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Transcriptome-wide analysis of trypanosome mRNA decay reveals complex degradation kinetics and suggests a role for co-transcriptional degradation in determining mRNA levels

Abstract (Expand)

African trypanosomes are an excellent system for quantitative modelling of post-transcriptional mRNA control. Transcription is constitutive and polycistronic; individual mRNAs are excised by trans … splicing and polyadenylation. We here measure mRNA decay kinetics in two life cycle stages, bloodstream and procyclic forms, by transcription inhibition and RNASeq. Messenger RNAs with short half-lives tend to show initial fast degradation, followed by a slower phase; they are often stabilized by depletion of the 5'-3' exoribonuclease XRNA. Many longer-lived mRNAs show initial slow degradation followed by rapid destruction: we suggest that the slow phase reflects gradual deadenylation. Developmentally regulated mRNAs often show regulated decay, and switch their decay pattern. Rates of mRNA decay are good predictors of steady state levels for short mRNAs, but mRNAs longer than 3 kb show unexpectedly low abundances. Modelling shows that variations in splicing and polyadenylation rates can contribute to steady-state mRNA levels, but this is completely dependent on competition between processing and co-transcriptional mRNA precursor destruction.

Authors: , M. Ryten, D. Droll, , V. Farber, , C. Merce, , ,

Date Published: 26th Aug 2014

Publication Type: Not specified

Dissecting the Catalytic Mechanism of Trypanosoma brucei Trypanothione Synthetase by Kinetic Analysis and Computational Modelling

Abstract (Expand)

In pathogenic trypanosomes, trypanothione synthetase (TryS) catalyzes the synthesis of both glutathionylspermidine (Gsp) and trypanothione [bis(glutathionyl)spermidine, T(SH)2]. Here we present a … thorough kinetic analysis of Trypanosoma brucei TryS in a newly developed phosphate buffer system at pH 7.0 and 37 °C, mimicking the physiological environment of the enzyme in the cytosol of bloodstream parasites. Under these conditions, TryS displays Km-values for GSH, ATP, spermidine and Gsp of 34, 18, 687, and 32 μM, respectively, as well as Ki-values for GSH and T(SH)2 of 1 mM and 360 μM, respectively. As Gsp hydrolysis has a Km-value of 5.6 mM, the in vivo amidase activity is probably negligible. To obtain a deeper insight in the molecular mechanism of TryS, we have formulated alternative kinetic models, with elementary reaction steps represented by linear kinetic equations. The model parameters were fitted to the extensive matrix of steady-state data obtained for different substrate/product combinations under the in vivo-like conditions. The best model describes the full kinetic profile and is able to predict time course data that were not used for fitting. This systems biology approach to enzyme kinetics led us to conclude that (i) TryS follows a ter-reactant mechanism, (ii) the intermediate Gsp dissociates from the enzyme between the two catalytic steps and (iii) T(SH)2 inhibits the enzyme by remaining bound at its product site and, as does the inhibitory GSH, by binding to the activated enzyme complex. The newly detected concerted substrate and product inhibition suggests that TryS activity is tightly regulated.

Editor:

Date Published: 3rd Jul 2013

Publication Type: Not specified

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

Abstract (Expand)

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the … parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Gl​ycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

Editor:

Date Published: 19th Jan 2012

Publication Type: Not specified

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