Copy Number Variants in Alzheimer's Disease

Neuronal cell cycle reentry events in the aging brain are more prevalent in neurodegeneration and lead to cellular senescence

Increasing evidence indicates that terminally differentiated neurons in the brain may recommit to a cell cycle-like process during neuronal aging and under disease conditions. Because of the rare existence and random localization of these cells in the brain, their molecular profiles and disease-specific heterogeneities remain unclear. Through a bioinformatics approach that allows integrated analyses of multiple single-nucleus transcriptome datasets from human brain samples, these rare cell populations were identified and selected for further characterization. Our analyses indicated that these cell cycle-related events occur predominantly in excitatory neurons and that cellular senescence is likely their immediate terminal fate. Quantitatively, the number of cell cycle re-engaging and senescent neurons decreased during the normal brain aging process, but in the context of late-onset Alzheimer's disease (AD), these cells accumulate instead. Transcriptomic profiling of these cells suggested that disease-specific differences were predominantly tied to the early stage of the senescence process, revealing that these cells presented more proinflammatory, metabolically deregulated, and pathology-associated signatures in disease-affected brains. Similarly, these general features of cell cycle re-engaging neurons were also observed in a subpopulation of dopaminergic neurons identified in the Parkinson's disease (PD)-Lewy body dementia (LBD) model. An extended analysis conducted in a mouse model of brain aging further validated the ability of this bioinformatics approach to determine the robust relationship between the cell cycle and senescence processes in neurons in this cross-species setting.

Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice

MOTIVATION

Copy-number variations (CNVs) are common genetic alterations in cancer and their detection may impact tumor classification and therapeutic decisions. However, detection of clinically relevant large and focal CNVs remains challenging when sample material or resources are limited. This has motivated us to create a software tool to infer CNVs from DNA methylation arrays which are often generated as part of clinical routines and in research settings.

RESULTS

We present our R package, conumee 2.0, that combines tangent normalization, an adjustable genomic binning heuristic, and weighted circular binary segmentation to utilize DNA methylation arrays for CNV analysis and mitigate technical biases and batch effects. Segmentation results were validated in a lung squamous cell carcinoma dataset from TCGA (n = 367 samples) by comparison to segmentations derived from genotyping arrays (Pearson's correlation coefficient of 0.91). We further introduce a segmented block bootstrapping approach to detect focal alternations that achieved 60.9% sensitivity and 98.6% specificity for deletions affecting CDKN2A/B (60.0% and 96.9% for RB1, respectively) in a low-grade glioma cohort from TCGA (n = 239 samples). Finally, our tool provides functionality to detect and summarize CNVs across large sample cohorts.

AVAILABILITY AND IMPLEMENTATION

Conumee 2.0 is available under open-source license at: https://github.com/hovestadtlab/conumee2.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Copy Number Variations in Hereditary Spastic Paraplegia-Related Genes: Evaluation of an Iranian Hereditary Spastic Paraplegia Cohort and Literature Review

Introduction

In human genetic disorders, copy number variations (CNVs) are considered a considerable underlying cause. CNVs are generally detected by array-based methods but can also be discovered by read-depth analysis of whole-exome sequencing (WES) data. We performed WES-based CNV identification in a cohort of 35 Iranian families with hereditary spastic paraplegia (HSP) patients.

Methods

Thirty-five patients whose routine single-nucleotide variants (SNVs) and insertion/deletion analyses from exome data were unrevealing underwent a pipeline of CNV analysis using the read-depth detection method. Subsequently, a comprehensive search about the existence of CNVs in all 84 known HSP-causing genes was carried out in all reported HSP cases, so far.

