Feral chickens are a-changing: updates on the rapid evolution of Kauai’s hybrid Gallus gallus

This post is by MSU research associate Eben Gering.

The author, a collaborator (Kayleigh Chalkowski), and an especially ‘domestic-like’ feral chicken in the Florida Keys

After an hour of trying to trap chickens at Hanalei Beach Park, we had only caught odd looks from locals. Finally, one bold rooster approached our buried net, cautiously tapped the spring-loaded frame, and gave an ear-splitting crow. The whole flock disappeared into the bushes, and I won a high-stakes wager with Kathyrn Fiedler, a local collaborator. “Fine,” she grumbled “they’re not so stupid after all.”

We will contemplate feral chickens’ tenacity and intelligence a bit further on. Meanwhile, if you’re curious what these animals were doing on a beach immortalized by Peter, Paul and Mary1, join the crowd. In an earlier blog entry, I described how DNA sequencing of Kauai chickens and fossils helped unravel their fascinating origins. Like the Hanalei locals, it turns out, these birds are exceptionally diverse. They are descended from both Red Junglefowl, brought to Hawaii by ancient Polynesians, and domestic chickens that arrived later, with western explorers and farmers. Recent hybridization between these ‘wild type’ and domestic chickens produced the highly variable, extremely successful, and ‘not-so-stupid’ flocks that occupy Kauai today2.

Understanding how populations came to be where they are is the central goal of biogeographical research, and it serves many purposes. Distributions of living organisms led Charles Darwin and Alfred Russel Wallace to a theory of evolution supported by countless comparative and experimental studies. Among them are studies of bacterial evolution led by Michigan State colleague Richard Lenski, who also co-founded BEACON to “illuminate and harness the power of evolution in action to advance science and technology and benefit society”.

Feral chickens inhabit most of Kauai’s scenic and variable habitats. This image from Kōkeʻe State Park highlights the mixture of wild (forest) and artificial (asphalt) elements feral chickens navigate on a daily basis.

In support of this mission, biogeographical analyses can help predict where invasive or endangered species might one day thrive.  It is not always clear though, whether and how we should apply this powerful insight. Consider the case of Kauai and its neighboring (Hawaiian) islands. There are both cultural and environmental reasons for conserving the archipelago’s heritage species, which arrived many centuries ago with Polynesian colonists. Today, these animals and plants serve a variety of useful purposes, and they are embedded in the Hawaiian ecosystem and culture. On the other hand, these non-native species also have negative impacts on native wildlife and local residents. Feral pigs, for example, are wreaking havoc on Hawaiian forests and farms. They also create breeding grounds for non-native mosquitoes, which spread human and wildlife diseases.

What about the feral chickens? Local attitudes towards these charismatic and often noisy neighbors are as variable as the chickens themselves. Kathryn, who still owes me a cocktail, is concerned they may transmit bacterial pathogens to local crops. However, as many farmers already know, chickens can also provide ecosystem services like pest control and soil improvement.

For better or worse, feral chickens’ ecological impacts have not been well studied –  this is something my collaborators and I are currently exploring. For now, let me give one fairly simple observation: it’s a pity that so many well-intentioned humans feed feral animals haphazardly. This only boosts populations to higher densities than local resources can support. In Hawaii and elsewhere, I have seen humanitarian efforts backfire, causing both unnecessary animal suffering and environmental degradation. So, if you are moved to help feral animals – please coordinate your efforts with local animal control and wildlife authorities. Thanks!

Getting back to our research update: Dr. Fielder may have lost our bet, but her research is succeeding. Her findings from Kauai will soon help guide intelligent management of feral populations, both in and beyond Kauai. But let’s get back to the apparent intelligence of the Kauai chickens themselves, and some basic questions about its recent evolution. Steven Gould, a celebrated author and evolutionary thinker, once asked what would happen if we could restart earth’s history and watch life evolve again. Would it take similar paths to produce life as we know it? Research from the Lenski lab has given us some clues. If evolution is replicated under carefully controlled conditions, adaptation to novel environments is indeed repeatable… if, and only if, a population crosses a stochastic (randomly attained) threshold of ”background’ evolutionary change.

It turns out evolution is also somewhat repeatable outside of the laboratory. For instance, studies of Caribbean lizards by Jonathan Losos’ group show that animals often evolve predictable forms and ways of life when they colonize similar ecosystems. Surprisingly, these evolutionary changes can occur very quickly – in just a handful of generations or less. If you’re interested in learning more, have a look at Dr. Losos’ new book, about the paradoxical predictability and capriciousness of evolution3.

By studying feral animals, my collaborators and I ask a related, but different question from Steven Gould: what happens when we reverse domestication (a human-directed evolutionary process)? Can evolution in natural environments undo behaviors, or other traits, instilled by centuries of selective breeding? Can we predict such changes, and use them to build better livestock, or protect wild populations? Feral animals abound nearly everywhere humans do, yet neither Lenski’s lab studies, nor Losos’ lizards can tell us precisely how these human-altered organisms will evolve in a human-altered landscape. This is one reason for my fascination with Kauai’s feral chickens. Now that we know where they came from, we can better understand how they are evolving.

DNA sequencing revealed that Kauai chickens are hybrid descendants of wild Red Junglefowl and domesticated breeds. It also suggested Kauai hens might inherit a gene from the Red Junglefowl that enhances their ability to raise their young in the wild. This hen’s offspring showcase the Kauai population’s impressive diversity, which was enhanced by hybridization between domesticated and wild populations.

To learn how chickens have adapted to feral environments, several European collaborators and I are now searching their genomes for signatures of recent, rapid evolution. Analyses by Martin Johnsson, a former graduate student in Dominic Wright’s lab (Linköping University, Sweden) found that genes controlling chicken reproduction and behavior are evolving quickly in the Kauai population4. We have also made progress in identifying the sources of the favored variants (i.e. versions) of the quickly-evolving genes. Care to bet where the winners came from?

