Biology subjects

Greene, J.

Publications and source records attributed to Greene, J..

11 recordsLinked to original sources

bioRxiv2025

Dual-Channel Event Microscopy for Ultrafast Biological Imaging

Many fundamental biological processes--spanning immune-tumor interactions, neuronal signaling, and microvascular flow--exhibit fast, multiscale dynamics among diverse cell types within three-dimensional tissue environments. Capturing such activity requires imaging systems that simultaneously achieve high temporal resolution, multicolor capability, and volumetric coverage over large fields of view (FOVs). However, existing modalities remain limited by trade-offs among imaging speed, spectral capacity, depth of field (DOF), and spatial resolution. Here, we present Dual-Channel Event Microscopy (DEM), which integrates digital micromirror device (DMD)-based pulsed illumination, extended-DOF optics, and event-based sensing for ultrafast, dual-channel volumetric imaging across a 2.3 x 1.3 mm2 FOV with an effective 200 {micro}m DOF. Using dual-color fluorescent phantoms and microsphere flow assays, DEM achieves accurate spectral separation and reconstruction of rapid motion at kilohertz frame rates. In vivo, DEM enables simultaneous visualization of neutrophils and premalignant tumors in freely swimming zebrafish. In immobilized specimens, it provides robust, sensor-level optical sectioning near the heart, suppressing diffuse background to reveal fine vascular networks and active blood circulation into and out of the cardiac chambers. DEM further enables quantitative mapping of blood-flow dynamics in the zebrafish tail, resolving arterial-venous differences and capturing heartbeat-driven oscillations that reflect cardiac pumping with high temporal fidelity. By uniting ultrafast acquisition, dual-channel capability, volumetric coverage, and intrinsic optical sectioning within a single event-driven architecture, DEM offers a powerful platform for visualizing rapid multicellular interactions and physiological dynamics in living systems.

bioengineering↗

bioRxiv2025

Covalent Degraders of Immune Regulatory Transcription Factors IRF8 and IRF5

Transcription factors are among the most challenging targets for drug discovery due to their lack of classical binding pockets and high degree of intrinsic disorder, despite the therapeutic importance of many of these proteins. IRF5 and IRF8 are key transcriptional regulators of innate immune signaling that orchestrate pro-inflammatory gene expression programs in response to stimuli such as toll-like receptor activation, making them central players in autoimmune and inflammatory diseases. Despite their therapeutic interest, direct targeting of IRF5 and IRF8 has remained challenging. Through a screen originally intended to identify cysteine-reactive electrophilic ligands that could directly and covalently engage and degrade IRF5, we identified an acrylamide hit, EN1033, that not only targeted and degraded IRF5 but also degraded a related inflammatory transcription factor, IRF8, more robustly and rapidly. EN1033 destabilized and degraded IRF5 and IRF8 by covalently targeting C28 and C223, respectively, as evidenced by the attenuation of their degradation through mutagenesis of these cysteines. We also found that IRF8 loss led to downregulation and inhibition of IRF5 activity, suggesting crosstalk between these two transcription factors, both of which are targeted by EN1033. Upon exploring structure-activity relationships, we identified an optimized compound, TH-B10, that more potently and selectively targeted and degraded IRF8 and, secondarily, downregulated IRF5 by targeting C223 on IRF8. Overall, we identify an early-stage pathfinder molecule, TH-B10, that directly covalently targets and degrades IRF8 and secondarily modulates IRF5 to shut down pro-inflammatory transcriptional activity.

biochemistry↗

bioRxiv2024

Understanding therapeutic tolerance through a mathematical model of drug-induced resistance

There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the persistence of an initially sensitive population of cancer cells in the presence of therapeutic agents. Understanding the implications of this druginduced resistance is essential for predicting transient and long-term tumor tumor dynamics subject to treatment. This paper introduces a mathematical model of this phenomenon of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations, and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses which were not used when fitting parameters. The model is then used, in conjunction with optimal control techniques, in order to discover dosing strategies that might lead to better outcomes as quantified by lower total cell volume.

systems biology↗

bioRxiv2024

Yersinia pseudotuberculosis growth arrest during type-III secretion system expression is associated with altered ribosomal protein expression and decreased gentamicin susceptibility

