Biology subjects

Park, S.

Publications and source records attributed to Park, S..

At least 307 records · Page 17Linked to original sources

bioRxiv2020

Identification of antiviral drug candidates against SARS-CoV-2 from FDA-approved drugs

COVID-19 is an emerging infectious disease and was recently declared as a pandemic by WHO. Currently, there is no vaccine or therapeutic available for this disease. Drug repositioning represents the only feasible option to address this global challenge and a panel of 48 FDA-approved drugs that have been pre-selected by an assay of SARS-CoV was screened to identify potential antiviral drug candidates against SARS-CoV-2 infection. We found a total of 24 drugs which exhibited antiviral efficacy (0.1 M < IC50 < 10 M) against SARS-CoV-2. In particular, two FDA-approved drugs - niclosamide and ciclesonide - were notable in some respects. These drugs will be tested in an appropriate animal model for their antiviral activities. In near future, these already FDA-approved drugs could be further developed following clinical trials in order to provide additional therapeutic options for patients with COVID-19.

microbiology↗

bioRxiv2020

A gut sensor for sugar preference

Summary Paragraph/AbstractAnimals innately prefer caloric sugars over non-caloric sweeteners. Such preference depends on the sugar entering the intestine.1-4 Although the brain is aware of the stimulus within seconds,5-8 how the gut discerns the caloric sugar to guide choice is unknown. Recently, we discovered an intestinal transducer, known as the neuropod cell.9,10 This cell synapses with the vagus to inform the brain about glucose in the gut in milliseconds.10 Here, we demonstrate that neuropod cells distinguish a caloric sugar from a non-caloric sweetener using the electrogenic sodium glucose co-transporter 1 (SGLT1) or sweet taste receptors. Activation of neuropod cells by non-caloric sucralose leads to ATP release, whereas the entry of caloric sucrose via SGLT1 stimulates glutamate release. To interrogate the contribution of the neuropod cell to sugar preference, we developed a method to record animal preferences in real time while using optogenetics to silence or excite neuropod cells. We discovered that silencing these cells, or blocking their glutamatergic signaling, renders the animals unable to recognize the caloric sugar. And, exciting neuropod cells leads the animal to consume the non-caloric sweetener as if it were caloric. By transducing the precise identity of the stimuli entering the gut, neuropod cells guide an animals internal preference toward the caloric sugar.

neuroscience↗

bioRxiv2020

Dual functions of Aire CARD multimerization in the transcriptional regulation of T cell tolerance

Aggregate-like biomolecular assemblies are emerging as new conformational states with functionality. Aire, a transcription factor essential for central T cell tolerance, is known to form large aggregate-like assemblies visualized as nuclear foci. We demonstrate that Aire utilizes Caspase Activation Recruitment Domain (CARD) to form filamentous homo-multimers in vitro, and this assembly mediates foci formation and transcriptional activity. However, CARD-mediated multimerization is a double-edged sword as it also makes Aire susceptible to interaction with PML bodies, sites of many nuclear processes including protein quality control of nuclear aggregates. Several loss-of-function Aire mutants, including those causing autoimmune polyendocrine syndrome type-1, form foci with increased PML body association. Directing Aire to PML bodies impairs Aires transcriptional activity, while dispersing PML bodies with a viral antagonist restores it. Thus, our study reveals a new regulatory role of PML bodies in Aire function and highlights the interplay between nuclear aggregate-like assemblies and PML-mediated quality control.

immunology↗

bioRxiv2020

Reliable data collection in participatory trials to assess digital healthcare apps.

