• Journal of Life Science
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• v.31 no.6
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• pp.568-573
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• 2021

This study identified genomic factors associated with endoplasmic reticulum protein (ERp)29 gene expression in a genome-wide association study (GWAS) of genetic variants, including single-nucleotide polymorphisms (SNPs). In total, 373 European genes from the 1000 Genomes Project were analyzed. SNPs with an allelic frequency of less than or more than 5% were removed, resulting in 5,913,563 SNPs including in the analysis. The following expression quantitative trait loci (eQTL) from the long noncoding RNA LOC105372577 were strongly associated with ERp29 expression: rs6138266 (p<4.172e10), rs62193420 (p<1.173e10), and rs6138267 (p<2.041e10). These were strongly expressed in the testis and in the brain. The three eQTL were identified through a transcriptome-wide association study (TWAS) and showed a significant association with ERp29 and osteosarcoma amplified 9 (OS9) expression. Upstream sequences of rs6138266 were recognized by ChIP-seq data, while HaploReg was used to demonstrate how its regulatory DNA binds upstream of transcription factor 1 (USF1). There were no changes in the expression of OS9 or USF1 following ER stress.

• BMB Reports
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• v.55 no.9
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• pp.465-471
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• 2022

Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of large-scale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment.

• Molecules and Cells
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• v.42 no.3
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• pp.237-244
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• 2019

Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial-mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).