Humans
and chimpanzees differ in only one percent of their DNA. Human
accelerated regions (HARs) are parts of the genome with an unexpected
amount of these differences. HARs were stable in mammals for millennia
but quickly changed in early humans. Scientists have long wondered why
these bits of DNA changed so much, and how the variations set humans
apart from other primates.
Now, researchers have analyzed thousands of human and chimpanzee HARs
and discovered that many of the changes that accumulated during human
evolution had opposing effects from each other.
“This helps answer a longstanding question about why HARs evolved so
quickly after being frozen for millions of years,” says the lead author
of the new study published in Neuron. “An initial variation in a HAR might have turned up its activity too much, and then it needed to be turned down.”
The findings have implications for understanding human evolution. In
addition—because the team discovered that many HARs play roles in brain
development—the study suggests that variations in human HARs could
predispose people to psychiatric disease.
“These results required cutting-edge machine learning tools to integrate
dozens of novel datasets generated by our team, providing a new lens to
examine the evolution of HAR variants,” says the first author of the
study.
The authors discovered HARs in 2006 when comparing the human and
chimpanzee genomes. While these stretches of DNA are nearly identical
among all humans, they differ between humans and other mammals. The lab
went on to show that the vast majority of HARs are not genes, but
enhancers— regulatory regions of the genome that control the activity of
genes.
More recently, the group wanted to study how human HARs differ from
chimpanzee HARs in their enhancer function. In the past, this would have
required testing HARs one at a time in mice, using a system that stains
tissues when a HAR is active.
Instead, the authors input hundreds of known human brain enhancers, and
hundreds of other non-enhancer sequences, into a computer program so
that it could identify patterns that predicted whether any given stretch
of DNA was an enhancer. Then they used the model to predict that a
third of HARs control brain development. “Basically, the computer was
able to learn the signatures of brain enhancers,” says the lead author.
Knowing that each HAR has multiple differences between humans and
chimpanzees, the team questioned how individual variants in a HAR
impacted its enhancer strength. For instance, if eight nucleotides of
DNA differed between a chimpanzee and human HAR, did all eight have the
same effect, either making the enhancer stronger or weaker?
“We’ve wondered for a long time if all the variants in HARs were
required for it to function differently in humans, or if some changes
were just hitchhiking along for the ride with more important ones,” says
the senior author.
To test this, the authors applied a second machine learning model, which
was originally designed to determine if DNA differences from person to
person affect enhancer activity. The computer predicted that 43 percent
of HARs contain two or more variants with large opposing effects: some
variants in a given HAR made it a stronger enhancer, while other changes
made the HAR a weaker enhancer.
This result surprised the team, who had expected that all changes would
push the enhancer in the same direction, or that some “hitchhiker”
changes would have no impact on the enhancer at all.
To validate this compelling prediction, the researchers fused each HAR
to a small DNA barcode. Each time a HAR was active, enhancing the
expression of a gene, the barcode was transcribed into a piece of RNA.
Then, the researchers used RNA sequencing technology to analyze how much
of that barcode was present in any cell—indicating how active the HAR
had been in that cell.
“This method is much more quantitative because we have exact barcode
counts instead of microscopy images,” says another author. “It’s also
much higher throughput; we can look at hundreds of HARs in a single
experiment.”
When the group carried out their lab experiments on over 700 HARs in
precursors to human and chimpanzee brain cells, the data mimicked what
the machine learning algorithms had predicted.
“We might not have discovered human HAR variants with opposing effects
at all if the machine learning model hadn’t produced these startling
predictions,” said the senior author.
The idea that HAR variants played tug-of-war over enhancer levels fits
in well with a theory that has already been proposed about human
evolution: that the advanced cognition in our species is also what has
given us psychiatric diseases.
“What this kind of pattern indicates is something called compensatory
evolution,” says the senior author. “A large change was made in an
enhancer, but maybe it was too much and led to harmful side effects, so
the change was tuned back down over time—that’s why we see opposing
effects.”
If initial changes to HARs led to increased cognition, perhaps
subsequent compensatory changes helped tune back down the risk of
psychiatric diseases, the senior author speculates. The data, can’t
directly prove or disprove that idea. But in the future, a better
understanding of how HARs contribute to psychiatric disease could not
only shed light on evolution, but on new treatments for these diseases.
“We can never wind the clock back and know exactly what happened in
evolution,” says the lead author. “But we can use all these scientific
techniques to simulate what might have happened and identify which DNA
changes are most likely to explain unique aspects of the human brain,
including its propensity for psychiatric disease.”
https://www.cell.com/neuron/fulltext/S0896-6273(22)01123-0
http://sciencemission.com/site/index.php?page=news&type=view&id=publications%2Fmachine-learning_6&filter=22