Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Guo, Tiannan; Luna, Augustin; Rajapakse, Vinodh N.; Koh, Ching Chiek; Wu, Zhicheng; Wei, Liu; Sun, Yaoting; Gao, Huanhuan; Menden, Michael P.; Xu, Chao; Calzone, Laurence; Martignetti, Loredana; Auwerx, Chiara; Buljan, Marija; Banaei‐Esfahani, Amir; Ori, Alessandro; Iskar, Murat; Gillet, Ludovic; Bi, Ran; Zhang, Jiangnan; Zhang, Huanhuan; Yu, Chunjing; Zhong, Qing; Varma, Sudhir; Schmitt, Uwe; Qiu, Peng; Zhang, Qiushi; Zhu, Yi; Wild, Peter J.; Garnett, Mathew J.; Bork, Peer; Beck, Martin; Liu, Kexin; Sáez-Rodríguez, Julio; Elloumi, Fathi; Reinhold, William C.; Sander, Chris; Pommier, ﻿Yves; Aebersold, Ruedi

doi:10.1016/j.isci.2019.10.059

Cited by 61 publications

(88 citation statements)

References 88 publications

(183 reference statements)

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“…However, no structural data at any resolution have been presented for the intact complex. The subunit stoichiometry is also uncertain; recent studies using label-free mass spectrometry (33)(34)(35)(36)(37)(38) have yielded variable results that are often at odds with the stoichiometries demonstrated in the known subcomplex structures.…”

Section: Ino80mentioning

confidence: 99%

“…Some structural information is available for portions of the NuRD complex ( Figure S1b): (i) HDAC1 forms a 2:2 complex with an N-terminal segment of MTA1 (MTA1 162-335 -the ELM-SANT region) (27); (ii) single particle electron microscopy (SPEM) and X-ray crystallography data show that two copies of RBBP4 can bind the C-terminal portion of MTA1 (28-30); (iii) MBD2 and GATAD2A form a heterodimeric coiled-coil (24, 31); and (iv) a cryo-EM structure has been determined for the complex between the catalytic domain of CHD4 and a nucleosome particle (32).However, no structural data at any resolution have been presented for the intact complex. The subunit stoichiometry is also uncertain; recent studies using label-free mass spectrometry (33)(34)(35)(36)(37)(38) have yielded variable results that are often at odds with the stoichiometries demonstrated in the known subcomplex structures.The mechanisms by which NuRD selects target sites are also poorly understood. Transcriptional regulators such as FOG1 (22,39) and BCL11A (40,41) can bind to NuRD via the RBBP subunits but this mechanism is likely to account for only a small proportion of NuRD-genome interactions.Recently, we demonstrated that the chromatin-binding protein PWWP2A, which can selectively recognize H2A.Z-containing nucleosomes (42,43), interacts robustly with the MTA, HDAC and RBBP subunits of NuRD.…”

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confidence: 99%

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The Nucleosome Remodeling and Deacetylase complex has an asymmetric, dynamic, and modular architecture

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Silva

Tabar

et al. 2020

Preprint

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The Nucleosome Remodeling and Deacetylase (NuRD) complex is essential for development in complex animals but has been refractory to biochemical analysis. We present the first integrated analysis of the architecture of the native mammalian NuRD complex, combining quantitative mass spectrometry, covalent cross-linking, protein biochemistry and electron microscopy. NuRD is built around a 2:2:4 pseudo-symmetric deacetylase module comprising MTA, HDAC and RBBP subunits. This module interacts asymmetrically with a remodeling module comprising one copy each of MBD, GATAD2 and CHD subunits. The previously enigmatic GATAD2 controls the asymmetry of the complex and directly recruits the ATP-dependent CHD remodeler. Unexpectedly, the MTA-MBD interaction acts as a point of functional switching. The transcriptional regulator PWWP2A modulates NuRD assembly by competing directly with MBD for binding to the MTA-HDAC-RBBP subcomplex, forming a 'moonlighting' PWWP2A-MTA-HDAC-RBBP complex that likely directs deacetylase activity to PWWP2A target sites. Taken together, our data describe the overall architecture of the intact NuRD complex and reveal aspects of its structural dynamics and functional plasticity. INO80(1, 2) and SWR1 complexes (3), as well as to the Snf2 (4) and Chromodomain-Helicase-DNA-binding 1 (CHD1) remodelers (5), our understanding of how such enzymes bring about remodeling is still underdeveloped. This is particularly true for the nucleosome remodeling and deacetylase (NuRD) complex.The NuRD complex is widely distributed among Metazoans and is expressed in most, if not all, tissues. It is essential for normal development (6, 7) and is a key regulator in the reprogramming of differentiated cells into pluripotent stem cells (8-10). Age-related reductions in NuRD subunit levels are strongly associated with memory loss, metastatic potential in human cancers (11), and the accumulation of chromatin defects (12, 13).The mammalian NuRD complex comprises at least six subunits (Figure S1a), and for each subunit there are at least two paralogues, giving the potential for significant compositional heterogeneity. CHD4 (and its paralogues CHD3 and -5) is the ATP-dependent DNA translocase in the complex and harbours several regulatory and targeting domains. For example, the PHD domains of CHD4 can recognize histone H3 N-terminal tails bearing methyllysine marks (14-16), and the HMG domain has been shown to bind to poly-ADP(ribose) (17). What distinguishes NuRD from many other remodelers is that it harbours a second catalytic activity, imparted by the histone deacetylases HDAC1 and -2. MBD2 and -3 can bind hydroxymethylated and/or methylated , and RBBP4 and -7 can each bind histone tails (21) and other transcriptional regulators (22,23). The metastasis-associated proteins MTA1, -2 and -3 contain several domains that are associated with nucleosome recognition, whereas GATAD2A and GATAD2B bind to both 25) and CHD proteins (25, 26) but otherwise do not have known functions. Some structural information is available for portions of t...

