The Role of PICALM in Alzheimer's Disease

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, suggesting that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, suggesting that DEGs exert more impact on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we observe an overall downregulation of astrocyte and microglia modules across all brain regions in AD, indicating a prevailing trend of functional repression in glial cells across these regions. Notable genes from the CALM and HSP90 families emerged as hub genes across neuronal modules in all brain regions, suggesting conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis to comprehensively understand the cell-type-specific roles of genes in AD-related biological processes.

Enhancing of cerebral Abeta clearance by modulation of ABC transporter expression: a review of experimental approaches

Clearance of amyloid-beta (Aβ) from the brain is impaired in both early-onset and late-onset Alzheimer's disease (AD). Mechanisms for clearing cerebral Aβ include proteolytic degradation, antibody-mediated clearance, blood brain barrier and blood cerebrospinal fluid barrier efflux, glymphatic drainage, and perivascular drainage. ATP-binding cassette (ABC) transporters are membrane efflux pumps driven by ATP hydrolysis. Their functions include maintenance of brain homeostasis by removing toxic peptides and compounds, and transport of bioactive molecules including cholesterol. Some ABC transporters contribute to lowering of cerebral Aβ. Mechanisms suggested for ABC transporter-mediated lowering of brain Aβ, in addition to exporting of Aβ across the blood brain and blood cerebrospinal fluid barriers, include apolipoprotein E lipidation, microglial activation, decreased amyloidogenic processing of amyloid precursor protein, and restricting the entrance of Aβ into the brain. The ABC transporter superfamily in humans includes 49 proteins, eight of which have been suggested to reduce cerebral Aβ levels. This review discusses experimental approaches for increasing the expression of these ABC transporters, clinical applications of these approaches, changes in the expression and/or activity of these transporters in AD and transgenic mouse models of AD, and findings in the few clinical trials which have examined the effects of these approaches in patients with AD or mild cognitive impairment. The possibility that therapeutic upregulation of ABC transporters which promote clearance of cerebral Aβ may slow the clinical progression of AD merits further consideration.

Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

miR-155-5p promotes hepatic steatosis via PICALM-mediated autophagy in aging hepatocytes

BACKGROUND

Hepatic steatosis, a lipid disorder characterized by the accumulation of intrahepatic fat, is more prevalent in the elderly population. This study investigates the role of miR-155-5p in the autophagy dysregulation of aging hepatic steatosis.

METHODS

We established an aging mouse model in vivo and a hepatocellular senescence model induced by low serum and palmitic acid in vitro. The fluctuations of microRNAs were derived from RNA-seq data and confirmed by qPCR in 4- and 18-month-old mouse liver tissues. Hematoxylin-eosin (H&E) staining observed pathological changes. Markers of senescence, autophagy, and lipolysis genes were analyzed using Western blot and qPCR. Bioinformatics analysis predicted miR-155-5p's target gene PICALM, confirmed by dual luciferase reporter assay and transfection of miR-155-5p mimic/inhibitor into senescent hepatocytes.

RESULTS

Senescent markers (p21, p16, and p-P53) and miR-155-5p were up-regulated in aging liver tissues and senescent hepatocytes. Bioinformatics analysis identified PICALM as a target gene of miR-155-5p, a finding further supported by dual luciferase reporter assays. Inhibition of miR-155-5p reduced expression of senescent marker genes (p16, p21, p-P53), improved autophagy (evidenced by increased LC3B-II and ATG5, and decreased P62), and enhanced lipolysis (indicated by increased ATGL and p-HSL) in senescent hepatocytes. Oil red O staining confirmed that miR-155-5p inhibition significantly reduced lipid accumulation in these cells.

CONCLUSIONS

This study suggests a potential new therapeutic approach for age-related hepatic steatosis through the inhibition of miR-155-5p to enhance autophagy.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.

Iron Overload in Brain: Transport Mismatches, Microbleeding Events, and How Nanochelating Therapies May Counteract Their Effects

Iron overload in many brain regions is a common feature of aging and most neurodegenerative diseases. In this review, the causes, mechanisms, mathematical models, and possible therapies are summarized. Indeed, physiological and pathological conditions can be investigated using compartmental models mimicking iron trafficking across the blood-brain barrier and the Cerebrospinal Fluid-Brain exchange membranes located in the choroid plexus. In silico models can investigate the alteration of iron homeostasis and simulate iron concentration in the brain environment, as well as the effects of intracerebral iron chelation, determining potential doses and timing to recover the physiological state. Novel formulations of non-toxic nanovectors with chelating capacity are already tested in organotypic brain models and could be available to move from in silico to in vivo experiments.

Endocytosis and Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and is the most common cause of dementia. The pathogenesis of AD still remains unclear, including two main hypotheses: amyloid cascade and tau hyperphosphorylation. The hallmark neuropathological changes of AD are extracellular deposits of amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles (NFTs). Endocytosis plays an important role in a number of cellular processes including communication with the extracellular environment, nutrient uptake, and signaling by the cell surface receptors. Based on the results of genetic and biochemical studies, there is a link between neuronal endosomal function and AD pathology. Taking this into account, we can state that in the results of previous research, endolysosomal abnormality is an important cause of neuronal lesions in the brain. Endocytosis is a central pathway involved in the regulation of the degradation of amyloidogenic components. The results of the studies suggest that a correlation between alteration in the endocytosis process and associated protein expression progresses AD. In this article, we discuss the current knowledge about endosomal abnormalities in AD.

Region-based analysis with functional annotation identifies genes associated with cognitive function in South Asians from India

The prevalence of dementia among South Asians across India is approximately 7.4% in those 60 years and older, yet little is known about genetic risk factors for dementia in this population. Most known risk loci for Alzheimer's disease (AD) have been identified from studies conducted in European Ancestry (EA) but are unknown in South Asians. Using whole-genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD), we performed a gene-based analysis of 84 genes previously associated with AD in EA. We investigated associations with the Hindi Mental State Examination (HMSE) score and factor scores for general cognitive function and five cognitive domains. For each gene, we examined missense/loss-of-function (LoF) variants and brain-specific promoter/enhancer variants, separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores) using the variant-Set Test for Association using Annotation infoRmation (STAAR). In the missense/LoF analysis without annotation weights and controlling for age, sex, state/territory, and genetic ancestry, three genes had an association with at least one measure of cognitive function (FDR q<0.1). APOE was associated with four measures of cognitive function, PICALM was associated with HMSE score, and TSPOAP1 was associated with executive function. The most strongly associated variants in each gene were rs429358 (APOE ε4), rs779406084 (PICALM), and rs9913145 (TSPOAP1). rs779406084 is a rare missense mutation that is more prevalent in LASI-DAD than in EA (minor allele frequency=0.075% vs. 0.0015%); the other two are common variants. No genes in the brain-specific promoter/enhancer analysis met criteria for significance. Results with and without annotation weights were similar. Missense/LoF variants in some genes previously associated with AD in EA are associated with measures of cognitive function in South Asians from India. Analyzing genome sequence data allows identification of potential novel causal variants enriched in South Asians.

