To be or not to be? The question of virulence as posed by polio

In a previous post we discussed the identity of polio.
If you haven’t yet read it and aren’t well-versed in who or what polio is, might I recommend a brief glance? If you’re already up to date on cVDPVs, let’s proceed, deep into the tangled web of RNA that is… poliovirus.

(Previous article: )

Step One – Find the Trouble Maker

Of the three types of attenuated poliovirus contained within the oral vaccine, it appears that Type II is by far the most likely to regain virulence. With this in mind, many researchers have focused on Type II, trying to pin down precisely what is occurring, at the genetic level.
Note that poliovirus is an RNA virus rather than a DNA virus, which means it is more prone to mutate, as during replication many errors are made and not corrected, unlike with DNA.

Some background on viruses (differences in DNA vs. RNA viruses), as well as an explanation of live vaccines, can be found here:

Step Two – What Makes a Trouble-Maker Troublesome

For the rest of this blog, most of the material discussed is referenced from an article titled: The Evolutionary Pathway to Virulence of an RNA Virus.

Combining genetic sequencing and experimental evolution (E.E.) – a handy method of studying evolutionary processes under experimental conditions – authors attempted to identify whether or not the mutations found in cVDPVs were liable to reoccur. By doing this they were able to help answer an important question – was there a parallelism between independent cVDPVs?

(In short, could there be a common element in the many cases of attenuated poliovirus reverting to virulence?)

Looking first at cVDPV sequences from Belarus, China, Egypt, Madagascar, and Nigeria, a series of nine common mutations were seen across the countries. This indicated that even without any interaction, avirulent vaccine strains were undergoing similar evolutionary changes to revert to virulence.
To delve deeper, researchers set up a model where human cells (a cell culture, not a human trial) were infected with the attenuated Type II. After a set time these cells were taken and the virus extracted. Some of the viral particles were then reintroduced into new cells, and some of them were sequenced. This cycle was repeated several times, with both a 33°C and 39.5°C model.

Retrieved from: Stern et al. “The Evolutionary Pathway to Virulence of an RNA Virus“, 2016.
Fig 1. A diagram showing the path of mutation from attenuated vaccine strain to virulence.

(These two apparently arbitrary temperatures might cause a raised eyebrow, but the rational is that vaccines are ordinarily produced at 33°C , and a human body under an immune response often reaches a 39.5°C febrile state.)

Whilst little synonymous mutation was seen at 33°C (there’s as always a base rate but no particular mutation was steadily seen significantly more than others), the 39.5°C model followed a similar trajectory to that seen in the cVDPVs analysed. Of the nine noted mutations seen in cVDPVs, four were observed to occur at heightened levels in cell culture, as shown below.

Retrieved from: Stern et al. “The Evolutionary Pathway to Virulence of an RNA Virus“, 2016.
Fig 2. The four mutations seen at heightened levels at 39.5°C (as indicated by the bold, coloured line). The baseline (in grey) shows the expected frequency, were the mutation not being actively selected. Dashes indicate levels at 33°C .

Three of these four were dubbed “gateway” mutations (seen as red lines in the above image) and were found to occur much more frequently than by random chance – indicating they were being selected.
The term “gateway” was used by the authors to clarify that in order of evolution, these mutations tended to precede further mutations. It appears that they are acting as an opening passage, where once they occur a series of further mutations can then occur, leading to reversion. To clarify whether or not these gatekeeper mutations were indeed likely to be “leading the charge”, samples were taken from vaccinated individuals, 14 days after vaccination. The sequences of these samples were compared to the initial attenuated Type II sequence used in vaccine production, and it was found that not only were these gatekeeper mutations tending to precede further mutations, A481G was usually seen prior to the other two gatekeeper mutations.

Taming the Shrew – Ahem, Trouble

All of this suggests a very delicate evolutionary pattern is taking place, and lends hope that with understanding, prevention can occur.
Now that particular key sites have been identified, one option would be to proactively remove an additional portion of the attenuated Type II strain, adding in another step it must take in order to regain virulence. A good target for this would be the gatekeeper mutation A481G, as it has been shown to be a key player through cVDPV analysis, cell culture E.E. and screening of vaccinated individuals.

In expanding this work to other vaccines, it is likely that similar patterns are detectable and could be prevented, before events progress to the point where polio currently is. This might include running a short E.E. experiment to see if there’s a sudden reversion when the vaccine of interest is taken from it’s 33°C production environment into a ~39.5°C environment. Any rapid reversion to a sequence resembling the initial virulent virus would be a red flag indicating a need to alter the vaccine.

It can be concerning to hear of a vaccine causing illness, but as is the case here, there is often much more to the picture. The number of cases of polio has dropped drastically world-wide since vaccinations began and were there a more effective vaccination system in place, cVDPVs wouldn’t have had the opportunity to develop. This, more than any other outcome, may be the most important finding, and something that needs to be amended for further eradication efforts.

Thank you for taking the time to read this (and the previous) post, I hope it was informative and left you wanting to do more of your own research in the future.

Posted in Experimental Evolution | Tagged , , , | 7 Comments

Experimental Evolution OF Evolution.

Evolution is something the vast majority of the 21st century agrees on.
TV shows like the big bang theory have created a cosy little bandwagon where after an 8 hour shift, Joe blow can switch off to the terminology and just go with the flow of the episode.

Knowledge of the basics and by extension their appreciation, is a luxury afforded to only those who sought it out in the first place. Without this, the ‘flow of the episode’ takes us where it wants us to go.

Going against the grain, and fighting against the flow of the episode is something that sets people apart, and this is not a concept lost on evolution itself.

We both know Joe blow isn’t going to be making any power moves in his life time, but what about his children?
If they are anything like their father, probably not. But if for some reason, their genes are expressed differently, they might just have the opportunity to ‘blow’ up the family name.

The world they grow up in will of course be different to their dad, but what if it wasn’t?
Would they still have the ability to change?
By how much would they change?

The study I chose to write this blog about asks that same question but in the context of Drosophila melanogaster fruit flies.

How does the expression of genes change when the environment is kept constant?
How does the expression of genes change when the environment itself changes regularly?

The Evolution of evolution guys yes.

Inception etc.

As a side note like any other dream, some parts are more memorable in the morning when you wake up than others, and my rendition of this paper works out a bit like that. Certain things have been left out for the greater good of the take home message. The take home message here is that he was stuck to the floor . . .  never mind the holes he knows are at the bottom of everyone’s feet. It’s deeper than that. Moooooving along . . .  .

To recreate the idea of our hypothetical Joe Blow, researchers used a field collected Drosophila Melanogaster raised on a standard corneal food to establish two other large populations. One was given the time to adapt to a salt-enriched diet and the other was given the time to adapt to a cadmium-rich diet.
A cross was made between both of these to create 20 smaller populations.

These were split into four different environments,

A Cadmium-rich diet and a Salt-rich diet where the flies were given either of the two every generation as a food source.
A temporally variable environment where flies were reared in alternating generations of Salt-rich followed by Cadmium-rich food.
And a Spatially variable environment where in each generation, half of the flies were fed on one diet and the other half on the other diet, separated from one another up until the point of mating.

Before a gene can become what it needs to be, it must first be written down like a post it note. At any given point in time researchers are able to take a snapshot of all the post it notes that are around in a cell before whoever needs to do what’s on them finally gets them done.

After 130 generations researchers took all the post it notes of the drosophila populations and compared them to see if they prioritized having certain things done more often than others.

This is what they called ‘RNA-seq data.’

This would be an example of what the post it notes would like in an inflamed leg muscle that is under exercise

In the same way the culture of the world would change between Joe Blow and his kids, the times and the place affected these genes within the drosophila. Different post it notes become more and more prevalent amongst the evolved populations.

When they compared the populations that were given Cadmium-rich OR Salt-rich food every generation, they found 546 genes that showed what they called a selection history effect.
This is essentially an evolved difference in the amount of a specific post it note relative to the other diets. 
In a previous study by the same researchers they measured how often certain alleles showed up between an ancestral salt-rich and cadmium-rich fed populations. These alleles are simply different versions of the same gene (before they are written down in a post it note). A bit like how you would get skim milk, soy milk, rice milk, oat milk, goat milk, and finally cow milk. It’s all drinkable but a little bit different. 

