Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis

Integration of genomic and transcriptomic layers in RNA-Seq data leads to protein interaction modules with improved Alzheimer's disease associations

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it is currently untreatable. RNA sequencing (RNA-Seq) is commonly used in the literature to identify AD-associated molecular mechanisms by analysing changes in gene expression. RNA-Seq data can also be used to detect genomic variants, enabling the identification of the genes with a higher load of deleterious variants in patients compared with controls. Here, we analysed AD RNA-Seq datasets to obtain differentially expressed genes and genes with a higher load of pathogenic variants in AD, and we combined them in a single list. We mapped these genes on a human protein-protein interaction network to discover subnetworks perturbed by AD. Our results show that utilizing gene pathogenicity information from RNA-Seq data positively contributes to the disclosure of AD-related mechanisms. Moreover, dividing the discovered subnetworks into highly connected modules reveals a clearer picture of altered molecular pathways that, otherwise, would not be captured. Repeating the whole pipeline with human metabolic network genes led to results confirming the positive contribution of gene pathogenicity information and enabled a more detailed identification of altered metabolic pathways in AD.

Identification of novel risk genes for Alzheimer's disease by integrating genetics from hippocampus

Alzheimer's disease (AD) stands as the most prevalent neurodegenerative ailment, presently lacking a definitive cure. Given that primary medications for AD patients in the early or middle stages demonstrate optimal efficacy, it becomes crucial to delve into the identification of risk genes associated with early onset. In our study, we compiled and integrated three transcriptomics datasets (GSE48350, GSE36980, GSE5281) originating from the hippocampus of 37 AD patients and 66 healthy controls (CTR) for comprehensive bioinformatics analysis. Comparative analysis with CTR revealed 25 up-regulated genes and 291 down-regulated genes in AD. Those down-regulated genes were notably enriched in processes related to the transmission and transport of synaptic signals. Intriguingly, 27 differentially expressed genes implicated in AD were also correlated with the Braak stage, establishing a connection with various immune cell types that exhibit differences in AD, including cytotoxic T cells, neutrophils, CD4 T cells, Th1, Th2, and Tfh. Significantly, a Cox model, constructed using nine feature genes, effectively stratified AD samples (HR = 2.72, 95% CI 1.94 ~ 3.81, P = 3.6e-10), highlighting their promising potential for risk assessment. In conclusion, our investigation sheds light on novel genes intricately linked to the onset and progression of AD, offering potential biomarkers for the early detection of this debilitating condition. This study contributes valuable insights toward enhancing the strategies for preventing and treating AD.

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer's disease

Alzheimer's disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

Integrated analyses of 5 mC, 5hmC methylation and gene expression reveal pathology-associated AKT3 gene and potential biomarkers for Alzheimer's disease

AIMS

5 mC methylation and hydroxymethylation (5hmC) are associated with Alzheimer's disease (AD). However, previous studies were limited by the absence of a 5hmC calculation. This study aims to find AD associated predictors and potential therapeutic chemicals using bioinformatics approach integrating 5 mC, 5hmC, and expression changes, and an AD mouse model.

METHODS

Gene expression microarray and 5 mC and 5hmC sequencing datasets were downloaded from GEO repository. 142 AD and 52 normal entorhinal cortex specimens were enrolled. Data from oxidative bisulfite sequencing (oxBS)-treated samples, which represent only 5 mC, were used to calculate 5hmC level. Functional analyses, random forest supervised classification and methylation validation were applied. Potential chemicals were predicted by CMap. Morris water maze, Y maze and novel object recognition behavior tests were performed using FAD4T AD mice model. Cortex and hippocampus tissues were isolated for immunohistochemical staining.

RESULTS

C1QTNF5, UBD, ZFP106, NEDD1, AKT3, and MBP genes involving 13 promoter CpG sites with 5mc, 5hmC methylation and expression difference were identified. AKT3 and MBP were down-regulated in both patients and mouse model. Three CpG sites in AKT3 and MBP showed significant methylation difference on validation. FAD4T AD mice showed recession in brain functions and lower AKT3 expression in both cortex and hippocampus. Ten chemicals were predicted as potential treatments for AD.

