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1.
Nucleic Acids Res ; 2024 Jun 14.
Article in English | MEDLINE | ID: mdl-38874491

ABSTRACT

RNA helicases-central enzymes in RNA metabolism-often feature intrinsically disordered regions (IDRs) that enable phase separation and complex molecular interactions. In the bacterial pathogen Pseudomonas aeruginosa, the non-redundant RhlE1 and RhlE2 RNA helicases share a conserved REC catalytic core but differ in C-terminal IDRs. Here, we show how the IDR diversity defines RhlE RNA helicase specificity of function. Both IDRs facilitate RNA binding and phase separation, localizing proteins in cytoplasmic clusters. However, RhlE2 IDR is more efficient in enhancing REC core RNA unwinding, exhibits a greater tendency for phase separation, and interacts with the RNase E endonuclease, a crucial player in mRNA degradation. Swapping IDRs results in chimeric proteins that are biochemically active but functionally distinct as compared to their native counterparts. The RECRhlE1-IDRRhlE2 chimera improves cold growth of a rhlE1 mutant, gains interaction with RNase E and affects a subset of both RhlE1 and RhlE2 RNA targets. The RECRhlE2-IDRRhlE1 chimera instead hampers bacterial growth at low temperatures in the absence of RhlE1, with its detrimental effect linked to aberrant RNA droplets. By showing that IDRs modulate both protein core activities and subcellular localization, our study defines the impact of IDR diversity on the functional differentiation of RNA helicases.

2.
Nucleic Acids Res ; 49(9): 5159-5176, 2021 05 21.
Article in English | MEDLINE | ID: mdl-33893802

ABSTRACT

The eIF4E are a family of initiation factors that bind the mRNA 5' cap, regulating the proteome and the cellular phenotype. eIF4E1 mediates global translation and its activity is controlled via the PI3K/AKT/mTOR pathway. mTOR down-regulation results in eIF4E1 sequestration into an inactive complex with the 4E binding proteins (4EBPs). The second member, eIF4E2, regulates the translatome during hypoxia. However, the exact function of the third member, eIF4E3, has remained elusive. We have dissected its function using a range of techniques. Starting from the observation that it does not interact with 4EBP1, we demonstrate that eIF4E3 recruitment into an eIF4F complex occurs when Torin1 inhibits the mTOR pathway. Ribo-seq studies demonstrate that this complex (eIF4FS) is translationally active during stress and that it selects specific mRNA populations based on 5' TL (UTR) length. The interactome reveals that it associates with cellular proteins beyond the cognate initiation factors, suggesting that it may have 'moon-lighting' functions. Finally, we provide evidence that cellular metabolism is altered in an eIF4E3 KO background but only upon Torin1 treatment. We propose that eIF4E3 acts as a second branch of the integrated stress response, re-programming the translatome to promote 'stress resistance' and adaptation.


Subject(s)
Eukaryotic Initiation Factor-4E/metabolism , Eukaryotic Initiation Factor-4F/metabolism , Protein Biosynthesis , Stress, Physiological/genetics , Animals , Cells, Cultured , Eukaryotic Initiation Factors/metabolism , Humans , Mice , Naphthyridines/pharmacology , RNA Caps/metabolism , TOR Serine-Threonine Kinases/antagonists & inhibitors
3.
Biosystems ; 98(1): 37-42, 2009 Oct.
Article in English | MEDLINE | ID: mdl-19607876

ABSTRACT

We present an efficient algorithm for individual-based, stochastic simulation of biological populations in continuous time. A simple method for its implementation is given and it is compared to Gillespie's commonly used Direct Method. These two methods are proven to be exactly equivalent and, using a basic evolutionary model, it is demonstrated that the new algorithm can run thousands of times faster. Furthermore, while computational cost per event increases linearly with population size under the Direct Method, this cost is independent of population size under the new algorithm. We argue that this gain in efficiency opens up the possibility to explore a new class of models in population biology.


Subject(s)
Algorithms , Biological Evolution , Ecosystem , Models, Biological , Population Dynamics , Selection, Genetic , Computer Simulation , Stochastic Processes
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