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Session 4

2014· article· en· W6994647490 sur OpenAlexaboutno aff

Notice bibliographique

RevuePubMed Central · 2014
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueEpigenetics and DNA Methylation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEpigeneticsHistoneProteomicsHistone methyltransferaseQuantitative proteomicsGene expressionMethyltransferaseHistone methylation
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

7.1 Proteomic Advances for Epigenetics Research Benjamin Garcia University of Pennsylvania, Philadelphia, PA, USA Epigenetic mechanisms such as histone post-translational modifications (PTMs), small non-coding RNAs and DNA methylation play crucial roles in the establishment and induction of gene expression patterns that regulate several aspects of cellular biology. Here we will present methodology advances to gain eve more accurate descriptions of these epigenetic networks. Quantification of histone PTMs is performed by shotgun proteomics favoring discovery of novel/low level PTMs, or targeted analyses (SRM) of known PTM sites. Each workflow has strengths and weaknesses for PTM quantification. Data independent acquisition (DIA) for comprehensive data generation combined with targeted data processing has recently been demonstrated to provide very high quality quantitative data. The advantages of this approach for targeted PTM quantification include no upfront assay development, quantitative data on all analytes and no dynamic exclusion of isobaric peptides. In this study, we develop a SWATH™ acquisition platform for quantitating histone PTMs and isoforms under distinct biological conditions. In a second project, we have recently developed novel quantitative affinity mass spectrometry (MS) based proteomics approaches to characterize in vivo protein lysine methyltransferase (KMT) activity in human cancers. Currently, there are approximately 50 proteins encoded in the human genome that contain the catalytic SET (Suppressor of variegation, Enhancer of zeste, Trithorax) domain, characteristic of nearly all KMTs. Roughly about a fourth of these KMTs have been shown to methylate histone proteins substrates to regulate gene expression. However, despite widespread efforts, only a small number of non-histone protein substrates for any KMT are defined. The significance of KMT activity in both normal physiology and disease is emerging as highly significant, as association of over 25 KMTs with a multitude of different human cancers have been reported. Therefore, an unambiguous determination of KMT activity, target sites and induced cellular responses or phenotypes would be highly beneficial to the chromatin, cancer and clinical biology communities, as this information is severely limited. Discussed will be the methods that we have developed to identify over hundreds non-histone methylated proteins in human cells, the most comprehensive large scale global analysis of protein lysine methylation (e.g. the “methylome”) to date, including KMT specific methylomes. 7.2 Dissecting the Role of the Extracellular Matrix in Cancer Progression: A Proteomics-based Approach Alexandra Naba(1), Karl Clauser(2), Steven A. Carr(2), Richard Hynes(3) (1)Massachusetts Institute of Technology, Cambridge, MA, USA; (2)Broad Institute of MIT and Harvard, Cambridge, MA, USA; (3)Howard Hughes Medical Institute, MIT, Cambridge, MA, USA The extracellular matrix (ECM) is a complex meshwork of cross-linked proteins that provides biophysical and biochemical cues that are major regulators of cell behaviors. ECM deposition (desmoplasia) is a hallmark of tumor progression and pathologists have used excessive ECM as a marker of tumors with poor prognosis long before the composition and the complexity of the ECM was even uncovered. However, the biochemical properties of ECM proteins (large size, insolubility) have compromised systematic characterization of ECM composition. We previously reported the development of a proteomic strategy to characterize the composition of in vivo ECMs and have shown that we can reproducibly identify 150+ ECM proteins in any given tissue or tumor type [1]. Using human tumor xenografts in mice, we demonstrated that both tumor cells and stromal cells contribute to the production of the tumor matrix and that tumors of differing metastatic potential differ in both the tumor- and the stroma-derived contributions. We have also demonstrated that a high proportion of the proteins differentially expressed between tumors of differing metastatic potential have causal effects on metastasis [2]. In this study, we applied this proteomic approach to characterize the ECM of patient-derived primary metastatic colorectal tumors, paired metastases to liver and normal colon and liver samples. We identified consistent differences in the ECMs of i) colon tumors as compared to normal colon, ii) colon cancer-derived metastases to the liver and normal liver, and iii) primary tumors as compared with metastases derived from them. Based on these changes, we demonstrate that robust signatures of ECM proteins characteristic of each tissue, normal and malignant, can be defined using relatively small samples (25mg) and from small numbers of patients [3]. The ECM proteins defined here represent candidate serological or tissue biomarkers, potential targets for imaging of occult