Translation is a key regulatory step, linking transcriptome and proteome. Two major methods of translatome investigations are RNC-seq (sequencing of translating mRNA) and Ribo-seq (ribosome profiling). To facilitate the investigation of translation, we built a comprehensive database TranslatomeDB (http://www.translatomedb.net/) which provides collection and integrated analysis of published and user-generated translatome sequencing data. The current version includes 2453 Ribo-seq, 10 RNC-seq and their 1394 corresponding mRNA-seq datasets in 13 species. The database emphasizes the analysis functions in addition to the dataset collections. Differential gene expression (DGE) analysis can be performed between any two datasets of same species and type, both on transcriptome and translatome levels. The translation indices translation ratios, elongation velocity index and translational efficiency can be calculated to quantitatively evaluate translational initiation efficiency and elongation velocity, respectively. All datasets were analyzed using a unified, robust, accurate and experimentally-verifiable pipeline based on the FANSe3 mapping algorithm and edgeR for DGE analyzes. TranslatomeDB also allows users to upload their own datasets and utilize the identical unified pipeline to analyze their data. We believe that our TranslatomeDB is a comprehensive platform and knowledgebase on translatome and proteome research, releasing the biologists from complex searching, analyzing and comparing huge sequencing data without needing local computational power.

The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

In recent years, the development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits its application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. However, the transcriptome and proteome of most cell lines shift during culturing, which limits their applicability to serve as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (R2=0.966-0.995) and proteome (R2=0.934-0.976 for DDA, R2=0.942-0.986 for DIA) after 9 subculturing generations, which allows this stable standard sample to be stably produced on an industrial scale for several decades. Moreover, this stability was maintained across labs and platforms. In sum, our results justified a omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

Bacterial antibiotic resistance sets a great challenge to human health. It seems that the bacteria can spontaneously evolve resistance against any antibiotic within a short time without the horizontal transfer of heterologous genes and before accumulating drug-resistant mutations. We have shown that the tRNA-mediated translational regulation counteracts the reactive oxygen species (ROS) in bacteria. In this study, we demonstrated that isolated and subcultured Escherichia coli elevated its tRNAs under antibiotic stress to rapidly provide antibiotic resistance, especially at the early stage, before upregulating the efflux pump and evolving resistance mutations. The DNA recombination system repaired the antibiotic-induced DNA breakage in the genome, causing numerous structural variations. These structural variations are overrepresented near the tRNA genes, which indicated the cause of tRNA up-regulation. Knocking out the recombination system abolished the up-regulation of tRNAs, and coincidently, they could hardly evolve antibiotic resistance in multiple antibiotics, respectively. With these results, we proposed a multi-stage model of bacterial antibiotic resistance in an isolated scenario: the early stage (recombination—tRNA up-regulation—translational regulation); the medium stage (up-regulation of efflux pump); the late stage (resistant mutations). These results also indicated that the bacterial DNA recombination system and tRNA could be targeted to retard the bacterial spontaneous drug resistance.

Bacterial antibiotic resistance sets a great challenge to human health. It seems that the bacteria can spontaneously evolve resistance against any antibiotic within short time without the horizontal transfer of heterologous genes and before accumulating drug-resistant mutations. We have shown that the tRNA-mediated translational regulation counteracts the reactive oxygen species in bacteria. In this study, we demonstrated that isolated and subcultured Escherichia coli elevated its tRNAs under antibiotic stress to rapidly provide antibiotic resistance, especially at the early stage, before upregulating the efflux pump and evolving resistance mutations. The DNA recombination system repaired the antibiotic-induced DNA breakage in the genome, causing numerous structural variations. These structural variations are overrepresented near the tRNA genes, which indicated the cause of tRNA up-regulation. The strains knocking out the recombination system could not up-regulate tRNAs, and coincidently, they could hardly evolve antibiotic resistance in multiple antibiotics, respectively. With these results, we proposed a multi-stage model of bacterial antibiotic resistance in an isolated scenario: the early stage (recombination – tRNA up-regulation – translational regulation); the medium stage (up-regulation of efflux pump); the late stage (resistance mutations). These results also indicated that the bacterial DNA recombination system and tRNA could be targeted to retard the bacterial spontaneous drug resistance.