Tag: optimization

Origin and Design of the Genetic Code: A One-Two Punch for Creation

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By Fazale Rana – October 23, 2019

So, in the spirit of the endless debates that take place on sports talk radio, I ask: What duo is the greatest one-two punch in NBA history? Is it:

  • Kareem and Magic?
  • Kobe and Shaq?
  • Michael and Scottie?

Another confession: I am a science-faith junkie. I never tire when it comes to engaging in discussions about the interplay between science and the Christian faith. From my perspective, the most interesting facet of this conversation centers around the scientific evidence for God’s existence.

So, toward this end, I ask: What is the most compelling biochemical evidence for God’s existence? Is it:

  • The complexity of biochemical systems?
  • The eerie similarity between biomolecular motors and machines designed by human engineers?
  • The information found in DNA?

Without hesitation I would say it is actually another feature: the origin and design of the genetic code.

The genetic code is a biochemical code that consists of a set of rules defining the information stored in DNA. These rules specify the sequence of amino acids used by the cell’s machinery to synthesize proteins. The genetic code makes it possible for the biochemical apparatus in the cell to convert the information formatted as nucleotide sequences in DNA into information formatted as amino acid sequences in proteins.

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Figure: A Depiction of the Genetic Code. Image credit: Shutterstock

In previous articles (see the Resources section), I discussed the code’s most salient feature that I think points to a Creator’s handiwork: it’s multidimensional optimization. That optimization is so extensive that evolutionary biologists struggle to account for it’s origin, as illustrated by the work of biologist Steven Massey1.

Both the optimization of the genetic code and the failure of evolutionary processes to account for its design form a potent one-two punch, evincing the work of a Creator. Optimization is a marker of design, and if it can’t be accounted for through evolutionary processes, the design must be authentic—the product of a Mind.

Can Evolutionary Processes Generate the Genetic Code?

For biochemists working to understand the origin of the genetic code, its extreme optimization means that it is not the “frozen accident” that Francis Crick proposed in a classic paper titled “On the Origin of the Genetic Code.”2

Many investigators now think that natural selection shaped the genetic code, producing its optimal properties. However, I question if natural selection could evolve a genetic code with the degree of optimality displayed in nature. In the Cell’s Design (published in 2008), I cite the work of the late biophysicist Hubert Yockey in support of my claim.3 Yockey determined that natural selection would have to explore 1.40 x 1070 different genetic codes to discover the universal genetic code found in nature. Yockey estimated 6.3 x 1015 seconds (200 million years) is the maximum time available for the code to originate. Natural selection would have to evaluate roughly 1055 codes per second to find the universal genetic code. And even if the search time was extended for the entire duration of the universe’s existence, it still would require searching through 1052 codes per second to find nature’s genetic code. Put simply, natural selection lacks the time to find the universal genetic code.

Researchers from Germany raised the same difficulty for evolution recently. Because of the genetic code’s multidimensional optimality, they concluded that “the optimality of the SGC [standard genetic code] is a robust feature and cannot be explained by any simple evolutionary hypothesis proposed so far. . . . the probability of finding the standard genetic code by chance is very low. Selection is not an omnipotent force, so this raises the question of whether a selection process could have found the SGC in the case of extreme code optimalities.”4

Two More Evolutionary Mechanisms Considered

Life scientist Massey reached a similar conclusion through a detailed analysis of two possible evolutionary mechanisms, both based on natural selection.9

If the genetic code evolved, then alternate genetic codes would have to have been generated and evaluated until the optimal genetic code found in nature was discovered. This process would require that coding assignments change. Biochemists have identified two mechanisms that could contribute to coding reassignments: (1) codon capture and (2) an ambiguous intermediate mechanism. Massey tested both mechanisms.

Massey discovered that neither mechanism can evolve the optimal genetic code. When he ran computer simulations of the evolutionary process using codon capture as a mechanism, they all ended in failure, unable to find a highly optimized genetic code. When Massey ran simulations with the ambiguous intermediate mechanism, he could evolve an optimized genetic code. But he didn’t view this result as success. He learned that it takes between 20 to 30 codon reassignments to produce a genetic code with the same degree of optimization as the genetic code found in nature.

The problem with this evolutionary mechanism is that the number of coding reassignments observed in nature is scarce based on the few deviants of the genetic code thought to have evolved since the origin of the last common ancestor. On top of this problem, the structure of the optimized codes that evolved via the ambiguous intermediate mechanism is different from the structure of the genetic code found in nature. In short, the result obtained via the ambiguous intermediate mechanism is unrealistic.

As Massey points out, “The evolution of the SGC remains to be deciphered, and constitutes one of the greatest challenges in the field of molecular evolution.”10

Making Sense of Explanatory Models

In the face of these discouraging results for the evolutionary paradigm, Massey concludes that perhaps another evolutionary force apart from natural selection shaped the genetic code. One idea Massey thinks has merit is the Coevolution Theory proposed by J. T. Wong. Wong argued that the genetic code evolved in conjunction with the evolution of biosynthetic pathways that produce amino acids. Yet, Wong’s theory doesn’t account for the extreme optimization of the genetic code in nature. And, in fact, the relationships between coding assignments and amino acid biosynthesis appear to result from a statistical artifact, and nothing more.11 In other words, Wong’s ideas don’t work.

That brings us back to the question of how to account for the genetic code’s optimization and design.

As I see it, in the same way that two NBA superstars work together to help produce a championship-caliber team, the genetic code’s optimization and the failure of every evolutionary model to account for it form a potent one-two punch that makes a case for a Creator.

And that is worth talking about.

Resources

Endnotes
  1. Steven E. Massey, “Searching of Code Space for an Error-Minimized Genetic Code via Codon Capture Leads to Failure, or Requires at Least 20 Improving Codon Reassignments via the Ambiguous Intermediate Mechanism,” Journal of Molecular Evolution 70, no. 1 (January 2010): 106–15, doi:10.1007/s00239-009-9313-7.
  2. F. H. C. Crick, “The Origin of the Genetic Code,” Journal of Molecular Biology 38, no. 3 (December 28, 1968): 367–79, doi:10.1016/0022-2836(68)90392-6.
  3. Hubert P. Yockey, Information Theory and Molecular Biology (Cambridge, UK: Cambridge University Press, 1992), 180–83.
  4. Stefan Wichmann and Zachary Ardern, “Optimality of the Standard Genetic Code Is Robust with Respect to Comparison Code Sets,” Biosystems 185 (November 2019): 104023, doi:10.1016/j.biosystems.2019.104023.
  5. Massey, “Searching of Code Space.”
  6. Massey, “Searching of Code Space.”
  7. Ramin Amirnovin, “An Analysis of the Metabolic Theory of the Origin of the Genetic Code,” Journal of Molecular Evolution 44, no. 5 (May 1997): 473–76, doi:10.1007//PL00006170.

