An improved analysis methodology for translational profiling by microarray

Abstract

Translational regulation plays a central role in the global gene expression of a cell, and detection of such regulation has allowed deciphering of critical biological mechanisms. Genome-wide studies of the regulation of translation (translatome) performed on microarrays represent a substantial proportion of studies, alongside with recent advances in deep-sequencing methods. However, there has been a lack of development in specific processing methodologies that deal with the distinct nature of translatome array data. In this study, we confirm that polysome profiling yields skewed data and thus violates the conventional transcriptome analysis assumptions. Using a comprehensive simulation of translatome array data varying the percentage and symmetry of deregulation, we show that conventional analysis methods (Quantile and LOESS normalizations) and statistical tests failed, respectively, to correctly normalize the data and to identify correctly deregulated genes (DEGs). We thus propose a novel analysis methodology available as a CRAN package; Internal Control Analysis of Translatome (INCATome) based on a normalization tied to a group of invariant controls. We confirm that INCATome outperforms the other normalization methods and allows a stringent identification of DEGs. More importantly, INCATome implementation on a biological translatome data set (cells silenced for splicing factor PSF) resulted in the best normalization performance and an improved validation concordance for identification of true positive DEGs. Finally, we provide evidence that INCATome is able to infer novel biological pathways with superior discovery potential, thus confirming the benefits for researchers of implementing INCATome for future translatome studies as well as for existing data sets to generate novel avenues for research.

Inhibition of Sec61-dependent translocation by mycolactone uncouples the integrated stress response from ER stress, driving cytotoxicity via translational activation of ATF4

“…To better understand this reprogramming, we performed translational profiling, analysing genome-wide redistribution of gene transcripts in mycolactone-exposed cells 22 , 23 . This approach determines changes in the relative abundance of polysomal vs. sub-polysomal transcripts as a surrogate measure of translational efficiency 23 . Of probes that detected targets, approximately 5% were significantly altered between the different pools in response to mycolactone ( p < 0.05, Fig.…”

“…Data was analysed by the method described in ref. 23 , with some variations. Briefly, background was corrected by the normexp method.…”

“…Transcripts showing a significant change in translational efficiency after exposure mycolactone were identified by Rank Product analysis of the entire data set of three biological repeats. This rank product analysis rules out changes in transcription that may occur and, while it yields a lower number of positive hits than other approaches, it leads to a higher validation rate for translationally regulated targets 23 . Gene transcripts that were enriched in the polysomal or sub-polysomal pools (reflecting enhanced and reduced translation, respectively) were subject to PANTHER Overrepresentation Test (release 20160715) to identify significantly overrepresented gene ontology (GO) groups ( p < 0.05).…”

The pathogenesis of mesothelioma is driven by a dysregulated translatome

“…Data were considered to be significantly changing if the fold change was at least two at pfp <0.2. This analysis pipeline from raw arrays to lists of significantly changing probes is available as the INCATome R package from CRAN 70 .…”

Discovery of Selenocysteine as a Potential Nanomedicine Promotes Cartilage Regeneration With Enhanced Immune Response by Text Mining and Biomedical Databases

“…GSE117999 contains 12 patients with osteoarthritis, and 12 patients without osteoarthritis (arthroscopic partial meniscectomy). The normalized log2 ratio expressive OA/N-OA of the GSE117999 dataset, normalized by the L and Q process (Sbarrato et al, 2017), was taken. Analysis interested gene IDs were changed to gene symbols on the Agilent-072363 SurePrint G3 Human GE v3 8x60K Microarray 039494 (Feature Number Version).…”