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Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease


Rached Alkallas, Lisa Fish, Hani Goodarzi, Hamed S. Najafabadi

ABSTRACT

The abundance of mRNA is mainly determined by the rates of RNA transcription and decay. Here, we present a method for unbiased estimation of differential mRNA decay rate from RNA-sequencing data by modeling the kinetics of mRNA metabolism. We show that in all primary human tissues tested, and particularly in the central nervous system, many pathways are regulated at the mRNA stability level. We present a parsimonious regulatory model consisting of two RNA-binding proteins and four microRNAs that modulate the mRNA stability landscape of the brain, which suggests a new link between RBFOX proteins and Alzheimer’s disease. We show that down-regulation of RBFOX1 leads to destabilization of mRNAs encoding for synaptic transmission proteins, which may contribute to the loss of synaptic function in Alzheimer’s disease. RBFOX1 down-regulation is more likely to occur in older and female individuals, consistent with the association of Alzheimer’s disease with age and gender.

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SUPPLEMENTARY DATA FILES

Data for 20 human tissues

Intronic, exonic, and stability measurements across 20 human tissues

The following matrices are obtained by analysis of RNA-seq data from SRA dataset SRP056969.

Human20Tissues.exonic....txt.gz
(gzipped text file, 1.6 MB)
The normalized exonic read counts across 20 human tissues for the filtered set of genes used in this work. The values represent variance-stabilized log2 fold-change relative to the average of 20 tissues.

Human20Tissues.intronic....txt.gz
(gzipped text file, 1.6 MB)
The normalized intronic read counts across 20 human tissues for the filtered set of genes used in this work. The values represent variance-stabilized log2 fold-change relative to the average of 20 tissues.

Human20Tissues.stability....txt.gz
(gzipped text file, 1.6 MB)
The unbiased stability measurements across 20 human tissues for the filtered set of genes used in this work. The values represent differential half-life relative to the average of 20 tissues.

Data for Alzheimer's disease

Intronic, exonic, and stability measurements across six AD and five control individuals

The following matrices are obtained by analysis of RNA-seq data from GEO dataset GSE53697.

AD.exonic....txt.gz
(gzipped text file, 921 KB)
The normalized exonic read counts across AD and control brain samples for the filtered set of genes used in this work. The values represent variance-stabilized log2 fold-change relative to the average of 11 samples.

AD.intronic....txt.gz
(gzipped text file, 922 KB)
The normalized intronic read counts across AD and control brain samples for the filtered set of genes used in this work. The values represent variance-stabilized log2 fold-change relative to the average of 11 samples.

AD.stability....txt.gz
(gzipped text file, 997 KB)
The unbiased stability measurements across AD and control brain samples for the filtered set of genes used in this work. The values represent differential half-life relative to the average of 11 samples.

High-confidence stability network 

High-confidence stability network of human brain

The following file is obtained by combined analysis of RNA-seq data from SRA dataset SRP056969, RBP motifs from CISBP-RNA, and miRNA seed sequences from TargetScan.

→ HC_brain_network.cys
(Cytoscape session file, 204 KB)
Viewing this file requires Cytoscape v3.4.0 or later. Each edge of the network represents a regulatory interaction between a stability factor (either an RBP or a miRNA), and a gene.

ADDITIONAL LINKS

  • The scripts for obtaining unbiased estimates of mRNA stability from RNA-seq data are available as an open-source package from Github.