Biology subjects

Chung, R.

Publications and source records attributed to Chung, R..

16 recordsLinked to original sources

bioRxiv2026

A Chairside Multimodal Platform for Temporomandibular Joint Biomechanical Assessment: Technical Evaluation and Illustrative Application

BackgroundTemporomandibular joint (TMJ) biomechanics can be characterized by mandibular motion, masticatory muscle activity, and bite force generation. When acquired synchronously, these functional variables can serve as model-ready inputs for subject-specific computational analyses of internal joint mechanics. However, existing tools typically measure these signals using separate hardware and software platforms, limiting synchronized acquisition within a clinically practical chairside workflow. MethodsWe developed and technically evaluated a compact multimodal platform for chairside acquisition of TMJ functional data and demonstrated its analytical utility in an illustrative orthognathic surgery application. The platform integrates motion, bite force, muscle activity, acoustic, and event-timing measurements with software for real-time preview, protocol guidance, and synchronized export. We assessed technical performance and chairside feasibility and analyzed representative pre- and postoperative data from an orthognathic surgery patient using kinematic, force-control, and computational modeling workflows. FindingsMotion capture demonstrated submillimeter accuracy, with static and dynamic errors of approximately 0.04 mm and 0.12 mm. Bite force sensors showed excellent linearity (R{superscript 2} = 0.998). Chairside deployment required approximately 15 minutes each for setup and data collection. The illustrative case demonstrated that synchronized chairside data can support preoperative and postoperative kinematic analysis, bite force control capacity assessment, and estimation of TMJ disc stress. InterpretationThe proposed platform enables time-efficient chairside acquisition of synchronized, model-ready multimodal datasets for quantitative TMJ biomechanical assessment. This platform and workflow could support subject-specific biomechanical analysis and future clinical studies of temporomandibular joint function.

bioengineering↗

bioRxiv2026

Neutrophils in patients with chronic coronary syndrome exhibit delayed apoptosis and resistance to regulatory T cell-induced apoptosis - Brief Report

BackgroundPersistent inflammation is linked to poor outcomes in patients with chronic coronary syndrome (CCS). The inflammatory state in atherosclerotic disease has been associated with activation of neutrophils as well as with regulatory T cell (Treg) deficiency. The role of Tregs in the regulation of neutrophil survival has been postulated recently. Here, we investigated neutrophil apoptosis along with the potential impact of Tregs on neutrophil apoptosis in patients with CCS, compared to healthy controls. MethodsTwenty patients with CCS and 19 healthy controls were included. Neutrophil apoptosis was assessed after 5h culture with or without interleukin(IL-)10, TNF or LPS. Neutrophil phenotype was evaluated through flow cytometry analysis of surface receptors (CD66b and CXCR4) and ex vivo release of cytokines and granule proteins. The ability of Tregs to induce neutrophil apoptosis was examined in autologous neutrophil-Treg co-cultures. ResultsSpontaneous neutrophil apoptosis was significantly delayed in patients compared to controls (10.3% vs. 19.2%, p=0.025). Also, neutrophils from patients overexpressed CD66b and CXCR4 and were more prone to release proinflammatory mediators. Notably, Tregs induced neutrophil apoptosis in healthy subjects, but not in patients, indicating a loss of Treg-mediated regulation in the latter. There was no evidence that IL-10 had any influence on neutrophil apoptosis. However, cell-to-cell contact was found essential for Treg-induced neutrophil apoptosis. ConclusionsPatients with CCS display delayed neutrophil apoptosis and a pro-inflammatory neutrophil phenotype that is resistant to Treg-mediated apoptosis. Neutrophil dysfunction may contribute to persistent inflammation in patients with CCS, and as such constitute a novel therapeutic target.

immunology↗

bioRxiv2025

Spatiotemporal Microbial Ecoevolutionary Dynamics on the International Space Station

