Biology subjects

Brady, R.

Publications and source records attributed to Brady, R..

12 recordsLinked to original sources

bioRxiv2026

Network Formation Dynamics in Thiol-ene Crosslinked Hyaluronic Acid Hydrogels: Design Principles for In Vitro Tissue Models

Hydrogels are widely used as three-dimensional cell culture systems to understand the impact of cellular mechanotransduction for tissue engineering applications. Photoinitiated thiol-ene click chemistry is a commonly utilized hydrogel crosslinking mechanism that provides spatial and temporal control over hydrogel network formation and resulting mesh size and compressive properties. Despite historically documented efficiency as step-growth reactions, these reactions do not always proceed as predicted. To understand the impact of cell confinement and microenvironmental mechanics on cellular function, thiol-ene network formation must be thoroughly characterized. To this end, the objective of this work was to investigate the crosslinking dynamics to determine hydrogel network formation as assessed via mesh size and mechanical properties using a pentenoate-functionalized hyaluronic acid thiol-ene reaction. Hydrogel parameters including polymer concentration and thiol:-ene crosslinker molar ratio were modulated (4, 6, or 8 polymer weight percent and 0.15:1, 0.5:1, or 1:1 molar ratio of thiol groups to reactive -ene groups) to tune network properties including shear storage modulus and relative mesh size. Molecular Dynamics (MD) simulations were used to simulate the thiol-ene crosslinking reaction and establish a method for predicting thiol-ene reaction efficiency. Lastly, the feasibility of this hydrogel system for in vitro modeling was confirmed via assessment of metabolic activity of encapsulated primary human meniscal cells.

bioengineering↗

bioRxiv2025

Design and fabrication of demountable 3D microphysiological systems for modeling barrier function and underlying tissue interactions

Several recent advances in microphysiological systems (MPSs) or organ-on-chip technology have demonstrated its potential for replacing traditional in vitro and animal models in the coming years. Despite the physiological relevance and cost-effectiveness of organ chips, there are several hurdles that must be overcome for widespread adoption for biological studies. Many shortcomings of manufacturing and scalability have been overcome by a transition from PDMS to thermoplastics. However, challenges have arisen in these sealed, brittle systems related to end-point tissue analyses, harvest, and high-resolution imaging, which is particularly difficult for multi-layer organ chips. Here, we present low-cost organ chips that are fluidically sealed but demountable, fabricated using a cut-and-assemble method without the need for cleanroom technologies. We have validated the capabilities of this method by demonstrating the culture of human aortic smooth muscle cells and induced pluripotent stem cell-derived neural cells, encapsulated in gelatin methacryloyl (GelMA) hydrogel on chip, for up to 27 days. The 3D culture layer of the organ chip was removed, and high-resolution images were obtained via immunostaining. Furthermore, these organ chips facilitate rapid redesign and manufacture for alternative tissue and/or interface systems. To our knowledge, this is the first innervated organ chip with multiple removable cell culture layers, as well as the first humanized nerve-artery model that includes a three-dimensional hydrogel culture. In future work, these unique features of our platform can be utilized for investigating the crosstalk mechanisms between different cell types in co-culture. Impact StatementWe present here a new method for fabricating low-cost demountable organ-on-a-chip platforms. This method leverages our recent cut & assemble method for layered 3D organ chips comprised of gas impermeable thermoplastics.

bioengineering↗

bioRxiv2024

Cerebellar-Prefrontal Connectivity Predicts Negative Symptom Severity Across the Psychosis Spectrum

BackgroundNegative symptom severity predicts functional outcome and quality life in people with psychosis. However, negative symptoms are poorly responsive to antipsychotic medication and existing literature has not converged on their neurobiological basis. Previous work in small schizophrenia samples has observed that lower cerebellar-prefrontal connectivity is associated with higher negative symptom severity and demonstrated in a separate neuromodulation experiment that increasing cerebellar-prefrontal connectivity reduced negative symptom severity. We sought to expand this finding to test associations between cerebellar-prefrontal connectivity with negative symptom severity and cognitive performance in a large, transdiagnostic sample of individuals with psychotic disorders. MethodsIn this study, 260 individuals with psychotic disorders underwent resting-state MRI and clinical characterization. Negative symptom severity was measured using the Positive and Negative Symptoms Scale, and cognitive performance was assessed with the Screen for Cognitive Impairment in Psychiatry. Using a previously identified cerebellar region as a seed, we performed seed to whole brain analyses and regressed connectivity against negative symptom severity, using age and sex as covariates. ResultsConsistent with prior work, we identified relationships between higher cerebellar-prefrontal connectivity and lower negative symptom severity (r=-0.17, p=.007). Higher cerebellar-prefrontal connectivity was also associated with better delayed verbal learning (r=.13, p=.034). ConclusionsOur results provide further evidence supporting the relationship between cerebellar-prefrontal connectivity and negative symptom severity and cognitive performance. Larger, randomized, sham-controlled neuromodulation studies should test if increasing cerebellar-prefrontal connectivity leads to reductions in negative symptoms in psychosis.

