Biology subjects

Kwon, S.

Publications and source records attributed to Kwon, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Harnessing RNA-based DNA repair pathways for targeted gene editing

Recent studies have revealed a role for RNA in the repair of DNA double-strand breaks. Here, we show that the asymmetric DNA overhangs generated by the small TevSaCas9 dual nuclease informs a simple and robust editing strategy in human cells whereby Pol{theta} and Rad52 are recruited to repair the double-strand break. The 2-nt, 3 DNA overhang generated by the I-TevI nuclease domain of TevSaCas9 hybridizes with the 3 end of a co-localized repair template guide RNA to specifically license repair. Substitutions that destabilize the repair duplex reduce editing efficiency. Targeted RNA-templated repair (rep-editing) harnesses cellular RNA-based DNA repair pathways to introduce precise nucleotide edits, deletions and insertions in human cells with high efficiency and fidelity independent of co-delivered repair functions. The small size of TevSaCas9 and RNA repair template offers delivery advantages over size-constrained or multi-component editing systems.

molecular biology↗

bioRxiv2024

Identification of B cell subsets based on antigen receptor sequences using deep learning

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.

bioinformatics↗

bioRxiv2024

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

bioinformatics↗

bioRxiv2023

Stereotypic persistent B cell receptor clonotypes in Alzheimer's Disease

We constructed B cell receptor (BCR) repertoires in silico using peripheral blood (PB) samples collected from 44 Alzheimers Disease (AD) patients at baseline and 37 patients at follow-up. For the control group (CG), we used BCR repertoire data from the chronologically collected PB samples of 55 healthy volunteers vaccinated with SARS-CoV-2 mRNA. The AD patients shared 3,983 stereotypic non-naive BCR clonotypes not found in CG, and their degree of overlap between patient pairs were significantly higher than that of CG pairs, even with the SARS-CoV-2 spike protein triggering a concerted BCR response. Twenty stereotypic non-naive AD patient-specific BCR clonotypes co-existed in more than four patients and persisted throughout two sampling points. One of these BCR clonotypes encoded an antibody reactive to the A{beta}42 peptide. Our findings strongly suggest that AD patients are exposed to common (auto)antigens associated with disease pathology, and their BCR repertoires show unique signatures with diagnostic potential.

immunology↗

bioRxiv2023

Structure and heterogeneity of a highly cargo-loaded encapsulin shell

Encapsulins are self-assembling protein nanocompartments able to selectively encapsulate dedicated cargo enzymes. Encapsulins are widespread across bacterial and archaeal phyla and are involved in oxidative stress resistance, iron storage, and sulfur metabolism. Encapsulin shells exhibit icosahedral geometry and consist of 60, 180, or 240 identical protein subunits. Cargo encapsulation is mediated by the specific interaction of targeting peptides or domains, found in all cargo proteins, with the interior surface of the encapsulin shell during shell self-assembly. Here, we report the 2.53 [A] cryo-EM structure of a heterologously produced and highly cargo-loaded T3 encapsulin shell from Myxococcus xanthus and explore the systems structural heterogeneity. We find that exceedingly high cargo loading results in the formation of substantial amounts of distorted and aberrant shells, likely caused by a combination of unfavorable steric clashes of cargo proteins and shell conformational changes. Based on our cryo-EM structure, we determine and analyze the targeting peptide-shell binding mode. We find that both ionic and hydrophobic interactions mediate targeting peptide binding. Our results will guide future attempts at rationally engineering encapsulins for biomedical and biotechnological applications.

biochemistry↗

bioRxiv2023

Effects of repeated developmental GLP-1R agonist exposure on adult behavior and hippocampal structure in mice

Glucagon-like peptide-1 receptor (GLP-1R) agonists are common type 2 diabetes medications that have been repurposed for adult chronic weight management. Clinical trials suggest this class may also be beneficial for obesity in pediatric populations. Since several GLP-1R agonists cross the blood-brain barrier, it is important to understand how postnatal developmental exposure to GLP-1R agonists might affect brain structure and function in adulthood. Toward that end, we systemically treated male and female C57BL/6 mice with the GLP-1R agonist exendin-4 (0.5 mg/kg, twice daily) or saline from postnatal day 14 to 21, then allowed uninterrupted development to adulthood. Beginning at 7 weeks of age, we performed open field and marble burying tests to assess motor behavior and the spontaneous location recognition (SLR) task to assess hippocampal-dependent pattern separation and memory. Mice were sacrificed, and we counted ventral hippocampal mossy cells, as we have recently shown that most murine hippocampal neuronal GLP-1R is expressed in this cell population. We found that GLP-1R agonist treatment did not alter P14-P21 weight gain, but modestly reduced adult open field distance traveled and marble burying. Despite these motor changes, there was no effect on SLR memory performance or time spent investigating objects. Finally, we did not detect any changes in ventral mossy cell number using two different markers. These data suggest developmental exposure to GLP-1R agonists might have specific rather than global effects on behavior later in life and that extensive additional study is necessary to clarify how drug timing and dose affect distinct constellations of behavior in adulthood.

