Genetic variants in Alzheimer disease - molecular and brain network approaches

Genetic-based patient stratification in Alzheimer's disease

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.

Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

INTRODUCTION

This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).

METHODS

This analysis consisted of 150 normal controls (CN), 257 MCI, and 205  AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.

RESULTS

Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.

DISCUSSION

Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.

Spatial transcriptomic patterns underlying regional vulnerability to amyloid-β and tau pathologies and their relationships to cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We studied the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies leveraging two large independent cohorts (n = 715) of participants along the AD continuum. We identified several AD susceptibility genes and gene modules in a gene co-expression network with expression profiles related to regional vulnerability to Aβ and tau pathologies in AD. In particular, we found that the positive APOE -to-tau association was only seen in the AD cohort, whereas patients with AD and frontotemporal dementia shared similar positive MAPT -to-tau association. Some AD candidate genes showed sex-dependent negative gene-to-Aβ and gene-to-tau associations. In addition, we identified distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. Finally, we proposed a novel analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations. Taken together, our study identified distinct gene expression profiles and biochemical pathways that may explain the discordance between regional Aβ and tau pathologies, and filled the gap between gene-to-pathology associations and cognitive dysfunction in individual AD patients that may ultimately help identify novel personalized pathogenetic biomarkers and therapeutic targets.

One Sentence Summary

We identified replicable cognition-related associations between regional gene expression profiles and selectively regional vulnerability to amyloid-β and tau pathologies in AD.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data

Background

Alzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level.

Methods

We collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and “A/T/N” was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes.

Results

We identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability.

Conclusion

The transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD.

Early impairment of cortical circuit plasticity and connectivity in the 5XFAD Alzheimer's disease mouse model

Genetic risk factors for neurodegenerative disorders, such as Alzheimer's disease (AD), are expressed throughout the life span. How these risk factors affect early brain development and function remain largely unclear. Analysis of animal models with high constructive validity for AD, such as the 5xFAD mouse model, may provide insights on potential early neurodevelopmental effects that impinge on adult brain function and age-dependent degeneration. The 5XFAD mouse model over-expresses human amyloid precursor protein (APP) and presenilin 1 (PS1) harboring five familial AD mutations. It is unclear how the expression of these mutant proteins affects early developing brain circuits. We found that the prefrontal cortex (PFC) layer 5 (L5) neurons in 5XFAD mice exhibit transgenic APP overloading at an early post-weaning age. Impaired synaptic plasticity (long-term potentiation, LTP) was seen at 6-8 weeks age in L5 PFC circuit, which was correlated with increased intracellular APP. APP overloading was also seen in L5 pyramidal neurons in the primary visual cortex (V1) during the critical period of plasticity (4-5 weeks age). Whole-cell patch clamp recording in V1 brain slices revealed reduced intrinsic excitability of L5 neurons in 5XFAD mice, along with decreased spontaneous miniature excitatory and inhibitory inputs. Functional circuit mapping using laser scanning photostimulation (LSPS) combined with glutamate uncaging uncovered reduced excitatory synaptic connectivity onto L5 neurons in V1, and a more pronounced reduction in inhibitory connectivity, indicative of altered excitation and inhibition during VC critical period. Lastly, in vivo single-unit recording in V1 confirmed that monocular visual deprivation-induced ocular dominance plasticity during critical period was impaired in 5XFAD mice. Our study reveals plasticity deficits across multiple cortical regions and indicates altered early cortical circuit developmental trajectory as a result of mutant APP/PS1 over-expression.

Network Comparison with Interpretable Contrastive Network Representation Learning

Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another. However, existing contrastive learning methods cannot be directly applied to networks as they are designed only for high-dimensional data analysis. To address this problem, we introduce a new analysis approach called contrastive network representation learning (cNRL). By integrating two machine learning schemes, network representation learning and contrastive learning, cNRL enables embedding of network nodes into a low-dimensional representation that reveals the uniqueness of one network compared to another. Within this approach, we also design a method, named i-cNRL, which offers interpretability in the learned results, allowing for understanding which specific patterns are only found in one network. We demonstrate the effectiveness of i-cNRL for network comparison with multiple network models and real-world datasets. Furthermore, we compare i-cNRL and other potential cNRL algorithm designs through quantitative and qualitative evaluations.

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease

Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer's disease neuropathology biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68, P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer's disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer's disease.

Two Opposing Functions of Angiotensin-Converting Enzyme (ACE) That Links Hypertension, Dementia, and Aging

A 2018 report from the American Heart Association shows that over 103 million American adults have hypertension. The angiotensin-converting enzyme (ACE) (EC 3.4.15.1) is a dipeptidyl carboxylase that, when inhibited, can reduce blood pressure through the renin–angiotensin system. ACE inhibitors are used as a first-line medication to be prescribed to treat hypertension, chronic kidney disease, and heart failure, among others. It has been suggested that ACE inhibitors can alleviate the symptoms in mouse models. Despite the benefits of ACE inhibitors, previous studies also have suggested that genetic variants of the ACE gene are risk factors for Alzheimer’s disease (AD) and other neurological diseases, while other variants are associated with reduced risk of AD. In mice, ACE overexpression in the brain reduces symptoms of the AD model systems. Thus, we find two opposing effects of ACE on health. To clarify the effects, we dissect the functions of ACE as follows: (1) angiotensin-converting enzyme that hydrolyzes angiotensin I to make angiotensin II in the renin–angiotensin system; (2) amyloid-degrading enzyme that hydrolyzes beta-amyloid, reducing amyloid toxicity. The efficacy of the ACE inhibitors is well established in humans, while the knowledge specific to AD remains to be open for further research. We provide an overview of ACE and inhibitors that link a wide variety of age-related comorbidities from hypertension to AD to aging. ACE also serves as an example of the middle-life crisis theory that assumes deleterious events during midlife, leading to age-related later events.

