Systematic review and meta-analysis of human transcriptomics reveals neuroinflammation, deficient energy metabolism, and proteostasis failure across neurodegeneration

Abstract

Neurodegenerative disorders such as Alzheimer's disease (AD), Lewy body diseases (LBD), and the amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD) spectrum are defined by the accumulation of specific misfolded protein aggregates. However, the mechanisms by which each proteinopathy leads to neurodegeneration remain elusive. We hypothesized that there is a common "pan-neurodegenerative" gene expression signature driving pathophysiology across these clinically and pathologically diverse proteinopathies. To test this hypothesis, we performed a systematic review of human CNS transcriptomics datasets from AD, LBD, and ALS-FTD patients and age-matched controls in the Gene Expression Omnibus (GEO) and ArrayExpress databases, followed by consistent processing of each dataset, meta-analysis, pathway enrichment, and overlap analyses. After applying pre-specified eligibility criteria and stringent data pre-processing, a total of 2600 samples from 26 AD, 21 LBD, and 13 ALS-FTD datasets were included in the meta-analysis. The pan-neurodegenerative gene signature is characterized by an upregulation of innate immunity, cytoskeleton, and transcription and RNA processing genes, and a downregulation of the mitochondrial electron transport chain. Pathway enrichment analyses also revealed the upregulation of neuroinflammation (including Toll-like receptor, TNF, and NFκB signaling) and phagocytosis, and the downregulation of mitochondrial oxidative phosphorylation, lysosomal acidification, and ubiquitin-proteasome pathways. Our findings suggest that neuroinflammation and a failure in both neuronal energy metabolism and protein degradation systems are consistent features underlying neurodegenerative diseases, despite differences in the extent of neuronal loss and brain regions involved.

Keywords: Alzheimer's disease; Amyotrophic lateral sclerosis; Frontotemporal dementia; Lewy body diseases; Meta-analysis; Mitochondrial energy metabolism; Neurodegeneration; Neuroinflammation; Proteostasis; Transcriptomics.

Fig. 1.. Methods Overview.

Summary of the data analysis pipeline applied in this study.

Fig. 2.. Dataset Selection Workflow.

Description of the systematic review of human transcriptomics from AD, LBD, and ALS-FTD patients in the Gene Expression Omnibus (GEO) and ArrayExpress databases according to PRISMA guidelines. After applying pre-specified inclusion and exclusion criteria, our systematic review yielded 1677 control and 1563 disease samples from 26 AD, 21 LBD, and 13 ALS-FTD datasets.

Fig. 3.. Pan-Neurodegenerative Genes.

The top 1000 upregulated and top 1000 downregulated genes by meta-analytic z-score for AD, LBD, and ALS-FTD were intersected to define the pan-neurodegenerative gene signature, which was composed of 88 upregulated and 45 downregulated genes. The color for each gene corresponds to the cell-type with the highest expression for that gene by average FPKM (Zhang et al., 2016).

Fig. 4.. Pan-Neurodegenerative Pathways.

Genes with the top z-scores of the pan-neurodegenerative pathways were visualized in heatmaps, where columns represent individual datasets included in the meta-analysis, and rows represent moderated z-scores of differential expression for a specific gene across these datasets. Annotation bars represent z-scores, disease label, and CNS region (EC = entorhinal cortex, HIPP = hippocampus, PCG = posterior cingulate gyrus, TEMP = temporal cortex, FRONT = frontal cortex, PAR = parietal cortex, DMNV = dorsal motor nucleus of the vagus, LC = locus coeruleus, SN = substantia nigra, STR = striatum, MOT = motor cortex, SC = spinal cord).

Fig. 4.. Pan-Neurodegenerative Pathways.

Genes with the top z-scores of the pan-neurodegenerative pathways were visualized in heatmaps, where columns represent individual datasets included in the meta-analysis, and rows represent moderated z-scores of differential expression for a specific gene across these datasets. Annotation bars represent z-scores, disease label, and CNS region (EC = entorhinal cortex, HIPP = hippocampus, PCG = posterior cingulate gyrus, TEMP = temporal cortex, FRONT = frontal cortex, PAR = parietal cortex, DMNV = dorsal motor nucleus of the vagus, LC = locus coeruleus, SN = substantia nigra, STR = striatum, MOT = motor cortex, SC = spinal cord).

Fig. 5.. Disease-Predominant Pathways.

Gene set enrichment analysis (GSEA) normalized enrichment scores (NES) for representative disease-predominant pathways, averaged across the grouped GO: Biological Processes, are shown.