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Blog / 02.13.15

Dynamic profiling of the protein life cycle in response to pathogens

By Lisa Girard
Cellular protein levels are dictated by the net balance of mRNA expression (the type of RNA that provides genetic information for proteins), protein synthesis, and protein degradation. While changes in protein levels are commonly inferred from measuring changes in mRNA levels (due to the...

Cellular protein levels are dictated by the net balance of mRNA expression (the type of RNA that provides genetic information for proteins), protein synthesis, and protein degradation. While changes in protein levels are commonly inferred from measuring changes in mRNA levels (due to the difficulties involved in measuring protein levels), it’s not often clear whether determining RNA levels is actually a good proxy for measuring protein levels.

In their recent article in the journal Science, Broad Institute researchers working in core member Aviv Regev’s and institute member Nir Hacohen’s laboratories, along with the Broad’s Proteomics Platform led by Steve Carr describe a quantitative genomic model that lets them explain the abundance of proteins in cells based on mRNA expression, translation, and degradation. They performed their study in mouse dendritic cells stimulated with LPS, a component of bacteria.

While previous studies had looked at global levels of regulation in rapidly-dividing, unstimulated cells, this work focuses on understanding how much of the change in protein levels is due to a change in mRNA expression, translation, and degradation in specific genes and classes of genes in response to a stimulus - in this case, LPS. For example, would the changes in levels of one class of proteins be mostly driven by changes in the levels of the mRNAs that encode them? On the other hand, would changes in the levels of other groups of proteins occur without changes in mRNA, but rather due to faster translation or slower degradation of the protein? These were the type of questions the scientists were interested in.

Explains co-first author Marko Jovanovic, “Can we, in a dynamic system, integrate RNA and protein life cycle data? People rarely do this, and never systematically. Can we really make a global model of gene expression where we know, in the end, how much each type of regulatory layer is contributing to each gene? You can get a global answer too, but straight percentages of global contribution of RNA levels and the protein life cycle to final protein levels was not my goal. My question was really, do we see that certain classes of genes are controlled one way and certain other classes another way and therefore gain new regulatory insight?”

Since changes in protein levels are not as dramatic and fast as changes in RNA levels, one of the greatest challenges they faced in their study was distinguishing actual signal from noise. Co-first author Michael Rooney explains how they tackled this problem: “While the quantitative accuracy of mass spectrometry has grown tremendously, we realized that statistical strategies for handling stochastic and systematic errors in the data would still be critical to getting correct results. As a first step, we developed a generative statistical model for the data. This allowed us to leverage the entire time course in a manner that was robust to missing values and stochastic variation. Second, we saw that the contribution of translation might be over-estimated if we allowed translation rates and protein levels to be calculated from the same experimental system, because in such a case they would both be confounded by the same systematic errors, making them appear more similar than they actually are. This led us to the novel strategy of creating biological replicates prepared by distinct peptide library protocols.”

In this way, the team was able to robustly build a dynamic model in which the mRNA synthesis rate, the translation rate and the protein degradation rate change over time. Based on this model, it was possible to predict how much of each of the three types of regulation are contributing to the change in the level of each protein and from that measure both globally, per gene class, and per gene, the relative contributions of each type of regulation.

Analyzing the LPS-stimulated dendritic cells, the researchers found that overall mRNA expression dominates the regulation strategies, accounting for up to 90% of the fold changes in protein level variation. This is a significant increase from their pre-stimulation measurements showing regulation of mRNA expression contributing 60-70%, translation 15-25%, and degradation also 10-20%.

What appeared to be regulated more substantially by the protein lifecycle (translation, degradation) were highly expressed genes. And, looking at changes in the number of protein molecules rather than just the relative fold changes in pre- versus post-stimulated cells, what emerges is that post-stimulation, regulation at the level of the protein lifecycle begins to dominate.

The findings lead to a model for the LPS-stimulated system in which protein expression associated with functions critical for a dendritic cell-specific functions is taken care of by regulation at the level of RNA expression. However, the readjustment of the pre-existing proteome when the cells enter a new state (for example, in response to pathogen stimulation) is controlled via regulation of the protein life cycle (translation, degradation) rather than RNA expression.

“We termed this the ‘cupcake model’,” says Jovanovic. “You have to forgive me, this is my European view on how I see people buy cupcakes. They go into the store and choose the cupcake based on the icing, so the icing is kind of the identity of the cupcake. So from one cupcake to another you are basically changing the icing. In our model, the identity of cell states is adjusted by mRNA regulation so mRNA regulation is basically contributing to the icing. However, there’s also the cake part. The cake part is often specifically adjusted to the icing on top of the cupcake. The cake part, analogous to “housekeeping genes’, also needs to change and this is mainly through the protein life cycle. I’m very biased because I don’t like the icing on cupcakes, just the cake part, and so in the same vein, I wanted to know more about how the protein lifecycle contributes to gene expression. I think people have focused too much on the icing. “

So, mRNA changes drive new cell state identity. Protein lifecycle regulation drives readjustment of preexisting “housekeeping genes” such as those encoding ribosomes and factors involved in metabolism to adjust the cell to its new state.

This approach is extensible to test the regulation of gene expression in other perturbed systems as well, and allows researchers for the first time to assess the relative contributions of each of the three levels of protein level regulation - mRNA expression, translation, and degradation - in any perturbed system.

Paper cited: Jovanovic, M et al. Dynamic profiling of the protein life cycle in response to pathogens. Science. Feb. 12, 2015. DOI: 10.1126/science.1259038