Biology subjects

Nam, Y.

Publications and source records attributed to Nam, Y..

16 recordsLinked to original sources

bioRxiv2026

Limits of marker information transfer]{Limits of marker information transfer in cross-panel cytometry imputation

Marker imputation can bridge cytometry datasets acquired with different panels and platforms. Its validity depends on whether common markers retain information about unmeasured target markers and whether those relationships transfer across assay contexts. We evaluated these limits in two paired flow cytometry-CyTOF cohorts comprising 134 patients and 257 specimens. A patient-held-out framework distinguished within-platform same-cell reconstruction from cross-platform distribution translation without cell correspondence. We compared conventional machine-learning and deep generative estimators with median imputation and used permutation controls to estimate the contributions of patient matching and source-exclusive markers. Evaluation covered marker values, specimen distributions, phenotype utility, and directional predictive relationships among markers. Within platforms, exact k-nearest neighbors reduced mean absolute error by 12.8-35.0% relative to median imputation. However, the median-based reconstructions were contracted, with a median ratio of predicted to observed interquartile range below 1 in every context and evaluable population. Patient matching improved cross-platform distribution recovery, whereas adding source-exclusive markers or higher-capacity estimators did not consistently produce net gains. In the independently annotated acute myeloid leukemia (AML) cohort, reconstructed markers largely preserved broad-lineage classification but did not recover fine-phenotype information available through direct measurement. In AML-CyTOF, the measured full panel outperformed the reconstructed representation by 10.25 percentage points for fine phenotypes. Predictive relationships among markers were context-specific. Most targets were better explained by several markers than by one. Marker selection based on these relationships showed a trade-off between panel size and pairwise coverage. Paired samples and imputation can therefore connect selected marker distributions across panels but should not be treated as substitutes for direct measurement. Cross-panel cytometry should be evaluated with patient-held-out tests matched to the resolution of available ground truth and the intended downstream use.

bioinformatics↗

bioRxiv2026

Deep learning representations of human Immune Health for precision immunology

The human immune system is composed of [~]30-50 distinct cell types, each of which can exist in different states of activation or differentiation. Indeed, the mammalian immune system has evolved to sense and respond to infections, cancers, injuries, and changes in tissue or host homeostasis (1). Moreover, an increasingly large fraction of approved drugs target the immune system directly, and/or cause immune changes (2-4). A key feature of the immune system is to store some of this information, for example as innate or adaptive immune memory (5). In addition, rewiring of immune network architecture induced by disease, environmental exposures, drug treatments, and/or chronological age allows the immune system to store information in the pattern of connections and activity across populations of immune cells. This ensemble information storage, in addition to changes to individual cells, functions as a major way the immune system encodes aspects of immune history and future potential. Genetic information can identify inherited risk alleles, but cannot capture the continual remodeling of the immune system shaped by exposures, infection, inflammation, therapy, and aging (6, 7). To define and use such ensemble immunotypes, we developed a self-supervised deep learning framework that transforms high-dimensional immune profiles into representations of immune health. MAESTRO (MAsked Encoding Set TRansformer with self-distillatiOn) encodes a set of cells from an individual into an embedding that captures immune cell population-level organization. Pretrained on 1,792 peripheral blood samples comprising over 418 million immune cells across 13 clinical diagnoses, MAESTRO learns immune fingerprints that are stable within individuals yet diverse across populations, states of health, disease, and treatment, providing a quantitative basis for comparing immune states across individuals and over time. These fingerprints capture immune architecture beyond coarse cell type proportions, enabling patient-efficient clinical prediction using simple task specific models. MAESTRO model embeddings retain a temporal dimension of immune history and potential, reflecting signatures of past exposures and baseline features that predict future immune responses. Finally, we demonstrate a translational precision immunotherapy application by testing this approach in metastatic Pancreatic Ductal Adenocarcinoma (PDAC), where pretreatment immune landscape circuitry maps enable patient stratification and therapeutic response prediction. Overall, we developed a large, attention-based model that captures deep network architecture of immune states through self- supervised representations of immune cytometry data as a reusable foundation for precision immunology, converting immune complexity into clinically actionable embeddings for diagnosis, monitoring, and therapy selection.

immunology↗

bioRxiv2026

MagNet: Computational Methods for Constructing High-Confidence Protein-Protein Interaction Networks in Magnaporthe oryzae

