Biology subjects

Heo, L.

Publications and source records attributed to Heo, L..

14 recordsLinked to original sources

bioRxiv2026

Organoid modeling of tumor-associated macrophages reveals phagocytosis checkpoint blockade-induced conversion to an immunosuppressive SPP1+ phenotype

Tumor-associated macrophages (TAM) exert essential functions during the immune response to cancer. However, investigations of TAM within a native human tumor microenvironment (TME) have been impeded by a lack of appropriate model systems. Here, patient-derived organoids (PDO) from air-liquid interface (ALI)-grown tumor fragments, containing a human TME that encompassed stroma and immune subsets, robustly preserved TAM that were maintained by endogenous CSF-1 and appropriately responded to polarization signals. Antibody blockade of the CD47 regulatory checkpoint in organoids stimulated phagocytosis and remodeled TAM cytokine secretion profiles that were confirmed in anti-CD47 phase I trial patients. Amongst PDO histologies screened, anti-CD47 tumor killing was notable in clear cell renal cell carcinoma (ccRCC) which was associated with increased TAM infiltration. PDO contained diverse previously described TAM subsets; however, anti-CD47 reprogrammed organoid TAM toward an immunosuppressive SPP1+ phenotype, highlighting a negative feedback mechanism. Our findings uncover a resistance circuit engaged by macrophage checkpoint blockade and position ALI PDO as a robust translational platform for dissecting human macrophage biology and informing precision immunotherapy.

cancer biology↗

bioRxiv2025

LOSS OF ROR2 TYROSINE KINASE RECEPTOR IS ASSOCIATED WITH ENDOTHELIAL DYSFUNCTION IN PAH VIA INAPPROPRIATE INTEGRIN BETA 1 ACTIVATION

RationaleEndothelial dysfunction is a key feature of pulmonary arterial hypertension (PAH). We previously identified Wnt7a, a ligand of the Wnt planar cell polarity (PCP) pathway, as essential for pulmonary angiogenesis, with its loss linked to PAH. Given the importance of Wnt/PCP to lung endothelial function and angiogenesis, our goal is to elucidate how Wnt/PCP regulates angiogenic responses in pulmonary microvascular endothelial cells (PMVECs). ROR2, a tyrosine kinase receptor specific to Wnt/PCP, is crucial for cardiovascular development, but its role in PAH is unclear. We hypothesized that ROR2 supports endothelial homeostasis, and its loss would impair angiogenesis, contributing to PAH. MethodsEndothelial-specific ROR2 knockout (ROR2 ECKO) and wild-type (WT) mice were studied under normoxia and chronic hypoxia using echocardiography, hemodynamics, and lung morphometry. PMVECs from healthy and PAH lungs were transfected with ROR2 siRNA/constructs for functional and molecular studies. Focal adhesion (FA) activation and force generation were assessed via FRET-based methods. Bulk and single-cell transcriptomic analyses were performed on siROR2 PMVECs and ROR2 ECKO lungs. ResultsROR2 ECKO mice exhibited worsened pulmonary hypertension, right ventricular remodeling, microvascular loss, and muscularization in hypoxia. Single-cell RNA sequencing of lung endothelial cells showed dysregulation of pathways involved in barrier formation and angiogenesis. Evans blue dye extravasation confirmed reduced endothelial barrier integrity in ROR2 ECKO mice. ROR2-deficient PAH PMVECs displayed increased adhesion, permeability, and FA numbers, with reduced VE-cadherin at cell junctions. Confocal imaging revealed ROR2 localization in FAs, interacting with integrin {beta}1 (ITGB1). FRET analysis showed that ITGB1 remained in an active, adhesion-promoting state in ROR2-deficient cells. Restoring ROR2 in PAH PMVECs normalized adhesion, barrier function, and FA abundance. Transcriptomic analysis identified Rab12 as a key mediator of ROR2-ITGB1 crosstalk, with Rab12 knockdown mimicking ROR2 deficiency in PMVECs. ConclusionsROR2 regulates pulmonary angiogenesis by maintaining endothelial barrier integrity and facilitating integrin recycling. Restoring ROR2 signaling could be a potential therapeutic approach for PAH.

