This is really cool and I think it might be foreshadowing of what part of the proteomics field may look like in the future! You can check it out ASAP at JPR here.
What is it? They took saliva from a bunch of people (almost 200, I think). They digested the proteins and did single shot proteomics on the peptides with an Orbitrap Velos with label free quantification. All normal stuff.
Where it gets really interesting is in the downstream analysis. They took all the info that they had about these participants they got the saliva proteomes from -- including the data that you can see in the title above -- and did fancy statistics based on the proteins identified and their relative quantification.
The genomics people are doing TONS of stuff like this. I bet you've heard of the GWAS stuff (Wikipedia article here). In these studies they take a snapshot of the genes, typically via SNP arrays or low read genome sequencing, of a bunch of people. In the simplest example, they do this on a group of people without a disease and a group with a disease and they try to figure out what areas of the genome associate with the disease. In the bigger and more ambitious studies, they just collect lots of info about people so they can separate them into classifications and then get genomic information on every participant they can afford.
This cool study is a page out of this, but instead of getting a picture of the area in the genome that there might be more copies of (which...might be transcribed...and that part might be translated...) they cut out the middle steps and go right to the proteins!
What do they find? Protein expression that strongly associates with some of these characteristics mentioned in the paper title! For example, 30 proteins can be associated with the saliva donor's BMI!
This is a nice method paper and proof-of-principle for this kind of study. The exciting part to me -- It doesn't take much imagination to come up with a way to apply it in a clinical sense, right? Collection of the sample couldn't be easier. We already know how to do the sample prep and analysis. Association with different diseases could be used to point us to individual proteins or patterns of proteins that could be early disease predictors. And maybe patterns are the key point here, and we can easily steal the tools the genomics teams are using for GWAS and divert them to finding patterns in the protein data.
This is a great forward-thinking study that I couldn't be more excited about!
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