Results and Discussion

CNV analysis of exome data indicated that 1 patient harbored a heterozygous deletion in exon 17 of the SPAST gene. Multiplex ligation-dependent probe amplification analysis confirmed this deletion in the proband and his affected father. Literature review demonstrated that, to date, pathogenic CNVs have been identified in 30 out of 84 HSP-causing genes (∼36%). However, CNVs in only 17 of these genes were specifically associated with the HSP phenotype. Among them, CNVs were more common in L1CAM, PLP1, SPAST, SPG7, SPG11, and REEP1 genes. The identification of the CNV in 1 of our patients suggests that WES allows the detection of both SNVs and CNVs from a single method without additional costs and execution time. However, because of intrinsic issues of WES in the detection of large rearrangements, it may not yet be exploited to replace the CNV detection methods in standard clinical practice.

Step by step: towards a better understanding of the genetic architecture of Alzheimer's disease

Alzheimer's disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants - even in genes like APOE - on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.

The contribution of copy number variants to psychiatric symptoms and cognitive ability

Copy number variants (CNVs) are deletions and duplications of DNA sequence. The most frequently studied CNVs, which are described in this review, are recurrent CNVs that occur in the same locations on the genome. These CNVs have been strongly implicated in neurodevelopmental disorders, namely autism spectrum disorder (ASD), intellectual disability (ID), and developmental delay (DD), but also in schizophrenia. More recent work has also shown that CNVs increase risk for other psychiatric disorders, namely, depression, bipolar disorder, and post-traumatic stress disorder. Many of the same CNVs are implicated across all of these disorders, and these neuropsychiatric CNVs are also associated with cognitive ability in the general population, as well as with structural and functional brain alterations. Neuropsychiatric CNVs also show incomplete penetrance, such that carriers do not always develop any psychiatric disorder, and may show only mild symptoms, if any. Variable expressivity, whereby the same CNVs are associated with many different phenotypes of varied severity, also points to highly complex mechanisms underlying disease risk in CNV carriers. Comprehensive and longitudinal phenotyping studies of individual CNVs have provided initial insights into these mechanisms. However, more work is needed to estimate and predict the effect of non-recurrent, ultra-rare CNVs, which also contribute to psychiatric and cognitive outcomes. Moreover, delineating the broader phenotypic landscape of neuropsychiatric CNVs in both clinical and general population cohorts may also offer important mechanistic insights.

Whole genome sequencing-based copy number variations reveal novel pathways and targets in Alzheimer's disease

INTRODUCTION

A few copy number variations (CNVs) have been reported for Alzheimer's disease (AD). However, there is a lack of a systematic investigation of CNVs in AD based on whole genome sequencing (WGS) data.

METHODS

We used four methods to identify consensus CNVs from the WGS data of 1,411 individuals and further investigated their functional roles in AD using the matched transcriptomic and clinicopathological data.

RESULTS

We identified 3,012 rare AD-specific CNVs whose residing genes are enriched for cellular glucuronidation and neuron projection pathways. Genes whose mRNA expressions are significantly correlated with common CNVs are involved in major histocompatibility complex class II receptor activity. Integration of CNVs, gene expression, and clinical and pathological traits further pinpoints a key CNV that potentially regulates immune response in AD.

DISCUSSION

We identify CNVs as potential genetic regulators of immune response in AD. The identified CNVs and their downstream gene networks reveal novel pathways and targets for AD.

Targeted copy number variant identification across the neurodegenerative disease spectrum

Background

Although genetic factors are known to contribute to neurodegenerative disease susceptibility, there remains a large amount of heritability unaccounted for across the diagnoses. Copy number variants (CNVs) contribute to these phenotypes, but their presence and influence on disease state remains relatively understudied.

Methods

Here, we applied a depth of coverage approach to detect CNVs in 80 genes previously associated with neurodegenerative disease within participants of the Ontario Neurodegenerative Disease Research Initiative (n = 519).

