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Systems-level analysis predicts no autotrophy-linked protein-RNA interactions in Clostridium autoethanogenum

Authors: Re, Angela; Politano, Gianfranco Michele Maria; Reinmets, Kristina; Benso, Alfredo; Girbal, Laurence; Cocaign-Bousquet, Muriel; Valgepea, Kaspar.;

Systems-level analysis predicts no autotrophy-linked protein-RNA interactions in Clostridium autoethanogenum

Abstract

Burning fossil fuels drives climate change with detrimental consequences on humankind while waste accumulation from increasing consumption threatens biosustainability globally. Gas fermentation enables to recycle carbon oxides (CO and CO2) from industrial waste gases and gasified waste into value-added products using gas-fermenting microbes, namely acetogens. However, our limited understanding of gene function and metabolic regulation is hindering rational engineering of acetogen cell factories. In this work, we aimed to identify genome-wide protein-RNA interactions contributing to autotrophy in the model-acetogen Clostridium autoethanogenum by combining steady-state chemostat cultivation, functional genomics, and computational methods. We first detected limited and uncoupled transcriptional and translational regulation between autotrophy and heterotrophy. Rigorous mapping of genome-wide transcriptional architecture revealed both differential usage and signal strength of transcriptional start and termination sites between genes and growth substrates. We then used computational tools to reconstruct protein-RNA interactions for differentially regulated genes, predicting 14 trans-acting regulatory RNA-binding proteins (RBPs) involved in post-transcriptional regulation but not linked to genes key for autotrophy. Most RBPs, one of which is translationally regulated, perform RNA modifications and regulate mRNA stability while others target translation-related genes. Our work provides valuable knowledge for metabolic engineering of acetogens and potentially contributes towards understanding primordial life on Earth.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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