Biology subjects

Ong, C.

Publications and source records attributed to Ong, C..

14 recordsLinked to original sources

bioRxiv2026

IFNγ-CXCL9-CXCR3 axis links T cell recruitment to skeletal muscle remodelling in human physical frailty

Introduction: Physical frailty is increasingly prevalent in our ageing society, and results in higher morbidity and healthcare costs. Yet, targeted therapeutics are lacking because underlying mechanisms are poorly defined. Human cohort studies have linked inflammation with frailty, but do not define the mechanisms by which the ageing immune system affects skeletal muscle and physical function. Defining this immune-muscle interaction is therefore crucial for identifying therapeutics for human physical frailty. Methods: We first studied two independent human ageing cohorts (discovery n=22; validation n=33). Physical frailty was quantified with age-adjusted timed-up-and-go (TUG) residuals. Peripheral blood was analysed for plasma OLINK proteomics, high-dimensional spectral immunophenotyping and in a subset, single-cell RNA-sequencing of PBMCs with paired TCR profiling. We then examined an independent human skeletal-muscle cohort (n=340) by bulk RNA-sequencing to derive our proposed immune-muscle axis. Finally, we interrogated individual steps of this axis in primary human in vitro models using IFN{gamma}-conditioned myotube-fibroblast co-cultures, PBMC transwell migration assays with CXCL9 neutralisation (anti-CXCL9) and receptor CXCR3 antagonism (AMG487). Macrophage co-cultured with CD8 were sequentially co-cultured with myotube/fibroblast co-culture, supernatant was then analysed for fibrosis mediators. We used fixed-effect meta-analysis, multivariable models and sequential mediation for analyses. Results: Physical frailty, by single-cell analyses, was associated with a compartmentalised immune-remodelling state involving inflammatory, tissue-remodelling and trafficking programmes in myeloid and T-cells. This finding was validated with plasma proteomics of the same human cohort, where physical frailty was significantly positively associated with an interferon-gamma (IFN{gamma})/CXCR3 (IL-18R1, IFN{gamma}, CXCL9) module, within which plasma CXCL9 was the strongest correlate. CD8-TEMRA and {gamma}{delta} T-cells were most responsive to CXCL9 in physical frailty. This expanded CD8-TEMRA compartment was polyclonal, consistent with bystander IFN{gamma} production rather than a shared antigen-specific response. In human muscle, CXCL9 was significantly positively associated with T-cell module, and through this, fibrosis. Conversely, fibrosis was associated with macrophages. Sequential mediation was consistent with a CXCL9-T-cell-macrophage-fibrosis pathway. Finally, in in-vitro human primary cell experiments, IFN{gamma}-conditioned myotube-fibroblast cultures produced CXCL9 and attracted CD8-TEMRA subsets. Anti-CXCL9 and CXCR3 antagonism significantly reduced this migration, indicating that recruitment of CD8-TEMRA cells was dependent on the CXCL9-CXCR3 axis. Incubating with activated CD8 cells shifted macrophages towards mixed CD163+CD86+ and pro-inflammatory CD163-CD86+ phenotypes. These CD8-co-cultured macrophages in myotube/fibroblast co-culture were associated with increased fibrosis markers transforming growth factor beta-1 and plasminogen activator inhibitor-1. Conclusion: We identified a CD8-TEMRA T-cell migration to human muscle dependent on IFN{gamma}-CXCL9-CXCR3 axis that is associated with human TUG-defined physical frailty which presents potential therapeutic avenues.

immunology↗

bioRxiv2026

Deep learning models detect neuromuscular disease and quantify functional impairment from video-derived biomechanics data

Measurements of human movement are critical for evaluating patients with neuromuscular diseases. However, current clinical tools are limited in sensitivity and can fail to detect the full spectrum of movement changes patients may exhibit. These limitations can obscure early signs of decline, delaying interventions that could preserve function and independence. OpenCap, a smartphone application that performs video-based markerless motion capture, can help address this gap by enabling scalable, out-of-lab measurements of human movement. Here, we present a method for both detecting disease and quantifying the severity of impaired movement from smartphone videos of patient motion. We used OpenCap to measure kinematics from 337 individuals with diagnoses spanning 12 rare, genetically-defined neuromuscular diseases and 78 age-matched healthy controls. Our dataset is the largest existing publicly available resource of body segment kinematics for this class of diseases. These data were collected in neuromuscular clinics during regular clinic visits as well as at conferences and community events across the United States. We trained deep learning models on these kinematic time series to differentiate between participants with neuromuscular disease and controls (AUROC = 0.97 [0.93-1.00]). We also developed a movement impairment severity score that correlates with both participant ratings of their own functional abilities ({rho} = -0.81, p < 0.001) and existing clinical tests of movement capabilities such as run speed ({rho} = -0.83, p < 0.001). Our methods for detecting disease and deriving quantitative biomarkers of movement impairment from smartphone videos have the potential to complement clinical assessments, inform screening protocols, and improve clinical trial endpoints.