Results

In total, we identified and validated four CNVs in the cohort, including: (1) a heterozygous deletion of exon 5 in OPTN in an Alzheimer's disease participant; (2) a duplication of exons 1–5 in PARK7 in an amyotrophic lateral sclerosis participant; (3) a duplication of >3 Mb, which encompassed ABCC6, in a cerebrovascular disease (CVD) participant; and (4) a duplication of exons 7–11 in SAMHD1 in a mild cognitive impairment participant. We also identified 43 additional CNVs that may be candidates for future replication studies.

Conclusion

The identification of the CNVs suggests a portion of the apparent missing heritability of the phenotypes may be due to these structural variants, and their assessment is imperative for a thorough understanding of the genetic spectrum of neurodegeneration.

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.

Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

MCKAT: a multi-dimensional copy number variant kernel association test

Background

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods.

Results

We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets.

Conclusion

A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case.

The contribution of CNVs to the most common aging-related neurodegenerative diseases

Alzheimer and Parkinson's diseases are neurodegenerative aging-related pathological conditions, mainly caused by the interplay of genetic and non-genetic factors and whose incidence rate is going to drastically increase given the growing life expectancy. To address these complex multifactorial traits, a systems biology strategy is needed to highlight genotype-phenotype correlations as well as overlapping gene signatures. Copy number variants (CNVs) are structural chromosomal imbalances that can have pathogenic nature causing or contributing to the disease onset or progression. Moreover, neurons affected by CNVs have been found to decline in number depending on age in healthy controls and may be selectively vulnerable to aging-related cell-death. In this review, we aim to update the reader on the role of these variations in the pathogenesis of Alzheimer and Parkinson diseases. To widen the comprehension of pathogenic mechanisms underlying them, we discuss variations detected from blood or brain specimens, as well as overlapped signatures between the two pathologies.

Genetic testing in dementia - utility and clinical strategies

Techniques for clinical genetic testing in dementia disorders have advanced rapidly but remain to be more widely implemented in practice. A positive genetic test offers a precise molecular diagnosis, can help members of an affected family to determine personal risk, provides a basis for reproductive choices and can offer options for clinical trials. The likelihood of identifying a specific genetic cause of dementia depends on the clinical condition, the age at onset and family history. Attempts to match phenotypes to single genes are mostly inadvisable owing to clinical overlap between the dementias, genetic heterogeneity, pleiotropy and concurrent mutations. Currently, the appropriate genetic test in most cases of dementia is a next-generation sequencing gene panel, though some conditions necessitate specific types of test such as repeat expansion testing. Whole-exome and whole-genome sequencing are becoming financially feasible but raise or exacerbate complex issues such as variants of uncertain significance, secondary findings and the potential for re-analysis in light of new information. However, the capacity for data analysis and counselling is already restricting the provision of genetic testing. Patients and their relatives need to be given reliable information to enable them to make informed choices about tests, treatments and data sharing; the ability of patients with dementia to make decisions must be considered when providing this information.

A Comprehensive Analysis of Unique and Recurrent Copy Number Variations in Alzheimer's Disease and its Related Disorders

BACKGROUND

Copy number variations (CNVs) play an important role in the genetic etiology of various neurological disorders, including Alzheimer's disease (AD). Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) were shown to have share mechanisms and signaling pathways with AD.

OBJECTIVE

We aimed to assess CNVs regions that may harbor genes contributing to AD, T2DM, and MDD in 67 Saudi familial and sporadic AD patients, with no alterations in the known genes of AD and genotyped previously for APOE.

METHODS

DNA was analyzed using the CytoScan-HD array. Two layers of filtering criteria were applied. All the identified CNVs were checked in the Database of Genomic Variants (DGV).

RESULTS

A total of 1086 CNVs (565 gains and 521 losses) were identified in our study. We found 73 CNVs harboring genes that may be associated with AD, T2DM or MDD. Nineteen CNVs were novel. Most importantly, 42 CNVs were unique in our studied cohort existing only in one patient. Two large gains on chromosomes 1 and 13 harbored genes implicated in the studied disorders. We identified CNVs in genes that encode proteins involved in the metabolism of amyloid-β peptide (AGRN, APBA2, CR1, CR2, IGF2R, KIAA0125, MBP, RER1, RTN4R, VDR and WISPI) or Tau proteins (CACNAIC, CELF2, DUSP22, HTRA1 and SLC2A14).

