Dataset Information

Proteomic profiling of retinoblastoma by high resolution mass spectrometry

ABSTRACT: We performed a high throughput comparative proteomics study to identify differentially expressed proteins that could serve as putative targets in retinoblastoma. We used iTRAQ based quantitative proteomics approach and analyzed the samples on orbitrap velos mass spectrometer. We identified and quantified a total of 3733 proteins in retinoblastoma when compared with normal adult retina. In total, we identified 987 proteins that were differentially expressed in retinoblastoma with a fold change of ≥2 of which 392 proteins were upregulated and 595 were downregulated.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Retina

DISEASE(S): Retinoblastoma

SUBMITTER: Sailaja Elchuri

LAB HEAD: Dr. Sailaja V Elchuri

PROVIDER: PXD002774 | Pride | 2016-12-23

REPOSITORIES: Pride

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Proteomic profiling of retinoblastoma by high resolution mass spectrometry.

Danda Ravikanth R Ganapathy Kalaivani K Sathe Gajanan G Madugundu Anil K AK Ramachandran Sharavan S Krishnan Uma Maheswari UM Khetan Vikas V Rishi Pukhraj P Keshava Prasad T S TS Pandey Akhilesh A Krishnakumar Subramanian S Gowda Harsha H Elchuri Sailaja V SV

Clinical proteomics 20161026

<h4>Background</h4>Retinoblastoma is an ocular neoplastic cancer caused primarily due to the mutation/deletion of RB1 gene. Due to the rarity of the disease very limited information is available on molecular changes in primary retinoblastoma. High throughput analysis of retinoblastoma transcriptome is available however the proteomic landscape of retinoblastoma remains unexplored. In the present study we used high resolution mass spectrometry-based quantitative proteomics to identify proteins ass ...[more]

PMID: 27799869

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Project description:Retinal cells are specified in a zebrafish recessive mutant called young (yng) but they fail to terminally differentiate; i.e. extend neurites and make synaptic contacts. A point mutation in a brahma-related gene 1 (brg1) is responsible for this phenotype. In this microarray study, a three-factor factorial design was utilized to investigate the effects of 1) mutation, 2) change in time (36 vs. 52hpf), and 3) change in tissue (retina vs. whole embryos), and their interactions on gene expression. Significant probesets were inferred by using both specific contrasts of the fitted Analysis of Variance (ANOVA) models and a corresponding 2-fold expression cutoff. The probesets were grouped into three broad categories: 1) Brg1-regulated retinal differentiation genes (731 probsets), 2) Retinal specific genes but independent of Brg1 regulation (3038 probesets) and 3) Genes regulated by Brg1 but outside the retina (107 probesets). Four gene groups/pathways including neurite outgrowth regulators, Delta-Notch signalling molecules, Irx family members and specific cell cycle regulators were identified in the first group, and their relevance for retinal differentiation functionally validated. This study demonstrates that an approach such as ours can identify relevant genes and pathways involved in retinal development as well as the development of other tissues at the same time. Experiment Overall Design: The gene expression levels of three independent replicates of retina consisting of ten samples each at 36 and 52hpf retinas from WT (WR36 & WR52) and yng (YR36 & YR52) larvae, and stage-matched whole embryos: WT at 36hpf (WA36), WT at 52hpf (WA52), yng at 36hpf (YA36), and yng at 52hpf (YA52) were measured by Affymetrix Zebrafish Whole Genome Arrays. We analyzed the effects of these factors on gene expression levels: 1) mutation (M), 2) tissue (R), and 3) time (T). Each of these factors have two levels: mutant and WT (M); retinas and whole body (R); and 36 and 52 hpf (T). As a result, there are totally eight conditions for all the comparisons and the design is called a 2x2x2 factorial design. The aim of a factorial analysis is to delineate the effects of each factor and their combinations (interaction) on gene expression. The overall analysis strategy is to first fit a full Analysis of Variance (ANOVA) model with all possible main effects (T, M, R) and their interactions (T*M, M*R, R*M, T*M*R) to the gene expression data for each probeset. The estimates of the model are obtained through a maximum likelihood estimation method. Then a backward elimination strategy is used to remove insignificant main effects and interactions to get the most parsimonious models. Finally, a group of contrasts and their corresponding fold changes are used to infer whether that probeset is significantly associated with a particular biological process.

			Action	DRS
	Retino_INV_iTRAQ_1.raw	Raw
	Retino_INV_iTRAQ_10.raw	Raw
	Retino_INV_iTRAQ_11.raw	Raw
	Retino_INV_iTRAQ_12.raw	Raw
	Retino_INV_iTRAQ_13.raw	Raw