Biology subjects

Lim, J.

Publications and source records attributed to Lim, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Mechanotherapeutic Potential of Survivin in Glioblastoma

Glioblastoma Multiforme (GBM) is a highly aggressive brain cancer characterized by rapid proliferation and extensive remodeling of the extracellular matrix (ECM), leading to progressive tissue stiffening. Although ECM stiffness is known to promote GBM progression, the molecular mechanisms linking mechanical cues to tumor growth remain insufficiently defined. In this study, transcriptomic comparison of GBM tumors and non-neoplastic brain tissue revealed coordinated upregulation of cell cycle regulators and matrisome-associated genes, with survivin (BIRC5) identified as a central node linking proliferative signaling and ECM remodeling networks. Analysis of GBM patient specimens further showed strong nuclear survivin expression in regions with elevated collagen deposition. To directly evaluate stiffness-dependent regulation of survivin, GBM cells were cultured on fibronectin-infused hydrogels with tunable stiffness. Stiff matrices increased survivin expression along with cyclin D1 and cyclin A, consistent with increased cell cycle progression. Pharmacologic inhibition or siRNA-mediated suppression of survivin reduced stiffness-induced proliferation and attenuated expression of matrisome components, including collagens and lysyl oxidase. These findings indicate that survivin functions as a mechanosensitive regulator that coordinates cell cycle progression with ECM production in stiff tumor microenvironments. Collectively, this study identifies survivin as a key mediator linking ECM stiffness to GBM growth and matrisome remodeling. Targeting survivin and its effectors may offer a mechanosensitive strategy to limit GBM growth.

pathology↗

bioRxiv2026

Kinetic Modeling of Target-Amplification-Free CRISPR-Cas-Based Autocatalysis Reactions

CRISPR-Cas-based diagnostics utilize the Cas enzymes trans-cleavage activity to generate signal and have become popular platforms for sensitive nucleic acid detection. Recently, autocatalytic systems have been demonstrated to improve the time to response and sensitivity in some cases. However, mechanistic description of these assays is limited and optimization relies on simple trial-and-error. In this work, we present the first comprehensive kinetic model that integrates all major biochemical processes involved in these assays, including cleavage reactions, nucleic acid equilibrium kinetics, inhibition of trans-cleavage by single-stranded DNA, and degradation of single-stranded reaction components. We discuss the biochemical foundations and implementation of the ordinary differential equation model, which is built for adaptation to different reaction schemes. We use the full model to investigate the role of nucleic acid stability in assay performance for a typical nucleic acid design and show that our model demonstrates inhibition effects consistent with experimental data. We describe the reaction behavior, derive a simplified analytical model and compare its performance to the full analytical model. Finally, we demonstrate tools developed for rapid in silico optimization to guide the rational design of future target-amplification-free CRISPR-Cas-based autocatalysis assays.

bioengineering↗

bioRxiv2026

TAZ (Wwtr1) deficiency leads to ER stress and mitochondrial dysfunction in a mouse model of Fuchs' endothelial corneal dystrophy

