Dataset Information

Quantitative proteomics study of Mecp2 KO versus wild type mice

ABSTRACT: Rett syndrome (RTT) is an X-linked neurodevelopmental disorder caused by mutations in the transcriptional regulator MeCP2. RTT is characterized by having apparently normal development until 6-18 months, when a progressive decline in motor and language functions begins and breathing abnormalities and seizures present. Here we present the first proteomic analysis in a RTT mouse model. Examining whole cortex tissue in symptomatic males (Mecp2Jae/y) and wild-type littermates, we have identified 465 proteins significantly altered. Pathway analysis identified biological pathways ubiquitous to multiple cell types as well as cell type specific pathways, underscoring the contributions of multiple central nervous system (CNS) cell populations to the disease pathogenesis.

OTHER RELATED OMICS DATASETS IN: PRJNA379366

INSTRUMENT(S): Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Michael Heaven

LAB HEAD: Michelle Olsen

PROVIDER: PXD006460 | Pride | 2017-11-02

REPOSITORIES: Pride

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Publications

RNA sequencing and proteomics approaches reveal novel deficits in the cortex of <i>Mecp2</i>-deficient mice, a model for Rett syndrome.

Pacheco Natasha L NL Heaven Michael R MR Holt Leanne M LM Crossman David K DK Boggio Kristin J KJ Shaffer Scott A SA Flint Daniel L DL Olsen Michelle L ML

Molecular autism 20171024

<h4>Background</h4>Rett syndrome (RTT) is an X-linked neurodevelopmental disorder caused by mutations in the transcriptional regulator MeCP2. Much of our understanding of MeCP2 function is derived from transcriptomic studies with the general assumption that alterations in the transcriptome correlate with proteomic changes. Advances in mass spectrometry-based proteomics have facilitated recent interest in the examination of global protein expression to better understand the biology between transc ...[more]

PMID: 29090078

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Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration. Of 198 samples, 129 represented the discovery phase and 69 represented the validation phase.

Dataset Information

Quantitative proteomics study of Mecp2 KO versus wild type mice

Publications

RNA sequencing and proteomics approaches reveal novel deficits in the cortex of <i>Mecp2</i>-deficient mice, a model for Rett syndrome.

Similar Datasets

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