This article is from
Journal of Creation 31(3):82–89, December 2017

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Can biologically active sequences come from random DNA?

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DNA
Illustration ©iStock.com/BlackJack3D

A recent report that random DNA sequences can be a source of biological novelty is being used to support evolution. The authors concluded that biologically important novelty was trivial to generate. However, they drew multiple premature conclusions from their work, and they made no attempt to correlate their sequences with known biological function. In this follow-up study, the standard sequence comparison tool BLASTn was used to probe for similarities between their random sequences and the E. coli genome. In most cases, a 20–40-bp section was identified that had a high degree of similarity (up to 100%) to a small portion of a known E. coli gene. In the majority of cases, the random DNA ran in the reverse direction from that of the gene. This strongly indicates that a specific subsection of the RNA transcript, and not the protein product of the randomized DNA, was the active agent. This size range resembles that of many biologically active RNA molecules, specifically microRNAs, that are known to have a major influence in regulating expression of many different genes. There is no evidence here that random DNA supports evolutionary theory. Instead, random RNAs inserted into the cell help us learn about the amazing complexity of genetic regulation.

Recently, Biologos1 fellow Dennis Venema reiterated the common evolutionary claim that new biological functions can easily arise from random mutation.2 As his first example, he used the nylonase gene. For several decades, evolutionists have been claiming the existence of the nylonase gene as prima facie evidence for evolution. The fact that a bacterium was able to ‘evolve’ the ability to digest a man-made polymer in just a few years was seen as a triumph of evolutionary predictions. But the early claims that attempted to describe how the gene arose fell short of reality. Instead of a ‘frame shift’ in a gene that caused the new ability to arise, an enzyme that already had the ability to digest similar molecules was fine-tuned by the bacterium to break the nylon bond. But this was done in a copy of the original gene on a plasmid. The original was left untouched.3 Since some bacteria already had the ability to degrade a similar bond (the amide bond found in all proteins), and since the enzyme already had a limited ability to degrade nylon, it only took a few minor changes in the backup copy of the enzyme to allow for more efficient nylon degradation. Thus, the nylonase gene is much better suited to supporting design arguments than to supporting evolution in general.

However, Venema brought up a second example, which comes from new research, where the experimenters supposedly found a high frequency of biologically active properties in random DNA sequences. An analysis of this new study will be the focus of this paper. But Venema shows his bias by asking, “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?” In both sentences he uses the term ‘gene’ without grappling with the nuances of the modern concept of genes and genetic information. Is it true that a random sequence, when inserted into a cell, has the capacity to take on the role of a ‘gene’?

box-and-whisker
Figure 1. Box-and-whisker plot of the nucleotide frequencies within the 713 biologically active random sequences reported by Neme et al. The mean is represented by the horizontal line within the box. The data are divided into quartiles, represented by the top and bottom edges of the boxes and the top and bottom ends of the whiskers.

The authors of the study under question, Neme et al., state, “Intriguingly, the highest rates of de novo emergence are always found in the evolutionarily youngest lineages.”4 This defies evolution, for it would mean that evolutionary rates are speeding up over time. Using circular logic, they are claiming that more ancient sequences evolve more slowly because they are more conserved.5 This does nothing to help their argument that new function can arise easily from random DNA and illustrates how our opponents often play fast and loose with important concepts and definitions.

In their study, Neme et al. generated millions of random 150-bp DNA sequences and inserted them into a bacterial plasmid. They then induced E. coli to absorb these plasmids. The plasmid carries an ampicillin resistance gene so any non-transformed bacteria would die when grown in the presence of the antibiotic. It also carries an inducible promoter that would turn on transcription of the random DNA sequence when exposed to IPTG.6 The plasmid also carries a built-in stop codon. This guarantees that a protein with a randomized centre comprising 50 amino acids would be made after the gene was transcribed. This is about the size of a typical protein domain, but note that evolution must explain how entire proteins evolve, not just disconnected subsections of proteins. Also, three of the 64 codons are stop codons; thus, stops should occur every 21.3 bases on average. Therefore, most of their sequences would not have been expected to produce a full-length protein.