Reprinted with permission by the author

Original article at:
https://reasons.org/explore/blogs/the-cells-design

New Insights into Genetic Code Optimization Signal Creator’s Handiwork

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By Fazale Rana – October 16, 2019

I knew my career as a baseball player would be short-lived when, as a thirteen-year-old, I made the transition from Little League to the Babe Ruth League, which uses official Major League Baseball rules. Suddenly there were a whole lot more rules for me to follow than I ever had to think about in Little League.

Unlike in Little League, at the Babe Ruth level the hitter and base runners have to know what the other is going to do. Usually, the third-base coach is responsible for this communication. Before each pitch is thrown, the third-base coach uses a series of hand signs to relay instructions to the hitter and base runners.

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Credit: Shutterstock

My inability to pick up the signs from the third-base coach was a harbinger for my doomed baseball career. I did okay when I was on base, but I struggled to pick up his signs when I was at bat.

The issue wasn’t that there were too many signs for me to memorize. I struggled recognizing the indicator sign.

To prevent the opposing team from stealing the signs, it is common for the third-base coach to use an indicator sign. Each time he relays instructions, the coach randomly runs through a series of signs. At some point in the sequence, the coach gives the indicator sign. When he does that, it means that the next signal is the actual sign.

All of this activity was simply too much for me to process. When I was at the plate, I couldn’t consistently keep up with the third-base coach. It got so bad that a couple of times the third-base coach had to call time-out and have me walk up the third-base line, so he could whisper to me what I was to do when I was at the plate. It was a bit humiliating.

Codes Come from Intelligent Agents

The signs relayed by a third-base coach to the hitter and base runners are a type of code—a set of rules used to convert and convey information across formats.

Experience teaches us that it takes intelligent agents, such as baseball coaches, to devise codes, even those that are rather basic in their design. The more sophisticated a code, the greater the level of ingenuity required to develop it.

Perhaps the most sophisticated codes of all are those that can detect errors during data transmission.

I sure could have used a code like that when I played baseball. It would have helped me if the hand signals used by the third-base coach were designed in such a way that I could always understand what he wanted, even if I failed to properly pick up the indicator signal.

The Genetic Code

As it turns out, just such a code exists in nature. It is one of the most sophisticated codes known to us—far more sophisticated than the best codes designed by the brightest computer engineers in the world. In fact, this code resides at the heart of biochemical systems. It is the genetic code.

This biochemical code consists of a set of rules that define the information stored in DNA. These rules specify the sequence of amino acids that the cell’s machinery uses to build proteins. In this process, information formatted as nucleotide sequences in DNA is converted into information formatted as amino acid sequences in proteins.

Moreover, the genetic code is universal, meaning that all life on Earth uses it.1

Biochemists marvel at the design of the genetic code, in part because its structure displays exquisite optimization. This optimization includes the capacity to dramatically curtail errors that result from mutations.

Recently, a team from Germany identified another facet of the genetic code that is highly optimized, further highlighting its remarkable qualities.2

The Optimal Genetic Code

As I describe in The Cell’s Design, scientists from Princeton University and the University of Bath (UK) quantified the error-minimization capacity of the genetic code during the 1990s. Their work indicated that the universal genetic code is optimized to withstand the potentially harmful effects of substitution mutations better than virtually any other conceivable genetic code.3

In 2018, another team of researchers from Germany demonstrated that the universal genetic code is also optimized to withstand the harmful effects of frameshift mutations—again, better than other conceivable codes.4

In 2007, researchers from Israel showed that the genetic code is also optimized to harbor overlapping codes.5 This is important because, in addition to the genetic code, regions of DNA harbor other overlapping codes that direct the binding of histone proteins, transcription factors, and the machinery that splices genes after they have been transcribed.

The Robust Optimality of the Genetic Code

With these previous studies serving as a backdrop, the German research team wanted to probe more deeply into the genetic code’s optimality. These researchers focused on potential optimality of three properties of the genetic code: (1) resistance to harmful effects of substitution mutations, (2) resistance to harmful effects of frameshift mutations, and (3) capacity to support overlapping genes.

As with earlier studies, the team assessed the optimality of the naturally occurring genetic code by comparing its performance with sets of random codes that are conceivable alternatives. For all three property comparisons, they discovered that the natural (or standard) genetic code (SGC) displays a high degree of optimality. The researchers write, “We find that the SGC’s optimality is very robust, as no code set with no optimised properties is found. We therefore conclude that the optimality of the SGC is a robust feature across all evolutionary hypotheses.”6

On top of this insight, the research team adds one other dimension to multidimensional optimality of the genetic code: its capacity to support overlapping genes.

Interestingly, the researchers also note that the results of their work raise significant challenges to evolutionary explanations for the genetic code, pointing to the code’s multidimensional optimality that is extreme in all dimensions. They write:

We conclude that the optimality of the SGC is a robust feature and cannot be explained by any simple evolutionary hypothesis proposed so far. . . . the probability of finding the standard genetic code by chance is very low. Selection is not an omnipotent force, so this raises the question of whether a selection process could have found the SGC in the case of extreme code optimalities.7

While natural selection isn’t omnipotent, a transcendent Creator would be, and could account for the genetic code’s extreme optimality.

The Genetic Code and the Case for a Creator

In The Cell’s Design, I point out that our common experience teaches us that codes come from minds. It’s true on the baseball diamond and true in the computer lab. By analogy, the mere existence of the genetic code suggests that biochemical systems come from a Mind—a conclusion that gains additional support when we consider the code’s sophistication and exquisite optimization.

The genetic code’s ability to withstand errors that arise from substitution and frameshift mutations, along with its optimal capacity to harbor multiple overlapping codes and overlapping genes, seems to defy naturalistic explanation.

As a neophyte playing baseball, I could barely manage the simple code the third-base coach used. How mind-boggling it is for me when I think of the vastly superior ingenuity and sophistication of the universal genetic code.

And, just like the hitter and base runner work together to produce runs in baseball, the elegant design of the genetic code and the inability of evolutionary processes to account for its extreme multidimensional optimization combine to make the case that a Creator played a role in the origin and design of biochemical systems.

With respect to the case for a Creator, the insight from the German research team hits it out of the park.

