Biology subjects

Shin, B.

Publications and source records attributed to Shin, B..

11 recordsLinked to original sources

bioRxiv2026

Metabolic signals regulate resuscitation speed of antibiotic persister bacteria during infection

All living organisms adjust their metabolism in response to environmental changes. Under unfavorable conditions, organisms enter a state of dormancy by halting metabolism, enabling survival. Dormant bacteria become highly tolerant to antibiotics-a phenomenon called persistence. Here, we demonstrate that selective metabolic reprogramming controls the resuscitation speed of persister after antibiotic exposure. Using multi-omics and in silico modeling, we found that dormant bacteria reprogram metabolic pathways to modulate persister awakening. Accumulation of L-serine and reduction of arginine drive rapid resuscitation. L-serine promotes cysteine biosynthesis and motility while reducing energy metabolism to facilitate rapid resuscitation. In contrast, arginine slows regrowth from dormancy by enhancing ethanol-aldehyde and energy metabolism. L-serine and arginine can, respectively, promote or inhibit the regrowth of antibiotic persister cells in macrophages and mouse models, and regulate the awakening speed of Salmonella, E. coli, and methicillin-resistant Staphylococcus aureus (MRSA). These findings suggest new strategies to target chronic bacterial infections. TeaserL-serine speeds and arginine slows the awakening of antibiotic persisters, revealing targets for chronic infection.

microbiology↗

bioRxiv2025

T cell development from expanded hematopoietic progenitors reveals progression control by Lmo2, Erg, Spi1, Hoxa9, and Meis1

To gain access to the earliest stages of T cell development, we adapted a serum-free culture system that expands hematopoietic stem and progenitor-like cells. These expanded cells efficiently undergo normal T-cell differentiation in vivo and in vitro, verified by early gene expression trajectories from single-cell RNA sequencing, though their absolute differentiation speed is slower than that of fresh progenitors and can be modulated with cytokine priming. Leveraging this expansion system to observe the first T-lineage events, we revealed that initial Notch activation immediately induces chromatin opening and transcriptional activation of the TCR-C{beta} locus. Additionally, acute CRISPR knockouts confirmed T-lineage entry requirements for Ikzf1, Hes1, Gabpa, and Myb while revealing that Lmo2, Erg, Spi1, Hoxa9, and Meis1 retard developmental progression with differing effects on proliferation. Endogenous expression of the stem, progenitor, and leukemia-associated factor Lmo2 markedly restrains initiation of the T cell program, with Lmo2 knockout greatly accelerating germline TCR{beta} locus transcription and expression of Tcf7, Gata3, Runx family, and E protein genes and their targets.

immunology↗

bioRxiv2024

Enhanced antifungal activity of NK-92 cells against Candida albicans mediated by a mannan-specific chimeric antigen receptor

Chimeric antigen receptors (CARs) offer promising prospects for innovative cell-based therapies against invasive fungal infections such as invasive candidiasis. Here, we have developed four CARs targeting Candida albicans with distinct single-chain variable fragments (scFvs): scFv3-CAR, scFv5-CAR, scFv12-CAR, and scFv{kappa}3-1-CAR. In T cells, scFv5-CAR induced IL-2 expression in response to C. albicans hyphae, while scFv3-CAR and scFv12-CAR did not mediate cell activation against C. albicans. Notably, scFv{kappa}3-1-CAR mediated the strongest cell activation against C. albicans yeast, hyphae, and other clinically relevant Candida species. scFv{kappa}3-1-CAR-NK-92 cells exhibited elevated IFN-{gamma} and CD107a expression, reducing C. albicans viability. NOD scid gamma (NSG) mice treated with scFv{kappa}3-1-CAR-NK-92 cells had reduced C. albicans burden in the kidney 24 hours postinfection. We showed that scFv{kappa}3-1-CAR targets C. albicans mannan but no other glycans in glycan microarray screening analyses. These findings reveal the scFv{kappa}3-1-CAR potential as a therapeutic strategy for treating Candida spp. by modifying peripheral blood mononuclear cells. ImportanceRecent studies on novel immunotherapies, including chimeric antigen receptor (CAR)-T cells, have shown promising results in preclinical models against invasive fungal infections (IFIs). However, the application of CAR technology in natural killer (NK) cells for treating IFIs remains unexplored. NK cells play a key role in early fungal clearance due to their antifungal activity mediated by granzymes, perforins, and the secretion of proinflammatory cytokines. This study is the first to demonstrate the feasibility and efficacy of CAR-modified NK cells targeting Candida spp. We provided proof-of-concept data showing that CAR-expressing NK cells exhibit enhanced activation and antifungal effects against clinically relevant Candida species by targeting mannan in the fungal cell wall. These findings are significant as they open new avenues for developing CAR-NK-based therapies to treat invasive candidiasis - a severe infection with limited treatment options and high mortality rates, particularly in immunocompromised patients.

