Biology subjects

Park, H.

Publications and source records attributed to Park, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Whole-brain protein profiling using organ-scale multiplexed immunolabeling and image co-registration

Proteins are major drivers of biological functions. Single-cell, organ-scale multiplexed protein imaging can reveal high-dimensional molecular and structural features of individual cells and their interactions, enabling an in-depth understanding of complex biological systems. However, achieving such imaging has remained elusive due to hurdles in multiplexed immunolabeling (mIF) of intact organs and integrative image analysis. Here, we present 3D CYCLIC, an organ-scale multiplexed immunolabeling technique, and TACTIC, a single-cell-level, organ-scale image co-registration algorithm. 3D CYCLIC combines ultrafast, versatile 3D immunolabeling with a cleavable crosslinker that preserves signals by protecting bound antibodies during optical clearing while enabling their detachment for subsequent rounds of immunolabeling. TACTIC uses deep warping networks coupled with a propagation-based cell-pair search to co-register individual cells across whole-brain images acquired from multiple rounds of 3D CYCLIC labeling. 3D CYCLIC enabled 6-plex protein profiling of a mouse brain hemisphere, with images that can be combined with TACTIC for integrative analysis. 3D CYCLIC and TACTIC are versatile techniques and will therefore enable a holistic, unbiased understanding of diverse organs.

neuroscience↗

bioRxiv2026

Label-free quantitative 3D mapping of collagen architecture by holotomography

Fibrillar collagen architecture regulates tissue mechanics and cell-matrix interactions, but following how collagen-rich matrices assemble and remodel in three dimensions requires non-destructive measurements that can link structure to matrix-associated mass without exogenous labels. Here we present holotomography (HT) as a label-free platform for volumetric imaging and quantitative profiling of reconstituted fibrillar collagen. Volumetric RI tomograms resolved fibrillar collagen networks without exogenous labels, and co-registration with second-harmonic generation (SHG) microscopy supported the correspondence between HT-resolved RI structures and SHG-positive fibrillar collagen. Curvelet-transform-based segmentation of representative axial sections converted the tomograms into fiber-level descriptors, including morphology, orientation and RI-derived fiber-associated dry mass. At the matched 0.8 mg/mL condition, HT resolved distinct network organization, with type I forming thicker, more sparsely distributed fibers and type III a finer, denser network, while mean fiber-associated RI remained comparable between subtypes. Across the concentration series, type I showed stronger increases in fiber width and RI-derived mass, whereas type III remained comparatively fine. Time-lapse HT further captured RI dynamics during type I fibrillogenesis and visualized local cell-matrix reorganization in HT1080 cell-embedded gels, capturing cells and surrounding matrix in a single label-free measurement. Together, these results position HT as a complementary label-free approach for volumetric visualization and RI-based quantification of collagen-rich extracellular matrices.

cell biology↗

bioRxiv2026

Transformed reactivation of latent working memory enables hierarchical language processing

Human language comprehension requires tracking words across intervening material to construct grammatical structures, yet two fundamental questions remain unresolved: whether maintenance of earlier words relies on sustained neural activity or activity-silent working memory mechanisms, and whether memory retrieval during integration engages domain-general or language-selective networks. Using magnetoencephalography with time-resolved decoding, we tracked main-clause subjects as participants heard sentences with embedded clauses (e.g., "The dog, who chases the cat, jumps over the mud."). The subject representation ("dog") decayed to baseline during the embedded clause but reactivated after the main-clause verb ("jumps"), with transformed rather than reinstated neural codes. Critically, reactivation emerged first in right dorsolateral prefrontal cortex before engaging frontotemporal language regions, and reactivation strength was modulated by syntactic structure and predicted comprehension accuracy. These findings demonstrate activity-silent working memory maintenance, structure-dependent retrieval, and cooperative function of domain-general and language-selective networks during hierarchical language processing.

neuroscience↗

bioRxiv2026

Deep Learning-Guided Holotomography Reveals Early Structural Remodelling During Pluripotency Exit