Resources:

Endnotes
  1. Some organisms have a genetic code that deviates from the universal code in one or two of the coding assignments. Presumably, these deviant codes originate when the universal genetic code evolves, altering coding assignments.
  2. Stefan Wichmann and Zachery Ardern, “Optimality of the Standard Genetic Code Is Robust with Respect to Comparison Code Sets,” Biosystems 185 (November 2019): 104023, doi:10.1016/j.biosystems.2019.104023.
  3. David Haig and Laurence D. Hurst, “A Quantitative Measure of Error Minimization in the Genetic Code,” Journal of Molecular Evolution 33, no. 5 (November 1991): 412–17, doi:1007/BF02103132; Gretchen Vogel, “Tracking the History of the Genetic Code,” Science 281, no. 5375 (July 17, 1998): 329–31, doi:1126/science.281.5375.329; Stephen J. Freeland and Laurence D. Hurst, “The Genetic Code Is One in a Million,” Journal of Molecular Evolution 47, no. 3 (September 1998): 238–48, doi:10.1007/PL00006381; Stephen J. Freeland et al., “Early Fixation of an Optimal Genetic Code,” Molecular Biology and Evolution 17, no. 4 (April 2000): 511–18, 10.1093/oxfordjournals.molbev.a026331.
  4. Regine Geyer and Amir Madany Mamlouk, “On the Efficiency of the Genetic Code after Frameshift Mutations,” PeerJ 6 (May 21, 2018): e4825, doi:10.7717/peerj.4825.
  5. Shalev Itzkovitz and Uri Alon, “The Genetic Code Is Nearly Optimal for Allowing Additional Information within Protein-Coding Sequences,” Genome Research 17, no. 4 (April 2007): 405–12, doi:10.1101/gr.5987307.
  6. Wichmann and Ardern, “Optimality.”
  7. Wichmann and Ardern, “Optimality.”

Reprinted with permission by the author

Original article at:
https://reasons.org/explore/blogs/the-cells-design

Is the Optimal Set of Protein Amino Acids Purposed by a Mind?

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By Fazale Rana – October 9, 2019

To get our assays to work properly, we had to carefully design and optimize each test before executing it with exacting precision in the laboratory. Optimizing these assays was no easy feat. It could take weeks of painstaking effort to get the protocols just right.

My experiences working in the lab taught me some important lessons that I carry with me today as a Christian apologist. One of these lessons has to do with optimization. Optimized systems don’t just happen, whether they are laboratory procedures, manufacturing operations, or well-designed objects or devices. Instead, optimization results from the insights and efforts of intelligent agents, and therefore serves as a sure indicator of intelligent design.

As it turns out, nearly every biochemical system appears to be highly optimized. For me, this fact indicates that life stems from a Mind. And as life scientists continue to characterize biochemical systems, they keep discovering more and more examples of biochemical optimization, as recent work by a large team of collaborators working at the Earth-Life Science Institute (ELSI) in Tokyo, Japan, illustrates.1

These researchers uncovered more evidence that the twenty amino acids encoded by the genetic code possess the optimal set of physicochemical properties. If not for these properties, it would not be possible for the cell to build proteins that could support the wide range of activities required to sustain living systems. This insight gives us important perspective into the structure-function relationships of proteins. It also has theological significance, adding to the biochemical case for a Creator.

Before describing the ELSI team’s work and its theological implications, a little background might be helpful for some readers. For those who are familiar with basic biochemistry, just skip ahead to Why These Twenty Amino Acids?

Background: Protein Structure

Proteins are large, complex molecules that play a key role in virtually all of the cell’s operations. Biochemists have long known that the three-dimensional structure of a protein dictates its function. Because proteins are such large, complex molecules, biochemists categorize protein structure into four different levels: primary, secondary, tertiary, and quaternary structures.

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Figure 1: The Four Levels of Protein Structure. Image credit: Shutterstock

  • A protein’s primary structure is the linear sequence of amino acids that make up each of its polypeptide chains.
  • The secondary structure refers to short-range three-dimensional arrangements of the polypeptide chain’s backbone arising from the interactions between chemical groups that make up its backbone. Three of the most common secondary structures are the random coil, alpha (α) helix, and beta (β) pleated sheet.
  • Tertiary structure describes the overall shape of the entire polypeptide chain and the location of each of its atoms in three-dimensional space. The structure and spatial orientation of the chemical groups that extend from the protein backbone are also part of the tertiary structure.
  • Quaternary structure arises when several individual polypeptide chains interact to form a functional protein complex.

Background: Amino Acids

The building blocks of proteins are amino acids. These compounds are characterized by having both an amino group and a carboxylic acid bound to a central carbon atom. Also bound to this carbon are a hydrogen atom and a substituent that biochemists call an R group.

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Figure 2: The Structure of a Typical Amino Acid. Image credit: Shutterstock

The R group determines the amino acid’s identity. For example, if the R group is hydrogen, the amino acid is called glycine. If the R group is a methyl group, the amino acid is called alanine.

Close to 150 amino acids are found in proteins. But only 19 amino acids (plus 1 imino acid, called proline) are specified by the genetic code. Biochemists refer to these 20 as the canonical set.

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Figure 3: The Protein-Forming Amino Acids. Image credit: Shutterstock

A protein’s primary structure forms when amino acids react with each other to form a linear chain, with the amino group of one amino acid combining with the carboxylic acid of another to form an amide linkage. (Sometimes biochemists call the linkage a peptide bond.)

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Figure 4: The Chemical Linkage between Amino Acids. Image credit: Shutterstock

The repeating amide linkages along the amino acid chain form the protein’s backbone. The amino acids’ R groups extend from the backbone, creating a distinct physicochemical profile along the protein chain for each unique amino acid sequence. To first approximation, this unique physicochemical profile dictates the protein’s higher-order structures and, hence, the protein’s function.

Why These Twenty Amino Acids?

Research has revealed that the set of amino acids used to build proteins is universal. In other words, the proteins found in every organism on Earth are made up of the same canonical set.

Biochemists have long wondered: Why these 20 amino acids?

In the early 1980s biochemists discovered that an exquisite molecular rationale undergirds the amino acid set used to make proteins.2 Every aspect of amino acid structure has to be precisely the way it is for life to be possible. On top of that, biochemists concluded that the set of 20 amino acids possesses the “just-right” physical and chemical properties that evenly and uniformly vary across a broad range of size, charge, and hydrophobicity (water resistance). In fact, it appears as if the amino acids selected for proteins seem to form a uniquely optimal set of 20 amino acids compared to random sets of amino acids.3

With these previous studies as a backdrop, the ELSI investigators wanted to develop a better understanding of the optimal nature of the universal set of amino acids used to build proteins. They also wanted to gain insight into the origin of the canonical set.

To do this they used a library of 1,913 amino acids (including the 20 amino acids that make up the canonical set) to construct random sets of amino acids. The researchers varied the set sizes from 3 to 20 amino acids and evaluated the performance of the random sets in terms of their capacity to support: (1) the folding of protein chains into three-dimensional structures; (2) protein catalytic activity; and (3) protein solubility.

They discovered that if a random set of amino acids included even a single amino acid from the canonical set, it dramatically out-performed random sets of the same size without any of the canonical amino acids. Based on these results, the researchers concluded that each of the 20 amino acids used to build proteins stands out, possessing highly unusual properties that make them ideally suited for their biochemical role, confirming the results of previous studies.

An Evolutionary Origin for the Canonical Set?