Fuchs endothelial corneal dystrophy (FECD) impacts over 300 million individuals worldwide with corneal transplantation as the primary treatment. There is a dire need to establish non-surgical alternatives which are dependent on mouse models. Transcriptional co-activator with PDZ-binding motif (TAZ, encoded by Wwtr1) is a mechanotransducer implicated in maintaining homeostasis of corneal endothelial cells (CEnC). Wwtr1-/- (TAZ KO) mice serve as an animal model for late-onset FECD. We combined single-cell transcriptomics, transmission electron microscopy, and immunofluorescence staining to elucidate the mechanisms driving pathogenesis in young (2-month-old) and geriatric (11-month-old) mice. A progressive stress response was observed in TAZ KOs defined by endoplasmic reticulum (ER) stress, mitochondrial structural and functional abnormalities, and impaired Na+/K+ ATPase localization. These changes were accompanied by an altered expression of genes involved in extracellular matrix (ECM) organization, oxidative phosphorylation, macroautophagy and response to oxidative stress. Additionally, we noted age-related differences in cellular response with young TAZ KO CEnCs upregulating macroautophagy and downregulating ECM organization while geriatric TAZ KO CEnCs downregulated macroautophagy, and ECM organization. Both TAZ KO groups downregulated response to oxidative stress and cell-substrate adhesion. Together, these findings establish a mechanistic link between disrupted mechanotransduction and organelle stress in CEnC degeneration, further elaborating on potential mechanisms driving FECD pathogenesis. This positions TAZ KO mice as a translational platform for evaluating non-surgical therapeutic strategies targeting FECD. Significance statementFuchs endothelial corneal dystrophy (FECD) is a common, age-related cause of vision loss involving a depletion of corneal endothelial cells (CEnC) that necessitates corneal transplantation. Understanding why corneal endothelial cells progressively fail in this disease is essential for developing non-surgical therapies. Using transcriptomics, electron microscopy and immunofluorescence staining, we demonstrate that loss of the mechanotransducer TAZ disrupts cellular homeostasis by inducing endoplasmic reticulum stress, mitochondrial dysfunction and improper extracellular matrix and functional protein organization in CEnCs. By linking altered mechanotransduction to organelle stress and endothelial cell loss, these findings provide insight into fundamental disease mechanisms and identify pathways that may be targeted to preserve corneal endothelial function in FECD.

pathology↗

bioRxiv2026

Accurate somatic variant detection from formalin fixed, paraffin embedded tissue (FFPE) derived WES and WGS by DeepOmicsFFPE-PLUS, a sequence context-based transformer

Formalin-fixed paraffin-embedded (FFPE) tissues represent a vast archival resource for genomic studies, yet their utility remains constrained by fixation-induced DNA damage and subsequent sequencing artifacts. To comprehensively characterize and address this challenge, we analyzed matched FFPE and fresh-frozen tumor samples from two institutions, spanning different storage durations, DNA qualities, sequencing platforms (WES and WGS), exome capture kits, and somatic variant callers. We found that FFPE-induced artifacts exhibit strong batch- and age-specific patterns, with a predominance of C:G>T:A substitutions, which particularly complicate the accurate identification of low allele frequency true variants. Enzymatic repair methods partially alleviated these artifacts but remained insufficient. To overcome these limitations, we developed DeepOmicsFFPE-PLUS(https://github.com/Theragen-Bio/DeepOmicsFFPE-PLUS), an advanced AI-based tool to accurately distinguish true somatic variants from FFPE-specific artifacts. DeepOmicsFFPE-PLUS demonstrated consistently superior performance across diverse conditions, achieving high sensitivity and specificity--even for low-frequency variants--outperforming existing tools. Application of our model to WGS data further enabled recovery of biologically relevant mutational signatures, including restoration of microsatellite instability (MSI)-associated signatures initially obscured by FFPE artifacts. Our findings underscore the necessity of artifact-aware variant calling in FFPE genomics and establish DeepOmicsFFPE-PLUS as a robust tool for artifact removal, enabling high-fidelity downstream analyses and personalized therapeutic target discovery.

cancer biology↗

bioRxiv2026

ro-crate-rs: Development of a Lightweight RO-Crate Rust Library for Automated Synthetic Biology

