Flow-Seq Evaluation of Translation Driven by a Set of Natural Escherichia coli 5'-UTR of Variable Length.
Ekaterina S KomarovaAnna N SlesarchukMaria P RubtsovaIlya A OstermanAlexey E TupikinDmitriy V PyshnyiOlga A DontsovaMarsel Rasimovich KabilovPetr V SergievPublished in: International journal of molecular sciences (2022)
Flow-seq is a method that combines fluorescently activated cell sorting and next-generation sequencing to deduce a large amount of data about translation efficiency from a single experiment. Here, we constructed a library of fluorescent protein-based reporters preceded by a set of 648 natural 5'-untranslated regions (5'-UTRs) of Escherichia coli genes. Usually, Flow-seq libraries are constructed using uniform-length sequence elements, in contrast to natural situations, where functional elements are of heterogenous lengths. Here, we demonstrated that a 5'-UTR library of variable length could be created and analyzed with Flow-seq. In line with previous Flow-seq experiments with randomized 5'-UTRs, we observed the influence of an RNA secondary structure and Shine-Dalgarno sequences on translation efficiency; however, the variability of these parameters for natural 5'-UTRs in our library was smaller in comparison with randomized libraries. In line with this, we only observed a 30-fold difference in translation efficiency between the best and worst bins sorted with this factor. The results correlated with those obtained with ribosome profiling.
Keyphrases
- single cell
- genome wide
- rna seq
- escherichia coli
- double blind
- open label
- dna methylation
- wastewater treatment
- phase iii
- copy number
- clinical trial
- placebo controlled
- phase ii
- stem cells
- quantum dots
- electronic health record
- magnetic resonance imaging
- mesenchymal stem cells
- big data
- cystic fibrosis
- small molecule
- deep learning
- single molecule
- klebsiella pneumoniae
- machine learning
- circulating tumor cells