Cusp-core transformations in dwarf galaxies: observational predictions

The presence of a dark matter core in the central kiloparsec of many dwarf galaxies has been a long standing problem in galaxy formation theories based on the standard cold dark matter paradigm. Recent cosmological simulations, based on Smooth Particle Hydrodynamics and rather strong feedback recipes have shown that it was indeed possible to form extended dark matter cores using baryonic processes related to a more realistic treatment of the interstellar medium. Using adaptive mesh refinement, together with a new, stronger supernovae feedback scheme that we have recently implemented in the RAMSES code, we show that it is also possible to form a prominent dark matter core within the well-controlled framework of an isolated, initially cuspy, 10 billion solar masses dark matter halo. Although our numerical experiment is idealized, it allows a clean and unambiguous identification of the dark matter core formation process. Our dark matter inner profile is well fitted by a pseudo-isothermal profile with a core radius of 800 pc. The core formation mechanism is consistent with the one proposed recently by Pontzen & Governato. We highlight two key observational predictions of all simulations that find cusp-core transformations: (i) a bursty star formation history with peak to trough ratio of 5 to 10 and a duty cycle comparable to the local dynamical time; and (ii) a stellar distribution that is hot with v/sigma=1. We compare the observational properties of our model galaxy with recent measurements of the isolated dwarf WLM. We show that the spatial and kinematical distribution of stars and HI gas are in striking agreement with observations, supporting the fundamental role played by stellar feedback in shaping both the stellar and dark matter distribution.Comment: Accepted for publication in MNRA

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

Investigation at the molecular level of soft cheese quality and ripening by infrared and fluorescence spectroscopies and chemometrics-relationships with rheology properties

International audienceTwelve traditional and 12 stabilised experimental soft cheeses were made according to a factorial design to two levels of dry matter (44% and 48%) and two fat on dry matter ratios (51% and 55%). Cheese samples were analysed using various methods to give physico-chemical data, uniaxial compression test data, emission spectra of tryptophan residues, excitation spectra of vitamin A, and the 900–1500, 2800–3000 and 1500–1700 cm−1 infrared regions. Common components and specific weights analysis showed that the common component 1 discriminating young and ripened cheeses explained 95%, 92%, 73% of the inertia of the 900–1500, 2800–3000 and 1500–1700 cm−1 infrared regions, respectively, and 51% of the rheology data. Common component 2 discriminating cheeses as a function of the technology explained 88%, 23% and 11% of the inertia of vitamin A spectra, chemical data and rheology data, respectively. The spectral patterns allowed molecular interpretations of the discrimination

Checkpoint kinase 1 inhibition sensitises transformed cells to dihydroorotate dehydrogenase inhibition

International audienceReduction in nucleotide pools through the inhibition of mitochondrial enzyme dihydroorotate dehydrogenase (DHODH) has been demonstrated to effectively reduce cancer cell proliferation and tumour growth. The current study sought to investigate whether this antiproliferative effect could be enhanced by combining Chk1 kinase inhibition. The pharmacological activity of DHODH inhibitor teriflunomide was more selective towards transformed mouse embryonic fibroblasts than their primary or immortalised counterparts, and this effect was amplified when cells were subsequently exposed to PF477736 Chk1 inhibitor. Flow cytometry analyses revealed substantial accumulations of cells in S and G2/M phases, followed by increased cytotoxicity which was characterised by caspase 3-dependent induction of cell death. Associating PF477736 with teriflunomide also significantly sensitised SUM159 and HCC1937 human triple negative breast cancer cell lines to dihydroorotate dehydrogenase inhibition. The main characteristic of this effect was the sustained accumulation of teriflunomide-induced DNA damage as cells displayed increased phospho serine 139 H2AX (γH2AX) levels and concentration-dependent phosphorylation of Chk1 on serine 345 upon exposure to the combination as compared with either inhibitor alone. Importantly a similar significant increase in cell death was observed upon dual siRNA mediated depletion of Chk1 and DHODH in both murine and human cancer cell models. Altogether these results suggest that combining DHODH and Chk1 inhibitions may be a strategy worth considering as a potential alternative to conventional chemotherapies

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

International audienceIntroduction The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

DataSheet_2_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.pdf

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p

DataSheet_1_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.xlsx

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p