Combing that data set with their results from the current evolution experiment, they were able separate genes based on whether the particular variation of that allele was located in coding regions, noncoding regions, or located in the DNA sections between genes (intergenic).
They found that the genes that were expressed more (or had more post it notes) also were the same genes they noticed previously had higher numbers of variations in their intergenic regions. Why would a mutation that appears between genes affect them?
Cis acting factors. Like little poltergeists, you might not see them initially, but after months and months of confusing results and occurrences you’re left thinking somethings toying with you .

These are regions that are near-by certain genes which can impose a regulatory function, giving them an idea on just how many post it notes are left in the booklet, and how many they can afford to take out to write on.
The reoccurrence of mutations in these intergenic regions, together with the overlap of an increase in gene expression, suggests that cis acting factors contribute significantly to the evolved difference in the amount of post it notes between populations.
They next chose to examine whether the genes that have more post it notes in cadmium exposure relative to salt exposure in the Ancestral Populations, were also upregulated in the 5 constant Cad populations.

Using the Grand Ancestor population as a point of reference (because it was naïve to BOTH diets), researchers identified 905 genes that showed a significant change in expression when the flies are reared on cadmium-rich food compared to salt.

They then again checked for overlap with the 546 genes that showed that selection history effect from before.
108 genes overlapped between the two gene sets, and further computer analysis showed that they had a reoccurring theme of being involved with the cell membrane.
In 91% of these genes, their response in terms of how many post it notes they had was opposite and contradictory to what appeared in the ancestral populations.

That is to say for example, A gene with more post it notes in the Ancestral populations will have evolved to have less post it notes in the populations that have been given the time to adapt. 
It is an example of what they called counter gradient evolution.
Where the genetic influences on a trait, oppose the environmental influences, creating far less of a change than what you would expect from the environment.

We expect that in a new environment, there will be far much more to do i.e. – more post it notes to deal with the changes, but what researchers found was the opposite of that.
There were less post it notes for the same genes even though the environments were different.
There are two common reasons behind the emergence of a counter gradient pattern.
If natural selection favours the same amount of post it notes across all the environments, but one environment induces a change, then opposing genetic changes are expected to evolve from what was normal.

The other reason is related to the stress that is experienced by a population exposed to a new environment. We expect that this stress would cause different post it notes to appear in higher amounts to cope with the changes. The result would be an abnormal display as above.

Abnormal displays like this build character, and after enough of them you might fight yourself changed for the better.
Someone who had already adapted however, would not warrant the same stress response as someone who wasn’t ‘built for it’.
The likely scenario is that after the 5 Cadmium populations had been given the time to adapt to the diet, they no longer needed to exhibit this stress specific response that was seen in the Naïve Ancestor.
This is the reason behind there being less post it notes for the same genes between the populations that are stressed and those who are not, who have already adapted.

The level of change that could appear in the amount of post it notes for a particular gene set is what these researchers termed its expression plasticity. We would expect a higher expression plasticity for the diets that vary spatially and temporally, compared to the two that were fed consistently every generation.

The researchers could not test this however just using the complete set of post it notes from a given population. They needed to first, identify specific genes where they expected either an increase or decrease before they were written down.

To do this, the screened for genes that could meet two criteria.
First, they required a significant difference in the amount of post it notes from the optimal that is seen in the Ancestral Salt/Cadmium populations.
Second, they needed to exclude genes that naturally have high levels of change in their number of post it notes between populations.

109 genes passed this screening test and for each gene in each population they calculated the change across diets in such a way that a positive value indicates the post it notes changed adaptively.

For each population they averaged the values across all 109 genes to obtain a single measure of adaptive plasticity that they could then compare between diets.
The mean score for both the spatially and temporally varying diets was significantly greater than 0.
This same mean score for the consistent diets was practically zero.

Their hypothesis that adaptive changes arise more readily and to a greater extent in these heterogeneous environments was confirmed once they assessed these results.
The change in expression that has been measures thus far is not the same as measuring how adaptive expression is in either diet.

To do this, the researchers figured out a mathematical formula that allowed them to represent how far the expression levels of genes are from their optimum.
As before the formula equates to a number that is a unique distance from zero, where zero represents optimal gene expression.

The Metric Φ in the Temporally and spatially variable diets was close to 0 for both, indicating them being close to optimal expression – i.e. the perfect amount of post it notes for what needs doing in that environment.
The same metric in a consistently fed population that has been transferred to either of the variable diets, was found to be significantly higher and much further away from optimal expression.

Obviously because they haven’t had the time to adapt.

The patterns of counter gradient variation that they see represent evolutionary responses that attempt to restore just the right amount of post it notes to handle the situation.

If the Goldilocks was built to handle the different types of porridge, then we wouldn’t have had our beloved nursery rhyme. It’s fair to say that when she sampled each of them, a bit of counter gradient variation occurred. She stalled too long making the post it notes that she needed to digest the meal, fell asleep, and the rest is history.

Ok so enough about Wolves, Joe blows, T-rexes and Leonardo DiCaprio, the bottom line really is as expected. Environments that have spatial or temporal variation elicit adaptive responses in the individuals who are naive. If an organism is already adapted to an environment then its response in terms of post it notes will not be adaptive in nature. It will know exactly what to do, how much to do, and at what times. No problem.

They mentioned some of the limitations they noticed in retrospect after having conducted the study.
They chose to count post it notes only from very young drosophila larvae, and this offers with it a problem when you attempt to extrapolate across other developmental stages.
The collections of genes they used were all reasonably highly expressed and that they could have been more liberal with their thresholds. This is a reoccurring problem in expression studies, where accepting a few more false positives would have given a higher resolution picture and idea of the post it notes involved in the specific response.

They also only took a single snapshot of the post it notes at a single time point in the populations and had they taken multiple, they would have noticed the inconsistencies that arise – as the tasks on the post it notes are being performed.

Moving forward, follow up studied perhaps using the same drosophila populations should have a focus on understanding (1) Why plasticity occurs

(2) What is the main cascade of events and who are the star players that underlie adaptation – i.e. where is the quarterback??

(3) How these events relate back to the change in post it notes that eventually cause different behaviour patterns and hopefully speciation away from the Joe Blow Lineage.

Thanks for reading !


Posted in Uncategorized | 8 Comments

Want to make a new species? Put a little pressure on it…

A fascinating event that can occur in evolution is speciation; mutations bringing about new species that are significantly different to their ancestor. But how does this happen, what causes it, and how can we watch it happen?

Observing the processes of evolution can be a daunting task. After all, in an experiment you need to be able to measure the results, but evolution occurs over a long, long, LOOONNG time… doesn’t it?

evolution of man

A depiction of the evolution of man. Image credit: 

Meyer et al. [1]  got around the challenge of waiting for organisms to evolve by studying those with a much shorter lifespan. Bacteriophage λ (lambda) is a virus that reproduces by infecting its bacterial host Escherichia coli.

You’ve probably heard of E. coli. – they live in our gut, often with us none the wiser, but some types can make us sick. E. coli. is readily available and easy to grow, and boy do they grow quickly! Each new generation of E. coli. can be ‘born’ in less than 20 minutes [2], and can have a different genetic fingerprint to their parent due to things like mutations and genetic material being switched around during reproduction. This short generation time allows scientists to observe THOUSANDS of generations worth of change within a few weeks.

But back to the star of the show (E. coli. is just the host after all): bacteriophage λ.

lambda phage

Artist’s interpretation of bacteriophage λ on an E. coli. cell. Image credit: 

This virus (also known as ‘phage’) has the ability to attach itself to an E. coli. cell, inject its DNA, then use the host machinery to replicate itself. Once there are many copies of phage λ the cell bursts open, releasing new phage progeny into the environment. Phage λ does this by having binding proteins on its ‘feet’ (we call them tail fibers) that match up to receptor proteins on the cell’s outer membrane. Most λ phage can only match to one receptor called ‘LamB’, but some types of λ can use another receptor as well.