CONCLUSION

The present work provided information on the presence of CNVs related to AD, T2DM, and MDD in Saudi Alzheimer's patients.

Exposure to the Heavy-Metal Lead Induces DNA Copy Number Alterations in Zebrafish Cells

DNA copy number variants are associated with the development of complex neurological diseases and disorders including autism spectrum disorder, schizophrenia, Alzheimer's disease, and Parkinson's disease. Exposure to multiple environmental chemicals including various heavy metals is suggested as a risk factor in these neurological diseases and disorders, but few studies have addressed if heavy-metal exposure can result in de novo DNA copy number changes as a genetic mechanism contributing to these disease outcomes. In this study to further investigate the relationship between heavy-metal exposure and de novo copy number alterations (CNAs), zebrafish fibroblast cells were exposed to the neurotoxicant lead (Pb). A crystal violet assay was first used to determine exposure concentrations with >80% cell confluency. Then a zebrafish-specific array comparative genomic hybridization platform was used to detect CNAs following a 72 h Pb exposure (0.24, 2.4, or 24 μM). The Pb exposure resulted in 72 CNA amplifications ranging in size from 5 to 329 kb. No deletions were detected. CNAs resulted in 15 CNA regions (CNARs), leaving 7 singlet CNAs. Two of the singlets were within high repeat genomic locations. The number of CNAs tended to increase in a concentration-dependent manner. Several CNARs encompassed genes previously reported to have altered expression with Pb exposure, suggesting a mechanistic link. In addition, almost all genes are associated within a molecular network with amyloid precursor protein, a key molecular target associated with the pathophysiology of Alzheimer's disease. Overall, these findings show that Pb exposure results in de novo CNAs that could serve as a mechanism driving adverse health outcomes associated with Pb toxicity including neurological disease pathogenesis for further study.

Transcriptomics in Alzheimer's Disease: Aspects and Challenges

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

Pathological Aspects of Neuronal Hyperploidization in Alzheimer's Disease Evidenced by Computer Simulation

When subjected to stress, terminally differentiated neurons are susceptible to reactivate the cell cycle and become hyperploid. This process is well documented in Alzheimer's disease (AD), where it may participate in the etiology of the disease. However, despite its potential importance, the effects of neuronal hyperploidy (NH) on brain function and its relationship with AD remains obscure. An important step forward in our understanding of the pathological effect of NH has been the development of transgenic mice with neuronal expression of oncogenes as model systems of AD. The analysis of these mice has demonstrated that forced cell cycle reentry in neurons results in most hallmarks of AD, including neurofibrillary tangles, Aβ peptide deposits, gliosis, cognitive loss, and neuronal death. Nevertheless, in contrast to the pathological situation, where a relatively small proportion of neurons become hyperploid, neuronal cell cycle reentry in these mice is generalized. We have recently developed an in vitro system in which cell cycle is induced in a reduced proportion of differentiated neurons, mimicking the in vivo situation. This manipulation reveals that NH correlates with synaptic dysfunction and morphological changes in the affected neurons, and that membrane depolarization facilitates the survival of hyperploid neurons. This suggests that the integration of synaptically silent, hyperploid neurons in electrically active neural networks allows their survival while perturbing the normal functioning of the network itself, a hypothesis that we have tested in silico. In this perspective, we will discuss on these aspects trying to convince the reader that NH represents a relevant process in AD.

