Using protein turnover to expand the applications of transcriptomics

Area of Science:

• Genomics
• Proteomics
• Systems Biology

Background:

• Transcriptomics and proteomics data often show discrepancies, limiting functional interpretation.
• Understanding the relationship between RNA expression and protein abundance is crucial for biological insights.

Purpose of the Study:

• To introduce a novel metric, 'persistence,' integrating RNA expression and protein half-life.
• To improve the prediction of protein abundance from RNA expression data.
• To enhance the functional interpretation of transcriptomics.

Main Methods:

• Developed a 'persistence' metric by combining RNA expression levels with protein half-life data.
• Applied the persistence metric to schizophrenia (SCZ) datasets.
• Evaluated the metric's ability to predict protein abundance and identify relevant genes/pathways.

Main Results:

• Persistence significantly improved the prediction of protein abundance from RNA expression.
• The metric successfully identified known impactful genes and pathways in SCZ.
• Demonstrated enhanced functional insight into transcriptome changes.

Conclusions:

• Persistence offers a valuable metric for bridging the gap between RNA expression and protein abundance.
• This approach provides deeper functional insights into transcriptomic alterations.
• The concept of persistence has broad applicability across various research fields.