Biology subjects

Antebi, Y. E.

Publications and source records attributed to Antebi, Y. E..

8 recordsLinked to original sources

bioRxiv2026

Epithelial mesenchymal transition initiates precancer states in BRCA1 mutation carriers

Epithelial-to-mesenchymal transition (EMT) is activated to equip cells with the capacity to adapt to and escape hostile conditions. While EMT is required for cancer progression, its role in breast cancer initiation remains elusive. Given the basal-like phenotype of breast cancers arising in female carriers of germline BRCA1 pathogenic variants (BRCA1 carriers), we hypothesized that enhanced EMT susceptibility underlies precancerous initiation in mammary epithelium. Perturbation of patient-derived normal mammary organoids from BRCA1 carriers and non-carriers with inflammatory cytokines induced copy number variations (CNV) and the acquisition of oncogenic mutations in both groups. However, in organoids derived from BRCA1 carriers, cytokine exposure induced morphological, transcriptomic, and functional EMT, accompanied by a transition to basal-like phenotype. Concomitant DNA damage accumulation in organoids from BRCA1-carriers demonstrated PARP inhibitor sensitivity. EMT-primed states were identified in a subpopulation of normal mammary epithelium from BRCA1 carriers. We demonstrate the utility of patient-derived normal BRCA1 heterozygous mammary organoids to reveal a plastic, high-risk epithelial state that is associated with a transient, targetable vulnerability.

cancer biology↗

bioRxiv2026

A low-dimensional transcriptional code enables decoding of BMP/TGFβ signaling from single-cell transcriptomes

Cells interpret complex extracellular environments through signaling pathways that compress diverse ligand inputs into a limited set of intracellular mediators. How this information is encoded in transcriptional responses, and whether it can be decoded to infer the original signaling environment, remains unclear. Here, we systematically map BMP/TGF{beta} transcriptional responses using single-cell RNA sequencing across 48 ligand-concentration conditions, generating a multi-ligand dose-response atlas. We find that, for each ligand, transcriptional responses collapse onto a one-dimensional, highly coordinated program in which concentration modulates response amplitude without altering gene identity. Across ligands, we find a small number of distinct, ligand-dependent transcriptional programs. We leverage this structured encoding to define a quantitative perception score that captures pathway activity at single-cell resolution. Finally, we train a machine learning model to decode extracellular ligand concentrations from single-cell transcriptomes. The model further generalizes to in vivo intestinal epithelium, where it reconstructs the spatial BMP gradient profile. Together, these results reveal a low-dimensional transcriptional code in BMP/TGF{beta} signaling that constrains cellular responses and enables quantitative inference of extracellular signaling environments from gene expression.

systems biology↗

bioRxiv2026

Asymmetric crosstalk between the BMP and TGFβ pathways resolves signaling ambiguity

The BMP and TGF{beta} signaling pathways control cellular fate decisions in diverse biological contexts, often playing opposing roles. Despite extensive knowledge of these pathways, understanding how cells respond to environments containing these opposing cues remains a challenge. Here, we systematically analyze the activation of these pathways under combinatorial signaling environments. We find that TGF{beta} ligands inhibit BMP signaling, while BMP ligands enhance TGF{beta} transcriptional response across concentrations, ligand variants, and cell types. This asymmetric crosstalk results in the activation of a TGF{beta}-biased transcriptional response, even under mixed signaling conditions, effectively reducing signal ambiguity, with implications for processes such as EMT. We show that this crosstalk originates downstream of the SMAD proteins phosphorylation. Using mathematical models, we predict, and experimentally verify, that promiscuous interactions between SMAD proteins provide the mechanism for the observed crosstalk. Our findings challenge the canonical models, suggesting an active role for mediator proteins in determining biological responses.

systems biology↗

bioRxiv2025

Receptor dimerization enables ligand discrimination through tunable response heterogeneity

Signaling pathways enable cells to coordinate collective behaviors by exchanging specific information. Many pathways utilize multiple ligand variants to activate the same intracellular signaling cascade, raising the question of how cells discriminate between these seemingly redundant signals. It has been shown that individual cells can discriminate between signals based on their induced level of activity, temporal dynamics or combinatorial effect. Here, we demonstrate that ligand discrimination could also emerge at the population level. Using mathematical models of ligand-receptor interactions, we examine how response heterogeneity at the population level can encode ligand identity. We introduce a local scaling metric to quantify how variation in pathway components affects the cellular response. Our results reveal that for pathways with dimeric receptors, and more significantly for heterodimeric receptors, biochemical parameters of the ligands control the resulting heterogeneity in the response of a population of cells. Furthermore, we show that the population-level heterogeneity encodes the enzymatic activity of the resulting receptor complex. This suggests a functional advantage for utilizing heterodimeric receptor complexes in pathways acting across a population of cells, such as the type I interferon pathway, which shows several of the characteristics of our model. This contrasts to juxtacrine pathways, such as Notch, that do not act at the population level and use a single component receptor. Overall, our findings highlight a novel mechanism by which receptor architecture enables cells to encode ligand-specific information through population-level heterogeneity, offering insights into immune regulation, tissue development, and synthetic biology. Significance StatementCells often communicate using distinct molecular signals (ligands) that activate the same pathway. Yet, how cells distinguish between these signals remains unclear. This study reveals that ligand discrimination can emerge at the level of entire cell populations, not just individual cells. By analyzing mathematical models, we show that certain receptor architectures, especially those involving heterodimeric receptors, allow signals to control the heterogeneity of the response level across a population. This mechanism enables cell populations to decode specific information about their environment, impacting processes like immune responses and tissue development. Our findings provide new insights into how signals coordinate collective behaviors and suggest strategies for designing synthetic systems to precisely control biological responses.

