Bulletin mensuel de statistique / Institut national de la statistique et des études économiques...

juillet 19801980/07 (N7,A31)

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells-0

) of two different samples on Affymetrix (a) and Illumina (b) platforms. The blue line on each plot represents a regression line that best fits the plotted set of points. Both array types provide high inter-replicates reproducibility of the relative gene expression intensities.<p><b>Copyright information:</b></p><p>Taken from "Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells"</p><p>http://www.biomedcentral.com/1471-2164/9/302</p><p>BMC Genomics 2008;9():302-302.</p><p>Published online 25 Jun 2008</p><p>PMCID:PMC2464609.</p><p></p

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells-3

He list was constituted by selecting DEG (Pc < 0.05), then within this list genes were ranked according to decreasing fold change. The number of overlapping genes between lists was calculated for increasing list size. When the number of probes in the lists was approximately 3800, the number of overlapping genes reached a plateau. The "best 3800" set of probes was defined accordingly.<p><b>Copyright information:</b></p><p>Taken from "Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells"</p><p>http://www.biomedcentral.com/1471-2164/9/302</p><p>BMC Genomics 2008;9():302-302.</p><p>Published online 25 Jun 2008</p><p>PMCID:PMC2464609.</p><p></p

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells-5

) of two different samples on Affymetrix (a) and Illumina (b) platforms. The blue line on each plot represents a regression line that best fits the plotted set of points. Both array types provide high inter-replicates reproducibility of the relative gene expression intensities.<p><b>Copyright information:</b></p><p>Taken from "Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells"</p><p>http://www.biomedcentral.com/1471-2164/9/302</p><p>BMC Genomics 2008;9():302-302.</p><p>Published online 25 Jun 2008</p><p>PMCID:PMC2464609.</p><p></p

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells-4

Examined; Genes present in the top-left and bottom right quarters of each plots show changes in opposite direction. These genes are expected to overlap by chance.<p><b>Copyright information:</b></p><p>Taken from "Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells"</p><p>http://www.biomedcentral.com/1471-2164/9/302</p><p>BMC Genomics 2008;9():302-302.</p><p>Published online 25 Jun 2008</p><p>PMCID:PMC2464609.</p><p></p

RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research

<p><strong>Abstract:</strong></p> <p>Despite many examples of excellent practice, rare disease (RD) research is still frequently fragmented by data type and disease. Individual efforts often have little interoperability and almost no systematic connection of detailed clinical information with genetic information, biomaterial availability or research/trial datasets. Linking data at both an individual-patient and whole-cohort level enables researchers to gain a better overview of their disease of interest, while providing access to data from other research groups in a secure fashion allows researchers in multiple institutions to compare results and gain new insights. Funded by the EU Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project which links databases, registries, biobanks and clinical bioinformatics data used in RD research into a central research resource. RD-Connect’s primary objectives are:</p> <p>• Harmonisation and development of common standards for RD patient registries by developing a common registry infrastructure and data elements</p> <p>• Harmonisation and development of common standards and catalogue for RD biobanks that collect and provide standardised, quality-controlled biomaterials for translational research</p> <p>• Development of clinical bioinformatics tools for analysis and integration of molecular and clinical data to discover new disease genes, pathways and therapeutic targets</p> <p>• Development of an integrated platform to host and analyse data from omics research projects</p> <p>• Development of mechanisms for incorporating patient interests and engaging with stakeholders</p> <p>• Development of best ethical practices and a proposal for a regulatory framework for linking medical and personal data related to RD.</p> <p>RD-Connect will accept data generated by IRDiRC projects such as EURenOmics, which focuses on causes, diagnostics, biomarkers and disease models for rare kidney disorders, and Neuromics, which uses next generation whole exome sequencing to increase genetic knowledge of rare neurodegenerative and neuromuscular disorders. The “siloed” nature of individual research efforts is a continued bottleneck for cutting-edge research towards diagnosis and therapy development in RD. RD-Connect aims to unite existing infrastructures and integrate the latest tools in order to create a comprehensive combined omics data, biobanking, data analysis and patient registry platform for RD used by researchers across the world.</p

Effect of mitochondrial respiration inhibitors on azole resistance.

<p>Itraconazole resistance is shown as IC₅₀^ITRA of fungi grown on sub-inhibitory concentrations of respiratory inhibitors. Results represent averages of three biological replicates with three technical replicates for each. Error bars represent standard deviation. C: control consisting of addition of water, DMSO or acetone to the medium. Only the water control is shown here for simplicity as all solvent controls showed indistinguishable growth rates. R: Rotenone, P: piericidin A, Az: sodium azide As: azoxystrobin, An: antimycin A, CN: potassium cyanide Ol: oligomycin. Error bars represent standard deviation of IC₅₀ values.</p

Impact of the 29.9KD deletion on CYP51A and CYP51B expression and ergosterol levels.

<p>A. Induction of CYP51A in parental (A1163) and deletion mutant (Δ29.9) isolates 4 h after addition of 1 mg/L itraconazole (Δ29.9+I and A1160+I) or DMSO solvent control (Δ29.9 and A1160). B. Induction of CYP51B in parental (A1160) and deletion mutant (Δ29.9) isolates 4h after addition of 1 mg/L itraconazole (Δ29.9+I and A1160+I) or DMSO solvent control (Δ29.9 and A1160). C. Ergosterol levels of parental (A1160), knockout (Δ29.9) CYP51A (CYP51A) knockout, CYP51B knockout (CYP51B) and reconstituted knockout (Δ29.9::29.9) strains 4 h after addition of a range of itraconazole concentrations (levels given as mg/l in panel C). Results represent averages of three biological replicates with three technical replicates for each. Error bars shown represent standard deviation.</p

Pathogenicity of mutants, knockouts and reconstituted strains in neutropenic mice.

<p>Kaplan Meier plot of survival of mice infected via inhalation.</p

Proposed mechanism for azole resistance via loss of complex I activity.

<p>A. In low O₂ sterol biosynthesis is reduced leading to SREBP signalling and complex I activity is reduced. Both regulatory systems contribute to a balanced oxygen stress response. B Azoles mimic low oxygen by reducing sterol biosynthesis. However oxygen levels are normal and complex I regulation does not occur leading to an unbalanced response. C. Azole reduces sterol biosynthesis but this is balanced by loss of complex I activity due either to rotenone treatment or failure to reactivate the complex because of deletion of the 29.9 KD subunit.</p