Biology subjects

Bagheri, N.

Publications and source records attributed to Bagheri, N..

At least 19 recordsLinked to original sources

bioRxiv2026

The human circular Tau 12->7 RNA is translated in mouse neurons and promotes tau aggregation

In addition to numerous mRNA isoforms, the human microtubule associated protein tau (MAPT) gene generates circular RNAs that are not known to be expressed in rodents. Using adeno associated viruses, we expressed human circular Tau RNA generated from backsplicing of exon 12 to exon 7 (circTau 12->7) in mouse brain. We observed rolling circle translation of circTau 12->7, similar to cultured human HEK293 cells. Despite using a ubiquitously active promoter, circTau 12->7 protein and a control circEGFP protein were only observed in mouse neurons, indicating neuron-specific translation of these circular RNAs. CircTau 12->7 expression resulted in tau aggregates resembling neurofibrillary tangles in mouse brain that included endogenous mouse tau protein. Our data support a model where neuron-specific translation of circTau RNAs induces 'linear', mRNA-translated tau aggregation, which could contribute to tauopathies, including Alzheimer's disease. Thus, targeting circTau RNAs could be a therapeutic approach for Alzheimer's disease.

neuroscience↗

bioRxiv2025

Altering cell size asymmetry in Drosophila neural stem cells creates supernumerary stem cells with limited lineage expansion potential

Asymmetrically dividing invertebrate and vertebrate stem cells can generate unequal sized sibling cells. However, the functional implications of cell size asymmetry (CSA) are underexplored. Here, we use Drosophila neural stem cells (NSCs) to investigate how changes in CSA impact NSC proliferation and cell fate decisions. Using live cell imaging, NSC lineage analysis, and gene expression profiling, we find that altering CSA increases the NSC pool but decreases lineage size and the number of differentiating progeny cells. Modeling CSA in silico with a volume-sensitivity and NSC self-inhibition model can recapitulate these findings. Gene expression profiling further revealed that the NSC growth regulator Imp and the G1-S cell cycle regulator CycE are upregulated in NSCs with altered CSA, providing a potential molecular link to the volume-sensitivity model. We propose that cell size and position regulate NSC proliferation and differential potential, impacting lineage progression and progeny cell differentiation in the developing Drosophila brain.

cell biology↗

bioRxiv2025

Covalent Drug binding in Live Cells Monitored by Mid-IR Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System

The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Using this approach, we detect small-molecule binding in E. coli, with particular focus on the interaction between para-coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm-1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometers ability to enhance sensitivity for monitoring drug-protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research. Authors are required to submit a graphic entry for the Table of Contents (TOC) that, in conjunction with the manuscript title, should give the reader a representative idea of one of the following: A key structure, reaction, equation, concept, or theorem, etc., that is discussed in the manuscript. Consult the journals Instructions for Authors for TOC graphic specifications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/670201v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@13bd30forg.highwire.dtl.DTLVardef@9db2bcorg.highwire.dtl.DTLVardef@141590forg.highwire.dtl.DTLVardef@13dfcf2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Rewiring DNA Repair Activity into CRISPR Signal Transduction via Synthetic DNA Transducers

Harnessing the precision of CRISPR systems for diagnostics has transformed nucleic acid detection. However, the integration of upstream cellular signals into CRISPR-based circuits remains largely underexplored. Here, we introduce a synthetic transduction platform that directly links endogenous DNA repair activity to CRISPR-Cas12a activation. By coupling base excision repair (BER) events to a programmable DNA-based transducer, our system converts the activity of DNA glycosylases-- such as uracil-DNA glycosylase (UDG) and human 8-oxoguanine glycosylase (hOGG1)--into a robust fluorescence signal via Cas12a-mediated trans-cleavage. This one-step CRISPR-based assay operates directly in cell lysates, enabling rapid and sensitive readout of enzymatic activity with high specificity. Additionally, it also enables in 15 minutes the throughput screening of novel potential inhibitors with high sensitivity. The modular design allows adaptation to diverse repair enzymes, offering a generalizable strategy for transforming intracellular repair events into programmable outputs. This approach lays the foundation for activity-based molecular diagnostics, synthetic gene circuits responsive to cellular states, and new tools for monitoring DNA repair in real time and drug screening.