Breakdown of specific functional brain networks in clinical variants of Alzheimer's disease

Alzheimer's disease (AD) is characterized by different clinical entities. Although AD phenotypes share a common molecular substrate (i.e., amyloid beta and tau accumulation), several clinicopathological differences exist. Brain functional networks might provide a macro-scale scaffolding to explain this heterogeneity. In this review, we summarize the evidence linking different large-scale functional network abnormalities to distinct AD phenotypes. Specifically, executive deficits in early-onset AD link with the dysfunction of networks that support sustained attention and executive functions. Posterior cortical atrophy relates to the breakdown of visual and dorsal attentional circuits, while the primary progressive aphasia variant of AD may be associated with the dysfunction of the left-lateralized language network. Additionally, network abnormalities might provide in vivo signatures for distinguishing proteinopathies that mimic AD, such as TAR DNA binding protein 43 related pathologies. These network differences vis-a-vis clinical syndromes are more evident in the earliest stage of AD. Finally, we discuss how these findings might pave the way for new tailored interventions targeting the most vulnerable brain circuit at the optimal time window to maximize clinical benefits.

Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies

Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer's Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require "harmonized" phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer's Disease.

Cross-sectional and longitudinal associations between adherence to Mediterranean diet with physical performance and cognitive function in older adults: A systematic review and meta-analysis

OBJECTIVES

The present study investigated the association between adherence to Mediterranean diet (MeDi) and physical performance and cognitive function in older adults.

METHODS

We conducted a systematic review and meta-analysis of cross-sectional and longitudinal studies that investigated older adults aged 60+ years and assessed adherence to MeDi diet using validated composite scores. Observational studies, including cross-sectional, case-control, and longitudinal cohort studies, if crude baseline data was available, which investigated as a primary or secondary outcome the association of MeDi diet adherence with physical performance and/or cognitive function in non-demented older adults were included in the cross-sectional analysis. For the longitudinal analysis, case-control and longitudinal cohort studies that investigated the longitudinal associations between adherence to MeDi diet with the incidence of mild cognitive impairment (MCI), dementia, and/or Alzheimer's disease (AD), and/or changes in physical performance and cognition in non-demented older adults were included. Studies published in other languages than English were excluded. Studies were retrieved from MEDLINE, SCOPUS, CINAHL, and AgeLine databases until May 19, 2021. The risk of bias was evaluated using the Newcastle - Ottawa Quality Assessment Scale (NOS). A pooled effect size was calculated based on standard mean differences (SMD), log odds ratio (OR) and log risk ratio (RR). This study is registered on PROSPERO (CRD42021250254).

RESULTS

Nineteen cross-sectional studies that investigated 19.734 community-dwelling and institutionalized older adults free of disability and dementia were included. A high adherence to MeDi was cross-sectionally associated with better walking speed (SMD = 0.42; 95 % Confidence Interval (CI) = 0.12-0.72, P = 0.006; I² = 65 %, P = 0.06), knee muscle strength speed (SMD = 0.26; 95 % CI = 0.17-0.36, P < 0.00001; I² = 0 %, P = 0.69), global cognition (SMD = 0.24; 95 % CI = 0.15-0.33, P < 0.00001; I² = 85 %, P < 0.00001), and memory (SMD = 0.18; 95 % CI = 0.13-0.25, P < 0.00001; I² = 100 %, P < 0.00001). The association between MeDi adherence and global cognition remained significant after stratifying the analysis by the region where the study was conducted, MeDi diet adherence composite score, and Mini Mental State Examination (MMSE). Studies had a moderate to low risk of bias. In relation to longitudinal analysis, thirty-four prospective studies with an average follow-up period that varied from 3.0 to 12.6 years and investigated 98.315 community-dwellers were included. Results indicated that older adults with high MeDi scores had a lower decline in global cognition RR = 0.26; 95 % CI = 0.23-0.29, P < 0.00001; I² = 100 %, P < 0.00001). In contrast, no significant associations between MeDi and mobility, MCI, dementia were found. A low risk of bias was found in the longitudinal studies.

DISCUSSION

Findings of the present study indicated that high adherence to MeDi was cross-sectionally associated with physical performance and cognitive function. Results of the pooled analysis of longitudinal studies revealed that high adherence to MeDi reduced the risk of global cognitive decline in non-demented older adults. However, no significant associations between MeDi adherence and the incidence of mobility problems, MCI, and dementia were found. Although important, our findings should be carefully interpreted due to the presence of heterogeneity and publication bias.

