Biology subjects

Cho, S.

Publications and source records attributed to Cho, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Single-Cell Atlas of Transcription and Chromatin States Reveals Regulatory Programs in the Human Brain

Directly measuring chromatin states alongside transcription is essential for understanding how cell-type-specific regulatory programs are established and maintained in the adult human brain. We present a large-scale single-cell multimodal atlas generated by jointly profiling transcriptome with active (H3K27ac) and repressive (H3K27me3) histone modifications across 18 brain regions. We profile >750,000 nuclei spanning 160 cell types and integrate these data with chromatin accessibility, DNA methylation, 3D genome architecture, and spatial transcriptome. This framework annotates >500,000 regulatory elements and resolves cell-type-specific chromatin states. We link enhancers to target genes, infer gene regulatory networks, and classify chromatin interactions, revealing neuron-enriched long-range Polycomb repression of developmental genes. Integrating these maps with GWAS data and sequence-based model prioritizes noncoding variants, effector genes, and vulnerable cell types for neuropsychiatric disorders. Finally, cross-species comparisons show conserved activation but more divergent repression. Together, this study provides a functional reference for interpreting noncoding variants, epigenetic memory, and brain organization. HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes with active or repressive histone modification in >750,000 nuclei across adult human brain. C_LIO_LIChromatin state annotation of >500,000 candidate cis-regulatory elements distinguishes active enhancers from accessible and Polycomb-repressed regions. C_LIO_LICell-type-resolved regulatory networks and sequence-based deep learning model prioritize functional neuropsychiatric risk variants. C_LIO_LISpatial epigenomic imputation reveals laminar layer-specific Polycomb repression programs. C_LIO_LIIntegration with 3D genome architecture reveals neuron-specific super long-range chromatin loops silencing early developmental genes. C_LIO_LIEvolutionary analysis uncovers conserved active regulatory grammar but divergent repressive landscape. C_LI

genomics↗

bioRxiv2026

Development of a NanoBiT based high throughput screening assay for discovery of NOS1-CAPON interaction inhibitors

The interaction between neuronal nitric oxide synthase (NOS1) and its adaptor protein CAPON (NOS1AP) plays a critical role in various neurological processes and has been implicated in cardiovascular and neuropsychiatric disorders. Disruption of this protein-protein interaction represents a potential therapeutic strategy, yet identifying small molecule inhibitors has been challenging. Here, we present the development and validation of a NanoBiT-based luminescence complementation assay optimized for high-throughput screening (HTS) of NOS1-NOS1AP interaction inhibitors. We engineered NOS1 and NOS1AP fusion proteins with HiBiT and LgBiT complementary subunits, respectively, and established stable CHO-K1 cell lines for robust signal generation. The assay demonstrated excellent performance characteristics with a signal-to-background ratio exceeding 240-fold, and was validated using TAT-GESV, a known peptide inhibitor that showed time- and dose-dependent inhibition. We successfully screened a diverse library of 10,240 compounds and identified 19 validated hits with IC50 values ranging from 2.54 to greater than 30 M, with the majority exhibiting IC50 values below 30 M. The top three compounds exhibited potent inhibitory activity with IC50 values of less than 5 M. This NanoBiT-based assay provides a reliable and efficient platform for discovering novel NOS1-NOS1AP interaction inhibitors and can be adapted for other protein-protein interaction studies.

pharmacology and toxicology↗

bioRxiv2026

Small Molecule Agonists of TREM2 Reprogram Microglia and Protect Synapses in Human Alzheimer's Models