cell biology↗

bioRxiv2025

CES1 DEFICIENCY IS ASSOCIATED WITH METABOLIC REPROGRAMING AND ENDOTHELIAL DYSFUNCTION IN PULMONARY ARTERIAL HYPERTENSION

BackgroundPulmonary arterial hypertension (PAH) is a progressive disease characterized by pulmonary microvascular loss and obliterative remodeling, driven by metabolic reprogramming, oxidative stress, and endothelial dysfunction. While BMPR2 mutations contribute to metabolic shifts in pulmonary microvascular endothelial cells (PMVECs), their low penetrance suggests additional genetic modifiers play a role. A genetic screen of PAH PMVECs identified carboxylesterase 1 (CES1)--an endoplasmic reticulum (ER) enzyme involved in lipid metabolism and detoxification--as a candidate regulator of endothelial metabolism and angiogenesis. We hypothesize that CES1 loss promotes endothelial dysfunction via metabolic reprogramming, lipotoxicity, and oxidative stress. MethodsPAH and healthy PMVECs and lung tissues were obtained from transplant donors and commercial sources. CES1 expression was modulated in PMVECs using siRNA knockdown and plasmid overexpression. Mitochondrial and ER function were assessed via confocal microscopy and proteomics. CES1 heterozygous knockout (HET KO) and endothelial-specific knockout (ECKO) mice were exposed to normoxia or hypoxia, with lung tissues analyzed by single-cell RNA sequencing (scRNA-seq) and histopathology. ResultsCES1 expression was significantly reduced in PAH PMVECs and vascular lesions. CES1-deficient PMVECs exhibited increased apoptosis, reactive oxygen species (ROS) production, mitochondrial fragmentation, ER stress, and impaired angiogenesis. Confocal imaging and metabolic studies revealed lipid droplet accumulation, reduced fatty acid oxidation, and a glycolytic shift-- phenotypes reversed by CES1 restoration. Mechanistically, CES1 transcription was induced by BMPR2 via NRF2 activation, a key regulator of redox and metabolic homeostasis. In vivo, CES1-deficient mice developed severe pulmonary hypertension (PH) under hypoxia, with extensive vascular remodeling, right ventricular dysfunction, and dysregulated angiogenesis and lipid metabolism pathways, as confirmed by lung scRNA-seq. ConclusionsCES1 is essential for pulmonary endothelial homeostasis and serves as a critical modifier of BMPR2 signaling. Given the limited efficacy of current PAH therapies in reversing endothelial dysfunction and small-vessel loss, restoring CES1 expression represents a promising therapeutic strategy.

biochemistry↗

bioRxiv2025

Single Cell RNA Sequencing Analysis of Human Melanoma Reveals A Distinct Prognostic Myeloid Cell

Abstract/SummaryMyeloid-derived cells and tumor-associated macrophages (TAMs) represent an emerging field in melanoma research. Single-cell sequencing has enhanced our understanding of interactions within the tumor microenvironment (TME). Although there is no current consensus, several solid-tumor associated TAM subtypes have been identified by single-cell RNA sequencing (scRNA-seq). Due to the lack of agreement across models and tumor types, we aimed to characterize the myeloid sub-compartment in melanoma by analyzing compiled public scRNA-seq datasets. In this study, we identified six myeloid cell clusters, designated M_C1 through M_C6, with distinct gene expression profiles indicating differences in structure and function. The frequency of the M_C1 subtype was found to be predictive of overall survival (OS) in brain metastatic melanoma. This finding was externally validated by deconvolution of publicly available bulk RNA sequencing data from a clinical trial of advanced melanoma patients treated with nivolumab, as well as data from The Cancer Genome Atlas (TCGA) for melanoma stages I-IV. The M_C1 cell type appears to be a hybrid of dendritic cells and macrophages, demonstrating prognostic relevance across all melanoma stages and predictive potential for immunotherapy response. These data suggest that M_C1 may have utility as a prognostic biomarker in melanoma and as a target for future cell-based therapies.

immunology↗

bioRxiv2024

Patient-derived colon epithelial organoids reveal lipid-related metabolic dysfunction in pediatric ulcerative colitis

