Biology subjects

Park, H.

Publications and source records attributed to Park, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

The Role of Pavlovian Bias in Smoking Cessation Success and Neurocognitive Alterations During Nicotine Withdrawal

BackgroundInability to manage conflicts between multiple decision-making systems is associated with various mental disorders including addiction. The imbalance between the Pavlovian and instrumental systems may contribute to smoking cessation failure and relapse, as excessive Pavlovian motivation may override goal-directed quitting attempts. However, the exact role of the Pavlovian bias in smoking cessation and its neurocognitive mechanisms remain poorly understood. MethodsEighty-six smokers participated in a smoking cessation clinic, completing the well-established orthogonalized go-nogo task before and after the intervention. We applied computational modeling and functional magnetic resonance imaging (fMRI) analyses to investigate the role of Pavlovian bias and its neural correlates in predicting smoking cessation outcomes. ResultsComputational modeling revealed that higher levels of Pavlovian bias were associated with treatment failures. Importantly, Pavlovian bias significantly moderated the relationship between clinic participation rate and the likelihood of smoking cessation, suggesting that such bias can undermine motivational effort to quit. Furthermore, individuals who successfully quit smoking exhibited increased Pavlovian bias toward reward following cessation, indicating a potential cognitive mechanism underlying the relapse cycle. Complementary fMRI analyses demonstrated that Pavlovian bias influenced valence encoding in the ventromedial prefrontal cortex (vmPFC), suggesting altered decision-making neural circuits. ConclusionsPavlovian bias can be a critical risk factor of relapse in the smoking population, while this bias can be increased in response to nicotine abstinence. Moreover, the results highlight the roles of the vmPFC in decision-making under Pavlovian-instrumental conflict, providing insights into the neural underpinnings of relapse vulnerability. Understanding these mechanisms may guide targeted interventions to enhance smoking cessation outcomes.

neuroscience↗

bioRxiv2025

Shared Binding Site but Divergent Resistance Profiles Uncover Novel Resistance Mechanisms in Plasmodium HSP90 Inhibitors

Drug resistance is a widespread problem across therapeutic areas including malaria, but what accounts for resistance propensity remains poorly understood. Here, we reveal that two HSP90 inhibitors targeting the identical ATP-binding site exhibit dramatically different resistance profiles in P. falciparum. Geldanamycin readily selected 10 distinct resistance mutations conferring up to 22-fold resistance, while AUY-922 required 44 weeks to yield a single A41S mutation with only 2-fold resistance to AUY-922 but not to geldanamycin. Resistance mapping in parasites and yeast revealed geldanamycin resistance mutations distributed throughout the binding pocket whereas AUY-922 resistance mutations localized close to the ATP-binding site. Unexpectedly, the A41S mutation enhanced AUY-922 binding affinity without changing geldanamycin binding. In silico analysis suggested this enhancement occurs through additional hydrogen bonding, yet stronger binding correlated with resistance. In yeast, A41S had opposite effects, hypersensitizing cells to all HSP90 inhibitors tested. Additionally, conditional HSP90 knockdown increased geldanamycin sensitivity but left AUY-922 activity unaffected, indicating different target dependencies despite shared binding sites. Based on these data, we propose a multi-target hypothesis where AUY-922s lower resistance risk stems from engaging multiple HSP90 family members. Our findings reveal how enhanced drug-target binding can paradoxically correlate with resistance and demonstrate that resistance risk cannot be predicted from binding site identity alone, providing insights for developing more durable drugs across therapeutic areas.

microbiology↗

bioRxiv2025

Allele-specific correction of ATAD3A pathogenic variants via template-free CRISPR-Cas9 editing and gene conversion

