BEACON Researchers at Work: Carnivore Skull Evolution

This week’s BEACON Researchers at Work blog post is by MSU graduate student Nikki Cavalieri.

CNCavalieri2015_UrsusMustelaWhy?

Why do tree frogs stick to glass but toads don’t? Why are baby skinks tail’s blue but adult’s not? Why are puppies and kittens born with their eyes closed but calves and foals born with their eyes open? Why did a girl from Cloudy, Oklahoma become a scientist? I was a child with many questions, which no one could answer. I grew up, went to college and still no one could answer all of my questions. So I became a scientist to find out WHY?

The essential question of my dissertation research is “Why don’t all carnivorans have a strong skull with formidable teeth?” Carnivorans are members of the mammalian Order Carnivora. They are some of the most recognizable animals on earth: cats, dogs, bears, seals and many others. They range in size from the least weasel (100 g) to the polar bear (800 Kg). They live on all major landmasses and occur in every ocean. Carnivoran diets range from completely herbivorous to completely carnivorous and include every combination in between.

Nikki showing the full range of carnivore skull sizes

Nikki showing the full range of carnivore skull sizes

The ancestral features of this group are a robust cranium and jaw with large canines and sharp cheek teeth. Many modern carnivorans have more delicate features. Why lose a good thing? We know that species evolve via natural selection. That is, individuals that perform better, through morphological, physiological, and behavioral traits, survive and have more opportunities to produce offspring. Those offspring inherit the traits of their parents. This differential survival and reproduction of individuals with different traits is what determines how species function and make a living.

We would expect that species would evolve only traits that perform best. However, this is not what we observe. So why? We know that there is a limited amount of energy available in the environment. In order to reproduce and pass traits to the next generation an individual must grow and survive first. Individuals must balance energy demands between traits for survival, growth and reproduction. These traits are competing. When traits compete we expect to see trade-offs. A trade-off is a compromise between conflicting selection pressures on a trait. This can result in the phenotype being suboptimal for one or more traits. To further complicate matters, selection pressures on a trait may be different during different times in life.

Not only do we expect trade-offs between what traits species have, but also when those traits develop. There is no advantage to developing secondary sexual characteristics to attract mates if reproductive organs are not fully developed. Thus, the evolution of timing of life history events (i.e. weaning, independence, age at first reproduction) is influenced by trade-offs between competing traits for survival, growth and reproduction. I believe that understanding the interaction and timing of competing traits will help us decipher why there so much variation in the carnivoran cranium and jaw.

Since energy is limited, natural selection should act strongly on traits for obtaining energy. The main structure for obtaining energy for carnivorans is the skull. The Carnivora skull is a multipurpose structure that serves as a feeding apparatus plus houses and protects the brain and sensory organs. The carnivoran skull is not completely developed at birth and must go through extensive post-natal growth before reaching morphological maturity. It has to meet demands at each life stage and to develop between life stages (e.g. morphology for nursing versus procuring food). In its role as a feeding apparatus, the skull must procure and process food. Many studies have found a strong relationship between diet and morphology of the carnivoran skull.

Specifically I study the patterns of interspecific variation in growth and development of the carnivoran skull. I focus on two questions: (1) whether interspecific differences in the timing of morphological maturity are reflected in life history schedules and (2) whether timing of morphological maturity is influenced by diet.

CNCavalieri2015_setupI hypothesize that delayed morphological maturity shifts reproduction to later in life. To test this I get to travel to natural history collections and photograph specimens ranging from one day to several years of age. From these photographs I construct ontogenetic skull series. Shape and size are quantified for the skull series using geometric morphometrics. I calculate age at morphological maturity for skull shape and skull size, for each species. I examine changes of ontogenetic trajectories, allometric trajectories, and disparity. I compare the timing of morphological maturity of the skull relative to life history schedules across the Order Carnivora, taking into account body size and longevity. I plan to examine some of the behavioral correlates (e.g. level of maternal care) that accompany these shifts.

My second hypothesis is that there is a trade-off between dietary challenge (i.e., difficulty in procurement and processing) and timing of skull development, such that species with more challenging diets reach morphological landmarks (e.g., age at adult skull morphology) later in their life cycle than species with less challenging diets. I measure dietary challenge with two components: 1) procuring difficulty and 2) processing difficulty. Procuring difficulty is quantified using a score that takes into account the physical and behavioral aspects of the predator and food item. A higher score indicates a more difficult diet to procure. Processing difficulty is quantified using empirical measurements of food items. I get to use engineering machinery, usually used to test material properties of steel beams, to measure the toughness, tensile strength, and hardness of food items.

