[Paleopsych] PNAS: Evolvability is a selectable trait -- Earl and Deem 101 (32): 11531

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PNAS: Evolvability is a selectable trait -- Earl and Deem 101 (32): 11531 

[I don't know what the connection with group selection is.]

    Published online before print August 2, 2004, 10.1073/pnas.0404656101
    PNAS | August 10, 2004 | vol. 101 | no. 32 | 11531-11536

    Evolvability is a selectable trait

    David J. Earl and Michael W. Deem^ [28]*

    Department of Bioengineering and Department of Physics and Astronomy,
    Rice University, Houston, TX 77005-1892

    Communicated by David Chandler, University of California, Berkeley,
    CA, June 30, 2004 (received for review April 19, 2004)

    Concomitant with the evolution of biological diversity must^ have been
    the evolution of mechanisms that facilitate evolution,^ because of the
    essentially infinite complexity of protein sequence^ space. We
    describe how evolvability can be an object of Darwinian^ selection,
    emphasizing the collective nature of the process.^ We quantify our
    theory with computer simulations of protein^ evolution. These
    simulations demonstrate that rapid or dramatic^ environmental change
    leads to selection for greater evolvability.^ The selective pressure
    for large-scale genetic moves such as^ DNA exchange becomes
    increasingly strong as the environmental^ conditions become more
    uncertain. Our results demonstrate that^ evolvability is a selectable
    trait and allow for the explanation^ of a large body of experimental

      Darwin was obsessed with variation. His books, considered as^ an
      ensemble, devote much more attention to variation than to^ natural
      selection, because he knew that no satisfactory theory^ of
      evolutionary change could be constructed until the causes^ of
      variation and the empirical rule of its form and amount had^ been
      elucidated ([37]1).


    Whether the propensity to evolve, or evolvability ([38]2-[39]4),^ can
    be an object of Darwinian natural selection is a topic of^ interest.
    Causality would suggest not because of the apparently^ anticipatory
    nature of evolvability ([40]5, [41]6). Many within the^ field of
    evolutionary biology are uncomfortable with the concept^ that
    evolvability is a selectable trait. A growing body of experimental^
    data, however, would be explained if evolvability were a selectable^
    trait ([42]7-[43]15).^

    Higher organisms cannot evolve, or adapt, by germ-line mutation^ to an
    environmental change within their own lifetime. Does this^ mean that
    lineages and individuals cannot be under selection^ for evolvability?
    Although viability is the selection criterion,^ the genotype that
    determines the viability arises in a mutated,^ evolved way from that
    of the previous generation as a result^ of base substitution,
    recombination, transposition, and horizontal^ gene transfer. These
    mutational processes are the driving forces^ of evolution, and their
    rates fundamentally determine evolvability.^ The perspective we offer
    here is that the evolvability of an^ organism is defined by the rates
    of genetic change, that genetic^ change is not always deleterious, and
    that these rates of genetic^ change are not fixed and are under
    selective pressure. That^ is, the mechanisms that define the rates of
    change are encoded^ in the genotype, and so they are selectable. An
    analogy with^ thermodynamics illuminates the issue: How is free energy
    minimized^ in a physical system of particles despite the difficulty
    in^defining the entropy of a given configuration of the particles?^ An
    ensemble of particle configurations allows the definition^ of free
    energy and the approach to thermodynamic equilibrium^ just as a
    population of evolving organisms allows the definition^ of and
    selection for evolvability.^