Real-time assessment of human pluripotent stem cell (hPSC) quality is critical for reproducibility and safety in regenerative medicine, yet current methods are invasive, labor-intensive, or highly operator-dependent. We present DeepHOPE (Deep-learning-guided Holotomography for Pluripotency Evaluation), a non-invasive, automatizable, and statistics-driven platform that integrates three-dimensional (3D) refractive index imaging with deep learning to assess pluripotency. DeepHOPE performs robustly across diverse contexts, including germ-layer differentiation, retinoic acid-induced differentiation, and mid-reprogramming cultures, enabling streamlined cell production workflows and improving the efficiency of midbrain dopaminergic neuron differentiation through informed colony selection. Mechanistically, DeepHOPE detects minute colony-scale topological changes that precede molecular loss of pluripotency. These early changes are associated with rapid F-actin remodeling, including apical-to-basal redistribution during early differentiation. Consistent with a functional role for cytoskeletal regulation in state transitions, sustained reduction of actomyosin tension decreases pluripotency, identifying cytoskeletal dynamics as an upstream determinant of early pluripotency exit. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/720508v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1d73508org.highwire.dtl.DTLVardef@1dab29aorg.highwire.dtl.DTLVardef@1039f76org.highwire.dtl.DTLVardef@da29a1_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2026

Double Mutations in Plasmodium falciparum Kelch13 drive resistance to next-generation artemisinin derivatives in malaria parasites

New antimalarial compounds are urgently required to overcome artemisinin partial resistance that has emerged in Asia and now Africa. Ozonides are promising next-generation artemisinins that offer the improved pharmacokinetic property of a prolonged in vivo half-life. To assess the potential for parasite resistance to ozonides in an artemisinin-resistant background, we subjected Cambodian Kelch13 (K13) mutant parasites to increasing artefenomel (OZ439) pressure up to in vivo physiological concentrations. Whole-genome sequencing identified a novel non-propeller K13 A212T mutation in OZ439-resistant parasites. Gene editing and drug susceptibility assays revealed that the K13 double mutation R539T+A212T is a determinant of OZ439 resistance. In extended parasite recovery assays, this resistance mechanism was associated with accelerated parasite recrudescence following OZ439 or OZ277 exposure. This phenotype was also observed in K13 C580Y+A212T double mutant parasites. Global metabolomic profiling revealed no changes in the levels of hemoglobin-derived peptides in OZ439-resistant parasites, suggesting that resistance is not associated with drug activation. Instead, double mutant parasites exhibited increased levels of metabolites linked to glutathione, nucleotide, and aspartate-glutamate metabolism, suggesting a higher capacity for redox regulation to tolerate drug-induced oxidative damage. Our findings demonstrate that ozonide resistance can emerge through a novel K13 mutation on the background of existing artemisinin-resistance k13 alleles.

microbiology↗

bioRxiv2026

FNIP1 Modulates B Cell Receptor Signaling Strength by Coordinating Metabolism During Development.

B cell development relies on stringent checkpoints that ensure immune competence and eliminate autoreactive clones. Transitional B cells (B220CD93), which emerge from the bone marrow, migrate to the spleen and differentiate into follicular (FO) or marginal zone (MZ) B cells, a process governed by B cell receptor (BCR) signaling strength, metabolic fitness, and survival cues. Here, we identify Folliculin Interacting Protein 1 (Fnip1) as a key regulator of this developmental transition. Using conditional Fnip1-deficient mice (Fnip1fl/flCD21Cre), loss of Fnip1 results in a developmental arrest at the transitional B220CD93mid stage, severely limiting differentiation into FO and MZ B cells and leading to accumulation of a distinct enlarged CD19high, RAG negative B cells. Fnip1 modulates BCR signaling thresholds and metabolic programming by regulating the AMPK/FLCN/TFEB and CD19/PI3K/Akt/mTORC1 pathways through restricting TFEB access to the nucleus. Using the MD4/mHEL/sHEL tolerance model, we show that Fnip1 is dispensable for negative selection but is essential for maintaining peripheral tolerance. Together, our findings define Fnip1 as a metabolic gatekeeper that integrates nutrient-sensing pathways with BCR signaling to orchestrate transitional B cell fate decisions, promote peripheral tolerance, and maintain immune homeostasis.

immunology↗

bioRxiv2026

G-screen: Scalable Receptor-Aware Virtual Screening through Flexible Ligand Alignment

