Biology subjects

Bleris, L.

Publications and source records attributed to Bleris, L..

8 recordsLinked to original sources

bioRxiv2026

EMTscore infers divergent EMT pathways from omics data and enables rapid screening for correlated gene sets

SummaryQuantitative analyses of epithelial-mesenchymal transition (EMT) have been widely used in several areas of biomedical sciences due to its importance in development and cancer progression, but its multi-contextual nature requires standardization and implementation of gene set scoring methods beyond capacities of conventional tools. We developed EMTscore, a package that provides an efficient implementation of unbiased scoring methods for multiple EMT pathways using individual single-cell or bulk omics data, and the package allows rapid screening for relationships between EMT and other cellular processes. Availability and ImplementationEMTscore is available from GitHub https://github.com/wenmm/EMTscore under the GNU General Public License, and it will be deposited to Zenodo upon acceptance. It is also under review at Bioconductor. ContactTian Hong (hong@utdallas.edu)

bioinformatics↗

bioRxiv2025

NAB2-STAT6 Fusion Proteins Drive Nuclear Condensate Formation and Transcriptional Reprogramming in Solitary Fibrous Tumors

Solitary fibrous tumor (SFT) is a rare and aggressive sarcoma driven by NAB2-STAT6 gene fusions, yet effective targeted therapies remain unavailable. Here, we report that the NAB2ex4-STAT6ex2 fusion variant forms nuclear condensates via liquid-liquid phase separation (LLPS) in engineered fibroblast models and primary SFT cells. These condensates co-localize with BRD4S and EGR1, key transcriptional regulators, and are functionally active, driving widespread transcriptional reprogramming. Treatment with Mithramycin A, a compound that disrupts EGR1-DNA interactions, dissolves NAB2-STAT6 condensates and reverses their aberrant gene expression and chromatin binding signatures. Our findings uncover a previously unrecognized role for NAB2-STAT6 in condensate-mediated oncogenic signaling and provide a mechanistic rationale for condensate-targeted therapy in SFT.

cancer biology↗

bioRxiv2025

Identification of BET Inhibitors (BETi) Against Solitary Fibrous Tumor (SFT) Through High-Throughput Screening (HTS)

Cancers, especially fusion oncoprotein (FO)-driven hematological cancers and sarcomas, often develop from a low number of key mutations. Solitary Fibrous Tumor (SFT) is a rare mesenchymal tumor driven by the NAB2-STAT6 oncofusion gene. Currently, the treatment options for SFT remain limited, with anti-angiogenic drugs providing only partial responses and an average survival of two years. To address this challenge, we constructed SFT cell models harboring specific NAB2-STAT6 fusion transcripts using the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology. High-throughput drug screens demonstrated that the BET inhibitor Mivebresib can differentially reduce proliferation in SFT cell models. Subsequently, BET inhibitors Mivebresib and BMS-986158 efficiently reduced tumor growth in an SFT patient-derived xenograft (PDX) animal model. Furthermore, our data showed that NAB2-STAT6 fusions may lead to higher levels of DNA damage in SFTs. Consequently, combining BET inhibitors with PARP (Poly (ADP-ribose) polymerase) or ATR inhibitors significantly enhanced anti-proliferative effects in SFT cells. Taken together, our study established BET inhibitors Mivebresib and BMS-986158 as promising anti-SFT agents. SignificanceNew therapies are a clinical need for patients with Solitary Fibrous Tumor. We demonstrated that BET inhibitors are highly active in the preclinical setting for the treatment of this sarcoma entity.