The number of digital healthcare mobile apps on the market is increasing exponentially owing to the development of the mobile network and widespread usage of smartphones. However, only a few of these apps have undergone adequate validation. As with many mobile apps, healthcare apps are generally considered safe to use, making them easy for developers and end-users to exchange them in the marketplace. The existing platforms are not suitable to collect reliable data for evaluating the effectiveness of the apps. Moreover, these platforms only reflect the perspectives of developers and experts, not of end-users. For instance, data collection methods typical of clinical trials are not appropriate for participant-driven assessment of healthcare apps because of their complexity and high cost. Thus, we identified a need for a participant-driven data collection platform for end-users that is interpretable, systematic, and sustainable --as a first step to validate the effectiveness of the apps. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. Interpretable data preparation consists of a protocol database system and semantic feature retrieval method to create a protocol without professional knowledge. Collected data reliability weight calculation belongs to the systematic data storage stage. For sustainable data collection, we integrated the weight method and the future reward distribution function. We validated the methods through statistical tests conducted on 718 human participants. The validation results demonstrate that the methods have significant differences in the comparative experiment and prove that the choice of the right method is essential for reliable data collection. Furthermore, we created a web-based system for our pilot platform to collect reliable data in an integrated pipeline. We validate the platform features with existing clinical and pragmatic trial data collection platforms. In conclusion, we show that the method and platform support reliable data collection, forging a path to effectiveness validation of digital healthcare apps.

bioinformatics↗

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↗

bioRxiv2020

Dynamic interactions between the RNA chaperone Hfq, small regulatory RNAs and mRNAs in live bacterial cells

RNA binding proteins play myriad roles in controlling and regulating RNAs and RNA-mediated functions, often through simultaneous binding to other cellular factors. In bacteria, the RNA chaperone Hfq modulates post-transcriptional gene regulation. Absence of Hfq leads to the loss of fitness and compromises the virulence of bacterial pathogens. Using live-cell super-resolution imaging, we are able to distinguish Hfq binding to different sizes of cellular RNAs. We demonstrate that under normal growth conditions, Hfq exhibits widespread mRNA binding activity. Particularly, the distal face of Hfq contributes mostly to the mRNA binding in vivo. In addition, binding of Hfq to these mRNAs can recruit RNase E to promote turnover of these mRNAs in an sRNA-independent manner, providing one mechanism to release Hfq from the pre-bound mRNAs. Finally, our data indicate that sRNAs, once expressed, can either co-occupy Hfq with the mRNA or displace the mRNA from Hfq, suggesting mechanisms through which sRNAs rapidly access Hfq to induce sRNA-mediated gene regulation. Our data collectively demonstrate that Hfq dynamically changes its interactions with different RNAs in response to changes in cellular conditions.

biophysics↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗

bioRxiv2020

Viscous medium-based crystal support in sample holder for fixed-target serial femtosecond crystallography

Serial femtosecond crystallography (SFX) enables the determination of a room-temperature crystal structure of macromolecules without causing radiation damage, as well as provides time-resolved molecular dynamics data in pump-probe experiments. Fixed-target SFX (FT-SFX) can minimize sample consumption and physical effects to crystals during sample delivery. Various types of sample holders have been developed and applied in FT-SFX; however, no sample holder has been developed that can universally mount crystals of various sizes and shapes. Here, we introduce a viscous media-based crystal support in a sample holder for FT-SFX. Crystal samples were embedded in viscous media such as gelatin and agarose, which were enclosed in a polyimide film. In the vertically placed sample holder, the viscous medium stably supported crystals between the two polyimide films without crystal sinking due to gravity. Using this method, we performed FT-SFX experiments with glucose isomerase and lysozyme embedded in gelatin and agarose, respectively. The room-temperature crystal structures of glucose isomerase and lysozyme were successfully determined at 1.75 and 1.80 [A] resolutions, respectively. Viscous media used in this experiment showed negligible background scattering in data processing. This method is useful for delivering crystal samples of various sizes and shapes in FT-SFX experiments.

biophysics↗

bioRxiv2020

Overcoming Genetic Drop-outs in Variants-based Lineage Tracing from Single-cell RNA Sequencing Data