bioengineering↗

bioRxiv2026

Airway Diameter-Matched Injury Improves Severity and Reproducibility of Experimental Rabbit Tracheal Stenosis

ObjectiveReliable animal models of tracheal stenosis are necessary for the development and translational testing of anti-fibrotic and regenerative therapies, but existing rabbit models frequently demonstrate substantial variability in stenosis severity, which limits their translational utility. The objective of our study was to determine whether airway diameter-matched mechanical injury improves the severity and reproducibility of experimental tracheal stenosis in a rabbit model, and to evaluate whether rabbit body weight is a reliable surrogate for tracheal luminal diameter during model creation. MethodsFourteen male New Zealand White rabbits (weight range, 2.7-3.5 kg) underwent tracheal injury using steel-bristle brushes introduced through a tracheotomy. Animals were assigned to receive either airway diameter-matched injury, in which brush size was selected to closely approximate the directly measured tracheal lumen diameter, or non-matched injury, in which brush size was selected without regard to measured lumen diameter. At postoperative day 21 (POD21), the injured tracheal segment and a native uninjured segment from the same animal were harvested and compared. Stenosis degree was quantified grossly, and lamina propria-to-cartilage (LP:C) ratio was quantified histologically by three blinded reviewers. The relationship between rabbit weight and airway diameter was assessed, and inter-rater reliability was calculated using the intraclass correlation coefficient (ICC). ResultsTwelve of fourteen rabbits reached the POD21 endpoint; two were euthanized early for severe airway compromise meeting humane endpoint criteria, both with approximately 80% stenosis. Injured tracheas demonstrated significantly greater stenosis than native controls (66.0 {+/-} 13.0% vs 16.0 {+/-} 2.7%; p = 0.00012), with a corresponding increase in LP:C ratio (p = 0.031). Airway diameter-matched injury produced significantly greater stenosis than non-matched injury (74.6 {+/-} 6.1% vs 50.6 {+/-} 4.0%; p = 0.001), while LP:C ratio did not differ between injury techniques (p = 1.0). Rabbit weight did not correlate with airway diameter (r = 0.176, p = 0.515; R2 = 0.031). Inter-rater reliability was excellent for both stenosis degree (ICC = 0.989) and LP:C ratio (ICC = 0.992). ConclusionsDirect measurement and matching of injury instrument diameter to native airway diameter substantially improves both the severity and the reproducibility of stenosis in a rabbit tracheal injury model, whereas body weight is an unreliable surrogate for airway size. This optimized, standardized protocol offers a reproducible platform for future translational studies of airway fibrosis and anti-fibrotic or regenerative therapies.