Advances in laboratory automation and AI-driven experimental design have increased the scale and complexity of data generated in synthetic biology. Whilst biofoundries provide significant resources and infrastructure to execute these experiments, most laboratories rely on isolated automated instruments and software systems that operate as disconnected silos, producing heterogeneous data formats with little structured metadata. This fragmentation hinders data integration, reproducibility, and downstream computational workflows. A potential solution is RO-Crate, which offers a lightweight, extensible framework for packaging research data with machine-readable metadata, but existing tooling remains immature for automation-orientated, cloud-native, or high-throughput laboratory workflows. Here, we introduce ro-crate-rs, a new suite of tools centred on a performant Rust library for constructing, validating and packaging RO-Crates across diverse compute environments and automated hardware. The library enforces RO-Crate 1.1 constraints through strong typing while enabling flexible extensions, and is complemented by a Python API and CLI for interactive use and pipeline integration. We demonstrate this combined approach through a semi-automated Old Yellow Enzyme characterisation workflow, showing how RO-Crates can capture data and metadata across multiple independent instruments. Together, these tools provide a robust foundation for FAIR-compliant, automation-ready data management and enable reproducible reconstruction of experimental workflows even in non-biofoundry settings. Availabilityhttps://github.com/intbio-ncl/ro-crate-rs

synthetic biology↗

bioRxiv2025

Amplification-Free Dual-Blocking Autocatalytic CRISPR-Cascade for Atto-Molar DNA Detection with Low Nonspecific Signal

Autocatalytic CRISPR architecture offers amplification-free nucleic acid detection by directly linking target recognition to self-reinforcing ribonucleoprotein (RNP) generation. However, spontaneous background activation remains a key barrier, because strand invasion or unwinding events can initiate unintended amplification and diminish assay specificity. Here, we introduce a dual-blocking CRISPR-Cascade design that independently cages both the guide RNA and trigger DNA, establishing an intrinsic AND gate to raise the effective kinetic barrier for unintended RNP formation. This strategy suppresses leakage by approximately 3- to 18-fold relative to single blocking configurations in full Cascade reactions, while preserving rapid detection (10 min), achieving single-copy sensitivity, and enabling quantitative detection. When paired with a competitive guide RNA decoy, the system further reduces background signals without affecting true target detection. Finally, we demonstrate robust Methicillin-resistant Staphylococcus aureus (MRSA) detection from whole blood in under 40 minutes including the sample purification and extraction. These results establish dual-blocking as a generalizable molecular gating framework for constructing leakage-resistant, amplification-free CRISPR systems suitable for rapid and decentralized diagnostics. SignificanceAmplification-free CRISPR diagnostics are often presented as simple positive feedback circuits, but most existing systems treat leakage, defined as target independent background activation that arises when blocked CRISPR components spontaneously form active ribonucleoprotein (RNP), as an unavoidable side effect rather than as a designable property. In particular, prior work has not explicitly accounted for two key sources of background signal in autocatalytic assays: Cas driven unwinding of blocked constructs and transient breathing of nucleic acid duplexes that intermittently expose trigger sites. Our study directly analyzes these leakage pathways in the context of switchable-cage-gRNA (scgRNA) and Cascade probe design and shows that blocking a single component is fundamentally vulnerable to both enzyme-driven strand invasion and equilibrium breathing. By contrast, we introduce a dual-blocking strategy in which both the guide RNA and the trigger DNA are gated. We further add a decoy guide RNA that competes for Cas12a. This multi-layer architecture demonstrates that robust amplification-free operation requires several coordinated barriers rather than a single switch, providing a new design principle for constructing self-amplifying CRISPR circuits with low background and robust signal-to-noise ratio.

