The effects of individual mutations may depend on the genetic context in which they occur. This phenomenon, known as epistasis, is illustrated in . This table presents several mutants of E. coli
β-lactamase and the levels of resistance they confer to aztreonam, a monobactam antibiotic that is not the preferred substrate for β-lactamase. These mutants have different combinations of the following three mutations: E104K, R164H, and G267R. Mutations E104K and R164H by themselves increase resistance to aztreonam by ~2.5 fold. In combination, though, their effect on resistance is 40-fold. The third mutation, G267R has no effect on its own or in the presence of E104K or R164H, but it doubles the level of aztreonam resistance in the presence of both E104K and R164H (Bloom et al., 2006
). In this case all three mutations have epistatic effects because their effect on aztreonam resistance varies depending on the presence of the other two mutations.
Epistatic effects of β-lactamase mutations on aztreonam resistance.1
There are two types of epistasis, magnitude epistasis and sign epistasis. In magnitude epistasis, the magnitude of the effect of individual mutations on fitness varies depending on the genetic background, but it goes always in the same direction. In the example presented above, E104K and R164H would fall into this category, as they always have a positive effect on aztreonam resistance. In sign epistasis, not only the magnitude, but the sign of the effect (i.e. positive, negative or neutral) changes depending on the genetic context (Camps et al., 2003
). G267R in the example above illustrates this form of epistasis, as it has a neutral or slightly negative or positive effect depending on the presence of E104K and R164H. Sign epistasis limits the number of mutational trajectories available to selection because some paths to an optimum contain fitness decreases (Weinreich & Chao, 2005
). This was shown experimentally in the model enzyme β-lactamase. This study investigated all possible mutational pathways leading to five point mutations controlling resistance to cefotaxime. Strikingly, of the 120 possible direct mutational trajectories linking these alleles, only 18 were found to be accessible to selection (Weinreich & Chao, 2005
). These constraints on the mutational trajectories matched the structure of sign epistasis of the five mutations.
In this study, the mechanistic basis of sign epistasis was traced back to one compensatory mutation, M182T. Alone, this mutation modestly reduced cefotaxime hydrolysis. M182T, however, suppressed the reduced thermodynamic stability associated with G238S, the mutation increasing cefotaxime hydrolysis. Thus, M182T has a dramatically different effect on cefotaxime resistance depending on the presence or absence of G238S. As illustrated by the M182T mutation, compensatory mutations are expected to exhibit frequent sign epistasis because they are selected to suppress effects of other mutations, which makes them context-dependent.
Sign epistasis associated with compensatory mutations arising during periods of adaptation has the following two implications: a) It “locks in” deleterious mutations, precluding reversion to wild-type sequence; b) It constrains possible trajectories to an optimum, dramatically limiting genetic diversity resulting from adaptation.
a) Reduced reversion to wild-type sequence
The presence of compensatory mutations rapidly creates selective valleys that preclude reversion to the ancestral, wild-type sequence. In phage, fixation of compensatory mutations was estimated to be twice as likely as reversion to wild-type sequence (Weinreich et al., 2006
). In experimental microbial cultures, mutants selected under drug pressure often exhibit reduced fitness. Examples include HIV resistance to protease inhibitors (Poon & Chao, 2005
), streptomycin resistance in E. coli
(Borman et al., 1996
; Schrag et al., 1997
), isoniazid or rifampicin resistance in mycobacteria (Maisnier-Patin et al., 2002
), fucidin resistance in Staphylococcus aureus
), and resistance to fluconazole in Saccaromyces cerevisiae
(Nagaev et al., 2001
). In all these cases, growth in the absence of selective pressure resulted in a partial compensation of the fitness defect but not in reversion, indicating the presence of additional (presumably compensatory) mutations that created an adaptive valley before the original mutation had a chance to revert.
b) Constrained evolutionary trajectories
As discussed above, sign epistasis associated with compensatory mutations severely limits the number of evolutionary pathways available for selection. This has two consequences: it restricts the diversity of mutants coming out of positive selections and it increases the reproducibility of adaptation, at least under identical conditions and with a large population.
Selections for drug resistance typically yield a very limited number of mutants. For example, only 8 extended-spectrum mutants of β-lactamase (out of more than 90 known mutants from clinical isolates) were obtained in a selection in vitro
under conditions resembling natural selection, and many of them shared mutations (Anderson et al., 2003
). Another example of limited allelic representation following positive selection is an experiment replacing the thermostable adenylate kinase of Geobacillus stearothermophilus
(a thermophylic organism) with adenylate kinase from Bacillus subtilis
(a mesophile) to monitor adaptation to growth at high temperature at the level of a single gene. Only 6 alleles exhibiting increased thermal stability were observed, representing less than 1% of the total possible (Barlow & Hall, 2003
). In both cases, the observed limited allelic representation likely reflects restrictions in the pathways available for selection, as each allele involves more than one mutation and a much larger number of mutants producing the desired effects is known. Multiple selective pressures, simultaneous or sequential, further restrict the outcome of positive selections. In the case of β-lactamase, this scenario would arise with alternating exposure to different β-lactam antibiotics in the clinic. Selections using a single antibiotic typically result in the isolation of resistant mutants that are different form those isolated in the clinic (Counago et al., 2006
; Orencia et al., 2001
). Exposing a culture of E. coli
to amoxicillin and ceftazidime, however, resulted in the isolation of only naturally occurring β-lactamase mutants (Blazquez et al., 2000
), suggesting that “fluctuating selection” likely contributed to restricting the allelic repertoire observed in clinical isolates. Fluctuating selection should favor “generalist” mutations, i.e. mutations that increase resistance to multiple antibiotics. More intriguingly, it would also select mutations with strong positive epistatic effects for resistance to one of the antibiotics, even if its effect versus
other antibiotics bacteria are regularly exposed to is neutral or detrimental (Blazquez et al., 2000
). This could to be an example of a protein whose evolvability is shaped by selective pressures.
Parallel evolution, i.e
. the independent occurrence of the same substitution in two independent lineages has been observed in natural and experimental populations of insects, bacteria and phage. It is commonly seen in selections for drug resistance under conditions that mimic natural evolution (Anderson et al., 2003
; Blazquez et al., 1998
). Similarly, during adaptation of adenylate kinase to thermostability, the same three major mutants were observed in two independent experimental runs (Barlow & Hall, 2003
). However, the most striking examples of parallel evolution come from adaptation studies in two closely-related phages of E. coli
, ΦX174 and S13. Adaptation of ΦX174 to higher temperature and to a new host (Salmonella
) resulted in 50% identical changes between independent lineages (Counago et al., 2006
) and long-term adaptation to culture in the laboratory resulted in 40% identical independent changes (Wichman et al., 1999
). In contrast, no such reproducibility has been observed in more complex situations such as the adaptation of E. coli
to growth in liquid culture (Wichman et al., 2005
) or to growth in glycerol (Woods et al., 2006
), or in the development of human cancer (Herring et al., 2006
). This lack of reproducibility is likely due to the plasticity built into networks of functional interactions.