The human connectome in Alzheimer disease - relationship to biomarkers and genetics

The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Genetic Counselling Improves the Molecular Characterisation of Dementing Disorders

Dementing disorders are a complex group of neurodegenerative diseases characterised by different, but often overlapping, pathological pathways. Genetics have been largely associated with the development or the risk to develop dementing diseases. Recent advances in molecular technologies permit analyzing of several genes in a small time, but the interpretation analysis is complicated by several factors: the clinical complexity of neurodegenerative disorders, the frequency of co-morbidities, and the high phenotypic heterogeneity of genetic diseases. Genetic counselling supports the diagnostic path, providing an accurate familial and phenotypic characterisation of patients. In this review, we summarise neurodegenerative dementing disorders and their genetic determinants. Genetic variants and associated phenotypes will be divided into high and low impact, in order to reflect the pathologic continuum between multifactorial and mendelian genetic factors. Moreover, we report a molecular characterisation of genes associated with neurodegenerative disorders with cognitive impairment. In particular, the high frequency of rare coding genetic variants in dementing genes strongly supports the role of geneticists in both, clinical phenotype characterisation and interpretation of genotypic data. The smart application of exome analysis to dementia patients, with a pre-analytical selection on familial, clinical, and instrumental features, improves the diagnostic yield of genetic test, reduces time for diagnosis, and allows a rapid and personalised management of disease.

Identification of Conserved Proteomic Networks in Neurodegenerative Dementia

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer’s disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership – Alzheimer’s Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery.

Swarup et al. use a multi-omic, multi-cohort approach to identify robust early and late proteomic changes in AD and other neurodegenerative dementias and find that genetic risk is differentially enriched across disorders. Shared co-expression modules showing consistent molecular alterations at multi-omic levels are ripe for future investigation as drug targets.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

Functional nanoassemblies for the diagnosis and therapy of Alzheimer's diseases

Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects populations around the world. Many therapeutics have been investigated for AD diagnosis and/or therapy, but the efficacy is largely limited by the poor bioavailability of drugs and by the presence of the blood-brain barrier. Recently, the development of nanomedicines enables efficient drug delivery to the brain, but the complex pathological mechanism of AD prevents them from successful treatment. As a type of advanced nanomedicine, multifunctional nanoassemblies self-assembled from nanoscale imaging or therapeutic agents can simultaneously target multiple pathological factors, showing great potential in the diagnosis and therapy of AD. To help readers better understand this emerging field, in this review, we first introduce the pathological mechanisms and the potential drug candidates of AD, as well as the design strategies of nanoassemblies for improving AD targeting efficiency. Moreover, the progress of dynamic nanoassemblies that can diagnose and/or treat AD in response to the endogenous or exogenous stimuli will be described. Finally, we conclude with our perspectives on the future development in this field. The objective of this review is to outline the latest progress of using nanoassemblies to overcome the complex pathological environment of AD for improved diagnosis and therapy, in hopes of accelerating the future development of intelligent AD nanomedicines. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging.

Exploiting Common Aspects of Obesity and Alzheimer's Disease

Alzheimer’s disease (AD) is an example of age-related dementia, and there are still no known preventive or curative measures for this disease. Obesity and associated metabolic changes are widely accepted as risk factors of age-related cognitive decline. Insulin is the prime mediator of metabolic homeostasis, which is impaired in obesity, and this impairment potentiates amyloid-β (Aβ) accumulation and the formation of neurofibrillary tangles (NFTs). Obesity is also linked with functional and morphological alterations in brain mitochondria leading to brain insulin resistance (IR) and memory deficits associated with AD. Also, increased peripheral inflammation and oxidative stress due to obesity are the main drivers that increase an individual’s susceptibility to cognitive deficits, thus doubling the risk of AD. This enhanced risk of AD is alarming in the context of a rapidly increasing global incidence of obesity and overweight in the general population. In this review, we summarize the risk factors that link obesity with AD and emphasize the point that the treatment and management of obesity may also provide a way to prevent AD.

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease

Alzheimer's disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).

Phosphodiesterase 4D Gene Modifies the Functional Network of Patients With Mild Cognitive Impairment and Alzheimer's Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is affected by several genetic variants. It has been demonstrated that genetic variants affect brain organization and function. In this study, using whole genome-wide association studies (GWAS), we analyzed the functional magnetic resonance imaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative dataset (ADNI) dataset and identified genetic variants associated with the topology of the functional brain network http://www.adni-info.org. We found three novel loci (rs2409627, rs9647533, and rs11955845) in an intron of the phosphodiesterase 4D (PDE4D) gene that contribute to abnormalities in the topological organization of the functional network. In particular, compared to the wild-type genotype, the subjects carrying the PDE4D variants had altered network properties, including a significantly reduced clustering coefficient, small-worldness, global and local efficiency, a significantly enhanced path length and a normalized path length. In addition, we found that all global brain network attributes were affected by PDE4D variants to different extents as the disease progressed. Additionally, brain regions with alterations in nodal efficiency due to the variations in PDE4D were predominant in the limbic lobe, temporal lobe and frontal lobes. PDE4D has a great effect on memory consolidation and cognition through long-term potentiation (LTP) effects and/or the promotion of inflammatory effects. PDE4D variants might be a main reasons underlyling for the abnormal topological properties and cognitive impairment. Furthermore, we speculated that PDE4D is a risk factor for neural degenerative diseases and provided important clues for the earlier detection and therapeutic intervention for AD.