Teacup evolution

Meyer and his colleagues wanted to explore what would happen if they took a type of phage λ that could match with two different receptors (LamB and OmpF), and let it go through many cycles of reproduction with E. coli. cells that have the LamB receptor, OmpF receptor, or both. The type of E. coli. cells that were provided represent a selection pressure for each set of phage to change the way that it attaches to host cells – evolution towards being better able to utilise the resources available.

The phage that could use both receptors was grown with each of three different types of E. coli. (LamB only, OmpF only, or both receptors), with six replicates of each scenario. Every 8 hours the phage population was moved to a new culture of bacteria, to prevent the E. coli. progeny from evolving resistance to the phage and affecting its ability to adapt. They wanted to see how phage λ would respond to only having one receptor available, and what it would do when it had access to both.

With only one option available, phage λ almost always evolved to specialise to that receptor. All six of the phage populations grown with the LamB-only E. coli. lost their ability to use the OmpF receptor, and five out of six grown with the OmpF-only cells became unable to use LamB.

Pretty easy to make a decision when you only have one option! However, when both receptors were available, most λ populations developed weaker specialisations to either of the two receptors, and some retained the function to use both.



Figure 1 [1]. In the ‘Allopatric’ group, red bars represent phage λ evolved exclusively with OmpF receptor while blue represents phage λ evolved with LamB. In the ‘Sympatric’ group, phage λ evolved with both OmpF and LamB were then isolated in equal proportions from cultures with one or the other receptor (red & blue bars respectively). The length of the bars shows the extent to which each group specialised, with -1 (OmpF) and 1 (LamB) indicating complete specialisation.


More LamB specialists evolved than OmpF, which the authors attributed to fewer mutations being required to become a LamB specialist, making it a simpler adaptation to achieve. This was determined by looking at the DNA sequence of J, a key gene in host recognition, from one phage progeny of each experimental group and comparing differences in the sequence that could bring about a change in function.

To test that these genetic sequence changes were responsible for the phage specialising on one receptor, Meyer et al. created phage with engineered genomes containing the mutations observed for each group. These modified phage were found to specialise or generalise in the same way as their evolved counterparts. A constructed hybrid with both sets of specialist mutations was not viable, indicating genetic incompatibility between the two specialists.

While this experiment didn’t go so far as to bring about the creation of a new species, it demonstrates how easily mutations can render two members of a species distinct from, and even incompatible with, each other. Genetic incompatibility is one of the criteria considered when defining speciation, with two members of different species being unable to produce offspring. While the phage λ progeny in this experiment were still far too genetically similar to qualify as distinct species, they are an example of how selective pressures can bring about adaptive mutations in a relatively short period of time, which has the potential to lead to speciation events. Let’s give them a few more weeks and see what happens…



  1. Meyer, J.R., et al., Ecological speciation of bacteriophage lambda in allopatry and sympatry. Science, 2016. 354(6317): p. 1301-1304.
  2. Todar, K. The Growth of Bacterial Populations. Online Textbook of Bacteriology 2012; Available from:
Posted in Experimental Evolution | Tagged , , , , , , , , | 10 Comments

What in the heck is Convergent evolution? An explanation, and how scientists study it.

Growing up I was always obsessed with the universe and the life it birthed. Atoms forged and fired from dying stars at the outer reaches of the cosmos travelling eons to coalesce into a cloud of universal dust. Gravity taking hold; pressurizing atoms until rock is formed; then Earth. A single pale blue dot orbiting a 4.6 billion year old nuclear fusion reaction we call the “Sun”; It’s awesome. The baron rock we call Earth slowly morphing into the one we know today. Lush and full of life. But how did life get there? The answer is deceptively simple; Evolution, baby!

A Pikachu evolving into Raichu after encountering a thunderstone. Credit

Have you ever noticed unrelated animals share the same traits?

Did I say deceptively simple? What so deceptive about it? Animals have traits, and those traits are selected for by the environment and passed down to offspring. Not very deceptive at all you might say; until you think about Bats, Birds, and Butterflies. Mammals, aves, and insects respectfully. How is it that each of these three organisms all share a common trait so distinguishable as wings and yet be completely different in every other way? Surely long ago an ancient ancestor of each of these animals evolved wings right? What if told you that Birds, Bats, and butterflies each evolved wings independently of one another? This is what is known as convergent evolution and its something that researchers led by Pedro Simoes out of Portugal decided to elucidate through their published journal article.

What are these Scientists actually researching?

So lets look at this incredible image I have made to better understand what it is these researchers are looking for. The orange background is the environment, the circles are the generations of species A and species B; the colour changes represent new adaptations that have been selected for. So what do we see between species A and species B? Well they both stem from different coloured circles (which means both species have different traits to begin with) and as they evolve, they begin to differ more and more, until! Aha! In the top right corner, can you see it? Even though the starting circle In each box was a different colour, both boxes have ended with a circle that is yellow! This is convergent evolution in action! Two separate organisms developing the same trait independently of one another.

That’s great and all but how does this relate to the research by Pedro Simoes?

Well with the knowledge you now have on convergent evolution I’m sure you’ve already put two and two together. Since we can make an environment homogenous (make completely the same), could we then breed animals in that environment for 100s of generations, add new organisms, and then predict the traits the new organisms will develop based off the 100s of older generations? Well this is exactly what Pedro Simoes’ team set out to discover!

They took flies and recorded their fecundity traits (the number of eggs laid, the age of first reproduction, peak number of eggs laid) over the generations and compared that to the results of the older generations of flies that had been living in the lab for a much longer time. If the traits of the new flies became similar (converged) to the older flies, then that would mean we could theoretically predict how the flies would evolve when placed in the lab! How cool is that?

Here’s what they found!

Image source
Moving clockwise starting on the far left we have: The difference to the control, The different populations of flies, the traits they studied, and the number of generations. The 0 represents identical traits to the older generations of flies that have lived int he lab previously. The dotted lines represent the smaller number of generations, and the solid line represents the larger number of generations
It should also be mentioned that early fecundity is the number of eggs laid in the first week, and peak fecundity is the number of eggs laid after that week.

In order to get this data Simoes et al caught flies from separate regions in Portugal, brought them back to the lab, bred them, and after two generations began collecting data (this is to help standardize the data, which is done to decrease variables in the experiment). To collect data Simoes et al performed phenotypic assays of the flies in each generation, by transferring mated flies to fresh media every day, and counting the number of eggs laid in the first week of life (early fecundity), the number of eggs laid between days 8 and 12 (peak fecundity), and the number of days before the first eggs were laid (age of first reproduction).

How did they make these graphs?

In order to normalize the data and plot it on this graph some pretty heavy statistical methods were used.

Source: Giphy

In interest of transparency i couldn’t accurately explain it to you, so instead i will give you a very simple explanation that gives you the gist. The data was averaged, and then a linear regression of the data was performed, mix that in with some statistics that go way over my head and bingo bango, you’ve got this graph.

In the graph, we can see that as time goes on the traits begin to converge (as seen by the dotted lines, and solid lines moving towards the 0 mark) but then as more time passes (more generations are born) they begin to diverge again e.g., move away from the 0 line (as seen in the very late generations; specifically in the NARA and TW populations). Some traits don’t even converge at all! (looking at you; peak fecundity for the NARA population) The writers of this article have stated there is “an overall theme of convergence” which is true. Overall some convergence is occurring. Unfortunately, due to the transient divergence seen between the early, and late generations of the populations; there is no way you could accurately predict the fly’s evolutionary trajectory.

So all in all, as cool as it would be to be able to predict traits in animals, there is just so much variety when it comes to evolution that it leaves us struggling to predict the trajectory animals will take along their evolutionary path. Perhaps with time, and more generations, the traits of the new flies will converge in a manner more predictable. Until then we will just have to remain ignorant of our evolutionary trajectory. Despite the results. I hope you can walk away from your computer screen knowing you’ve learnt something about convergent evolution, and how experimental evolution can shed light on how this phenomena arises.