    Within the framework of point mutation, base substitution, and^
    recombination, correlations of adaptation with function have^ been
    observed. It is known that immunoglobins have evolved such^ that the
    mutation rates in complementary determining regions,^ in which
    mutation is most likely to generate useful variants,^ are much higher
    than those in framework regions ([44]14, [45]16). Recent^ data point
    to a role for DNA polymerases in regulating the somatic^ hypermutation
    rate of immunoglobin genes ([46]13, [47]17). Similarly,^ codon usage
    within the influenza hemagglutinin protein seems^ to be biased to
    favor more rapid antigenic drift ([48]14). Furthermore,^ in HIV-1
    protease, the probability of mutation is not randomly^ distributed
    within the structure but rather concentrated at^ sites that alter the
    geometry of the protein-binding domain,^ conferring significant
    propensity for antigenic drift ([49]18).^ Such behavior is not mere
    curiosity and has widespread implications^ for drug design and the
    evolution of drug resistance ([50]19). Stressful^ conditions may
    generally provoke activation of error-prone polymerases,^ triggering a
    large increase in adaptive rates ([51]20). Not only^ point mutation
    but also recombination are widely appreciated^ to confer increased
    evolvability ([52]9, [53]21, [54]22). Recombination^ among the
    hemagglutinin and neuraminidase proteins, for example,^ is believed to
    be a significant mechanism leading to the emergence^ of new virulent
    strains of influenza ([55]23). Computational and^ theoretical studies
    have also shown the persistence under selection^ of
    evolvability-enhancing moves in the context of point mutation^ and
    recombination evolutionary dynamics ([56]24-[57]29).^

    The selective forces that lead to the evolution and maintenance^ of
    mechanisms for rearrangement, deletion, transfer, and transposition^
    of genetic material, inasmuch as they lead to even greater evolution^
    than point mutation and recombination alone, are of great interest.^
    For example, the development of antibiotic resistance in bacteria^ has
    evolved mainly through the swapping of DNA pieces between^ the
    evolving bacteria ([58]8, [59]30). Similarly, the evolution of
    Escherichia^ coli from Salmonella is thought to have occurred
    exclusively^ from DNA swapping ([60]31). It has been proposed that the
    success^ of bacteria as a group stems from a capacity to acquire
    genes^from a large and diverse range of species ([61]32). It would
    seem,^ then, that large genetic moves are pervasive and crucial to^
    evolutionary dynamics ([62]6, [63]8, [64]10-[65]12, [66]15, [67]30,
    [68]31, [69]33-[70]39).^ Concomitantly, evolvability is enhanced by
    these larger moves,^ as shown experimentally for the case of DNA
    shuffling ([71]32, [72]40-[73]44).^ A key question is whether
    selection for evolvability fosters^ the husbandry of these moves.^

    We address here, from a theoretical point of view, selection^ of
    evolvability in the presence of large-scale genetic moves.^ Although
    the use of the term evolvability has only recently^ come into vogue in
    the scientific community, investigations^ into the evolution of
    adaptation go back several decades ([74]45-[75]47).^ Prominent from a
    theoretical perspective are works in population^ genetics ([76]48,
    [77]49) and game theory ([78]50-[79]52). Despite the^ insights that
    these studies give as to the origin and maintenance^ of evolvability,
    evolution of and selection for evolvability^ remains a contested issue
    primarily because of the causality^ principle ([80]5, [81]6). We show
    here that evolvability is selected^ for, notwithstanding the
    constraints imposed by causality, when^ a system is subject to a
    constant, random environmental change.^ This selection for
    evolvability occurs even when viability as^ a function of genotype is
    an extremely complex function, with^ exponentially many optima, and
    when the evolving system is unable^ to reach the global optimum of
    viability in any one instance^ of the environment. We demonstrate our
    results by using computer^ simulations of protein molecular evolution
    that incorporate^ selection in a varying environment. The genotype of
    a protein^ molecule is mapped to a complex phenotype by using a
    generalized^ NK model in which all assumptions and relevant parameters
    are^ known. The selective pressure for evolvability is shown to be^
    greater for larger rates of environmental change. Interestingly,^ a
    generalized susceptibility of the system correlates with the^
    fluctuations in the environment, albeit not as a result of
    generalized^linear response theory ([82]53). The addition of selection
    for evolvability^ as a phenomenological law to the toolbox of
    evolutionary theory^ allows for the explanation of a large body of
    experimental results.^

       The Generalized Block NK Model

                                                      [83][uarrow.gif] Top
                                                 [84][uarrow.gif] Abstract
                                     [dot.gif] The Generalized Block NK...
                                  [85][darrow.gif] System Evolution and...
                               [86][darrow.gif] Selection for Evolvability
                                           [87][darrow.gif] Susceptibility
                               [88][darrow.gif] Implications for Evolution
                                                  [89][darrow.gif] Summary
                                               [90][darrow.gif] References

