Biology subjects

Noh, J.

Publications and source records attributed to Noh, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Modulation of Oncogenic KRAS Signaling by Branched Actin-driven Cell Membrane Protrusions

For over three decades, we have known that oncogenic RAS alters the actin cytoskeleton organization and cell surface morphology1,2. RAS activates the GTPase RAC1, which triggers the growth of branched actin networks to promote cell membrane protrusions3,4. In melanoma, the hyperactive RAC1 mutant, Rac1P29S, was recently shown to drive extended lamellipodia, which then empower cell proliferation through sequestration and localized inhibition of the merlin tumor suppressor5. This discovery illustrates cell morphological programs not only as outputs but also as regulators of human oncogenic signals. Hence, we wondered whether the pronounced branched actin-driven membrane protrusions (BAMPs) downstream of oncogenic RAS are not mere outputs of RAS signaling but rather an active component in mediating the oncogenic penetrance of RAS mutants. We used volumetric light sheet microscopy and biochemical approaches to investigate the role of BAMPs in regulating the molecular signaling of oncogenic KRAS in pancreatic and lung cancer models. We found that elevated BAMP formation regulated the interaction of oncogenic KRAS with downstream effectors, specifically with the RAC1 GEF TIAM1. This implies that BAMPs amplify their own upstream regulators in a positive feedback. This meritorious cycle upregulates cyclin D1 expression by inactivating the merlin tumor suppressor, independently of the mitogen activated protein kinase pathway (MAPK). In the absence of BAMPs, cells carrying oncogenic KRAS mutations are unable to attain their full penetrance in proliferation. Overall, this work unveils the long-overlooked role of branched actin-driven cell morphology in the functionalization of KRAS mutants as potent oncogenes.

cancer biology↗

bioRxiv2026

The Nkx2.3-Nr5a1 gene cascade plays a crucial role in spleen-specific vascular architecture and marginal zone formation

NR5A1 is a nuclear receptor and master regulator of steroidogenic tissue development and steroid hormone biosynthesis in the adrenal glands and gonads. Although NR5A1 is expressed in splenic vascular endothelial cells, its function in the adult spleen remains unclear. Using single-cell transcriptomics in mice, we show that NR5A1-positive endothelial cells are heterogeneous and exhibit distinct phenotypes according to their localization in the splenic sinus or marginal sinus. To assess NR5A1 function, we generated a spleen-specific Nr5a1 enhancer deletion model. Enhancer loss resulted in reduced spleen size and ablation of the marginal zone and marginal sinus. In addition, 21-hydroxylase activity in splenic sinusoidal endothelial cells was reduced, particularly in females, leading to decreased levels of adrenocortical steroids in the spleen. Enhancer deletion also induced ectopic lymphatic-like and high endothelial venule-like vasculature similar to those observed in Nkx2.3-deficient mice. Consistently, mutation of the NKX2.3-binding motif within the enhancer abolished NR5A1 expression. Together, these findings identify an Nkx2.3[->]Nr5a1 regulatory axis essential for spleen-specific endothelial cell differentiation and splenic architecture required for blood filtration and marginal zone formation.

developmental biology↗

bioRxiv2026

Reconstructing Actin Dynamics of the Leading Edge from Observational Data

Mesenchymal cell migration starts with the protrusion of the cells leading edge, enabled by the branching growth of the actin network. Two crucial molecular players in protrusion are actin filaments and the Arp2/3 protein complex, the branching agent. Traditionally, protrusion models are intuited from several perturbative experiments. Recent multiplex microscopy imaging data promise to drastically change this paradigm by providing measurements of naturally fluctuating densities of key proteins at the leading edge of unperturbed cells. We report the first attempt to reconstruct the mechanistic protrusion model from such data. We analyze fluctuations of F-actin and Arp2/3 densities and cell edge velocity using phase space and regression analysis to reconstruct linearized stochastic actin-Arp2/3-velocity coupled dynamics. We then build a nonlinear partial differential equation model of these dynamics based on previous knowledge of this system and parsimony. The resulting model recovers the rates of reactions and mechanical and transport processes in the lamellipodium from a single non-perturbative experiment. The model posits that the only essential nonlinearities in the lamellipodial dynamics stem from the retrograde flow of F-actin. The model suggests that the protrusion is dominated by a damped oscillatory cycle resulting from a combination of positive feedback from Arp2/3 to protrusion velocity and negative feedback from F-actin to the velocity. Significance statementTraditionally, models of intracellular mechanochemistry are intuited from perturbative experiments. Multiplex microscopy imaging changes this by providing measurements of naturally fluctuating densities of key proteins in unperturbed cells. We use such data to reconstruct a mechanistic model of cell leading edge. We build equations for actin system and edge velocity using data, previous knowledge and parsimony. The resulting model recovers the rates of reactions and mechanical and transport processes at the leading edge. The model suggests that the protrusion is dominated by a damped oscillatory cycle resulting from a combination of positive feedback from Arp2/3 to protrusion velocity and negative feedback from F-actin to the velocity.

