Biothiols and oxidative stress markers and polymorphisms of TOMM40 and APOC1 genes in Alzheimer's disease patients

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries

INTRODUCTION

Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry.

METHODS

We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed.

RESULTS

We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations.

DISCUSSION

We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

Exploring the complexities of 1C metabolism: implications in aging and neurodegenerative diseases

The intricate interplay of one-carbon metabolism (OCM) with various cellular processes has garnered substantial attention due to its fundamental implications in several biological processes. OCM serves as a pivotal hub for methyl group donation in vital biochemical reactions, influencing DNA methylation, protein synthesis, and redox balance. In the context of aging, OCM dysregulation can contribute to epigenetic modifications and aberrant redox states, accentuating cellular senescence and age-associated pathologies. Furthermore, OCM's intricate involvement in cancer progression is evident through its capacity to provide essential one-carbon units crucial for nucleotide synthesis and DNA methylation, thereby fueling uncontrolled cell proliferation and tumor development. In neurodegenerative disorders like Alzheimer's and Parkinson's, perturbations in OCM pathways are implicated in the dysregulation of neurotransmitter synthesis and mitochondrial dysfunction, contributing to disease pathophysiology. This review underscores the profound impact of OCM in diverse disease contexts, reinforcing the need for a comprehensive understanding of its molecular complexities to pave the way for targeted therapeutic interventions across inflammation, aging and neurodegenerative disorders.

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression

BACKGROUND

Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies.

METHODS

We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.

RESULTS

Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression.

DISCUSSION

The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.

Selected Biomarkers of Oxidative Stress and Energy Metabolism Disorders in Neurological Diseases

Neurological diseases can be broadly divided according to causal factors into circulatory system disorders leading to ischemic stroke; degeneration of the nerve cells leading to neurodegenerative diseases, such as Alzheimer's (AD) and Parkinson's (PD) diseases, and immune system disorders; bioelectric activity (epileptic) problems; and genetically determined conditions as well as viral and bacterial infections developing inflammation. Regardless of the cause of neurological diseases, they are usually accompanied by disturbances of the central energy in a completely unexplained mechanism. The brain makes up only 2% of the human body's weight; however, while working, it uses as much as 20% of the energy obtained by the body. The energy requirements of the brain are very high, and regulatory mechanisms in the brain operate to ensure adequate neuronal activity. Therefore, an understanding of neuroenergetics is rapidly evolving from a "neurocentric" view to a more integrated picture involving cooperativity between structural and molecular factors in the central nervous system. This article reviewed selected molecular biomarkers of oxidative stress and energy metabolism disorders such as homocysteine, DNA damage such as 8-oxo2dG, genetic variants, and antioxidants such as glutathione in selected neurological diseases including ischemic stroke, AD, PD, and epilepsy. This review summarizes our and others' recent research on oxidative stress in neurological disorders. In the future, the diagnosis and treatment of neurological diseases may be substantially improved by identifying specific early markers of metabolic and energy disorders.

S-Glutathionylation and S-Nitrosylation in Mitochondria: Focus on Homeostasis and Neurodegenerative Diseases

Redox post-translational modifications are derived from fluctuations in the redox potential and modulate protein function, localization, activity and structure. Amongst the oxidative reversible modifications, the S-glutathionylation of proteins was the first to be characterized as a post-translational modification, which primarily protects proteins from irreversible oxidation. However, a growing body of evidence suggests that S-glutathionylation plays a key role in core cell processes, particularly in mitochondria, which are the main source of reactive oxygen species. S-nitrosylation, another post-translational modification, was identified >150 years ago, but it was re-introduced as a prototype cell-signaling mechanism only recently, one that tightly regulates core processes within the cell’s sub-compartments, especially in mitochondria. S-glutathionylation and S-nitrosylation are modulated by fluctuations in reactive oxygen and nitrogen species and, in turn, orchestrate mitochondrial bioenergetics machinery, morphology, nutrients metabolism and apoptosis. In many neurodegenerative disorders, mitochondria dysfunction and oxidative/nitrosative stresses trigger or exacerbate their pathologies. Despite the substantial amount of research for most of these disorders, there are no successful treatments, while antioxidant supplementation failed in the majority of clinical trials. Herein, we discuss how S-glutathionylation and S-nitrosylation interfere in mitochondrial homeostasis and how the deregulation of these modifications is associated with Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis and Friedreich’s ataxia.

