Biology subjects

Yeung, E.

Publications and source records attributed to Yeung, E..

14 recordsLinked to original sources

bioRxiv2026

Pediatric traumatic brain injury elicits acute neuroinflammation and long-term changes in social, cognitive, and decision-making behaviors in male and female rats

Traumatic brain injury (TBI) is one of the leading causes of emergency room visits in children under 10. Children are potentially more vulnerable to the adverse effects of TBI, given that their brains are still developing at the time of injury. Indeed, early life TBI has been linked to cognitive, social, and mood-related impairments later in life. The neuroimmune system has been implicated in adult TBI mechanisms and plays numerous key roles in brain development, making it an interesting candidate for linking pediatric TBI and prolonged behavioral alterations. Here we establish a rat model of mild pediatric TBI to investigate the relationship between early life TBI, acute responses of neuroimmune cells, and chronic behavioral dysregulation. At postnatal day 15, which is roughly equivalent to toddler age, male and female rat pups received a TBI via lateral fluid percussion injury. At 3 days post injury, TBI increased microglia and astrocyte coverage locally in the Perilesional Cortex but not in more distant corticolimbic regions. However, the hippocampus and prefrontal cortex did exhibit increased expression of the phagocytic marker CD68 in microglia, suggesting widespread glial activation even in the absence of gross coverage change. TBI also impacted mast cells, early-response innate immune cells, increasing their number and degranulation in multiple regions. In the juvenile and early adult periods, TBI impaired cognitive function, reduced sociability, and increased avoidance, with no change in anxiety-like behavior. Later in adulthood, TBI continued to impact cognitive behavior, increasing risky decision-making and impairing optimization months after injury. Together, these results suggest that pediatric TBI causes lasting cognitive and social dysregulation, possibly via acute neuroimmune alterations following injury at a critical period of brain development.

neuroscience↗

bioRxiv2025

Tuning a Genetic Circuit with Double Negative Feedforward Loops to Approximate Square Waves

Precise temporal control of gene expression is important for programming cellular functions. Existing circuits, however, often couple amplitude and duration. This prevents independent tuning of key dynamic features. We therefore set out to design a genetic system capable of generating square-wave outputs with externally programmable geometry. To achieve this, we constructed a layered regulatory design that separates production and removal into two independently controlled modules. An activating input turns expression on and builds the reporter. A repressing input stops synthesis and removes existing protein. Together, these actions confine the active window and produce square-wave outputs or pulses on demand. Waveform geometry depends on input dose and timing. Analysis of limiting cases reveals that strong repression compresses dynamic range, and finite degradation capacity restricts decay rate. Enhancing removal capacity alleviates this bottleneck and sharpens the off-transition. Together, this work establishes a general framework for programming gene-expression dynamics with independently tunable amplitude and duration.

synthetic biology↗

bioRxiv2025

Single-Molecule Imaging Reveals Transcription-Driven Supercoiling in Unconstrained DNA

In vitro studies of supercoiling dynamics have relied on externally applied force to twist constrained DNA. It is thus unknown whether transcription alone can generate supercoiling in topologically unconstrained DNA, and whether RNAP complexes could act as the topological barriers required to confine this stress. Here, using single-molecule imaging of 20k base pair long double strand DNA, we reveal that RNAP dynamically generates and confines supercoiling in unconstrained DNA. We demonstrate that multiple transcription events create transient topological domains, which allow plectonemes to stabilize between them; in contrast, a single transcription event does not lead to plectoneme formation. Furthermore, we show that this transcription-induced supercoiling is modulated by topoisomerase activity. Crucially, by observing that RNAP itself provides sufficient topological barriers, we establish that transcription-induced supercoiling is an inherently localized phenomenon. These findings redefine the physical basis of transcription-coupled supercoiling, providing a new mechanistic framework for modeling gene regulation based on local topology, with broader implications for genome editing and genome evolution.

molecular biology↗

bioRxiv2025

Topoisomerase Based Control of Cellular Transcription and Growth

Global cellular transcription and growth are tightly coupled to the supercoiling state of a cells genomic DNA. Direct processes that modulate the supercoiling state of genomic DNA thus provide a novel mechanistic route for biological control of cellular dynamics. In this work, we develop a stochastic model of genome-wide transcriptional responses to local DNA supercoiling and show that, despite heterogeneity in the transcription of each individual gene, the overall system converges to a unique stationary distribution. From the stochastic system, we derive a corresponding nonlinear population model with both local and global supercoiling states, demonstrate that the proposed model exhibits a unique, globally stable equilibrium point, and identify key parameters through sensitivity analysis. We further extend the model by considering the temperature-sensitive gyrase mutant E. coli strain nalA43 and illustrate how shifting the gyrase-topoisomerase balance via temperature acts as an effective control knob on the population, from which we derive a control law. Numerical simulations confirm the effectiveness of the derived control law and provide insight into the dynamics of the system. These results help establish a theoretical foundation for understanding the crucial equilibrium dynamics of topoisomerase enzymes and their implications for cell function.