Background & AimsUlcerative colitis (UC) is associated with epithelial metabolic derangements which exacerbate gut inflammation. Patient-derived organoids recapitulate complexities of the parent tissue in health and disease; however, whether colon organoids (colonoids) model metabolic impairments in the pediatric UC epithelium is unclear. This study determined the functional metabolic differences in the colon epithelia using epithelial colonoids from pediatric patients. MethodsWe developed biopsy-derived colonoids from pediatric patients with endoscopically active UC, inactive UC, and those without endoscopic or histologic evidence of colon inflammation (non-IBD controls). We extensively interrogated metabolic dysregulation through extracellular flux analyses and tested potential therapies that recapitulate or ameliorate such metabolic dysfunction. ResultsEpithelial colonoids from active UC patients exhibit elevated oxygen consumption and proton leak supported by enhanced glycolytic capacity and dysregulated lipid metabolism. The hypermetabolic features in active UC colonoids were associated with increased cellular stress and chemokine secretion, specifically during differentiation. Transcriptomic and pathway analyses indicated a role for PPAR- in lipid-induced hypermetabolism in active UC colonoids, which was validated by PPAR- activation in non-IBD colonoids. Accordingly, limiting neutral lipid accumulation in active UC colonoids through pharmacological inhibition of PPAR- induced a metabolic shift towards glucose consumption, suppressed hypermetabolism and chemokine secretion, and improved cellular stress markers. Control and inactive UC colonoids had similar metabolic and transcriptomic profiles. ConclusionsOur pediatric colonoids revealed significant lipid-related metabolic dysregulation in the pediatric UC epithelium that may be alleviated by PPAR- inhibition. This study supports the advancement of colonoids as a preclinical human model for testing epithelial-directed therapies against such metabolic dysfunction. What You Need to KnowO_ST_ABSBackground and ContextC_ST_ABSColon mucosa healing in pediatric UC requires reinstating normal epithelial function but a lack of human preclinical models of the diseased epithelium hinders the development of epithelial-directed interventions. New FindingsUsing colon biopsy-derived epithelial organoids, samples from pediatric patients with active UC show hyperactive metabolic function largely driven by enhanced lipid metabolism. Pharmacologic inhibition of lipid metabolism alleviates metabolic dysfunction, cellular stress, and chemokine production. LimitationsThough our epithelial colon organoids from active UC patients show targetable metabolic and molecular features from non-IBD controls, they were cultured under sterile conditions, which may not fully capture any potential real-time contributions of the complex inflammatory milieu typically present in the disease. Clinical Research RelevanceCurrent therapies for pediatric UC mainly target the immune system despite the need for epithelial healing to sustain remission. We identified a pharmacologic target that regulates epithelial metabolism and can be developed for epithelial-directed therapy in UC. Basic Research RelevancePediatric UC patient tissue adult stem cell-derived colon epithelial organoids retain disease-associated metabolic pathology and can serve as preclinical human models of disease. Excess reliance on lipids as an energy source leads to oxidative and inflammatory dysfunction in pediatric UC colon organoids. Preprint: This manuscript is currently on bioRxiv. doi: https://doi.org/10.1101/2024.08.22.609271 Lay Summary: Using patient tissue-derived colon epithelial organoids, the investigators identified epithelial metabolic dysfunction and inflammation in pediatric ulcerative colitis that can be alleviated by PPAR-a inhibition.

physiology↗

bioRxiv2023

One particle per residue is sufficient to describe all-atom protein structures

Atomistic resolution is considered the standard for high-resolution biomolecular structures, but coarse-grained models are often necessary to reflect limited experimental resolution or to achieve feasibility in computational studies. It is generally assumed that reduced representations involve a loss of detail, accuracy, and transferability. This study explores the use of advanced machine-learning networks to learn from known structures of proteins how to reconstruct atomistic models from reduced representations to assess how much information is lost when the vast knowledge about protein structures is taken into account. The main finding is that highly accurate and stereochemically realistic all-atom structures can be recovered with minimal loss of information from just a single bead per amino acid residue, especially when placed at the side chain center of mass. High-accuracy reconstructions with better than 1 [A] heavy atom root-mean square deviations are still possible when only C coordinates are used as input. This suggests that lower-resolution representations are essentially sufficient to represent protein structures when combined with a machine-learning framework that encodes knowledge from known structures. Practical applications of this high-accuracy reconstruction scheme are illustrated for adding atomistic detail to low-resolution structures from experiment or coarse-grained models generated from computational modeling. Moreover, a rapid, deterministic all-atom reconstruction scheme allows the implementation of an efficient multi-scale framework. As a demonstration, the rapid refinement of accurate models against cryoEM densities is shown where sampling at the coarse-grained level is guided by map correlation functions applied at the atomistic level. With this approach, the accuracy of standard all-atom simulation based refinement schemes can be matched at a fraction of the computational cost. STATEMENT OF SIGNIFICANCEThe fundamental insight of this work is that atomistic detail of proteins can be recovered with minimal loss of information from highly reduced representations with just a single bead per amino acid residue. This is possible by encoding the existing knowledge about protein structures in a machine-learning model. This suggests that it is not strictly necessary to resolve structures in atomistic detail in experiments, computational modeling, or the generation of protein conformations via neural networks since atomistic details can inferred quickly via the neural network. This increases the relevance of experimental structures obtained at lower resolutions and broadens the impact of coarse-grained modeling.