molecular biology↗

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

Clonal dynamics of monozygotic twinning in early human embryogenesis

Monozygotic twins are derived from the split of a single zygote early in embryogenesis. Although it was hypothesized that the timing of twining is overall associated with fetal membrane configuration of twins, i.e., chorionicity and amnionicity, our understanding of early embryonic clonal dynamics underlying human twinning is limited. Here we explored the segregations of early embryonic lineages in 7 dichorionic diamniotic (DCDA), 7 monochorionic diamniotic (MCDA), 8 monochorionic monoamniotic (MCMA) monozygotic twins, and 1 dichorionic triamniotic (DCTA) monozygotic triplets, using post-zygotic early embryonic mutations (EEMs) as endogenous lineage barcodes. Patterns of the early lineage distributions among monozygotic twins revealed three apparent clonal categories, referred to as para-identical, sub-identical, and full-identical twins, which largely correlated with the amnionicity of the twins. Rather, despite conventional wisdom, chorionicity was not substantially associated with early clonal compositions, but with blood exchanges in utero. In sub-identical twins, where one co-twin was clonally a part of the other, our data suggested that the foundation of the latter co-twin was established after acquisition of a median of 6 additional post-zygotic mutations (range: 2-13), corresponding to [~]5 early cell divisions. Additional in-depth analysis on the matched placenta from an MCDA twin suggested that separation of two co-twins can precede the separation of the placenta and embryonic proper, and a single chorion can be formed even with multiclonal origin. Our findings provide insights into the clonal dynamics, twinning processes, and cell fate decisions in early human embryogenesis.

genomics↗

bioRxiv2025

Rapid discovery of antiviral targets through dimensionality reduction of genome-scale metabolic models

The COVID-19 pandemic underscored the urgent need for rapid and broadly applicable strategies to identify antiviral targets against emerging pathogens. Conventional approaches, which rely on detailed viral characterization and large-scale drug screening, are too slow to address this challenge. Here, we introduce a transcriptome-based computational framework that integrates genome-scale metabolic models with dimensionality reduction to uncover host metabolic vulnerabilities that support viral replication. Applying this approach to bulk and single-cell RNA-seq data from HCoV-OC43-infected cells and organoids identified oxidative phosphorylation as a key vulnerability, and pharmacological inhibition of complex I effectively curtailed viral replication. Extending the framework to SARS-CoV-2 and MERS-CoV revealed pyrimidine catabolism as a conserved antiviral pathway, with inhibition of its rate-limiting enzyme DPYD suppressing replication in organoid models. Re-analysis of SARS-CoV-2 patient metabolome data further confirmed elevated DPYD activity, underscoring its clinical relevance. Together, these findings establish a generalizable and rapid strategy for host-directed antiviral discovery, providing a foundation for precision therapeutics and pandemic preparedness. Significance StatementHost-directed antiviral therapies offer several advantages in antiviral research, but identifying key host factors poses a significant challenge. By integrating genome-scale metabolic models with single-gene knockout simulation and dimensionality reduction, we developed a computational framework based on single and bulk RNA-seq data that can systematically pinpoint host pathways whose downregulation is predicted to rewire virus-induced metabolic alterations. Applying this approach to multiple human coronaviruses reveals unique metabolic vulnerabilities, and we experimentally demonstrate that inhibiting these host metabolic pathways reduces viral replication. This framework provides a generalizable antiviral strategy to discern effective targets and can be further extended to investigate virus-host metabolic interactions.

systems biology↗

bioRxiv2025

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a weakly-supervised multiple instance learning (MIL) machine learning method to close this gap in interpretable subcellular discovery. siMILe identifies condition-specific changes in protein assemblies by leveraging their shape and network features, without requiring structure-level supervision. siMILe improves structure classification by extending embedded instance selection (MILES) through adversarial erasing and a symmetric classifier. We validated siMILe by detecting caveolae from caveolin-1 (Cav1) labeled PC3 prostate cancer cells differentially expressing cavin-1. In PC3-CAVIN1 cells, cavin-1 closely associates with siMILe-identified caveolae, to a lesser extent with higher-order non-caveolar Cav1 scaffolds, but not small Cav1 oligomers corresponding to 8S complexes, supporting a role for progressive cavin-1 interaction in 8S complex oligomerization. We also validated siMILe on simulated SMLM data and in detecting inhibitor-induced structural variations within clathrin-coated pit data. These results highlight siMILes potential to identify differential molecular structures in distinct cell conditions. siMILe extends the SuperResNET SMLM software platform with the ability to detect interpretable structural differences across conditions.

bioinformatics↗

bioRxiv2025

Structural analysis of HERC2/UBE3A and HERC2/DOCK10 complexes provides new insights into the molecular basis of Angelman, Angelman-like and Dup15q Syndromes

