Alzheimer's Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci

A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

A Single-Nucleus Transcriptome-Wide Association Study Implicates Novel Genes in Depression Pathogenesis

BACKGROUND

Depression, a common psychiatric illness and global public health problem, remains poorly understood across different life stages, which hampers the development of novel treatments.

METHODS

To identify new candidate genes for therapeutic development, we performed differential gene expression analysis of single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of older adults (n = 424) in relation to antemortem depressive symptoms. Additionally, we integrated genome-wide association study results for depression (n = 500,199) along with genetic tools for inferring the expression of 14,048 unique genes in 7 cell types and 52 cell subtypes to perform a transcriptome-wide association study of depression followed by Mendelian randomization.

RESULTS

Our single-nucleus transcriptome-wide association study analysis identified 68 candidate genes for depression and showed the greatest number being in excitatory and inhibitory neurons. Of the 68 genes, 53 were novel compared to previous studies. Notably, gene expression in different neuronal subtypes had varying effects on depression risk. Traits with high genetic correlations with depression, such as neuroticism, shared more transcriptome-wide association study genes than traits that were not highly correlated with depression. Complementing these analyses, differential gene expression analysis across 52 neocortical cell subtypes showed that genes such as KCNN2, SCAI, WASF3, and SOCS6 were associated with late-life depressive symptoms in specific cell subtypes.

CONCLUSIONS

These 2 sets of analyses illustrate the utility of large single-nucleus RNA sequencing data both to uncover genes whose expression is altered in specific cell subtypes in the context of depressive symptoms and to enhance the interpretation of well-powered genome-wide association studies so that we can prioritize specific susceptibility genes for further analysis and therapeutic development.

MTCH2 in Metabolic Diseases, Neurodegenerative Diseases, Cancers, Embryonic Development and Reproduction

Mitochondrial carrier homolog 2 (MTCH2) is a member of the solute carrier 25 family, located on the outer mitochondrial membrane. MTCH2 was first identified in 2000. The development in MTCH2 research is rapidly increasing. The most well-known role of MTCH2 is linking to the pro-apoptosis BID to facilitate mitochondrial apoptosis. Genetic variants in MTCH2 have been investigated for their association with metabolic and neurodegenerative diseases, however, no intervention or therapeutic suggestions were provided. Recent studies revealed the physiological and pathological function of MTCH2 in metabolic diseases, neurodegenerative diseases, cancers, embryonic development and reproduction via regulating mitochondrial apoptosis, metabolic shift between glycolysis and oxidative phosphorylation, mitochondrial fusion/fission, epithelial-mesenchymal transition, etc. This review endeavors to assess a total of 131 published articles to summarise the structure and physiological/pathological role of MTCH2, which has not previously been conducted. This review concludes that MTCH2 plays a crucial role in metabolic diseases, neurodegenerative diseases, cancers, embryonic development and reproduction, and the predominant molecular mechanism is regulation of mitochondrial function. This review gives a comprehensive state of current knowledgement on MTCH2, which will promote the therapeutic research of MTCH2.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features

BACKGROUND

Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

METHODS

A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics.

RESULTS

The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel).

CONCLUSION

The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

Gene expression investigation of four key regulators of polyadenylation and alternative adenylation in the periphery of late-onset Alzheimer's disease patients

BACKGROUND

Alzheimer's disease (AD) is a genetic and sporadic neurodegenerative disease considered by an archetypal cognitive impairment and a decrease in less common cognitive impairment. Notably, the discovery of goals in this paradigm is still a challenge, and understanding basic mechanisms is an important step toward improving disease management. Polyadenylation (PA) and alternative polyadenylation (APA) are two of the most critical RNA processing stages in 3'UTRs that influence various AD-related genes.

METHODS

In this study, we assessed Cleavage and polyadenylation specificity factors 1 and 6 (CPSF1 and CPSF6), cleavage stimulation factor 1 (CSTF1), and WD Repeat Domain 33 (WDR33) genes expression in the periphery of 50 AD patients and 50 healthy individuals with age and gender-matched by quantitative real-time PCR.

RESULTS

Comparing AD patients with healthy people using expression analysis revealed a substantial increase in CSTF1 (posterior beta = 0.773, adjusted P-value = 0.042). Significant positive correlations were found between CSTF1 and CPSF1 (r = 0.365, P < 0.001), WDR33 (r = 0.506, P < 0.001), and CPSF6 (r = 0.446, P < 0.001) expression levels.

CONCLUSION

Although further research is required to determine their potential contribution to AD, our findings offer a fresh perspective on molecular regulatory pathways associated with AD pathogenic mechanisms associated with PA and APA.

Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer's Disease

BACKGROUND

Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation.

METHODS

To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF1/SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays.

RESULTS

We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1, convergent evidence indicated that 6 target genes (MTCH2, ACP2, NDUFS3, PSMC3, C1QTNF4, and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2.

CONCLUSIONS

Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD.

Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer's disease brains

BACKGROUND

The genetic underpinnings of late-onset Alzheimer's disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD.

METHODS

Here, we present the largest parallel single-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co-accessibility networks (CCANs).

RESULTS

Integrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype-specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which, including ELK1, JUN, and SMAD4 in excitatory neurons, were also LOAD-DEGs. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs, including APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes.

CONCLUSIONS

To our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specific cis-trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.

