• BMB Reports
• /
• 제44권3호
• /
• pp.182-186
• /
• 2011

Exogenous stimuli such as nerve growth factor (NGF) exert their effects on neurite outgrowth via Trk neurotrophin receptors. TrkA receptors are known to be ubiquitinated via proteasome inhibition in the presence of NGF. However, the effect of proteasome inhibition on neurite outgrowth has not been studied extensively. To clarify these issues, we investigated signaling events in PC12 cells treated with NGF and the proteasome inhibitor MG132. We found that MG132 facilitated NGF-induced neurite outgrowth and potentiated the phosphorylation of the extracellular signal-regulated kinase/mitogen-activated protein kinase (ERK/MAPK) and phosphatidylinositol-3-kinase (PI3K)/AKT pathways and TrkA receptors. MG132 stimulated internalization of surface TrkA receptor and stabilized intracellular TrkA receptor, and the $Ub^{K63}$ chain was found to be essential for stability. These results indicate that the ubiquitin-proteasome system potentiated neurite formation by regulating the stability of TrkA receptors.

• Mass Spectrometry Letters
• /
• 제9권1호
• /
• pp.1-16
• /
• 2018

Microfluidic technologies hold high promise and emerge as a potential molecular tool to facilitate the progress of fundamental and applied biomedical researches by enabling miniaturization and upgrading current biological research tools. In this review, we summarize the state of the art of existing microfluidic technologies and its' application for characterizing biophysical properties of individual cells. Microfluidic devices offer significant advantages and ability to handle in integrating sample processes, minimizing sample and reagent volumes, and increased analysis speed. Therefore, we first present the basic concepts and summarize several achievements in new coupling between microfluidic devices and mass spectrometers. Secondly, we discuss the recent applications of microfluidic chips in various biological research field including cellular and molecular level. Finally, we present the current challenge of microfluidic technologies and future perspective in this study field.

• Journal of Preventive Medicine and Public Health
• /
• 제54권4호
• /
• pp.259-264
• /
• 2021

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

• Genomics & Informatics
• /
• 제18권4호
• /
• pp.46.1-46.4
• /
• 2020

We developed the BaSDAS (Barcode-Seq Data Analysis System), a GUI-based pooled knockout screening data analysis system, to facilitate the analysis of pooled knockout screen data easily and effectively by researchers with limited bioinformatics skills. The BaSDAS supports the analysis of various pooled screening libraries, including yeast, human, and mouse libraries, and provides many useful statistical and visualization functions with a user-friendly web interface for convenience. We expect that BaSDAS will be a useful tool for the analysis of genome-wide screening data and will support the development of novel drugs based on functional genomics information.

• BMB Reports
• /
• 제56권1호
• /
• pp.43-48
• /
• 2023

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.655-664
• /
• 2022

On the motivation by an integrative study of multi-omics data, we are interested in estimating the structure of the sparse cross correlation matrix of two high-dimensional random vectors. We rewrite the problem as a multiple testing problem and propose a new method to estimate the sparse structure of the cross correlation matrix. To do so, we test the correlation coefficients simultaneously and threshold the correlation coefficients by controlling FRD at a predetermined level α. Further, we apply the proposed method and an alternative adaptive thresholding procedure by Cai and Liu (2016) to the integrative analysis of the protein expression data (X) and the mRNA expression data (Y) in TCGA breast cancer cohort. By varying the FDR level α, we show that the new procedure is consistently more efficient in estimating the sparse structure of cross correlation matrix than the alternative one.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2023년도 춘계학술발표대회
• /
• pp.490-492
• /
• 2023

시퀀싱(sequencing) 기술의 발달로 다양한 오믹스(omics) 데이터의 축적과 인공 지능 기술의 발달로 인하여 다양한 드라이버 유전자 분류기법이 제안되어왔다. 최근에는 암 데이터가 대용량으로 축적되며 기계 학습 기반의 다양한 기법들이 활발히 제안되었다. 특히 다양한 오믹스 데이터를 결합한 고차원 데이터에서 높은 정확도를 확보하기 위한 시도가 활발히 이루어지고 있다. 본 논문에서는 멀티 오믹스와 네트워크 관련 특징을 기반으로 암의 증식 및 발생에 중요한 역할을 하는 드라이버 유전자를 분류하는 딥러닝 모델을 제시한다. 또한 The Cancer Genome Atlas(TCGA) 데이터를 통해서 모델 학습 후 기존 통계 및 머신러닝 기반 기법과 비교하여 성능이 개선되었음을 확인하였다.

• Mass Spectrometry Letters
• /
• 제14권4호
• /
• pp.121-140
• /
• 2023

It is becoming more and more clear that each cell, even those of the same type, has a unique identity. This sophistication and the diversity of cell types in tissue are what are pushing the necessity for spatially distributed omics at the single-cell (SC) level. Single-cell chemical assessment, which also provides considerable insight into biological, clinical, pharmacodynamic, pathological, and toxicity studies, is crucial to the investigation of cellular omics (genomics, metabolomics, etc.). Mass spectrometry (MS) as a tool to image and profile single cells and subcellular organelles facilitates novel technical expertise for biochemical and biomedical research, such as assessing the intracellular distribution of drugs and the biochemical diversity of cellular populations. It has been illustrated that ambient mass spectrometry (AMS) is a valuable tool for the rapid, straightforward, and simple analysis of cellular and sub-cellular constituents and metabolites in their native state. This short review examines the advances in ambient mass spectrometry (AMS) and ambient mass spectrometry imaging (AMSI) on single-cell analysis that have been authored in recent years. The discussion also touches on typical single-cell AMS assessments and implementations.

• 한국미생물생명공학회소식지:생물산업
• /
• 제21권2호
• /
• pp.4-11
• /
• 2008

• 한국미생물학회:학술대회논문집
• /
• 한국미생물학회 2009년도 International Meeting of the Microbiological Society of Korea
• /
• pp.133-134
• /
• 2009