    Whether evolvability is selectable has been a difficult question^ to
    answer, primarily because observations in evolutionary biology^ tend
    to be correlative in nature and difficult on which to make^
    mechanistic conclusions. Therefore, we consider here the dynamics^ of
    evolvability in a well defined theoretical model of protein^ evolution
    ([91]54). Within this model of protein structure and function,^ we
    have a fixed population of proteins, which we take to be^ 1,000. We
    have a microscopic selection criterion, which we take^ to be the
    folding and binding of a protein to a substrate. And^ we have a means
    of inducing constant, random environmental change.^

    We model the molecular evolution of protein systems by using^ a
    generalization of the NK ([92]55-[93]57) and block NK ([94]58) models^
    that has been used previously to study protein molecular evolution^
    strategies ([95]54) and the immune-system response to vaccination^ and
    disease ([96]59). The model includes a population of sequences,^ upon
    which selection acts and in which occur genetic mutations.^ The
    mutational hierarchy includes both point mutations and large-scale^
    swapping moves, akin to transposition or translocation events.^
    Although the model does not include recombination, such inclusion^ is
    not expected to change the results because swapping can be^ viewed as
    a powerful form of recombination ([97]54). For example,^ linkage
    effects are mitigated even more rapidly by swapping^ in our model than
    they would be by recombination. The selection^ for greater swapping
    rates in more rapidly changing environments^ observed in our model
    parallels results found in studies of^ the evolution of sex, for which
    adaptation and variation in^ a heterogeneous environment is well
    researched ([98]60).^

    In the generalized block NK model, each individual evolving^ protein
    sequence has an energy that is determined by secondary^ structural
    subdomain energies, U^sd, subdomain-subdomain^ interaction energies,
    U^sd-sd, and chemical binding energies,^ U^c. This energy is used as
    the selection criteria in our studies^ and is given by ^

    [fd1.gif] [1]

    Within our generalized^ block NK model, each protein molecule is
    composed of M = 10^ secondary structural subdomains of N = 10 aa in
    length. We consider^ five chemically distinct amino acid classes
    (negative, positive,^ polar, hydrophobic, and other) and L = 5
    different types of^ subdomains (helices, strands, loops, turns, and
    others). We^ therefore have L different subdomain energy functions of
    the^ NK form ^

    [fd2.gif] [2]

    where a[j] is the amino acid^ type of the jth amino acid in the
    subdomain, and {alpha} [i] is the type^ of the ith subdomain. As in
    previous studies, we consider the^ case in which the range of the
    interactions within a subdomain^ is specified by K = 4 ([99]54,
    [100]59). Here {sigma} [{alpha} i] is a quenched Gaussian^ random
    number with zero mean and a variance of unity, and it^ is different
    for each value of its argument for each of the^ L subdomain types,
    {alpha} [i]. The interaction energy between secondary^ subdomain
    structures is given by ^

    [fd3.gif] [3]

    where^ we consider D = 6 interactions between secondary structures^
    ([101]54, [102]59). The zero-mean, unit-variance Gaussian [f4.gif] and
    the interacting amino acids, j[1],..., j[K], are selected at^ random
    for each interaction (i, j, k). In our model, P = 5 aa^ contribute
    directly to a binding event, as in a typical pharmacophore,^ where the
    chemical binding energy of each amino acid is given^ by ^

    [fd5.gif] [4]

    where the zero-mean, unit-variance^ Gaussian {sigma} [i] and the
    contributing amino acid, i, are chosen at^ random.^

       System Evolution and Environmental Change

                                                     [103][uarrow.gif] Top
                                                [104][uarrow.gif] Abstract
                             [105][uarrow.gif] The Generalized Block NK...
                                         [dot.gif] System Evolution and...
                              [106][darrow.gif] Selection for Evolvability
                                          [107][darrow.gif] Susceptibility
                              [108][darrow.gif] Implications for Evolution
                                                 [109][darrow.gif] Summary
                                              [110][darrow.gif] References