You might have guessed that the Red Junglefowl gene variants would outperform the domesticated alternatives. After all, Red Junglefowl thrive in wild Asian jungles without any human help. This prediction is partially accurate: certain ‘wild-type’ (Red Junglefowl) gene variants are, indeed, over-represented in the Kauai population. And included among the genes that follow this pattern is one that critically affects brain development and maternal care behavior. Why is the domesticated chicken’s version of this gene disadvantaged in feral hybrids? We predict experiments will show this reflects their comparative inability to hatch and nurture young in the wild. Perhaps ‘wild-type’ (Red Junglefowl) versions of this gene also help hybrids build brains that can respond to dangers of the wild, such as nosy biologists’ traps. Together with Dominic Wright and Rie Henrickson, we will soon begin testing these ideas, and learn how feral brains and behaviors evolve.

In other compartments of feral chicken genomes, evolution is taking a very different path. For example, evolution has favored the domesticated version of a gene cluster affecting bone growth, comb mass, and egg production in Kauai chickens. We think this may reflect the domesticated chicken’s ability to outgrow and out-reproduce Red Junglefowl, a legacy of selective breeding for streamlined poultry production. For an educational activity exploring this idea (using data you can gather from tourist photos), have a look at our Data Nugget activity.

In this entry, I have focused on what genomic data tell us about the role of hybridization in Kauai chickens’ recent adaptive evolution. Ultimately, though, experimental studies are still needed to truly understand the environmental and social challenges that drive observed genomic changes. Fortunately, with practice and stealth, our trapping abilities have vastly improved. Unfortunately, we still get strange looks when we put them to use. Our understanding of feral chicken behavior and adaptation are also evolving quickly. Stay tuned for the next update, if you’d like to know more.

1 https://en.wikipedia.org/wiki/Puff,_the_Magic_Dragon

2 Gering E, Johnsson M, Willis P, Getty T, & Wright, D (2015). Mixed‐ancestry and admixture in Kauai’s feral chickens: invasion of domestic genes into ancient Red Junglefowl reservoirs. Molecular ecology.

3 Losos, J. B. (2017). Improbable destinies: Fate, chance, and the future of evolution. Riverhead Books.

4 Johnsson, M, Gering E, Willis P, Lopez S, Van Dorp L, Hellenthal G, Henriksen R, Friberg U, Wright D (2016). Feralisation targets different genomic loci to domestication in the chicken. Nature Communications 7: 12950.

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Can birdsong signal immune gene quality?

This post is by MSU postdoc Joel Slade. 

Joel recording bird song in 2014.

“BEEP! BEEP! BEEP!” – I wake up to the dreaded sound of my alarm clock at 3:45 am in my cabin. Even though it’s mid-April at the Queen’s University Biological Station in Elgin Ontario, the chilly air still penetrates my bones as I rapidly put on my warm field gear and grab my flashlight to meet the field team. In the back of our field vehicle are two parabolic microphones, and kits to collect blood, take body measurements, and band our study species, the song sparrow (Melospiza melodia). This relatively plain looking songbird has been the main model species for the MacDougall-Shackleton lab in the biology department at Western University in London, Ontario (where I completed my PhD). However, where this bird is lacking in coloration, it makes up for in melodic beauty. Male song sparrows are one of the first songbird species to arrive to their breeding ground at our field site around late March/early April to stake out their territory. When you arrive early in the morning all you can hear are males defending their territory and trying to attract females that are slowly arriving. However, not all males are of equal acoustic quality, and this may be determined by a family of genes called the major histocompatibility complex (hereafter, MHC).

Listen to one of Joel’s recorded song sparrows Photo Credit: Tosha Kelly.

In jawed vertebrates, MHC genes produce cell-surface proteins that recognize pathogen-derived antigens, which are presented to T cells to initiate an adaptive immune response. What’s fascinating about MHC is its incredible genetic diversity. The MHC is one of the most polymorphic gene families in existence, whereby many species contain multiple alleles. For example, in my PhD study species, the song sparrow, we characterized up to 26 MHC alleles per individual, and 517 different alleles across the population. It is posited that pathogen-mediated balancing selection is responsible for this immense diversity at MHC in many animals. For example, risk of disease by multiple pathogens should favor heterozygosity at MHC loci. Likewise, antagonistic coevolution (host-pathogen arms races) should generate new MHC alleles. So, there is no doubt that expressing a wide-variety of MHC molecules should confer fitness benefits, but could being too diverse be a bad thing?

Juvenile male song sparrows experience a critical learning period during their first year of development. Within this critical period, males will listen to the songs of local males and create their own song types. After a year, males are no longer capable of learning new songs. Therefore, their condition in early life is critical for them to increase their song repertoire size. Males range from singing as little as five song types and as much as 12 song types. Females are more likely to choose males with more song types, as it is considered an honest signal of quality. Since MHC diversity is critical for the adaptive immune health of vertebrates, I postulated that MHC diversity of male song sparrows is related to their song repertoire size. I predicted that males with high diversity could fight off more pathogens while learning songs in early life, and therefore would be able to invest more energy into song learning instead of immune defense.

Figure 1: Relationship between song repertoire size and the number of MHC alleles in male song sparrows. (#MHC t = 2.20, p =0.036; #MHC2 t = -2.26. p =0.031)

When I compared song repertoire size to MHC diversity, I found that MHC diversity is related to song diversity, but in a direction that I did not anticipate. It was males with intermediate MHC diversity that sang the most amount of songs, rather than males that were maximally-diverse at MHC. This created a non-linear (quadratic) trend whereby males with low and high MHC diversity sang the least amount of song types (Figure 1). What may explain this trend?