Triggering receptor expressed on myeloid cells-2 (TREM2) is a key immune receptor in the central nervous system that regulates microglial phagocytosis, survival, and neuroinflammatory responses. TRME2 variants have been established as genetic risk factors for Alzheimers disease (AD). However, the therapeutic development of TREM2 modulators has been limited to antibody-based approaches that face limitations in blood-brain barrier penetration and manufacturing scalability. Furthermore, there are no FDA approved TREM2 therapeutics available to date marking an unmet therapeutic gap. Herein, we report the identification of the first TREM2 small molecule submicromolar binders as a result of optimizing compound 4a to yield S9 with TREM2 binding affinity of 0.95 {micro}M. S9 demonstrated robust TREM2 agonism in cellular assays where it induced proximal Syk phosphorylation, activated downstream NFAT transcriptional signaling, enhanced APOE internalization and microglial phagocytic capacity. Pharmacokinetic profiling of the optimized hits revealed S9 to exhibit improved drug-likeness compared to 4a with 7-fold enhanced aqueous solubility, superior metabolic stability, reduced intrinsic clearance and a 9-fold improved hERG safety margin. Functional validation in human iPSC-derived microglia confirmed that S9 suppresses amyloid-beta (A{beta})-induced IL-1{beta} secretion through a TREM2-dependent mechanism. In human neuron-microglia co-culture models exposed to amyloid stress, S9 treatment preserved synaptic integrity as measured by PSD95 expression that indicates promising neuroprotective activity. Together, these findings establish S9 as a first-TREM2 submicromolar small molecule TREM2 agonist which is orally bioavailable with favorable pharmacokinetic properties and promising therapeutic potential for the treatment of Alzheimers disease.

pharmacology and toxicology↗

bioRxiv2026

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

BackgroundStage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guarantee prolonged disease-free survival. ObjectiveThe spatial, quantitative, and qualitative characteristics of various cell types within tumour tissues could be key to developing accurate prognostic AI models. DesignTissue microarrays created from primary tumour tissues collected during surgical resection from a cohort of 493 stage III colorectal cancer (CRC) patients were analysed for 61 protein markers at the single-cell level using multiplexed immunofluorescence imaging via the Cell DIVE platform. Subsequent cell-type classification enabled quantitative cell-type analyses, co-localisation neighbourhood assessments, and cell-type-specific protein signature discoveries that distinguish between early and late/non-recurring patient samples. ResultsThis study identifies a stem cell protein profile that drives tumour relapse. A deep neural network (DNN) model, based on a stem cell protein signature composed of BAX, MLKL, FLIP, GLUT1, and CDX2, provided accurate prognosis for stage III CRC patients in both discovery and validation cohorts and in an independent validation cohort. Nodal count-based metric further increased prognosis accuracy. Our study also revealed distinct spatial arrangements of immune, endothelial, and stem cells that were linked to early tumour recurrence. ConclusionOur findings propose a clinically promising prognostic tool based on a five-protein stem cell signature. These markers not only predict chemotherapy resistance in cancer stem cells but also suggest potential therapeutic strategies such as combinatorial treatments incorporating small molecule inhibitors targeting FLIP and GLUT1. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIMore than 20% of stage III colorectal cancer patients will experience early tumour recurrence within the first 3 years post treatment that includes surgery and adjuvant 5-FU based chemotherapy treatment. C_LIO_LISeveral studies pointed towards involvements of number of cell type specific spatial neighbourhoods in tumour progression where some immune tumour microenvironment promoting angiogenesis and intravasation events, some may provide immunosuppression. C_LIO_LICancer stem cells could be responsible for metastatic tumour spread, early recurrence and chemoresistance. C_LI What this study addsO_LISpatial single cell quantitative multiplex profiling of 45 cancer hallmark proteins and 15 cell identity markers in 493 stage III CRC patients tissue samples demonstrated significant differences in cellular proximity neighbourhoods, cell type specific abundance and expression between the early and late recurrence samples. C_LIO_LIWe discover that macrophages show spatial association with the blood vessels in early recurrence samples. Moreover, we observed conglomeration of B cells and macrophages with Tregulatory, Thelper and Tcytotoxic cells in association with early recurrences. C_LIO_LIWe showed that stromal abundance of Tregulatory, Thelper, Tcytotoxic cells and monocytes are significantly in late, and no recurrence samples compared to early recurrence samples. C_LIO_LIThe most differential expression profile that differentiates late and no recurrence samples from the early recurrence samples is related to the stem cell population. Particularly, we found overexpression of GLUT1, FLIP and downregulation of BAX, BAK, MLKL and CDX2 proteins in the cancer stem cell of early recurrence samples. C_LIO_LIWe built a neural network based on the cancer stem cell protein signature (BAX, MLKL, FLIP, GLUT1 and CDX2 proteins) that delivers a high-performance prognostic classifier. C_LI How this study might affect research, practice or policyO_LIOur results propose a clinically promising prognostic tool based on a five-protein stem cell signature that outperforms existing clinical and proposed transcriptomic based signatures for separation between risk groups. C_LIO_LIMoreover, our five-protein signature markers not only predict stem cell chemotherapy resistance and therefore tumour recurrence but also suggest potential therapeutic strategies. For instance, this approach could guide combinatorial treatments at high risk of chemoresistance, such as incorporating small molecule inhibitors targeting FLIP (currently in discovery phase) and GLUT1 (already under preclinical trial evaluation). C_LI