Regional genetic correlations highlight relationships between neurodegenerative disease loci and the immune system

Neurodegenerative diseases, including Alzheimer's and Parkinson's disease, are devastating complex diseases resulting in physical and psychological burdens on patients and their families. There have been important efforts to understand their genetic basis leading to the identification of disease risk-associated loci involved in several molecular mechanisms, including immune-related pathways. Regional, in contrast to genome-wide, genetic correlations between pairs of immune and neurodegenerative traits have not been comprehensively explored, but could uncover additional immune-mediated risk-associated loci. Here, we systematically assess the role of the immune system in five neurodegenerative diseases by estimating regional genetic correlations between these diseases and immune-cell-derived single-cell expression quantitative trait loci (sc-eQTLs). We also investigate correlations between diseases and protein levels. We observe significant (FDR < 0.01) correlations between sc-eQTLs and neurodegenerative diseases across 151 unique genes, spanning both the innate and adaptive immune systems, across most diseases tested. With Parkinson's, for instance, RAB7L1 in CD4+ naïve T cells is positively correlated and KANSL1-AS1 is negatively correlated across all adaptive immune cell types. Follow-up colocalization highlight candidate causal risk genes. The outcomes of this study will improve our understanding of the immune component of neurodegeneration, which can warrant repurposing of existing immunotherapies to slow disease progression.

RNA-binding proteins in autoimmunity: From genetics to molecular biology

Autoimmune diseases (ADs) are chronic pathologies generated by the loss of immune tolerance to the body's own cells and tissues. There is growing recognition that RNA-binding proteins (RBPs) critically govern immunity in healthy and pathological conditions by modulating gene expression post-transcriptionally at all levels: nuclear mRNA splicing and modification, export to the cytoplasm, as well as cytoplasmic mRNA transport, storage, editing, stability, and translation. Despite enormous efforts to identify new therapies for ADs, definitive solutions are not yet available in many instances. Recognizing that many ADs have a strong genetic component, we have explored connections between the molecular biology and the genetics of RBPs in ADs. Here, we review the genetics and molecular biology of RBPs in four major ADs, multiple sclerosis (MS), type 1 diabetes mellitus (T1D), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA). We anticipate that gaining insights into the genetics and biology of ADs can facilitate the discovery of new therapies. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Microglia in Alzheimer's disease: pathogenesis, mechanisms, and therapeutic potentials

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by protein aggregation in the brain. Recent studies have revealed the critical role of microglia in AD pathogenesis. This review provides a comprehensive summary of the current understanding of microglial involvement in AD, focusing on genetic determinants, phenotypic state, phagocytic capacity, neuroinflammatory response, and impact on synaptic plasticity and neuronal regulation. Furthermore, recent developments in drug discovery targeting microglia in AD are reviewed, highlighting potential avenues for therapeutic intervention. This review emphasizes the essential role of microglia in AD and provides insights into potential treatments.

BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity

Alzheimer's disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.

MTCH2 is a mitochondrial outer membrane protein insertase

In the mitochondrial outer membrane, α-helical transmembrane proteins play critical roles in cytoplasmic-mitochondrial communication. Using genome-wide CRISPR screens, we identified MTCH2, and its paralog MTCH1, and showed that it is required for insertion of biophysically diverse tail-anchored (TA), signal-anchored, and multipass proteins, but not outer membrane β-barrel proteins. Purified MTCH2 was sufficient to mediate insertion into reconstituted proteoliposomes. Functional and mutational studies suggested that MTCH2 has evolved from a solute carrier transporter. MTCH2 uses membrane-embedded hydrophilic residues to function as a gatekeeper for the outer membrane, controlling mislocalization of TAs into the endoplasmic reticulum and modulating the sensitivity of leukemia cells to apoptosis. Our identification of MTCH2 as an insertase provided a mechanistic explanation for the diverse phenotypes and disease states associated with MTCH2 dysfunction.

We showed that MTCH2 was both necessary and sufficient for insertion of diverse α-helical proteins into the mitochondrial outer membrane, and was the defining member of a family of insertases that have co-opted the SLC25 transporter fold.

E2F4DN Transgenic Mice: A Tool for the Evaluation of E2F4 as a Therapeutic Target in Neuropathology and Brain Aging

E2F4 was initially described as a transcription factor with a key function in the regulation of cell quiescence. Nevertheless, a number of recent studies have established that E2F4 can also play a relevant role in cell and tissue homeostasis, as well as tissue regeneration. For these non-canonical functions, E2F4 can also act in the cytoplasm, where it is able to interact with many homeostatic and synaptic regulators. Since E2F4 is expressed in the nervous system, it may fulfill a crucial role in brain function and homeostasis, being a promising multifactorial target for neurodegenerative diseases and brain aging. The regulation of E2F4 is complex, as it can be chemically modified through acetylation, from which we present evidence in the brain, as well as methylation, and phosphorylation. The phosphorylation of E2F4 within a conserved threonine motif induces cell cycle re-entry in neurons, while a dominant negative form of E2F4 (E2F4DN), in which the conserved threonines have been substituted by alanines, has been shown to act as a multifactorial therapeutic agent for Alzheimer’s disease (AD). We generated transgenic mice neuronally expressing E2F4DN. We have recently shown using this mouse strain that expression of E2F4DN in 5xFAD mice, a known murine model of AD, improved cognitive function, reduced neuronal tetraploidization, and induced a transcriptional program consistent with modulation of amyloid-β (Aβ) peptide proteostasis and brain homeostasis recovery. 5xFAD/E2F4DN mice also showed reduced microgliosis and astrogliosis in both the cerebral cortex and hippocampus at 3-6 months of age. Here, we analyzed the immune response in 1 year-old 5xFAD/E2F4DN mice, concluding that reduced microgliosis and astrogliosis is maintained at this late stage. In addition, the expression of E2F4DN also reduced age-associated microgliosis in wild-type mice, thus stressing its role as a brain homeostatic agent. We conclude that E2F4DN transgenic mice represent a promising tool for the evaluation of E2F4 as a therapeutic target in neuropathology and brain aging.

