All three types of protein are essential for the ring to spin. To find out how this complex structure evolved, Thornton and his colleagues compared the proteins with related versions in other organisms, such as animals. Fungi and animals share a common ancestor that lived around a billion years ago.
In animals, the vacuolar ATPase complexes also have spinning rings made of six proteins. But those rings are different in one crucial way: instead of having three types of proteins in their rings, they have only two. Each animal ring is made up of five copies of Vma3 and one of Vma They have no Vma By McShea and Brandon's definition of complexity, fungi are more complex than animals—at least when it comes to their vacuolar ATPase complexes.
The scientists looked closely at the genes encoding the ring proteins. Vma11, the ring protein unique to fungi, turns out to be a close relative of the Vma3 in both animals and fungi.
The genes for Vma3 and Vma11 must therefore share a common ancestry. Thornton and his colleagues concluded that early in the evolution of fungi, an ancestral gene for ring proteins was accidentally duplicated. Those two copies then evolved into Vma3 and Vma By comparing the differences in the genes for Vma3 and Vma11, Thornton and his colleagues reconstructed the ancestral gene from which they both evolved. They then used that DNA sequence to create a corresponding protein—in effect, resurrecting an million-year-old protein.
The scientists called this protein Anc. They wondered how the protein ring functioned with this ancestral protein. To find out, they inserted the gene for Anc. They also shut down its descendant genes, Vma3 and Vma Normally, shutting down the genes for the Vma3 and Vma11 proteins would be fatal because the yeast could no longer make their rings.
But Thornton and his co-workers found that the yeast could survive with Anc. It combined Anc. Experiments such as this one allowed the scientists to formulate a hypothesis for how the fungal ring became more complex. Fungi started out with rings made from only two proteins—the same ones found in animals like us. The proteins were versatile, able to bind to themselves or to their partners, joining up to proteins either on their right or on their left. Later the gene for Anc.
These new proteins kept doing what the old ones had done: they assembled into rings for pumps. But over millions of generations of fungi, they began to mutate.
Some of those mutations took away some of their versatility. Vma11, for example, lost the ability to bind to Vma3 on its clockwise side. Vma3 lost the ability to bind to Vma16 on its clockwise side. These mutations did not kill the yeast, because the proteins could still link together into a ring. They were neutral mutations, in other words. But now the ring had to be more complex because it could form successfully only if all three proteins were present and only if they arranged themselves in one pattern.
Thornton and his colleagues have uncovered precisely the kind of evolutionary episode predicted by the zero-force evolutionary law. Over time, life produced more parts—that is, more ring proteins. And then those extra parts began to diverge from one another.
The fungi ended up with a more complex structure than their ancestors had. But it did not happen the way Darwin had imagined, with natural selection favoring a series of intermediate forms. Instead the fungal ring degenerated its way into complexity. Gray has found another example of constructive neutral evolution in the way many species edit their genes.
When cells need to make a given protein, they transcribe the DNA of its gene into RNA, the single-stranded counterpart of DNA, and then use special enzymes to replace certain RNA building blocks called nucleotides with other ones. RNA editing is essential to many species, including us—the unedited RNA molecules produce proteins that do not work. But there is also something decidedly odd about it.
Why don't we just have genes with the correct original sequence, making RNA editing unnecessary? Take mammals. They come from a common ancestor, and have taken strikingly similar forms even though they have evolved on different continents.
Clearly, there is an apparent contradiction at the heart of evolutionary biology. On one hand, the mechanisms of evolution have no predisposition for change in any particular direction. On the other hand, let those mechanisms get going, and beyond some threshold, the interwoven ecological and developmental systems they generate tend to yield more and more species with greater maximum complexity.
So can we expect more diversity and complexity going forward? We are now at the beginning of a sixth mass extinction , caused by humans and showing no signs of stopping — wiping out the results of millions of years of evolution.
Despite this, humans themselves are too numerous, widespread and adaptable to be at serious risk of extinction any time soon. It is far more likely that we will extend our distribution yet further by engineering habitable biospheres on other planets. On other planets, we may one day find alien life.
Would that follow the same evolutionary trajectory as life on Earth? From one cell, the transition to multi-cellularity may be an easy hurdle to jump. Although it came quite late on Earth, it nevertheless happened many times. Population extinction was likely enhanced by the increased growth in genome size in these experiments as compared to the original experiments. Finally, we performed experiments to test whether the effect of a deletion bias a higher fraction of deletions among all indels alters the relationship between population size and the evolution of complexity.