cancer biology↗

bioRxiv2025

Capturing Rapid Behavioral Adaptation in an Updated Theory of the Hippocampal Cognitive Map and its Neural Implementation

Animals sometimes adapt their behavior after a single exposure to novel information. Here, we integrate abstract theory with recent mechanistic insights to understand this and other examples of rapid adaptation. We ground our investigation in studies of animal learning in spatial contexts, where it is widely held that learning depends on the formation of a cognitive map reflected in learned representations in hippocampal areas CA1 and CA3. We extend the successor representation (SR) theory of the cognitive map, incorporating the weighting of this representation by perceived salience (PS) signals, and using it to represent future availability of environmental features that allow behavior to be guided flexibly by an animals goals and needs at later times. To capture rapid adaptation, we rely on behavioral time-scale synaptic plasticity (BTSP), a form of plasticity that captures one-shot formation of new place fields. Through simulations, we show that our hippocampal model network can learn the PS-weighted SR rapidly and set up the ability to replay experiences in the environment after a single exposure through BTSP. Spontaneous replay activity during subsequent offline periods strengthens the PS-weighted SR. Predictions of environmental features also develop rapidly, and behavioral simulations show how these learned representations give rise to need-dependent choice, rapid behavioral adaptation to changes in the availability of reward, and one-trial shock avoidance learning.

neuroscience↗

bioRxiv2025

HTS-Oracle: Experimentally validated AI-enabled prioritization for generalizable small molecule hit discovery

High-throughput screening (HTS) remains a central pillar of small molecule discovery yet routinely fails for immune receptors and protein-protein interaction-driven targets. Here, we introduce HTS-Oracle, an experimentally validated AI system for prospective hit discovery that integrates molecular language modeling with cheminformatics to prioritize bioactive compounds at scale. We deploy HTS-Oracle across three clinically validated yet historically intractable immune targets, TREM2, CHI3L1, and CD28, representing cryptic binding pockets, intrinsically disordered proteins, and protein-protein interaction-driven immune checkpoint, respectively. Across the tested targets, HTS-Oracle reduces experimental screening requirements by up to >99% while increasing hit rates by up to 176-fold relative to traditional HTS. Notably, the platform remains predictive under extreme data sparsity, achieving an eightfold improvement for CD28 despite fewer than 2% actives in training. By consistently enriching for experimentally validated hits, HTS-Oracle establishes a new performance benchmark for hit discovery and unlocks small molecule access to immune targets long regarded as chemically inaccessible.

bioinformatics↗

bioRxiv2025

Distributed activity in the human posterior putamen distinguishes goal-directed from habitual control in humans

How do some individuals rapidly form habits while others maintain flexible, goal-directed control? Using multivariate fMRI decoding in 199 participants, we show that distributed neural activity patterns in the left posterior putamen during initial learning predict individual behavioral strategy on a subsequent outcome devaluation test. This prediction generalized across two independent cohorts of healthy adults and psychiatric patients with heterogeneous diagnoses and was anatomically specific to the posterior putamen. Critically, predictive neural signatures were present during training, before strategy expression after devaluation, enabling prospective classification of habitual versus goal-directed behavior. These findings demonstrate that stable individual differences in behavioral control are reflected in circumscribed brain activity during learning, highlighting the posterior putamen as a candidate neural marker of habit propensity with potential clinical relevance.

neuroscience↗

bioRxiv2025

High-Throughput Fragment Screening Identifies a New Small Molecule Scaffold that Modulates TREM2 Signaling

