Breast cancer, COVID-19 and autism may seem unrelated, but they share surprising links. Some of the same genes that are mutated in breast cancer are also hijacked by COVID-19, and some other cancer-mutated genes are also implicated in autism.
Commonalities like these have led Nevan KroganPhD, Director of UCSF Institute for Quantitative Biosciencesto examine in detail the effects of a handful of genes that seem to play an outsized role in a wide range of diseases.
These effects depend on proteins, of which genes are the models. When a gene is mutated, so is its protein.
“Our genome is relatively static, but proteins are not,” Krogan said. “They constantly interact with other proteins in different contexts that change over time.”
Many conditions involve dozens of mutations, he added. Seeing a person’s full disease landscape means piecing together how each of these mutated proteins contributes to it.
More than ten years ago, Krogan started using sophisticated quantitative approaches to create “cellular maps” that compare thousands of these protein-protein interactions, or PPIs, in healthy and diseased cells across a range of mutations in cancer, autism and infectious diseases.
He believes focusing on these PPIs can elucidate how mutations disrupt cellular functions and uncover entry points for safer and more effective treatments.
Already, collaborations between Krogan and researchers in the United States and around the world have revealed how mutations in different genes sometimes scramble the same cellular pathways, illuminating connections between diseases that can look very different at the genetic level.
In other cases, the same gene is implicated in more than one disease: a mutation at point A can contribute to cancer, while a mutation at point B can create a predisposition to a psychiatric disorder.
“We find the Achilles’ heels of the genome,” Krogan said. “By going beyond DNA and looking at these networks of protein interaction, we are able to connect dots that we didn’t even know existed before.”
Map the network
To find these dots and draw the lines between them, Krogan, along with his collaborators, uses his cellular maps to see exactly how a specific mutation in a particular gene translates into changes in protein interactions.
A gene called PIK3CA, for example, is implicated in a significant percentage of cancers, as well as autism and other brain disorders. There are hundreds of known mutations in PIK3CA, each having a specific effect on the protein machinery.
Krogan cataloged not only how each of these mutations leads to disease, but also how different PIK3CA pathways operate in healthy cells, allowing him to identify the intersection where each of these mutations disrupts the cell’s protein interactions.
Achieving this granular approach involves overlaying large datasets and finding patterns that identify the molecular moment when a cellular process goes wrong. Krogan teams use mass spectrometry to weigh protein molecules and combine it with other methods that assess protein structure. Advanced computational techniques are required to process the huge amount of data involved.
These maps can help provide a protein-based prognosis resulting from mutations found in a particular patient’s genes; help clinicians choose one treatment over another; and reveal where a drug might be able to stop a disease without interfering with other healthy cell functions.
A new vision of the disease
While some researchers have studied PPIs associated with individual genetic mutations, Krogan has spent his career studying them on a large scale. “There is great value in looking at the big picture,” he said. “It makes those analyzes exponentially more powerful.”
Krogan compares protein maps to a computer-generated geographic map. You can zoom out to see a large area, then zoom in to see local details, then zoom out again to put those details in context.
Being able to see these different levels of detail can potentially help researchers identify FDA-approved drugs that could be tested for unexpected applications, Krogan said. “These cell maps are a whole new way to look at disease and drug discovery.”
Ultimately, Krogan’s goal is to allow researchers to apply artificial intelligence to these maps, so they can predict a patient’s prognosis and the best combination of drugs to treat it.
“Once we understand this underlying biology, attacking disease becomes so much easier,” Krogan said. “We are perfectly positioned to build this bridge between the genome and the clinic for a whole range of disorders.”
“We are on the cusp of these great things.”
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