Gene conversion is a specific form of homologous recombination (HR), involving the unidirectional transfer of genetic information from one genomic locus to another. CRISPR-Cas9-directed double strand breaks (DSBs) induce both interallelic and interlocus gene conversion in early human embryos and somatic cells, suggesting its potential for correcting pathogenic mutations. However, the key features in mitotic gene conversion, including its efficiency, the length of conversion track, and its dependency on specific recombination proteins, remain largely undefined. Here, we show that allele-specific CRISPR-Cas9-induced DSBs, without exogenous donor templates, can efficiently correct a heterozygous pathogenic variant (c.1582C>T; p.Arg528Trp) in the ATAD3A gene of patient-derived induced pluripotent stem cells (iPSCs). Amplicon-based next-generation sequencing (NGS) revealed that approximately 38%[~]53% of edited iPSCs carried two wild-type ATAD3A alleles. Notably, over 99% of the corrected alleles derived from the homologous chromosome, indicating that the repair occurred mainly via interallelic gene conversion. Long-range amplicon nanopore sequencing coupled with haplotype analysis showed that the majority of gene conversion tracts was less than 2 kilobases in length. Whole-genome sequencing of three corrected iPSC clones showed the absence of large deletions or structural rearrangements at the ATAD3A target site. However, one clone carried a heterozygous deletion in ATAD3B locus, suggesting that CRISPR-Cas9 can introduce off-target genomic alterations. Knockdown of key HR proteins, including RAD51, CtIP, and BRCA1/2, significantly reduced the correction efficiency, indicating that the gene conversion relies on a RAD51-dependent HR pathway. Together, our findings provide compelling evidence that template-free CRISPR-Cas9-mediated interallelic gene conversion can be harnessed to correct disease causing variants in human iPSCs.

genomics↗

bioRxiv2025

Organelle-Aware Representation Learning Enables Label-Free Detection of Mitochondrial Dysfunction in Live Human Neurons

Mitochondrial dysfunction is a convergent hallmark of neurodegenerative diseases and represents a promising biomarker for early diagnosis and therapy. However, current in vitro assays rely on fluorescence or electron microscopy, which are invasive, low-throughput, and incompatible with longitudinal analysis. Here, we present a noninvasive framework by integrating label-free optical diffraction tomography (ODT) with organelle-aware representation learning to detect subtle mitochondrial dysfunction in live human induced pluripotent stem cell (hiPSC)-derived neurons. Through virtual staining of the nuclei, lysosomes, and mitochondria, we establish two complementary and interpretable classification pipelines: a deep learning model with organelle-aware encoder and a logistic regression model on morphometric descriptors. Both models achieve approximately 85% accuracy: the deep model provides end-to-end prediction with minimal feature engineering, whereas the logistic regression model offers a more interpretable, feature-based approach. To our knowledge, this is the first demonstration of ODT-based organelle-resolved virtual staining in live human neurons, establishing a scalable, non-invasive platform for mitochondrial disease modeling, drug discovery, and neurodegeneration research.

neuroscience↗

bioRxiv2025

A Pangenomic Method for Establishing a Somatic Variant Detection Resource in HapMap Mixtures

Somatic mosaicism is essential in human biology and disease, yet robust benchmarks are scarce. The SMaHT Consortium mixed six HapMap cell lines to create artificial somatic variants spanning 0.25% to 16.5% variant allele fractions. We developed a technology-agnostic method that builds pangenome graphs from individual assemblies to create unified benchmarking sets: > 6M single-nucleotide variants, 1.8M small insertions/deletions, 49K structural variations, and 10K mobile element insertions across autosomes, X, and mitochondrial chromosomes. We validated the variants using ultra-deep simulated reads and developed a binomial-based model to estimate coverage requirements for variant detection. Evaluating multiple callers showed CHM13 alignment improves structural variant detection and offers advantages in difficult-to-map regions compared to GRCh38. Systematic characterization showed regions with low detection rate are enriched in centromeres, satellite sequences, tandem repeats, and falsely duplicated genes. This accurate, versatile resource enables systematic evaluation of somatic variant detection technologies.

genomics↗

bioRxiv2025

VEGFR2 blockade overcomes acquired KRAS G12D inhibitor resistance driven by PI3Kγ activation

