Research Background

In the field of neuroscience, amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two highly heterogeneous neurodegenerative diseases. Studies indicate that non-coding hexanucleotide repeat expansions in the c9orf72 gene are the most common genetic causes of these diseases. However, the specific consequences of these repeat expansions at the transcriptome level remain unclear. Although previous studies have suggested that c9orf72 repeat expansions may trigger diseases through mechanisms such as reducing gene expression, forming RNA foci, and aggregating dipeptide-repeat proteins (DPRs), the ways in which these mechanisms lead to extensive clinical and pathological variations remain undetermined.

Recently, researchers have gradually recognized the importance of the cerebellum in ALS and FTLD studies. Although traditionally the cerebellum was thought to be less affected in these diseases, recent research shows that TDP-43 protein levels are reduced in this brain area, while RNA foci and DPR proteins are widely present in the cerebellum. Therefore, studying the transcriptome changes in cerebellar tissue could help to reveal pathological mechanisms that may be masked in other brain areas with extensive neuronal death.

This study, conducted by Evan Udine and colleagues at the Mayo Clinic and published in the journal “Acta Neuropathologica,” aims to identify and analyze transcriptome changes in the cerebellum of c9orf72 repeat expansion patients using transcriptome sequencing technology, hoping to further understand the pathogenesis of ALS and FTLD. The paper presents the RNA sequencing (RNA-seq) results of patient samples and reveals many new findings, exploring potential molecular mechanisms associated with neurodegenerative diseases.

Research Process

The main processes of this study are as follows:

1. Sample Collection and RNA Extraction

The study used 193 cerebellar tissue samples from the Mayo Clinic Brain Bank, including 60 c9orf72 repeat expansion patients, 108 non-c9orf72 repeat expansion patients, and 25 control samples with no neurological diseases. RNA was extracted using an rRNAseasy Plus Kit (Qiagen), and quality control was performed before sequencing to ensure RNA integrity values (RIN values) were above 6.0.

1. RNA Sequencing and Data Processing

RNA sequencing was performed using the Illumina HiSeq 4000 platform, generating RNA-seq data for the 193 samples. Data were processed using Map-RSeq (v3.1.3) and STAR aligner (v2.5.2c) to map reads to the reference human genome (hg38). Gene and exon expression levels were calculated using Subread (v1.5.1), resulting in raw read counts and RPKM (reads per kilobase of transcript per million reads).

1. Data Normalization and Differential Gene Expression Analysis

Conditional quantile normalization (CQN) was used to remove low-expression and non-expressing genes. Differential gene expression analysis was conducted using a linear regression model, considering variables such as gender, gene count, RIN value, age, and using marker genes of the five major brain cell types as covariates.

1. Co-expression Analysis

Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify gene modules associated with disease states. Different modules represent different biological processes, such as small molecule metabolism, mitochondrial structure, and protein localization.

1. Differential Splicing Analysis

Differential splicing analysis was conducted using LeafCutter (v0.2.6), identifying numerous differential splicing events, including exon skipping and cryptic splicing in specific genes.

1. Immunofluorescence Staining and Validation Experiments

Olig2+ cell immunofluorescence staining was performed on 12 samples to confirm oligodendrocyte proportion changes observed through deconvolution analysis. Some cryptic exons were validated using cDNA sequencing and long-read RNA-seq.

Main Results

1. Differential Gene Expression

The study found 6,911 genes significantly differentially expressed in c9orf72 repeat expansion patients compared to controls, including several known ALS and FTLD-related genes such as c9orf72, TARDBP (encoding TDP-43), and FUS. Co-expression analysis of disease-related modules showed significant associations with small molecule metabolic processes and RNA processing pathways, suggesting these biological pathways play critical roles in the disease.

1. Transcript Splicing Changes

Splicing analysis identified multiple differential splicing events in ALS and FTLD-related genes, including CAMTA1 and DCTN1, which are involved in the same disease spectrum. For instance, exon skipping in CAMTA1 is related to clinical modifiers of ALS survival time.

1. Cryptic Exons

Analysis revealed significantly more cryptic exons in c9orf72 repeat expansion patients compared to non-c9orf72 patients and controls, with 77 of 105 cryptic splicing events concentrated in c9 patients. Further studies indicate these cryptic splicing events might be related to the expression levels of RNA-binding proteins like TDP-43.

1. Changes in Cell Type Proportions

Deconvolution and immunostaining results suggest that oligodendrocyte proportions might be reduced in c9orf72 repeat expansion patients, which may be related to the disease’s pathological mechanisms.

Research Conclusions

This study reveals extensive transcriptome changes in the cerebellum of c9orf72 repeat expansion patients, particularly disruptions in gene expression and RNA splicing. These findings further indicate that, despite lacking significant neuronal death, the cerebellum plays an important role in the pathology of ALS and FTLD. Moreover, the study confirms differential expression and splicing modifications in several known and potential disease-related genes, providing new perspectives on understanding the molecular mechanisms of these complex diseases.

The research underscores the importance of conducting transcriptomic and functional studies in different brain areas, pointing future studies towards exploring the relationships between pathological characteristics and transcriptional changes using techniques like single-cell sequencing and long-read sequencing.

By conducting in-depth analyses of transcriptome data, the study offers new insights into the heterogeneity of ALS and FTLD, potentially providing theoretical support for the development of future therapeutic strategies.