Biology subjects

Park, Y.

Publications and source records attributed to Park, Y..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

An oligodendrocyte silencer element underlies the pathogenic impact of lamin B1 structural variants

The role of non-coding regulatory elements and how they might contribute to tissue type specificity of disease phenotypes is poorly understood. Autosomal Dominant Leukodystrophy (ADLD) is a fatal, adult-onset, neurological disorder that is characterized by extensive CNS demyelination. Most cases of ADLD are caused by tandem genomic duplications involving the lamin B1 gene (LMNB1) while a small subset are caused by genomic deletions upstream of the gene. Utilizing data from recently identified families that carry LMNB1 gene duplications but do not exhibit demyelination, ADLD patient tissues, CRISPR modified cell lines and mouse models, we have identified a novel silencer element that is lost in ADLD patients and that specifically targets overexpression to oligodendrocytes. This element consists of CTCF binding sites that mediate three-dimensional chromatin looping involving the LMNB1 and the recruitment of the PRC2 repressor complex. Loss of the silencer element in ADLD identifies a previously unknown role for silencer elements in tissue specificity and disease causation.

genetics↗

bioRxiv2023

Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index

Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated inter-individual variations in BMI to be associated with altered brain structure and function. However, how the structure-function correspondence is altered according to BMI is under-investigated. In this study, we combined structural and functional connectivity using Riemannian optimization with varying diffusion time parameters and assessed their association with BMI. First, we simulated functional connectivity from structural connectivity and generated low-dimensional principal gradients of the simulated functional connectivity across diffusion times, where low and high diffusion times indirectly reflected mono- and polysynaptic communication. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the simulated gradients and BMI revealed the strongest relationship when the hierarchical structure was most evident. Moreover, the functional gradient-BMI association map showed significant correlations with the cytoarchitectonic measures of the microstructural gradient and moment features, indicating that BMI-related functional connectome alterations were remarkable in higher-order cognitive control-related brain regions. Finally, transcriptomic association analysis provided potential biological underpinnings, specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is most apparent, and the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.

neuroscience↗

bioRxiv2023

Disentangling gene expression burden identifies generalizable phenotypes induced by synthetic gene networks

Large-scale genetic circuits are rapidly becoming critical components for the next generation of biotechnologies and living therapeutics. However, the relationship between synthetic and host gene expression is poorly understood. To reveal the impact of genetic circuits on their host, we measure the transcriptional response of wild-type and engineered E. coli MG1655 subject to seven genomically integrated circuits and two plasmid-based circuits across 4 growth time points and 4 circuit input states resulting in 1007 transcriptional profiles. We train a classifier to distinguish profiles from wild-type or engineered strains and use the classifier to identify synthetic construct burdened genes, i.e., genes whose dysregulation is dependent on the presence of a genetic circuit and not what is encoded on the circuit. We develop a deep learning architecture, capable of disentangling influence of combinations of perturbations, to model the impact that synthetic genes have on their host. We use the model to hypothesize a generalizable, synthetic cell state phenotype and validate the phenotype through antibiotic challenge experiments. The synthetic cell state results in increased resistance to {beta}-lactam antibiotics in gram-negative bacteria. This work enhances our understanding of circuit impact by quantifying the disruption of host biological processes and can guide the design of robust genetic circuits with minimal burden or uncover novel biological circuits and phenotypes.

systems biology↗

bioRxiv2023

Hybrid machine-learning framework for volumetric segmentation and quantification of vacuoles in individual unlabeled yeast cells using holotomography

The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of unlabeled cell data. We applied this method to unlabeled live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.

biophysics↗

bioRxiv2023

Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters

Precise modulation of brain activity is fundamental for the proper establishment and maturation of the cerebral cortex. To this end, cortical organoids are promising tools to study circuit formation and the underpinnings of neurodevelopmental disease. However, the ability to manipulate neuronal activity with high temporal resolution in brain organoids remains limited. To overcome this challenge, we introduce a bioelectronic approach to control cortical organoid activity with the selective delivery of ions and neurotransmitters. Using this approach, we sequentially increased and decreased neuronal activity in brain organoids with the bioelectronic delivery of potassium ions (K+) and {gamma}-aminobutyric acid (GABA), respectively, while simultaneously monitoring network activity. This works highlights bioelectronic ion pumps as tools for high-resolution temporal control of brain organoid activity toward precise pharmacological studies that can improve our understanding of neuronal function.