A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction

MOTIVATION

Linear mixed models (LMMs) have long been the method of choice for risk prediction analysis on high-dimensional data. However, it remains computationally challenging to simultaneously model a large amount of variants that can be noise or have predictive effects of complex forms.

RESULTS

In this work, we have developed a penalized LMM with generalized method of moments (pLMMGMM) estimators for prediction analysis. pLMMGMM is built within the LMM framework, where random effects are used to model the joint predictive effects from all variants within a region. Different from existing methods that focus on linear relationships and use empirical criteria for variable screening, pLMMGMM can efficiently detect regions that harbor genetic variants with both linear and non-linear predictive effects. In addition, unlike existing LMMs that can only handle a very limited number of random effects, pLMMGMM is much less computationally demanding. It can jointly consider a large number of regions and accurately detect those that are predictive. Through theoretical investigations, we have shown that our method has the selection consistency and asymptotic normality. Through extensive simulations and the analysis of PET-imaging outcomes, we have demonstrated that pLMMGMM outperformed existing models and it can accurately detect regions that harbor risk factors with various forms of predictive effects.

AVAILABILITY AND IMPLEMENTATION

The R-package is available at https://github.com/XiaQiong/GMMLasso.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.

Accurate disease risk prediction is an essential step towards precision medicine. Deep learning models have achieved the state-of-the-art performance for many prediction tasks. However, they generally suffer from the curse of dimensionality and lack of biological interpretability, both of which have greatly limited their applications to the prediction analysis of whole-genome sequencing data. We present here an explainable deep transfer learning model for the analysis of high-dimensional genomic data. Our proposed method can detect predictive genes that harbor genetic variants with both linear and non-linear effects via the proposed group-wise feature importance score. It can also efficiently and accurately model disease risk based on the detected predictive genes using the proposed transfer-learning based network architecture. Our proposed method is built at the gene level, and thus is much more biologically interpretable. It is also computationally efficiently and can be applied to whole-exome sequencing data that have millions of potential predictors. Through both simulation studies and the analysis of whole-exome data obtained from the Alzheimer’s Disease Neuroimaging Initiative, we have demonstrated that our method can efficiently detect predictive genes and it has better prediction performance than many existing methods.

Variation of genes encoding nitric oxide synthases and antioxidant enzymes as potential risks of multiple sclerosis development: a preliminary study

Multiple sclerosis (MS) is a neurodegenerative disease characterized by a variable clinical course and diverse pathophysiology, including nitrative and oxidative stresses as well as inflammation. We aimed to detect the potential association between five selected single-nucleotide polymorphisms (SNPs) in genes encoding nitric oxide synthetases as well as antioxidant enzymes and the development of MS in a Polish population. Genomic DNA was isolated from peripheral blood collected from 142 MS patients and 140 controls. Using Taq-Man® probes, we genotyped the following SNPs: rs1879417 in NOS1, and rs2297518 in NOS2 as well as rs4880 in SOD2, rs7943316 in CAT, rs713041 in GPX4. In the case of rs2297518, the C/C genotype and C allele SNP were associated with an enhanced occurrence of MS, while the C/T, T/T genotypes, and T allele of the same polymorphism reduced this risk. Moreover, the C/C homozygote and C allele of the rs4880 SNP reduced MS risk, while the T allele increased the risk. In addition, the A/T heterozygote of rs7943316 polymorphism was associated with an increased risk of MS occurrence. We also detected that the C/C genotype and C allele of rs713041 decreased the risk of MS, whereas the T/T genotype and T allele increased this risk. In conclusion, the results of our study suggest some links between polymorphic variability in the nitrative/oxidative stress-related genes and the risk of MS development in the Polish population.

Altered central and blood glutathione in Alzheimer's disease and mild cognitive impairment: a meta-analysis

BACKGROUND

Increasing evidence implicates oxidative stress (OS) in Alzheimer disease (AD) and mild cognitive impairment (MCI). Depletion of the brain antioxidant glutathione (GSH) may be important in OS-mediated neurodegeneration, though studies of post-mortem brain GSH changes in AD have been inconclusive. Recent in vivo measurements of the brain and blood GSH may shed light on GSH changes earlier in the disease.