The ELSI researchers believe that—from an evolutionary standpoint—these results also shed light as to how the canonical set of amino acids emerged. Because of the unique adaptive properties of the canonical amino acids, the researchers speculate that “each time a CAA [canonical amino acid] was discovered and embedded during evolution, it provided an adaptive value unusual among many alternatives, and each selective step may have helped bootstrap the developing set to include still more CAAs.”4

In other words, the researchers offer the conjecture that whenever the evolutionary process stumbled upon one of the amino acids in the canonical set and incorporated it into nascent biochemical systems, the addition offered such a significant evolutionary advantage that it became instantiated into the biochemistry of the emerging cellular systems. Presumably, as this selection process occurred repeatedly over time, members of the canonical set would be added, one by one, to the evolving amino acid set, eventually culminating in the full canonical set.

Scientists find further support for this scenario in the following observation: some of the canonical amino acids seemingly play a more important role in optimizing smaller sets of amino acids, some play a more important role in optimizing intermediate size sets of amino acids, and others play a more prominent role in optimizing larger sets. They argue that this difference may reflect the sequence by which amino acids were added to the evolving set of amino acids as life emerged.

On the surface, this evolutionary explanation is not unreasonable. But more careful consideration of the idea raises concerns. For example, just because a canonical amino acid becomes incorporated into a set of amino acids and improves its adaptive value doesn’t mean that the resulting set of amino acids could produce the range of proteins with the solubility, foldability, and catalytic range needed to support life processes. Intuitively, it seems to me as a biochemist, that there must be a threshold for the number of canonical amino acids in any set of amino acids for it to have the range of physicochemical properties needed to build all the proteins needed to support minimal life.

I also question this evolutionary scenario because some of the amino acids that optimize smaller sets would not have been the ones present initially on the early Earth because they cannot be made by prebiotic reactions. Instead, many of the amino acids that optimize smaller sets can only be generated through biosynthetic routes that must have emerged much later in any evolutionary scenario for the origin of life.5 This limitation also means that the only way for some of the canonical amino acids to become incorporated into the canonical set is that multi-step biosynthetic routes for those amino acids evolved first. But if the full canonical set isn’t available, then it is questionable if the proteins needed to catalyze the biosynthesis of these amino acid would exist, resulting in a chicken-and-egg dilemma.

In light of these concerns, is there a better explanation for the highly optimized canonical set of amino acids?

A Creator’s Role?

Optimality of the universal set of protein amino acids finds explanation if life stems from a Creator’s handiwork. As noted, optimization is an indicator of intelligent design, achieved through foresight and preplanning. Optimization requires inordinate attention to detail and careful craftsmanship. By analogy, the optimized biochemistry epitomized by the amino acid set that makes up proteins rationally points to the work of a Creator.

Is There a Biochemical Anthropic Principle?

This discovery also leads to another philosophical implication: It lends support to the existence of a biochemical anthropic principle.

The ELSI researchers speculate that no matter the starting point in the evolutionary process, the pathways will all converge at the canonical set of amino acids because of the acids’ unusual adaptive properties. In other words, the amino acids that make up the universal set of protein-coding amino acids are not the outworking of an historically contingent evolutionary process, but instead seem to be fundamentally prescribed by the laws of nature. To put it differently, it appears as if the canonical set of amino acids has been preordained in some way.6 One of the study’s authors, Rudrarup Bose, suggests that “Life may not be just a set of accidental events. Rather, there may be some universal laws governing the evolution of life.”7

Though I prefer to see the origin of life as a creation event, it is important to recognize that even if one were to adopt an evolutionary perspective on life’s origin, it looks as if a Mind is responsible for jimmy-rigging the process to a predetermined endpoint. It looks as if a Mind purposed for life to be present in the universe and structured the laws of nature so that, in this case, the uniquely optimal canonical set of amino acids would inevitably emerge.

Along these lines, it is remarkable to think that the canonical set of amino acids has the precise properties needed for life to exist. This “coincidence” is eerie, to say the least. As a biochemist, I interpret this coincidence as evidence that our universe has been designed for a purpose. It is provocative to think that regardless of one’s perspective on the origin of life, the evidence converges toward a single conclusion: namely that life manifests from an intelligent agent—God.

Resources

The Optimality of Biochemical Systems

The Biochemical Anthropic Principle

Endnotes
  1. Melissa Ilardo et al., “Adaptive Properties of the Genetically Encoded Amino Acid Alphabet Are Inherited from Its Subset,” Scientific Reports 9, no. 12468 (August 28, 2019), doi:10.1038/s41598-019-47574-x.
  2. Arthur L. Weber and Stanley L. Miller, “Reasons for the Occurrence of the Twenty Coded Protein Amino Acids,” Journal of Molecular Evolution 17, no. 5 (September 1981): 273–84, doi:10.1007/BF01795749; H. James Cleaves II, “The Origin of the Biologically Coded Amino Acids,” Journal of Theoretical Biology 263, no. 4 (April 2010): 490–98, doi:10.1016/j.jtbi.2009.12.014.
  3. Gayle K. Philip and Stephen J. Freeland, “Did Evolution Select a Nonrandom ‘Alphabet’ of Amino Acids?” Astrobiology 11, no. 3 (April 2011), 235–40, doi:10.1089/ast.2010.0567; Matthias Granhold et al., “Modern Diversification of the Amino Acid Repertoire Driven by Oxygen,” Proceedings of the National Academy of Sciences, USA 115, no. 1 (January 2, 2018): 41–46, doi:10.1073/pnas.1717100115.
  4. Ilardo et al., “Adaptive Properties.”
  5. J. Tze-Fei Wong and Patricia M. Bronskill, “Inadequacy of Prebiotic Synthesis as Origin of Proteinous Amino Acids,” Journal of Molecular Evolution 13, no. 2 (June 1979): 115–25, doi:10.1007/BF01732867.
  6. Tokyo Institute of Technology, “Scientists Find Biology’s Optimal ‘Molecular Alphabet’ May Be Preordained,” ScienceDaily, September 10, 2019, http://www.sciencedaily.com/releases/2019/09/190910080017.htm.
  7. Tokyo Institute, “Scientists Find.”

Reprinted with permission by the author

Original article at:
https://reasons.org/explore/blogs/the-cells-design

New Insights into Endothermy Heat Up the Case for a Creator

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By Fazale Rana – August 7, 2019

When I was younger, I was always hot. I needed to be in air conditioning everywhere I went. I could never get the temperature cold enough. But now that I am older, I feel like a frail person who is always chilled, needing to drape myself with a blanket to keep warm.

Nevertheless, like all human beings, I am still warm-blooded. I am an endotherm, as are all mammals and birds.

For many biologists, endothermy represents a bit of an enigma. Maintaining a constant body temperature requires an elevated basal metabolic rate. But the energy needed to preserve a constant body temperature doesn’t come cheap. In fact, warm-blooded animals demand 30 times the energy per unit time compared to cold-blooded (ectothermic) creatures.

Though biologists have tried to account for endothermy, no model has adequately explained why birds and mammals are warm-blooded. The advantages of being warm-blooded over being cold-blooded have not seemed to adequately outweigh costs—until now.