CONCLUSIONS

AKT3 and MBP may be associated with AD pathology and could serve as biomarkers. The ten predicted chemicals might offer new therapeutic approaches. Our findings could contribute to identifying novel markers and advancing the understanding of AD mechanisms.

Neuropathological mRNA Expression Changes after Single Mild Traumatic Brain Injury in Pigs

Traumatic brain injury (TBI) is a public health concern, with an estimated 42 million cases globally every year. The majority of TBIs are mild TBIs, also known as concussion, and result from the application of mechanical forces on the head. Most patients make a complete recovery and mortality is rare; therefore, studies investigating cellular changes after mild TBI in a clinical setting are limited. To address this constraint, our group utilized a pig model of closed-head rotational acceleration-induced TBI, which recreated the biomechanical loading parameters associated with concussion on a large gyrencephalic brain similar to humans. While our previous research has focused on immunohistochemical characterization of neuropathology, the current study utilized transcriptomic assays to evaluate an array of TBI-induced neurodegenerative analytes. Pigs subjected to mild TBI were survived for 3 days post-injury (DPI) (n = 3), 30 DPI (n = 3), or 1 year post-injury (YPI) (n = 3) and compared to animals undergoing a sham procedure (n = 8). RNA was isolated from whole coronal sections of fixed tissue and multiplexed on a Nanostring neuropathology panel. Differential expression analysis revealed 11 differentially expressed genes at 3 DPI versus sham, including downregulation of the synaptotagmin calcium sensor gene (SYT1), upregulation of the neurofibromin gene (NF1), and upregulation of the Alzheimer's disease-associated receptor gene (SORL1). There were no differentially expressed genes at 30 DPI or 1 YPI compared to shams. Additionally, high-magnitude undirected global significance scores (GSS) were detected at 3 DPI for chromatin modification and autophagy gene sets, and at 30 DPI for cytokine gene sets, while many dysregulated gene sets were highlighted by directed GSSs out to 1 YPI. This study adds to a growing body of literature on transcriptomic changes in a clinically relevant large animal model of closed-head TBI, which highlights potential therapeutic targets following mild TBI.

Multilayer Analysis of RNA Sequencing Data in Alzheimer's Disease to Unravel Molecular Mysteries

Alzheimer's disease (AD) is a complex disease, and numerous cellular events may be involved in etiology. RNAseq-based transcriptome data hold multilayer information content, which could be crucial in unraveling molecular mysteries of AD. It enables quantification of gene expression levels, identification of genomic variants, and elucidation of splicing anomalies such as exon skipping and intron retention. Additional integration of this information into protein-protein interaction networks and genome-scale metabolic models from the literature has potential to decipher functional modules and affected mechanisms for complex scenarios such as AD. In this chapter, we review the application areas of the multilayer content of RNAseq and associated integrative approaches available, with a special focus on AD.

The impact of aging and oxidative stress in metabolic and nervous system disorders: programmed cell death and molecular signal transduction crosstalk