When grown in mixed culture, they were surprised to discover many clones where the growth rates were affected by the presence of the random DNA. Although most of the random DNA sequences they scored caused a decrease in growth rate, some did the opposite. They took this to indicate that some of the random sequences affected the cells enough that selection (either purifying or positive) could have acted upon them.

genomes-multiple-E-coli-strains
Figure 2. Graphical results for a BLAST search comparing clone #2 (the first clone in the database) against E. coli. The genomes of multiple E. coli strains are in the NCBI database, hence the multiple identical hits. This small 27-bp segment is part of the sensor histidine kinase gene that is involved in citrate metabolism.

The experiment is ingenious, and, as an intellectual exercise, reveals intriguing lines for future enquiry. Technically, they did nothing wrong. However, they made several critical errors when attempting to extract evolutionary connotations.

Their first error was one of applicability. We know that nothing in life produces truly random sequences, and no part of evolutionary theory (after the origin of life) starts with randomized nucleotides. The typical protein consists of multiple interspersed functional domains and disordered regions.7 This does not mean the intrinsically disordered regions (IDRs) have no function, however; they are involved in multiple important cellular processes from affecting protein folding to influencing protein assembly. IDRs also have distinct compositional biases (i.e. they have more charged and polar amino acids and fewer amino acids with bulky hydrophobic groups). They are not truly ‘random’ (see previous reference for a detailed discussion) and should not serve as a source of truly random DNA for evolutionary purposes. Unlike humans and higher organisms, bacteria have little ‘junk DNA’,8 so this cannot be the source of new functional novelty.

four-nucleotides
Table 1. The expected usage of each of the four nucleotides in the proteins coded in the biologically active random DNA sequences, after adjusting for codon usage. The first row shows the frequency of each nucleotide among all codons in E. coli. The second row shows the frequencies among the amino acids specifically flagged as less abundant (R, D, C, S, and V) in E. coli compared to the random sequences. The third row shows the frequencies among the amino acids specifically flagged as more abundant (N, E, Q, I, and T).

Second, the authors failed to address how much time would be required to sample these random sequences in real life. Sanford et al. studied how long it would take a random functional string to appear in a human-like population.9 Their model results indicate that it would take approximately 84 million years for random mutation to produce, and for natural selection to fix, even a strongly favoured 2-nucleotide string. It would take more time than the history of life on Earth to fix a 6-nucleotide string.10 In a similar vein, O’Micks studied the evolution of bacterial gene promoters via random mutation and concluded it was virtually impossible.11

This ‘waiting time problem’ is a significant hurdle for evolution to cross. Bacteria like E. coli have much shorter generation times and much higher population sizes than humans, and so might be able to experiment with much more random DNA over time. Yet, Neme et al. made no estimate concerning how much time this might take, even allowing for the sudden appearance of 150-bp random sequences that can be transcribed and translated in the cell.

Third, the sequence space they explored was probably orders of magnitude greater than what life could ever experience. There are four nucleotides in DNA, thus the potential for 4150 (>2 × 1090) theoretical sequences 150 nucleotides in length. Since they were dealing with μg quantities of DNA, they did not even begin to exhaust the possibilities. However, they did test tens of millions of different sequences.12 Also, most genes do not have to be perfect to manufacture either a functional RNA or protein. Thus, they may have sampled a much greater proportion of protein or functional RNA space than one might assume at first.

To develop these thoughts further, another standard laboratory procedure needed to be applied to their sequence data, one which is available to them, yet they curiously failed to perform: BLAST.

neme
Table 2. BLAST results for multiple clones compared to E. coli. Included here are the first 10 ‘up’ and the first 10 ‘down’ strains, and each strain assayed by Neme et al. in competition experiments. The matching gene name is dependent on the annotation data provided by the contributor of the sequence, thus not all annotations are of the same quality. ‘Direction’ indicates whether the clone runs in the same direction as the gene in question. Due to the potential for frameshifting, only ⅓ of the clones that match in the forward direction are expected to produce a protein that matches the relevant section of the gene in question.

