Biology subjects

Caiment, F.

Publications and source records attributed to Caiment, F..

3 recordsLinked to original sources

bioRxiv2026

Microphysiological Flow Batteries For Dynamic EDC Screening Of mESC-derived Thyroid Organoids

Endocrine disrupting chemicals (EDCs) are ubiquitous environmental contaminants capable of dysregulating the production of thyroid hormones. Traditional thyroid toxicological assays rely on 2D cell cultures and animal models, both of which fail to accurately recapitulate human thyroid physiology and provide limited mechanistic insight into EDC toxicity. To overcome these limitations, we report a novel thyroid-on-chip platform integrating mouse embryonic stem cell-derived thyroid organoids with advanced organ-on-chip (OoC) technology and downstream multi-omics analysis. The platform leverages a reversibly-sealed microphysiological flow battery (MFB) to allow scale up of dynamic organoid culture and controlled chemical exposure while reducing operational complexity. Upon EDC exposure, transcriptomic and proteomic analysis revealed new molecular signatures of thyroid disruption across four different EDC classes, even at very low EDC concentrations (1nM), validating the capacity of this system to mechanistically dissect EDC-induced responses. This represents an integrated platform consists of an advanced physiologically relevant assay framework for next-generation endocrine toxicity testing, bridging the gap between in vitro screening and in vivo thyroid physiology.

bioengineering↗

bioRxiv2025

Vascular contribution to cognitive impairment in heart failure with preserved ejection fraction: TRPV4 and KLF2 as key mediators of neurovascular dysfunction in the ZSF1 model

Vascular cognitive impairment (VCI) shares major risk factors with heart failure with preserved ejection fraction (HFpEF), including obesity, diabetes and hypertension. Yet VCI research often relies on single-stimulus models, whereas patients experience combined risk factors. We therefore assessed cerebrovascular and cognitive phenotypes in an HFpEF model and investigated underlying mechanisms. Male Lean and Obese ZSF1 rats underwent longitudinal assessments of blood pressure, glucose, cardiac function, and behavioural performance. Cerebral blood flow and neurovascular coupling were assessed by laser speckle contrast imaging. White matter integrity, blood-brain barrier (BBB) permeability, and vascular density were analysed by (immuno)histochemistry. Cortical microvessels were isolated for transcriptomic profiling, and selected targets were validated using multiplex in-situ hybridization. Obese rats exhibited neurovascular uncoupling and impaired short- and long-term memory and spatial learning, accompanied by brain atrophy and reduced myelin. BBB permeability increased at 22-23 weeks and vascular density at 34-35 weeks in Obese vs Lean rats. Transcriptomic analysis of brain microvessels revealed altered processes related to angiogenesis, vasoreactivity, immune mechanisms and vascular remodelling, with consistent downregulation of Trpv4 and Klf2. Obese ZSF1 rats develop progressive neurovascular dysfunction associated with HFpEF onset and reduced Trpv4 and Klf2 expression in cerebral microvessels, two key vasoprotective genes.

neuroscience↗

bioRxiv2022

Quantifying the number of translatable transcripts through the use of OMICs involved in post-transcriptional regulation.

Transcriptomics is nowadays frequently used as an analytical tool to study the extent of cell expression changes between two phenotypes or between different conditions. However, an important portion of the significant changes observed in transcriptomics at the gene level is usually not consistently detected at the protein level by proteomics. This poor correlation between the measured transcriptome and proteome is probably mainly due to post-transcriptional regulation, among which miRNA and circRNA have been proposed to play an important role. Therefore, since both miRNA and circRNA are also quantified by transcriptomics, we proposed to build a model taking those factors into account to estimate, for each transcript, the fraction of transcripts that would be available for translation. Using a dataset of cells exposed to diverse compounds, we evaluated how our model was able to improve the correlation between the assessed transcriptome and proteome expression level. The results show that the model improved the correlation for a subset of genes, probably due to the regulation of different miRNAs across the genome.

bioinformatics↗