Biology subjects

Oh, C.

Publications and source records attributed to Oh, C..

14 recordsLinked to original sources

bioRxiv2026

The human papillomavirus L2 protein in the capsid is an ensemble of related structures poised to exit the virus particle

The human papillomavirus (HPV) capsid contains 360 molecules of the L1 protein, which assembles into pentamers that form the capsid shell, and up to 72 molecules of the L2 protein. The C-terminus of the L2 protein must emerge from the capsid, insert into the endosome membrane, and protrude into the cytoplasm to bind to cellular proteins that mediate trafficking of the virus to the nucleus during virus entry. Computational analysis predicts that much of L2 is disordered, but the stoichiometry, structure and location of L2 in the capsid remain elusive. Here, we use cryo-EM single particle analysis of HPV16 pseudovirus capsids with and without the L2 protein combined with AlphaFold3 predictions and molecular dynamics simulations to develop a structural model of L2 within the capsid. Our results revealed that a single molecule of L2 associates with the inner surface of most L1 pentamers. The relatively rigid structure of the pentamer forces the long intrinsically disordered segments of L2 to adopt numerous related, compact, stable conformations in the capsid, whereas the structured elements of L2 remain folded but adopt varied positions in space largely beneath the pentamer. A finger-like projection of L2 extends into the central pore of the pentamer, but many different segments of the disordered domains of L2 can form fingers. In some models the C-terminus of L2 extends through the pore to the exterior surface of the capsid positioned to insert into the endosomal membrane. Consistent with these models, analysis of purified pseudoviruses showed that the C-terminus of multiple L2 molecules is exposed on the surface of each capsid. These findings provide a structural basis for understanding how L2 is organized in the capsid poised to initiate its action during virus entry.

microbiology↗

bioRxiv2026

Cargo-Adaptor Cooperation Programs Retromer Coat Architecture

Retromer drives endosomal cargo retrieval in combination with sorting nexin (SNX) adaptors, but how adaptor-cargo combinations specify coat architecture remains unclear. We identify sorting nexin 12 (SNX12) as the retromer adaptor required for human papillomavirus 16 (HPV16) infection and show that the viral L2 capsid protein tail directly engages SNX12-retromer complexes to trigger membrane tubulation. The crystal structure reveals a conserved cargo-recognition mode, whereas cryo-electron tomography of reconstituted assemblies shows retromer arches organized into two lattice configurations stabilized by membrane-proximal interfaces. These lattices assemble as multi-start helices and accommodate curvature through hinge-like motions between arches. These findings establish cargo and adaptor identity as co-determinants of retromer coat architecture, revealing retromer as a programmable system capable of generating route-specific transport carriers.

cell biology↗

bioRxiv2026

Toroidal Search Algorithm: A Topology-Inspired Metaheuristic with Applications to ODE Parameterization in Mathematical Oncology

We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA addresses this limitation by embedding the search domain into a toroidal geometry, thereby eliminating artificial boundaries and enabling continuous cyclic exploration. Beyond boundary handling, TSA uses winding numbers to capture the history of agent movement across periodic dimensions, which are exploited to adaptively refine local search. A modified sigmoid control function regulates the transition between global and local search. Performance of TSA is evaluated on a collection of unimodal and multimodal benchmark functions at various dimensions. It consistently outperforms established metaheuristics. Notably, TSA demonstrates exceptional robustness to increasing dimensionality, maintaining fast convergence and low variance where competing methods deteriorate. To assess real-world applicability, we apply TSA to an inverse problem from mathematical oncology. With both synthetic and clinical data, TSA reliably recovers physiologically plausible parameters with greater stability and predictive accuracy than competing algorithms. These results demonstrate that TSA is a powerful and robust tool for large-scale global optimization in computational modelling applications. Striking Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/709766v1_ufig1.gif" ALT="Figure 1"> View larger version (131K): org.highwire.dtl.DTLVardef@bb604aorg.highwire.dtl.DTLVardef@1ae9cb3org.highwire.dtl.DTLVardef@b1c126org.highwire.dtl.DTLVardef@1ffa938_HPS_FORMAT_FIGEXP M_FIG C_FIG Image generated with Google Gemini.

