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Biology subjects

Chung, C. H.

Publications and source records attributed to Chung, C. H..

16 recordsLinked to original sources

Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and therapeutics remain limited. We present a generalizable framework combining machine-learning and genome-scale metabolic modeling to prioritize combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum (Fn) and other pathogenic, probiotic, and commensal microbes. Trained on 6,514 drug combinations in microbe-free CRC cell lines, the model predicted synergistic combinations in both microbe-free and microbe-associated contexts and generalized to immunotherapy-associated conditions. Predictions were validated using an asymmetric co-culture system that mimics the colons normoxic-anaerobic gradient, confirming synergistic combinations in HCT116 cells with Fn, including drugs not typically used in CRC therapy. Mechanistic analysis and targeted pharmacological perturbations revealed phospho-inositol metabolism and cysteine transport as key determinants of Fn-dependent drug synergy. Together, this work introduces a scalable, microbiome-aware framework to enable discovery of context-specific combination therapies.

bioinformatics↗

Collagen remodeling promotes a GPCR-mediated mechanosensory immune checkpoint in ADGRG1+ CD8+ T cells and serves as a spatial biomarker of response to immunotherapy

Accessibility of immune cells to the tumor microenvironment (TME) in many solid tumors can be influenced by extracellular matrix (ECM) deposition, organization, and remodeling within tumor stroma. Specifically, lysyl hydroxylase-2 (LH2)-catalyzed lysine hydroxylation in type-I collagen telopeptides leads to formation of intermolecular collagen cross-links creating a stiffened and proteolytically resistant, stable ECM. Advanced head and neck squamous cell carcinomas (HNSCC) frequently exhibit desmoplasia with elevated LH2 expression, and Immune-checkpoint (ICI) therapy is effective in only <20% patients, suggesting that ECM remodeling mechanistically governs the composition and function of TME infiltrates. We show that elevated LH2 and collagen alignment promotes stromal accumulation of CD8 T-cells, and poor response to ICI in HPV-HNSCCs. Integration of clinical biopsies, transcriptomic datasets, and an immunocompetent syngeneic mouse model revealed that aligned collagen spatially restricts adhesion G protein-coupled receptor positive (ADGRG1) CD8 T-cells to activate a non-canonical GPCR-mediated mechanosensory program that drives dysfunction and exhaustion. Statement of significanceDesmoplasia is common in solid tumors, and immune checkpoint inhibitors benefit only some patients. Current biomarkers like PD-L1 and TMB have limited value. Our findings reveal a previously unrecognized collagen-ADGRG1 mechanosensory immune checkpoint, offering a clinically tractable, spatially resolved biomarker to better stratify patients for immunotherapy.

cancer biology↗

C. elegans huntingtin, htt-1, promotes robust autophagy induction and survival under stress conditions

Huntingtin (HTT) is the gene responsible for Huntingtons disease (HD), a neurodegenerative disorder caused by a CAG trinucleotide repeat expansion mutation. While HD pathogenesis has traditionally been attributed to the toxic gain-of-function effects of mutant huntingtin (mHTT), increasing evidence underscores the critical role of wild-type HTT loss-of-function. Understanding the physiological roles of HTT is essential for elucidating HD mechanisms and developing effective therapeutic strategies. The C. elegans htt-1 gene, an ortholog of human HTT, remains largely uncharacterized. Here, we demonstrate that htt-1 promotes survival under stress conditions that requires autophagy as a defense mechanism. Specifically, we identify intestinal htt-1 as a key regulator of C. elegans survival during Pseudomonas aeruginosa PA14 infection. Our findings reveal that htt-1 functions downstream of the MPK-1/ERK pathway to induce systemic autophagy and enhance host defense during immune challenges. Moreover, expression of wild-type human HTT in htt-1 mutant worms rescues the survival defect during PA14 infection. Expression of mutant human HTT, on the other hand, exacerbates survival deficits, underscoring the conserved function of HTT across species. Additionally, we found that htt-1 affects survival and autophagy under heat shock stress conditions. These results establish htt-1 as a critical regulator of survival and autophagy in response to both pathogenic bacterial infection and thermal stress. Author summaryHuntingtin plays essential roles in selective autophagy across various organisms, including Drosophila, mice, and humans. Notably, the C. elegans huntingtin ortholog, htt-1, remains largely uncharacterized. Here, we describe a novel pro-survival role htt-1 in stress resistance. We show that htt-1 mutants exhibit significantly reduced survival during Pseudomonas aeruginosa infection. Expression of wild-type human HTT rescues the reduced survival of htt-1 mutants, whereas mutant HTT further exacerbates the phenotype. These findings establish C. elegans htt-1 as a valuable model for studying huntingtin biology and its roles in stress resistance. Downregulation of ERK in htt-1 mutants had no additional effect on survival, suggesting that htt-1 functions downstream of ERK. Interestingly, htt-1 mutants display reduced autophagy, indicating that htt-1 functions in the ERK-autophagy signaling cascade during infection. Mechanistically, we propose that htt-1 regulates autophagy at the protein level. HTT-1 remains cytoplasmic, and autophagy-related gene transcripts remain stable, without downregulation, in htt-1 mutants following infection. Additionally, htt-1 mutants exhibit reduced survival and impaired autophagy under heat stress. These results suggest that htt-1 may have a broader role in stress resilience extending beyond immune defense.