PICALM as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltration in Breast Cancer

PICALM (phosphatidylinositol-binding clathrin assembly protein) mutations have been linked to a number of human disorders, including leukemia, Alzheimer's disease, and Parkinson's disease. Nevertheless, the effect of PICALM on cancer, particularly on prognosis and immune infiltration in individuals with BRCA, is unknown. We obtained the data of breast cancer patients from The Cancer Genome Atlas (TCGA) database, and analyzed the expression of PICALM in breast cancer, its impact on survival' and its role in tumor immune invasion. Finally, in vitro cellular experiments were performed to validate the results. Research has found that PICALM expression was shown to be downregulated in BRCA and to be substantially linked with clinical stage, histological type, PAM50, and age. PICALM downregulation was linked to a lower overall survival (OS) and disease-specific survival (DSS) in BRCA patients. A multivariate Cox analysis revealed that PICALM is an independent predictor of OS. The enriched pathways revealed by functional enrichment analysis included oxidative phosphorylation, angiogenesis, the TGF signaling pathway, and the IL-6/JAK/STAT3 signaling system. Furthermore, the amount of immune cell infiltration by B cells, eosinophils, mast cells, neutrophils, and T cells was positively linked with PICALM expression. Finally, we experimentally verified that low expression of PICALM can reduce proliferation, migration, and invasion in tumor cells. This evidence shows that PICALM expression impacts prognosis, immune infiltration, and pathway expression in breast cancer patients, and it might be a potential predictive biomarker for the disease.

Simple model systems reveal conserved mechanisms of Alzheimer's disease and related tauopathies

The lack of effective therapies that slow the progression of Alzheimer's disease (AD) and related tauopathies highlights the need for a more comprehensive understanding of the fundamental cellular mechanisms underlying these diseases. Model organisms, including yeast, worms, and flies, provide simple systems with which to investigate the mechanisms of disease. The evolutionary conservation of cellular pathways regulating proteostasis and stress response in these organisms facilitates the study of genetic factors that contribute to, or protect against, neurodegeneration. Here, we review genetic modifiers of neurodegeneration and related cellular pathways identified in the budding yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster, focusing on models of AD and related tauopathies. We further address the potential of simple model systems to better understand the fundamental mechanisms that lead to AD and other neurodegenerative disorders.

Smoothed quantile regression for partially functional linear models in high dimensions

Practitioners of current data analysis are regularly confronted with the situation where the heavy-tailed skewed response is related to both multiple functional predictors and high-dimensional scalar covariates. We propose a new class of partially functional penalized convolution-type smoothed quantile regression to characterize the conditional quantile level between a scalar response and predictors of both functional and scalar types. The new approach overcomes the lack of smoothness and severe convexity of the standard quantile empirical loss, considerably improving the computing efficiency of partially functional quantile regression. We investigate a folded concave penalized estimator for simultaneous variable selection and estimation by the modified local adaptive majorize-minimization (LAMM) algorithm. The functional predictors can be dense or sparse and are approximated by the principal component basis. Under mild conditions, the consistency and oracle properties of the resulting estimators are established. Simulation studies demonstrate a competitive performance against the partially functional standard penalized quantile regression. A real application using Alzheimer's Disease Neuroimaging Initiative data is utilized to illustrate the practicality of the proposed model.

A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is characterized by the intra- and extracellular accumulation of amyloid-β (Aβ) peptides. How Aβ aggregates perturb the proteome in brains of patients and AD transgenic mouse models, remains largely unclear. State-of-the-art mass spectrometry (MS) methods can comprehensively detect proteomic alterations, providing relevant insights unobtainable with transcriptomics investigations. Analyses of the relationship between progressive Aβ aggregation and protein abundance changes in brains of 5xFAD transgenic mice have not been reported previously.

METHODS

We quantified progressive Aβ aggregation in hippocampus and cortex of 5xFAD mice and controls with immunohistochemistry and membrane filter assays. Protein changes in different mouse tissues were analyzed by MS-based proteomics using label-free quantification; resulting MS data were processed using an established pipeline. Results were contrasted with existing proteomic data sets from postmortem AD patient brains. Finally, abundance changes in the candidate marker Arl8b were validated in cerebrospinal fluid (CSF) from AD patients and controls using ELISAs.

RESULTS

Experiments revealed faster accumulation of Aβ42 peptides in hippocampus than in cortex of 5xFAD mice, with more protein abundance changes in hippocampus, indicating that Aβ42 aggregate deposition is associated with brain region-specific proteome perturbations. Generating time-resolved data sets, we defined Aβ aggregate-correlated and anticorrelated proteome changes, a fraction of which was conserved in postmortem AD patient brain tissue, suggesting that proteome changes in 5xFAD mice mimic disease-relevant changes in human AD. We detected a positive correlation between Aβ42 aggregate deposition in the hippocampus of 5xFAD mice and the abundance of the lysosome-associated small GTPase Arl8b, which accumulated together with axonal lysosomal membranes in close proximity of extracellular Aβ plaques in 5xFAD brains. Abnormal aggregation of Arl8b was observed in human AD brain tissue. Arl8b protein levels were significantly increased in CSF of AD patients.

CONCLUSIONS

We report a comprehensive biochemical and proteomic investigation of hippocampal and cortical brain tissue derived from 5xFAD transgenic mice, providing a valuable resource to the neuroscientific community. We identified Arl8b, with significant abundance changes in 5xFAD and AD patient brains. Arl8b might enable the measurement of progressive lysosome accumulation in AD patients and have clinical utility as a candidate biomarker.

Gene Expression Profiling as a Novel Diagnostic Tool for Neurodegenerative Disorders

There is a lack of effective diagnostic biomarkers for neurodegenerative disorders (NDDs). Here, we established gene expression profiles for diagnosing Alzheimer’s disease (AD), Parkinson’s disease (PD), and vascular (VaD)/mixed dementia. Patients with AD had decreased APOE, PSEN1, and ABCA7 mRNA expression. Subjects with VaD/mixed dementia had 98% higher PICALM mRNA levels, but 75% lower ABCA7 mRNA expression than healthy individuals. Patients with PD and PD-related disorders showed increased SNCA mRNA levels. There were no differences in mRNA expression for OPRK1, NTRK2, and LRRK2 between healthy subjects and NDD patients. APOE mRNA expression had high diagnostic accuracy for AD, and moderate accuracy for PD and VaD/mixed dementia. PSEN1 mRNA expression showed promising accuracy for AD. PICALM mRNA expression was less accurate as a biomarker for AD. ABCA7 and SNCA mRNA expression showed high-to-excellent diagnostic accuracy for AD and PD, and moderate-to-high accuracy for VaD/mixed dementia. The APOE E4 allele reduced APOE expression in patients with different APOE genotypes. There was no association between PSEN1, PICALM, ABCA7, and SNCA gene polymorphisms and expression. Our study suggests that gene expression analysis has diagnostic value for NDDs and provides a liquid biopsy alternative to current diagnostic methods.

Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks

Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on ElasticNet and OMP (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks. We evaluate these methods on four Microarray datasets from the DREAM5 benchmark, using both internal and external metrics. The results obtained by both methods are comparable to those obtained by state-of-the-art tools, but are fast to train and exhibit high levels of sparsity, which make them easier to interpret. Moreover we applied these methods to three complex datasets containing RNA-seq informations from different mammalian tissues/cell-types. Lastly, we applied our methodology to compare a normal vs. a disease condition (Alzheimer), which allowed us to detect differential expression of genes’ sub-networks between these two biological conditions. Globally, the gene networks obtained exhibit a sparse and modular structure, with inner communities of genes presenting statistically significant over/under-expression on specific cell types, as well as significant enrichment for some anatomical GO terms, suggesting that such communities may also drive important functional roles.

Lipid metabolism and Alzheimer's disease: clinical evidence, mechanistic link and therapeutic promise

Alzheimer's disease (AD) is an age-associated neurodegenerative disorder with multifactorial etiology, intersecting genetic and environmental risk factors, and a lack of disease-modifying therapeutics. While the abnormal accumulation of lipids was described in the very first report of AD neuropathology, it was not until recent decades that lipid dyshomeostasis became a focus of AD research. Clinically, lipidomic and metabolomic studies have consistently shown alterations in the levels of various lipid classes emerging in early stages of AD brains. Mechanistically, decades of discovery research have revealed multifaceted interactions between lipid metabolism and key AD pathogenic mechanisms including amyloidogenesis, bioenergetic deficit, oxidative stress, neuroinflammation, and myelin degeneration. In the present review, converging evidence defining lipid dyshomeostasis in AD is summarized, followed by discussions on mechanisms by which lipid metabolism contributes to pathogenesis and modifies disease risk. Furthermore, lipid-targeting therapeutic strategies, and the modification of their efficacy by disease stage, ApoE status, and metabolic and vascular profiles, are reviewed.

Alzheimer's Disease: An Updated Overview of Its Genetics

Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.

Deep learning predicts the impact of regulatory variants on cell-type-specific enhancers in the brain

Motivation

Previous studies have shown that the heritability of multiple brain-related traits and disorders is highly enriched in transcriptional enhancer regions. However, these regions often contain many individual variants, while only a subset of them are likely to causally contribute to a trait. Statistical fine-mapping techniques can identify putative causal variants, but their resolution is often limited, especially in regions with multiple variants in high linkage disequilibrium. In these cases, alternative computational methods to estimate the impact of individual variants can aid in variant prioritization.

Results

Here, we develop a deep learning pipeline to predict cell-type-specific enhancer activity directly from genomic sequences and quantify the impact of individual genetic variants in these regions. We show that the variants highlighted by our deep learning models are targeted by purifying selection in the human population, likely indicating a functional role. We integrate our deep learning predictions with statistical fine-mapping results for 8 brain-related traits, identifying 63 distinct candidate causal variants predicted to contribute to these traits by modulating enhancer activity, representing 6% of all genome-wide association study signals analyzed. Overall, our study provides a valuable computational method that can prioritize individual variants based on their estimated regulatory impact, but also highlights the limitations of existing methods for variant prioritization and fine-mapping.

Availability and implementation

The data underlying this article, nucleotide-level importance scores, and code for running the deep learning pipeline are available at https://github.com/Pandaman-Ryan/AgentBind-brain.

Contact

mgymrek@ucsd.edu.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

PICALM Variation Moderates the Relationships of APOE ɛ4 with Alzheimer's Disease Cerebrospinal Biomarkers and Memory Function Among Non-Demented Population

BACKGROUND

APOE ɛ4 and PICALM are established genes associated with risk of late-onset Alzheimer's disease (AD). Previous study indicated interaction of PICALM with APOE ɛ4 in AD patients.

OBJECTIVE

To explore whether PICALM variation could moderate the influences of APOE ɛ4 on AD pathology biomarkers and cognition in pre-dementia stage.

METHODS

A total of 1,034 non-demented participants (mean age 74 years, 56% females, 40% APOE ɛ4 carriers) were genotyped for PICALM rs3851179 and APOE ɛ4 at baseline and were followed for influences on changes of cognition and cerebrospinal fluid (CSF) AD markers in six years. The interaction effects were examined via regression models adjusting for age, gender, education, and cognitive diagnosis.

RESULTS

The interaction term of rs3851179×APOE ɛ4 accounted for a significant amount of variance in baseline general cognition (p = 0.039) and memory function (p = 0.002). The relationships of APOE ɛ4 with trajectory of CSF Aβ42 (p = 0.007), CSF P-tau181 (p = 0.003), CSF T-tau (p = 0.001), and memory function (p = 0.017) were also moderated by rs3851179 variation.

CONCLUSIONS

APOE ɛ4 carriers experienced slower clinical and pathological progression when they had more protective A alleles of PICALM rs3851179. These findings firstly revealed the gene-gene interactive effects of PICALM with APOE ɛ4 in pre-dementia stage.

PICALM and Alzheimer's Disease: An Update and Perspectives

Genome-wide association studies (GWAS) have identified the PICALM (Phosphatidylinositol binding clathrin-assembly protein) gene as the most significant genetic susceptibility locus after APOE and BIN1. PICALM is a clathrin-adaptor protein that plays a critical role in clathrin-mediated endocytosis and autophagy. Since the effects of genetic variants of PICALM as AD-susceptibility loci have been confirmed by independent genetic studies in several distinct cohorts, there has been a number of in vitro and in vivo studies attempting to elucidate the underlying mechanism by which PICALM modulates AD risk. While differential modulation of APP processing and Aβ transcytosis by PICALM has been reported, significant effects of PICALM modulation of tau pathology progression have also been evidenced in Alzheimer's disease models. In this review, we summarize the current knowledge about PICALM, its physiological functions, genetic variants, post-translational modifications and relevance to AD pathogenesis.

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

Transcriptomic and epigenomic landscapes of Alzheimer's disease evidence mitochondrial-related pathways

Alzheimers disease (AD) is the main cause of dementia and it is defined by cognitive decline coupled to extracellular deposit of amyloid-beta protein and intracellular hyperphosphorylation of tau protein. Historically, efforts to target such hallmarks have failed in numerous clinical trials. In addition to these hallmark-targeted approaches, several clinical trials focus on other AD pathological processes, such as inflammation, mitochondrial dysfunction, and oxidative stress. Mitochondria and mitochondrial-related mechanisms have become an attractive target for disease-modifying strategies, as mitochondrial dysfunction prior to clinical onset has been widely described in AD patients and AD animal models. Mitochondrial function relies on both the nuclear and mitochondrial genome. Findings from omics technologies have shed light on AD pathophysiology at different levels (e.g., epigenome, transcriptome and proteome). Most of these studies have focused on the nuclear-encoded components. The first part of this review provides an updated overview of the mechanisms that regulate mitochondrial gene expression and function. The second part of this review focuses on evidence of mitochondrial dysfunction in AD. We have focused on published findings and datasets that study AD. We analyzed published data and provide examples for mitochondrial-related pathways. These pathways are strikingly dysregulated in AD neurons and glia in sex-, cell- and disease stage-specific manners. Analysis of mitochondrial omics data highlights the involvement of mitochondria in AD, providing a rationale for further disease modeling and drug targeting.