Apparently maximal MHC diversity may be bad for your health. Two main hypotheses exist to explain this phenomenon: (1) too many MHC alleles may cause autoimmune disorders whereby MHC proteins start recognizing self-antigens as non-self antigens, and (2) too many MHC alleles expressed may cause a dilution effect whereby the most important MHC alleles required to recognize a pathogen are not in high abundance, and therefore you may not be able to recognize the invading pathogen. My study is not the first to discover an intermediate MHC diversity advantage. In fact, in three-spined sticklebacks (Gasterosteus aculeatus), individuals with intermediate MHC diversity had the lowest parasite loads than those that had low or high levels of MHC alleles (Wegner et al. 2003). My study may therefore reflect a balance between pathogen-mediated selection and selection against maximal MHC diversity, which is critical for song learning in these sparrows.

At BEACON, I am bringing my expertise on avian immune genes by working with Dr. Danielle Whittaker and Dr. Kevin Theis. We are exploring how host immune genotype can shape their microbiome, and thus shape the semiochemicals produced by commensal bacteria.

This blog post is based on Slade et al.’s (2017) study in Biology Letters.


Wegner KM, Kalbe M, Kurtz J, Reusch TBH, Milinski M. 2003 Parasite selection for immunogenetic optimality. Science 301, 1343.

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BEACON collaboration to study amphibian-associated skin bacteria

This post is by MSU grad student Patric Vaelli

Rough-skinned newt (Taricha granulosa) along the edge of a pond near Moscow, ID.

Animal bodies are inhabited by diverse communities of microorganisms that we collectively call the microbiome. These communities consist of bacteria, fungi, and viruses, all of which can affect the physiology, behavior, and even the evolution of their animal hosts. In our research, we seek to understand what roles the skin microbiome of amphibians, specifically newts and salamanders, play in the production of specialized toxins that protect the animal host from predators.

We set out to address this question through a BEACON-supported collaboration between the Eisthen lab at Michigan State University and the Foster lab at the University of Idaho. The Eisthen lab has been working on the sensory biology and behavior of a poisonous salamander called the rough-skinned newt (Taricha granulosa) for a number of years, while the Foster lab specializes in the preparation, sequencing, and analysis of bacterial DNA. Details of the project can be found in an earlier BEACON blog post.

BEACON graduate student Patric Vaelli, with a rough-skinned newt (Taricha granulosa)

In September of 2016, I boarded a plane from Detroit to Moscow, ID, where I began a five week “mini-sabbatical” at the University of Idaho. As part of this trip, I would learn how to extract and prepare bacterial DNA for high-throughput sequencing. This was the easy part – the more challenging endeavor was to begin developing the computational and statistical skills necessary for reconstructing and interpreting the newt microbiome from the short pieces of DNA sequence generated by high-throughput sequencing. Last but not least, there were also wild populations of rough-skinned newts near Moscow! How lucky could we be?

When I arrived at the Pullman-Moscow regional airport I was greeted by our friend and collaborator Dr. Janet Williams. She brought me to my new home for the next five weeks: an apartment that I shared with a fellow graduate student from U Idaho. Within two days of arriving in Moscow, I was catching wild newts near a forested park north of town and collecting bacterial samples from every newt I could find. We collected bacterial samples using sterile swabs, from which we later extracted bacterial DNA for sequencing. We also collected small skin biopsies from each animal that would later allow us to measure the amount of toxin in the skin of each newt.

Patric Vaelli and BEACONite Yannik Roell (now at Aarhus University in Denmark) collect bacterial samples from wild newts near Moscow, ID. Bacterial samples will be used for DNA-based analysis of the amphibian skin microbiome. Samples are also collected from the pond and soil for comparison.

For the next 3 weeks, I spent each day carefully extracting bacterial DNA from each swab. This process is extremely sensitive to contamination; bacteria are everywhere, even in many commercially available DNA extraction kits (a problem now referred to as the “Kitome”). Once I had extracted the bacterial DNA, I performed targeted sequencing of the 16S rRNA gene, a “phylogenetic marker” gene present in all bacteria. The sequence of this gene can be used by biologists to identify the species of bacteria within a sample, similar to a species “barcode”. Once we obtained the sequence data, I used a combination of data processing pipelines including dbcAmplicons and mothur to assemble the raw sequence data into the “barcode” sequences that I could then classify into different bacterial groups.

From here, we could ask broader ecological questions about the skin microbiome. What’s the total diversity (alpha diversity) of the microbiome within each animal? How different is the composition or structure of the microbiome across different groups of animals (Beta diversity)? Do these measures correlate with traits in the animal host such as toxicity?

As a BEACON graduate student, I’m extremely grateful for the opportunity to conduct interdisciplinary research at a partner BEACON school. Through this BEACON-supported “mini-sabbatical”, I was able to conduct field work with my model animal in its natural habitat, gain lab experience in preparing bacterial samples for sequencing, and begin to develop computational skills in sequence processing and data analysis. This training is invaluable for my success in future projects. I also had the opportunity to meet and work with a new lab group and live in a new place. I strongly encourage new graduate students to seek out and develop collaborations, especially with your fellow BEACONites!


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A Tail of Two Ascidians

This post is written by UW grad student Alexander Fodor

Figure 1. Photographs of the ascidian Molgula occidentalis taken in an isolation tank at the University of Washington’s Friday Harbor Labs. Photo by Alexander Fodor.

Searching through the lower intertidal and subtidal rocky beaches you notice a small strange creature attached to the underside of a rock in a small pool of water. It has a round body and two tubes pointing straight at you. You reach down and touch it, it feels leathery and almost plant like; you squeeze it and it shoots a stream of water right in your face. You have found a sea squirt (also called ascidians or tunicates) (Figure 1). These creatures appear very different from us and you may not even recognize it as an animal, but they are chordates, members of our own phylum that you can easily see in their tadpole larvae (Figure 2L). When it comes time to reproduce they are free spawners, spraying sperm and eggs into the water where the eggs are fertilized by another individual and develop into tadpoles. Ascidian development (Figure 2) is determinant and invariant across all solitary sea squirts where the cellular fate is set up with each division and each cell is always destined to become the same structures; so if you trace the cellular fate of a cell in one solitary ascidian, if you look in a different species, that same cell will become the exact same tissue. As they develop they form a tadpole that looks much like a tiny larval frog, complete with a notochord in the center of the tail and gravity sensing and light sensing structures in the head. They spend anywhere from a few hours to several days as a tailed larva with a notochord, searching for the appropriate substrate, using their light sensing and gravity sensing structures to swim underneath an object (like a dock or rock) and attach their head to the bottom. They then absorb their tail and completely change their anatomy, absorbing all of their larval structures, growing two feeding siphons, and surrounding themselves with a tough tunic, containing cellulose. The cellulose operon, consisting of three genes, was retro-virally spliced into the tunicate ancestor’s genome (Dehal et al. 2002).

