Biology subjects

Vanneste, M.

Publications and source records attributed to Vanneste, M..

6 recordsLinked to original sources

bioRxiv2024

An MITF- and mTOR-dependent FLCN pathway suppresses TFE3-driven metastasis in melanoma

Melanoma cells have the ability to switch from a melanocytic and proliferative state to a mesenchymal and invasive state and back again. This plasticity drives intra-tumoral heterogeneity, progression, and therapeutic resistance. Microphthalmia-associated Transcription Factor (MITF) promotes the melanocytic/proliferative phenotype, but factors that drive the mesenchymal/invasive phenotype and the mechanisms that effect the switch between cell states are unclear. Here, we identify the MITF paralog TFE3 and the non-canonical mTORC1 pathway as regulators of the mesenchymal state. We show that TFE3 expression drives the metastatic phenotype in melanoma cell lines and tumors. Deletion of TFE3 in MITF-low melanoma cell lines suppresses their ability to migrate and metastasize. Further, MITF suppresses the mesenchymal phenotype by directly or indirectly activating expression of FNIP1, FNIP2, and FLCN, which encode components of the non-canonical mTORC1 pathway, thereby promoting cytoplasmic retention and lysosome-mediated degradation of TFE3. These findings highlight a molecular pathway controlling melanoma plasticity and invasiveness.

cancer biology↗

bioRxiv2024

Mapping genes for human face shape: exploration of univariate phenotyping strategies

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits. Author SummaryAdvancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.

genetics↗

bioRxiv2023

Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population

Human craniofacial shape is highly variable yet highly heritable with genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing new insights into the genetic intersection of complex traits and Mendelian disorders.

genetics↗

bioRxiv2022

A 3D Clinical Face Phenotype Space of Genetic Syndromes using a Triplet-Based Singular Geometric Autoencoder

Clinical diagnosis of syndromes benefits strongly from objective facial phenotyping. This study introduces a novel approach to enhance clinical diagnosis through the development and exploration of a low-dimensional metric space referred to as the clinical face phenotypic space (CFPS). As a facial matching tool for clinical genetics, such CFPS can enhance clinical diagnosis. It helps to interpret facial dysmorphisms of a subject by placing them within the space of known dysmorphisms. In this paper, a triplet loss-based autoencoder developed by geometric deep learning (GDL) is trained using multi-task learning, which combines supervised and unsupervised learning approaches. Experiments are designed to illustrate the following properties of CFPSs that can aid clinicians in narrowing down their search space: a CFPS can 1) classify syndromes accurately, 2) generalize to novel syndromes, and 3) preserve the relatedness of genetic diseases, meaning that clusters of phenotypically similar disorders reflect functional relationships between genes. The proposed model consists of three main components: an encoder based on GDL optimizing distances between groups of individuals in the CFPS, a decoder enhancing classification by reconstructing faces, and a singular value decomposition layer maintaining orthogonality and optimal variance distribution across dimensions. This allows for the selection of an optimal number of CFPS dimensions as well as improving the classification capacity of the CFPS.

bioengineering↗

bioRxiv2020

Locally-invasive, castrate-resistant prostate cancer in a Pten/Trp53 double knockout mouse model of prostate cancer monitored with non-invasive bioluminescent imaging

Here we have improved an existing mouse model of prostate cancer based on prostate-specific deletion of Pten and Trp53 by incorporating a Cre-activatable luciferase reporter. By coupling the deletion of those genes to the activation of a luciferase reporter, we were able to monitor tumor burden non-invasively over time. We show that, consistent with previous reports, deletion of both Pten and Trp53 on a C57BL/6 background accelerates tumor growth and results in both the loss of androgen receptor expression and castrate resistant tumors as compared with loss of Pten alone. Loss of Trp53 results in the development of sarcomatoid histology and the expression of markers of epithelial-to-mesenchymal transition Zeb1 and vimentin, with kinetics and penetrance dependent on whether one or both alleles of Trp53 were deleted. Homozygous deletion of Trp53 and Pten resulted in uniformly lethal disease by 25 weeks. While we were able to detect locally invasive disease in the peritoneal cavity in aggressive tumors from the double knockout mice, we were unable to detect lymphatic or hematogenous metastatic disease in lymph nodes or at distant sites.

cancer biology↗

bioRxiv2019

Cancer cells resist mechanical destruction in the circulation via RhoA-myosin II axis

During metastasis cancer cells are exposed to potentially destructive hemodynamic forces including fluid shear stress (FSS) while en route to distant sites. However, prior work indicates that cancer cells are more resistant to brief pulses of high-level fluid shear stress (FSS) in vitro relative to non-transformed epithelial cells. Herein we identify a mechanism of FSS resistance in cancer cells, and extend these findings to mouse models of circulating tumor cells (CTCs). We show that cancer cells acutely isolated from primary tumors are resistant to FSS. Our findings demonstrate that cancer cells activate the RhoA-myosin II axis in response to FSS, which protects them from FSS-induced plasma membrane damage. Moreover, we show that the myosin II activity is protective to CTCs in mouse models. Collectively our data indicate that viable CTCs actively resist destruction by hemodynamic forces and are likely to be more mechanically robust than is commonly thought.

cancer biology↗