What is the best genetic strategy of making offspring? Sex is a great way of acquiring new, adaptive combinations of genes, but the disadvantage is that by recombining you also risk breaking apart the adaptive combinations. So which strategy is better from an evolutionary point of view? Turns out bacteria can provide one answer to this question. We can learn more about this in a recent publication that is a result of collaboration between researchers from the Malopolska Centre of Biotechnology and from the Big Data Institute at University of Oxford.
Many biologists find the answer to the question “is sex better than no sex?” obvious: sex is better because it increases genetic diversity on which selection can act, hence accelerating adaptation of populations to changing environmental conditions. But the problem is that sex need not increase diversity and increased diversity need not be beneficial. And yet almost all known organisms engage in some form of “genetic sex”. So if sex and recombination are so widespread, perhaps we should not be asking “what is the best genetic strategy of making offspring”, and instead we should ask “what rates of gene exchange are evolutionarily optimal”?
Bacteria do not engage in sexual reproduction per se, but they undergo horizontal gene transfer, or HGT, which is typically defined as a combination of three distinct processes: transformation, conjugation and transduction (see below).
Drawing by Dorota Pacześniak
HGT is fundamentally different from eukaryotic recombination: two of the three HGT mechanisms (conjugation and transduction) are not autonomous bacterial processes, driven by the movement of selfish mobile genetic elements, while transformation has been argued to have evolved as a mechanism of preventing – not enabling – the increase of genetic diversity.
Importantly, different bacterial lineages undergo HGT at different rates, but the HGT rate is not the only trait that can vary between bacterial lineages. So when conducting a genomic study one observes a positive correlation between HGT rate and that trait (e.g., frequency of antibiotic resistance), one is tempted to conclude that this is because HGT is adaptive and helps these lineages adapt by acquiring that trait (helping bacteria become resistant to antibiotics). But if there is one thing we learned from 150 years of studies on the evolution of sex it is that more sex is not always better. So are bacterial lineages that undergo HGT at higher rates better at adapting to altered circumstances?
To address these two hypotheses, Dr Rafał Mostowy from MCB UJ collaborated with Dr Sonja Lehtinen and Professor Christophe Fraser from the Big Data Institute at University of Oxford as well as several other scientists from other institutions. They analysed a collection of isolates of infectious bacteria Streptococcus pneumoniae from Mae La, a refugee camp on the Thai-Burmese border. Samples were isolated from mothers and babies over 3 years time. Researchers estimated HGT rate in different bacterial lineages and looked at the relationship between frequency of antibiotic resistance and duration of bacterial carriage. They found that HGT rate does not explain the observed frequency of antibiotic resistance, and that in turn is best explained by the properties of bacterial carriage - the longer the bacterium is carried, the more likely it is to evolve resistance to antibiotics.
Conclusions from the publication
One important conclusion from the publication is not the most controversial one, namely that correlation is not causation. In genomic studies where HGT rates are inferred, positive correlations between HGT rate and a given trait should not be immediately interpreted as evidence of adaptive value of HGT. We of course know that HGT is a powerful evolutionary tool, but being more prone to gene exchange itself is not always beneficial. These results also show how little we still understand about the impact of HGT rate on evolutionary dynamics of bacterial populations and the evolution of HGT rate itself. The rapid increase in size of genomic databases offers an incredible opportunity to investigate these questions. However, with millions of genomes around the corner, there is a temptation to reach for the low-hanging fruit and report on observations inferred from newly sequenced data rather than to better understand associations between previously estimated quantities. We will need all of these to provide a coherent intellectual framework to understand the role of HGT in bacterial adaptation.