A genome-wide association study identifying single nucleotide polymorphisms in the PPFIBP2 gene was predictive for interstitial lung disease in rheumatoid arthritis patients

Objective

Genetic polymorphisms might serve as useful prognostic markers for the timely diagnosis of RA. The purpose of this study was to identify genomic factors predictive of the occurrence of interstitial lung disease (ILD) in RA by performing a genome-wide association study of genetic variants, including single nucleotide polymorphisms (SNPs).

Methods

The study population included 306 RA patients. All patients were treated with conventional DMARDs, including 6–16 mg MTX per week. Clinical data and venous blood samples were collected from all patients before administration of DMARDs. A total of 278 347 SNPs were analysed to determine their association with ILD occurrence.

Results

Several SNPs were strongly associated with ILD occurrence (P < 10⁻⁵). rs6578890, which is located on chromosome 11 in the intronic region of the gene encoding tyrosine phosphatase receptor type F polypeptide-interacting protein-binding protein 2 (PPFIBP2), showed the strongest association with ILD occurrence (odds ratio 4.32, P = 10^−7.93).

Conclusion

PPFIBP2 could be a useful genetic marker for occurrence of interstitial pneumonia in RA patients and might help to identify the risk of ILD occurrence before RA treatment, thereby improving patient outcomes.

Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80[Formula: see text]. The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome.

RESULTS

We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer's Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method.

CONCLUSION

We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.

MethReg: estimating the regulatory potential of DNA methylation in gene transcription

Epigenome-wide association studies often detect many differentially methylated sites, and many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated that CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here, we present MethReg, an R/Bioconductor package that analyzes matched DNA methylation and gene expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize and annotate CpG sites with high regulatory potential. At these CpG sites, TF-target gene associations are often only present in a subset of samples with high (or low) methylation levels, so they can be missed by analyses that use all samples. Using colorectal cancer and Alzheimer's disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

Manifestations of Alzheimer's disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

Genetics play an important role in late-onset Alzheimer's Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

A Mutant Variant of E2F4 Triggers Multifactorial Therapeutic Effects in 5xFAD Mice

Alzheimer’s disease (AD) has a complex etiology, which requires a multifactorial approach for an efficient treatment. We have focused on E2 factor 4 (E2F4), a transcription factor that regulates cell quiescence and tissue homeostasis, controls gene networks affected in AD, and is upregulated in the brains of Alzheimer’s patients and of APP^swe/PS1^dE9 and 5xFAD transgenic mice. E2F4 contains an evolutionarily conserved Thr-motif that, when phosphorylated, modulates its activity, thus constituting a potential target for intervention. In this study, we generated a knock-in mouse strain with neuronal expression of a mouse E2F4 variant lacking this Thr-motif (E2F4DN), which was mated with 5xFAD mice. Here, we show that neuronal expression of E2F4DN in 5xFAD mice potentiates a transcriptional program consistent with the attenuation of the immune response and brain homeostasis. This correlates with reduced microgliosis and astrogliosis, modulation of amyloid-β peptide proteostasis, and blocking of neuronal tetraploidization. Moreover, E2F4DN prevents cognitive impairment and body weight loss, a known somatic alteration associated with AD. We also show that our finding is significant for AD, since E2F4 is expressed in cortical neurons from Alzheimer patients in association with Thr-specific phosphorylation, as evidenced by an anti-E2F4/anti-phosphoThr proximity ligation assay. We propose E2F4DN-based gene therapy as a promising multifactorial approach against AD.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

The Potential of Ferroptosis-Targeting Therapies for Alzheimer's Disease: From Mechanism to Transcriptomic Analysis

Alzheimer’s disease (AD), the most common form of dementia, currently affects 40–50 million people worldwide. Despite the extensive research into amyloid β (Aβ) deposition and tau protein hyperphosphorylation (p-tau), an effective treatment to stop or slow down the progression of neurodegeneration is missing. Emerging evidence suggests that ferroptosis, an iron-dependent and lipid peroxidation-driven type of programmed cell death, contributes to neurodegeneration in AD. Therefore, how to intervene against ferroptosis in the context of AD has become one of the questions addressed by studies aiming to develop novel therapeutic strategies. However, the underlying molecular mechanism of ferroptosis in AD, when ferroptosis occurs in the disease course, and which ferroptosis-related genes are differentially expressed in AD remains to be established. In this review, we summarize the current knowledge on cell mechanisms involved in ferroptosis, we discuss how these processes relate to AD, and we analyze which ferroptosis-related genes are differentially expressed in AD brain dependant on cell type, disease progression and gender. In addition, we point out the existing targets for therapeutic options to prevent ferroptosis in AD. Future studies should focus on developing new tools able to demonstrate where and when cells undergo ferroptosis in AD brain and build more translatable AD models for identifying anti-ferroptotic agents able to slow down neurodegeneration.

Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions

Introduction

As new late‐onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell–type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single‐cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD‐genome‐wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD‐GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding.

Methods

Active enhancers within LOAD‐associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP‐seq signals. Finally, the high‐priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease‐relevant tissues.

Results

We catalogued 61 strong enhancers in LOAD‐GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy‐seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF‐binding sites showed ChIP‐seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL.

Discussion

This study provides a framework to catalogue noncoding variations in enhancers located in LOAD‐GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single‐cell multi‐omics assays and gene editing.