Life expectancy is increasing throughout the world and coincides with a rise in non-communicable diseases (NCDs), especially for metabolic disease that includes diabetes mellitus (DM) and neurodegenerative disorders. The debilitating effects of metabolic disorders influence the entire body and significantly affect the nervous system impacting greater than one billion people with disability in the peripheral nervous system as well as with cognitive loss, now the seventh leading cause of death worldwide. Metabolic disorders, such as DM, and neurologic disease remain a significant challenge for the treatment and care of individuals since present therapies may limit symptoms but do not halt overall disease progression. These clinical challenges to address the interplay between metabolic and neurodegenerative disorders warrant innovative strategies that can focus upon the underlying mechanisms of aging-related disorders, oxidative stress, cell senescence, and cell death. Programmed cell death pathways that involve autophagy, apoptosis, ferroptosis, and pyroptosis can play a critical role in metabolic and neurodegenerative disorders and oversee processes that include insulin resistance, β-cell function, mitochondrial integrity, reactive oxygen species release, and inflammatory cell activation. The silent mating type information regulation 2 homolog 1 (Saccharomyces cerevisiae) (SIRT1), AMP activated protein kinase (AMPK), and Wnt1 inducible signaling pathway protein 1 (WISP1) are novel targets that can oversee programmed cell death pathways tied to β-nicotinamide adenine dinucleotide (NAD+), nicotinamide, apolipoprotein E (APOE), severe acute respiratory syndrome (SARS-CoV-2) exposure with coronavirus disease 2019 (COVID-19), and trophic factors, such as erythropoietin (EPO). The pathways of programmed cell death, SIRT1, AMPK, and WISP1 offer exciting prospects for maintaining metabolic homeostasis and nervous system function that can be compromised during aging-related disorders and lead to cognitive impairment, but these pathways have dual roles in determining the ultimate fate of cells and organ systems that warrant thoughtful insight into complex autofeedback mechanisms.

Analyzing alternative splicing in Alzheimer's disease postmortem brain: a cell-level perspective

Alzheimer's disease (AD) is a neurodegenerative disease with no effective cure that attacks the brain's cells resulting in memory loss and changes in behavior and language skills. Alternative splicing is a highly regulated process influenced by specific cell types and has been implicated in age-related disorders such as neurodegenerative diseases. A comprehensive detection of alternative splicing events (ASEs) at the cellular level in postmortem brain tissue can provide valuable insights into AD pathology. Here, we provided cell-level ASEs in postmortem brain tissue by employing bioinformatics pipelines on a bulk RNA sequencing study sorted by cell types and two single-cell RNA sequencing studies from the prefrontal cortex. This comprehensive analysis revealed previously overlooked splicing and expression changes in AD patient brains. Among the observed alterations were changed in the splicing and expression of transcripts associated with chaperones, including CLU in astrocytes and excitatory neurons, PTGDS in astrocytes and endothelial cells, and HSP90AA1 in microglia and tauopathy-afflicted neurons, which were associated with differential expression of the splicing factor DDX5. In addition, novel, unknown transcripts were altered, and structural changes were observed in lncRNAs such as MEG3 in neurons. This work provides a novel strategy to identify the notable ASEs at the cell level in neurodegeneration, which revealed cell type-specific splicing changes in AD. This finding may contribute to interpreting associations between splicing and neurodegenerative disease outcomes.

RNAseq Analysis of FABP4 Knockout Mouse Hippocampal Transcriptome Suggests a Role for WNT/β-Catenin in Preventing Obesity-Induced Cognitive Impairment

Microglial fatty-acid binding protein 4 (FABP4) is a regulator of neuroinflammation. We hypothesized that the link between lipid metabolism and inflammation indicates a role for FABP4 in regulating high fat diet (HFD)-induced cognitive decline. We have previously shown that obese FABP4 knockout mice exhibit decreased neuroinflammation and cognitive decline. FABP4 knockout and wild type mice were fed 60% HFD for 12 weeks starting at 15 weeks old. Hippocampal tissue was dissected and RNA-seq was performed to measure differentially expressed transcripts. Reactome molecular pathway analysis was utilized to examine differentially expressed pathways. Results showed that HFD-fed FABP4 knockout mice have a hippocampal transcriptome consistent with neuroprotection, including associations with decreased proinflammatory signaling, ER stress, apoptosis, and cognitive decline. This is accompanied by an increase in transcripts upregulating neurogenesis, synaptic plasticity, long-term potentiation, and spatial working memory. Pathway analysis revealed that mice lacking FABP4 had changes in metabolic function that support reduction in oxidative stress and inflammation, and improved energy homeostasis and cognitive function. Analysis suggested a role for WNT/β-Catenin signaling in the protection against insulin resistance, alleviating neuroinflammation and cognitive decline. Collectively, our work shows that FABP4 represents a potential target in alleviating HFD-induced neuroinflammation and cognitive decline and suggests a role for WNT/β-Catenin in this protection.