Lineage tracing provides key insights into the fates of individual cells in complex tissues. Recent works on lineage reconstruction based on the single-cell expression data are suitable for short time frames while tracing lineage based on more stable genetic markers is needed for studies that span time scales over months or years. However, variant calling from the single-cell RNA sequencing (scRNA-Seq) data suffers from "genetic drop-outs", including low coverage and allelic bias, which presents significant obstacles for lineage reconstruction. Prior studies focused only on mitochondrial (chrM) variants and need to be expanded to the whole genome to capture more variants with clearer physiological meaning. However, non-chrM variants suffer even more severe drop-outs than chrM variants, although drop-outs affect all variants. We developed strategies to overcome genetic drop-outs in scRNA-Seq-derived whole genomic variants for accurate lineage tracing, and we developed SClineger, a Bayesian Hierarchical model, to implement our approach. Our validation analyses on a series of sequencing protocols demonstrated the necessity of correction for genetic drop-outs and consideration of variants in the whole genome, and also showed the improvement that our approach provided. We showed that genetic-based lineage tracing is applicable for single-cell studies of both tumors and non-tumor tissues using our approach, and can reveal novel biological insights not afforded by expressional analyses. Interestingly, we showed that cells of various lineages grew under the spatial constraints of their respective organs during the developmental process. Overall, our work provides a powerful tool that can be applied to the large amounts of already existing scRNA-Seq data to construct the lineage histories of cells and derive new knowledge.

bioinformatics↗

bioRxiv2019

Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites

Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features--providing orthogonal information to physicochemical properties--illustrate that many possess significantly different feature temporal dynamics (fTDs) across buildings, with rapid and unpredictable deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features with unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.

bioinformatics↗

bioRxiv2019

Geometric principles underlying the proliferation of a model cell system

Wall deficient variants of many bacteria, called L-forms, divide by a simple mechanism that does not depend on the complex FtsZ-based cell division machine. We have used microfluidic systems to probe the growth, chromosome cycle and division mechanism of Bacillus subtilis L-forms. The results show that forcing cells into a narrow linear configuration greatly improves the efficiency of cell growth and chromosome segregation. This reinforces the view that L-form division is driven by an excess accumulation of surface area over volume. Cell geometry was also found to play a dominant role in controlling the relative positions and movement of segregating chromosomes. The presence of the nucleoid appears to influence division both via a cell volume effect and by nucleoid occlusion, even in the absence of the FtsZ machine. Overall, our results emphasise the importance of geometric effects for a range of critical cell functions and are of relevance for efforts to develop artificial or minimal cell systems.

microbiology↗

bioRxiv2019

An interactive retrieval system for clinical trial studies with context-dependent protocol elements

A clinical trial protocol defines the procedures that should be performed during a clinical trial. Every clinical trial begins with the design of its protocol. While designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art retrieval systems only offer text-based searches for user-entered keywords. In this study, we present an interactive retrieval system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains various semantic information that enables the protocols to be filtered during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips), which enables an interactive search by utilizing protocol elements. CLIPS provides the options to select the next element according to the previous element in the form of a connected tree, thus enabling an interactive search for combinations of protocol elements. The validation results show that our method achieves better performance than existing databases in predicting phenotypic features.

bioinformatics↗

bioRxiv2019

Comparison of BOLD and CBV using 3D EPI and 3D GRASE for cortical layer fMRI at 7T.

PurposeFunctional MRI (fMRI) at the mesoscale of cortical layers and columns requires both sensitivity and specificity, which can be compromised if the imaging method is affected by vascular artifacts, particularly cortical draining veins at the pial surface. Recent studies have shown that cerebral blood volume (CBV) imaging is more specific to the actual laminar locus of neural activity than BOLD imaging when using standard gradient-echo (GE) EPI sequences. Gradient and Spin Echo (GRASE) BOLD imaging has also shown greater specificity when compared with GE-BOLD.\n\nMethodsHere we directly compare CBV and BOLD contrasts in high-resolution imaging of the primary motor cortex for laminar fMRI in four combinations of signal labeling, VASO (CBV) and BOLD with 3D GE-EPI and zoomed 3D GRASE image readouts.\n\nResultsWe find that both CBV imaging using EPI-VASO and BOLD imaging using GRASE-BOLD, show similar specificity and sensitivity and are thus useful tools for mesoscopic fMRI in the human cortex.\n\nConclusionThese techniques demonstrate sufficient sensitivity and specificity to allow layer-fMRI to be used by neuroscientists in a wide range of investigations of depth-dependent neural circuitry in the human brain.