bioengineering↗

bioRxiv2023

PIP2 electrostatically triggers vesicle fusion: arresting full SNARE assembly and vesicle fusion by PIP2-masking

SNARE proteins drive vesicle fusion and neurotransmitters release. Given that exocytosis is fast, and vesicle docking is tight, SNARE proteins are likely pre-assembled before fusion. However, the molecular mechanisms of the partially-assembled SNARE complex remain controversial. We use amperometry and the reconstitution of native vesicle fusion to show that MARCKS arrests basal fusion by masking PIP2 in a vesicle docking state where the SNARE complex is partially assembled. Ca2+/CaM or PKC-epsilon unmask PIP2 through the MARCKS dissociation, and thus rescue basal fusion and potentiates synaptotagmin-1-mediated Ca2+-dependent vesicle fusion. Our data provide the novel model that PIP2 electrostatically triggers vesicle fusion by lowering the hydration energy, and that masking PIP2 arrests vesicle fusion in a state of the partial SNARE assembly. Vesicle-mimicking liposomes fail to arrest vesicle fusion by masking PIP2, indicating that native vesicles are essential for the reconstitution of physiological vesicle fusion. One Sentence SummaryMasking PIP2 by MARCKS arrests the full SNARE assembly and vesicle fusion.

neuroscience↗

bioRxiv2023

Sequencing, fast and slow: profiling microbiomes in human samples with nanopore sequencing

Rapid and accurate pathogen identification is crucial in effectively combating infectious diseases. However, the current diagnostic tools for bacterial infections predominantly rely on century-old culture-based methods. Furthermore, recent research highlights the significance of host-microbe interactions within the host microbiota in influencing the outcome of infection episodes. As our understanding of science and medicine continues to advance, there is a pressing need for innovative diagnostic methods that can identify pathogens and also rapidly and accurately profile the microbiome landscape in human samples. In clinical settings, such diagnostic tools will become a powerful predictive instrument in directing the diagnosis and prognosis of infectious diseases by providing comprehensive insights into the patients microbiota. Here, we explore the potential of long-read sequencing in profiling the microbiome landscape from various human samples in terms of speed and accuracy. Using nanopore sequencers, we generate native DNA sequences from saliva and stool samples rapidly, from which each long-read is basecalled in real-time to provide downstream analyses such as taxonomic classification and antimicrobial resistance through the built-in software (< 12 hours). Subsequently, we utilize the nanopore sequence data for in-depth analysis of each microbial species in terms of host-microbe interaction types and deep learning-based classification of unidentified reads. We find that the nanopore sequence data encompass complex information regarding the microbiome composition of the host and its microbial communities, and also shed light on the unexplored human mobilome including bacteriophages. In this study, we use two different systems of long-read sequencing to give insights into human microbiome samples in the slow and fast modes, which raises additional inquiries regarding the precision of this novel technology and the feasibility of extracting native DNA sequences from other human microbiomes.

microbiology↗

bioRxiv2023

Diversity of the bacterial and viral communities in the tropical horse tick, Dermacentor nitens in Colombia

Ticks are obligatory hematophagous ectoparasites that transmit pathogens among various vertebrates, including humans. The composition of the microbial and viral communities in addition to the pathogenic microorganisms is highly diverse in ticks, but the factors driving the diversity are not well understood. The tropical horse tick, Dermacentor nitens, is distributed throughout the Americas and it is recognized as a natural vector of Babesia caballi and Theileria equi, the causal agents of equine piroplasmosis. We characterized the bacterial and viral communities associated with partially-fed D. nitens females collected by a passive survey on horses from field sites representing three distinct geographical areas in Colombia (Bolivar, Antioquia, and Cordoba). RNA-seq and sequencing of the V3 and V4 hypervariable regions of the 16S rRNA gene were performed using the Illumina-Miseq platform. A total of 356 operational taxonomic units (OTUs) were identified, in which the presumed endosymbiotic Francisellaceae/Francisella spp. was predominantly found. Nine contigs corresponding to six different viruses were identified in three viral families: Chuviridae, Rhabdoviridae, and Flaviviridae. Differences in the relative abundance of the microbial composition among the geographical regions were found to be independent of the presence of Francisella-Like Endosymbiont (FLE). The most prevalent bacteria found on each region were Corynebacterium in Bolivar, Staphylococcus in Antioquia, and Pseudomonas in Cordoba. Rickettsia-like endosymbionts, mainly recognized as the etiological agent of rickettsioses in Colombia were detected in the Cordoba samples. Metatranscriptomics revealed 13 contigs containing FLE genes, suggesting a trend of regional differences. These findings suggest regional distinctions among the ticks and their bacterial compositions.