cell biology↗

bioRxiv2026

Simulation-guided design of exotendons to reduce the energetic cost of running

A passive device that attaches to the feet, called an exotendon, can reduce the energetic cost of running at moderate speeds, but its efficacy and optimal design parameters at higher speeds are unknown. Identifying optimal parameters at new speeds experimentally would require many experimental trials with different exotendon designs, which is challenging for participants at higher running speeds. We developed a muscle-driven simulation framework to predict the effect of various exotendon designs on the energetic cost of running at an experimentally untested speed (4 m/s). We used these predictions to select four designs, which we evaluated experimentally as users ran at this speed. The framework correctly predicted that an exotendon that reduced energetic cost at 2.7 m/s would also reduce energetic cost at 4 m/s (10% predicted vs. 5.7% measured) and that a short, stiff exotendon and a long, compliant exotendon would not significantly reduce energetic cost. However, exotendon parameters predicted by the simulation to maximize energetic savings did not significantly reduce energetic cost when evaluated experimentally. There was variability between participants in both the magnitude of maximum energy savings and the exotendon condition associated with those savings. In a 5-km time trial performed with and without the exotendon condition that elicited the largest energy savings for each participant during the experiment, we observed a lower average heart rate (-3.9 {+/-} 3.8 beats/min; P=0.03; mean {+/-} standard deviation) and increased cadence (15.9 {+/-} 9.6 steps/min; P=0.002) when participants ran with the exotendon but did not observe a statistically significant difference in finishing time (-13.5 {+/-} 24.6 sec; P=0.3). These results demonstrate exotendons can reduce energetic cost across multiple running speeds and that predictive simulations provide a framework for guiding experiments to evaluate assistive device designs. Author summaryDesigning assistive devices that help people move more efficiently usually requires many experimental trials. These studies can be time-consuming and physically demanding, especially when testing multiple device designs. In this study, we explored whether computer simulations could help guide the design of an assistive device for running called an exotendon. The exotendon is a simple elastic band that connects the feet and can help runners use less energy. Previous experiments showed that the device reduces the energy needed to run at moderate speeds, but it was unclear whether it would also work at faster speeds or which design would lead to energetic savings. We first used simulations of human running to test many possible exotendon designs at a faster speed. These simulations allowed us to identify promising designs before conducting experiments. We then tested a small number of these designs with runners. The experiments confirmed that the exotendon can reduce the energy required to run at faster speeds, although the efficacy of different designs varied between individuals. Our results show that computer simulations can help researchers rapidly evaluate a variety of assistive device ideas and focus experimental testing on the most promising designs.

bioengineering↗

bioRxiv2025

A New Paradigm of Transcriptional Regulation by the SufR-Like Iron-Sulfur Transcription Factors

SufR is an iron-sulfur ([4Fe-4S]) cluster-containing transcription factor belonging to an uncharacterized domain family (COG2345). It has been shown to negatively regulate the sulfur utilization factor (SUF) Fe-S biogenesis system in several Gram-negative and Gram-positive bacteria including Cyanobacteria, Mycobacteria and Streptomyces. The structural basis for its DNA recognition and transcriptional regulation by the SufR-like proteins remains enigmatic. In this study, we present the cryo-EM structure of Mycobacterium tuberculosis SufR bound to its promoter, revealing a new domain architecture. Our structural, biochemical and molecular analyses show that SufR possesses an unusual [4Fe-4S] cluster coordination environment in the sensory domain and recognizes its promoter via a dual-module mechanism using both the AT-hook and the helix-turn-helix (HTH) DNA-binding motif in the DNA-binding domain. This DNA recognition strategy differs from a canonical winged HTH transcription factor. Moreover, our bioinformatic analysis and structural modeling suggest that SufR and SufR-like proteins in the COG2345 family represent a large, previously uncharacterized family of transcription factors widely distributed across bacteria and archaea. Together, these findings establish SufR-like proteins as a new model for transcriptional regulation by Fe-S transcription factors in prokaryotes and uncover the underappreciated evolutionary versatility of AT-hooks.

biochemistry↗

bioRxiv2025

Development of a point-of-care field diagnostic test for DFT1 and DFT2

The Tasmanian devil (Sarcophilus harrisii) population has undergone a major decline in the wild due to the epidemics of two transmissible cancers known as devil facial tumours 1 (DFT1) and devil facial tumour 2 (DFT2). Understanding the distribution and prevalence of DFT1 and DFT2 is challenging as they can only be detected in field conditions when tumours are large enough to be visually observed. This impedes our ability to understand how the devil population is evolving in response to the tumours. PCR-based diagnostics are available, but the technique is not field-applicable. To overcome these hurdles, we developed an ultrasensitive and field-deployable rapid diagnostic tool. We used a technique called Specific High-Sensitivity Enzymatic Reporter Unlocking (SHERLOCK), which combines an isothermal nucleic acid amplification step with CRISPR-based recognition and activation of reporter probes to indicate target sequence detection. This system requires only portable laboratory equipment, making it more amenable to field conditions. The results showed that our test can detect DFT1 and DFT2 DNA within the attomolar range. We then confirmed that the test can detect DFT1 in the field using non-invasive swab samples. The SHERLOCK test can be used for more active monitoring and management approaches that are not currently possible.