Genome-wide association study identifies genomic loci associated with flight reaction in cattle

Lower flight reaction is closely related to higher production in cattle, but the genetic basis of lower flight reaction is not clearly understood. Here, we sampled a total of 45 Brahman cattles and 166 Yunling cattles with flight distance (FD), and 73 Brahman cattles and 288 Yunling cattles with crush score (CS) and flight speed (FS), whereas there were 45 Brahman cattles and 161 Yunling cattles with all three traits. The FD, CS and FS in Brahman cattle were significantly lower than those in Yunling cattle. The flight reaction traits had negative correlation with conformational traits (e.g., body weight, withers height and body length). Based on SNPs derived from a subset of 162 whole genomes (25 Brahman genomes and 100 Yunling genomes with FD, 30 Brahman and 131 Yunling genomes with CS and FS), genome-wide association study with mixed linear model was performed to test potential associations between flight reaction traits and genomic variants. We identified five, two and two genomic loci suggestively associated with FD, CS and FS, respectively. Five out of five candidate genes for FD (LOC789753, LRP6, CTIF, SLC9A9 and ZEB1) were reported to be related to Alzheimer's disease representing cognitive impairment in human, which was consistent with the finding that cognitive-behavioural intervention decreased the FD of cows to human. In CS, a very strong association locus was assigned to CDH8, a cadherin involved in synaptic adhesion, axon outgrowth and guidance, whose deletion was associated with autism spectrum disorder. In FS, a very strong association locus was assigned to GABRG2, a gamma-aminobutyric acid (a major inhibitory neurotransmitter in brain) receptor, whose polymorphisms were associated with suicidal behaviour in schizophrenia patients. Our findings will provide targets for molecular-marker selection and genetic manipulation of cattle improvement to meeting the growing demand for lower flight reaction to human.

Critical evaluation of copy number variant calling methods using DNA methylation

Recent technological and methodological developments have enabled the use of array-based DNA methylation data to call copy number variants (CNVs). ChAMP, Conumee, and cnAnalysis450k are popular methods currently used to call CNVs using methylation data. However, so far, no studies have analyzed the reliability of these methods using real samples. Data from a cohort of individuals with genotype and DNA methylation data generated using the HumanMethylation450 and MethylationEPIC BeadChips were used to assess the consistency between the CNV calls generated by methylation and genotype data. We also took advantage of repeated measures of methylation data collected from the same individuals to compare the reliability of CNVs called by ChAMP, Conumee, and cnAnalysis450k for both the methylation arrays. ChAMP identified more CNVs than Conumee and cnAnalysis450k for both the arrays and, as a consequence, had a higher overlap (~62%) with the calls from the genotype data. However, all methods had relatively low reliability. For the MethylationEPIC array, Conumee had the highest reliability (57.6%), whereas for the HumanMethylation450 array, cnAnalysis450k had the highest reliability (43.0%). Overall, the MethylationEPIC array provided significant gains in reliability for CNV calling over the HumanMethylation450 array but not for overlap with CNVs called using genotype data.

Identification of intermediate-sized deletions and inference of their impact on gene expression in a human population

Background

Next-generation sequencing has allowed for the identification of different genetic variations, which are known to contribute to diseases. Of these, insertions and deletions are the second most abundant type of variations in the genome, but their biological importance or disease association is not well-studied, especially for deletions of intermediate sizes.

Methods

We identified intermediate-sized deletions from whole-genome sequencing (WGS) data of Japanese samples (n = 174) with a novel deletion calling method which considered multiple samples. These deletions were used to construct a reference panel for use in imputation. Imputation was then conducted using the reference panel and data from 82 publically available Japanese samples with gene expression data. The accuracy of the deletion calling and imputation was examined with Nanopore long-read sequencing technology. We also conducted an expression quantitative trait loci (eQTL) association analysis using the deletions to infer their functional impacts on genes, before characterizing the deletions causal for gene expression level changes.

