Biology subjects

Kim, S.

Publications and source records attributed to Kim, S..

At least 325 records · Page 18Linked to original sources

bioRxiv2023

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA

Interrogating gene mutations and aberrant DNA methylation in plasma cell-free DNA (cfDNA) has become increasingly common for monitoring tumor burden in cancer patients. However, no tests currently target chromosomal structural variants (SVs) genome-wide. Here, we report a simple molecular and sequencing workflow, Genome-wide Analysis of Palindrome Formation (GAPF-seq), to probe DNA palindromes, a type of SV that often demarcates gene amplification. Low-coverage next-generation sequencing of palindrome-enriched DNA uncovered skewed chromosomal distributions of high-coverage 1-kb bins (HCBs) in tumor DNA. When combined with traditional machine learning, GAPF-seq differentiated 39 breast tumors from matched control DNA with an Area Under the Curve (AUC) of 0.955. A proof-of-concept liquid biopsy study using cfDNA from 27 prostate cancer patients and 24 control individuals yielded an average AUC of 0.896. HCBs on the X chromosome emerged as a nearly decisive feature and were linked to androgen receptor gene amplification. As a simple and agnostic liquid biopsy approach, GAPF-seq could fill this technological gap, offering unique cancer-specific SV profiles.

cancer biology↗

bioRxiv2023

Learning gene networks under SNP perturbation using SNP and allele-specific expression data

Allele-specific expression quantification from RNA-seq reads provides opportunities to study the control of gene regulatory networks by cis-acting and trans-acting genetic variants. Many existing methods performed a single-gene and single-SNP association analysis to identify expression quantitative trait loci (eQTLs), and placed the eQTLs against known gene networks for functional interpretation. Instead, we view eQTL data as a capture of the effects of perturbation of gene regulatory system by a large number of genetic variants and reconstruct a gene network perturbed by eQTLs. We introduce a statistical framework called CiTruss for simultaneously learning a gene network and cis-acting and trans-acting eQTLs that perturb this network, given population allele-specific expression and SNP data. CiTruss uses a multi-level conditional Gaussian graphical model to model trans-acting eQTLs perturbing the expression of both alleles in gene network at the top level and cis-acting eQTLs perturbing the expression of each allele at the bottom level. We derive a transformation of this model that allows efficient learning for large-scale human data. Our analysis of the GTEx and LGxSM advanced intercross line mouse data for multiple tissue types with CiTruss provides new insights into genetics of gene regulation. CiTruss revealed that gene networks consist of local subnetworks over proximally located genes and global subnetworks over genes scattered across genome, and that several aspects of gene regulation by eQTLs such as the impact of genetic diversity, pleiotropy, tissue-specific gene regulation, and local and long-range linkage disequilibrium among eQTLs can be explained through these local and global subnetworks.

genetics↗

bioRxiv2023

Mapping ovarian cancer spatial organization uncovers immune evasion drivers at the genetic, cellular, and tissue level

Immune exclusion and evasion are central barriers to the success of immunotherapies and cell therapies in solid tumors. Here we applied single cell spatial and perturbational transcriptomics alongside clinical, histological, and genomic profiling to elucidate immune exclusion and evasion in high-grade serous tubo-ovarian cancer (HGSC). Using high-plex spatial transcriptomics we profiled more than 1.3 million cells from 95 tumors and 60 patients, revealing generalizable principles in HGSC tumor tissue organization. Our data demonstrates that effector T cells resist stroma-mediated trapping and sequestration. However, upon infiltration into the tumor, T cells, as well as Natural Killer (NK) cells, preferentially co-localize only with a subset of malignant cells that manifest a distinct transcriptional cell state. The latter consists of dozens of co-regulated genes and is repressed under various copy number alterations. Performing CRISPR Perturb-seq screens in ovarian cancer cells, we identified functionally diverse genetic perturbations - including knockout of the insulin sensing repressor PTPN1 and the epigenetic regulator ACTR8 - that de-repress the proposed immunogenic malignant cell state identified in patients and indeed sensitize ovarian cancer cells to T cell and NK cell cytotoxicity. Taken together, our study uncovered a profound connection between somatic genetic aberrations, malignant cell transcriptional dysregulation, and immune evasion at the cellular and tissue level, allowing us to identify targets that reprogram malignant cell states as an avenue to unleash anti-tumor immune responses.