Methods

In their supplementary information, Neme et al. provided a list of 713 random 150-bp sequences (and the 50-amino acid translated proteins) they determined were biologically active. They also flagged each sequence ‘up’ or ‘down’ to indicate whether it would have a positive (+) or negative (–) effect on bacterial numbers over time. They cloned the random sequences into a specific plasmid vector, leaving a DNA sequence with this formula:

ATGAAGCTTAGC…N150…GCATTGGTCGACTACAAGGACGATGACGACAAGTGA

where N150 represents the 150-bp randomized DNA sequence. This translates into a protein with this formula:

MetLysLeuSer…AA50…AlaLeuValAspTyrLysAspAspAspAspLysSTOP

where AA50 represents the randomized string of 50 amino acids. In their paper, they reported analyses on a small subset of the active sequences. Specifically, they tested the activity of clones 3 (+), 8 (+), 53 (–) and 119 (–). They also assayed clones 4 (+), 32 (+), and 600 (+) in competition experiments. They did not include clone 600 in the sequence list, for unexplained reasons. Clone 605 was used here instead, since they listed it as ‘similar to 600’.

The >700 biologically active clones Neme et al. listed should not have been in any particular order, so the first 10 ‘up’-regulating and the first ten ‘down’-regulating clones were treated as a representative sample. I also examined all seven of the clones they specifically assayed in competition experiments. I searched for similar sequences among these 27 clones using the standard BLASTn tool (v. 2.6.1).13 There are many different parameter settings that affect BLAST results, but, knowing that they used short sequences with potentially little similarity to living things, and after some experimentation, I set the Expect Threshold to 20 (higher than normal) and the Word Size to 11 (smaller than normal) to account for these difficulties. At low word sizes, the trailing FLAG sequence received many hits due to the popular use of this vector in many different studies, so the leading and trailing plasmid vector sequences were trimmed prior to any reported BLAST search. I used BLAST directly on the E. coli genome first. To broaden the applicability of these results, I also used BLAST against a set of curated diverse genomes (refseq_representative_genomes). I also used the random sequence generator at bioinformatics.org14 to create multiple random nucleotide strings 150 to 1,500 long. This was done to create a set of random sequences that were not first filtered for activity in E. coli. After a few initial trials, I opted to not search the entire NCBI nucleotide collection (with the exception of the longest random string) because this generates many non-biological, engineered, and duplicate hits. The purpose was not to identify every biological sequence that matched these random sequences, but only to identify and characterize a few high-scoring matches, if they existed.

Results

Neme et al. claimed their random sequences were synthesized as “equimolar mixes of A, C, G, and T at every position”, but we do not know if they validated this. The 713 biologically active sequences they reported had decidedly non-random nucleotide frequencies (figure 1). An even distribution would mean all nucleotides should have a frequency of 0.25, but the reported sequences were rich in G (0.33 +/– 0.03 SD) and depauperate in A (0.18 +/– 0.03 SD). The other two nucleotides were exactly at expectation (0.25 +/– 0.04). They did not perform this simple measure and may have noticed something was amiss if they had. Instead of ‘random’ sequences showing functionality, the ‘biologically active’ sequences had highly skewed nucleotide ratios, indicating that something decidedly non-random was occurring with the E. coli populations that carried these sequences.

They did not analyze the nucleotide composition of their clones, but they did perform an analysis on amino acid frequencies. Since one of their (and Venema’s) assumptions was that the synthesized proteins would be the active agents in their assay, they incorrectly state that the amino acid composition provides “potentially more information than nucleotide composition of the underlying RNAs”. They found no significant differences from random expectations, but they did note that specific amino acids were less common (E, I, N, Q, and T) or more common (C, D, G, R, and S) in the random sequences than in E. coli. This pattern does not match that found in IDRs (see Discussion). After adjusting for codon frequency,15 I calculated the nucleotide frequency within the 64 codons used in E. coli. I then calculated the nucleotide frequency of the codons for the amino acids that were more and less common than expected. The results were an exceedingly close match to that of the nucleotide composition within the clones. That is, the codons for the amino acids that appeared at higher-than-expected frequencies had less A and more G than average, and vice versa (table 1). Thus, the amino acid composition in the putative protein products was a simple function of the uneven nucleotide composition in the random sequences. This is evidence that the random sequences are acting on the RNA/DNA level.

5CRE51
Figure 3. Details of the 27-bp region highlighted in figure 1, showing 89% identity at the nucleotide level. ‘Query’ is the test sequence (clone #2). The match was generated for nucleotides 80–106 (out of 150) in the test sequence against the ‘Subject’ E. coli genome (strain 5CRE51).

The very first BLAST search produced a startling result: clone 2 contains a 27-bp subsection of the E. coli sensor histidine kinase gene (figure 2). This gene happens to be involved in citrate metabolism.

