Biology subjects

Kang, H.

Publications and source records attributed to Kang, H..

106 records · Page 6Linked to original sources

bioRxiv2020

The Transition from Acute to Chronic Pain: Dynamic Epigenetic Reprogramming of the Mouse Prefrontal Cortex up to One Year Following Nerve Injury

Chronic pain is associated with persistent structural and functional changes throughout the neuroaxis, including in the prefrontal cortex (PFC). The PFC is important in the integration of sensory, cognitive and emotional information and in conditioned pain modulation. We previously reported wide-spread epigenetic reprogramming in the PFC many months following nerve injury in rodents. Epigenetic modifications, including DNA methylation, can drive changes in gene expression without modifying DNA sequences. To date, little is known about epigenetic dysregulation at the onset of acute pain or how it progresses as pain transitions from acute to chronic. We hypothesize that acute pain following injury results in rapid and persistent epigenetic remodelling in the PFC that evolves as pain becomes chronic. We further propose that understanding epigenetic remodelling will provide insights into the mechanisms driving pain-related changes in the brain. Epigenome-wide analysis was performed in the mouse PFC 1 day, 2 weeks, 6 months, and 1 year following peripheral injury using the spared nerve injury (SNI) in mice. SNI resulted in rapid and persistent changes in DNA methylation, with robust differential methylation observed between SNI and sham-operated control mice at all time points. Hundreds of differentially methylated genes were identified, including many with known function in pain. Pathway analysis revealed enrichment in genes related to stimulus response at early time points, immune function at later time points and actin and cytoskeletal regulation throughout the time course. Increased attention to pain chronicity as a factor is recommended for both pain research and management.

neuroscience↗

bioRxiv2020

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Advances in technology have allowed for the study of metabolomics in the context of disease, enabling the discovery of new potential risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular biomedical importance as it is in direct contact with the brain and spinal cord. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid compared to blood or urine studies. Here, we present a metabolome-wide association study (MWAS), an analysis using individual-level genetic and metabolomic data from two cohorts to impute metabolites into large samples with genome-wide association summary statistics. We conducted a metabolome-wide genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations, 6 of which were novel. Using these results, we then built prediction models for all available CSF metabolites and tested for associations with 27 neurological and psychiatric phenotypes in large cohorts, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the potential of MWAS to overcome the logistic challenges inherent in cerebrospinal fluid research to study the role of metabolomics in brain-related phenotypes and the feasibility of this framework for similar studies of omic data in scarce sample types.

genomics↗

bioRxiv2020

A common problem in the purification of calicivirus raised by molecular sieving: balance between purification and loss of viral particles

Caliciviridae is a group of RNA viruses could lead to gastrointestinal disease in humans and acute oral or upper respiratory tract disease in felines, no efficient vaccine so far. The preparation of immunogen is a challenge to obtain a safe and efficient vaccine candidate. Molecular sieving, due to the advantage of simple and fast purifying, is considered as a very powerful tool in calicivirus purification; however, also introduce mechanical damage to the viron. In this study, we took Feline Calicivirus (FCV), a common natural pathogen in cats as well as a classic model for caliciviruses studies, as a research object. Here, with the help of the cryo-electron microscope (cryo-EM), we collected particle images in both conditions of with and without molecular sieving post-processing, the impact of molecular sieving on FCV particles was evaluated by building a flexibility evaluation system after 3D classification. The results indicated that the molecular sieving will impact the stability of P domains through increasing flexibility. In addition, we successfully built up a scoring system to describe this flexibility of FCV P-domains, which can be extended to assess the purification effect to the entire calicivirus.

microbiology↗

bioRxiv2020

Calsyntenin-3 directly interacts with neurexins to orchestrate excitatory synapse development in the hippocampus

Calsyntenin-3 (Clstn3) is a postsynaptic adhesion molecule that induces presynaptic differentiation via presynaptic neurexins (Nrxns), but whether Nrxns directly bind to Clstn3 has been a matter of debate. Here, we show that {beta}-Nrxns directly interact via their LNS domain with Clstn3 and Clstn3 cadherin domains. Expression of splice site 4 (SS4) insert-positive {beta}-Nrxn variants, but not insert-negative variants, reversed the impaired Clstn3 synaptogenic activity observed in Nrxn-deficient neurons. Consistently, Clstn3 selectively formed complexes with SS4-positive Nrxns in vivo. Neuron-specific Clstn3 deletion caused significant reductions in number of excitatory synaptic inputs, and moderate impairment of light-induced anxiety-like behaviors in mice. Moreover, expression of Clstn3 cadherin domains in CA1 neurons of Clstn3 conditional knockout mice rescued structural deficits in excitatory synapses, especially within the stratum radiatum layer. Collectively, our results suggest that Clstn3 links to SS4-positive Nrxns to induce presynaptic differentiation and orchestrate excitatory synapse development in specific hippocampal neural circuits.