systems biology↗

bioRxiv2025

DINOcell: Learning Generalizable Perturbation Effects through Self-Distillation

AO_SCPLOWBSTRACTC_SCPLOWPredicting cellular responses to therapeutics is a promising approach for novel target discovery. However, state-of-the-art computational models designed to predict perturbation effects struggle to generalize and outperform simple baselines. We present DINOcell, a weakly supervised framework that adapts self-distillation to single-cell transcriptomics for predicting perturbation effects. We demonstrate that DINOcell outperforms baselines in predicting the effects of single gene perturbations. Furthermore, DINOcell accurately predicts non-additive effects of combination perturbations, indicating its capacity to model complex genetic interactions. Finally, we show that DINOcell learns representations that capture biological signals and is a promising, generalizable approach for in silico perturbation modeling, providing a valuable tool for accelerating therapeutic target discovery.

bioinformatics↗

bioRxiv2023

Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages

Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like (PRKCA, SOX6, RGS6, KCNQ3) and luminal A/B (FAM155A, LRP1B) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.

cancer biology↗

bioRxiv2023

Rescue of blood coagulation Factor VIII exon-16 mis-splicing by antisense oligonucleotides

The human Factor VIII (F8) protein is essential for the blood coagulation cascade and specific F8 mutations cause the rare bleeding disorder Hemophilia A (HA). Here, we investigated the impact of HA-causing single-nucleotide mutations on F8 pre-mRNA splicing. We found that 14/97 ([~]14.4%) coding sequence mutations tested in our study induced exon skipping. Splicing patterns of 4/11 ([~]36.4%) F8 exons tested were especially sensitive to the presence of common disease-causing mutations. RNA-chemical probing analyses revealed a three-way junction structure at the 3' end of intron 15 (TWJ-3-15). TWJ-3-15 sequesters the polypyrimidine tract, a key determinant of 3' splice site strength. Using exon-16 of the F8 gene as a model, we designed specific antisense oligonucleotides (ASOs) that target TWJ-3-15 and identified three that promote the splicing of F8 exon-16. Interaction of TWJ-3-15 with ASOs increases accessibility of the polypyrimidine tract and inhibits the binding of hnRNPA1-dependent splicing silencing factors. Moreover, ASOs targeting TWJ-3-15 rescue diverse splicing-sensitive HA-causing mutations, most of which are distal to the 3 splice site being impacted. The TWJ-3-15 structure and its effect on mRNA splicing provide a model for HA etiology in patients harboring specific F8 mutations and provide a framework for precision RNA-based HA therapies.

molecular biology↗

bioRxiv2023

Sequence independent activity of a predicted long disordered segment of the human papillomavirus L2 capsid protein during virus entry

The papillomavirus L2 capsid protein protrudes through the endosome membrane into the cytoplasm during virus entry to bind cellular factors required for intracellular virus trafficking. Cytoplasmic protrusion of HPV16 L2, virus trafficking, and infectivity are inhibited by large deletions in an [~]110 amino acid segment of L2 that is predicted to be disordered. The activity of these mutants can be restored by inserting protein segments with diverse compositions and chemical properties into this region, including scrambled sequences, a tandem array of a short sequence, and the intrinsically disordered region of a cellular protein. The infectivity of mutants with small in-frame insertions and deletions in this segment directly correlates with the size of the segment. These results indicate that the length of the disordered segment, not its sequence or its composition, determines its activity during virus entry. Sequence independent but length dependent activity has important implications for protein function and evolution.

biochemistry↗

bioRxiv2021

Inactivation mechanism and efficacy of grape seed extract for Human Norovirus surrogate

