Protein atlas doubles variety of identified interactions in mice | Spectrum
Contact list: A new online tool describes numerous novel (blue) and already identified (orange) connections between proteins in mice.
A new atlas catalogs more than 125,000 protein-protein interactions in the brain and six other types of tissue in mice – more than double the previously known number. The data set, described in Cell in July, could prove useful for autism researchers aiming to understand which proteins “talk” to each other and how these interactions differ between tissues and different parts of the mouse .
Scientists typically only measured one protein-protein interaction at a time, grabbed one of the proteins, pulled it out of a cell, and saw what other proteins it interacts with. But this method is slow and arduous, says study researcher Leonard Foster, professor of biochemistry at the University of British Columbia in Vancouver, so that “5,000 proteins is 5,000 times more work than one.”
To create the new atlas, Foster and his colleagues used a method called “protein correlation profiling – stable isotopic labeling of mammals,” which allows thousands of molecular interactions to be measured simultaneously.
The team’s full dataset is available in the supplemental information of the paper and online, and researchers can also query specific proteins via a web application.
“By making this resource public, people who already have data on some of these proteins can look it up and say, ‘Hey, look! My protein is in, ”says Foster. “And now things are suddenly making sense because this protein may be interacting with this other protein that they hadn’t thought of before.”
Protein “gold mine”:
To map protein-protein interactions en masse, the team fed two generations of mice with “heavy” forms of amino acids that can assimilate into proteins over time and serve as markers in any of the seven types of tissue tested: brain, Heart, liver, lungs, kidney, muscle and thymus gland.
The team extracted labeled and unlabeled protein complexes from the tissues and used an electrified gel to sort them by size and charge. They used mass spectrometry to identify each protein. A comparison of the labeled proteins with the control “background” shows, according to Foster, which proteins interact.
“We’re doing this on a large scale and looking at thousands of proteins,” he says. “We can monitor hundreds or maybe even up to a thousand different protein complexes at the same time.”
The researchers cataloged between 20,000 and 35,000 protein-protein interactions in each of the seven tissues, for a total of more than 125,000 unique interactions.
In additional experiments, Foster’s team compared its atlas with nine databases that together contain more than 82,000 protein-protein interactions. Only 4,354 of the interactions found by Foster’s team were documented in these databases, suggesting that much of the data set consists of new information.
The researchers found that proteins associated with diseases such as Huntington’s disease are significantly more likely than those not associated with disease to have different protein-protein interactions between tissues. In other words, disease-related proteins change their interaction partners or with whom they “speak” from one tissue type to the next. These tissue-specific communication networks could explain why some diseases manifest only in certain parts of the body, the authors say, even though mutations in germline cells such as eggs and sperm can destroy a protein throughout the body.
Foster says he plans to study how a genetic mutation, for example in a gene linked to autism, can alter the interactions of the encoded protein with other proteins in a cell.
“If you really look at the data, there are interactions between proteins that appear to be involved in various neuronal disorders that no one has reported on before,” he says. “We really have the feeling that this is some kind of gold mine. But it’s a gold mine with lots and lots of different tunnels and it’s not even clear where to begin. “
Quote this article: https://doi.org/10.53053/HCXF2814