Biology subjects

Kang, K.

Publications and source records attributed to Kang, K..

67 records · Page 4Linked to original sources

bioRxiv2020

Fine-scale Population Structure and Demographic History of Han Chinese Inferred from Haplotype Network of 111,000 Genomes

ABSTRACTHan Chinese is the most populated ethnic group across the globe with a comprehensive substructure that resembles its cultural diversification. Studies have constructed the genetic polymorphism spectrum of Han Chinese, whereas high-resolution investigations are still missing to unveil its fine-scale substructure and trace the genetic imprints for its demographic history. Here we construct a haplotype network consisted of 111,000 genome-wide genotyped Han Chinese individuals from direct-to-consumer genetic testing and over 1.3 billion identity-by-descent (IBD) links. We observed a clear separation of the northern and southern Han Chinese and captured 5 subclusters and 17 sub-subclusters in haplotype network hierarchical clustering, corresponding to geography (especially mountain ranges), immigration waves, and clans with cultural-linguistic segregation. We inferred differentiated split histories and founder effects for population clans Cantonese, Hakka, and Minnan-Chaoshanese in southern China, and also unveiled more recent demographic events within the past few centuries, such as Zou Xikou and Chuang Guandong. The composition shifts of the native and current residents of four major metropolitans (Beijing, Shanghai, Guangzhou, and Shenzhen) imply a rapidly vanished genetic barrier between subpopulations. Our study yields a fine-scale population structure of Han Chinese and provides profound insights into the nation’s genetic and cultural-linguistic multiformity.Competing Interest StatementThe authors AL, KK, ST, XW, LW, TL, HW, JD, QZ, XY, and GC work for WeGene (Shenzhen Zaozhidao Technology Co. Ltd. or Shenzhen WeGene Clinical Laboratory).View Full Text

genomics↗

bioRxiv2020

Azole use in agriculture reduces Aspergillus fumigatus abundance but does not alter its population structure

Antibiotic resistance is an increasing threat to human health. In the case of Aspergillus fumigatus, which is both an environmental saprobe and an opportunistic human fungal pathogen, resistance is suggested to arise from fungicide use in agriculture, as the azoles used for plant protection are almost identical to the frontline antifungals used clinically. However, limiting azole fungicide use on crop fields to preserve their activity for clinical use could threaten the global food supply via a reduction in yield. In this study we clarify the link between fungicide use on crop fields and resistance in a prototypical human pathogen through systematic soil sampling on farms in Germany and surveying fields before and after azole application. We observed a reduction in the abundance of A. fumigatus on fields following fungicide treatment in 2017--a finding that was not observed on an organic control field applying only natural plant protection agents. However, this finding was less pronounced during our 2018 sampling, indicating that the impact of fungicides on A. fumigatus population size is variable and influenced by additional factors. The overall resistance frequency among agricultural isolates is low, with only 1-3% of isolates from 2016-2018 displaying resistance to medical azoles. Isolates collected after the growing season and azole exposure show a subtle, but consistent decrease in susceptibility to medical and agricultural azoles. Whole genome sequencing indicates that, despite the alterations in antifungal susceptibility, fungicide application does not significantly affect the population structure and genetic diversity of A. fumigatus in fields. Given the low observed resistance rate among agricultural isolates, as well the lack of genomic impact following azole application, we do not find evidence that azole use on crops is significantly driving resistance in A. fumigatus in this context. IMPORTANCEAntibiotic resistance is an increasing threat to human health. In the case of Aspergillus fumigatus, which is an environmental fungus that also causes life-threatening infections in humans, antimicrobial resistance is suggested to arise from fungicide use in agriculture, as the chemicals used for plant protection are almost identical to the antifungals used clinically. However, removing azole fungicides from crop fields threatens the global food supply via a reduction in yield. In this study, we survey crop fields before and after fungicide application. We find a low overall azole resistance rate among agricultural isolates, as well a lack of genomic and population impact following fungicide application, leading us to conclude azole use on crops does not significantly contribute to resistance in A. fumigatus.

microbiology↗

bioRxiv2020

Cell-type specific gene expression profiling in heterogeneous in vitro cultures using epitope-tagged RPL22

Genetic and genomic studies of brain disease increasingly demonstrate disease-associated interactions between the cell types of the brain. Increasingly complex and more physiologically relevant human induced pluripotent stem cell (hiPSC)-based models better explore the molecular mechanisms underlying disease, but also challenge our ability to resolve cell-type specific perturbations. Here we report an extension of the RiboTag system, first developed to achieve cell-type restricted expression of epitope-tagged ribosomal protein (RPL22) in mouse tissue, to a variety of in vitro applications, including immortalized cell lines, primary mouse astrocytes, and hiPSC-derived neurons. RiboTag expression enables efficient depletion of off-target RNA in mixed species primary co-cultures and in hiPSC-derived neural progenitor cells, motor neurons, and GABAergic neurons. Nonetheless, depletion efficiency varies across independent experimental replicates. The challenges and potential of implementing RiboTags in complex in vitro cultures are discussed.

