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Study Suggests Protein Synthesis, Degradation Info Could Improve Use of RNA Data in Proteomics

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A study by researchers at the University of British Columbia has found that levels of RNA transcripts can be better correlated with their corresponding proteins by taking into account protein synthesis and degradation levels.

Described in a paper published last week in Molecular Systems Biology, the finding suggests that the addition of such information could significantly improve the usefulness of transcriptomic data in proteomics work, Leonard Foster, a UBC researcher and author on the paper, told ProteoMonitor.

RNA transcripts are an attractive proxy for protein expression levels due to the relative ease of transcriptomic techniques like microarrays and RNA-seq compared to shotgun proteomics. However, as Foster noted, many studies have demonstrated poor correlation between transcript levels and protein expression levels.

The MSB work, however, indicates that integrating protein synthesis and degradation data with transcriptomics datasets "gives you a much better predictive power about what the ultimate protein expression levels are," he said.

"Having the transcriptomic data by itself gives you slightly more than half the data you need to make a good prediction [of protein expression]," Foster said. "But if [with degradation and synthesis data] you can go from [for instance] 60 percent accuracy to 80 percent accuracy, that is something well worth doing."

Importantly, he noted, protein degradation and synthesis rates appear to be fairly stable across species and between proliferating and differentiating cells, meaning that these values wouldn't have to be calculated for each individual transcriptomic dataset.

Rather, Foster said, "we think that if you could do a widescale measurement of protein [synthesis and degradation] rates, then those could be reasonably added to a database like Uniprot as sort of de facto rates for that protein."

"While that might not be precise for every cell type, it would at least be a pretty good initial starting point if you need to use that data for layering on top of a transcriptomic measurement," he added. "Someone would have to do those [synthesis and degradation] measurements, but once they are measured they could be widely applied."

The finding emerged from research by Foster and his colleagues into using quantitative proteomics to study protein metabolism and the factors involved in regulation of protein expression during cellular differentiation.

Traditionally, researchers have investigated protein metabolism by using isotope-labeled amino acids and quantifying the incorporation of these labeled amino acids as a function of time. However, this approach, the UBC team felt, was too time- and labor-intensive for tracking protein expression in the two different cell types – human THP-1 cells and mouse C2C12 cells – across the five time points they were interested in.

Instead, they used a SILAC-based method in which they switched cell populations from growth in medium to heavy amino acids at the moment they induced differentiation, allowing them to calculate relative protein degradation and synthesis rates by monitoring the decrease in medium amino acid levels.

As the researchers began designing their experiment, they came across a 2009 Nature Genetics paper featuring a transcriptomic dataset that they realized could complement their effort.

"That made us go back and rethink the design of our experiment a little bit so that we could have an exact parallel in terms of time courses and cell type," Foster said.

In addition to investigating the relationship between transcriptomic data and protein expression data, the UBC researchers also examined the nature of protein turnover within protein complexes, employing a high-throughput protein complex identification approach that combines size-exclusion chromatography and SILAC mass spec.

First detailed in a paper published by Foster's lab last year in Nature Methods (PM 8/12/2012), the method uses high-resolution size-exclusion chromatography to collect a series of SILAC-labeled sample fractions separated by analyte size. With this data they can build chromatograms of the levels of each protein across all the different size exclusion fractions, with proteins with similar chromatograms identified as likely co-eluting. They can then develop a protein interaction matrix based on co-eluting proteins, from which they are able to identify proteins in the same complex.

Combining this method with their SILAC-based protein dynamics measurements, the researchers examined the turnover rates of proteins within complexes, finding, somewhat surprisingly, Foster noted, that synthesis and degradation of such proteins are co-regulated – meaning that components of a complex are typically all synthesized and degraded together.

"You might suspect that if one protein in a complex becomes unfolded or something then maybe that complex gets disassembled and that protein is targeted for degradation, but you would reasonably assume that the other [still intact] components of the complex would just be used to make a new complex," Foster said.

However, he noted, based on the MSB study, while "we can't say that doesn't happen, it seems in general that [components of protein complexes] are co-synthesized and co-degraded, as well."

Moving forward, the UBC team is now looking to continue this protein complex work in different organisms and tissues, looking for differences in interaction networks, Foster said. They have also begun work calculating protein synthesis and degradation levels in a variety of different tissues, he said.

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