A biased ratio of deletion to insertion mutations is found in biological organisms across the tree of life, especially in bacteria [ 45 , 46 ]. In these experiments we set the ratio of deletions to insertions as , but kept the total indel mutation rate as in the original experiments. In this treatment, only one population with ten individuals went extinct, as opposed to 47 populations in the original treatment.
However, the advantage towards evolving complexity previously enjoyed by small populations vanished S7 Fig. The median genome size increased as the population size increased for all populations sizes. Only the largest populations evolved a median number of novel phenotypic traits greater than zero.
These results suggest that it is not only the role of genetic drift, but the equal frequency of insertions and deletions that results in the increased genome size and phenotypic complexity in small populations.
The idea that small populations could have an evolutionary advantage over large populations dates back to Wright and his Shifting Balance theory [ 25 ]. More recently, a potential small-population advantage has been demonstrated both theoretically [ 27 ] and experimentally [ 26 ], but only in regard to short-term increases in fitness.
The Mutational Burden hypothesis provides an evolutionary mechanism that gives small populations an advantage towards increased phenotypic complexity [ 4 , 33 ]. However, an experimental demonstration of this advantage is lacking. Our study provides further insight into the conditions that give small populations such an evolutionary advantage.
We confirmed that small populations do evolve larger genomes due to the increased fixation of slightly deleterious mutations, as predicted [ 28 ]. We also showed how small populations have an increased potential to later evolve increased phenotypic complexity in small populations through the larger genomes generated by increased genetic drift [ 3 , 4 ].
As phenotypic traits are strongly beneficial in the Avida environment used here, these small populations used slightly deleterious genome expansions to cross fitness valleys and eventually reach novel fitness peaks. Our work also shows that this evolutionary advantage of small populations is limited by an increased rate of population extinction. Such a trend between the evolution of large genomes and an increased rate of extinction is seen in some multicellular eukaryote clades [ 47 , 48 ].
Ecological stressors increase extinction risk [ 52 ] and small populations are less able to adapt to detrimental environmental changes [ 53 ]. Our results concerning extinction, combined with the risk of other factors not examined here, suggest that the likelihood of a small population using genetic drift to evolve greater complexity without an increased risk of extinction may be limited. However, it is possible that multiple small populations could reduce the risk of extinction without reducing the evolution of complexity; future work should consider the interplay between population size and the evolution of complexity within a metapopulation of small populations.
Large populations also evolved greater genome sizes and phenotypic complexity. In our original experiments, genome evolution in large populations was driven by the fixation of rare beneficial insertions Fig 4.
While it is likely that many gene duplications are not under positive selection and lost due to genetic drift and mutation accumulation [ 54 ], some, especially those resulting in the amplification of gene expression, can be immediately beneficial and later lead to increased phenotypic complexity [ 55 — 58 ].
Due to the increased mutation supply, these events would occur at a greater frequency in large populations [ 59 ] and possibly lead to an increased probability of the evolution of complexity there.
However, we also found that large populations did not require this large supply of beneficial insertions. Even when insertion mutations added non-functional instructions and further point mutations were required to evolve functional traits, large populations still evolved complexity similar to that evolved in small populations.
These results suggest that purifying selection may not limit the evolution of complexity in large populations. Finally, we found that when deletions occur at a much greater frequency than insertions, only large populations have an evolutionary advantage towards complexity. As many bacteria do have a bias towards deletions [ 60 , 61 ], this result suggests that large microbial populations can have an evolutionary advantage over small microbial populations for evolving novel traits after all.
Such a trend where both large and small, but not intermediate-sized populations have an evolutionary advantage has already been theoretically proposed elsewhere. Weissman et al. Small populations valley-crossed due to genetic drift and large populations did so due to an increased supply of double mutants. Ochs and Desai also showed that intermediate-sized populations evolved to a lower fitness peak compared to small or large populations when valley-crossing was required for reaching a higher peak [ 36 ].
We found similar results, but from different evolutionary mechanisms. Here, populations needed to increase in genome size in order to evolve phenotypic complexity. Additionally, our populations evolved in a complex fitness landscape with many different possible paths to phenotypic complexity. While small populations did fix deleterious insertions to increase genome size, large populations evolved on a different path, either through beneficial insertions Fig 3 or neutral insertions S4 Fig.
