What do adaptations result from

Structural and Behavioral Adaptations All organisms have adaptations that help them survive and thrive. Some adaptations are structural. Structural adaptations are physical features of an organism like the bill on a bird or the fur on a bear.

Other adaptations are behavioral. Behavioral adaptations are the things organisms do to survive. For example, bird calls and migration are behavioral adaptations. Did You Know? Many of the things that impress us most in nature are thought to be adaptations. Mimicry of leaves by insects is an adaptation for evading predators.

This example is a katydid from Costa Rica. The creosote bush is a desert-dwelling plant that produces toxins that prevent other plants from growing nearby, thus reducing competition for nutrients and water. Echolocation in bats is an adaptation for catching insects. The answer: a lot of things. One example is vestigial structures.

Because these networks result from billions of years of evolution, one wonders whether the predominance of phenotypic reversion is attributable to the evolutionary history of the species studied, especially the environments in which the species and its ancestors have been selected in the past, or an intrinsic property of any functional system. To address this question, we applied the same analysis to functional random metabolic networks previously generated These networks were constructed from i AF by swapping its reactions with randomly picked reactions from the universe of all metabolic reactions in Kyoto Encyclopedia of Genes and Genomes 38 as long as the network has a non-zero FBA-predicted fitness in the glucose environment upon each reaction swap Only 20 new environments that i AF can adapt to from the glucose environment are adaptable by at least 20 of the random networks.

We thus analyzed the adaptations of random networks to each of these 20 new environments, with the glucose environment being the original environment. For each new environment, the median C RV of all random networks that can adapt to this environment is generally around 0.

By contrast, median C RI across random networks for a new environment is generally below 0. Clearly, the predominance of flux reversion is also evident in functional random networks, suggesting that this property is intrinsic to any functional metabolic network rather than a product of particular evolutionary histories.

Indeed, the mechanistic explanation for this property in actual organisms Fig. Furthermore, f p is generally more different from 1 than is f a in a log 10 scale, because log 10 f p — log 10 f a is largely positive Fig. Predominance of flux reversion in random metabolic networks.

For each new environment, values estimated from different random networks are shown by a box plot, with symbols explained in the legend to Fig. The corresponding values for the E. Intriguingly, however, for 19 of the 20 new environments, C RV in the E. A similar but less obvious trend holds for C RI Fig. Hence, although both the E. Mechanistically, this disparity is explainable at least qualitatively by our model in the previous section.

Specifically, for 15 of the 20 new environments, the fraction of E. For all 20 new environments, f p of E. But, why is f p of E. One potential explanation is that the composition and structure of the E.

As a result, when glucose is replaced with a new carbon source in a new environment, the fitness of E. Although the absolute fitness in the plastic stage may well be higher for E. Thus, the higher prevalence of flux reversion relative to reinforcement in E.

In the foregoing analyses of transcriptomes Fig. In comparative and evolutionary studies, however, phenotypes at stage p are typically inaccessible. As a result, comparative and evolutionary biologists usually focus on traits whose phenotypic values differ between stages o and a, despite that the other traits could have also experienced adaptive changes from the values at stage p to those at stage a.

Of the 50 environmental adaptations of the E. Because all flux changes observed in the maximization of fitness are required and therefore are by definition beneficial, even the adaptation to a simple carbon source change apparently involves much more than a few reactions.

Fraction of reinforcing traits C RI is no greater than that of reversing traits C RV in adaptations even when the total change exceeds a preset cutoff. In both panels, the equality in the fraction of reinforcing and reversing reactions is tested by a two-tailed binomial test. Using the transcriptome data collected in a total of 44 cases of six different experimental evolutionary adaptations of three species E.

Our fluxome analyses have several caveats worth discussion. First, because MOMA minimizes the total squared flux difference from the original flux, PCs could have been underestimated, but this bias would only make our conclusion more conservative. Second, a bias could exist owing to potentially different accuracies of MOMA and FBA that are respectively used to predict plastic and genetic flux changes. Third, we considered only single-carbon source environments in our analyses while the natural environments of E.

We thus simulated adaptations from the glucose environment to environments with mixed carbon sources see Methods , but found our conclusion unaltered Supplementary Fig. But, the fact that our fluxome-based conclusion qualitatively match the transcriptome-based conclusion suggests that our fluxome analysis is reliable. Furthermore, some of our metabolic analyses are largely immune to flux prediction errors. For example, because the E.

As mentioned, our transcriptome analysis also has a potential shortcoming. Because the organisms were not fully adapted to the new environments at the end of experimental evolution, it is possible that a trait currently not considered to show reversion or reinforcement due to insufficient GC would show one of these two patterns if allowed to adapt further. However, because our results are robust to different cutoffs used 0.

Another concern is that expression levels of many genes strongly correlate with organismal growth rate and may simply reflect the growth rate 40 , 41 ; it is interesting to ask whether removing these genes would alter our result.

Esquerre et al. Focusing on these genes in 30 cases of E. Thus, our finding also holds for growth-rate-independent genes. In all analyses, we regarded phenotypic reinforcement as evidence for the stepping stone role of plasticity in adaptation and phenotypic reversion as evidence against this hypothesis One could argue that although reinforcement supports the hypothesis, reversion is not necessarily against the hypothesis.

Specifically, if a PC moves the organismal phenotype closer to the optimum in the new environment but overshoots, the GC required to bring the phenotype to the optimum may be smaller than that in the absence of plasticity. To investigate this scenario, we considered all traits with PC and GC both larger than the cutoff as was done in the definition of reinforcement and reversion.