Virtual screening has long been a central computational tool for rational ligand discovery, enabling the systematic prioritization of candidate molecules from large chemical libraries. Although docking and related approaches that explicitly account for protein-ligand interactions have been developed and refined over several decades, achieving both reliable protein-aware interaction modeling and computational scalability remains an open challenge, particularly for ultra-large chemical spaces. Ligand-based methods are fast and robust but do not explicitly incorporate protein structure, whereas docking-based approaches model protein-ligand interactions more directly at substantially higher computational cost. Here, we present G-screen, a freely available and scalable protein-aware virtual screening framework designed for cases in which an experimentally determined or predicted reference protein-ligand complex structure is available. Rather than performing computationally intensive full docking with explicit pose sampling and optimization, G-screen rapidly generates alignment-guided pose hypotheses using a flexible global alignment algorithm (G-align). The resulting aligned poses are subsequently evaluated using protein-aware pharmacophore interactions derived from the reference complex, enabling explicit atomic-level interaction analysis while retaining the scalability and robustness of ligand-based alignment methods. Benchmarking on DUD-E, LIT-PCBA, and MUV datasets demonstrates that G-screen achieves competitive discrimination and early enrichment relative to representative ligand-based and docking-based methods, while maintaining millisecond-scale per-molecule runtimes under multi-threaded execution. These results position G-screen as a practical and scalable intermediate strategy between conventional ligand-based virtual screening and computationally intensive docking workflows for efficiently filtering ultra-large chemical libraries when a reference complex structure is available. G-screen and G-align are freely available at https://github.com/seoklab/gscreen and https://github.com/seoklab/galign, respectively. Scientific ContributionWe have developed a scalable virtual screening framework for efficiently filtering ultra-large chemical libraries using a flexible global alignment algorithm combined with protein- aware pharmacophore evaluations and alignment-guided pose hypotheses. Despite explicitly capturing atomic-level interactions, the method remains highly efficient, maintaining millisecond-scale per-molecule runtimes under parallel execution. It achieves competitive discrimination and early enrichment, serving as an intermediate strategy between conventional ligand-based virtual screening and computationally intensive docking while combining the speed of ligand-based approaches with the structural context of traditional docking.

biophysics↗

bioRxiv2026

Insulin resistance modifies longitudinal multi-omics responses to habitual diet

How habitual diet influences the gut microbiome and plasma metabolome across insulin resistance states remains unclear. We conducted year-long multi-omics profiling of 71 deeply phenotyped adults, integrating repeated assessments of diet, metabolome, gut microbiome, clinical laboratory measures, and inflammatory markers. Using gold-standard insulin suppression tests and machine learning-derived dietary patterns, we examined how dietary patterns relate to metabolic and microbial landscapes by insulin resistance status. Insulin-sensitive individuals exhibited stronger and more numerous diet-omics associations than insulin-resistant individuals, identifying metabolic flexibility as a central determinant of dietary responsiveness. Parabacteroides emerged as a candidate microbial mediator between refined carbohydrate-rich dietary patterns and host metabolic signatures. Integrated into a cardiovascular risk prediction model, diet, metabolites, microbial taxa, and immune markers each contributed to 10-year atherosclerotic cardiovascular disease risk. These findings show that inter-individual variation in cardiometabolic risk partly reflects differences in molecular responsiveness to habitual diet, informing precision nutrition and cardiovascular prevention.

