Epigenome-wide association studies of Alzheimer’s disease have highlighted neuropathology-associated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six DNA methylomic studies of Alzheimer’s disease (N = 1453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. We identify 236 CpGs in the prefrontal cortex, 95 CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex at Bonferroni significance, with none in the cerebellum. Our cross-cortex meta-analysis (N = 1408 donors) identifies 220 CpGs associated with neuropathology, annotated to 121 genes, of which 84 genes have not been previously reported at this significance threshold. We have replicated our findings using two further DNA methylomic datasets consisting of a further >600 unique donors. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).
Epigenome-wide association studies of Alzheimer's disease have highlighted neuropathologyassociated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six methylomic studies of Alzheimer's disease (N=1,453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. At an experiment-wide significance threshold (P<1.238 x10 -7 ) we identified 236 CpGs in the prefrontal cortex, 95CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex, with none in the cerebellum.Our cross-cortex meta-analysis (N=1,408 donors) identified 220 CpGs associated with neuropathology, annotated to 121 genes, of which 96 genes had not been previously reported at experiment-wide significance. Polyepigenic scores derived from these 220 CpGs explain 24.7% of neuropathological variance, whilst polygenic scores accounted for 20.2% of variance in these samples. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).
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.
The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, Friedreich’s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.
Alzheimer’s disease is a neurodegenerative disorder clinically defined by gradual cognitive impairment and alteration in executive function. We conducted an epigenome-wide association study (EWAS) of a clinically and neuropathologically characterized cohort of 296 brains, including Alzheimer’s disease (AD) and non-demented controls (ND), exploring the relationship with the RNA expression from matched donors. We detected 5246 CpGs and 832 regions differentially methylated, finding overlap with previous EWAS but also new associations. CpGs previously identified in ANK1, MYOC, and RHBDF2 were differentially methylated, and one of our top hits (GPR56) was not previously detected. ANK1 was differentially methylated at the region level, along with APOE and RHBDF2. Only a small number of genes showed a correlation between DNA methylation and RNA expression statistically significant. Multiblock partial least-squares discriminant analysis showed several CpG sites and RNAs discriminating AD and ND (AUC = 0.908) and strongly correlated with each other. Furthermore, the CpG site cg25038311 was negatively correlated with the expression of 22 genes. Finally, with the functional epigenetic module analysis, we identified a protein–protein network characterized by inverse RNA/DNA methylation correlation and enriched for “Regulation of insulin-like growth factor transport”, with IGF1 as the hub gene. Our results confirm and extend the previous EWAS, providing new information about a brain region not previously explored in AD DNA methylation studies. The relationship between DNA methylation and gene expression is not significant for most of the genes in our sample, consistently with the complexities in the gene expression regulation. Graphical Abstract
Background Clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time‐consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores thereby reducing health care costs by ruling out or streamlining additional tests such as imaging. Biological processes leading to neuropathology are reflected in both the central nervous system and vascular peripheral system and progress silently for many years. Because Alzheimer’s disease (AD) symptoms often overlap with common disorders that may be treatable or reversible, an accurate diagnosis at the earliest time is crucial. A blood‐based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. Additionally, an inexpensive blood panel could also monitor disease progression, for example, marking the advancement beyond mild cognitive impairment (MCI). Method The study of blood‐based changes in mRNA gene expression presents a good strategy for differentiating patients of any neurodegenerative disease regardless of the proteins or their post‐translational modifications occurring in disease. Information about features selected for the classification may also guide insight into possible treatment strategies. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood. Result Our machine learning algorithm generated small sets of transcripts which distinguish a number of neurodegenerative diseases with high sensitivity and specificity. Conclusion Neurodegenerative diseases such as AD are associated with changes in specific molecular pathways and signatures. Using RNA expression as blood biomarkers can also provide information for a pathophysiological relationship with disease and gives us the advantage of using the vast knowledge of gene expression to not only predict disease but also analyze the pathways involved. Our chosen transcripts reveal that neurodegenerative diseases have common themes which after removal bare the unique transcripts of each disease.
Background Neurodegenerative diseases such as Alzheimer’s disease are associated with changes in specific molecular pathways involving metabolomic and biological processes active in both the central nervous system and vascular peripheral system. Each disease results in neurodegeneration in vulnerable regions and cell types specific to that disease and are expressed in whole blood as disease progresses. Machine learning can be applied to the entire blood transcriptome of neurodegenerative diseases providing information for a pathophysiological relationship and gives us the advantage of using the vast knowledge of gene expression to analyze pathways affected thereby guiding insight into possible treatment strategies. We applied our unique machine learning algorithm to public data sets for selection of transcripts that guide our discovery of affected pathways. Methods We used large public blood microarray mRNA expression datasets, with platforms of more than 30,000 probes covering 10,000 genes, to discover disease transcripts using a novel Random Forest algorithm which selected supervised predictors for six different neurodegenerative diseases including Alzheimer’s disease (AD), Parkinson’s disease (PD), behavioral variant frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and Friedreich’s ataxia (FRDA). Results Our machine learning selection of transcript disease classifiers reveals that neurodegenerative diseases have common themes as well as unique individual differences. Molecular components which overlap across diseases might be considered neurodegeneration generalities or a result of mixed pathology, while differential components identified by selected feature transcripts may represent unique dysfunctions. Conclusion Our Random Forest algorithm selected important features hidden in mRNA expression values from blood of affected individuals of six neurodegenerative diseases compared to healthy controls. For each of the six diseases studied here, the selected features were categorized by pathway analysis into eight main functional groups. The dysfunction of any or multiple functional groups combined can lead to synapse and cell loss in these neurodegenerative diseases each with specific vulnerabilities in particular tissue and cell types. Vulnerability is usually associated with those cells vulnerable to the disease pathology and are often first to exhibit cell death from cytotoxic events such as neuroinflammation, mitochondrial dysfunction, transcription alterations, apoptosis, protein synthesis dysfunction, cytoskeletal changes, and ubiquitylation/proteasome stalls.
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