Promising protein biomarkers in the early diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is an insidious, multifactorial disease that involves the devastation of neurons leading to cognitive impairments. Alzheimer's have compounded pathologies of diverse nature, including proteins as one important factor along with mutated genes and enzymes. Although various review articles have proposed biomarkers, still, the statistical importance of proteins is missing. Proteins associated with AD include amyloid precursor protein, glial fibrillary acidic protein, calmodulin-like skin protein, hepatocyte growth factor, matrix Metalloproteinase-2. These proteins play a crucial role in the AD hypothesis which includes the tau hypothesis, amyloid-beta (Aβ) hypothesis, cholinergic neuron damage, etc. The present review highlights the role of major proteins and their physiological functions in the early diagnosis of AD. Altered protein expression results in cognitive impairment, synaptic dysfunction, neuronal degradation, and memory loss. On the medicinal ground, efforts of making anti-amyloid, anti-tau, anti-inflammatory treatments are on the peak, having these proteins as putative targets. Few proteins, e.g., Amyloid precursor protein results in the formation of non-soluble sticky Aβ₄₀ and Aβ₄₂ monomers that, over time, aggregate into plaques in the cortical and limbic brain areas and neurogranin is believed to regulate calcium-mediated signaling pathways and thus modulating synaptic plasticity are few putative and potential forthcoming targets for developing effective anti-AD therapies. These proteins may help to diagnose the disease early, bode well for the successful discovery and development of therapeutic and preventative regimens for this devasting public health problem.

Evolution of Human-Specific Alleles Protecting Cognitive Function of Grandmothers

The myelomonocytic receptor CD33 (Siglec-3) inhibits innate immune reactivity by extracellular V-set domain recognition of sialic acid (Sia)-containing "self-associated molecular patterns" (SAMPs). We earlier showed that V-set domain-deficient CD33-variant allele, protective against late-onset Alzheimer's Disease (LOAD), is derived and specific to the hominin lineage. We now report multiple hominin-specific CD33 V-set domain mutations. Due to hominin-specific, fixed loss-of-function mutation in the CMAH gene, humans lack N-glycolylneuraminic acid (Neu5Gc), the preferred Sia-ligand of ancestral CD33. Mutational analysis and molecular dynamics (MD)-simulations indicate that fixed change in amino acid 21 of hominin V-set domain and conformational changes related to His45 corrected for Neu5Gc-loss by switching to N-acetylneuraminic acid (Neu5Ac)-recognition. We show that human-specific pathogens Neisseria gonorrhoeae and Group B Streptococcus selectively bind human CD33 (huCD33) as part of immune-evasive molecular mimicry of host SAMPs and that this binding is significantly impacted by amino acid 21 modification. In addition to LOAD-protective CD33 alleles, humans harbor derived, population-universal, cognition-protective variants at several other loci. Interestingly, 11 of 13 SNPs in these human genes (including CD33) are not shared by genomes of archaic hominins: Neanderthals and Denisovans. We present a plausible evolutionary scenario to compile, correlate, and comprehend existing knowledge about huCD33-evolution and suggest that grandmothering emerged in humans.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

scEpiLock: A Weakly Supervised Learning Framework for cis-Regulatory Element Localization and Variant Impact Quantification for Single-Cell Epigenetic Data

Recent advances in single-cell transposase-accessible chromatin using a sequencing assay (scATAC-seq) allow cellular heterogeneity dissection and regulatory landscape reconstruction with an unprecedented resolution. However, compared to bulk-sequencing, its ultra-high missingness remarkably reduces usable reads in each cell type, resulting in broader, fuzzier peak boundary definitions and limiting our ability to pinpoint functional regions and interpret variant impacts precisely. We propose a weakly supervised learning method, scEpiLock, to directly identify core functional regions from coarse peak labels and quantify variant impacts in a cell-type-specific manner. First, scEpiLock uses a multi-label classifier to predict chromatin accessibility via a deep convolutional neural network. Then, its weakly supervised object detection module further refines the peak boundary definition using gradient-weighted class activation mapping (Grad-CAM). Finally, scEpiLock provides cell-type-specific variant impacts within a given peak region. We applied scEpiLock to various scATAC-seq datasets and found that it achieves an area under receiver operating characteristic curve (AUC) of ~0.9 and an area under precision recall (AUPR) above 0.7. Besides, scEpiLock’s object detection condenses coarse peaks to only ⅓ of their original size while still reporting higher conservation scores. In addition, we applied scEpiLock on brain scATAC-seq data and reported several genome-wide association studies (GWAS) variants disrupting regulatory elements around known risk genes for Alzheimer’s disease, demonstrating its potential to provide cell-type-specific biological insights in disease studies.

Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A and NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.

PICALM exerts a role in promoting CRC progression through ERK/MAPK signaling pathway

Background

Colorectal cancer (CRC) is a common malignant tumor in gastrointestinal tract with high incidence and mortality. In this study, the functions and potential mechanism of phosphatidylinositol-binding clathrin assembly protein (PICALM) in CRC were preliminarily explored.

Methods

Based on the Cancer Genome Atlas database and immunohistochemistry staining, revealing that the expression level of PICALM in CRC tissues was higher than that in adjacent normal tissues.

Results

Moreover, loss-of-function and gain-of-function assays in HCT 116 and RKO cells found that PICALM promotes proliferation and migration of CRC cells and inhibits apoptosis. Consistently, knockdown of PICALM inhibited tumorigenicity of CRC cells in vivo. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that knockdown of PICALM resulted in the enrichment of MAPK signaling pathway. Treatment of CRC cells with MAPK inhibitor reversed the effects of PICALM overexpression on proliferation and apoptosis. In addition, overexpression of PICALM upregulated the protein levels of ERK1/2 (p-ERK1/2), MEK1/2 (p-MEK1/2), p38 (p-p38) and JNK (p-JNK), and these effects were partially alleviated by the treatment of MAPK inhibitor.

Conclusions

In summary, the study presented the new discovery that PICALM promoted CRC progression through ERK/MAPK signaling pathway, which drew further interest regarding its clinical application as a promising therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02577-z.

