HIV And The Mystery Of Its Phylogenetic Tree

When I hear the term “global pandemic” I tend to not so fondly associate it with unrelenting COVID-19. Thanks to the ability of COVID-19 to take over both our respiratory systems and all media outlets, the everyday person is now an expert. Terms such as RNA virus, genome sequencing, PCR testing and viral evolution are household names. Yet, SARS-CoV-2, the virus responsible for COVID-19, is not the first RNA virus to inflict an enormous burden of disease on humanity. The original RNA viral pandemic belongs to the persevering 1981 Human Immunodeficiency Virus (HIV). While the manifestation of disease and method of infection vary between these two RNA viruses, many comparisons can be drawn between their basic genetics, mutation rate, and the way in which we can track their evolution to guide clinical prevention and treatment. 


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HIV is a virus that interferes with the human immune system by impairing its ability to fight infection and disease. It does so by infecting T-cells which are the mini fighters in our body. HIV leads to the development of Acquired Immunodeficiency Syndrome (AIDS) which currently has no cure, and in 2020 caused approximately 680,000 deaths (Joint United Nations Project on HIV AIDS, 2018). Not only is HIV highly prevalent and almost always fatal, but it has an incredible talent at evolving rapidly. This is thanks to the combined activity of three key biological factors :

  1. HIV is a retrovirus

    HIV has two single strands of RNA which encode for the entire HIV genome of only nine genes! (German Advisory Committee Blood, 2016). As a retrovirus, HIV uses cellular machinery to convert its RNA into DNA (the stuff humans have) to infect human host cells. This process is called reverse transcription and it lacks a proof reading ability. As a result, errors, known as mutations are introduced into the DNA sequence (Andrews & Rowland-Jones, 2017).  

  2. HIV has a short generation time

    HIV has a generation time of 1-2 days. A shorter generation time results in quicker evolution as mutations can accumulate in a population faster. 

  3. HIV undergoes recombination

    Recombination is where HIV’s two RNA strands exchange genetic material with each other. This reshuffling of genetic material can provide unique combinations of different versions of genes (alleles), increasing diversity. 

These three factors contribute to HIV’s extensive viral diversity which allow it to successfully evade the human immune system, evolve drug resistance, and bypass vaccinations (Andrews & Rowland-Jones, 2017).  HIV’s rapid evolutionary rate has gained attention from various researchers such as Bertels et el. (2020) who performed a long term evolutionary experiment on HIV. In a long term evolution experiment, organisms are transferred for many generations in defined and reproducible conditions. This allows us to understand basic evolutionary principals or to see how an organism’s evolutionary history might help us infer its evolutionary future.  

Tracing HIVs evolutionary history is a bit like looking at an enemy’s past tactics to better prepare ourselves for future battle. If we can work out where they came from and how well they did, then we can develop an airtight war plan against them. However, it becomes harder to prepare when the intel isn’t clear. Did they come from the north, or the north west? Do they have 100 or 1000 men? Should we bring guns or knives? Trying to determine the evolutionary history of a virus is much like trying to get a message across a radio with a weak signal. We know parts, but there’s interference and certain factors we can’t quite see to account for. One of these factors is parallel evolution. Parallel evolution is where two geographically distinct groups of organisms develop the same mutations or traits (Westram & Johannesson, 2016). As a result, these two groups can look more closely related than what they are, interfering with our ability to trace how they really evolved. Having an inaccurate evolutionary history, presents challenges when using it to inform our battle plan of clinical management and development of treatment.

Image and caption from Bertels et al. (2020)

 Bertels et al. (2020) wanted to investigate the extent of parallel evolution in HIV in a constant environment. Over the course of 315 days, they serially transferred HIV to Human T-cell leukemia cell lines called MT-2 and MT-4. The HIV was left to replicate for a few days and then transferred to fresh MT-2 and MT-4 cell cultures twice a week. At the end of 315 days, 90 transfers had taken place (approximately 180 viral replications). At every 10th transfer, Illumina genome sequencing took place. Genome sequencing – there’s a term all of us COVID-19 armchair experts should be familiar with by now! If you haven’t been keeping up with the 1pm news conferences, genome sequencing is where the entire RNA sequence is essentially read out by a computer. To measure if a mutation occurs or not, the researchers look at nucleotides. These are structural components of RNA and can exist in one of four forms – Adenine (A), Uracil (U), Cytosine (C) and Guanine (G). Looking at a certain point along the genome, the researchers compare what the original nucleotide was, with the current nucleotide.  If they find that a new nucleotide is present at a higher frequency than the original one, this is determined to be a “majority mutation”. 

Two replicates of each T-cell line were used to see if the same mutations appear even though they are separated from one another i.e to observe parallel evolution. To increase the power of this experiment more replicates of each T-cell line could have been tested. This would have boosted confidence that mutations occurring in multiple cell lines were as a result of evolutionary advantage rather than appearing by chance. 

Image and caption from Bertels et al. (2020)

Over the course of 315 days, 92 majority mutations appeared (figure 2A). Of these mutations, several were found to exist in more than one of the strains, and one mutation existed in all four (Figure 2C). So what does this mean? The same mutations occurring in geographically separated cultures is indicative of parallel evolution. Interestingly, the authors expected the accumulation of mutations in HIV to decelerate towards the end of the experiment as HIV reaches an “adaptive peak.” By adaptive peak, they mean that the HIV strains have evolved to do really great in their new environments! So great, that there is almost no more mutations that could occur to make them better. 

I found this statement a bit strange considering that HIV can live for years within a host continuing to mutate and evolve. The expectation that mutations begin to cease after only 315 days (180 viral replications) seems to be arbitrary and assumes that the end of the experiment coincides with a natural evolutionary peak. There is nothing cited in this paper to support this assumption either. The HIV genome consist of approximately 9800 nucleotides. With 3 possible mutations at every base, there is a total of 29,400 mutations that are able to occur (German Advisory Committee Blood, 2016). A very similar long term evolution experiment conducted by Bons et al. (2020), found over the course of up to 600 generations only 3% of all possible mutations reach majority. Additionally, mutations are still continuing to accumulate towards the end of their experiment. Given this is over 3 times what Bertels et al. (2020) performed, we might say that 180 generations really isn’t long enough to see an adaptive peak!

To further investigate why the mutation accumulation rate did not decrease, they had a look at the fitness of each majority mutation (figure 2B). Fitness is how well a mutation survives in a particular environment and persists into the next generation. The more the mutation helps HIV – the better the fitness.  How big of an impact the mutation has, depends on whether it changes the amino acid sequence. Amino acids are VERY important molecules that are the building blocks of proteins. Each set of three nucleotides encodes for one amino acid. If there is a change in the nucleotide, this has the potential to change the amino acid and thus change the protein. Here, each mutation is plotted as Synonymous (there is no change in the amino acid, so considered neutral or silent), Non – synonymous (there is a change in the amino acid and the mutation should impact the function of the protein) and Untranslated (the mutation occurs in a region that does not code for a protein). 

The lines on the graph of figure 2B are linear regressions which demonstrate the relationship between the fitness of mutations across the time of the experiment. In short – the downwards sloping line tells us that as the experiment proceeds, the fitness gains of the mutations decrease.  This is expected to occur for populations that adapt to new environments. As mutations occur, those that help the virus in its environment are selected to stick around. As this continues, the virus becomes better suited to the environment and so the increase in new beneficial mutations starts to decline. 

So taking these two figures together – the mutations continue to accumulate towards the end of the experiment, but the fitness decreases. What does this tell us? The authors suggest that the continued increase in mutations is due to neutral mutations occurring as opposed to beneficial majority mutations – therefore, no fitness gains are expected. 

The – very pretty – Venn Diagram in figure 2C gives a great visual demonstration of the mutations. We can see here how several mutations arise in more than one cell line. This is parallel evolution occurring. There is more overlap in replicates of the same cell line i.e MT2-1 with MT2-2 and MT4-1 with MT4-2 which we would expect as mutations help HIV in one particular environment. The more similar the environment, the more likely similar mutations will arise and persist.

Image and caption from Bertels et al. (2020)

As previously mentioned, recombination is another one of HIVs special immune evading tactics. Bertels et al. (2020) concluded that there was a frequent occurrence of recombination throughout their experiment. While Figure 3 may look a little confusing, take notice of the yellow and black line in the first box for MT2-1. We can see that initially, their frequency increases at the same rate. However, around transfer 20, this decouples and they increase at different rates. The likely explanation is recombination. As RNA strands exchange genetic material, some combinations of mutations perform better than others. It’s like having a really great tennis partner that helps you win a lot of games, but at half time you get swapped out. Your new partner (or new set of mutations) isn’t as great and so the frequency at which you win games decreases. Therefore recombination can both improve and impede HIV’s evolution by creating combinations of alleles that aid or hinder HIV. 

So we want to know if parallel evolution influences evolutionary history. How do we figure this out? Bertels et al. (2020) used phylogenetic trees which are diagrams that depict the evolutionary relationship of one organism to another. 

They constructed three different trees to show how evolution can look different for the exact same organisms.  

Image and caption from Bertels et al. (2020)
  • Phylogeny A 

 This phylogenetic tree is constructed from the experimental sequence data. At every 10th transfer, Illumina sequencing is performed for each strain. A consensus sequence is determined which represents the most common sequence among all the individual HIV viruses for a particular T-cell line.  Using a computer program called PhyML, the phylogenetic tree is constructed based on maximum likelihood – a statistical analysis that ensures the outcome is the most probable.

  • Phylogeny B 

This phylogeny is considered to be the correct evolutionary history constructed from simulated sequence data.  This tree represents the true set up of the experiment. 

When comparing phylogenies A and B, we can see that there are some obvious differences between the inferred history (A) and the correct history (B). In A, the MT-4 line clusters together according to environment as opposed to the two distinct MT4-1 MT4-2 lines as in B. Why is this? Parallel evolution! The emergence of the same majority mutations in different cell lines creates sequences that appear more closely related than what they are in reality. This results in the inferred tree clustering the MT4 sequences together. On the other hand, the inferred tree looks similar to what we would expect if the MT4-1 and MT4-2 lines were not separate in the lab or had experienced some kind of cross contamination – oops!

  • Phylogeny C

Phylogeny C includes minority mutations. Minority mutations are still changes in nucleotides, however these changes don’t become more frequent than the original nucleotide. The method in which this tree was constructed is again entirely different to either of the other trees. 