Magnaporthe oryzae, the rice blast fungus, plays a role as a model organism for molecular plant-microbe interaction research. Studies on the pathogenic mechanism of this fungus revealed many genes involved in signaling pathways. As multi-omics data are being available, genomic-level researches have been conducted to uncover the underlying biological processes during the pathogenesis of M. oryzae. Identifying the genome-wide protein-protein interaction (PPI) network is one of the omics-level approaches, which helps to understand signaling and regulatory pathways. However, existing biological network resources of M. oryzae are not sufficient to decipher pathogenesis mechanisms due to the abundance of false positives/negatives. In this study, a reliable PPI network database of M. oryzae, MagNet, was constructed with three methods, including homology-based Interolog search, co-expression network construction, and domain-domain interaction (DDI)-based prediction. With three approaches altogether, the pan-network with 5,600,976 interactions was generated, including 217,531 highly confident interactions supported by all three methods. Experimental data on M. oryzae PPIs supported that our PPI network can predict PPIs with higher accuracy compared to the previously constructed databases. MagNet would provide integrated biological network data, which can help to understand the molecular mechanisms of the rice blast fungus. The PPI data can be accessed via https:/magnet.scnu.ac.kr.

genomics↗

bioRxiv2026

IFIT3 Associates with m⁶A-Modified RNA to Restrict Hepatitis C Virus Infection

Interferon-induced proteins with tetratricopeptide repeats (IFITs) are RNA-binding effectors that restrict infection by diverse RNA viruses. Among the IFIT family, how IFIT3 recognizes RNA remains the least understood. Here, we identify IFIT3 as preferentially associating with N6-methyladenosine (mA)-modified hepatitis C virus (HCV) genomic RNA and host transcripts to restrict HCV infection. IFIT3 cellular RNA binding sites and mA sites, mapped transcriptome-wide by HyperTRIBE-seq during HCV infection, showed significant overlap. This mA preference was further supported by findings that IFIT3 binding sites significantly overlapped those of established mA-binding proteins; that inhibiting mA installation reduced IFIT3 association with mA-modified HCV RNA and cellular transcripts; that IFIT3 co-purified more efficiently with mA-modified short RNA probes than with unmodified controls; and that mutating mA consensus motifs in the HCV genome reduced IFIT3 association with viral RNA. Structure-function analyses identified two regions required for RNA probe binding: tetratricopeptide repeat domains 1-2 (TPR1-2) and a previously uncharacterized predicted helical hairpin between TPRs 6 and 7. In infected cells, the helical hairpin was required for IFIT3 association with HCV RNA but dispensable for interactions with other IFIT proteins. Conversely, TPR1-2 was dispensable for HCV RNA binding but essential for IFIT2 interaction, establishing that these functions are structurally separable. Loss of either region diminished antiviral activity, as indicated by increased levels of HCV RNA in clarified supernatants. Consistent with models of mA-linked restriction of late stages of infection, extracellular HCV RNA showed reduced mA and decreased IFIT3 association relative to intracellular RNA. Together, these findings define an mA-linked mechanism by which IFIT3 engages viral RNA and reveal an unexpected role for mA in antiviral effector function. SIGNIFICANCERNA-binding proteins are critical effectors of antiviral defense, yet for many of these proteins the mechanisms of viral RNA recognition remain unclear. Here, we show that m6A modification on both host and viral RNA promotes recognition by the interferon stimulated gene, IFIT3. RNA recognition did not require interaction with IFIT1 or IFIT2, although IFIT3 antiviral function required both RNA binding and interaction with IFIT2. These findings identify m6A as a new regulator of IFIT protein function and broaden our understanding of how RNA modifications shape antiviral restriction.

immunology↗

bioRxiv2025

Disrupted Lipid Homeostasis as a Pathogenic Mechanism in ABCA7-Associated Alzheimers Disease Risk

INTRODUCTIONABCA7 (ATP-binding cassette sub-family A member 7) encodes a lipid transporter linked to Alzheimers disease (AD). While common variants confer modest risk in Europeans, a 44-base pair deletion (rs142076058; p.Arg578Alafs) is a strong risk factor in African Americans (AA). Despite this, the biological consequences of this ancestry-specific variant are not well understood. METHODSWe expressed the truncated ABCA7 protein in HEK and HepG2 cells to assess localization and lipid metabolism. Additionally, induced pluripotent stem cell (iPSC)-derived neurons carrying the deletion were compared with isogenic controls. RESULTSThe truncated ABCA7 localized to the plasma membrane similarly to wild type but induced significant lipid droplet accumulation in HepG2 cells and iPSC-derived neurons. DISCUSSIONThese findings show that the AA-specific ABCA7 deletion disrupts lipid regulation despite normal localization, suggesting a mechanistic link between impaired lipid homeostasis and increased AD risk. This work underscores the importance of ancestry-specific studies in AD research. HighlightsO_LITruncated ABCA7 protein remains stable and correctly localizes to the plasma membrane in HEK293T cells. C_LIO_LITruncated ABCA7 disrupts lipid droplet regulation in HepG2 cells. C_LIO_LIABCA7 shows the highest expression in neurons among brain cell types. C_LIO_LIABCA7 truncation impairs lipid metabolism in neurons. C_LI