One of the cool analyses I will be able to do is to map dietary challenge and timing of skull maturity on a phylogeny of the Order Carnivora. By using Bayesian inference to estimate ancestral character states, I can explore transitions between high and low dietary challenge and early or late skull maturity through evolution. This is exciting because it can allow me to understand why and when variation in carnivorans evolved.

As of right now I still don’t know “Why all carnivorans don’t have a strong skull with formidable teeth?”. I have collected enough data to know that it is going to be interesting but not enough to see the whole picture. I will just have to wait a little longer to find out WHY?

For more information about Nikki’s work, you can contact her at cavali13 at msu dot edu.

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BEACON Researchers at Work: Tools for mapping rare mutations

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin postdoc Daniel Deatherage.

Dan in a suit and without a beard. It may be surprising to those that don’t work with him, but the suit is a more common sighting than the lack of beard these days.

Dan in a suit and without a beard. It may be surprising to those that don’t work with him, but the suit is a more common sighting than the lack of beard these days.

My doctoral work focused on epigenetic changes in ovarian cancer in the lab of Dr. Tim Huang at The Ohio State University. A common theme of the journal clubs I attended was the rise of next-generation sequencing technologies, the enormous amounts of data they produced, and questions they could answer that were unanswerable by any other method. One thing that always struck me was how small variations in constructing sequencing libraries could have profound effects on the data the sequencing runs would produce. Because of this, one of my primary interests became how to generate sequencing libraries capable of answering questions not possible even with “standard” next-generation sequencing. Now in my post-doctoral work with Dr. Jeffrey Barrick at The University of Texas at Austin I have sought to use modified Illumina sequencing adapters to monitor how mutations within evolving populations of E. coli spread while they are still very rare (<0.01%) rather than having to wait until they reach 1% of the total population as standard Illumina sequencing requires. Hopefully by the time you are finished reading this, you will have a new appreciation for just how powerful next-generation sequencing can be even if you are already using it in your own research.

Identifying mutations in next-generation sequencing data can be very difficult regardless of what method of analysis you choose. This is compounded when looking at mixed populations where both mutations and wild type sequences exist. If I were going to use this blog post as nothing but a bit of shameless self-promotion, the rest of it would likely talk about all the benefits of the breseq computational program that our lab has developed. I could go on and on about how well it automates the identification of mutations particularly in organisms with genomes smaller than 20 mega bases that have good reference sequences, about how its being actively developed as a tool for the entire community that is already being used by many BEACON and non-BEACON researchers alike, and about how it is freely available for both linux and mac operating systems with tutorials for understanding how to use it. At the end of this hypothetical self promotion I would put in a hyper link to the breseq page where you can find instructions on how to install the program and the tutorials for how to use it, and say something about how I hope anyone using next-generation sequencing in their research considers using breseq and contacts our lab if they run into difficulties.

Figure 1. Computer simulation of E. coli population evolving under LTEE like conditions. Each line represents the percentage a single mutation at any given generational time point. Horizontal dashed line represents the 1% minimum threshold a mutation must eclipse to be detectable by standard Illumina sequencing.

Figure 1. Computer simulation of E. coli population evolving under LTEE like conditions. Each line represents the percentage a single mutation at any given generational time point. Horizontal dashed line represents the 1% minimum threshold a mutation must eclipse to be detectable by standard Illumina sequencing.

Instead, I’d like to talk about a key limitation of all standard next-generation sequencing experiments, and how I am overcoming this limitation in my research. Next-generation sequencing error rates are something that can quite often be completely ignored, particularly when sequencing a sample that you expect to have only a single genotype (such as a bacterial culture grown up from a single colony) as the predominantly random distribution of sequencing errors are unlikely to yield false-positive mutations. When you begin sequencing samples that are mixed populations, the error rate sets a floor on the minimum frequency you can confidently call no matter how much sequencing you perform. In the case of standard Illumina sequencing, although looking at different subsets of data can lower the error rate somewhat, the overall error rate is commonly reported to be ~1% [1]. Computer simulations show that only a small fraction of total mutations in an evolving bacterial population rise above a frequency of 1%, meaning that a study which does not compensate for sequencing error rate is only looking at the tip of the iceberg and ignoring several orders of magnitude more mutations (see figure at left). As mentioned previously, because the error rate is randomly distributed among all reads, methods such as duplex sequencing [2], which incorporate random nucleotides into the adapters as the first bases sequenced, allow reads corresponding to the same original fragment of DNA to be grouped together based on the “molecular index” and more accurate consensus sequences (with error rates of < 0.01%) can be generated for each read group. This means that mutations can reliably be detected while they are at least 100 times more rare, or only present in a single cell out of more than 10,000.