A review ofimaging genetics in Alzheimer's disease

Determining the association between genetic variation and phenotype is a key step to study the mechanism of Alzheimer's disease (AD), laying the foundation for studying drug therapies and biomarkers. AD is the most common type of dementia in the aged population. At present, three early-onset AD genes (APP, PSEN1, PSEN2) and one late-onset AD susceptibility gene apolipoprotein E (APOE) have been determined. However, the pathogenesis of AD remains unknown. Imaging genetics, an emerging interdisciplinary field, is able to reveal the complex mechanisms from the genetic level to human cognition and mental disorders via macroscopic intermediates. This paper reviews methods of establishing genotype-phenotype to explore correlations, including sparse canonical correlation analysis, sparse reduced rank regression, sparse partial least squares and so on. We found that most research work did poorly in supervised learning and exploring the nonlinear relationship between SNP-QT.

Cognitive Resilience in Brain Health and Dementia Research

With the expected rise in Alzheimer's disease and related dementias (ADRD) in the coming decades due to the aging population and a lack of effective disease-modifying treatments, there is a need for preventive strategies that may tap into resilience parameters. A wide array of resilience strategies has been proposed including genetics, socioeconomic status, lifestyle modifications, behavioral changes, and management of comorbid disease. These different strategies can be broadly classified as distinguishing between modifiable and non-modifiable risk factors, some of which can be quantified so that their clinical intervention can be effectively accomplished. A clear shift in research focus from dementia risk to addressing disease resistance and resilience is emerging that has provided new potential therapeutic targets. Here we review and summarize the latest investigations of resilience mechanisms and methods of quantifying resilience for clinical research. These approaches include identifying genetic variants that may help identify novel pathways (e.g., lipid metabolism, cellular trafficking, synaptic function, inflammation) for therapeutic treatments and biomarkers for use in a precision medicine-like regimen. In addition, innovative structural and molecular neuroimaging analyses may assist in detecting and quantifying pathological changes well before the onset of clinical symptoms setting up the possibility of primary and secondary prevention trials. Lastly, we summarize recent studies demonstrating the study of resilience in caregivers of persons living with dementia may have direct and indirect impact on the quality of care and patient outcomes.

Influence of PICALM and CLU risk variants on beta EEG activity in Alzheimer's disease patients

PICALM and CLU genes have been linked to alterations in brain biochemical processes that may have an impact on Alzheimer’s disease (AD) development and neurophysiological dynamics. The aim of this study is to analyze the relationship between the electroencephalographic (EEG) activity and the PICALM and CLU alleles described as conferring risk or protective effects on AD patients and healthy controls. For this purpose, EEG activity was acquired from: 18 AD patients and 12 controls carrying risk alleles of both PICALM and CLU genes, and 35 AD patients and 12 controls carrying both protective alleles. Relative power (RP) in the conventional EEG frequency bands (delta, theta, alpha, beta, and gamma) was computed to quantify the brain activity at source level. In addition, spatial entropy (SE) was calculated in each band to characterize the regional distribution of the RP values throughout the brain. Statistically significant differences in global RP and SE at beta band (p-values < 0.05, Mann–Whitney U-test) were found between genotypes in the AD group. Furthermore, RP showed statistically significant differences in 58 cortical regions out of the 68 analyzed in AD. No statistically significant differences were found in the control group at any frequency band. Our results suggest that PICALM and CLU AD-inducing genotypes are involved in physiological processes related to disruption in beta power, which may be associated with physiological disturbances such as alterations in beta-amyloid and neurotransmitter metabolism.

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment

OBJECTIVES

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis.

METHODS

In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used.

RESULTS

We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50%, 98.39% ± 2.50%, and 99.44% ± 1.11% using the DLG model, while 71.38% ± 0.63%, 63.13% ± 2.87%, and 85.59% ± 6.66% using traditional GWAS. Similar results were obtained from the other two intergroup classifications.

CONCLUSION

The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

Protective genes and pathways in Alzheimer's disease: moving towards precision interventions

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that is characterized by neurodegeneration, cognitive impairment, and an eventual inability to perform daily tasks. The etiology of Alzheimer's is complex, with numerous environmental and genetic factors contributing to the disease. Late-onset AD is highly heritable (60 to 80%), and over 40 risk loci for AD have been identified via large genome-wide association studies, most of which are common variants with small effect sizes. Although these discoveries have provided novel insight on biological contributors to AD, disease-modifying treatments remain elusive. Recently, the concepts of resistance to pathology and resilience against the downstream consequences of pathology have been of particular interest in the Alzheimer's field as studies continue to identify individuals who evade the pathology of the disease even into late life and individuals who have all of the neuropathological features of AD but evade downstream neurodegeneration and cognitive impairment. It has been hypothesized that a shift in focus from Alzheimer's risk to resilience presents an opportunity to uncover novel biological mechanisms of AD and to identify promising therapeutic targets for the disease. This review will highlight a selection of genes and variants that have been reported to confer protection from AD within the literature and will also discuss evidence for the biological underpinnings behind their protective effect with a focus on genes involved in lipid metabolism, cellular trafficking, endosomal and lysosomal function, synaptic function, and inflammation. Finally, we offer some recommendations in areas where the field can rapidly advance towards precision interventions that leverage the ideas of protection and resilience for the development of novel therapeutic strategies.

The Differential Influence of Immune, Endocytotic, and Lipid Metabolism Genes on Amyloid Deposition and Neurodegeneration in Subjects at Risk of Alzheimer's Disease

BACKGROUND

Over 20 single-nucleotide polymorphisms (SNPs) are associated with increased risk of Alzheimer's disease (AD). We categorized these loci into immunity, lipid metabolism, and endocytosis pathways, and associated the polygenic risk scores (PRS) calculated, with AD biomarkers in mild cognitive impairment (MCI) subjects.

OBJECTIVE

The aim of this study was to identify associations between pathway-specific PRS and AD biomarkers in patients with MCI and healthy controls.

METHODS

AD biomarkers ([18F]Florbetapir-PET SUVR, FDG-PET SUVR, hippocampal volume, CSF tau and amyloid-β levels) and neurocognitive tests scores were obtained in 258 healthy controls and 451 MCI subjects from the ADNI dataset at baseline and at 24-month follow up. Pathway-related (immunity, lipid metabolism, and endocytosis) and total polygenic risk scores were calculated from 20 SNPs. Multiple linear regression analysis was used to test predictive value of the polygenic risk scores over longitudinal biomarker and cognitive changes.

RESULTS

Higher immune risk score was associated with worse cognitive measures and reduced glucose metabolism. Higher lipid risk score was associated with increased amyloid deposition and cortical hypometabolism. Total, immune, and lipid scores were associated with significant changes in cognitive measures, amyloid deposition, and brain metabolism.

CONCLUSION

Polygenic risk scores highlights the influence of specific genes on amyloid-dependent and independent pathways; and these pathways could be differentially influenced by lipid and immune scores respectively.