    Our model system maintains a constant population of 1,000 proteins,^
    each protein of 100 aa in length and initially distinct in sequence.^
    The system evolves through the base substitution of single amino^
    acids and through DNA swapping of amino acid subdomains from^
    structural pools representing the five different subdomain types,^
    each containing 250 low-energy subdomain sequences. These moves^
    represent the small-scale adaptation and the large-scale, dramatic^
    evolution that occur in nature. For protein i, n[mut](i) point^
    mutations occur per sequence per round of selection. In addition,^ for
    protein i, subdomain sequences are replaced randomly with^ sequences
    from the same-type low-energy pools with probability^ p[swap](i).^

    After pool swapping and point mutations, selection occurs, and^ the
    20% lowest-energy protein sequences are kept and amplified^ to form
    the population of 1,000 proteins for the next round^ of selection. The
    parameters p[swap](i) and n[mut](i) are allowed^ to take a
    log-Gaussian random walk for each protein sequence.^ This process is
    repeated for N[gen] rounds of selection, after^ which an environmental
    change is imposed on the system with^ a severity that is characterized
    by the parameter p ([111]59). The^ parameter p is the probability of
    (i) changing the type of each^ of the 10 subdomains in the protein
    sequences, {alpha} [i] in Eq. 2, (ii)^ changing the amino acids and
    energies that are involved in subdomain-subdomain^ interactions, j[k]
    and {sigma} ^(k)[ij] in Eq. 3, and (iii) changing the^ amino acids and
    energies that are involved in the chemical binding,^ i and {sigma} [i]
    in Eq. 4. We repeat the process for a total of 100 environmental^
    changes and average our results over 1,000 instances of the^ ensemble.
    For each system studied, a steady state in n[mut], p[swap],^ and the
    average energies at the beginning, < U > [start], and end,^ < U >
    [end], of the dynamics in a single instance of the environment^ is
    reached after <80 environmental changes in all cases.^ We average the
    data over the last 20 environmental changes.^ We study how the
    frequency of environmental change, 1/N[gen],^ and the severity of
    environmental change, p, affect the evolvability^ of the protein
    sequences. A schematic diagram showing the molecular^ evolution of our
    protein system can be seen in [112]Fig. 1.^


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    [113][in this window]
    [114][in a new window]
      Fig. 1. Schematic diagram showing the evolution of the protein

       Selection for Evolvability

                                                     [115][uarrow.gif] Top
                                                [116][uarrow.gif] Abstract
                             [117][uarrow.gif] The Generalized Block NK...
                                 [118][uarrow.gif] System Evolution and...
                                      [dot.gif] Selection for Evolvability
                                          [119][darrow.gif] Susceptibility
                              [120][darrow.gif] Implications for Evolution
                                                 [121][darrow.gif] Summary
                                              [122][darrow.gif] References

    Shown in [123]Fig. 2 are the steady-state values of p[swap] and
    n[mut]^that our protein system selects as a function of imposed
    frequency^ of environmental change, 1/N[gen], and severity of
    environmental^ change, p. The DNA swapping moves that we propose have
    a high^ capacity for evolutionary change, because a significant
    number^of amino acids may be altered in a protein sequence in one
    swap^move. It is clear that our systems select for higher
    probabilities^ of DNA swapping moves, and thus evolvability, as the
    frequency^ and severity of environmental change increases. We stress
    the^ importance of this result. Mainstream evolutionary theory does^
    not recognize a need for the selection of evolvability. More^
    generally, we see that only in the limit of little or no
    environmental^change, p[swap] -> 0, do large-scale changes tend to be
    disfavored.^ The role of base substitution in our evolving system is
    more^ complex. For more severe environmental changes and for higher^
    frequencies of environmental change, the system depends more^ on DNA
    swapping than on point mutation to produce low-energy^ proteins. In
    these cases, because the protein must make large^ changes to its
    sequence to adapt to the environmental change,^ selection results in
    high values of p[swap], with base substitution^ having only a small
    effect on the energy of the protein. For^ less severe environmental
    changes and lower frequencies of environmental^ change, base
    substitution is sufficient to achieve the small^ modifications in
    protein sequence that are required for adaptation^ to the
    environmental change. Thus, we observe the higher dependence^ on
    n[mut]and lower dependence on p[swap] for small p. In addition,^ as
    1/N[gen] -> 0, n[mut] -> 0, because mutations tend to be deleterious^
    in stable systems with no environmental fluctuations.^


