Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis

An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved.

DNA methylation in amyotrophic lateral sclerosis: where do we stand and what is next?

Genes involved in immune response, inflammation and metabolism are among those most likely affected by changes in DNA methylation (DNAm) and expression levels in amyotrophic lateral sclerosis (ALS) tissues. Unfortunately, it is still largely unclear whether any of these changes precede the onset of disease symptoms or whether most of them are the result of the muscular and metabolic changes that follow symptoms onset. In this article the author discusses the strengths and limitations of the available studies of DNAm in ALS and provides some suggestions on what, in his opinion, could be done in the near future for a better understanding of the DNAm changes occurring in ALS, their link with environmental exposures and their potential clinical utility.

Advancing personalized medicine in neurodegenerative diseases: The role of epigenetics and pharmacoepigenomics in pharmacotherapy

About 80 % of brain disorders have a genetic basis. The pathogenesis of most neurodegenerative diseases is associated with a myriad of genetic defects, epigenetic alterations (DNA methylation, histone/chromatin remodeling, miRNA dysregulation), and environmental factors. The emergence of new sequencing technologies and tools to study the epigenome has led to identifying predictive biomarkers for earlier diagnosis, opening up the possibility of prophylactical interventions. As a result, advances in pharmacogenetics and pharmacoepigenomics now allow for personalized treatments based on the profile of each patient and the specific genetic and epigenetic mechanisms involved. This Review highlights the complexity of neurodegenerative diseases and the variability in patient responses to pharmacotherapy, emphasizing the influence of genetic polymorphisms on the pharmacokinetics and pharmacodynamics of drugs used to treat those conditions. We specifically discuss the potential modulatory effect of several genetic polymorphisms associated with an increased risk of developing different neurodegenerative diseases. We explore genetic and genomic technologies and the potential of analyzing individual-specific drug metabolism to predict and influence drug response and associated clinical outcomes. We also provide insights into the mechanism of action of the drugs under investigation and their potential impact on disease-modifying pathways. Finally, the Review underscores the great potential of this field to enhance the effectiveness and safety of drug treatments through personalized medicine.

The implications of DNA methylation for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a complex and serious neurodegenerative disorder that develops in consequence of the progressive loss of the upper and lower motor neurons. Cases of ALS are classified as sporadic (sALS), or familial (fALS). Over 90% of cases are sALS, while roughly 10% are related to inherited genetic mutations (fALS). Approximately 70% of the genetic mutations that contribute to fALS have been identified. On the other hand, the majority of the sALS cases have an undetermined genetic contributor and few mutations have been described, despite the advanced genetic analysis methods. Also, several factors contribute to the onset and progression of ALS. Numerous lines of evidence indicate that epigenetic changes are linked to aging, as well as neurodegenerative disorders, such as ALS. In most cases, they act as the heritable regulation of transcription by DNA methylation, histone modification and expression of noncoding RNAs. Mechanisms involving aberrant DNA methylation could be relevant to human ALS pathobiology and therapeutic targeting. Despite advances in research to find factors associated with ALS and more effective treatments, this disease remains complex and has low patient survival. Here, we provide a narrative review of the role of DNA methylation for this complex neurodegenerative disorder.

Global endometrial DNA methylation analysis reveals insights into mQTL regulation and associated endometriosis disease risk and endometrial function

Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Herein, we characterize variation in DNA methylation (DNAm) and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genotype data and methylation in endometrial samples from 984 deeply-phenotyped participants. We estimate that 15.4% of the variation in endometriosis is captured by DNAm and identify significant differences in DNAm profiles associated with stage III/IV endometriosis, endometriosis sub-phenotypes and menstrual cycle phase, including opening of the window for embryo implantation. Menstrual cycle phase was a major source of DNAm variation suggesting cellular and hormonally-driven changes across the cycle can regulate genes and pathways responsible for endometrial physiology and function. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis, highlighting candidate genes contributing to disease risk. Our work provides functional evidence for epigenetic targets contributing to endometriosis risk and pathogenesis. Data generated serve as a valuable resource for understanding tissue-specific effects of methylation on endometrial biology in health and disease.

DNA Methylation Analysis in Monozygotic Twins Discordant for ALS in Blood Cells

ALS is a fatal motor neuron disease that displays a broad variety of phenotypes ranging from early fatal courses to slowly progressing and rather benign courses. Such divergence can also be seen in genetic ALS cases with varying phenotypes bearing specific mutations, suggesting epigenetic mechanisms like DNA methylation act as disease modifiers. However, the epigenotype dictated by, in addition to other mechanisms, DNA methylation is also strongly influenced by the individual's genotype. Hence, we performed a DNA methylation study using EPIC arrays on 7 monozygotic (MZ) twin pairs discordant for ALS in whole blood, which serves as an ideal model for eliminating the effects of the genetic-epigenetic interplay to a large extent. We found one CpG site showing intra-pair hypermethylation in the affected co-twins, which maps to the Glutamate Ionotropic Receptor Kainate Type Subunit 1 gene (GRIK1). Additionally, we found 4 DMPs which were subsequently confirmed using 2 different statistical approaches. Differentially methylated regions or blocks could not be detected within the scope of this work. In conclusion, we revealed that despite a low sample size, monozygotic twin studies discordant for the disease can bring new insights into epigenetic processes in ALS, pointing to new target loci for further investigations.