KRAS G12D mutation is a key oncogenic driver in many solid tumors, including pancreatic, gastric, and colorectal cancers. While recent studies have characterized features associated with primary and acquired resistance to KRAS inhibitors, strategies to overcome such resistance, particularly in the context of gastrointestinal cancers, remain underexplored. Here, we have generated nine human gastrointestinal cancer models, including three patient-derived organoids (PDOs), with acquired resistance to the KRAS G12D-selective inhibitor MRTX1133. Using single-cell RNA sequencing analysis, we identified the enrichment of angiogenesis, hypoxia, and epithelial-to-mesenchymal transition (EMT) signatures in the resistant model compared to the parental PDO. Across all resistant models, VEGFA expression and VEGFR2 phosphorylation were uniformly elevated, which were driven by AKT activation and SP1 nuclear translocation. Mechanistic investigations uncovered increased PI3K{gamma} activity in MRTX1133-resistant models via complex formation of KRAS with p110{gamma} and p101. This leads to an autocrine VEGFA-VEGFR2 signaling loop formation and EMT induction. Therapeutically, the disruption of VEGFA-VEGFR2 signaling restored MRTX1133 sensitivity and inhibited EMT. Furthermore, cancer-endothelial paracrine signaling amplified angiogenesis, hypoxia, and EMT signatures in cancer cells and simultaneously promoted endothelial cell proliferation, reinforcing an adaptive feedback mechanism. In a mouse model of MRTX1133-resistant tumor xenograft, a combination of anti-VEGFR2 therapy and MRTX1133 more effectively reduced tumor growth, angiogenesis, and proliferation markers than monotherapy without significant body weight change. These findings establish VEGFA-VEGFR2 signaling by PI3K{gamma} activation as a key driver of acquired resistance to KRAS G12D inhibition and provide a rationale for combining VEGFA-VEGFR2 inhibition with KRAS blockade in KRAS-mutant cancers. HighlightO_LIVEGFA-VEGFR2 signaling activation is a common feature of MRTX1133 resistance in KRASG12D cancer cells C_LIO_LINuclear translocation of SP1 by AKT activation promotes VEGFA transcription in MRTX1133-resistant models C_LIO_LIInteraction of p110{gamma}-p101 with KRAS activates PI3K{gamma} in the resistant models C_LIO_LIVEGFA-VEGFR2 inhibition reverses MRTX1133 resistance in vitro and in vivo C_LI

cancer biology↗

bioRxiv2025

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles

In microbiome studies, sample processing errors are frequent and difficult to detect, especially in large studies involving multiple sites, personnel, and sample types. We present two complementary approaches to identify such errors using host DNA profiled via metagenomic sequencing of microbiome samples. The first approach compares host SNPs inferred from metagenomics to independently obtained genotypes (e.g., microarray genotypes) to match samples to their donors, while the second method compares metagenomics-inferred SNPs between samples to identify samples supplied by the same donor. Furthermore, we demonstrate that combining these methods with experimental metadata provides greater confidence in the identification of errors. Analyzing a longitudinal vaginal microbiome dataset, we demonstrate the ability of our approach to identify mislabeled samples. Using subsampling, we further show that our methods are robust to low sequencing coverage. Overall, our analysis highlights the frequency of processing errors in microbiome studies. We therefore recommend applying error-detection methods in all studies with suitable data.

bioinformatics↗

bioRxiv2025

Loss of presenilin 2 function age-dependently increases susceptibility to kainate-induced acute seizures and blunts hippocampal kainate-type glutamate receptor expression

Presenilin 2 (PSEN2) variants increase risk of Alzheimers disease (AD) and unprovoked seizures. Yet, age-related PSEN2 contributions to seizure susceptibility are understudied. Critically, PSEN proteolytic capacity may regulate hippocampal kainate-type glutamate receptor (KARs) availability. Kainic acid (KA) is a KAR agonist that evokes severe seizures in mice. We hypothesized that PSEN2 knockout (KO) mice would show reduced latency to KA-induced seizures, increased seizure burden, worsened 7-day survival, and altered hippocampal KAR expression compared to wild-type (WT) controls. Using repeated low-dose systemic KA administration to 3-4- and 12-15-month-old PSEN2 KO versus WT mice, we quantified acute seizure latency and neuropathology. GluK2 and GluK5 KAR subunit expression was colocalized in astrocytes 7 days after seizures or sham to assess the impact of PSEN2 loss and seizures on hippocampal KARs. Young PSEN2 KO mice were more seizure-prone than WTs, while genotype did not change latency to first seizure. Aged females seized faster than young females and experienced greater mortality, unlike males. There was no difference in KAR subunit expression between young mouse genotypes and regardless of seizure history. In both genotypes, hippocampal CA3 astrocytes expressed GluK5 after seizures, however, astrocytic GluK2 upregulation only occurred in WTs. GluK5 expression was significantly reduced in aged seizure-naive PSEN2 KO versus WT mice, while total GluK2 expression did not differ. Seizure-induced astrocytic GluK5 expression only occurred in WT mice in CA3, while astrocytic GluK2 expression occurred in both. Thus, PSEN2 loss may impair age-related hippocampal KAR expression, implicating KARs as understudied contributors to AD-related seizures. Highlights.- Alzheimers disease (AD) and epilepsy share many pathophysiological links, but the contributions of distinct AD-risk factors are understudied. - Loss of normal presenilin 2 function is associated with AD; its role in late-life seizures and neuropathology have been minimally evaluated. - Loss of presenilin 2 increases susceptibility to kainic-acid induced acute seizures in young adult mice, mirroring late life risk. - Increased susceptibility to kainic acid induced acute seizures due to loss of PSEN2 is lost in late life, suggesting earlier senescence. - Loss of presenilin 2 function in aged mice is associated with reduced GluK5 expression and greater astrogliosis under basal conditions.

