How Nature Streamlines Evolution by Katya Poltarak | January 2019

One of the key features of life on our planet is variability—the existence of multiple versions of the same trait. A classic example is the enormous variety of finches that the originator of the theory of evolution, Charles Darwin, observed in the Galápagos Islands and later discussed in his book On the Origin of Species. While all of the birds descended from a common ancestor, groups on different islands eventually developed beaks that gave them the best shot at survival on their particular island. As Darwin realized, the differences were the result of natural selection—a process that gradually weeds out characteristics that put a given organism at a disadvantage while promoting advantageous traits. As a result, what was once a minor variation becomes more and more entrenched, eventually leading the carriers of those traits to become separate species. [See If the Beak Fits, May 2016.]

Shutterstock One of the key features of life on our planet is variability—the existence of multiple versions of the same trait. A classic example is the enormous variety of finches that the originator of the theory of evolution, Charles Darwin, observed in the Galápagos Islands. As Darwin realized, the differences were the result of natural selection—a process that gradually weeds out characteristics that put a given organism at a disadvantage while promoting advantageous traits.

Since Darwin's discoveries, scientists have come a long way toward understanding the molecular mechanism of evolution by natural selection. However, figuring out what sets it in motion is considerably harder. In a recent issue of Molecular Systems Biology, Andreas Wagner and his team fill in some crucial gaps in our understanding of the mechanisms of trait variability. By using computer simulations of a well-known genetic mechanism found in the bacterium Escherichia coli (E. coli), they explain why evolution seems to "prefer" certain types of changes over others.

Building Blocks of Life

To understand variability at the molecular level, we have to look at how the genotype—the entire set of a particular organism's genes—gives rise to a specific phenotype, or set of physical traits. All of a particular organism's traits are encoded in DNA—a long, double-stranded molecule packaged into tiny structures, called chromosomes, which are found in the nucleus of every cell. DNA strands consist of small units known as nucleotides and are strung together in a specific sequence, with stretches known as genes serving as the blueprint for making proteins—the basic building blocks of all living tissues. Variations in the genetic code in organisms of the same species, in turn, account for the variability that is central to Darwin's theory. Some are caused by mutations, while others are the result of recombination—a reshuffling of genes during reproduction that leaves offspring with a unique combination of parental DNA.

Shutterstock

Variations in the genetic code in organisms of the same species account for the variability that is central to Darwin's theory. Some are caused by mutations, while others are the result of recombination—a reshuffling of genes during reproduction that leaves offspring with a unique combination of parental DNA.

The process of building proteins based on genetic instructions involves two crucial steps. The double-stranded DNA is first "transcribed" into RNA—a single-stranded version of the genome—which, in turn, is "translated" into chains of amino acids assembled according to the order of the nucleotides in the RNA. If all goes as planned, a gene—the stretch of DNA that corresponds to one particular protein—gives the cell instructions for assembling that protein from amino acids strung together in the order dictated by the gene.

Running the Gene Factory

As Wagner and his colleagues point out, however, there are still large gaps in our understanding of the details of this process in the broader context of evolution. While the significance of trait diversity is clear, it's much more difficult to explain how variability emerges in the first place. It's great that it does—otherwise we'd still be single-celled amoebas—but what is the molecular mechanism that sets it in motion? And, most importantly, why is there such a thing as "evolutionary bias"—nature's apparent preference for some new phenotypes over others?

Wagner and his team began looking for answers to these questions decades ago. While they pursued several lines of research, the one central to the current study has to do with "genetic regulatory networks," or GRNs. These consist of transcription factors—special proteins dedicated entirely to running the process of gene expression. Some bind to the DNA molecule directly to induce or suppress the transcription of a particular gene. Others increase or decrease the rate of transcription through the parallel processes of upregulation and downregulation. Others, in turn, recruit additional proteins to act as "coactivators" or "corepressors" in controlling the rate of transcription.

PNAS

In the current study, Andreas Wagner and his team sought to discover the molecular mechanism that sets trait variability in motion. They focused on genetic regulatory networks or GRNs—special proteins dedicated entirely to running the process of gene expression.

One of the main roles of GRNs has to do with cell development and differentiation. Since the of every one of an organism's cells that is not a sex cell contains an entire copy of that organism's genome, almost all cells should be able to make any protein encoded by their DNA. However, if all the genes were expressed, the result would be a bit like an overzealous traveler buying camping gear, kayaking supplies, skis, diving equipment and snowshoes in preparation for one trip. The same is true for cells: to maximize efficiency, each one only produces the specific proteins it needs to perform its particular function.

In addition to setting a particular cell's protein-making machinery on the right track, GRNs can turn on certain genes in response to changes in the organism's environment. For example, when a yeast cell happens to be in a sugar solution, its GRNs will turn on genes that code for proteins designed for digestion in order to take advantage of the presence of the sugar.

Evolution in the Lab

Because GRNs play such a vital role in how a cell acquires its identity, it's logical to assume that they may also play a part in determining—and possibly restricting—the ways in which that identity evolves over time. Previous studies had shown that mutations in certain regions of GRNs "play an important part in evolutionary adaptation and innovation"— a quality that makes them "primary candidates for systems that might lead to the production of constrained variation," Wagner and his colleagues write.