This is because GC will probably rise in further adaptations while TC will either rise by at most the same amount as the increase in GC or reduce. For the adaptations of the E. Thus, the comparison between facilitating and hindering plasticity also refutes the hypothesis that plasticity is a stepping stone to adaptation. It is also possible that the PC of a trait can move its phenotypic value to the optimal state in the new environment such that no GC is needed.

The corresponding value is only 0. Hence, even considering these cases does not alter our conclusion. We provided evidence that the cause for the preponderance of phenotypic reversion is that, even with plasticity, organismal fitness drops precipitously after environmental shifts, but more or less recovers through subsequent evolution; such fitness trajectories dictate that many fitness-associated traits are drastically altered at the plastic stage but are then restored via adaptive evolution.

Our model is consistent with the observation that stress response is frequently associated with growth cessation as well as reductions in the expression levels of growth-related genes and concentrations of central metabolites 43 , 44 , It is also consistent with the notion that genetic adaptation tends to rebalance the energy allocation in growth that is broken in stress response and that the physiological state of organisms after the rebalance in the new environment is similar to that in the original environment 16 , 18 , 44 , 46 , Together, these considerations suggest that plastic phenotypic changes in new environments represent emergency stress responses that may be important for organismal survival, but are otherwise not stepping stones for genetic adaptations to the new environments.

The similar observation in functional random metabolic networks suggests that our conclusion is likely to be general to most functional systems regardless of the specific evolutionary histories of the systems. Evolutionary biologists may contend that they are interested only in traits that differ between organisms living in different environments, because these traits have most likely experienced adaptive evolution. We showed that even for such traits i. In other words, traits with similar values in stages o and a may have had cryptic adaptations unrevealed due to the lack of information about stage p.

Hence, the observation that a trait looks similar among organisms living in different environments does not necessarily mean that it experiences no adaptive changes in organismal adaptations to their respective environments. It is important to emphasize that our study focuses exclusively on adaptations to new environments that have not been experienced at least in the recent past. For those environments that have been repeatedly experienced by the organisms in the recent past, it is possible that mutations conferring plastic phenotypic changes that are beneficial in these environments have been fixed and there is no controversy that adaptive plasticity can evolve under this scenario.

The importance of plasticity in adaptation has also been discussed in theories of genetic assimilation 48 and accommodation 6 , which refer to the evolutionary process by which a phenotype induced by an environmental stimulus becomes stably expressed even without the evoking environmental stimulus.

Because the experimental evolution data analyzed do not contain information on the phenotypic plasticity of the organisms adapted to the new environment, our study cannot test genetic assimilation or accommodation. A related hypothesis that we did not test regarding the role of plasticity in adaptation is that upon an environmental shift, organisms with a relatively high plasticity adapt faster or are more likely to adapt than organisms with a relatively low plasticity.

It would be interesting to test this hypothesis in the future when comparable organisms with contrasting levels of plasticity become available for experimental evolution studies. Due to the limitation of the available data, our transcriptome and fluxome analyses focused primarily on unicellular microbes with the exception of guppies.

Compared with unicellulars, multicellulars are more complex because of differential gene expressions among cell types and because the biomass production rate of a cell type may not correlate well with organismal fitness. Therefore, it will be important to confirm the generality of our findings in the future when more data sets from multicellulars become available. Transcriptome data sets from six experimental adaptations were acquired from five studies.

For each replicate of each adaptation, the data included gene expression levels of ancestral organisms in the original environment stage o , ancestral organisms in the new environment stage p , and evolved organisms in the new environment stage a. For each data set, we removed genes with any missing expression levels and then normalized gene expression levels such that the mean expression level of all genes is the same across all data sets. The first data set came from the experimental evolution of E.

The second data set came from the experimental evolution of E. We respectively used i to estimate L o , ii to estimate L p , and both iii and iv to estimate L a of genes. All expression levels measured by DESeq were provided by the authors. The third and fourth data sets came from the experimental evolution of E.

The authors used Affymetrix E. Each line has three replicates, except that profile iii has six replicates. We averaged gene expression levels across replicates for each line.

For the adaptation to the glycerol medium, we respectively used i to estimate L o , ii to estimate L p , and v to estimate L a. For the adaptation to the lactate medium, we respectively used i to estimate L o , iii to estimate L p , and vii to estimate L a.

In total, genes were considered. The fifth data set came from the experimental evolution of 12 different strains of S. The authors performed RNA-seq using i 12 ancestral lines in a glucose medium, ii 12 ancestral lines in the xylulose medium, and iii 12 evolved lines in the xylulose medium. Each line has two replicates, and the averaged expression levels of the two replicates were used. We respectively used i to estimate L o , ii to estimate L p , and iii to estimate L a of genes.

The sixth data set came from the experimental evolution of P. We respectively used i to estimate L o , ii to estimate L p , and iii to estimate L a of 37, genes. All expression levels in terms of TMM-normalized counts measured by edgeR were provided by the authors. The codes are available upon request. We used FBA to estimate the fluxes of the E. FBA assumes a metabolic steady state and maximizes the rate of biomass production Mathematically, FBA is a linear programming question in the following form.

The model i AF includes exchange reactions, each of which allows the uptake of one carbon source. The uptake rates of non-carbon sources such as water, oxygen, carbon dioxide, and ammonium were set as in the previous study An adaptation is a feature produced by natural selection for its current function.

Is the evidence consistent with this hypothesis? There are several relevant lines of evidence that must be examined:. If a trait has been shaped by natural selection, it must increase the fitness of the organisms t hat have it — since natural selection only increases the frequency of traits that increase fitness. Are birds more fit with feathers than without? We could do experiments to test each of these criteria of adaptation.

So far so good — the feature could have been shaped by natural selection. But we also have to look at historical questions about what was going on when it arose.

Did feathers arise in the context of natural selection for flight?