Recently, a biologist from the University of Nevada, Reno, Michael L. Logan, published a model that helps make sense of this enigma.1 His work evokes the optimal design and elegant rationale for endothermy in birds and mammals—and ectothermy in amphibians and reptiles.

An Explanation for Endothermy

For endothermy to exist, it must confer some significant advantage for animals’ constant, elevated body temperatures.

Logan argues that endothermy maintains mammalian and bird body temperatures close to the thermal optimum for immune system functionality. The operations of the immune system are temperature-dependent. If the temperature is too low or too high, the immune system responds poorly to infectious agents. But an elevated and stable body temperature primes mammalian and bird immune systems to rapidly and effectively respond to pathogens. When birds and mammals acquire a pathogen, their bodies mount a fever response. This slight elevation in temperature places their body temperature at the thermal optimum.

In other words, the fever response plays a critical role when animals battle infectious agents. And warm-blooded animals have the advantage of possessing body temperatures close to ideal.

Temperature and Immune System Function

A body of evidence indicates that the immune system’s components display temperature-dependent changes in activity. As it turns out, fever optimizes immune system function by:

  1. Increasing the flow of blood through the bloodstream because of the vasodilation (blood vessel expansion) associated with fever. This increased blood flow accelerates the movement of immune cells throughout the body, giving them more timely access to pathogens.
  2. Increasing binding of immune system proteins to immune cells, assisting their trafficking to lymph tissue.
  3. Increasing cellular activity, such as proliferation and differentiation of immune cells and phagocytosis.

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Figure: The Human Immune System. Image credit: Shutterstock

Other studies indicate that some pathogens, such as fungi, lose virulence at higher temperatures, further accounting for elevated body temperatures and the importance of the fever response. Of course, if body temperature becomes too high, it will compromise immune system function, moving it away from the temperature optimum and leading to other complications. So, the fever response must be carefully regulated.

Here’s the key point: the metabolic costs of endothermy are justified because warm-bloodedness allows the immune systems of birds and mammals to be near enough to the temperature optimum that infectious agents can be quickly cleared from their bodies.

Fever Response in Ectotherms

Cold-blooded animals (ectotherms) also mount a fever response to infectious agents for the same reason as endotherms. However, the body temperature of ectotherms is set by their surroundings. This limitation means that ectotherms need to regulate their body temperature and mount the fever response through their behavior by moving into spaces with elevated temperatures. Doing so places them at the mercy of environmental changes. This condition means that cold-blooded creatures experience a significant time lag between the onset of infection and the fever response. It also means that, in some cases, ectotherms can’t elevate their body temperature to the immune system optimum if, for example, it is night or overcast.

Finally, in an attempt to elevate their body temperatures, ectotherms need to be out from under cover, making themselves vulnerable to predators. So, according to Logan’s model, endothermy offers some tangible advantages compared to ectothermy.

But endothermy comes at a cost. As mentioned, the metabolic cost of endothermy is extensive compared to ectothermy. Pathogen virulence marks another disadvantage. Logan points out that pathogens that infect cold-blooded animals are much less virulent than pathogens that infect warm-blooded creatures.

Endothermy and Ectothermy Trade-Offs

So, when it comes to regulation of animal body temperature, a set of trade-offs exists that include:

  • Metabolic costs
  • Immune system responsiveness and effectiveness
  • Pathogen virulence
  • Vulnerability to predators

These trade-offs can be managed by two viable strategies: endothermy and ectothermy. Each has advantages and disadvantages. And each is optimized in its own right.

Regulation of Body Temperature and the Case for a Creator

Logan seeks to account for the evolutionary origins of endothermy by appealing to the advantages it offers organisms battling pathogens. But, examining Logan’s scenario leaves one feeling as if the explanation is little more than an evolutionary just-so story.

When endothermy presented an enigma for biologists, it would have been hard to argue that it reflected the handiwork of a Creator, particularly in light of its large metabolic cost. But now that scientists understand the trade-offs in play and the optimization associated with the endothermic lifestyle, we can also interpret the optimization of endothermy and ectothermy as evidence for design.

From my vantage point, optimization signifies the handiwork of a Creator. As I discuss in The Cell’s Design, saying something is optimized is equivalent to saying it is well-designed. The optimization of an engineered system doesn’t just happen. Rather, such systems require forethought, planning, and careful attention to detail. In the same way, the optimized designs of biological systems like endothermy and ectothermy reasonably point to the work of a Creator.

And I am chill with that.

Resources

Endnotes
  1. Michael L. Logan, “Did Pathogens Facilitate the Rise of Endothermy?” Ideas in Ecology and Evolution 12 (June 4, 2019): 1–8, https://ojs.library.queensu.ca/index.php/IEE/article/view/13342.

Reprinted with permission by the author

Original article at:
https://reasons.org/explore/blogs/the-cells-design

The Optimal Design of the Genetic Code

theoptimaldesign

BY FAZALE RANA – OCTOBER 3, 2018

Were there no example in the world of contrivance except that of the eye, it would be alone sufficient to support the conclusion which we draw from it, as to the necessity of an intelligent Creator.

–William Paley, Natural Theology

In his classic work, Natural Theology, William Paley surveyed a range of biological systems, highlighting their similarities to human-made designs. Paley noticed that human designs typically consist of various components that interact in a precise way to accomplish a purpose. According to Paley, human designs are contrivances—things produced with skill and cleverness—and they come about via the work of human agents. They come about by the work of intelligent designers. And because biological systems are contrivances, they, too, must come about via the work of a Creator.

For Paley, the pervasiveness of biological contrivances made the case for a Creator compelling. But he was especially struck by the vertebrate eye. For Paley, if the only example of a biological contrivance available to us was the eye, its sophisticated design and elegant complexity alone justify the “necessity of an intelligent creator” to explain its origin.

As a biochemist, I am impressed with the elegant designs of biochemical systems. The sophistication and ingenuity of these designs convinced me as a graduate student that life must stem from the work of a Mind. In my book The Cell’s Design, I follow in Paley’s footsteps by highlighting the eerie similarity between human designs and biochemical systems—a similarity I describe as an intelligent design pattern. Because biochemical systems conform to the intelligent design pattern, they must be the work of a Creator.

As with Paley, I view the pervasiveness of the intelligent design pattern in biochemical systems as critical to making the case for a Creator. Yet, in particular, I am struck by the design of a single biochemical system: namely, the genetic code. On the basis of the structure of the genetic code alone, I think one is justified to conclude that life stems from the work of a Divine Mind. The latest work by a team of German biochemists on the genetic code’s design convinces me all the more that the genetic code is the product of a Creator’s handiwork.1

To understand the significance of this study and the code’s elegant design, a short primer on molecular biology is in order. (For those who have a background in biology, just skip ahead to The Optimal Genetic Code.)

Proteins

The “workhorse” molecules of life, proteins take part in essentially every cellular and extracellular structure and activity. Proteins are chain-like molecules folded into precise three-dimensional structures. Often, the protein’s three-dimensional architecture determines the way it interacts with other proteins to form a functional complex.