Microglia in Alzheimer's Disease: a Key Player in the Transition Between Homeostasis and Pathogenesis

Immune activation accompanies the development of proteinopathy in the brains of Alzheimer's dementia patients. Evolving from the long-held viewpoint that immune activation triggers the pathological trajectory in Alzheimer's disease, there is accumulating evidence now that microglial activation is neither pro-amyloidogenic nor just a simple reactive process to the proteinopathy. Preclinical studies highlight an interesting aspect of immunity, i.e., spurring immune system activity may be beneficial under certain circumstances. Indeed, a dynamic evolving relationship between different activation states of the immune system and its neuronal neighbors is thought to regulate overall brain organ health in both healthy aging and progression of Alzheimer's dementia. A new premise evolving from genome, transcriptome, and proteome data is that there might be at least two major phases of immune activation that accompany the pathological trajectory in Alzheimer's disease. Though activation on a chronic scale will certainly lead to neurodegeneration, this emerging knowledge of a potential beneficial aspect of immune activation allows us to form holistic insights into when, where, and how much immune system activity would need to be tuned to impact the Alzheimer's neurodegenerative cascade. Even with the trove of recently emerging -omics data from patients and preclinical models, how microglial phenotypes are functionally related to the transition of a healthy aging brain towards progressive degenerative state remains unknown. A deeper understanding of the synergism between microglial functional states and brain organ health could help us discover newer interventions and therapies that enable us to address the current paucity of disease-modifying therapies in Alzheimer's disease.

Prioritization of human well-being spectrum related GWAS-SNVs using ENCODE-based web-tools predict interplay between PSMC3, ITIH4, and SERPINC1 genes in modulating well-being

Several traits related to positive and negative affect show a high genetic as well as phenotypic correlation with well-being in humans, and are therefore collectively termed as "Well-being spectrum". Genome-Wide Association studies (GWA studies) on "well-being measurement" have led to identification of several genomic variants (Single Nucleotide Variants - SNVs), but very little has been explained with respect to their functionality and mode of alteration of well-being. Utilizing a pool of 1258 GWA studies based SNVs on "well-being measurement", we prioritized the SNVs and tried to annotate well-being related functionality through several bioinformatic tools to predict whether a protein sequence variation affects protein function, as well as experimentally validated datasets available in ENCODE based web-tools namely rSNPBase, RegulomeDB, Haploreg, along with GTEx Portal and STRING based protein interaction networks. Prioritization yielded three key SNVs; rs3781627-A, rs13072536-T and 5877-C potentially regulating three genes, PSMC3, ITIH4 and SERPINC1, respectively. Interestingly, the genes showed well clustered protein-protein interaction (maximum combined confidence score >0.4) with other well-being candidate genes, namely TNF and CRP genes suggesting their important role in modulation of well-being. PSMC3 and ITIH4 genes are also involved in driving acute phase responses signifying a probable cross-talk between well-being and psychoneuroimmunological system. To best of our knowledge this study is the first of its kind where the well-being associated GWA studies-SNVs were prioritized and functionally annotated, majorly based on functional data available in public domain, which revealed PSMC3, ITIH4 and SERPINC1 genes as probable candidates in regulation of well-being spectrum.

E2F4-Based Gene Therapy Mitigates the Phenotype of the Alzheimer's Disease Mouse Model 5xFAD

After decades of unfruitful work, no effective therapies are available for Alzheimer's disease (AD), likely due to its complex etiology that requires a multifactorial therapeutic approach. We have recently shown using transgenic mice that E2 factor 4 (E2F4), a transcription factor that regulates cell quiescence and tissue homeostasis, and controls gene networks affected in AD, represents a good candidate for a multifactorial targeting of AD. Here we show that the expression of a dominant negative form of human E2F4 (hE2F4DN), unable to become phosphorylated in a Thr-conserved motif known to modulate E2F4 activity, is an effective and safe AD multifactorial therapeutic agent. Neuronal expression of hE2F4DN in homozygous 5xFAD (h5xFAD) mice after systemic administration of an AAV.PHP.B-hSyn1.hE2F4DN vector reduced the production and accumulation of Aβ in the hippocampus, attenuated reactive astrocytosis and microgliosis, abolished neuronal tetraploidization, and prevented cognitive impairment evaluated by Y-maze and Morris water maze, without triggering side effects. This treatment also reversed other alterations observed in h5xFAD mice such as paw-clasping behavior and body weight loss. Our results indicate that E2F4DN-based gene therapy is a promising therapeutic approach against AD.

Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer's disease brains

BACKGROUND

In the post-GWAS era, there is an unmet need to decode the underpinning genetic etiologies of late-onset Alzheimer's disease (LOAD) and translate the associations to causation.

METHODS

We conducted ATAC-seq profiling using NeuN sorted-nuclei from 40 frozen brain tissues to determine LOAD-specific changes in chromatin accessibility landscape in a cell-type specific manner.

RESULTS

We identified 211 LOAD-specific differential chromatin accessibility sites in neuronal-nuclei, four of which overlapped with LOAD-GWAS regions (±100 kb of SNP). While the non-neuronal nuclei did not show LOAD-specific differences, stratification by sex identified 842 LOAD-specific chromatin accessibility sites in females. Seven of these sex-dependent sites in the non-neuronal samples overlapped LOAD-GWAS regions including APOE. LOAD loci were functionally validated using single-nuclei RNA-seq datasets.