This study presents the most comprehensive spatiotemporal analysis of the ISS microbiome to date. Over a seven-year period, 184 surface samples were collected for live/dead metagenomic profiling, phenotypic characterization of antimicrobial resistance (AMR) and virulence among 102 cultured isolates, and metagenome-assembled genome (MAG) analysis to evaluate evolutionary selection pressures. Despite ecological stability, comparative genomics revealed ongoing microevolution through lateral gene transfer and selection for traits (e.g., radiation resistance and biocide tolerance). Importantly, predictions of AMR and virulence frequently misaligned with experimental outcomes, underscoring the need for functional validation. This dataset highlights a stable core microbiome that persists across years, punctuated by localized adaptation and gene flow. The ISS microbiome exemplifies both ecological resilience and microevolutionary innovation that can inform risk management for long-duration spaceflight.

microbiology↗

bioRxiv2025

An Nrf2/Yap1-like bZIPprotein drives UV-induced oxidative stress response in a Sporobolomyces yeast with evolutionary conservation

Research backgroundUltraviolet radiation represents a major environmental hazard that damages DNA and promotes oxidative stress in all living organisms. In animals, this stress is counteracted by the Nrf2 signalling pathway, while in yeasts such as Saccharomyces cerevisiae, the Yap1 transcription factor coordinates oxidative stress responses. The extent to which these mechanisms are conserved in UV-tolerant yeasts from the Basidiomycota lineage remains poorly understood. Experimental approachWe investigated the stress response of a UV-tolerant Sporobolomyces sp. isolates exposed to long-term ultraviolet-B irradiation. Yeast survival and antioxidant capacity were assessed, followed by proteome-wide quantification of protein expression using tandem mass tag labelling and multidimensional protein identification technology. Proteins were annotated for biological function through gene ontology and pathway analyses to identify stress-related factors and transcriptional regulators. Results and conclusionsThe LEV-2 Sporobolomyces yeast isolate displayed remarkable survival under conditions lethal to S. cerevisiae and showed induction of antioxidant activity during prolonged exposure. Proteomic profiling revealed large-scale adjustments in protein expression, including the upregulation of enzymes involved in antioxidant biosynthesis, enzymatic antioxidants, DNA repair proteins, heat shock proteins, and multiple signalling pathways. Importantly, several basic leucine zipper proteins with similarity to animal Nrf2 and yeast Yap1 were detected, with one factor showing a distinct induction in response to ultraviolet stress. These findings suggest that the oxidative stress response of Sporobolomyces is mediated by conserved regulatory modules comparable to those of both yeasts and animals, highlighting the evolutionary conservation of stress adaptation mechanisms. Novelty and scientific contributionThis work provides the first proteome-level description of the ultraviolet stress response in a Basidiomycota yeast. It demonstrates that bZIP transcription factors drive a complex antioxidant and repair programme in Sporobolomyces, functionally reminiscent of the animal Nrf2 pathway. The results support the view that fundamental oxidative stress responses arose early in eukaryotic evolution and have been preserved across distant fungal and animal lineages.

microbiology↗

bioRxiv2025

Comparison of the effects of lithium orotate and lithium carbonate on locomotion and memory in a Drosophila melanogaster model of Alzheimer's disease

AbstractAlzheimers disease (AD) is a terminal neurodegenerative disease characterized by cognitive decline and memory loss resulting from the buildup of amyloid-beta plaques and tau protein tangles. Lithium salts, particularly lithium carbonate (Li2CO3), inhibit the enzyme Glycogen Synthase Kinase-3, which is upregulated in AD, reducing tau and amyloid-beta accumulation, inflammation, and oxidative stress. However, Li2CO3 has declined in use due to long-term neurotoxicity. Lithium orotate (LiOr), an alternative lithium salt, has greater bioavailability-delivering more ions to the brain-but was discontinued in the 1970s due to toxicity concerns at equivalent doses. It was hypothesized that LiOr would be more effective than Li2CO3 at lower doses (5 mM) due to higher lithium ion transfer, while Li2CO3 may outperform LiOr at higher doses (10 mM) due to LiOrs toxicity. This study compares LiOr and Li2CO3 in treating locomotion and memory in a Drosophila melanogaster model produced by crossing UAS amyloid-beta 42 and Act5c GAL4 flies via the GAL4/UAS system. Once aged to 25 days, flies were assessed for locomotion using the negative geotaxis assay and for short-term memory using the aversive phototaxic suppression (APS) assay. These behavioral assays aim to quantify changes in cognitive and motor function resulting from treatment. Results suggest Li2CO3 improves cognitive and motor performance in healthy flies, but LiOr was not found to be significantly more effective. This study offered a novel comparison of two lithium compounds in an Alzheimers model and may guide research into safer, more effective AD and lithium treatments.