Proteins form when the cellular machinery links together (in a head-to-tail fashion) smaller subunit molecules called amino acids. To a first approximation, the cell employs 20 different amino acids to make proteins. The amino acids that make up proteins possess a variety of chemical and physical properties.

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Figure 1: The Amino Acids. Image credit: Shutterstock

Each specific amino acid sequence imparts the protein with a unique chemical and physical profile along the length of its chain. The chemical and physical profile determines how the protein folds and, therefore, its function. Because structure determines the function of a protein, the amino acid sequence is key to dictating the type of work a protein performs for the cell.

DNA

The cell’s machinery uses the information harbored in the DNA molecule to make proteins. Like these biomolecules, DNA consists of chain-like structures known as polynucleotides. Two polynucleotide chains align in an antiparallel fashion to form a DNA molecule. (The two strands are arranged parallel to one another with the starting point of one strand located next to the ending point of the other strand, and vice versa.) The paired polynucleotide chains twist around each other to form the well-known DNA double helix. The cell’s machinery forms polynucleotide chains by linking together four different subunit molecules called nucleotides. The four nucleotides used to build DNA chains are adenosine, guanosine, cytidine, and thymidine, familiarly known as A, G, C, and T, respectively.

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Figure 2: The Structure of DNA. Image credit: Shutterstock

As noted, DNA stores the information necessary to make all the proteins used by the cell. The sequence of nucleotides in the DNA strands specifies the sequence of amino acids in protein chains. Scientists refer to the amino-acid-coding nucleotide sequence that is used to construct proteins along the DNA strand as a gene.

The Genetic Code

A one-to-one relationship cannot exist between the 4 different nucleotides of DNA and the 20 different amino acids used to assemble polypeptides. The cell addresses this mismatch by using a code comprised of groupings of three nucleotides to specify the 20 different amino acids.

The cell uses a set of rules to relate these nucleotide triplet sequences to the 20 amino acids making up proteins. Molecular biologists refer to this set of rules as the genetic code. The nucleotide triplets, or “codons” as they are called, represent the fundamental communication units of the genetic code, which is essentially universal among all living organisms.

Sixty-four codons make up the genetic code. Because the code only needs to encode 20 amino acids, some of the codons are redundant. That is, different codons code for the same amino acid. In fact, up to six different codons specify some amino acids. Others are specified by only one codon.

Interestingly, some codons, called stop codons or nonsense codons, code no amino acids. (For example, the codon UGA is a stop codon.) These codons always occur at the end of the gene, informing the cell where the protein chain ends.

Some coding triplets, called start codons, play a dual role in the genetic code. These codons not only encode amino acids, but also “tell” the cell where a protein chain begins. For example, the codon GUG encodes the amino acid valine and also specifies the starting point of the proteins.

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Figure 3: The Genetic Code. Image credit: Shutterstock

The Optimal Genetic Code

Based on visual inspection of the genetic code, biochemists had long suspected that the coding assignments weren’t haphazard—a frozen accident. Instead it looked to them like a rationale undergirds the genetic code’s architecture. This intuition was confirmed in the early 1990s. As I describe in The Cell’s Design, at that time, scientists from the University of Bath (UK) and from Princeton University quantified the error-minimization capacity of the genetic code. Their initial work indicated that the naturally occurring genetic code withstands the potentially harmful effects of substitution mutations better than all but 0.02 percent (1 out of 5,000) of randomly generated genetic codes with codon assignments different from the universal genetic code.2

Subsequent analysis performed later that decade incorporated additional factors. For example, some types of substitution mutations (called transitions) occur more frequently in nature than others (called transversions). As a case in point, an A-to-G substitution occurs more frequently than does either an A-to-C or an A-to-T mutation. When researchers included this factor into their analysis, they discovered that the naturally occurring genetic code performed better than one million randomly generated genetic codes. In a separate study, they also found that the genetic code in nature resides near the global optimum for all possible genetic codes with respect to its error-minimization capacity.3

It could be argued that the genetic code’s error-minimization properties are more dramatic than these results indicate. When researchers calculated the error-minimization capacity of one million randomly generated genetic codes, they discovered that the error-minimization values formed a distribution where the naturally occurring genetic code’s capacity occurred outside the distribution. Researchers estimate the existence of 1018 (a quintillion) possible genetic codes possessing the same type and degree of redundancy as the universal genetic code. Nearly all of these codes fall within the error-minimization distribution. This finding means that of 1018 possible genetic codes, only a few have an error-minimization capacity that approaches the code found universally in nature.

Frameshift Mutations

Recently, researchers from Germany wondered if this same type of optimization applies to frameshift mutations. Biochemists have discovered that these mutations are much more devastating than substitution mutations. Frameshift mutations result when nucleotides are inserted into or deleted from the DNA sequence of the gene. If the number of inserted/deleted nucleotides is not divisible by three, the added or deleted nucleotides cause a shift in the gene’s reading frame—altering the codon groupings. Frameshift mutations change all the original codons to new codons at the site of the insertion/deletion and onward to the end of the gene.

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Figure 4: Types of Mutations. Image credit: Shutterstock

The Genetic Code Is Optimized to Withstand Frameshift Mutations

Like the researchers from the University of Bath, the German team generated 1 million random genetic codes with the same type and degree of redundancy as the genetic code found in nature. They discovered that the code found in nature is better optimized to withstand errors that result from frameshift mutations (involving either the insertion or deletion of 1 or 2 nucleotides) than most of the random genetic codes they tested.

The Genetic Code Is Optimized to Harbor Multiple Overlapping Codes

The optimization doesn’t end there. In addition to the genetic code, genes harbor other overlapping codes that independently direct the binding of histone proteins and transcription factors to DNA and dictate processes like messenger RNA folding and splicing. In 2007, researchers from Israel discovered that the genetic code is also optimized to harbor overlapping codes.4

The Genetic Code and the Case for a Creator

In The Cell’s Design, I point out that common experience teaches us that codes come from minds. By analogy, the mere existence of the genetic code suggests that biochemical systems come from a Mind. This conclusion gains considerable support based on the exquisite optimization of the genetic code to withstand errors that arise from both substitution and frameshift mutations, along with its optimal capacity to harbor multiple overlapping codes.

The triple optimization of the genetic code arises from its redundancy and the specific codon assignments. Over 1018 possible genetic codes exist and any one of them could have been “selected” for the code in nature. Yet, the “chosen” code displays extreme optimization—a hallmark feature of designed systems. As the evidence continues to mount, it becomes more and more evident that the genetic code displays an eerie perfection.5

An elegant contrivance such as the genetic code—which resides at the heart of biochemical systems and defines the information content in the cell—is truly one in a million when it comes to reasons to believe.