Results

We obtained a set of polymorphic 4378 high-confidence deletions and constructed a reference panel. The deletions were successfully imputed into the Japanese samples with high accuracy (97.3%). The eQTL analysis identified 181 deletions (4.1%) suggested as causal for gene expression level changes. The causal deletion candidates were significantly enriched in promoters, super-enhancers, and transcription elongation chromatin states. Generation of deletions in a cell line with the CRISPR-Cas9 system confirmed that they were indeed causative variants for gene expression change. Furthermore, one of the deletions was observed to affect the gene expression levels of a gene it was not located in.

Conclusions

This paper reports an accurate deletion calling method for genotype imputation at the whole genome level and shows the importance of intermediate-sized deletions in the human population.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0656-4) contains supplementary material, which is available to authorized users.

Cross-examining candidate genes implicated in multiple system atrophy

Multiple system atrophy (MSA) is a devastating neurodegenerative disease characterized by the clinical triad of parkinsonism, cerebellar ataxia and autonomic failure, impacting on striatonigral, olivopontocerebellar and autonomic systems. At early stage of the disease, the clinical symptoms of MSA can overlap with those of Parkinson's disease (PD). The key pathological hallmark of MSA is the presence of glial cytoplasmic inclusions (GCI) in oligodendrocytes. GCI comprise insoluble proteinaceous filaments composed chiefly of α-synuclein aggregates, and therefore MSA is regarded as an α-synucleinopathy along with PD and dementia with Lewy bodies. The etiology of MSA is unknown, and the pathogenesis of MSA is still largely speculative. Much data suggests that MSA is a sporadic disease, although some emerging evidence suggests rare genetic variants increase susceptibility. Currently, there is no general consensus on the susceptibility genes as there have been differences due to geographical distribution or ethnicity. Furthermore, many of the reported studies have been conducted on patients that were only clinically diagnosed without pathological verification. The purpose of this review is to bring together available evidence to cross-examine the susceptibility genes and genetic pathomechanisms implicated in MSA. We explore the possible involvement of the SNCA, COQ2, MAPT, GBA1, LRRK2 and C9orf72 genes in MSA pathogenesis, highlight the under-explored areas of MSA genetics, and discuss future directions of research in MSA.

Protective Variants in Alzheimer's Disease

PURPOSE OF REVIEW

Over the last decade over 40 loci have been associated with risk of Alzheimer's disease (AD). However, most studies have either focused on identifying risk loci or performing unbiased screens without a focus on protective variation in AD. Here, we provide a review of known protective variants in AD and their putative mechanisms of action. Additionally, we recommend strategies for finding new protective variants.

RECENT FINDINGS

Recent Genome-Wide Association Studies have identified both common and rare protective variants associated with AD. These include variants in or near APP, APOE, PLCG2, MS4A, MAPT-KANSL1, RAB10, ABCA1, CCL11, SORL1, NOCT, SCL24A4-RIN3, CASS4, EPHA1, SPPL2A, and NFIC.

SUMMARY

There are very few protective variants with functional evidence and a derived allele with a frequency below 20%. Additional fine mapping and multi-omic studies are needed to further validate and characterize known variants as well as specialized genome-wide scans to identify novel variants.

Alzheimer's Disease and Frontotemporal Dementia: The Current State of Genetics and Genetic Testing Since the Advent of Next-Generation Sequencing

The advent of next-generation sequencing has changed genetic diagnostics, allowing clinicians to test concurrently for phenotypically overlapping conditions such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). However, to interpret genetic results, clinicians require an understanding of the benefits and limitations of different genetic technologies, such as the inability to detect large repeat expansions in such diseases as C9orf72-associated FTD and amyotrophic lateral sclerosis. Other types of mutations such as large deletions or duplications and triple repeat expansions may also go undetected. Additionally, the concurrent testing of multiple genes or the whole exome increases the likelihood of discovering variants of unknown significance. Our goal here is to review the current knowledge about the genetics of AD and FTD and suggest up-to-date guidelines for genetic testing for these dementias. Despite the improvements in diagnosis due to biomarkers testing, AD and FTD can have overlapping symptoms. When used appropriately, genetic testing can elucidate the diagnosis and specific etiology of the disease, as well as provide information for the family and determine eligibility for clinical trials. Prior to ordering genetic testing, clinicians must determine the appropriate genes to test, the types of mutations that occur in these genes, and the best type of genetic test to use. Without this analysis, interpretation of genetic results will be difficult. Patients should be counseled about the benefits and limitations of different types of genetic tests so they can make an informed decision about testing.