Gene expression of serotonergic markers in peripheral blood mononuclear cells of patients with late-onset Alzheimer's disease

Serotonin or 5-hydroxytryptamine (5-HT) is primarily involved in the regulation of learning and memory. Pathological changes in metabolism or functional imbalance of 5-HT has been associated with Alzheimer's disease (AD). The hypothesis tested is that in peripheral blood, markers of the serotonergic pathway can be used as a diagnostic tool for AD. The current study measured the relative expression of 5-HT receptors (5-HTR2A and 5-HTR3A) as well as the 5-HT catalytic enzyme, Monoamine oxidase A (MAO-A) mRNA in Peripheral Blood Mononuclear Cells (PBMCs) of patients with late-onset Alzheimer's disease (LOAD) and age-matched controls. 5-HTR2A, 5-HTR3A, and MAO-A mRNA expressions were examined in PBMCs of 30 patients with LOAD and 30 control individuals. Real-time quantitative PCR was used to measure mRNA expression. The dementia status of patients in this study was assessed using a Mini-Mental State Examination (MMSE). Mean data of relative mRNA expression of 5-HTR2A, 5-HTR3A and MAO-A were significantly lower in PBMCs of patients with LOAD compared with controls. Based on the down-regulation of serotonergic markers in PBMCs, our findings may be another claim to the systemic nature of LOAD. The role of peripheral serotonergic downregulation, in the pathogenesis of AD, needs to be further studied. Given the extremely convenient access to PBMCs, these molecular events may represent more complete dimensions of AD neuropathophysiology or possibly lead to a new direction in studies focused on blood-based markers.

Ketogenic therapy in neurodegenerative and psychiatric disorders: From mice to men

Ketogenic diet is a low carbohydrate and high fat diet that has been used for over 100 years in the management of childhood refractory epilepsy. More recently, ketogenic diet has been investigated for a number of metabolic, neurodegenerative and neurodevelopmental disorders. In this comprehensive review, we critically examine the potential therapeutic benefits of ketogenic diet and ketogenic agents on neurodegenerative and psychiatric disorders in humans and translationally valid animal models. The preclinical literature provides strong support for the efficacy of ketogenic diet in a variety of diverse animal models of neuropsychiatric disorders. However, the evidence from clinical studies, while encouraging, particularly in Alzheimer's disease, psychotic and autism spectrum disorders, is limited to case studies and small pilot trials. Firm conclusion on the efficacy of ketogenic diet in psychiatric disorders cannot be drawn due to the lack of randomised, controlled clinical trials. The potential mechanisms of action of ketogenic therapy in these disorders with diverse pathophysiology may include energy metabolism, oxidative stress and immune/inflammatory processes. In conclusion, while ketogenic diet and ketogenic substances hold promise pre-clinically in a variety of neurodegenerative and psychiatric disorders, further studies, particularly randomised controlled clinical trials, are warranted to better understand their clinical efficacy and potential side effects.

Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.

Central and Peripheral Metabolic Defects Contribute to the Pathogenesis of Alzheimer's Disease: Targeting Mitochondria for Diagnosis and Prevention

Significance: Epidemiological studies indicate that metabolic disorders are associated with an increased risk for Alzheimer's disease (AD). Metabolic remodeling occurs in the central nervous system (CNS) and periphery, even in the early stages of AD. Mitochondrial dysfunction has been widely accepted as a molecular mechanism underlying metabolic disorders. Therefore, focusing on early metabolic changes, especially from the perspective of mitochondria, could be of interest for early AD diagnosis and intervention. Recent Advances: We and others have identified that the levels of several metabolites are fluctuated in the periphery before their accumulation in the CNS, which plays an important role in the pathogenesis of AD. Mitochondrial remodeling is likely one of the earliest signs of AD, linking nutritional imbalance to cognitive deficits. Notably, by improving mitochondrial function, mitochondrial nutrients efficiently rescue cellular metabolic dysfunction in the CNS and periphery in individuals with AD. Critical Issues: Peripheral metabolic disorders should be intensively explored and evaluated for the early diagnosis of AD. The circulating metabolites derived from mitochondrial remodeling represent novel potential diagnostic biomarkers for AD that are more readily detected than CNS-oriented biomarkers. Moreover, mitochondrial nutrients provide a promising approach to preventing and delaying AD progression. Future Directions: Abnormal mitochondrial metabolism in the CNS and periphery is involved in AD pathogenesis. More clinical studies provide evidence for the suitability and reliability of circulating metabolites and cytokines for the early diagnosis of AD. Targeting mitochondria to rewire cellular metabolism is a promising approach to preventing AD and ameliorating AD-related metabolic disorders.

Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease

Alzheimer's disease (AD)-a complex disease showing multiple pathomechanistic alterations-is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use-including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD "signatures" through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.
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Molecular Pathogenesis and Interventional Strategies for Alzheimer's Disease: Promises and Pitfalls

Alzheimer's disease (AD) is a debilitating disorder characterized by age-related dementia, which has no effective treatment to date. β-Amyloid depositions and hyperphosphorylated tau proteins are the main pathological hallmarks, along with oxidative stress, N-methyl-d-aspartate (NMDA) receptor-mediated excitotoxicity, and low levels of acetylcholine. Current pharmacotherapy for AD only provides symptomatic relief and limited improvement in cognitive functions. Many molecules have been explored that show promising outcomes in AD therapy and can regulate cellular survival through different pathways. To have a vivid approach to strategize the treatment regimen, AD physiopathology should be better explained considering diverse etiological factors in conjunction with biochemical disturbances. This Review attempts to discuss different disease modification approaches and address the novel therapeutic targets of AD that might pave the way for new drug discovery using the well-defined targets for therapy of the disease.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Rodent models for Alzheimer disease

Animal models are indispensable tools for Alzheimer disease (AD) research. Over the course of more than two decades, an increasing number of complementary rodent models has been generated. These models have facilitated testing hypotheses about the aetiology and progression of AD, dissecting the associated pathomechanisms and validating therapeutic interventions, thereby providing guidance for the design of human clinical trials. However, the lack of success in translating rodent data into therapeutic outcomes may challenge the validity of the current models. This Review critically evaluates the genetic and non-genetic strategies used in AD modelling, discussing their strengths and limitations, as well as new opportunities for the development of better models for the disease.

Induction of Amyloid-β42 Production by Fipronil and Other Pyrazole Insecticides

Generation of amyloid-β peptides (Aβs) by proteolytic cleavage of the amyloid-β protein precursor (AβPP), especially increased production of Aβ42/Aβ43 over Aβ40, and their aggregation as oligomers and plaques, represent a characteristic feature of Alzheimer's disease (AD). In familial AD (FAD), altered Aβ production originates from specific mutations of AβPP or presenilins 1/2 (PS1/PS2), the catalytic subunits of γ-secretase. In sporadic AD, the origin of altered production of Aβs remains unknown. We hypothesize that the 'human chemical exposome' contains products able to favor the production of Aβ42/Aβ43 over Aβ40 and shorter Aβs. To detect such products, we screened a library of 3500 + compounds in a cell-based assay for enhanced Aβ42/Aβ43 production. Nine pyrazole insecticides were found to induce a β- and γ-secretase-dependent, 3-10-fold increase in the production of extracellular Aβ42 in various cell lines and neurons differentiated from induced pluripotent stem cells derived from healthy and FAD patients. Immunoprecipitation/mass spectrometry analyses showed increased production of Aβs cleaved at positions 42/43, and reduced production of peptides cleaved at positions 38 and shorter. Strongly supporting a direct effect on γ-secretase activity, pyrazoles shifted the cleavage pattern of another γ-secretase substrate, alcadeinα, and shifted the cleavage of AβPP by highly purified γ-secretase toward Aβ42/Aβ43. Focusing on fipronil, we showed that some of its metabolites, in particular the persistent fipronil sulfone, also favor the production of Aβ42/Aβ43 in both cell-based and cell-free systems. Fipronil administered orally to mice and rats is known to be metabolized rapidly, mostly to fipronil sulfone, which stably accumulates in adipose tissue and brain. In conclusion, several widely used pyrazole insecticides enhance the production of toxic, aggregation prone Aβ42/Aβ43 peptides, suggesting the possible existence of environmental "Alzheimerogens" which may contribute to the initiation and propagation of the amyloidogenic process in sporadic AD.

Novel methods for integration and visualization of genomics and genetics data in Alzheimer's disease

INTRODUCTION

Numerous omics studies have been conducted to understand the molecular networks involved in Alzheimer's disease (AD), but the pathophysiology is still not completely understood; new approaches that enable neuroscientists to better interpret the results of omics analysis are required.

METHODS

We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways.

RESULTS

We have analyzed over 50 public human gene expression data sets, spanning 19 different brain regions and encompassing three separate cohorts. We integrated genome-wide association studies with expression data to identify important genes in the pathophysiology of AD, which provides further insight into the calcium signaling and calcineurin pathways.

DISCUSSION

Biologists can use these freely-available tools to obtain a comprehensive, information-rich view of the pathways in AD.

A Network of Genetic Effects on Non-Demented Cognitive Aging: Alzheimer's Genetic Risk (CLU + CR1 + PICALM) Intensifies Cognitive Aging Genetic Risk (COMT + BDNF) Selectively for APOEɛ4 Carriers

BACKGROUND

Trajectories of complex neurocognitive phenotypes in preclinical aging may be produced differentially through selective and interactive combinations of genetic risk.

OBJECTIVE

We organize three possible combinations into a "network" of genetic risk indices derived from polymorphisms associated with normal and impaired cognitive aging, as well as Alzheimer's disease (AD). Specifically, we assemble and examine three genetic clusters relevant to non-demented cognitive trajectories: 1) Apolipoprotein E (APOE), 2) a Cognitive Aging Genetic Risk Score (CA-GRS; Catechol-O-methyltransferase + Brain-derived neurotrophic factor), and 3) an AD-Genetic Risk Score (AD-GRS; Clusterin + Complement receptor 1 + Phosphatidylinositol-binding clathrin assembly protein).

METHOD

We use an accelerated longitudinal design (n = 634; age range = 55-95 years) to test whether AD-GRS (low versus high) moderates the effect of increasing CA-GRS risk on executive function (EF) performance and change as stratified by APOE status (ɛ4+ versus ɛ4-).

RESULTS

APOEɛ4 carriers with high AD-GRS had poorer EF performance at the centering age (75 years) and steeper 9-year decline with increasing CA-GRS but this association was not present in APOEɛ4 carriers with low AD-GRS.

