| Autor: |
Ponomarenko EA; Institute of Biomedical Chemistry, Moscow 119121, Russia., Krasnov GS; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia., Kiseleva OI; Institute of Biomedical Chemistry, Moscow 119121, Russia., Kryukova PA; Institute of Biomedical Chemistry, Moscow 119121, Russia., Arzumanian VA; Institute of Biomedical Chemistry, Moscow 119121, Russia., Dolgalev GV; Institute of Biomedical Chemistry, Moscow 119121, Russia., Ilgisonis EV; Institute of Biomedical Chemistry, Moscow 119121, Russia., Lisitsa AV; Institute of Biomedical Chemistry, Moscow 119121, Russia., Poverennaya EV; Institute of Biomedical Chemistry, Moscow 119121, Russia. |
| Abstrakt: |
Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction. |
