Three-dimensional cell culture conditions promoted the Mesenchymal-Amoeboid Transition in the Triple-Negative Breast Cancer cell line MDA-MB-231.

Introduction: Breast cancer (BC) is the leading cause of death among women, primarily due to its potential for metastasis. As BC progresses, the extracellular matrix (ECM) produces more type-I collagen, resulting in increased stiffness. This alteration influences cellular behaviors such as migration, invasion, and metastasis. Specifically, cancer cells undergo changes in gene expression that initially promote an epithelial-to-mesenchymal transition (EMT) and subsequently, a transition from a mesenchymal to an amoeboid (MAT) migration mode. In this way, cancer cells can migrate more easily through the stiffer microenvironment. Despite their importance, understanding MATs remains challenging due to the difficulty of replicating in vitro the conditions for cell migration that are observed in vivo. Methods: To address this challenge, we developed a three-dimensional (3D) growth system that replicates the different matrix properties observed during the progression of a breast tumor. We used this model to study the migration and invasion of the Triple-Negative BC (TNBC) cell line MDA-MB-231, which is particularly subject to metastasis. Results: Our results indicate that denser collagen matrices present a reduction in porosity, collagen fiber size, and collagen fiber orientation, which are associated with the transition of cells to a rounder morphology with bleb-like protrusions. We quantified how this transition is associated with a more persistent migration, an enhanced invasion capacity, and a reduced secretion of matrix metalloproteinases. Discussion: Our findings suggest that the proposed 3D growth conditions (especially those with high collagen concentrations) mimic key features of MATs, providing a new platform to study the physiology of migratory transitions and their role in BC progression.

Fibronectin induces a transition from amoeboid to a fan morphology and modifies migration in Entamoeba histolytica.

Cell migration modes can vary, depending on a number of environmental and intracellular factors. The high motility of the pathogenic amoeba Entamoeba histolytica is a decisive factor in its ability to cross the human colonic barrier. We used quantitative live imaging techniques to study the migration of this parasite on fibronectin, a key tissue component. Entamoeba histolytica amoebae on fibronectin contain abundant podosome-like structures. By using a laminar flow chamber, we determined that the adhesion forces generated on fibronectin were twice those on non-coated glass. When migrating on fibronectin, elongated amoeboid cells converted into fan-shaped cells characterized by the presence of a dorsal column of F-actin and a broad cytoplasmic extension at the front. The fan shape depended on the Arp2/3 complex, and the amoebae moved laterally and more slowly. Intracellular measurements of physical variables related to fluid dynamics revealed that cytoplasmic pressure gradients were weaker within fan-shaped cells; hence, actomyosin motors might be less involved in driving the cell body forward. We also found that the Rho-associated coiled-coil containing protein kinase regulated podosome dynamics. We conclude that E. histolytica spontaneously changes its migration mode as a function of the substrate composition. This adaptive ability might favour E. histolytica's invasion of human colonic tissue. By combining microfluidic experiments, mechanical modelling, and image analysis, our work also introduces a computational pipeline for the study of cell migration.

Sensitivity-Aware Density Estimation in Multiple Dimensions.

We formulate an optimization problem to estimate probability densities in the context of multidimensional problems that are sampled with uneven probability. It considers detector sensitivity as an heterogeneous density and takes advantage of the computational speed and flexible boundary conditions offered by splines on a grid. We choose to regularize the Hessian of the spline via the nuclear norm to promote sparsity. As a result, the method is spatially adaptive and stable against the choice of the regularization parameter, which plays the role of the bandwidth. We test our computational pipeline on standard densities and provide software.We also present a new approach to PET rebinning as an application of our framework.

The genetic architecture of multimodal human brain age.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.