Figure 2. Development of Boltenia villosa up to tadpole larva (L) then metamorphosis (M) though adulthood (N and O). The orange color in the developing embryos (A-N) is caused by myoplasm of cells destined to become muscle cells. Larva photos taken during an Undergraduate Apprenticeship at the University of Washington’s Friday Harbor Labs spring of 2001, adult photo by Alexander Fodor at Friday Harbor Labs.

Molgulids are a special family of sea squirts where the larval tail and notochord has been lost several times in species in the family completely independently of one another. The tailless species all have a similar phenotype where they only have fewer than 40 notochord cells, and they do not converge and extend into a notochord, but rather sit on the side of the embryo in what has been described as a “notoball”. In addition they have lost their gravity and light sensing organs (Figure 3C). These animals typically live in northern waters where there are very strong tidal currents so it is conceivable that animals could still disperse enough even without a swimming tail (Huber et al. 2000). We are uncovering the molecular mechanisms underlying this tail loss by studying two species, Molgula oculata and Molgula occulta, in the Swalla lab at the University of Washington’s Friday Harbor Labs. These two species are sister species, but they have very different looking larvae: M. oculata has a fully functioning tail with a 40 cell notochord inside and gravity and light sensors in the head, but M. occulta is lacking all of the larval structures. The species are still closely enough related that they can be hybridized them in the lab. If the egg of the tailed M. oculata is used, then the resulting larva always has a fully functioning tail and notochord; but if the egg of the tailless M. occulta is used, then some of the time the resulting hybrid has a half tail which is only composed of 20 notochord cells, but it still converges and extends out (Swalla et al. 1990), in addition, the paternal expression of the tyrosinase gene saves the gravity and light sensors (Raccicopi et al. 2017) (Figure 3).

In collaboration with the C. Titus Brown Lab (formally MSU, now UC Davis), our lab has recently sequenced the genomes and transcriptomes of M. oculata, M. occulta, the hybrid made with M. occulta eggs, and M. occidentalis (the outgroup for the Molgula family) (Stolfi et al. 2014). We are currently searching through the genomes and transcriptomes, looking for the molecular mechanisms responsible for this change in morphology. We have identified a number of genes that could be involved in this tail loss, and examining their sequences, and are testing their expression profiles. In the summer of 2018, Dr. Billie Swalla and I are going to go to Roscoff, France where M. oculata and M. occulta live to dissect gravid adults and obtain embryos for transgenic experiments. We will use gene-editing techniques to express tailed M. oculata genes in the tailless M. occulta and the hybrid made with the occulta eggs to see if we can recapitulate the tailed expression. It is very intriguing to think about how evolution can make a small number of changes to a gene network, which can in turn change the expression of a whole structure and the life history of the organism. It is nice to use sea squirts for such experimentation as they are very closely related to the vertebrates so can teach us much about how complex gene networks can be altered, and in turn change complex evolutionary traits. We are grateful for the BEACON funding that we have received for this project and are looking forward to making progress on the project in the coming years.

Figure 3. Pictures and cartoons of the larvae of A. M. oculata C. M. occulta and B. The hybrid made with M. occulta eggs and M. oculata sperm. Adapted from Swalla and Jeffery 1996


Dehal P, Satou Y, Campbell RK, Chapman J, Degnan B, De Tomaso A, Davidson B, Di Gregorio A, Gelpke M, Goodstein DM, Harafuji N, Hastings KE, Ho I, Hotta K, Huang W, Kawashima T, Lemaire P, Martinez D, Meinertzhagen IA, Necula S, Nonaka M, Putnam N, Rash S, Saiga H, Satake M, Terry A, Yamada L, Wang HG, Awazu S, Azumi K, Boore J, Branno M, Chin-Bow S, DeSantis R, Doyle S, Francino P, Keys DN, Haga S, Hayashi H, Hino K, Imai KS, Inaba K, Kano S, Kobayashi K, Kobayashi M, Lee BI, Makabe KW, Manohar C, Matassi G, Medina M, Mochizuki Y, Mount S, Morishita T, Miura S, Nakayama A, Nishizaka S, Nomoto H, Ohta F, Oishi K, Rigoutsos I, Sano M, Sasaki A, Sasakura Y, Shoguchi E, Shin-i T, Spagnuolo A, Stainier D, Suzuki MM, Tassy O, Takatori N, Tokuoka M, Yagi K, Yoshizaki F, Wada S, Zhang C, Hyatt PD, Larimer F, Detter C, Doggett N, Glavina T, Hawkins T, Richardson P, Lucas S, Kohara Y, Levine M, Satoh N, Rokhsar DS. 2002. The draft genome of Ciona intestinalis: insights into chordate and vertebrate origins. Science. Dec 13; 298(5601):2157-67.

Huber, J. L.; da Silva, K. B.; Bates, W. R.; and Swalla, B. J. 2000: The evolution of anural larvae in molgulid ascidians. Seminars in Cell & Developmental Biology 11(6):419–426.

