made us able to interact with [microbial] wildlife that we never would’ve interacted with before.”

“This made me think about how there could be viruses in almost anything in the world around you.”

“It opened my mind”

“It gave us a chance to look at what real scientists do.”

What long-term benefits could the phage hunting (viruses) project provide to you?

“It gives me something to look back on in the future and make me remember all the things I saw that I’ve never seen or knew existed before.”

“I think it could provide a better understanding of the micro level of this planet.”

“It could show me a possible career to go into.”

“It could help you to see if you want to go into that kind of science thing.”

“It made me more interested in it.”

What skills have you gained from the phage hunting project that may help you become a better student, scientist or citizen?

“It made me realize that being patient brings good things” – similarly, “to be patient while waiting for the results.”

“I learned how to work with others in my class, how to investigate things, and how to use the equipment.”

“It made me think more deeply about questions and to go out and search and experience.”

“Always taking notes on what I did so that if I were to go back to it in like 12 months that I would know what I was doing.” [totally agree with this!]

We’ll keep hunting phage in the future, though next time we could encourage students to sample from their own home or neighborhood and then compare that to what they found at the high school. The more classes we do this with, the more we’ll refine our methods and strengthen the partnership between university and high school.

“As we go through the techniques more it begins to be a bigger part of our curriculum,” said Mr. Schrad. “Having real scientists leading the project and actually being part of the process has a tremendous impact on how the students view the project. If we spark the interest to go into science of just one student it is a worthy accomplishment.”

Interested in trying this for your college or high school class? Feel free to talk to us on Twitter @Phage4Lyfe or visit the Parent lab website, which includes video protocols for phage isolation.

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In the Light of Evolution: Connecting Genotype to Phenotype and Fitness in an Introductory Biology Class

Posted on April 17, 2018 by hageyt

Katie Dickinson

This post is by UW research scientist Katie Dickinson

“It was through the [Bio180 CURE] class that biology truly came to life and I felt that our time in [the] lab was interesting and relevant to our world today. The large lecture halls felt smaller as our table group grew closer together…”  Former Bio180 CURE student.

I am helping to develop a set of labs that enables undergraduate students, early in their academic career, to experience what it is like to do research. Ultimately, we aim for this CURE (Course-based Undergraduate Research Experience) to be woven into the Introductory Biology series (BIOL 180 and BIOL 200) at the University of Washington. In addition to trying to make research accessible to large numbers of students, students are able to observe evolution in action to better understand a global health crisis, antibiotic resistance.

In this blog I wanted to provide a general overview of the Intro Bio CURE lab series. Students use bacteria to investigate the evolution of antibiotic resistance at the population level and connection to cellular/molecular mechanisms.

Students hard at work

In the first set of labs, students expose E.coli to specific drug regimes, which select for resistant mutants. These mutants, along with a sensitive ancestor, are transferred daily in drug-free media for several weeks. Samples of each isolate are frozen down enabling students to make comparisons between the progenitors (from the beginning of the transfers) and the descendants (from the end of the transfers). Then assays are done to determine competitive fitness and the level of drug resistance of each isolate. The resistance level will be measured in two drugs enabling students to gauge whether they see evidence for cross-drug interactions; where resistance to one drug (the drug in the Petri dish that was used to isolate the strain) confers increased or decreased resistance to another drug. These labs highlight evolutionary phenomena at a population level.

Alumni students assisting with lab prep

In the second series of labs, students will analyze the products of their own evolution experiments (evolved bacterial isolates from the first course). Activities include: PCR/gel of a candidate gene, DNA sequence analysis, exploring protein sequence and structure analysis. The goal is to enable students to trace genotype to phenotype at the cellular level, and connect evolution to molecular biology.