Fragment-based drug discovery (FBDD) remains a powerful tool in drug development for targeting a wide range of proteins and identifying new small molecule-based scaffolds. Here, we explored a fragment library of 3,200 compounds using a temperature-related intensity change (TRIC)-based high-throughput screening (HTS) approach, and successfully identified new scaffolds that bind to triggering receptor expressed on myeloid cells 2 (TREM2), a relevant target in neurodegenerative diseases and cancer immunotherapy. We first validated the hits with dose-dependent assays, then chose the three most promising compounds (2M06, 6B10, 7G19) with binding affinities in the low to medium micromolar range for a "SAR by catalog" study. We screened 29 selected derivatives and subsequently evaluated them with dose-dependent experiments, a thermal shift assay (TSA), selectivity studies with off-targets (LAG-3, TREM1), and finally, in vitro TREM2 activation assays. In this SAR study, derivative 6B10-9 emerged as the lead compound with moderate TREM2 binding affinity (KD = 68.3 {micro}M) and significant effects on TREM2-dependent phosphorylation of SYK and DAP12 in HEK cells as wells as on microglial phagocytosis in HMC3 cells. Additionally, an in silico analysis revealed that 6B10-9 forms a stable complex with TREM2 via hydrogen bonding, which maintains its structural integrity during extended molecular dynamics (MD) simulations. These results suggest that 6B10-9 could serve as a promising lead for future optimizations in the development of small molecule-based TREM2 modulators.

pharmacology and toxicology↗

bioRxiv2025

Annotation-Free Prediction of Cancer Cells and Glands and Spatial Analysis of Immune Cells.

Prostate cancer is classified as "immune-cold" due to limited infiltration of immune cells and no clear correlation between immune cells and clinical outcomes. However, immune cells are found in prostate cancers and the spatial relationships between these immune cells and cancer cells/glands have not been investigated, partly due to a lack of automated tools that classify both cancerous cells/glands. In this paper, we have developed an end-to-end tool (TOPAZ: Tissue Organization identification using sPAtial proteomics) that combines multiplexed single-cell protein data with histology images to: 1) predict cancerous versus non-cancerous epithelial cells using a Gaussian-mixture model; 2) predict cancerous/non-cancerous gland type using a principal curve estimation. Using TOPAZ to assign cancer and non-cancerous labels to cells and glands, we extracted multiscale spatial features from the classification results--including immune dense-region geometrical features and cell-to-gland distances-- and correlated the features with risk of biochemical recurrence and cancer grade. Tissue-microarrays containing 753 cores from 217 prostate cancer patients underwent multiplexed immunofluorescent imaging (Cell DIVE, Leica) for epithelial cell markers (panCK26, S6, NaKATPase), basal cell markers (p63, CK5), a cancer cell marker (AMACR), and T cell markers (CD3, CD4, CD8, FOXP3, CD68). Cancerous/non-cancerous cell classification from TOPAZ achieved 82% sensitivity and 99% specificity against expert annotation, and the pipeline further predicted cancerous/non-cancerous glands without manual threshold tuning. Regulatory-T-cell and helper-T-cell percentages decreased, and macrophage percentage increased with grade increase (P < 0.05). When the median distance from cancerous gland centroids to the nearest regulatory or helper T-cell exceeded approximately 50 {micro}m, the hazard of biochemical recurrence doubled (log-rank P < 0.01). The open-source Shiny app TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) packages the workflow, predicting individual cell types and gland shapes. By combining probabilistic cell typing with gland-shape modeling, TOPAZ yields interpretable multiscale spatial features linked to prognosis and is released as an open web app for unrestricted use. Author SummarySpatial distribution of cancerous cells/glands and immune cell distribution has not been considered as a prognosticator in prostate cancer. In addition, automated tools that can quantify and integrate these distributions are lacking. We combined high-dimensional single-cell protein measurements with histology images to map gland structure in prostate cancer tissue. Our web-based tool, TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) predicts whether each epithelial cell and gland in virtual H&E image is cancerous or not. Once the predictions are made, a spatial analysis workflow helps quantify spatial features of immune cells relative to the glands and correlate with recurrence risk and grades. Across 753 tissue cores from 217 prostate cancer patients, helper and regulatory-T-cells located more than about 50 {micro}m away from cancerous epithelial glands were associated with a higher risk of biochemical recurrence. The pipeline provides new insights for researchers and pathologists into prostate cancer progression and biochemical recurrence through integration of spatial location of cancer glands and immune cells.