biophysics↗

bioRxiv2022

Modeling concentration-dependent phase separation processes involving peptides and RNA via residue-based coarse-graining

Biomolecular condensation, especially liquid-liquid phase separation, is an important physical process with relevance for a number of different aspects of biological functions. Key questions of what drives such condensation, especially in terms of molecular composition, can be addressed via computer simulations, but the development of computationally efficient, yet physically realistic models has been challenging. Here, the coarse-grained model COCOMO is introduced that balances the polymer behavior of peptides and RNA chains with their propensity to phase separate as a function of composition and concentration. COCOMO is a residue-based model that combines bonded terms with short- and long-range terms, including a Debye-Huckel solvation term. The model is highly predictive of experimental data on phase-separating model systems. It is also computationally efficient and can reach the spatial and temporal scales on which biomolecular condensation is observed with moderate computational resources.

biophysics↗

bioRxiv2022

Characterizing Transient Protein-Protein Interactions by Trp-Cys Quenching and Computer Simulations

Transient protein-protein interactions occur frequently under the crowded conditions encountered in biological environments, yet they remain poorly understood. Here, tryptophan-cysteine quenching is introduced as an experimental approach that is ideally suited to characterize such interactions between proteins with minimal labeling due to its sensitivity to nano- to microsecond dynamics on sub-nanometer length scales. The experiments are paired with computational modeling at different resolutions including fully atomistic molecular dynamics simulations to provide interpretation of the experimental observables and add further insights at the molecular level. This approach is applied to model systems, villin variants and the drkN SH3 domain, in the presence of protein G crowders. It is demonstrated that Trp-Cys quenching experiments are able to not only distinguish between overall attractive and repulsive interactions between different proteins, but they can also discern variations in interaction preferences at different protein surface locations. The close integration between experiment and simulations also provides an opportunity to evaluate different molecular force fields for the simulation of concentrated protein solutions. Significance StatementBiological environments typically involve a variety of different proteins at very high concentrations where non-specific interactions are unavoidable. These interactions may go beyond simple crowding effects and involve transient contacts that may impact structure, dynamics, and ultimately function of proteins in vivo. While computer simulations have partially characterized such interactions, experimental data remain limited because established techniques are generally not well-suited to the characterization of dynamic processes on microsecond time and nanometer length scales. Tryptophan quenching by cysteine is introduced here as a new approach for studying transient protein encounters under concentrated conditions with the support of computational modeling. The study demonstrates that such experiments can resolve not just differences between different proteins but also residue-specific interaction preferences.

biophysics↗

bioRxiv2022

Direct Generation of Protein Conformational Ensembles via Machine Learning

Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle a generative adversarial network based on a transformer architecture with self-attention was trained on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent ensembles for any sequence demonstrating that transferability can be achieved beyond the limited training data. idpGAN was also retrained on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.

biophysics↗

bioRxiv2021

Multi-state Modeling of G-protein Coupled Receptors at Experimental Accuracy

The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased towards either the active or inactive conformation depending on the GPCR class. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 [A] and 1.41 [A] for the active and inactive state models, respectively. The models were more suitable for protein-ligand docking than the original AlphaFold2 models and template-based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR-peptide complex structures in GPCR Dock 2021, a blind GPCR-ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.

biophysics↗

bioRxiv2021

Inhibitory Proteins Block Substrate Access to the Active Site of Bacillus subtilis Intramembrane Metalloprotease SpoIVFB

