Biology subjects

Kang, K.

Publications and source records attributed to Kang, K..

4 recordsLinked to original sources

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↗