cancer biology↗

bioRxiv2025

Structure-Guided Optimization and Functional Characterization of Small Molecule Antagonists Targeting CD28 Costimulation

CD28 is the prototypical costimulatory receptor that integrates with TCR signaling to sustain T-cell activation, proliferation, and survival. While indispensable for adaptive immunity, persistent CD28 signaling drives autoimmunity, graft-versus-host disease, and inflammatory pathology. Despite its therapeutic relevance, CD28 has long been regarded as an undruggable target due to its flat, solvent-exposed dimer interface, restricting modulation to biologics. Here, we describe a structure-activity relationship (SAR) campaign to optimize a small molecule CD28 inhibitor. Guided by biophysical profiling and functional assays, derivatives of the 8VS and 22VS chemotypes were evaluated, leading to the identification of BPU11 as a chemically tractable lead with improved pharmacokinetic stability, aqueous solubility, and plasma persistence. BPU11 consistently disrupted CD28-B7 interactions across biochemical and cellular systems, and potently suppressed T-cell activation in both a tumor-PBMC co-culture and a human PBMC- mucosal tissue model, functionally mimicking the biologic antagonist FR104. Molecular docking and dynamics simulations revealed engagement of the lipophilic canyon of CD28 through stabilizing hydrogen-bonding and hydrophobic interactions. These findings expand the pharmacological space of immune checkpoint blockade beyond antibodies and position BPU11 as a foundation for next-generation immunotherapies.

pharmacology and toxicology↗

bioRxiv2025

Modelling Discrete States and Long-Term Dynamics in Functional Brain Networks

Functional brain network dynamics underlie fundamental aspects of human cognition and behaviour, including memory, ageing, and a range of clinical disorders. It has been shown that ongoing brain network dynamics can be reliably inferred at fast, sub-second timescales from electrophysiological data using unsupervised machine learning. However, these methods often struggle with inherent trade-offs. For example, Hidden Markov Models (HMMs) have been used to infer categorical brain network states that provide good interpretability but do not model long-range temporal structure. Recently, deep learning approaches using recurrent neural networks (e.g., Dynamic Network Modes) have been proposed to model long-range temporal dependencies, but at the expense of interpretability. In this paper, we introduce Dynamic Network States (DyNeStE) to address this problem. This new model employs amortised Bayesian inference with recurrent neural networks to model long-range temporal structure and uses a Gumbel-Softmax distribution to enforce categorical states for greater interpretability. In both simulations and real resting-state magnetoencephalography data, DyNeStE was able to recover plausible dynamic brain network states and showed superior performance over the HMM in capturing long-range temporal dependencies in network dynamics. These dynamic networks were reproducible across independent data splits and build on established HMM-based findings. Together, these results highlight DyNeStE as an interpretable and temporally informative framework, capable of representing large-scale neural activity as discrete state transitions while capturing transient and long-range brain network dynamics.

neuroscience↗

bioRxiv2025

AI-Guided Design of Cyclic Peptide Binders Targeting TREM2 Using CycleRFdiffusion and Experimental Validation

Triggering receptor expressed on myeloid cells 2 (TREM2) plays a central role in regulating microglial function in the central nervous system and has emerged as a promising therapeutic target for Alzheimers disease. Despite advances in antibody-based therapeutics, small molecules and peptides capable of modulating TREM2 remain limited. Here, we present a cyclic peptide design pipeline that integrates CycleRFdiffusion, ProteinMPNN for sequence design, and HighFold for structural prediction and screening. Using the TREM2 structure as input, we generated and screened 1,500 peptide-target complexes, prioritizing four candidates that met structural and energetic criteria. Subsequent biophysical evaluation identified TP4 as a TREM2 binder, demonstrating consistent binding in spectral shift, microscale thermophoresis, and surface plasmon resonance. Pharmacokinetic profiling indicated that TP4 possesses favorable plasma stability and moderate metabolic stability, supporting its tractability for further optimization. This study establishes a generalizable framework for AI-driven cyclic peptide discovery and provides the first proof-of-concept demonstration of TREM2-targeted cyclic peptide binders.