Association of Alzheimer's disease risk variants on the PICALM gene with PICALM expression, core biomarkers, and feature neurodegeneration

It is still unclear how PICALM mutations influence the risk of Alzheimer’s disease (AD). We tested the association of AD risk variants on the PICALM gene with PICALM expression and AD feature endophenotypes. Bioinformatic methods were used to annotate the functionalities and to select the tag single nucleotide polymorphisms (SNPs). Multiple regressions were used to examine the cross-sectional and longitudinal influences of tag SNPs on cerebrospinal fluid (CSF) AD biomarkers and neurodegenerations. A total of 59 SNPs, among which 75% were reported in Caucasians, were associated with AD risk. Of these, 73% were linked to PICALM expression in the whole blood (p < 0.0001) and/or brain regions (p < 0.05). Eleven SNPs were selected as tag SNPs in Caucasians. rs510566 (T allele) was associated with decreased CSF ptau and ptau/abeta42 ratio. The G allele of rs1237999 and rs510566 was linked with greater reserve capacities of the hippocampus, parahippocampus, middle temporal lobe, posterior cingulate, and precuneus. The longitudinal analyses revealed four loci that could predict dynamic changes of CSF ptau and ptau/abeta42 ratio (rs10501610, p = 0.0001) or AD feature neurodegeneration (rs3851179, rs592297, and rs7480193, p < 0.005). Overall, the genetic, bioinformatic, and association studies tagged four SNPs (rs3851179, rs7480193, rs510566, and rs1237999) as the most prominent PICALM loci contributing to AD in Caucasians.

The first Antechinus reference genome provides a resource for investigating the genetic basis of semelparity and age-related neuropathologies

Antechinus are a genus of mouse-like marsupials that exhibit a rare reproductive strategy known as semelparity and also naturally develop age-related neuropathologies similar to those in humans. We provide the first annotated antechinus reference genome for the brown antechinus (Antechinus stuartii). The reference genome is 3.3 Gb in size with a scaffold N50 of 73Mb and 93.3% complete mammalian BUSCOs. Using bioinformatic methods we assign scaffolds to chromosomes and identify 0.78 Mb of Y-chromosome scaffolds. Comparative genomics revealed interesting expansions in the NMRK2 gene and the protocadherin gamma family, which have previously been associated with aging and age-related dementias respectively. Transcriptome data displayed expression of common Alzheimer's related genes in the antechinus brain and highlight the potential of utilising the antechinus as a future disease model. The valuable genomic resources provided herein will enable future research to explore the genetic basis of semelparity and age-related processes in the antechinus.

Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and Parkinson's diseases

Genome-wide association studies of neurological diseases have identified thousands of variants associated with disease phenotypes. However, most of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional SNPs for Alzheimer's and Parkinson's diseases, nominating target genes and cell types for previously orphaned loci from genome-wide association studies. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) Parkinson's disease risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

Association of PICALM Gene Polymorphisms with Alzheimer's Disease: Evidence from an Updated Meta-Analysis

BACKGROUND

Previous studies have examined the roles of three polymorphisms (rs3851179, rs541458, and rs592297) of the PICALM gene in susceptibility to Alzheimer's disease (AD) with inconclusive findings.

OBJECTIVE

We performed a meta-analysis to explore whether these three polymorphisms in the PICALM gene were associated with susceptibility to AD.

METHODS

Bibliographical searches were conducted in the PubMed, Embase, Web of Science, and China National Knowledge Infrastructure (CNKI) databases. Summary Odds Ratios (ORs) with 95% Confidence Intervals (CIs) were used to assess the strength of association in a random effects model. Potential sources of heterogeneity were identified by subgroup and meta-regression analyses.

RESULTS

Twenty studies (9,017 cases and 15,448 controls) on rs3851179, 12 studies (8,077 cases and 12,022 controls) on rs541458, and 4 studies (2,106 cases and 2,234 controls) on rs592297 were considered eligible for meta-analyses. For both rs3851179 and rs541458, the overall ORs were significant under all genetic models with mild heterogeneity. Compared with G carriers, A carriers of rs3851179 were associated with a decreased risk of AD (OR = 0.88; 95% CI 0.84, 0.91, P for Z-test <0.001, I2 = 0.0%). Compared with T carriers, C carriers of rs541458 were inversely associated with AD risk (OR = 0.86; 95% CI 0.81, 0.92, P for Z-test <0.001, I2 = 39.5%). No association was observed for rs592297. Subgroup and meta-regression analyses indicated that the protective effect of the rs541458 C allele was observed only among Caucasians, not among Asians (P for interaction: 0.021~<0.001).

CONCLUSION

rs3851179 and rs541458 appear to be associated with decreased AD risk. The null associations for rs592297 with AD risk need further confirmation with a larger number of participants.

The dark side of Alzheimer's disease: unstructured biology of proteins from the amyloid cascade signaling pathway

Alzheimer's disease (AD) is a leading cause of age-related dementia worldwide. Despite more than a century of intensive research, we are not anywhere near the discovery of a cure for this disease or a way to prevent its progression. Among the various molecular mechanisms proposed for the description of the pathogenesis and progression of AD, the amyloid cascade hypothesis, according to which accumulation of a product of amyloid precursor protein (APP) cleavage, amyloid β (Aβ) peptide, induces pathological changes in the brain observed in AD, occupies a unique niche. Although multiple proteins have been implicated in this amyloid cascade signaling pathway, their structure-function relationships are mostly unexplored. However, it is known that two major proteins related to AD pathology, Aβ peptide, and microtubule-associated protein tau belong to the category of intrinsically disordered proteins (IDPs), which are the functionally important proteins characterized by a lack of fixed, ordered three-dimensional structure. IDPs and intrinsically disordered protein regions (IDPRs) play numerous vital roles in various cellular processes, such as signaling, cell cycle regulation, macromolecular recognition, and promiscuous binding. However, the deregulation and misfolding of IDPs may lead to disturbed signaling, interactions, and disease pathogenesis. Often, molecular recognition-related IDPs/IDPRs undergo disorder-to-order transition upon binding to their biological partners and contain specific disorder-based binding motifs, known as molecular recognition features (MoRFs). Knowing the intrinsic disorder status and disorder-based functionality of proteins associated with amyloid cascade signaling pathway may help to untangle the mechanisms of AD pathogenesis and help identify therapeutic targets. In this paper, we have used multiple computational tools to evaluate the presence of intrinsic disorder and MoRFs in 27 proteins potentially relevant to the amyloid cascade signaling pathway. Among these, BIN1, APP, APOE, PICALM, PSEN1 and CD33 were found to be highly disordered. Furthermore, their disorder-based binding regions and associated short linear motifs have also been identified. These findings represent important foundation for the future research, and experimental characterization of disordered regions in these proteins is required to better understand their roles in AD pathogenesis.