neuroscience↗

bioRxiv2019

Transcriptomic changes due to early, chronic ethanol exposure during cortical development implicates arealization, cell-type specification, Axon Guidance and Synaptogenesis as primary determinants of Fetal Alcohol Spectrum Disorders

Fetal alcohol spectrum disorders (FASD) are described by a cluster of deficits following in utero alcohol exposure, whose effects disproportionately target the cerebral cortex. In vitro and in vivo models of FASD have successfully recapitulated multiple facets of clinical presentations, including morphological and behavioral deficits, but far less is understood regarding the molecular and genetic bases of FASD. In this study, we utilize an in vitro human pluripotent stem cell-based (hPSC) model of corticogenesis to probe the effect of early, chronic alcohol exposure on the transcriptome of developing cortical neurons. We here identify a relatively limited number of significantly altered biological pathways, including regional patterning, cell-type specification, axon guidance and synaptic function. Significant upregulation of WNT signaling-related transcripts, to the exclusion of other secreted morphogens was also observed in alcohol exposed cultures. Lastly, an overall alcohol-associated shift towards an increased caudal profile, at the expense of rostral molecular identity was observed, representing a potentially previously underappreciated FASD phenotype.

neuroscience↗

bioRxiv2019

Weak-Instrument Robust Tests in Two-Sample Summary-Data Mendelian Randomization

Mendelian randomization (MR) is a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most popular due to privacy. Unfortunately, many MR methods for two-sample summary data are not robust to weak instruments, a common phenomena with genetic instruments; many of these methods are biased and no existing MR method has Type I error control under weak instruments. In this work, we propose test statistics that are robust to weak instruments by extending Anderson-Rubin, Kleibergen, and conditional likelihood ratio tests in econometrics to the two-sample summary data setting. We conclude with a simulation and an empirical study and show that the proposed tests control size and have better power than current methods.

genetics↗

bioRxiv2019

Probabilistic Assessment of Nerve Regeneration with Diffusion MRI: Validation in Rat Models of Peripheral Nerve Trauma

Nerve regeneration after injury must occur in a timely fashion to restore function. Unfortunately, current methods (e.g., electrophysiology) provide limited information following trauma, resulting in delayed management and suboptimal outcomes. Herein, we evaluated the ability of diffusion MRI to monitor nerve regeneration after injury/repair. Sprague-Dawley rats were divided into three treatment groups (sham=21, crush=23, cut/repair=19) and ex vivo diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) was performed 1-12 weeks post-surgery. Behavioral data showed a distinction between crush and cut/repair nerves at 4 weeks. This was consistent with DTI, which found that thresholds based on the ratio of radial and axial diffusivities (RD/AD=0.40{+/-}0.02) and fractional anisotropy (FA=0.53{+/-}0.01) differentiated crush from cut/repair injuries. By the 12th week, cut/repair nerves whose behavioral data indicated a partial recovery were below the RD/AD threshold (and above the FA threshold), while nerves that did not recover were on the opposite side of each threshold. Additional morphometric analysis indicated that DTI-derived normalized scalar indices report on axon density (RD/AD: r=-0.54, p<1e-3; FA: r=0.56, p<1e-3). Interestingly, higher-order DKI analyses did not improve our ability classify recovery. These findings suggest that DTI can distinguish successful/unsuccessful nerve repairs and potentially identify cases that require reoperation.

neuroscience↗

bioRxiv2019

Single molecule dynamics of Dishevelled at the plasma membrane and Wnt pathway activation