molecular biology↗

bioRxiv2025

Longitudinal single-cell analysis reveals RUNX1T1 as an early driver in treatment-induced neuroendocrine transdifferentiation

Treatment-induced neuroendocrine prostate cancer (t-NEPC) is a lethal form of advanced prostate cancer that can emerge from adenocarcinoma under androgen receptor pathway inhibition, but the tumor-cell states and regulators underlying this transition remain incompletely defined. Here, we analyzed longitudinal single-cell RNA sequencing data from the LTL331/331R patient-derived xenograft model across seven disease stages, comprising 32,269 tumor-cell transcriptomes from treatment-naive adenocarcinoma, post-castration regression, and relapsed NEPC. We identified an AR-low/NE-low intermediate state that emerged after castration and before overt relapse, as well as two transcriptionally distinct terminal NEPC states marked by ASCL1high/FOXA2low and ASCL1low/FOXA2high programs. RUNX1T1 was induced in the intermediate state and remained elevated across both terminal states. RUNX1T1 enhanced NE-associated features and cell viability during enzalutamide treatment in adenocarcinoma cells, whereas its knockdown in NCI-H660 cells reduced NE-associated transcriptional programs, proliferation, and survival and shifted the transcriptome toward an intermediate-like state. Chromatin interactome analysis and co-immunoprecipitation linked RUNX1T1 to G9A/LASP1-containing chromatin repressor complexes. These findings define a temporal framework for treatment-induced NEPC progression and identify RUNX1T1 as an early-induced and sustained regulator of NE lineage transition and maintenance of established NEPC.

cancer biology↗

bioRxiv2025

OPTIMIZING AN EXOTENDON AT AN INCREASED RUNNING SPEED

The exotendon is a passive device that reduces the energetic cost of running at 2.7 m/s, but its potential benefits at higher speeds remain unknown. Experimental testing is challenging because of the wide range of conditions that must be tested. Here, we use muscle-driven simulations to overcome this challenge and inform exotendon design. We validated a simulation framework that estimates changes in energy expenditure, body kinematics, and muscle activations when simulated subjects run with and without an exotendon. Simulations of people running at 4 m/s with the exotendon that saved energy at 2.7 m/s predicted a 10% reduction in energy cost compared to natural running. We then performed simulations of 25 designs and found that many of the designs saved energy. A longer, stiffer exotendon yielded slightly greater energy savings (12%). Longer and more compliant exotendons offered little savings. We plan to test a limited set of our simulation predictions in an experiment to evaluate their accuracy and assess how an exotendon impacts running performance at 4 m/s. The purpose of this paper is to present the simulation results and to make predictions about the performance of the runner-exotendon system in experiments. This paper has been posted before the experiments have begun to avoid informing the predictions from the experimental results.

bioengineering↗

bioRxiv2024

Investigating cerebellar control in slow gait adaptations: Insights from predictive simulations of split-belt walking

BackgroundDuring split-belt treadmill walking, neurotypical humans exhibit adaptations characterized by a gradual decrease in step length asymmetry (SLA) toward or beyond symmetry, whereas individuals with cerebellar damage do not show these motor adaptations. Neuromusculoskeletal simulations may help to better understand individual aspects of the underlying neural control, but are currently incapable of predicting adaptations to the continuous perturbations imposed by split-belt walking. MethodsWe extend a spinal reflex model with a biologically inspired model of the cerebellum, which enables error-based motor adaptation by modulating spinal control parameters in response to mismatches between a continuously updated internal prediction and actual motor outcomes. In this work, the cerebellum modulates only a single spinal control parameter, the timing of swing initiation in each leg, which allows examining its isolated contribution to gait adaptation as all other reflex pathways are held constant. We created 80 s predictive simulations of the model walking on a split-belt treadmill with a 2:1 belt-speed ratio, and compared predicted spatiotemporal parameters and kinematics to the reflex-only model and literature. ResultsThe reflex-only model could walk on the split-belt treadmill, but showed no step length adaptations. In contrast, the extended model adapted SLA from an initial asymmetric value toward symmetry or beyond, following an exponential time course similar to that observed in experiments. The model could adapt at varying rates and converge to different asymmetry levels. We found that, in simulation, SLA adaptation during split-belt walking is possible without changes in reflex gains, by adapting the timing of swing initiation. The modulation of timing alone also predicted the experimentally observed exponential adaptation in the temporal domain, but only a linear change in the spatial domain, indicating that additional control mechanisms are likely required to reproduce the full spatial adaptation observed in split-belt walking. ConclusionWe propose a computational model of the cerebellum which, when integrated into a spinal reflex model, autonomously drives feedforward gait adaptations during split-belt walking. This advances the current state of predictive simulations and may eventually help to better understand specific adaptation processes. The modular framework can be extended to test different hypotheses about motor control and adaptation during continuous perturbation tasks.

