Biology subjects

Cho, M.

Publications and source records attributed to Cho, M..

33 records · Page 2Linked to original sources

bioRxiv2022

Adaptive Optical Coherent Raman Imaging of Axons through Mouse Cranial Bone

Coherent Raman scattering imaging has provided inherent chemical information of biomolecules without the need for any external labels.1-3 However, its working depth in deep-tissue imaging is extremely shallow because both the intrinsic scattering cross-section and image contrast are so small that even weak perturbation of the pump and Stokes beam focusing by the complex tissue causes the loss of the resolving power.4,5 Here, we propose a deep-tissue coherent Raman scattering (CRS) microscopy equipped with an advanced adaptive optics (AO) system measuring complex tissue aberration from elastic backscattering. Using this label-free AO-CRS microscopy, we demonstrate the vibrational imaging of lipid-rich substances such as myelin inside the mouse brain even through the thick and opaque cranial bones.

bioengineering↗

bioRxiv2022

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

As of June 2022, the GISAID database contains more than one million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.

bioinformatics↗

bioRxiv2022

Long-term cellular cargo tracking reveals intricate trafficking through active cytoskeletal networks

A eukaryotic cell is a microscopic world within which efficient material transport is essential. To examine cargo transport in a crowded cellular environment, we tracked unlabeled cargos in directional motion in a massively parallel fashion using the interferometric scattering microscopy. Our label-free, cargo-tracing method revealed not only dynamic cargo transportation but also the fine architecture of the actively used cytoskeletal highways and the long-term evolution of the associated traffic at sub-diffraction resolution via a myriad of molecular strokes. Cargos experience a traffic jam, but they have an effective strategy to circumvent it: moving together in tandem or migrating collectively. All taken together, a cell is an incredibly complex and busy space where the principle and practice in transportation intriguingly parallel those of our macroscopic world.

biophysics↗

bioRxiv2022

SciViewer- An interactive browser for visualizing single cell datasets

Single-cell sequencing improves our ability to understand biological systems at single-cell resolution and can be used to identify novel drug targets and optimal cell-types for target validation. However, tools that can interactively visualize and provide target-centric views of these large datasets are limited. We present SciViewer (Single-cell Interactive Viewer), a novel tool to interactively visualize, annotate and share single-cell datasets. SciViewer allows visualization of cluster, gene and pathway level information such as clustering annotation, differential expression, pathway enrichment, cell-type specificity, cellular composition, normalized gene expression and comparison across datasets. Further, we provide APIs for SciViewer to interact with publicly available pharmacogenomics databases for systematic evaluation of potential novel drug targets. We provide a module for non-programmatic upload of single-cell datasets. SciViewer will be a useful tool for data exploration and target discovery from single-cell datasets. It is available on GitHub (https://github.com/Dhawal-Jain/SciViewer).

bioinformatics↗

bioRxiv2022

A Statistical Physics Approach for Disease Module Detection

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the human protein-protein interaction (PPI) network (a.k.a. the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach with genome-wide association studies (GWAS) of six complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

systems biology↗

bioRxiv2021

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ~50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837328. Author summaryGene regulation is a critical mechanism underlying complex traits. Transcriptome-wide association studies (TWAS) have helped elucidate potential mechanisms because each association connects a gene rather than a variant to the complex trait. Like genome-wide association studies (GWAS), most TWAS are still conducted exclusively in populations of European ancestry, which misses the opportunity to test the full spectrum of human genetic variation for associations with complex traits. Here, move beyond the transcriptome and because protein measurement assays are growing to allow interrogation of the proteome, we use data from TOPMed MESA to develop genetic predictors of protein abundance in diverse ancestry populations. We compare model-building strategies with the goal of providing the best resource for protein association discovery with available data. We demonstrate how these prediction models can be used to perform proteome-wide association studies (PWAS) in diverse populations. We show the most protein-trait associations were discovered, colocalized, and replicated in independent cohorts using proteome prediction model training populations with similar ancestries to individuals in the GWAS. We shared our protein prediction models and performance statistics publicly to facilitate future proteome mapping studies in diverse populations.

genomics↗

bioRxiv2020

Improved Prediction of Smoking Status via Isoform-Aware RNA-seq Deep Learning Models

Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in over 2,000 subjects in the COPDGene Study. We observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published five gene prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, our exon-based models including an exon-to-isoform mapping layer achieved a test set AUC of 0.88 using data from the same 5 genes and an AUC of 0.94 using a larger set of exon quantifications. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models. Author summaryPredictive models based on gene expression are already a part of medical decision making for selected situations such as early breast cancer treatment. Most of these models are based on measures that do not capture critical aspects of gene splicing, but with RNA sequencing it is possible to capture some of these aspects of alternative splicing and use them to improve clinical predictions. Building on previous models to predict cigarette smoking status, we show that measures of alternative splicing significantly improve the accuracy of these predictive models.

genomics↗

bioRxiv2020

Integrity of anterior corpus callosum is well related to language impairment after traumatic brain injury