AIM

To quantitatively review in vivo GSH in AD and MCI compared to healthy controls (HC) using meta-analyses.

METHOD

Studies with in vivo brain or blood GSH levels in MCI or AD with a HC group were identified using MEDLINE, PsychInfo, and Embase (1947-June 2020). Standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for outcomes using random effects models. Outcome measures included brain GSH (Meshcher-Garwood Point Resolved Spectroscopy (MEGA-PRESS) versus non-MEGA-PRESS) and blood GSH (intracellular versus extracellular) in AD and MCI. The Q statistic and Egger's test were used to assess heterogeneity and risk of publication bias, respectively.

RESULTS

For brain GSH, 4 AD (AD=135, HC=223) and 4 MCI (MCI=213, HC=211) studies were included. For blood GSH, 26 AD (AD=1203, HC=1135) and 7 MCI (MCI=434, HC=408) studies were included. Brain GSH overall did not differ in AD or MCI compared to HC; however, the subgroup of studies using MEGA-PRESS reported lower brain GSH in AD (SMD [95%CI] -1.45 [-1.83, -1.06], p<0.001) and MCI (-1.15 [-1.71, -0.59], z=4.0, p<0.001). AD had lower intracellular and extracellular blood GSH overall (-0.87 [-1. 30, -0.44], z=3.96, p<0.001). In a subgroup analysis, intracellular GSH was lower in MCI (-0.66 [-1.11, -0.21], p=0.025). Heterogeneity was observed throughout (I2 >85%) and not fully accounted by subgroup analysis. Egger's test indicated risk of publication bias.

CONCLUSION

Blood intracellular GSH decrease is seen in MCI, while both intra- and extracellular decreases were seen in AD. Brain GSH is decreased in AD and MCI in subgroup analysis. Potential bias and heterogeneity suggest the need for measurement standardization and additional studies to explore sources of heterogeneity.

Transcriptome aberration associated with altered locomotor behavior of zebrafish (Danio rerio) caused by Waterborne Benzo[a]pyrene

Waterborne Benzo[a]pyrene (B[a]P) pollution is a global threat to aquatic organisms. The exposure to waterborne B[a]P can disrupt the normal locomotor behavior of zebrafish (Danio rerio), however, how it affect the locomotor behavior of adult zebrafish remains unclear. Herein, B[a]P at two concentrations (0.8 μg/L and 2.0 μg/L) were selected to investigate the molecular mechanisms of the affected locomotor behavior of zebrafish by B[a]P based on transcriptome profiling. Adverse effects of B[a]P exposure affecting locomotor behavior in zebrafish were studied by RNA sequencing, and the locomotion phenotype was acquired. The gene enrichment results showed that the differentially highly expressed genes (atp2a1, cdh2, aurka, fxyd1, clstn1, apoc1, mt-co1, tnnt3b, and fads2) of zebrafish are mainly enriched in adrenergic signaling in cardiomyocytes (dre04261) and locomotory behavior (GO:0007626). The movement trajectory plots showed an increase in the locomotor distance and velocity of zebrafish in the 0.8 μg/L group and the opposite in the 2.0 μg/L group. The results showed that B[a]P affects the variety of genes in zebrafish, including motor nerves, muscles, and energy supply, and ultimately leads to altered locomotor behavior.

Modulation of retinoid-X-receptors differentially regulates expression of apolipoprotein genes apoc1 and apoeb by zebrafish microglia

Transcriptome analyses performed in both human and zebrafish indicate strong expression of Apoe and Apoc1 by microglia. Apoe expression by microglia is well appreciated, but Apoc1 expression has not been well-examined. PPAR/RXR and LXR/RXR receptors appear to regulate expression of the apolipoprotein gene cluster in macrophages, but a similar role in microglia in vivo has not been studied. Here, we characterized microglial expression of apoc1 in the zebrafish central nervous system (CNS) in situ and demonstrate that in the CNS, apoc1 expression is unique to microglia. We then examined the effects of PPAR/RXR and LXR/RXR modulation on microglial expression of apoc1 and apoeb during early CNS development using a pharmacological approach. Changes in apoc1 and apoeb transcripts in response to pharmacological modulation were quantified by RT-qPCR in whole heads, and in individual microglia using hybridization chain reaction (HCR) in situ hybridization. We found that expression of apoc1 and apoeb by microglia were differentially regulated by LXR/RXR and PPAR/RXR modulating compounds, respectively, during development. Our results also suggest RXR receptors could be involved in endogenous induction of apoc1 expression by microglia. Collectively, our work supports the use of zebrafish to better understand regulation and function of these apolipoproteins in the CNS.