It is possible that even larger populations than those evolved here would fix more deleterious insertions, as the likelihood of a further, beneficial mutation arising on the background of a segregating deleterious mutation increases as population size increases.
However, our results emphasize that large populations may not be dependent on valley-crossing in some fitness landscapes if alternative evolutionary trajectories exist, even if these trajectories are rare.
While the first maps of fitness landscapes suggested mutational paths are small in number [ 62 ], more recent work suggests that many indirect evolutionary trajectories exist in larger fitness landscapes [ 63 ]. The population sizes that led to the evolution of greater phenotypic complexity via drift are very small 10 individuals. As biological populations of that size are unrealistic, we may wonder whether such populations can actually evolve greater complexity due to increased genetic drift.
However, there are reasons to believe that these results would generally hold for biological systems. The limited range of small population sizes that led to complexity is an Avida-specific result due to the severe fitness effect of insertion mutations in avidians with small sequence length. Insertion mutations in biological genomes are not nearly as detrimental, and therefore the critical population size to see evolution of complexity via drift is much larger. We can therefore expect to see the effect of increased complexity due to drift in biological populations that are small, but not unreasonably small.
Another possible avenue for future work suggested by this study is to use a simpler population genetics model to explore the same questions we attempted to answer here. Many previous theoretical studies have examined the relevance of valley-crossing to the evolution of complex traits in simple fitness landscapes [ 34 — 36 ].
One benefit of a simpler model is that it allows for a broader exploration of the relevant parameters involved in the interplay between population size, genome size, and the evolution of phenotypic complexity. While we were not able to perform large parameter searches using the Avida system, our work here establishes a possible relationship between the factors that influence the evolution of complexity in a fitness landscape with many possible mutational trajectories to novel traits [ 65 ].
These results should drive future theoretical studies on the evolution of genome size and phenotypic complexity using population genetics models with simpler fitness landscapes.
Here we studied the evolution of complexity in haploid asexual digital organisms with an ancestral minimal genome on a frequency-independent fitness landscape. While beyond the scope of this work, it is worth considering how adjusting these genotype characteristics would alter our results.
It is likely that the ancestral minimal genomes are a requirement for small populations to evolve the same number of novel traits as large populations. If the ancestor organism had a significant amount of non-functional genome content, the mutation supply advantage that large populations have should result in an accelerated rate of phenotypic evolution in large populations [ 66 ]. The organisms used here, as in all Avida experiments, are haploid.
It is possible that polyploidy would alter the results found here. However, the implementation of a ploidy cycle in Avida is non-trivial due to the mechanistic style of replication, and so presently other experimental systems would have to be used to explore the role of ploidy in the evolution of phenotypic complexity. It is unclear how sexual, instead of asexual, reproduction would change the results.
While sexual reproduction can enhance adaptation by combining beneficial mutations that arise in different background, it can also break up beneficial combinations of mutations [ 67 ]. One result that may be altered by sexual reproduction is the rate of extinction in small populations, as sex has been found to reduce the rate of mutational meltdowns [ 68 ].
Sexual reproduction has previously been studied using Avida, but it is more akin to homologous recombination in bacteria [ 69 ] as there is no ploidy cycle. Future work should address the role of sexual recombination on the results shown here. Finally, the experiments performed here had no frequency-dependent fitness effects. Previous Avida studies showed that frequency-dependent interactions enhanced the evolution of complexity for a given population size [ 70 , 71 ].
It is worth exploring how the presence of frequency-dependent selection alters the evolution of complexity, especially in small populations. The benefits of the diversity seen in frequency-dependent fitness landscapes may be reduced in small populations. The extensions to the experiments performed here would provide a more complete understanding of the role of adaptive and non-adaptive evolutionary processes in the origins of complexity.
In order to experimentally test the role of population size and genetic drift in the evolution of complexity, we used the digital evolution system Avida version 2. In Avida, self-replicating computer programs avidians compete in a population for a limited supply of CPU Central Processing Unit time needed to successfully reproduce.