Surprisingly, there is no commonly used software that can consider both majority and minority mutations. This meant that the authors had to come up with their own method of constructing a phylogeny to include these minority mutations. If you are anything like me, then understanding the intricacies of phylogenetic construction is not your thing! But bear with me as I try to explain this one. They started by calculating the difference in frequency between the original nucleotide and all three possible mutations (remember a nucleotide can be one of four things – A, U, C, G!). This gave them the genetic distance between each of the different cell lines at each transfer. From there they used “neighbor joining” which is an algorithm that constructs a tree based on these genetic differences. Confused? Me too!

The take home message from these three trees is much simpler to understand. The presence of parallel evolution and shared majority mutations causes the MT-4 lines to look more closely related than what they are (Tree A). Tree C shows than when we include minority mutations, we get a tree much more similar to the correct history of tree B.   This indicates that the cell lines diverge early at the level of minority mutations, not at the level of majority mutations. Tree C’s similarity to tree B helps to somewhat absolve our Tree A contamination theory – phew!

This brings us to a nice conclusion that parallel evolution does indeed influence evolutionary reconstruction. But if anything, the chopping and changing between reconstruction methods due to the unavailability of appropriate software for all situations, enhances the opinion that we should be careful in how much weight we place on a phylogenetic tree. Like most things in life, using a different method is likely to give you a slightly different result. 

Additionally, as discovered above, there is a certain amount of recombination occurring in this experiment which does influence phylogenetic reconstruction (Posada, 2000; Wang & Liu, 2016). While the authors discuss the occurrence of recombination in their evolutionary lines, they do not discuss how this may impact the phylogenetic inference.  

The comparison of the three trees teaches us a lesson about the reliability of phylogenetic trees. While all three are constructed from the exact same strains, they tell a different story. The presence of parallel evolution in these strains depicts the limitations of phylogenetic trees when using them to infer evolutionary history. This is critical to acknowledge when deciphering our clinical battle plan. When treatment of deadly viruses is reliant on the knowledge that we gain from phylogenies, it is important to remember that in each instance there is shrouded information. 

Image and caption from Bertels et al. (2020)

Bertels et al. (2020) didn’t stop investigating evolutionary predictability there! They suspected that looking at nucleotide diversity at different time points could forecast mutation rate. Nucleotide diversity is the difference in nucleotides at the same site across one or many sequences – in this case, the diversity that exists between different sequences of the same evolutionary T-cell line. They correlated the nucleotide diversity of all time points, with the number of majority mutations at transfer 90. They found a correlation between the two in that a higher nucleotide diversity, indicates a higher number of parallel majority mutations.  How great does this sound!? It means we have a bit of a leg up on being able to predict how HIV might mutate and therefore send our army in to stop it in its tracks! Right? Well maybe not so right…and here’s why. 

They found that the strongest correlation occurs at transfer 30 in the MT-2-1 line with a p value of 0.0003 and an R-squared of 0.79. While the stats on this particular location seem to be convincing, it is not a pattern followed by any other cell line. At most, there would be an expectation that the MT2-2 cell line would also have some similarity in correlation at this point. Interestingly, it is at transfer 30 that this line has the lowest correlation between nucleotide diversity and mutation. Additionally, all evolution lines have peaks and troughs in the R- squared value at different transfer points. So while the authors argue that earlier nucleotide diversity can be predictive of the number of majority mutations, we must ask – what classifies as an early predictive time point? If this changes from strain to strain, then it can hardly be a good predictor of evolution in the wider HIV population. How do we know when and where to look? Additionally, the length of the experiment is very short when compared to the typical occupancy of HIV within a host – which can last decades. Therefore, the ability to predict the accumulation of mutations at transfer 90 (180 generations) does not provide a necessarily clinically relevant prediction mechanism. The authors conclusion that a prediction can be performed based on nucleotide diversity doesn’t appear to be a widely applicable concept. 

While the paper is centered around HIV, it is only too easy to draw comparisons to the trendier RNA virus – SARS CoV-2. The COVID-19 Pandemic is fast approaching its second birthday, yet it’s origin and evolutionary history still remain to be fully elucidated. Sequencing of genomes has been essential in tracing the pathway of infection as well as participating in the design of prevention strategies.  However, this research by Bertels et al. (2020) demonstrates the inability of a phylogenetic tree to correctly identify the evolutionary path that an RNA virus has taken due to parallel evolution. Additionally, the genetics of an RNA virus are complex and act to interfere with our ability to form a battle plan. While the authors are confident in coming to conclusions surrounding the predictability of mutations, I would debate that this paper does more for the argument against prediction. If anything, it demonstrates the need to exercise caution when using an evolutionary history to predict the evolutionary future of a RNA virus.

So next time you are developing your viral pandemic expertise by listening to the 1pm news conference, stop and reflect on this. How might parallel evolution be influencing our own efforts in tracing and preventing COVID-19? 

This blog post and included figures were based on the research article by Bertels, F., Leemann, C., Metzner, K. J., & Regoes, R. R. (2019). Parallel Evolution of HIV-1 in a Long-Term Experiment. Molecular Biology and Evolution, 36(11), 2400-2414. doi:10.1093/molbev/msz155

Joint United Nations Project on HIV AIDS. (2018). Global report: UNAIDS report on the global AIDS epidemic 2018. Retrieved from

Andrews, S. M., & Rowland-Jones, S. (2017). Recent advances in understanding HIV evolution. F1000Research, 6, 597-597. doi:10.12688/f1000research.10876.1

Baum, D. (2008) Reading a Phylogenetic Tree: The Meaning of Monophyletic Groups. Nature Education 1(1):190

Bertels, F., Leemann, C., Metzner, K. J., & Regoes, R. R. (2019). Parallel Evolution of HIV-1 in a Long-Term Experiment. Molecular Biology and Evolution, 36(11), 2400-2414. doi:10.1093/molbev/msz155

Bons, E., Leemann, C., Metzner, K. J., & Regoes, R. R. (2021). Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history. PLOS Biology, 18(12), e3001010. doi:10.1371/journal.pbio.3001010

Britannica, T. Editors of Encyclopaedia (2020, January 31). T cell. Encyclopedia Britannica.

Britannica, T. Editors of Encyclopaedia (2019, March 1). Retrovirus. Encyclopedia Britannica.

German Advisory Committee Blood, S. A. o. P. T. b. B. (2016). Human Immunodeficiency Virus (HIV). Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie, 43(3), 203-222. doi:10.1159/000445852

Guindon S, Delsuc F, Dufayard JF, Gascuel O. Estimating maximum likelihood phylogenies with PhyML. Methods Mol Biol. 2009;537:113-37. doi: 10.1007/978-1-59745-251-9_6. PMID: 19378142.

Posada, D. (2000). How does recombination affect phylogeny estimation? Trends in Ecology & Evolution, 15(12), 489-490. doi:

Wang, Z., & Liu, K. J. (2016). A performance study of the impact of recombination on species tree analysis. BMC Genomics, 17(10), 785. doi:10.1186/s12864-016-3104-5

Westram, A. M., & Johannesson, K. (2016). Parallel Speciation. In R. M. Kliman (Ed.), Encyclopedia of Evolutionary Biology (pp. 212-219). Oxford: Academic Press.

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The war on superbugs

Imagine getting injured by an enemy bullet while battling it out for your country in the war zone of the Middle East. But it turns out that’s the least of your worries because there’s a new killer on the battle-torn streets, an invisible one that does not carry a gun. The culprit is one of the deadliest, most resistant bacteria, or better called “superbugs“, in the world that has found a home in your wound. Treating this invisible enemy is not exactly the easiest thing to do because it is resistant to basically all antibiotics. Now the chances of dying have risen even higher, having this on top of already being close to death from being shot. Doesn’t exactly seem like the nicest situation to be in, does it?

Well, this is the case for many soldiers in the Middle East injured on the battlefield. And it’s not just in the Middle East this is happening. All over the world, people in hospitals who are already critically ill are being thrown even closer to the edge of death by being infected with these invisible enemies. And the odds of this happening increases dramatically each decade. With the estimated number of deaths from superbug associated infections currently being at least 700,000 a year (O’Neill, 2016). However, scientists are predicting that by 2050 superbugs could kill 10 million people a year!! That’s approximately one person dying from these superbugs every three seconds, which is more than the number of people who die from cancer today (CRAZY, RIGHT?!). This is due to a phenomenon called antimicrobial resistance (AMR).

The consequence of superbugs and how they gain antimicrobial resistance

Essentially, antimicrobial resistance (AMR) is the main challenge facing modern medicine today. Antimicrobial resistance occurs when microbes, like bacteria, evolve mechanisms that protect them from the effects of antimicrobials, like antibiotics. Antimicrobial-resistant bacteria decrease the effectiveness of antibiotics so that they are no longer performing correctly, e.g., killing bacterial cells. AMR bacteria are commonly called “superbugs” due to their ability to defeat the drugs designed to kill them. Essential medical procedures such as joint replacements, caesarean sections, gut surgery, and treatments that weaken the immune system, such as chemotherapy for cancer, could become too dangerous to perform, as some opportunistic bacteria prey on a weakened immune system (O’Neill, 2016).

Cartoon representation of a superbug microorganism, being strong and tough because of AMR. Image credit: Finkelstein, J. A., 2017.

The leading cause of these superbugs creating drug-resistant infections is due to antibiotic overuse and misuse. These superbugs have also managed to gain their antibiotic resistance through a process called horizontal gene transfer (HGT). This process happens by two bacteria getting together for a little friendly conjugation (the microbial form of snuggling). They form a connection and “squirt” DNA into each other. However, some bacteria get a little too friendly, which could be making it so nasty, as they’re receiving antibiotic resistance genes from other bacteria. With the addition of new genes, these bacteria become more lethal and harder to defeat at the same time.

Schematic of how antibiotic resistance develops. Image credit: Theresa Tam, 2019

The superbug Acinetobacter baumannii

I recently read the article “Evolutionary pathways to antibiotic resistance are dependent upon environmental structure and bacterial lifestyle” by Santos-Lopez et al. The authors undertook an experimental evolution study on antimicrobial resistance in a particular bacterium, Acinetobacter baumannii. A. baumannii is commonly known as an opportunistic bacterium. It only infects hosts with compromised immune systems through infections in the skin, bloodstream, urinary tract, and other soft tissues (UC San Diego School of Medicine, 2021). Most of these infections occur in critically ill patients in the intensive care unit (ICU), accounting for 20% of infections in ICUs worldwide! (UC San Diego School of Medicine, 2021). It is also commonly referred to as “Iraqibacter” due to its prevalence in US military personnel returning from the Middle East (it’s also because Iraqibacter is easier to pronounce, which definitely helps!)