pharmacology and toxicology↗

bioRxiv2025

Development of human iPSC-derived cardiomyocytes-based heart-on-a-chip with customized multi-electrode array for drug-induced cardiotoxicity evaluations

The heart is an essential organ at the beginning and end of life. Cardiotoxicity directly related to life is emphasized as an absolute safety factor in drug development, so it is indispensable to evaluate it accurately. That is why establishing a novel platform that can precisely recapitulate human cardiophysiological properties is necessary. Here, we report a human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes-based heart-on-a-chip integrated with a customized multi-electrode array (cMEA), capable of actualizing a cardiac muscle fascicle and surrounding capillaries tridimensionally. The differences in experimental reproducibility, scale of field potential wave, and gene expression patterns according to the two-dimensional cell-level and three-dimensional tissue-level culture are confirmed. Excellent accuracy of drug efficacy/toxicity of the chip is also verified relative to other conventional cell culture platforms by looking at heart rate changes depending on drug type and concentration/time. Additionally, we checked that kinetic/electrophysiological/molecular biological analysis can be performed with our chip by applying motion tracking, cMEA, and RNA sequencing. Thus, our platform is expected to be used as a tool for more exact cardiotoxicity tests with high reproducibility and multi-angle analysis in line with new pharmacological safety assessments such as comprehensive cardiac arrhythmias assessment (CiPA).

bioengineering↗

bioRxiv2025

AI-Assisted Discovery and Optimization of Small Molecule TREM2 Agonists with Functional Microglial Activity

Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglia-specific receptor whose loss-of-function variants increase Alzheimers disease (AD) risk by impairing plaque compaction, survival, and protective microglial programming. While antibody-based agonists have shown promise, their translation is hindered by poor brain penetration and high cost. Here, we report the discovery and optimization of small molecule TREM2 agonists through an AI-assisted virtual screening strategy. Deep Docking of over five million purchasable compounds identified a structurally novel hit, T2K-014, which engaged TREM2 with modest affinity. A SAR-by-catalog campaign led to the identification of T2M-010 as a potent binder. T2M-010 demonstrated favorable in vitro PK properties, including high solubility, passive BBB permeability, moderate metabolic stability, and minimal safety liabilities. Functionally, T2M-010 activated receptor-proximal signaling, inducing SYK phosphorylation in TREM2-expressing cells, and promoted microglial phagocytosis. Together, these findings establish T2M-010 as the most potent small molecule TREM2 binder reported to date capable of driving protective microglial responses relevant to AD.

pharmacology and toxicology↗

bioRxiv2025

Polyelectrolyte domains in intrinsically disordered regions differentially regulate intracellular biomolecular condensation

Intracellular condensation is a hallmark of many proteins containing intrinsically disordered regions (IDRs). Decoding the regulatory mechanisms of condensation encoded within the amino acid sequences of condensate-forming proteins is essential for understanding both how intracellular condensates assemble and how they contribute to various disease pathologies. Here, we investigated the budding yeast P-body proteins Nst1 and Edc3, which exhibit pronounced intracellular self-condensation, and identified charged amino acid clusters in the low complexity regions (LCRs) that modulate their phase behavior. Guided by the stickers-and-spacers framework, we hypothesized that clusters of charged residues within IDRs function in promoting intracellular condensation process. Using a sliding-window approach, we designated and dissected the polyelectrolyte (PE) domains of Nst1 and Edc3 and established their essential contributions to condensate coalescence. In Nst1, the polyanionic (PA) subdomain enhances intermolecular attraction and promotes coalescence, whereas the polyampholyte (PAM) subdomain imparts rigidity to the condensate as a distinct modulatory element and functions as a platform in condensation process of other P-body components. PA of Edc3 also regulates coalescence. Extending these findings to the neurodegenerative disease-associated protein TDP-43, we show that its PA regions are critical for coalescence, consistent with the presence of negatively charged amino acid clusters as potent molecular stickers functioning in condensate coalescence in cells. These results highlight charged-residue clusters as important sequence determinants of protein phase behavior and suggest that they represent promising novel regulatory targets for modulating TDP-43 condensation in cells.

