Application of ID: Leveraging Design Triangulation to Anticipate Biological Redundancy
In previous posts (here and here), I’ve covered how neo-Darwinism can make biological redundancy more confusing than it should be. I also argued that intelligent design can bring clarity by providing a strong rationale for studying biological redundancy while also simplifying the types of biological redundancy. Today I am going to give examples of each category of biological redundancy and demonstrate how using design triangulation can help scientists know when and where to expect biological redundancy.
Design triangulation allows one to infer the likelihood of a component based on the observation of a similar component from a different engineered system. For biological redundancy this is especially important because the function may be obscure. The key is to think about function instead of fitness 一 a subtle but important difference.
Let me provide an example of the first category.
True Redundancy: Backup Devices to Prevent Failure When Key System Components Fail
“Responsive backup circuits” are a prime example of the first category of true redundancy. (Kafri, Levy, and Pilpel 2006) Responsive backup circuits are duplicate genes that are designed to detect when one gene goes out and then fire up transcription of the second. However, compensation for the primary gene’s inactivation is not the only functional advantage of responsive backup circuits. These responsive backup circuits have also been shown to filter non-genetic noise, providing extreme precision in gene expression. (Kafri, Levy, and Pilpel 2006) Several examples occur in key metabolic pathways, and upregulation of the second gene may also follow an environmental cue to push things to the max.
Recursive backup circuits are a great example of true redundancy with some additional bonuses. But now, can design triangulation predict where to find more examples in this category?
Design Triangulating to Predict True Redundancy
All genetic information has a cellular cost of storage, maintenance, and transmission. Therefore in order to justify a backed-up gene, from a design perspective, the gene must be performing a safety-critical function for the organism or a multiplicity of functions (called “moonlighting”). Safety-critical functions in engineering often demand redundancy because of the magnitude of danger if the component fails. An example would be the fly-by-wire control systems in commercial airlines which have triple redundancy. Using design triangulation, a biologist might expect to find true redundancy in safety-critical organismal features likely to be enriched in developmental and repair processes. In order to qualify as true redundancy, the gene’s primary function needs to be identical, although regulation and sequence may vary.
Generic Redundancy: Anticipatory Systems for Rare Conditions
My example of generic redundancy comes from the work of Elizabeth Mueller, a postdoc at Stanford. She recently published beautiful work uncovering specialized niche roles for cell wall building enzymes in E. coli. (Mueller et al. 2019) There are 9 periplasmic steps to peptidoglycan synthesis but 36 periplasmic enzymes! Why so many periplasmic enzymes when the cytoplasmic steps have a nearly 1:1 ratio of enzymes to steps? Are these extra genes just redundant, unimportant enzymes?
Dr. Mueller showed in a series of experiments that this variety pack of enzymes allow for optimal cell wall synthesis in different pH environments. Her discovery of these seemingly redundant enzymes reveals an anticipatory system where the extra enzymes allow E.coli to optimally build its cell wall when faced with different pH environments.
I know of no evidence that Dr. Mueller is supportive of intelligent design. But I find it courageous that she pressed forward to discover the function of these seemingly redundant enzymes in the E. coli peptidoglycan synthesis framework. Although these enzymes at first glance seem to do very similar things, they actually help E. coli adapt precisely and be prepared for specific conditions. Now, let’s see how design triangulation can help us predict where generic redundancy might be needed.
Design Triangulating to Predict Anticipatory Systems for Rare Conditions
Based on the purpose of anticipatory systems for rare conditions, one can use design triangulation to predict that systems for rare conditions are probably not frequently expressed and the specialists are likely to have subtle but unique performance spectrums around the environmental variable. Biologists might look for these systems in rarely expressed genes which do not affect organismal health under standard laboratory conditions. Inferences about function are likely to be gained from the type of environmental cue triggering gene expression.
Closing out our design triangulation for generic redundancy takes us to our final category of biological redundancy, for which I will use my own thesis work as an example.