The text output of a search includes information on the organism and/or strain name, where the match occurs along the search and target string, and in which nucleotides are identical. In this case, 24 of the 27 nucleotides (89%) are identical between the two (figure 3):

The gene in question is on the antisense strand. Thus, compared to the search string, the gene runs in the reverse direction and the short protein produced by clone #2 should have nothing to do with the full-length sensor histidine kinase protein (the alignment of the two sets of codons are also off by one nucleotide). However, the short RNA produced during the transcription of clone #2 will have strong affinity for the double-stranded DNA within this portion of the gene, potentially affecting its regulation.

eight-diverse-bacterial-genomes
Figure 4. Graphical results for a BLAST search comparing clone 2 against a curated set of representative genomes. In order to increase specificity and reduce the number of hits, the Expect Threshold was set at 10 and the Word Size at 15. Eight diverse bacterial genomes are represented here, including representatives from genera Steptomyces, Lysobacter, Blastococcus, Dietzia, Geodermatophilus, and Cupriavidus.

When expanding the search to include a list of representative genomes curated by NCBI, portions of this clone can be seen in diverse organisms. The first search brought up hits from 30 different bacterial and one fungal species. This was reduced to high-scoring hits only, from four bacterial species, by changing the Expect Threshold and Word Size (figure 4). Interestingly, these results did not overlap with those from a search of E. coli specifically, nor was E. coli in these search results. This indicates that short, random search strings have a high probability of aligning with known DNA sequences.

BLAST results for the remaining clones compared to E. coli are summarized in table 2. BLAST comparisons for the seven assay clones compared to a curated list of representative genomes are given in table 3.

Among the multiple random test sequences I generated that had not been filtered for activity in E. coli, no significant matches with the E. coli genome were found. But, as in the other tests, short sections of 20–30 nucleotides had significant matches to a range of other organisms (figure 5 and table 4).

Discussion

Though the sequences Neme et al. tested were randomized, intelligently designed sequences were placed on both sides of each random sequence to facilitate its integration into the bacterial genome. Our concept of what a gene is has changed dramatically over the past few decades. The ‘one gene, one enzyme’ mantra is a thing of the past. The modern definition of a gene includes alternative splicing variants of the protein for which the gene codes,16 as well as the regulatory regions, which may include enhancer regions far away from the gene itself. Evolutionists generally try to downplay the idea of functional information in biology. This does not mean that biblical creationists have not mishandled the subject over time,17 but the information content in living things is a subject evolutionists invariably avoid. Neme et al. did exactly that, and this led to fatal mistakes in their analysis.

seven-assay-clones
Table 3. Significant BLAST hits from a curated list of representative genomes for the seven assay clones

Most of the clones examined received highly significant matches to the E. coli genome using BLASTn. However, the matching sections were all small (18–43 nucleotides). Percent identity ranged up to 100% over those small sections, meaning that the authors unknowingly identified real portions of real genes. The diversity of organisms represented in these matches was surprising. A few microorganisms, at best, other than E. coli were expected on the list, yet species that received significant hits ranged from beaver to bacilli (table 1). The fact that 20–40 nucleotide sections of different genomes were highlighted indicates their experimental setup was sufficient to explore a considerable portion of gene space in that size range.

NCBI
Figure 5. BLAST results generated by comparing a long string of random nucleotides to the entire nucleotide collection at NCBI. The lengths and percent matches of the flagged sections are similar to the others discovered above.

The statistics pertaining to this situation seem perplexing at first. On the one hand, a 15-nucleotide sequence would be expected to be found once in a billion random nucleotides, and a 30-nucleotide sequence once in every 1018 random nucleotides. These numbers are much larger than the E. coli genome (of approximately 4.6 million bases). But there are several mitigating factors that greatly increase the probability of a significant hit.

First, the matching sequences do not have to be exact. There are many permutations of a 15-bp nucleotide string with one or more allowed ambiguous bases in random positions along that string.

REL606
Figure 6. The frequencies of the 1,024 pentamers in the E. coli genome (strain REL606) are far from random. They range from 0.0025% to 0.29%. With a genome size of 4,629,812 nucleotides, there is more than enough data to generate a robust average of each frequency, so the data presented are not sampling artifacts. The range of frequencies only increases with increased word size.

