The Probable Cell of Origin of NF1- and PDGF-Driven Glioblastomas

Primary glioblastomas are subdivided into several molecular subtypes. There is an ongoing debate over the cell of origin for these tumor types where some suggest a progenitor while others argue for a stem cell origin. Even within the same molecular subgroup, and using lineage tracing in mouse models, different groups have reached different conclusions. We addressed this problem from a combined mathematical modeling and experimental standpoint. We designed a novel mathematical framework to identify the most likely cells of origin of two glioma subtypes. Our mathematical model of the unperturbed in vivo system predicts that if a genetic event contributing to tumor initiation imparts symmetric self-renewing cell division (such as PDGF overexpression), then the cell of origin is a transit amplifier. Otherwise, the initiating mutations arise in stem cells. The mathematical framework was validated with the RCAS/tv-a system of somatic gene transfer in mice. We demonstrated that PDGF-induced gliomas can be derived from GFAP-expressing cells of the subventricular zone or the cortex (reactive astrocytes), thus validating the predictions of our mathematical model. This interdisciplinary approach allowed us to determine the likelihood that individual cell types serve as the cells of origin of gliomas in an unperturbed system

Utveckling av bioinformatiska analysflöden för helgenomsekvenserade bakterieisolat i Python

This study investigates the analyses and clustering of Campylobacter spp., Listeria monocytogenes and Shiga toxin-producing Escherichia coli (STEC) at Livsmedelsverket. Livsmedelsverket is a control authority in Sweden. They work with eating habits, what food contains and safe food and good drinking water, where outbreak investigations of the above-mentioned bacterial types is a part of the work. For the investigations Livsmedelsverket uses a pipeline that is written in the programming language Python. The purpose of this project is to add identification of virulence genes and analysis of the STEC bacterium to the script. But also to develop the existing method to be able to cluster more isolates without losing information, enable the user to adjust parameters in the pipeline and write an ethical analysis to the work that is done. Our study shows the analysis and clustering of the three different types of bacteria, clustering of the samples from the analysis, both adaptively and statically, and that it can determine serotype, sequence type and virulence genes. We therefore conclude that STEC can be added to outbreak investigations at Livsmedelsverkets in-house pipeline. The clustering method has also been modified to be able to use more of the information given from the samples with the restriction of having lower accuracy

Utveckling av bioinformatiska analysflöden för helgenomsekvenserade bakterieisolat i Python

This study investigates the analyses and clustering of Campylobacter spp., Listeria monocytogenes and Shiga toxin-producing Escherichia coli (STEC) at Livsmedelsverket. Livsmedelsverket is a control authority in Sweden. They work with eating habits, what food contains and safe food and good drinking water, where outbreak investigations of the above-mentioned bacterial types is a part of the work. For the investigations Livsmedelsverket uses a pipeline that is written in the programming language Python. The purpose of this project is to add identification of virulence genes and analysis of the STEC bacterium to the script. But also to develop the existing method to be able to cluster more isolates without losing information, enable the user to adjust parameters in the pipeline and write an ethical analysis to the work that is done. Our study shows the analysis and clustering of the three different types of bacteria, clustering of the samples from the analysis, both adaptively and statically, and that it can determine serotype, sequence type and virulence genes. We therefore conclude that STEC can be added to outbreak investigations at Livsmedelsverkets in-house pipeline. The clustering method has also been modified to be able to use more of the information given from the samples with the restriction of having lower accuracy