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease

Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer’s disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.

Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 down-regulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets.

Mapping the sequence specificity of heterotypic amyloid interactions enables the identification of aggregation modifiers

Heterotypic amyloid interactions between related protein sequences have been observed in functional and disease amyloids. While sequence homology seems to favour heterotypic amyloid interactions, we have no systematic understanding of the structural rules determining such interactions nor whether they inhibit or facilitate amyloid assembly. Using structure-based thermodynamic calculations and extensive experimental validation, we performed a comprehensive exploration of the defining role of sequence promiscuity in amyloid interactions. Using tau as a model system we demonstrate that proteins with local sequence homology to tau amyloid nucleating regions can modify fibril nucleation, morphology, assembly and spreading of aggregates in cultured cells. Depending on the type of mutation such interactions inhibit or promote aggregation in a manner that can be predicted from structure. We find that these heterotypic amyloid interactions can result in the subcellular mis-localisation of these proteins. Moreover, equilibrium studies indicate that the critical concentration of aggregation is altered by heterotypic interactions. Our findings suggest a structural mechanism by which the proteomic background can modulate the aggregation propensity of amyloidogenic proteins and we discuss how such sequence-specific proteostatic perturbations could contribute to the selective cellular susceptibility of amyloid disease progression.

In this work, Louros et al. uncover a rule book for interactions of amyloids with other proteins. This grammar was shown to promote cellular spreading of tau aggregates in cells, but can also be harvested to develop structure-based aggregation blockers.

An enriched environment delays the progression from mild cognitive impairment to Alzheimer's disease in senescence-accelerated mouse prone 8 mice

A previous study demonstrated that middle-aged (5-6 months of age) senescence-accelerated mouse prone 8 (SAMP8) mice can be used as animal models of mild cognitive impairment (MCI). An enriched environment (EE) can mitigate cognitive decline and decrease the pathological changes associated with various neurodegenerative diseases. In the present study, the learning-memory abilities of SAMP8 mice during the MCI phase (5 months of age) was evaluated and neuropathological changes in the hippocampus were examined after the mice were exposed to an EE for 60 days. In the Morris water maze test, EE-exposed mice demonstrated significantly decreased escape latency and increased time spent in the target quadrant and number of platform crossings compared with control mice. Terminal deoxynucleotidyl transferase dUTP nick end labeling and Nissl staining showed that EE-exposed mice had reduced neuronal apoptosis and increased number of surviving neurons compared with control mice. Golgi staining, transmission electron microscopy, and immunohistochemical staining demonstrated that EE-exposed mice exhibited increased dendritic spine densities among secondary and tertiary apical dendrites; increases in synaptic numerical density, synaptic surface density, and expression of synaptophysin; and reduced deposition of amyloid-β (Aβ) and expression of amyloid-precursor protein (APP) in the hippocampal CA1 region compared with control mice. These results demonstrate that EE exposure effectively decreases neuronal loss and regulates neuronal synaptic plasticity by reducing the expression of APP and the deposition of Aβ in the hippocampal CA1 region, thereby mitigating cognitive decline in SAMP8 mice during the MCI phase and delaying the progression from MCI to Alzheimer's disease.

RNA Dynamics in Alzheimer's Disease

Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder that heavily burdens healthcare systems worldwide. There is a significant requirement to understand the still unknown molecular mechanisms underlying AD. Current evidence shows that two of the major features of AD are transcriptome dysregulation and altered function of RNA binding proteins (RBPs), both of which lead to changes in the expression of different RNA species, including microRNAs (miRNAs), circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs). In this review, we will conduct a comprehensive overview of how RNA dynamics are altered in AD and how this leads to the differential expression of both short and long RNA species. We will describe how RBP expression and function are altered in AD and how this impacts the expression of different RNA species. Furthermore, we will also show how changes in the abundance of specific RNA species are linked to the pathology of AD.