Source Material

Simoes, P., Fragata, I., Santos, J., Santos, M. A., Santos, M., Rose, M. R., Matos, M. (2019) How phenotypic convergence arises in experimental evolution. Evolution, 73(9), 1839-1849.  doi: 10.1111/evo.13806

Posted in Uncategorized | 9 Comments

How much do small changes actually matter?

Many, many small things in the world add up to big things. I’m sure you’ve heard of some of these:

  • Acts of kindness
  • Pieces of plastic
  • Mutations in bacteria

Wait what?? One of these isn’t like the others…

A brief run-down on bacteria: single-celled organisms that are found everywhere (even on you!). They have a singular chromosome, and occasionally a plasmid – which holds genetic information. On the chromosome is genes – things that code for proteins, and small changes (mutations) in these genes can have drastic effects. Overall, things on this chromosome is termed the genome.

Long term evolution experiments (LTEE) are commonly used to study evolution. Researchers sample parallel populations across thousands of generations, hence bacteria such as Escherichia Coli are often used [2, 3]. Organisms need to adapt to their environment to survive, which allows for divergence of populations, even if they remain in an unchanging environment. These genetic differences then may lead to further adaption [2]. Changes in the genome are believed to cause evolution, but mutations are not always positive for the individual. It is also thought that neutral mutations (ones that are neither beneficial nor harmful) would accumulate at a constant rate [3].

These papers investigated the OVERALL impact of beneficial mutations in E. Coli. Many studies have been done on the how fitness improves over time, but less work has focused on specific genes and how the genetic background of mutations affects gene fitness. Comparatively, Peng et al. looked at a specific gene (pyfK) and the effect of moving the gene into different genetic backgrounds (i.e. other populations of the same bacteria!). Genetic background is simple – just looking at the mutations which came before the one that gave a fitness boost. These mutations may rely on previous sequence changes to work.

Focusing on a specific gene allowed these researchers to directly measure the impact of certain mutations. This was important because it meant we could see how mutation effects change in different backgrounds. It also proves that mutations are not isolated events that randomly improve fitness. Instead, although mutations can have major beneficial effects, this may differ when placed into a new genetic background. They found that the evolved mutations had variable effects in their own background (neutral to +25%) however it varied when placed into the ancestral background [1].

[1] – Peng et al. 2018. Placing two mutations into new strains has differing effects on fitness.

You can see in the picture above (Fig4) that mutation alleles have varying effects when placed into a variety of different genetic backgrounds. The authors chose to take the ‘A301S point mutation’ or ‘deletion mutation’ allele and place in into new strains. This meant that they could compare the evolved alleles, the indel allele and the A301S allele and their effect on fitness.

As a broad generalization, the deletion had the greatest effect on fitness, and the point mutation was less beneficial than the original allele. The A301S point mutation was chosen to be transferred into other strains as it evolved independently in three strains of E. Coli. The deletion mutation was used as a proxy for the insertion mutation which arose [1]. Fitness effects were shown to be affected mainly by the genetic background, rather than the specific mutations being hugely beneficial.

The authors preformed a similar experiment, where they took all the evolved alleles and put them into the ancestor strains, measuring the difference in fitness. This shows a rise in RELATIVE fitness over the first 10-15,000 generations, which begins to drop off after 20,000 generations [1]. From six different populations where mutations had arisen, the pykF allele was taken and reverted to the ancestral gene. Hence the effect of the mutation within pykF could be measured. The evolved allele was placed into the ancestor and fitness effects were measured (Fig5 – below). Whilst pyfK mutations occurred early in the LTEE, the genetic background was less well understood, and so it is hard to understand the relationship between mutations in pyfK and fitness effects of other mutations. However, an understanding of the trend between pyfK and fitness can be inferred [1].

[1] – Peng et al. 2018. How mutations within pyfK can change fitness over time.

Fitness was measured by undertaking competitive fitness assays for both figures. These head to head competitions had relative fitness calculated by growth rate within the environment [1]. Therefore, the fitness effect of the mutation was calculated by “relative fitness minus one”.

Small changes within the genome can influence genes, even if these changes are not in a gene. How well bacteria survive in an unchanging environment is affected by mutations. Evolution is a complex process, with many factors at play, and long-term evolution experiments in bacteria are simply one way to help unravel the mystery of how small changes can have large effects – just like you can. Be kind, be happy and always be open to change.

1.            Peng, F., et al., Effects of Beneficial Mutations in pykF Gene Vary over Time and across Replicate Populations in a Long-Term Experiment with Bacteria. Mol Biol Evol, 2018. 35(1): p. 202-210.

2.            Richard E. Lenski, et al., Long-Term Experimental Evolution in Escherichia coli. I. Adaptation and Divergence During 2,000 Generations. The American Naturalist, 1991. 138(6): p. 1315-1341.

3.            Barrick, J.E., et al., Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature, 2009. 461(7268): p. 1243-7.

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You’ve heard about measles, you’ve probably even heard about smallpox… but what do you know about polio?

Feeling feverish? It could be the outcome of a fevered excitement to read this article, or it could be polio.
Polio, also known as poliomyelitis (let’s stick with polio), is a highly contagious illness that can, along with fever, induce a paralytic state – one could say it freezes lungs in their tracks. It’s caused by poliovirus, which before vaccines were available was free to have its way with the general populace, particularly during the tail-end of WWII. As is often the case, woes bred woes and with a general decrease in hygiene accompanying an increase in crowding, polio caused many deaths – particularly amongst young children.

Children in an “iron lung” after suffering from polio induced paralysis of the lungs.

Fortunately, an effective vaccine was created and in this modern age polio is almost eradicated, with the very illness becoming somewhat of an “ailment of old times”. Less fortunately, there’s been a slight issue with vaccinations…

In essence, the vaccine is causing polio.

Obviously, that’s not ideal.
However, before sharpening any “anti-vaccination” pitchforks, allow me to slip into a little first person prose (the benefits of a blog).

Our fast-paced, high-tech life can make it hard to navigate and sift fact from fiction. The constant plying of information can be overwhelming, with many sources having an agenda of persuasion. For example some sites may simply state “the vaccine is causing polio”, and that could quite understandably leave you with feelings of concern or trepidation when considering whether or not to vaccinate your children.
I would much rather provide you with a summation of information and allow you, the reader, to form your own opinion.

So, if you please, read on for an explanation.

The vaccine is not causing any immunised individuals to develop polio. What’s happening is that in 1988 the W.H.O. (World Health Organisation) made a plan, a very ambitious plan of globally eradicating polio. One challenging part of this was that even remote, impoverished areas needed to be reached – a tough call when staffing and sanitation levels were dubious. The only practical way to implement vaccination in these areas was through use of an oral vaccine – drops taken by mouth.

Within this liquid are a combination of attenuated poliovirus: Type I, II & III. They are called attenuated because their genetic make-up has been altered to the point where they are no longer considered virulent/are not capable of causing polio. However, they are still live viruses, and will for a short time be present in the newly immunised person’s stools. In situations where there is a public sewerage system and good hygiene practices this wouldn’t be an issue, but for some isolated areas this isn’t the case so coupled with many individuals not getting the vaccine, we end up with transmission. As this cycle repeats itself, the attenuated strands are undergoing mutations and eventually we see a reversion to virulence. These variants are referred to as cVDPVs (a mouthful of “circulating vaccine derived polio virus”). Now, any cVDPV infected individuals are at risk of developing polio.

This means that it’s still the unimmunised that are at risk, but that the vaccine is responsible for exposing these unimmunised individuals to the virus. There are a few ways to work around this, one being more thorough immunisation of these remote regions. Another way, and this is the area that my next post focuses on, is identifying how this virulence is regained.

In the mean-time feel free to peruse the following sites for more information on polio, both the illness and the virus.

Update – Following article can be found here:

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Hybrids: a fast track to evolution?