UBE3A and HERC2 are two mutually interacting HECT E3 Ubiquitin ligases whose genes are altered in 15q11.2-13.1 Duplication (Dup15q) Syndrome, Angelman Syndrome (AS) and other phenotypically-related mental retardation syndromes. Using quantitative binding assays, X-ray crystallography and sequence conservation analysis, we show that the HERC2/UBE3A complex occurs in probably most animals with a central nervous system, via a conserved interface involving the RLD2 domain of HERC2 and a "DxDKDxD" motif of UBE3A. We found that HERC2 also recognizes and binds to similar DxDKDxD motifs within a handful of other proteins relevant to brain development (DOCK10, PCM1, USP35, BAZ2B, ARID4A, ARIP4, RERE and MYT1). We further investigated the interaction of HERC2 with DOCK10, a RAC1- and CDC42-GEF protein that regulates dendritic spine morphogenesis in hippocampal neurons. Both disruption of the HERC2-binding motif in DOCK10 and knockdown of HERC2 affected the GEF activity of DOCK10. We also show that the DOCK10-induced dendritic spine formation is dependent on its ability to bind HERC2. Structural modeling of full-length DOCK10, free or bound to either RLD2, CDC42 or RAC1 indicates that the GEF activation of full-length DOCK10 requires a conformational change that is stimulated by binding to HERC2. Based on our data, we propose that under pathological conditions, in developing brains with an abnormal dosage of either HERC2 or its dominant partner UBE3A, increased or decreased amounts of HERC2/DOCK10 complexes could lead to altered GTPase activation. This in turn could affect dendritic spine formation and neurodevelopment.

biochemistry↗

bioRxiv2025

Accelerating Drug Discovery with HyperLab: An Easy-to-Use AI-Driven Platform

HyperLab, developed by HITS, is a web-based, AI-driven drug discovery platform designed to increase research efficiency for experimental drug discovery researchers. The platform features an intuitive user interface and experience (UI/UX), enabling researchers without specialized expertise in AI or computational methods to readily generate essential discovery outcomes. By employing a Structure-Based Drug Discovery (SBDD) methodology, HyperLab streamlines the complete discovery workflow. Its core functionalities include the prediction of ligand-protein structures and binding activities (Hyper Binding, Covalent Hyper Binding), protein structure-based molecular optimization (Hyper Design), structure-activity-relationship (SAR) analysis, screening of extensive chemical libraries ranging from one million to seven trillion compounds (Hyper Screening and Hyper Screening X), prediction of ADMET properties (Hyper ADME/T), and an AI-driven assistant designed to boost researcher productivity and efficiency. In benchmark evaluations, Hyper Binding demonstrated 77% accuracy for binding pose prediction on the PoseBuster v2 benchmark, surpassing conventional docking techniques and closely approaching the 84% accuracy of AlphaFold3, while offering a considerable advantage in computational speed. Furthermore, for binding affinity prediction, HyperBinding showed superior performance over both deep learning and physics-based docking models on two distinct FEP datasets, achieving Pearson correlation coefficients of 0.70 and 0.53, respectively. For experimental validation, an internal study utilized Hyper Screening to identify top-ranked compounds without without any post-analysis or visual inspection by human experts. The screening process was completed in 24 hours. Through experimental validation of top-ranked compounds, five compounds with IC50 values ranging from 70 to 600 nM were identified. Hyper Design was employed to generate novel derivatives with improved predicted binding scores and structural novelty. Of five synthesized compounds, in vitro assays confirmed that three demonstrated over 75% inhibition at a 1 {micro}M concentration, with IC50 values in the 200 to 400 nM range. Notably, one compound exhibited activity comparable or superior to the reference compound. HyperLab is therefore positioned to substantially lower the barriers of time, cost, and specialized expertise inherent in modern drug discovery initiatives.