Proper disinfection of harvested food and water is critical to minimize infectious disease. Grape seed extract (GSE), a commonly used health supplement, is a mixture of plant-derived polyphenols. Polyphenols possess anti-microbial and -fungal properties, but anti-viral effects are not well-known. Here we show that GSE outperformed chemical disinfectants (e.g., free chlorine and peracetic acids) in inactivating Tulane virus, a human norovirus surrogate. GSE induced virus aggregation, an event that correlated with a decrease in virus titers. This aggregation and disinfection was not reversible. Molecular docking simulations indicate that polyphenols potentially formed hydrogen bonds and strong hydrophobic interactions with specific residues in viral capsid proteins. Together, these data suggest that polyphenols physically associate with viral capsid proteins to aggregate viruses as a means to inhibit virus entry into the host cell. Plant-based polyphenols like GSE are an attractive alternative to chemical disinfectants to remove infectious viruses from water or food. ImportanceHuman noroviruses are major food- and water-borne pathogens, causing approximately 20% of all cases of acute gastroenteritis cases in developing and developed countries. Proper sanitation or disinfection are critical strategies to minimize human norovirus-caused disease until a reliable vaccine is created. Grape seed extract (GSE) is a mixture of plant-derived polyphenols that is used as a health supplement. Polyphenols are known for antimicrobial, antifungal, and antibiofilm activities, but antiviral effects are not well-known. In studies here, plant-derived polyphenols outperformed chemical disinfectants (e.g., free chlorine and peracetic acids) in inactivating Tulane virus, a human norovirus surrogate. Based on data from additional molecular assays and molecular docking simulations, the current model is that the polyphenols in GSE bind to the Tulane virus capsid, an event that triggers virion aggregation. It is thought that this aggregation prevents Tulane virus from entering host cells.

biochemistry↗

bioRxiv2021

Predicting drug concentrations in PDMS microfluidic organ chips

Microfluidic organ-on-a-chip (Organ Chip) cell culture devices are often fabricated using polydimethylsiloxane (PDMS) because it is biocompatible, transparent, elastomeric, and oxygen permeable; however, hydrophobic small molecules can absorb to PDMS, which makes it challenging to predict drug responses. Here, we describe a combined simulation and experimental approach to predict the spatial and temporal concentration profile of a drug under continuous dosing in a PDMS Organ Chip containing two parallel channels separated by a porous membrane that is lined with cultured cells, without prior knowledge of its log P value. First, a three-dimensional finite element simulation of drug loss into the chip was developed that incorporates absorption, adsorption, convection, and diffusion, which predicts changes in drug levels over time and space as a function of potential PDMS diffusion coefficients and log P values. By then experimentally measuring the diffusivity of the compound in PDMS and determining its partition coefficient through mass spectrometric analysis of the drug concentration in the channel outflow, it is possible to estimate the effective log P range of the compound. The diffusion and partition coefficients were experimentally derived for the antimalarial drug and potential SARS-CoV-2 therapeutic, amodiaquine, and incorporated into the model to quantitatively predict the drug-specific concentration profile over time measured in human Lung Airway Chips lined with bronchial epithelium interfaced with pulmonary microvascular endothelium. The same strategy can be applied to any device geometry, surface treatment, or in vitro microfluidic model to predict the spatial and temporal gradient of a drug in 3D without prior knowledge of the partition coefficient or the rate of diffusion in PDMS. Thus, this approach may expand the use of PDMS Organ Chip devices for various forms of drug testing.

bioengineering↗

bioRxiv2021

Spatial drivers and pre-cancer populations collaborate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is a lethal disease with limited treatment options and poor survival. We studied 73 samples from 21 patients (7 treatment-naive and 14 treated with neoadjuvant regimens), analyzing distinct spatial units and performing bulk proteogenomics, single cell sequencing, and cellular imaging. Spatial drivers, including mutant KRAS, SMAD4, and GNAQ, were associated with differential phosphosignaling and metabolic responses compared to wild type. Single cell subtyping discovered 12 of 21 tumors with mixed basal and classical features. Trefoil factor family members were upregulated in classical populations, while the basal populations showed enhanced expression of mesenchymal genes, including VIM and IGTB1. Acinar-ductal metaplasia (ADM) populations, present in 95% of patients, with 46% reduction of driver mutation fractions compared to tumor populations, exhibited suppressive and oncogenic features linked to morphologic states. We identified coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin receptor expression in tumor cells. Higher expression of angiogenic and stress response genes in dendritic cells compared to tumor cells suggests they have a pro-tumorigenic role in remodeling the microenvironment. Treated samples contain a three-fold enrichment of inflammatory CAFs when compared to untreated samples, while other CAF subtypes remain similar. A subset of tumor and/or ADM-specific biomarkers showed differential expression between treatment groups, and several known drug targets displayed potential cross-cell type reactivities. This resolution that spatially defined single cell omics provides reveals the diversity of tumor and microenvironment populations in PDAC. Such understanding may lead to more optimal treatment regimens for patients with this devastating disease. HIGHLIGHTSO_LIAcinar-ductal metaplasia (ADM) cells represent a genetic and morphologic transition state between acinar and tumor cells. C_LIO_LIInflammatory cancer associated fibroblasts (iCAFs) are a major component of the PDAC TME and are significantly higher in treated samples C_LIO_LIReceptor-ligand analysis reveals tumor cell-TME interactions through NECTIN4-TIGIT C_LIO_LITumor and ADM cell proteogenomics differ between treated and untreated samples, with unique and shared potential drug targets C_LI