cancer biology↗

bioRxiv2023

NODDI-derived measures of microstructural integrity in medial temporal lobe white matter pathways are associated with Alzheimer's disease pathology and cognitive outcomes

INTRODUCTIONDiffusion tensor imaging has been used to assess white matter (WM) changes in the early stages of Alzheimers disease (AD). However, the tensor model is necessarily limited by its assumptions. Neurite Orientation Dispersion and Density Imaging (NODDI) can offer insights into microstructural features of WM change. We assessed whether NODDI more sensitively detects AD-related changes in medial temporal lobe WM than traditional tensor metrics. METHODSStandard diffusion and NODDI metrics were calculated for medial temporal WM tracts from 199 older adults drawn from ADNI3 who also received PET to measure pathology and neuropsychological testing. RESULTSNODDI measures in medial temporal tracts were more strongly correlated to cognitive performance and pathology than standard measures. The combination of NODDI and standard metrics exhibited the strongest prediction of cognitive performance in random forest analyses. CONCLUSIONSNODDI metrics offer additional insights into contributions of WM degeneration to cognitive outcomes in the aging brain.

neuroscience↗

bioRxiv2023

MurSS: Multi-resolution Selective Segmentation Model for Breast Cancer

We propose the Multi-resolution Selective Segmentation model (MurSS) for segmenting benign, Ductal Carcinoma In Situ, and Invasive Ductal Carcinoma in breast resection Hematoxylin and Eosin stained Whole Slide Images. MurSS simultaneously trains on context information from a wide area at low resolution and content information from a local area at high resolution, aiming for a more accurate diagnosis. Additionally, through the selection stage, it provides solutions for ambiguous tissue regions. Our proposed MurSS achieves a mean Intersection of Union performance of 91.1%, which is at least 16.8% and at most 19.0% higher than well-known image segmentation models.

bioinformatics↗

bioRxiv2023

Time- and lineage-resolved transcriptional profiling uncovers gene expression programs and clonal relationships that underlie human T lineage specification

T cells develop from multi-potent hematopoietic progenitors in the thymus and provide adaptive protection against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors, and their subsequent specification to the T lineage, has been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process. Time-resolved single cell RNA sequencing revealed that cell-cycle exit, downregulation of the multipotent hematopoietic program, and upregulation of >90 lineage-associated transcription factors all occur within a highly co-ordinated and narrow developmental window. Computational gene-regulatory network inference elucidated the transcriptional logic of T lineage specification, uncovering an important role for YBX1. We mapped the differentiation cell fate hierarchy using transcribed lineage barcoding and mathematical trajectory inference and discovered that mast and myeloid potential bifurcate from each other early in haematopoiesis, upstream of T lineage restriction. Collectively, our analyses provide a quantitative, time-resolved model of human T cell specification with relevance for regenerative medicine and developmental immunology.

immunology↗

bioRxiv2023

A method of identifying false positives in the strain-specific variant calling of rice

In this study, we investigated the strain-specific effect in genetic variant calling from next-generation sequencing data. For this purpose, we used two major strains of the rice genome, Indica and Japonica, to build different variant calling models that differ in the composition of samples from the two strains. We found that the more the samples differed in their strains from the reference sequence, the more variants were predicted. In particular, the increase in predicted variants was noticeable when the samples that differed in their strains from the reference were included. We used machine learning approaches to understand this finding and compared the performance of different variant calling models using confusion matrices constructed from the predicted variants. We found that a significant proportion of the incrementally predicted variants are potential false positives, which becomes more pronounced the more phylogenetically different accessions from the reference are included in the samples. For the accuracy of the predicted variants, we proposed a method to identify the false positives that can be excluded from the potential false positives if necessary. The proposed method involves calling true variants from the purebred samples. We demonstrated the validity of the proposed method on the different variant calling models and showed a reduction of false positives in the predicted variants. As an example of practical utility, we applied the method to the dbSNP, a database of known variants, and demonstrated a way to identify false positives in the dbSNP. In these respects, this study provides general recommendations for effective practices in strain-specific variant calling in rice.