Summary: Here we investigate expression of two apolipoprotein genes by microglia in the zebrafish model during normal development, and in contexts of pharmacological manipulations that target candidate regulatory receptors.

TOMM40 and APOE variants synergistically increase the risk of Alzheimer's disease in a Chinese population

BACKGROUND

The apolipoprotein E (APOE) ε4 allele is a strong risk factor for Alzheimer's disease (AD) in Caucasian and African American populations. It suggests that other genetic factors may modulate AD pathogenesis in Chinese populations, among which the frequency of this allele is reduced but the AD prevalence is maintained. The translocase of outer mitochondrial membrane 40 (TOMM40), which is located adjacent to APOE, may play an APOE-dependent role in modulating AD pathogenesis.

AIMS

This work aimed to investigate whether TOMM40 polymorphisms modulate AD risk independently of, or in conjunction with APOE polymorphisms in Chinese populations.

METHODS

We conducted a case-control study including 834 patients with AD recruited from the Memory Clinic and 643 cognitively normal participants recruited from the community. The Taqman SNP method was used for APOE genotyping, while TOMM40 polymorphism genotyping was conducted via a polymerase chain reaction-ligase detection reaction.

RESULTS

The TOMM40 rs10119 and rs71352238 alleles were associated with AD independently of the patient APOE status. The rs10119 AA genotype and rs71352238 CC genotype were risk genotypes of AD. Individuals carrying a TOMM40 rs10119 GG/APOE ε4+ (OR, 3.73; 95% CI 1.49-9.37; P = 0.005), TOMM40 rs10119 AG/APOE ε4+ (OR, 4.16; 95% CI 3.30-5.24; P < 0.001), or TOMM40 rs10119 AA/APOE ε4+ (OR, 14.78; 95% CI 8.56-25.54; P < 0.001) genotype exhibited a significantly higher AD risk. Those carrying a TOMM40 rs71352238 TT/APOE ε4+ (OR, 3.82; 95% CI 2.32-6.29; P < 0.001), TOMM40 rs71352238 CT/APOE ε4+ (OR, 4.40; 95% CI 3.46-5.56; P < 0.001), or TOMM40 rs71352238 CC/APOE ε4+ (OR, 14.02; 95% CI 7.81-25.17; P < 0.001) genotype also exhibited a significantly increased AD risk.

DISCUSSION AND CONCLUSIONS

This study provides invaluable insights into the mechanisms underlying the prevalence of AD in Chinese populations, and supports that simultaneous TOMM40 and APOE genotyping in the clinical setting may identify individuals at high risk of developing AD.

Molecular Factors Mediating Neural Cell Plasticity Changes in Dementia Brain Diseases

Neural plasticity-the ability to alter a neuronal response to environmental stimuli-is an important factor in learning and memory. Short-term synaptic plasticity and long-term synaptic plasticity, including long-term potentiation and long-term depression, are the most-characterized models of learning and memory at the molecular and cellular level. These processes are often disrupted by neurodegeneration-induced dementias. Alzheimer's disease (AD) accounts for 50% of cases of dementia. Vascular dementia (VaD), Parkinson's disease dementia (PDD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD) constitute much of the remaining cases. While vascular lesions are the principal cause of VaD, neurodegenerative processes have been established as etiological agents of many dementia diseases. Chief among such processes is the deposition of pathological protein aggregates in vivo including β-amyloid deposition in AD, the formation of neurofibrillary tangles in AD and FTD, and the accumulation of Lewy bodies composed of α-synuclein aggregates in DLB and PDD. The main symptoms of dementia are cognitive decline and memory and learning impairment. Nonetheless, accurate diagnoses of neurodegenerative diseases can be difficult due to overlapping clinical symptoms and the diverse locations of cortical lesions. Still, new neuroimaging and molecular biomarkers have improved clinicians' diagnostic capabilities in the context of dementia and may lead to the development of more effective treatments. Both genetic and environmental factors may lead to the aggregation of pathological proteins and altered levels of cytokines, such that can trigger the formation of proinflammatory immunological phenotypes. This cascade of pathological changes provides fertile ground for the development of neural plasticity disorders and dementias. Available pharmacotherapy and disease-modifying therapies currently in clinical trials may modulate synaptic plasticity to mitigate the effects neuropathological changes have on cognitive function, memory, and learning. In this article, we review the neural plasticity changes seen in common neurodegenerative diseases from pathophysiological and clinical points of view and highlight potential molecular targets of disease-modifying therapies.