neuroscience↗

bioRxiv2025

O-Acyltransferase Genes Involved in the Production of Volatile Sex Pheromones in Caenorhabditis elegans

Gene family expansions are critical for functional diversification, yet paralog contributions to metabolic pathways are often unclear. In Caenorhabditis, the expanded O-acyltransferase (OAC) family--enzymes that transfer acyl groups to hydroxylated substrates--remains poorly characterized despite having been implicated in lipid metabolism. Using CRISPR-Cas9 mutagenesis, behavioral assays, gas chromatographic-mass spectral (GC-MS) analyses, and metabolomics, we systematically analyzed 59 OAC-family protein-coding genes to define their roles in regulating signaling molecules. We found that four adjacent paralogs (oac-13, oac-16, oac-25, and oac-28) on chromosome I are required for synthesizing volatile sex pheromones (VSPs)--airborne signals critical for male mate-searching. Specifically, oac-13 and oac-16 are necessary for producing both major pheromone components, while the identical tandem paralogs oac-25 and oac-28 regulate the production of the later-eluting component in gas chromatography. Disruption of these genes reduced production of key pheromone components and impaired male attraction. Metabolomics revealed that oac-16 and other OACs also modulate synthesis and secretion of non-volatile ascaroside pheromones, indicating dual roles in chemical signaling. This work uncovers functional specialization within an expanded gene family, illustrating how redundancy and divergence enable adaptive evolution of communication systems.

biochemistry↗

bioRxiv2025

SHARP: Generating Synthesizable Molecules via Fragment-based Hierarchical Action-space Reinforcement Learning for Pareto Optimization

Designing drug-like molecules that satisfy multiple objectives--such as high binding affinity, synthesizability, and drug-likeness--poses a complex global optimization problem over an astronomically large chemical space. Existing deep learning-based molecular generative models often treat this task as distribution modeling, relying on atom-level autoregressive actions with less consideration of explicit optimization feedback. Consequently, they frequently generate invalid structures, converge to local optima, or produce synthetically infeasible candidates. Here, we introduce SHARP (Synthesizable Hierarchical Action-space Reinforcement learning for Pareto optimization), a molecular generator that addresses these limitations via a fragment-based hierarchical action space and reinforcement learning. SHARP ensures synthetic accessibility by applying action masks guided by a pretrained Synthesizability Estimation Model (SEM). The reinforcement learning (RL) policy is trained using a composite reward function integrating docking scores, pharmacophore matching, and solvent accessibility to generate functionally relevant and experimentally tractable molecules. Furthermore, across four lead optimization tasks--fragment growing, linker design, scaffold hopping, and sidechain decoration--on a diverse receptor set, SHARP consistently outperforms prior methods in producing molecules at high affinity and synthesizability. These results demonstrate that reinforcement learning with a chemically intuitive action space design can be an effective solution to the optimization challenges in AI-driven drug discovery, offering a robust framework for rational molecular design in structure-based applications.

bioinformatics↗

bioRxiv2025

Restoring compromised Blood-Retina-Barrier integrity with Netrin-1 overexpression