Because this method of error reduction requires multiple reads from each fragment of DNA, E. coli such as the thoroughly studied REL606 with its ~4.6 megabase genome could easily require more than 1 billion Illumina reads to give 10,000 fold coverage of the entire genome. While it is certainly possible to generate such a quantity of reads, it is not necessarily the wisest investment of money particularly when so much more is known about the organism. The decades of research performed by Richard Lenski and colleagues on REL606 and its evolved descendants in the E. coli long-term evolution experiment (LTEE) has amassed a list of genes that can be mutated to provide a selective advantage. Using some of this knowledge, I designed iDT xGen biotinylated probes against several genes I expected to have beneficial mutations within 500 generations. These probes were hybridized with Illumina libraries containing molecular indexes and enriched for the targeted genes with streptavidin beads. This caused on average ~70-80% of reads to map to the 8 genes of interest corresponding to just ~0.7% of the genome making it highly economical to deep sequence numerous mixed populations.

Despite the enormous power of the “frozen fossil record” of the LTEE experiments performed by Richard Lenski, populations have only been frozen every 500 generations which is a very long time when looking at rare mutations. To overcome this, I allowed six replicate populations of REL606 to evolve under nearly identical conditions to the LTEE for 500 generations, but froze each day’s culture taking up more than half of a large –80°C chest freezer much to the annoyance of other lab members. Sequencing libraries were generated at ~13 to 25 generation increments over the course of the experiment for each of the different populations and analyzed with breseq revealing unprecedented insights into the beneficial mutational landscapes of individual genes and epistatic interactions. These results should be published soon, but to underscore just how powerful this approach has proven I’ll share two highlights. First, more than 150 beneficial mutations were identified in just 3 genes which is significantly more than have previously been reported for these genes. Second, the fitness effect of all 150+ mutations has been determined based on sequencing data alone, and it is in agreement with clones verified to have individual mutations and competed against the ancestor in conventional fitness assays. These findings would not have been possible if we restricted our analysis to mutations that reach 1% frequency because clonal interference quickly becomes the key force acting on the population and the majority of mutations are outcompeted by a single clone often harboring multiple mutations before they can reach 1% of the total population. As sequencing costs continue to fall this type of analysis should make it possible to map the entire single step beneficial mutational landscape that is available to an organism in a single experiment.

References:

  1. Lou, D. I. et al. (2013)High-throughput dna sequencing errors are reduced by orders of magnitude using circle sequencing. Proc Natl Acad Sci USA, 110: 19872–19877.
  2. Schmitt MW, et al. (2012) Detec­tion of ultra-rare mutations by next-generation sequencing.Proc Natl Acad Sci USA, 109:14508–14513.

For more information about Dan’s work, you can contact him at daniel dot deatherage at gmail dot com.

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BEACON’s NSF grant renewed for another 5 years

From the press release at MSU Today

Michigan State University has been awarded $22.5 million by the National Science Foundation to continue the research, education and outreach activities of the BEACON Center for the Study of Evolution in Action.

Since 2010, BEACON has brought together evolutionary biologists, computer scientists and engineers to explore evolution going on in today’s world. BEACON researchers have provided insights into the evolution of disease, reducing the evolution of antibiotic resistance and predicting how populations of organisms respond to climate change.

The use of digital organisms – self-reproducing computer programs operating in a controlled computer environment – allows researchers to explore evolutionary dynamics much more rapidly than studies in the lab or field. Understanding these processes contributes to better solutions of design and engineering problems of industrial and societal importance using evolutionary computational tools, said BEACON Director Erik Goodman.