neuroscience↗

bioRxiv2025

Human TDP-43 overexpression in zebrafish motor neurons triggers MND-like phenotypes through gain-of-function mechanism

Dysregulation of the TAR DNA-binding protein 43 (TDP-43), including intraneuronal cytoplasmic mislocalisation and aggregation is a feature of multiple neurodegenerative diseases including amyotrophic lateral sclerosis (ALS), frontotemporal lobar dementia (FTLD), limbic-predominant age-related TDP-43 encephalopathy (LATE) and alzheimers disease (AD). Unravelling the causes and functional consequences of TDP-43 dysregulation is paramount to understanding disease mechanisms as well as identifying effective therapeutic targets. Here we present a comprehensive in vivo characterisation of three stable transgenic zebrafish models that express human TDP-43 variants in motor neurons. We demonstrate that overexpression of predominantly nuclear wildtype TDP-43, cytoplasm-targeted TDP-43, and an ALS-linked variant (G294V) each induce toxic gain-of-function effects, leading to impaired motor function, motor neuron loss, and muscle atrophy. Importantly, these models reveal distinct phenotypes, with the ALS-linked mutant exhibiting axonal transport deficits and neuromuscular junction disruption, while cytoplasmic mislocalised TDP-43 heightened susceptibility to oxidative stress. Two FDA-approved drugs used to treat ALS, edaravone and riluzole, were examined in these models and revealed that edaravone, but not riluzole, was effective in rescuing motor deficits associated with cytoplasmic TDP-43 expression and, to a lesser extent, mutant TDP-43G294V. Collectively, these findings reveal distinct pathological consequences of TDP-43 dysregulation, providing neuron-centric mechanistic insights, and establish the humanised TDP-43 zebrafish as an efficient system for preclinical therapeutic testing. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/663393v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@18c4a50org.highwire.dtl.DTLVardef@162df7forg.highwire.dtl.DTLVardef@3f2848org.highwire.dtl.DTLVardef@6d9439_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Design of Novel Dehalogenases using Protein Large Language Models

Per- and polyfluoroalkyl substances (PFAS) are toxic compounds linked to cancers, infertility, and vaccine resistance at concentrations above 1 part per trillion. Owing to strong carbon-fluorine bonds, they are persistent in the environment, taking centuries to millennia to degrade. As part of the 2024 iGEM competition, several high school students local to the Pasadena area leveraged recently-developed bioinformatic tools and large language models to discover and design novel reductive dehalogenases predicted to degrade perfluorooctanoic acid (PFOA), a long chain PFAS whose manufacture is prohibited, yet still persists in the environment and drinking water. The team identified 68 enzymes with structural similarity to Acidimicrobium sp. Strain A6 RdhA, the only specific known PFAS degrading enzyme in nature, expressed and refolded 5 of these enzymes in addition to 1 rational, and 3 large language model designs. These designs all have diverse sequences yet all are predicted to retain key substrate and cofactor binding pockets. These enzymes will be assayed on the ability to defluorinate PFOA.

bioengineering↗

bioRxiv2024

Fine-tuning sequence-to-expression models onpersonal genome and transcriptome data

Genomic sequence-to-expression deep learning models, which are trained to predict gene expression and other molecular phenotypes across the reference genome, have recently been shown to have poor out-of-the-box performance in predicting gene expression variation across individuals based on their personal genome sequences. Here we explore whether additional training (fine-tuning) on paired personal genome and transcriptome data improves the performance of such sequence-to-expression models. Using Enformer as a representative pretrained model, we explore various fine-tuning strategies. Our results show that fine-tuning improves cross-individual prediction performance over the baseline Enformer model for held-out individuals on genes seen during fine-tuning, with comparable performance to variant-based linear models commonly used in transcriptome-wide association studies. However, fine-tuning does not improve model generalizability on held-out genes, which contain sequences and variants unseen during fine-tuning, highlighting a remaining open challenge in the field.

genomics↗

bioRxiv2024

Current genomic deep learning models display decreased performance in cell type specific accessible regions