It has been long appreciated that expression of the Yersinia type-III secretion system (T3SS) in culture is associated with growth arrest. Here we sought to understand whether this impacts expression of ribosomal protein genes, which were among the most highly abundant transcripts in exponential phase Yersinia pseudotuberculosis based on RNA-seq analysis. To visualize changes in ribosomal protein expression, we generated a fluorescent transcriptional reporter with the promoter upstream of rpsJ/S10 fused to a destabilized gfp variant. We confirmed reporter expression significantly increases in exponential phase and decreases as cells transition to stationary phase. We then utilized a mouse model of systemic Y. pseudotuberculosis infection to compare T3SS and S10 reporter expression during clustered bacterial growth in the spleen, and found that cells expressing high levels of the T3SS had decreased S10 levels, while cells with lower T3SS expression retained higher S10 expression. In bacteriological media, growth inhibition with T3SS induction and a reduction in S10 expression were observed in subsets of cells, while cells with high expression of both T3SS and S10 were also observed. Loss of T3SS genes resulted in rescued growth and heightened S10 expression. To understand if clustered growth impacted bacterial gene expression, we utilized droplet-based microfluidics to encapsulate bacteria in spherical agarose droplets, and also observed growth inhibition with high expression of T3SS and reduced S10 levels that better mirrored phenotypes observed in the mouse spleen. Finally, we show that T3SS expression is sufficient to promote tolerance to the ribosome-targeting antibiotic, gentamicin. Collectively, these data indicate that the growth arrest associated with T3SS induction leads to decreased expression of ribosomal protein genes, and this results in reduced antibiotic susceptibility. Author SummarySlow-growing bacterial cells have reduced antibiotic susceptibility, rendering them very difficult to eliminate during antibiotic treatment. However, for many key virulence factors (bacterial factors required to promote infection), it remains unclear whether expression is sufficient to slow bacterial growth and impact antibiotic susceptibility. Using Yersinia pseudotuberculosis, we found ribosomal protein expression fluctuated based on growth rate, and we generated a fluorescent reporter construct to detect altered ribosomal protein expression within individual bacterial cells. We then asked if expression of a key virulence factor in Yersinia, the type-III secretion system (T3SS), is sufficient to lower ribosomal protein expression, since it has been well established that T3SS induction results in growth arrest. We found high levels of T3SS expression promotes slowed growth and antibiotic tolerance, and bacterial cells that survive treatment with a ribosome-targeting antibiotic, gentamicin, have heightened levels of T3SS and lower levels of S10 expression.

microbiology↗

bioRxiv2024

Evaluating the Importance of Glucagon in the Insulin-Glucose Regulatory System: A Mechanistic Modeling Approach

The dynamics of insulin and glucose are tightly regulated. The pancreatic islets of Langerhans contain both beta and alpha cells which produce insulin and glucagon, respectively. Insulin is the only hormone in the body that lowers blood glucose levels by acting like a key for glucose to enter cells. Without insulin, cells cannot utilize glucose, their primary source of energy. In contrast, glucagon functions as a hormone which elevates blood glucose levels by promoting the breakdown of glycogen in the liver. Maintaining blood glucose within a safe range is vital since both excessively high and low levels can be life-threatening (hyperglycemia and hypoglycemia, respectively), and these two hormones work together to achieve this balance. In this work we aim to underscore the significance of glucagon in the insulin-glucose regulatory system. We construct a three-compartment mechanistic model that includes insulin, glucose, and glucagon, which is then validated by fitting to publicly available from an intravenous glucose tolerance test (IVGTT). After model validation, we investigate how removing glucose feedback from insulin secretion, as seen in insulin-dependent diabetes, disrupts the regulation of glucose and glucagon. To do this, we simulate the model (a) when insulin secretion is reduced to mimic an insufficient dose of insulin, (b) when the peak of insulin action is delayed mimicking a dosing delay of insulin, and (c) when both occur simultaneously. Lastly, we test different half-lives of insulin to evaluate how an increased half-life of manufactured insulin may further disrupt the system. We find that when insulin secretion is decreased, glucagon still responds to high glucose levels by decreasing glucagon production. This suggests that in cases of type 2 diabetes, where glucagon secretion is elevated despite high levels of glucose, a lack of insulin response may not be the sole cause for glucagon dysfunction. We also find that delaying insulin secretion increases the risk of a hypoglycemic event through a suppression of glucagon production. Initially, the spike in glucose causes glucagon secretion to be reduced; this is then followed by the delay in insulin peak which then continues to suppress glucagon despite blood glucose levels falling, leading to a lack of response by glucagon and a subsequent hypoglycemic event. Furthermore, we find that a higher half-life of insulin causes it to remain longer in the blood stream, inhibiting glucagons response to severely low glucose levels (glucose levels less than 3.9 mmol/L). This sheds light on why patients taking exogenous insulin, which has a longer half-life than endogenous insulin, may have difficulty recovering from hypoglycemic events. Hence, our model suggests that keeping the half-life of exogenous insulin below 10 minutes and administering it immediately after meals could help reduce the risk of hypoglycemic events in patients with type 1 or insulin dependent diabetes. Overall, we highlight how a disruption in the feedback between insulin and glucose not only alters blood glucose levels, but also glucagon response, which may lead to further disruption of the system.