synthetic biology↗

bioRxiv2025

Magnetic resonance control of reaction yields through genetically-encoded protein:flavin spin-correlated radicals in a live animal

Radio-frequency (RF) magnetic fields can influence reactions involving spin-correlated radical pairs. This provides a mechanism by which RF fields can influence living systems at the biomolecular level. Here we report the modification of the emission of various red fluorescent proteins (RFPs), in the presence of a flavin cofactor, induced by a combination of static and RF magnetic fields. Resonance features in the protein fluorescence intensity were observed near the electron spin resonance frequency at the corresponding static magnetic field strength. This effect was measured at room temperature both in vitro and in the nematode C. elegans, genetically modified to express the RFP mScarlet. These observations suggest that the magnetic field effects measured in RFP-flavin systems are due to quantum-correlated radical pairs. Our experiments demonstrate that RF magnetic fields can influence dynamics of reactions involving RFPs in biologically relevant conditions, and even within a living animal. These results have implications for the development of a new class of genetic tools based on RF manipulation of genetically-encoded quantum systems.

biophysics↗

bioRxiv2025

All-Optical Strategies to Minimize Photo-Bleaching in Reversibly Switchable Fluorescent Proteins

Photo-bleaching is a general hurdle of fluorescence-based techniques that becomes even more severe in high- resolution microscopy that relies on prolonged, focused and complex illumination sequences. Strategies to reduce photo-bleaching require chemical modifications of the cell media, which often stave off physiological cellular conditions. Here, we outline an all-optical strategy to minimize photo-bleaching in reversibly switching fluorescent proteins (RSFPs), a class of probes used in several super-resolution and protein-multiplexing imaging techniques. By identifying the photobleaching pathways, we developed novel imaging schemes to increase the number of ON- OFF photo-switching cycles based on a designed modulation of the on-switching light or a co-irradiation with red- shifted light. By rationalizing the photo-cycle, we expand multiplexing strategies with RSFPs to high- spatiotemporal resolutions while maintaining the accuracy and recording longer time-lapse imaging of sub-cellular structures with both confocal microscopy and parallelized RESOLFT nanoscopy.

biophysics↗

bioRxiv2024

4D hybrid model interrogates agent-level rules and parameters driving hiPS cell colony dynamics

Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate cell shape and colony dynamics in human induced pluripotent stem cell (hiPS cell) colonies. This model aimed to first mimic and then explore the dynamics observed in real-world hiPS cell cultures. Initial outputs showed great potential, seeming to mimic small colony behaviors relatively well. However, longer simulations and quantitative comparisons revealed limitations, particularly with the CPM component, which lacked long-range interactions that might be necessary for accurate simulations. This challenge led us to thoroughly examine the hybrid models potential and limitations, providing insights and recommendations for systems where cell-wide mechanics play significant roles. The CPM supports 2D and 3D cell shapes using a Monte Carlo algorithm to prevent cell fragmentation. Basic "out of the box" CPM Hamiltonian terms of volume and adhesion were insufficient to match live cell imaging of hiPS cell cultures. Adding substrate adhesion resulted in flatter colonies, highlighting the need to consider environmental context in modeling. High-throughput parameter sweeps identified regimes that produced consistent simulated shapes and demonstrated the impact of specific model decisions on emergent dynamics. Full-scale simulations showed that while certain agent rules could form a hiPS cell monolayer in 3D, they could not maintain it over time. Our study underscores that "out of the box" 3D CPMs, which do not natively incorporate long-range cell mechanics like elasticity, may be insufficient for accurately simulating hiPS cell and colony dynamics. To address this limitation, future work could add mechanical constraints to the CPM Hamiltonian or integrate global agent rules. Alternatively, replacing the CPM with a methodology that directly represents cell mechanics might be necessary. Documenting and sharing our model development process fosters open team science and supports the broader research community in developing computational models of complex biological systems.