The blood-retina barrier (BRB) protects retinal neuronal function and enables vision. A compromised, leaky BRB is a hallmark of vision-threatening retinal diseases such as diabetic retinopathy (DR) and wet age-related macular degeneration (AMD) that affect millions of persons worldwide. Strategies to enhance BRB integrity hold promise as therapeutic interventions to prevent vision loss. Previous studies identified Netrin-1 (NTN1) as a key regulator of BRB stability and revealed reduced Netrin-1 signaling in DR patients, suggesting that Netrin-1 supplementation could help preserve BRB function and prevent disease progression. Herein, we used genetic NTN1 overexpression to investigate effects on BRB development and maintenance. We show that global NTN1 overexpression converted leaky vessels at the P5 angiogenic front into a non-leaky state. In pathological settings, NTN1 overexpression reinforced BRB integrity in oxygen- induced retinopathy (OIR), improving electroretinogram (ERG) amplitudes and rescued vascular leak in laser-induced choroidal neovascularization (CNV). Mechanistically, we determined that NTN1 overexpression minimally and transiently affected retinal angiogenesis in an Unc5b independent manner, while Netrins barrier stabilizing effects were Unc5b dependent. These findings establish Netrin-1 as a promising therapeutic target for preventing BRB breakdown in retinal vascular diseases and suggest that reinforcing the Netrin-1/Unc5b signaling pathway may provide a strategy to selectively stabilize the BRB.

molecular biology↗

bioRxiv2025

Impact of Dysbiosis and Antiseizure Medication on Seizure Pathophysiology in a Viral Infection-Induced Epilepsy Model

Viral encephalitis is a major worldwide cause of acquired epilepsy, yet the impact of environmental and non-neuronal factors on an individuals risk for epilepsy is understudied. For example, the gut microbiome influences immune system function yet the impact of the gut microbiome on seizure-associated neuropathology remains poorly understood. Using the Theilers murine encephalomyelitis virus (TMEV) mouse model of infection-induced acute symptomatic seizures (ASyS), we aimed to investigate how antibiotic (ABX)-induced gut dysbiosis during a brain viral infection could influence resulting hippocampal neuropathology. Further, we included co-administration of carbamazepine (CBZ) to assess the extent to which antiseizure medicines could also shift the neurological impact of ASyS and gut dysbiosis. Brain tissue from TMEV-infected mice with and without gut dysbiosis was assessed for neurodegeneration and glial response (astrocytes and microglia). TMEV infection primarily drove neuroinflammatory changes in CA1, including increased astrogliosis, microgliosis, and microglial activation. ABX-induced dysbiosis exacerbated neuroinflammation across hippocampal subregions, markedly increasing microgliosis in CA3 and DG, and elevating microglial and neuronal proliferation in CA1. TMEV infection-induced astrogliosis was impacted by dysbiosis in a region-dependent manner, being worsened in DG while being alleviated in CA3. CBZ was neuroprotective selectively within DG, reducing neurodegeneration and microglial immunoreactivity with dysbiosis. Astroglial proliferation occurred regardless of gut microbiome integrity. Altogether, gut dysbiosis shapes hippocampal neuroimmune responses following viral infection-induced ASyS in a region-dependent manner, and CBZ may confer a neuroprotective effect. Together this work highlights the acute neuroinflammatory impact of infection-induced ASyS and reveals an underappreciated contribution of the gut-brain-axis to seizure-related neuropathology.

neuroscience↗

bioRxiv2025

Neuroticism Heterogeneity Through Item-Level Associations in Resting-State Functional Connectivity

Neuroticism is characterized by emotional instability and increased susceptibility to stress-related disorders. While traditionally treated as a unitary construct, growing evidence suggests that neuroticism is heterogeneous in both its genetic basis and its effects on social and health outcomes. To quantify this heterogeneity at the neurofunctional level, we analyzed resting-state functional connectivity (RSFC) in a large sample (n = 33,180) from the UK Biobank dataset. Using machine learning regression analysis, we identified RSFC patterns associated with item-level responses from the neuroticism scale of the Eysenck Personality Questionnaire. The pattern of RSFC associations across questionnaire items reflected genetically defined clusters (Worry and Depressed Affect), showing a significant correlation (r = 0.767, p < 0.001). It also aligned with psycholometrically derived factors (Anxiety/Tension and Worry/Vulnerability), reflecting a factor structure consistent with prior psychological studies. These findings were replicated in a separate MRI scan session from the UK biobank dataset. Associated connectivity was primarily observed in cognitive control, sensory integration, and self-referential processing networks. These neurofunctional signatures position RSFC as a robust intermediate phenotype, bridging genetic predisposition for neuroticism and psychological states. This framework could enhance precision in predicting psychiatric vulnerability and inform tailored therapeutic interventions.