Gold-Modified Micellar Composites as Colorimetric Probes for the Determination of Low Molecular Weight Thiols in Biological Fluids Using Consumer Electronic Devices

This work describes a new, low-cost and simple-to-use method for the determination of free biothiols in biological fluids. The developed method utilizes the interaction of biothiols with gold ions, previously anchored on micellar assemblies through electrostatic interactions with the hydrophilic headgroup of cationic surfactant micelles. Specifically, the reaction of AuCl4− with the cationic surfactant cetyltrimethyl ammonium bromide (CTAB) produces an intense orange coloration, due to the ligand substitution reaction of the Br− for Cl− anions, followed by the coordination of the AuBr4− anions on the micelle surface through electrostatic interactions. When biothiols are added to the solution, they complex with the gold ions and disrupt the AuBr4−–CTAB complex, quenching the initial coloration and inducing a decrease in the light absorbance of the solution. Biothiols are assessed by monitoring their color quenching in an RGB color model, using a flatbed scanner operating in transmittance mode as an inexpensive microtiter plate photometer. The method was applied to determine the biothiol content in urine and blood plasma samples, with satisfactory recoveries (i.e., >67.3–123% using external calibration and 103.8–115% using standard addition calibration) and good reproducibility (RSD < 8.4%, n = 3).

Genome-wide association studies of brain imaging data via weighted distance correlation

MOTIVATION

Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships.

RESULTS

To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI).

AVAILABILITY AND IMPLEMENTATION

Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Aging-Related Single-Nucleotide Polymorphism is Associated With Altered Clinical Outcomes and Distinct Inflammatory Profiles in Aged Blunt Trauma Patients

The contribution of individual genetic determinants of aging to the adverse clinical outcomes and altered inflammation mediator networks characteristic of aged trauma patients is unknown. The AA genotype of the aging-related single-nucleotide polymorphism (SNP) rs2075650 in TOMM40 has been associated with longevity, while the AG and GG genotypes are associated with an increased risk of Alzheimer disease. Here, we studied the effect of rs2075650 on clinical outcomes and dynamic biomarker patterns after traumatic injury. Genomic DNA was obtained from blunt trauma patients admitted to the ICU and examined for 551,839 SNPs using an Illumina microarray kit. Plasma was sampled from each patient three times within the first 24 h and daily from day 1 to 7 then assayed for 31 biomarkers using Luminex. Aged patients (65-90 years) were segregated into AA (n = 77) and AG/GG (n = 17) genotypes. Additional comparisons were made with matched groups of young patients (18-30 years), controlling for injury severity score (ISS) and sex ratio, and also segregated into AA (n = 56) and AG/GG (n = 19) genotypes. Aged patients with the AA genotype had a significantly lower requirement for ventilation and fewer days on mechanical ventilation, as well as significantly higher levels of one mediator and lower levels of two mediators. Dynamic Bayesian Network inference revealed IL-23 as a central node in each network regardless of age or genotype, with MIG and IP-10 also as key mediators in the networks of the aged patients. These findings suggest that an aging-related SNP, rs2075650, may influence clinical outcomes and inflammation networks in aged patients following blunt trauma, and thus may serve as a predictive outcome biomarker in the setting of polytrauma.

