Biology subjects

Park, N.

Publications and source records attributed to Park, N..

13 recordsLinked to original sources

bioRxiv2026

The anatomical compartment defines distinct immune remodeling within human visceral adipose tissue during aging

Age-associated inflammation varies across tissues, but whether distinct visceral adipose tissue (VAT) depots undergo inflammatory remodeling in a depot-specific manner remains unclear. Here we profiled human peri-organ VAT, comparing kidney-associated fat (KF, from kidney transplantation donors) and gallbladder-associated fat (GBF, from asymptomatic cholecystectomy patients with incidental polyps), using single-cell RNA sequencing, flow cytometry, intracellular protein profiling and in situ analysis. GBF showed broad tissue-residency and granzyme K-associated remodeling across conventional, regulatory and innate-like lymphocyte compartments, whereas KF showed stronger B cell remodeling, greater myeloid representation and more compartmentalized changes within resident effector-like CD8+ T cell states. We further identified age-associated CD20+ T cells with features consistent with local B-T cell interaction and an antigen-experienced, granzyme K-associated inflammatory memory phenotype. In situ analysis revealed age-associated myeloid accumulation and crown-like structure remodeling, accompanied by distinct myeloid inflammatory programs in KF and GBF. Finally, depot-specific immune signatures associated with clinical indices of adjacent kidney and liver function. These findings indicate that age-associated distinct immune programs within peri-organ VAT depots track with local tissue context and the state of the adjacent organ.

immunology↗

bioRxiv2026

An in vivo platform to jointly monitor cellular and metabolic responses to chemotherapy.

How drug treatments reshape immune and metabolic states within intact tumors remains difficult to study with existing methods. We introduce a spatial pharmacology platform that enables parallel analysis of multiple agents within a single tumor, linking local drug exposure to immune and metabolic remodeling. Using a microdevice for localized drug delivery, we created a large-scale paired CyCIF-MALDI dataset spanning 1.5 million cells across 27 MMTV-PyMT tumor sections and nine treatment programs, enabling integrated spatial pharmacology at unprecedented scale. Metabolic signatures robustly predict proteomic spatial neighborhoods establishing metabolism as a powerful predictor of tumor organization and immune phenotype. Within this framework, we identify a dominant metabolic axis defined by the myeloid polarization between CSF1R+ tumor-associated macrophages and MPO+ infiltrating myeloid cells localized near regions of drug-induced tumor cell death. Finally, we detect putative lipid-associated macrophage (LAM)-like populations within drug-resistant treatment regions.

bioengineering↗

bioRxiv2025

IMMUNIA: A Multi-LLM Reasoning Agent for Immunoregulatory Surfaceome Discovery

Biomarker discovery for immunotherapy often requires reasoning across complex immune contexts. We present IMMUNIA, a multi-large-language-model (multi-LLM) reasoning agent designed to identify immunoregulatory surfaceome genes through interpretable, biologically grounded analysis. The term IMMUNIA originates from the fusion of Immune and Noeia (the Greek concept of perception and understanding), defining an AI system that perceives, reasons, and interprets the immune landscape with human-like cognition. IMMUNIA integrates structured prompting, contextual scoring across immunotherapy, inflammation, and NF-{kappa}B signaling, and consensus reasoning across GPT-4o, GPT-5, and Gemini 2.5 Pro. Benchmarking with positive (HLA) and negative (contactin) controls confirmed model consistency and contextual discrimination. Consensus evaluation prioritized IL1R1, BSG, CD276, ALCAM, B2M, PTPRS, VCAN, and MXRA5 as high-confidence candidates. Among these, PTPRS, VCAN, and MXRA5 emerged as previously unrecognized stromal immune checkpoint-like regulators, shaping tumor-immune crosstalk via phosphatase, ECM, and cytokine signaling networks. IMMUNIA thus establishes a reasoning-centric AI paradigm that bridges computational inference with biological plausibility, offering a scalable approach for precision immunotherapy biomarker discovery.

cancer biology↗

bioRxiv2025

Timing of immune checkpoint blockade shapes anti-tumor immunity via a clock-dependent chemokine axis

Circadian clocks regulate immunity, yet how they shape the tumor immune microenvironment and influence cancer immunotherapy remains unclear. Here, we show that tumor immune infiltration and immune checkpoint inhibitor efficacy vary by time of day in mice, driven by intrinsic clocks in dendritic cells and CD8+ T cells. Time-of-day modulates the abundance, spatial organization, and cytokine-chemokine production of tumor-infiltrating immune cells. Mechanistically, dendritic cell clocks control expression of Cx3cl1, driving recruitment of CX3CR1+CD8+ T cells and thereby reshaping the tumor immune microenvironment to enhance immunotherapy efficacy. Disruption of this axis abolishes time-of-day-dependent differences in treatment response. These findings identify a circadian mechanism of immune cell recruitment to tumors and provide mechanistic insight into clinical observations linking treatment timing to immunotherapy outcomes. One sentence summaryTime of day determines cancer immunotherapy efficacy through a circadian clock-dependent chemokine axis.

