Conserved cross-domain protein-to-mRNA ratios enable proteome prediction in microbes

Abstract

Microbial communities are often studied by measuring gene expression (mRNA levels), but translating these data into functional insights is challenging because mRNA abundance does not always predict protein levels. Here, we present a strat egy to bridge this gap by deriving gene-specific RNA-to-protein conversion factors that improve the prediction of protein abundance from transcriptomic data. Using paired mRNA–protein data sets from seven bacteria and one archaeon, we identified orthologous genes where mRNA levels poorly predicted protein abundance, yet each gene’s protein-to-RNA ratio was consistent across these diverse organisms. Applying the resulting conversion factors to mRNA levels dramatically improved protein abundance predictions, even when the conversion factors were obtained from distantly related species. Remarkably, conversion factors derived from bacteria also enhanced protein prediction in an archaeon, demonstrating the robustness of this approach. This cross domain framework enables more accurate functional inference in microbiomes without requiring organism-specific proteomic data, offering a powerful new tool for microbial ecology, systems biology, and functional genomics.