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

UNLABELLED

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 strategy 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.

IMPORTANCE

Deciphering the biology of natural microbial communities is limited by the lack of functional data. While transcriptomics enables gene expression profiling, mRNA levels often fail to predict protein abundance, the primary indicator of microbial function. Prior studies addressed this by calculating RNA-to-protein (RTP) conversion factors using conserved protein-to-RNA (ptr) ratios across bacterial strains, but their cross-species and cross-domain utility remained unknown. We generated comprehensive transcriptomic and proteomic data sets from seven bacteria and one archaeon spanning diverse metabolisms and ecological niches. We identified orthologous genes with conserved ptr ratios, enabling the discovery of RTP conversion factors that significantly improved protein prediction from mRNA, even between distant species and domains. This reveals previously unrecognized conservation in ptr ratios across domains and eliminates the need for paired proteomic data in many cases. Our approach offers a broadly applicable framework to enhance functional prediction in microbiomes using only transcriptomic data.