biophysics↗

bioRxiv2026

Cross-cohort analysis reveals conserved gut virome signatures and phage-encoded auxiliary functions in ulcerative colitis

While gut bacteriome dysbiosis is a well-established hallmark of ulcerative colitis (UC), the ecological and functional remodeling of the gut virome conserved across diverse populations remains unclear. Here, by constructing a cross-cohort atlas of the UC-associated fecal virome, we show that viral reorganization closely parallels bacterial dysbiosis, with enrichment of phages targeting disease-associated bacterial taxa. We identified a conserved set of UC-associated viral signatures that robustly distinguished UC from healthy controls using machine-learning-based classification across independent cohorts. Functional profiling further revealed that phages enriched in UC carried higher densities of auxiliary metabolic genes (AMGs) related to virulence and antibiotic resistance than phages depleted in UC, with immune evasion and glycopeptide resistance genes particularly overrepresented. Together, our cross-cohort approach highlights the value of the gut virome-based diagnostic framework for UC and suggests that phage-encoded AMGs may contribute to shaping the gut ecosystem under inflammatory conditions.

microbiology↗

bioRxiv2025

Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design

The precise de novo design of antibodies remains a therapeutic challenge. The AI platform, GaluxDesign, was evaluated in a high-efficiency Precision-Scale Workflow by synthesizing and testing only 50 full-length IgG candidates per epitope across eight distinct epitopes from six therapeutic targets. This campaign yielded a 10.5% binder rate (estimated EC50 < 100 nM), identifying target-specific binders for seven of eight epitopes, with multiple candidates exhibiting sub-nanomolar to single-digit nanomolar dissociation constants (Kd). We further assessed the same workflow on nine shared benchmark targets selected for external comparison, where GaluxDesign identified target-specific binders for eight of nine targets, demonstrating strong target-level performance relative to previously reported de novo antibody design approaches. Together, these results establish a high-efficiency, precision-scale workflow for generating novel, high-affinity therapeutic antibodies.

bioengineering↗

bioRxiv2025

TITANiAN: Robust Prediction of T-cell Epitope Immunogenicity using Adversarial Domain Adaptation Network

T-cell immunogenicity, the ability of peptide fragments to elicit T-cell responses, is a critical determinant of the safety and efficacy of protein therapeutics and vaccines. While deep learning shows promise for in silico prediction, the scarcity of comprehensive immunogenicity data is a major challenge. We present T-SCAPE, a novel multi-domain deep learning framework that leverages adversarial domain adaptation to integrate diverse immunologically relevant data sources, including MHC presentation, peptide-MHC binding affinity, TCR-pMHC interaction, source organism information, and T-cell activation. Validated through rigorous leakage-controlled benchmarks, T-SCAPE demonstrates exceptional performance in predicting T-cell activation for specific peptide-MHC pairs. Remarkably, it also accurately predicts the ADA-inducing potential of therapeutic antibodies without requiring MHC inputs. This success is attributed to T-SCAPEs biologically grounded and data-driven multi-domain pretraining. Its consistent and robust performance highlights its potential to advance the development of safer and more effective vaccines and protein therapeutics.