Darwin’s theory of evolution by natural selection is, in my opinion, the most awe-inspiring of scientific theories. Sure, it doesn’t quite have the indispensable quality of gravitational theory in literally holding us to the Earth… But, to look around at the endless forms most beautiful with which we share our planet, and know that every single one of them (and us) shares a common ancestor that lived approximately 4 billion years ago, usually has my brain going something a little like this:

What Darwin got wrong…

Or rather, what he didn’t get quite right.

The branches of Darwin’s classic tree notably extend outwards: simplicity giving rise to greater complexity: one ancestral species giving rise to multiple different species.

Charles Darwin’s hand-drawn “tree of life”
from On the Origin of Species.

But we know that this is not always the case.

Hybrids can be formed through the mating between species (interspecific hybridisation), or between distinct populations/breeds within species (intraspecific hybridisation).

The labradoodle: an intraspecific hybrid of the labrador and poodle. Image credit: thelabradorsite

Life as a hybrid.

Hybridisation has the potential to impact evolution through a variety of ways. After all, the sudden mixing of two dissimilar sets of genetic material has repercussions not otherwise associated with conventional Darwinian evolution. The Nobel Prize winner Barbara McClintock coined the term genome shock, which aptly describes the multitude of potential challenges faced by a genome following hybridisation.

These potential challenges most notably include complications during the phase of cell division where chromosomes are required to pair up; the set of chromosomes the hybrid inherits from each parent may be too different from one another that it interferes with this pairing. In spite of the potential challenges they face, there must be some benefits to being a hybrid, as they can be found throughout the plant, animal and fungal kingdoms.

In fact, the prevalence of hybrid species within the plant kingdom means there’s a good chance that you ate one within the last week (and even the brewer’s yeast used in wine and beer making is a hybrid!)

Or, you may have even admired one in your garden without realising it.
Take the humble sunflower, for instance.

Part of my green-thumb efforts of Summer 2019.

Sunflowers are a crop heavily reliant on hybrid breeding.

Can hybridisation act as an evolutionary stimulus?

A study published just earlier this year used an experimental evolution approach to investigate if naturally-occurring hybridisation can accelerate adaptive evolution in sunflowers.

Experimental evolution studies biological populations under defined environmental conditions and over multiple generations, to understand their evolutionary responses.

But how can someone measure evolution, you ask?

This study tracked 27 traits in a hybrid population, and closely-related non-hybrid control population, of sunflowers. The sunflowers were planted in open fields; the two populations separated by a dense copse of trees to prevent any cross-pollination.

Leaf samples were taken yearly and used in genetic analyses, to track the progression of trait evolution. Seed samples were also taken yearly and stored for usage in a common garden in the final stage of the experiment.

A common garden allows for the comparison of biological populations without the confounding effects of their corresponding environments.

Mitchell et al (2019) Figure 1: the experimental setup.

As neither population was originally from the testing area, the change of the 27 traits over the course of the experiment (which I should add, was eight years long!) in the hybrids relative to the controls, when analysed at each time-point and in the final common garden, would give an indication of how well they were evolving in their new local environment.

If the traits were observed to evolve more rapidly in the hybrid population over the experimental period, this would suggest that hybridisation was accelerating evolution.

What did they find?

Mitchell et al (2019) Figure 2: the evolution of fitness.

Hybrid fitness was shown to increase over time (dark blue and light blue lines), while control fitness (gold line) did not change overall. H. a. texanus (black horizontal line) is a locally-adapted wild sunflower hybrid used as a standard for comparison. The second hybrid population (and the reason for which there are two blue lines) was established to assess the generality of the results.

Fitness is a measure of reproductive success; how well a biological organism or population is adapted to a given environment in order to survive to a reproductive age and to produce offspring.

From the above figure, hybridisation does appear to have accelerated evolution!

So which of the 27 traits evolved most rapidly?

Some key traits associated with
• leaf area
• bud initiation time
• seed maturation time
• flower disk diameter
• herbivore resistance
evolved faster in the hybrids relative to the controls, which could have enabled the hybrids to acquire resources (e.g. sunlight), avoid pest-based damage, and produce viable offspring more effectively: all qualities that would promote the propagation and continuation of the population (which is, of course, the inherent goal of life itself!)

But was it adaptive evolution, or just evolution?

And how can you tell?

Although traits were observed to evolve in the hybrids, this evolution was not necessarily adaptive. To prove adaptive evolution, the researchers needed to show that the traits were under selection, otherwise the evolution could have been driven by additional forces, such as genetic drift or a genetic correlation with other traits under selection.

Selection is the preferential survival and reproduction (or, preferential elimination) of individuals with particular characteristics or features.

Genetic drift is a type of evolution that typically occurs in small populations, whereby certain genetic frequencies change over time due to chance or random processes.

The researchers performed some statistical analyses of the data, focusing on trait selection, and determined that some traits had indeed evolved adaptively.

Mitchell et al (2019) Figure 3: individual trait evolution.

In the above-left figure (3a), colour is used to differentiate whether trait values increased (blue) or decreased (pink), along the shading gradient that corresponds to values derived from the statistical analyses. Traits with an ‘a’ were determined to have evolved adaptively. In the above-right figure (3b), colour is instead used to differentiate the rate of trait evolution and whether it was steeper in the control (orange) or hybrid (blue) population, along the shading gradient. We see in Figure 3b, from the prominent blue shading, that most traits evolved more rapidly in the hybrid populations. In both figures, ‘LBJ’ and ‘BFL’ correspond to the two hybrid populations, and ‘control’ to the control population. A solid black outline of a square denotes a highly statistically significant result, a dashed black outline a statistically significant result, and no outline a non-significant result.

When considering Figure 3a, we see that all adaptively evolved traits are associated with high statistical significance, which gives us confidence in the results. However, sunflower disk diameter (‘DiskDiam’) and overall plant size (‘Volume’) are the only traits for which the hybrid populations both demonstrated adaptive evolution and the control population didn’t. Thus, the title ‘Hybridization speeds adaptive evolution in an eight-year field experiment’ is perhaps a bit of a stretch, and could have done without the inclusion of the word ‘adaptive’, in my opinion.

Some final thoughts.

It is important to remember that the researchers’ conclusions have been drawn from a single study using a single control and two hybrid populations, making it entirely possible that the observed effects are due to species-specific factors at play, or are not completely representative of reality.

Experimental evolution studies in higher organisms (e.g. plants and animals) are impeded by their long generation times; a barrier not encountered in studies of microorganisms such as E. coli, which has a generation time of 15-20 minutes in the lab and has subsequently been used to great effect in the E. coli long-term experimental evolution project. Field-based studies, such as that discussed in this blog post, encounter further barriers in ensuring that conditions are held constant for the duration of the experiment, according to the conditions defined at its outset. Aptly highlighting this was the need for this case study to be terminated after eight years due to a change in land usage at the host site. Conversely, the benefit of field experimental evolution over lab experimental evolution is the ability to study a population in its natural environment, and without the introduction of artifacts that may occur in lab settings.

An artifact, in biological science, is a misleading observation or misrepresentation in the data, introduced by the experimental equipment or techniques utilised.

Scientific knowledge, and its countless applications, would benefit significantly from further field-based experimental evolution studies; particularly those on higher organisms, as well as those on hybrid populations. Such studies are largely under-represented in the literature, as reviewed in Kawecki et al (2012). A follow-up to this case study would also help to clarify and consolidate its results.

As I touched on towards the beginning of this post, hybrid species are found throughout the plant, animal and fungal kingdoms. They are also imperative to the functioning of many industries, and to humanity itself. The more we can learn about hybrids and their evolution, therefore, the greater ability we have to make informed decisions to benefit our ever-increasing global population.

When we consider the data from this study, however, it does suggest that hybridisation accelerates evolution, at least in sunflowers. Thus it appears, in quite a circle-of-life type of way, that the evolutionary processes responsible for shaping sunflower populations able to hybridise with one another, are now being influenced by that very hybridisation.

And for that, I have just three words: science. is. awesome.

