Caught in the act: how drug-resistance mutations sweep through populations of HIV

Blog author Meredith Carpenter is a postdoc in Carlos Bustamante's lab.

Blog author Meredith Carpenter is a postdoc in Carlos Bustamante’s lab.

It has been over 30 years since the emergence of HIV/AIDS, yet the disease continues to kill over one million people worldwide per year [UNAIDS report]. One of the reasons that this epidemic has been so difficult to control is because HIV evolves quickly—it has a short replication time and a high mutation rate, so viruses harboring new mutations that confer drug resistance tend to arise often and spread quickly.

However, the likelihood of one of these beneficial mutations popping up and subsequently “sweeping” through the viral population—i.e., becoming more common because of the survival advantage—also depends on the underlying population genetics, much of which is still poorly understood. In a paper just published in PLoS Genetics, Pleuni Pennings, postdoc in the Petrov lab, and colleagues Sergey Kryazhimskiy and John Wakeley from Harvard tracked the genetic diversity in adapting populations of HIV to better understand how and when new mutations arise.

Mutations and populations

Mutations are usually caused by either DNA damage (e.g., from environmental factors like UV radiation) or by a mistake during DNA replication. Because HIV is a retrovirus, meaning it must copy its RNA genome into DNA before it can be reproduced in the host cell, it is especially prone to errors that happen during the replication process. The rate that these errors occur, also called the mutation rate, is constant on a per-virus basis —for example, a specific mutation might happen in one virus in a million. As a consequence, the overall number of viruses in the population determines how many new mutations will be present, with a larger population harboring more mutations at any given time.

Whether these mutations will survive, however, is related to what population geneticists call the “effective population size” (also known as Ne), which takes into account genetic diversity. Due to a combination of factors, including the purely random destruction of some viruses, not all mutations will be preserved in the population, regardless of how beneficial they are. The Ne is a purely theoretical measure that can tell us how easily and quickly a new mutation can spread throughout a population. Because it accounts for factors that affect diversity, it is usually smaller than the actual (or “census”) population size.

Pennings and colleagues wanted to determine the Ne for HIV in a typical patient undergoing drug treatment. This is a contentious area: previous researchers examining this question using different methods, including simply summing up overall mutation numbers, came up with estimates of Ne ranging from one thousand to one million (in contrast, the actual number of virus-producing cells in the body is closer to one hundred million, but more on that later). To get a more exact estimate, Pennings took a new approach. Using previously published DNA sequences of HIV sampled from patients over the course of a drug treatment regimen, she looked at the actual dynamics of the development of drug-resistant virus populations over time.

Swept away

Specifically, Pennings focused on selective sweeps, wherein an advantageous mutation appears and then rises in frequency in the population. Features of these sweeps can give estimates of Ne because they reveal information about the diversity present in the initial population. Pennings sought to distinguish between “hard” and “soft” selective sweeps occurring as the viruses became drug resistant. A hard sweep occurs when a mutation appears in one virus and then rises in frequency, whereas a soft sweep happens when multiple viruses independently gain different mutations, which again rise in frequency over time (see Figure 1). These two types of sweeps have distinct fingerprints, and their relative frequencies depend on the underlying effective population size—soft sweeps are more likely when a population is larger it becomes more likely for different beneficial mutations to independently arise in two different viruses. Soft sweeps also leave more diversity in the adapted population compared to hard sweeps (Figure 1).

Figure 1, an illustration of a hard sweep (left) and a soft sweep (right).

Figure 1, an illustration of a hard sweep (left) and a soft sweep (right).

To tell these types of sweeps apart, Pennings took advantage of a specific amino acid change in the HIV gene that encodes reverse transcriptase (RT). This change can result from two different nucleotide changes, either one of which will change the amino acid from lysine to asparagine and confer resistance to drugs that target the RT protein.  Pennings used this handy feature to identify hard and soft sweeps: if she observed both mutations in the same drug-resistant population, then the sweep was soft. If only one mutation was observed, the sweep could be soft or hard, so she also factored in diversity levels to tell these apart. Pennings found evidence of both hard and soft sweeps in her study populations. Based on the frequencies of each, she estimated the Ne of HIV in the patients. Her estimate was 150,000, which is higher than some previous estimates but still lower than the actual number of virus-infected cells in the body. Pennings suggests that this discrepancy could be due to the background effects of other mutations in the viruses that gain the drug-resistance mutation—that is, even if a virus gets the valuable resistance mutation, it might still end up disappearing from the population because it happened to harbor some other damaging mutation as well. This would reduce the effective population size as measured by selective sweeps.