Dvl (Dishevelled) is one of several essential non-enzymatic components of the Wnt signaling pathway. In most current models, Dvl forms complexes with Wnt ligand receptors, Fzd and LRP5/6 at the plasma membrane, which then recruits other components of the destruction complex leading to inactivation of {beta}-catenin degradation. Although this model is widespread, direct evidence for this process is lacking. In this study, we tagged mEGFP to C-terminus of dishevlled2 gene using CRISPR/Cas9 induced homologous recombination and observed its dynamics directly at the single molecule level with Total Internal Reflection Fluorescence (TIRF) microscopy. We focused on two questions: 1) What is the native size and the dynamic features of membrane-associated Dvl complexes during Wnt pathway activation? 2) What controls the behavior of these complexes? We found that membrane bound Dvl2 is predominantly monomer in the absent of Wnt (mean size 1.10). Wnt3a stimulation leads to an increase in the total concentration of membrane-bound Dvl2 from 0.08/m2 to 0.34/m2. Wnt3a also leads to increased oligomerization which raises the weighted averaged mean size of Dvl2 complexes to 1.4; with 65% of Dvl still as monomers. The driving force for Dvl2 oligomerization is the increased concentration of Dvl2 at the membrane caused by increased affinity of Dvl2 for Fzd, the Dvl2 and Fzd binding is independent of LRP5/6. The oligomerized Dvl2 complexes have greatly increased dwell time, 2~3 minutes compared to less than 1 second for monomeric Dvl2. These properties make Dvl a unique scaffold dynamically changing its state of assembly and stability at the membrane in response to Wnt ligands.\n\nSignificance StatementCanonical Wnt signaling is one of the most widely distributed pathways in metazoan development. Despite intense genetic and biochemical study for over 35 years, the major features of signaling across the plasma membrane are still poorly understood. Dishevelled serves as an essential bridge between the membrane receptors and downstream signaling components. Attempts to reconstruct the pathway and analyze its biochemical features in vitro have been hampered by Dishevelleds tendency to aggregate in vitro and to form large aggregates of dubious significance in vivo. To obtain a molecular understanding of the role of Dvl in Wnt signaling, while circumventing these aggregation problems we have expressed a fluorescent tagged Dishevelled in cells at their physiological concentration and quantified the size distribution of Dishevelled before and after Wnt treatment. We found that limited oligomerization in response to the Wnt ligand is very dynamic and provides a key step of signal transduction.

systems biology↗

bioRxiv2018

Consensus interpretation of the Met34Thr and Val37Ile variants in GJB2 by the ClinGen Hearing Loss Expert Panel

PURPOSEPathogenic variants in GJB2 are the most common cause of autosomal recessive sensorineural hearing loss. The classification of c.101T>C/p.Met34Thr and c.109G>A/p.Val37Ile in GJB2 are controversial. Therefore, an expert consensus is required for the interpretation of these two variants.\n\nMETHODSThe ClinGen Hearing Loss Expert Panel (HL-EP) collected published data and shared unpublished information from participating laboratories regarding the two variants. Functional, computational, allelic, and segregation data were also obtained.\n\nRESULTSThe panel reviewed the synthesized information, and classified the Met34Thr and Val37Ile variants according to professional variant interpretation guidelines. We found that Met34Thr and Val37Ile are significantly overrepresented in hearing loss patients, compared to the general population. Met34Thr or Val37Ile homozygotes or compound heterozygotes typically manifest mild to moderate hearing loss. Several other types of evidence also support pathogenic roles for those two variants.\n\nCONCLUSIONResolving controversies in variant classification requires coordinated effort among a panel of international multi-institutional experts to share data, standardize classification rules, review evidence, and reach a consensus. The ClinGen HL-EP concluded that Met34Thr and Val37Ile variants in GJB2 are pathogenic for autosomal recessive nonsyndromic hearing loss with variable expressivity and age-dependent penetrance.

genetics↗

bioRxiv2018

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

PurposeFiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do not capture variabilities because of in vivo physiological factors.\n\nMethodsTo date, a large-scale reproducibility analysis has not been performed for the assessment of the newest generation of tractography algorithms with in vivo data. Reproducibility does not assess the validity of a brain connection however it is still of critical importance because it describes the variability for an algorithm in group studies. The ISMRM 2017 TraCED challenge was created to fulfill the gap. The TraCED dataset consists of a single healthy volunteer scanned on two different scanners of the same manufacturer. The multi-shell acquisition included b-values of 1000, 2000 and 3000 s/mm2 with 20, 45 and 64 diffusion gradient directions per shell, respectively.\n\nResultsNine international groups submitted 46 tractography algorithm entries. The top five submissions had high ICC > 0.88. Reproducibility is high within these top 5 submissions when assessed across sessions or across scanners. However, it can be directly attributed to containment of smaller volume tracts in larger volume tracts. This holds true for the top five submissions where they are contained in a specific order. While most algorithms are contained in an ordering there are some outliers.\n\nConclusionThe different methods clearly result in fundamentally different tract structures at the more conservative specificity choices (i.e., volumetrically smaller tractograms). The data and challenge infrastructure remain available for continued analysis and provide a platform for comparison.