Experimental Overview Schematic

Lab Activity Key Concepts
Lab1 Screen for resistance by spreading a sample of bacteria on Petri dishes with antibiotics and without. Natural selection, mutations, antibiotic resistance, sterile technique
Lab 2 Pick resistant mutants (and a sensitive isolate as a control), freeze down a sample and begin serial transfers in the absence of drug. Experiment design, fitness, the cost of drug resistance, evolution and population dynamics

 
Lab 3 & 4 Calculate relative fitness with mock data, learn how to determine a minimal inhibitory concentration (MIC) value, use R/Rstudio to graph and gauge significances. Serial transfers continue. Basic statistics, graphing, introduce cross-drug interactions (collateral sensitive/resistance)
Lab 5 Serial transfers end. Competition and MIC assays comparing the progenitors to their descendants Relative fitness, Levels of drug resistance, importance of collaboration
Lab 6 Data analysis, suggest future research Data interpretation, determine if there is evidence for a fitness cost associated with resistance, compensatory mutations, reversion, look for collateral effects
Lab 7 PCR, gel electrophoreses, sequencing Central dogma, genetic techniques
Lab 8 Analyze sequencing data DNA sequence analysis, identify mutations, translation, evolution, genotype
Lab 9 Protein structure Resistance mutation effect protein structure, enzyme function, phenotype and fitness.
Lab 10 Poster presentation Integrate connection between genotype, phenotype, and fitness. Scientific communication, collaboration

Competition Petri dishes and MIC microtiter plates

Last year we ran several pilot classes (single lab sections with 24 students). This winter quarter we scaled up, four randomly selected BIOL 180 lab sections swapping out the tradition lab material for the CURE modules. Currently, three BIOL 200 sections are continuing the CURE labs this spring quarter. Throughout the Intro Bio CURE labs, students are collaborating and communicating to collect and analyze their own data and propose follow-up experiments. In addition, students are introduced to career-transferrable skills.

Presently, data is being gathered on student outcomes. We are specifically measuring: core concepts, competencies, and affect. Are students gaining a better grasp of evolution via natural selection? Are they able to connect genotype to phenotype to fitness? Is there evidence for improved understanding of the experimental process, and how to gather and interpret data? Do students gain an appreciation for the importance of data visualization, statistics, scientific communication & collaboration? Does the Intro Bio CURE series enhance a sense of belonging in science/college and does this translate to retention in STEM fields? Do students identity as scientists? Are we successfully enabling students to cultivate a positive attitude towards value of research and practice of science?

What is next?

If outcomes are looking promising, we plan to go forward with scaling. By winter 2019 all students enrolled in the UW Introductory Biology series will be provided with the opportunity to engage in authentic research experience, serving roughly 2000 students per quarter!

 

 

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Congratulations to Prof. Percy Pierre, Chair of the BEACON Diversity Steering Committee

Posted on April 12, 2018 by hageyt

On April 10, 2018, BEACON’s own Prof. Percy Pierre, Chair of the BEACON Diversity Steering Committee and contributor to BEACON from its earliest proposal days, was honored with the Historical Leader Award of the MSU Black Faculty, Staff and Administrators Association (BFSAA). BEACON’s Business Manager, Connie James, who is Recording Secretary of the group, was on hand to describe to the group some of Percy’s most significant contributions.  His distinguished career has spanned serving as a White House Fellow, where he was deputy to the Assistant to the President for Urban Affairs; as Dean of Engineering at Howard University; Assistant Secretary of the Army for Research, Development and Acquisition, and later Acting Secretary of the Army; President of Prairie View A&M University; Vice President for Research and Graduate Studies at MSU, and Professor of Electrical and Computer Engineering at MSU.  In the latter role, he has mentored over 200 minority graduate students to degrees in Engineering, the accomplishment of which he is most proud. Dr. Pierre was a principal architect of the national minority engineering effort, co-chairing the 1973 National Academy of Engineering Symposium that launched the effort. He served as the program officer for minority engineering at the Alfred P. Sloan Foundation, where his efforts resulted in funding of many minority engineering organizations, including NACME, GEM, MESA, DAPCEP, and SECME. He was elected to membership in the National Academy of Engineering, and has received many other awards and honors.

Percy’s wisdom and vast experience have helped to shape BEACON’s diversity efforts to be among the most successful of any of NSF’s Science and Technology Centers. We all owe him a debt of gratitude for the positive and supportive atmosphere that pervades all of BEACON today.

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Exploring the evolution of troglodytes?

Posted on April 10, 2018 by hageyt

This post is by MSU postdoc John Phillips

Some of you may be familiar with the term ‘troglodyte’, which is a somewhat old-timey derogatory term for an unintelligent person. The Greek root troglo- means “cave” so a troglodyte is a cave person. While we use(d?) this as an insult, caves are actually fascinating places to study, explore, and even earn a Ph.D!

