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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background This study undertakes a systematic and comprehensive analysis of brain gene expression profiles of immune/inflammation-related genes in aging and Alzheimer’s disease (AD). Methods In a well-powered microarray study of young (20 to 59 years), aged (60 to 99 years), and AD (74 to 95 years) cases, gene responses were assessed in the hippocampus, entorhinal cortex, superior frontal gyrus, and post-central gyrus. Results Several novel concepts emerge. First, immune/inflammation-related genes showed major changes in gene expression over the course of cognitively normal aging, with the extent of gene response far greater in aging than in AD. Of the 759 immune-related probesets interrogated on the microarray, approximately 40% were significantly altered in the SFG, PCG and HC with increasing age, with the majority upregulated (64 to 86%). In contrast, far fewer immune/inflammation genes were significantly changed in the transition to AD (approximately 6% of immune-related probesets), with gene responses primarily restricted to the SFG and HC. Second, relatively few significant changes in immune/inflammation genes were detected in the EC either in aging or AD, although many genes in the EC showed similar trends in responses as in the other brain regions. Third, immune/inflammation genes undergo gender-specific patterns of response in aging and AD, with the most pronounced differences emerging in aging. Finally, there was widespread upregulation of genes reflecting activation of microglia and perivascular macrophages in the aging brain, coupled with a downregulation of select factors (TOLLIP, fractalkine) that when present curtail microglial/macrophage activation. Notably, essentially all pathways of the innate immune system were upregulated in aging, including numerous complement components, genes involved in toll-like receptor signaling and inflammasome signaling, as well as genes coding for immunoglobulin (Fc) receptors and human leukocyte antigens I and II. Conclusions Unexpectedly, the extent of innate immune gene upregulation in AD was modest relative to the robust response apparent in the aged brain, consistent with the emerging idea of a critical involvement of inflammation in the earliest stages, perhaps even in the preclinical stage, of AD. Ultimately, our data suggest that an important strategy to maintain cognitive health and resilience involves reducing chronic innate immune activation that should be initiated in late midlife.

Numbers of neurons and glia were counted in the cerebral cortex of one case of autism and two age- and sex-matched controls. Cell counts were made in primary auditory cortex, Broca's speech area, and auditory association cortex. No consistent differences in cell density were found between brains of autistic and control patients. (Author/CL)

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. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common cause of dementia in older adults. However, the pathogenesis of AD remains to be fully understood and clinically effective treatments are lacking. Recent advances in single cell RNA sequencing offers an opportunity to characterize the heterogeneity of cell response and explore the molecular mechanism of complex diseases at a single cell level. Here, we present the application of the Ion AmpliSeq transcriptome approach to profile gene expression in single laser captured neurons as well as pooled 10 and 100 neurons from hippocampal CA1 of AD brains versus matching normal aged brains. Our results demonstrated the high sensitivity and high genome coverage of the AmpliSeq transcriptome in single cell sequencing. In addition to capturing the known changes related to AD, our data confirmed the diversity of neuronal profiles in AD brain, which allow the potential identification of single cell response that might be hidden in population analyses. Notably, we also revealed the extensive inhibition of olfactory signaling and confirmed the reduction of neurotransmitter receptors in AD hippocampus. We conclude that although single neuron data show more variance than data from 10 or 100 pooled neurons, single neuron data can be informative. These findings support the utility of the Ion AmpliSeq method for obtaining and analyzing gene expression data from single defined laser captured neurons. [ABSTRACT FROM AUTHOR]

We used Illumina Human HT-12 v4 arrays to compare RNA expression of middle temporal gyrus (MTG; BA21) in Alzheimer's disease (AD = 97) and non-demented controls (ND = 98). A total of 938 transcripts were highly differentially expressed (adj p < 0.01; log2 FC ≥ |0.500|, with 411 overexpressed and 527 underexpressed in AD. Our results correlated with expression profiling in neurons from AD and ND obtained by laser capture microscopy in MTG from an independent dataset (log2 FC correlation: r = 0.504; p = 2.2e-16). Additionally, selected effects were validated by qPCR. ANOVA analysis yielded no difference between genders in response to AD, but some gender specific genes were detected (e.g., IL8 and AGRN in males, and HSPH1 and GRM1 in females). Several transcripts were associated with Braak staging (e.g., AEBP1 and DNALI1), antemortem MMSE (e.g., AEBP1 and GFAP), and tangle density (e.g., RNU1G2, and DNALI1). At the pathway level, we detected enrichment of synaptic vesicle processes and GABAergic transmission genes. Finally, applying the Weighted Correlation Network Analysis, we identified four expression modules enriched for neuronal and synaptic genes, mitochondria-associated membrane, chemical stimulus and olfactory receptor and non-coding RNA metabolism genes. Our results represent an extensive description of MTG mRNA profiling in a large sample of AD and ND. These data provide a list of genes associated with AD, and correlated to neurofibrillary tangles density. In addition, these data emphasize the importance of mitochondrial membranes and transcripts related to olfactory receptors in AD. [ABSTRACT FROM AUTHOR]