Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease

BACKGROUND: The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer’s disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific. OBJECTIVE: Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature. METHODS: Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls. RESULTS: The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7–64.6), 41.9% specificity (95% CI = 26.8–54.3), 13.6% PPV (95% CI = 9.9–18.5), and 81.1% NPV (95% CI = 73.3–87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5–52.0), 95.3% specificity (95% CI = 93.3–97.1), 61.4% PPV (95% CI = 53.8–69.6), and 89.7% NPV (95% CI = 87.8–91.4). CONCLUSIONS: This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required

A Meta-Analysis of Alzheimer’s Disease Brain Transcriptomic Data

BACKGROUND: Microarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer's disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations. OBJECTIVE: Meta-analyze publicly available transcriptomic data from multiple brain-related disorders to identify robust transcriptomic changes specific to AD brains. METHODS: Twenty-two AD, eight schizophrenia, five bipolar disorder, four Huntington's disease, two major depressive disorder, and one Parkinson's disease dataset totaling 2,667 samples and mapping to four different brain regions (temporal lobe, frontal lobe, parietal lobe, and cerebellum) were analyzed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction. RESULTS: Meta-analysis identified 323, 435, 1,023, and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe, and cerebellum brain regions, respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-Seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. In addition, biological pathways involved in the "metabolism of proteins" and viral components were significantly enriched across AD brains. CONCLUSION: This study identified transcriptomic changes specific to AD brains, which could make a significant contribution toward the understanding of AD disease mechanisms and may also provide new therapeutic targets

DNAscan: personal computer compatible NGS analysis, annotation and visualisation.

BACKGROUND: Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step. RESULTS: We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub. CONCLUSIONS: DNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available

Transcriptomic analysis of probable asymptomatic and symptomatic alzheimer brains

Individuals with intact cognition and neuropathology consistent with Alzheimer's disease (AD) are referred to as asymptomatic AD (AsymAD). These individuals are highly likely to develop AD, yet transcriptomic changes in the brain which might reveal mechanisms for their AD vulnerability are currently unknown. Entorhinal cortex, frontal cortex, temporal cortex and cerebellum tissue from 27 control, 33 AsymAD and 52 AD human brains were microarray expression profiled. Differential expression analysis identified a significant increase of transcriptomic activity in the frontal cortex of AsymAD subjects, suggesting fundamental changes in AD may initially begin within the frontal cortex region prior to AD diagnosis. Co-expression analysis identified an overactivation of the brain "glutamate-glutamine cycle", and disturbances in the brain energy pathways in both AsymAD and AD subjects, while the connectivity of key hub genes in this network indicates a shift from an already increased cell proliferation in AsymAD subjects to stress response and removal of amyloidogenic proteins in AD subjects. This study provides new insight into the earliest biological changes occurring in the brain prior to the manifestation of clinical AD symptoms and provides new potential therapeutic targets for early disease intervention

Plasma based markers of [11C] PiB-PET brain amyloid burden.

PublishedJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tChanges in brain amyloid burden have been shown to relate to Alzheimer's disease pathology, and are believed to precede the development of cognitive decline. There is thus a need for inexpensive and non-invasive screening methods that are able to accurately estimate brain amyloid burden as a marker of Alzheimer's disease. One potential method would involve using demographic information and measurements on plasma samples to establish biomarkers of brain amyloid burden; in this study data from the Alzheimer's Disease Neuroimaging Initiative was used to explore this possibility. Sixteen of the analytes on the Rules Based Medicine Human Discovery Multi-Analyte Profile 1.0 panel were found to associate with [(11)C]-PiB PET measurements. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes. Thirteen of these markers of brain amyloid burden--c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E--were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. When a threshold was used to classify subjects as PiB positive, the regression model was found to predict actual PiB positive individuals with a sensitivity of 0.918 and a specificity of 0.545. The number of APOE [Symbol: see text] 4 alleles and plasma apolipoprotein E level were found to contribute most to this model, and the relationship between these variables and brain amyloid burden was explored.Alzheimer's Disease Neuroimaging Initiative (ADNI)Canadian Institutes of Health ResearchFoundation for the National Institutes of HealthNational Institutes of HealthInnoMed, European Union of the Sixth Framework programNational Institutes for Health Research Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation TrustInstitute of Psychiatry, King's College Londo

An epigenome-wide association study of Alzheimer's disease blood highlights robust DNA hypermethylation in the HOXB6 gene

This is the final version. Available from the publisher via the DOI in this record.A growing number of epigenome-wide association studies have demonstrated a role for DNA methylation in the brain in Alzheimer's disease. With the aim of exploring peripheral biomarker potential, we have examined DNA methylation patterns in whole blood collected from 284 individuals in the AddNeuroMed study, which included 89 nondemented controls, 86 patients with Alzheimer's disease, and 109 individuals with mild cognitive impairment, including 38 individuals who progressed to Alzheimer's disease within 1 year. We identified significant differentially methylated regions, including 12 adjacent hypermethylated probes in the HOXB6 gene in Alzheimer's disease, which we validated using pyrosequencing. Using weighted gene correlation network analysis, we identified comethylated modules of genes that were associated with key variables such as APOE genotype and diagnosis. In summary, this study represents the first large-scale epigenome-wide association study of Alzheimer's disease and mild cognitive impairment using blood. We highlight the differences in various loci and pathways in early disease, suggesting that these patterns relate to cognitive decline at an early stage.Alzheimer's Society, United KingdomMedical Research Council (MRC)NIH, United States, R01 grantAlzheimer's Research U

