RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

Nazarov, Petr V.; Muller, Arnaud; Kaoma, Tony; Nicot, Nathalie; Máximo, Cristina; Birembaut, Philippe; Tran, Nhan L.; Dittmar, Gunnar; Vallar, Laurent

doi:10.1186/s12864-017-3819-y

Cited by 72 publications

(55 citation statements)

References 61 publications

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“…Through analysis of gene expression at the level of individual exons and exon-exon junctions, we identified AS events associated with gestational age and fetal sex. The exon junction arrays used in this study were previously compared to RNA-Seq for the purpose of differential splicing and found to have higher power when quantifying low-abundance transcripts as well as long non-coding RNAs that tend to be shorter than protein-coding gene counterparts [126]. Of interest, we found more genes (17.5% of the genes detected) displaying differential splicing than differential expression (10%) with gestational age.…”

Section: Amniotic Fluid Differential Splicing With Advancing Gestationmentioning

confidence: 75%

Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy

et al. 2020

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Background: The amniotic fluid (AF) cell-free transcriptome is modulated by physiologic and pathologic processes during pregnancy. AF gene expression changes with advancing gestation reflect fetal development and organ maturation; yet, defining normal expression and splicing patterns for biomarker discovery in obstetrics requires larger heterogeneous cohorts, evaluation of potential confounding factors, and novel analytical approaches. Methods: Women with a normal pregnancy who had an AF sample collected during midtrimester (n = 30) or at term gestation (n = 68) were included. Expression profiling at exon level resolution was performed using Human Transcriptome Arrays. Differential expression was based on moderated t-test adjusted p < 0.05 and fold change > 1.25; for differential splicing, a splicing index > 2 and adjusted p < 0.05 were required. Functional profiling was used to interpret differentially expressed or spliced genes. The expression of tissue-specific and cell-type specific signatures defined by single-cell genomics was quantified and correlated with covariates. In-silico validation studies were performed using publicly available datasets. Results: 1) 64,071 genes were detected in AF, with 11% of the coding and 6% of the non-coding genes being differentially expressed between midtrimester and term gestation. Expression changes were highly correlated with those previously reported (R > 0.79, p < 0.001) and featured increased expression of genes specific to the trachea, salivary glands, and lung and decreased expression of genes specific to the cardiac myocytes, uterus, and fetal liver, among others. 2) Single-cell RNA-seq signatures of the cytotrophoblast, Hofbauer cells, erythrocytes, monocytes, T and B cells, among others, showed complex patterns of modulation with gestation (adjusted p < 0.05). 3) In 17% of the genes detected, we found differential splicing with advancing gestation in genes related to brain development processes and immunity pathways, including some that were missed based on differential expression analysis alone.

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Section: Amniotic Fluid Differential Splicing With Advancing Gestationmentioning

confidence: 75%

Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy

et al. 2020

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“…26,27 This approach is also advantageous when the protein products of genes are either recalcitrant to proteomic analysis or of low abundance, which can result in their under-representation in some conditions. 28 The proteomic analysis is widely used in the analyses of differential expression of proteins in order to understand the changes of protein expression between experimental and control group. The proteins are the ultimate executors of cellular function.…”

Section: Discussionmentioning

confidence: 99%

Notch3 promotes 3T3‐L1 pre‐adipocytes differentiation by up‐regulating the expression of LARS to activate the mTOR pathway

Guo

Tan

Xiong

et al. 2019

J Cellular Molecular Medi

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Adipocytes constitute a major component of the tumour microenvironment. Numerous studies have shown that adipocytes promote aggressiveness and invasion by stimulating cancer cells proliferation and modulating their metabolism. Herein, we reported that Notch3 promotes mouse 3T3‐L1 pre‐adipocytes differentiation by performing the integrative transcriptome and TMT‐based proteomic analyses. The results revealed that aminoacyl‐tRNA_biosynthesis pathway was significantly influenced with Nocth3 change during 3T3‐L1 pre‐adipocytes differentiation, and the expression of LARS in this pathway was positively correlated with Notch3. Published studies have shown that LARS is a sensor of leucine that regulates the mTOR pathway activity, and the latter involves in adipogenesis. We therefore supposed that Notch3 might promote 3T3‐L1 pre‐adipocytes differentiation by up‐regulating LARS expression and activating mTOR pathway. CHIP and luciferase activity assay uncovered that Notch3 could transcriptionally regulate the expression of LARS gene. Oil Red staining identified a positive correlation between Notch3 expression and adipocytic differentiation. The activation of mTOR pathway caused by Notch3 overexpression could be attenuated by knocking down LARS expression. Altogether, our study revealed that Notch3 promotes adipocytic differentiation of 3T3‐L1 pre‐adipocytes cells by up‐regulating LARS expression and activating the mTOR pathway, which might be an emerging target for obesity treatment.

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“…Taken together, consensus ICA approach represents a versatile tool to dissect complex data cohorts into individual components allowing for better use of such datasets. (Nazarov, Muller et al, 2017), we shifted and scaled the microarray data:…”

Section: Discussionmentioning

confidence: 99%

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

Nazarov

Wienecke-Baldacchino

Zinovyev

et al. 2018

Preprint

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The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) -an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes celltype specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible. RNA-seq and miRNA-seq of investigation set:GDC data portal: https://portal.gdc.cancer.gov/ Expression data of validation set:Array Express: https://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-19234/ New expression data of investigation set:The sequencing data for 3 primary melanoma tumours and 2 controls are freely available under the GEO accession number GSE116111. Data for miRNAs are in the Supplementary Table S5. Tools: Consensus parallel ICA: https://gitlab.com/biomodlih/consica R/Bioconductor v.3.4.3 with packages fastICA, doMC, doSNOW, topGO, randomForest, survival https://cran.r-project.org/ Enrichr ACKNOWLEDGEMENT We would like to thank the patients for providing clinical material and the clinical staff of the Dermatology Unit at the University of Freiburg for professionally taking and handling all the samples. Bioinformatics analyses presented in this paper were partly carried out using the high-performance computing facilities of the University of Luxembourg (http://hpc.uni.lu). Furthermore, we thank Demetra Philippidou and Dr. Susanne Reinsbach for processing clinical samples and NHEM cells forming the investigation dataset. We acknowledge Dr. Aurélien Ginolhac and Cristina Maximo for critically reading the manuscript and for valuable input.

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RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

Cited by 72 publications

References 61 publications

Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy

Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy

Notch3 promotes 3T3‐L1 pre‐adipocytes differentiation by up‐regulating the expression of LARS to activate the mTOR pathway

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

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