metastases, and potential novel targets for therapies. Altogether, our results illustrate that the proteomic analysis of the composition of tumor ECMs offers promise for development of diagnostic and prognostic signatures of the metastatic potential of tumors. In addition, the fact that reliable results can be obtained using small tissue samples from limited numbers of patients opens the way to application of these methods to other tumor types. [1] Naba A, Clauser K.R, Hoersch S, Liu H, Carr S.A and Hynes RO. (2012) The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Molecular and Cellular Proteomics, 11(4):M111.014647. [2] Naba A, Clauser K.R, Lamar J.M, Carr S.A and Hynes RO. (2014) Extracellular matrix signatures of human mammary carcinoma identify novel metastasis promoters. eLife, 3:e01308. [3] Naba A, Clauser K.R, Whittaker C.A, Carr S.A, Tanabe K.K and Hynes RO. (2014) Extracellular matrix signatures of human primary metastatic colon cancers and their metastases to liver. BMC Cancer, accepted. 7.3 Signaling Interactome Dynamics in Health and Disease Anne-Claude Gingras Lunenfeld-Tanenbaum Research Institute, Toronto, ONT, CA Kinases and phosphatases coordinate critical cellular decisions, including whether to grow and divide, to differentiate into a specific cell type, or to die. They must respond to environmental cues and transmit precise signals. Deregulation of the phosphorylation balance is implicated in multiple diseases, including cancer and neurodegenerative diseases. This deregulation can involve mutations directly in the kinase or phosphatase proteins, changes in their splicing patterns, or may involve expression modulation; all these events can lead to network rewiring. Since kinases and phosphatases frequently associate with regulators, scaffolding molecules and substrates, a possible outcome of response to a cue, or a mutation or splicing alteration (besides modulation of intrinsic catalytic activity) is a change in these physical interactions. In the past several years, we have developed proteomics methods to monitor these regulated interactions for key signaling molecules. These include a coupling of affinity purification (AP) with Selected Reaction Monitoring or with the data independent acquisition approach SWATH. We recently introduced a normalization strategy to automatically calculate fold change and confidence in the regulated interactomes. With our collaborators, we have also been developing software tools to perform identification from SWATH data, leading in a more efficient utilization of the instrument time, while increasing sensitivity in the detection of the regulated interactions. We have harnessed the AP-SWATH approach to probe the dysregulation of kinases and phosphatase interactions induced by mutations, and to analyze the consequences of pharmacological treatment on the interactions established by signaling proteins. We will discuss these approaches in the context of cancer and vascular disease. 7.4 Structure of RNA Polymerase II – Mediator Holoenzyme Investigated through an Integrated Mass Spectrometry and Electron Microscopy Approach Michael J Trnka(1), Philip J. J. Robinson(2), Riccardo Pellarin(1), Andrej Sali(1), Roger D. Kornberg(2), A. L. Burlingame(1) (1)University of California, San Francisco, San Francisco, CA, USA; (2)Stanford University School of Medicine, Stanford, CA, USA Transcription of mRNA coding genes requires the assembly at the promoter of a large protein complex consisting of RNA polymerase II (pol II), the general transcription factors (GTFs), and the mediator of transcriptional regulation (Mediator). The Mediator complex plays a central role in transcriptional regulation by relaying gene specific regulatory signals to the general transcriptional machinery. The yeast holoenzyme between mediator and pol II consists of 33 subunits totaling over 1.5 MDa in size. The large size precludes atomic resolution crystallography of the entire assembly. Hence, hybrid methods of structure determination that integrate data from crystallography of stable subassemblies, cryoEM of the entire complex, and mass spectrometry (MS) are necessary to map protein complexes of this size. Mass spectrometry based structural techniques such as crosslinking and native-MS provide spatial restraints and stoichiometry information that guide the modeling process. Crosslinking-MS samples interacting surfaces by identifying amino acid residues that have been covalently modified through application of bifunctional reagents, while native MS permits the determination of stably associated assemblies of proteins in the gas-phase in a manner reflective of solution-state conformation and quaternary structure. The application of these structural MS tools to the holoenzyme system has necessitated the development of several technologies including: robust bioinformatics strategies for determining crosslinked peptides in large database searches, enrichment methods and chemical reagents for crosslinking, as we

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,252
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,000
Communication savante0,0050,003
Science ouverte0,0020,005
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,7480,676

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,224
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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