biochemistry↗

bioRxiv2025

BiGER: Bayesian Rank Aggregation in Genomics with Extended Ranking Schemes

With the rise of large-scale genomic studies, large gene lists targeting important diseases are increasingly common. While evaluating each study individually gives valuable insights on specific samples and study designs, the wealth of available evidence in the literature calls for robust and efficient meta-analytic methods. Crucially, the diverse assumptions and experimental protocols underlying different studies require a flexible but rigorous method for aggregation. To address these issues, we propose BiGER, a fast and accurate Bayesian rank aggregation method for the inference of latent global rankings. Unlike existing methods in the field, BiGER accommodates mixed gene lists with top-ranked and top-unranked genes as well as bottom-tied and missing genes, by design. Using a Bayesian hierarchical framework combined with variational inference, BiGER efficiently aggregates large-scale gene lists with high accuracy, while providing valuable insights into source-specific reliability for researchers. Through both simulated and real datasets, we show that BiGER is a useful tool for reliable meta-analysis in genomic studies.

genomics↗

bioRxiv2025

Multiplexed, scalable analog recording of gene regulation dynamics over weeks using intracellular protein tapes

Gene expression is constantly regulated by gene regulatory networks that consist of multiple regulatory components to mediate cellular functions. An ideal tool for analyzing gene regulation processes would provide simultaneous measurements of the dynamics of many components in the gene regulatory network, but existing methodologies fall short of simultaneously tracking the dynamics of components over long periods of time. Here, we present CytoTape--a genetically encoded, modular, and scalable analog recorder for continuous, multiplexed in situ recording of gene regulation dynamics over multiple days and weeks at single-cell resolution. CytoTape consists of a flexible, thread-like, elongating intracellular protein self-assembly engineered via AI-guided rational design. Gene regulation dynamics, together with timestamps for reconstruction of the continuous time axis, are directly encoded via distinct molecular tags distributed along single CytoTape assemblies in live cells, to be readout at scale after fixation via standard immunofluorescence imaging. CytoTape recorders are modularly designed to record gene expression driven by a variety of activity-dependent promoters. We demonstrated the utility of CytoTape in mammalian embryonic kidney cells, cancer cells, glial cells, and neurons, achieving simultaneous recording of five cell plasticity-associated transcription factor activities and immediate early gene expression levels, namely CREB, c-fos, Arc, Egr1, and Npas4 activities, within single cells in a spatiotemporally scalable manner. CytoTape revealed complex waveforms and nonlinear temporal couplings among these cellular activities, enabling investigations of how gene regulation histories and intrinsic signaling states shape transcriptional logics. We envision CytoTape to have broad applications in both basic and disease-related cell biology research.

synthetic biology↗

bioRxiv2025

H2O2-induced astrocytic collagen triggers neuronal death via fucosylation-dependent glial barrier formation upon ischemic stroke

The cascade of molecular and cellular events leading to neuronal death after a focal ischemic stroke remains enigmatic. Although astrocytes form glial barriers that may protect surrounding tissue, how barriers develop and contribute to neuronal death is unclear. Here, we show that H2O2 induces astrocytic type I collagen (COL1)-production via miR-29-mediated post-transcriptional and fucosylation-dependent post-translational regulations, leading to integrin activation and neuronal death. In photothrombosis (PT)-induced cortical stroke model, PT triggered H2O2-surge, astrogliosis, glial barrier formation, COL1 expression, fibrotic scarring, altered N-glycosylation, neuronal loss, and neurological deficits. Remarkably, these effects were reversed by astrocyte-specific COL1 or FUT8 gene-silencing or treatment with KDS12025, an H2O2-decomposing peroxidase enhancer. KDS12025s neuroprotective effects were also recapitulated in non-human primate stroke model. These findings delineate a previously unrecognized astrocyte-driven mechanism in which oxidative stress induces COL1 production, promoting neuronal death, and position H2O2 and astrocytic COL1 and FUT8 as promising therapeutic targets for ischemic stroke.