cell biology↗

bioRxiv2025

Structure-Based Virtual Screening Identifies TREM2-Targeted Small Molecules that Enhance Microglial Phagocytosis

Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglia-specific receptor whose activation promotes phagocytosis and neuroprotection in Alzheimers disease (AD) and related neurodegenerative disorders. While therapeutic efforts have largely focused on antibodies, small molecule TREM2 modulators remain limited. Here, we applied a structure- based virtual screening workflow targeting a putative allosteric site on TREM2, guided by PyRod-derived pharmacophores from molecular dynamics simulations. Screening of the Enamine Collection yielded 20 candidate compounds, three of which demonstrated binding in TRIC assays. The top hit, EN020, exhibited a KD of 14.2 {micro}M (MST) and 35.9 {micro}M (SPR), and significantly enhanced microglial phagocytosis in BV2 cells outperforming the known TREM2 agonist VG-3927. A preliminary structure-activity relationship (SAR) study, including synthetic and catalog-derived analogs, highlighted a narrow tolerance for scaffold modifications, with only T2V002 retaining partial TREM2 binding affinity. This work identifies EN020 as a novel small molecule TREM2 modulator with functional activity, providing a framework for rational optimization toward potential AD therapeutics.

pharmacology and toxicology↗

bioRxiv2025

As48, a First-in-Class Dual-Function TREM2 Modulator: Receptor Activation and Shedding Inhibition

Triggering receptor expressed on myeloid cells 2 (TREM2) dysfunction contributes to Alzheimers disease pathogenesis, yet current therapeutics cannot prevent ADAM-mediated receptor shedding that diminishes signaling efficacy. Using Affinity Selection-Mass Spectrometry (AS-MS) screening, we identified As48, a novel small molecule that binds TREM2 with high affinity. Biophysical validation confirmed s 7-fold selectivity over TREM1. Cellular assays demonstrated that As48 functions as a TREM2 agonist, activating SYK phosphorylation and enhancing microglial phagocytosis. Molecular docking and molecular dynamics simulations revealed that As48 binds near the cleavage region, establishing hydrogen bonds with Gly68 and reducing conformational flexibility in regions 58-102. Based on this structural insight, we investigated the effect of As48 on TREM2 ectodomain shedding and discovered inhibition of receptor shedding without affecting ADAM10/17 protease activities, representing the first small molecule with anti-shedding properties through conformational restriction of protease accessibility. Importantly, As48 displayed favorable pharmacokinetics with potential for brain permeability, supporting its translational relevance. Through its dual and simultaneous promotion of receptor activation and prevention of shedding, As48 represents a paradigm shift in TREM2 modulation and neuroinflammatory drug discovery. Abstract figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/671919v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1b67c28org.highwire.dtl.DTLVardef@1913034org.highwire.dtl.DTLVardef@f3bf58org.highwire.dtl.DTLVardef@97448a_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Single-Cell Network Analysis Identifies CLEC4E as a Key Mediator of Proinflammatory mDC Responses in Influenza Infection

The severity of influenza is often driven by an excessive host immune response rather than the virus itself, yet the key molecular drivers within specific immune cells remain poorly understood. While recent single-cell RNA sequencing studies have successfully identified immune populations involved, they have largely not identified the upstream drivers modulating their pro-inflammatory functions. Here we employed an integrated single-cell co-expression network to address this gap. Our analysis identified myeloid dendritic cells (mDCs) as central to pro-inflammatory response during infection. Through a multi-layered key driver analysis, we pinpointed C-type lectin, CLEC4E as a top candidate modulating this pathological inflammatory response. The role of CLEC4E was confirmed in an independent single-cell dataset from influenza-infected patients and further validated in vivo. Pharmacological inhibition of CLEC4E in a murine influenza model significantly reduced disease severity and lower viral titers in the lungs. This study not only clarifies that CLEC4E overexpression in mDCs contributes to pro-inflammatory signaling pathways influencing influenza severity but also shows the power of single-cell network approaches to uncover novel and robust therapeutic targets hidden within complex immune responses.

systems biology↗