An overview of DNA methylation-derived trait score methods and applications

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

Parsimonious model for mass-univariate vertexwise analysis

Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses. Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. Results: We showed that when performed on a large sample ( N = 8662 , UK Biobank), GLMs yielded greatly inflated false positive rate (cluster false discovery rate > 0.6 ). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate but at a cost of increased computation. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence, and smoking status) and LMM yielded fewer and more localized associations. We identified 19 significant clusters displaying small associations with age, sex, and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes. Conclusions: The published literature could contain a large proportion of redundant (possibly confounded) associations that are largely prevented using LMMs. The parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance.

Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer's disease

To better understand DNA methylation in Alzheimer's disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10^-5). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.

Association between DNA methylation variability and self-reported exposure to heavy metals

Individuals encounter varying environmental exposures throughout their lifetimes. Some exposures such as smoking are readily observed and have high personal recall; others are more indirect or sporadic and might only be inferred from long occupational histories or lifestyles. We evaluated the utility of using lifetime-long self-reported exposures for identifying differential methylation in an amyotrophic lateral sclerosis cases-control cohort of 855 individuals. Individuals submitted paper-based surveys on exposure and occupational histories as well as whole blood samples. Genome-wide DNA methylation levels were quantified using the Illumina Infinium Human Methylation450 array. We analyzed 15 environmental exposures using the OSCA software linear and MOA models, where we regressed exposures individually by methylation adjusted for batch effects and disease status as well as predicted scores for age, sex, cell count, and smoking status. We also regressed on the first principal components on clustered environmental exposures to detect DNA methylation changes associated with a more generalised definition of environmental exposure. Five DNA methylation probes across three environmental exposures (cadmium, mercury and metalwork) were significantly associated using the MOA models and seven through the linear models, with one additionally across a principal component representing chemical exposures. Methylome-wide significance for four of these markers was driven by extreme hyper/hypo-methylation in small numbers of individuals. The results indicate the potential for using self-reported exposure histories in detecting DNA methylation changes in response to the environment, but also highlight the confounded nature of environmental exposure in cohort studies.

Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation-based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.

Functional characterisation of the amyotrophic lateral sclerosis risk locus GPX3/TNIP1

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a complex, late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies (GWAS) have identified ten risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Given association analysis data alone cannot determine the most plausible risk gene for this locus, we undertook a comprehensive suite of in silico, in vivo and in vitro studies to address this.

METHODS

The Functional Mapping and Annotation (FUMA) pipeline and five tools (conditional and joint analysis (GCTA-COJO), Stratified Linkage Disequilibrium Score Regression (S-LDSC), Polygenic Priority Scoring (PoPS), Summary-based Mendelian Randomisation (SMR-HEIDI) and transcriptome-wide association study (TWAS) analyses) were used to perform bioinformatic integration of GWAS data (Ncases = 20,806, Ncontrols = 59,804) with 'omics reference datasets including the blood (eQTLgen consortium N = 31,684) and brain (N = 2581). This was followed up by specific expression studies in ALS case-control cohorts (microarray Ntotal = 942, protein Ntotal = 300) and gene knockdown (KD) studies of human neuronal iPSC cells and zebrafish-morpholinos (MO).

RESULTS

SMR analyses implicated both TNIP1 and GPX3 (p < 1.15 × 10-6), but there was no simple SNP/expression relationship. Integrating multiple datasets using PoPS supported GPX3 but not TNIP1. In vivo expression analyses from blood in ALS cases identified that lower GPX3 expression correlated with a more progressed disease (ALS functional rating score, p = 5.5 × 10-3, adjusted R2 = 0.042, Beffect = 27.4 ± 13.3 ng/ml/ALSFRS unit) with microarray and protein data suggesting lower expression with risk allele (recessive model p = 0.06, p = 0.02 respectively). Validation in vivo indicated gpx3 KD caused significant motor deficits in zebrafish-MO (mean difference vs. control ± 95% CI, vs. control, swim distance = 112 ± 28 mm, time = 1.29 ± 0.59 s, speed = 32.0 ± 2.53 mm/s, respectively, p for all < 0.0001), which were rescued with gpx3 expression, with no phenotype identified with tnip1 KD or gpx3 overexpression.

CONCLUSIONS

These results support GPX3 as a lead ALS risk gene in this locus, with more data needed to confirm/reject a role for TNIP1. This has implications for understanding disease mechanisms (GPX3 acts in the same pathway as SOD1, a well-established ALS-associated gene) and identifying new therapeutic approaches. Few previous examples of in-depth investigations of risk loci in ALS exist and a similar approach could be applied to investigate future expected GWAS findings.