bioinformatics↗

bioRxiv2023

ElegansBot: Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans

Locomotion is a fundamental behavior of Caenorhabditis elegans (C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

animal behavior and cognition↗

bioRxiv2023

Single cell analysis uncovers striking cellular heterogeneity of lung infiltrating Tregs during eosinophilic vs. neutrophilic allergic airway inflammation

Allergic airway inflammation is a chronic inflammatory condition resulting from uncontrolled immune responses to environmental antigens. While it is well-established that allergic immune responses exhibit a high degree of diversity, driven by primary effector cell types like eosinophils, neutrophils, or CD4 T cells with distinct effector signatures, the exact mechanisms responsible for such pathogenesis remain largely elusive. Foxp3+ regulatory T cells (Treg) is an essential immune regulator during chronic inflammation including allergic airway inflammation. Emerging evidence suggests that Tregs infiltrating inflamed tissues exhibit distinct phenotypes dependent on the specific tissue sites and can display significant heterogeneity and tissue residency. Whether diverse allergic inflammatory responses in the lung influences infiltrating Treg heterogeneity or Treg lung residency has not previously been explored. We employed an unbiased single-cell RNAseq approach to investigate lung-infiltrating Tregs in models of eosinophilic and neutrophilic airway inflammation models, in which Tregs are critical regulators of inflammation. We found that lung-infiltrating Tregs are highly heterogeneous and that Tregs displaying lung resident phenotypes are significantly different depending on the types of inflammation. Tregs expression of ST2, a receptor for alarmin cytokine IL-33, was predominantly induced by eosinophilic inflammation and by tissue residency. However, Treg-specific ST2 deficiency did not affect the development of eosinophilic allergic inflammation nor the generation of lung resident Tregs. These results uncover a striking heterogeneity among Tregs infiltrating the lungs during allergic airway inflammation. The results also indicate that varying types of inflammation may give rise to phenotypically distinct lung resident Tregs, underscoring a novel mechanism by which inflammatory cues may shape the composition of infiltrating Tregs, allowing them to regulate inflammatory responses through tissue-adapted mechanisms.

immunology↗

bioRxiv2023

Intronic miR342 is a master regulator of cellular glycolysis in Foxp3+ regulatory CD4 T cells

Foxp3+ regulatory T (Treg) cells are a subset of CD4 T cells that play a potent and indispensable role in regulating immunity and tolerance. The precise mechanisms by which Treg cells mediate such functions have extensively been explored, and there are many cellular and molecular factors that are instrumental for adequate Treg cell functions. miRNAs, small non-coding RNA molecules, are one of the factors capable of controlling Treg cell functions. In this study, we report that miR-342 is essential for Treg cells to mitigate autoimmune inflammation in the central nervous system. Utilizing novel mouse models with Treg cell-specific miR-342 deficiency or overexpression, we demonstrate that miR-342 expression in Treg cells, while dispensable for immune homeostasis at steady-state conditions, is necessary for Treg cells to control inflammatory responses. Mechanistically, we found that Treg cells deficient in miR-342 display dysregulated metabolic profiles, elevated glycolysis and decreased oxidative phosphorylation, a metabolic phenotype associated with functionally defective Treg cells. Interestingly, miR-342-dependent metabolic dysregulation was observed in Treg but not in Th1 type cells. In support, miR-342-mediated Rictor targeting was found in Treg but not in Th1 type cells. Collectively, our findings uncover that miR-342 may serve a master regulator specific for metabolism and functions in Treg cells.

immunology↗

bioRxiv2023

Temporal Generative Models for Learning Heterogeneous Group Dynamics of Ecological Momentary Data

SO_SCPLOWUMMARYC_SCPLOWOne of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of Ecological Momentary Assessments (EMAs) that capture multiple responses in real-time at high frequency. However, EMA data is often multi-dimensional, correlated, and hierarchical. Mixed-effects models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The Recurrent Temporal Restricted Boltzmann Machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the Heterogeneous-Dynamics Restricted Boltzmann Machine (HDRBM), to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world EMA data sets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for EMA studies.