“BEACON’s world-class faculty members, all pulling toward common goals, enable our high level of scientific innovation, attracting top-notch graduate students and postdoctoral researchers to the best place in the world to study evolution in action,” he said. “Researchers are drawn to BEACON because the frequent exchange of ideas makes it worthwhile for their research, energizing everyone involved. NSF’s renewal of BEACON will enable continuing breakthroughs in our understanding and harnessing of evolution in action.”

BEACON’s development of more-sophisticated evolutionary computational models to solve intractable problems in science and industry is creating new partnerships between biologists and engineers, said George Gilchrist, program director in NSF’s Division of Environmental Biology.

“In the first five years, BEACON has changed the landscape of evolutionary computation, creating a set of multidisciplinary scientists making strong contributions in both biology and engineering,” he said. “The second five years promises new advances in taking inspiration from the algorithmic nature of the evolutionary process to deliver robust solutions to some of the most-difficult problems in both science and industry.”

For example, Richard Lenski, MSU Hannah Distinguished Professor of microbiology and molecular genetics, continues to provide important insights about evolution and the process of natural selection. His long-term E. coli experiment distills the essence of evolution in petri dishes and has received a great deal of media attention from outlets including NPR, the New York Times, Science and New Scientist. In fact, Science referred to Lenski as “The Man Who Bottled Evolution.”

Kay Holekamp, University Distinguished Professor of zoology, continues to serve as one of the world’s leading behavioral ecologists through her studies of spotted hyenas in Kenya. Her long-running study has accumulated more than 25 years of data, covering nearly 10 generations, of spotted hyenas. She and her students have published more than 150 scientific papers.

BEACONITES also have made great strides toward understanding one of the major transitions in evolution – transforming from single-celled to multicellular life. Charles Ofria, director of MSU’s Digital Evolution Laboratory, and fellow BEACONITE Heather Goldsby, showed how reproductive division of labor could possibly have evolved. Their research suggests that separating germ cells – sperm and eggs – from somatic cells – all other cells – preserves genetic building blocks while allowing organisms to flourish.

Overall, BEACON researchers have published more than 565 peer-reviewed papers and written proposals that have netted nearly $47 million in external funds.

BEACON is headquartered at MSU, and its partners include University of Idaho, North Carolina A&T State University, University of Texas at Austin and University of Washington. BEACON stands as one of 14 NSF Science and Technology Centers, an elite group of research partnerships meant to unite and transform fields across science and engineering.

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BEACON Researchers at Work: Combining Evolution and Engineering in Aquatic Robots

This week’s BEACON Researchers at Work blog post is by MSU Visiting Scholar René Draschwandtner.

René after a BEACON seminar

René after a BEACON seminar

Guten Tag, god dag, and good day to all readers. I am René Draschwandtner, a visiting scholar from Salzburg, Austria, although I came to Michigan State University and BEACON by way of Stockholm, Sweden. More on that circuitous route shortly. Since March I have been investigating the behavior of snake-like aquatic robots in Prof. Philip McKinley’s research group. My research involves integrating behaviors observed in animals with the search capability of evolutionary algorithms, enabling robots to conduct complex tasks efficiently.

Genetic Programming output with HeuristicLab.

Genetic Programming output with HeuristicLab.

The first time I encountered evolutionary computation (EC) was at the University of Applied Sciences Upper Austria (UASUA), where I earned a master’s degree in Biomedical Informatics. Ever since I attended a lecture on EC, I have been excited about applying mechanisms from natural evolution to computer algorithms. Especially intriguing to me is exploiting the search capabilities of genetic algorithms for parameter optimization. While at UASUA, I conducted a course project, supervised by Prof. Michael Affenzeller and Prof. Stephan Winkler, where we examined human blood data with evolutionary algorithms in order to find so called Virtual Tumor Markers [1]. We used HeuristicLab, a user-friendly open source tool developed by the Heuristic and Evolutionary Algorithms Laboratory (HEAL), to conduct evolutionary experiments with different parameter settings. Specifically, we explored the effect of different evolutionary parameters on producing Tumor Marker estimation models.

René enjoying the midday sun on a frozen lake above the Arctic Circle.

René enjoying the midday sun on a frozen lake above the Arctic Circle.