BackgroundThe corpus callosum (CC) serves as the bridge that relays information between the two cerebral hemispheres, and is one of the most commonly injured areas after traumatic brain injury (TBI). This study was designed to investigate the association between the CC integrity and language function after TBI. MethodsWe retrospectively enrolled 30 patients with TBI who underwent diffusion tensor imaging and language function evaluation using the Western Aphasia Battery. The CC was divided into five segments (C1-C5) according to its projecting fibers using Hofers method, and fractional anisotropy (FA) values were measured using DSI studio software. The FA values of the left arcuate fasciculus and cingulum for language function and executive function, respectively, were also evaluated. Twelve healthy controls were also enrolled to compare the FA values of these tracts. ResultsThe FA values of the cingulum and left arcuate fasciculus were significantly correlated with all language scores. The FA values of the entire CC were significantly correlated with the fluency, repetition, and aphasia quotient scores. The FA values of the anterior CC segment (C1 and C2) significantly correlated with the aphasia quotient score; C1 with the fluency score; and C2 with the fluency, comprehension, and repetition scores. However, the FA values of the posterior CC (C3-C5) were not significantly correlated with any of the language subset scores. ConclusionThe language function in patients with TBI is correlated with the integrity of the white matter tracts important for language and attention processes. Moreover, disruption of the CC is common after TBI, and the anterior CC segment plays an important role in language impairment after TBI. Therefore, analyzing CC integrity using diffusion tensor imaging can help predict language impairment in patients with TBI.

neuroscience↗

bioRxiv2020

Suspension of A Point-Mass-Loaded Filament in Non-uniform Flows: Passive Dynamics of a Ballooning Spider

Spiders utilize their fine silk fibres for their aerial dispersal, known as ballooning. With this method, spiders can disperse hundreds of kilometres, reaching as high as 4.5 km. However, the passive dynamics of a ballooning model (a highly flexible filament and a spider body at the end of it) are not well understood. The previous study (Rouse model: without taking into account anisotropic drag of a fibre) suggested that the flexible and extendible fibres reduce the settling speed of the ballooning model in homogeneous turbulence. However, the exact cause of the reduction of the settling speed is not explained and the assumed isotropic drag of a fibre is not realistic in the low Reynolds number flow. Here we introduce a bead-spring model that takes into account the anisotropic drag of a fibre to investigate the passive behaviour of the ballooning model in the various non-uniform flows (a shear flow, a periodic vortex flow field and a homogeneous turbulent flow). For the analysis of the wide range of parameters, we defined a dimensionless parameter, which is called a ballooning number. The ballooning number means the ratio of Stokes fluid-dynamic force on a fibre by the non-uniform flow field to the gravitational force of a body at the end of the fibre. Our simulation shows that the settling speed of the present model in the homogeneous turbulent flows shows the biased characters of slow settling as the influence of the turbulent flow increases. The causes of this slow settling are investigated by simulating it in a wide range of shear flows. We revealed that the cause of this is the drag anisotropy of the filament structure (spider silk). In more detail, the cause of reduced settling speed lies not only in the deformed geometrical shape of the ballooning silk but also in its generation of fluid-dynamic force in a non-uniform flow (shear flow). Additionally, we found that the ballooning structure could become trapped in a vortex flow. This seemed to be the second reason why the ballooning structure settles slowly in the homogeneous turbulent flow. These results can help deepen our understanding of the passive dynamics of spiders ballooning in the atmospheric boundary layer.

biophysics↗

bioRxiv2020

locStra: Fast analysis of regional/global stratification in whole genome sequencing (WGS) studies

locStra is an R-package for the analysis of regional and global population stratification in whole genome sequencing studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared to the global ones, based on user-defined window sizes and metrics, e.g. the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million RVs per subject), the runtime for the genome-wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.

genetics↗

bioRxiv2019

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called \"switch genes\" - for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

bioinformatics↗

bioRxiv2018

Structural basis of ECF-sigma-factor-dependent transcription initiation.

Extracytoplasmic (ECF) {sigma} factors, the largest class of alternative {sigma} factors, are related to primary {sigma} factors, but have simpler structures, comprising only two of the six conserved functional modules present in primary {sigma} factors: region 2 ({sigma}R2) and region 4 ({sigma}R4). Here, we report crystal structures of transcription initiation complexes containing Mycobacterium tuberculosis RNA polymerase (RNAP), M. tuberculosis ECF {sigma} factor {sigma}L, and promoter DNA. The structures show that {sigma}R2 and {sigma}R4 of the ECF {sigma} factor occupy the same sites on RNAP as in primary {sigma} factors, show that the connector between {sigma}R2 and {sigma}R4 of the ECF {sigma} factor--although unrelated in sequence--follows the same path through RNAP as in primary {sigma} factors, and show that the ECF {sigma} factor uses the same strategy to bind and unwind promoter DNA as primary {sigma} factors. The results define protein-protein and protein-DNA interactions involved in ECF-{sigma}-factor-dependent transcription initiation.