CONCLUSIONS

Using brain sorted-nuclei enabled the identification of sex-dependent cell type-specific LOAD alterations in chromatin structure. These findings enhance the interpretation of LOAD-GWAS discoveries, provide potential pathomechanisms, and suggest novel LOAD-loci.

GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs

Several genome-wide association studies (GWAS) have been carried out with late-onset Alzheimer's disease (LOAD), mainly in European and Asian populations. Different polymorphisms were associated, but several of them without a functional explanation. GWAS are fundamental for identifying loci associated with diseases, although they often do not point to causal polymorphisms. In this sense, functional investigations are a fundamental tool for discovering causality, although the failure of this validation does not necessarily indicate a non-causality. Furthermore, the allele frequency of associated genetic variants may vary widely between populations, requiring replication of these associations in other ethnicities. In this sense, our study sought to replicate in 150 AD patients and 114 elderly controls from the South Brazilian population 18 single-nucleotide polymorphisms (SNPs) associated with AD in European GWAS, with further functional investigation using bioinformatic tools for the associated SNPs. Of the 18 SNPs investigated, only four were associated in our population: rs769449 (APOE), rs10838725 (CELF1), rs6733839, and rs744373 (BIN1-CYP27C1). We identified 54 variants in linkage disequilibrium (LD) with the associated SNPs, most of which act as expression or splicing quantitative trait loci (eQTLs/sQTLs) in genes previously associated with AD or with a possible functional role in the disease, such as CELF1, MADD, MYBPC3, NR1H3, NUP160, SPI1, and TOMM40. Interestingly, eight of these variants are located within long non-coding RNA (lncRNA) genes that have not been previously investigated regarding AD. Some of these polymorphisms can result in changes in these lncRNAs' secondary structures, leading to either loss or gain of microRNA (miRNA)-binding sites, deregulating downstream pathways. Our pioneering work not only replicated LOAD association with polymorphisms not yet associated in the Brazilian population but also identified six possible lncRNAs that may interfere in LOAD development. The results lead us to emphasize the importance of functional exploration of associations found in large-scale association studies in different populations to base personalized and inclusive medicine in the future.

Pathway-Specific Genetic Risk for Alzheimer's Disease Differentiates Regional Patterns of Cortical Atrophy in Older Adults

Brain aging is highly variable and represents a challenge to delimit aging from disease processes. Moreover, genetic factors may influence both aging and disease. Here we focused on this issue and investigated effects of multiple genetic loci previously identified to be associated with late-onset Alzheimer's disease (AD) on brain structure of older adults from a population sample. We calculated a genetic risk score (GRS) using genome-wide significant single-nucleotide polymorphisms from genome-wide association studies of AD and tested its effect on cortical thickness (CT). We observed a common pattern of cortical thinning (right inferior frontal, left posterior temporal, medial occipital cortex). To identify CT changes by specific biological processes, we subdivided the GRS effect according to AD-associated pathways and performed follow-up analyses. The common pattern from the main analysis was further differentiated by pathway-specific effects yielding a more bilateral pattern. Further findings were located in the superior parietal and mid/anterior cingulate regions representing 2 unique pathway-specific patterns. All patterns, except the superior parietal pattern, were influenced by apolipoprotein E. Our step-wise approach revealed atrophy patterns that partially resembled imaging findings in early stages of AD. Our study provides evidence that genetic burden for AD contributes to structural brain variability in normal aging.

Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is an incurable neurodegenerative disease currently affecting 1.75% of the US population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION transcriptome-wide association study (TWAS) pipeline to ingest gene expression values from multiple neocortical regions.

METHODS

A combined dataset of 2003 genotypes clustered to 1000 Genomes individuals from Utah with Northern and Western European ancestry (CEU) was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles from temporal cortex (TCX = 248), prefrontal cortex (FP = 50), inferior frontal gyrus (IFG = 41), superior temporal gyrus (STG = 34), parahippocampal cortex (PHG = 34), and dorsolateral prefrontal cortex (DLPFC = 461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a gene's surrounding cis-variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and support cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues.

RESULTS

Cis-variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N = 6775 (99.92%), Bonferroni: N = 6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05): APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, and KNOP1.

CONCLUSIONS

We provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue

Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1 Mb of genes was evaluated using linear regression models for unrelated subjects and linear-mixed models for related subjects. Cell-type-specific eQTL (ct-eQTL) models included an interaction term for the expression of "proxy" genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell types is supported by the observation that a large portion of GWS ct-eQTLs map within 1 Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type-specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type-specific analysis.

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell–cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer’s disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell–cell relationships. It is also a powerful framework that can be applied to general scRNA-Seq analyses.

Single-cell RNA-Seq suffers from heterogeneity in sequencing sparsity and complex differential patterns in gene expression. Here, the authors introduce a graph neural network based on a hypothesis-free deep learning framework as an effective representation of gene expression and cell–cell relationships.

rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues

BACKGROUND

It has been well established that the TMEM106B gene rs1990622 variant was a frontotemporal dementia (FTD) risk factor. Until recently, growing evidence highlights the role of TMEM106B in Alzheimer's disease (AD). However, it remains largely unclear about the role of rs1990622 variant in AD.

METHODS

Here, we conducted comprehensive analyses including genetic association study, gene expression analysis, eQTLs analysis, and colocalization analysis. In stage 1, we conducted a genetic association analysis of rs1990622 using large-scale genome-wide association study (GWAS) datasets from International Genomics of Alzheimer's Project (21,982 AD and 41,944 cognitively normal controls) and UK Biobank (314,278 participants). In stage 2, we performed a gene expression analysis of TMEM106B in 49 different human tissues using the gene expression data in GTEx. In stage 3, we performed an expression quantitative trait loci (eQTLs) analysis using multiple datasets from UKBEC, GTEx, and Mayo RNAseq Study. In stage 4, we performed a colocalization analysis to provide evidence of the AD GWAS and eQTLs pair influencing both AD and the TMEM106B expression at a particular region.

