How Did Birds Get Their Wings? Bacteria May Provide a Clue to the Genomic Basis of Evolutionary Innovation, Say Evolutionists
That evolution occurred is known to be a fact but how evolution occurred is not known. In particular we are ignorant of how evolutionary innovations arose. Of course biological novelties and innovations arose from a series of random chance events, but it is less than reassuring that we cannot provide more detail. How exactly did the most complex designs spontaneously arise? What mechanisms overcame, over and over, the astronomical entropy barriers, by sheer luck of the draw? As Craig MacLean’s and Andreas Wagner’s, and coworker’s, new PLOS Genetics paper begins, “Novel traits play a key role in evolution, but their origins remain poorly understood.” Could it be that evolution is not actually a fact? No, not according to evolutionists. And this new paper claims to provide the basis for how the seemingly impossible became the mundane.
The paper begins by summarizing the many proposed genetic mechanisms for the evolution of biological innovations:
An evolutionary innovation is a new trait that allows organisms to exploit new ecological opportunities. Some popular examples of innovations include flight, flowers or tetrapod limbs [1,2]. Innovation has been proposed to arise through a wide variety of genetic mechanisms, including: domain shuffling [3], changes in regulation of gene expression [4], gene duplication and subsequent neofunctionalization [5,6], horizontal gene transfer [7,8] or gene fusion [9]. Although innovation is usually phenotypically conspicuous, the underlying genetic basis of innovation is often difficult to discern, because the genetic signature of evolutionary innovation erodes as populations and species diverge through time.
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5. Zhang J. Evolution by gene duplication: An update. Trends Ecol Evol. 2003;18: 292–298. doi: 10.1016/S0169-5347(03)00033-8.
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8. Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35: 957–976. doi: 10.1111/j.1574-6976.2011.00292.x. pmid:21711367
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The unspoken problem here is, as usual, serendipity. The various proposed genetic mechanisms for the evolution of biological innovations all suggest an amazing bit of fortuitous luck. For random chance events just happened to create these various complicated structures and mechanisms (such as horizontal gene transfer and protein domains their shuffling) which then produced new evolutionary breakthroughs.
Evolution didn’t know what was coming. Evolution did not plan this out, it did not realize that horizontal gene transfer would lead the way to new biological worlds. The evolution of horizontal gene transfer would require a long sequence of random mutations, many of which would not provide any fitness advantage. And when the construction project was completed, and the first horizontal gene transfer capability was possible, there would be no immediate advantage.
This is because there would have been no genes to transfer. The mechanism works only when it is present in more than one, neighboring, cells. One cell gives, and another cells receives. By definition the mechanism involves multiple cells.
But it doesn’t stop there. Even if the first horizontal gene transfer capability was able to spread across a population, and even if it did provide a fitness advantage to the fortunate citizens, there would not be even a hint of the enormous world of biological innovations that had just been opened.
In other words, what this evolutionary narrative entails is monumental serendipity. Biological structures and mechanisms (horizontal gene transfer in this case, but it is the same story with the other hypotheses listed above) are supposed to have evolved as a consequence of a local, proximate, fitness advantage: a bacteria could now have a gene it didn’t have before.
But it just so happened that the new structures and mechanisms would also, as a free bonus, be just what was needed to produce all manner of biological innovations, far beyond assisting a lowly bacteria increase its fecundity.
This is monumental serendipity.
Undaunted, the new paper finds that one of the other mechanisms, gene duplication and subsequent neofunctionalization, is a key enabler and pathway to biological innovations.
That conclusion resulted from what otherwise was a fine piece of research work. The experimenters exposed different populations of Pseudomonas aeruginosa, a dangerous infectious bacteria, to 95 new sources of its favorite food: carbon.
The bacteria had to adjust to the new flavors of carbon and they did so with various genetic modifications, including various genetic mutations. In the most challenging cases (where the new carbon sources were most difficult for the bacteria to adjust to), the bacteria often produced mutations in genes involved in transcription and metabolism. And these mutations often occurred in genes where there were multiple copies, so the mutations occurred in one copy while the other copy could continue in its normal duties.