Explicating anti-amyloidogenic role of curcumin and piperine via amyloid beta (Aβ) explicit pathway: recovery and reversal paradigm effects

Previously, we reported the synergistic effects of curcumin and piperine in cell cultures as potential anti-cholinesterase and anti-amyloidogenic agents. Due to limited findings on the enrolment of these compounds on epigenetic events in AD, we aimed at elucidating the expression profiles of Aβ42-induced SH-SY5Y cells using microarray profiling. In this study, an optimized concentration of 35 µM of curcumin and piperine in combination was used to treat Aβ42 fibril and high-throughput microarray profiling was performed on the extracted RNA. This was then compared to curcumin and piperine used singularly at 49.11 µM and 25 µM, respectively. Our results demonstrated that in the curcumin treated group, from the top 10 upregulated and top 10 downregulated significantly differentially expressed genes (p < 0.05; fold change ≥ 2 or ≤ -2), there were five upregulated and three downregulated genes involved in the amyloidogenic pathway. While from top 10 upregulated and top 10 downregulated significantly differentially expressed genes (p < 0.05; fold change ≥ 2 or ≤ - 2) in the piperine treated group, there were four upregulated and three downregulated genes involved in the same pathway, whereas there were five upregulated and two downregulated genes involved (p < 0.05; fold change ≥ 2 or ≤ - 2) in the curcumin-piperine combined group. Four genes namely GABARAPL1, CTSB, RAB5 and AK5 were expressed significantly in all groups. Other genes such as ITPR1, GSK3B, PPP3CC, ERN1, APH1A, CYCS and CALM2 were novel putative genes that are involved in the pathogenesis of AD. We revealed that curcumin and piperine have displayed their actions against Aβ via the modulation of various mechanistic pathways. Alterations in expression profiles of genes in the neuronal cell model may explain Aβ pathology post-treatment and provide new insights for remedial approaches of a combined treatment using curcumin and piperine.

Single Nucleotide Polymorphisms in Alzheimer's Disease Risk Genes Are Associated with Intrinsic Connectivity in Middle Age

BACKGROUND

It is critical to identify individuals at risk for Alzheimer's disease (AD) earlier in the disease time course, such as middle age and preferably well prior to the onset of clinical symptoms, when intervention efforts may be more successful. Genome-wide association and candidate gene studies have identified single nucleotide polymorphisms (SNPs) in APOE, CLU, CR1, PICALM, and SORL1 that confer increased risk of AD.

OBJECTIVE

In the current study, we investigated the associations between SNPs in these genes and resting-state functional connectivity within the default mode network (DMN), frontoparietal network (FPN), and executive control network (ECN) in healthy, non-demented middle-aged adults (age 40 -60; N = 123; 74 females).

METHODS

Resting state networks of interest were identified through independent components analysis using a template-matching procedure and individual spatial maps and time courses were extracted using dual regression.

RESULTS

Within the posterior DMN, functional connectivity was associated with CR1 rs1408077 and CLU rs9331888 polymorphisms (p's < 0.05). FPN connectivity was associated with CR1 rs1408077, CLU rs1136000, SORL1 rs641120, and SORL1 rs689021 (p's < 0.05). Functional connectivity within the ECN was associated with the CLU rs11136000 (p < 0.05). There were no APOE- or PICALM-related differences in any of the networks investigated (p's > 0.05).

CONCLUSION

This is the first demonstration of the relationship between intrinsic network connectivity and AD risk alleles in CLU, CR1, and SORL1 in healthy, middle-aged adults. These SNPs should be considered in future investigations aimed at identifying potential preclinical biomarkers for AD.

The cellular machinery of post-endocytic APP trafficking in Alzheimer's disease: A future target for therapeutic intervention?

Recent data establish multiple defects in endocytic functions as early events initiating various neurodegenerative disorders, including Alzheimer's disease (AD). The genetic landscape resulting from genome-wide association studies (GWAS) reveals changes in post-endocytic trafficking of amyloid precursor protein (APP) in neurons leading to an increase in amyloidogenic processing, deficits in amyloid beta (Aβ) clearance, increases in intracellular Aβ, and other endosomal pathogenic phenotypes. Multiple genetic factors regulate each segment of endosomal and post-endosomal trafficking. Intriguingly, several studies indicate endosomal dysfunctions preceding Aβ pathology and tau phosphorylation. In this chapter we highlight the role of various GWAS-identified endosomal and post-endosomal gene products in initiating AD pathologies. We also summarize the functions of various genetic modifiers of post-endocytic trafficking of APP that may work as targets for therapeutic intervention in AD.

High glucose-mediated PICALM and mTORC1 modulate processing of amyloid precursor protein via endosomal abnormalities

BACKGROUND AND PURPOSE

Although diabetes mellitus (DM) is an important risk factor for Alzheimer's disease (AD), the detailed mechanism(s) by which DM regulates amyloid β (Aβ) processing is still unclear. The longer residence time of amyloid precursor protein (APP) in endosomes is critical for Aβ production and DM is known to cause endosomal dysregulation. Here we have examined the effects of high glucose on APP-producing endosomes and related signaling pathways.

EXPERIMENTAL APPROACH

To identify the underlying mechanisms, we investigated the effects of high glucose on abnormalities in early endosomes and related signalling pathways in human neuroblastoma cells. In vivo, diabetic mice treated with pharmacological inhibitors were used to examine endosomal dysfunction.

KEY RESULTS

The hippocampus of diabetic animals presented endosomal abnormalities and Aβ up-regulation. High glucose increased Aβ production through early endosomal enlargement achieved by increased lipid raft-mediated APP endocytosis. High glucose induced ROS-stimulated Sp1 activation, up-regulating phosphatidylinositol binding clathrin assembly protein (PICALM), clathrin heavy chain, and adaptor-related protein complex 2 alpha 1. PICALM facilitated clathrin-mediated APP endocytosis resulting in early endosomal enlargement. Meanwhile, AMPK/mTORC1-mediated autophagy defect and ROS- and mTORC1-mediated lysosomal dysfunction aggravated early endosomal enlargement under high glucose. Moreover, the increased Aβ production and cognitive deficits in diabetic mice were reversed by inhibition of early endosomal enlargement.

CONCLUSION AND IMPLICATIONS

High glucose induces early endosomal abnormalities through PICALM-induced APP endocytosis and mTORC1-inhibited endosomal clearance, up-regulating Aβ production. Thus, targeting PICALM and mTORC1 to prevent endosomal disorders is a promising strategy for managing diabetes-induced AD.

Autophagy in Neurodegenerative Diseases: A Hunter for Aggregates

Cells have developed elaborate quality-control mechanisms for proteins and organelles to maintain cellular homeostasis. Such quality-control mechanisms are maintained by conformational folding via molecular chaperones and by degradation through the ubiquitin-proteasome or autophagy-lysosome system. Accumulating evidence suggests that impaired autophagy contributes to the accumulation of intracellular inclusion bodies consisting of misfolded proteins, which is a hallmark of most neurodegenerative diseases. In addition, genetic mutations in core autophagy-related genes have been reported to be linked to neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. Conversely, the pathogenic proteins, such as amyloid β and α-synuclein, are detrimental to the autophagy pathway. Here, we review the recent advances in understanding the relationship between autophagic defects and the pathogenesis of neurodegenerative diseases and suggest autophagy induction as a promising strategy for the treatment of these conditions.