neuroscience↗

bioRxiv2023

An ancestral vaccine induces anti-Omicron antibodies by hypermutation

The immune escape of Omicron variants significantly subsides by the third dose of an mRNA vaccine. However, it is unclear how Omicron variant-neutralizing antibodies develop under repeated vaccination. We analyzed blood samples from 41 BNT162b2 vaccinees following the course of three injections and analyzed their B-cell receptor (BCR) repertoires at six time points in total. The concomitant reactivity to both ancestral and Omicron receptor-binding domain (RBD) was achieved by a limited number of BCR clonotypes depending on the accumulation of somatic hypermutation (SHM) after the third dose. Our findings suggest that SHM accumulation in the BCR space to broaden its specificity for unseen antigens is a counter protective mechanism against virus variant immune escape.

bioengineering↗

bioRxiv2023

GRIP: Graph representation of immune repertoire using graph neural network and transformer

The immune repertoire is a collection of immune receptors that has emerged as an important biomarker for both the diagnostic and therapeutic of cancer patients. In terms of deep learning, analyzing immune repertoire is a challenging multiple-instance learning problem in which the immune repertoire of an individual is a bag, and the immune receptor is an instance. Although several deep learning methods for immune repertoire analysis are introduced, they consider the immune repertoire as a set-like structure that doesnt take into account the nature of the immune response. When the immune response occurs, mutations are introduced to the immune receptor sequence sequentially to optimize the immune response against the pathogens that enter our body. As a result, immune receptors for the specific pathogen have the lineage of evolution; thus, the immune repertoire is better represented as a graph-like structure. In this work, we present our novel method, graph representation of immune repertoire (GRIP), which analyzes the immune repertoire as a hierarchical graph structure and utilize the collection of graph neural network followed by graph pooling and transformer to efficiently represents the immune repertoire as an embedding vector. We show that GRIP predicts the survival probability of cancer patients better than the set-based methods, and graph-based structure is critical for performance. Also, GRIP provides interpretable results, which prove that GRIP adequately uses the prognosis-related immune receptor and gives the further possibility to use the GRIP as the novel biomarker searching tool.

bioinformatics↗

bioRxiv2022

3D Graph Contrastive Learning for MolecularProperty Prediction

MotivationSelf-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (1) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (2) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (3) Previous models that apply contrastive learning to molecules use the augmentation of permuting atoms and bonds. Therefore, molecules having different characteristics can be in the same positive samples. We propose a novel contrastive learning framework, small-scale 3D Graph Contrastive Learning (3DGCL) for molecular property prediction, to solve the above problems. Results3DGCL learns the molecular representation by reflecting the molecules structure through the pre-training process that does not change the semantics of the drug. Using only 1,128 samples for pre-train data and 0.5 million model parameters, we achieved state-of-the-art or comparable performance in six benchmark datasets. Extensive experiments demonstrate that 3D structural information based on chemical knowledge is essential to molecular representation learning for property prediction. AvailabilityData and codes are available in https://github.com/moonkisung/3DGCL. Contactsy.kwon@pusan.ac.kr

biochemistry↗

bioRxiv2022

A Novel Mouse Model that Recapitulates the Heterogeneity of Human Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new targeted therapies, but few TNBC mouse models exist. Here, we developed a novel TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl murine model develops TN mammary tumors that display histological and molecular features commonly found in human TNBC. We performed deep omic analyses on Myc;Ptenfl tumors including machine learning for morphologic features, bulk and single-cell RNA-sequencing, multiplex immunohistochemistry and single-cell phenotyping. Through comparison with human TNBC, we demonstrated that this new genetic mouse model develops mammary tumors with differential survival that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC; providing a unique pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response. Statement of significanceThe development of cancer models that mimic triple-negative breast cancer (TNBC) microenvironment complexities is critical to develop effective drugs and enhance disease understanding. This study addresses a critical need in the field by identifying a murine model that faithfully mimics human TNBC heterogeneity and establishing a foundation for translating preclinical findings into effective human clinical trials.