The problem is, these genetic duplicates were preexisting in the P. aeruginosa genome. This is yet another instance of serendipity.
Why? Because preexisting duplicates are not common. Only about 10% of the genes have duplicates lying around, and fortunately, the genes needed for adaptation (involving transcription and metabolism) just happened to have such duplicates.
Now there were a few instances of de novo gene duplication. That is, once the experiment began, and after the P. aeruginosa populations were exposed to the challenging diets, a total of six genes underwent duplication events. But in each and every case, the duplication events occurred repeatedly and independently, in different populations (for each of the 95 different carbon sources, the experimenters ran four parallel trials with independent populations).
This result indicates directed gene duplication. This is because it is highly unlikely that random, chance, gene duplication events just happened hit on the same gene in different populations. Here is an example calculation.
Let’s assume that in the course of the experiment, which ran for 30 days and about 140 generations of P. aeruginosa, some genes may undergo duplication events by chance. Next assume there is a particular gene that needs to be duplicated and modified in order to for P. aeruginosa to adapt to the new food source. (Note that there may be several such genes, but as we shall see that will not affect the conclusion). Given that there are four separate, independent trials, what is the probability that the gene will be duplicated in two or more of those trials?
Let P_dup be the probability that any gene is duplicated in the course of the experiment. For our gene of interest, it may be duplicated in 0, 1, 2, 3, or all 4 of the trials. The binomial distribution describes the probability, P, of each of these outcomes. To answer our question (i.e., What is the probability that the gene will be duplicated in two or more of those trials?) we sum the binomial distribution’s value for N = 2, 3 and 4. In other words, we calculate P(2) + P(3) + P(4).
This will give us the probability of observing what was observed in the experiment (i.e., the duplication events occurred repeatedly and independently, in different populations, in all 6 cases where duplication events were observed).
Well for a reasonable value of P_dup, the probability that any gene is duplicated in the course of the experiment, such as 0.0001, the probability of observing multiple duplications events for any given food source (i.e., P(2) + P(3) + P(4)) is about 60 in one billion, or 6 times 10^-8. Even worse, the probability of observing this in all 6 cases where duplication events were observed is about 5 times 10^-44.
It isn’t going to happen.
Exceptionally high rates of gene duplication, in particular genomic regions of Salmonella typhimurium, in a high growth rate medium, were observed to be about 0.001 and even slightly above 0.01 in rare cases.
If we go all out and set P_dup to an unrealistically high 0.1, our results are still unlikely. The P(2) + P(3) + P(4)) is .05, and the probability of observing this in all 6 cases where duplication events were observed is about 2 times 10^-8.
In order to raise these probabilities to reasonable levels, such that what was observed in the experiment is actually likely to have occurred, we need to raise P_dup to much higher values. For example, for a P_dup of .67 (two-thirds probability), P(2) + P(3) + P(4)) is .89, and the probability of observing this in all 6 cases where duplication events were observed is about .5.
But even this doesn’t work. For if we were to imagine unrealistically high P_dup values of 0.1 or higher, then massive numbers of duplication events would have been observed in the experiments.
But they weren’t.
Once again, the science contradicts the theory. Our a priori assumption that evolution is a fact, and that the P. aeruginosa adaptations to the new food sources were driven by random mutations, did not work. The theory led to astronomically low probabilities of the observed results.
What the observed gene duplications are consistent with is directed gene duplications. Just as mutations have been found to be directed in cases of environmental challenges, it appears that gene duplications may also be directed.
The paper’s premise, that biological innovations such as flowers and wings are analogous to bacteria adapting to new nutrient sources, is fallacious. But setting that aside, the experimental results do not make sense on evolution’s mechanism of random mutations and natural selection. Instead, the results indicate directed adaptation.
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