cell biology↗

Transfer learning predicts species-specific drug interactions in emerging pathogens

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross- species and species-specific drug interaction outcomes. Upon analyzing [~]600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram- negative (e.g., A. baumannii) and non-tuberculous mycobacteria (NTM) pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam against M. abscessus, an emerging pathogen with growing levels of antibiotic resistance. Lastly, we leveraged TACTIC to propose selectively synergistic drug combinations to treat bacterial eye infections (endophthalmitis).

systems biology↗

Neuroprotective role of Hippo signaling by microtubule stability control in C. elegans

The evolutionarily conserved Hippo (Hpo) pathway has been shown to impact early development and tumorigenesis by governing cell proliferation and apoptosis. However, its post-developmental roles are relatively unexplored. Here, we demonstrate its roles in post-mitotic cells by showing that defective Hpo signaling accelerates age-associated structural and functional decline of neurons in C. elegans. Loss of wts-1/LATS, the core kinase of the Hpo pathway, resulted in premature deformation of touch neurons and impaired touch responses in a yap-1/YAP-dependent manner, the downstream transcriptional co-activator of LATS. Decreased movement as well as microtubule destabilization by treatment with colchicine or disruption of microtubule stabilizing genes alleviated the neuronal deformation of wts-1 mutants. Colchicine exerted neuroprotective effects even during normal aging. In addition, the deficiency of a microtubule-severing enzyme spas-1 also led to precocious structural deformation. These results consistently suggest that hyper-stabilized microtubules in both wts-1-deficient neurons and normally aged neurons are detrimental to the maintenance of neuronal structural integrity. In summary, Hpo pathway governs the structural and functional maintenance of differentiated neurons by modulating microtubule stability, raising the possibility that the microtubule stability of fully developed neurons could be a promising target to delay neuronal aging. Our study provides potential therapeutic approaches to combat age- or disease-related neurodegeneration.

developmental biology↗

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of immune cell trafficking patterns and tumor-immune interactions is unlikely in human tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen formation evolve as a mechanism of immune escape, but the exact nature of the interaction between immune cells and collagen is poorly understood. Spatial data quantifying the degree of collagen fiber alignment in squamous cell carcinomas indicates that late stage disease is associated with highly aligned fibers. Here, we introduce a computational modeling framework (called Lenia) to discriminate between two hypotheses: immune cell migration that moves 1) parallel or 2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-ECM interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. We also illustrate the capabilities of Lenia to model the evolution of tumor progression and immune predation. Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining a kernel cell-cell interaction function that governs tumor growth dynamics under immune predation with immune cell migration. Mathematical modeling provides important mechanistic insights into cell interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interactions drives tumor growth and infiltration.

cancer biology↗

Unveiling targeted cell-free DNA methylation regions through paired methylome analysis of tumor and normal tissues