Almost Redundancy: Optimization Machinery for Organismal Robustness
In my PhD work, I considered a DNA binding protein called RefZ, a sporulation-specific protein from Bacillus subtilis. (Brown et al. 2019) This protein doesn’t cause an obvious fitness defect when deleted, but chromosome capture is affected, meaning the portion of the chromosome captured inside the developing spore is altered. Also, deleting RefZ causes another subtle functional defect: it takes the bacterium longer to form a spore. There are no identical proteins to RefZ (ruling it out as true redundancy) but there are other Bacillus proteins with similar features (suggesting it might be a case of generic or almost redundancy). As a sporulation protein, RefZ, is not expressed all the time but it is expressed every time the bacterium sporulates. This suggests its expression isn’t particularly sensitive to environmental conditions ruling out generic redundancy. Taken together the data point towards RefZ being an example of almost redundancy where it is important for precise chromosome capture and timely sporulation. Importantly, these types of studies can help us understand the optimality of biological systems.
Design Triangulating to Predict Almost Redundancy
Design triangulation predicts that a good design will have many optimization components whose loss will cause less optimal function overall but not a system defect. The fact that a system defect does not occur does not necessarily mean the part is unimportant. It could be that the redundant part’s purpose is only related to beauty, elegance, or precision but isn’t absolutely necessary for the system to work. These types of mechanisms are analogous to automotive details like catalytic converters, speakers, or keyless entry systems for vehicles. Loss doesn’t result in system failure but the organism will not be as optimal or elegant as before.
Outside the Box
So, let’s think outside the box. Assume redundancy in biological systems is intentional, test that hypothesis, and consider reasons such as optimality, environment variability, and robustness when trying to justify its importance to your colleagues. It’s time for these underappreciated system components to enter the limelight, raising our appreciation for the fine-tuning and robustness of biological systems.
- Brown, Emily E., Allyssa K. Miller, Inna V. Krieger, Ryan M. Otto, James C. Sacchettini, and Jennifer K. Herman. 2019. “A DNA-Binding Protein Tunes Septum Placement during Bacillus Subtilis Sporulation.” Journal of Bacteriology201 (16). https://doi.org/10.1128/JB.00287-19.
- Kafri, Ran, Melissa Levy, and Yitzhak Pilpel. 2006. “The Regulatory Utilization of Genetic Redundancy through Responsive Backup Circuits.” Proceedings of the National Academy of Sciences of the United States of America103 (31): 11653–58.
- Magadum, Santoshkumar, Urbi Banerjee, Priyadharshini Murugan, Doddabhimappa Gangapur, and Rajasekar Ravikesavan. 2013. “Gene Duplication as a Major Force in Evolution.” Journal of Genetics 92 (1): 155–61.
- Mueller, Elizabeth A., Alexander Jf Egan, Eefjan Breukink, Waldemar Vollmer, and Petra Anne Levin. 2019. “Plasticity of Escherichia Coli Cell Wall Metabolism Promotes Fitness and Antibiotic Resistance across Environmental Conditions.” eLife 8 (April). https://doi.org/10.7554/eLife.40754.
- Nei, M. 1969. “Gene Duplication and Nucleotide Substitution in Evolution.” Nature 221 (5175): 40–42.
- Nowak, M. A., M. C. Boerlijst, J. Cooke, and J. M. Smith. 1997. “Evolution of Genetic Redundancy.” Nature 388 (6638): 167–71.
- Snoke, David W., Jeffrey Cox, and Donald Petcher. 2015. “Suboptimality and Complexity in Evolution.” Complexity 21 (1): 322–27.
- Weir, J. A. 1946. “Sparing Genes for Further Evolution.” Proceedings of the Iowa Academy of Science 53 (1): 313–19.
- Zhang, Jianzhi. 2003. “Evolution by Gene Duplication: An Update.” Trends in Ecology & Evolution 18 (6): 292–98.