Racioppi, C., Valoroso, M. C., Coppola, U., Lowe, E. K., Brown, C. T., Swalla, B. J., Christiaen, L., Stolfi, A., Ristoratore, F. 2017. Evolutionary loss of melanogenesis in the tunicate Molgula occulta. EvoDevo. 8:11

Spring 2001 Undergraduate Apprenticeship at FHL. 2018. Retrieved February 8, 2018. http://faculty.washington.edu/bjswalla/fhl_sp01/boltenia.html

Stolfi, A., Lowe, E., Racioppi, C., Ristoratore, F., Swalla, B. J., Brown, C. T. and Christiaen, L. (2014) Divergent mechanisms regulate conserved cardiopharyngeal development and gene expression in distantly related ascidians. eLife 2014:3:e03728

Swalla, B. J., and Jeffery, W. R. 1990. Interspecific hybridization between an anural and urodele ascidian: Differential expression of urodele features suggests multiple mechanisms control anural development. Dev. Biol. 142: 319-334.

Swalla, B. J., and Jeffery, W.R. 1996. Requirement of the manx gene for expression of chordate features in a tailless ascidian larva. Science 274: 1205-1209.

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A CRAL-TRIO domain gene involved in butterfly vision

This post is written by UCI grad student Aide Macias

Graduate student Aide Macias presenting a poster at SMBE 2017

Butterflies have extremely diverse wing color patterns which cause us to wonder, what do these brightly colored insects see? The Briscoe lab at the University of California, Irvine aims to study the evolution of vision in butterflies. As a graduate student in Adriana Briscoe’s lab my projects have involved quantifying the expression of vision-related genes. In my latest research project, I used a variety of methods to identify a gene that encodes a chromophore-binding protein.

Cartoon of a photoreceptor cell membrane with a rhodopsin before and after light absorption

In vision, light information is converted into an electrical signal by a process called phototransduction. Photoreceptor cells have a light-sensitive receptor called rhodopsin which is made up of an opsin protein bound to a retinal molecule known as chromophore. The vertebrate chromophore is 11-cis-retinal, while the Drosophila chromophore is 11-cis-3-hydroxyretinal. When light is absorbed by rhodopsin, the chromophore undergoes a configurational change from 11-cis to all-trans, which triggers the phototransduction signaling cascade. In vertebrates and Drosophila, chromophores are transported by a group of proteins that have a structural region in common, a CRAL-TRIO domain. A previous study investigating the CRAL-TRIO domain gene family in insect genomes found many copies of these genes in moths and butterflies, but none with a common evolutionary history to those found in Drosophila (Smith and Briscoe 2015). Do any of these moth and butterfly gene copies have the same chromophore binding function?

In order to identify a candidate CRAL-TRIO domain gene involved in butterfly chromophore transport, we explored the evolution and expression of this gene family in the butterfly Heliconius melpomene. H. melpomene is a good species to use because it has a reference genome and a lot of sequencing data is available for this species.

Heliconius butterfly

We began our study by searching for CRAL-TRIO domain genes in genomes of the butterfly H. melpomene. We found 43 CRAL-TRIO domain genes in the H. melpomene reference genome, but we also found that other genomes had more or fewer copies of the genes, termed “copy number variation”. To see which of these copies functions in vision, we looked at their expression in four different tissues: head, antennae, legs and mouth parts. We hypothesized that genes involved in vision would be expressed more highly in heads. We found only one gene that had high expression in butterfly heads. To visualize where this gene was expressed, we designed an antibody against the gene encoded protein. We looked at sections of the butterfly eye for fluorescence showing that an antibody is binding the protein target. The protein stain showed that the product of our candidate gene was expressed in the primary and secondary pigment cells surrounding the photoreceptor cells. Thus, we were able to provide the first evidence for a visual function for any member of this large and rapidly-evolving gene family in butterflies. Details of this study are published in Macias-Muñoz et al. (2017)

Graphical abstract summarizing results


Smith G, Briscoe AD. 2015. Molecular evolution and expression of the CRAL_TRIO protein family in insects. Insect Biochem. Mol. Biol. 62:168–173.

Macias-Muñoz A, McCulloch KJ, Briscoe AD. 2017. Copy number variation and expression analysis reveals a non-orthologous pinta gene family member involved in butterfly vision. Genome Biology and Evolution 9:3398-3412.



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Living Laboratories: Using islands to track natural selection in wild lizards

This post is by MSU postdocs Melissa Kjelvik and Liz Schultheis, and BEACON education director Louise Mead

The National Association of Biology Teachers (NABT) annual Professional Development Conference provides biology educators from across the nation the opportunity to join other leaders in biology and life science education for four days of renowned speakers, hands-on workshops, informative sessions, and special events. In November 2017 the BEACON Center partnered with the American Society of Naturalists to sponsor a special symposium highlighting cutting edge evolutionary research and introducing attendees to a new study system, research questions, and related resources they could incorporate into their classrooms.

Bob presenting his lab’s research on reproduction-survival tradeoffs in anole lizards.

This year’s Evolution Symposium: Emerging Research in Evolutionary Biology at NABT featured a research talk by Dr. Robert Cox (Bob) on selection and fitness in anole lizards, followed by a Data Nugget activity highlighting data from his research. Bob, an evolutionary biologist from the University of Virginia, studies these charismatic lizards, native to Cuba and the Bahamas. He describes anoles as “ecological popcorn” because they are so abundant and are eaten by many organisms. In Florida, where the anoles are invasive, you can shake a bush and 10 will fall out! Bob shared an amazing talk describing his lab’s research on brown anoles and the challenges and opportunities of studying natural selection in the wild. Bob uses real-time studies of wild animal populations to understand the ecological basis of natural selection as it happens. He chose to work with anoles because they are ideal organisms for studies of natural selection; they are abundant, easy to catch, and have short lifespans.

