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GSE121569

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ALS implicated protein TDP-43 sustains levels of STMN2 a mediator of motor neuron growth and repair

Organism: Homo sapiens
Platform: GPL11154
Samples: 18
Experiment Types:
Expression profiling by high throughput sequencing
Submitted: Oct 22 2018
Last Updated: Mar 27 2019
Status: Public on Jan 15 2019
Contact: Rory,,Kirchner (Harvard T.H. Chan School of Public Health)

Relations

BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA497804 SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRP166282

Summary

The discovery that TDP-43 mutations cause familial ALS and that many patients display pathological TDP-43 mislocalization has nominated altered RNA metabolism as a potential disease mechanism. Despite its importance, the identity of RNAs regulated by TDP-43 in motor neurons remains poorly understood. Here, we report transcripts whose abundances in human motor neurons are sensitive to TDP-43 depletion. Notably, we found STMN2, which encodes a microtubule regulator, declined after TDP-43 knockdown, in patient-specific motor neurons, following TDP-43 mislocalization, and in the postmortem patient spinal cords. Loss of STMN2 upon reduced TDP-43 function was due to the emergence of a cryptic exon, which is of substantial functional importance, as we further demonstrate that STMN2 is necessary for both axonal outgrowth and repair. Importantly, post-translational stabilization of STMN2 could rescue neurite outgrowth and axon regeneration deficits induced by TDP-43 depletion. We propose restoring STMN2 expression warrants future examination as an ALS therapeutic strategy.

Overall Design

Transcriptome differential expression analysis in HUES3 cells with and without siRNA knockdown of TDP-43

Analysis (12 steps)

View Data Processing
Processing steps for GSE121569
  1. All FASTQ files were analyzed using the bcbioRNASeq workflow and toolchain91.
  2. The FASTQ files were aligned to the GRCh37/hg19 reference genome.
  3. Differential expression testing was performed using DESeq2 suite of bioinformatics tools38.
  4. The DEXSeq module of Bioconductor was used to identify differential splicing92.
  5. We used salmon to generate the counts and tximport to load them at gene level93,94.
  6. All p-values are then corrected for multiple comparisons using the method of Benjamini and Hochberg95.
  7. We used an adjusted p-value cutoff of 0.05 with no log fold-change ratio cutoff for differential gene expression analysis and a 0.1 cutoff with no log fold-change ratio cutoff for differential exon usage.
  8. [tpm.csv.gz] gene x sample matrix of TPM for each sample
Showing first 8 steps.

Supplementary Files (5)

GSE121569_category_als_vs_control_all.csv.gz Download
GSE121569_combined.dexseq.txt.gz Download
GSE121569_dexseq-all.csv.gz Download
GSE121569_raw_counts.csv.gz Download
GSE121569_tpm.csv.gz Download
GEO Samples (18)

Dataset Citations (1)

Potentially Related Publications (1)

This accession was text-mined from the PMC full text of these publications. It may be referenced for comparison or reused from another study rather than generated for them.

Data Files (10)

Accession File Name Stored Type Output Type Mapping Assembly Size Download
— BD_22_1.fastq.gz RNA-Seq 2.3 GB link
— BD_23_1.fastq.gz RNA-Seq 1.6 GB link
— BD_24_1.fastq.gz RNA-Seq 1.9 GB link
— BD_25_1.fastq.gz RNA-Seq 1.7 GB link
— BD_30_1.fastq.gz RNA-Seq 1.1 GB link
— BD_31_1.fastq.gz RNA-Seq 1.3 GB link
— BD_32_1.fastq.gz RNA-Seq 1.4 GB link
— BD_33_1.fastq.gz RNA-Seq 1.2 GB link
— BD_34_1.fastq.gz RNA-Seq 1.2 GB link
— BD_35_1.fastq.gz RNA-Seq 1.2 GB link