 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Mar 17, 2021 |
| Title |
Multi-influential interactions controls behaviour and cognition through a limited number of pathways in Down syndrome mouse models (RNA-Seq reanalysis II) |
| Sample organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
Third-party reanalysis
|
| Summary |
Down syndrome (DS) is the most common genetic form of intellectual disability with additional clinical features, caused by the presence of an additional copy of human chromosome 21 (Hsa21). A few number of patients with DS features, carry partial duplication of Hsa21 and their study provided novel insights into genotype–phenotype correlations. Despite the progress of genome analysis, the rareness of patients with partial duplication, and the human genetic heterogeneity, makes difficult to achieve a more detailed phenotypic map at present. As a complementary approach, we screened the in vivo DS mouse library with highly standardized behavioural tests, magnetic resonance imaging (MRI) and digged into hippocampal gene expression to go further in dissecting the genotype–phenotype correlations and in deciphering misregulated genes, functional pathways and biological cascades in DS models. Altogether this approach bring novel insights into the field. First, we unravelled the complexity of the genetic interactions between different regions of the chromosome 21 and how they play an important role in modulating the outcome of the behavioural and molecular phenotypes. Then, in depth analysis of misregulated expressed genes involved in synaptic dysfunction highlitghed six biological cascades centered around DYRK1A, GSK3beta, NPY, RHOA, NPAS4 and the SNARE complex. Finally, we provide a novel vision of the existing altered gene-gene crosstalk and molecular mechanisms that could be at play for both the DS clinical features and the rescue mechanisms by targeting specific hubs or well connected nodes that may be central to advance in our understanding of DS and therapies development.
|
| |
|
| Overall design |
I downloaded and re-analysed the gene expression profile of human foetal fibroblast derived datasets published by Letourneau et al., (2014) and adult fibroblast published by Prandini et al., 2007 with GEO accession number GSE55426. We re-analysed the expression of 3 biological replicates + 1 technical replicate of foetal cultured fibroblasts from monozygotic twins discordant for Down syndrome and adult cultured fibroblasts samples from 8 Down syndrome and Euploid adults. The re-analysis was performed using our bioinformatics pipeline, developed over the R environment. As a brief sumup, the pipeline consisted in eight major steps: Alignment & counts generation, Quality control (QC), pre-processing and normalization of the raw data, analysis of the structure and homogenicity of the data, differential expression analysis (DEA), differential functional analysis (DFA), Network connectivity analyses of pathways and genes and finally network topology and centrality analyses.Alignment & counts generation: To perform the aligning I downloaded from ENSEMBL the human genome assembly version GRCh38 release 96 together with the corresponding gene location file in .gtf format files. The alignment was performed using HISAT2 with the options -p 8 –dta, -t –summary-file –no-unal, --no-hd. Samtools version 1.9 was used to generate the bam file and index and the counts were generated using HTSeq-Count 0.9.1 with the options –mode=intersection-nonempty ---type=exon --idattr=gene_id –aditional-attr=gene_name. Data quality control: I performed an initial quality control analysis (QC) of the raw .fastq files using fastQC. I also checked that there were none overrepresented sequences matching to any other organism, the levels of GC content, the duplication levels were good, and no residual adapters sequences were identified. Then, DESeq2 was chosen for the normalization and both DESeq2 and FCROS for the differential expression analysis (DEA). Ultimately, we decided to carry on all downstream analyses using FCROS68results although we maintained the DEGs annotations identified by DESeq2 (RNA-Seq) for the purpose of maintained the results of one of the DEA method most widely used by the community for RNA-Seq analyses.FCROS is a fold change (FC) rank based method that works well with noisy datasets and gives strong reproducible results. FCROS computes for each pair of test/control samples (K pairs), a statistic associated with the k ranks of the FC values for each gene, and the obtained probability (f-value) is used to identify the differential expressed genes (DEGs) within an error level fixed by the analyst. Our fixed error level corresponds to a 5% False Discovery Rate (FDR), and is attained by setting the Ɑ parameter within this range 0.025<Ɑ>0.975.Differential functional analysis (DFA): To identify the altered functions due to the changes in gene expression and possibly get new insights into the mechanistic changes along the pathways between the two conditions trisomic vs. wild type on each model we used the generally applicable gene set enrichment for pathway analysis (GAGE, Luo et al., 2009) R package to carry on the functional expression analysis.The main aim of re-analysing this data was to get new insights into DS gene deregulation first in humans applying a more updated computational pipeline and secondly, by applying a comparative genomic approach with rodent Down syndrome datasets identify the conserved gene and functional deregulated profiles.
|
| |
|
| Contributor(s) |
Muñiz Moreno MM |
| Citation(s) |
33693642 |
| Submission date |
Apr 27, 2020 |
| Last update date |
Mar 18, 2021 |
| Contact name |
maria del mar muñiz moreno |
| E-mail(s) |
munizmom@igbmc.fr
|
| Organization name |
IGBMC
|
| Department |
Translational medicine and neurogenetics
|
| Lab |
Physiopathology of aneuploidy, gene dosage effect and Down syndrome - Yann Herault team
|
| Street address |
1 Rue Laurent Fries
|
| City |
Strasbourg |
| State/province |
Alsace |
| ZIP/Postal code |
67400 |
| Country |
France |
| |
|
| This SubSeries is part of SuperSeries: |
| GSE149470 |
Multi-influential interactions controls behaviour and cognition through a limited number of pathways in Down syndrome mouse models |
|
| Relations |
| Reanalysis of |
GSM1338311 |
| Reanalysis of |
GSM1338312 |
| Reanalysis of |
GSM1338313 |
| Reanalysis of |
GSM1338314 |
| Reanalysis of |
GSM1338315 |
| Reanalysis of |
GSM1338316 |
| Reanalysis of |
GSM1338317 |
| Reanalysis of |
GSM1338318 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE149469_monozygotic_twins_fibroblasts_HTSeqCounts_matrix.txt.gz |
537.4 Kb |
(ftp)(http) |
TXT |
| GSE149469_monozygotic_twins_fibroblasts_Letourneau.xls.gz |
17.4 Kb |
(ftp)(http) |
XLS |
| GSE149469_monozygotic_twins_fibroblasts_subSeries_DESeq2_normalization_matrix.txt.gz |
1.9 Mb |
(ftp)(http) |
TXT |
| GSE149469_monozygotic_twins_fibroblasts_subSeries_FCROS_results_matrix.txt.gz |
176.3 Kb |
(ftp)(http) |
TXT |
| Processed data provided as supplementary file |
|
|
|
|
 |