molecular biology↗

bioRxiv2026

Dual targeting a LIN28B:β-catenin axis in acute myeloid leukaemia

BackgroundWnt/{beta}-catenin signalling is dysregulated in acute myeloid leukaemia (AML), where it lacks effective targeting strategies. Previously, we discovered that {beta}-catenin interacts with several RNA-binding proteins (RBP), indicating post-transcriptional influence which is yet to be therapeutically interrogated in AML. MethodsCo-immunoprecipitation confirmed protein interactions, and TCF/LEF reporters were used to assess Wnt signalling output in leukaemia cells. Regulatory crosstalk was assessed using immunoblotting and RT-qPCR approaches following lentiviral transduction of myeloid cell lines. Targeting of {beta}-catenin and LIN28B was tested through combinations of genetic and pharmacological inhibition in AML cells. ResultsThe most frequent RBP-binding motif amongst {beta}-catenin-bound mRNAs was the GGAG motif targeted by oncofetal miRNA-regulating RBP; LIN28B. {beta}-Catenin:LIN28B interactions were detected in lymphoid and myeloid cell lines, plus primary human CD34 fetal-liver HSCs. LIN28B positively regulated Wnt signalling output through LEF1 regulation involving a post-transcriptional let7 miRNA mechanism. Further miRNA sequencing of {beta}-catenin- and LIN28B-depleted myeloid cells revealed potential cooperative and antagonistic function in miRNA regulation. Finally, dual-targeting both {beta}-catenin and LIN28B through either genetic and/or pharmacological means preferentially reduced AML cell viability. ConclusionThe {beta}-catenin:LIN28B axis could represent a novel synthetically lethal relationship in AML which could be exploited in rare subtypes where LIN28B expression becomes reactivated.

cancer biology↗

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↗

bioRxiv2026

Microstate-specific functional connectivity in alcohol use disorder using resting-state EEG

Alcohol Use Disorder (AUD) is often described as a "disconnection syndrome", reflecting neural dysfunction associated with chronic alcohol use. However, previous studies on functional connectivity of resting-state EEG (rsEEG) in AUD have reported inconsistent findings--some showing hyperconnectivity, others hypoconnectivity or no significant differences--possibly due to overlooking the brains dynamic nature even at its resting state. Here, we examined rsEEG in 26 young adults with AUD and 35 healthy controls (HC), using microstate analysis to characterize transient brain states and to estimate functional connectivity within each state. We showed that microstate map topologies were comparable between the two groups. However, the temporal dynamics in the AUD group were biased toward microstates C and D, characterized by anterior-to-posterior configurations, within which between-channel functional connectivity differed from that of HC. Particularly within microstate C, the AUD group showed significantly reduced functional connectivity compared to the HC group in the majority of channel pairs. Moreover, compared with conventional functional connectivity estimates, microstate-specific functional connectivity yielded superior classification performance in distinguishing individuals with AUD from HC. Subsequent computation of graph-theoretical measures revealed that individuals with AUD showed less stable hub-like properties and reduced small-worldness in microstate C, indicating diminished network efficiency. Our findings suggest that transient EEG properties, exemplified by microstate-specific functional connectivity, provide informative neural markers for characterizing the impact of alcohol use on the brain. HighlightsO_LIResting-state EEG microstates reveal altered brain dynamics in alcohol use disorder C_LIO_LIMicrostate C exhibits widespread reductions in functional connectivity in AUD C_LIO_LIMicrostate-specific connectivity outperforms conventional measures in AUD classification C_LIO_LIGraph analysis reveals reduced network efficiency and unstable hubs in AUD C_LI

neuroscience↗

bioRxiv2026

Induction of ferroptotic and amyloidogenic signatures linked to Alzheimers disease by chemically distinct air pollutants