Each avidian consists of a circular haploid genome of computer instructions. During its lifespan, an avidian executes the instructions that compose its genome. After executing certain instructions, it begins to copy its genome. This new copy will eventually be divided off from its mother reproduction in most Avida experiments is asexual. Because an avidian passes on its genome to its descendants, there is heredity in Avida. As an avidian copies its genome, mutations may occur, resulting in imperfect transmission of hereditary information.
This error-prone replication introduces variation into Avida populations. Finally, avidians that differ in instructions their genetic code also likely differ in their ability to self-replicate; this results in differential fitness. Therefore, because there is differential fitness, variation, and heredity, an Avida population undergoes evolution by natural selection [ 72 ].
This allows researchers to perform experimental evolution in Avida as in microbial systems [ 19 , 73 ]. Avida has been successfully used as a model system to explore many topics concerning the evolution of complexity [ 2 , 65 , 71 , 74 , 75 ]. Twenty-six different instructions compose the Avida instruction set see [ 42 ] for a more complete overview.
These include instructions for genome replication, such as an instruction to allocate memory for a new daughter genome, an instruction to copy instructions from the mother genome into the daughter genome, and an instruction to divide off the new avidian. There are instructions that allow for the input, output, and manipulation of random numbers that are used in the performance of certain Boolean logic calculations see below.
There are also instructions for altering instruction execution, including conditional instructions and instructions for changing the next instruction location in the genome to be executed. It is important to note that the Avida instruction set was not designed to mimic any biological organism. Instead, it was created in order to have an organism with mechanistic reproduction in a non-specified fitness landscape that allows for studies of evolutionary dynamics.
The Avida world consists of a toroidal grid of N cells, where N is the maximum population size. When an avidian successfully divides, its offspring is placed into a cell in the population. While the default setting places the offspring into one of nine neighboring cells of the parent, here the offspring is placed into any cell in the entire population. This simulates a well-mixed environment without spatial structure.
When there are empty cells in the population, new offspring are preferentially placed in an empty cell. However, if the population is at its carrying capacity, the individual who is currently occupying the selected cell is replaced by the new offspring a new individual can also eliminate its parent if that cell is selected.
This adds an element of genetic drift into the population as the individual to be removed is selected without regard to fitness. A population can also decrease in size by the death of individuals. This can lead to population extinction in very small populations. Time in Avida is divided into updates, not generations. This method of keeping time was implemented in order to allow individuals to execute their genomes in parallel.
During one update, a fixed number of instructions is executed across the entire population. By default, there are 30 N SIPs available to the entire population per update, where N is the population size.
SIPs are distributed among the individual genotypes within a population in proportion to the trait or traits displayed by an individual. In a homogeneous population of one genotype clones where each individual has the same merit, each individual will obtain approximately 30 SIPs per update.
However, in a heterogeneous population where merit differs between individuals, SIPs will be distributed in an uneven manner. This places a strong selection pressure on evolving a greater merit.
One generation has passed when the population has produced N offspring. Typically depending on the complexity of an avidian between 5 and 10 updates pass in one generation. These phenotypic traits are the ability or lack there-of to perform certain Boolean logic calculations on random binary numbers that the environment provides.
Further instructions should manipulate those numbers so as to perform the rewarded computations. When a number is then written to the output, the Avida program checks to see whether a logic operation was successfully performed. If so, the the individual that performed the computation consumes a resource tied to the performance of that trait there are many different codes, that is, combinations of instructions, that will trigger the reward.
Resource consumption causes the offspring of that individual to have their merit modified by a factor set by the experimenter. Each individual only gains a benefit from performing each function once per generation. There is an infinite supply of resources for the performance of each logic function in the present experiments, making fitness frequency-independent. Because the performance of these logic functions increases merit, they also increase fitness and are under strong positive selection.
The only way to measure the fitness of an avidian is to run it through its lifecycle and examine its phenotype. Genotypes that can reproduce faster will out-compete other genotypes, all else being equal. Evolution often takes away rather than adding. For instance, cave fish lose their eyes, while parasites like tapeworms lose their guts. Such simplification might be much more widespread than realised. Some apparently primitive creatures are turning out to be the descendants of more complex creatures rather than their ancestors.
For instance, it appears the ancestor of brainless starfish and sea urchins had a brain. Nevertheless, there is no doubt that evolution has produced more complex life-forms over the past four billion years. It is usually simply assumed to be the result of natural selection, but recently a few biologists studying our own bizarre and bloated genomes have challenged this idea.
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