Biology subjects

Raimundez, E.

Publications and source records attributed to Raimundez, E..

3 recordsLinked to original sources

bioRxiv2025

From Brown to White: Brown Adipose Tissue Endothelial Cells whiten in Culture Conditions

Endothelial cells (ECs) are central regulators of vascular and metabolic homeostasis, yet their organ- and depot-specific diversity remains underexplored. Two major types of adipose tissue (AT) can be distinguished that differ substantially in their physiological function and vascularization: white AT (WAT), which is the major energy storage and brown AT (BAT), which is highly vascularized and dissipates energy [1-5]. While ECs from these depots likely contribute to adipose function, their characterization has been hindered by technical limitations in isolation and culture. Here, we establish a protocol for isolating and expanding ECs from murine BAT and WAT, enabling transcriptomic and functional analyses across depots. We demonstrate that freshly isolated BAT-ECs express depot-specific gene signatures, including Rgcc, Cdkn1c, Tcf15, Meox2, and Efnb1, several of which are dynamically regulated during cold-induced BAT activation. These findings reveal novel BAT-EC markers and highlight specialized endothelial programs that may support thermogenesis. However, we also uncover that culturing BAT-ECs profoundly remodels EC identity. Transcriptomic profiling shows that BAT-ECs rapidly downregulate BAT-specific markers and acquire features resembling WAT- and lymphatic ECs. This dedifferentiation is accompanied by signatures of proliferation, adhesion remodeling, and endothelial-to-mesenchymal transition. While these changes present challenges for maintaining depot-specific identity in culture, they also provide a framework to better interpret experimental outcomes and to investigate EC plasticity. Taken together, our study delivers a novel isolation and culture protocol for adipose ECs, defines BAT-EC markers, and demonstrates how culture conditions reshape their identity. These insights build the foundation for future research of AT vasculature. HighlightsO_LIFast method to purify endothelial cells (ECs) for direct RNA or protein extraction. C_LIO_LIEasy method to isolate and culture ECs specifically from thermogenic adipose tissue. C_LIO_LINovel signature of BAT-ECs regulated upon browning and lost in culture. C_LIO_LIRGC-32, p57kip2, TCF15/MEOX2 and EFNB1 as novel BAT-EC-specific markers. C_LI

cell biology↗

bioRxiv2022

Posterior marginalization accelerates Bayesian inference for dynamical systems

Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter uncertainties, and thereby the reliability of models and their predictions. Yet, generating representative samples from the Bayesian posterior distribution is often computationally challenging. Here, we present an approach that lowers the computational complexity of sample generation for problems with scaling, offset and noise parameters. The proposed method is based on the marginalization of the posterior distribution, which reduces the dimensionality of the sampling problem. We provide analytical results for a broad class of problems and show that the method is suitable for a large number of applications. Subsequently, we demonstrate the benefit of the approach for various application examples from the field of systems biology. We report a substantial improvement up to 50 times in the effective sample size per unit of time, in particular when applied to multi-modal posterior problems. As the scheme is broadly applicable, it will facilitate Bayesian inference in different research fields.

systems biology↗

bioRxiv2019

Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines

Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides accurate predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a powerful basis for the assessment of gastric cancer signaling and for the development and discovery of predictive biomarkers.\n\nAuthor SummaryUnraveling the causal differences between drug responders and non-responders is an important challenge. The information can help to understand molecular mechanisms and to guide the selection and design of targeted therapies. Here, we approach this problem for cetuximab treatment for gastric cancer using mechanistic mathematical modeling. The proposed model describes multiple gastric cancer cell lines and can accurately predict the response in several validation experiments. Our analysis provides a differentiated view on mutations and explains, for instance, the relevance of MET mutations and the insignificance of PIK3CA mutation in the considered cell lines. The model might provide the basis for understanding the recent failure of several clinical studies.

systems biology↗