Liquid biopsy analysis of cell-free DNA (cfDNA) has revolutionized cancer research by enabling non-invasive assessment of tumor-derived genetic and epigenetic changes. In this study, we conducted a comprehensive paired-sample differential methylation analysis (psDMR) on reprocessed methylation data from two large datasets, CPTAC and TCGA, to identify and validate differentially methylated regions (DMRs) as potential cfDNA biomarkers for head and neck squamous cell carcinoma (HNSC). Our hypothesis is that the paired sample test provides a more suitable and powerful approach for the analysis of heterogeneous cancers like HNSC. The psDMR analysis revealed a significant number of overlapped hypermethylated DMRs between two datasets, indicating the reliability and relevance of these regions for cfDNA methylation biomarker discovery. We identified several candidate genes, including CALCA, ALX4, and HOXD9, which have been previously established as liquid biopsy methylation biomarkers in various cancer types. Furthermore, we demonstrated the efficacy of targeted region analysis using cfDNA methylation data from oral cavity squamous cell carcinoma and nasopharyngeal carcinoma patients, further validating the utility of psDMR analysis in prioritizing cfDNA methylation biomarkers. Overall, our study contributes to the development of cfDNA-based approaches for early cancer detection and monitoring, expanding our understanding of the epigenetic landscape of HNSC, and providing valuable insights for liquid biopsy biomarker discovery not only in HNSC and other cancer types.

genomics↗

Using Mathematical Modeling to Distinguish Intrinsic and Acquired Targeted Therapeutic Resistance in Head and Neck Cancer

The promise of precision medicine has been limited by the pervasive therapeutic resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies the mechanism and timing of cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

cancer biology↗

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

cancer biology↗

An interpretable flux-based machine learning model of drug interactions across metabolic space and time

AO_SCPLOWBSTRACTC_SCPLOWDrug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (AUC = 0.83 for synergy, AUC = 0.98 for antagonism) in predicting drug interactions for E. coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two sub-populations of E. coli cells defined by the levels of three proteins (eno, fadB and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use. SO_SCPLOWIGNIFICANCEC_SCPLOWWorldwide, 700,000 people die each year from drug-resistant infections. Drug combinations have great potential to reduce the spread of drug-resistant bacteria. However, their potency is impacted by both the pathogen growth environment and the heterogeneity in pathogen metabolism. The metabolic heterogeneity in a pathogen population allows them to survive antibiotic treatment. Here we present a flexible machine-learning framework that utilizes diverse data types to effectively search through the large design space of both sequential and simultaneous combination therapies across hundreds of simulated growth conditions and pathogen metabolic states. Our approach can serve as a useful guide for the selection of robustly synergistic drug combinations.

systems biology↗

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease

The rate of nonalcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically-driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis. Genome-wide histone acetylation is increased in steatotic livers of rodents fed high fructose or high fat diet. In vitro, steatosis relaxes chromatin and increases DNA damage marker {gamma}H2AX, which is reversed by inhibiting acetyl-CoA production. Steatosis-associated acetylation and {gamma}H2AX are enriched at gene clusters in telomere-proximal regions which contain HCC tumor suppressors in hepatocytes and human fatty livers. Regions of metabolically-driven epigenetic change also have increased levels of DNA mutation in non-cancerous tissue from NAFLD patients. Finally, genome-scale network modelling indicates that redox balance is a key contributor to this mechanism. Thus abnormal histone hyperacetylation is a potential initiating event in HCC carcinogenesis.

cancer biology↗

The deacylase SIRT5 supports melanoma viability by regulating chromatin dynamics

Cutaneous melanoma remains the most lethal skin cancer, and ranks third among all malignancies in terms of years of life lost. Despite the advent of immune checkpoint and targeted therapies, only roughly half of patients with advanced melanoma achieves a durable remission. SIRT5 is a member of the sirtuin family of protein deacylases that regulate metabolism and other biological processes. Germline Sirt5 deficiency is associated with mild phenotypes in mice. Here we show that SIRT5 is required for proliferation and survival across all cutaneous melanoma genotypes tested, as well as uveal melanoma, a genetically distinct melanoma subtype that arises in the eye and is incurable once metastatic. Likewise, SIRT5 is required for efficient tumor formation by melanoma xenografts and in an autochthonous mouse Braf;Pten-driven melanoma model. Via metabolite and transcriptomic analyses, we find that SIRT5 is required to maintain histone acetylation and methylation levels in melanoma cells, thereby promoting proper gene expression. SIRT5-dependent genes notably include MITF, a key lineage-specific survival oncogene in melanoma, and the c-MYC proto-oncogene. SIRT5 may represent a novel, druggable genotype-independent addiction in melanoma.