Genetic Association Between Alzheimer's Disease Risk Variant of the PICALM Gene and EEG Functional Connectivity in Non-demented Adults

Genome wide association studies (GWAS) have identified and validated the association of the PICALM genotype with Alzheimer's disease (AD). The PICALM rs3851179 A allele is thought to have a protective effect, whereas the G allele appears to confer risk for AD. The influence of the PICALM genotype on brain functional connectivity in non-demented subjects remains largely unknown. We examined the association of the PICALM rs3851179 genotype with the characteristics of lagged linear connectivity (LLC) of resting EEG sources in 104 non-demented adults younger than 60 years of age. The EEG analysis was performed using exact low-resolution brain electromagnetic tomography (eLORETA) freeware (Pascual-Marqui et al., 2011). We found that the carriers of the A PICALM allele (PICALM AA and AG genotypes) had higher widespread interhemispheric LLC of alpha sources compared to the carriers of the GG PICALM allele. An exploratory correlation analysis showed a moderate positive association between the alpha LLC interhemispheric characteristics and the corpus callosum size and between the alpha interhemispheric LLC characteristics and the Luria word memory scores. These results suggest that the PICALM rs3851179 A allele provides protection against cognitive decline by facilitating neurophysiological reserve capacities in non-demented adults. In contrast, lower functional connectivity in carriers of the AD risk variant, PICALM GG, suggests early functional alterations in alpha rhythm networks.

Intracellular Trafficking Mechanisms of Synaptic Dysfunction in Alzheimer's Disease

Alzheimer’s disease (AD) is the most common neurodegenerative disease characterized by progressive memory loss. Although AD neuropathological hallmarks are extracellular amyloid plaques and intracellular tau tangles, the best correlate of disease progression is synapse loss. What causes synapse loss has been the focus of several researchers in the AD field. Synapses become dysfunctional before plaques and tangles form. Studies based on early-onset familial AD (eFAD) models have supported that synaptic transmission is depressed by β-amyloid (Aβ) triggered mechanisms. Since eFAD is rare, affecting only 1% of patients, research has shifted to the study of the most common late-onset AD (LOAD). Intracellular trafficking has emerged as one of the pathways of LOAD genes. Few studies have assessed the impact of trafficking LOAD genes on synapse dysfunction. Since endocytic traffic is essential for synaptic function, we reviewed Aβ-dependent and independent mechanisms of the earliest synaptic dysfunction in AD. We have focused on the role of intraneuronal and secreted Aβ oligomers, highlighting the dysfunction of endocytic trafficking as an Aβ-dependent mechanism of synapse dysfunction in AD. Here, we reviewed the LOAD trafficking genes APOE4, ABCA7, BIN1, CD2AP, PICALM, EPH1A, and SORL1, for which there is a synaptic link. We conclude that in eFAD and LOAD, the earliest synaptic dysfunctions are characterized by disruptions of the presynaptic vesicle exo- and endocytosis and of postsynaptic glutamate receptor endocytosis. While in eFAD synapse dysfunction seems to be triggered by Aβ, in LOAD, there might be a direct synaptic disruption by LOAD trafficking genes. To identify promising therapeutic targets and biomarkers of the earliest synaptic dysfunction in AD, it will be necessary to join efforts in further dissecting the mechanisms used by Aβ and by LOAD genes to disrupt synapses.

Bioinformatics strategy to advance the interpretation of Alzheimer's disease GWAS discoveries: The roads from association to causation

INTRODUCTION

Genome-wide association studies (GWAS) discovered multiple late-onset Alzheimer's disease (LOAD)-associated SNPs and inferred the genes based on proximity; however, the actual causal genes are yet to be identified.

METHODS

We defined LOAD-GWAS regions by the most significantly associated SNP ±0.5 Mb and developed a bioinformatics pipeline that uses and integrates chromatin state segmentation track to map active enhancers and virtual 4C software to visualize interactions between active enhancers and gene promoters. We augmented our pipeline with biomedical and functional information.

RESULTS

We applied the bioinformatics pipeline using three ∼1 Mb LOAD-GWAS loci: BIN1, PICALM, CELF1. These loci contain 10-24 genes, an average of 106 active enhancers and 80 CTCF sites. Our strategy identified all genes corresponding to the promoters that interact with the active enhancer that is closest to the LOAD-GWAS-SNP and generated a shorter list of prioritized candidate LOAD genes (5-14/loci), feasible for post-GWAS investigations of causality.

DISCUSSION

Interpretation of LOAD-GWAS discoveries requires the integration of brain-specific functional genomic data sets and information related to regulatory activity.

The role of CD2AP in the Pathogenesis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease characterized by irreversible decline in cognition with unclear pathogenesis. Recently, accumulating evidence has revealed that CD2 associated protein (CD2AP), a scaffolding molecule regulates signal transduction and cytoskeletal molecules, is implicated in AD pathogenesis. Several single nucleotide polymorphisms (SNPs) in CD2AP gene are associated with higher risk for AD and mRNA levels of CD2AP are decreased in peripheral lymphocytes of sporadic AD patients. Furthermore, CD2AP loss of function is linked to enhanced Aβ production, Tau-induced neurotoxicity, abnormal neurite structure modulation and reduced blood-brain barrier integrity. This review is to summarize the recent discoveries about the genetics and known functions of CD2AP. The recent evidence concerning the roles of CD2AP in the AD pathogenesis is summarized and CD2AP can be a promising therapeutic target for AD.

The role of PTB domain containing adaptor proteins on PICALM-mediated APP endocytosis and localization

One hallmark of Alzheimer's disease (AD) is the presence of amyloid plaques, which mainly consist of the amyloid precursor protein (APP) cleavage product amyloid β (Aβ). For cleavage to occur, the APP must be endocytosed from the cell surface. The phosphatidylinositol binding clathrin assembly protein (PICALM) is involved in clathrin-mediated endocytosis and polymorphisms in and near the gene locus were identified as genetic risk factors for AD. PICALM overexpression enhances APP internalization and Aβ production. Furthermore, PICALM shuttles into the nucleus, but its function within the nucleus is still unknown. Using co-immunoprecipitation, we demonstrated an interaction between PICALM and APP, which is abrogated by mutation of the APP NPXY-motif. Since the NPXY-motif is an internalization signal that binds to phosphotryrosine-binding domain-containing adaptor proteins (PTB-APs), we hypothesized that PTB-APs can modulate the APP-PICALM interaction. We found that interaction between PICALM and the PTB-APs (Numb, JIP1b and GULP1) enhances the APP-PICALM interaction. Fluorescence activated cell sorting analysis and internalization assays revealed differentially altered APP cell surface levels and endocytosis rates that depended upon the presence of PICALM and co-expression of distinct PTB-APs. Additionally, we were able to show an impact of PICALM nuclear shuttling upon co-expression of PTB-APs and PICALM, with the magnitude of the effect depending on which PTB-AP was co-expressed. Taken together, our results indicate a modulating effect of PTB-APs on PICALM-mediated APP endocytosis and localization.