Zhang Mengshi, Zhang Changyi, Ramos Anayancy, Whitaker Rachel J, Whiteley Marvin
School of Biological Sciences and Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, Georgia, USA.
Emory-Children's Cystic Fibrosis Center, Atlanta, Georgia, USA.
mBio. 2025 Aug 13;16(8):e0141125. doi: 10.1128/mbio.01411-25. Epub 2025 Jul 24.
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.
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.
微生物群落通常通过测量基因表达（mRNA水平）来进行研究，但将这些数据转化为功能见解具有挑战性，因为mRNA丰度并不总是能预测蛋白质水平。在此，我们提出一种策略来弥合这一差距，即通过推导基因特异性的RNA到蛋白质的转换因子，来改善从转录组数据预测蛋白质丰度的能力。利用来自七种细菌和一种古菌的配对mRNA-蛋白质数据集，我们鉴定出了直系同源基因，这些基因的mRNA水平对蛋白质丰度的预测效果不佳，但每个基因的蛋白质与RNA的比率在这些不同的生物体中是一致的。将所得的转换因子应用于mRNA水平，显著改善了蛋白质丰度的预测，即使转换因子是从远缘物种获得的。值得注意的是，从细菌衍生的转换因子也增强了对古菌中蛋白质的预测，证明了这种方法的稳健性。这种跨域框架能够在无需特定生物体蛋白质组数据的情况下，在微生物群落中进行更准确的功能推断，为微生物生态学、系统生物学和功能基因组学提供了一个强大的新工具。
由于缺乏功能数据，对自然微生物群落生物学的解读受到限制。虽然转录组学能够进行基因表达谱分析，但mRNA水平往往无法预测蛋白质丰度，而蛋白质丰度是微生物功能的主要指标。先前的研究通过使用跨细菌菌株的保守蛋白质与RNA（ptr）比率来计算RNA到蛋白质（RTP）转换因子来解决这一问题，但它们的跨物种和跨域实用性仍然未知。我们从七种细菌和一种古菌中生成了涵盖不同代谢和生态位的全面转录组和蛋白质组数据集。我们鉴定出了具有保守ptr比率的直系同源基因，从而发现了RTP转换因子，这些因子显著改善了从mRNA预测蛋白质的能力，即使在远缘物种和域之间也是如此。这揭示了跨域ptr比率中以前未被认识到的保守性，并在许多情况下消除了对配对蛋白质组数据的需求。我们的方法提供了一个广泛适用的框架，仅使用转录组数据就能增强对微生物群落的功能预测。