Intramembrane proteases function in numerous signaling pathways that impact health, but knowledge about regulation of intramembrane proteolysis is limited. We examined inhibition of intramembrane metalloprotease SpoIVFB by proteins BofA and SpoIVFA. We found that BofA residues in and near a predicted transmembrane segment are required for SpoIVFB inhibition, and cross-linking experiments indicated that this transmembrane segment occupies the SpoIVFB active site cleft. SpoIVFA is also required for SpoIVFB inhibition. The inhibitory proteins block access of the substrate N-terminal Proregion to the membrane-embedded SpoIVFB active site, based on additional cross-linking experiments; however, the inhibitory proteins did not prevent interaction between the substrate C-terminal region and the SpoIVFB soluble domain. A structural model was built of SpoIVFB in complex with BofA and parts of SpoIVFA and substrate, using partial homology and constraints from cross-linking and co-evolutionary analyses. The model predicts that conserved BofA residues interact to stabilize a transmembrane segment and a membrane-embedded C-terminal region. SpoIVFA is predicted to bridge the BofA C-terminal region and SpoIVFB, forming a membrane-embedded inhibition complex. Our results reveal a novel mechanism of intramembrane protease inhibition with clear implications for relief from inhibition in vivo and design of inhibitors as potential therapeutics.

biochemistry↗

bioRxiv2020

Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement

Protein structures are crucial for understanding their biological activities. Since the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need to understand the biological behavior of the virus and provide a basis for developing effective therapies. Since the proteome of the virus was determined, some of the protein structures could be determined experimentally, and others were predicted via template-based modeling approaches. However, tertiary structures for several proteins are still not available from experiment nor they could be accurately predicted by template-based modeling because of lack of close homolog structures. Previous efforts to predict structures for these proteins include efforts by DeepMind and the Zhang group via machine learning-based structure prediction methods, i.e. AlphaFold and C-I-TASSER. However, the predicted models vary greatly and have not yet been subjected to refinement. Here, we are reporting new predictions from our in-house structure prediction pipeline. The pipeline takes advantage of inter-residue contact predictions from trRosetta, a machine learning-based method. The predicted models were further improved by applying molecular dynamics simulation-based refinement. We also took the AlphaFold models and refined them by applying the same refinement method. Models based on our structure prediction pipeline and the refined AlphaFold models were analyzed and compared with the C-I-TASSER models. All of our models are available at https://github.com/feiglab/sars-cov-2-proteins.

biophysics↗

bioRxiv2019

Direct therapeutic targeting of SWI/SNF induces epigenetic reprogramming and durable tumor regression in rhabdoid tumor

PurposeRhabdoid tumor is a pediatric cancer characterized by the biallelic inactivation of SMARCB1, a subunit of the SWI/SNF chromatin remodeling complex. SMARCB1 inactivation leads to SWI/SNF redistribution to favor a proliferative dedifferentiated cellular state. Although this deletion is the known oncogenic driver, SWI/SNF therapeutic targeting remains a challenge. Experimental DesignWe define a novel epigenetic mechanism for mithramycin using biochemical fractionation, chromatin immunoprecipitation sequencing (ChIP-seq), and a dual spike-in assay for transposase accessible chromatin sequencing (ATAC-seq). We correlate epigenetic reprogramming with changes with chromatin A/B compartments and promoter accessibility with chromHMM models and RNA-seq. Finally, we demonstrate durable, marked tumor response in an intramuscular rhabdoid tumor xenograft model. ResultsHere we show mithramycin and a second-generation analogue EC8042 evict mutated SWI/SNF from chromatin and are effective in rhabdoid tumor. SWI/SNF blockade triggers chromatin compartment remodeling and promoter reprogramming leading to differentiation and amplification of H3K27me3, the catalytic mark of PRC2. Treatment of rhabdoid rumor xenografts with EC8042 leads to marked, durable tumor regression and differentiation of the tumor tissue into benign mesenchymal tissue, including de novo bone formation. ConclusionOverall, this study identifies a novel therapeutic candidate for rhabdoid tumor and an approach that may be applicable to the 20% of cancers characterized by mutated SWI/SNF. STATEMENT OF TRANSLATIONAL RELEVANCEThere is a tremendous need for novel therapeutic approaches for rhabdoid tumor and the more than 20% of human cancers characterized by dysregulation of the SWI/SNF chromatin remodeling complex. While approaches to target associated complexes, such as PRC2, known to be influenced by dysregulated SWI/SNF are currently being evaluated in the clinic, the direct therapeutic targeting of SWI/SNF has not been explored. Here we identify an inhibitor of SWI/SNF and thoroughly explore the therapeutic development of this compound from a mechanistic and translational perspective thus providing insight into the targeting of this complex as well as a dose, schedule, and biomarker of target inhibition that is immediately clinically translatable.

cancer biology↗

bioRxiv2019

High-Accuracy Protein Structures By Combining Machine-Learning With Physics-Based Refinement

Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from co-evoluttionary analysis via machine learning have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences were templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structure, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

biophysics↗