Immune signatures and disorder-specific patterns in a cross-disorder gene expression analysis

BACKGROUND: Recent studies point to overlap between neuropsychiatric disorders in symptomatology and genetic aetiology. AIMS: To systematically investigate genomics overlap between childhood and adult attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and major depressive disorder (MDD). METHOD: Analysis of whole-genome blood gene expression and genetic risk scores of 318 individuals. Participants included individuals affected with adult ADHD (n = 93), childhood ADHD (n = 17), MDD (n = 63), ASD (n = 51), childhood dual diagnosis of ADHD-ASD (n = 16) and healthy controls (n = 78). RESULTS: Weighted gene co-expression analysis results reveal disorder-specific signatures for childhood ADHD and MDD, and also highlight two immune-related gene co-expression modules correlating inversely with MDD and adult ADHD disease status. We find no significant relationship between polygenic risk scores and gene expression signatures. CONCLUSIONS: Our results reveal disorder overlap and specificity at the genetic and gene expression level. They suggest new pathways contributing to distinct pathophysiology in psychiatric disorders and shed light on potential shared genomic risk factors

Behaviour in therapeutic medical care: evidence from general practitioners in Austria

Aim: The present study examines monetary effects of general practioners’ behaviour in therapeutic medical care to identify sample characteristics that allow differentiating between the individual general practitioner and the basic population. Subjects and methods: Medical services, provided by 3,919 general practitioners in Austria, were operationalized by means of the dependent variable “costs per patient”. Statistical outliers were identified using Chebyshev’s inequality and categorized by investigating bivariate correlations between the dependent variable and the personal characteristics of each physician. Results: Variables that relate to the size of the customer base such as number of consultations (r = 0.385) and office days (r = 0.376), correlate positively with the costs for medical services. By analyzing the portfolio of the general practitioners, we found a correlation of 0.451 between this coefficient and the costs. Statistical outliers feature an average portfolio of 44.5 different services, compared to 30.45 among non-outliers. Laboratory services especially were identified as cost drivers (r = 0.408). Statistical outliers generate at least one laboratory parameter for 44.34% of their patients, opposed to 27.2% within the rest of the sample. Consequently outliers produce higher laboratory costs than their counterparts. Conclusion: We found some evidence that physicians have influence in the provision of their services. Considering entrepreneurial objectives, the extension of the portfolio can increase their profit. Our findings indicate supplier-induced demand for several groups of services. We assume that the effect is consolidated by the fee for service system and could be compensated by adequate reform

Tumor Necrosis Factor-Alpha G308α Gene Polymorphism and Essential Hypertension: A Meta-Analysis Involving 2244 Participants

BACKGROUND: The tumor necrosis factor-alpha (TNFα) G308A gene polymorphism has been implicated in susceptibility to essential hypertension (EH), but study results are still controversial. OBJECTIVE AND METHODS: The present meta-analysis is performed to investigate the relationship between the TNFα G308A gene polymorphism and EH. Electronic databases were searched and seven separate studies on the association of the TNF α G308A gene polymorphism with EH were analyzed. The meta-analysis involved 1092 EH patients and 1152 controls. The pooled odds ratios (ORs) and their corresponding 95% confidence interval (CI) were calculated by a fixed or random effect model. RESULTS: A significant relationship between the TNFα G308A gene polymorphism and EH was found in an allelic genetic model (OR: 1.45, 95% CI: 1.17 to 1.80, P = 0.0008), a recessive genetic model (OR: 3.181, 95% CI: 1.204 to 8.408, P = 0.02), and a homozygote model (OR: 3.454, 95% CI: 1.286 to 9.278, P = 0.014). No significant association between them was detected in both a dominant genetic model (OR: 1.55, 95% CI: 0.99 to 2.42, P = 0.06) or a heterozygote genetic model (OR: 1.45, 95% CI: 0.90 to 2.33, P = 0.13). CONCLUSION: The TNFα G308A gene polymorphism is associated with EH susceptibility

ALSgeneScanner: a pipeline for the analysis and interpretation of DNA sequencing data of ALS patients

Amyotrophic lateral sclerosis (ALS, MND) is a neurodegenerative disease of upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two years of first symptoms. Genetic factors are an important cause of ALS, with variants in more than 25 genes having strong evidence, and weaker evidence available for variants in more than 120 genes. With the increasing availability of next-generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyze and interpret it. Furthermore, the relevance of novel or existing variants in ALS genes is not always apparent. Here we present ALSgeneScanner, a tool that is easy to install and use, able to provide an automatic, detailed, annotated report, on a list of ALS genes from whole-genome sequencing (WGS) data in a few hours and whole exome sequence data in about 1 h on a readily available mid-range computer. This will be of value to non-specialists and aid in the interpretation of the relevance of novel and existing variants identified in DNA sequencing data