When Charles Darwin talked about the “struggle for existence” he was making the observation that many individuals in the wild don’t survive long enough to reach adulthood. Many die before they have the chance to reproduce and pass on their genes to the next generation. Darwin also noted that in every species there is variation in physical traits such as size, color, and shape. Is it simply that those who survive to reproduce are lucky, or do these traits affect which individuals have a greater or lesser chance of surviving? While evolutionary biology is often viewed as a historical science, exploring processes that have played out over millions of years, Bob stressed that natural selection, the primary force of adaptive evolution, is happening all the time and can be measured in natural populations! Along with field studies, Bob and his lab use genetic methods to help them track the reproductive success of thousands of individuals across multiple generations. Experimental manipulations of predators and competitors also help in understanding the ecological basis of natural selection.

Aaron sharing engaging stories about his experience working in the field during grad school.

The talk was followed by a hands-on workshop, led by Aaron Reedy, Elizabeth Schultheis, and Melissa Kjelvik, where participants worked together as students on two Data Nugget activities. This open time gives teachers the opportunity to have discussions about connections to educational standards or pedagogical strategies that are helpful for them to translate the research and associated data back to their classroom settings. Data Nuggets (http://datanuggets.org) are free classroom activities, designed to improve the scientific and quantitative abilities of K-16 students by providing them with authentic data collected by practicing scientists. The research in Data Nuggets on the anole lizards focused on two traits – their size, and their dewlaps. The Data Nuggets take students through the process of exploring two different hypotheses. In Part I students calculate and graph % survival of lizards according to their size at the beginning of the season, to explore the hypothesis that size influences survival and overall fitness. In Part II, students graph % survival as a function of dewlap size. The dewlap is an extendable red and yellow flap of skin on their throat. To communicate with other brown anoles, they extend their dewlap and move their head and body. Males have particularly large dewlaps, which they often display in territorial defense against other males and during courtship with females. Females have much smaller dewlaps and use them less often. This trait comes with a trade off – while it attracts the desired attention of females, it also attracts predators. There could be sexual selection favoring this trait, while natural selection works against it.

Participants working through Data Nuggets highlighting Bob and Aaron’s research.

In these and other Data Nugget activities, students read background information on a study system and scientist, graph and interpret authentic data from their research, and use their graphs to construct explanations based on sound reasoning and evidence. By relying on authentic research and data, Data Nuggets’ innovative approach reveals to students how the process of science really works, while building their quantitative abilities and interest in science. One teacher from the workshop shared that, “the beauty of the activity is in the simplicity,” which is a great testament to Bob and Aaron’s ability to take complex evolution research and distill it down to a core message. The Data Nuggets include the story behind Aaron and Bob’s research to further engage students in the journey taken by the scientist as they formulated their research questions and ideas.

For more information, and to check out the talk slides and Data Nuggets, check out this page (http://datanuggets.org/2017/10/nabt-2017/). To learn more about Bob and the research in his lab, check out his webpage (https://www.coxlabuva.org), and to learn more about Aaron’s research and outreach, check out his website (http://aaronmreedy.com).




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Determining functionality in a genome

This post is written by MSU grad student Bethany Moore

Bethany Moore, at work programming

Imagine you are traveling in space, searching for a hospitable planet. Not only does the planet have to have elements present on earth, but it has to be the right distance from a star in order to avoid extreme temperatures, and has to have the correct proportions of water, oxygen, and carbon. There are millions light years of empty space between stars and planets, and you cannot see a planet given its close proximity to a bright star. How can you know where to search for such a planet? A similar conundrum is experienced in finding out the function of the DNA in a genome. First, you have to find where a gene (or planet) is in the midst of a galaxy of DNA that includes many non-functional regions (empty space) and thousands of genes (potentially hospitable planets). As you might imagine, matching up genes to their function is tricky to say the least.

Gene expression, or the measurement of RNA a gene produces or “expresses” is one way to determine function—genes that are expressed at high levels might be doing something important, while genes expressed at the same time or under similar conditions (called co-expression) might be involved in the same kinds of processes. A more direct approach is gene knockout, an experimental procedure where a gene is mutated in some way to make it non-functional, and the phenotype of the mutant is recorded. While this shows a more definitive relationship between the gene and the function, this process can take weeks or months for each gene in question.

The Shiu lab focuses on predicting gene function using computational approaches such as machine-learning. Given a set of example inputs and desired outputs, a computer program “learns” the general rule by which you can get from the input to the output. When the computer program is thus “trained” by given inputs, the type of machine-learning is called supervised-learning. How can this approach be applied to finding gene function? If we train our program with inputs from genes whose function we know, we receive output as to whether an unknown gene looks like our known gene. This approach can be highly efficient and accurate in predicting gene function and narrowing down a set of candidate genes that can be experimentally validated using more time-consuming techniques, such as making a gene knockout.

Predicting Lethal Gene Phenotypes

A previous graduate student in our lab to predicted essential genes in plants (Lloyd et al., 2015). Only a small proportion (15%) of genes in the well-annotated genome of the model plant A. thaliana have experimental evidence that connects a gene to a function in the plant. The goal of our project was then to predict what genes are essential, or in other words cause a lethal phenotype, in A. thaliana. Characteristics of known essential genes and non-functional genes (pseudogenes) were used to create a model capable of predicting the likelihood of an uncharacterized gene to be functional. Characteristics such as mechanisms of gene duplication, gene expression, evolution and conservation, and gene networks were compared between lethal phenotype genes and pseudogenes. Using a supervised machine-learning approach, we combined these characteristics to model what lethal phenotype genes and pseudogenes look like. Finally, we applied the model to genes with unknown function, predicting 1,970 undocumented genes to have a lethal phenotype. Not only did this model enable us to document the functionality of genes without a known phenotype, but can help future research in prioritizing candidate genes for further study.

Predicting Gene Regulation

Cis-elements (CREs) important in predicting the up-regulation of salt stress in the shoots of A. thaliana from Uygun, et al., 2017. This first and second columns are sequence logos, which represent the sequence of a CRE, and their corresponding reverse complement sequence. The third column contains the sequence logos and transcription factor family of the best matching transcription factor binding site.