Plasma GSH levels and Alzheimer's disease. A prospective approach.: Results from the HELIAD study

BACKGROUND

Potential links between oxidative stress and the pathophysiology of Alzheimer's disease (AD) have been reported in the existing literature. Biological markers of oxidative stress, such as the reduced form of glutathione (GSH), may have a potential role as predictive biomarkers for AD development. The aim of the present study was to explore the longitudinal associations between plasma GSH and the risk of developing AD or cognitive decline, in a sample of community-dwelling, non-demented older adults.

METHODS

Participants from the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) were included in the present prospective study. The sample used in the analyses consisted of 391 non-demented individuals over the age of 64 (mean age = 73.85 years; SD = 5.06), with available baseline GSH measurements and longitudinal follow-up. Plasma GSH was treated both as a continuous variable and as tertiles in our analyses. Cox proportional hazards models were used to evaluate the hazard ratio (HR) for AD incidence as a function of baseline plasma GSH. Generalized estimating equations (GEE) models were deployed to explore the associations between baseline plasma GSH and the rate of change of performance scores on individual cognitive domains over time. Models were adjusted for age, years of education and sex. Supplementary exploratory models were also adjusted for mild cognitive impairment (MCI) at baseline, risk for malnutrition, physical activity and adherence to the Mediterranean dietary pattern.

RESULTS

A total of 24 incident AD cases occurred during a mean (SD) of 2.99 (0.92) years of follow-up. Individuals in the highest GSH tertile group (highest baseline plasma GSH values) had a 70.1% lower risk for development of AD, compared to those in the lowest one [HR = 0.299 (0.093-0.959); p = 0.042], and also demonstrated a slower rate of decline of their executive functioning over time (5.2% of a standard deviation less decline in the executive composite score for each additional year of follow-up; p = 0.028). The test for trend was also significant suggesting a potential dose-response relationship.

CONCLUSION

In the present study, higher baseline plasma GSH levels were associated with a decreased risk of developing AD and with a better preservation of executive functioning longitudinally.

DNA methylation analysis of candidate genes associated with dementia in peripheral blood

Aim: To investigate whether genes implicated in dementia pathogenesis are differently methylated in peripheral blood. Materials & methods: Participants included 160 cognitively healthy individuals aged 70+ years: 73 who were subsequently diagnosed with dementia and 87 controls matched on age, gender, education, smoking and baseline cognition. A total of 49 participants also provided blood samples at diagnosis. Blood DNA methylation of APOE, APP, BDNF, PIN1, SNCA and TOMM40 was examined. Results: A total of 56 of 299 probes were differentially methylated in dementia compared with controls and 39 probes prior to diagnosis. The greatest effect size was in APP (cg19423170, Δ-8.32%, adjusted p = 0.009 at diagnosis; cg19933173, Δ-4.18%, adjusted p < 0.0001 prediagnosis). Conclusion: Genes implicated in dementia pathogenesis show differential blood methylation in dementia, even prior to diagnosis.

A Robust Blood-based Signature of Cerebrospinal Fluid Aβ42 Status

Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid β_1-42 (Aβ₄₂) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aβ₄₂ status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aβ₄₂ robustly predict CSF Aβ₄₂ with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.

Association of Uncommon, Noncoding Variants in the APOE Region With Risk of Alzheimer Disease in Adults of European Ancestry

IMPORTANCE

The ε2 and ε4 alleles of the apolipoprotein E (APOE) gene are associated with Alzheimer disease (AD) risk. Although nearby genetic variants have also been shown to be associated with AD, including rs2075650 in the TOMM40 gene and rs4420638 near the APOC1 gene, it is unknown whether these associations are independent of the ε2 and ε4 alleles.

OBJECTIVE

To assess whether variants near APOE are associated with AD independently of the ε2/ε3/ε4 genotype.

DESIGN, SETTING, AND PARTICIPANTS

In this genetic association study of the Alzheimer's Disease Genetics Consortium imputed genotype at data, 14 415 variants near APOE (±500 kilobase) for 18 795 individuals with European ancestry were tested for association with AD using 4 logistic mixed models adjusting for sex, cohort, population structure, and relatedness. Model 1 had no APOE adjustment, and model 2 adjusted for the count of ε2 and ε4 alleles. Model 3 was restricted to ε3 homozygotes, and model 4 was restricted to ε4 homozygotes. Data were downloaded from May 31, 2018, to June 3, 2018, and analyzed from November 1, 2018, to June 24, 2020.