bioengineering↗

bioRxiv2023

Multiplex, quantitative, high-resolution imaging of protein:protein complexes via hybridization chain reaction

Signal amplification based on the mechanism of hybridization chain reaction (HCR) facilitates spatial exploration of gene regulatory networks by enabling multiplex, quantitative, high-resolution imaging of RNA and protein targets. Here, we extend these capabilities to the imaging of protein:protein complexes, using proximity-dependent cooperative probes to conditionally generate a single amplified signal if and only if two target proteins are colocalized within the sample. HCR probes and amplifiers combine to provide automatic background suppression throughout the protocol, ensuring that even if reagents bind nonspecifically in the sample, they will not generate amplified background. We demonstrate protein:protein imaging with high signal-to-background in human cells, mouse proT cells, and highly autofluorescent formalin-fixed paraffin-embedded (FFPE) human breast tissue sections. Further, we demonstrate multiplex imaging of 3 different protein:protein complexes simultaneously and validate that HCR enables accurate and precise relative quantitation of protein:protein complexes with subcellular resolution in an anatomical context. Moreover, we establish a unified framework for simultaneous multiplex, quantitative, high-resolution imaging of RNA, protein, and protein:protein targets, with 1-step, isothermal, enzyme-free HCR signal amplification performed for all target classes simultaneously. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/550181v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@dee6f4org.highwire.dtl.DTLVardef@cc6a6org.highwire.dtl.DTLVardef@c5ea79org.highwire.dtl.DTLVardef@1a4edee_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

Runx factors launch T-cell and innate lymphoid programs via direct and gene network-based mechanisms

Runx factors are essential for lineage specification of various hematopoietic cells, including T lymphocytes. However, they regulate context-specific genes and occupy distinct genomic regions in different cell types. Here, we show that dynamic Runx binding shifts in early T-cell development are mostly not restricted by local chromatin state but regulated by Runx dosage and functional partners. Runx co-factors compete to recruit a limited pool of Runx factors in early T-progenitors, and a modest increase in Runx protein availability at pre-commitment stages causes premature Runx occupancy at post-commitment binding sites. This results in striking T-lineage developmental acceleration by selectively activating T-identity and innate lymphoid cell programs. These are collectively regulated by Runx together with other, Runx-induced transcription factors that co-occupy Runx target genes and propagate gene network changes.

immunology↗

bioRxiv2022

Distinct super-enhancer elements differentially control Il2ra gene expression in a cell-type specific fashion

The IL-2 receptor -chain (IL-2R/CD25) is constitutively expressed on DN2/DN3 thymocytes and Treg cells but induced by IL-2 on mature T and NK cells. Il2ra expression is regulated by a super-enhancer extensively bound by STAT5 in mature T cells. Here, we demonstrate that STAT5 cooperates with Notch to induce/maintain Il2ra/CD25 expression in DN2/DN3 cells. Moreover, we systematically investigated CD25 regulation using a series of mice with deletions spanning STAT5 binding elements. Deleting the upstream super-enhancer region mainly affected constitutive CD25 expression on DN2/DN3 thymocytes and Tregs, whereas deleting an intronic region primarily decreased IL-2-induced CD25 on peripheral T and NK cells. Thus, distinct elements preferentially control constitutive versus inducible expression in a cell-type-specific manner, with the MED1 coactivator co-localizing with specific STAT5 binding sites. Moreover, the intronic region was a dominant element whose deletion altered the structure throughout the super-enhancer in mature T cells. These results demonstrate differential functions for distinct super-enhancer elements, thereby indicating ways to manipulate CD25 expression in a cell-type specific fashion.

immunology↗

bioRxiv2022

ST6GAL1 sialyltransferase promotes acinar to ductal metaplasia and pancreatic cancer progression

The role of aberrant glycosylation in pancreatic ductal adenocarcinoma (PDAC) remains an under-investigated area of research. In this study, we determined that the ST6GAL1 sialyltransferase, which adds 2,6-linked sialic acids to N-glycosylated proteins, is upregulated in patients with early-stage PDAC, and further increased in advanced disease. A tumor-promoting function for ST6GAL1 was elucidated using tumor xenograft models with human PDAC cells. Additionally, we developed a genetically-engineered mouse (GEM) with transgenic expression of ST6GAL1 in the pancreas, and found that mice with dual expression of ST6GAL1 and oncogenic KRASG12D have greatly accelerated PDAC progression and mortality compared with mice expressing KRASG12D alone. As ST6GAL1 imparts progenitor-like characteristics, we interrogated ST6GAL1s role in acinar to ductal metaplasia (ADM), a process that fosters neoplasia by reprogramming acinar cells into ductal, progenitor-like cells. We confirmed that ST6GAL1 promotes ADM using multiple models including the 266-6 cell line, GEM-derived organoids and tissues, and an in vivo model of inflammation-induced ADM. EGFR is a key driver of ADM and is known to be activated by ST6GAL1-mediated sialylation. Importantly, EGFR activation was dramatically increased in acinar cells and organoids from mice with transgenic ST6GAL1 expression. These collective results highlight a novel glycosylation-dependent mechanism involved in early stages of pancreatic neoplasia.