Some regions of DNA in the genome that are not genes can play a role in how and when genes are expressed. This is known as gene regulation, and can be thought of as turning genes on or off. Many genes can be described as “cryptic”, in that they are only turned on under certain conditions, for example during viral infection, so both the gene function and how it is regulated can be difficult to detect unless a given stress is present. This sort of cryptic expression has allowed plants to adapt to many diverse environments around the globe, from deserts to alpine regions to marshes. What regions of a plant genome can actually respond to drought, or cold, or flooding? Does this have implications for our crop plants? What if we could grow crops that under a particular stress, like drought, turn on genes that increase that crop’s resistance to that stress?

To answer these questions, we looked at DNA sequences in specific regions that are frequently involved in regulation. These regions are adjacent to genes, and commonly known as the cis region. We asked if there was a cis-regulatory “code” that can turn on a gene under a given stress. Using gene expression data from plants under salt stress, we were able to determine important cis-elements that tend to regulate genes under this condition, and elements that responded in specific parts of the plant, including the root and the shoot (Uygun, et. al., 2017). We then used machine-learning to predict how well our putative cis-regulatory codes explained plant gene expression under salt stress, and found our putative cis-elements explained approximately 79% of expression. Currently the Shiu lab is working on finding cis-elements that regulate wounding, heat, and drought stress.


When I first joined the Shiu lab in 2014, I had no previous computational experience, only a desire to learn more about genes and genomes of plants. By learning how to program and how to deal with big datasets, I developed my skill set in bioinformatics and with it the future job market looks a little brighter. Additionally, I have gained insight into the biology and complexity of what is happening in a given genome, and tools of how to parse that complexity into meaningful data.

As a lab, we have found computational methods, particularly machine-learning, can be combined with gene expression data to make powerful predictions of gene function. If we can predict the function of a gene, or a genomic region, this can provide a starting point for experimental validation, reducing the amount of guesswork and time involved in the validation process. As many genes and functional regions of the genome remain unknown or uncharacterized, gene prediction or predictions of functional regions are important to discover and is a way to navigate the seas of genomic data.


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Bringing Together a World through Science

This post is written by UT undergraduate researchers Zachary Martinez and Andrew Ly

UT Austin undergraduates (L->R) Rachel Johnson, Zachary Martinez, Andrew Ly, Andrea Martinez, and Milki Negeri. Behind them is their poster, entitled “Yo GABA GABA”. The student researchers also presented their work orally.

The University of Texas at Austin is known for many things: from being a powerhouse in Division 1 sports, to leading the world in innovation and cutting-edge research. However, there is one historic fact that many Longhorns do not know, and that is the success of the UT Austin iGEM team. For the past six years at the International Genetically Engineered Machine (iGEM) conference, UT Austin has earned a gold medal each year, an honor bestowed only to teams fulfilling the highest and strictest research requirements. This annual synthetic biology conference takes place in Boston, where over 300 teams from universities around the world present their research. This year, the UT Austin team consisted of a wide range of students, from underclassmen that have just started doing research, to more seasoned upperclassmen that have participated in iGEM previously. The 2017 project focused on engineering an effective GABA-producing probiotic.

The indigenous gut flora of humans possesses the ability to synthesize neurotransmitters, such as GABA, that are hypothesized to influence behavioral, cognitive, and emotional processes of the body via the gut-brain axis. The microbiome-gut-brain axis is a bi-directional communication system in which the microbiome of the gut affects the central nervous system, and vice-versa. Using this information along with our background in microbiology, molecular biology, and synthetic biology, we set out to engineer this microbiome as a way to potentially treat mental illnesses.

Gamma-Aminobutyric acid, or GABA, is the chief inhibitory neurotransmitter in the body and is responsible for reducing neuronal signaling in the central nervous system. Medications, such as alprazolam and diazepam, that increase GABA signaling are typically used for treating anxiety disorders. However, such drugs can lead to a physical dependence, and if given to children, a “pill-popping” habit. Due to these reasons, we began researching potential probiotics that we could study and engineer in order to produce GABA. We ended up picking Lactobacillus plantarum, which is not only indigenous to the human gut, but also expresses GABA in small amounts by converting glutamate to GABA via a glutamate decarboxylase enzyme encoded by the gadB gene. Our goal was to engineer this microbe to produce high levels of GABA and implement it into fermentable foods (such as kombucha, kimchi, or yogurt), which could then be ingested as an alternative form of medicine for patients suffering from anxiety.

Bacterial plate of transformed L. plantarum.

In order to engineer our probiotic to produce high levels of GABA in the human gut, we first wanted to assemble a plasmid in which the gadB gene was overexpressed. To accomplish this, we employed a cloning technique called Golden Gate Assembly, which utilizes type IIS restriction enzymes that cut adjacent to the recognition sites. This allows for the scarless and simultaneous interchanging of different DNA parts, such as origins of replication, antibiotic resistance cassettes, coding sequences, and promoters, all while maintaining directionality in a single reaction. As such, we chose this assembly method due to its ability to rapidly create functional plasmid prototypes that would allow us to interchange parts quickly as we begin experimenting with L. plantarum. After successfully assembling our intended gadB overexpression plasmid using Golden Gate Assembly, we would then introduce it into our probiotic.

While trying to overexpress GABA, we observed various mutational inactivations of our gadB gene. Given that glutamate is an important substrate in biosynthesis and that GABA production requires the conversion of glutamate into GABA, we hypothesized that the functionally active form of gadB was ultimately toxic to cells. As a result, cells containing a mutated gadB gene were more evolutionarily fit and thus selected for. This explains why we were only able to obtain cells with the mutated gadB gene. We then constructed plasmids with either lower copy numbers and/or inducible promoters that would downregulate or control the expression of the gadB gene. However, we still found mutations within the gadB gene. Some possible solutions to address this issue are to utilize an inducible promoter with tighter regulation in our plasmid assembly, perform DNA transformations with a strain with a lower mutation rate, or even simply growing the bacteria in media supplemented with high levels of glutamate. Our future directions include developing a quorum sensing system in our engineered probiotic for controlled GABA production and potentially introducing our probiotic into the microbial ecosystem of the fermented beverage, kombucha, which was the main focus of our iGEM project last year. This year’s project, much like our 2015 iGEM project regarding evolutionary stability, has highlighted the importance of creating evolutionarily stable genetic circuits with low metabolic burdens: a problem synthetic biology has long had.