Caves serve as fascinating evolutionary laboratories and are home to a variety of species, many of which have converged on adaptations allowing them to thrive underground. From invertebrates to fish to salamanders, cave-obligate species have repeatedly lost vision/eyes, deactivated pigments, slowed metabolic rates, and evolved behaviors to survive in a nutrient poor environment where most organic material gets washed in from the surface! Many cave food webs are based on bat guano, thus highlighting the importance of bats to the persistence of many cave species. Additionally, cave species are often overlooked when it comes to conservation efforts. This can be a HUGE problem because cave species are imperiled when you combine habitat-specialization, high rates of endemism and low rates of dispersal with a suite of anthropogenic threats (think groundwater pollution or climate change).

Cave crayfish

Cave millipede

Much of my research involves the study of biogeography–I like discovering when and how species got to where they are. Cave systems seemed like an excellent ecosystem which has been relatively ignored in genetic studies. Furthermore, I was living only an hour from the beautiful Ozark Plateau, which is known as a biodiversity hotspot with many endemic species (including several cave species) but was lacking for studies testing biogeographic hypotheses which can be crucial for conservation efforts. The Ozarks are made from limestone karst, which is easily fragmentable rock and often dissolves in ways that produce amazing caves and subsequently their fauna. There are over 10,000 caves in the Ozark Plateau, many of which have not been well-studied to understand their biodiversity.

Gyrinophilus palleucus

In one of my studies, I looked at the Grotto Salamander (Eurycea spelaea) that is unique among salamanders. While there are only 12 described cave-obligate species of salamanders in the world almost are paedomorphic, which means they retain characteristics of larvae throughout their life (predominantly gills and a fully aquatic lifestyle, see picture of the Tennessee Cave Salamander (Gyrinophilus palleucus)). Typically, these salamanders will never leave the cave. However, the Grotto Salamander larvae (pictured) can inhabit surface streams and possess fully functional eyes. After several years as larvae they metamorphose into adults, losing their gills, pigments, and eyes, whereupon they leave the water and are free to climb about the cave walls.

 

Grotto Salamander larvae (Eurycea spelaea)

As a group, salamanders employ various life-history strategies, but none as unique as this. All grotto salamanders obligately metamorphose, indicating this an evolved strategy as opposed to something environmentally driven. Because of this unique life-history shift, my colleagues (Ron Bonett: University of Tulsa, Sarah Emel: UMass – Amherst, and Danté Fenolio: San Antonio Zoo) were interested in testing colonization patterns of Grotto Salamanders across the Ozark Plateau. Grotto Salamanders occupy a much larger range than other cave salamanders (See #1 on the map below). Could this be due to the surface-dwelling larvae following drainage patterns? Or do the terrestrial adults disperse underground more readily that their fully aquatic relatives? SPOILER ALERT: It is actually hard to distinguish between the two causes, but using the DNA of these salamanders we find that the geologic history of the Ozarks and major changes in drainage basins of the regions combine to explain a majority of genetic variation.

How much genetic variation? Well we have discovered three highly divergent lineages of grotto salamanders dating back 10–15 million years. While these three groups have not changed noticeably in their morphological features (so far as we can tell yet), they are considerably more different genetically than many other species of salamanders are to one another. Therefore, my colleagues and I have “re-elevated” each lineage to species status based on strongly supported genetic differences and geographical separation (see lower map). This phenomenon–where multiple species are unknowingly classified as a single species–is known as “cryptic speciation”. This has turned out to be quite common in cave species. Partially due to their lack of study. Hopefully our efforts here will help conservation agencies (in Oklahoma, Arkansas, Missouri, and Kansas) better manage these lineages across their range.

Eurycea braggi

Eurycea nerea

For more info on my Grotto Salamander work, feel free to read: Phillips, J.G., Fenolio, D.B., Emel, S.L., Bonett, R.M., 2017. Hydrologic and geologic history of the Ozark Plateau drive phylogenomic patterns in a cave-obligate salamander. J. Biogeogr. 44, 2463–2474.

This work was done as part of my Ph.D. at the University of Tulsa in Oklahoma. Currently I am a postdoc at MSU with BEACON, EEBB, and the Department of Integrative Biology where I study evolution in Stickleback fish! Hopefully I will have another blog post down the line as my work here progresses.

 

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