neuroscience↗

bioRxiv2025

Localized Reactivity on Protein as Riemannian Manifolds:A Geometric and Quantum-Informed Basis for Deterministic, Metal-Aware Reactive-Site Prediction

We introduce a unified framework for analysing molecular reactivity based on a geometric, quantum-inspired environment representation and a fully deterministic, metalaware implementation. Proteins and ribonucleoprotein complexes are treated as configurations in [R]3 x T, and each residue or nucleotide p is mapped to an environment vector Ep that encodes a coarse-grained, DFT-inspired density surrogate together with metal/phosphate fields, solvent exposure and local geometry. A block-streamed, GPU-optional Python pipeline maps arbitrary PDB/mmCIF structures to fixed-dimensional environment vectors without stochastic training and scales to supramolecular assemblies: the 6Q97 tmRNA-SmpB-ribosome rescue complex (11,618 residues) can be processed in a single pass on commodity cloud hardware, demonstrating practical feasibility at ribosome scale. In a strict unbound, zero-shot setting on the Docking Benchmark 5.5 (DB5.5), a simple classifier trained on top of Ep achieves a macro-averaged area under the precision-recall curve of ~0.53 and a ROC-AUC of ~0.86 for residue-level interface vs. non-interface classification, competitive with specialised interface-prediction architectures despite using no evolutionary profiles, MSAs or task-specific retraining on DB5.5. Across mechanistically curated case studies (Rubisco, GroEL/GroES, SecA, p53- DNA and ribosomal pockets), untuned Random Forests used purely as probes under site-grouped cross-validation yield ROC-AUC values exceeding 0.95 for catalytic and anchor cores (e.g., SecA ATPase, GroES IVL), while diffuse regulatory and fitness-defined labels are substantially harder to separate. For 6Q97, a Tier 1/Tier 2 labelling scheme over tmRNA/SmpB pockets, decoding-centre rRNA, the 23S peptidyl transferase centre and helicase-like uS3/uS4/uS5 pockets, together with a curated hard-negative panel of 323 buried hydrophobic, electrostatic and stacking decoys, yields global AUCs of ~0.94 (Tier 1+2 vs. all) and ~0.98 (Tier 1+2 vs. hard negatives). These results support the view that the environment representation defines an interpretable "reactivity manifold" in which genuinely functional pockets occupy regions that cannot be mimicked by generic dense or charged environments, and that this structure remains accessible even for full ribosomes on modest hardware.

bioinformatics↗

bioRxiv2025

Deaminase-based RNA recording enables high throughput mutational profiling of protein-RNA interactions

Protein-RNA interactions govern nearly every aspect of RNA metabolism and are frequently dysregulated in disease. While individual protein residues and RNA nucleotides critical for these interactions have been characterized, scalable methods that jointly map protein- and RNA-level determinants remain limited. RNA deaminase fusions have emerged as a powerful strategy to identify transcriptome-wide targets of RNA-binding proteins by converting binding events into site-specific nucleotide edits. Here, we demonstrate that this RNA recording approach enables high-throughput mutational scanning of protein-RNA interfaces. Using the {lambda}N-boxB system as a model, we show that editing by a fused TadA adenosine deaminase directly correlates with binding affinity between protein and RNA variants in vitro. Systematic variation of RNA sequence context reveals a strong bias for editing at UA dinucleotides by the engineered TadA8.20, mirroring wild-type TadA preferences. We further demonstrate that stepwise recruitment of the deaminase using nanobody and protein A/G fusions maintains both sequence and binding specificity. Stable expression of the TadA fusion in human cells reproduces in vitro editing patterns across a library of RNA variants. Finally, comprehensive single amino acid mutagenesis of {lambda}N in human cells reveals critical residues mediating RNA binding. Together, our results establish RNA recording as a versatile and scalable tool for dissecting protein-RNA interactions at nucleotide and residue resolution, both in vitro and in cells.

molecular biology↗

bioRxiv2025

In situ cell-only bioprinting of patterned prevascular tissue into bioprinted high-density stem cell-laden microgel bioinks for vascularized bone tissue regeneration