A. baumannii is a bacterium that, until the last couple of decades, was considered to be pretty wimpy. However, it has a clever ability to steal antibiotic resistance genes from other bacteria through the process of microbial “snuggling”— it’s like a bacterial kleptomaniac, and this is its superpower! Acinetobacter is one of the most successful pathogens responsible for hospital-acquired infections in the modern healthcare system. It hangs around in places like doorknobs and pillowcases etc., where it can survive on surfaces for weeks (scary, aye?!).

Acinetobacter baumannii. Image credit: Centers for Disease Control and Prevention (CDC)

It is the #1 priority pathogen in the World Health Organization’s (WHO) “Global Priority Pathogens List for Combatting Antibiotic Resistance” (UC San Diego School of Medicine, 2021). This is due to A. baumannii often being naturally resistant to antibiotics or has been reported to rapidly evolve resistance to them through its kleptomaniac like abilities. It is also naturally resistant to the antibiotics of last resort, like carbapenem (Yang et al., 2018). This poses a global threat to human health and a therapeutic challenge due to emerging and constantly increasing resistance.

Today with this current COVID-19 pandemic, there are cases in which COVID-19 patients in ICUs have been reported with having carbapenem-resistant A. baumannii (CRAb) infections that can increase the mortality rates of these patients (Shinohara et al., 2021).

So, when antibiotics fail to fight superbugs, finding a solution is a top priority in this day and age. One possible solution is through using personalized bacteriophage to fight infection, called phage therapy. It is an experimental approach, but bacteriophages have been shown to defeat bacteria when antibiotics fail. Phages are viruses that have naturally evolved to become predators of bacteria. It’s like nature’s own alternative to antibiotics. The phage latches on to and hijacks the bacterial cell, where it takes over the cell’s machinery and turns it into a phage manufacturing plant (Khan Academy, n.d.). The newly produced phages then burst out of the cell, killing it. For a short video on how phages infect bacteria, click here.

Basics of phage therapy. Image credit: Cleveland Clinic

An individual called Tom Patterson became severely sick after being infected with A. baumannii. Yet, he was cured through phage therapy after all other treatments, including antibiotics, did not work (TEDx Talks, 2017; Schooley et al., 2017). Tom was in a coma, and he started going into multi-system organ failure; his lungs, heart, and kidneys had begun to fail. He had been in hospital for 4 months when doctors began phage therapy. A few days later, he woke up. After 3 months, his own body cleared the infection, and he was discharged. If it wasn’t for phage therapy he likely would have died.

In this COVID-19 pandemic world, the word virus does not exactly bring good thoughts and feelings; instead, it’s like an enemy to society. So, it makes it quite hard to wrap your head around how viruses, in fact, can be good. In this instance of phage therapy, viruses are like our friend here instead of our enemy. As Steffanie Strathdee, a global health expert at the University of California, San Diego, and also Tom’s wife says, “you have a miniature Godzilla, the bacteria, and we’re sending in a miniature King Kong [phage] to attack it” (Pawlowski, 2019). So, they battle against each other, with the phage eventually winning by attacking and killing the bacteria. It not only worked in this case study against A. baumannii but has also been helping people who have cystic fibrosis.

So how do bacteria live?

In nature, bacteria have two different lifestyles: (1) Planktonic cells, which are well-mixed populations described as free-swimming, so you could imagine them like little fish swimming around in the ocean. These are commonly the cells used in test tube cultures in the laboratory. These cells can form biofilms, which is the next lifestyle. (2) Biofilms are planktonic cells that are non-moving as they typically adhere to a surface or tissue in communities where there’s plenty of them all congregating. They are protected from external stresses, like antibiotics, by a matrix layer that can be considered as a slimy layer covering the community. The cells inside these biofilms are then able to disperse out of it to reverse back into a planktonic lifestyle. For a closer look into how biofilms are formed, click here.

Schematic representation of a biofilm formation. Image credit: British Society for Immunology

The way A. baumannii can survive on surfaces or tissues for weeks is by building these biofilms, which is the dominant mode of growth for most microbes. Therefore, a biofilm lifestyle is essential to antimicrobial resistance. This emergence of AMR in biofilms is important because:

  1. The environmental structure of biofilms can enhance genetic diversity.
  2. Ecological conditions within the biofilm can favour adaptations to different places.
  3. The biofilm itself can protect its residents from exposure to external stresses like antibiotics through the matrix produced.
  4. Biofilms allow bacteria to communicate with each other and exchange genetic matter that helps it develop resistance to a wide range of antibiotics. 

The Experiment

Santos-Lopez et al. underwent an experimental evolution approach to examine how the lifestyle of bacteria, specifically A. baumannii, influences the dynamics, diversity, identity of genetic mechanisms and the effects of resistance to a common antibiotic. This experiment’s drive is that when undergoing laboratory tests on the evolution of AMR in different bacteria, planktonic conditions are used. However, in nature and clinical settings, biofilms are the most common mode of growth. Therefore, the authors wanted to see how these two bacterial lifestyles differ by using an experimental evolution approach that stimulates the effects of both biofilm and planktonic conditions on the evolution of AMR. Experimental evolution allows scientists to explore the dynamics of evolution through conducting experiments in the laboratory or in nature. Santos-Lopez et al. predicted that evolution in biofilms would lead to different pathways of antibiotic resistance compared to planktonic cells.

To determine if their prediction was correct, the authors took a single colony of A. baumannii (which is a small group of bacteria grown on a petri dish, shown here) and let it grow overnight in a liquid medium specifically for microbial growth, to make lots of copies of bacteria (Figure 1). They then took this culture containing lots of bacteria and divided it into 20 replicate populations. 10 of these populations of bacteria underwent reproduction every 24h in fresh media, containing subinhibitory concentrations of an antibiotic called ciprofloxacin (CIP) (basically means the antibiotic does not completely inhibit bacterial growth). The subinhibitory concentration of CIP is 0.5x the minimum inhibitory concentration (MIC) of CIP – with MIC being the lowest concentration of an antibiotic that will stop the growth of a microorganism.

Figure 1. Experimental design. Image credit: Adapted from Santos-Lopez et al., 2019.

Of these 10 populations, 5 had a planktonic lifestyle, and the remaining 5 had a biofilm lifestyle. 10 other populations (5 planktonic and 5 biofilm) were grown without antibiotics to use as a comparison (top of Figure 1). The planktonic lifestyle consists of daily 1:100 dilutions, which takes a small volume of the 24h growth media and adds it to a larger volume of fresh media. This keeps populations constantly growing over the experiment.

Whereas to stimulate the biofilm life cycle, a bead biofilm model is used. The bead biofilm model works by having a polystyrene bead in a test tube with fresh media containing A. baumannii. Cells of A. baumannii are meant to attach to this bead and grow for a 24h period. The bead is then transferred to a new tube containing fresh media and new beads to establish itself onto, creating a new bacterial population. Click here to see a fun video explaining how the bead biofilm model works!

Bead biofilm model as proposed by Vaugh Cooper. Image credit: Adapted from Turner, C. B., Marshall, C. W., & Cooper, V. S., 2018.

However, I couldn’t get my head around trusting this model as an example to replicate biofilm formation in the laboratory. This is because it only gives bacteria 24h to attach and assemble a biofilm on a polystyrene bead and then disperse from it to reattach onto a different bead when placed in a new tube. This is a very short amount of time to get bacteria to complete these steps. So, for me, it does not seem clinically relevant of how bacteria make biofilms in nature, as they likely wouldn’t have this short time frame to create a biofilm then disperse to form another. Therefore, it makes me wonder if the results gathered from the biofilm populations may not be representative of what happens in nature and clinical settings. For better results, it might help giving bacteria more than 24h to undergo the biofilm life cycle.

Anyway, after 3 days of bacterial reproduction in this subinhibitory concentration, the differing bacterial lifestyle populations were exposed to two different antibiotic regimes for 9 more days:

  1. Constant subinhibitory concentrations of ciprofloxacin (middle of Figure 1)
  2. Increasing concentrations of ciprofloxacin (bottom of Figure 1)

Santos-Lopez et al. first tested evolutionary consequences associated with A. baumannii gaining resistance to the antibiotic ciprofloxacin. To do this, they took 5 clones (a population of identical cells) from each bacterial lifestyle at the end of the evolution experiment and measured their resistance and fitness. This term ‘fitness’ is the capability of an organism to reproduce and contribute its genetics to a population. At the same time, ‘resistance’ in this case is the ability of an organism to withstand the effects of antibiotics. Resistance and fitness were measured by mixing the original starting strain of A. baumannii with the evolved clones both in planktonic and biofilm conditions without antibiotics. Susceptible populations were calculated as the difference between the total population (number of colonies/mL growing on the nonselective plates) and the resistant population (number of colonies/mL growing on the plates containing CIP). Figure 2 shows that:

Figure 2. Evolved trade-off between resistance level and fitness. Image credit: Santos-Lopez et al., 2019.
  1. Biofilm clones were much less resistant than planktonic clones. This is shown by the biofilm populations (blue) having low MIC (resistance) values on the x-axis. However, they were fitter relative to the ancestral strain (black dot) than the planktonic-evolved clones (red), demonstrated by higher values of the selection rate (fitness) on the y-axis.
  2. Planktonic clones were less fit because of fitness trade-offs of antibiotic resistance. Sounds complicated, right? Well, a trade-off, in this case, means that you can either have high fitness OR high resistance and tend to not have both as one compromises the other. So, in this case, high resistance is causing lower fitness.

The authors also wanted to see if lifestyle determines the selected mechanisms of resistance. In clinical settings, A. baumannii samples acquire resistance to ciprofloxacin by two principal mechanisms: (1) modification of the direct antibiotic targets — gyrase A or B and topoisomerase IV — or (2) by the overexpression of efflux pumps that reduce the concentrations of the antibiotic inside the cell. To directly associate the genetic makeup of an organism with its physical resistance properties, Santos-Lopez et al. sequenced 49 clones isolated at the end of the 12-day experiment. Figure 3 shows the highest frequency of mutations observed in the biofilm populations being adeL and adeS, with adeL being the most common (blue). These are two mutations involved with the overexpression of efflux pumps. The implications of adeL mutation are predicted to be an increase in both biofilm and antibiotic resistance.