CONCLUSIONS

APOEɛ4 carriers with high AD-GRS are at elevated risk of cognitive decline when they also possess higher CA-GRS risk. Genetic risk from both common cognitive aging and AD-related indices may interact in intensification networks to differentially predict (1) level and trajectories of EF decline and (2) potential selective vulnerability for transitions into impairment and dementia.

Causes and Patterns of Dementia: An Update in the Era of Redefining Alzheimer's Disease

The burden of dementia continues to increase as the population ages, with no disease-modifying treatments available. However, dementia risk appears to be decreasing, and progress has been made in understanding its multifactorial etiology. The 2018 National Institute on Aging-Alzheimer's Association (NIA-AA) research framework for Alzheimer's disease (AD) defines AD as a biological process measured by brain pathology or biomarkers, spanning the cognitive spectrum from normality to dementia. This framework facilitates interventions in the asymptomatic space and accommodates knowledge that many additional pathologies (e.g., cerebrovascular) contribute to the Alzheimer's dementia syndrome. The framework has implications for how we think about risk factors for "AD": Many commonly accepted risk factors are not related to AD pathology and would no longer be considered risk factors for AD. They may instead be related to other pathologies or resilience to pathology. This review updates what is known about causes, risk factors, and changing patterns of dementia, addressing whether they are related to AD pathology/biomarkers, other pathologies, or resilience.

Integrative approach to sporadic Alzheimer's disease: deficiency of TYROBP in cerebral Aβ amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular pathology without reducing Aβ burden

Integrative gene network approaches enable new avenues of exploration that implicate causal genes in sporadic late-onset Alzheimer's disease (LOAD) pathogenesis, thereby offering novel insights for drug-discovery programs. We previously constructed a probabilistic causal network model of sporadic LOAD and identified TYROBP/DAP12, encoding a microglial transmembrane signaling polypeptide and direct adapter of TREM2, as the most robust key driver gene in the network. Here, we show that absence of TYROBP/DAP12 in a mouse model of AD-type cerebral Aβ amyloidosis (APP^KM670/671NL/PSEN1^Δexon9) recapitulates the expected network characteristics by normalizing the transcriptome of APP/PSEN1 mice and repressing the induction of genes involved in the switch from homeostatic microglia to disease-associated microglia (DAM), including Trem2, complement (C1qa, C1qb, C1qc, and Itgax), Clec7a and Cst7. Importantly, we show that constitutive absence of TYROBP/DAP12 in the amyloidosis mouse model prevented appearance of the electrophysiological and learning behavior alterations associated with the phenotype of APP^KM670/671NL/PSEN1^Δexon9 mice. Our results suggest that TYROBP/DAP12 could represent a novel therapeutic target to slow, arrest, or prevent the development of sporadic LOAD. These data establish that the network pathology observed in postmortem human LOAD brain can be faithfully recapitulated in the brain of a genetically manipulated mouse. These data also validate our multiscale gene networks by demonstrating how the networks intersect with the standard neuropathological features of LOAD.

The Therapeutic Potential of Ketogenic Diet Throughout Life: Focus on Metabolic, Neurodevelopmental and Neurodegenerative Disorders

This chapter reviews the efficacy of the ketogenic diet in a variety of neurodegenerative, neurodevelopmental and metabolic conditions throughout different stages of life. It describes conditions affecting children, metabolic disorders in adults and disorderrs affecting the elderly. We have focused on application of the ketogenic diet in clinical studies and in preclinical models and discuss the benefits and negative aspects of the diet. Finally, we highlight the need for further research in this area with a view of discovering novel mechanistic targets of the ketogenic diet, as a means of maximising the potential benefits/risks ratio.

When pitch adds to volume: coregulation of transcript diversity predicts gene function

Background

Genes corregulate their overall transcript volumes to perform their physiological functions. However, it is unknown if they additionally coregulate their transcript diversities. We studied the reliability, consistency and functional associations of co-splicing correlations of genes of interest, across two independent studies, multiple tissues and two statistical methods. We thoroughly investigated the reproducibility of co-splicing correlations of APP, the candidate gene of Azheimer’s disease (AD). We then studied how co-splicing correlations in different tissues contributed to predict functional interactions of three other genes and finally computed co-splicing frequency for 17 thousand genes across 52 human tissues.

Results

We replicated co-splicing correlations between APP and 5 AD-related genes and reproduced expected enrichment of APP co-splicing in synaptic vesicle cycle and proteosome pathways. We observed novel associations for tissue vulnerability to disease with enrichment in APP co-splicing, co-expression and epistasis in AD. APP co-splicing was the strongest predictor and replicated between studies. We confirmed known gene interactions of PRPF8 and GRIA1 in testis and brain cortex, and observed a novel interaction of FGFR2, in breast and prostate, modulated by cancer risk-variants. We produced a co-splicing map across 52 human tissues to help predict the function of over 17 thousand genes.

Conclusions

We show that coregulation of transcript diversities provides novel biological insights in gene physiology and helps to interpret GWAS results. Co-splicing correlations are reliable and frequent and should be further pursued to help predict gene function. Our results additionally support current AD interventions aiming at the ubiquitin proteosome pathway but unveil the need to consider transcript diversity in addition to volume to assess treatment response and susceptibility to the disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5263-z) contains supplementary material, which is available to authorized users.