One-carbon epigenetics and redox biology of neurodegeneration

One-carbon metabolism provides the methyl groups for both DNA and histone tail methylation reactions, two of the main epigenetic processes that tightly regulate the chromatin structure and gene expression levels. Several enzymes involved in one-carbon metabolism, as well as several epigenetic enzymes, are regulated by intracellular metabolites and redox cofactors, but their expression levels are in turn regulated by epigenetic modifications, in such a way that metabolism and gene expression reciprocally regulate each other to maintain homeostasis and regulate cell growth, survival, differentiation and response to environmental stimuli. Increasing evidence highlights the contribution of impaired one-carbon metabolism and epigenetic modifications in neurodegeneration. This article provides an overview of DNA and histone tail methylation changes in major neurodegenerative disorders, namely Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis, discussing the contribution of oxidative stress and impaired one-carbon and redox metabolism to their onset and progression.

Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer's Disease

Background:

The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019).

Objective:

Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation.

Methods:

Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations.

Results:

AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression.

Conclusion:

This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

BACKGROUND

People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease.

RESULTS

We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson's disease (and none with Alzheimer's disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights.

CONCLUSIONS

We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

Circulating cell-free DNA as potential diagnostic tools for amyotrophic lateral sclerosis

DNA methylation has garnered much attention in recent years for its diagnostic potential in multiple conditions including cancer and neurodegenerative diseases. Conversely, advances regarding the potential diagnostic relevance of DNA methylation status have been sparse in the field of amyotrophic lateral sclerosis (ALS) even though patients diagnosed with this condition would significantly benefit from improved molecular assays aimed at furthering the current diagnostic and therapeutic options available. This review will provide an overview of the current diagnostic approaches available for ALS diagnosis and discuss the potential clinical usefulness of DNA methylation. We will also present examples of DNA methylation as a diagnostic tool in various types of cancer and neurodegenerative conditions and expand on how circulating cfDNA methylation may be leveraged for the early detection of ALS. In general, this article will reinforce the importance of cfDNA methylation as diagnostic tools and will further highlight its clinical relevance for persons diagnosed with ALS.

Could Polygenic Risk Scores Be Useful in Psychiatry?: A Review

IMPORTANCE

Polygenic risk scores (PRS) are predictors of the genetic susceptibility to diseases, calculated for individuals as weighted counts of thousands of risk variants in which the risk variants and their weights have been identified in genome-wide association studies. Polygenic risk scores show promise in aiding clinical decision-making in many areas of medical practice. This review evaluates the potential use of PRS in psychiatry.

OBSERVATIONS

On their own, PRS will never be able to establish or definitively predict a diagnosis of common complex conditions (eg, mental health disorders), because genetic factors only contribute part of the risk and PRS will only ever capture part of the genetic contribution. Combining PRS with other risk factors has potential to improve outcome prediction and aid clinical decision-making (eg, determining follow-up options for individuals seeking help who are at clinical risk of future illness). Prognostication of adverse physical health outcomes or response to treatment in clinical populations are of great interest for psychiatric practice, but data from larger samples are needed to develop and evaluate PRS.

CONCLUSIONS AND RELEVANCE

Polygenic risk scores will contribute to risk assessment in clinical psychiatry as it evolves to combine information from molecular, clinical, and lifestyle metrics. The genome-wide genotype data needed to calculate PRS are inexpensive to generate and could become available to psychiatrists as a by-product of practices in other medical specialties. The utility of PRS in clinical psychiatry, as well as ethical issues associated with their use, should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. Clinical psychiatry has lagged behind other fields of health care in its use of new technologies and routine clinical data for research. Now is the time to catch up.

Epigenetics of neuromuscular disorders

Neuromuscular disorders are a heterogeneous group of conditions affecting the neuromuscular system. The aim of this article is to review the major epigenetic findings in motor neuron diseases and major hereditary muscular dystrophies. DNA methylation changes are observed in both hereditary and sporadic forms, and combining DNA methylation analysis with mutational screening holds the potential for better diagnostic and prognostic accuracy. Novel, less toxic and more selective epigenetic drugs are designed and tested in animal and cell culture models of neuromuscular disorders, and non-coding RNAs are being investigated as either disease biomarkers or targets of therapeutic approaches to restore gene expression levels. Overall, neuromuscular disorder epigenetic biomarkers have a strong potential for clinical applications in the near future.

What do we know about the variability in survival of patients with amyotrophic lateral sclerosis?

INTRODUCTION

ALS is a fatal neurodegenerative disease. However, patients show variability in the length of survival after symptom onset. Understanding the mechanisms of long survival could lead to possible avenues for therapy.

AREAS COVERED

This review surveys the reported length of survival in ALS, the clinical features that predict survival in individual patients, and possible factors, particularly genetic factors, that could cause short or long survival. The authors also speculate on possible mechanisms.

EXPERT OPINION

a small number of known factors can explain some variability in ALS survival. However, other disease-modifying factors likely exist. Factors that alter motor neurone vulnerability and immune, metabolic, and muscle function could affect survival by modulating the disease process. Knowing these factors could lead to interventions to change the course of the disease. The authors suggest a broad approach is needed to quantify the proportion of variation survival attributable to genetic and non-genetic factors and to identify and estimate the effect size of specific factors. Studies of this nature could not only identify novel avenues for therapeutic research but also play an important role in clinical trial design and personalized medicine.