While in planktonic populations, the highest frequency mutations observed were adeN and gyrA, with adeN being the most common (red). adeN is a mutation involved with the overexpression of efflux pumps, while gyrA is a ciprofloxacin-resistant mutation in DNA gyrase. Implications of the adeN mutation are that it can contribute resistance to biocides, hospital disinfectants, and to both natural and acquired antibiotic resistance in A. baumannii. It may also decrease biofilm formation, which could explain its prevalence in planktonic populations here.

These results show that bacterial lifestyle influences evolutionary dynamics and targets of selection of AMR.

Figure 3. Lifestyle-dependent mutations under CIP selection. Image credit: Adapted from Santos-Lopez et al., 2019.

Lastly, the authors wanted to determine if there were evolutionary interactions with other antibiotics. When a bacterium acquires resistance to one antibiotic, the mechanism of resistance can also give resistance to other antibiotics (cross-resistance) or increase the susceptibility to other antibiotics (collateral sensitivity). They tested the resistance of the evolved populations to different antibiotics in planktonic conditions and reported changes in susceptibility. Changes in susceptibilities are shown by this heatmap in Figure 4. This figure may look a little confusing, but essentially it shows anything that is pink and red having resistance to an antibiotic. In contrast, anything blue has a sensitivity to a antibiotic. Figure 4 shows that cross-resistance is exhibited in planktonic-evolved populations to antibiotics POD (cefpodoxime) and CAZ (ceftazidime) by the pink coloured boxes. Both of these antibiotics are in the cephalosporin class of antibiotics. Whereas in biofilm-evolved populations the opposite is shown, with there being collateral sensitivity to POD and CAZ, visible by the blue-coloured boxes.

The results show that biofilm growth, commonly thought to broaden resistance, may generate collateral sensitivity during treatment with ciprofloxacin and potentially other antibiotics. This gives possibilities for developing new treatments against AMR.

Figure 4. Collateral sensitivities and cross resistances to various antibiotics. Image credit: Santos-Lopez et al., 2019.

So now we know that:

  1. Antibiotic resistance is one of the main problems facing modern medicine today.
  2. Most infections are caused by biofilms on surfaces or tissues.
  3. Studying bacteria in their natural biofilm lifestyle may help researchers develop new approaches that limit the spread of antibiotic resistance and improve treatment.
  4. The key mutations selected here may indicate they are the fittest mutations in A. baumannii.
  5. The more diverse biofilm-evolved populations revealed collateral sensitivity to the cephalosporin class of antibiotics, providing a new strategy for treatment.
  6. The relationship between fitness and resistance can be altered by mode of growth, where biofilms can align resistance and fitness traits.

We have a growing crisis of superbugs coming — antibiotic resistance is becoming a bigger and bigger problem. Therefore, finding ways to treat these superbugs is of great importance to the health of humanity. Whether it’s through a collateral sensitivity approach to help achieve this, or through unconventional cures that could become the new best thing, like more use of phage therapy. It doesn’t matter how, just so long as an approach becomes available, so that we don’t have the next epidemic being superbugs against humankind. I don’t know about you, but I’m not wanting to go through another global health crisis anytime soon.

Are you?


Khan Academy. (n.d.). Bacteriophages.

O’Neill, J. (2016). Tackling drug-resistant infections globally: Final report and recommendations. The Review on Antimicrobial Resistance.

Pawlowski, A. (2019). How to treat antibiotic-resistant superbugs: Woman saves husband with phage therapy. TODAY.

Santos-Lopez, A., Marshall, C. W., Scribner, M. R., Snyder, D. J., & Cooper, V. S. (2019). Evolutionary pathways to antibiotic resistance are dependent upon environmental structure and bacterial lifestyle. ELife, 8.

Schooley, R. T., Biswas, B., Gill, J. J., Hernandez-Morales, A., Lancaster, J., Lessor, L., Barr, J. J., Reed, S. L., Rohwer, F., Benler, S., Segall, A. M., Taplitz, R., Smith, D. M., Kerr, K., Kumaraswamy, M., Nizet, V., Lin, L., McCauley, M. D., Strathdee, S. A., Benson, C. A., Pope, R. K., Leroux, B. M., Picel, A. C., Mateczun, A. J., Cilwa, K. E., Regeimbal, J. M., Estrella, L. A., Wolfe, D. M., Henry, M. S., Quinones, J., Salka, S., Bishop-Lilly, K. A., Young, R., & Hamilton, T. (2017). Development and Use of Personalized Bacteriophage-Based Therapeutic Cocktails To Treat a Patient with a Disseminated Resistant Acinetobacter baumannii Infection. Antimicrobial Agents and Chemotherapy, 61(10), e00954-00917.

Shinohara, D. R., Dos Santos Saalfeld, S. M., Martinez, H. V., Altafini, D. D., Costa, B. B., Fedrigo, N. H., & Tognim, M. C. B. (2021). Outbreak of endemic carbapenem-resistant Acinetobacter baumannii in a coronavirus disease 2019 (COVID-19)-specific intensive care unit. Infection control and hospital epidemiology, 1-3.

TEDx Talks. (2017, October 18). How Sewage Saved My Husband’s Life from a Superbug | Steffanie Strathdee | TEDxNashville [Video]. YouTube.

UC San Diego School of Medicine. (2021). Collaborative to Halt Antibiotic-Resistant Microbes (CHARM).

Yang, P., Chen, Y., Jiang, S., Shen, P., Lu, X., & Xiao, Y. (2018, 2018/11/19). Association between antibiotic consumption and the rate of carbapenem-resistant Gram-negative bacteria from China based on 153 tertiary hospitals data in 2014. Antimicrobial Resistance & Infection Control, 7(1), 137.

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Evolutionary Trade Off: A solution to anti-biotic resistance?

We all have heard of a saying, “That which doesn’t kill us makes us stronger” by Friedrich Nietzsche. This is especially true in case of bacteria, organisms that are found everywhere ranging from “on us” to “inside us”. these microscopic organisms are responsible for many of the most deadliest diseases to humankind. Advent of antibiotics provided relief as they were considered to be the best solution to the diseases caused by bacteria. however, on a longer run it was established that these organisms indeed develop resistance to the antibiotics making them invulnerable to the effects of antibiotics and making them more stronger in the process.

Antibiotic Resistance. Image Source: PHARMAC, NZ

This antibiotic resistance is the result of mutations that takes place in a bacterial population exposed to a specific drug leading to a evolved population which becomes impenetrable by the drug. The infections that are caused by these newly evolved strains of bacteria become very challenging to cure emanating longer stays in hospitals. This very evolution however, has opened channels to explore for solutions to the issue of antibiotic resistance as the phenomena of evolution which causes to build resistance against one type of drug might also result in developing hypersensitivity to others, thereby preventing multi-drug resistance. This strategy is termed as collateral sensitivity, which is a two way trade-off amounting to increased resistance to one drug and causing increased sensitivity to the other drug (Szybalski & Bryson, 1952).

Barbosa, Roemhild et al. attempted to exploit this aspect to decipher that evolving collateral sensitivity will ultimately slowdown the evolution of resistance by combination and therapies that are sequenced (Rodriguez De Evgrafov et al., 2015). Their biggest challenge was to determine the stability of their system such that at one point the bacterial population goes extinct or at least render them invulnerable to develop multi-drug resistance. In order to test their system they incorporated the bacterium Pseudomonas aeruginosa as it is believed to develop collateral sensitivity to to different drug treatments

The Experiment:

They subjected Pseudomonas aeruginosa to a two-step evolution, they utilized already evolved extremely resistant populations of P. aeruginosa, procured by serial passage experiments having increased concentrations of bactericidal antibiotics which were clinically relevant. They tested collateral sensitivity which would reciprocate i.e. the first target drug would create resistance while the second drug would show impend hypersensitivity on in first set-up and vice-versa. They also treated the bacteria by switching the antibiotics to collateral sensitivity but the administration of first drug remained continued along with the administration of the second drug hence they confirmed a constraint environment. Altogether, they laid down a total of four conditions which were running parallelly i.e. minimal or maximal increase of second drug with and without the presence of first drug. Simultaneously run control experiments i.e. without any antibiotic ensured treatment success. incorporation of quantification of extinct population frequency, absorbance measurements and characterization of changes amounting to antibiotic resistance of the evolving bacteria in comparison to what was previously observed in Pseudomonas aeruginosa further ensured the proofing of the experiment.

Authors conducted experiment to test stability in the evolution of reciprocal/reverse collateral sensitivity by exposing the clones from previous resistant populations with new set of antibiotics at high concentrations which resulted hypersensitivity in resistant populations. they performed a series of evolution experiments for 12 days following serial transfer protocol with beginning population size of approximately 106 CFU/ml. They evaluated each population in eight replicates and 5 treatment groups i.e.:

  1. Controls (without anti-biotic)
  2. Low level of increasing concentration of antibiotic (Unconstrained Evolution)
  3. High level of increasing concentration of antibiotic (Unconstrained Evolution)
  4. Low level of increasing concentration of antibiotic (Constrained Evolution)
  5. High level of increasing concentration of antibiotic (Constrained Evolution)

The authors also validated their findings by conducting repetitive evolution experiments by incorporating resistant population as the initial material. they used approximately 107 cells in contrast to a single clone, but reduced the treatments. In total they utilised 38 resistant populations.

Experimental design for testing collateral sensitivity
Image Source: (Barbosa et al., 2019)

Drug Combination:

  1. (PIT) Piperacillin/tazobactam and (STR) streptomycin
  2. (CAR) Carbenicillin and (GEN) Gentamicin

The authors in their experiment found that it is highly circumstantial that collateral sensitivity could be exploited for sequence based treatments as it validity is dependent on the combination of drugs used and also the order in which they are used, they also found that epistatic genetic interactions also play a role in their selection. The increased extinction rates determines that bacterial adaptation was constrained in treatments when a switch to ß-lactam was made. This effect was maximised when the second drug was administered in constraint environment. Their findings opened doors to the possibilities of further dwelling into deeper research of an unexplored strategy of treatment i.e. using single drug therapy after being treated with combination treatment. in this manner, the evolutionary trade-off of drug sensitivity could be maximised.

To be specific, the drug pair of CAR/GEN after deep analysis showed high extinction in comparison to growth improvements when given strong dose administration over mild dose. It is interesting to see how rate of extinction are often ignored and left unreported as a part of evolutionary outcome in corresponding studies (Yen & Papin, 2017). The authors are convinced upon the fact that, since antibiotic therapy is aimed towards attempt to eliminate bacterial pathogens, the extinction frequencies from dated experiments on evolution have shown variance depending upon different types of treatment and hence these deliberations could serve in order to refine the understanding of efficacy of the treatment.