neuroscience↗

bioRxiv2018

Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimers disease (AD; N=61,212). Among 1,738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Further, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

genetics↗

bioRxiv2018

Efficient Multivariate Analysis Algorithms for Longitudinal Genome-wide Association Studies

MotivationCurrent dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.\n\nResultsWe herein developed efficient genome-wide multivariate association algorithms (GMA) for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed new method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes.\n\nAvailability and ImplementationWe wrote a software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA), which is available freely for interested users in relevant fields.

bioinformatics↗

bioRxiv2018

Mining unknown porcine protein isoforms by tissue-based map of proteome enhances the pig genome annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model. We developed the tissue-based proteome maps using 34 major normal pig tissues. A total of 7,319 unknown protein isoforms were identified and systematically characterized, including 3,703 novel protein isoforms, 669 protein isoforms from 460 genes symbolized beginning with LOC, and 2,947 protein isoforms without clear NCBI annotation in current pig reference genome. These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing (RNA-seq) of the same pig tissues, further improving the genome annotation of corresponding protein coding genes. Combining the well-annotated genes that having parallel expression pattern and subcellular witness, we predicted the tissue related subcellular components and potential function for these unknown proteins. Finally, we mined 3,656 orthologous genes for 49.95% of unknown protein isoforms across multiple species, referring to 65 KEGG pathways and 25 disease signaling pathways. These findings provided valuable insights and a rich resource for enhancing studies of pig genomics and biology as well as biomedical model application to human medicine.

genomics↗

bioRxiv2018

Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness

Facial attractiveness is a complex human trait of great interest in both academia and industry. Literature on sociological and phenotypic factors associated with facial attractiveness is rich, but its genetic basis is poorly understood. In this paper, we conducted a genome-wide association study to discover genetic variants associated with facial attractiveness using 3,928 samples in the Wisconsin Longitudinal Study. We identified two genome-wide significant loci and highlighted a handful of candidate genes, many of which are specifically expressed in human tissues involved in reproduction and hormone synthesis. Additionally, facial attractiveness showed strong and negative genetic correlations with BMI in females and with blood lipids in males. Our analysis also suggested sex-specific selection pressure on variants associated with lower male attractiveness. These results revealed sex-specific genetic architecture of facial attractiveness and provided fundamental new insights into its genetic basis.

genetics↗

bioRxiv2017

Advanced NeuroGPS-Tree: dense reconstruction of brain-wideneuronal population close to ground truth

Recent progresses allow imaging specific neuronal populations at single-axon level across mouse brain. However, digital reconstruction of neurons in large dataset requires months of human labor. Here, we developed a tool to solve this problem. Our tool offers a special error-screening system for fast localization of submicron errors in densely packed neurites and along long projection across the whole brain, thus achieving reconstruction close to the ground-truth. Moreover, our tool equips algorithms that significantly reduce intensive manual interferences and achieve high-level automation, with speed 5 times faster compared to semi-automatic tools. We also demonstrated reconstruction of 35 long projection neurons around one injection site of a mouse brain at an affordable time cost. Our tool is applicable with datasets of 10 TB or higher from various light microscopy, and provides a starting point for the reconstruction of neuronal population for neuroscience studies at a single-cell level.

neuroscience↗

bioRxiv2017

Predicting aging of brain metabolic topography using variational autoencoder

Predicting future brain topography can give insight into neural correlates of aging and neurodegeneration. Due to variability in aging process, it has been challenging to precisely estimate brain topographical change according to aging. Here, we predict age-related brain metabolic change by generating future brain 18F-Fluorodeoxyglucose PET. A cross-sectional PET dataset of cognitively normal subjects with different age was used to develop a generative model. The model generated PET images using age information and characteristic individual features. Predicted regional metabolic changes were correlated with the real changes obtained by follow-up data. This model was applied to produce a brain metabolism aging movie by generating PET at different ages. Normal population distribution of brain metabolic topography at each age was estimated as well. In addition, a generative model using APOE4 status as well as age as inputs revealed a significant effect of APOE4 status on age-related metabolic changes particularly in the calcarine, lingual cortex, hippocampus and amygdala. It suggested APOE4 could be a factor affecting individual variability in age-related metabolic degeneration in normal elderly. This predictive model may not only be extended to understanding cognitive aging process, but apply to development of a preclinical biomarker for various brain disorders.

neuroscience↗