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    [124][in this window]
    [125][in a new window]
      Fig. 2. n[mut] (dashed lines) and p[swap] (solid lines) as a
    function of the frequency of environmental change, 1/N[gen], for
    different values of the severity of environmental change, p. The
    statistical errors in the results are smaller than the symbols on the

    Evolvability is intimately related to the diversity of a population.^
    At short times, evolvability can be quantified by the diffusion^
    coefficient in protein sequence space, D[0], which is given by^ the
    combined diffusion due to swapping of the subdomains and^ the point
    mutation of individual amino acids ([126]61): ^

    [fd6.gif] [5]

    The overwhelming contribution to D[0] comes from^ the swapping step,
    because the swapping move far more dramatically^ changes the sequence.
    The short-time diffusion rate selected^ for reflects, as a function of
    environmental change, a balance^ between staying within a favorable
    basin of attraction, or niche,^ and adaptation to a newly created,
    superior niche. As [127]Fig. 2^ shows, greater environmental change
    favors greater local diffusion,^ as indicated by the monotonic
    increase of p[swap] with p.^

    It is useful to regard base substitution as a means of fine^ tuning
    the protein sequences, whereas DNA swapping can be considered^ a
    source of more substantial evolutionary change. This hierarchy^ within
    the space of evolutionary moves becomes more apparent^ when studying
    the difference between starting and ending protein^ sequences within
    one environment as a function of p, p[swap],^ and n[mut]. The distance
    between protein sequences is characterized^ by the Hamming distance
    between the respective amino acid sequences.^ For a given p, the
    Hamming distance decreases only slightly^ as the frequency of
    environmental change, 1/N[gen], increases,^ but it has a very strong
    dependence on the severity of the environmental^ change, p, as shown
    in [128]Fig. 3a. The sensitivity of the Hamming^ distance also shows
    markedly different behavior to p[swap] and^ n[mut], as shown in
    [129]Fig. 3b. For state points with fixed n[mut],^ 1/N[gen], and p,
    the Hamming distance strongly depends on the^ value of p[swap].
    However, for state points with fixed p[swap],^ 1/N[gen], and p, the
    Hamming distance displays little or no variation^ with n[mut]. The
    Hamming distance is a long-time measure of the^ evolvability of the
    system. The long-time diffusion coefficient^ can be defined as the
    square of the Hamming distance multiplied^ by the frequency of
    environmental change. As [130]Fig. 3a implies,^ the long-time
    evolvability, as measured by the long-time diffusion^ coefficient,
    increases with both the severity and frequency^ of environmental


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    [131][in this window]
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      Fig. 3. Hamming distance and average variance. (a) Hamming distance
    as a function of the severity of environmental change, p, for the
    state points shown in [133]Fig. 2. (b) Hamming distance as a function
    of n[mut] (dashed lines) and p[swap] (solid lines) for fixed N[gen] =
    15 and for different severities of environmental change, p. In
    displaying the Hamming distance dependence on n[mut] (p[swap]), we fix
    p[swap] (n[mut]) to the selected values from [134]Fig. 2. The selected
    values of n[mut] and p[swap] at each state point are shown by light
    and dark circles, respectively. (c) Average variance, {sigma}
    ^2[U][end], of the energy of a population at the end of an evolution,
    U[end], as a function of the severity of environmental change, p, for
    different frequencies of environmental change, 1/N[gen].