MAIN OUTCOMES AND MEASURES

Alzheimer disease affectation status was defined by clinicians using standard National Institute of Neurological and Communicative Disorders and Stroke and Alzheimer Disease and Related Disorders Association criteria. Association was evaluated using Score tests; results with P < .05 divided by the number of independent tests per model were considered statistically significant.

RESULTS

Among the 18 795 individuals in the study, 9704 were affected by AD and 9066 were control individuals; the median age at onset/evaluation was 76 (interquartile range, 70-82) years; and 11 167 were female (59.4%). Associations with AD were found for rs2075650 (odds ratio [OR], 2.59; 95% CI, 2.45-2.75; P = 3.19 × 10-228) and rs4420638 (OR, 2.77; 95% CI, 2.62-2.94; P = 2.99 × 10-254) without APOE adjustment. Although rs2075650 was nominally associated with AD among the ε4 homozygotes (OR, 1.33; 95% CI, 1.00-1.77; P = .047), the association between rs4420638 and AD was eliminated by APOE adjustment (model 2 OR, 1.06 [95% CI, 0.96-1.18; P = .24]; model 3 OR, 1.13 [95% CI, 0.95-1.34; P = .18]; model 4 OR, 0.90 [95% CI, 0.56-1.45; P = .66]). There was a significant association between rs192879175 and AD among ε3 homozygotes (OR, 0.50; 95% CI, 0.37-0.68; P = 8.30 × 10-6).

CONCLUSIONS AND RELEVANCE

The results of this genetic association study suggest that ε2/ε3/ε4 alleles are not the only variants in the APOE region that are associated with AD risk. Additional work with independent data is needed to replicate these results.

A TOMM40/APOE allele encoding APOE-E3 predicts high likelihood of late-onset Alzheimer's disease in autopsy cases

BACKGROUND

The APOE-ε4 allele is an established risk factor for Alzheimer's disease (AD). TOMM40 located adjacent to APOE has also been implicated in AD but reports of TOMM40 associations with AD that are independent of APOE-ε4 are at variance.

METHODS

We investigated associations of AD with haplotypes defined by three TOMM40 and two APOE single nucleotide polymorphisms in 73 and 71 autopsy cases with intermediate and high likelihood of AD (defined by BRAAK stages

RESULTS

We observed eight haplotypes with a frequency >0.02. The two haplotypes encoding APOE-E4 showed strong associations with AD that did not differ between intermediate and high likelihood AD. In contrast, a TOMM40 haplotype encoding APOE-E3 was identified as risk haplotype of high- (p = .0186), but not intermediate likelihood AD (p = .7530). Furthermore, the variant allele of rs2075650 located in intron 2 of TOMM40, increased the risk of high-, but not intermediate likelihood AD on the APOE-ε3/ε3 background (p = .0230).

CONCLUSION

The striking association of TOMM40 only with high likelihood AD may explain some contrasting results for TOMM40 in clinical studies and may reflect an association with more advanced disease and/or suggest a role of TOMM40 in the pathogenesis of neurofibrillary tangles.

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Key Words

• APOE
• Alzheimer’ disease
• TOMM40
• beta-amyloid
• genetics
• haplotypes
• neurofibrillary tangles

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MESH Headings

• Aged
• Aged, 80 and over
• Alzheimer Disease/genetics
• Alzheimer Disease/pathology
• Apolipoproteins E/genetics
• Female
• Gene Frequency
• Haplotypes
• Humans
• Male
• Membrane Transport Proteins/genetics
• Mitochondrial Precursor Protein Import Complex Proteins
• Polymorphism, Single Nucleotide

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Grants

• V 344 Austrian Science Fund FWF
• Austrian Science Fund
• Kurt und Senta Herrmann Stiftung

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Affiliation(s)

Selma M. Soyal Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Markus Kwik Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Ognian Kalev Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria
Stefan Lenz Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria
Greta Zara Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Peter Strasser Institute of Laboratory MedicineParacelsus Medical UniversitySalzburgAustria
Wolfgang Patsch Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Serge Weis Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria

