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Biology subjects

Flores, K. B.

Publications and source records attributed to Flores, K. B..

3 recordsLinked to original sources

P95-HER2 promotes metastatic progression by biasing MRTFA dependent signaling

Naturally occurring isoforms of the proto-oncogene Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2, HER2) incite unique pathways of tumor progression in mouse models of breast cancer. Although each isoform has been shown to progress to metastasis, the N-terminally truncated isoform (p95) was previously shown to be biased toward early distant dissemination prior to detection. Here we show how HER2 isoforms differentially promote go-or-grow phenotypes through biased utilization of tyrosine autophosphorylation sites in the intracellular tail of HER2. Fluorescently barcoded humanized full-length HER2 (WT), exon-16 splice HER2 isoform (d16), and p95 expressing tumor cell lines were derived from HER2 Crainbow mice, then utilized for a series of functional assays including proliferation, tumor growth rate, cell motility, collective cell migration, cellular morphology, and invasive potential. Quantitative analysis reveals biased tumor cell behaviors across each genotype. WT tumor cells are biased toward proliferation and collective migration, d16 cells are biased toward proliferation and individual motility, and p95 tumor cells are biased toward individual motility and invasion. Single cell analysis reveals a myogenic-like state transition in p95 cells accompanied by an increased nuclear translocation of the Myocardin Related Transcription Factor A (MRTFA). MRTFA knockdown, as well as knockdown of a downstream effector, Transforming growth factor beta 1 induced transcript 1 (TGFB1I1), both inhibit the p95 invasive phenotype. Furthermore, intracellular residue tyrosine 1139 (Y1139) is necessary for MRTFA translocation, and when edited in p95 cells (Y1139F) invasion and motility phenotypes are subsequently lost. Our data illustrate the importance of functionally selective signaling in HER2 and highlight the need for therapeutics that intercept metastasis by targeting HER2 biased signaling.

cell biology↗

Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data

Advances in spatiotemporal single-cell imaging have enabled detailed observations of cell population dynamics and intercellular interactions. However, translating these rich data sets into mechanistic insight remains a significant challenge. Agent-based models (ABMs) are a bottom-up computational framework for investigating the emergent behavior of cell populations that can arise from rules defining the interactions between individual neighboring cells, while topological data analysis (TDA) provides robust descriptors of spatial organization. We present TOPAZ (TOpologically-based Parameter inference for Agent-based model optimiZation), a computational pipeline that integrates TDA with approximate Bayesian computation (ABC), approximate approximate Bayesian computation (AABC), and Bayesian model selection to identify biologically plausible ABMs from spatiotemporal cellular data. TOPAZ uses persistent homology to quantify spatial features of cell trajectories and combines this topological information with parameter inference via ABC and AABC and model comparison using the Bayesian information criterion. We validate TOPAZ using simulations of collective fibroblast movement, demonstrating its ability to accurately recover model parameters and distinguish between a baseline ABM and an extended model that incorporates an alignment interaction. Our results and open-source code demonstrate the utility of TOPAZ as an extensible framework for mechanistic inference and model discrimination in spatial single-cell analysis.

cell biology↗

Deep learning-enabled phenotyping reveals distinct patterns of neurodegeneration induced by aging and cold-shock

Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative assessment of phenotypes, particularly for complex subcellular morphologies. In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans. We apply a Convolutional Neural Network algorithm (Mask R-CNN) to identify neurodegenerative sub-cellular protrusions that appear after cold-shock or as a result of aging. A multiparametric phenotypic profile captures the unique morphological changes induced by each perturbation. We identify that acute cold-shock-induced neurodegeneration is reversible and depends on rearing temperature, and importantly, that aging and cold-shock induce distinct neuronal beading patterns.

systems biology↗