Early Life Stress and Epigenetics in Late-onset Alzheimer's Dementia: A Systematic Review

Involvement of life stress in Late-Onset Alzheimer's Disease (LOAD) has been evinced in longitudinal cohort epidemiological studies, and endocrinologic evidence suggests involvements of catecholamine and corticosteroid systems in LOAD. Early Life Stress (ELS) rodent models have successfully demonstrated sequelae of maternal separation resulting in LOAD-analogous pathology, thereby supporting a role of insulin receptor signalling pertaining to GSK-3beta facilitated tau hyper-phosphorylation and amyloidogenic processing. Discussed are relevant ELS studies, and findings from three mitogen-activated protein kinase pathways (JNK/SAPK pathway, ERK pathway, p38/MAPK pathway) relevant for mediating environmental stresses. Further considered were the roles of autophagy impairment, neuroinflammation, and brain insulin resistance. For the meta-analytic evaluation, 224 candidate gene loci were extracted from reviews of animal studies of LOAD pathophysiological mechanisms, of which 60 had no positive results in human LOAD association studies. These loci were combined with 89 gene loci confirmed as LOAD risk genes in previous GWAS and WES. Of the 313 risk gene loci evaluated, there were 35 human reports on epigenomic modifications in terms of methylation or histone acetylation. 64 microRNA gene regulation mechanisms were published for the compiled loci. Genomic association studies support close relations of both noradrenergic and glucocorticoid systems with LOAD. For HPA involvement, a CRHR1 haplotype with MAPT was described, but further association of only HSD11B1 with LOAD found; however, association of FKBP1 and NC3R1 polymorphisms was documented in support of stress influence to LOAD. In the brain insulin system, IGF2R, INSR, INSRR, and plasticity regulator ARC, were associated with LOAD. Pertaining to compromised myelin stability in LOAD, relevant associations were found for BIN1, RELN, SORL1, SORCS1, CNP, MAG, and MOG. Regarding epigenetic modifications, both methylation variability and de-acetylation were reported for LOAD. The majority of up-to-date epigenomic findings include reported modifications in the well-known LOAD core pathology loci MAPT, BACE1, APP (with FOS, EGR1), PSEN1, PSEN2, and highlight a central role of BDNF. Pertaining to ELS, relevant loci are FKBP5, EGR1, GSK3B; critical roles of inflammation are indicated by CRP, TNFA, NFKB1 modifications; for cholesterol biosynthesis, DHCR24; for myelin stability BIN1, SORL1, CNP; pertaining to (epi)genetic mechanisms, hTERT, MBD2, DNMT1, MTHFR2. Findings on gene regulation were accumulated for BACE1, MAPK signalling, TLR4, BDNF, insulin signalling, with most reports for miR-132 and miR-27. Unclear in epigenomic studies remains the role of noradrenergic signalling, previously demonstrated by neuropathological findings of childhood nucleus caeruleus degeneration for LOAD tauopathy.

The Molecular and Neuropathological Consequences of Genetic Risk for Alzheimer's Dementia

Alzheimer's dementia commonly impacts the health of older adults and lacks any preventative therapy. While Alzheimer's dementia risk has a substantial genetic component, the specific molecular mechanisms and neuropathologies triggered by most of the known genetic variants are unclear. Resultantly, they have shown limited influence on drug development portfolios to date. To facilitate our understanding of the consequences of Alzheimer's dementia susceptibility variants, we examined their relationship to a wide range of clinical, molecular and neuropathological features. Because the effect size of individual variants is typically small, we utilized a polygenic (overall) risk approach to identify the global impact of Alzheimer's dementia susceptibility variants. Under this approach, each individual has a polygenic risk score (PRS) that we related to clinical, molecular and neuropathological phenotypes. Applying this approach to 1,272 individuals who came to autopsy from one of two longitudinal aging cohorts, we observed that an individual's PRS was associated with cognitive decline and brain pathologies including beta-amyloid, tau-tangles, hippocampal sclerosis, and TDP-43, MIR132, four proteins including VGF, IGFBP5, and STX1A, and many chromosomal regions decorated with acetylation on histone H3 lysine 9 (H3K9Ac). While excluding the APOE/TOMM40 region (containing the single largest genetic risk factor for late-onset Alzheimer's dementia) in the calculation of the PRS resulted in a slightly weaker association with the molecular signatures, results remained significant. These PRS-associated brain pathologies and molecular signatures appear to mediate genetic risk, as they attenuated the association of the PRS with cognitive decline. Notably, the PRS induced changes in H3K9Ac throughout the genome, implicating it in large-scale chromatin changes. Thus, the PRS for Alzheimer's dementia (AD-PRS) showed effects on diverse clinical, molecular, and pathological systems, ranging from the epigenome to specific proteins. These convergent targets of a large number of genetic risk factors for Alzheimer's dementia will help define the experimental systems and models needed to test therapeutic targets, which are expected to be broadly effective in the aging population that carries diverse genetic risks for Alzheimer's dementia.