neuroscience↗

bioRxiv2024

Effects of METTL3-METTL14 on primary microRNA processing by Drosha-DGCR8

MicroRNAs modulate most protein-coding genes, and many are regulated during maturation. Chemical modifications of primary transcripts containing microRNAs have been implicated in altering Microprocessor processing efficiency, a key initiating endonucleolytic step performed by Drosha and DGCR8. METTL3-METTL14 produces N6-methyladenosine which is the most common methylation for mRNAs. Genetic experiments suggested that METTL3-METTL14 promotes primary microRNA processing by Microprocessor, but the molecular mechanism still needs to be elucidated. We tested the hypothesis that METTL3-METTL14 or m6A may directly impact Drosha or DGCR8 function during primary microRNA processing. After reconstituting the methyltransferase and processing activities, we show that the presence of METTL3-METTL14 complexes does not affect the processing efficiency of Drosha-DGCR8. We also established a method to prepare m6A-modified primary microRNAs and used them to show that the processing of the transcripts with m6A is similar to those without any modification. Recombinant METTL3-METTL14 and DGCR8 do not form stable complexes, challenging the previous model that depends on enhanced DGCR8 recruitment. Therefore, METTL3-METTL14 or m6A modification does not generally promote Microprocessor-mediated microRNA processing, although they may impact certain cases.

biochemistry↗

bioRxiv2024

Single-cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper

High-throughput single-cell cytometry data are crucial for understanding involvement of immune system in diseases and responses to treatment. Traditional methods for annotating cytometry data, specifically manual gating and clustering, face challenges in scalability, robustness, and accuracy. In this study, we propose a cytometry masked autoencoder (cyMAE), which offers an automated solution for immunophenotyping tasks including cell type annotation. The cyMAE model is designed to uphold user-defined cell type definitions, thereby facilitating easier interpretation and cross-study comparisons. The cyMAE model operates on a pre-train and fine-tune approach. In the pre-training phase, cyMAE employs Masked Cytometry Modelling (MCM) to learn relationships between protein markers in immune cells solely based on protein expression, without relying on prior information such as cell identity and cell type-specific marker proteins. Subsequently, the pre-trained cyMAE is fine-tuned on multiple specialized tasks via task-specific supervised learning. The pre-trained cyMAE addresses the shortcomings of manual gating and clustering methods by providing accurate and interpretable predictions. Through validation across multiple cohorts, we demonstrate that cyMAE effectively identifies co-occurrence patterns of bound labeled antibodies, delivers accurate and interpretable cellular immunophenotyping, and improves the prediction of subject metadata status. Specifically, we evaluated cyMAE for cell type annotation and imputation at the cellular-level and SARS-CoV-2 infection prediction, secondary immune response prediction against COVID-19, and prediction of the infection stage in COVID-19 progression at the subject-level. The introduction of cyMAE marks a significant step forward in immunology research, particularly in large-scale and high-throughput human immune profiling. This approach offers new possibilities for predicting and interpreting cellular-level and subject-level phenotypes in both health and disease.

bioinformatics↗

bioRxiv2023

U6 snRNA m6A modification is required for accurate and efficient cis- and trans-splicing of C. elegans mRNAs

pre-mRNA splicing is a critical feature of eukaryotic gene expression. Many eukaryotes use cis-splicing to remove intronic sequences from pre-mRNAs. In addition to cis-splicing, many organisms use trans-splicing to replace the 5' ends of mRNAs with a non-coding spliced-leader RNA. Both cis- and trans-splicing rely on accurately recognising splice site sequences by spliceosomal U snRNAs and associated proteins. Spliceosomal snRNAs carry multiple RNA modifications with the potential to affect different stages of pre-mRNA splicing. Here, we show that m6A modification of U6 snRNA A43 by the RNA methyltransferase METT-10 is required for accurate and efficient cis- and trans-splicing of C. elegans pre-mRNAs. The absence of U6 snRNA m6A modification primarily leads to alternative splicing at 5' splice sites. Furthermore, weaker 5' splice site recognition by the unmodified U6 snRNA A43 affects splicing at 3' splice sites. U6 snRNA m6A43 and the splicing factor SNRNP27K function to recognise an overlapping set of 5' splice sites with an adenosine at +4 position. Finally, we show that U6 snRNA m6A43 is required for efficient SL trans-splicing at weak 3' trans-splice sites. We conclude that the U6 snRNA m6A modification is important for accurate and efficient cis- and trans-splicing in C. elegans.