systems biology↗

bioRxiv2024

TGFβ determines morphology and key cellular processes of activated CD4+ T cells

During an immune response, cells are simultaneously exposed to multiple cytokine signals that collectively determine their phenotype. Transforming growth factor {beta} (TGF{beta}) is a pleiotropic cytokine acting as a key regulator of T-cell differentiation with activating and suppressive effects on their immune function. Here, we systematically analyze the cellular responses of CD4+ T cells to TGF{beta} across diverse cytokine environments in the presence or absence of TGF{beta}. We found that TGF{beta} had a profound dominant effect independent of the presence of other cytokines, modulating the expression of more than 4,000 genes. In the presence of TGF{beta}, cells exhibit lower expression of translation-related and apoptosis-related genes, accompanied by increased survival of activated T cells. Notably, cells cultured in the presence of TGF{beta} were smaller in size while preserving their proliferative ability. Accordingly, we identified a dense network of transcription factors that were modulated by TGF{beta}, suggesting a core gene set connecting TGF{beta} signaling to the regulation of T-cell size. We found N-Myc to be at the center of this network, and we directly show that TGF{beta} regulates its gene expression level, protein level, and nuclear localization. Our work provides a system to study cell size control and demonstrate the profound effect of TGF{beta} in the modulation and regulation of T-cell properties, expanding its role beyond guiding their phenotype. Significance StatementTGF{beta} is a key determinant of CD4+ T-cell differentiation; however, understanding its effect on additional aspects of T-cell state is lacking. Here, we systematically studied the role of TGF{beta} in regulating T-cell physiology. Exposing cells to diverse combinations of cytokines enabled us to distill the core effect of TGF{beta}. We found TGF{beta} to have a profound effect on multiple cellular processes critical to T-cell function. Significantly, TGF{beta} induced smaller T-cells both in vitro and in vivo, suggesting that TGF{beta} could skew the population towards tissue infiltration and residency. Furthermore, TGF{beta} can be used to fine-tune T-cell size, providing a system for studying cell size control. Overall, our findings demonstrate the profound effect of TGF{beta} in the regulation of T-cell physiology.

immunology↗

bioRxiv2022

From pseudotime to true dynamics: reconstructing a real-time axis for T cells differentiation

Numerous methods have recently emerged for ordering single cells along developmental trajectories. However, accurate depiction of developmental dynamics can only be achieved after rescaling the trajectory according to the relative time spent at each developmental point. We formulate a model which estimates local cell densities and fluxes, and incorporates cell division and apoptosis rates, to infer the real time dimension of the developmental trajectory. We validate the model using mathematical simulations, and apply it on experimental high dimensional cytometry data obtained from the mouse thymus to construct the true time-profile of the thymocyte developmental process. Our method can easily be implemented in any of the existing tools for trajectory inference.

systems biology↗

bioRxiv2020

Ligand-receptor promiscuity enables cellular addressing

In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, each defined by its receptor expression profile. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capacity. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increasing receptor multiplicity, and is maximized by specific biochemical parameter relationships. Together, these results identify fundamental design principles governing cell addressing by ligand combinations.

systems biology↗

bioRxiv2020

The context-dependent, combinatorial logic of BMP signaling

Cell-cell communication systems typically comprise families of ligand and receptor variants that function together in combinations. Pathway activation depends in a complex way on which ligands are present and what receptors are expressed by the signal-receiving cell. To understand the combinatorial logic of such a system, we systematically measured pairwise Bone Morphogenetic Protein (BMP) ligand interactions in cells with varying receptor expression. Ligands could be classified into equivalence groups based on their profile of positive and negative synergies with other ligands. These groups varied with receptor expression, explaining how ligands can functionally replace each other in one context but not another. Context-dependent combinatorial interactions could be explained by a biochemical model based on competitive formation of alternative signaling complexes with distinct activities. Together, these results provide insights into the roles of BMP combinations in developmental and therapeutic contexts and establish a framework for analyzing other combinatorial, context-dependent signaling systems.

systems biology↗