cancer biology↗

bioRxiv2023

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

ObjectiveThe safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. ApproachA literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main ResultsWe compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. SignificanceThis novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

neuroscience↗

bioRxiv2023

What can we learn when fitting a complex gene expressionmodel to a simple telegraph model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

biophysics↗

bioRxiv2021

Impact of Variability in Cell Cycle Periodicity on Cell Population Dynamics

The cell cycle consists of a series of orchestrated events controlled by molecular sensing and feedback networks that ultimately drive the duplication of total DNA and the subsequent division of a single parent cell into two daughter cells. The ability to block the cell cycle and synchronize cells within the same phase has helped understand factors that control cell cycle and the properties of each individual phase. Intriguingly, when cells are released from a synchronized state, they do not maintain synchronized cell division and rapidly become asynchronous. The rate and factors that control cellular desynchronization remain largely unknown. In this study, using a combination of experiments and simulations, we investigate the desynchronization properties in cervical cancer cells (HeLa) starting from the G1/S boundary following double-thymidine block. Propidium iodide (PI) DNA staining was used to perform flow cytometry cell cycle analysis at regular intervals of 8 hours, and a custom auto-similarity function to assess the desynchronization and quantify the convergence to asynchronous state. In parallel, we developed a single-cell phenomenological model the returns the DNA concentration across the cell cycle stages and fitted the parameters using experimental data. Simulations of population of cells reveal that the cell cycle desynchronization rate is primarily sensitive to the variability of cell cycle duration within a population. To validate the model prediction, we introduced lipopolysaccharide (LPS) to increase cell cycle noise. Indeed, we observed an increase in cell cycle variability under LPS stimulation in HeLa cells, accompanied with an enhanced rate of cell cycle desynchronization. Our results show that the desynchronization rate of artificially synchronized in-phase cell populations can be used a proxy of the degree of variance in cell cycle periodicity, an underexplored axis in cell cycle research.

systems biology↗

bioRxiv2021

Provenance Attestation of Human Cells Using Physical Unclonable Functions

We introduce a novel methodology, namely CRISPR-Engineered Attestation of Mammalian Cells using Physical Unclonable Functions (CREAM-PUFs), which can serve as the cornerstone for formally verifying transactions in human cell line distribution networks. A PUF is a physical entity which provides a measurable output that can be used as a unique and irreproducible identifier for the artifact wherein it is embedded. Popularized by the electronics industry, silicon PUFs leverage the inherent physical variations of semiconductor manufacturing to establish intrinsic security primitives for attesting integrated circuits. Owing to the stochastic nature of these variations and the multitude of steps involved, photo-lithographically manufactured silicon PUFs are impossible to reproduce (thus unclonable). Inspired by the success of silicon PUFs, we sought to exploit a combination of sequence-restricted barcodes and the inherent stochasticity of CRISPR-induced non-homologous end joining DNA error repair to create the first generation of genetic physical unclonable functions in three distinct human cells (HEK293, HCT116, and HeLa). We demonstrate that these CREAM-PUFs are robust (i.e., they repeatedly produce the same output), unique (i.e., they do not coincide with any other identically produced PUF), and unclonable (i.e., they are virtually impossible to replicate). Accordingly, CREAM-PUFs can serve as a foundational principle for establishing provenance attestation protocols for protecting intellectual property and confirming authenticity of engineered cell lines.

synthetic biology↗

bioRxiv2018

CRISPR-based editing reveals edge-specific effects in biological networks

Unraveling the properties of biological networks is central to understanding normal and disease cellular phenotypes. Networks consist of functional elements (nodes) that form a variety of diverse connections (edges) with each node being a hub for multiple edges. Herein, in contrast to node-centric network perturbation and analysis approaches, we present a high-throughput CRISPR-based methodology for delineating the role of network edges. Ablation of network edges using a library targeting 93 miRNA target sites in 71 genes reveals numerous edges that control, with variable importance, cellular survival under stress. To compare the impact of removing nodes versus edges in a biological network, we dissect a specific p53-microRNA pathway. In summary, we demonstrate that network edges are critical to the function and stability of biological networks. Our results introduce a novel genetic screening opportunity via edge ablation and highlight a new dimension in biological network analysis.

systems biology↗