Search bioRxiv⌕ Search

Biology subjects

Tamburini, C.

Publications and source records attributed to Tamburini, C..

2 recordsLinked to original sources

Oxysterol-liver X receptor signaling mediates CYFIP1 regulation of cortical neurogenesis

Dysregulation in neural progenitor proliferation and neuronal differentiation has been increasingly recognised as a common pathology in neural cells harboring genetic risks to neuropsychiatric and neurodevelopmental disorders, yet the underlying molecular mechanisms remains largely unknown. Deletions and duplications of the 15q11.2 region containing the CYFIP1 gene have been associated with autism and schizophrenia. Using patient-derived iPSCs carrying 15q11.2 deletion and genetically manipulated hESCs with CYFIP1 gain- and loss-of-function (GoF and LoF), we show that 15q11.2 deletion and CYFIP1-LoF leads to premature neuronal differentiation while CYFIP1-GoF favours neural progenitor maintenance. We identified cholesterol biosynthesis and metabolism as a biological process disturbed by CYFIP1 dosage change, leading to altered neuro-oxysterol profiles. 24S,25-epoxycholesterol, which was decreased in CYFIP1-GoF and increased in CYFIP1-LoF and 15q11.2del neural cells, can mimic the 15q11.2del and CYFIP1-LoF phenotype by promoting cortical neuronal differentiation and restore the impaired neuronal differentiation of CYFIP1-GoF neural progenitors. Moreover, the neurogenic activity of 24S,25-epoxycholesterol is lost following genetic deletion of the brain expressed isoform of the liver X receptor LXRb while compound deletion of LXRb in CYFIP1-/- background rescued their premature neurogenesis. This work delineates LXR mediated oxysterol regulation of neurogenesis as a novel pathological mechanism in neural cells carrying 15q11.2CNV and provides a potential target for therapeutic strategies for genetic disorders associated with this risk locus.

neuroscience↗

A ready-to-use logistic Verhulst model implemented in R shiny to estimate growth parameters of microorganisms

In microbiology, the estimation of the growth rate of microorganisms is a critical parameter to describe a new strain or characterize optimal growth conditions. Traditionally, this parameter is estimated by selecting subjectively the exponential phase of the growth, and then determining the slope of this curve section, by linear regression. However, for some experiments, the number of points to describe the growth can be very limited, and consequently such linear model will not fit, or the parameters estimation can much lower and strongly variable. In this paper, we propose a tools to estimate growth parameters using a logistic Verhulst model that take into account the entire growth curve for the estimation of the growth rate. The efficiency of such model is compared to the linear model. Finally, the novelty of our work is to propose a "Shiny-web application", online, without any programming or modelling skills, to allow estimating growth parameters including growth rate, maximum population, and beginning of the exponential phase, as well as an estimation of their variability. The final results can be displayed in the form of a scatter plot representing the model, its efficiency and the estimated parameters are downloadable.

microbiology↗