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

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

Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection

Microbiota provide their hosts with a range of beneficial services, including defense from external pathogens. However, host-associated microbial communities themselves can act as a source of opportunistic pathogens depending on the environment. Marine poikilotherms and their microbiota are strongly influenced by temperature, but experimental studies exploring how temperature affects the interactions between both parties are rare. To assess the effects of temperature, temperature stress and infection on diversity, composition and dynamics of the hemolymph microbiota of Pacific oysters (Crassostrea gigas), we conducted an experiment in a fully-crossed, three-factorial design, in which the temperature acclimated oysters (8 or 22 °C) were exposed to temperature stress and to experimental challenge with a virulent Vibrio sp. Strain. We monitored oyster survival and repeatedly collected hemolymph of dead and alive animals to determine the microbiome composition by 16s rRNA gene amplicon pyrosequencing. We found that the microbial dynamics and composition of communities in healthy animals (including infection survivors) were significantly affected by temperature and temperature stress, but not by infection. The response was mediated by changes in the incidence and abundance of operational taxonomic units (OTUs) and accompanied by little change at higher taxonomic levels, indicating dynamic stability of the hemolymph microbiome. Dead and moribund oysters, on the contrary, displayed signs of community structure disruption, characterized by very low diversity and proliferation of few OTUs. We can therefore link short-term responses of host-associated microbial communities to abiotic and biotic factors and assess the potential feedback between microbiota dynamics and host survival during disease

The splicing factor SRSF1 regulates apoptosis and proliferation to promote mammary epithelial cell transformation

The splicing-factor oncoprotein SRSF1 (also known as SF2/ASF or ASF/SF2) is upregulated in breast cancers. We investigated the ability of SRSF1 to transform human and mouse mammary epithelial cells in vivo and in vitro. SRSF1-overexpressing COMMA-1D cells formed tumors, following orthotopic transplantation to reconstitute the mammary gland. In three-dimensional (3D) culture, SRSF1-overexpressing MCF-10A cells formed larger acini than control cells, reflecting increased proliferation and delayed apoptosis during acinar morphogenesis. These effects required the first RNA-recognition motif and nuclear functions of SRSF1. SRSF1 overexpression promoted alternative splicing of BIM (also known as BCL2L11) and BIN1 to produce isoforms that lack pro-apoptotic functions and contribute to the phenotype. Finally, SRSF1 cooperated specifically with MYC to transform mammary epithelial cells, in part by potentiating eIF4E activation, and these cooperating oncogenes are significantly coexpressed in human breast tumors. Thus, SRSF1 can promote breast cancer, and SRSF1 itself or its downstream effectors may be valuable targets for the development of therapeutics