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Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

Alief Moulana, Adriana Scanteianu, DeAnalisa Jones, Alan D. Stern, Mehdi Bouhaddou, Marc R. Birtwistle
doi: https://doi.org/10.1101/399816
Alief Moulana
1Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
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Adriana Scanteianu
1Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
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DeAnalisa Jones
1Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
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Alan D. Stern
1Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
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Mehdi Bouhaddou
2Cellular and Molecular Pharmacology, University of California, San Francisco, CA
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  • For correspondence: mehdi.bouhaddou@ucsf.edu mbirtwi@clemson.edu
Marc R. Birtwistle
3Chemical and Biomolecular Engineering, Clemson University, Clemson, SC
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  • For correspondence: mehdi.bouhaddou@ucsf.edu mbirtwi@clemson.edu
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Abstract

Transcriptomic data are widely available, and the extent to which they are predictive of protein abundances remains debated. Using multiple public databases, we calculate mRNA and mRNA-to-protein ratio variability across human tissues to quantify and classify genes for protein abundance predictability confidence. We propose that such predictability is best understood as a spectrum. A gene-specific, tissue-independent mRNA-to-protein ratio plus mRNA levels explains ∼80% of protein abundance variance for more predictable genes, as compared to ∼55% for less predictable genes. Protein abundance predictability is consistent with independent mRNA and protein data from two disparate cell lines, and mRNA-to-protein ratios estimated from publicly-available databases have predictive power in these independent datasets. Genes with higher predictability are enriched for metabolic function, tissue development/cell differentiation roles, and transmembrane transporter activity. Genes with lower predictability are associated with cell adhesion, motility and organization, the immune system, and the cytoskeleton. Surprisingly, many genes that regulate mRNA-to-protein ratios are constitutively expressed but also exhibit ratio variability, suggesting a general autoregulation mechanism whereby protein expression profile changes can be implemented quickly, or homeostatic sensing stabilizes protein abundances under fluctuating conditions. Gene classifications and their mRNA-to-protein ratios are provided as a resource to facilitate protein abundance predictions by others.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 24, 2018.
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Gene-Specific Predictability of Protein Levels from mRNA Data in Humans
Alief Moulana, Adriana Scanteianu, DeAnalisa Jones, Alan D. Stern, Mehdi Bouhaddou, Marc R. Birtwistle
bioRxiv 399816; doi: https://doi.org/10.1101/399816
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Gene-Specific Predictability of Protein Levels from mRNA Data in Humans
Alief Moulana, Adriana Scanteianu, DeAnalisa Jones, Alan D. Stern, Mehdi Bouhaddou, Marc R. Birtwistle
bioRxiv 399816; doi: https://doi.org/10.1101/399816

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