systems biology↗

bioRxiv2024

Developing, characterizing and modelingCRISPR-based point-of-use pathogen diagnostics

Recent years have seen intense interest in the development of point-of-care nucleic acid diagnostic technologies to address the scaling limitations of laboratory-based approaches. Chief among these are combinations of isothermal amplification approaches with CRISPR-based detection and readouts of target products. Here, we contribute to the growing body of rapid, programmable point-of-care pathogen tests by developing and optimizing a one-pot NASBA-Cas13a nucleic acid detection assay. This test uses the isothermal amplification technique NASBA to amplify target viral nucleic acids, followed by Cas13a-based detection of amplified sequences. We first demonstrate an in-house formulation of NASBA that enables optimization of individual NASBA components. We then present design rules for NASBA primer sets and LbuCas13a guide RNAs for fast and sensitive detection of SARS-CoV-2 viral RNA fragments, resulting in 20 - 200 aM sensitivity without any specialized equipment. Finally, we explore the combination of high-throughput assay condition screening with mechanistic ordinary differential equation modeling of the reaction scheme to gain a deeper understanding of the NASBA-Cas13a system. This work presents a framework for developing a mechanistic understanding of reaction performance and optimization that uses both experiments and modeling, which we anticipate will be useful in developing future nucleic acid detection technologies.

synthetic biology↗

bioRxiv2024

Long term rescue of Alzheimer deficits in vivo by one-time gene-editing of App C-terminus.

Gene-editing technologies promise to create a new class of therapeutics that can achieve permanent correction with a single intervention. Besides eliminating mutant alleles in familial disease, gene-editing can manipulate upstream pathophysiologic events and alter disease-course in wider patient populations. Here we use CRISPR-Cas9 to edit the last exon of amyloid precursor protein (App), relevant for Alzheimers disease (AD). Our strategy effectively eliminates an endocytic (YENPTY) motif at APP C-terminus in mouse and human neurons, while preserving N-terminus and compensatory APP-homologues. This manipulation favorably alters events along the amyloid-pathway; inhibiting toxic APP-beta-cleavage fragments (including Abeta) and upregulating neuroprotective APP-alpha-cleavage. AAV-editing ameliorates neuropathologic, electrophysiologic, and behavioral deficits in an AD knockin mouse model. Effects persist for many months with no detectable abnormalities in germline-edited WT mice, and pathologic alterations in glial-transcriptomes are also normalized. Our strategy takes advantage of innate transcriptional rules that render terminal exons insensitive to nonsense-decay, and this upstream manipulation is expected to be effective for all forms of AD.

neuroscience↗

bioRxiv2024

Incorporating temporal information during featureengineering bolsters emulation of spatio-temporalemergence

MotivationEmergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies--e.g. live cell imaging, scRNAseq, agent-based models--requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. ResultsDespite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. AvailabilityAll source codes for the agent-based model, emulation, and analyses are publicly available at github.com/bagherilab/ARCADE, github.com/bagherilab/emulation, and github.com/bagherilab/emulation_analysis, respectively. Contactnbagheri@uw.edu

systems biology↗

bioRxiv2023

Energy metabolism modulates the regulatory impact of activators on gene expression

Gene expression is a regulated process fueled by ATP consumption. Therefore, regulation must be coupled to constraints imposed by the level of energy metabolism. Here, we explore this relationship both theoretically and experimentally. A stylized mathematical model predicts that activators of gene expression have variable impact depending on metabolic rate. Activators become less essential when metabolic rate is reduced and more essential when metabolic rate is enhanced. We find that in the Drosophila eye, expression dynamics of the yan gene are less affected by loss of EGFR-mediated activation when metabolism is reduced, and the opposite effect is seen when metabolism is enhanced. The effects are also seen at the level of pattern regularity in the adult eye, where loss of EGFR-mediated activation is mitigated by lower metabolism. We propose that gene activation is tuned by energy metabolism to allow for faithful expression dynamics in the face of variable metabolic conditions.