bioinformatics↗

bioRxiv2023

GAN-MAT: Generative Adversarial Network-based Microstructural Profile Covariance Analysis Toolbox

AO_SCPLOWBSTRACTC_SCPLOWMultimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community, and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

neuroscience↗

bioRxiv2023

Epistasis facilitates functional evolution in an ancient transcription factor

A proteins genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factors specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptors capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

evolutionary biology↗

bioRxiv2023

Engrailed-1 Promotes Pancreatic Cancer Metastasis

Engrailed-1 (EN1) is a critical homeodomain transcription factor (TF) required for neuronal survival, and EN1 expression has been shown to promote aggressive forms of triple negative breast cancer. Here, we report that EN1 is aberrantly expressed in a subset of pancreatic ductal adenocarcinoma (PDA) patients with poor outcomes. EN1 predominantly repressed its target genes through direct binding to gene enhancers and promoters, implicating a role in the acquisition of mesenchymal cell properties. Gain- and loss-of-function experiments demonstrated that EN1 promoted PDA transformation and metastasis in vitro and in vivo. Our findings nominate the targeting of EN1 and downstream pathways in aggressive PDA.

cancer biology↗

bioRxiv2023

Inducing an oxidized redox-balance improves anti-tumor CD8+ T cell function

Cancer immunotherapy using antigen-specific CD8+ T cells depends on long-lasting anti-tumor function of the in vitro expanded T cells. T cell function is intricately linked to the activity of many metabolic pathways directly impacting the ability of CD8+ T cells to kill tumor cells. Metabolic conditioning in vitro better prepares CD8+ T cells for in vivo survival, tumor infiltration and tumor clearance. The mechanism underlying in vitro metabolic conditioning-induced augmented in vivo T cell function remains poorly understood. Here we show that metabolic conditioning of CD8+ effector T cells induces an oxidized cellular redox balance at least in part mediated by increased mitochondrial reactive oxygen species (ROS). This redox shift contributes to enhanced in vivo persistence and tumor clearance. In human tumour-infiltrating T cells, altering the redox balance ex-vivo reinvigorated pro-inflammatory cytokine production. Therefore, we believe that redox alterations present a targetable pathway to increase T cell-based anti-tumor immunotherapy efficacy.

immunology↗

bioRxiv2023

DNA nanopores as artificial membrane channels for origami based bioelectronics

Biological membrane channels mediate information exchange between cells and facilitate molecular recognition1-4. While tuning the shape and function of membrane channels for precision molecular sensing via de-novo routes is complex, an even more significant challenge is interfacing membrane channels with electronic devices for signal readout5-8. This challenge at the biotic-abiotic interface results in low efficiency of information transfer - one of the major barriers to the continued development of high-performance bioelectronic devices9. To this end, we integrate membrane spanning DNA nanopores with bioprotonic contacts to create programmable, modular, and efficient artificial ion-channel interfaces that resolve the iono-electronic disparity between the biotic environment and electronics. Through simulations and experiments, we show that cholesterol modified DNA nanopores spontaneously and with remarkable affinity span the lipid bilayer formed over the planar bio-protonic electrode surface and mediate proton transport across the bilayer. Using the ability to easily modify DNA nanostructures, we illustrate that this bioelectronic device can be programmed for electronic recognition of biomolecular signals such as presence of Streptavidin, without disrupting the native environment of the biomolecule. We anticipate this robust biotic-abiotic interface will allow facile electronic measurement of inter-cellular ionic communication and also open the door for active control of cell behavior through externally controlled selective gating of the channels.

