Scientists have long known that human genes
spring into action through instructions delivered by the precise order of our
DNA, directed by the four different types of individual links, or
"bases," coded A, C, G and T.
Nearly 25% of our genes are widely known to
be transcribed by sequences that resemble TATAAA, which is called the
"TATA box." How the other three-quarters are turned on, or promoted,
has remained a mystery due to the enormous number of DNA base sequence
possibilities, which has kept the activation information shrouded.
Now, with the help of artificial
intelligence, researchers at the University of California San Diego have
identified a DNA activation code that's used at least as frequently as the TATA
box in humans. Their discovery, which they termed the downstream core promoter
region (DPR), could eventually be used to control gene activation in
biotechnology and biomedical applications. The details are described September
9 in the journal Nature.
"The identification of the DPR reveals
a key step in the activation of about a quarter to a third of our genes,"
said James T. Kadonaga, a distinguished professor in UC San Diego's Division of
Biological Sciences and the paper's senior author. "The DPR has been an
enigma—it's been controversial whether or not it even exists in humans.
Fortunately, we've been able to solve this puzzle by using machine
learning."
In 1996, Kadonaga and his colleagues
working in fruit flies identified a novel gene activation sequence, termed the
DPE (which corresponds to a portion of the DPR), that enables genes to be
turned on in the absence of the TATA box. Then, in 1997, they found a single
DPE-like sequence in humans. However, since that time, deciphering the details
and prevalence of the human DPE has been elusive. Most strikingly, there have
been only two or three active DPE-like sequences found in the tens of thousands
of human genes. To crack this case after more than 20 years, Kadonaga worked
with lead author and post-doctoral scholar Long Vo ngoc, Cassidy Yunjing Huang,
Jack Cassidy, a retired computer scientist who helped the team leverage the
powerful tools of artificial intelligence, and Claudia Medrano.
The results, as Kadonaga describes them,
were "absurdly good." So good, in fact, that they created a similar
machine learning model as a new way to identify TATA box sequences. They
evaluated the new models with thousands of test cases in which the TATA box and
DPR results were already known and found that the predictive ability was
"incredible," according to Kadonaga.
These results clearly revealed the
existence of the DPR motif in human genes. Moreover, the frequency of occurrence
of the DPR appears to be comparable to that of the TATA box. In addition, they
observed an intriguing duality between the DPR and TATA. Genes that are
activated with TATA box sequences lack DPR sequences, and vice versa.
Kadonaga says finding the six bases in the
TATA box sequence was straightforward. At 19 bases, cracking the code for DPR
was much more challenging.
"The DPR could not be found because it
has no clearly apparent sequence pattern," said Kadonaga. "There is
hidden information that is encrypted in the DNA sequence that makes it an
active DPR element. The machine learning model can decipher that code, but we
humans cannot."
Going forward, the further use of
artificial intelligence for analyzing DNA sequence patterns should increase
researchers' ability to understand as well as to control gene activation in
human cells. This knowledge will likely be useful in biotechnology and in the
biomedical sciences, said Kadonaga.
"In the same manner that machine
learning enabled us to identify the DPR, it is likely that related artificial
intelligence approaches will be useful for studying other important DNA
sequence motifs," said Kadonaga. "A lot of things that are
unexplained could now be explainable."