RESULTS

We found (1) rs1990622 variant T allele contributed to AD risk. A sex-specific analysis in UK Biobank further indicated that rs1990622 T allele only contributed to increased AD risk in females, but not in males; (2) TMEM106B showed different expression in different human brain tissues especially high expression in cerebellum; (3) rs1990622 variant could regulate the expression of TMEM106B in human brain tissues, which vary considerably in different disease statuses, the mean ages at death, the percents of females, and the different descents of the selected donors; (4) colocalization analysis provided suggestive evidence that the same variant contributed to AD risk and TMEM106B expression in cerebellum.

CONCLUSION

Our comprehensive analyses highlighted the role of FTD rs1990622 variant in AD risk. This cross-disease approach may delineate disease-specific and common features, which will be important for both diagnostic and therapeutic development purposes. Meanwhile, these findings highlight the importance to better understand TMEM106B function and dysfunction in the context of normal aging and neurodegenerative diseases.

SCFD1 expression quantitative trait loci in amyotrophic lateral sclerosis are differentially expressed

Evidence indicates that common variants found in genome-wide association studies increase risk of disease through gene regulation via expression Quantitative Trait Loci. Using multiple genome-wide methods, we examined if Single Nucleotide Polymorphisms increase risk of Amyotrophic Lateral Sclerosis through expression Quantitative Trait Loci, and whether expression Quantitative Trait Loci expression is consistent across people who had Amyotrophic Lateral Sclerosis and those who did not. In combining public expression Quantitative Trait Loci data with Amyotrophic Lateral Sclerosis genome-wide association studies, we used Summary-data-based Mendelian Randomization to confirm that SCFD1 was the only gene that was genome-wide significant in mediating Amyotrophic Lateral Sclerosis risk via expression Quantitative Trait Loci (Summary-data-based Mendelian Randomization beta = 0.20, standard error = 0.04, P-value = 4.29 × 10^-6). Using post-mortem motor cortex, we tested whether expression Quantitative Trait Loci showed significant differences in expression between Amyotrophic Lateral Sclerosis (n = 76) and controls (n = 25), genome-wide. Of 20 757 genes analysed, the two most significant expression Quantitative Trait Loci to show differential in expression between Amyotrophic Lateral Sclerosis and controls involve two known Amyotrophic Lateral Sclerosis genes (SCFD1 and VCP). Cis-acting SCFD1 expression Quantitative Trait Loci downstream of the gene showed significant differences in expression between Amyotrophic Lateral Sclerosis and controls (top expression Quantitative Trait Loci beta = 0.34, standard error = 0.063, P-value = 4.54 × 10^-7). These SCFD1 expression Quantitative Trait Loci also significantly modified Amyotrophic Lateral Sclerosis survival (number of samples = 4265, hazard ratio = 1.11, 95% confidence interval = 1.05-1.17, P-value = 2.06 × 10^-4) and act as an Amyotrophic Lateral Sclerosis trans-expression Quantitative Trait Loci hotspot for a wider network of genes enriched for SCFD1 function and Amyotrophic Lateral Sclerosis pathways. Using gene-set analyses, we found the genes that correlate with this trans-expression Quantitative Trait Loci hotspot significantly increase risk of Amyotrophic Lateral Sclerosis (beta = 0.247, standard deviation = 0.017, P = 0.001) and schizophrenia (beta = 0.263, standard deviation = 0.008, P-value = 1.18 × 10^-5), a disease that genetically correlates with Amyotrophic Lateral Sclerosis. In summary, SCFD1 expression Quantitative Trait Loci are a major factor in Amyotrophic Lateral Sclerosis, not only influencing disease risk but are differentially expressed in post-mortem Amyotrophic Lateral Sclerosis. SCFD1 expression Quantitative Trait Loci show distinct expression profiles in Amyotrophic Lateral Sclerosis that correlate with a wider network of genes that also confer risk of the disease and modify the disease's duration.

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.

Stop codon read-through of mammalian MTCH2 leading to an unstable isoform regulates mitochondrial membrane potential

Stop codon read-through (SCR) is a process of continuation of translation beyond a stop codon. This phenomenon, which occurs only in certain mRNAs under specific conditions, leads to a longer isoform with properties different from that of the canonical isoform. MTCH2, which encodes a mitochondrial protein that regulates mitochondrial metabolism, was selected as a potential read-through candidate based on evolutionary conservation observed in the proximal region of its 3' UTR. Here, we demonstrate translational read-through across two evolutionarily conserved, in-frame stop codons of MTCH2 using luminescence- and fluorescence-based assays, and by analyzing ribosome-profiling and mass spectrometry (MS) data. This phenomenon generates two isoforms, MTCH2x and MTCH2xx (single- and double-SCR products, respectively), in addition to the canonical isoform MTCH2, from the same mRNA. Our experiments revealed that a cis-acting 12-nucleotide sequence in the proximal 3' UTR of MTCH2 is the necessary signal for SCR. Functional characterization showed that MTCH2 and MTCH2x were localized to mitochondria with a long t1/2 (>36 h). However, MTCH2xx was found predominantly in the cytoplasm. This mislocalization and its unique C terminus led to increased degradation, as shown by greatly reduced t1/2 (<1 h). MTCH2 read-through-deficient cells, generated using CRISPR-Cas9, showed increased MTCH2 expression and, consistent with this, decreased mitochondrial membrane potential. Thus, double-SCR of MTCH2 regulates its own expression levels contributing toward the maintenance of normal mitochondrial membrane potential.