Implications and future work

Pennings’ findings have several implications. The first is that HIV populations have a limited supply of resistance mutations, as evidenced by the presence of hard sweeps (which, remember, occur when a sweep starts from a single mutation). This means that even small reductions in Ne, such as those produced by combination drug therapies, could have a big impact on preventing drug resistance. The second relates to the fact that, as described above, the likelihood that a mutation will sweep the population may be affected by background mutations in the virus in which it appears. This finding suggests that mutagenic drugs, given in combination with standard antiretrovirals, could be particularly useful for reducing drug resistance.  Now, Pennings is using larger datasets to determine whether some types of drugs lead to fewer soft sweeps (presumably because they reduce Ne). She is also trying to understand why drug resistance in HIV evolves in a stepwise fashion (one mutation at a time), even if three drugs are used in combination.

Paper author Pleuni Pennings is a postdoc in the lab of Dmitri Petrov.

Paper author Pleuni Pennings is a postdoc in the lab of Dmitri Petrov.


Pennings, PS, Kryazhimskiy S, Wakeley J. Loss and recovery of genetic diversity in adapting HIV populations. 2014, PLoS Genetics.

The fruit fly and its microbiome


Philipp Messer is a research associate in the Petrov lab

This post was written by Philipp Messer.

Although fruit flies are one of the most important model organisms in genetics, evolution, and immunology, surprisingly little is known about their associated microorganisms (their microbiome). This is even the more surprising if you consider that the microbiome can strongly affect quantitative traits in flies, for example their growth rate and cold tolerance. Furthermore, the natural environment of fruit flies – rotting fruit – is very rich in microorganisms.

All organisms interact with associated microbes

Because microbes can influence the phenotype of organisms, we expect such interactions to be subject to natural selection. Genes involved in pathogen defense are indeed amongst the fastest evolving genes. But interactions with microbes do not always just lead to an evolutionary arms race between microbes and their hosts, they can also facilitate major evolutionary innovations. Prominent examples of such innovations are the light organ of the bobtail squid that arose through a symbiotic relationship between squids and bioluminescent bacteria, or cellulose digestion in termites which relies on microbes in their guts. Hence, to improve our understanding of the evolution of fruit flies, we need to better understand how they interact and coevolve with their associated microorganisms.

In their paper “Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment”, Fabian Staubach and his colleagues at Stanford and the Max Planck Institute for Evolutionary Biology in Plön shed light on some of the major questions regarding Drosophila associated microbes. Beyond finding out which bacteria are present in flies, they assess the relative roles of host species and environmental effects on bacterial communities, detect candidate natural pathogens, and find interesting results regarding lab-of-origin-effects on the fly microbial community.

The microbiome of fruitflies

We need more studies like this

These results are not only highly relevant for everyone working with Drosophila, but are also a strong reminder that we cannot understand any model organism without taking its associated microbiota into account. We therefore need more microbiome studies like that of Staubach et al to identify the microbes that coevolve with their hosts and understand how the genomes of hosts and microbes interact in the evolutionary process. I would not be surprised if interactions between microbes and their hosts turn out to be among the biggest selective forces in many organisms.

The paper is a fun and easy read and can be found at here. Fabian was a postdoc in the Petrov lab from 2010 to 2013 and has just moved to the University of Freiburg in Germany to start his own group, where he plans to follow his interest to deepen our understanding of the role of microbes in adaptation.

Citation: Staubach F, Baines JF, Künzel S, Bik EM, Petrov DA (2013) Host Species and Environmental Effects on Bacterial Communities Associated with Drosophila in the Laboratory and in the Natural Environment. PLoS ONE 8(8): e70749. doi:10.1371/journal.pone.0070749


Fabian Staubach studies the microbiome of fruitflies.