In their experiment on CAR/GEN pair at clonal level demonstrated stability in collateral sensitivity as a result of slow adaptation and efficiency of re-sensitization. however, it was the negative epistasis of drug-specificity that re-sensitization was seen during switch from aminoglycoside to a ß-lactam. On the contrary the reciprocal collateral sensitivity test showed less stability as evident from low levels of extinction and absence of re-sensitisation. Few outcomes of their findings were unexplained with their current data set and the high instability could not be interpreted. Their work has drawn certainly drawn attention towards the need of careful evaluation of novel options of treatment.

Evolution is unique and inevitable, and so is the development of new methodologies to tackle the issue of drug-resistance. With many factors to play a role in achieving success to slow down the rate of drug-resistance or to completely eliminate the pathogenic bacteria, evolution amounting to collateral sensitivity has drawn attention towards designing a sustainable and stable approach of drug therapy to control infection in coalescence with traditional and other newly developed techniques.

Barbosa, C., Romhild, R., Rosenstiel, P., & Schulenburg, H. (2019, Oct 29). Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa. Elife, 8.

Rodriguez De Evgrafov, M., Gumpert, H., Munck, C., Thomsen, T. T., & Sommer, M. O. A. (2015). Collateral Resistance and Sensitivity Modulate Evolution of High-Level Resistance to Drug Combination Treatment in Staphylococcus aureus. Molecular Biology and Evolution, 32(5), 1175-1185.

Szybalski, W., & Bryson, V. (1952). Genetic studies on microbial cross resistance to toxic agents. I. Cross resistance of Escherichia coli to fifteen antibiotics. Journal of bacteriology, 64(4), 489-499.

Yen, P., & Papin, J. A. (2017). History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment. PLOS Biology, 15(8), e2001586.

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Why make it when you can take it?

I don’t know about you, but when I think of evolution I think of gaining the ability to do something. Just look at humans who live at high altitudes; they make a massive amount of red blood cells (among other things) so that they can breathe when the air is thinner. Sure the ability to breathe may not necessarily seem like a whole lot, we can all do it after all, but put a person who has adapted this ability at an altitude where the air is “normal” and step back and watch the magic. They don’t fatigue like the rest of us. Any aerobic athlete would KILL for this ability – look at Lance Armstrong, he ruined his reputation and cost himself 10 million dollars for the chance to exchange oxygen as efficiently as a Tibetan native. But what if adapting to a certain environment involves the loss of an ability? Not necessarily because the adaption is harmful, but because it is simply unnecessary. Athletes who train at high altitudes for just two weeks begin to adapt to life at low oxygen levels very quickly. Among these adaptions, the elusive increase in red blood cell count. Take these freshly adapted humans, put them back in life at sea level and there you have it, a super human ability for oxygen transport. But there’s a catch: these changes aren’t permanent. Want to maintain this wonderful ability to breathe super well? Back up the mountain you go! 

If you’re anything like me, you’re probably wondering why this happens. It seems to me that this specific adaption is really, really helpful. So why would we lose the ability to keep an abnormally high red blood cell count when it so clearly increases our fitness (both literally and evolutionarily). For this I have one simple answer: it’s just not an ability we need to have. The average human doesn’t have an evolutionary need to run a fast 10K, we just need to be able to be fit to survive long enough to reproduce. And the effort it takes our body’s to make these extra cells isn’t worth the benefit that we get from it. Evolution isn’t about being exceptionally good at living – it’s just about being able to survive. 

Personally, this came as a bit of a surprise to me. We are so used to associating evolution with natural selection and then in turn associating natural selection with the idea of the “survival of the fittest”. So what exactly is natural selection? How does it work and why have we not ended up with a super human population? 

Essentially the theory of natural selection is as follows:

  1. Traits, or characteristics are heritable. This means we get things from our parents. Eye colour, nose size, beak shape, short temper – all thanks to mum and dad.
  2. In any population (human, elephant, bacterial) certain individuals will inherit traits that make them better at reproducing and surviving than the rest of the population. These guys go on to have the most offspring.
  3. Because these good traits are heritable, they will pass them onto their children and because these traits allow them to have the most children, eventually these traits will become more common.
  4. Over generations, the original population will become adapted to its environment.

So basically natural selection doesn’t give us the fittest possible population available, it gives us the fittest population FOR THAT ENVIRONMENT. Rather than survival of the fittest we get survival of the least likely to die.

Let’s look at an example:

So if natural selection works to make sure that beneficial traits are passed on, we can assume that every trait we inherit from our parents is beneficial, right? Not quite. Natural selection isn’t the only evolutionary force in action, in fact some evolution happens purely by random chance. Now this may be a bit confusing seeing as I’ve just been going on about evolution ensuring that good traits get passed on and bad ones don’t. But there’s one little thing I didn’t mention about natural selection – it’s only really effective when we’re talking about large populations. Occasionally, a population can suddenly decrease in size. This can happen for a number of reasons: maybe a handful of individuals decide to move away from the rest to make their own new population, a predator might come in and eat all but a few or perhaps a natural disaster strikes. Either way, populations can suddenly go from huge to small, and when this sudden size decrease happens the individuals that remain are there purely by chance. This chance survival can mean that we end with a lot of individuals within this left over population that have traits we don’t necessarily want. And this random change in the prevalence of a certain trait in the population, due to “random population sampling” (i.e. taking only a small portion of the population) is called genetic drift.

If we refer back to the beetle colour example, natural selection would ensure that the population ends up being mostly green because the green beetles are the ones who are most likely to survive. But suppose one day massive rain fall caused a flood that killed 75% of the beetle population, with the remaining 25% surviving by chance and chance alone. The random survival of these individuals could mean that we end up with most of these surviving individuals being black rather than green, even though green is more advantageous. Then when these individuals reproduce, they will pass their colour trait down to their offspring and the population will remain mostly black in colour. So here we’ve ended up with a population that is mostly black, despite the fact that being black is actually a disadvantage. 

So how can we tell if a trait is present because it’s beneficial or because of random chance? This is where a little something called experimental evolution comes in. Experimental evolution allows scientist to use lab experiments or controlled field environments to explore the dynamics of evolution. This particular question was asked by a couple of scientists ( Glen D’Souza and Christian Kost) who noticed that bacteria sometimes lose the ability to make some nutrients and/or molecules that they need to survive. Bacteria are known for their ever-changing genome (think of a genome as a genetic blue print), so the loss and gain of genetic information is not a new thing for bacteria. But it does seem odd that they would lose the ability to carry out a task as important as making a molecule essential for their survival. So the question was asked: is the loss of this ability favoured by natural selection or does it occur as a result of random genetic drift?

D’Souza and Kost set out to answer this question by performing experimental evolution on a type of bacteria calledE.coli. Personally I think E.coli are underappreciate and misunderstood. Yea sure if the wrong strain populates your insides in the wrong place then they could kill you. But they’re not all out to get you, in fact we all contain a population ofE. coli in our gut that helps us break down and digest the food we eat. If that isn’t enough to change your mind then go and look at a video of them growing under a microscope – adorable. Plus, on top of all of that, their tiny genomes, super-fast reproductive rate and the fact that they’re super easy to manipulate make them the perfect model organism to perform evolutionary experiments on. 

D’Souza and Kost’s particular question of interest was to see whether or not E. coli would evolve to stop making amino acids (think of amino acids as the building blocks used to make proteins, and thus VERY essential) when they were provided by the environment. Firstly they looked at whether or not bacteria grow faster in an environment that lacks amino acids, compared to an environment that contains amino acids. They found that E.coli grown in the presence of amino acids reached a much greater cell density, or population size, than those that weren’t (figure 1A.). Remember how natural selection needs big populations to work? A larger population means it’s more likely that natural selection is actually happening and that any adaptions and mutations that happen are likely to be beneficial. 

So then the next question was – do these populations grow larger because of the increase in available nutrients? To determine this they subtracted the growth rate of populations in the presence of environmental amino acids from the growth rate of populations not in the presence of environmental amino acids and calculate the difference. If this number was a negative value it showed better growth in the presence of amino acids but if this number was positive it showed better growth in the absence of amino acids. If you look at the figure 1B you can see how these values over time. Initially the presence of amino acids was not the cause of increased growth rate, but over time that reversed. So by the time 2000 generations had passed, the E. coli grew far better when amino acids were available to them from the environment. So what does that mean? It means that over time the bacteria in this population have become dependent on the environmental amino acids for growth. 

A possible answer for this dependence is that the bacteria evolved amino acid auxotrophies. This means that they lost the ability to make these amino acids themselves and now rely on the environment to provide them. To confirm this, the genomes of 1000 bacterial colonies were analysed. The results were converted into a heat map (figure 2.) which shows the percentage of the population that had developed an amino acid auxotrophy along with how many generations it took them to obtain that auxotrophic mutation. Any square that is black shows no auxotrophies, any square that is pink or darker indicates auxotrophies. If you look at figure 2, you can see A and B – A shows populations grown in the presence of amino acids, B shows populations grown in the absence of amino acids. Both of these heat maps show pink squares – indicating auxotrophies evolve when grown in either environment (although more when amino acids were provided). Coupled with the large population sizes, these results suggest that the development of auxotrophs are in fact evolutionarily advantageous. 

So how can we confirm that having these auxotrophic mutations are advantageous? Well we can compare the fitness of the cells that don’t have them with the cells that do have them, which is exactly what D’Souza and Kost did. This figure below may look a little bit confusing, but essentially it shows the fitness of one strain compared to another in either of the two possible environments. The red boxes are auxotrophs, the bacteria that can no longer make certain amino acids, and the green boxes are bacteria that can make all of the amino acids themselves (these guys are called prototrophs). If the box sits higher up on the graph it has a greater fitness. So now looking at this figure we can see that when grown in the presence of amino acids, the auxotrophs have a greater fitness than the prototrophs, but when grown in the absence of amino acids this is reversed. The fitness of the phototrophs doesn’t really change at all. And this pattern was seen regardless of whether the strains evolved in the presence (A) or absence (B) of amino acids. 