    Due to the roughness of viability as a function of sequence,^ the
    exploration performed by any particular individual is limited^ to a
    local basin of attraction defined by the short-time mutation^ rates,
    and thus more independent traces through sequence space^ allow for
    more thorough evolution. In other words, the more^ diverse the
    starting population of individuals, the greater^ potential there is
    for evolution. [135]Fig. 3c shows the average^ variance of the energy
    values at the end of the dynamics within^ a single instance of the
    environment as a function of the severity^ and frequency of
    environmental change. It is clear that the^ diversity increases
    monotonically with p and 1/N[gen].^

    As we have seen, evolvability is quantifiable at any point in^ time
    through measurement of diversity and the local mutation^ rates. For
    this reason, causality does not prevent selection^ for evolvability.
    Because evolvability is an observable property,^ it can be selected


                                                     [136][uarrow.gif] Top
                                                [137][uarrow.gif] Abstract
                             [138][uarrow.gif] The Generalized Block NK...
                                 [139][uarrow.gif] System Evolution and...
                              [140][uarrow.gif] Selection for Evolvability
                                                  [dot.gif] Susceptibility
                              [141][darrow.gif] Implications for Evolution
                                                 [142][darrow.gif] Summary
                                              [143][darrow.gif] References

    A further measure of long-time evolvability is the response,^ or
    susceptibility, of the system to environmental change. In^ [144]Fig.
    4a we plot the average energy at the start, < U > [start], and^ end, <
    U > [end], of the dynamics within a single instance of the
    environment.^ This quantity is shown as a function of the severity, p,
    and^ frequency, 1/N[gen], of environmental change. It is apparent
    that^at low frequencies of environmental change, populations with^
    greater diversity and variation, which are more evolvable, have^
    slightly lower values of < U > [end]. There is also a clear
    increasing^trend in < U > [start] as a function of p, which is a
    feature of the^ generalized NK model. Considering the ending energy of
    a protein^ molecule within one instance of the environment to be
    roughly^ the sum of n Gaussian terms from the generalized NK model, ^

    [fd7.gif] [6]

    The starting energy of this protein molecule^ after an environmental
    change is given by ^

    [fd8.gif] [7]

    ^where ^

    [fd9.gif] [8]

    and where [f10.gif] are random Gaussian variables with zero mean (
    [f11.gif] ), whereas x[i] are evolved variables that are better than
    random^ and typically negative. Thus, the average starting energy of^
    this protein molecule is ^

    [fd12.gif] [9]

    Thus, averaging^ over the values in the new environment ^

    [fd13.gif] [10]

    ^or, averaging over many environmental changes ^

    [fd14.gif] [11]



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      Fig. 4. Average energy, average change in energy, and probability
    distribution. (a) Average energy immediately after, < U > [start], and
    immediately before, < U > [end], an environmental change as a function
    of the severity of environmental change, p, for different frequencies
    of environmental change. (b) Average change in energy, < {Delta} U > ,
    multiplied by the frequency of environmental change, 1/N[gen], as a
    function of the severity of environmental change, p. (c) Probability
    distribution of the susceptibility for different values of the
    severity of environmental change, p, for a fixed frequency of
    environmental change, 1/N[gen] = 0.1.

    This average reduction in the energy is a measure of the
    susceptibility^ of a system, < {Delta} U > /N[gen] = ( < U > [end] - <
    U > [start])/N[gen]. In [147]Fig. 4b^ we plot the susceptibility of
    our system as a function of the^ severity of environmental change, p.
    For a fixed frequency of^ environmental change, the susceptibility is
    a linear function^ of the severity of environmental change, as in Eq.
    11. This^ simple analysis captures the essence of the dynamics that
    occurs^ in the correlated, generalized NK model. [148]Fig. 4c shows
    that^ the probability distribution of the susceptibility is Gaussian^
    in shape. Note also that the variance of the susceptibility^ increases
    with p in [149]Fig. 4c, and thus the linearity of the susceptibility^
    in [150]Fig. 4b is not simply the result of a generalized
    fluctuation-dissipation^ theorem.^

       Implications for Evolution

                                                     [151][uarrow.gif] Top
                                                [152][uarrow.gif] Abstract
                             [153][uarrow.gif] The Generalized Block NK...
                                 [154][uarrow.gif] System Evolution and...
                              [155][uarrow.gif] Selection for Evolvability
                                          [156][uarrow.gif] Susceptibility
                                      [dot.gif] Implications for Evolution
                                                 [157][darrow.gif] Summary
                                              [158][darrow.gif] References