Air pollution (AirP) exposure is associated with increased Alzheimers disease (AD) risk, yet AirP is chemically heterogeneous, complicating identification of shared pathogenic drivers. We examined acute cortical responses to two metal-rich AirP sources, diesel exhaust particles (DEP) and World Trade Center (WTC) dust, and compared them to woodsmoke (WS), a particulate exposure with low metal content. DEP and WTC elicited highly convergent transcriptional responses, sharing over 1200 differentially expressed genes linked to inflammation, ferroptosis, neuronal remodeling, and amyloid processing. These changes were accompanied by impaired antioxidant activity and increased lipid peroxidation within lipid rafts, a membrane microdomain critical for amyloid processing, resulting in increased A{beta} generation. In contrast, WS produced a distinct transcriptional signature and failed to induce ferroptotic priming or lipid peroxidation, consistent with its low metal composition. Together, these findings implicate metals as a shared driver linking diverse AirP exposures to amyloidogenic vulnerability and elevated AD risk. Graphical AbstractAcute AirP exposure converges on ferroptotic priming, amyloidogenic processing, and white-matter vulnerability. Acute exposure to metal-rich AirP, such as DEP or WTC introduces redox-active metals and particulate matter that promote lipid peroxidation, amyloidogenesis, and altered transcriptional regulation in the brain. AirP exposure engages xenobiotic metabolism pathways (AhR/ARNT), activates iron and heme handling through ferritinophagy (NCOA4) and heme oxygenase activity (HMOX1), and blunts lipid peroxide detoxification systems, including glutathione peroxidase 4 (GPx4), ferroptosis suppressor protein 1 (FSP1), and glutathione (GSH) synthesis. These changes promote ferroptotic priming and lipid raft oxidation, facilitating amyloid precursor protein (APP) processing by secretases (ADAM10, BACE1, {gamma}-secretase) and increasing amyloid-{beta} (A{beta}) generation. In parallel, transcriptional and cell-state remodeling involving neuronal and oligodendrocyte responses contribute to selective white-matter vulnerability, particularly within the corpus callosum. Together, these pathways provide a mechanistic framework linking acute AirP exposure to convergent oxidative, amyloidogenic, and microstructural changes relevant to Alzheimers disease pathology. O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/696601v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@3a22dcorg.highwire.dtl.DTLVardef@c6ae26org.highwire.dtl.DTLVardef@1d3412forg.highwire.dtl.DTLVardef@5cb011_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Reactive Astrocytes Drive Extracellular Acidification to Mediate α-Synuclein Neurodegeneration

Astrocytes are increasingly recognized as key players in neurodegeneration1-3, yet the molecular mechanisms by which they drive disease remain elusive. Here, we uncover a fundamental pathway in which reactive astrocytes fuel neurodegeneration in -synucleinopathies--including Dementia with Lewy bodies and Parkinsons disease dementia--by acidifying the brains extracellular environment. We demonstrate that both human patient tissue and a gut-to-brain -synuclein mouse model exhibit accumulation of reactive astrocytes and extracellular acidosis. Mechanistically, we show that astrocytic lysosomal exocytosis releases acidic contents, driving a drop in pH that activates neuronal acid-sensing ion channel 1a (ASIC1a), resulting in neuronal loss and behavioral decline. Blocking this pathway--either by inhibiting astrocytic lysosomal exocytosis or genetically or pharmacologically targeting neuronal ASIC1a--mitigates pathology and rescues neurodegenerative phenotypes in vivo. These findings provide a conceptual advance by establishing a mechanistic link between glial inflammation, acid-base homeostasis, and neuronal vulnerability, and suggest that targeting astrocyte-driven acidification or ASIC1a signaling could offer new avenues for disease modification in -synucleinopathies.

neuroscience↗

bioRxiv2025

FAM84B facilitates tau propagation via RYR3-mediated exocytosis in response to neuroinflammation

The increasing prevalence of dementia underscores the pressing need for effective therapeutic interventions. Despite extensive research into A{beta}, the limited efficacy of existing treatment modalities necessitates a paradigm shift toward tau pathology including tau propagation to slow dementia progression. Here, we identified a novel protein, FAM84B (also known as LRATD2), involved in tau propagation. Elevated FAM84B levels were detected in the postmortem cortex, cerebrospinal fluid, and induced pluripotent stem cell-derived cortical neurons of patients with tauopathy. FAM84B facilitates tau secretion through exosome assembly and RYR3-mediated exocytosis, followed by exosomal uptake into neighboring cells via clathrin-mediated endocytosis, thereby driving tau propagation. We confirmed that neuroinflammation induced by heightened STAT3 activity and cytokine treatment upregulated FAM84B expression, stimulating tau propagation, as evidenced by reduced propagation upon RYR3 inhibition. These findings provide invaluable insights into potential avenues for targeting tau propagation to ameliorate disease progression in tauopathies.

neuroscience↗

bioRxiv2025

Automated, modular assembly of reconstituted cell-free systems from in vitro-produced components