cancer biology↗

bioRxiv2022

Engineered protein nanocages for concurrent RNA and protein packaging in vivo

Protein nanocages have emerged as an important engineering platform for biotechnological and biomedical applications. Among naturally occurring protein cages, encapsulin nanocompartments have recently gained prominence due to their favorable physico-chemical properties, ease of shell modification, and highly efficient and selective intrinsic protein packaging capabilities. Here, we expand encapsulin function by designing and characterizing encapsulins for concurrent RNA and protein encapsulation in vivo. Our strategy is based on modifying encapsulin shells with nucleic acid binding peptides without disrupting the native protein packaging mechanism. We show that our engineered encapsulins reliably self-assemble in vivo, are capable of efficient size-selective in vivo RNA packaging, can simultaneously load multiple functional RNAs, and can be used for concurrent in vivo packaging of RNA and protein. Our engineered encapsulation platform has potential for co-delivery of therapeutic RNAs and proteins to elicit synergistic effects, and as a modular tool for other biotechnological applications.

synthetic biology↗

bioRxiv2022

Glucagon-like peptide-1 receptor differentially controls mossy cell activity across the dentate gyrus longitudinal axis

Understanding the role of dentate gyrus (DG) mossy cells (MCs) in learning and memory has rapidly evolved due to increasingly precise methods for targeting MCs and for in vivo recording and activity manipulation in rodents. These studies have shown MCs are highly active in vivo, strongly remap to contextual manipulation, and that their inhibition or hyperactivation impairs pattern separation and location or context discrimination. What is not well understood is how MC activity is modulated by neurohormonal mechanisms, which might differentially control the participation of MCs in cognitive functions during discrete states, such as hunger or satiety. In this study, we demonstrate that glucagon-like peptide-1 (GLP-1), a neuropeptide produced in the gut and the brain that regulates food consumption and hippocampal-dependent mnemonic function, might regulate MC function through selective expression of its receptor, GLP-1R. RNA-seq demonstrated that most Glp1r in hippocampal principal neurons is expressed in MCs, and in situ hybridization revealed strong expression of Glp1r in hilar neurons. Glp1r-ires-Cre mice crossed with Ai14D reporter mice followed by co-labeling for the MC marker GluR2/3 revealed that almost all MCs in the ventral DG expressed Glp1r and that almost all Glp1r-expressing hilar neurons were MCs. However, only ~60% of dorsal DG MCs expressed Glp1r, and Glp1r was also expressed in small hilar neurons that were not MCs. Consistent with this expression pattern, peripheral administration of the GLP-1R agonist exendin-4 (5 g/kg) increased cFos expression in ventral but not dorsal DG hilar neurons. Finally, whole-cell patch-clamp recordings from ventral MCs showed that bath application of exendin-4 (200 nM) depolarized MCs and increased action potential firing. Taken together, this study identifies a potential neurohormonal mechanism linking a critically important satiety signal with activity of MCs in the ventral DG that might have functional effects on learning and memory during distinct states.

neuroscience↗

bioRxiv2022

Existence of blood circulating immune-cell clusters (CICs) comprising antigen-presenting cells and B cells

Cell-to-cell physical interactions are involved in almost every physiological processes in multicellular organisms. Although the dynamics of these interactions could be highly diverse and complex in many circumstances, certain cell-to-cell interactions among immune cells have been well studied due importance in understanding disease pathogenesis and immune therapy development1. Dendritic cells (DCs) and B cells are directly involved in adaptive immune response against pathogens. Interaction mechanism between these two celltypes is well-known to occur in germinal centers either indirectly via helper T (Th) cells or directly via cell contact. However, there are animal in vitro and in vivo evidence that such direct DC-to-B cell contact can occur outside germinal centers like in peripheral blood or collagen matrix and display antiviral immune-related activity2,3. Here, we provide evidence that certain types of antigen presenting cells (APCs) can form robust cell clusters with B cells and circulate in blood. From healthy human blood immune single cell RNA-seq datasets, we detected APC subpopulations (0.34{+/-}0.19% of total PBMCs) that were also enriched with well-known naive B cell markers. We visually observed DC:B doublets and multiplets ([~]0.69% of total live PBMCs) in wildtype mouse blood using flow cytometry and microscopic imaging, thus proving the existence of circulating immune-cell clusters (CICs) composed of APCs and B cells. BCR repertoire of these healthy mouse CICs were similar to circulating B cells. Noticeably, frequency of these APC:B CICs were higher COVID-19 patients than healthy donors and their B cell subtype composition (e.g. naive, plasmablast, IgM+, IgG+) varied with disease severity.

immunology↗

bioRxiv2022

One-step pipetting of barcoded planar microparticles into compact monolayer assembling chip for efficient readout of multiplexed immunoassay