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Section: Ino80mentioning

confidence: 99%

mentioning

confidence: 99%

The Nucleosome Remodeling and Deacetylase complex has an asymmetric, dynamic, and modular architecture

Low

Silva

Tabar

et al. 2020

Preprint

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“…About ten thousand redundant and low-quality assays were removed. Then we extracted the chromatography of these fragments and MS1 signals using OpenSWATHWorkflow, followed by curation using DIA-expert [106]. Briefly, the chromatography of all fragments and MS1 signals were subject to scrutiny by empirically developed expert rules.…”

Section: Peptide Quantification Using Openswathmentioning

confidence: 99%

Convergent network effects along the axis of gene expression during prostate cancer progression

Charmpi

Guo

Zhong

et al. 2020

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36Tumor-specific genomic aberrations are routinely determined by high throughput genomic 37 measurements. However, it is unclear how complex genome alterations affect molecular networks 38 through changing protein levels, and consequently biochemical states of tumor tissues. Here, we 39 investigated how tumor heterogeneity evolves during prostate cancer progression. In this study, we 40 performed proteogenomic analyses of 105 prostate samples, consisting of both benign prostatic 41 hyperplasia regions and malignant tumors, from 39 prostate cancer (PCa) patients. Exome sequencing, 42 copy number analysis, RNA sequencing and quantitative proteomic data were integrated using a network-43 based approach and related to clinical and histopathological features. In general, the number and 44 magnitude of alterations (DNA, RNA and protein) correlated with histopathological tumor grades. 45Although common sets of proteins were affected in high-grade tumors, the extent to which these proteins 46 changed their concentrations varied considerably across tumors. Our multi-layer network integration 47 identified a sub-network consisting of nine genes whose activity positively correlated with increasingly 48 aggressive tumor phenotypes. Importantly, although the effects on individual gene members were barely 49 detectable, together the perturbation of this sub-network was predictive for recurrence-free survival time. 50The multi-omics profiling of multiple tumor sites from the same patients revealed cases of likely shared 51 clonal origins as well as the occasional co-existence of multiple clonally independent tumors in the same 52 prostate. Overall, this study revealed molecular networks with remarkably convergent alterations across 53 tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single 54 sub-network that was perturbed in all high-grade tumor regions. 55 56 57

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“…As a benchmark, we used transcriptomics datasets are from the default dataset from bladderbatch package and ComBat package. We also applied the BatchServer to a published proteomic data of NCI-60 dataset cell lines [17]. The missing values of all the four data sets were replaced by '1'.…”

Section: Illustrative Applications Of Batchsevermentioning

confidence: 99%

“…The proteomics data set are from NCI-60 cell lines [17], containing 3,171 proteins and 60 samples of 12 batches. This data set was generated by Pressure Cyling Technology coupled with data independent acquisition (PCT-DIA) [18].…”

Section: Illustrative Applications Of Batchsevermentioning

confidence: 99%

BatchServer: a web server for batch effect evaluation, visualization and correction

Zhu

Chen

Yuan

et al. 2020

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Background: Batch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects is thus necessary for omics data analysis, especially in the context of large cohort of thousands of samples with different experimental platforms. Existing batch effect reducing tools mainly focus on the development of algorithms, while requiring programming skills and the knowledge of data distribution limits their application for many researchers. In order to facilitate evaluation and correction of batch effects, we provided an user-friendly and easy-to-use graphical batch effects analysis web platform. Results:We developed an open-source R/Shiny based web server --BatchServer that allows users to graphical interactively evaluate, visualize and correct of the batch effects in high-throughput data sets. BatchServer including a modified ComBat, which was a popular batch effect adjustment tool to correct batch effects, PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) to evaluate and visualize batch effects. BatchServer is an efficient batch effects processing platform, as its application in three publicly available data sets. Conclusion:Our user-friendly online open-source web server BatchServer supports comprehensive batch effects analysis facilitating the batch effect evaluations and corrections for biologists.BatchServer is deployed at https://lifeinfo.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/zhutiansheng/batch_server.

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Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Cited by 61 publications

References 88 publications

The Nucleosome Remodeling and Deacetylase complex has an asymmetric, dynamic, and modular architecture

The Nucleosome Remodeling and Deacetylase complex has an asymmetric, dynamic, and modular architecture

Convergent network effects along the axis of gene expression during prostate cancer progression

BatchServer: a web server for batch effect evaluation, visualization and correction

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