Can we quantify a relationship between PTMs and phenotypes?

Here is an interesting line of thought -- can we map the relationship between PTMs and phenotypes?  It feels like a big step.  As a microbiologist in my past life, we often worked on the link between the genotype and the phenotype.  In bacteria, these things can line up pretty well, "this gene helps shape the bacteria's size or what sugars they can utilize."

They don't always line up, of course, but we often chalk that up to redundant mechanisms.  In eukaryotes, it is even worse.  We have fail safe after fail safe protecting essential functions.  We also know that our proteomes are orders of magnitude more complex than our genomes.  But what if you could take a step forward and out of the system and look at the PTMs and how they line up with phenotypes.  Would this assemble some sort of a bigger picture for what is happening?

This is the question that is examined in this new paper in MCP from Warren Albertin et al., out of a multi-department effort mostly centered at the University of Paris.

In this study, they use a lot of fancy statistics to analyze acetylation of proteins in yeast and look at the correlation between theses and the phenotypes of these yeast.  The differential was determined by 2D gels and the mass spec employed is an undisclosed detail.  I find this funny:

"Spots of interest were quantified using Progenesis software (Nonlinear Dynamics, Newcastle, UK) and identified using mass spectrometry (MS)."

Yeah, cause who cares what MS was used and how? ;)

It is clear that the statistics are the stars of this paper.  And it all appears to be quite good (and way above my head).  It is an interesting pixel into the potential of what the big picture could be here.