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. 2020 Jan 8;9(1):159.
doi: 10.3390/cells9010159.

Splicing Players Are Differently Expressed in Sporadic Amyotrophic Lateral Sclerosis Molecular Clusters and Brain Regions

Affiliations

Splicing Players Are Differently Expressed in Sporadic Amyotrophic Lateral Sclerosis Molecular Clusters and Brain Regions

Valentina La Cognata et al. Cells. .

Abstract

Splicing is a tightly orchestrated process by which the brain produces protein diversity over time and space. While this process specializes and diversifies neurons, its deregulation may be responsible for their selective degeneration. In amyotrophic lateral sclerosis (ALS), splicing defects have been investigated at the singular gene level without considering the higher-order level, involving the entire splicing machinery. In this study, we analyzed the complete spectrum (396) of genes encoding splicing factors in the motor cortex (41) and spinal cord (40) samples from control and sporadic ALS (SALS) patients. A substantial number of genes (184) displayed significant expression changes in tissue types or disease states, were implicated in distinct splicing complexes and showed different topological hierarchical roles based on protein-protein interactions. The deregulation of one of these splicing factors has a central topological role, i.e., the transcription factor YBX1, which might also have an impact on stress granule formation, a pathological marker associated with ALS.

Keywords: SALS molecular subtypes; YBX1; amyotrophic lateral sclerosis; hub-bottlenecks; motor cortex; non hub-bottlenecks; spinal cord; spliceosome; splicing factors; tissue-specific program.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flow chart illustrates the analysis methods used in the present study to characterize the differentially expressed genes encoding splicing factors (left green box) and investigate their functional significance (right orange box).
Figure 2
Figure 2
Venn diagram of differentially expressed splicing factor genes in tissue districts (motor cortex and spinal cord) of control and SALS subtype patients. Detailed information for the lists of gene probes is provided in Supplementary Data S2.
Figure 3
Figure 3
Supervised hierarchical clustering was used to visualize the gene expression changes in differentially expressed genes (302 entities) in the motor cortexes and spinal cords of control and ALS (SALS1 and SALS2 subtypes) patients. As shown in the color bar, red indicates up-regulation, and blue down-regulation.
Figure 4
Figure 4
Venn diagrams of the total number of deregulated genes in SALS subtypes versus controls (a) and in motor cortex versus spinal cord (c). Tables show the numbers of up and down-regulated genes in SALS subtypes versus controls (b) and in motor cortex versus spinal cord (d), respectively.
Figure 5
Figure 5
PPI networks in (a) SALS1 and (b) SALS2 cortexes. To better visualize the generated networks we used the Force Atlas layout algorithm. Sphere size is proportional to the degree of connection, whereas the color (red for up-regulated and green for down-regulated in SALS subtypes) represents the expression logFC value. Details about nodes properties are listed in Supplementary Data S4.
Figure 6
Figure 6
Spinal cord-specific protein–protein interaction (PPI) networks are shown for both (a) SALS1 and (b) SALS2. To better visualize generated networks, we used the Auto layout algorithm. Sphere size is proportional to the degree of connection, whereas the color (red for up-regulated and green for down-regulated in SALS subtypes) represents the expression logFC value. Details about node properties are listed in Supplementary Data S4.
Figure 7
Figure 7
Literature-based network of tissue-specific deregulated genes involved in stress granule formations generated by the Genomatix Pathways System (GePS). The hierarchical layout has been used to highlight the main direction or information flow of the network. The legend below reports information about elements in figure.

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