Second, one major mistake the authors made was to assume that DNA is random. It is not. Certain combinations of letters are favoured, and others disfavoured, at all levels of organization. Unlike the DNA of higher organisms, the four nucleotides in E. coli are found at approximately the same frequency (24.6–25.4%). However, this is not true of the 16 dimers (4.6–8.3%), and the spread increases with increasing word size (figure 6). In fact, departures from random expectations can be found among any set of n-mers, even after accounting for the frequencies of the smaller n-mers. Thus, even though there is an astronomical number of nucleotides 150-bp in length, due to the non-random nature of biological DNA a certain subset of those combinations are highly likely to match significant portions of DNA.

Failure to take into account the non-randomness of biological DNA at all levels led a team of computer scientists at IBM to mistakenly identify millions of ‘pyknons’ in the human genome.18 These seemed like a ‘code within a genetic code’, and would have been an exciting discovery.19 However, they merely found repeating subunits of the already-known and well-characterized Alu elements that happened to permeate the genome.

Neme et al. made additional errors when saying things like, “Contrary to expectations, we find that random sequences with bioactivity are not rare.” This is patently untrue. They discovered approximately 700 active sequences. Out of the millions of sequences they started with, this represents a very small percentage of all sequences assayed (literally ‘one in a million’). While we have no idea how many of these random sequences were severely detrimental to the cell because these would quickly disappear from the culture, one would expect that most random sequences would have no effect at all.

They make an additional error by assuming that the random sequences add biological novelty to the cell. There is, in fact, no evidence for this. The majority of sequences I analyzed had a highly significant match to a known gene or what might be assumed to be a control region of a known gene. If this were not the case, one might be able to argue that short, random proteins can create biological novelty. Instead, it appears that short, random nucleotides interfere with cellular operations.

The high proportion of sequences that match the reverse compliment of a known gene demonstrate that orientation is unimportant. But functional areas can include non-genic areas like promotor regions. Thus, the protein sequence, at least in most cases, though perhaps all, is also unimportant.

Table-4-BLAST-result
Table 4. BLAST results generated from random sequences. Tests 1–5 used 150-nucleotide test sequences. Test 6 used a 1,500-nucleotide test sequence. Genomic contexts (if available) are provided for test 6.

If these ‘bioactive’ DNA sequences are not producing functional proteins, they must be acting on the level of RNA–RNA or RNA–DNA interactions. The annealing temperatures of ribonucleic acids depend on their length and percent identity. Biological function in this case does not depend on sequence specificity. Also, the triple-hydrogen bonding G and C bind more tightly than the double-hydrogen bonding A and T, meaning sequences rich in G and C have a higher melting temperature (the temperature at which the two nucleic acids will separate in solution). The placement of G and C along the strand also impacts annealing, with terminal Gs and Cs serving to anchor the strand more so than internal ones. The skewed frequencies of A (low) and G (high) seen in the data are quite interesting in this context.

Why do we not see longer or shorter ‘bioactive’ sequences? First, due to the sheer number of permutations along a DNA strand, as the search string gets longer, the expected number of matches drops off exponentially. Second, it may be that the BLAST algorithm is cutting off less-than-perfect, but still functional, leading or trailing sequences that are beneath the detection threshold. Third, shorter sequences will not have a high enough annealing temperature to interact directly with the genome.

What we see are the sequences at just the right length. Their RNA transcripts are long enough (20–40 nucleotides) that they could bind tightly to both RNA and DNA under physiological conditions (e.g. 37°C). The two RNA ends that have no match to the surrounding sequence would not anneal, however. This will affect the annealing of the ‘random’ RNA strand, but to an unknown extent. The RNAs produced in their experiment were on the order of 700 nucleotides, only 150 of which were the ‘random’ component. Since these are long compared to the oligomers flagged by BLAST, it is quite possible that they might not anneal to the bacterial DNA directly. Instead, they may operate through RNA interference, soaking up regulatory RNAs that would otherwise anneal to those 20–30 bp sections of the bacterial genome. It is also possible that they could interfere with translation by annealing to the mRNA in those short target areas.

Our understanding of the role of RNA in the cell has exploded over the previous decade. Specifically, microRNAs are short, non-coding RNAs, approximately 22-bp in size, that play multiple roles in genomic regulation.20 They bind to transcribed mRNA, rendering them inactive and preventing protein translation. But short RNAs can also bind to DNA. The evidence presented in this paper suggests that Neme et al. stumbled upon a set of short RNA sequences that interfere with normal cellular gene regulation patterns.

