Harnessing cross-species data on protein evolution can help understand subtle genetic variants in people with autism and identify hundreds of new genes that may contribute to the disease, new analysis shows.
The work focuses on ‘missense’ variants, which alter a single amino acid in a protein and often have mild effects. Although researchers have identified thousands of missense variants in people with autism, analyzing which ones contribute to the disease has been difficult.
“The impact of a mutation that turns one amino acid into another in a protein is difficult to interpret,” says Olivier Lichtarge, professor of molecular and human genetics at Baylor College of Medicine in Houston, who led the recent research. This could alter the way the protein folds, breaks down, is transported, or interacts with other molecules, and predicting this outcome is complex, he says.
To identify autism-related missense variants, Lichtarge and his colleagues used an approach known as “evolutionary action,” which involves comparing the amino acid sequences of a protein across different species. to infer the likely impact of a missense variant.
Using this strategy, the team identified missense variants in 398 genes that could contribute to autism. Some are known genes linked to autism, such as RELN, PTEN, and SYNGAP1, but others have not been previously linked to the disease.
The approach “does indeed seem to identify important mutations in the context of autism,” explains Ivan Iossifov, associate professor at the Cold Spring Harbor Laboratory in New York, who was not involved in the work. Although researchers have developed many ways to assess the impact of missense variants, spotting autism-related ones is still a big problem in the field, he says, and “it seems like a good way to go. approach it “.
Lichtarge and colleagues looked for uninherited, or de novo, missense variants in 2,384 people with autism and 1,792 of their unaffected siblings. The data came from the Simons Simplex Collection, a repository of genetic and trait information from families with an autistic child. (The dataset is funded by the Simons Foundation, Spectrumparent organization of.)
Participants with autism have 1,418 de novo missense variants that affect 1,269 genes, and their siblings have 976 missense variants that affect 911 genes, the researchers reported in May in Science Translational Medicine.
The team calculated the evolutionary action score for each variant on a scale of 0 to 100; the higher the score, the more likely a variant is to damage the corresponding protein in the gene. The score takes into account two factors: the sensitivity of a particular point in a protein’s amino acid sequence to variants and the severity of the disturbance caused by an amino acid change.
To measure sensitivity, the team used existing databases to compare amino acid sequences in proteins from various species to the protein associated with each mutated gene. They then measured the evolutionary distance associated with a change at a specific location in the sequence. If an alteration was associated with a large evolutionary distance, variants at this site were considered likely to alter the function of the corresponding protein.
To assess the severity of a disturbance caused by an amino acid change, the team measured how often a particular amino acid is swapped for another in any protein from one species to another. A change that rarely occurred in evolution suggested that the new amino acid has different properties than the one it replaced and that the alteration may have been damaging.
Although people with autism have more de novo missense variants than their siblings, the distribution of scores in the two groups was not significantly different, and the researchers say that without relying on existing knowledge about them. genes linked to autism, they would have been unable to identify which of the affected genes contributes to the disease.
So the team pooled variants based on 368 biological pathways – focusing on variants in the 1,792 people with autism who have matched siblings – and examined the distribution of evolutionary action scores. Only 23 pathways were skewed toward high-impact variants, many of which are related to neurodevelopment, neural signaling, and the development of neural projections called axons.
High-impact pathways include 398 genes, many of which appear in the SFARI Gene database of genes linked to autism. (SFARI Gene is funded by the Simons Foundation.) Of these 398 genes, 28 were not classified as “high confidence” in 2017, but were listed as such in 2020.
These results suggest that the evolutionary action approach could help identify candidate genes for future research, the researchers said.
In a more in-depth analysis, the team divided people with autism into three groups based on their intelligence quotients (IQs). For each person, they counted the evolutionary action score of the variants within the 398 genes.
People with the lowest IQs have the most impacting variants in priority genes, supporting a link between the variants and autism, the researchers say. Scores plus rare and inherited missense variants are also tracked with IQ, according to another test.
Previous studies have not found a statistically significant link between rare and hereditary missense variants and the severity of autism traits, says Yufeng Shen, associate professor of systems biology and biomedical informatics at the University. Columbia, who was not involved in the research. So, the evolutionary approach may be useful in discovering the role that these variants play in disease, he says.
One limitation of the study is that the researchers did not systematically compare the evolutionary action approach with other methods commonly used to identify harmful missense variants, Shen says. “Without comparison with other methods, it is very difficult to assess the contribution of this method to autism research.”
The researchers also warn that IQ is only a measure of the severity of autism traits and that a person’s impact score is not necessarily predictive of their IQ.
The results are just the âtip of the iceberg,â says Lichtarge. As researchers analyze more data, they can potentially use evolutionary action scores to find out how a person’s variants affect their genetic traits in more individualized ways.
Quote this article: https://doi.org/10.53053/VESX6574