Predicting the protein half-life in tissue from its cellular properties

Rahman, Mohammad A.; Sadygov, Rovshan G.

doi:10.1371/journal.pone.0180428

Cited by 13 publications

(17 citation statements)

References 23 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1c ). Along the same lines, a recent work that has analyzed the variables that can predict protein lifetimes in vivo has revealed that protein lifetimes measured in vitro hold the highest predicting power 47 , suggesting that for some homeostasis parameters there are intrinsic similarities that are conserved both in vitro and in vivo .…”

Section: Resultsmentioning

confidence: 99%

The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain

Mandad

Rahman

Centeno

et al. 2018

Sci Rep

View full text Add to dashboard Cite

The homeostasis of the proteome depends on the tight regulation of the mRNA and protein abundances, of the translation rates, and of the protein lifetimes. Results from several studies on prokaryotes or eukaryotic cell cultures have suggested that protein homeostasis is connected to, and perhaps regulated by, the protein and the codon sequences. However, this has been little investigated for mammals in vivo. Moreover, the link between the coding sequences and one critical parameter, the protein lifetime, has remained largely unexplored, both in vivo and in vitro. We tested this in the mouse brain, and found that the percentages of amino acids and codons in the sequences could predict all of the homeostasis parameters with a precision approaching experimental measurements. A key predictive element was the wobble nucleotide. G-/C-ending codons correlated with higher protein lifetimes, protein abundances, mRNA abundances and translation rates than A-/U-ending codons. Modifying the proportions of G-/C-ending codons could tune these parameters in cell cultures, in a proof-of-principle experiment. We suggest that the coding sequences are strongly linked to protein homeostasis in vivo, albeit it still remains to be determined whether this relation is causal in nature.

show abstract

Section: Resultsmentioning

confidence: 99%

The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain

Mandad

Rahman

Centeno

et al. 2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This would allow us to use machine learning to integrate RNA expression and protein turnover ratios in the way that would most accurately predict protein expression in the same tissue 35 , 36 . A more complex relationship undoubtedly exists between these factors, so training a model to more accurately predict this relationship would be highly beneficial 10 – 12 . It would also be useful to expand the number and variety of protein turnover ratios to put into this model.…”

Section: Discussionmentioning

confidence: 99%

“…Here, we propose a novel method of using protein turnover ratios to infer protein abundance from RNAseq and microarray data, thereby extending its functional value. Every protein has a particular half-life, and therefore exists in a cell to exert its functions for a relatively predictable amount of time 9 , 10 . We propose that using this information about protein half-life in tandem with transcriptomics data could offer insights into protein abundance.…”

Section: Introductionmentioning

confidence: 99%

Using protein turnover to expand the applications of transcriptomics

Smail

Reigle

McCullumsmith

2021

Sci Rep

View full text Add to dashboard Cite

RNA expression and protein abundance are often at odds when measured in parallel, raising questions about the functional implications of transcriptomics data. Here, we present the concept of persistence, which attempts to address this challenge by combining protein half-life data with RNA expression into a single metric that approximates protein abundance. The longer a protein’s half-life, the more influence it can have on its surroundings. This data offers a valuable opportunity to gain deeper insight into the functional meaning of transcriptome changes. We demonstrate the application of persistence using schizophrenia (SCZ) datasets, where it greatly improved our ability to predict protein abundance from RNA expression. Furthermore, this approach successfully identified persistent genes and pathways known to have impactful changes in SCZ. These results suggest that persistence is a valuable metric for improving the functional insight offered by transcriptomics data, and extended application of this concept could advance numerous research fields.

show abstract

“…This would allow us to use machine learning to integrate RNA expression and protein turnover ratios in the way that would most accurately predict protein expression in the same tissue [35,36]. A more complex relationship undoubtedly exists between these factors, so training a model to more accurately predict this relationship would be highly beneficial [10][11][12]. It would also be useful to expand the number and variety of protein turnover ratios to put into this model.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…Here, we propose a novel method of using protein turnover ratios to infer protein expression from RNAseq and microarray data. Every protein has a particular half-life, and therefore exists in a cell to exert its functions for a relatively predictable amount of time [9,10]. We propose that using this information about protein half-life in tandem with transcriptomics data could offer insights into protein expression.…”

Section: Introductionmentioning

confidence: 99%

Persistence: Using Protein Turnover to Expand the Applications of Transcriptomics

Smail

Reigle

McCullumsmith

2020

Preprint

View full text Add to dashboard Cite

ABSTRACTOne of the major issues with RNA sequencing is the lack of reproducibility between RNA and protein expression. Transcriptomics offers a holistic view of the molecular landscape of a tissue at an RNA level. However, RNA and protein expression are often at odds when measured in the same sample, raising the question whether or not changes in RNA expression translate to functional differences. This problem creates a need to devise a way to approximate protein abundance from transcriptomics data, in order to create a more complete picture of the functional landscape of a tissue. One additional measure that could be useful here is protein turnover or half-life. Once RNA is transcribed into protein, that protein can either be quickly degraded or remain in the cell for an extended period of time. The longer a protein’s half-life, the more influence it can have on its surroundings. Recently, a study used stable isotope labeling in mammals (SILAM) in combination with mass spectrometry to determine the turnover ratio of ∼2200 protein in mouse synaptosomes. This data offers a valuable opportunity to integrate protein turnover with RNA expression to gain deeper insight into the functional meaning of RNA expression changes. Here, we present the concept of this combination of protein turnover and RNA expression, which we coined as persistence. We then demonstrate the application of persistence using schizophrenia (SCZ) transcriptomics datasets. Calculating persistence for these datasets greatly improved our ability to predict protein expression from RNA expression. Furthermore, this approach successfully identified persistent genes and pathways known to have impactful changes in SCZ. These results suggest that persistence is a valuable metric for improving the functional insight that can be gained from transcriptomics data.

show abstract

Predicting the protein half-life in tissue from its cellular properties

Cited by 13 publications

References 23 publications

The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain

The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain

Using protein turnover to expand the applications of transcriptomics

Persistence: Using Protein Turnover to Expand the Applications of Transcriptomics

Contact Info

Product

Resources

About