immunology↗

bioRxiv2025

A Systematic Multi-LLM AI Framework for Immunotherapy Biomarker Discovery and Target Identification

Immune checkpoint inhibitors have revolutionized cancer therapy, yet their efficacy is frequently limited by primary and acquired resistance. This underscores the need for novel immune regulatory targets that can improve or broaden patient responsiveness to immunotherapy. Among these, immunoglobulin (Ig) domain-containing proteins play central roles in immune signaling and tumor immune evasion, but many remain underexplored as therapeutic targets. Although recent advances in RNA sequencing, single-cell technologies, and multi-omics have expanded our understanding of gene and protein expression, integrating these data into actionable therapeutic insights remains a major challenge. Recent developments in Artificial Intelligence (AI), particularly in natural language processing and large language models (LLMs), offer a promising solution. Models such as GPT-4o, Llama 3.1-8B, and Gemini 1.5 Flash have demonstrated exceptional reasoning capabilities in biomedical applications. However, their use in immunotherapy biomarker discovery has yet to be fully realized. In this study, we introduce a novel AI-based framework that employs a multi-LLM approach to systematically identify and prioritize candidate biomarkers from a curated set of Ig-domain-containing genes. By integrating multi-omics data with structured prompt engineering and comparative model reasoning, our platform enhances the robustness, reproducibility, and interpretability of gene selection while minimizing bias inherent to single-model predictions. To our knowledge, this is the first study to apply a multi-LLM strategy to immunotherapy biomarker discovery. Our findings support the broader utility of LLMs in precision oncology and highlight their potential to accelerate the identification of novel, clinically actionable targets.

cancer biology↗

bioRxiv2025

Implementing N-terminomics and machine learning to probe in vivo Nt-arginylation

N-terminal arginylation (Nt-arginylation) serves as a protein degradation signal in both the ubiquitin-proteasome system and the autophagy-lysosomal pathway. However, the scarcity of arginylated proteins in cells and limitations in current identification methods have hindered progress in this field. In this study, we developed a novel integrated approach that combines N-terminomics (N-terminomic enrichment, LC-MS/MS, and tandem database search) with advanced machine learning-based filtering strategies to successfully identify in vivo Nt-arginylation with unprecedented sensitivity and specificity. By utilizing Arg-starting peptides from missed cleavage products as physicochemical proxies for ATE1-mediated Nt-arginylation, we trained a transfer learning-based model to predict MS2 spectra and retention times of candidate peptides. Additionally, near-isobaric modifications were filtered by statistically analyzing mass error deviations in MS2 fragment ions. Using this approach, we identified 134 Nt-arginylation sites in thapsigargin-treated HeLa cells, revealing a significant increase of Nt-arginylome under unfolded protein response (UPR) stress. Arginylated proteins originate from various organelles, including ER, nucleus and mitochondria. Notably, arginylation frequently occurred at sites processed by caspases or where signal peptides had been cleaved. Several proteins identified in our arginylome study were validated for their interaction with the R-catcher, an Nt-Arginylation bait derived from the p62 ZZ domain. Temporal profiling of N-terminal arginylation sites post-stress induction via targeted proteomics revealed a sequential response pattern. The UPR signal transducer ATF4 exhibited the most rapid increase, followed by N-terminal arginylation of caspase-3 substrates, and subsequently by arginylation at signal peptide cleavage sites among endoplasmic reticulum proteins. Our novel machine learning-based filtering methodologies enable the discovery of rare post-translational modifications by implementing specialized filtering strategies tailored to the unique physicochemical properties of terminal modifications, with potential applications in biomarker discovery, drug target identification, and elucidation of disease-specific protein regulation mechanism.

systems biology↗

bioRxiv2025

Targeted genomic surveillance of insecticide resistance in African malaria vectors

