Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease.
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ABSTRACT:
SUBMITTER: Elsheikh SSM
PROVIDER: S-EPMC8734427 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We previously reported strong genetic linkage on chromosome 14q to Alzheimer's disease (AD) using the presence of co-morbid hallucinations as a covariate. Those results suggested the presence of a gene increasing the risk for a genetically homogeneous form of AD characterized by the absence of comorbid hallucinations. Here we report our follow up of that study through the analysis of single nucleotide polymorphisms (SNPs) in five functional candidate genes. This work provides significant evidence of association for the gene coding for neuroglobin (NGB), a nervous system globin known to protect cells against amyloid toxicity and to attenuate the AD phenotype of transgenic mice. On further experiments we found that NGB expression is reduced with increasing age and lower in women consistent with their increased risk. NGB expression is up-regulated in the temporal lobe of AD patients consistent with a response to the disease process, as reported for NGB and hypoxia. We speculate that a compromised response due to DNA variation might increase the risk for AD. Our and others' data strongly support the involvement of NGB in AD.
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Project description:BackgroundPrevious studies have shown that CpG dinucleotides are enriched in a subset of promoters and the CpG content of promoters is positively correlated with gene expression levels. But the relationship between divergence of CpG content and gene expression evolution has not been investigated. Here we calculate the normalized CpG (nCpG) content in DNA regions around transcription start site (TSS) and transcription terminal site (TTS) of genes in nine organisms, and relate them with expression levels measured by RNA-seq.ResultsThe nCpG content of TSS shows a bimodal distribution in all organisms except platypus, whereas the nCpG content of TTS only has a single peak. When the nCpG contents are compared between different organisms, we observe a different evolution pattern between TSS and TTS: compared with TTS, TSS exhibits a faster divergence rate between closely related species but are more conserved between distant species. More importantly, we demonstrate the link between gene expression evolution and nCpG content changes: up-/down- regulation of genes in an organism is accompanied by the nCpG content increase/decrease in their TSS and TTS proximal regions.ConclusionsOur results suggest that gene expression changes between different organisms are correlated with the alterations in normalized CpG contents of promoters. Our analyses provide evidences for the impact of nCpG content on gene expression evolution.
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Project description:The unintended consequences of gene targeting in mouse models have not been thoroughly studied and a more systematic analysis is needed to understand the frequency and characteristics of off-target effects. Using RNA-seq, we evaluated targeted and neighboring gene expression in tissues from 44 homozygous mutants compared with C57BL/6N control mice. Two allele types were evaluated: 15 targeted trap mutations (TRAP); and 29 deletion alleles (DEL), usually a deletion between the translational start and the 3' UTR. Both targeting strategies insert a bacterial beta-galactosidase reporter (LacZ) and a neomycin resistance selection cassette. Evaluating transcription of genes in +/- 500 kb of flanking DNA around the targeted gene, we found up-regulated genes more frequently around DEL compared with TRAP alleles, however the frequency of alleles with local down-regulated genes flanking DEL and TRAP targets was similar. Down-regulated genes around both DEL and TRAP targets were found at a higher frequency than expected from a genome-wide survey. However, only around DEL targets were up-regulated genes found with a significantly higher frequency compared with genome-wide sampling. Transcriptome analysis confirms targeting in 97% of DEL alleles, but in only 47% of TRAP alleles probably due to non-functional splice variants, and some splicing around the gene trap. Local effects on gene expression are likely due to a number of factors including compensatory regulation, loss or disruption of intragenic regulatory elements, the exogenous promoter in the neo selection cassette, removal of insulating DNA in the DEL mutants, and local silencing due to disruption of normal chromatin organization or presence of exogenous DNA. An understanding of local position effects is important for understanding and interpreting any phenotype attributed to targeted gene mutations, or to spontaneous indels.
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Project description:How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. One approach starts from the perspective of biological experiments where only the local statistics of connectivity motifs between small groups of neurons are accessible. Another approach is based instead on the perspective of artificial neural networks where the global connectivity matrix is known, and in particular its low-rank structure can be used to determine the resulting low-dimensional dynamics. A direct relationship between these two approaches is however currently missing. Specifically, it remains to be clarified how local connectivity statistics and the global low-rank connectivity structure are inter-related and shape the low-dimensional activity. To bridge this gap, here we develop a method for mapping local connectivity statistics onto an approximate global low-rank structure. Our method rests on approximating the global connectivity matrix using dominant eigenvectors, which we compute using perturbation theory for random matrices. We demonstrate that multi-population networks defined from local connectivity statistics for which the central limit theorem holds can be approximated by low-rank connectivity with Gaussian-mixture statistics. We specifically apply this method to excitatory-inhibitory networks with reciprocal motifs, and show that it yields reliable predictions for both the low-dimensional dynamics, and statistics of population activity. Importantly, it analytically accounts for the activity heterogeneity of individual neurons in specific realizations of local connectivity. Altogether, our approach allows us to disentangle the effects of mean connectivity and reciprocal motifs on the global recurrent feedback, and provides an intuitive picture of how local connectivity shapes global network dynamics.
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Project description:Dementia is a major public health concern affecting approximately 47 million people worldwide. Mild cognitive impairment (MCI) is one form of dementia that affects an individual's memory with or without affecting their daily life. Alzheimer's disease dementia (ADD) is a more severe form of dementia that usually affects elderly individuals. It remains unclear whether MCI is a distinct disorder from or an early stage of ADD. Gene expression data from blood were analyzed to identify potential biomarkers that may be useful for distinguishing between these two forms of dementia. A meta-analysis revealed 91 genes dysregulated in individuals with MCI and 387 genes dysregulated in ADD. Pathway analysis identified seven pathways shared between MCI and ADD and nine ADD-specific pathways. Fifteen transcription factors were associated with MCI and ADD, whereas seven transcription factors were specific for ADD. Mir-335-5p was specific for ADD, suggesting that it may be useful as a biomarker. Diseases that are associated with MCI and ADD included developmental delays, cognition impairment, and movement disorders. These results provide a better molecular understanding of peripheral changes that occur in MCI and ADD patients and may be useful in the identification of diagnostic and prognostic biomarkers.