Conclusion

By introducing random RNAs into the cell, Neme et al. inadvertently changed the genomic regulation patterns of already existing genes. No new functions were added. No evolution has taken place. While the experiment was ingenious, the conclusions they derived from it were unwarranted. Venema was premature in his praise.

Acknowledgments

I thank Shaun Doyle for his critical review of an earlier draft of this manuscript as well as the efforts of two anonymous reviewers.

Note added after publication:

I found the original study fascinating for multiple reasons. First and foremost, I was familiar with every step they performed, and I used each of these techniques in my doctoral work. Second, I missed an opportunity to perform this experiment myself. I must give a ‘hat tip’ to the authors for an ingenious experimental design. It was so obvious, after the fact, but I did not see it and so they deserve compliments.

When preparing my analysis, I wrote the corresponding author to ask a question about the number of sequences they tested. His reply was that their “library contained several million different clones”. All I wanted to know was how many sequences they tested, and his answer gave me a good indication. I did not need to ask any other questions to understand what they were doing.

As a courtesy, I forwarded a PDF of the paper to this person after it was printed in the Journal of Creation.

I wrote:

Thank you for the earlier correspondence. I wanted to give you a heads-up before you heard it from anybody else, but your paper was recently cited and critiqued in the Journal of Creation. I am attaching a PDF for you to share with the other authors, if you so desire. Again, I thought the experiment itself was brilliant, and since I have performed nearly every one of those experimental steps, I also know how much work it must have taken. I do, however, have some reservations about several of the conclusions you drew, which should be evident in this review, but I could always be wrong…

He replied:

You had asked me only one question about our paper, but you criticize many things. Actually, practically all of what you say is not correctly presented, including the answer that I gave you – why?

Just to set this straight: of course we have done BLAST searches, but we did not find any significant hit. As far as I can see from your results, none of these is significant either (BLAST povides an e-value to judge significance, but you do not report it).

Further, nobody expects that a completely random sequence can be produced by the simple synthesis scheme that we have used, since there are always slight asymmetries in chemical affinities and reagent delivery – it is not even worth to mention this in a serious publication.

But I assume nothing can change your opinion anyway…

Let the reader understand that I meant the authors no ill will. I did use a non-CMI e-mail address for my correspondence, but this was not to hide my identity so much as it was to prevent a knee-jerk negative reaction in the mind of the correspondent. I did not misrepresent his answer to my question, and his comment about e-values is irrelevant. From the NIH website, “The Expect value (E) is a parameter that describes the number of hits one can ‘expect’ to see by chance when searching a database of a particular size. It decreases exponentially as the Score (S) of the match increases. Essentially, the E value describes the random background noise.” But there was no reason for me to consider the e-value. There was no reason to consider “significance” either. A sequence either matches or it does not. And I found a lot of short sequence matches. Changing the search parameters may have caused me to find more or to miss the ones I found, but that does not change the fact that the ones that were found were truly present in the E. coli genome. They found that adding already-existing elements to a complex system can affect the working of the system. This has nothing to do with evolution.