cancer biology↗

bioRxiv2020

Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model

The infection of a novel coronavirus found in Wuhan of China (2019-nCoV) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for 2019-nCoV, various strategies are being tested in China, including drug repurposing. In this study, we used our pretrained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of 2019-nCoV. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing a inhibitory potency with Kd of 94.94 nM against the 2019-nCoV 3C-like proteinase, followed by efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of 2019-nCoV with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra, could be used for the treatment of 2019-nCoV, although there is no real-world evidence supporting the prediction. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for 2019-nCoV.

microbiology↗

bioRxiv2019

The polyQ expansion modulates the configuration and phosphorylation of huntingtin

The polyQ-expansion at the N-terminus of huntingtin (HTT) is the prime cause of Huntingtons disease. The recent cryo-EM structure of HTT with HAP40 provides information on the proteins prominent HEAT-repeats. Here, we present analyses of the impact of polyQ-length on the conformation of HTT by cryo-EM, the domain-interactions by cross-linking mass spectrometry and the phosphorylation of HTT. The cryo-EM analysis of normal (Q23-) and disease (Q78-) type HTTs in their apo forms shows that the structures of apo HTTs significantly differ from the structure of HTT-HAP40, and that the polyQ expansion induces global structural changes consisting of significant domain movements of the C-HEAT domain relative to the N-HEAT domain. In addition, we show that the polyQ-expansion alters the phosphorylation pattern across the full-length HTT and that the specific phosphorylation (Ser2116p) in turn affects the global structure of HTT, which influences the activity of polyQ-expanded HTT. These results provide a molecular basis for the effect of the N-terminal polyQ segment on HTT structure and activity, that may be important for the cell-selective toxicity of mutant HTT.

neuroscience↗

bioRxiv2017

Wx: a neural network-based feature selection algorithm for next-generation sequencing data

MotivationNext-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. For example, an NGS-based transcriptomic assay called RNA-seq can be used to estimate the abundance of approximately 190,000 transcripts together. As the cost of next-generation sequencing sharply declines, researchers in many fields have been conducting research using NGS. The amount of information produced by NGS has made it difficult for researchers to choose the optimal set of target genes (or genomic loci).\n\nResultsWe have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker (universal gene-expression biomarkers) candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method.\n\nAvailabilityhttps://github.com/deargen/DearWX\n\nContactkangk1204@dankook.ac.kr\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗

bioRxiv2017

Natural Language Processing for Classification of Acute, Communicable Findings on Unstructured Head CT Reports: Comparison of Neural Network and Non-Neural Machine Learning Techniques

Background and PurposeTo evaluate the accuracy of non-neural and neural network models to classify five categories (classes) of acute and communicable findings on unstructured head computed tomography (CT) reports.\n\nMaterials and MethodsThree radiologists annotated 1,400 head CT reports for language indicating the presence or absence of acute communicable findings (hemorrhage, stroke, hydrocephalus, and mass effect). This set was used to train, develop, and evaluate a non-neural classifier, support vector machine (SVM), in comparisons to two neural network models using convolutional neural networks (CNN) and neural attention model (NAM) Inter-rater agreement was computed using kappa statistics. Accuracy, receiver operated curves, and area under the curve were calculated and tabulated. P-values < 0.05 was significant and 95% confidence intervals were computed.\n\nResultsRadiologist agreement was 86-94% and Cohens kappa was 0.667-0.762 (substantial agreement). Accuracies of the CNN and NAM (range 0.90-0.94) were higher than SVM (range 0.88-0.92). NAM showed relatively equal accuracy with CNN for three classes, severity, mass effect, and hydrocephalus, higher accuracy for the acute bleed class, and lower accuracy for the acute stroke class. AUCs of all methods for all classes were above 0.92.\n\nConclusionsO_LINeural network models (CNN & NAM) generally had higher accuracies compared to the non-neural models (SVM) and have a range of accuracies that comparable to the inter-annotator agreement of three neuroradiologists.\nC_LIO_LIThe NAM method adds ability to hold the algorithm accountable for its classification via heat map generation, thereby adding an auditing feature to this neural network.\nC_LI\n\nAbbreviations

neuroscience↗