molecular biology↗

bioRxiv2023

Cancer mutations rewire the RNA methylation specificity of METTL3-METTL14

Chemical modification of RNAs is important for post-transcriptional gene regulation. The METTL3-METTL14 complex generates most N6-methyladenosine (m6A) modifications in mRNAs, and dysregulated methyltransferase expression has been linked to numerous cancers. Here we show that changes in m6A modification location can impact oncogenesis. A gain-of-function missense mutation found in cancer patients, METTL14R298P, promotes malignant cell growth in culture and in transgenic mice. The mutant methyltransferase preferentially modifies noncanonical sites containing a GGAU motif and transforms gene expression without increasing global m6A levels in mRNAs. The altered substrate specificity is intrinsic to METTL3-METTL14, helping us to propose a structural model for how the METTL3-METTL14 complex selects the cognate RNA sequences for modification. Together, our work highlights that sequence-specific m6A deposition is important for proper function of the modification and that noncanonical methylation events can impact aberrant gene expression and oncogenesis.

biochemistry↗

bioRxiv2022

Dog Size and Patterns of Disease History Across the Canine Age Spectrum: Results from the Dog Aging Project

Age in dogs is associated with the risk of many diseases, and canine size is a major factor in that risk. However, the size effect is not as simple as the age effect. While small size dogs tend to live longer, some diseases are more prevalent among small dogs. Utilizing owner-reported data on disease history from a substantial number of companion dogs, we investigate how body size, as measured by weight, associates with the prevalence of a reported condition and its pattern across age for various disease categories. We found significant positive associations between weight and prevalence of skin, bone/orthopedic, gastrointestinal, ear/nose/throat, cancer/tumor, brain/neurologic, endocrine, and infectious diseases. Similarly, weight was negatively associated with the prevalence of eye, cardiac, liver/pancreas, and respiratory disease categories. Kidney/urinary disease prevalence did not vary by weight. We also found that the association between age and disease prevalence varied by dog size for many conditions including eye, cardiac, orthopedic, ear/nose/throat, and cancer. Controlling for sex, purebred/mixed breed, and geographic region made little difference in all disease categories we studied. Our results align with the reduced lifespan in larger dogs for most of the disease categories but suggest potential avenues for further examination.

physiology↗

bioRxiv2021

netCRS: Network-based comorbidity risk score for prediction of myocardial infarction using biobank-scaled PheWAS data

The polygenic risk score (PRS) can help to identify individuals genetic susceptibility for various diseases by combining patient genetic profiles and identified single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Although multiple diseases will usually afflict patients at once or in succession, conventional PRSs fail to consider genetic relationships across multiple diseases. Even multi-trait PRSs, which take into account genetic effects for more than one disease at a time, fail to consider a sufficient number of phenotypes to accurately reflect the state of disease comorbidity in a patient, or are biased in terms of the traits that are selected. Thus, we developed novel network-based comorbidity risk scores to quantify associations among multiple phenotypes from phenome-wide association studies (PheWAS). We first constructed a disease-SNP heterogeneous multi-layered network (DS-Net), which consists of a disease network (disease-layer) and SNP network (SNP-layer). The disease-layer describes the population-level interactome from PheWAS data. The SNP-layer was constructed according to linkage disequilibrium. Both layers were attached to transform the information from a population-level interactome to individual-level inferences. Then, graph-based semi-supervised learning was applied to predict possible comorbidity scores on disease-layer for each subject. The SNP-layer serves as receiving individual genotyping data in the scoring process, and the disease-layer serves as the propagated output for an individuals multiple disease comorbidity scores. The possible comorbidity scores were combined by logistic regression, and it is denoted as netCRS. The DS-Net was constructed from UK Biobank PheWAS data, and the individual genetic profiles were collected from the Penn Medicine Biobank. As a proof-of-concept study, myocardial infarction (MI) was selected to compare netCRS with the PRS with pruning and thresholding (PRS-PT). The combined model (netCRS + PRS-PT + covariates) achieved an AUC improvement of 6.26% compared to the (PRS-PT + covariates) model. In terms of risk stratification, the combined model was able to capture the risk of MI up to approximately eight-fold higher than that of the low-risk group. The netCRS and PRS-PT complement each other in predicting high-risk groups of patients with MI. We expect that using these risk prediction models will allow for the development of prevention strategies and reduction of MI morbidity and mortality.

