Biology subjects

Boutinaud, P.

Publications and source records attributed to Boutinaud, P..

4 recordsLinked to original sources

bioRxiv2024

Sphingomyelins in mosquito saliva modify the host lipidome to enhance transmission of flaviviruses by promoting viral protein levels

Mosquito saliva plays a determining role in flavivirus transmission. Here, we discover and elucidate how salivary lipids enhance transmission. Building upon our discovery of salivary extracellular vesicles (EV), we determined that lipids within mosquito EVs, and neither within human EVs nor virions, enhance infection for flaviviruses in primary cell types relevant for transmission. Mechanistically, mosquito EV-lipids specifically promote viral protein levels by reducing ER-associated degradation. Infection enhancement is caused by sphingomyelins within mosquito salivary EVs that elevate sphingomyelin concentration within host cells. Transmission assays showed that mosquito EV-lipids exacerbate disease severity. Our study reveals that EV-associated sphingomyelins within mosquito saliva enhance transmission for multiple flaviviruses by reconfiguring the host lipidome to promote viral protein levels and the resulting skin infection. Our findings open a new dimension centered on lipids in the interplay between hosts, mosquitoes and flaviviruses that determine transmission, unveiling lipids as a new pan-flavivirus target. HighlightsO_LILipids within mosquito extracellular vesicles (EVs) enhance infection in primary skin and immune cells for multiple flaviviruses. C_LIO_LIMosquito EV-lipids increase flaviviral protein levels by dampening ER-associated degradation. C_LIO_LISphingomyelins within salivary EVs are responsible for the infection enhancement by altering host lipidome. C_LIO_LICo-injection of mosquito EV-lipids exacerbate disease severity. C_LI

pathology↗

bioRxiv2023

Early detection of white matter hyperintensities using SHIVA-WMH detector

White matter hyperintensities (WMH) are well-established markers of cerebral small vessel disease (cSVD), and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e. with confluency) in evaluation datasets from three distinct databases: MRi-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LST-LPA: Schmidt, 2017), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al, 2021), and HyperMapper tool (HPM: Mojiri Forooshani et al., 2022), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH. HighlightsO_LIWe propose a novel 3D Unet-based model, SHIVA-WMH detector, with much improved detection of small WMH across subjects with a wide range of WMH burden compared to existing methods C_LIO_LIWe characterize microstructural properties of small white matter hyperintensities in young adults from MRi-Share study C_LI

neuroscience↗

bioRxiv2020

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

neuroscience↗

bioRxiv2020

Deep learning-based classification of resting-state fMRI independent-component analysis

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92%. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96% of the gray matter. Second, we proposed an individualbased analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

neuroscience↗