In previous posts in this series, we’ve explored the claim made by the Intelligent Design (ID) movement that evolutionary mechanisms are not capable of generating the information-rich sequences in genes. One example that we have explored is nylonase – an enzyme that allows the bacteria that have it to digest the human-made chemical nylon, and use it as a food source. As we have seen, nylonase is a good example of a de novo gene – a gene that arose suddenly and came under natural selection because of its new and advantageous function. Since nylonase is a folded protein with a demonstrable function, it should be beyond the ability of evolution to produce, according to ID.
The implications of nylonase for ID arguments are clear, and they have caught the notice of several ID supporters. In recent weeks a number of posts on the subject have appeared on ID websites. ID biologist Anne Gauger, for example, is writing a series of posts in an attempt to rebut the evidence that nylonase is in fact a de novo gene. Her motivation for this work is clear:
Venema is right. If the nylonase enzyme did evolve from a frameshifted protein, it would genuinely be a demonstration that new proteins are easy to evolve. It would be proof positive that intelligent design advocates are wrong, that it’s not hard to get a new protein from random sequence.
As we can see, the issue at hand is not so much the specific origins of nylonase, but rather the relative ease by which new, functional proteins can come into existence. If it is easy to evolve them, ID advocates are wrong. If new protein functions are vanishingly rare and inaccessible to evolution, ID would be strongly supported. With nylonase, we are dealing with events that happened in the past, so our inferences are limited to working with the evidence we have in the present. What would be better, of course, would be controlled experiments in the present that we can observe in real time.
New functions, in real time, from randomness
As technology improves, it becomes possible to design and execute experiments that were only a pipe dream even a few years ago. Sequencing technology has advanced to the point where it is now possible – even trivial – to sequence a population of DNA molecules as a mix without separating them from each other. In the past, DNA samples needed to be nearly pure (i.e. contain only multiple copies of one DNA sequence). Now, sequencing technology has advanced to the point where a mixture of many different DNA molecules can be sequenced without purification. This process also is also capable of determining the proportions of the different DNA sequences in the impure mixture.
This ability to sequence a population of DNA molecules over time and track the relative proportions of various individual DNA sequences was recently used to address the very questions at issue for ID advocates: just how easy is it to obtain a functional gene from random DNA sequence? And consequently how likely is it that de novo gene origination is a common occurrence?
To investigate this question, a group of researchers assembled a large set of randomized DNA sequences, introduced them into a population of bacteria, and sampled the population over time to see which bacteria increased or decreased in abundance over the course of the experiment. The randomized sequences were set up with engineered sequences on either side of the randomized portion to allow the random portion to be transcribed into RNA and translated into protein. (If you need a refresher on transcription and translation, see here).
In effect, the researchers were creating a large number of brand-new, random genes and seeing if any of them had an effect on the bacteria that received them. Because these new genes were both transcribed (into RNA) and translated (into protein), it would be possible for any of them that had a function to be acting as either a functional RNA molecules or as translated protein. In other words, any biological effect noted could be due to the RNA or protein product. (As we have seen, RNA can fold up into functional shapes, and as such, new randomized genes might function as RNAs rather than as proteins.) The motivation for creating a large number of randomized genes came from the expectation that functional sequences within the mix would be rare – and thus a large number of sequences would have to be explored if there was to be any hope of success.
One of the scientists involved with the study, Rafik Neme, recounts how he envisioned setting up the experiment:
During my early months in the Tautz lab, while still a Master’s student, I contemplated the possibility of doing an experiment that could support de novo evolution as a general process, and so I came up with a thought experiment. I would insert random sequences in living cells, together with enough regulatory machinery to make sure they would be transcribed and translated by the host. Then, I would wait until any of those would mutate enough to “acquire a function.” It occurred to me that starting with a sufficiently large pool of random sequences would reduce the waiting time, because some would exhibit some biochemical activity upon their introduction.
The results, to put it mildly, were surprising. The experiment found functional, beneficial genes in the mix – genes that increased the ability of bacteria to replicate and compete against other bacteria in the population. What was most surprising, however, was the sheer number of beneficial random genes identified in the experiments. Overall, the researchers report over 600 randomized genes that were beneficial to the bacteria that received them. Rather than new functions being rare, they were common. In one experiment, 25% of the random sequences tested were beneficial. Rafik relates the surprise that these results produced:
I can remember clearly the moment when the first results came in. Not only had we found beneficial activities over and over, but the sheer amount detected was beyond our imagination. […] We had expected that the sequence space would be largely devoid of functions, but it seems to be quite the opposite.
Further work by Rafik and colleagues would show that in some cases the new functional genes acted at the RNA level, and in some cases through the new protein that was produced. They examined four of the new beneficial genes closely, and found that three of them exerted their beneficial effects through the RNA form of the gene - the transcription product. One of the four, however, exerted its beneficial effect through the translated protein product. This demonstrates that transcription of random DNA sequences into RNA has significant potential to produce new functions. The confirmation that one of the new beneficial genes acts through the protein product also confirms that random DNA sequences can be a ready source of functional proteins. Though there are several hundred other functional de novo genes left to analyze from this study, these results are a demonstration that new functions are easy for evolution to find. Though the researchers had expected new functional genes to be rare in a pool of random sequences, they were everywhere.
The importance of these results for ID arguments is clear. By direct experimental test, new biological functions have been shown to be common, not rare, within random sequences - and that these functions may be found in either RNA transcripts or de novo protein products. By Gauger’s own measure, ID advocates have been shown to be wrong. Since this particular ID claim undergirds a large proportion of the ID argument that biological information cannot have arisen through evolution, the consequences for ID are significant.
So, we can see that the nylonase issue is something of a distraction - a missing of the forest to focus on one particular tree. Even if this particular example could have an alternate explanation, as Gauger argues, the problems for ID do not go away. In light of these new experimental results showing widespread function in random sequences, as well as the accumulating evidence that de novo genes are common, ID has far greater problems on its hands.
In the next and final post in this series, we’ll reflect on how an evolutionary creationist understanding of the origins of biological information can lead to wonder and worship.