neuroscience↗

bioRxiv2025

The Adolescent Functional Connectome is Dynamically Controlled by a Sparse Core of Cognitive and Topological Hubs

Fundamental mechanisms that control the brains ability to dynamically respond to cognitive demands are poorly understood, especially during periods of accelerated neural and cognitive maturation, such as adolescence. Using a sparsity-promoting feedback control framework we investigated the controllability of the adolescence functional connectome. Critical feedback costs associated with a regions control action on itself and the rest of the brain were estimated using resting-state fMRI data from an early longitudinal sample in the Adolescent Brain Cognitive Development (ABCD) study (n = 1394; median (IQR) age = 10.1 (1.1) years at baseline and 12.1 (1.1) years at follow-up). A highly reproducible, core set of predominantly highly connected regions retained their control action over the connectome under high feedback costs. They included posterior visual areas, retrosplenial cortex, cuneus and precuneus, superior parietal lobule, temporal ventral cortex and dorsolateral and lateral prefrontal cortices, i.e., both developed and developing brain regions. These regions were central to the topological organization of the connectome, consistently engaged during spontaneous coordination of resting-state networks, and overlapped with cognitive and topological brain hubs that play ubiquitous roles in cognitive function and the organization of the connectome. Also, most received (integrated) and distributed approximately equal amounts of neural information. These regions control action was developmentally stable, i.e., critical feedback costs did not change significantly during puberty, suggesting that, despite ongoing maturation and topological changes in the adolescent brain, fundamental mechanisms of system controllability may be well developed to facilitate information processing and response to cognitive demands.

neuroscience↗

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

Temporally overlapping mechanisms diversify clonal B cell responses in vivo.

Naive B cells amplify and diversify their responses when activated by cognate antigen, via Myc-dependent clonal expansion, immunoglobulin class switch recombination (CSR), phenotypic variation, and somatic hypermutation (SHM). Whether these mechanisms act combinatorially in vivo to diversify clonal responses to a single pathogen remains unclear. Since diversity in the antigenic targets, functional classes, and production kinetics of parasite-specific antibodies influences immunity to malaria, we test here whether individual B cell clones diversify over time during Plasmodium infection and treatment. During the first week of infection, amid widespread Type I Interferon (IFN)-mediated bystander activation, CSR initiates soon after Myc up-regulation, and overlaps partially with clonal expansion, resulting in isotype variegation amongst clones. During the second week of infection, expanded clones that seed germinal centres (GC) bifurcate into extra-follicular plasmablasts, exhibit isotype variegation, and initiate SHM, revealing substantial intra-clonal diversification. Over the following month, GC clones exhibit SHM at approximately four mutations per week, with IgG mutational diversity and IgM+ cells also preserved in GCs over time. Anti-malarial intervention does not impede SHM, but instead exerts quantitative limits on GC size, plasma cell emergence, circulating IgG levels, and protection against re-infection. Finally, contemporaneous B cell development relocates from bone marrow to spleen during infection and treatment. Thus, multiple temporally overlapping mechanisms combine in vivo to amplify, diversify, and safeguard humoral immune responses. We present this data as a temporal, multi-parameter atlas of B cell differentiation in vivo: https://bcell-dynamics.science.unimelb.edu.au Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/628863v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@c57890org.highwire.dtl.DTLVardef@6e5936org.highwire.dtl.DTLVardef@a2e872org.highwire.dtl.DTLVardef@14abbac_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPartial temporal overlap of CSR with clonal expansion leads to isotype variegation in clones. C_LIO_LIClones seeding GCs bifurcate into plasmablasts and exhibit isotype variegation. C_LIO_LIGC B cells accrue [~]4 mutations/week, a rate unaffected by anti-malarials. C_LIO_LIPlasmodium infection triggers antigen-independent Type I IFN-mediated bystander activation. C_LIO_LIB cell development is preserved in malaria by shifting from bone marrow to spleen. C_LI

immunology↗