High-performance cell-free protein synthesis has transformative potential for synthetic biology, yet the prohibitive costs of PURE kits and the labor intensity of in-house preparation have restricted accessibility and scalability. We developed i-POPFLEX (Purified components OPtimized for FLEXible protein expression), a modular cell-free protein synthesis system in which 34 translation factors, individually synthesized in vitro, are assembled using automated liquid handling. This workflow minimizes manual input and supports parallelized production, generating complete, ready-to-use systems within two days. i-POPFLEX achieves up to 5-fold higher protein yields and a 95 % cost reduction (20-fold lower cost) compared with commercial kits. Its flexible architecture also enables selective component inclusion for genetic code reprogramming and site-specific incorporation of non-canonical amino acids. By coupling modular design with automation, i-POPFLEX provides an accessible, customizable, and economically viable platform for next-generation biomanufacturing workflows. Technology readinessi-POPFLEX is a reconstituted cell-free system (CFS) composed of 34 translational components that are individually synthesized in vitro and assembled by automation. This system achieves up to 5-fold higher protein yields with a 95 % cost reduction (20-fold lower; <USD 0.06 {micro}L-1) compared with commercial PURE kits (USD 1.36 {micro}L-1) and reduces preparation time from four days to two. The workflow has been validated across diverse protein and peptide synthesis applications, including genetic code reprogramming and site-specific incorporation of non-canonical amino acids, and integrates seamlessly with benchtop automated liquid-handling platforms. These capabilities place i-POPFLEX at Technology Readiness Level (TRL) 4-5: integrated components validated in a laboratory setting and advancing toward pilot-scale readiness. For full-scale deployment, several key challenges remain. Matching crude lysate costs (USD 0.03 {micro}L-1) will require bulk reagent production, optimized procurement strategies, and specialized equipment for high-throughput component synthesis. Enabling milliliter- and liter-scale production will demand improvements in upstream synthesis, downstream purification, and quality control to ensure consistent performance at industrial throughput. Overcoming these barriers would enable production of thousands to millions of fully assembled i-POPFLEX systems for biofoundry-scale enzyme engineering, metabolic pathway prototyping, and large-scale variant screening, democratizing reconstituted CFS technology, and accelerating adoption in next-generation biomanufacturing pipelines.

synthetic biology↗

bioRxiv2025

TOE1 influences canonical Wnt signalling in myeloid leukaemia cells through LEF-1 modulation and regulates the proliferation of haematopoietic cells through PAK2.

Acute myeloid leukaemia (AML) is an aggressive haematological malignancy characterised by the clonal proliferation of myeloid progenitor cells in the bone marrow and peripheral blood. Dysregulation of the Wnt/{beta}-catenin pathway has been implicated in the establishment and maintenance of leukaemic stem cells in AML, where higher expression of {beta}-catenin promotes clonogenic capacity, drug resistance and inferior survival. The finding that low levels of Wnt signalling are necessary to maintain normal haematopoiesis makes {beta}-catenin an attractive therapeutic target; however, drug design has been hampered by a poor understanding of its molecular interactions in leukaemia cells. To address this, we previously characterised the {beta}-catenin interactome in myeloid cells and identified a plethora of novel interacting proteins. One such interactor was Target of EGR1 (TOE1), a member of the Asp-Glu-Asp-Asp (DEDD) family of deadenylases with previously uncharacterised function in haematopoietic cells. The {beta}-catenin:TOE1 interaction was detected in the nuclear and cytosolic compartments of myeloid cell lines and primary AML samples, and {beta}-catenin depletion was found to promote the cytosolic accumulation of TOE1. Furthermore, TOE1 levels were found to be overexpressed in primary AML blasts versus normal cord-blood derived CD34+ haematopoietic stem and progenitor cells (HSPCs), suggesting that TOE1 levels may be dysregulated in leukaemia. TOE1 depletion abrogated Wnt signalling capacity (TCF/LEF activity), potentially via reduced stability/translatability of the Wnt transcription factor lymphoid enhancing factor 1 (LEF-1). TOE1 depletion further suppressed the proliferation and survival of myeloid leukaemia cell lines (HEL and OCI-AML2) and primary human CD34+ HSPC; however, this could not be fully explained through LEF-1 alone, since OCI-AML2 do not express LEF-1. Using tandem mass tag (TMT)-labelling coupled to mass spectrometry analysis in TOE1 deficient HEL and OCI-AML2 cells, we identified and validated p21 (RAC1) activated kinase 2 (PAK2) as a downregulated target that could reduce the proliferation (but not survival) of AML cell lines. Interestingly, ectopic expression of PAK2 was able to partially rescue the proliferation defect in TOE1 depleted myeloid cell lines and primary human CD34+ HSPC. In summary, these data reveal TOE1 as novel interacting partner for {beta}-catenin in haematological cells capable of modulating Wnt signalling output via LEF-1, and as a novel mediator of growth and survival in human HSPC and AML cells partly through PAK2 regulation.