Pulse-Chase Proteomics of the App Knockin Mouse Models of Alzheimer's Disease Reveals that Synaptic Dysfunction Originates in Presynaptic Terminals

Compromised protein homeostasis underlies accumulation of plaques and tangles in Alzheimer's disease (AD). To observe protein turnover at early stages of amyloid beta (Aβ) proteotoxicity, we performed pulse-chase proteomics on mouse brains in three genetic models of AD that knock in alleles of amyloid precursor protein (APP) prior to the accumulation of plaques and during disease progression. At initial stages of Aβ accumulation, the turnover of proteins associated with presynaptic terminals is selectively impaired. Presynaptic proteins with impaired turnover, particularly synaptic vesicle (SV)-associated proteins, have elevated levels, misfold in both a plaque-dependent and -independent manner, and interact with APP and Aβ. Concurrent with elevated levels of SV-associated proteins, we found an enlargement of the SV pool as well as enhancement of presynaptic potentiation. Together, our findings reveal that the presynaptic terminal is particularly vulnerable and represents a critical site for manifestation of initial AD etiology. A record of this paper's transparent peer review process is included in the Supplemental Information.

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning

INTRODUCTION

Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments.

OBJECTIVE

The primary purpose of this study is to develop a classification model using machine learning for predicting the deleterious effect of variants with respect to AD.

METHODS

We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants.

RESULTS

Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range of 54%-62%.

CONCLUSION

Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/. VEPAD can be used to predict the effect of new variants associated with AD.

Transcriptomics in Alzheimer's Disease: Aspects and Challenges

Alzheimer's disease (AD) is the most common cause of dementia. Although the heritability of AD is high, the knowledge of the disease-associated genes, their expression, and their disease-related pathways remain limited. Hence, finding the association between gene dysfunctions and pathological mechanisms, such as neuronal transports, APP processing, calcium homeostasis, and impairment in mitochondria, should be crucial. Emerging studies have revealed that changes in gene expression and gene regulation may have a strong impact on neurodegeneration. The mRNA-transcription factor interactions, non-coding RNAs, alternative splicing, or copy number variants could also play a role in disease onset. These facts suggest that understanding the impact of transcriptomes in AD may improve the disease diagnosis and also the therapies. In this review, we highlight recent transcriptome investigations in multifactorial AD, with emphasis on the insights emerging at their interface.

Neurodegenerative Diseases - Is Metabolic Deficiency the Root Cause?

Neurodegenerative diseases, including Alzheimer, Parkinson, Huntington, and amyotrophic lateral sclerosis, are a prominent class of neurological diseases currently without a cure. They are characterized by an inexorable loss of a specific type of neurons. The selective vulnerability of specific neuronal clusters (typically a subcortical cluster) in the early stages, followed by the spread of the disease to higher cortical areas, is a typical pattern of disease progression. Neurodegenerative diseases share a range of molecular and cellular pathologies, including protein aggregation, mitochondrial dysfunction, glutamate toxicity, calcium load, proteolytic stress, oxidative stress, neuroinflammation, and aging, which contribute to neuronal death. Efforts to treat these diseases are often limited by the fact that they tend to address any one of the above pathological changes while ignoring others. Lack of clarity regarding a possible root cause that underlies all the above pathologies poses a significant challenge. In search of an integrative theory for neurodegenerative pathology, we hypothesize that metabolic deficiency in certain vulnerable neuronal clusters is the common underlying thread that links many dimensions of the disease. The current review aims to present an outline of such an integrative theory. We present a new perspective of neurodegenerative diseases as metabolic disorders at molecular, cellular, and systems levels. This helps to understand a common underlying mechanism of the many facets of the disease and may lead to more promising disease-modifying therapeutic interventions. Here, we briefly discuss the selective metabolic vulnerability of specific neuronal clusters and also the involvement of glia and vascular dysfunctions. Any failure in satisfaction of the metabolic demand by the neurons triggers a chain of events that precipitate various manifestations of neurodegenerative pathology.