bioengineering↗

bioRxiv2023

Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning

For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In this study, we propose a label-free method that combines optical diffraction tomography (ODT) and deep learning (DL) to automate the histological quantification process. The DL model classifies ODT image patches with 95% accuracy, and the collective prediction generates a whole-slide map of red blood cells and fibrin. The resulting whole-slide composition displays an average error of 1.1% and does not experience staining variability, facilitating faster analysis with reduced labor. The present approach will enable rapid and quantitative evaluation of blood clot composition, expediting the preclinical research and diagnosis of cardiovascular diseases.

bioengineering↗

bioRxiv2023

High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy

A critical requirement for studying cell mechanics is three-dimensional (3D) assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy (TFM) with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow 3D acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), a high-speed volumetric technique that simultaneously quantifies the 3D morphology and traction force of cells. RI-TFM reconstructs a 3D tomogram of single cells on a planar hydrogel elastomer using a high-speed illumination scheme with 0.5-Hz temporal resolution. This allows for quantitative analyses of 3D dry-mass distributions and shear (in-plane) and normal (out-of-plane) tractions of single cells on the extracellular matrix without labelling. When combined with a constrained total variation-based deconvolution algorithm, the proposed method provides 0.55-Pa shear (in-plane) and 1.59-Pa normal (out-of-plane) traction sensitivity for a 1-kPa hydrogel substrate. We demonstrate the utility of RI-TFM by assessing the effects of compromised intracellular stress due to actin disruption or low temperature and capturing the rapid dynamics of cellular junction formation in the spatiotemporal changes in non-planar traction components.

biophysics↗

bioRxiv2023

Verification of nucleotide sequence reagent identities in original publications in high impact factor cancer research journals

Human gene research studies that describe wrongly identified nucleotide sequence reagents have been mostly identified in journals of low to moderate impact factor, where unreliable findings could be considered to have limited influence on future research. This study examined whether papers describing wrongly identified nucleotide sequences are also published in high impact factor cancer research journals. We manually verified nucleotide sequence identities in original Molecular Cancer articles published in 2014, 2016, 2018 and 2020, including nucleotide sequence reagents that were claimed to target circRNAs. Using keywords identified in problematic 2018 and 2020 Molecular Cancer papers, we also verified nucleotide sequence identities in 2020 Oncogene papers that studied miRNA(s) and/or circRNA(s). Overall, 3.8% (253/6,647) and 4.3% (50/1,165) nucleotide sequences that were verified in Molecular Cancer and Oncogene papers, respectively, were found to be wrongly identified. These wrongly identified nucleotide sequences were distributed across 18% (92/500) original Molecular Cancer papers, including 38% Molecular Cancer papers from 2020, and 40% (21/52) selected Oncogene papers from 2020. Original papers with wrongly identified nucleotide sequences were therefore unexpectedly frequent in two high impact factor cancer research journals, highlighting the risks of employing journal impact factors or citations as proxies for research quality.

cancer biology↗

bioRxiv2023

Volumetric refractive index measurement and quantitative density analysis of mouse brain tissue with sub-micrometer spatial resolution

High-resolution structural imaging of brain tissue is important for neuroscience research. However, conventional approaches have several limitations, such as the need for exogenous staining, limited accessibility to volumetric information, and qualitative analysis. Herein, we present high-resolution label-free volumetric imaging and analysis of mouse brain tissue using three-dimensional quantitative phase imaging. Measurement of the refractive index distribution of tissue enables direct imaging of the cellular and subcellular structures. Quantification of subcellular organelles is performed in the anatomical regions of the somatosensory cortex, corpus callosum, caudoputamen, and thalamus regions.

biophysics↗

bioRxiv2023

CRISPR-Cas-Docker: Web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins

MotivationCRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRN sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. ResultsCRISPR-Cas-Docker is an optimized and integrated platform that provides users with 1) 3D-predicted crRNA structures and AlphaFold-predicted Cas protein structures, 2) the top-10 docking models for a particular crRNA-Cas protein pair, and 3) machine learning-based classification of crRNA into its Cas system type. Availability and implementationCRISPR-Cas-Docker is available as an open-source tool under the GNU General Public License v3.0 on GitHub. It is also available as a web server.

bioinformatics↗