Dataset Information

Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease.

ABSTRACT:

Background

Alzheimer's disease (AD) is a common neurodegenerative disorder that has a multi-step disease progression. Differences between moderate and advanced stages of AD have not yet been fully characterized.

Materials and methods

Herein, we performed a transcript-resolution analysis in 454 AD-related samples, including 145 non-demented control, 140 asymptomatic AD (AsymAD), and 169 AD samples. We comparatively characterized the transcriptome dysregulation in AsymAD and AD samples at transcript level.

Results

We identified 4,056 and 1,200 differentially spliced alternative splicing events (ASEs) that might play roles in the disease progression of AsymAD and AD, respectively. Our further analysis revealed 287 and 222 isoform switching events in AsymAD and AD, respectively. In particular, a total of 163 and 119 transcripts showed increased usage, while 124 and 103 transcripts exhibited decreased usage in AsymAD and AD, respectively. For example, gene APOA2 showed no expression changes between AD and non-demented control samples, but expressed higher proportion of transcript ENST00000367990.3 and lower proportion of transcript ENST00000463812.1 in AD compared to non-demented control samples. Furthermore, we constructed RNA binding protein (RBP)-ASE regulatory networks to reveal potential RBP-mediated isoform switch in AsymAD and AD.

Conclusion

In summary, our study provided transcript-resolution insights into the transcriptome disturbance of AsymAD and AD, which will promote the discovery of early diagnosis biomarkers and the development of new therapeutic strategies for patients with AD.

SUBMITTER: Wu H

PROVIDER: S-EPMC10308376 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease.

Wu Hao H Wang Jiao J Hu Xiaoyuan X Zhuang Cheng C Zhou Jianxin J Wu Peiru P Li Shengli S Zhao Robert Chunhua RC

Frontiers in aging neuroscience 20230615

<h4>Background</h4>Alzheimer's disease (AD) is a common neurodegenerative disorder that has a multi-step disease progression. Differences between moderate and advanced stages of AD have not yet been fully characterized.<h4>Materials and methods</h4>Herein, we performed a transcript-resolution analysis in 454 AD-related samples, including 145 non-demented control, 140 asymptomatic AD (AsymAD), and 169 AD samples. We comparatively characterized the transcriptome dysregulation in AsymAD and AD samp ...[more]

PMID: 37396652

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Project description:BackgroundOne primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer's disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes.Methodology/principal findingsWe re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis.Conclusions/significanceOur analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.

Project description:BackgroundIdentifying biomarkers associated with Alzheimer's disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome-wide association studies have revealed correlations between DNA methylation at cytosine-phosphate-guanine (CpG) sites and AD pathology and diagnosis. Here, we report relationships between peripheral blood DNA methylation profiles measured using Infinium® MethylationEPIC BeadChip and AD progression in participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.ResultsThe rate of cognitive decline from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACCdigit and mPACCtrailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. In addition, diagnosis conversion status was assessed using a dichotomized endpoint. Two CpG sites were significantly associated with the slope of mPACC in CN participants (P < 5.79 × 10-8 [Bonferroni correction threshold]); cg00386386 was associated with the slope of mPACCdigit, and cg09422696 annotated to RP11-661A12.5 was associated with the slope of CDR-SB. No significant CpG sites associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 19, 13, and 5 differentially methylated regions (DMRs) associated with the slopes of mPACCtrailsB, mPACCdigit, and CDR-SB, respectively, were identified by both comb-p and DMRcate algorithms; these included DMRs annotated to HOXA4. Furthermore, 5 and 19 DMRs were associated with conversion status in CN and MCI participants, respectively. The most significant DMR was annotated to the AD-associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak-corrected P = 7.74 × 10-24), which was associated with both the slope of CDR-SB and the MCI conversion status.ConclusionCandidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in participants in the ADNI cohort. While we did not identify a single CpG site with sufficient clinical utility to be used by itself due to the observed effect size, a biosignature composed of DNA methylation changes may have utility as a prognostic biomarker for AD progression.

Project description:The liver undergoes a slow process for lipid deposition during chick embryonic period. However, the underlying physiological and molecular mechanisms are still unclear. Therefore, the aim of the current study was to reveal the epigenetic mechanism of hepatic transcriptional reprogramming changes based on the integration analysis of RNA-seq and H3K27ac labeled CUT&Tag. Results showed that lipid contents increased gradually with the embryonic age (E) 11, E15, and E19 based on morphological analysis of Hematoxylin-eosin and Oil Red O staining as well as total triglyceride and cholesterol detection. The hepatic protein level of SREBP-1c was higher in E19 when compared with that in E11 and E15, while H3K27ac and H3K4me2 levels declined from E11 to E19. Differential expression genes (DEGs) among these 3 embryonic ages were determined by transcriptome analysis. A total of 107 and 46 genes were gradually upregulated and downregulated respectively with the embryonic age. Meanwhile, differential H3K27ac occupancy in chromatin was investigated. But the integration analysis of RNA-seq and CUT&Tag data showed that the overlap genes were less between DEGs and target genes of differential peaks in the promoter regions. Further, some KEGG pathways enriched from target genes of typical enhancer were overlapped with those from DEGs in transcriptome analysis such as insulin, FoxO, MAPK signaling pathways which were related to lipid metabolism. DNA motif analysis identify 8 and 10 transcription factors (TFs) based on up and down differential peaks individually among E11, E15, and E19 stages where 7 TFs were overlapped including COUP-TFII, FOXM1, FOXA1, HNF4A, RXR, ERRA, FOXA2. These results indicated that H3K27ac histone modification is involved in the transcriptional reprogramming regulation during embryonic development, which could recruit TFs binding to mediate differential enhancer activation. Differential activated enhancer impels dynamic transcriptional reprogramming towards lipid metabolism to promote the occurrence of special phenotype of hepatic lipid deposition.

Dataset Information

Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease.

Background

Materials and methods

Results

Conclusion

Publications

Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease.

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