Neurodegenerative Diseases: Regenerative Mechanisms and Novel Therapeutic Approaches

Regeneration refers to regrowth of tissue in the central nervous system. It includes generation of new neurons, glia, myelin, and synapses, as well as the regaining of essential functions: sensory, motor, emotional and cognitive abilities. Unfortunately, regeneration within the nervous system is very slow compared to other body systems. This relative slowness is attributed to increased vulnerability to irreversible cellular insults and the loss of function due to the very long lifespan of neurons, the stretch of cells and cytoplasm over several dozens of inches throughout the body, insufficiency of the tissue-level waste removal system, and minimal neural cell proliferation/self-renewal capacity. In this context, the current review summarized the most common features of major neurodegenerative disorders; their causes and consequences and proposed novel therapeutic approaches.

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene-gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer's disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer's disease.

Integrated analysis of human genetic association study and mouse transcriptome suggests LBH and SHF genes as novel susceptible genes for amyloid-β accumulation in Alzheimer's disease

Alzheimer's disease (AD) is a common neurological disease that causes dementia in humans. Although the reports of associated pathological genes have been increasing, the molecular mechanism leading to the accumulation of amyloid-β (Aβ) in human brain is still not well understood. To identify novel genes that cause accumulation of Aβ in AD patients, we conducted an integrative analysis by combining a human genetic association study and transcriptome analysis in mouse brain. First, we examined genome-wide gene expression levels in the hippocampus, comparing them to amyloid Aβ level in mice with mixed genetic backgrounds. Next, based on a GWAS statistics obtained by a previous study with human AD subjects, we obtained gene-based statistics from the SNP-based statistics. We combined p values from the two types of analysis across orthologous gene pairs in human and mouse into one p value for each gene to evaluate AD susceptibility. As a result, we found five genes with significant p values in this integrated analysis among the 373 genes analyzed. We also examined the gene expression level of these five genes in the hippocampus of independent human AD cases and control subjects. Two genes, LBH and SHF, showed lower expression levels in AD cases than control subjects. This is consistent with the gene expression levels of both the genes in mouse which were negatively correlated with Aβ accumulation. These results, obtained from the integrative approach, suggest that LBH and SHF are associated with the AD pathogenesis.

Advances in the discovery of genetic risk factors for complex forms of neurodegenerative disorders: contemporary approaches, success, challenges and prospects

Neurodegenerative diseases constitute a large proportion of disorders in elderly, majority being sporadic in occurrence with ∼5-10% familial. A strong genetic component underlies the Mendelian forms but nongenetic factors together with genetic vulnerability contributes to the complex sporadic forms. Several gene discoveries in the familial forms have provided novel insights into the pathogenesis of neurodegeneration with implications for treatment. Conversely, findings from genetic dissection of the sporadic forms, despite large genomewide association studies and more recently whole exome and whole genome sequencing, have been limited. This review provides a concise account of the genetics that we know, the pathways that they implicate, the challenges that are faced and the prospects that are envisaged for the sporadic, complex forms of neurodegenerative diseases, taking four most common conditions, namely Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and Huntington disease as examples. Poor replication across studies, inability to establish genotype-phenotype correlations and the overall failure to predict risk and/or prevent disease in this group poses a continuing challenge. Among others, clinical heterogeneity emerges as the most important impediment warranting newer approaches. Advanced computational and system biology tools to analyse the big data are being generated and the alternate strategy such as subgrouping of case-control cohorts based on deep phenotyping using the principles of Ayurveda to overcome current limitation of phenotype heterogeneity seem to hold promise. However, at this point, with advances in discovery genomics and functional analysis of putative determinants with translation potential for the complex forms being minimal, stem cell therapies are being attempted as potential interventions. In this context, the possibility to generate patient derived induced pluripotent stem cells, mutant/gene/genome correction through CRISPR/Cas9 technology and repopulating the specific brain regions with corrected neurons, which may fulfil the dream of personalized medicine have been mentioned briefly. Understanding disease pathways/biology using this technology, with implications for development of novel therapeutics are optimistic expectations in the near future.

Store depletion-induced h-channel plasticity rescues a channelopathy linked to Alzheimer's disease

Voltage-gated ion channels are critical for neuronal integration. Some of these channels, however, are misregulated in several neurological disorders, causing both gain- and loss-of-function channelopathies in neurons. Using several transgenic mouse models of Alzheimer's disease (AD), we find that sub-threshold voltage signals strongly influenced by hyperpolarization-activated, cyclic nucleotide-gated (HCN) channels progressively deteriorate over chronological aging in hippocampal CA1 pyramidal neurons. The degraded signaling via HCN channels in the transgenic mice is accompanied by an age-related global loss of their non-uniform dendritic expression. Both the aberrant signaling via HCN channels and their mislocalization could be restored using a variety of pharmacological agents that target the endoplasmic reticulum (ER). Our rescue of the HCN channelopathy helps provide molecular details into the favorable outcomes of ER-targeting drugs on the pathogenesis and synaptic/cognitive deficits in AD mouse models, and implies that they might have beneficial effects on neurological disorders linked to HCN channelopathies.

Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure

Background

Alzheimer’s disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the specific effects that pathological mutations and coding variants associated with AD have on the cellular composition of the brain are often ignored.

Methods

We developed and optimized a cell-type-specific expression reference panel and employed digital deconvolution methods to determine brain cellular distribution in three independent transcriptomic studies.

Results

We found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies.

Conclusions

These findings suggest that genetic risk factors associated with AD etiology have a specific imprinting in the cellular composition of AD brains. Our digital deconvolution reference panel provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition and suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0551-4) contains supplementary material, which is available to authorized users.