Final Report Summary - CNVIMPACTGEXP (Deep surveying of CNV impact on Mouse transcriptome complexity and regulation)
Structural variants (SV) are defined as stretches of DNA larger than 1 kilobase that display differences between individuals in the normal population. These differences include insertions, deletions, translocations, inversions and complex rearrangements of genetic material. In the past decade, much effort has been put into the identification and mapping of these regions among seemingly normal individuals in humans and a number of model organisms that has allowed uncovering the widespread nature of SV in mammalian genomes. In human, more than 66 000 SV from approximately 16 000 regions have been identified to date (see http://projects.tcag.ca/variation/(se abrirá en una nueva ventana) for details). SV cover as much as 10 % of the human genome and encompass hundreds of genes underscoring their significant contribution to genetic diversity. Moreover, they have been associated with multiple human phenotypes and susceptibility to disease, while more recent reports demonstrated their involvement in complex traits such as schizophrenia, autism, mental retardation and cancer for instance.
Phenotypic effects of SV are supposedly brought about by changes at expression levels, either directly affecting the genes located in the SV regions, or indirectly through downstream effects on regulatory networks. Thus, transcriptome analyses seemed appropriate proxies to study the consequences of SV. The recently described RNA-seq method has brought transcriptome analysis to a new level, as it addresses gene expression at nucleotide resolution and alternative splicing events simultaneously. In our project, we used these advantages to unravel the effects of SV on gene transcription at high resolution in mouse inbred strains as a model.
Combining two distinct sets of structural variants in mouse, we assessed the influence of structural variants on transcriptomes from liver and brain of different mouse inbred strains using the Illumina Genome Analyser IIx, one of the most common high throughput sequencer. In this context, we monitored expression changes for transcripts as well as differences in splicing considering the gene structures - i.e. coding or regulatory regions - that are affected by structural variants.
Our findings suggest that large structural variants affecting the whole gene region seem to have a more direct impact on their expression, while smaller structural variants occurring in regulatory regions are more likely to influence alternative splicing, leading especially to a higher number of isoforms for these genes.
With the new dataset we are currently producing we will be able to assess not only coding transcripts but also long non-coding RNAs to further investigate the way structural variants can impact their regulatory function on transcription.
This study provides a unique opportunity to extensively gauge the influence of structural variants on the transcriptome complexity and regulation.
Phenotypic effects of SV are supposedly brought about by changes at expression levels, either directly affecting the genes located in the SV regions, or indirectly through downstream effects on regulatory networks. Thus, transcriptome analyses seemed appropriate proxies to study the consequences of SV. The recently described RNA-seq method has brought transcriptome analysis to a new level, as it addresses gene expression at nucleotide resolution and alternative splicing events simultaneously. In our project, we used these advantages to unravel the effects of SV on gene transcription at high resolution in mouse inbred strains as a model.
Combining two distinct sets of structural variants in mouse, we assessed the influence of structural variants on transcriptomes from liver and brain of different mouse inbred strains using the Illumina Genome Analyser IIx, one of the most common high throughput sequencer. In this context, we monitored expression changes for transcripts as well as differences in splicing considering the gene structures - i.e. coding or regulatory regions - that are affected by structural variants.
Our findings suggest that large structural variants affecting the whole gene region seem to have a more direct impact on their expression, while smaller structural variants occurring in regulatory regions are more likely to influence alternative splicing, leading especially to a higher number of isoforms for these genes.
With the new dataset we are currently producing we will be able to assess not only coding transcripts but also long non-coding RNAs to further investigate the way structural variants can impact their regulatory function on transcription.
This study provides a unique opportunity to extensively gauge the influence of structural variants on the transcriptome complexity and regulation.