Precision medicine still struggles with a basic problem: understanding how genomic context can amplify or mute the effects of isolated mutations. And so, precision medicine is more than willing to take lessons from other disciplines, namely, comparative genomics and evolutionary biology. These disciplines may help explain why mutations that are frequently pathogenic in the human genome are harmless in other genomes. For example, they may identify genomic features that compensate for potentially disease-causing mutations.

Making sense of genomic context was at the heart of a recent study conducted by researchers from Duke University and Brigham and Women’s Hospital. In this study, the researchers compared thousands of human disease-causing mutations with the analogous sequences of some 100 animal species.

Focusing on missense variants, the researchers found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. Although generally in line with earlier observations, the scale of the findings motivated geneticists to find the explanation for this apparent mystery.

“We found many examples in which an entire species should have a serious genetic ailment, but instead were healthy,” said Duke’s Nicholas Katsanis, Ph.D. “So, if we can understand how animals escape illness from such severe genetic mutations, we might have a way to make humans better. What we considered is that for many mutations, there must be a buffering mechanism—another mutation that protects the animal from the detrimental effects of the disease-causing mutation.”

The researchers considered two possible explanations: Disease suppression might be the result of one or two additional substitutions on the same gene that buffer the harmful effect of the mutation; or suppression may be caused by numerous small substitutions throughout the genome that form an aggregate “shield.”

The details of the researchers’ investigation appeared June 29 in Nature, in an article entitled, “Identification of cis-suppression of human disease mutations by comparative genomics.” The article described how the researchers developed a computational tool to predict candidate residues subject to compensation—that is, to describe how a shield might work.

The researchers began with a model of genetic interactions that predicts most disease-causing alleles to be simple pairwise compensations. “Functional testing of this model on two known human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo,” stated the authors of the Nature article. “This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species.”

Essentially, the team tracked changes in protein sequence that travelled with the disease-causing mutation and were candidates for offering protection. If a species lost the “traveler,” it would also have to eliminate the mutation or become extinct. The researchers tested this notion using molecular tools to identify these sites. They first engineered mutant proteins that were defective, then added secondary sites and were able to completely restore protein function.

“In the end, it looks like you can shield mutations with a single change elsewhere in the same gene, creating a single champion.” Dr. Katsanis noted.

With the advent of genome engineering, scientists are now introducing hundreds of different human mutations in other species to study their effects and develop new drugs. Because the effect of mutations on protein function might be dependent on the broader context of the human sequence, this approach will also lead to serious false negative conclusions, Dr. Katsanis warned.

“We are really beginning to appreciate the fundamental complexity of the human genome and genomes in general,” Dr. Katsanis added. “It used to be black-and-white: mutation, bad; no mutation, good. But it's far more complex. We are now beginning to be able to compute the effect of mutations in the context of the rest of the genome. There is no question that this will improve our ability to interpret human genomes and inform clinical practice”

Co-author Shamil Sunyaev, Ph.D., of Brigham and Women's Hospital, agrees that the results highlight the complexity of interpreting DNA sequences.

“Our study provides an example of the utility of comparative genomics and evolutionary models for medicine,” Dr. Sunyaev explained. “At the same time, it shows how data accumulated by medical geneticists can be used to better understand evolution. Beyond medicine and evolution, the logical next step would be to explain biochemical mechanisms underlying interactions between mutations.”

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