The emergence of insecticide resistance is threatening the efforts of malaria control programmes, which rely heavily on a limited arsenal of insecticidal tools, such as insecticide-treated bed nets. Importantly, genomic surveillance of malaria vectors can provide critical, policy-relevant insights into the presence and evolution of insecticide resistance, allowing us to maintain and extend the shelf life of these interventions. Yet the complex genetic architecture of resistance, combined with resource constraints in malaria-endemic settings, have thus far precluded the widespread use of genomics in routine surveillance. Meanwhile, stakeholders in sub-Saharan Africa are moving towards locally driven, decentralised generation of genomic data, underscoring the need for standardised and robust genomics workflows. To address this need, we demonstrate an approach to targeted genomic surveillance in Anopheles gambiae s.l with Illumina sequencing. We target 90 genomic loci in the Anopheles gambiae s.l genome, including 55 resistance-associated mutations and 35 ancestry informative markers. This protocol is coupled with advanced, automated software for accurate and reproducible variant analysis. We are able to elucidate population structure and ancestry in our cohorts and accurately identify most species in the An. gambiae species complex. We report frequencies of variants at insecticide-resistance loci and explore the continued evolution of the pyrethroid target site, the Voltage-gated sodium channel. Applying the platform to a recently established colony of field-caught resistant mosquitoes (Siaya, Kenya), we identified seven independent resistance-associated variants contributing to reduced efficacy of insecticide-treated nets in East Africa. Additionally, we leverage a machine learning algorithm (XGBoost) to demonstrate the possibility of predicting bioassay mortality using genotypes alone. This achieved high accuracy (75%), demonstrating the potential of targeted genomics to predictively monitor insecticide resistance. Together these tools provide a practical, scalable solution for resistance monitoring while advancing the goal of building local genomic surveillance capacity in sub-Saharan Africa.

genomics↗

bioRxiv2024

Axolotl epigenetic clocks offer insights into the nature of negligible senescence

Renowned for their regenerative abilities, axolotls also exhibit exceptional longevity, resistance to age-related diseases and apparent lack of physiological declines through lifespan, and have thus been considered organisms of negligible senescence. Whether axolotls display epigenetic hallmarks of ageing remains unknown. Here, we probe the axolotl DNA methylome throughout lifespan and present its first epigenetic clocks. Both at tissue-specific or pan-tissue levels, the clocks are biphasic, capable of predicting age during early life but not for the rest of its lifespan. We show that axolotls exhibit evolutionarily conserved features of epigenetic ageing during early life, yet their methylome is remarkably stable across lifespan, including at Polycomb Repressive Complex 2 (PRC2) target sites, suggesting that this species deviates from known patterns of epigenetic ageing. This study provides molecular insights into negligible senescence and furthers our understanding of ageing dynamics in animals capable of extreme regeneration.

developmental biology↗

bioRxiv2024

Glioblastoma stem cells show transcriptionally correlated spatial organization

Glioblastoma (GBM) is an aggressive brain cancer with a poor survival rate. Despite hundreds of clinical trials, there is no effective targeted therapy. Glioblastoma stem cells (GSCs) are an important GBM model system. In culture, these cells form spatial structures that share morphological aspects with their source tumors. We collected 17,000 phase contrast images of 15 patient-derived GSC lines growing to confluence. We find that GSCs grow in characteristic multicellular patterns depending on their transcriptional state. Interpretable computer vision algorithms identified specific image features that predict transcriptional state across multiple cell confluency levels. This relationship will be useful in developing GSC screens where image features can be used to identify how GSC biology changes in response to perturbations simply by imaging cultured cells on plates.

cancer biology↗

bioRxiv2024

Extensively acquired antimicrobial resistant bacteria restructure the individual microbial community in post-antibiotic conditions

In recent years, the overuse of antibiotics has led to the emergence of antimicrobial resistant (AMR) bacteria. To evaluate the spread of AMR bacteria, the reservoir of AMR genes (resistome) has traditionally been identified from environmental samples, hospital environments, and human populations; however, the functional role of AMR bacteria in the human gut microbiome and their persistency within individuals has not been fully investigated. Here, we performed a strain-resolved in-depth analysis of the resistome changes by reconstructing a large number of metagenome-assembled genomes (MAGs) of antibiotics- treated individuals gut microbiome. Interestingly, we identified two bacterial populations with different resistome profiles, extensively acquired antimicrobial resistant bacteria (EARB) and sporadically acquired antimicrobial resistant bacteria (SARB), and found that EARB showed broader drug resistance and a significant functional role in shaping individual microbiome composition after antibiotic treatment. Furthermore, longitudinal strain analysis revealed that EARB bacteria were inherently carried by individuals and can reemerge through strain switching in the human gut microbiome. Our data on the presence of AMR bacteria in the human gut microbiome provides a new avenue for controlling the spread of AMR bacteria in the human community.