To explore this possibility in more detail, the researchers used the tools of synthetic biology—an interdisciplinary field focused on studying complex biological systems by engineering and testing various simulations. Back in the 1990s, Wagner began developing a synthetic gene network model programmed to simulate well-known interactions among members of real-life GRNs. The model makes it possible to test how the network responds to various modifications and has since become a valuable tool for investigating the molecular underpinnings of gene expression in an evolutionary context. Top: Shutterstock; Bottom: Schaerli, Y., et al./Molecular Systems Biology

Wagner and his team used two simulated GRN circuits, both of which were modeled after real-life GRNs found in E. coli. They showed that mutations resulted in distinct novel phenotypes in two circuits that previously produced the same phenotype before mutation. They concluded that this constrained phenotypic variation was caused by differences in the circuits’ regulatory mechanisms.

Recently, Wagner and his team used two simulated GRN circuits, both of which were modeled after real-life GRNs found in E. coli. Both systems produce a characteristic phenotype—a so-called "expression stripe," or pattern of gene expression induced by the regulatory network. In order to see how the synthetic GRNs influenced their target genes, the researchers tagged them with clearly visible fluorescent proteins which lit up the areas of interest, much like a word search feature highlights particular words in a web page or Word document. While both networks produced the same result—a clearly recognizable gene expression stripe—the specific interactions that helped them get the job done were different. The authors describe them as having "different topologies…qualitatively different patterns of interaction between a GRN's genes" that do not affect their overall function as regulators but could nonetheless have significant implications on a wider scale.

Tuning into the Differences

Wagner and his team then tested the possibility that the internal dynamics of a GRN might play a role in the bias toward certain phenotypes over others in an evolutionary context. To study this, they introduced mutations to the synthetic GRN systems and repeated the experiment to see what types of changes would occur.

As it turned out, after the mutations were introduced, interesting differences emerged in the output of the two networks—which had previously been the same gene expression stripe. As expected, the mutations gave rise to new patterns of expression in both cases. However, the new phenotypes seemed to be constrained or limited according to different criteria, with each network showing a bias for a particular set of new patterns.

The presence of such biases, in turn, suggests that the regulatory dynamics involved in GRN activities do, in fact, play a role in setting limits on new phenotypes that arise in the course of evolution. These tendencies make some new phenotypes more likely to emerge than others and shed light on the way variability of traits works at the molecular level.

To get a better sense of this situation, imagine a symphony orchestra and a jazz band playing a well-known piece of music that could be performed by either classical or jazz musicians. Here the equivalent of "phenotype" would be the performance of the piece; but the internal dynamics of the two performances would of course be very different. Now imagine both groups putting on a concert that includes other pieces—varying the "phenotypes," so to speak. It's likely that the two concerts will be different—with one leaning more toward classical music and the other more toward jazz.

Living Systems

By showing how nature places constraints on the types of variations that arise at a molecular level, the study significantly advances our understanding of the way evolution works in a more global sense. Wagner and his team plan to conduct follow-up experiments to explore the subject more deeply.

Moreover, the idea of constrained evolution itself relates to the broader idea of biological systems: how they develop, how they are maintained, and how they change with time. Nature tends to edit out anything unnecessary to maximize efficiency. The brain of an infant, for example, prunes away irrelevant or unnecessary connections as it develops—a process that slows down but never completely stops, meaning that our perception and experience of the world are, at some level, always in flux.

The research makes a valuable contribution to the field of synthetic biology and has important implications for future research. As Wagner himself puts it in Arrival of the Fittest: Solving Evolution's Greatest Puzzle, "Environmental change requires complexity, which begets robustness, which begets genotype networks, which enable innovations, the very kind that allow life to cope with environmental change, increase its complexity, and so on, in an ascending spiral of ever-increasing innovability."

Discussion Questions

Do you think the process described in the quote at the end is an accurate description of how living things evolve? If so, how does this tendency towards increasing complexity remain consistent with the tendency to prune away less efficient parts of an organism, as noted earlier.

Physicist Albert Einstein reportedly once said, "Everything should be made as simple as possible, but not simpler." Do you think this remark is consistent with the idea expressed in the closing quotation?

Journal Abstracts and Articles

(Researchers' own descriptions of their work, summary or full-text, on scientific journal websites.)

Schaerli, Y, et al. "Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution. Molecular Systems Biology." Molecular Systems Biology (September 10, 2018) [accessed September 20, 2018]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129954/.

Bibliography

Andreas Wagner Laboratory [accessed September 20, 2018]: http://www.ieu.uzh.ch/wagner/research.html.

Keywords synthetic biology, constrained evolution, phenotype, genotype, natural selection, gene regulatory networks, trait variability, expression stripe, evolutionary bias, Andreas Wagner

Citation Information ( MLA ) Poltarak, Katya. “How Nature Streamlines Evolution.” Today's Science, Infobase Learning, Jan. 2019, http://tsof.infobaselearning.com/recordurl.aspx?wid=99270&ID=41822. Accessed 8 Feb. 2019.