It seems odd that these bacteria would evolve to lose the ability to make amino acids, even when there are no amino acids available from the environment right? Absolutely! But the thing is, the environment doesn’t just mean the physical place in which the bacteria grow. The fact that there was the development of auxotrophs when amino acids weren’t provided in the growth liquid shows that these bacteria were able to get the amino acids they needed from somewhere else. But where? Well not all the bacteria in these populations evolved into auxotrophs, some still retained the ability to make all the amino acids they needed on their own (prototrophs). So even when there are no amino acids obviously available in the growth liquid, the prototrophic cells that evolved with them provide a source of amino acids to the cells that need them. This is evident when looking at the how the fitness of the auxotrophs changes when they are assessed with the prototrophs.

So now we know that:

  1. Bacterial populations grow faster and when grown in a liquid media that contains extra nutrients (like amino acids)
  2. Over time bacteria evolve to become reliant on the nutrients provided by the environment
  3. This reliance is due to a loss in the ability to make the nutrients on their own (auxotrophs).
  4. When auxotrophs are grown in an environment that provides the nutrients they have a higher fitness levels than the cells that make all of these themselves.
  5. Auxotrophs can get the nutrients they need not only from the liquid their grown in but from the cells that can still make all the nutrients required.

There is one more important take away from this paper, and it involves something called negative frequency dependent selection. Sounds fancy and complicated right? It’s actually pretty simple. Basically it means that a certain trait is maintained at a low frequency because the more common it becomes in a population, the more its fitness decreases. So basically the trait is only beneficial when it’s not very common. It was determined that this is the case with these auxotrophic bacteria found in the population – they only had an increased fitness when they were present in low amounts. Why? Because the more auxotrophic bacteria there are in the population, the more the whole population relies on the environment to provide what they need. Think of the environment like a grocery store – the more people that need to buy groceries, the less there are available for everyone.  But if 75% of the population can grow their own vegetables at home then only 25% of the population needs to buy them. The less people that buy, the longer the stock lasts.

So there you have it, sometimes losing the ability to do something can actually increase the fitness of an individual. Yea sure making an essential nutrient is a good thing, but why make it when you can just take it?  

This blog was based on the following paper:

D’Souza, G., & Kost, C. (2016). Experimental evolution of metabolic dependency in bacteria. PLoS genetics12(11), e1006364.

Find it here:

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An experimental evolution-inspired step towards saving our planet

Do you every look up into the night sky of a big city in the hope to see stars, only to be disappointed by bleak smoggy darkness? Fossil fuel emissions are not pretty, and they certainly are not good for our environment. Annual Carbon Dioxide (CO2) emissions from fossil fuels have increased dramatically in the past decade and continue to rise rapidly. This has obvious negative health and environmental impacts such as accelerating global warming and deforestation.

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Therefore, we need to find alternative, more sustainable sources of energy so that we can reduce our carbon footprint for future generations. Of such, bioethanol is a renewable biofuel energy source that could meet our requirements as well as being environmentally sustainable [4]. Bioethanol is currently the most highly produced biofuel, meaning demand is high [4]. The process of bioethanol generation, called second generation bioethanol production, involves utilising cellulose and hemicellulose (plant biomass); the structural components of plants, into a renewable energy source called bioethanol [1].

So, you may be wondering why bioethanol, derived from plants, is so much better than fossil fuels? Well, in addition to being renewable and highly abundant, bioethanol has much lower CO2 emissions in comparison to fossil fuels. Also, the little amount of carbon dioxide that is released into the air by bioethanol use can be absorbed and utilised by the plants themselves, so that we can grow more plants and generate more biofuel. Hence the word “renewable”, It is an endless cycle of give and take, which is better for both us, our planet, and our environment.

The general process of second-generation bioethanol production involves pre-treating the plant biomass to weaken their strong structure provided by cellulose. This is followed by hydrolysis (breaking apart) of these cellulose structures into individual sugars [2]. Next, these sugars are fermented into bioethanol, which is assisted by a fermentable agent or microbe such as the commonly used yeast (S. cerevisiae) [2]. However, a problem arises when the hydrolysis step exceedingly releases acetic acid as a by-product. Yeast do not like acetic acid, it stops them from growing and being able to ferment ☹ [1]. In the big picture, this is bad because it means that bioethanol production yield will be reduced!

Expanding on this problem, experimental evolution has been an attractive approach for researchers to improve the efficacy of second-generation bioethanol production. Experimental evolution means that scientists can manipulate an environment experimentally and explore how this affects the evolution of a certain species [3]. In saying this, what if we could utilise experimental evolution to improve the efficacy of second-generation bioethanol production? A study that I will discuss in this blog post did just this, and the results were impressive!

González‑Ramos et al. (2016) used an experimental evolution approach to modify yeast to evolve a lasting tolerance to acetic acid, meaning these yeasts could thrive and survive in higher concentrations of acetic acid. This would allow the fermentation step of bioethanol production to be much more efficient and also improve the outcome of bioethanol production.

As a starting point, the researchers first tested whether prior exposure to acetic acid helps yeast to acquire tolerance. They did this by growing yeast either in the presence or absence of acetic acid (of a tolerable concentration), and then testing how the pre-adapted yeast cells grow, in comparison to non-adapted yeast cells, in acetic acid. To quantify these results, the researchers measured Final OD600 as an indicator of yeast biomass yield and were also able to determine specific growth rates (see Figure 1). So, as expected, both pre-adapted and non-adapted yeast cells were not very tolerant at all, as shown in Figure 1. There was a significant negative correlation between acetic acid concentration and growth of both pre-adapted and non-adapted yeasts. In saying this, the “pre-adapted” yeasts were slightly more resistant in concentrations greater than 10g/L, where they sustained slightly more survival (see Figure 1). Also, this tolerance of pre-adapted yeasts was lost after one exposure to non-stressed (low acetic acid concentration) conditions. Considering that the concentration of acetic acid can be more than 10g/L during bioethanol production, we want them to thrive in higher concentrations than this, and in a lasting manner, so this yeast has a fair way to go yet!

Figure 1: Growth and survival of either pre-adapted (grey lines) or non-adapted (black line) yeasts in gradually increasing acetic acid concentrations.

So here is where experimental evolution comes in…the researchers wanted to find a way to make these yeast able to thrive in and tolerate higher concentrations of acetic acid. To combat this, yeast cells were UV mutagenized (irradiating the cells with UV light to introduce mutations) and grown being transferred between conditions alternating from the absence and presence of acetic acid (of gradually increasing concentration); a technique called serial microaerobic transfer. Just like we train at the gym to build fitness, the yeast cells were training in the lab to build acetic acid tolerance. Over time, and approximately 50 transfers back and forth, these yeast cells were finally able to thrive in the higher acetic acid concentrations! They could even survive in 18g/L acetic acid, nearly twice the initial concentration that most yeasts could not survive in (Figure 2). Check out Figure 2 to see an example of the progress of one of these experimentally evolved yeasts with improved tolerance to acetic acid.

Figure 2: Acquired acetic acid tolerance of experimentally evolved yeast over serial microaerobic batch transfer. Acetic acid concentration is indicated by the grey line. Black dots are final OD600 measurements in the absence of acetic acid, while grey squares are final OD600 measurements in the presence of acetic acid.

 So, they experimentally evolved this yeast to be able to survive and grow in higher acetic acid concentrations, which is pretty cool (for a science nerd like me, anyways…), but the questions remain; what is going on? How is this happening? To approach this, researchers turned to genetics in the hopes to answer their questions. The research group used fancy scientific techniques allowing them to get a big archive of all the genes in these tolerant yeasts and compared these to the original non-tolerant yeast. This helped them to pinpoint a few culprits, mutated genes, for this acquired tolerance to acetic acid. They observed that many of these tolerant yeasts contained similar specific mutated genes (or, in ‘sciency’ terms; single nucleotide polymorphisms).

But what if this was just a crazy coincidence, and these mutated genes actually had nothing at all to do with this acquired acetic acid tolerance? To prove that this was not just a coincidence, and that these specific mutated genes were causal of this tolerance to acetic acid, the researchers carried out more fancy scientific techniques. They introduced each of these special mutated genes, individually, back into the non-tolerant yeast and… voila, these non-tolerant yeasts became tolerant to higher acetic acid concentrations! What is also cool is that the more of these mutated genes that were introduced back into the yeast, the more tolerant they became to high acetic acid concentrations (see Figure 3).

Figure 3: Growth of yeast with different variations of the identified mutant genes for acquired acetic acid tolerance.

In summary, using an experimental evolution approach, González‑Ramos et al. (2016) was able to genetically engineer yeast cells there were able to constitutively survive higher concentrations of acetic acid, which normally impairs yeast productivity and stops yeast from growing. This has beneficial implications in improving the efficacy of second-generation bioethanol production.

I found this study interesting because of the potential applications that it has. This study struck out to me as it shed some light on the clever way that we can apply scientific techniques like experimental evolution to biotechnology for the benefit of our environment and our future. I hope that you found this study as interesting as I did. Hopefully with more applicable studies like this, our cities will look less like *first picture* and more like *bottom picture*.

Image URL:

Thanks for reading,


Here are the papers I referred to:

  1. González-Ramos, D., Gorter de Vries, A. R., Grijseels, S. S., van Berkum, M. C., Swinnen, S., van den Broek, M., . . . van Maris, A. J. A. (2016). A new laboratory evolution approach to select for constitutive acetic acid tolerance in Saccharomyces cerevisiae and identification of causal mutations. Biotechnology for Biofuels, 9(1), 173. 10.1186/s13068-016-0583-1
  2. Rastogi, M., Shrivastava, S. J. R., & Reviews, S. E. (2017). Recent advances in second generation bioethanol production: an insight to pretreatment, saccharification and fermentation processes. 80, 330-340.
  3. Kawecki, T. J., Lenski, R. E., Ebert, D., Hollis, B., Olivieri, I., Whitlock, M. C. J. T. i. e., & evolution. (2012). Experimental evolution. 27(10), 547-560.
  4. Branco, R. H., Serafim, L. S., & Xavier, A. M. J. F. (2019). Second generation bioethanol production: on the use of pulp and paper industry wastes as feedstock. 5(1), 4.
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Fight or flight, bacterial edition: adapting your defense to suit your foe

Imagine you were surrounded by enemies, and had a choice of two defenses. You could send out a targeted laser to kill each bad guy, or instead, put in a bit more effort to form a clever disguise so that none of them could find you. How many baddies would there have to be to make the disguise worth the extra effort?

I recently read an article; The effect of phage genetic diversity on bacterial resistance evolution[1]. The authors deliberately evolved viruses that infect bacteria, to see if exposure to a more diverse group of these viruses would result in a change to the bacteria’s defense strategies.