cancer biology↗

bioRxiv2025

Using experimental results of protein design to guide biomolecular energy-function development

Computational models of macromolecules have many applications in biochemistry, but physical inaccuracies limit their utility. One class of models uses energy functions rooted in classical mechanics. The standard datasets used to train these models are limited in diversity, pointing to a need for new training data. Here, we sought to explore a new paradigm for training an energy function, where the Rosetta energy function was used to design de novo proteins. Experimental results on these designs were then used to identify failure modes of design, which were subsequently used as a "guiding principle" to retrain the energy function. Specifically, we examined a diverse set of de novo protein designs experimentally tested for their ability to stably fold, identifying unstable designs that were predicted to be stable by the Rosetta energy function. Using deep mutational scanning, we identified single amino-acid mutations that rescued the stability of these designs, providing insight into common failure modes of the energy function. We identified one key failure mode, involving steric clashing in protein cores. We identified similar overpacking when using Rosetta to refine high-resolution protein crystal structures, quantified the degree of overpacking, and refit a small set of energy-function parameters to better recapitulate native-like packing. Following fitting, we largely eliminated the failure mode in the refinement task, while retaining performance on other benchmarks, resulting in an updated version of the Rosetta energy function. This work shows how learning from protein designs can guide energy-function development. Author summaryComputational models of macromolecules have many applications, such as predicting structures, predicting mutational effects, or designing new proteins. One type of model uses an energy function to explicitly model the physical forces at play. These models are trained using experimental data. However, available training data are limited in number and diversity, prompting a need for new sources of training data. In this paper, we explore a new paradigm for training an energy function, which involves using the energy function to design de novo proteins and then learning from which designs succeed and which fail when experimentally tested in the lab. We used experimental data to learn common failure modes in design, identified examples of a failure mode in a high-quality benchmark, and then used this benchmark to retrain the energy function. Using this strategy, we identified and largely resolved a bias in the Rosetta energy function that involved energetically unfavorable steric clashes in protein cores. Overall, this work helps establish a framework for how learning from design can be used to guide the development of macromolecular models.

biochemistry↗

bioRxiv2025

Comparative metagenomics using pan-metagenomics graphs

Identifying microbial genomic factors underlying human phenotypes is a key goal of microbiome research. Sequence graphs are a highly effective tool for genome comparisons because they enable high-resolution de novo analyses that capture and contextualize complex genomic variation. However, applying sequence graphs to complex microbial communities remains challenging due to the scale and complexity of metagenomic data. Existing multi-sample sequence graphs used in these settings are highly complex, computationally expensive, less accurate than single-sample alternatives, and often involve arbitrary coarse-graining. Here, we present copangraph, a multi-sample sequence-graph-based analysis framework for comprehensive comparisons of genomic variation across metagenomes. Copangraph uses a novel homology-based graph, which provides both non-arbitrary, evolutionary-motivated grouping of sequences into the same node as well as flexibility in the scale of variation represented by the graph. Its construction relies on hybrid coassembly, a new coassembly approach in which single-sample graphs are first constructed separately and are then merged to create a multi-sample graph. We also present an algorithm that uses paired-end reads to improve detection of contiguous genomic regions, increasing accuracy. Our results demonstrate that copangraph captures sequence and variant information more accurately than alternative methods, provides graphs that are more suitable for comparative analysis than de Bruijn graphs, and is computationally tractable. We show that copangraph reflects meaningful metagenomic variation across diverse scenarios. Importantly, it enables significantly better performance than other metagenomic representations when predicting the gut colonization trajectories of Vancomycin-resistant Enterococcus. Our results underscore the value of our multi-sample, graph-based framework for comparative metagenomic analyses.

bioinformatics↗