After graduation, I immediately enrolled for a second master’s program at UASUA, this time in Information Engineering and Management. I was particularly interested in how the natural sciences, including topics such as evolution, can inform business computation. Especially, the usage of machine learning methods in conjunction with data warehouses in order to generate and predict key figures was one of my favorite topics during my studies. A student exchange program with Stockholm University took me to Sweden in fall 2014. There, I focused on data mining and IT management. The focus on business-IT alignment and IT strategy broadened my mindset beyond my academic knowledge. By the way, if you ever visit Sweden, I highly recommend crossing the Arctic Circle! The frigid and untamed environment is breathtaking.

As I finished my studies in the business domain, I continued to be interested in evolutionary computing in other areas. This led me to another exciting area of study, evolutionary robotics, where evolutionary algorithms are used to produce behaviors, and even bodies, of robots. I wrote a proposal on evolving aquatic robots and submitted it to the Austrian Marshall Plan Foundation. Luckily, the proposal was funded, enabling me to come to Michigan State University and the BEACON Center to complete my final thesis. In addition to Prof. McKinley, I am collaborating closely with Anthony Clark and Jared Moore, who have applied evolutionary computation to several aspects of aquatic robots. Particularly, their work has explored the evolution of behavior in computer simulations [2] and optimization of flexible caudal fins for robotic fish [3].

Robot with evolved gaits transporting payload to a destination region. A video can be seen at https://youtu.be/xWSVBTm4m3c.

Robot with evolved gaits transporting payload to a destination region. A video can be seen at https://youtu.be/xWSVBTm4m3c.

My research project investigates the locomotion behavior of a snake-like robot consisting of several rigid links, in an aquatic environment. The project is based on the idea of creating complex behavior by composing several simple motions, namely the actuation of joints. The task for the simulated robot is to capture and transport a payload to a target. This behavior is useful in the real world such as rescuing an arbitrary shaped object floating in the water. For example, a rescue team on a boat could launch the robot, which would then swim to the object, grasp it, and deliver it to a destination area. Further, the robot would perform these subtasks by simply deforming its body in different ways, without the need for an external propeller or a specialized grasping apparatus.

A key aspect of my approach is to engineer certain behaviors, drawing on the literature, but use evolutionary computation to refine individual behaviors and combine primitive behaviors to form more complex ones. In nature, snake-like animals propagate sinusoidal waves through their bodies in order to generate forward velocity. This behavior is relatively easy to code manually, but we apply evolution to tune parameters so as to maximize performance. Similarly, we can encode a behavior for the snake to grasp an object, but use evolution to determine the precise timing of the grasping maneuver as the robot approaches the object.

My five-month visit to MSU and BEACON will end in late July, after which I will graduate from UASUA. Not only will I be happy to have finished my studies, but I will be able to look back on extraordinary times in Sweden and Michigan.

[1] Winkler, S. M., Affenzeller, M., Kronberger, G. K., Kommenda M., Wagner S., Jacak W., and Stekel H. (2013). On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary Algorithms. Advanced Methods and Applications in Computational Intelligence, Topics in Intelligent Engineering and Informatics, Vol. 6, 95-122.

[2] Moore, J. M., Clark, A. J., and McKinley, P. K. (2013). Evolution of station keeping as a response to flows in an aquatic robot. Proceedings of the 15th annual conference on Genetic and evolutionary computation:  239-246.

[3] Clark, A. J., Moore, J. M., Wang, J., Tan, X., and McKinley, P. K. (2012). Evolutionary design and experimental validation of a flexible caudal fin for robotic fish. Proceedings of the Thirteenth International Conference on the Synthesis and Simulation of Living Systems: 325-332.

For more information about René’s work, you can contact him at rdraschwandtner at hotmail dot com.

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BEACON Researchers at Work: The Age of Phage

This week’s BEACON Researchers at Work blog post is by MSU faculty member Kristin Parent, with John Dover. 

Kristin Parent (center), Natalia Porcek and Jason Schrad at the PVA meeting in Switzerland.

Jason Schrad, Kristin Parent, and Natalia Porcek at the PVA meeting in Switzerland.

This year marks the 100th anniversary of the discovery of viruses that infect bacteria—the bacteriophages. One may think (as many do) that there is little else to gain by continuing to study bacteriophages (often shortened to just phages), but like their bacterial hosts, there is actually still a lot to learn. In fact, I recently attended a Phage and Virus Assembly (PVA) meeting, and the same theme kept coming up: despite 100 years of research, there is still so much that we don’t know.

