Disturbance of phylogenetic layer-specific adaptation of human brain gene expression in Alzheimer's disease.
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ABSTRACT:
SUBMITTER: Jorge NAN
PROVIDER: S-EPMC8511061 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC7175937 | biostudies-literature
Project description:BACKGROUND:Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer's disease (AD) patients and looked for their specific functions. METHODS:In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules. RESULTS:We obtained 30?AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology. CONCLUSIONS:Through our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.
| S-EPMC6311927 | biostudies-literature
Project description:The dynamic processes of formatting long-term memory traces in the cortex are poorly understood. The investigation of these processes requires measurements of task-evoked neuronal activities from large numbers of neurons over many days. Here, we present a two-photon imaging-based system to track event-related neuronal activity in thousands of neurons through the quantitative measurement of EGFP proteins expressed under the control of the EGR1 gene promoter. A recognition algorithm was developed to detect GFP-positive neurons in multiple cortical volumes and thereby to allow the reproducible tracking of 4,000 neurons in each volume for 2 mo. The analysis revealed a context-specific response in sparse layer II neurons. The context-evoked response gradually increased during several days of training and was maintained 1 mo later. The formed traces were specifically activated by the training context and were linearly correlated with the behavioral response. Neuronal assemblies that responded to specific contexts were largely separated, indicating the sparse coding of memory-related traces in the layer II cortical circuit.
| S-EPMC3932903 | biostudies-literature
Project description:IntroductionMAPT encodes for tau, the predominant component of neurofibrillary tangles that are neuropathological hallmarks of Alzheimer's disease (AD). Genetic association of MAPT variants with late-onset AD (LOAD) risk has been inconsistent, although insufficient power and incomplete assessment of MAPT haplotypes may account for this.MethodsWe examined the association of MAPT haplotypes with LOAD risk in more than 20,000 subjects (n-cases = 9,814, n-controls = 11,550) from Mayo Clinic (n-cases = 2,052, n-controls = 3,406) and the Alzheimer's Disease Genetics Consortium (ADGC, n-cases = 7,762, n-controls = 8,144). We also assessed associations with brain MAPT gene expression levels measured in the cerebellum (n = 197) and temporal cortex (n = 202) of LOAD subjects. Six single nucleotide polymorphisms (SNPs) which tag MAPT haplotypes with frequencies greater than 1% were evaluated.ResultsH2-haplotype tagging rs8070723-G allele associated with reduced risk of LOAD (odds ratio, OR = 0.90, 95% confidence interval, CI = 0.85-0.95, p = 5.2E-05) with consistent results in the Mayo (OR = 0.81, p = 7.0E-04) and ADGC (OR = 0.89, p = 1.26E-04) cohorts. rs3785883-A allele was also nominally significantly associated with LOAD risk (OR = 1.06, 95% CI = 1.01-1.13, p = 0.034). Haplotype analysis revealed significant global association with LOAD risk in the combined cohort (p = 0.033), with significant association of the H2 haplotype with reduced risk of LOAD as expected (p = 1.53E-04) and suggestive association with additional haplotypes. MAPT SNPs and haplotypes also associated with brain MAPT levels in the cerebellum and temporal cortex of AD subjects with the strongest associations observed for the H2 haplotype and reduced brain MAPT levels (β = -0.16 to -0.20, p = 1.0E-03 to 3.0E-03).ConclusionsThese results confirm the previously reported MAPT H2 associations with LOAD risk in two large series, that this haplotype has the strongest effect on brain MAPT expression amongst those tested and identify additional haplotypes with suggestive associations, which require replication in independent series. These biologically congruent results provide compelling evidence to screen the MAPT region for regulatory variants which confer LOAD risk by influencing its brain gene expression.
| S-EPMC4198935 | biostudies-literature
Project description:Mild cognitive impairment (MCI) represents a cognitive state intermediate between normal aging and early Alzheimer's disease (AD). To investigate if the molecular signature of MCI parallels the clinical picture, we use microarrays to extensively profile gene expression in 4 cortical brain regions (entorhinal cortex, hippocampus, superior frontal gyrus, post-central gyrus) using the postmortem tissue from cognitively normal aged controls, MCI, and AD cases. Our data reveal that gene expression patterns in MCI are not an extension of aging, and for the most part, are not intermediate between aged controls and AD. Functional enrichment analysis of significant genes revealed prominent upregulation in MCI brains of genes associated with anabolic and biosynthetic pathways (notably transcription, protein biosynthesis, protein trafficking, and turnover) as well as mitochondrial energy generation. In addition, many synaptic genes showed altered expression in MCI, predominantly upregulation, including genes for central components of the vesicle fusion machinery at the synapse, synaptic vesicle trafficking, neurotransmitter receptors, and synaptic structure and stabilization. These data suggest that there is a rebalancing of synaptic transmission in the MCI brain. To investigate if synaptic gene expression levels in MCI were related to cognitive function, Pearson correlation coefficient between the Mini Mental State Examination (MMSE) and region-specific messenger RNA expression were computed for MCI cases. A number of synaptic genes showed strong significant correlations (r > 0.8, p < 0.01) most notably in the entorhinal cortex, with fewer in the hippocampus, and very few in neocortical regions. The synaptic genes with highly significant correlations were predominantly related to synaptic transmission and plasticity, and myelin composition. Unexpectedly, we found that gene expression changes that facilitate synaptic excitability and plasticity were overwhelmingly associated with poorer MMSE, and conversely that gene expression changes that inhibit plasticity were positively associated with MMSE. These data suggest that there are excessive excitability and apparent plasticity in limbic brain regions in MCI, that is associated with impaired synaptic and cognitive function. Such changes would be predicted to contribute to increased excitability, in turn leading to greater metabolic demand and ultimately progressive degeneration and AD, if not controlled.