NeuroArray, A Custom CGH Microarray to Decipher Copy Number Variants in Alzheimer's Disease

Background:

Copy Number Variants (CNVs) represent a prevailing type of structural variation (deletions or duplications) in the human genome. In the last few years, several studies have demonstrated that CNVs represent significant mutations in Alzheimer’s Disease (AD) hereditability. Currently, innovative high-throughput platforms and bioinformatics algorithms are spreading to screening CNVs involved in different neurological diseases. In particular, the use of custom arrays, based on libraries of probes that can detect significant genomic regions, have greatly improved the resolution of targeted regions and the identification of chromosomal aberrations.

Objective:

In this work, we report the use of NeuroArray, a custom CGH microarray useful to screening and further investigate the role of the recurring genomic aberrations in patients with confirmed or suspected AD.

Methods:

The custom oligonucleotide aCGH design includes 641 genes and 9118 exons, linked to AD. The genomic DNA was isolated from blood samples of AD affected patients. The entire protocol of custom NeuroArray included digestion, labelling and hybridization steps as a standard aCGH assay.

Results:

The NeuroArray analysis revealed the presence of amplifications in several genes associated with AD. In the coding regions of these genes, 14,586 probes were designed with a 348 bp median probe spacing. The majority of targeted AD genes map on chromosomes 1 and 10. A significant aspect of the NeuroArray design is that 95% of the total exon targets is covered by at least one probe, a resolution higher than CGH array platforms commercially available.

Conclusion:

By identifying with a high sensitivity the chromosomal abnormalities in a large panel of AD-related genes and other neurological diseases, the NeuroArray platform is a valid tool for clinical diagnosis.

NeuroArray: A Customized aCGH for the Analysis of Copy Number Variations in Neurological Disorders

BACKGROUND

Neurological disorders are a highly heterogeneous group of pathological conditions that affect both the peripheral and the central nervous system. These pathologies are characterized by a complex and multifactorial etiology involving numerous environmental agents and genetic susceptibility factors. For this reason, the investigation of their pathogenetic basis by means of traditional methodological approaches is rather arduous. High-throughput genotyping technologies, including the microarray-based comparative genomic hybridization (aCGH), are currently replacing classical detection methods, providing powerful molecular tools to identify genomic unbalanced structural rearrangements and explore their role in the pathogenesis of many complex human diseases.

METHODS

In this report, we comprehensively describe the design method, the procedures, validation, and implementation of an exon-centric customized aCGH (NeuroArray 1.0), tailored to detect both single and multi-exon deletions or duplications in a large set of multi- and monogenic neurological diseases. This focused platform enables a targeted measurement of structural imbalances across the human genome, targeting the clinically relevant genes at exon-level resolution.

CONCLUSION

An increasing use of the NeuroArray platform may offer new insights in investigating potential overlapping gene signatures among neurological conditions and defining genotype-phenotype relationships.

The Whole Exome Sequencing Clarifies the Genotype- Phenotype Correlations in Patients with Early-Onset Dementia