Resources

Endnotes

  1. Regine Geyer and Amir Madany Mamlouk, “On the Efficiency of the Genetic Code after Frameshift Mutations,” PeerJ 6 (2018): e4825, doi:10.7717/peerj.4825.
  2. David Haig and Laurence D. Hurst, “A Quantitative Measure of Error Minimization in the Genetic Code,” Journal of Molecular Evolution33 (1991): 412–17, doi:1007/BF02103132.
  3. Gretchen Vogel, “Tracking the History of the Genetic Code,” Science281 (1998): 329–31, doi:1126/science.281.5375.329; Stephen J. Freeland and Laurence D. Hurst, “The Genetic Code Is One in a Million,” Journal of Molecular Evolution 47 (1998): 238–48, doi:10.1007/PL00006381.; Stephen J. Freeland et al., “Early Fixation of an Optimal Genetic Code,” Molecular Biology and Evolution 17 (2000): 511–18, doi:10.1093/oxfordjournals.molbev.a026331.
  4. Shalev Itzkovitz and Uri Alon, “The Genetic Code Is Nearly Optimal for Allowing Additional Information within Protein-Coding Sequences,” Genome Research(2007): advanced online, doi:10.1101/gr.5987307.
  5. In The Cell’s Design, I explain why the genetic code cannot emerge through evolutionary processes, reinforcing the conclusion that the cell’s information systems—and hence, life—must stem from the handiwork of a Creator.
Reprinted with permission by the author
Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/10/03/the-optimal-design-of-the-genetic-code

Protein Amino Acids Form a “Just-Right” Set of Biological Building Blocks

proteinaminoacids

BY FAZALE RANA – FEBRUARY 21, 2018

Like most kids, I had a set of Lego building blocks. But, growing up in the 1960s, the Lego sets were nothing like the ones today. I am amazed at how elaborate and sophisticated Legos have become, consisting of interlocking blocks of various shapes and sizes, gears, specialty parts, and figurines—a far cry from the square and rectangular blocks that made up the Lego sets of my youth. The most imaginative things I could ever hope to build were long walls and high towers.

It goes to show: the set of building blocks make all the difference in the world.

This truism applies to the amino acid building blocks that make up proteins. As it turns out, proteins are built from a specialty set of amino acids that have the just-right set of properties to make life possible, as recent work by researchers from Germany attests.1 From my vantage point as a biochemist and a Christian, the just-right amino acid composition of proteins evinces intelligent design and is part of the reason I think a Creator must have played a direct role in the origin and design of life.

Why is the Same Set of Twenty Amino Acids Used to Build Proteins?

It stands as one of the most important insights about protein structure discovered by biochemists: The set of amino acids used to build proteins is universal. In other words, the proteins found in every organism on Earth are made up of the same 20 amino acids.

Yet, hundreds of amino acids exist in nature. And, this abundance prompts the question: Why these 20 amino acids? From an evolutionary standpoint, the set of amino acids used to build proteins should reflect:

1) the amino acids available on early Earth, generated by prebiotic chemical reactions;

2) the historically contingent outworking of evolutionary processes.

In other words, evolutionary mechanisms would have cobbled together an amino acid set that works “just good enough” for life to survive, but nothing more. No one would expect evolutionary processes to piece together a “just-right,” optimal set of amino acids. In other words, if evolutionary processes shaped the amino acid set used to build proteins, these biochemical building blocks should be much like the unsophisticated Lego sets little kids played with in the 1960s.

An Optimal Set of Amino Acids

But, contrary to this expectation, in the early 1980s biochemists discovered that an exquisite molecular rationale undergirds the amino acid set used to make proteins. Every aspect of the amino acid structure has to be precisely the way it is for life to be possible. On top of that, researchers from the University of Hawaii have conducted a quantitative comparison of the range of chemical and physical properties possessed by the 20 protein-building amino acids versus random sets of amino acids that could have been selected from early Earth’s hypothetical prebiotic soup.2 They concluded that the set of 20 amino acids is optimal. It turns out that the set of amino acids found in biological systems possesses the “just-right” properties that evenly and uniformly vary across a broad range of size, charge, and hydrophobicity. They also showed that the amino acids selected for proteins are a “highly unusual set of 20 amino acids; a maximum of 0.03% random sets outperformed the standard amino acid alphabet in two properties, while no single random set exhibited greater coverage in all three properties simultaneously.”3

A New Perspective on the 20 Protein Amino Acids

Beyond charge, size, and hydrophobicity, the German researchers wondered if quantum mechanical effects play a role in dictating the universal set of 20 protein amino acids. To address this question, they examined the gap between the HOMO (highest occupied molecular orbital) and the LUMO (lowest unoccupied molecular orbital) for the protein amino acids. The HOMO-LUMO gap is one of the quantum mechanical determinants of chemical reactivity. More reactive molecules have smaller HOMO-LUMO gaps than molecules that are relatively nonreactive.

The German biochemists discovered that the HOMO-LUMO gap was small for 7 of the 20 amino acids (histidine, phenylalanine cysteine, methionine, tyrosine, and tryptophan), and hence, these molecules display a high level of chemical activity. Interestingly, some biochemists think that these 7 amino acids are not necessary to build proteins. Previous studies have demonstrated that a wide range of foldable, functional proteins can be built from only 13 amino acids (glycine, alanine, valine, leucine, isoleucine, proline, serine, threonine, aspartic acid, glutamic acid, asparagine, lysine, and arginine). As it turns out, this subset of 13 amino acids has a relatively large HOMO-LUMO gap and, therefore, is relatively unreactive. This suggests that the reactivity of histidine, phenylalanine cysteine, methionine, tyrosine, and tryptophan may be part of the reason for the inclusion of the 7 amino acids in the universal set of 20.

As it turns out, these amino acids readily react with the peroxy free radical, a highly corrosive chemical species that forms when oxygen is present in the atmosphere. The German biochemists believe that when these 7 amino acids reside on the surface of proteins, they play a protective role, keeping the proteins from oxidative damage.

As I discussed in a previous article, these 7 amino acids contribute in specific ways to protein structure and function. And they contribute to the optimal set of chemical and physical properties displayed by the universal set of 20 amino acids. And now, based on the latest work by the German researchers, it seems that the amino acids’ newly recognized protective role against oxidative damage adds to their functional and structural significance in proteins.

Interestingly, because of the universal nature of biochemistry, these 7 amino acids must have been present in the proteins of the last universal common ancestor (LUCA) of all life on Earth. And yet, there was little or no oxygen present on early Earth, rendering the protective effect of these amino acids unnecessary. The importance of the small HOMO-LUMO gaps for these amino acids would not have become realized until much later in life’s history when oxygen levels became elevated in Earth’s atmosphere. In other words, inclusion of these amino acids in the universal set at life’s start seemingly anticipates future events in Earth’s history.

Protein Amino Acids Chosen by a Creator

The optimality, foresight, and molecular rationale undergirding the universal set of protein amino acids is not expected if life had an evolutionary origin. But, it is exactly what I would expect if life stems from a Creator’s handiwork. As I discuss in The Cell’s Design, objects and systems created and produced by human designers are typically well thought out and optimized. Both are indicative of intelligent design. In human designs, optimization is achieved through foresight and planning. Optimization requires inordinate attention to detail and careful craftsmanship. By analogy, the optimized biochemistry, epitomized by the amino acid set that makes up proteins, rationally points to the work of a Creator.