| S-EPMC4067010 | biostudies-literature
Project description:There is an increasing body of evidence suggesting that metal homeostasis is dysregulated in the pathology of Alzheimer's disease (AD). Although expression levels of several transporters belonging the SLC30 family, which comprises predominantly zinc transporters, have been studied in the AD brain, SLC30A10 (ZnT10) has not been studied in this context. To determine if dysregulated expression of ZnT10, which may transport both Zn and Mn, could be a factor that contributes to AD, we investigated if there were differences in ZnT10 mRNA levels in specimens of frontal cortex from AD patients and controls and also if brain tissue from the APP/PS1 transgenic (Tg) mouse model showed abnormal levels of ZnT10 mRNA expression. Our results show that ZnT10 is significantly (P<0.01) decreased in the frontal cortex in AD. Furthermore, we observed a significant decrease in ZnT10 mRNA levels in the APP/PS1-Tg mice compared with wild-type controls (P<0.01). Our results suggest that this dysregulation in ZnT10 could further contribute to disease progression.
| S-EPMC3669266 | biostudies-literature
Project description:Women are at a 2-fold risk of developing late-onset Alzheimer's disease (AD) (onset at 65 years of age or older) compared with men. During perimenopausal years, women undergo hormonal changes that are accompanied by metabolic, cardiovascular, and inflammatory changes. These all together have been suggested as risk factors for late-onset AD. However, not all perimenopausal women develop AD; we hypothesize that certain genetic factors might underlie the increased susceptibility for developing AD in postmenopausal women. We investigated the Androgen Receptor gene (AR) in a clinical cohort of male and female AD patients and normal control subjects by sequencing all coding exons and evaluating the length and distribution of the CAG repeat in exon 1. We could not establish a correlation between the repeat length, sex, and the disease status, nor did we identify possible pathogenic variants. AR is located on the X chromosome; to assess its role in AD, X-inactivation patterns will need to be studied to directly correlate the actual expressed repeat length to a possible sex-specific phenotypic effect.
| S-EPMC4012749 | biostudies-literature
Project description:Identifying ancestry-specific molecular profiles of late-onset Alzheimer's Disease (LOAD) in brain tissue is crucial to understand novel mechanisms and develop effective interventions in non-European, high-risk populations. We performed gene differential expression (DE) and consensus network-based analyses in RNA-sequencing data of postmortem brain tissue from 39 Caribbean Hispanics (CH). To identify ancestry-concordant and -discordant expression profiles, we compared our results to those from two independent non-Hispanic White (NHW) samples (n = 731). In CH, we identified 2802 significant DE genes, including several LOAD known-loci. DE effects were highly concordant across ethnicities, with 373 genes transcriptome-wide significant in all three cohorts. Cross-ancestry meta-analysis found NPNT to be the top DE gene. We replicated over 82% of meta-analyses genome-wide signals in single-nucleus RNA-seq data (including NPNT and LOAD known-genes SORL1, FBXL7, CLU, ABCA7). Increasing representation in genetic studies will allow for deeper understanding of ancestry-specific mechanisms and improving precision treatment options in understudied groups.
| S-EPMC10039465 | biostudies-literature
Project description:BackgroundAlzheimer's Disease (AD) is a neurodegenerative complex brain disease that represents a public health concern. AD is considered the fifth leading cause of death in Americans who are older than 65 years which prioritizes the importance of understanding the etiology of AD in its early stages before the onset of symptoms. This study attempted to further understand Alzheimer's disease (AD) etiology by investigating the dysregulated genes using gene expression data from multiple brain regions.MethodsA linear mixed-effects model for differential gene expression analysis was used in a sample of 15 AD and 30 control subjects, each with data from four different brain regions, in order to deal with the hierarchical multilevel data. Post-hoc Gene Ontology and pathway enrichment analyses provided insights on the biological implications in AD progression. Supervised machine learning algorithms were used to assess the discriminative power of the top 10 candidate genes in distinguishing between the two groups.ResultsEnrichment analyses revealed biological processes and pathways that are related to structural constituents and organization of the axons and synapses. These biological processes and pathways imply dysfunctional axon and synaptic transmission between neuronal cells in AD. Random Forest classification algorithm gave the best accuracy on the test data with F1-score of 0.88.ConclusionThe differentially expressed genes were associated with axon and synaptic transmissions which affect the neuronal connectivity in cognitive systems involved in AD pathophysiology. These genes may open ways to explore new effective treatments and early diagnosis before the onset of clinical symptoms.
| S-EPMC7717689 | biostudies-literature
Project description:Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.
| S-EPMC6422912 | biostudies-literature