genomics↗

bioRxiv2019

Plumage redness signals mitochondrial function in the House Finch

Carotenoid coloration is widely recognized as a signal of individual condition in various animals, but despite decades of study, the mechanisms that link carotenoid coloration to condition remain unresolved. Most birds with red feathers convert yellow dietary carotenoids to red carotenoids in an oxidation process requiring the gene encoding the putative cytochrome P450 enzyme CYP2J19. Here, we tested the hypothesis that the process of carotenoid oxidation and feather pigmentation is functionally linked to mitochondrial performance. Consistent with this hypothesis, we observed high levels of red ketolated carotenoids associated with the hepatic mitochondria of molting wild house finches (Haemorhous mexicanus), and upon fractionation, we found the highest concentration of ketolated carotenoids in the inner mitochondrial membrane. We further found that the redness of growing feathers was positively related to the performance of liver mitochondria. Structural modeling of CYP2J19 supports a direct role of this protein in carotenoid ketolation that may be functionally linked to cellular respiration. These observations suggest that feather coloration serves as a signal of core functionality through inexorable links to cellular respiration in the mitochondria.

evolutionary biology↗

bioRxiv2019

The Group B Streptococcal surface antigen I/II protein, BspC, interacts with host vimentin to promote adherence to brain endothelium and inflammation during the pathogenesis of meningitis

Streptococcus agalactiae (Group B Streptococcus, GBS) normally colonizes healthy adults but can cause invasive disease, such as meningitis, in the newborn. To gain access to the central nervous system, GBS must interact with and penetrate brain or meningeal blood vessels; however, the exact mechanisms are still being elucidated. Here, we investigate the contribution of BspC, an antigen I/II family adhesin, to the pathogenesis of GBS meningitis. Disruption of the bspC gene reduced GBS adherence to human cerebral microvascular endothelial cells (hCMEC), while heterologous expression of BspC in non-adherent Lactococcus lactis conferred bacterial attachment. In a murine model of hematogenous meningitis, mice infected with {Delta}bspC mutants exhibited lower mortality as well as decreased brain bacterial counts and inflammatory infiltrate compared with mice infected with WT GBS strains. Further, BspC was both necessary and sufficient to induce neutrophil chemokine expression. We determined that BspC interacts with the host cytoskeleton component vimentin, and confirmed this interaction using a bacterial two-hybrid assay, immunofluorescent staining, and imaging flow cytometry. Vimentin null mice were protected from WT GBS infection and also exhibited less inflammatory cytokine production in brain tissue. These results suggest that BspC and the vimentin interaction is critical for the pathogenesis of GBS meningitis. AUTHOR SUMMARYGroup B Streptococcus (GBS) typically colonizes healthy adults but can cause severe disease in immune compromised individuals, including newborns. Despite wide-spread intrapartum antibiotic prophylaxis given to pregnant women, GBS remains a leading cause of neonatal meningitis. To cause meningitis, GBS must interact with and penetrate the blood-brain barrier (BBB), which separates bacteria and immune cells in the blood from the brain. In order to develop targeted therapies to treat GBS meningitis, it is important to understand the mechanisms of BBB crossing. Here, we describe the role of the GBS surface factor, BspC, in promoting meningitis and discover the host ligand for BspC, vimentin, which is an intermediate filament protein that is constitutively expressed by endothelial cells. We determined that BspC interacts with the C-terminal domain of cell-surface vimentin to promote bacterial attachment to brain endothelial cells and that purified BspC protein can induce immune signaling pathways. In a mouse model of hematogenous meningitis, we observed that a GBS mutant lacking BspC was less virulent compared to WT GBS and resulted in less inflammatory disease. We also observed that mice lacking vimentin were protected from GBS infection. These results reveal the importance of the BspC-vimentin interaction in the progression of GBS meningitis disease.

microbiology↗

bioRxiv2018

Multiple laboratory mouse reference genomes define strain specific haplotypes and novel functional loci

The most commonly employed mammalian model organism is the laboratory mouse. A wide variety of genetically diverse inbred mouse strains, representing distinct physiological states, disease susceptibilities, and biological mechanisms have been developed over the last century. We report full length draft de novo genome assemblies for 16 of the most widely used inbred strains and reveal for the first time extensive strain-specific haplotype variation. We identify and characterise 2,567 regions on the current Genome Reference Consortium mouse reference genome exhibiting the greatest sequence diversity between strains. These regions are enriched for genes involved in defence and immunity, and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. Several immune related loci, some in previously identified QTLs for disease response have novel haplotypes not present in the reference that may explain the phenotype. We used these genomes to improve the mouse reference genome resulting in the completion of 10 new gene structures, and 62 new coding loci were added to the reference genome annotation. Notably this high quality collection of genomes revealed a previously unannotated gene (Efcab3-like) encoding 5,874 amino acids, one of the largest known in the rodent lineage. Interestingly, Efcab3-like-/- mice exhibit severe size anomalies in four regions of the brain suggesting a mechanism of Efcab3-like regulating brain development.

genomics↗