bioinformatics↗

bioRxiv2023

Linking Gba1 E326K mutation to microglia activation and mild age-dependent dopaminergic Neurodegeneration

Mutations in the GBA1 gene have been identified as a prevalent genetic risk factor for Parkinsons disease (PD). GBA1 mutations impair enzymatic activity, leading to lysosomal dysfunction and elevated levels of -synuclein (-syn). While most research has primarily focused on GBA1s role in promoting synucleinopathy, emerging evidence suggests that neuroinflammation may be a key pathogenic alteration caused by GBA1 deficiency. To examine the molecular mechanism underlying GBA1 deficiency-mediated neuroinflammation, we generated Gba1 E326K knock-in (KI) mice using the CRISPR/Cas9 technology, which is linked to an increased risk of PD and dementia with Lewy bodies (DLB). In the ventral midbrain and hippocampus of 24-month-old Gba1 E326K KI mice, we found a moderate decline in GBA1 enzymatic activity, a buildup of glucosylceramide, and an increase in microglia density. Furthermore, we observed increased levels of pro-inflammatory cytokines and formation of reactive astrocytes in primary microglia and astrocytes, respectively, cultured from Gba1 E326K KI mice following treatment with pathologic -syn preformed fibrils (PFF). Additionally, the gut inoculation of -syn PFF in Gba1 E326K KI mice significantly enhanced the accumulation of Lewy bodies in the dentate gyrus of the hippocampus, accompanied by aggravated neuroinflammation and exacerbated non-motor symptoms. This research significantly enhances our understanding of the Gba1 E326K mutations involvement in neuroinflammation and the cell-to-cell transmission of pathogenic -syn in the brain, thereby opening new therapeutic avenues.

neuroscience↗

bioRxiv2023

Highly localized chemical sampling at sub-second temporal resolution enabled with a silicon nanodialysis platform at exceedingly slow flows.

Microdialysis (MD) is a versatile and powerful technique for chemical profiling of biological tissues and is widely used for quantification of neurotransmitters, neuropeptides, metabolites, biomarkers, and drugs in the central nervous system as well as in dermatology, ophthalmology, and in pain research. However, MD performance is severely limited by fundamental tradeoffs between chemical sensitivity, spatial resolution, and temporal response. Here, by using wafer-scale silicon microfabrication, we develop and demonstrate a nanodialysis (ND) sampling probe that enables highly localized chemical sampling with 100m spatial resolution and sub-second temporal resolution at high recovery rates. These performance metrics, which are 100X-1000X superior to existing MD approaches, are enabled by a 100X reduction of the microfluidic channel cross-section, a corresponding drastic 100X reduction of flow rates to exceedingly slow few nL/min flows, and integration of a nanometer-thin nanoporous membrane with high transport flux into the probe sampling area. Miniaturized ND probes may allow for the minimally invasive and highly localized sampling and chemical profiling in live biological tissues with unprecedented spatio-temporal resolution for clinical, biomedical, and pharmaceutical applications.

bioengineering↗

bioRxiv2023

Sde Proteins Coordinate Ubiquitin Utilization and Phosphoribosylation to Promote Establishment and Maintenance of the Legionella Replication Vacuole

The Legionella pneumophila Sde family of translocated proteins promotes host tubular endoplasmic reticulum (ER) rearrangements that are tightly linked to phosphoribosyl-ubiquitin (pR-Ub) modification of Reticulon 4 (Rtn4). Sde proteins have two additional activities of unclear relevance to the infection process: K63 linkage-specific deubiquitination and phosphoribosyl modification of polyubiquitin (pR-Ub). We show here that the deubiquitination activity (DUB) stimulates ER rearrangements while pR-Ub protects the replication vacuole from cytosolic surveillance by autophagy. Loss of DUB activity is tightly linked to lowered pR-Ub modification of Rtn4, consistent with the DUB activity fueling the production of pR-Ub-Rtn4. In parallel, phosphoribosyl modification of polyUb, in a region of the protein known as the isoleucine patch, prevents binding by the autophagy adapter p62. An inability of Sde mutants to modify polyUb results in immediate p62 association, a critical precursor to autophagic attack. The ability of Sde WT to block p62 association decays quickly after bacterial infection, as predicted by the presence of previously characterized L. pneumophila effectors that inactivate Sde and remove polyUb. In sum, these results show that the accessory Sde activities act to stimulate ER rearrangements and protect from host innate immune sensing in a temporal fashion.

