Blood RNA transcripts reveal similar and differential alterations in fundamental cellular processes in Alzheimer's disease and other neurodegenerative diseases

Abstract

Background Dysfunctional processes in Alzheimer's disease and other neurodegenerative diseases lead to neural degeneration in the central and peripheral nervous system. Research demonstrates that neurodegeneration of any kind is a systemic disease that may even begin outside of the region vulnerable to the disease. Neurodegenerative diseases are defined by the vulnerabilities and pathology occurring in the regions affected. Method A random forest machine learning analysis on whole blood transcriptomes from six neurodegenerative diseases generated unbiased disease‐classifying RNA transcripts subsequently subjected to pathway analysis. Results We report that transcripts of the blood transcriptome selected for each of the neurodegenerative diseases represent fundamental biological cell processes including transcription regulation, degranulation, immune response, protein synthesis, apoptosis, cytoskeletal components, ubiquitylation/proteasome, and mitochondrial complexes that are also affected in the brain and reveal common themes across six neurodegenerative diseases. Conclusion Neurodegenerative diseases share common dysfunctions in fundamental cellular processes. Identifying regional vulnerabilities will reveal unique disease mechanisms. Highlights Transcriptomics offer information about dysfunctional processes. Comparing multiple diseases will expose unique malfunctions within diseases. Blood RNA can be used ante mortem to track expression changes in neurodegenerative diseases. Protocol standardization will make public datasets compatible.

Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis

“…Indeed, the advantage of blood cell-based transcriptomes has also been described in an elegant integrative study aimed at identifying signatures of vulnerability across different neurodegenerative disorders. 42 This evidence supports the need for producing more large-scale omics datasets for ALS 17,54 , which at the moment are very limited and often include a reduced number of healthy controls. This represents a limitation that has to be kept in mind even in regards to this study, since the inter-individual variability characterizing omics experiments is a source of larger inconsistency within healthy controls groups than in patients, which still share the underlying traits of the disease considered.…”

“…Such an approach is made possible by the development of novel mathematical and machine-learning tools that, together with the increased accessibility to deep-sequencing analysis, offer the possibility of integrating different layers of biological information with the final aim of identifying druggable targets, as well as diagnostic and prognostic markers for human diseases. 41,42,43 In order to apply this strategy to ALS, we performed the first integrative, machine-learning-based analysis of transcriptomic, epigenetic and genetic data obtained from human cultured MN, blood and post-…”

Biomarkers in neurodegeneration: A beginner’s guide into curated analyses using publicly available datasets

“…While we have presented an approach for curated gene expression analyses covering select studies in PD and other NDDs, there are two there are two resources (and approaches) that the readers will find considerably useful. The first one is the application of machine learning methods on GEO microarray datasets from blood samples (blood transcriptome) in NDDs (8). This elegant report revealed that perturbations in several cellular pathways (e.g., mitochondrial function, immune response, protein synthesis) were a shared feature in common NDDs (8).…”

“…The first one is the application of machine learning methods on GEO microarray datasets from blood samples (blood transcriptome) in NDDs (8). This elegant report revealed that perturbations in several cellular pathways (e.g., mitochondrial function, immune response, protein synthesis) were a shared feature in common NDDs (8). Adopting similar high-content analyses on the brainderived datasets, which we have compiled (Table S2) may unravel unique mechanistic findings in one disease or across NDDs.…”

“…The basic framework is demonstrated by curated analyses of microarray datasets from human studies available in the Gene Expression Omnibus (GEO) repository of the National Center for Biotechnology Information (NCBI) (Table 1). We have primarily focused on the studies reporting gene expression profiling in brain tissue in patient studies, since studies covering meta-analysis of blood samples (8) or from animal studies are covered elsewhere (9) (also see Discussion under Additional Resources).…”