Barcoded planar microparticles have many qualities suitable for developing cost-efficient multiplexed immunoassays. But at the translational research level, there are a number of technical aspects yet remain to be addressed which includes robustness and efficiency of the assay readout process. Assay readout process involves automated barcode identification and signal intensity values from each planar microparticle. For this, each microparticle has to be correctly aligned for correct barcode readout while being, ideally, compactly assembled for maximum microparticle imaging efficiency. To simultaneously achieve such alignment and assembly of microparticles but in a straightforward manner, we designed a microfluidic microparticle assembling chip that only requires a single pipetting step. Our design utilizes capillary flow based guided particle assembly, which allows maximum microparticle-based immunoassay readout efficiency. With the aid of image processing algorithms, we obtained good multiplex immunoassay readout accuracy similar to conventional imaging platforms. Our approach is applicable to both soft elastomer materials (e.g. PDMS) and rigid materials (e.g. polystyrene), the latter of which is frequently used for injection molding based mass production. We anticipate our device could help developing facile and user-friendly platform technologies based on barcoded planar microparticles.

bioengineering↗

bioRxiv2022

Whole genome sequencing of hematologically stained cells catapulted from Cell smears

Analyzing archived peripheral blood smears is a potential route towards gaining cell morphology and genome information of blood cell types from various diseases. Yet, acquiring whole genome information from morphologically targeted cells was difficult, especially for rare cell types. The main causes for such difficulty were the inevitable usage of cell stains leading to whole genome amplification inhibition, and insufficient cell isolation performance of previously introduced laser microdissection (LMD) techniques. Here, we introduce a new laser-based cell isolation technique and a whole genome amplification (WGA) protocol optimized for whole genome analysis from minute input of hematologically stained cells. We were able to perform whole genome copy number profiling and SNP analysis from as little as 5 cells.

genomics↗

bioRxiv2022

Robust hydrogel-integrated microsystems enabled by enhanced interfacial bonding strength

Noncovalent hydrogels, compared to covalent hydrogels, have distinctive advantages including biocompatibility and self-healing property but tend to have poor mechanical robustness, thus restricting their application spectrum. A clue to increase utility of such soft hydrogels without chemical bulk modification can be witnessed in biological organ walls where soft mucous epithelial layers are juxtaposed with tough connective tissues. Perhaps, similarly, bonding noncovalent hydrogels to stronger materials, such as tough hydrogels, might be a viable approach for increasing stability and scalability as well as creating novel functions for hydrogel-based systems. However when attempting to bond these two materials, each of the four existing hydrogel-hydrogel bonding method has practical shortcomings. In this work, we introduce a mucosa-inspired bonding method that realizes interfacial bonding of noncovalent hydrogels to tough, hybrid hydrogels without external glue or bulk modification of the noncovalent gel while preserving interfacial micropatterns. The procedure is simple and we confirmed broad applicability with various noncovalent hydrogels and tough hydrogels. We demonstrated the utility of our bonding method with novel applications regarding in vitro assay, soft robotics and biologically inspired systems.

bioengineering↗

bioRxiv2021

Spatially distributed computation in cortical circuits

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neurons response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation. TeaserInvestigating interference of neural waves helps to overcome limitations of the traditional view of cortical computation.

neuroscience↗

bioRxiv2021

Episodic memory precision and reality monitoring following stimulation of angular gyrus

The qualities of remembered experiences are often used to inform reality monitoring judgments, our ability to distinguish real and imagined events (Johnson & Raye, 1981). Previous experiments have tended to investigate only whether reality monitoring decisions are accurate or not, providing little insight into the extent to which reality monitoring may be affected by qualities of the underlying mnemonic representations. We used a continuous-response memory precision task to measure the quality of remembered experiences that underlie two different types of reality monitoring decisions: agency decisions that distinguish actions performed by participants and the experimenter, and perceptual decisions that distinguish perceived and imagined experiences. The data revealed memory precision to be associated with higher accuracy in both agency and perceptual reality monitoring decisions, with reduced precision linked with a tendency to misattribute self-generated experiences to external sources. We then sought to investigate the possible neurocognitive basis of these observed associations by applying brain stimulation to a region that has been implicated in precise recollection of personal events, left angular gyrus. Stimulation of angular gyrus selectively reduced the association between memory precision and self-referential reality monitoring decisions, relative to control site stimulation. Angular gyrus may, therefore, be important for the ability to imbue remembered experiences with a sense of self-agency, a key component of autonoetic consciousness that characterises episodic memory (Tulving, 1985).

neuroscience↗