bioinformatics↗

bioRxiv2025

Precise, Specific, and Sensitive De Novo Antibody Design Across Multiple Cases

The precision design of antibodies, which naturally recognize diverse molecules through six variable loops, remains a critical challenge in therapeutic molecule discovery. In this study, we demonstrate that precise, sensitive, and specific antibody design can be achieved without prior antibody information across eight distinct target proteins. For each target, binders were identified from a yeast display scFv library of approximately 106 sequences, constructed by combining 102 designed light chain sequences with 104 designed heavy chain sequences. Binders with varying binding strengths were identified for all eight targets, including a case where no experimentally resolved target protein structure was available, demonstrating the highest level of precision compared to previous de novo antibody design reports. To further validate the designed antibodies, they were characterized in the IgG format for five distinct targets. The antibodies exhibited favorable developability properties, positioning them as promising hit or lead candidates. Notably, for one target in particular, the IgG-formatted antibodies exhibited affinity, activity, and developability, comparable to a commercial antibody, highlighting the sensitivity of the design method. Furthermore, binders capable of distinguishing closely related protein subtypes or mutants were identified, demonstrating that the method can achieve high molecular specificity. Cryo-EM analysis experimentally validated the designs accuracy by confirming that the key binding interactions were precisely as designed. These findings underscore the effectiveness of precision molecular design based on atomic-accuracy structure prediction. This study establishes computational antibody design as a viable approach for generating therapeutic molecules with tailored properties, with promising potential for achieving the efficacy and safety required for successful therapeutics.

bioengineering↗

bioRxiv2024

Combinatorial responsiveness of single chemosensory neurons to external stimulation of mouse explants revealed by DynamicNeuronTracker

Calcium fluorescence imaging enables us to investigate how individual neurons of live animals encode sensory input or drive specific behaviors. Extracting and interpreting large-scale neuronal activity from imaging data are crucial steps in harnessing this information. A significant challenge arises from uncorrectable tissue deformation, which disrupts the effectiveness of existing neuron segmentation methods. Here, we propose an open-source software, DynamicNeuronTracker (DyNT), which generates dynamic neuron masks for deforming and/or incompletely registered 3D calcium imaging data using patch-matching iterations. We demonstrate that DyNT accurately tracks densely populated neurons, whereas a widely used static segmentation method often produces erroneous masks. DyNT also includes automated statistical analyses for interpreting neuronal responses to multiple sequential stimuli. We applied DyNT to analyze the responses of pheromone-sensing neurons in mice to controlled stimulation. We found that four bile acids and four sulfated steroids activated 15 subpopulations of sensory neurons with distinct combinatorial response profiles, revealing a strong bias toward detecting sulfated estrogen and pregnanolone. TeaserDynamicNeuronTracker extracts neuronal activity from deforming 3D calcium imaging datasets using patch-matching iterations.

neuroscience↗

bioRxiv2024

Accurate antibody loop structure prediction enables zero-shot design of target-specific antibodies

Protein loops, characterized by their versatile structures with varying sizes and shapes, can recognize a wide range of targets with high specificity and affinity. The variable loops of the antibody complementarity-determining region (CDR) are particularly crucial for immune responses and therapeutic applications due to their effective target recognition capabilities. Accurate structure prediction of these antibody loops is essential for the efficient in silico design of target-binding antibodies for therapeutic or industrial use. However, predicting antibody loop structures is challenging due to the lack of evolutionary information from related proteins. Thus, a successful ab initio structure prediction method, which operates without structural templates or related sequences, is crucial for the effective design of antibody loop-mediated interactions. This study demonstrates that highly accurate antibody loop structure prediction enables the effective zero-shot design of target-binding antibody loops. The performance of loop design has been shown to depend on the accuracy of ab initio loop structure prediction, as tested with two versions of our design model. The high affinity, diversity, novelty, and specificity of the antibody loops designed with these new methods were validated experimentally on four target proteins.

bioengineering↗

bioRxiv2023

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

biophysics↗

bioRxiv2023

An ancestral vaccine induces anti-Omicron antibodies by hypermutation

The immune escape of Omicron variants significantly subsides by the third dose of an mRNA vaccine. However, it is unclear how Omicron variant-neutralizing antibodies develop under repeated vaccination. We analyzed blood samples from 41 BNT162b2 vaccinees following the course of three injections and analyzed their B-cell receptor (BCR) repertoires at six time points in total. The concomitant reactivity to both ancestral and Omicron receptor-binding domain (RBD) was achieved by a limited number of BCR clonotypes depending on the accumulation of somatic hypermutation (SHM) after the third dose. Our findings suggest that SHM accumulation in the BCR space to broaden its specificity for unseen antigens is a counter protective mechanism against virus variant immune escape.