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Project description:Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.
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Project description:Frontotemporal dementia is a heterogeneous neurodegenerative disorder characterized by neuronal loss in the frontal and temporal lobes. Despite progress in understanding which genes are associated with the aetiology of frontotemporal dementia, the biological basis of how mutations in these genes lead to cell loss in specific cortical regions remains unclear. In this work we combined gene expression data for 16,772 genes from the Allen Institute for Brain Science atlas with brain maps of gray matter atrophy in symptomatic C9orf72, GRN and MAPT mutation carriers obtained from the Genetic Frontotemporal dementia Initiative study. No significant association was seen between C9orf72, GRN and MAPT expression and the atrophy patterns in the respective genetic groups. After adjusting for spatial autocorrelation, between 1,000 and 5,000 genes showed a negative or positive association with the atrophy pattern within each individual genetic group, with the most significantly associated genes being TREM2, SSBP3 and GPR158 (negative association in C9orf72, GRN and MAPT respectively) and RELN, MXRA8 and LPA (positive association in C9orf72, GRN and MAPT respectively). An overrepresentation analysis identified a negative association with genes involved in mitochondrial function, and a positive association with genes involved in vascular and glial cell function in each of the genetic groups. A set of 423 and 700 genes showed significant positive and negative association, respectively, with atrophy patterns in all three maps. The gene set with increased expression in spared cortical regions was enriched for neuronal and microglial genes, while the gene set with increased expression in atrophied regions was enriched for astrocyte and endothelial cell genes. Our analysis suggests that these cell types may play a more active role in the onset of neurodegeneration in frontotemporal dementia than previously assumed, and in the case of the positively-associated cell marker genes, potentially through emergence of neurotoxic astrocytes and alteration in the blood-brain barrier respectively.
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Project description:Frontotemporal dementia is a heterogeneous neurodegenerative disorder characterized by neuronal loss in the frontal and temporal lobes. Despite progress in understanding which genes are associated with the aetiology of frontotemporal dementia, the biological basis of how mutations in these genes lead to cell loss in specific cortical regions remains unclear. In this work we combined gene expression data for 16,772 genes from the Allen Institute for Brain Science atlas with brain maps of gray matter atrophy in symptomatic C9orf72, GRN and MAPT mutation carriers obtained from the Genetic Frontotemporal dementia Initiative study. No significant association was seen between C9orf72, GRN and MAPT expression and the atrophy patterns in the respective genetic groups. After adjusting for spatial autocorrelation, between 1,000 and 5,000 genes showed a negative or positive association with the atrophy pattern within each individual genetic group, with the most significantly associated genes being TREM2, SSBP3 and GPR158 (negative association in C9orf72, GRN and MAPT respectively) and RELN, MXRA8 and LPA (positive association in C9orf72, GRN and MAPT respectively). An overrepresentation analysis identified a negative association with genes involved in mitochondrial function, and a positive association with genes involved in vascular and glial cell function in each of the genetic groups. A set of 423 and 700 genes showed significant positive and negative association, respectively, with atrophy patterns in all three maps. The gene set with increased expression in spared cortical regions was enriched for neuronal and microglial genes, while the gene set with increased expression in atrophied regions was enriched for astrocyte and endothelial cell genes. Our analysis suggests that these cell types may play a more active role in the onset of neurodegeneration in frontotemporal dementia than previously assumed, and in the case of the positively-associated cell marker genes, potentially through emergence of neurotoxic astrocytes and alteration in the blood-brain barrier respectively.
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Project description:OBJECTIVE:Previous gene expression analysis identified a network of coexpressed genes that is associated with β-amyloid neuropathology and cognitive decline in older adults. The current work targeted influential genes in this network with quantitative proteomics to identify potential novel therapeutic targets. METHODS:Data came from 834 community-based older persons who were followed annually, died, and underwent brain autopsy. Uniform structured postmortem evaluations assessed the burden of β-amyloid and other common age-related neuropathologies. Selected reaction monitoring quantified cortical protein abundance of 12 genes prioritized from a molecular network of aging human brain that is implicated in Alzheimer's dementia. Regression and linear mixed models examined the protein associations with β-amyloid load and other neuropathological indices as well as cognitive decline over multiple years preceding death. RESULTS:Average age at death was 88.6 years. Overall, 349 participants (41.9%) had Alzheimer's dementia at death. A higher level of PLXNB1 abundance was associated with more β-amyloid load (p = 1.0 × 10-7 ) and higher PHFtau tangle density (p = 2.3 × 10-7 ), and the association of PLXNB1 with cognitive decline is mediated by these known Alzheimer's disease pathologies. On the other hand, higher IGFBP5, HSPB2, and AK4 and lower ITPK1 levels were associated with faster cognitive decline, and, unlike PLXNB1, these associations were not fully explained by common neuropathological indices, suggesting novel mechanisms leading to cognitive decline. INTERPRETATION:Using targeted proteomics, this work identified cortical proteins involved in Alzheimer's dementia and begins to dissect two different molecular pathways: one affecting β-amyloid deposition and another affecting resilience without a known pathological footprint. Ann Neurol 2018;83:78-88.
| S-EPMC6119500 | biostudies-literature