It might be surprising to learn that the number of phages (~1031) is ten times greater than the number of bacteria (~1030). In fact, one handful of water from lake Michigan contains more phages than there are people on Earth. Think about that the next time you go swimming!

Frederick Twort, in 1915, discovered that bacteria were susceptible to phages, along with Felix d’Herelle’s independent discovery two years later. The next few decades proved to be a robust period for phage research, with an avalanche of studies culminating in >62,700 published papers (as of June 23rd 2015).

  • The 1920s saw the birth of “phage therapy”, in which phages are used to combat bacterial disease.
  • In the 1930s, the phage life cycle was established and is now an introductory biology textbook staple.
  • The 1940s and 1950s saw the use of phages in the famous Hershey/Chase experiment that conclusively showed that DNA is the genetic material, and phages were used in the explosion of molecular genetics studies that followed. In actuality, phages were used as tools that directly contributed to the vast majority of genetic manipulations that are now routine in the laboratory.
  • Phage work continued strong into the 1960s and 1970s where phages again led the way in the creation of methods that are commonplace in the modern laboratory: negative staining in electron microscopy, the use of transposable elements, and DNA sequencing, wherein the first complete genome sequenced by the eponymous Sanger was a phage genome.
  • Research in the 1980s and 1990s revealed that phages were everywhere—in all terrestrial and aquatic biomes.
  • The metagenomics boom starting in the early 2000s gave clues to phages in the human microbiome, and they are now actively being investigated as part of the virome of the microbiome. There are more bacterial cells and phages in your body than there are human cells, and phage/host interactions contribute an enormous amount to our bacterial diversity, and yet, we know so little about them and how they evolve.
  • In the last ten years, advances in cryo-electron microscopy have given us images of the beautiful structures of entire phage particles, which are complexes of thousands and thousands of proteins. We are only now starting to understand how these elegantly assembled structures work as molecular machines.

Despite the huge mass of information gained during the past “phage century”, there are still numerous aspects of phage biology that remain a mystery. One part of this mystery is how a phage, or any virus, recognizes and successfully infects its “favorite” host. Such a process is critical to virus survival. In all environments there is great diversity in both viruses and hosts resulting in an enormous challenge for viruses to encounter and infect suitable targets. My laboratory is focused on how phages efficiently recognize their hosts and transfer their genomes into those hosts. We use a combination of microbiology, biochemistry, structural biology, and experimental evolution to investigate these processes.

One of the phages that we use as a model system is Sf6, which infects Shigella flexneri. Natalia Porcek, a graduate student in my lab, has shown that Sf6 uses a host cell outer membrane protein, or “Omp”, for infection. Her work has shed light on protein-protein interactions critical to Sf6 entry into its host, and her work has contributed to an understanding of host range—specifically, how Sf6 can recognize Shigella and Salmonella species but not E. coli. Another graduate student in my lab, Jason Schrad, is also working toward understanding this process by using cryo-electron microscopy to look at Sf6 during the process of infection.

John Dover counting plaques in the lab.

John Dover counting plaques in the lab.

John Dover, a technician in my lab, and Alita Burmeister, a collaborating student from the Lenski lab, are using Sf6 for experimental evolution studies aimed at understanding how phages can adapt to infect different hosts. These studies have revealed a potentially novel evolutionary mechanism distinct from other phages such as lambda. Sf6 is a member of a class of phages that packages “headfuls of DNA” in its capsid, a protein shell that encloses the DNA. This means that the phage packages DNA until no more can fit in the capsid container, which is more DNA than needed to encode a single genome. We have seen parallel evolution across ten phage lineages that show whole gene deletions as a path to fitness. In some cases, as much as 15% of the ancestral genome was deleted. Since the phage packages headful, that would mean that a new genomic composition (replacing 15% of the DNA), is contributing in some way to a faster life cycle. Their work has also found parallel evolution of cell lysis timing, which is the temporarily controlled stage of the phage life cycle that breaks open the bacteria and releases new phage “babies”. Faster lysis allows the phages to infect the next group of cells in its controlled environment earlier, making the phage more fit.

We still have much more to do to fully understand bacteriophages. We are in an exciting time as experimental advances have evolved to provide many robust tools for dissecting phage biology, and we are looking forward to the next 100 years of phage discovery.

For more information about work in the Parent lab, you can contact Kristin at kparent at msu dot edu.

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