References and notes

  1. Cosner, L., Evolutionary syncretism: a critique of BioLogos, creation.com/biologos-evolutionary-syncretism, 7 September 2010. Return to text.
  2. Venema, D., Biological Information and Intelligent Design: new functions are everywhere, biologos.org/blogs/dennis-venema-letters-to-the-duchess/biological-information-and-intelligent-design-new-functions-are-everywhere, 18 May 2017. Return to text.
  3. Batten, D., Nylon-degrading bacteria: update, creation.com/nylonase-update, 19 May 2017. Return to text.
  4. Neme, R. et al., Random sequences are an abundant source of bioactive RNAs or peptides, Nature Ecology and Evolution 1:0127, 2017. Return to text.
  5. Cserháti, M., Creation aspects of conserved non-coding sequences, J. Creation 21(2):101–108, 2007. Return to text.
  6. ITPG stands for Isopropyl β-D-1-thiogalactopyranoside. It is a chemical mimic of allolactose that is used to induce protein expression in this system. IPTG binds to the lac repressor, freeing up the lac gene for transcription while at the same time exposing a strong promoter just upstream of the engineered sequence. Return to text.
  7. Van der Lee, R. et al., Classification of Intrinsically Disordered Regions and Proteins, Chem. Rev. 114(13):6589–6631, 2014. Return to text.
  8. Of course, this is a misnomer and the term would be retired were it not for evolutionary intransigence. See Carter, R.W., The slow, painful death of junk DNA, J. Creation 23(3):12–13, 2009; creation.com/junk-dna-slow-death. Return to text.
  9. Sanford, J. et al., The waiting time problem in a model hominin population, Theoret. Biol. and Med. Modelling 12:18, 2015. Return to text.
  10. Note that this is not the ‘time to first appearance’, which is much less than the time to fixation since >99.9% of all new mutations in a hominin-like population are lost over time. See Rupe, C.L. and Sanford, J.C., Using numerical simulation to better understand fixation rates, and establishment of a new principle; in: Horsetmeyerm M. (Ed.) Haldane’s Ratchet, Proceedings of the Seventh International Conference on Creationism, Creation Science Fellowship, Pittsburgh, PA, 2013. Return to text.
  11. O’Micks, J., Promoter evolution is impossible by random mutations, J. Creation 30(2):60–66, 2016. Return to text.
  12. Personal communication with the corresponding author (D. Tautz) confirmed that at least ‘millions’ of clones were in their library. How many were tested is unknown, since they did not report the molar concentrations of the DNA, nor how large a quantity they used, nor the estimated transformation efficiency in the electroporation step. However, in similar experiments I would typically take 30 μl of cells at 109 to 1010 cells/ml, transform with a plasmid in pg/ml to μg/ ml concentration, and almost always get > 50% transformation efficiency. This would put the number of clones at least in the millions per transformation. Return to text.
  13. Altschul, S.F. et al., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucl. Acids Res. 25:3389–3402, 1997. Return to text.
  14. bioinformatics.org/sms2/random_dna.html. Return to text.
  15. I used the codon frequency table at www.kazusa.or.jp/codon. Return to text.
  16. Carter, R.W., Splicing and dicing the human genome: Scientists begin to unravel the splicing code, 29 June 2010. Return to text.
  17. Carter, R.W., Can mutations create new information? J. Creation 25(2):92–98, 2011; creation.com/mutations-new-information. Return to text.
  18. Rigoutsos, I. et al., Short blocks from the noncoding parts of the human genome have instances within nearly all known genes and relate to biological processes, PNAS 103(17):6605–6610, 2006. Return to text.
  19. Meynert, A. and Birney, E., Picking pyknons out of the human genome, Cell 125:836–838, 2006. Return to text.
  20. Arneigh, M.R., It’s a small world—microRNA cuts evolution down to size, J. Creation 27(2):85–90, 2013. Return to text.
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Readers’ comments

Eric P.
This is fascinating stuff but I must confess it could be written more clearly for the average punter. I am able to pick up the general shape of the argument but many of the basic premises remain opaque when just a tad more explanation on terms etc would have rendered them crystal clear.
Robert Carter
Very sorry about the level. We try to write different things for different audiences, and this was Journal of Creation-level material, so about as technical as we get. Take home point: there is no evidence that random DNA sequences are at all useful for evolution.
Russell N.
Dr. Carter,

I know you are a fantastic scientist and researcher, and this kind of study involving DNA, RNA, mRNA, nucleotides, the genome, & etc., all of this has to be truly exciting seeing God's handiwork at the molecular level. A part of me envies you, but in a good way, of course. So many places in God's Word that tells us about how God is directly involved in our lives from the very beginning, even from eternity past. God is the Originator, Creator & Sustainer of all life. I especially appreciate John 1:4a, "In Him was life..." Only God has life in Himself, & no amount of any kind of experiment by any scientist will ever bring forth life. So I never get scared by what any evolutionist says. I so often think of this verse whenever I hear or read you, Dr. Sarfati, Dr. Batton, Dr. Sanford & others go in depth on your various studies within the cells of our bodies. So much of what I now know is due to all of you here at CMI.

I also want to commend you on how cordial & gentlemanly you were with Venema & his associates. They--probably Venema--seemed to be on the peck, so to speak, right from the start. I also like your final sentence: "This has nothing to do with evolution." Exactly!