Recently, microgels have been widely used in three-dimensional (3D) bioprinting as both supporting baths and bioinks. As a bioink, microgels have several unique properties, such as shear-thinning and self-healing behaviors with tunable mechanics, making them useful in 3D bioprinting. While cell encapsulated microgels offer many advantages in 3D bioprinting, they also have some limitations. It is still challenging to produce large quantities of cell encapsulated microgels with consistent quality and properties due to processes that are often complex and time-consuming. In this study, stem cell encapsulated, photocrosslinkable, shear-thinning and self-healing alginate microgel (SSAM) bioinks have been successfully fabricated via simple mixing of an oxidized and methacrylated alginate solution with suspended stem cells and a supersaturated calcium sulfate slurry solution through a custom-made spiral mixing unit. The SSAM bioinks can be bioprinted into complex 3D structures with both high resolution and shape fidelity due to their shear-thinning and self-healing properties. The 3D bioprinted SSAM bioinks can then serve as a supporting bath for the creation of prevascular network patterns using an individual cell-only prevasculogenic bioink within the 3D printed constructs. The prevascular network patterned 3D bioprinted constructs can be further stabilized by secondary photocrosslinking of the SSAMs, which enables long-term culture of the printed constructs for functional vascularized osteogenic tissue formation by differentiation of the bioprinted cells. The SSAM bioinks and individual cell-only printing technique enable in situ bioprinting of prevascularized tissue constructs in a mouse calvarial bone defect, achieving mechanical stability and ensuring the in situ bioprinted constructs remain within the defect.

bioengineering↗

bioRxiv2025

NADPH oxidase inhibitor enhances brain resilience in Alzheimer's disease by reducing tauopathy and neuroinflammation

Alzheimers disease associates closely with activation of NADPH oxidase (Nox) isozymes. We identified that CRB-2131, a novel oxadiazole derivative, potently suppresses Nox isozymes. It inhibits reactive oxygen species production (ROS) by hippocampal neuronal and microglial cells and reduces microglial activation. Prophylactic (starting at 3.5 months of age) and therapeutic (starting at 6 months of age) oral administration with CRB-2131 for 10 weeks in 5XFAD mice reduced hippocampal superoxide levels, lipid peroxidation, Tau phosphorylation, and neuroinflammation. Prophylactic and therapeutic CRB-2131 treatment of 5XFAD mice restored their impaired cognition as shown by the novel-object recognition, Y-maze, and Morris water-maze tests. CRB-2131 treatment increased mature neurons, reduced apoptotic mature neurons, and elevated immature neurons in the hippocampus. Positron-emission tomography/computed-tomography imaging confirmed that CRB-2131 stimulated neuronal regeneration. CRB-2131 suppresses brain oxidation, tauopathy, and neuroinflammation, thereby preventing mature neuron death and promoting neuron regeneration. Ultimately, this fosters a resilient brain and protects cognition.

neuroscience↗

bioRxiv2025

Longitudinal analysis of antibody titers after primary and booster mRNA COVID-19 vaccination can identify individuals at risk for breakthrough infection

A key issue in the post-COVID-19 era is the ongoing administration of COVID-19 vaccines. Repeated vaccination is essential for preparing against currently circulating and newly emerging SARS-CoV-2 variants while enabling people to continue with daily life. Optimizing vaccination strategies is crucial to efficiently manage medical resources and establish an effective vaccination framework. Therefore, it is important to quantitatively understand vaccine-induced immunity dynamics and to be able to identify poor responders with lower sustained antibody titers as potential priorities for revaccination. We investigated longitudinal antibody titer data in a cohort of 2,526 people in Fukushima, Japan, from April 2021 to November 2022 for whom basic demographic and health information was available. Using mathematical modeling and machine learning, we stratified the time-course patterns of antibody titers after 2 primary doses and 1 booster dose of mRNA COVID-19 vaccines. We identified 3 notable populations, which we refer to as the durable, the vulnerable, and the rapid-decliner populations, approximately half of which remained in the same population after the booster dose. Notably, the rapid-decliner population experienced earlier infections than the others. Furthermore, when comparing IgG(S) titers, IgA(S) titers, and T-spot counts between participants who experienced breakthrough infections after booster vaccination and those who did not, we found that IgA(S) titers were significantly lower in breakthrough infected participants during the early stage after booster vaccination. Our computational approach is adaptable to various types of vaccinations. This flexibility can inform policy decisions on vaccine distribution to enhance immunity both in future pandemics and in the post-COVID-19 era.

immunology↗