Overall, the iGEM conference was an invaluable experience where we were able to meet and network with numerous people from around the world, ranging from China to Ghana. We spoke to researchers who were looking into creating more robust genetic systems in a wide array of bacteria, something we have had an interest in for several years. Additionally, students from Vilnius, Lithuania discussed how they were able to use multiple plasmids within a single bacterium while controlling the copy number and maintaining this entire set of plasmids (five in total!) over multiple generations. As we prepare for next year’s iGEM competition, we hope to take what we have learned from this year’s experience and apply it to our 2018 research project.



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Lessons from your parents: “Fool me once, shame on you. Fool me twice, shame on me” – Randall Terry.

This post is by UW faculty Cynthia Chang and Thelma Madzima, research tech Colin Feng, and undergraduate researcher Jackelyn Garcia

“I told you so” – All parents?

Can the lessons from your parent’s experiences be passed on to you for your benefit, and if so, how?

In many organisms, the memory of our experiences often influences our behavior and how we respond to similar situations in the future. In humans, these ‘learned’ lessons are often passed on verbally from one generation to the next. (Sometimes, no matter what our parents tell us, we have to experience things for ourselves). However, in some organisms like plants, life lessons and behavior are not ‘verbally communicated’, but rely on other methods so that clues of past experiences are passed on to offspring.

In living organisms, memory information can be passed on from one generation to the next at the molecular level; through signals added on top of DNA, referred to as ‘epigenetic modifications’ (Greek for epi = on top or above. Therefore, on top of genetic information). Epigenetic modifications (such as DNA methylation) can be inherited, are reversible and can influence phenotype or how an organism develops. Epigenetic modifications can also be induced by the environment, therefore, by studying the inheritance of epigenetic signals, we can understand how the environment experienced in one generation (parents) can impact developmental responses in the offspring. Thus, epigenetic modification may prove to be an important signal to understanding how species remember and respond to a rapidly changing climate (Donelson et al. 2017).

Climate change: “It’s getting hot in her(r)e” – Nelly

Current climate change models predict greater periods of drought as well as a more variable environment, both of which will drive the evolution of how plants respond to changing environmental conditions (Jump and Penuelas 2005). Our project focuses on determining if plants that experience high environmental stress (drought) pass on molecular signals (epigenetic modifications) to their offspring which allows the offspring to learn from their parents, and better adapt to a variable environment.

To do this, we are using the model plant Arabidopsis thaliana, a fast-growing, primarily selfing plant. As part of our experimental design, we will collect physiological and epigenetic data from Arabidopsis plants exposed to different stresses over multiple generations. In the first 3 generations, we will expose half of our plants to high-drought conditions, and the other half to normal conditions (low-stress; non-drought). Offspring seeds will be collected from each plant and planted in the same treatment their parent experienced. In the fourth generation, we will determine if these life experiences are inherited. We will compare historically stressed plants to non-stressed plants, when grown in either a low, high, or variable water stress environment. We hypothesize that historically stressed plants will grow better than non-stressed plants when grown in a high or variable water stress environment. However, it is also possible that plants have to ‘learn for themselves’ each time.

What’s in it for me?”

This research will provide insight into how a plant population’s past experiences can help or hinder its ability to adapt to a rapidly changing environment. Understanding how plants will respond to climate change is a major motivation for our whole research team.

“The molecular mechanisms of epigenetic inheritance are particularly relevant to all plants, especially in agriculturally important plants. But first, it’s important and more feasible to study these mechanisms in a model plant like Arabidopsis thaliana”. – Thelma Madzima

“I am excited to see how the epigenetic modifications will affect Arabidopsis in the future generations of this project. More specifically, I want to see what genetic differences do stressed plants have compared to unstressed (if any) and how that impacts their ability to respond to stresses.” – Colin Feng

With an interdisciplinary team, we are able to tackle this research question with our different areas of expertise.

Jackelyn Garcia, 2nd year undergraduate researcher at the University of Washington-Bothell, watering the first generation of experimental plants.

“Understanding the evolutionary implications of epigenetics is an exciting way to bridge the gap between molecular biology and ecology.” – Cynthia Chang

Finally, this Beacon research is providing first-hand research experience to young undergraduate researchers. Jackelyn Garcia, a 2nd year UW-Bothell aspiring Biology major has dedicated her time to understanding the phenotypic (trait) patterns of the plants. She hopes to use this research to connect her coursework to real research, and learn more about evolution, ecology, and genetics.

Scientific experience and inheritance

This research is being conducted in collaboration with the undergraduates phenotyping Arabidopsis knockouts (unPAK) network (http://arabidopsisunpak.org/). unPAK is a network of undergraduate research institutions working towards to goal of understanding the relationship between genotype and phenotype, using Arabidopsis. In addition to answering our own research questions, the plants grown in our experiment will provide data for this growing database of unPAK genotype-phenotype data. Both Assistant Professors are particularly excited to incorporate this research in their undergraduate Investigative Biology courses with the hope of adding to our growing understand of how plants can adapt to climate change and the molecular signals that are transmitted, and inspiring new researchers to tackle this complex problem.

Literature Cited

Donelson, J. M., S. Salinas, P. L. Munday, and L. N. S. Shama. 2017. Transgenerational plasticity and climate change experiments: Where do we go from here? Global Change Biology.

Jump, A. S., and J. Penuelas. 2005. Running to stand still: adaptation and the response of plants to rapid climate change. Ecology Letters 8:1010-1020.

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