I would like to leave a final thought with all of you scientists here at CMI. CMI has had such a tremendous part in educating--and convincing--me that life truly does start at conception. Life & life's activity in everyone truly does start at that very second, & it never stops until the very second we die. Whenever I read Psalm 139, I see God's life-long plan He has for me, and it started in His mind even from eternity past. What a God we serve! Being pro-Life today has never meant so much for me as it does right now. And for that I am most grateful to all of you! Thank you so much!
Reverend Robert W.
Random phrases, clauses, sentences, paragraphs, letters, can lead gradually to a masterpiece essay on Relativity theory? I should have used that laid-back approach to qualify. I think not.
Ferdinand I.
i work in IT. Code is designed, programmed, maintained. Mutations destroy code, they do not enhance. If DNA and the genome are akin to code and operating systems, the idea that something as complicated as sequence information can self-create; 'mutate'; extend; maintain and randomly improve itself, is unfounded. There is no observational evidence within IT that this happens. In fact it is the opposite. Over time all IT systems degrade if left alone - and usually quickly. This is why patches, upgrades, updates and constant operational intervention is necessary.
Why would the programmatic code of God and nature be any different?
In my opinion, extinctions are mainly due to the collapse of software and operating systems due to mutations.
That is more sensible than the evolutionary religion's dogma of random chance improvement.

Great article.
Mike D.
Let's just quit messing around & expose evolution's fallacy by getting to the bottom line. All the machines they use, the people doing the experiments, analyzing the results etc. Every part & parcel of that takes an INTELLIGENCE/INTELLIGENT DESIGNER for the machines, etc. So going from that, to claim what they are investigating that is so irreducible complex could have occurred by random trial & error is absurd. It's absurd as thinking the machinery etc. used could have created itself by random trial & error is patently absurd too! I've asked many engineers & computer programmers to prove evolution is real to me. They first have to show me any project they've worked on that could have succeeded without an Intelligence involved & happen by random trial & error. I then make them realize that's what evolution has sold them. I also ask them for just one example sans evolution's just so story where an explosion of energy created order & precision. I say for example, tornado, hurricane, nuclear bomb ending WWII. I see no examples. So do an experiment proving that is possible. I ask, If walking on a beach or anywhere & you walk upon a cell phone, iPad, etc., is your first thought, "Look at what evolved over eons of time," or do you automatically see evidences of intelligent design? DNA is still more complex a "computer program" than man can match, and you think it occurred by random trial & error. Man's robotics takes intelligence yet can't beat the original & even if could, it took an INTELLIGENT DESIGNER, proving our point. Evolution depends on faith & the supernatural to be possible. It breaks science laws. So it takes the supernatural & faith for evolution to work. Its foundational premises are fatally deadly flawed!
David R.
Genetically, how do we get so many different races of people in such a very very short time? The early Egyptians identified five distinct races, the Romans seven. In the Book of Jeremiah 13:23 we have "Can Ethiopian change his skin or a leopard its spots". From Noah to Jeremiah we have only about 1,500 years. Even less if you take in what the Egyptians and Babylonians said about different races. How do we get Inuits, East Africans, West Africans, Native Americans, Chinese, Australian Aborigines, Polynesians, all the other Orientals groups, and the Basque with their unique blood type in less than 1500 years? I am not a Troll or trouble maker. I have been a Bible studying Christian since 1968. I love the sciences. So far, I have found no solution in genetics or elsewhere, for the above dilemma.
Robert Carter
The 'races' would have formed early and quickly after the Tower of Babel due to isolation from one another and the resultant inbreeding within the many small subpopulations. But since there are hardly any genetic differences among the so-called-races, geneticists do not like using the term much anymore. See:

Inbreeding and the origin of races

Skin colour surprises

Extensive mixing among Israelites and non-Israelites in biblical history
David R.
OK, I agree about Genetic differences. But, that does not explain the dilemma as to how many different races of people arose to rapidly, in such a very very short period of time. I am looking at all the evidence. Both historical and biological. Many people have lived separated from each other in isolated groups since before 500 BC. Yet, no new races of people have arisen in the world population. Only the mixing of races has taken place. This is the dilemma I have been trying to find answers to for the last 50 years. There must be something we are missing in our hypothesis. We now have over 5,000 years of very detailed Chinese history in the mix.
Robert Carter
There is no dilemma. Differences would have arisen in the various post-Babel people groups quickly through inbreeding and founder effects. Remember, we are talking about something that happened around 2,500 BC or earlier. There is plenty of time for the process to work itself out and still leave lots of room for recorded history.

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