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Proteome-wide remodeling of protein location and function by stress
Lee, KiYoung,Sung, Min-Kyung,Kim, Jihyun,Kim, Kyung,Byun, Junghyun,Paik, Hyojung,Kim, Bongkeun,Huh, Won-Ki,Ideker, Trey National Academy of Sciences 2014 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF Vol.111 No.30
<P>Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress.</P>
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Lau, E.,Kluger, H.,Varsano, T.,Lee, K.,Scheffler, I.,Rimm, David L.,Ideker, T.,Ronai, Ze'ev A. Cell Press ; MIT Press 2012 Cell Vol.148 No.3
The transcription factor ATF2 elicits oncogenic activities in melanoma and tumor suppressor activities in nonmalignant skin cancer. Here, we identify that ATF2 tumor suppressor function is determined by its ability to localize at the mitochondria, where it alters membrane permeability following genotoxic stress. The ability of ATF2 to reach the mitochondria is determined by PKCε, which directs ATF2 nuclear localization. Genotoxic stress attenuates PKCε effect on ATF2; enables ATF2 nuclear export and localization at the mitochondria, where it perturbs the HK1-VDAC1 complex; increases mitochondrial permeability; and promotes apoptosis. Significantly, high levels of PKCε, as seen in melanoma cells, block ATF2 nuclear export and function at the mitochondria, thereby attenuating apoptosis following exposure to genotoxic stress. In melanoma tumor samples, high PKCε levels associate with poor prognosis. Overall, our findings provide the framework for understanding how subcellular localization enables ATF2 oncogenic or tumor suppressor functions.
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Genome Wide Proteomics of ERBB2 and EGFR and Other Oncogenic Pathways in Inflammatory Breast Cancer
Zhang, Emma Yue,Cristofanilli, Massimo,Robertson, Fredika,Reuben, James M.,Mu, Zhaomei,Beavis, Ronald C.,Im, Hogune,Snyder, Michael,Hofree, Matan,Ideker, Trey,Omenn, Gilbert S.,Fanayan, Susan,Jeong, S American Chemical Society 2013 Journal of proteome research Vol.12 No.6
<P>In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a model system for the evaluation of selective integration of subsets of transcriptomic and proteomic data. We assessed the oncogene status with reads per kilobase per million mapped reads (RPKM) values for ERBB2 (14.4, 400, and 300 for SUM149, SUM190, and SKBR3, respectively) and for EGFR (60.1, not detected, and 1.4 for the same 3 cell lines). We then used RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines (total 31) and interrogated the corresponding proteomics data sets for proteins with significant interaction values with these oncogenes. The number of observed interactors for each oncogene showed a significant range, e.g., 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a fraction of the total protein interactions in a given data set vs total interactors for that oncogene in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D (Interologous Interaction Database, version 1.95). This approach allowed us to focus on 4 main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used bioinformatics sites GeneGo, PathwayCommons and NCI receptor signaling networks to identify pathways that contained the four main oncogenes and had good coverage in the transcriptomic and proteomic data sets as well as a significant number of oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-Seq values allowed the use of transcript ratios to correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for ERBB signaling; caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings; branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD), nucleolin (NCL) (high levels of EGFR transcript); transferrin receptor (TFRC), metadherin (MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 subpathway. Future studies of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to explore the significance of these observations.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2013/jprobs.2013.12.issue-6/pr4001527/production/images/medium/pr-2013-001527_0010.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr4001527'>ACS Electronic Supporting Info</A></P>
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Conservation and Rewiring of Functional Modules Revealed by an Epistasis Map in Fission Yeast
Roguev, A.,Bandyopadhyay, S.,Zofall, M.,Zhang, K.,Fischer, T.,Collins, S. R.,Qu, H.,Shales, M.,Park, H.-O.,Hayles, J.,Hoe, K.-L.,Kim, D.-U.,Ideker, T.,Grewal, S. I.,Weissman, J. S.,Krogan, N. J. American Association for the Advancement of Scienc 2008 Science Vol.322 No.5900
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GPS 시스템 및 센서를 활용한 임베디드 영상처리에 기반한 이동장치의 주행유도
김수용,이승수,Kamil Židek,Vladislav Maxim 한국기계기술학회 2013 한국기계기술학회지 Vol.15 No.5
최근에 개발된 시스템 온칩 프로세서는 통합 성능을 요구하는 작업의 가능성을 제공하였으며, 이러한 작업은 예전에는 우수한 성능을 가진 컴퓨터의 도움만으로 수행 할 수 있는 것이었다. 본 논 문에서는 실제 환경 하에서 자율 이동 장치의 GPS 위치측정을 개선하기 위해 임베디드 영상처리기 법을 활용하는 고급 제어 시스템을 소개한다. 메인 컨트롤 시스템은 Raspberry PI 개발 보드에 통합 된 ARM(SoC.) 아키텍처를 기반으로 한다. 제시한 제어 시스템은 실시간 비디오 캡처, 전력-효율적 이미지 처리 작 업, 예를 들어 (임계 값 처리, 이진화, 모션 감지 등) 및 비디오와 같은 스트리밍 결과 이미지를 처리 할 수 있다. GPS 정밀도는 WAAS(EGNOS) 위성을 활용하여 다만 3 미터의 정밀도를 제공 할 수 있다. 제안한 솔루션은 도로와 보도의 경계를 감지하기 위해 GPS 솔루션 및 임베디드 이 미지 처리를 사용한다. 일부 도로나 통로가 길가의 흰색 선을 제공하지 않기 때문에 제시한 알고리 즘은 길가의 흰색 선을 검출하지 않고 보편적인 도로나 보도를 감지한다. 제안한 시스템은 소형 이 동장치에 사용할 수 있다. 예를 들어, 생산 공간 사이의 긴 거리를 가진 중공업 산업 단지에서 부품 수송을 위한 이동장치 등에 사용할 수 있다.
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