AbstractO_ST_ABSBackgroundC_ST_ABSA number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), which play critical roles in gene regulation, make up only a small fraction of the genome. Furthermore, cell type specific CREs contain a large proportion of complex disease heritability. ResultsWe evaluate genomic deep learning models in chromatin accessibility regions with varying degrees of cell type specificity. We assess two modeling directions in the field: general purpose models trained across thousands of outputs (cell types and epigenetic marks), and models tailored to specific tissues and tasks. We find that the accuracy of genomic deep learning models, including two state-of-the-art general purpose models - Enformer and Sei - varies across the genome and is reduced in cell type specific accessible regions. Using accessibility models trained on cell types from specific tissues, we find that increasing model capacity to learn cell type specific regulatory syntax - through single-task learning or high capacity multi-task models - can improve performance in cell type specific accessible regions. We also observe that improving reference sequence predictions does not consistently improve variant effect predictions, indicating that novel strategies are needed to improve performance on variants. ConclusionsOur results provide a new perspective on the performance of genomic deep learning models, showing that performance varies across the genome and is particularly reduced in cell type specific accessible regions. We also identify strategies to maximize performance in cell type specific accessible regions.

genomics↗

bioRxiv2024

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function

Kidney disease is highly heritable; however, the causal genetic variants, the cell types in which these variants function, and the molecular mechanisms underlying kidney disease remain largely unknown. To identify genetic loci affecting kidney function, we performed a GWAS using multiple kidney function biomarkers and identified 462 loci. To begin to investigate how these loci affect kidney function, we generated single-cell chromatin accessibility (scATAC-seq) maps of the human kidney and identified candidate cis-regulatory elements (cCREs) for kidney podocytes, tubule epithelial cells, and kidney endothelial, stromal, and immune cells. Kidney tubule epithelial cCREs explained 58% of kidney function SNP-heritability and kidney podocyte cCREs explained an additional 6.5% of SNP-heritability. In contrast, little kidney function heritability was explained by kidney endothelial, stromal, or immune cell-specific cCREs. Through functionally informed fine-mapping, we identified putative causal kidney function variants and their corresponding cCREs. Using kidney scATAC-seq data, we created a deep learning model (which we named ChromKid) to predict kidney cell type-specific chromatin accessibility from sequence. ChromKid and allele specific kidney scATAC-seq revealed that many fine-mapped kidney function variants locally change chromatin accessibility in tubule epithelial cells. Enhancer assays confirmed that fine-mapped kidney function variants alter tubule epithelial regulatory element function. To map the genes which these regulatory elements control, we used CRISPR interference (CRISPRi) to target these regulatory elements in tubule epithelial cells and assessed changes in gene expression. CRISPRi of enhancers harboring kidney function variants regulated NDRG1 and RBPMS expression. Thus, inherited differences in tubule epithelial NDRG1 and RBPMS expression may predispose to kidney disease in humans. We conclude that genetic variants affecting tubule epithelial regulatory element function account for most SNP-heritability of human kidney function. This work provides an experimental approach to identify the variants, regulatory elements, and genes involved in polygenic disease.

genetics↗

bioRxiv2024

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2023

Personal transcriptome variation is poorly explained by current genomic deep learning models

Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the reference genome, their ability to explain expression variation between individuals due to cis-regulatory genetic variants remains largely unexplored. Here we evaluate four state-of-the-art models on paired personal genome and transcriptome data and find limited performance when explaining variation in expression across individuals.

genomics↗

bioRxiv2022

The post-translational modification SUMO affects TDP-43 phase separation, compartmentalization, and aggregation in a zebrafish model

TDP-43 is a nuclear RNA-binding protein that can undergo liquid-liquid phase separation (LLPS) and forms pathological insoluble aggregates in frontotemporal dementia and amyotrophic lateral sclerosis (ALS). Perturbations of TDP-43 function are linked to mislocalization and neurodegeneration. By studying TDP-43 in vivo, we confirmed for the first time that TDP-43 undergoes LLPS and forms biomolecular condensates in spinal motor neurons (MNs). Importantly, we discovered that interfering with the K136 SUMOylation site of TDP-43 altered its phase separation behavior, reducing cytoplasmic mislocalization and aggregation. Introduction of the ALS-linked mutation G294V did not alter these LLPS characteristics, indicating that posttranslational modifications such as lysine-specific alterations can modulate TDP-43 pathogenesis through regulating phase separation. Altogether, our in vivo characterization of TDP-43 confirms the formation of dynamic nuclear TDP-43 condensates in zebrafish spinal neurons and establishes a critical platform to validate the molecular grammar of phase separation that underpins TDP-43 aggregation in ALS and other proteinopathies.