Interaction between physical exercise and APOE gene polymorphism on cognitive function in older people

We aimed to present an overview of the literature regarding the interaction between physical exercise and APOE gene polymorphism on cognitive function, particularly in patients with Alzheimer's disease (AD). Firstly, this review focused on the effect of the physical exercise on cognitive function, regardless of APOE gene polymorphism. Some studies have shown that a high level of cardiorespiratory fitness is associated with less neuronal damage with an improvement in memory score tests whereas other studies failed to detect any association between physical exercise and cognitive improvement either in healthy individuals or patients with AD. Taken together, standardized protocols and more longitudinal studies are required to provide a better insight into the effects of physical exercise on cognitive function. Although there is no agreement in the literature regarding the effects of physical exercise on cognitive function, it is well established that it improves social interaction and the feeling of well-being, thereby positively contributing to the quality of life of the elderly. Regarding the influence of physical exercise on cognitive function in APOE ε4 allele carriers, the data trend shows that the carriers of allele ε4 for APOE gene were more responsive to the beneficial effects of physical exercise on cognitive function compared with non-carriers. Nevertheless, studies with larger sample sizes will provide more accuracy about this relationship.

CELF2 regulates the species-specific alternative splicing of TREM2

Genetic variations of TREM2 have been implicated as a risk factor of Alzheimer’s disease (AD). Recent studies suggest that the loss of TREM2 function compromises microglial responses to the accumulation of amyloid beta. Previously, we found that exon 3 of TREM2 is an alternative exon whose skipping leads to a reduction in full-length TREM2 protein by inducing nonsense-mediated mRNA decay. Here, we aimed to identify factors regulating TREM2 splicing. Using a panel of RNA-binding proteins, we found that exon 3 skipping of TREM2 was promoted by two paralogous proteins, CELF1 and CELF2, which were both linked previously with risk loci of AD. Although the overexpression of both CELF1 and CELF2 enhanced exon 3 skipping, only CELF2 reduced the expression of full-length TREM2 protein. Notably, the TREM2 ortholog in the green monkey, but not in the mouse, showed alternative splicing of exon 3 like human TREM2. Similarly, splicing regulation of exon 3 by CELF1/2 was found to be common to humans and monkeys. Using chimeric minigenes of human and mouse TREM2, we mapped a CELF-responsive sequence within intron 3 of human TREM2. Collectively, our results revealed a novel regulatory factor of TREM2 expression and highlighted a species-dependent difference of its regulation.

Genetic architecture of Alzheimer's disease

Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.

Environment and Gene Association With Obesity and Their Impact on Neurodegenerative and Neurodevelopmental Diseases

Obesity is a multifactorial disease in which environmental conditions and several genes play an important role in the development of this disease. Obesity is associated with neurodegenerative diseases (Alzheimer, Parkinson, and Huntington diseases) and with neurodevelopmental diseases (autism disorder, schizophrenia, and fragile X syndrome). Some of the environmental conditions that lead to obesity are physical activity, alcohol consumption, socioeconomic status, parent feeding behavior, and diet. Interestingly, some of these environmental conditions are shared with neurodegenerative and neurodevelopmental diseases. Obesity impairs neurodevelopment abilities as memory and fine-motor skills. Moreover, maternal obesity affects the cognitive function and mental health of the offspring. The common biological mechanisms involved in obesity and neurodegenerative/neurodevelopmental diseases are insulin resistance, pro-inflammatory cytokines, and oxidative damage, among others, leading to impaired brain development or cell death. Obesogenic environmental conditions are not the only factors that influence neurodegenerative and neurodevelopmental diseases. In fact, several genes implicated in the leptin–melanocortin pathway (LEP, LEPR, POMC, BDNF, MC4R, PCSK1, SIM1, BDNF, TrkB, etc.) are associated with obesity and neurodegenerative and neurodevelopmental diseases. Moreover, in the last decades, the discovery of new genes associated with obesity (FTO, NRXN3, NPC1, NEGR1, MTCH2, GNPDA2, among others) and with neurodegenerative or neurodevelopmental diseases (APOE, CD38, SIRT1, TNFα, PAI-1, TREM2, SYT4, FMR1, TET3, among others) had opened new pathways to comprehend the common mechanisms involved in these diseases. In conclusion, the obesogenic environmental conditions, the genes, and the interaction gene–environment would lead to a better understanding of the etiology of these diseases.

Regional Variation of Splicing QTLs in Human Brain

A major question in human genetics is how sequence variants of broadly expressed genes produce tissue- and cell type-specific molecular phenotypes. Genetic variation of alternative splicing is a prevalent source of transcriptomic and proteomic diversity in human populations. We investigated splicing quantitative trait loci (sQTLs) in 1,209 samples from 13 human brain regions, using RNA sequencing (RNA-seq) and genotype data from the Genotype-Tissue Expression (GTEx) project. Hundreds of sQTLs were identified in each brain region. Some sQTLs were shared across brain regions, whereas others displayed regional specificity. These “regionally ubiquitous” and “regionally specific” sQTLs showed distinct positional distributions of single-nucleotide polymorphisms (SNPs) within and outside essential splice sites, respectively, suggesting their regulation by distinct molecular mechanisms. Integrating the binding motifs and expression patterns of RNA binding proteins with exon splicing profiles, we uncovered likely causal variants underlying brain region-specific sQTLs. Notably, SNP rs17651213 created a putative binding site for the splicing factor RBFOX2 and was associated with increased splicing of MAPT exon 3 in cerebellar tissues, where RBFOX2 was highly expressed. Overall, our study reveals a more comprehensive spectrum and regional variation of sQTLs in human brain and demonstrates that such regional variation can be used to fine map potential causal variants of sQTLs and their associated neurological diseases.