molecular biology↗

bioRxiv2018

The Sec1/Munc18 (SM) protein Vps45 is involved in iron uptake, mitochondrial function and virulence in the pathogenic fungus Cryptococcus neoformans

The battle for iron between invading microorganisms and mammalian hosts is a pivotal determinant of the outcome of infection. The pathogenic fungus, Cryptococcus neoformans, employs multiple mechanisms to compete for iron during cryptococcosis, a disease primarily of immunocompromised hosts. In this study, we examined the role of endocytic trafficking in iron uptake by characterizing a mutant defective in the Sec1/Munc18 (SM) protein Vps45. This protein is known to regulate the machinery for vesicle trafficking and fusion via interactions with SNARE proteins. As expected, a vps45 deletion mutant was impaired in endocytosis and showed sensitivity to trafficking inhibitors. The mutant also showed poor growth on iron-limited media and a defect in transporting the Cfo1 ferroxidase of the high-affinity iron uptake system from the plasma membrane to the vacuole. Remarkably, we made the novel observation that Vps45 also contributes to mitochondrial function in that a Vps45-Gfp fusion protein associated with mitotracker, and a vps45 mutant showed enhanced sensitivity to inhibitors of electron transport complexes as well as changes in mitochondrial membrane potential. Consistent with mitochondrial function, the vps45 mutant was impaired in calcium homeostasis. To assess the relevance of these defects for virulence, we examined cell surface properties of the vps45 mutant and found increased sensitivity to agents that challenge cell wall integrity and antifungal drugs. A change in cell wall properties was consistent with our observation of altered capsule polysaccharide attachment, and with attenuated virulence in a mouse model of cryptococcosis. Overall, our studies reveal a novel role for Vps45-mediated trafficking for iron uptake, mitochondrial function and virulence.

microbiology↗

bioRxiv2017

An Observational Study of Ballooning in Large Spiders: Nanoscale Multi-Fibres Enable Large Spiders’ Soaring Flight

The physical mechanism of aerial dispersal of spiders, \"ballooning behavior,\" is still unclear because of the lack of serious scientific observations and experiments. Therefore, as a first step in clarifying the phenomenon, we studied the ballooning behavior of relatively large spiders (heavier than 5 mg) in nature. Additional wind tunnel tests to identify ballooning silks were implemented in the laboratory. From our observation, it seems obvious that spiders actively evaluate the condition of the wind with their front leg (leg I) and wait for the preferable wind condition for their ballooning takeoff. In the wind tunnel tests, as yet unknown physical properties of ballooning fibers (length, thickness and number of fibers) were identified. Large spiders, 16-20 mg Xysticus species, spun 50 to 60 nanoscale fibers, with a diameter of 121 to 323 nm. The length of these threads was 3.22 {+/-} 1.31 m (N = 22). These physical properties of ballooning fibers can explain the ballooning of large spiders with relatively light updrafts, 0.1-0.5 m s-1, which exist in a light breeze of 1.5-3.3 m s-1. Additionally, in line with previous research on turbulence in atmospheric boundary layers and from our wind measurements, it is hypothesized that spiders use the ascending air current for their aerial dispersal, the \"ejection\" regime, which is induced by hairpin vortices in the atmospheric boundary layer turbulence. This regime is highly correlated with lower wind speeds. This coincides well with the fact that spiders usually balloon when the wind speed is lower than 3 m s-1.

biophysics↗

bioRxiv2017

Correction of autoimmune IL2RA mutations in primary human T cells using non-viral genome targeting

Human T cells are central to physiological immune homeostasis, which protects us from pathogens without collateral autoimmune inflammation. They are also the main effectors in most current cancer immunotherapy strategies1. Several decades of work have aimed to genetically reprogram T cells for therapeutic purposes2-5, but as human T cells are resistant to most standard methods of large DNA insertion these approaches have relied on recombinant viral vectors, which do not target transgenes to specific genomic sites6, 7. In addition, the need for viral vectors has slowed down research and clinical use as their manufacturing and testing is lengthy and expensive. Genome editing brought the promise of specific and efficient insertion of large transgenes into target cells through homology-directed repair (HDR), but to date in human T cells this still requires viral transduction8, 9. Here, we developed a non-viral, CRISPR-Cas9 genome targeting system that permits the rapid and efficient insertion of individual or multiplexed large (>1 kilobase) DNA sequences at specific sites in the genomes of primary human T cells while preserving cell viability and function. We successfully tested the potential therapeutic use of this approach in two settings. First, we corrected a pathogenic IL2RA mutation in primary T cells from multiple family members with monogenic autoimmune disease and demonstrated enhanced signalling function. Second, we replaced the endogenous T cell receptor (TCR) locus with a new TCR redirecting T cells to a cancer antigen. The resulting TCR-engineered T cells specifically recognized the tumour antigen, with concomitant cytokine release and tumour cell killing. Taken together, these studies provide preclinical evidence that non-viral genome targeting will enable rapid and flexible experimental manipulation and therapeutic engineering of primary human immune cells.

genetics↗