bioinformatics↗

bioRxiv2021

Characterization of aquaporin1b (AQP1b) mRNA in mud loach (Misgurnus mizolepis) in response to heavy metal and immunostimulant stimuli

Aquaporins (AQPs) facilitate the transport of water or other small solutes into cells in the presence of osmotic gradients. However, the current understanding of piscine AQP gene with cellular stress responses has been still limitedly exemplified. In present study, we characterized the mud loach AQP1b gene at the nucleotide and amino acid levels. We identified three AQP 1b transcript variants (mmAQP1b_tv1, mmAQP1b_tv2, and mmAQP1b_tv3). Then, we examined the AQP1b promoter region and observed several transcription factor binding sites (TFBS) for nuclear factor of activated T-cells (NFAT), SRY-box, c-AMP responsive element binding protein (CREB), GATA binding factor, and hepatic nuclear factor-1. Interestingly, mmAQP1b transcription was differentially modulated by heavy metal or immunostimulant challenge. Further studies to deepen the knowledge of fish AQP-mediated adaptation response potentially relevant to molecular pathogenesis are warranted. Summary statementWe identified mud loach AQP1b transcript variants and consensus sequences involved in stress or innate immunity in promotor region. AQP1b transcription was differentially modulated by heavy metal or immunostimulant challenge.

molecular biology↗

bioRxiv2021

Label-free monitoring of 3D cortical neuronal growth in vitro using optical diffraction tomography

The highly complex central nervous systems of mammals are often studied using three-dimensional (3D) in vitro primary neuronal cultures. A coupled confocal microscopy and immunofluorescence labeling are widely utilized for visualizing the 3D structures of neurons. However, this requires fixation of the neurons and is not suitable for monitoring an identical sample at multiple time points. Thus, we propose a label-free monitoring method for 3D neuronal growth based on refractive index tomograms obtained by optical diffraction tomography. The 3D morphology of the neurons was clearly visualized, and the developmental processes of neurite outgrowth in 3D spaces were analyzed for individual neurons. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/454602v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@b858baorg.highwire.dtl.DTLVardef@c946caorg.highwire.dtl.DTLVardef@468168org.highwire.dtl.DTLVardef@da022c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

In-vivo histology of iron and myelin in the brain using magnetic susceptibility source separation in MRI

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method,{chi} -separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.

neuroscience↗

bioRxiv2019

Measurements of 3-D morphology of individual red blood cells upon localized photothermal effects using gold nanorods

We investigated the in-situ photothermal response of human red blood cells (RBCs) by combining photothermal heat generation and 3D quantitative phase imaging techniques. Gold-nanorod-coated substrates were excited using near-infrared light to generate local heat to RBCs, and response was measured by imaging 3-D refractive index tomograms of cells under various near infrared (NIR) excitation conditions. On photothermal treatment, cell morphology changed from discoid to crescent shapes, cell volume and dry mass decreased, and hemoglobin concentration increased. We also investigated the irreversible deformation of RBCs when multiple intense excitation shocks are applied. These results provide a new understanding of thermodynamic aspects of cell biology and hematology. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2017

Non-viral induction of transient cell reprogramming in skeletal muscle to enhance tissue regeneration

Somatic cells can be reprogrammed to pluripotency in vivo by overexpression of defined transcription factors. While their sustained expression triggers tumorigenesis, transient reprogramming induces pluripotency-like features and proliferation only temporarily, without teratoma formation. We sought to achieve transient reprogramming within mouse skeletal muscle with a localized injection of plasmid DNA (pDNA) and hypothesized that this would enhance regeneration after severe injury. Intramuscular administration of reprogramming pDNA rapidly upregulated pluripotency (Nanog, Ecat1, Rex1) and early myogenesis genes (Pax3) in the healthy gastrocnemius of various mouse strains. Mononucleated cells expressing such markers appeared promptly in clusters among myofibers, but proliferated only transiently and did not lead to the generation of teratomas. Nanog was also upregulated in the gastrocnemius when reprogramming factors were administered 7 days after laceration of its medial head. Enhanced tissue regeneration after reprogramming was manifested by the accelerated appearance of centro-nucleated myofibers and reduced fibrosis. These results suggest that in vivo transient reprogramming may constitute a novel strategy towards the acceleration of regeneration following muscle injury, based on the induction of transiently-proliferative, pluripotent-like cells in situ. Further research to achieve clinically meaningful functional regeneration is warranted.

developmental biology↗