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Optimization of carbon and energy utilization through differential translational efficiency.

ABSTRACT: Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5'-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms.

SUBMITTER: Al-Bassam MM

PROVIDER: S-EPMC6203783 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Model topology is divided into two compartments, cell programming and performance testing. The cell programming compartment is split into history and pre-treatment. History ( History I or H1: E.coli grown for 18hrs in LB flask, transferred to fresh LB flask after that. History II or H2: E.coli grown for 18hrs in LB flask, transferred to fresh LB flask for 45 min. From this flask, 0.1O.D./ml transferred to rich medium and grown for 4 hours. From this,0.1O.D./ml transferred to fresh rich medium. History III or H3: E.coli grown for 18hrs in LB flask, transferred to fresh LB flask for 45 min. From this flask, 0.1O.D./ml transferred to starvation medium and grown for 4 hours. From this,0.1O.D./ml transferred to fresh starvation medium. Pre-treatment (Pre-treatment 1(T1): 2.5g glucose/litre 5mM NH4Cl. Pre-treatment 2 (T2): 2.5g glucose/litre 0.25mM NH4Cl. Pre-treatment 3 (T3): - 0.25g glucose/litre 5mmM NH4Cl. Pre-treatment 4 (T4): 0.25g glucose/litre 0.25mM NH4Cl). Each pre-treatment given for 2.5 hours.The culture nomenclature indicates the adaptive path followed, for example, H1T1 indicates the culture has encountered history I (H1) and then transferred to pre-treatment 1 (T1).RNA was extracted for selected combinations. Performance testing : Performance testing describes the type of analysis done which is the growth pattern study onto three substrates, glucose, succinate and pyruvate. This performance testing revealed specific history-pretreatment combinations to be better suited for growth on certain substrate and some not suited for growth. The samples were harvested for RNA isolation at peak growth points and named worst_glucose, best_glucose,worst_succinate, best_succinate, worst_pyruvate and best_pyruvate according to the growth shown after testing all 12 history-pretreatment combinations. The differences in physiology were studied in details using microarray analysis of 13 samples including 3 history samples, 4 pre-treatment samples and 6 samples at performance testing level. RNA extraction was done using Qiagen RNeasy minikit (Germany). Standard Affymetrix protocol was followed for hybridization on Affymetrix E. coli Genome 2.0 Array.

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Optimization of carbon and energy utilization through differential translational efficiency.

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