neuroscience↗

bioRxiv2024

Personalizing a computational upper body model improves kinematic tracking in high range-of-motion arm movements

Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions. Existing models can be scaled to represent the size of an individual person, but the kinematics are generic. We introduce a method to personalize the shoulder kinematics of a computational model of the upper body that defines the orientations of the clavicle and scapula based on glenohumeral joint angles. During five static calibration poses, we palpate and measure the orientation of the scapula. We explore the importance of representing shoulder elevation by introducing clavicle elevation as a degree of freedom that is independent of the glenohumeral angles. For ten subjects, we record the five calibration poses, ten additional static poses, and dynamic arm raises using optical motion capture. We examine the data using a dynamically-constrained inverse kinematics analysis. Personalization, independent clavicle elevation, and both in combination reduce the average upper body marker tracking error compared to the generic model in the static poses (26 mm to 17-20 mm) and in the dynamic trials (22 mm to 14-17 mm). Only personalization reduces the average scapula marker error (51 mm to 36-38 mm) and scapula axis-angle error (15{degrees} to 10{degrees}) in the static poses, and in the dynamic trials at instances that best match the static poses (53 mm to 37-40 mm, 15{degrees} to 9{degrees}). Our results show that personalizing upper body models improves kinematic tracking. We provide our experimental data, model, and methods to allow researchers to reproduce and build upon our results.

bioengineering↗

bioRxiv2023

Heparan sulfate promotes TRAIL-induced tumor cell apoptosis

TRAIL (TNF-related apoptosis-inducing ligand) is a potent inducer of tumor cell apoptosis through TRAIL receptors. While it has been previously pursued as a potential anti-tumor therapy, the enthusiasm subsided due to unsuccessful clinical trials and the fact that many tumors are resistant to TRAIL. In this report we identified heparan sulfate (HS) as an important regulator of TRAIL-induced apoptosis. TRAIL binds HS with high affinity (KD = 73 nM) and HS induces TRAIL to form higher-order oligomers. The HS-binding site of TRAIL is located at the N-terminus of soluble TRAIL, which includes three basic residues. Binding to cell surface HS plays an essential role in promoting the apoptotic activity of TRAIL in both breast cancer and myeloma cells, and this promoting effect can be blocked by heparin, which is commonly administered to cancer patients. We also quantified HS content in several lines of myeloma cells and found that the cell line showing the most resistance to TRAIL has the least expression of HS, which suggests that HS expression in tumor cells could play a role in regulating sensitivity towards TRAIL. We also discovered that death receptor 5 (DR5), TRAIL and HS can form a ternary complex and that cell surface HS plays an active role in promoting TRAIL-induced cellular internalization of DR5. Combined, our study suggests that TRAIL-HS interactions could play multiple roles in regulating the apoptotic potency of TRAIL and might be an important point of consideration when designing future TRAIL-based anti-tumor therapy.

cell biology↗

bioRxiv2021

OpenSense: An open-source toolbox for Inertial-Measurement-Unit-based measurement of lower extremity kinematics over long durations

BackgroundThe ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift. MethodsWe computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subjects RMS differences over time. ResultsIMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3-6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60 to 0.87). We observed minimal drift in the RMS differences over ten minutes; the average slopes of the linear fits to these data were near zero (-0.14 to 0.17 deg/min). ConclusionsOur workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, obviating the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

bioengineering↗

bioRxiv2020

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This "Learn to Move" competition, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.

bioengineering↗

bioRxiv2017

Designing an intuitive web application for drug discovery scientists

Although a scientific web application that is intuitive can help scientists utilize data more easily and advance their research, there is little guidance on how to design such an application in the academic literature. We discuss how we designed an intuitive application for bench scientists working in drug discovery following an approach that can be applied to the design and development of scientific resources in a broad range of disciplines.

bioinformatics↗