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Involvement of Lipids in Alzheimer's Disease Pathology and Potential Therapies

Lipids constitute the bulk of the dry mass of the brain and have been associated with healthy function as well as the most common pathological conditions of the brain. Demographic factors, genetics, and lifestyles are the major factors that influence lipid metabolism and are also the key components of lipid disruption in Alzheimer's disease (AD). Additionally, the most common genetic risk factor of AD, APOE ϵ4 genotype, is involved in lipid transport and metabolism. We propose that lipids are at the center of Alzheimer's disease pathology based on their involvement in the blood-brain barrier function, amyloid precursor protein (APP) processing, myelination, membrane remodeling, receptor signaling, inflammation, oxidation, and energy balance. Under healthy conditions, lipid homeostasis bestows a balanced cellular environment that enables the proper functioning of brain cells. However, under pathological conditions, dyshomeostasis of brain lipid composition can result in disturbed BBB, abnormal processing of APP, dysfunction in endocytosis/exocytosis/autophagocytosis, altered myelination, disturbed signaling, unbalanced energy metabolism, and enhanced inflammation. These lipid disturbances may contribute to abnormalities in brain function that are the hallmark of AD. The wide variance of lipid disturbances associated with brain function suggest that AD pathology may present as a complex interaction between several metabolic pathways that are augmented by risk factors such as age, genetics, and lifestyles. Herewith, we examine factors that influence brain lipid composition, review the association of lipids with all known facets of AD pathology, and offer pointers for potential therapies that target lipid pathways.

Mitochondrial and Nuclear DNA Oxidative Damage in Physiological and Pathological Aging

Mitochondria play an important role in numerous processes, including energy generation, regulating ion homeostasis, and cell signaling. Mitochondria are also the main source of reactive oxygen species (ROS). Due to the oxidative environment within mitochondria, the macromolecules therein, for example, mtDNA, proteins, and lipids are more susceptible to sustaining damage. During aging, mitochondrial functions decline, partly as a result of an accumulation of mtDNA mutations, decreased mtDNA copy number and protein expression, and a reduction in oxidative capacity. The aim of this study was to summarize the knowledge on DNA oxidative damage in aging and age-related neurodegenerative diseases. It has been hypothesized that various ROS may play an important role not only in physiological senescence but also in the development of neurodegenerative diseases, for example, Alzheimer's disease and Parkinson's disease. Thus, mitochondria seem to be a potential target of novel treatments for neurodegenerative diseases.

Apolipoprotein C1: Its Pleiotropic Effects in Lipid Metabolism and Beyond

Apolipoprotein C1 (apoC1), the smallest of all apolipoproteins, participates in lipid transport and metabolism. In humans, APOC1 gene is in linkage disequilibrium with APOE gene on chromosome 19, a proximity that spurred its investigation. Apolipoprotein C1 associates with triglyceride-rich lipoproteins and HDL and exchanges between lipoprotein classes. These interactions occur via amphipathic helix motifs, as demonstrated by biophysical studies on the wild-type polypeptide and representative mutants. Apolipoprotein C1 acts on lipoprotein receptors by inhibiting binding mediated by apolipoprotein E, and modulating the activities of several enzymes. Thus, apoC1 downregulates lipoprotein lipase, hepatic lipase, phospholipase A2, cholesterylester transfer protein, and activates lecithin-cholesterol acyl transferase. By controlling the plasma levels of lipids, apoC1 relates directly to cardiovascular physiology, but its activity extends beyond, to inflammation and immunity, sepsis, diabetes, cancer, viral infectivity, and-not last-to cognition. Such correlations were established based on studies using transgenic mice, associated in the recent years with GWAS, transcriptomic and proteomic analyses. The presence of a duplicate gene, pseudogene APOC1P, stimulated evolutionary studies and more recently, the regulatory properties of the corresponding non-coding RNA are steadily emerging. Nonetheless, this prototypical apolipoprotein is still underexplored and deserves further research for understanding its physiology and exploiting its therapeutic potential.

Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes

It is estimated that the impact of related genes on the risk of Alzheimer's disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Because of linkage disequilibrium (LD), neither the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data-based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs, which belong to 20 genes by eQTL analysis and 379 significant SNPs, which belong to seven known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.