systems biology↗

bioRxiv2023

Disentangling gene expression burden identifies generalizable phenotypes induced by synthetic gene networks

Large-scale genetic circuits are rapidly becoming critical components for the next generation of biotechnologies and living therapeutics. However, the relationship between synthetic and host gene expression is poorly understood. To reveal the impact of genetic circuits on their host, we measure the transcriptional response of wild-type and engineered E. coli MG1655 subject to seven genomically integrated circuits and two plasmid-based circuits across 4 growth time points and 4 circuit input states resulting in 1007 transcriptional profiles. We train a classifier to distinguish profiles from wild-type or engineered strains and use the classifier to identify synthetic construct burdened genes, i.e., genes whose dysregulation is dependent on the presence of a genetic circuit and not what is encoded on the circuit. We develop a deep learning architecture, capable of disentangling influence of combinations of perturbations, to model the impact that synthetic genes have on their host. We use the model to hypothesize a generalizable, synthetic cell state phenotype and validate the phenotype through antibiotic challenge experiments. The synthetic cell state results in increased resistance to {beta}-lactam antibiotics in gram-negative bacteria. This work enhances our understanding of circuit impact by quantifying the disruption of host biological processes and can guide the design of robust genetic circuits with minimal burden or uncover novel biological circuits and phenotypes.

systems biology↗

bioRxiv2022

Learning transcriptome dynamics for discovery of optimal genetic reporters of novel compounds

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the librarys ability to be useful outside the lab by detecting malathion in the environment. The library of living malathion sensors can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

synthetic biology↗

bioRxiv2021

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that takes the cell response to single perturbations as the input and predicts the whole cell transcriptional response to the combination of inducers. We find that the HRM is able to qualitatively predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of known prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). This tool will significantly reduce the number of high-throughput sequencing experiments that need to be run to characterize the transcriptional impact of the combination of perturbations on the host.

bioinformatics↗

bioRxiv2021

Discovery of Dynamical Network Models for Genetic Circuits from Time-Series Data with Incomplete Measurements

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements. We introduce a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a historical genetic circuit and a new E. coli based transcriptional event detector.

synthetic biology↗

bioRxiv2019

Precise temporal regulation of post-transcriptional repressors is required for an orderly Drosophila maternal-to-zygotic transition

In animal embryos the maternal-to-zygotic transition (MZT) hands developmental control from maternal to zygotic gene products. We show that the maternal proteome represents over half of the protein coding capacity of the Drosophila melanogaster genome and that 2% of this proteome is rapidly degraded during the MZT. Cleared proteins include the post-transcriptional repressors Cup, Trailer hitch (TRAL), Maternal expression at 31B (ME31B), and Smaug (SMG). While the ubiquitin-proteasome system is necessary for clearance of all four repressors, distinct E3 ligase complexes target them: the C-terminal to Lis1 Homology (CTLH) complex targets Cup, TRAL and ME31B for degradation early in the MZT; the Skp/Cullin/F-box-containing (SCF) complex targets SMG at the end of the MZT. Deleting the C-terminal 233 amino acids of SMG makes the protein immune to degradation. We show that artificially persistent SMG downregulates the zygotic re-expression of mRNAs whose maternal contribution is cleared by SMG. Thus, clearance of SMG permits an orderly MZT.

developmental biology↗

bioRxiv2018

A broad-host-range event detector: expanding and quantifying performance across bacterial species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli. It is clear that new applications - such as chemical sensing and event logging in complex environments - will benefit from new host chassis. This study quantitatively compared how a chemical event logger performed across multiple microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads - Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two bench-mark Escherichia coli chassis including the probiotic Nissle 1917 and common cloning strain DH5. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and down-stream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5 in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

synthetic biology↗

bioRxiv2018

A stress recovery signaling network for enhanced flooding tolerance in Arabidopsis thaliana

Abiotic stresses in plants are often transient and the recovery phase following stress removal is critical. Flooding, a major abiotic stress that negatively impacts plant biodiversity and agriculture, is a sequential stress where tolerance is strongly dependent on viability underwater and during the postflooding period. Here we show that in Arabidopsis thaliana accessions (Bay-0 and Lp2-6), different rates of submergence recovery correlate with submergence tolerance and fecundity. A genome-wide assessment of ribosome-associated transcripts in Bay-0 and Lp2-6 revealed a signaling network regulating recovery processes. Differential recovery between the accessions was related to the activity of three genes: RESPIRATORY BURST OXIDASE HOMOLOG (RBOHD), SENESCENCE-ASSOCIATED GENE113 (SAG113) and ORESARA1 (ORE1/NAC6) which function in a regulatory network involving a reactive oxygen species (ROS) burst upon de-submergence and the hormones abscisic acid and ethylene. This regulatory module controls ROS homeostasis, stomatal aperture and chlorophyll degradation during submergence recovery. This work uncovers a signaling network that regulates recovery processes following flooding to hasten the return to pre-stress homeostasis.\n\nSignificance statementFlooding due to extreme weather events can be highly detrimental to plant development and yield. Speedy recovery following stress removal is an important determinant of tolerance, yet mechanisms regulating this remain largely uncharacterized. We identified a regulatory network in Arabidopsis thaliana that controls water loss and senescence to influence recovery from prolonged submergence. Targeted control of the molecular mechanisms facilitating stress recovery identified here can potentially improve performance of crops in flood-prone areas.