molecular biology↗

bioRxiv2020

Hormone Receptor-status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer (BRCA) patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from the cancer genome atlas TCGA BRCA and METABRIC datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal BRCA. We also confirmed that non-matching subgroup classification affected the survival of BRCA patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

cancer biology↗

bioRxiv2020

Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model

The infection of a novel coronavirus found in Wuhan of China (2019-nCoV) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for 2019-nCoV, various strategies are being tested in China, including drug repurposing. In this study, we used our pretrained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of 2019-nCoV. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing a inhibitory potency with Kd of 94.94 nM against the 2019-nCoV 3C-like proteinase, followed by efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of 2019-nCoV with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra, could be used for the treatment of 2019-nCoV, although there is no real-world evidence supporting the prediction. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for 2019-nCoV.

microbiology↗

bioRxiv2019

Global kinome silencing combined with 3D invasion screening of the tumor microenvironment identifies fibroblast-expressed PIK3Cδ involvement in triple-negative breast cancer progression

As there is growing evidence for the tumor microenvironments (TME) role in tumorigenesis, we sought to investigate the role of fibroblast-expressed kinases in triple negative breast cancer (TNBC). Using a high-throughput kinome screen combined with 3D invasion assays, we identified fibroblast-expressed PIK3C{delta} (f-PIK3C{delta}) as a key regulator of progression. Although PIK3C{delta} has been mainly described in leucocytes, we detected high expression in primary fibroblasts derived from TNBC patients, while PIK3C{delta} was undetectable in cancer epithelial cell lines. Genetic and pharmacologic gain- and loss-of functions experiments verified the contribution of f-PIK3C{delta} in TNBC cell invasion. By employing an integrated secretomics and transcriptomics analysis, we revealed a paracrine mechanism via which f-PIK3C{delta} confers its pro-tumorigenic effects. Inhibition of f-PIK3C{delta} promoted the secretion of factors, including PLGF and BDNF, which subsequently led to upregulation of NR4A1 in TNBC cells where it acts as a tumor suppressor. Inhibition of PIK3C{delta} in an orthotopic BC mouse model reduced tumor growth only after inoculation with fibroblasts, indicating a role of f-PIK3C{delta} in cancer progression. Similar results were observed in the MMTV-PyMT transgenic BC mouse model, in addition to a decrease on tumor metastasis emphasizing the potential immune-independent effects of PIK3C{delta} inhibition. Finally, analysis of BC patient cohorts and TCGA datasets identified f-PIK3C{delta} (protein and mRNA levels) as an independent prognostic factor for overall and disease free survival, highlighting it as a therapeutic target for TNBC.

cancer biology↗

bioRxiv2019

Effects of active dry yeast on growth performance, rumen fermentation characteristics and slaughter performance in beef cattle

This study aimed to investigate the effects of active dry yeast (ADY) on rumen microbial composition and slaughter performance of beef cattle. Thirty-two finishing beef cattle (simmental crossbred cattle [male] x cattle-yaks [female]), with an average body weight of 110 {+/-} 12.85 kg, were randomly assigned to one of four treatments: the low plane of nutrition group (Control), low plane of nutrition group + ADY 2 g/head/d (ADY2), low plane of nutrition group + ADY 4 g/head/d (ADY4) and high plane of nutrition group (HPN). ADY supplementation increased average daily gain (P<0.001), and the carcass weight of ADY4 group had no significant difference with HPN group (P>0.05). The serum glutamic-pyruvic transaminase activity in control and ADY4 group was higher than HPN group (P=0.001). The neutral detergent fiber (P=0.022) and acid detergent fiber (P=0.043) digestibility in HPN group was greater than control, but no difference was obtained among ADY2, ADY4 and HPN group (P>0.05). The rumen ammonium nitrogen content in control was greater than ADY2 and ADY4 group (P=0.003), and no difference was obtained ADY2, ADY4 and HPN group (P>0.05). The propionic acid content in the rumen in ADY2, ADY4, and HPN group were greater than control group (P<0.001). The simpson (P=0.014) and shannon (P=0.045) indexes in control and HPN group were greater than ADY4 group. At the phylum level, the relative abundance of Firmicutes in the HPN group was higher than ADY4 group (P=0.015). At the genus level, HPN and ADY4 were clustered together, and the relative abundance of Ruminococcaceae UCG-002 in ADY4 group was higher than control and HPN group (P=0.004). In conclusion, supplementation ADY 4 g/head/d shift the rumen microbial composition of beef cattle fed low plane of nutrition to a more similar level with cattle fed with HPN diet, produced comparable carcass weight with HPN diet.

zoology↗

bioRxiv2019

Splotch: Robust estimation of aligned spatial temporal gene expression data.

Spatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments.

bioinformatics↗

bioRxiv2018

A novel computational complete deconvolution method using RNA-seq data

The cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our methods performance using several synthetic and experimental mixtures of varied but known cell type composition and compared its performance to the performance of two state-of-the art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-typespecificgene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue (MATLAB code is available at https://github.com/kkang7/CDSeq_011).

bioinformatics↗

bioRxiv2018

IFN-γ selectively suppresses a subset ofTLR4-activated genes and enhancers to potentiate M1-like macrophage polarization

Complete polarization of macrophages towards an M1-like proinflammatory and antimicrobial state requires combined action of IFN-{gamma} and LPS. Synergistic activation of canonical inflammatory NF-{kappa}B target genes by IFN-{gamma} and LPS is well appreciated, but less is known about whether IFN-{gamma} negatively regulates components of the LPS response, and how this affects polarization. A combined transcriptomic and epigenomic approach revealed that IFN-{gamma} selectively abrogates LPS-induced feedback and select metabolic pathways by suppressing TLR4-mediated activation of gene enhancers. In contrast to superinduction of inflammatory genes via enhancers that harbor IRF sequences and bind STAT1, IFN-{gamma}-mediated repression targeted enhancers with STAT sequences that bound STAT3. TLR4-activated IFN-{gamma}-suppressed enhancers comprised two subsets distinguished by differential regulation of histone acetylation and recruitment of STAT3, CDK8 and cohesin, and were functionally inactivated by IFN-{gamma}. These findings reveal that IFN-{gamma} suppresses feedback inhibitory and metabolic components of the TLR response to achieve full M1 polarization, and provide insights into mechanisms by which IFN-{gamma} selectively inhibits TLR4-induced transcription.

immunology↗

bioRxiv2018

Spatiotemporal Dynamics of Molecular Pathology in Amyotrophic Lateral Sclerosis

Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify novel pathway dynamics, regional differences between microglia and astrocyte populations at early time-points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.\n\nOne Sentence SummaryAnalysis of the ALS spinal cord using Spatial Transcriptomics reveals spatiotemporal dynamics of disease driven gene regulation.

neuroscience↗

bioRxiv2017

Wx: a neural network-based feature selection algorithm for next-generation sequencing data

MotivationNext-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. For example, an NGS-based transcriptomic assay called RNA-seq can be used to estimate the abundance of approximately 190,000 transcripts together. As the cost of next-generation sequencing sharply declines, researchers in many fields have been conducting research using NGS. The amount of information produced by NGS has made it difficult for researchers to choose the optimal set of target genes (or genomic loci).\n\nResultsWe have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker (universal gene-expression biomarkers) candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method.\n\nAvailabilityhttps://github.com/deargen/DearWX\n\nContactkangk1204@dankook.ac.kr\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗

bioRxiv2017

TraRECo: A Greedy Approach based de novo Transcriptome Assembler with Read Error Correction using Consensus Matrix

BackgroundChallenges in developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize de Bruijn graph, which has a complexity linearly growing with data size while suffers from errors and repeat. Although one can correct errors by inspecting topological structure of the graph, it is an uneasy task when there are too many branches. There are two research directions: improving either graph reliability or path search precision. We focused on improving the reliability.\n\nResultsWe present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. The idea is similar to overlap-layout-consensus approach used for genome assembly, but is different in that consensus is made through the entire graph construction step. Basically, we built contigs by direct read alignment within a distance margin and performed junction search to construct splicing graphs. While doing so, however, a contig of length l was represented by 4x1 matrix (called consensus matrix), of which each element was the base count of aligned reads so far. A representative sequence is obtained, by taking majority in each column of the consensus matrix, to be used for further read alignment. Once splicing graphs were obtained, we used IsoLasso to find paths with noticeable read depth. The experiments using real and simulated reads showed that the method provides considerable improvements in sensitivity and reasonably better performances when comparing both sensitivity and precision. This could be achieved by making more erroneous reads to be participated in graph construction, which, in turn, improved the depth information quality used for the subsequent path search step. The results for simulated reads showed also challenges are still remaining since non-negligible percentage of transcripts with high abundance were not recovered by the assemblers we considered.\n\nConclusionde novo assembly is mainly to explore not-yet-discovered isoforms and must be able to represent as much reads as possible in an efficient way. In this sense, TraRECo provides us a potential alternative to improve graph reliability, even though the computational burden can be much higher than single k-mer de Bruijn graph approach.

bioinformatics↗