Increased nuclear but not cytoplasmic activities of CELF1 protein leads to muscle wasting

mRNA processing is highly regulated during development through changes in RNA-binding protein (RBP) activities. CUG-BP, Elav-like family member 1 (CELF1, also called CUGBP1) is an RBP, the expression of which decreases in skeletal muscle soon after birth. CELF1 regulates multiple nuclear and cytoplasmic RNA processing events. In the nucleus, CELF1 regulates networks of postnatal alternative splicing (AS) transitions, while in the cytoplasm, CELF1 regulates mRNA stability and translation. Stabilization and misregulation of CELF1 has been implicated in human diseases including myotonic dystrophy type 1, Alzheimer's disease and multiple cancers. To understand the contribution of nuclear and cytoplasmic CELF1 activity to normal and pathogenic skeletal muscle biology, we generated transgenic mice for doxycycline-inducible and skeletal muscle-specific expression of active CELF1 mutants engineered to be localized predominantly to either the nucleus or the cytoplasm. Adult mice expressing nuclear, but not cytoplasmic, CELF1 are characterized by strong histopathological defects, muscle loss within 10 days and changes in AS. In contrast, mice expressing cytoplasmic CELF1 display changes in protein levels of targets known to be regulated at the level of translation by CELF1, with minimal changes in AS. These changes are in the absence of overt histopathological changes or muscle loss. RNA-sequencing revealed extensive gene expression and AS changes in mice overexpressing nuclear and naturally localized CELF1 protein, with affected genes involved in cytoskeleton dynamics, membrane dynamics, RNA processing and zinc ion binding. These results support a stronger role for nuclear CELF1 functions as compared to cytoplasmic CELF1 functions in skeletal muscle wasting.

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression.

METHODS

We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD.

RESULTS

We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions.

DISCUSSION

Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD.

A transcriptome-wide association study based on 27 tissues identifies 106 genes potentially relevant for disease pathology in age-related macular degeneration

Genome-wide association studies (GWAS) for late stage age-related macular degeneration (AMD) have identified 52 independent genetic variants with genome-wide significance at 34 genomic loci. Typically, such an approach rarely results in the identification of functional variants implicating a defined gene in the disease process. We now performed a transcriptome-wide association study (TWAS) allowing the prediction of effects of AMD-associated genetic variants on gene expression. The TWAS was based on the genotypes of 16,144 late-stage AMD cases and 17,832 healthy controls, and gene expression was imputed for 27 different human tissues which were obtained from 134 to 421 individuals. A linear regression model including each individuals imputed gene expression data and the respective AMD status identified 106 genes significantly associated to AMD variants in at least one tissue (Q-value < 0.001). Gene enrichment analysis highlighted rather systemic than tissue- or cell-specific processes. Remarkably, 31 of the 106 genes overlapped with significant GWAS signals of other complex traits and diseases, such as neurological or autoimmune conditions. Taken together, our study highlights the fact that expression of genes associated with AMD is not restricted to retinal tissue as could be expected for an eye disease of the posterior pole, but instead is rather ubiquitous suggesting processes underlying AMD pathology to be of systemic nature.

Vascular Inflammation Risk Factors in Retinal Disease

Inflammation of the blood vessels that serve the central nervous system has been increasingly identified as an early and possibly initiating event among neurodegenerative conditions such as Alzheimer's disease and related dementias. However, the causal relevance of vascular inflammation to major retinal degenerative diseases is unresolved. Here, we describe how genetics, aging-associated changes, and environmental factors contribute to vascular inflammation in age-related macular degeneration, diabetic retinopathy, and glaucoma. We highlight the importance of mouse models in studying the underlying mechanisms and possible treatments for these diseases. We conclude that data support vascular inflammation playing a central if not primary role in retinal degenerative diseases, and this association should be a focus of future research.

Enhancer variants associated with Alzheimer's disease affect gene expression via chromatin looping

BACKGROUND

Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer's disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops.

METHODS

To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10- 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments.

RESULTS

We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes.

CONCLUSION

Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

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.

Proteomic Atlas of the Human Brain in Alzheimer's Disease

The brain represents one of the most divergent and critical organs in the human body. Yet, it can be afflicted by a variety of neurodegenerative diseases specifically linked to aging, about which we lack a full biomolecular understanding of onset and progression, such as Alzheimer's disease (AD). Here we provide a proteomic resource comprising nine anatomically distinct sections from three aged individuals, across a spectrum of disease progression, categorized by quantity of neurofibrillary tangles. Using state-of-the-art mass spectrometry, we identify a core brain proteome that exhibits only small variance in expression, accompanied by a group of proteins that are highly differentially expressed in individual sections and broader regions. AD affected tissue exhibited slightly elevated levels of tau protein with similar relative expression to factors associated with the AD pathology. Substantial differences were identified between previous proteomic studies of mature adult brains and our aged cohort. Our findings suggest considerable value in examining specifically the brain proteome of aged human populations from a multiregional perspective. This resource can serve as a guide, as well as a point of reference for how specific regions of the brain are affected by aging and neurodegeneration.

GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations

PURPOSE OF REVIEW

Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.

RECENT FINDINGS

Several common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.

SUMMARY

GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.