bioengineering↗

bioRxiv2022

Existence of blood circulating immune-cell clusters (CICs) comprising antigen-presenting cells and B cells

Cell-to-cell physical interactions are involved in almost every physiological processes in multicellular organisms. Although the dynamics of these interactions could be highly diverse and complex in many circumstances, certain cell-to-cell interactions among immune cells have been well studied due importance in understanding disease pathogenesis and immune therapy development1. Dendritic cells (DCs) and B cells are directly involved in adaptive immune response against pathogens. Interaction mechanism between these two celltypes is well-known to occur in germinal centers either indirectly via helper T (Th) cells or directly via cell contact. However, there are animal in vitro and in vivo evidence that such direct DC-to-B cell contact can occur outside germinal centers like in peripheral blood or collagen matrix and display antiviral immune-related activity2,3. Here, we provide evidence that certain types of antigen presenting cells (APCs) can form robust cell clusters with B cells and circulate in blood. From healthy human blood immune single cell RNA-seq datasets, we detected APC subpopulations (0.34{+/-}0.19% of total PBMCs) that were also enriched with well-known naive B cell markers. We visually observed DC:B doublets and multiplets ([~]0.69% of total live PBMCs) in wildtype mouse blood using flow cytometry and microscopic imaging, thus proving the existence of circulating immune-cell clusters (CICs) composed of APCs and B cells. BCR repertoire of these healthy mouse CICs were similar to circulating B cells. Noticeably, frequency of these APC:B CICs were higher COVID-19 patients than healthy donors and their B cell subtype composition (e.g. naive, plasmablast, IgM+, IgG+) varied with disease severity.

immunology↗

bioRxiv2021

Analysis of B-cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Analyzing B cell receptor (BCR) repertoires is immensely useful in evaluating ones immunological status. Conventionally, repertoire analysis methods have focused on comprehensive assessments of clonal compositions, including V(D)J segment usage, nucleotide insertions/deletions, and amino acid distributions. Here, we introduce a novel computational approach that applies deep-learning-based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that this new approach enables us to not only accurately cluster BCR repertoires of coronavirus disease 2019 (COVID-19) patients and healthy subjects but also efficiently track minute changes in immune status over time as patients undergo treatment. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved a mean accuracy rate of over 87% given a repertoire of CDR3 sequences.

bioinformatics↗

bioRxiv2021

Inference of Granger-causal relations in molecular systems -- a case study of the functional hierarchy among actin regulators in lamellipodia

Many cell regulatory systems implicate nonlinearity and redundancy among components. The regulatory network governing lamellipodial and lamellar actin structures is prototypical of such a system, containing tens of actin-nucleating and -modulating molecules with functional overlap and feedback loops. Due to instantaneous and long-term compensation, phenotyping the system response to perturbation provides limited information on the roles the targeted component plays in the unperturbed system. Accordingly, how individual actin regulators contribute to lamellipodial dynamics remains ambiguous. Here, we present a perturbation-free reconstruction of cause-effect relations among actin regulators by applying Granger-causal inference to constitutive image fluctuations that indicate regulator recruitment as a proxy for activity. Our analysis identifies distinct zones of actin regulator activation and of causal effects on filament assembly and delineates actin-dependent and actin-independent regulator roles in controlling edge motion. We propose that edge motion is driven by assembly of two independently operating actin filament systems. A record of this papers Transparent Peer Review process is included in the Supplemental Information.

cell biology↗

bioRxiv2021

Cell cycle-independent integration of stress signals promotes Non-G1/G0 cellular quiescence

Cellular quiescence is a non-proliferative state required for cell survival under stress and during development. In most quiescent cells, proliferation is stopped in a reversible state of low Cdk1 kinase activity; in many organisms, however, quiescent states with high Cdk1 activity can also be established through still uncharacterized stress or developmental mechanisms. Here, we used a microfluidics approach coupled to phenotypic classification by machine learning to identify stress pathways associated with starvation-triggered high-Cdk1 quiescent states in Saccharomyces cerevisiae. We found that low- and high-Cdk1 quiescent states shared a core of stress-associated processes, such as autophagy, protein aggregation, and mitochondrial upregulation, but differed in the nuclear accumulation of the stress transcription factors Xbp1, Gln3, and Sfp1. The decision between low- or high-Cdk1 quiescence was controlled by cell cycle-independent accumulation of Xbp1, which acted as a time-delayed integrator of the duration of stress stimuli. Our results show how cell cycle-independent stress-activated factors promote cellular quiescence outside of G1/G0.