neuroscience↗

bioRxiv2019

A tale of winglets: evolution of flight morphology in stick insects

The evolutionary transition between winglessness and a full-winged morphology requires selective advantage for intermediate forms. Conversely, repeated secondary wing reductions among the pterygotes indicates relaxation of such selection. However, evolutionary trajectories of such transitions are not well characterized. The stick insects (Phasmatodea) exhibit diverse wing sizes at both interspecific and intersexual levels, and thus provide a system for examining how selection on flight capability, along with other selective forces, drives the evolution of flight-related morphology. Here, we examine variation in relevant morphology for stick insects using data from 1100+ individuals representing 765 species. Although wing size varies along a continuous spectrum, taxa with either long or miniaturized wings are the most common, whereas those with intermediate-sized wings are relatively rare. In a morphological space defined by wing and body size, the aerodynamically relevant parameter termed wing loading (the average pressure exerted on the air by the wings) varies according to sex-specific scaling laws; volant but also flightless forms are the most common outcomes in both sexes. Using phylogenetically-informed analyses, we show that relative wing size and body size are inversely correlated in long-winged insects regardless of sexual differences in morphology and ecology. These results demonstrate the diversity of flight-related morphology in stick insects, and also provide a general framework for addressing evolutionary coupling between wing and body dimensions. We also find indirect evidence for a fitness valley associated with intermediate-sized wings, suggesting relatively rapid evolutionary transitions between wingless and volant forms.

evolutionary biology↗

bioRxiv2019

Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes

Accurate identification of pathways associated with cancer phenotypes (e.g., cancer sub-types and treatment outcome) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix tri-factorization (NMTF) can be successfully applied to identify pathways associated with specific cancer types or disease classes as a prognostic and predictive biomarker. However, one key limitation of non-negative factorization methods, including various non-negative bi-factorization methods, is their lack of ability to handle non-negative input data. For example, many molecular data that consist of real-values containing both positive and negative values (e.g., normalized/log transformed gene expression data where negative value represents down-regulated expression of genes) are not suitable input for these algorithms. In addition, most previous methods provide just a single point estimate and hence cannot deal with uncertainty effectively.\n\nTo address these limitations, we propose a Bayesian semi-nonnegative matrix trifactorization method to identify pathways associated with cancer phenotypes from a realvalued input matrix, e.g., gene expression values. Motivated by semi-nonnegative factorization, we allow one of the factor matrices, the centroid matrix, to be real-valued so that each centroid can express either the up- or down-regulation of the member genes in a pathway. In addition, we place structured spike-and-slab priors (which are encoded with the pathways and a gene-gene interaction (GGI) network) on the centroid matrix so that even a set of genes that is not initially contained in the pathways (due to the incompleteness of the current pathway database) can be involved in the factorization in a stochastic way specifically, if those genes are connected to the member genes of the pathways on the GGI network. We also present update rules for the posterior distributions in the framework of variational inference. As a full Bayesian method, our proposed method has several advantages over the current NMTF methods which are demonstrated using synthetic datasets in experiments. Using the The Cancer Genome Atlas (TCGA) gastric cancer and metastatic gastric cancer immunotherapy clinical-trial datasets, we show that our method could identify biologically and clinically relevant pathways associated with the molecular sub-types and immunotherapy response, respectively. Finally, we show that those pathways identified by the proposed method could be used as prognostic biomarkers to stratify patients with distinct survival outcome in two independent validation datasets. Additional information and codes can be found at https://github.com/parks-cs-ccf/BayesianSNMTF.

bioinformatics↗

bioRxiv2019

The most common RNF43 mutant G659Vfs41 is fully functional in inhibiting Wnt signaling and unlikely to play a role in tumorigenesis