cancer biology↗

Estimation of immune cell content in tumor using single-cell RNA-seq reference data

BackgroundThe rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. However, the majority of previous and current studies related to translational and molecular oncology have only focused on the bulk tumor and there is a wealth of gene expression data accumulated with matched clinical outcomes.\n\nResultsIn this paper, we introduce a scheme for characterizing cell compositions from bulk tumor gene expression by integrating signatures learned from scRNA-seq data. We derived the reference expression matrix to each cell type based on cell subpopulations identified in head and neck cancer dataset. Our results suggest that scRNA-Req-derived reference matrix outperforms the existing gene panel and reference matrix with respect to distinguishing immune cell subtypes.\n\nConclusionsFindings and resources created from this study enable future and secondary analysis of tumor RNA mixtures in head and neck cancer for a more accurate cellular deconvolution, and can facilitate the profiling of the immune infiltration in other solid tumors due to the expression homogeneity observed in immune cells.

cancer biology↗

Meteorin-like (Metrnl) adipomyokine improves glucose tolerance in type 2 diabetes via AMPK pathway

Meteorin-like (metrnl) is a recently identified adipomyokine that has beneficial effects on glucose metabolism. However, its underlying mechanism of action is not completely understood. In this study, we have shown that a level of metrnl increase in vitro under electrical-pulse-stimulation (EPS) and in vivo in exercise mice, suggesting that metrnl is an exercise-induced myokine. In addition, metrnl increases glucose uptake through the calcium-dependent AMPK pathway. Metrnl also increases the phosphorylation of HDAC5, a transcriptional repressor of GLUT4, in an AMPK-dependent manner. Phosphorylated HDAC5 interacts with 14-3-3 proteins and sequesters them in the cytoplasm, resulting in the activation of GLUT4 transcription. The intraperitoneal injection of recombinant metrnl improves glucose tolerance in mice with high fat-induced obesity or type 2 diabetes (db/db), but this is not seen in AMPK {beta}1{beta}2 muscle-specific null mice (AMPK {beta}1{beta}2 MKO). In conclusion, we have demonstrated that metrnl induces beneficial effects on glucose metabolism via AMPK and is a promising therapeutic candidate for glucose-related diseases such as type 2 diabetes.

biochemistry↗

lncDIFF: a novel distribution-free method for differential expression analysis of long non-coding RNA

MotivationLong non-coding RNA expression data has been increasingly used in finding diagnostic and prognostic biomarkers in cancer studies. Existing differential analysis tools for RNA sequencing does not effectively accommodate low abundant genes, as commonly observed in lncRNA. We propose a novel and robust statistical method lncDIFF to detect differential expressed (DE) genes without assuming the true density on normalized counts.\n\nResultslncDIFF adopts the generalized linear model with zero-inflated exponential quasi likelihood to estimate group effect on normalized counts, and employs the likelihood ratio test to detect differential expressed genes. The proposed method and tool is suitable for data processed with standard RNA-Seq preprocessing and normalization pipelines. Simulation results illustrate that lncDIFF detects DE genes with more power and lower false discovery rate regardless of the data pattern. The analysis on a head and neck squamous cell carcinomas study also confirms that lncDIFF has better sensitivity in identifying novel lncRNA genes with relatively large fold change and prognostic value.\n\nAvailability and ImplementationlncDIFF is an R package available at https://github.com/qianli10000/lncDIFF.\n\nSupplementary InformationSupplementary Data are available at Bioinformatics online.

bioinformatics↗

Untangling The Gene-Epigenome Networks: Timing Of Epigenetic Regulation Of Gene Expression In Acquired Cetuximab Resistance

BACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.\n\nRESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.\n\nCONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

cancer biology↗