developmental biology↗

bioRxiv2022

Mapping CAR T-cell design space using agent-based models

Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features--CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression--individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.

bioengineering↗

bioRxiv2021

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce GAMES: a workflow for Generation and Analysis of Models for Exploring Synthetic systems that includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/465216v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@b1c8feorg.highwire.dtl.DTLVardef@29d13org.highwire.dtl.DTLVardef@1967eb7org.highwire.dtl.DTLVardef@1593bdd_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2020

Model-guided design of mammalian genetic programs

Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multi-functional proteins integrating both transcriptional and post-translational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

synthetic biology↗

bioRxiv2019

Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila

Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis. Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists.\n\nAuthor summaryBiologists use mosaic tissues to compare the behavior of genetically distinct cells within an otherwise equivalent context. The ensuing analysis is often limited to qualitative insight. However, it is becoming clear that quantitative models are needed to unravel the complexities of many biological systems. In this manuscript we introduce Fly-QMA, an open-source software framework that automates the quantification of mosaic analysis for Drosophila imaginal discs, a common setting for studies of developmental processes. The software automatically extracts quantitative measurements from confocal images of mosaic tissues, rectifies any cross-talk between fluorescent reporters, and identifies clonally-related subpopulations of cells. Together, these functions allow users to rigorously ascribe changes in gene expression to the presence or absence of particular genes. We validate the performance of our framework using both real and synthetic data. Through its publication, we aim to contribute to the current move toward automated quantitative analysis among biologists.

systems biology↗

bioRxiv2019

COMET: A toolkit for composing customizable genetic programs in mammalian cells

Engineering mammalian cells to carry out sophisticated and customizable genetic programs requires a toolkit of multiple orthogonal and well-characterized transcription factors (TFs). To address this need, we developed the COmposable Mammalian Elements of Transcription (COMET)--an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic. We also develop a mathematical model that provides mechanistic insights into COMET performance characteristics. Altogether, COMET enables the design and construction of customizable genetic programs in mammalian cells.

synthetic biology↗

bioRxiv2019

Macrophages employ quorum licensing to regulate collective activation

Macrophage-initiated inflammation is tightly regulated to eliminate threats such as infections while suppressing harmful immune activation. However, individual cells signaling responses to pro-inflammatory cues are heterogeneous; subpopulations emerge with high or low activation states, though why this occurs is unknown. To address this question, we used single-cell tracking and dynamical modeling to develop and validate a revised model for macrophage activation by lipopolysaccharide (LPS) that invokes a mechanism we term quorum licensing. Bimodal phenotypic partitioning of macrophages is primed during the resting state, depends on cumulative history of cell density, is predicted by extrinsic noise in transcription factor expression, and is independent of canonical LPS-induced intercellular feedback in the tumor necrosis factor (TNF) response. Our analysis shows how this density-dependent coupling produces a nonlinear effect on collective TNF production. We speculate that by linking macrophage density to activation, this mechanism could amplify local responses to threats and prevent false alarms.

systems biology↗

bioRxiv2019

Repressive gene regulation synchronizes development with cellular metabolism

Metabolic conditions affect the developmental tempo of most animal species. Consequently, developmental gene regulatory networks (GRNs) must faithfully adjust their dynamics to a variable time scale. We find evidence that layered weak repression of genes provides the necessary coupling between GRN output and cellular metabolism. Using a mathematical model that replicates such a scenario, we find that lowering metabolism corrects developmental errors that otherwise occur when different layers of repression are lost. Through mutant analysis, we show that gene expression dynamics are unaffected by loss of repressors, but only when cellular metabolism is reduced. We further show that when metabolism is lowered, formation of a variety of sensory organs in Drosophila is normal despite loss of individual repressors of transcription, mRNA stability, and protein stability. We demonstrate the universality of this phenomenon by experimentally eliminating the entire microRNA family of repressors, and find that all microRNAs are rendered unnecessary when metabolism is reduced. Thus, layered weak repression provides robustness through error frequency suppression, and may provide an evolutionary route to a shorter reproductive cycle.

developmental biology↗