microbiology↗

bioRxiv2023

Backmapping with Mapping and Isomeric Information

I present a powerful and flexible backmapping tool named Multiscale Simulation Tool (mstool) that converts a coarse-grained (CG) system into all-atom (AA) resolution and only requires AA to CG mapping and isomeric information (cis/trans/dihedral/chiral). The backmapping procedure includes two simple steps: a) AA atoms are randomly placed near the corresponding CG beads according to the provided mapping scheme. b) Energy minimization is performed with two modifications in the AA force field (FF). First, nonbonded interactions are replaced with cosine functions to ensure numerical stability. Second, additional torsions are imposed to maintain molecules isomeric properties. To test the simplicity and robustness of the tool, I backmapped multiple membrane and protein CG structures into AA resolution, including a four-bead CG lipid model (resolution increased by a factor of 34) without using intermediate resolution. The tool is freely available at github.com/ksy141/mstool. TABLE OF CONTENTS (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/556379v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@5704aforg.highwire.dtl.DTLVardef@f4bdf6org.highwire.dtl.DTLVardef@1e362feorg.highwire.dtl.DTLVardef@1781ade_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Effects of visual feedback on corticostriatal activity during continuous de novo motor skill learning

A corticostriatal network plays a pivotal role in visuomotor learning. However, it is unclear to what extent visual feedback for motor learning impacts corticostriatal activity. To address this, we conducted an fMRI experiment in which participants acquired a complex motor skill using either online or offline visual cursor feedback. We found a highly selective response in the entire striatum to visual performance feedback in both conditions and a relatively higher response in the caudate nucleus for the offline feedback. However, the ventromedial prefrontal cortex (vmPFC) response was significant only for online feedback. Furthermore, we also found functional distinctions of the striatal subregions in random versus goal-directed motor control. These findings collectively underscore the substantial impact of such simple visual feedback, as designed in our study, in eliciting corticostriatal responses, thereby elucidating its significance in reinforcement-based motor learning.

neuroscience↗

bioRxiv2023

Cortico-hippocampal representational similarity contributes to learning of multiple motor sequences

Cortico-hippocampal interaction is integral to memory consolidation during sleep or wakeful rest. However, the extent of its engagement in non-declarative motor memory during skill learning remains largely undefined. Specifically, it remains unclear how neural representations in cortical and hippocampal regions are related to learning multiple motor sequences. Here, we conducted an fMRI experiment in which participants learned four distinct motor sequences in a pseudo-random order with interleaved rest periods. We found pattern separation in the sensorimotor regions was significant, particularly in the anterior motor cortex, related to early learning performance. However, there was no pattern separation in the hippocampus during the rest periods. Intriguingly, the representational similarity between the motor cortex during task periods and the hippocampus during rest periods was highly correlated with overall learning performance. These findings suggest a new role of hippocampal replay in motor sequence learning, that is, learning a more abstract relational structure of sequences instead of representing sequences per se.

neuroscience↗

bioRxiv2023

A Novel Computational Approach for the Mining of Signature Pathways Using Species Co-occurrence Networks in Gut Microbiomes

Advances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological signals that pave the way for better and accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. There have been few attempts to uncover microbial community associations with certain health conditions. However, such attempts have mostly used partial or incomplete data to accurately capture those associations. This study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data. When comparing IBD to a control group, we show that co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment. The obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that classify or diagnose certain health conditions. CCS CONCEPTS* Applied computing [->] Biological networks; Bioinformatics; Systems biology. ACM Reference FormatSuyeon Kim, Ishwor Thapa, and Hesham Ali. 2023. A Novel Computational Approach for the Mining of Signature Pathways Using Species Co-occurrence Networks in Gut Microbiomes. In Proceedings of ACM Conference (Conference17). ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