molecular biology↗

bioRxiv2021

Tissue-specific impacts of aging and genetics on gene expression patterns in humans

Age is the primary risk factor for many common human diseases including heart disease, Alzheimers dementias, cancers, and diabetes. Determining how and why tissues age differently is key to understanding the onset and progression of such pathologies. Here, we set out to quantify the relative contributions of genetics and aging to gene expression patterns from data collected across 27 tissues from 948 humans. We show that age impacts the predictive power of expression quantitative trait loci across several tissues. Jointly modelling the contributions of age and genetics to transcript level variation we find that the heritability (h2) of gene expression is largely consistent among tissues. In contrast, the average contribution of aging to gene expression variance varied by more than 20-fold among tissues with [Formula] in 5 tissues. We find that the coordinated decline of mitochondrial and translation factors is a widespread signature of aging across tissues. Finally, we show that while in general the force of purifying selection is stronger on genes expressed early in life compared to late in life as predicted by Medawars hypothesis, a handful of highly proliferative tissues exhibit the opposite pattern. These non-Medawarian tissues exhibit high rates of cancer and age-of-expression associated somatic mutations in cancer. In contrast, gene expression variation that is under genetic control is strongly enriched for genes under relaxed constraint. Together we present a novel framework for predicting gene expression phenotypes from genetics and age and provide insights into the tissue-specific relative contributions of genes and the environment to phenotypes of aging.

genetics↗

bioRxiv2018

Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depression

Inflammatory markers such as cytokines represent potential biomarkers for major depressive disorder (MDD). Many, generally small studies have examined the role of single markers and found significant associations. We assessed 42 inflammatory markers, namely cytokines, in the blood of 321 control subjects and 887 MDD cases. We tested whether individual inflammatory marker levels were significantly affected by MDD case/control status, current episode, or current depression severity, co-varying for age, sex, body mass index (BMI), smoking, current antidepressant use, ethnicity, assay batch and study effects. We further used machine learning algorithms to investigate if we could use our data to blindly discriminate MDD patients, or those in a current episode. We found broad and powerful influences of confounding factors on log-protein levels. Notably, IL-6 levels were very strongly influenced by BMI (p = 1.37 x 10-43, variance explained = 18%), while Interleukin-16 was the most significant predictor of current depressive episode (p = 0.003, variance explained = 0.9%, q < 0.1). No single inflammatory marker predicted MDD case/control status when a subject was not in a depressed episode, nor did any predict depression severity. Machine learning results revealed that using inflammatory marker data with clinical confounder information significantly increased precision for differentiating MDD patients who were in an episode. To conclude, a wide panel of inflammatory markers alongside clinical information may aid in predicting the onset of symptoms, but no single inflammatory protein is likely to represent a clinically useful biomarker for MDD diagnosis or prognosis. We note that the potential influence of physical health related and population stratification related confounders on inflammatory biomarker studies in psychiatry is considerable.

neuroscience↗

bioRxiv2017

Fine-mapping of genetic loci driving spontaneous clearance of hepatitis C virus infection

Approximately three quarters of acute HCV infections evolve to a chronic state, while one quarter are spontaneously cleared. Genetic predispositions strongly contribute to the development of chronicity. We have conducted a genome-wide association study to identify genomic variants underlying HCV spontaneous clearance using Immunochip in European and African ancestries. We confirmed two previously reported significant associations, in the IL28B/IFNL41,2 and MHC regions, with spontaneous clearance in the European population. We further fine-mapped the MHC association to a region of about 50 kilo base pairs, down from 1 mega base pairs in the previous study. Additional analyses suggested that the association in the MHC locus might be significantly stronger for virus subtype 1a than 1b, suggesting that viral subtype may have influenced the genetic mechanism underlying the clearance of HCV.

immunology↗