Pseudomonas aeruginosa (pronounced “Soo-da-moan-us orig-in-osa”) is a bacteria that causes disease in both plants and animals, including humans[2]. It’s important for us to know more about it because it can have a negative impact on agricultural industries, and it’s able to form multi-cellular structures called biofilms to develop chronic infections on medical devices like catheters[3]. It’s an opportunistic pathogen, and one of the most common species associated with infections of immune-compromised or vulnerable patients such as those with cystic fibrosis[4].

Pseudomonas aeruginosa. Image credit: bioMérieux.

The ‘bad guys’ in this story are bacteriophages, or “phages” for short. Phages are viruses that specifically infect bacteria. They do this by attaching to the outside of the bacterial cell and injecting their DNA, before using the cell’s own machinery to replicate themselves. The newly formed phage then burst out of the cell to each infect their own targets, killing the host cell in the process[5]. Click here to see a fun video explaining how this happens & why studying phage is so important, as well as a sneaky peek at a P. aeruginosa chronic infection that was treated with phage therapy!

Transmission electron micrograph of Pseudomonas aeruginosa bacteriophage. Image credit: Watanabe et al., 2007.

A phage called DMS3vir was used in this study and is specific to P. aeruginosa, but bacteria can employ a variety of different strategies to protect themselves from infection.

One of these strategies is a general resistance mechanism called surface modification. Bacterial cells can make changes to the receptor proteins of their surface (which phages use to recognise the cell), to mask their identity as a target and evade infection[6]. There are also more targeted strategies where the cell actively damages the phage particle, such as the CRISPR-Cas adaptive immune system.

CRISPR-Cas (or CRISPR) is utilised when infecting phage are recognised by the bacteria; the cell uses an enzyme called Cas to cleave the viral DNA, and a short sequence is then incorporated into a special region of the bacterial cell’s genome (known as CRISPR). This short sequence is called a spacer, and bacteria can accumulate them over time. Spacers can be used to help the bacteria recognise and disable infecting phage sooner, by creating a guide to allow Cas enzymes to become targeted towards the phage which have that specific sequence[7]. This becomes a type of immune memory to allow for a more precise and prompt immune defense. You can learn more about CRISPR-Cas and how this targeted mechanism is being developed for gene editing here.

While CRISPR is an effective targeted strategy, the spacers acquired are specific to a particular phage’s DNA, and will only work against phage that also have that sequence. So bacteria have to accumulate multiple spacers to have a broader range of immunity against different phage, and the cost of using this defense increases with each spacer they accumulate. By contrast, surface modification has a fixed cost, as one change to the receptors on the cell surface will prevent recognition by a variety of phage. This could make it a very efficient system when there are a lot of different phage to defend against.

P. aeruginosa has been shown to readily evolve CRISPR-based immunity to phage DMS3vir in the lab, but the communities of bacteriophages we can isolate from the environment are likely to be far more diverse, which can impact the way that bacteria protect themselves. The authors of this article wanted to know whether phage diversity has an effect of the development of CRISPR-based immune defense by the host bacteria, and hypothesised that surface modification will be favoured in conditions when phage diversity is high.

They started by evolving the phage DMS3vir to be more diverse. To do this, they mixed the phage with a culture of host bacteria which lacked a DNA repair gene; they thought that if the bacteria was more likely to mutate, then the phage resulting from infections of that bacteria may be even more diverse than those evolved using a ‘normal’ or wild-type strain of P. aeruginosa.

Schematic diagram of the bacteriophage evolution experiment, conducted in 12 replicates over 17 days.

After letting the phage infect the bacteria for 24 hours, they killed and removed any remaining bacterial cells before using the new collection of phage to infect a fresh bacterial culture. Each time they did this, the resulting population of phage was able to accumulate mutations and become more diverse. They repeated this for 17 days using 12 separate replicate experiments. At the end of the evolution experiment, they also took one purified phage (referred to as clonal phage) from each diverse population for use as a control to show that it was the diversity of phage bringing about any change in the bacterial response, not the evolved state of the phage alone.

They then obtained DNA sequences for each of these 12 diverse populations and clonal phage, as well as the ‘ancestral’ phage that they started with, to determine how many single-nucleotide polymorphisms (changes), or SNPs, had occurred as a result of the phages evolving. As shown in Figure 1A, the diverse phage populations had a much higher frequency of SNPs than the clonal phage, indicating that each type of phage within the diverse populations is likely to have different SNPs to each other.

Figure 1, Broniewski et al., 2020.

They then tested each of these phage against bacteria that had some CRISPR-based immunity already. For the bacteria, they used lab strains that each had a unique spacer against phage DMS3vir (1-12 in Figure 1B), as well as 6 strains that each had 2 spacers & would be harder for the phage to infect (combined, ‘2sp BIM’), and one surface-modified mutant (‘SM’). While none of the phage could infect the 2-spacer strains or the SM mutant, the diverse phage had a greater infection capability than its paired clonal isolate against each of the single-spacer strains.

The authors wanted to see how phage diversity affects which defense strategy the bacteria will develop. For this, they used wild-type bacteria with no existing CRISPR spacers for the DMS3vir phage. After exposing this bacteria to infecting phage for 3 days to allow bacterial defenses to come into play, they then added them to both a wild-type phage and a modified phage that had a gene to disable CRISPR-based immunity. If the bacteria was resistant to both of these phages, the authors assumed it had developed a surface modification, whereas resistance to the wild-type but not the anti-CRISPR modified phage was assessed to be due to a CRISPR-based immunity. Figure 2B shows that bacteria challenged by a diverse phage population utilised surface modification (SM) more often than when challenged by clonal phage, whereas bacteria challenged by a clonal phage population almost always developed a CRISPR-based immunity.

Figure 2B, adapted from Broniewski et al., 2020.

It was also observed that when bacteria challenged by a diverse population did develop CRISPR-based immunity, they were more likely to acquire multiple rather than single spacers. As shown in Figure 3A, the majority of bacteria challenged by one type of phage (clonal phage) only acquired a single spacer, as that was all that was required, whereas exposure to a more diverse population of phages more often resulted in multiple spacers being accumulated by P. aeruginosa.

Figure 3, Broniewski et al., 2020.

Overall, this study showed that an increased phage diversity promoted surface modification as a form of generalised resistance to the phage. It was also demonstrated that when CRISPR-based immunity was developed, a diverse phage population increased the likelihood of multiple spacers being acquired in the bacterial host.

While I found the research interesting, I found the ‘scoring’ for type of bacterial resistance to be based on a huge assumption that other strategies were not coming into play. Bacteria have many different ways to protect themselves against phage, and the authors don’t really mention how they eliminated other mechanisms as possible explanations for what they observed.

I also don’t feel that a lab-evolved population of bacteriophages is ever going to be an accurate representation of the diversity in an environmental sample; while this team had a readily available stock of phage DMS3vir in the lab, I would have been interested to hear their justification for not isolating P. aeruginosa bacteriophages from the environment to use in testing the effect of phage diversity either alongside or instead of their lab-evolved phage.

What do you think?


1.      Broniewski, J.M., et al., The effect of phage genetic diversity on bacterial resistance evolution. The ISME Journal, 2020. 14(3): p. 828-836.

2. Wu, W., et al., Chapter 41 – Pseudomonas aeruginosa, in Molecular Medical Microbiology (Second Edition), Y.-W. Tang, et al., Editors. 2015, Academic Press: Boston. p. 753-767.

3.      Khatoon, Z., et al., Bacterial biofilm formation on implantable devices and approaches to its treatment and prevention. Heliyon, 2018. 4(12): p. e01067.

4.      Bassetti, M., et al., How to manage Pseudomonas aeruginosa infections. Drugs in context, 2018. 7: p. 212527-212527.

5.      White, H.E. and E.V. Orlova, Bacteriophages: Their Structural Organisation and Function, in Bacteriophages – Perspectives and Future, R. Savva, Editor. 2019.

6.      Westra, Edze R., et al., Parasite Exposure Drives Selective Evolution of Constitutive versus Inducible Defense. Current Biology, 2015. 25(8): p. 1043-1049.

7.      Westra, E.R., et al., Evolution and Ecology of CRISPR. Annual Review of Ecology, Evolution, and Systematics, 2016. 47(1): p. 307-331.

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Phage Hunt NZ Guest Post: A First Hand Experience with Phage Therapy, Post 2

The following is the second of two guest posts by SamD* from Auckland New Zealand who describes his first hand account of his unsuccessful Phage Therapy in Georgia. Sam had a complicated infection treated by antibiotics for many years. Post 1 is his tale and Post 2 is his thoughts about phage therapy considering his experiences.

Sam is very interested in thoughts or suggestions regarding his case and can be contacted at *This is a pseudomonym.

The Lessons I learned regarding Phages

  • Patients needing treatment of multi-pathogenic environments with a history of extensive antibiotic treatment require a treatment facility who are experienced, who are transparent about potential treatment outcomes, rigorously establish sensitivities to antibiotics and use them unfailingly concurrently, and persevere with the concurrent treatment until all pathogenic responses are resolved.
  • A phage treatment plan’s schedule must differentiate between physiologically complex, possibly multi-pathogenic environments like urogenital and airway systems, versus muscle-tissue infections like the classic lower limb examples often associated with phages, and allocate time and money accordingly, the first as much as a magnitude of 6 over the second.
  • That 6 over 1 assessment: Much of the time allotted to be spent at a treatment provider is spent waiting for the production of phages, if problems arise as a result of the actual use of the phage, there will very likely be no time to deal with the consequences unless significant extra funds are spent. Arrival >> tests taking up to 7 days for results >> Phage preparation time (if a match is available) 2 weeks to 3+ months >> Virulent reaction to phage >> the long process starts all over again.. and.. etc.
  • Phage in-Clinic treatment v. long-distance both have pros and cons that at first glance confers no distinct advantage either way. An in-Clinic presence should offer more prompt treatment and better communication but the enormous consideration of waiting time and charges should negate these advantages, until the virulent reactions are considered. In many countries, rigorous testing and the availability of a wide range of antibiotics for best sensitivity choice may be absent, so the distance patient upon taking the new bacteriophage** and antibiotic (only targeted to the last infection, not the next) would be in a difficult position medically if another infection appeared as a result.
  • The application of the first bacteriophage didn’t only bring about a new dominant infection, it meant the phage treatment of the original target had to be immediately discontinued and on retesting, the original bacteriophage was no longer highly-specific to the original infection and had to be re-produced. As well, antibiotic sensitivities of the original infection changed markedly. (Note: in an uncomplicated treatment schedule that phage would not be discontinued, and one would expect to remove the pathogen, albeit with months of phage-taking, alternating 3 weeks on, 3 weeks off, to avoid immune system responses).