Our study aimed to identify the underlying causes in patients with early onset dementia by clinical and genetic exploration. We recruited a group of 38 patients with early-onset dementia. Firstly, hexanucleotide repeat expansions in C9ORF72 gene were screened in all subjects to exclude the possibility of copy number variation. Then, the whole exome sequencing (WES) was conducted, and the data were analyzed focusing on 89 dementia-related causing and susceptible genes. The effects of identified variants were classified according to the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. There were no pathogenic expansions in C9ORF72 detected. According to the ACMG standards and guidelines, we identified five known pathogenic mutations, PSEN1 P284L, PSEN1c.857-1G>A, PSEN1 I143T, PSEN1 G209E and MAPT G389R, and one novel pathogenic mutation APP K687N. All these mutations caused dementia with the mean onset age of 38.3 (range from 27 to 51) and rapid progression. Eleven variants with uncertain significance were also detected and needed further verification. The clinical phenotypes of dementia are heterogeneous, with both onset ages and clinical features being influenced by mutation position as well as the causative gene. WES can serve as efficient diagnostic tools for different heterogeneous dementia.

Review: Impact of Helicobacter pylori on Alzheimer's disease: What do we know so far?

BACKGROUND

Helicobacter pylori has changed radically gastroenterologic world, offering a new concept in patients' management. Over time, more medical data gave rise to diverse distant, extragastric manifestations and interactions of the "new" discovered bacterium. Special interest appeared within the field of neurodegenerative diseases and particularly Alzheimer's disease, as the latter and Helicobacter pylori infection are associated with a large public health burden and Alzheimer's disease ranks as the leading cause of disability. However, the relationship between Helicobacter pylori infection and Alzheimer's disease remains uncertain.

METHODS

We performed a narrative review regarding a possible connection between Helicobacter pylori and Alzheimer's disease. All accessible relevant (pre)clinical studies written in English were included. Both affected pathologies were briefly analyzed, and relevant studies are discussed, trying to focus on the possible pathogenetic role of this bacterium in Alzheimer's disease.

RESULTS

Data stemming from both epidemiologic studies and animal experiments seem to be rather encouraging, tending to confirm the hypothesis that Helicobacter pylori infection might influence the course of Alzheimer's disease pleiotropically. Possible main mechanisms may include the bacterium's access to the brain via the oral-nasal-olfactory pathway or by circulating monocytes (infected with Helicobacter pylori due to defective autophagy) through disrupted blood-brain barrier, thereby possibly triggering neurodegeneration.

CONCLUSIONS

Current data suggest that Helicobacter pylori infection might influence the pathophysiology of Alzheimer's disease. However, further large-scale randomized controlled trials are mandatory to clarify a possible favorable effect of Helicobacter pylori eradication on Alzheimer's disease pathophysiology, before the recommendation of short-term and cost-effective therapeutic regimens against Helicobacter pylori-related Alzheimer's disease.

Free radicals and polyphenols: The redox chemistry of neurodegenerative diseases

The oxidation of bioorganic materials by air and, particularly, the oxidative stress involved in the cell loss and other pathologies associated with neurodegenerative diseases (NDs) are of enormous social and economic importance. NDs generally involve free radical reactions, beginning with the formation of an initiating radical by some redox, thermal or photochemical process, causing nucleic acid, protein and lipid oxidations and the production of harmful oxidative products. Physically, persons afflicted by NDs suffer progressive loss of memory and thinking ability, mood swings, personality changes, and loss of independence. Therefore, the development of antioxidant strategies to retard or minimize the oxidative degradation of bioorganic materials has been, and still is, of paramount importance. While we are aware of the importance of investigating the biological and medical aspects of the diseases, elucidation of the associated chemistry is crucial to understanding their progression, heading to intelligent chemical intervention to find more efficient therapies to prevent or delay the onset of the diseases. Accordingly, this review aims to provide the reader with a chemical base to understand the behavior and properties of the reactive oxygen species involved and of typical radical scavengers such as polyphenolic antioxidants. Some discussion on the structures of the various species, their formation, chemical reactivities and lifetimes is included. The ultimate goal is to understand how, when and where they form, how far they travel prior to react, which molecules are their targets, and how we can, eventually, control their activity to minimize their impact by means of chemical methods. Recent strategies explore chemical modifications of the hydrophobicity of potent, natural antioxidants to improve their efficiency by fine-tuning their concentrations at the reaction site.