Search bioRxiv⌕ Search

Biology subjects

Gauthier, I.

Publications and source records attributed to Gauthier, I..

2 recordsLinked to original sources

Development of metastases in mice induced by plasma sample collected during radiotherapy in patient with triple-negative breast cancer: Role of Rab4A

The relapse rate in early-stage triple-negative breast cancer (TNBC) is significantly higher than in other breast cancer subtypes. This study assessed the relevance to target RAB4A to prevent the development of metastases that occur after treatment. The ability of cancer cells to invade peritumoral tissue is associated with the expression of membrane-type matrix metalloproteinase-1 on their surface, which is regulated by RAB4A. When RAB4A was downregulated using shRNA in the TNBC cells D2A1 and MDA-MB-231, a significant reduction in the proteolytic activity of MT1-MMP and the invasion capacity of these TNBC cells were measured. Plasma samples from an early-stage TNBC patient, who developed metastases six months after treatment, were collected before radiotherapy and after the fourth radiation dose. Compared to the plasma collected before radiotherapy, the plasma collected during the treatment significantly enhanced the invasiveness of the TNBC cells, as assessed with Boyden chambers. The development of lung metastases was also stimulated when the D2A1 cells were preincubated with this plasma before their i.v. injection in female Balb/c mice. Importantly, these adverse effects of plasma collected during radiotherapy were significantly blocked by downregulating RAB4A. These results highlight the relevance of developing RAB4A inhibitors to prevent the development of metastases occurring after treatment in TNBC patients.

cancer biology↗

Thickness of deep layers in the fusiform face area predicts face recognition

People with superior face recognition have relatively thin cortex in face-selective brain areas, while those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) for abilities acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultra-high resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term MR layers, in the right fusiform face area (rFFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, while vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) while the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of rFFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development.\n\nSignificance StatementFace and object recognition vary in the normal population and are only modestly related to each other. The recognition of faces and vehicles are both positively related to neural responses in the fusiform face area (FFA), but show different relations to the cortical thickness of FFA. Here, we use very high-resolution MRI, and find that face recognition ability (a skill acquired early in life) is negatively correlated with thickness of FFAs deepest MR-defined layers, whereas recognition of vehicles (a skill acquired later in life) is positively related to thickness at of all cortical layers. Our methods can be used in the future to characterize sources of variability in human abilities and relate them to distinct mechanisms of neural plasticity.

neuroscience↗