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.
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.
I thank Shaun Doyle for his critical review of an earlier
draft of this manuscript as well as the efforts of two
1. Cosner, L., Evolutionary syncretism: a critique of BioLogos, creation.com/
biologos-evolutionary-syncretism, 7 September 2010.
2. Venema, D., Biological Information and Intelligent Design: new functions
are everywhere, biologos.org/blogs/dennis-venema-letters-to-the-duchess/
18 May 2017.
3. Batten, D., Nylon-degrading bacteria: update, creation.com/nylonase-update,
19 May 2017.
4. Neme, R. et al., Random sequences are an abundant source of bioactive RNAs
or peptides, Nature Ecology and Evolution 1:0127, 2017.
5. Cserháti, M., Creation aspects of conserved non-coding sequences, J. Creation21( 2):
101–108, 2007; creation.com/images/pdfs/tj/j21_2/j21_2_101-108.pdf.
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.
7. Van der Lee, R. et al., Classification of Intrinsically Disordered Regions and
Proteins, Chem. Rev. 114( 13):6589–6631, 2014.
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.
9. Sanford, J. et al., The waiting time problem in a model hominin population,
Theoret. Biol. and Med. Modelling 12: 18, 2015.
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.
11. O’Micks, J., Promoter evolution is impossible by random mutations, J. Creation 30( 2):
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.
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.
15. I used the codon frequency table at www.kazusa.or.jp/codon.
16. Carter, R. W., Splicing and dicing the human genome: Scientists begin to unravel
the splicing code, creation.com/splicing-and-dicing-the-human-genome, 29 June
17. Carter, R. W., Can mutations create new information? J. Creation 25( 2): 92–98,
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.
19. Meynert, A. and Birney, E., Picking pyknons out of the human genome, Cell 125:
20. Arneigh, M.R., It’s a small world—microRNA cuts evolution down to size,
J. Creation 27( 2): 85–90, 2013.
Robert Carter received his Bachelor of Science
in Applied Biology from the Georgia Institute of
Technology in 1992 and his Ph.D. in Coral Reef Ecology
from the University of Miami in 2003. He has studied
the genetics of fluorescent proteins in corals and
sea anemones and holds one patent on a particular
fluorescent protein gene. His current research involves
looking for genetic patterns in the human genome and
the development of a biblical model of human genetic
history. He works as a speaker and scientist at CMI-US.