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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]

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]

Introduction: Our laboratories have demonstrated that accumulation of oligomeric amyloid β (OAβ) in neurons is an essential step leading to OAβ‐mediated mitochondrial dysfunction. Methods: Alzheimer's disease (AD) and matching control hippocampal neurons, astrocytes, and microglia were isolated by laser‐captured microdissection from the same subjects, followed by whole‐transcriptome sequencing. Complementary in vitro work was performed in OAβ‐treated differentiated SH‐SY5Y, followed by the use of a novel CoQ10 analogue for protection. This compound is believed to be effective both in suppressing reactive oxygen species and also functioning in mitochondrial electron transport. Results: We report decreases in the same mitochondrial‐encoded mRNAs in Alzheimer's disease laser‐captured CA1 neurons and in OAβ‐treated SH‐SY5Y cells, but not in laser‐captured microglia and astrocytes. Pretreatment with a novel CoQ10 analogue, protects neuronal mitochondria from OAβ‐induced mitochondrial changes. Discussion: Similarity of expression changes in neurons from Alzheimer's disease brain and neuronal cells treated with OAβ, and the effect of a CoQ10 analogue on the latter, suggests a pretreatment option to prevent OAβ toxicity, long before the damage is apparent. [ABSTRACT FROM AUTHOR]

Introduction We have comprehensively described the expression profiles of mitochondrial DNA and nuclear DNA genes that encode subunits of the respiratory oxidative phosphorylation (OXPHOS) complexes (I–V) in the hippocampus from young controls, age matched, mild cognitively impaired (MCI), and Alzheimer's disease (AD) subjects. Methods Hippocampal tissues from 44 non-AD controls (NC), 10 amnestic MCI, and 18 AD cases were analyzed on Affymetrix Hg-U133 plus 2.0 arrays. Results The microarray data revealed significant down regulation in OXPHOS genes in AD, particularly those encoded in the nucleus. In contrast, there was up regulation of the same gene(s) in MCI subjects compared to AD and ND cases. No significant differences were observed in mtDNA genes identified in the array between AD, ND, and MCI subjects except one mt-ND6 . Discussion Our findings suggest that restoration of the expression of nuclear-encoded OXPHOS genes in aging could be a viable strategy for blunting AD progression. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Soluble amyloid β (Aβ) oligomers are identified as the main neurotoxic species in Alzheimer's Disease (AD). Currently, little is known about intracellular Aβ in human brains mainly due to a lack of a technology to access the proteomic cell content. Here, we developed a microfluidic assay for in situ mass spectrometry (MS) detection of immunocaptured Aβ from microdissected human brain cells using our previously established MIcrofluidic platform in tandem with MALDI‐TOF MS (MIMAS). Moreover, Aβ monomers (Aβ‐M) and oligomers (Aβ‐O) were characterized extensively in vitro with multiple analytical techniques in preparation for the MIMAS assay from brain sections. Methods: To load cells directly on‐chip, the MIMAS platform was coupled with laser capture microdissection (LCM). After all sample preparation steps on‐chip, the elastomer platform is peeled‐off from the conductive substrate, which serves as the target in the MS analysis. We employed antibody 6E10 for the affinity capture. Aβ‐M and Aβ‐O were prepared and analyzed by gel electrophoresis, MALDI‐MS, and atomic force microscopy (AFM). Photo‐induced cross‐linking of unmodified proteins (PICUP) was used to cross‐link Aβ. Results: The LCM‐MIMAS coupling allowed for cell loading on‐chip with 70% efficiency. We further implemented Aβ immunocapture on‐chip showing the functioning principle of the assay in the microfluidic format. Gel characterization of native Aβ did not resolve Aβ‐O, while the cross‐linked Aβ‐O species obtained with the PICUP technique were successfully observed. MALDI‐MS spectra of native Aβ‐O showed a range of different oligomeric species, up to 8‐mers for Aβ40 and 12‐mers for Aβ42, showing the capability to detect native Aβ‐O with cross‐linking. AFM imaging revealed oligomer heights of 1.38±1.03 nm for Aβ40‐O and 11.25±10.4 nm for Aβ42‐O. In agreement with MALDI, AFM showed larger oligomers for Aβ42‐O. Conclusions: The LCM‐MIMAS approach could overcome the current limitations of accessing protein cell content, allowing the study of Aβ from brain cells. The Aβ characterization will facilitate the optimization of the assay. We plan to study soluble Aβ species from specific cell subpopulations in post‐mortem brain tissue from the frontal cortex and hippocampus regions. The identification of AD‐related Aβ species in brain cells could provide valuable insights into the disease. [ABSTRACT FROM AUTHOR]

Background: Microarray technology has been an effective tool to capture gene expression of thousands of genes from multiple molecular pathways in human brain tissue. Many large expression datasets exist maintaining information about differences in gene expression related to Alzheimer's disease processes. Often groups employ comparative analysis strategies on the data resulting in differential expression between disease and controls. Method: Using available expression datasets with multiple brain regions as a function of Braak‐staging, we look for expression signatures that do not necessarily increase nor decrease with disease progression, but rather have highly variable patterns indicating a molecular process dysregulation. Here we use a variation on singular value decomposition (SVD) as a rigorous approach to this type of analysis thus providing a characterization of the structure of the data as well as information about the relationship among genes. Result: Analysis of the sequential singular values generated determines the number of components that capture the data essential features. The magnitude of the singular values identified dysfunctional expressed transcripts at early stage in the disease before the appearance of tau protein neurofibrillary tangles. It follows that by pathway analysis we can determine upstream players in the affected biological processes pathway and design appropriate experiments to validate the early dysregulated molecular pathways. Conclusion: Using a novel approach to analyze expression data, we identified transcripts displaying dysregulated expression signatures at early stages of disease in brain regions not yet affected by neurofibrillary tangles. The relationships of these transcripts and pathways enriched then guided us to explore upstream events in the early disease cascade. [ABSTRACT FROM AUTHOR]

Hypertension is a major health concern in the developed world, and its prevalence increases with advancing age. The impact of hypertension on the function of the renal and cardiovascular systems is well studied; however, its influence on the brain regions important for cognition has garnered less attention. We utilized the Cyp1a1‐Ren2 xenobiotic‐inducible transgenic rat model to mimic both the age of onset and rate of induction of hypertension observed in humans. Male, 15‐month‐old transgenic rats were fed 0.15% indole‐3‐carbinol (I3C) chow to slowly induce renin‐dependent hypertension over a 6‐week period. Systolic blood pressure significantly increased, eventually reaching 200 mmHg by the end of the study period. In contrast, transgenic rats fed a control diet without I3C did not show significant changes in blood pressure (145 mmHg at the end of study). Hypertension was associated with cardiac, aortic, and renal hypertrophy as well as increased collagen deposition in the left ventricle and kidney of the I3C‐treated rats. Additionally, rats with hypertension showed reduced savings from prior spatial memory training when tested on the hippocampus‐dependent Morris swim task. Motor and sensory functions were found to be unaffected by induction of hypertension. Taken together, these data indicate a profound effect of hypertension not only on the cardiovascular‐renal axis but also on brain systems critically important for learning and memory. Future use of this model and approach may empower a more accurate investigation of the influence of aging on the systems responsible for cardiovascular, renal, and neurological health. In this manuscript we demonstrate that an increase in blood pressure ‐ driven by a genetic upregulation of the renin‐angiotensin system ‐ in a fashion that closely mimics the timing and rate of onset of hypertension in the human alters spatial memory performance in the rat. [ABSTRACT FROM AUTHOR]