neuroscience↗

bioRxiv2023

Logistic tumor-population growth and ghost-points symmetry

The observed time evolution of a population is well approximated by a logistic function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential at a constant rate and capped at a limit size, i.e., the carrying capacity. In mathematical oncology, the carrying capacity has been postulated to be co-evolving and thus patient-specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic functions rotation symmetry can help estimate the populations growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against a classic oncology database of logistic tumor growth, achieving a 30% to 40% reduction in the time necessary to correctly estimate the logistic growth rate and carrying capacity. Our results will improve tumor dynamics forecasting and augment the clinical decision-making process.

cancer biology↗

bioRxiv2023

Evolution of promoter-proximal pausing enabled a new layer of transcription control

Promoter-proximal pausing of RNA polymerase II (Pol II) is a key regulatory step during transcription. Despite the central role of pausing in gene regulation, we do not understand the evolutionary processes that led to the emergence of Pol II pausing or its transition to a rate-limiting step actively controlled by transcription factors. Here we analyzed transcription in species across the tree of life. Unicellular eukaryotes display a slow acceleration of Pol II near transcription start sites that transitioned to a longer-lived, focused pause in metazoans. This event coincided with the evolution of new subunits in the NELF and 7SK complexes. Depletion of NELF in mammals shifted the promoter-proximal buildup of Pol II from the pause site into the early gene body and compromised transcriptional activation for a set of heat shock genes. Our work details the evolutionary history of Pol II pausing and sheds light on how new transcriptional regulatory mechanisms evolve.

evolutionary biology↗

bioRxiv2020

Innervated adrenomedullary microphysiological system to model prenatal nicotine and opioid exposure

The transition to extrauterine life results in a critical surge of catecholamines necessary for increased cardiovascular, respiratory, and metabolic activity. The mechanisms mediating adrenomedullary catecholamine release are poorly understood, given the sympathetic adrenomedullary control systems functional immaturity. Important mechanistic insight is provided by newborns delivered by cesarean section or subjected to prenatal nicotine or opioid exposure, demonstrating the impaired release of adrenomedullary catecholamines. To investigate mechanisms regulating adrenomedullary innervation, we developed compartmentalized 3D microphysiological systems (MPS) by exploiting the meniscus pinning effect via GelPins, capillary pressure barriers between cell-laden hydrogels. The MPS comprises discrete 3D cultures of adrenal chromaffin cells and preganglionic sympathetic neurons within a contiguous bioengineered microtissue. Using this model, we demonstrate that adrenal chromaffin innervation plays a critical role in hypoxia-medicated catecholamine release. Furthermore, opioids and nicotine were shown to affect adrenal chromaffin cell response to a reduced oxygen environment, but neurogenic control mechanisms remained intact. GelPin containing MPS represent an inexpensive and highly adaptable approach to study innervated organ systems and improve drug screening platforms by providing innervated microenvironments.

bioengineering↗

bioRxiv2019

Individual Variation in Brain Network Topology is Linked to Schizophrenia Symptomatology

BackgroundResting state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia negative symptom severity and network connectivity are actually due to individual differences in network spatial organization.\n\nMethods44 participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole brain functional connectivity correlates with negative symptom severity at the individual voxel level.\n\nResultsBrain connectivity to a region of the right dorso-lateral pre-frontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of two networks: the default mode network (DMN) and the task positive network (TPN). Both networks demonstrate strong (r[~]0.49) and significant (p<0.001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN.\n\nConclusionPreviously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (e.g. TMS).

neuroscience↗

bioRxiv2019

Prostate-Specific Antigen Dynamics Predict Individual Responses to Intermittent Androgen Deprivation