    Our results have implications for evolutionary theory. In our^ model
    system, populations of protein molecules that are subject^ to greater
    environmental change select for higher rates of evolvability.^ The
    selection criterion that we use is not a measure of evolvability^ in
    any way, yet the system selects for evolvability based on^ the
    implicit energetic benefits of adaptation to environmental^ change. In
    addition, there is no reason to assume that selection^ is optimal. In
    fact, systems optimal for one environment tend^ to have too little
    evolvability and tend to be selected against^ when faced with the
    inevitability of change.^

    Given our results, we propose that it is not mere chance that^ highly
    evolvable species tend to be found in rapidly changing^ environments
    or that an environmental crisis can trigger an^ increase in the rate
    of the evolution of a species. Indeed,^ selection for evolvability
    allows for the explanation of many^ data: the existence of somatic
    hypermutation in the immune system^ ([159]13, [160]14, [161]16,
    [162]17), the evolution of drug resistance in species^ of bacteria
    ([163]8, [164]30), and the occurrence and success of transpositional^
    events in bacterial evolution ([165]10, [166]31, [167]36). A recently
    studied^ example from mammals is the San Nicolas Island fox, which is^
    a highly endangered species and the most monomorphic sexually^
    reproducing animal known. This species, however, is found to^ have
    high levels of genetic variation within the major histocompatibility^
    complex loci ([168]62) that allows for increased pathogen resistance.^

    We believe that our results are of relevance to the field of^ vaccine
    and drug design. Currently, the design of new vaccines^ and drugs is
    largely based on the assumption that pathogens^ evolve by local space
    searching in response to therapeutic and^ immune selection. However,
    it is clear that we must anticipate^ the evolutionary potential of
    large DNA swapping events in the^ development of viruses, parasites,
    bacteria, and cancers if^ we are to engineer effective methods of
    treating them. How evolvability^ correlates with treatment strategy,
    and how to drive pathogens^ into regions of low evolvability where
    they are eradicated most^ easily, is of importance to efforts for
    vaccine and drug engineering.^

    Specific pathogenic examples of evolvability include the emergence^ of
    new influenza strains by a novel hemagglutinin neuraminidase^
    recombination, followed by antigenic drift to a highly infectious^
    strain ([169]23); emergence of many new HIV strains with the spread^
    of the disease from its site of origin in Africa ([170]63, [171]64);
    and^ the increased emergence of new infectious diseases associated^
    with modern, post-World War II travel ([172]65). Additionally, a^
    recent study of the dynamics of HIV-1 recombination suggests^ that
    HIV-1 may have evolved high recombination rates to foster^ rapid
    diversification and further its survival ([173]66).^

    Note that evolvability is not simply the observation that new^ strains
    occur; rather, it is the underlying probability with^ which new
    strains are created by genetic modification. These^ new strains may
    proliferate and be observed, or they may fail^ and not be observed to
    an appreciable extent. Fundamental study^ of evolvability, then,
    requires an appreciation of these underlying^ rates of genetic change.
    These underlying rates, such as polymerase^ error rates, recombination
    rates, and transposition rates, are^ what selection for increased
    evolvability may modulate ([174]67).^ These underlying rates of change
    are inheritable and can be^ altered by mutation. Study of these rates
    of genetic change,^ deconvoluted from observed rates of evolution,
    which are these^ rates multiplied by a probability of survival, is of
    fundamental^ interest.^