systems biology↗

bioRxiv2024

Learning from Machine Learning: advancing from static to dynamic facial function quantification

ObjectivesWe assess an open-source Python machine learning algorithms efficacy in image and video analysis of facial palsy (FP) patients. MethodsImages and videos of 60 patients with varying FP severities performing standard movements were obtained from MEEI Facial Palsy database. Landmarks generated on images by the open-source algorithm (adapted from OpenCV and Dlib libraries) and Emotrics (standard for two-dimensional FP analysis) were compared. Considering the human eye as the standard for accuracy, three raters marked perceived errors in each algorithms tracking of five facial features. Cumulative error distributions between both algorithms were compared via normalized root mean square error. FP severity and facial feature-specific error rates were compared using ANOVA tests. Raters also analyzed open-source algorithm-generated video landmarks; similar statistical comparisons between open-source algorithms image and video-based analyses were performed. ResultsCumulative error distribution between both algorithms image analyses was most robust for normal function; significant discrepancies were observed in mild/moderate flaccidity and nearly-normal/complete synkinesis. Both algorithms had similar error rates across all facial features (p=0.76) and FP severities (p=0.37). In the open-source algorithms video analysis, mild synkinesis (24.7%) and severe flaccidity (19.7%) had the highest error rates. Comparing image and video analyses generated by the open-source algorithm, video analyses had lower error rates across all FP severities (p<0.001). ConclusionsWe report for the first time the feasibility and relative accuracy of a Python open-source algorithm for dynamic facial landmark tracking in FP videos. The demonstrated superiority of landmark tracking with videos over images can improve objective FP quantification.

bioinformatics↗

bioRxiv2022

EDoF-Miniscope: pupil engineering for extended depth-of-field imaging in a fluorescence miniscope

Extended depth of field (EDoF) microscopy has emerged as a powerful solution to greatly increase the access into neuronal populations in table-top imaging platforms. Here, we present EDoF-Miniscope, which integrates an optimized thin and lightweight binary diffractive optical element (DOE) onto the gradient refractive index (GRIN) lens of a head-mounted fluorescence miniature microscope, i.e. "miniscope". We achieve an alignment accuracy of 70 m to allow a 2.8X depth-of-field extension between the twin foci. We optimize the phase profile across the whole back aperture through a genetic algorithm that considers the primary GRIN lens aberrations, optical property of the submersion media, and axial intensity loss from tissue scattering in a Fourier optics forward model. Compared to other computational miniscopes, our EDoF-Miniscope produces high-contrast signals that can be recovered by a simple algorithm and can successfully capture volumetrically distributed neuronal signals without significantly compromising the speed, signal-to-noise, signal-to-background, and maintain a comparable 0.9-m lateral spatial resolution and the size and weight of the miniature platform. We demonstrate the robustness of EDoF-Miniscope against scattering by characterizing its performance in 5-m and 10-m beads embedded in scattering phantoms. We demonstrate that EDoF-Miniscope facilitates deeper interrogations of neuronal populations in a 100-m thick mouse brain sample, as well as vessels in a mouse brain. Built from off-the-shelf components augmented by a customizable DOE, we expect that this low-cost EDoF-Miniscope may find utility in a wide range of neural recording applications.

bioengineering↗

bioRxiv2022

BMAL1 loss in oligodendroglial lineage cells dysregulates myelination and sleep

Myelination depends on maintenance of oligodendrocytes that arise from oligodendrocyte precursor cells (OPCs). We show that the dynamic nature of oligodendroglia and myelination are regulated by the circadian transcription factor BMAL1. Bmal1 knockdown in OPCs during development - but not adulthood - decreases OPC proliferation, whereas BMAL1 regulates OPC morphology throughout life. OPC-specific Bmal1 deficiency impairs remyelination in an age-dependent manner, suggesting that age-associated decrements in circadian regulation of oligodendroglia may contribute to the deficient remyelination potential in demyelinating diseases like multiple sclerosis (MS). This oligodendroglial dysregulation and dysmyelination increase sleep fragmentation in OPC-specific Bmal1 knockout mice, and sleep fragmentation is causally associated with MS. These findings have broad mechanistic and therapeutic implications for numerous brain disorders that include both myelin and sleep phenotypes. One-Sentence SummaryBMAL1 regulates the homeostatic maintenance of oligodendroglia and myelin, that subsequently controls sleep architecture.