Case study paper:

Mitchell, N., Owens, G. L., Hovick, S. M., Rieseberg, L. H., & Whitney, K. D. (2019). Hybridization speeds adaptive evolution in an eight-year field experiment. Scientific Reports, 9(1), 6746. doi:10.1038/s41598-019-43119-4

Further readings on this topic:

Barton, N. H. (2001). The role of hybridization in evolution. Molecular Ecology, 10(3), 551-568. doi:10.1046/j.1365-294x.2001.01216.x

Dimitrijevic, A., & Horn, R. (2018). Sunflower hybrid breeding: from markers to genomic selection. Frontiers in Plant Science, 8, 2238. doi:10.3389/fpls.2017.02238

Kawecki, T. J., Lenski, R. E., Ebert, D., Hollis, B., Olivieri, I., & Whitlock, M. C. (2012). Experimental evolution. Trends in Ecology & Evolution, 27(10), 547-560.

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As a scientist, I want to find a treatment for cancer. To do this I need to perform many experiments testing my new therapy on cancer. I can’t just use human patients, they’re too annoying. How about cancer cells from a tumour and grown in flasks? Sounds good, but through my literature search, I find that cell lines undergo substantial alterations within their genome. That surely won’t do, my therapy targets a specific gene. I need a model which does not undergo such genetic alterations. I probably should look for something else.

Cell Lines

My very own cancer cell line cultures I am currently using

Perhaps it’s because the environment of a flask is too different from a human? How about a mouse?

Mice are mammals, like humans. Sounds good.

If I can place the cancer cells from a human tumour into a mouse, since it is a similar environment, the cancer should not change as excessively. So, I take a sample of cells from a cancerous tumour and surgically graft it into a mouse. Great, now I have an accurate model of cancer. One that I can use to perform experiments on that does not involve those annoying humans. One that will better represent the original tumour than if grown in flasks. One that does not change its genetic make-up over time.

But wait!

Humans and mice seem more similar than humans and flasks. But still wouldn’t the change from a human environment to a mouse also cause changes?

I mean, I like cheese, but not as much as a mouse. Surely this would have an effect.

Apparently not.

It was assumed that Patient-derived Xenografts (PDX’s), where human tumours are surgically grafted into mice, accurately represent the genetics of the human primary tumour.

Well… until recently.

Uri, et al (2017) was not satisfied with this assumption. They set out to test if Patient-derived xenografts accurately represent the genetic landscape of a primary tumour and to compare with two established cancer models; cancer cell lines and cell line derived xenografts (CLDX’s).

Cancer models

Overview of the different cancer cell models.

First, we may need to establish some characteristics of cancer.

We need to view a cancerous tumour not as an individual entity, but as a population of individual cancer cells. The cancer cell population is subjected to evolutionary forces the same as any other population of individuals. Over passages (generations) of cells, certain genes will be lost or gained depending on evolutionary forces.

As you can imagine with a population of animals, if placed in a totally alien environment, the animals would undergo divergent evolution over generations in the new environment. Creating genetically different animals when compared to the original population. A population of cancer cells, in this aspect, is no different. When cancer cells are taken from a human tumour and grown in flasks (Cell Lines) they too will undergo evolution over passages – Gains/losses of DNA via mutations within the genome of cancer cells. This leads to cancer cells with altered genetic landscapes in comparison to the original tumour cells.

This is a current problem for cancer research.

Cancer research relies on models that accurately represent the genetic landscape of the primary tumour. If not, the observed responses may not be applicable to the original cancer in question, otherwise known as – useless.

Currently, cell lines and CLDX’s are known to undergo model specific mutations leading them to be genetically different from the primary tumour over time. It is therefore important for future research to develop a model which accurately represents the primary tumour.

As genetic changes in PDX models over passages had not been directly studied Uri, et al (2017) had to create a catalogue of large genetic alterations – gains or losses of genetic information – between early and late passages of PDX models from other papers. Each study had to include DNA-based genomic measurements from primary tumours and at least one PDX passage. However, this lead to a small data set, too small for a comprehensive analysis.

That won’t do…

To overcome the quantity issue they applied computational inference algorithms on studies with gene expression profiles. In other words, they predicted changes in the genomic landscape by inferring the expression of genes – somewhat concerning. It is unreliable to predict specific losses or gains of DNA based on gene expression. But until a direct study between PDX models and primary tumours is made this will have to do.

Overall, a final data set of 1110 PDX’s representing 24 different cancer types were used (Figure 1). This is a bit misleading as only fractions of this data set were suitable for many of their analyses leading to a much smaller sample size per analysis. This leads to an inability to make specific conclusions, however, general conclusions can be.

Data Set

Figure 1 – Cancer types in the PDX dataset from Uri, et al (2017)

By comparing the passaged models genetic landscapes to the primary tumour’s genetic landscape allowed Uri, et al (2017) to determine if each model still retains – and which best represents – the genetic landscape of the primary tumour. Alterations from the primary tumour’s genetic landscape are considered model-acquired alterations. The greater the amount of model-acquired genetic alterations leads to a poor representation of the primary tumour by that model.

Using the above reasoning the assumption of PDX models retaining the same genetic variation as their primary tumours was shown to be false. After a tumour is implanted within a mouse there is a dramatic shift in the genetic landscape of the PDX tumours with a median of 12.3% (range, 0-58.8%) within 4 passages (Figure 2). This is a reduction of total genetic alterations in PDX models as compared with cell lines.

However, given the labelling “early, medium, and late” of cell line passaging, the actual amount of passaging would be much greater than 4 even for “early”. This could account for some of the increase of genetic alterations when compared with PDX models.

Next, when PDX’s were compared to CLDX’s, it is shown that PDX has a much higher change in the genetic landscape. This does not necessarily mean that CLDX’s better represent the primary tumour, unfortunately. Certain aspects of the CLDX model, such as they are xenografted from well-established cell lines. Thus, before being xenografted each CLDX would have already undertaken large changes within their genetic landscapes, causing less observed changes when passaged within mice.

Figure 2 – Mosaic of Model-acquired genetic alterations per model type. Credit: Uri, et al (2017)

Overall this does not look good for PDX’s – It does not look good for any of the three models.

The change in environment from a human to a mouse does not seem to be spared from specific selection pressures causing genomic alterations. Thus, PDX models diverge from their primary tumours after being xenografted and do not accurately represent the primary tumour. PDX’s also do not necessarily represent the primary tumour better than cell lines of CLDX’s.

What is even more concerning for current cancer research is their next finding.

The PDX models can lose signature chromosomal aberrations through passaging – large gains or losses of chromosomes that are believed to cause the specific cancer type. We see an example of this is in figure 3, the hallmark chromosomal gains of breast cancer chromosomes 1q and 8q are lost in PDX models.

Figure 3

Figure 3 –  gain and losses of signature chromosomal aberrations within breast cancer PDX – Modified Figure 4 b  Uri, et al (2017)

This could have a huge therapeutic impact.

Most therapies target aspects like these signature alterations. If the signature alterations are no longer present in the models, it is highly likely that the targets of therapies are altered too. Potentially leading to many therapies having been discarded due to the failure of the model to accurately represent the primary tumour and not due to the therapy being ineffective.

Think of all the wasted time and research grants.

Most scientists already knew the flaws of working with cancer cells line and CLDX models but assumed the same flaws would not be present with tumours grown in mice. This assumption clearly was incorrect. Uri et al, 2017 has created a clear divide within cancer research where, after the publication, PDX’s (once thought infallible) are now subject to the same criticism subjected to cell lines and cell line derived xenografts. The search for cancers next model top model needs to continue.

I’m sorry to say.

Patient-derived Xenografts you are no longer in the running to be cancers next top model.


America’s Next Top Model elimination Credit:

Topic Paper; Uri et al. (2017) Patient-derived xenografts undergo mouse-specific tumor evolution. Nature Genetics, 49 (11) 1567-1575. doi:10.1038/ng.3967

Addition Reading

Cell line evolution’s effects on Drug Response: Uri, et al. (2018)Genetic and transcriptional evolution alters cancer cell line drug response, Nature, 560, 325-330.