BackgroundIntermittent androgen deprivation therapy (IADT) is an attractive treatment approach for biochemically recurrent prostate cancer (PCa), whereby cycling treatment on and off can reduce cumulative dose, limit toxicities, and delay development of treatment resistance. To optimize treatment within the context of ongoing intratumoral evolution, underlying mechanisms of resistance and actionable biomarkers need to be identified.\n\nMethodsWe have developed a quantitative framework to simulate enrichment of prostate cancer stem cell (PCaSC) dynamics during treatment as a plausible mechanism of resistance evolution.\n\nResultsSimulated dynamics of PCaSC and non-stem cancer cells demonstrate that stem cell proliferation patterns correlate with longitudinal serum prostate-specific antigen (PSA) measurements in 70 PCa patients undergoing multiple cycles of IADT. By learning the dynamics from each treatment cycle, individual model simulations predict evolution of resistance in the subsequent IADT cycle with a sensitivity and specificity of 57% and 94%, respectively and an overall accuracy of 90%. Additionally, we evaluated the potential benefit of docetaxel for IADT in biochemically recurrent PCa. Model simulations based on response dynamics from the first IADT cycle identify patients who would or would not benefit from concurrent docetaxel in subsequent cycles.\n\nConclusionOur results demonstrate the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics continuously updated with each treatment cycle.\n\nTranslational RelevanceCompared to continuous androgen deprivation therapy, intermittent androgen deprivation (IADT) has been shown to reduce toxicity and delay time to progression in prostate cancer. While numerous mathematical models have been developed to study the response to both continuous and intermittent androgen deprivation, very few have identified actionable biomarkers of resistance and exploited them to predict how patients will or will not respond to subsequent treatment. Here, we identify prostate-specific antigen (PSA) dynamics as the first such biomarker. Mechanistic mathematical modeling of prostate cancer stem cell dynamics that dictate prostate-specific antigen serum levels predicts individual responses to IADT with 90% overall accuracy and can be used to develop patient-specific adaptive treatment protocols, and potentially identify patients that may benefit from concurrent chemotherapy. Model results demonstrate the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics continuously updated with each treatment cycle.

cancer biology↗

bioRxiv2019

Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt

Renal cell carcinoma (RCC) is one of the ten most common and lethal cancers in the United States. Tumor heterogeneity and development of resistance to treatment suggest that patient-specific evolutionary therapies may hold the key to better patients prognosis. Mathematical models are a powerful tool to help develop such strategies; however, they depend on reliable biomarker information. In this paper, we present a dynamic model of tumor-immune interactions, as well as the treatment effect on tumor cells and the tumor-immune environment. We hypothesize that the neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker that can be used to predict an individual patients response to treatment. Using randomly sampled virtual patients, we show that the model recapitulates patient outcomes from clinical trials in RCC. Finally, we use in silico patient data to recreate realistic tumor behaviors and simulate various treatment strategies to find optimal treatments for each virtual patient.

cancer biology↗

bioRxiv2018

Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses

PurposeRadiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of nonidentifiability and clinically unrealistic results.\n\nMaterials and MethodsWe develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients for predicting response to subsequent radiation doses.\n\nResultsModel analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index=0.89).\n\nConclusionThe PSI model may be suited to forecast treatment response for individual patients and offer actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.

cancer biology↗

bioRxiv2018

Diverse Pathophysiological Processes Converge on Network Disruption in Mania

BackgroundNeuroimaging of psychiatric disease is challenged by the difficulty of establishing the causal role of neuroimaging abnormalities. Lesions that cause mania present a unique opportunity to understand how brain network disruption may cause mania in both lesions and in bipolar disorder.\n\nMethodsA literature search revealed 23 case reports with imaged lesions that caused mania in patients without history of bipolar disorder. We traced these lesions and examined resting-state functional Magnetic Resonance Imaging (rsfMRI) connectivity to these lesions and control lesions to find networks that would be disrupted specifically by mania-causing lesions. The results were then used as regions-of-interest to examine rsfMRI connectivity in patients with bipolar disorder (n=16) who underwent imaging longitudinally across states of both mania and euthymia alongside a cohort of healthy participants scanned longitudinally. We then sought to replicate these results in independent cohorts of manic (n=26) and euthymic (n=21) participants with bipolar disorder.\n\nResultsMania-inducing lesions overlap significantly in network connectivity. Mania-causing lesions selectively disrupt networks that include orbitofrontal cortex, dorsolateral prefrontal cortex, and temporal lobes. In bipolar disorder, the manic state was reflected in strong, significant, and specific disruption in network communication between these regions and regions implicated in bipolar pathophysiology: the amygdala and ventro-lateral prefrontal cortex.\n\nLimitationsThe was heterogeneity in the clinical characterization of mania causing lesions.\n\nConclusionsLesions causing mania demonstrate shared and specific network disruptions. These disruptions are also observed in bipolar mania and suggest a convergence of multiple disorders on shared circuit dysfunction to cause mania.

neuroscience↗

bioRxiv2018

Individual Variation in Brain Network Topology and Social Cognitive Ability

BackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.\n\nObjectiveUsing resting-state fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.\n\nMethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxels connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).\n\nResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.\n\nConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

neuroscience↗