    It is intriguing that we find that at low frequencies of
    environmental^change, populations that are subject to more severe
    environmental^ changes can produce lower-energy individuals than
    populations^ that are not subject to environmental changes ([175]Fig.
    4a). Thus,^ under some conditions, adaptability can provide global
    benefits.^ This finding can be contrasted to the more customary
    expectation^ that specialists are better than generalists ([176]68).
    In experimental^ studies of Chlamydomonas, generalists that were
    evolved in alternating^ light and dark conditions were found to be
    better than their^ ancestors in both light and dark conditions but
    less good than^ specialists that had evolved exclusively in one of the
    environmental^ conditions ([177]69). Studies of the evolution of E.
    coli at constant^ and alternating temperatures produced similar
    results ([178]70, [179]71).^ The nature of the environmental change in
    these studies is not^ completely random as in our model. In addition,
    the number of^ rounds of selected evolution under each environmental
    condition^ is perhaps better defined within our model. These
    experiments^ do point to possible tests of our theory. For a species
    that^ is capable of DNA swapping evolutionary moves, a systematic^
    study of competency as a function of the frequency of a random^
    environmental change would be of interest. We predict that under^ some
    conditions, certain frequencies of environmental change^ will produce
    better individuals, after a given number of rounds^ of evolution and
    selection, than would be produced by evolution^ in a constant
    environment. Different severities of environmental^ change could also
    be imposed by altering the change in environmental^ variables between
    samples, such as temperature, food concentrations,^ light conditions,
    and exposure to disease. With regard to susceptibility,^ we would
    expect the rate of change of viability within an environment^ to be
    higher in systems with more frequent and harsher environmental^
    changes because of greater evolvability.^


                                                     [180][uarrow.gif] Top
                                                [181][uarrow.gif] Abstract
                             [182][uarrow.gif] The Generalized Block NK...
                                 [183][uarrow.gif] System Evolution and...
                              [184][uarrow.gif] Selection for Evolvability
                                          [185][uarrow.gif] Susceptibility
                              [186][uarrow.gif] Implications for Evolution
                                                         [dot.gif] Summary
                                              [187][darrow.gif] References

    Not only has life evolved, but life has evolved to evolve. That^ is,
    correlations within protein structure have evolved, and^ mechanisms to
    manipulate these correlations have evolved in^ tandem. The rates at
    which the various events within the hierarchy^ of evolutionary moves
    occur are not random or arbitrary but^ are selected by Darwinian
    evolution. Sensibly, rapid or extreme^ environmental change leads to
    selection for greater evolvability.^ This selection is not forbidden
    by causality and is strongest^ on the largest-scale moves within the
    mutational hierarchy.^

    Many observations within evolutionary biology, heretofore considered^
    evolutionary happenstance or accidents, are explained by selection^
    for evolvability. For example, the vertebrate immune system^ shows
    that the variable environment of antigens has provided^ selective
    pressure for the use of adaptable codons and low-fidelity^ polymerases
    during somatic hypermutation. A similar driving^ force for biased
    codon usage as a result of productively high^ mutation rates is
    observed in the hemagglutinin protein of influenza^ A. Selection for
    evolvability explains the prevalence of transposons^ among bacteria
    and recombination among higher organisms. We^ suggest that
    therapeutics also confer selective pressure on^ the evolvability of
    pathogens, and that this driving force for^ antigenic drift should be
    considered in drug- and vaccine-design^ efforts.^


    We thank Kevin R. Foster for a careful reading of the manuscript.^
    This research is supported by the National Institutes of Health.^

    ^* To whom correspondence should be addressed at: Department of
    Bioengineering and Department of Physics and Astronomy, MS 142, Rice
    University, 6100 Main Street, Houston, TX 77005-1892. E-mail:
    [188]mwdeem at chinook.rice.edu.

    © 2004 by [189]The National Academy of Sciences of the USA


                                                     [190][uarrow.gif] Top
                                                [191][uarrow.gif] Abstract
                             [192][uarrow.gif] The Generalized Block NK...
                                 [193][uarrow.gif] System Evolution and...
                              [194][uarrow.gif] Selection for Evolvability
                                          [195][uarrow.gif] Susceptibility
                              [196][uarrow.gif] Implications for Evolution
                                                 [197][uarrow.gif] Summary
                                                      [dot.gif] References

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  276. http://www.pnas.org/cgi/external_ref?access_num=42055&link_type=MED
  278. http://www.pnas.org/cgi/external_ref?access_num=42057&link_type=MED
  280. http://www.pnas.org/cgi/external_ref?access_num=42062&link_type=MED
  284. http://www.pnas.org/cgi/external_ref?access_num=42056&link_type=MED

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