The study concludes that the evolution of unique human DNA has been a balancing act

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(a–c) Brightfield images of human iPSCs (A). iPSC differentiated into neuronal rosettes (B) and N2 cells (C) demonstrating typical morphology. (d) Human iPSCs show normal karyotypes. (e) Human N2 cells express paired box 6 (PAX6), a neuronal marker. (F) Human N3 cells express glial fibrillary acidic protein (GFAP), which is a glioma marker. (GI) Brightfield images of (G) chimpanzee iPSCs. iPSC differentiated into neuronal rosettes (H) and N2 cells (I) demonstrating typical morphology. (J) Chimpanzee iPSCs show normal karyotypes. (k) Chimpanzee N2 cells express PAX6. (L) Chimpanzee N3 cells express GFAP. (M) Percentage of cells in the scRNA-seq-expressing genes that are markers of the cell cycle or neuronal and diencephalon types. Human and chimpanzee N2 and N3 cells show similar expression of radial and telencephalic glial markers. For example, 50%–90% of the cells expressed FOXG1, which is a marker of telencephalon. (N and O) Coverage (calculated per million [CPM]) of H3K27ac ChIP-seq reads in HARs, ordered in order of maximum CPM, in human (N) and chimpanzee (O) N2 cells. (P) Footprints of human and chimpanzee N2 H3K27ac TF are largely identical, but some TF families with LIM, POU and homeodomains show species-biased enrichment. Select TFs expressed in NPCs and with significant differences in q-value between species are discriminated. Credit: Neuron (2023). DOI: 10.1016/j.neuron.2022.12.026

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 have been stable in mammals for thousands of years but changed rapidly in early humans. Scientists have long wondered why these pieces of DNA change so much, and how the differences distinguish humans from other primates.

Now, researchers at the Gladstone Institutes have analyzed thousands of human and chimpanzee HARs and discovered that many changes that accumulated during human evolution had opposing effects from each other.

says Katie Pollard, PhD, director of the Gladstone Institute for Data Science and Biotechnology and lead author of the new study published today in Neuron. “It may be that the initial HAR discrepancy overextended it and should therefore be dismissed.”

The findings, she says, have implications for understanding human evolution. Additionally — given her and her team’s discovery that several HARs play roles in brain development — the study suggests that differences in human HARs could predispose people to mental illness.

says Sean Wen, PhD, senior author of the study and senior staff research scientist in Pollard’s lab.

It is enabled by machine learning

Pollard discovered HARs in 2006 when comparing the human and chimpanzee genomes. While these DNA stretches are nearly identical among all humans, they differ between humans and other mammals. Pollard’s lab went on to show that the vast majority of HARs are not genes, but enhancers—regulatory regions of the genome that control gene activity.

Recently, Pollard’s group wanted to study how human HARs differ from chimpanzee HARs in their enhancer function. In the past, this required testing HARs one by one in mice, using a system that stains tissues when the HAR is active.

Instead, Whalen inserted hundreds of known human brain enhancers, and hundreds of other unenhanced sequences, into a computer program so he could identify patterns that predict whether any given stretch of DNA is enhanced or not. The model was then used to predict that one-third of HARs control brain development.

“Essentially, the computer was able to learn the signatures of the brain’s enhancers,” Wallen says.

Knowing that each HAR has multiple differences between humans and chimpanzees, Pollard and her team wondered how individual variants in the HAR might affect the potency of the enhancer. For example, if eight nucleotides of DNA differed between chimpanzee and human HAR, would all eight have the same effect, either making the enhancer stronger or weaker?

says Pollard, who is also corporate chair of bioinformatics in the Department of Epidemiology and Biostatistics at the University of California, San Francisco (UCSF), as well as a Chan Zuckerberg Biohub investigator.

Validation of the active HAR enhancer that regulates ROCK22xHAR. Credit: Neuron (2023). DOI: 10.1016/j.neuron.2022.12.026

To test this, Whalen applied a second machine learning model, which was originally designed to determine whether DNA differences from person to person affect enhancer activity. The computer predicted that 43 percent of the HARs contained two or more variables with significant opposing effects: some variables in a given HAR made it a stronger reinforcer, while other changes made the HAR a weaker reinforcer.

This result surprised the team, who had expected that all changes would push the optimizer in the same direction, or that some Wanderer changes would have no effect on the optimizer at all.

HAR force measurement

To validate this compelling prediction, Pollard collaborated with the laboratories of Nadav Ahituv, PhD, and Alex Pollen, PhD, at UCSF. The researchers combined each HAR into a small DNA barcode. Every time HAR is active, which enhances the expression of a gene, the barcode is copied into a piece of RNA. Next, the researchers used RNA-sequencing technology to analyze how much of this barcode was in any given cell – indicating how active HAR was in that cell.

“This method is more quantitative because we have precise numbers of bar codes rather than microscopic images,” says Ahituv. “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 more than 700 HARs in human and chimpanzee brain precursor cells, the data mimicked what machine learning algorithms predicted.

“We might not have discovered human HAR variants with opposing effects at all if the machine learning model had not produced such amazing predictions,” Pollard said.

Implications for understanding mental illness

The idea that HAR variants played a tug-of-war over enhancer levels fits nicely with a theory that has already been proposed about human evolution: that advanced cognition in our species is also what gave us psychopathology.

“What this kind of pattern indicates is something called compensatory evolution,” Pollard says. “A big change was made in an enhancer, but it might have been too much and led to adverse side effects, so the change has been adjusted back in over time — that’s why we’re seeing conflicting effects.”

If the initial changes to HARs increased cognition, then perhaps subsequent compensatory changes helped reduce psychiatric risk, Pollard speculates. She adds that her data cannot directly prove or disprove this idea. In the future, however, a better understanding of how HARs contribute to mental illness could shed light not only on development, but on new treatments for these illnesses.

“We can never turn back the clock and figure out exactly what happened in evolution,” Pollard says. “But we can use all of these scientific techniques to simulate what might happen and identify the DNA changes that are most likely to explain unique aspects of the human brain, including its propensity for mental illness.”

Further information: Sean Whalen et al, Machine Learning Anatomy of Human Accelerated Regions in Primate Neurodevelopment, Neurons (2023). DOI: 10.1016/j.neuron.2022.12.026

Provided by Gladstone Institutes

Citation: The evolution of unique human DNA has been a balancing act, study concludes (2023, January 13), Retrieved January 14, 2023 from https://phys.org/news/2023-01-evolution-uniquely-human-dna.html

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