RNF43 is an E3 ligase that inhibits Wnt signaling by ubiquitinating Wnt receptors for degradation. It is mutated in various cancer types with the most recurrent mutation being the frameshift G659Vfs41 with frequencies of ~5-8% in colon, stomach and endometrial cancers. The mutation has always been assumed to be loss-of-function that would lead to increased Wnt signaling and tumorigenesis, yet no functional characterization has been reported. We analyzed the distribution of the G659Vfs41 mutation and its association with other cancer gene mutations, and found that the mutation occurred nearly exclusively in tumors with low expression of the DNA mismatch repair gene MLH1. Mutant RNF43-G659Vfs41 was no different from wild type RNF43 in expression, localization, R-spondin binding, promotion of Wnt receptor degradation, inhibition of Wnt signaling. We also found that colon tumors with RNF43-G659Vfs41 had low expression of Wnt/{beta}-catenin signaling markers and were frequently mutated in BRAF. A colon cancer cell line with RNF43-G659Vfs41 and BRAF-V600E mutations was uniquely sensitively to activation of Wnt/{beta}-catenin signaling. These findings indicate that RNF43-G659Vfs41 is likely a passenger mutation in MLH1-down regulated tumors, not a loss-of-function driver mutation that facilitates tumorigenesis. Tumors with RNF43-G659Vfs41 and BRAF-V600E may be susceptible to activators of Wnt signaling.

cancer biology↗

bioRxiv2019

Genomic profiling of 553 uncharacterized neurodevelopment patients reveals a high proportion of recessive pathogenic variant carriers in an outbred population

BackgroundA substantial portion of Mendelian disease patients suffers from genetic variants that are inherited in a recessive manner. A precise understanding of pathogenic recessive variants in a population would assist in pre-screening births of such patients. However, a systematic understanding of the contribution of recessive variants to Mendelian diseases is still lacking.\n\nMethodsGenetic diagnosis and variant discovery of 553 undiagnosed Korean patients with complex neurodevelopmental problems (KND for Korean NeuroDevelopmental cohort) were performed using whole exome sequencing of patients and their parents. Pathogenic variants were selected and evaluated based on a comparison to patient symptoms and genetic properties of the variants were analyzed.\n\nResultsDisease-causing variants, including newly discovered variants, were identified in in 57.5% of the probands of the KND cohort. Of the 553 patients, 47.4% harbored variants that were previously reported as being pathogenic, and 35.1% of the previous reported pathogenic variants were inherited in a recessive manner. Genes that cause recessive disorders tend to be less constrained by loss-of-function variants and enriched in metabolic and mitochondrial pathways. This observation was applied to an estimation that approximately 1 in 17 healthy Korean individuals carry at least one of these pathogenic variants that develop severe neurodevelopmental problems in a recessive manner. Furthermore, the feasibility of these genes for carrier screening was evaluated.\n\nConclusionsWe suggest that the odds are high for healthy individuals carrying a potentially pathogenic variant, and its genetic properties. Our results will serve as a foundation for recessive variant screening to reduce occurrences of rare Mendelian disease patients. Additionally, our results highlight the utility and necessity of whole exome sequencing-based diagnostics for improving patient care in a country with a centralized medical system.

genetics↗

bioRxiv2019

Fraction and Spatial Organization of Tumor-Infiltrating Lymphocytes Correlating with Bladder Cancer Patient Survival

Recent studies based on deep learning on pathology images have shown the potential prognostic role of Tumor-infiltrating lymphocytes (TILs) in many cancer types. In general, most of these studies focused on detecting, quantifying and identifying TILs and spatial organization in entire whole slide images (WSIs) including both non-cancer and cancer regions. In this work, we hypothesize that TILs present in cancer regions not entire WSIs could have prognostic values. Here we present an automatic technique based on convolutional neural networks (CNN) that detects both cancer and TILs regions, and explores fraction and spatial organization of TILs in the detected cancer region from WSIs. A novel set of quantitative histological image features reflecting TILs fraction and spatial organization is extracted and used to stratify patients with distinct survival outcomes. In experiments using 277 different patient pathology slides selected from The Cancer Genome Atlas (TCGA) bladder cancer cohort, we show that bladder cancer patient survival is significantly correlated with our designed TIL-related image features, with a log-rank test p value below 0.05. Our method is extensible to histopathology images of other organs for predicting patient survival via TIL analysis.

bioinformatics↗