Project description:The first 4 samples belong to the RNA-IP using in situ TAP tagged ZC3H30 in procyclic (insect) form of the parasite T. brucei Lister 427, 2 samples are Elu or eluate, and 2 are FL or flowthrough (unbound) sample. The other 8 samples are also from procyclic cells. 4 samples belong to DKO(ZC3H30 gene double knockout), 2 are non-stressed and 2 are heat shocked samples; the rest 4 samples are DKO-ectopic (ZC3H30 double knockouts, expressing, ectopic copy of ZC3H30) 2 are non-stressed and 2 are heat shocked samples. Heat Shock experiment was done at 39 degree Celsius.
Project description:Achcar2012 - Glycolysis in bloodstream form T. brucei
Kinetic models of metabolism require quantitative knowledge of detailed kinetic parameters. However, the knowledge about these parameters is often uncertain. An analysis of the effect of parameter uncertainties on a particularly well defined example of a quantitative metablic model, the model of glycolysis in bloodstream form Trypanosoma brucei
, has been presented here.
This model is described in the article:
Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
Achcar F, Kerkhoven EJ; SilicoTryp Consortium, Bakker BM, Barrett MP, Breitling R.
PLoS Comput Biol. 2012 Jan; 8(1):e1002352.
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/Glycolysis). 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.
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Project description:Trypanosomes are a globally important group of parasites which together kill and debilitate millions of people world-wide. In trypanosomes, genes do not have individual promoters, rather ~10000 genes share ~200 promoters and all gene expression is thus regulated post-transcriptionally. While effector proteins which modulate the expression of many genes have been described, the mechanisms by which trypanosomes sense changes in their environment and manifest changes in gene expression remain elusive. This study demonstrates that trypanosomes sense changes in their environment through temperature sensitive RNA secondary structure. We show that the majority of observed mRNA abundance changes which distinguish insect adapted and bloodstream adapted life cycle stages can be explained through change in temperature alone.
Project description:RNA-coimmunopurifications with TAP-tagged Puf proteins from Saccharomyces cereviseae. Untagged strain (BY4741) served as a control. Cells were grown to midlog phase and harvested by centrifugation. TAP-tagged Puf proteins were affinity purified from cell-free extracts with IgG sepharose and eluted with TEV protease. RNA was isolated from extract (=input)and from purified protein samples by phenol-chloroform extraction. RNA samples were reverse transcribed using a mixture of oligo-dT and random nonamer oligos in the presence of amino-allyl dUTP/ dNTP mixture. cDNAs were fluorescently labeled and hybridized on yeast DNA microarrays over night at 65 degrees. For a detailed procedure see http://microarray-pubs.stanford.edu/yeast_puf and also Gerber AP et al. PLoS Biology, 2004.