cell biology↗

bioRxiv2020

Stereotypic Neutralizing VH Clonotypes Against SARS-CoV-2 RBD in COVID-19 Patients and the Healthy Population

In six of seven severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients, VH clonotypes, encoded by either immunoglobin heavy variable (IGHV)3-53 or IGHV3-66 and immunoglobin heavy joining (IGHJ)6, were identified in IgG1, IgA1, and IgA2 subtypes, with minimal mutations, and could be paired with diverse light chains, resulting in binding to the SARS-CoV-2 receptor-binding domain (RBD). Because most human antibodies against the RBD neutralized the virus by inhibiting host cell entry, we selected one of these clonotypes and demonstrated that it could potently inhibit viral replication. Interestingly, these VH clonotypes pre-existed in six of 10 healthy individuals, predominantly as IgM isotypes, which could explain the expeditious and stereotypic development of these clonotypes among SARS-CoV-2 patients.One Sentence Summary Stereotypic-naïve SARS-CoV-2 neutralizing antibody clonotypes, encoded by IGHV3-53/IGHV3-66 and IGHJ6, were identified in most patients and pre-exist in the majority of the healthy population, predominantly as an IgM isotype.Competing Interest StatementThe authors have declared no competing interest.View Full Text

immunology↗

bioRxiv2020

DASC, a sensitive classifier for measuring discrete early stages in clathrin-mediated endocytosis

Clathrin-mediated endocytosis (CME) in mammalian cells is driven by resilient machinery that includes >70 endocytic accessory proteins (EAP). Accordingly, perturbation of individual EAPs often results in minor effects on biochemical measurements of CME, thus providing inconclusive/misleading information regarding EAP function. Live-cell imaging can detect earlier roles of EAPs preceding cargo internalization; however, this approach has been limited because unambiguously distinguishing abortive-clathrin coats (ACs) from bona fide clathrin-coated pits (CCPs) is required but unaccomplished. Here, we develop a thermodynamics-inspired method, "disassembly asymmetry score classification (DASC)", that unambiguously separates ACs from CCPs without an additional marker. After extensive verification, we use DASC-resolved ACs and CCPs to quantify CME progression in 11 EAP knockdown conditions. We show that DASC is a sensitive detector of phenotypic variation in CCP dynamics that is orthogonal to the variation in biochemical measurements of CME. Thus, DASC is an essential tool for uncovering the function of individual EAPs.

cell biology↗

bioRxiv2019

Stepwise autophosphorylation regulates biased agonism of the insulin receptor

Insulin is a key regulator of energy metabolism in peripheral tissues but also functions as a growth factor. Insulin binding to the insulin receptor (IR) leads to autophosphorylation of intracellular tyrosine residues, which simultaneously initiates a multitude of signals and functions. In contrast, some artificial (non-insulin) ligands for the IR result in biased agonism, selectively activating the PI3K/AKT pathway and metabolic effects without activating mitogen-activated protein kinase (MAPK) pathway and mitogenic effects. However, the precise mechanism of biased agonism at the receptor level remains unclear. The biased agonist IR-A48 aptamer selectively induces mono-phosphorylation of Tyr1150 residue (m-pY1150) in the kinase domain of IR. Hence, we hypothesized that IR autophosphorylation is a stepwise process in which formation of m-pY1150 represents an intermediate step. To explore this idea, we used hybrid receptors in which insulin can bind only one of the two binding sites of the dimeric receptor. Asymmetric insulin binding selectively induced symmetric m-pY1150 in both kinase domains. Moreover, the juxtamembrane domain, which interacts with the kinase domain, restricted the full activation of IR, and symmetric m-pY1150 played a crucial role in the rearrangement of intracellular domains to release this restriction. Our findings demonstrate that the symmetry of insulin binding to a dimeric receptor determines the stepwise autophosphorylation of IR. Furthermore, considering that the degree of ligand symmetry on a receptor depends mainly on ligand concentration, our results suggest that IR may play metabolic-biased roles in peripheral tissues dependent on local insulin concentrations.

cell biology↗