Organ on a chip – new human drug model: Zhang, et al. (2018) Advances in organ-on-a-chip engineeringNature Reviews Materials3(8), 257.

Further PDX Info: Tentler, et al. (2012) Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338-350.

Clinical relevance of Cancer Cell Lines: Gillet et al (2013). The clinical relevance of cancer cell lines. J. Natl. Cancer Inst. 105, 452–458.


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Finding Sequels

I’ve always, always, always loved animals. As a kid, I wanted to be a vet (“also an actor,” thought this 30-something genetics student, “kids are weird”). My Mum worked at a Safari Park in the UK, which didn’t help, as I grew up in a house of darling dogs, cute cats, silly snakes and lovable lizards. My own, personal, private zoo, and I loved it. Of course, as a kid I’ve also always, always, always, always loved Disney movies, particularly of the Pixar variety. Finding Nemo, then, holds an especially dear, squishy, jellyfish-shaped place in my heart.

If you don’t know anything about Finding Nemo , then you probably haven’t lived, but besides that our story begins in Australia’s Great Barrier Reef: 2000+ kilometers of astounding ecological diversity; 1,500+ species of fish;  3000 species of mollusks; 350 species of hard corals, and never-mind all the soft corals, clams, sponges, etc (Gutierrez, 2014). Despite being a computer animated film, every shade and hue you see in Finding Nemo is no exaggeration about what’s really out there, ready to astound you.


Why, if you ain’t the bluest thing I ever did see. (c) 2004 Richard Ling

Finding Nemo released in 2003. I was around 15-years-old. Even now, however, I remember a thought I had while snug in those big cinema-chairs, holding the obligatory empty bucket of popcorn, eviscerated before the film even began:

“How much of it still looks like that?”

Yes, kids are weird, but I wasn’t an especially jaded 15-year-old. I’m sure a lot of people were aware of things like coral bleaching, but being the son of a conservationist perhaps made it more present in my mind. I’m sure a lot of people are still aware of bleaching events, because it’s still a thing. Climate change, among other things, contributes to these bleaching events, and refers to when the corals lose those fantastic colours. “Bleaching”, it turns out, is quite literal.


Corresponding healthy (background) and bleached (foreground) corals. (c) CC BY 3.0

Coral bleaching boils down to an endosymbiotitic relations between coral polyps (which we’ll refer to as corals for the sake of simplicity), and their micro-algae Symbiodinium. Corals are immobile or “sessile” creatures often referred to as “reef builders”: they pump out the calcium-carbonate responsible for these hard coral reefs forming. The micro-algae are photosynthetic (hopefully not surprising anybody) and can live inside the corals, exchanging photosynthates (sugary treats from photosynthesis) for some of the corals’ own inorganic molecules (ammonium, nitrate, etc).

Increasing water temperatures, as a result of climate change, causes stress on both the corals and the algae (I empathize, being English makes me about as thermally intolerant as you can imagine). The corals may expel their algae as a short term survival tactic, reducing their burden as a host. Alternatively, the algae my decide to jump ship, making things easier for themselves. Sadly as the corals are sessile, they rely on their symbiotic relationships for up to 90% of their nutritional needs (Falkowski et al, 1984). Prolonged bleaching events means the corals eventually to starve, leading to a slight case of death. While coral reefs can recover, climate change has resulted in these recovery periods becoming shorter and shorter, with many organisms unable to keep up (Hoegh-Guldberg and Bruno, 2010).

Disney is going to end up having a hard time Finding Sequels. Aha.

As mentioned, the thermal tolerance of the algae, as well as the corals, contribute to these bleaching events (Pandolfi et al, 2011). As the algae themselves have much shorter life-spans (days, Wilkerson, 1988) than the corals (years, Babcock, 1991), perhaps the algae can evolve their thermal tolerance quicker than the corals? If this so, then perhaps the algae’s increased thermal tolerance can help prevent the corals becoming bleached?


Your handy-dandy guide to coral bleaching. (c) National Oceanic and Atmospheric Administration

Experimental evolution can be thought of as a kind of indirect artificial selection: as opposed to picking and choosing which organisms can breed based on a shared desirable trait, we instead change the environment that our organisms inhabit, favouring the traits we wish to develop. In terms of Symbiodinium, this would involve growing a strain at an elevated temperature and hoping to observe positive growth (ie growth that is equal to or better than growth observed at a more common temperature). If positive growth is observed, we can try to grow a sample of this strain at an even higher temperature, and so on and so on.

This is precisely what Chakravarti and van Oppen tried to achieve in their recent 2017 study. They took 5 genetically distinct Symbiodinium strains and attempted to grow them at 30°C, a temperature that Symbiodinium are known to tolerate in the literature. If “positive growth” was observed for these strains (explained below), then a sample of those strains were then grown at 31°C, and so on and, indeed, so on. This process was repeated over the course of a year.


Chakravarti and van Oppen’s simplified experimental design. Blue indicates the control temperature (27), red the hotter experimental temperatures (30, 31, etc). The yellow colour refers to fresh media the strains were transplanted into at the corresponding elevated temperature, to compare the growth of the wild -type (WT) and experimental strains (SS).

“Positive growth” in this study was assessed in terms of “growth rate” and “photosynthetic efficiency”. Growth rate compared the initial cell density with cell density at a later time, as well as the cell doubling or generation time at a given  experimental temperature. This makes intuitive sense: if an experimental strain’s rates of growth are positive after prolonged exposure to an elevated temperature (where the corresponding wild-type may struggle), it suggests a stable, adaptive change for the experimental strains’ thermal tolerance.

“Photosynthetic efficiency” was assessed using common plant stress measurements : the maximum and effective quantum yield. Maximum quantum yield assesses how well our plants can photosynthesize after being “dark-adapted”, whereas the effective quantum yield assesses photosynthesis when light is available at a steady state.

Without going too much into the biochemistry of it all, photosynthesis involves the movement of electrons, excited by light energy, or “photons”. Electron movement is facilitated by “reaction centres” that carry the electrons. A dark-adapted plant should have more available reaction centers, as there’s been no light available to excite electrons for them to be carried by the reaction centres. When light is available at a steady state, however, there’s a constantly back-and-forth between reaction centers picking up and dropping off excited electrons. A stressed plant will have fewer available reaction centres, resulting in reduced maximum and effective quantum yields.

The figure below from another study (Roth, Goericke, and Deheyn, 2012) helps us visualize the effect of both heat and cold on the photophysiology (ie photosynthetic efficiency) of the coral Acropora yongei.

Figure 3

Here, we see the stress caused by both cold (downward arrows) and heat (upward arrows) have detrimental effects on the effective and maximum quantum yields of the corals, compared to the control temperature (white circles). Heat is shown as being more deleterious over time.

(We also see the effects of maximum excitation pressure of photosystem II, Qm, which from what I can understand involves the availability of some reaction centers in response to environmental factors such as heat and light, Grey et al, 1996).

This again makes intuitive sense: if a strain, after prolonged exprosure to a higher temperature, can photosynthesis better than its wild-type at the same temperature, it also suggests a stable adaptive change has taken place.

It is important to remember that, while Charkravarti and van Oppen’s claim to have observed stable adaptive changes in their experimental strains (a great start), it’s certainly not the end of the line. Experimental evolution, as with experiments as a whole, involves controlling a number of variables. Controlling variables means it’s easier to understand the relationship between our results (stable adaptive change) and the experimental conditions we did change (temperature).

Only two traits (growth rate and photosynthetic efficiency) were assessed in this experiment. In a real world setting, these two traits will not be the only ones that matter. Chakravarti and van Oppen discuss in their paper future experiments that need to be considered. Do these evolved strains contribute to the corals during heat stress? Being photosynthetically efficient in vivo is a great start, as many species of coral are “broadcast spawners“. Adapted strains could be released during a spawning period, surviving elevated temperatures ex hospite before being taken up by the coral spawn. Being photosynthetically efficient, however, doesn’t mean the strains release photosynthates when in hospite (Stat, Morris, and Gates, 2008). We also need to ask if these evolved strains can and will infect the corals and be retained by them (Gabay, Weis, and Davy, 2018).