Quantitative proteomics of synaptosome S-nitrosylation in Alzheimer's disease

Increasing evidence suggests that both synaptic loss and neuroinflammation constitute early pathologic hallmarks of Alzheimer's disease. A downstream event during inflammatory activation of microglia and astrocytes is the induction of nitric oxide synthase type 2, resulting in an increased release of nitric oxide and the post-translational S-nitrosylation of protein cysteine residues. Both early events, inflammation and synaptic dysfunction, could be connected if this excess nitrosylation occurs on synaptic proteins. In the long term, such changes could provide new insight into patho-mechanisms as well as biomarker candidates from the early stages of disease progression. This study investigated S-nitrosylation in synaptosomal proteins isolated from APP/PS1 model mice in comparison to wild type and NOS2^-/- mice, as well as human control, mild cognitive impairment and Alzheimer's disease brain tissues. Proteomics data were obtained using an established protocol utilizing an isobaric mass tag method, followed by nanocapillary high performance liquid chromatography tandem mass spectrometry. Statistical analysis identified the S-nitrosylation sites most likely derived from an increase in nitric oxide (NO) in dependence of presence of AD pathology, age and the key enzyme NOS2. The resulting list of candidate proteins is discussed considering function, previous findings in the context of neurodegeneration, and the potential for further validation studies.

Development of a Sensitive, Scalable Method for Spatial, Cell-Type-Resolved Proteomics of the Human Brain

While nearly comprehensive proteome coverage can be achieved from bulk tissue or cultured cells, the data usually lacks spatial resolution. As a result, tissue based proteomics averages protein abundance across multiple cell types and/or localizations. With proteomics platforms lacking sensitivity and throughput to undertake deep single-cell proteome studies in order to resolve spatial or cell type dependent protein expression gradients within tissue, proteome analysis has been combined with sorting techniques to enrich for certain cell populations. However, the spatial resolution and context is lost after cell sorting. Here, we report an optimized method for the proteomic analysis of neurons isolated from post-mortem human brain by laser capture microdissection (LCM). We tested combinations of sample collection methods, lysis buffers and digestion methods to maximize the number of identifications and quantitative performance, identifying 1500 proteins from 60 000 μm² of 10 μm thick cerebellar molecular layer with excellent reproducibility. To demonstrate the ability of our workflow to resolve cell type specific proteomes within human brain tissue, we isolated sets of individual Betz and Purkinje cells. Both neuronal cell types are involved in motor coordination and were found to express highly specific proteomes to a depth of 2800 to 3600 proteins.

A New Vision for Therapeutic Hypothermia in the Era of Targeted Temperature Management: A Speculative Synthesis

Three decades of animal studies have reproducibly shown that hypothermia is profoundly cerebroprotective during or after a central nervous system (CNS) insult. The success of hypothermia in preclinical acute brain injury has not only fostered continued interest in research on the classic secondary injury mechanisms that are prevented or blunted by hypothermia but has also sparked a surge of new interest in elucidating beneficial signaling molecules that are increased by cooling. Ironically, while research into cold-induced neuroprotection is enjoying newfound interest in chronic neurodegenerative disease, conversely, the scope of the utility of therapeutic hypothermia (TH) across the field of acute brain injury is somewhat controversial and remains to be fully defined. This has led to the era of Targeted Temperature Management, which emphasizes a wider range of temperatures (33–36°C) showing benefit in acute brain injury. In this comprehensive review, we focus on our current understandings of the novel neuroprotective mechanisms activated by TH, and discuss the critical importance of developmental age germane to its clinical efficacy. We review emerging data on four cold stress hormones and three cold shock proteins that have generated new interest in hypothermia in the field of CNS injury, to create a framework for new frontiers in TH research. We make the case that further elucidation of novel cold responsive pathways might lead to major breakthroughs in the treatment of acute brain injury, chronic neurological diseases, and have broad potential implications for medicines of the distant future, including scenarios such as the prevention of adverse effects of long-duration spaceflight, among others. Finally, we introduce several new phrases that readily summarize the essence of the major concepts outlined by this review—namely, Ultramild Hypothermia, the “Responsivity of Cold Stress Pathways,” and “Hypothermia in a Syringe.”