cancer biology↗

bioRxiv2020

Rapid SARS-CoV-2 Detection and Classification Using Phase Imaging with Computational Specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical pathlength sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A), HAdV (adenovirus), and ZIKV (Zika). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically. One Sentence SummaryThis work proposes a rapid (<1 min.), label-free testing method for SARS-CoV-2 detection, using quantitative phase imaging and deep learning.

bioengineering↗

bioRxiv2020

Converting Microwave Ovens into Plasma-Generating Decontamination Units for N-95 Respirators

We show how a common microwave oven, a coat-hanger and a coffee cup can be used to decontaminate N-95 respirators in 30 seconds. Tulane virus in the artificial saliva was reduced by >3 log and Geobacillus stearothermophilus spores were reduced by >6 log. Respirators maintained filtration and fit even after 10 cycles. Filtration and fit tests performed by the CDC confirmed there was no damage to the respirators. Spectroscopy of the plasma reveals that OH and C-containing radicals as the most prevalent active species.

bioengineering↗

bioRxiv2019

The Tangent copy-number inference pipeline for cancer genome analyses

MotivationSomatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas (TCGA), the Broad Institute Genome Characterization Center developed the Tangent copy-number inference pipeline to generate copy-number profiles using single-nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) data from over 10,000 pairs of tumors and matched normal samples. Here, we describe the Tangent pipeline, which begins with DNA sequencing data in the form of .bam files or raw SNP array probe-level intensity data, and ends with segmented copy-number calls to facilitate the identification of novel genes potentially targeted by SCNAs. We also describe a modification of Tangent, Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available.\n\nResultsTangent Normalization offers substantial signal-to-noise ratio (SNR) improvements compared to conventional normalization methods in both SNP array and WES analyses. The improvement in SNRs is achieved primarily through noise reduction with minimal effect on signal. Pseudo-Tangent also reduces noise when few normal samples are available. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data.\n\nAvailability and ImplementationTangent is available at https://github.com/coyin/tangent and as a Docker image (https://hub.docker.com/r/coyin/tangent). Tangent is also the normalization method for the Copy Number pipeline in Genome Analysis Toolkit 4 (GATK4).\n\nContactmatthew_meyerson@dfci.harvard.edu, rameen@broadinstitute.org, gadgetz@broadinstitute.org

bioinformatics↗

bioRxiv2017

Patient-derived xenografts undergo murine-specific tumor evolution

Patient-derived xenografts (PDXs) have become a prominent model for studying human cancer in vivo. The underlying assumption is that PDXs faithfully represent the genomic features of primary tumors, retaining their molecular characteristics throughout propagation. However, the genomic stability of PDXs during passaging has not yet been evaluated systematically. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We found that new CNAs accumulated quickly, such that within four passages an average of 12% of the genome was affected by newly acquired CNAs. Selection for preexisting minor clones was a major contributor to these changes, leading to both gains and losses of CNAs. The rate of CNA acquisition in PDX models was correlated with the extent of both aneuploidy and genetic heterogeneity observed in primary tumors of the same tissue. However, the specific CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients, suggesting that PDX tumors are subjected to distinct selection pressures compared to those that exist in human hosts. Specifically, several recurrent CNAs observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Finally, we found that the genomic stability of PDX models also affected their responses to chemotherapy and targeted drugs. Our findings thus highlight the need to couple the timing of PDX molecular characterization to that of drug testing experiments. These results suggest that while PDX models are powerful tools, they should be used with caution.

cancer biology↗