plant biology↗

bioRxiv2017

Accurate, Sensitive, and Precise Multiplexed Proteomics using the Complement Reporter Ion Cluster

Quantitative analysis of proteomes across multiple time points, organelles, and perturbations is essential for understanding both fundamental biology and disease states. The development of isobaric tags (e.g. TMT) have enabled the simultaneous measurement of peptide abundances across several different conditions. These multiplexed approaches are promising in principle because of advantages in throughput and measurement quality. However, in practice existing multiplexing approaches suffer from key limitations. In its simple implementation (TMT-MS2), measurements are distorted by chemical noise leading to poor measurement accuracy. The current state-of- the-art (TMT-MS3) addresses this, but requires specialized quadrupole-iontrap-Orbitrap instrumentation. The complement reporter ion approach (TMTc) produces high accuracy measurements and is compatible with many more instruments, like quadrupole-Orbitraps. However, the required deconvolution of the TMTc cluster leads to poor measurement precision. Here, we introduce TMTc+, which adds the modeling of the MS2- isolation step into the deconvolution algorithm. The resulting measurements are comparable in precision to TMT-MS3/MS2. The improved duty cycle, and lower filtering requirements make TMTc+ more sensitive than TMT-MS3 and comparable with TMT-MS2. At the same time, unlike TMT-MS2, TMTc+ is exquisitely able to distinguish signal from chemical noise even outperforming TMT-MS3. Lastly, we compare TMTc+ to quantitative label-free proteomics of total HeLa lysate and find that TMTc+ quantifies 7.8k versus 3.9k proteins in a 5-plex sample. At the same time the median coefficient of variation improves from 13% to 4%. Thus, TMTc+ advances quantitative proteomics by enabling accurate, sensitive, and precise multiplexed experiments on more commonly used instruments.

systems biology↗

bioRxiv2017

Implementation and System Identification of a Phosphorylation-Based Insulator in a Cell-Free Transcription-Translation System

An outstanding challenge in the design of synthetic biocircuits is the development of a robust and efficient strategy for interconnecting functional modules. Recent work demonstrated that a phosphorylation-based insulator (PBI) implementing a dual strategy of high gain and strong negative feedback can be used as a device to attenuate retroactivity. This paper describes the implementation of such a biological circuit in a cell-free transcription-translation system and the structural identifiability of the PBI in the system. We first show that the retroactivity also exists in the cell-free system by testing a simple negative regulation circuit. Then we demonstrate that the PBI circuit helps attenuate the retroactivity significantly compared to the control. We consider a complex model that provides an intricate description of all chemical reactions and leveraging specific physiologically plausible assumptions. We derive a rigorous simplified model that captures the output dynamics of the PBI. We perform standard system identification analysis and determine that the model is globally identifiable with respect to three critical parameters. These three parameters are identifiable under specific experimental conditions and we perform these experiments to estimate the parameters. Our experimental results suggest that the functional form of our simplified model is sufficient to describe the reporter dynamics and enable parameter estimation. In general, this research illustrates the utility of the cell-free expression system as an alternate platform for biocircuit implementation and system identification and it can provide helpful insights into future biological circuit designs.

bioengineering↗

bioRxiv2016

The Effect of Compositional Context on Synthetic Gene Net-works

It is well known that synthetic gene expression is highly sensitive to how comprising genetic elements (promoter structure, spacing regions between promoter and coding sequences, ribosome binding sites, etc.) are spatially configured. An important topic that has received far less attention is how the physical layout of entire genes within a synthetic gene network affects their individual expression levels. In this paper we show, both quantitatively and qualitatively, that compositional context can significantly alter expression levels in synthetic gene networks. We also show that these compositional context effects are pervasive both at the transcriptional and translational level. Further, we demonstrate that key characteristics of gene induction, such as ultra-sensitivity and dynamic range, are heavily dependent on compositional context. We postulate that supercoiling can be used to explain these interference effects and validate this hypothesis through modeling and a series of in vitro supercoiling relaxation experiments. On the whole, these results suggest that compositional context introduces feedback in synthetic gene networks. As an illustrative example, we show that a design strategy incorporating compositional context effects can improve threshold detection and memory properties of the toggle switch.

synthetic biology↗