“Finding Spawn” would have been a very different movie. (c) Jamie Craggs.

What about where these evolved strains exist on the symbiotic spectrum of parasitism to mutualism (Baker, 2018)? Studies have shown that an algae may be more or less beneficial to a coral depending on the coral’s life stage, such as one strain may be more symbiotic for juveniles (Suzuki et al, 2013), but more parasitic in mature corals (Stat, Morris, and Gates, 2008). Corals also rarely host only one strain of Symbiodinium, and so we ask how these corals perform with a single evolved strain compared to being given a “cocktail” of thermally tolerant strains.

What about the thermal tolerance of the corals themselves? Some recent papers by Liew et al and Yong et al have suggested bringing about epigenetic changes in the corals by using “coral nurseries”. It is thought that growing corals in nurseries at elevated temperatures (rather than relying on sporadic, “natural” temperature increases) may cause epigenetic changes that would increase expression of thermal tolerance genes. These young corals would then better react to haphazard temperature changes, rather than hoping these’s enough time between bleaching events for the corals to develop these changes more naturally. Yong et al go on to say that these epigenetic changes also causes changes in transcription that responds to algal symbiosis of the corals.

It’s all exciting stuff, as we consider the myriad of ways in which we might be able to help. Charkravarti and van Oppen have used experimental evolution here as a tool to improve the thermal tolerance of the endosymbiont Symbiodinium, claiming to have observed stable adaptive change in 3 of their 5 experimental strains. I believe combining some of the approaches discussed here, and other incredibly clever ways, is the best hope we have to aid these diverse ecological systems. I hope these efforts bare fruit. I hope there’s a time where I can watch Finding Nemo and no longer think: How much of it still looks like that?

“I hope”.

(Different movie, I know. Thanks for reading!)

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Antibiotics in Agriculture: We Can’t Afford It.

NZ nature

A snapshot from our Massey University Albany campus.

I am fairly certain my housemate saved my life that day. It was the first week of graduate school and she found me delirious, dehydrated and with a temperature above 40°C. Overconfident, I babbled about having the flu but being fine, the futon had hurt my back and that I just needed some water. It turned out I had a progressed kidney infection. That is how I came to start my graduate course in bacterial evolution; alongside courses of antibiotics, prescribed to combat the bacteria ravaging my internal organs.

We often take antibiotics for granted. But every day there are more and more people for whom antibiotics don’t work. Hundreds of thousands now die each year from bacterial infections that can no longer be treated with these life saving drugs. A recent report estimated that by the year 2050, more people will die of antibiotic resistant infections each year than from cancer (O’Neill 2016).

That is a truly scary prospect. We may still be able to avoid this fate but it will take real changes in the way we use these precious medicines. We have over used antibiotics in a deeply irresponsible way and it is time for us to reassess our values. You and I, lawmakers, doctors and farmers must all carefully consider the sacrifices that we are willing to make to save human lives. What is a life worth?

For global antibiotic awareness week this year I want to focus on antibiotics in agriculture. This is not what we generally think of first when we discuss this issue but it should be. According to the Food and Drug Administration, in the US, 80% of all antibiotics are used in agriculture (2009 FDA report).

How can that be? For reasons that are still not clear to science when low doses of antibiotics are given to the farm animals they increase the amount of meat that they put on their bones (2012 Review). This use of antibiotics is called “growth promotion” and it is no longer allowed in Europe or New Zealand. For those places where it is still permitted it is a huge boon for farmers who are looking for ways of producing cheap meat. Bigger animals mean bigger profits for individuals and corporations who are trying to feed the world meat products.

Not long ago the New Zealand Veterinary Association (NZVA) vowed “by 2030 New Zealand will not need antibiotics for the maintenance of animal health and wellness.” In the same report the NZVA also proudly declared that New Zealand is already the third lowest user of antibiotics for animals in the OECD (Organisation for Economic Co-operation and Development) (Hillerton) (Fig. 1). This sort of statistic is enough to swell the heart of any red blooded Kiwi soul. We are proud of our beautiful rolling hills and our happy grass fed sheep and cattle. “Of course we are doing well,” we sigh contentedly, “we are clean and green and so naturally we are miles ahead of places like the US” (ranked 28/30).

Antibiotics ag

Figure 1. Antimicrobial use in humans and agriculture by mg/Kg biomass in OECD countries, 2012 (Hillerton).

Unfortunately, as with many a national myth, this one does not hold up to closer inspection. The first tip off is the metric that is being used by the NZVA; Antibiotic use in milligrams per kilogram of biomass (mg/Kg). To be clear, the NZVA did not invent this reporting measurement, they are simply following a very cleverly opaque convention. By reporting the mg/Kg of biomass we benefit simply by being a country that has a lot of large animals to put in the denominator, a single chicken weighs a little less than 2 kg on average but a single cow weighs 160 times that. Any country in the OECD with a high proportion of cattle and sheep is going to have a lower reported mg/Kg, even if every chicken and pig in the nation is being force fed unnecessary antibiotics in it’s feed each day. As it happens, Cattle and Sheep, which are generally (but not always) free range in NZ, make up 98% of the weight of the animals that we raise for food production each year (Fig. 2).

Bio mass NZ

Figure 2. Animal biomass in New Zealand broken down by animal type.

Poultry and Pigs combined are 2% of the animal weight we put in our denominator but these two groups consume 34% or 22,000 kgs of antibiotics sold for agriculture each year. That is phenomenal to me. Ultimately, we may look good by this measure but we should not pat ourselves on the back for simply having a lot of cattle. We are still pouring huge amounts of antibiotics directly into the feed of many of the poultry and pigs here in NZ.

Why do we do it? This use of antibiotics is called “prophylactic use”. Essentially, we deliver antibiotics to these animals in their water or feed in order to prevent them from getting sick. This is common practice when animals are being kept in less than ideal conditions. Stressed, over crowded, close quartering of small animals is cheaper for farmers but these are also ideal conditions for infections to spread. Constant prophylactic antibiotic administration is one way of keeping these animals healthy.

“Who cares?” you may say, “I am no battery hen!”

Antibiotics Res spreads

Figure 3. Antibiotic use in one environment can affect antibiotic resistance rates in many other environments (adapted from Andersson and Hughes 2014).

I was recently involved in the drafting of the most recent white paper on ‘Antimicrobial Resistance – Implications for New Zealanders’ from the Royal Society of New Zealand. This is an excellent source of NZ relevantdata on this topic and I can recommend it. Two important points are that antibiotics that go into agricultural production are not isolated there and they don’t simply go away. They assert pressure on bacteria in the soil to become more resistant to those antibiotics (Fig. 3). If these are pathogens we can become infected when we ingest food that has been treated with antibiotics. The second point raised by the RSNZ report is that the rates of antibiotic resistant pathogens such as methicillin-resistant Staphylococcus aureus (MRSA), carbapenemase-producing Enterobacteriaceae and beta-lactamase resistant Enterobacteriaceae in New Zealand are already on the rise.

According to a 2015 study published in the Proceedings of the National Academy of Science, New Zealand is using more antibiotics in food animals per Km2 than many other OECD countries (Fig. 4). This means that we are running the same risks in terms of the affect of these antibiotics on the bacteria that infect us as any of our less favourably ranked OECD colleagues. This also means we still need to do better!

Select Counties Use-01

Figure 4. Antibiotic use in animals by land or per person in select OECD countries.

None of us wants our friends or loved ones to be the next person in hospital who will be told that the antibiotics aren’t working because the bacteria are already resistant to everything we have. It’s a nightmare scenario. What are we willing to give up to avoid the post-antibiotic era?

-I encourage lawmakers in New Zealand to take antibiotic use seriously and regulate agricultural use. This should include labelling antibiotic use in the foods we see in the supermarket.

-I implore farmers to reconsider their practices if they are using antibiotics for anything other than saving lives. Let us strive towards practices that will eliminate such use.

-As a consumer, ask yourself what cost we are all really paying for the cheap meat we find in the supermarket. Can you afford it at this price?




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