What happens next?

  • I had read the NZ Herald Massey Phages Project article in the closing days in Tbilisi and contacted Dr Hendrickson soon after my return, hence this post. She showed great interest and has been extremely helpful in analysing the elements of phage treatment and encouraging the treatment experience to be recorded on the Phage Hunt NZ blog.
  • I next wrote to the second “International” Bacteriophage Clinic in Tbilisi giving them the phage treatment/repeat infection details with no country/clinic details to see what they would say; a paraphrased summary of several emails is “With multi-pathogenic environments like the urogenital system, we must always administer antibiotics concurrent with a phage. It is common for new, ascendant infections to occur. Nevertheless, we continue producing phage solutions and administering concurrent antibiotics for each occurrence, without stopping until no more appear. We usually find the ascendant infections are variants of the original infection being treated, as against your second different bacteria infection.” (The current infection has similar antibiotic sensitivities to the last Staphylcoccus haemolyticus report which suggests it is now a variant of the last infection).
  • Being back in New Zealand with an unknown infection also means a return to a medical system which offers no answers or treatment ability for complex urogenital infections. As before, the infection became chronic in the two weeks that passed in returning to New Zealand from its first occurrence. My system seems to have seen so many antibiotic courses and repeat infections that an infection quickly moves to a state where symptoms are virulent and debilitating, but sub-acute, and the local simplistic detection method of urinalysis fails the WBC level entry requirement for culture. No positive culture, no treatment of any kind. I have failed over a long time to find anyone in New Zealand in public medical practice who understands the significant differences in physiological status, and detection methods required, between acute and chronic pathogenic urogenital conditions. Microscopy, EPS tests, a consideration of the role of bacterial slime and biofilms have all proved to be beyond the scope of locally available treatment. Post-phage I am back to depending on an antibiotic to poorly maintain a condition more virulent and with a wider tissue spread than at any time in the past.

HK97 from Life in our phage world

HK97 from “Life in Our Phage World”, available in full (for free) here:

Bacteriophage Therapy Protocol Summary

  • It would be an understatement to say I know how a phage guinea pig might feel after a failed experiment; but more recently received information confirms for me that although my experience cannot be easily learned from available internet-based information, the phage treatment Clinic kept the realities of treatment complications close to the chest, only revealing them when they had to and were quick to deny further treatment when the going required persistence and resilience.
    The second commercial Tbilisi phage Clinic has now offered to pick up where their peers left off, and rigorously progress treatment to a conclusion, however the cost involved so far has been fairly substantial and the money has run out.
  • A Bacteriophage protocol in this area appears to need a lot more work; ideally an in-vitro test of an environment sample with the newly produced bacteriophage before application, if such a thing were possible, and a phage cocktail of a much wider spectrum for any one bacterial species. Otherwise the current treatment method as seen in Georgia presents an unaffordable monetary cost for many prospective patients.

If anyone has questions or observations you’d like a reply to, you can write to me here:

**Where Bacteriophage is written in BOLD, Sam had initially used the word Autophage, a term that he came across in Georgia and which appears to be the way clinicians there refer to Bacteriophages used therapeutically.

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Phage Hunt NZ Guest Post: A First Hand Experience with Phage Therapy, Post 1

The following is the first of two guest posts by SamD* from Auckland New Zealand who describes his first hand account of his recent experiences with unsuccessful Phage Therapy in Georgia. Sam has a complicated infection that was treated with antibiotics for many years. Post 1 will be his tale and Post 2 will be his thoughts about phage therapy considering his experiences.

Sam is very interested in thoughts or suggestions regarding his case and can be contacted at *This is a pseudomonym.

Phage Therapy in Georgia: A Clinical Perspective

In this post I detail my recent experience with Bacteriophage treatment in Georgia, the challenges the treatment presented, the lessons learned, a “Where to from Here” and my thoughts about how Bacteriophage therapy needs to be developed for my situation.

Phage therapy has increasingly gripped the Western world lately with media headlines of great wonder and promise. After a longtime on the receiving end of increasingly severe, resistant, urogenital infections, the potential promise of bacteriophages was not new to me, it had long been my plan “Y”; when and if absolutely all else failed, phages would be my salvation.

Phage Treatment in Georgia
Plan “Y” came early in March 2015 in New Zealand, after 29 years of antibiotic-dependency, including 15 years of continuous antibiotics, subsequent complications from long-term antibiotic side-effects and finally, unyielding nosocomial infections, a casualty of prior successful medical treatment. Preparations were made to go to a Tbilisi phage Clinic with the only initial reservation being that my phage research had always lacked one vital factor; reported, verifiable (male) urogenital-related clinical outcomes were virtually non-existent. I had found most phage clinical material in general very dated with narrow repetitive treatment topics. A new perplexing factor crept in at the 11th hour; the treating Clinic mentioned they sometimes administered antibiotics concurrently with phages. Apart from a few very specific PAS studies, this conflicted with my idea of the principles of the specificity of bacteriophages v. broad-spectrum medications and got me thinking about a pathogen acquiring resistance in mid-course treatment with the two technologies. I attached no more importance to this apart from it remaining as a mild irritant in the back of my mind. But, I was to find it was probably to be one of two specific things crucial to my medical treatment success.

An image of Félix d’Herelle.

On arrival in Tbilisi, the high apartment window view, coincidentally, was very “phage”;  there was a leafy view of Félix d’Herelle’s residence, totally obscured at street level, having been a KGB and now State Security facility, walled up like Fort Knox and bristling with cameras, as well as an aerial view of the actual Eliava Institute building and more distantly, the Zoo which was soon to be destroyed in disastrous flooding.

The Tbilisi Phage Clinic took samples and using a third-party laboratory, identified a “strong” infection of Enterococcus faecalis and “non-pathogenic” Staphylococcus haemolyticus (both the infections a casualty of prior treatment referred to above) with detailed report results including a wide range of graduated drug sensitivities, antibody counts and other non-translated information. These test results confirmed results obtained two weeks earlier in China en route to Georgia. Frustratingly, in the preceding 18 months in New Zealand, all similar tests were pronounced negative, in spite of debilitating, ongoing symptoms.

My too-Simplistic Perception of Phages becomes Apparent
The Eliava Institute, as another third-party entity, successfully located a match for the Enterococcus; enhancing and producing an bacteriophage took 4 weeks and the three application techniques produced a near-miraculous result. Over three days, all the prior 22 months of urogenital pain and urinary urgency disappeared and a sense of normal life started to rapidly return; but it was short-lived, on the 4th day an acute bladder-urethral infection ensued and a sample was sent to pathology. A return flight home was due in 3 days, too late to change the departure date.Medical tourism is essentially uninsurable.Remaining in Tbilisi likely meant the time and cost would again be spent unable to do anything, waiting for production of another bacteriophage. The treating Clinic promised that another bacteriophage would be produced and sent by courier, and so a very uncomfortable 33 hour trip back to New Zealand followed.

The new infection was identified as the “non-pathogenic” Staphylococcus haemolyticus shortly after leaving. Once the immediate-area Enterococcus had been removed by its bacteriophage, it appeared the new environment enabled the Staphylococcus to become virulently ascendant. This new infection not only quickly and aggressively occupied the same areas as the previous Enterococcus, but spread outside the urogenital system. The laboratory report included a list of 23 antibiotic sensitivities, with just one of the 4 that showed good sensitivity being available in New Zealand, but proving ineffective as single therapy as its drug notes indicated.phage

Seven long weeks followed, with no information from the Clinic on the E.T.A. of the new bacteriophage. It had become obvious that a “long-distance” patient had either gone to the bottom of the treatment priorities, or that other forces were at play. So, a return flight ensued, and upon arrival the Clinic announced that the Institute (apparently the only bacteriophage producer in Tbilisi) had a phage match and it would take the standard 2 weeks to produce. It was very tempting to think that the coming phage would be the total solution after a bad start but the former thoughts of “will phages work for me” had well and truly changed to “what’s going to happen with the next phage” and the coming days passed slowly.

The new bacteriophage was delivered and a repeat of the first bacteriophage reaction followed, except this time, the next ascendant infection appeared even more acutely in just 24 hours, so much so that antibiotics were used immediately to control it and a two week course halted any other progress. By now the summer break holidays were approaching and soon, behind closed doors, one of the Doctors for the first time broached the subject that resistant multi-pathogenic environments can be a problem to treat with phages, that the environment is easily unbalanced by the high specificity of a phage. Apparently, the “sometimes” use of antibiotics at the same time as the bacteriophage was started was specifically for this reason. I wasn’t convinced the increasingly virulent ascendant pathogens were unavoidable, I was getting a distinct impression that each phage was conferring some kind of virulence on other bacteria. Further, it appeared that the high phage specificity, single phage dosing, a variable concurrent antibiotic policy and a lack of awareness of the microbiology of the targeted area required a higher-level phage treatment protocol than the Clinic was offering. They were appearing at this point as little more than a matching agency for 3rd-party laboratory and phage production services.

This very competent Doctor formed a plan, the second bacteriophage** should be resumed, it would surely cause a repeat of the last infection and so all-important urinalysis would be done, and we would continue like before.. another bacteriophage. Airtickets were extended for an extra 2 weeks, giving nearly 4 in all, to accommodate this, with my distinct unease that phage treatment may never bring the resolution I sought.

And this is how the plan went; the organising Doctor went off for the Summer holidays, the Clinic’s Director (Managing Doctor) took over, a sample from a very virulent ensuing infection was sent off for pathology, however after waiting 5 days the Director declared it “an unusable sample”, and by now 4 days of injected and oral antibiotics getting the forced new infection back under control made getting another sample of no use. Further, the Director refused over the next few days to arrange an EPS which may have revealed a non-antibiotic-affected culprit, holding out until I had to meet my airline departure deadline. Now came the long trip back to New Zealand, uncomfortable in the knowledge that the clinic didn’t have the medical bacteriophage resilience required to engage with my condition. Further, in comparison to when I first went to Georgia, I now had a far more virulent unidentified infection and was antibiotic-dependant again, a return to a past I had hoped I had left for good.

If anyone has questions or observations you’d like a reply to, you can write to me here:
**Where Bacteriophage is written in BOLD, Sam had initially used the word Autophage, a term that he came across in Georgia and which appears to be the way clinicians there refer to Bacteriophages used therapeutically.

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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 !


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