neuroscience↗

bioRxiv2020

Universal features of epidemic models under social distancing guidelines

Social distancing as a form of nonpharmaceutical intervention has been enacted in many countries as a form of mitigating the spread of COVID-19. There has been a large interest in mathematical modeling to aid in the prediction of both the total infected population and virus-related deaths, as well as to aid government agencies in decision making. As the virus continues to spread, there are both economic and sociological incentives to minimize time spent with strict distancing mandates enforced, and/or to adopt periodically relaxed distancing protocols, which allow for scheduled economic activity. The main objective of this study is to reduce the disease burden in a population, here measured as the peak of the infected population, while simultaneously minimizing the length of time the population is socially distanced, utilizing both a single period of social distancing as well as periodic relaxation. We derive a linear relationship among the optimal start time and duration of a single interval of social distancing from an approximation of the classic epidemic SIR model. Furthermore, we see a sharp phase transition region in start times for a single pulse of distancing, where the peak of the infected population changes rapidly; notably, this transition occurs well before one would intuitively expect. By numerical investigation of more sophisticated epidemiological models designed specifically to describe the COVID-19 pandemic, we see that all share remarkably similar dynamic characteristics when contact rates are subject to periodic or one-shot changes, and hence lead us to conclude that these features are universal in epidemic models. On the other hand, the nonlinearity of epidemic models leads to non-monotone behavior of the peak of infected population under periodic relaxation of social distancing policies. This observation led us to hypothesize that an additional single interval social distancing at a proper time can significantly decrease the infected peak of periodic policies, and we verified this improvement numerically. While synchronous quarantine and social distancing mandates across populations effectively minimize the spread of an epidemic over the world, relaxation decisions should not be enacted at the same time for different populations.

systems biology↗

bioRxiv2018

Molecular Process Diagram: a precise, scalable and compact visualization of rule-based models

Rule-based modeling allows representation and simulation of biological systems where molecular features (such as protein domains) and feature-specific details of molecular interactions are accounted for. The rule-based description is precise and can define very fine molecular details (e.g. how phosphorylation status of a single residue in a multi-protein complex can affect affinity of another binding site of another protein within the same complex), but makes it difficult to effectively combine the assumptions scribed within the multiple rules of a model into a diagrammatic view. Various visualization schemas have been suggested, but they are all highly rule-based centric (e.g. a visual list of unconnected rules, extended contact maps, or atom-rule graphs). None of them match the clarity of traditional reaction/pathway diagrams, where a researcher can easily visually track the transitions and modifications of chemical species occurring in the biological systems being modeled. Here we present a novel approach and software for precise, scalable and compact representation of rule-based models that we call Molecular Process Diagram. It is based on the three basic elements: interacting molecular complexes, molecular sites directly modified by a rule, and molecular sites that are not modified but contribute to a rule mechanism (e.g. a site that in a phosphorylated state changes binding affinity of another site). Multiple levels of resolution are available: pathway-like diagram of interactions among molecules, optional site-specific interactions, and additional contingencies for interactions. Inclusion of molecular sites enables unambiguous reconstruction of the rule descriptions from the visual diagram without additional supporting documentation, while still keeping a pathway-like visual appearance. The proposed approach for visualization has been implemented in the Virtual Cell (VCell) modeling and simulation framework. Our Molecular Process Diagrams extend the notion of Systems Biology Graphical Notation (SBGN) process diagrams and use SBGN-compliant conventions.\n\nSummaryKinetic models have provided significant insights into biological regulatory mechanisms even though they typically did not take into consideration the details of protein subcomponents such as binding domains and phosphorylation sites. However, these details are often required for an accurate understanding of the events that occur during cell signaling. Without such detailed understanding, intervention strategies to act on signaling pathways in pathological conditions are bound to have limited success. This need to include site-specific details into models led to the advance of rule-based modeling. While rules describe the details of interactions with unmatched precision, they often obscure the \"big picture\", i.e. a pathway-like description of the information flow through the biological system. An intuitive visual diagram is crucial for understanding the assumptions embodied into a model. Here we present a novel approach and software for precise, scalable and compact representation of rule-based models that we call Molecular Process Diagram. It allows visualizing in a pathway-like diagram of the interacting molecules, the molecular sites modified, and the molecular sites that affect the interactions. The approach is implemented in the Virtual Cell (VCell) modeling and simulation framework and suggested as an extension for the Systems Biology Graphical Notations (SBGN) standard.

systems biology↗

bioRxiv2018

Mathematical Details on a Cancer Resistance Model

The primary factor limiting the success of chemotherapy in cancer treatment is the phenomenon of drug resistance. We have recently introduced a framework for quantifying the effects of induced and non-induced resistance to cancer chemotherapy [11, 10]. In this work, we expound on the details relating to an optimal control problem outlined in [10]. The control structure is precisely characterized as a concatenation of bang-bang and path-constrained arcs via the Pontryagin Maximum Principle and differential Lie algebra techniques. A structural identifiability analysis is also presented, demonstrating that patient-specific parameters may be measured and thus utilized in the design of optimal therapies prior to the commencement of therapy. For completeness, a detailed analysis of existence results is also included.

cancer biology↗