Resources

Endnotes

  1. Matthias Granhold et al., “Modern Diversification of the Amino Acid Repertoire Driven by Oxygen,” Proceedings of the National Academy of Sciences USA 115 (January 2, 2018): 41–46, doi:10.1073/pnas.1717100115.
  2. Gayle K. Philip and Stephen J. Freeland, “Did Evolution Select a Nonrandom ‘Alphabet’ of Amino Acids?” Astrobiology 11 (April 2011): 235–40, doi:10.1089/ast.2010.0567.
  3. Philip and Freeland, “Did Evolution Select,” 235–40.
Reprinted with permission by the author
Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/02/21/protein-amino-acids-form-a-just-right-set-of-biological-building-blocks

Placenta Optimization Shows Creator’s Handiwork

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BY FAZALE RANA – OCTOBER 19, 2016

The Creator of the universe desires an intimate relationship with each of us.

It is one of the more outrageous claims of the Christian faith. And no passage of Scripture expresses the intimacy between Creator and creature more than Psalm 139:13.

A fresh perspective on this passage of Scripture comes from recent work by researchers from Cambridge University in the UK. This study reveals the central role the placenta plays in properly allocating nutritional resources between mother and child, illustrating the intimate care God provided for us through the elegant design of embryological development.1

This research also has important pro-life implications, providing a response to the claim that the fetus is nothing more than a harmful mass of tissue.

Nutritional Demands of the Fetus and the Mother

For a pregnancy to be successful, nutrients must be carefully distributed between the fetus and the mother. Yet sharing nutrients runs contrary to the biological tendencies of the mother and the unborn baby. The fetus has a genetic drive for growth and craves all the nutrients it can get. So does the mother. But for the fetus to grow and develop, the mother must provide it with the nutrients it needs, setting up a potential tug of war between the mother and the developing baby in her womb.

Ironically, if the fetus hoards nutrients excessively, the hoarding can backfire. If the mother doesn’t have access to sufficient nutrients during the pregnancy, it can negatively impact lactation and the mother’s long-term health, which, in turn, compromises her ability to care for the child after birth.

As it turns out, the placenta plays a critical role in managing this trade-off. Instead of being passive tissue that absorbs available nutrients from the mother, the placenta dynamically distributes nutrients between mother and fetus, optimally ensuring the health of both mother and developing baby. To do this, the placenta receives metabolic signals from both the mother and fetus and responds to this input by regulating the nutrient amounts made available to the fetus.

One of the key genes involved in nutrient regulation is called p110α. This gene codes for a protein that integrates the metabolic signals from mother and fetus. The Cambridge University researchers wanted to understand the role that the maternal and fetal versions of this gene play in parsing the nutrient supply between mother and developing baby.

What Happens When p110α Is Defective in Mother and Child?

What happens when p110α is defective in mother and child? To answer this question, the research team used mice as a model system, preparing genetic mutants, so that either the mother or fetus had a defective version of the p110α gene. If the mother had a healthy p110αgene, but the fetus a defective version, the placenta developed abnormally. But in spite of its defective appearance, the placenta compensated so that it would still take up the nutrients the fetus needed to develop. However, if the mother had a defective version of the p110αgene, the placenta (which formed abnormally even though the fetus had a healthy version of the p110α gene) transported fewer nutrients to the fetus.

In adult tissue, the p110α gene plays a role in regulating growth in relationship to nutrient supply and mediates the metabolic effects of insulin and insulin-like growth factors. That means that a defective version of this gene models conditions in which the mother’s health is compromised due to disease, poor nutrition, stress, or other factors.

On the basis of this study, it appears that when the mother is healthy, the placenta readily transports nutrients to the fetus and dynamically adjusts, even if it forms abnormally. On the other hand, if the mother’s health is compromised, the placenta restricts nutrient flow to the fetus to ensure the mother’s long-term health, with the prospects that the fetus can still grow and develop.

This insight has important biomedical implications. In the developing world, one in five pregnancy complications involve the placenta. In the developed world, this number is one in eight. The researchers hope that this insight will help them understand the etiologies behind problem pregnancies and also help them identify biomarkers that will alert physicians to problems earlier in the pregnancy.

This work also has important apologetics implications, as well.

Indeed, We Are Fearfully and Wonderfully Made

This work highlights the elegance of embryological development. It seems an exquisite rationale—a biological logic, if you will—undergirds every aspect of development. The optimal way the placenta partitions resources between mother and fetus, carefully managing trade-offs, evinces the handiwork of the Creator, and reveals the Creator’s intimate care for the fetus.

The devastating effects caused by mutations to the p110α gene raises questions about the capacity of evolutionary mechanisms to explain the origin of the reproductive system in placental mammals. Because the placenta is not a passive conduit for nutrients between mother and fetus, the challenges of explaining its genesis via unguided evolutionary process become insurmountable. If the placenta lacks the capability to effectively allocate resources between the mother and fetus—or even if this process operates in a suboptimal manner—the fetus may not survive, or the mother may not be healthy enough to nurse and rear the child once it’s born. In other words, it becomes difficult to imagine how the placenta’s role in embryological development could evolve from an imperfect system to an optimal system under the influence of natural selection because of the critical, dynamic role the placenta plays in embryological development. If this role isn’t properly executed, the child isn’t likely to make it to reproductive age.

Is the Fetus Like a Tumor?

This work also has implications for the pro-life debate. I have often heard pro-choice advocates argue that abortion is not murder, because the fetus is like a tumor. But the work by the scientists from Cambridge University makes this view impossible. Because the placenta dynamically allocates resources between the mother and the fetus in a way that preserves the mother’s health, the fetus cannot be viewed as a tumor robbing the mother of nutrients. Instead, it looks as if the placenta’s function has been designed in such a way to ensure optimal health for both the mother and the fetus. This study also shows that if the mother’s health is in jeopardy, the placenta actually compromises the health of the fetus so that the mother’s health is not unduly harmed by the pregnancy.

Resources
“Curvaceous Anatomy of the Female Spine Reveals Ingenious Obstetric Design” by Virgil Robertson (article)
“What Are the Odds of You Being You?” by Matthew McClure (article)
“Morning Sickness May Protect Embryos from Toxins” with Fazale Rana (podcast)

Endnotes

  1. Amanda Sferruzzi-Perri et al., “Maternal and Fetal Genomes Interplay through Phosphoinositol 3-Kinase (PI3K)-p110α Signaling to Modify Placental Resource Allocation,” Proceedings of the National Academy of Sciences, USA 113 (October 2016): 11255–60, doi:10.1073/pnas.1602012113.
Reprinted with permission by the author
Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2016/10/19/placenta-optimization-shows-creator’s-handiwork