Association of amyloid beta changes and gene expression during conversion to Alzheimer's disease
Poster No:
121
Submission Type:
Abstract Submission
Authors:
Sewook Oh1, Sunghun Kim1,2, Hyunjin Park1,2
Institutions:
1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 2Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea, Republic of
First Author:
Sewook Oh
Department of Electrical and Computer Engineering, Sungkyunkwan University
Suwon, Gyeonggi-do
Department of Electrical and Computer Engineering, Sungkyunkwan University
Suwon, Gyeonggi-do
Co-Author(s):
Sunghun Kim
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Gyeonggi-do|Suwon, Korea, Republic of
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Gyeonggi-do|Suwon, Korea, Republic of
Hyunjin Park
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Gyeonggi-do|Suwon, Korea, Republic of
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Gyeonggi-do|Suwon, Korea, Republic of
Introduction:
Alzheimer's disease (AD) is a highly complex disease influenced by various factors, including the aggregation of amyloid-beta (Aβ), which are is widely recognized as a key pathological markers. Genetic factors also play a significant role in the development of AD, with certain genes linked to the accumulation of Aβ.
Genetic factors are among the key contributors to the development of AD. In particular, specific genotypes such as APOE4 are closely associated with increased AD risk and progression. Recent studies have further revealed that certain genotypes and gene expression patterns are linked to both early-onset and late-onset AD, and these associations are also connected to Aβ levels [1].
In this study, we investigated the relationship between changes in Aβ levels and genes identified as AD risk genes during the conversion tofrom mild cognitive impairment (MCI) to AD. Specifically, we examined the spatial correlation between gene expression in the brain and Aβ accumulation, and we tested the significance of these relationships.
Genetic factors are among the key contributors to the development of AD. In particular, specific genotypes such as APOE4 are closely associated with increased AD risk and progression. Recent studies have further revealed that certain genotypes and gene expression patterns are linked to both early-onset and late-onset AD, and these associations are also connected to Aβ levels [1].
In this study, we investigated the relationship between changes in Aβ levels and genes identified as AD risk genes during the conversion tofrom mild cognitive impairment (MCI) to AD. Specifically, we examined the spatial correlation between gene expression in the brain and Aβ accumulation, and we tested the significance of these relationships.
Methods:
We utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the correlation between changes in Aβ levels and gene expression during the conversion in AD. We focused on ADNI subjects who exhibited no evidence of cognitive resilience and had longitudinal AV-45 PET data available. The study included 19 subjects who experienced conversion from MCI to AD and 90 censored subjects who remained in the MCI stage for more than two years from their baseline. Gene expression data were obtained from the Allen Human Brain Atlas [2]. To focus the analysis on genes associated with AD, only 20 genes provided by the Alzheimer's Disease Sequencing Project were included in the study.
The AV-45 PET images were normalized converted to standardized uptake value (SUVR). Subsequently, both the SUVR and gene expression were mapped to the Schaefer atlas with 300 parcels [3]. To measure changes in Aβ during the conversion from MCI to AD, we calculated the difference between baseline and follow-up data, and then divided it by the time interval in years between the sessions. The changes in Aβ and gene expression levels were averaged, and these values were utilized to identify spatial correlations within the brain.
The AV-45 PET images were normalized converted to standardized uptake value (SUVR). Subsequently, both the SUVR and gene expression were mapped to the Schaefer atlas with 300 parcels [3]. To measure changes in Aβ during the conversion from MCI to AD, we calculated the difference between baseline and follow-up data, and then divided it by the time interval in years between the sessions. The changes in Aβ and gene expression levels were averaged, and these values were utilized to identify spatial correlations within the brain.
Results:
We evaluated Aβ accumulation in the baseline and follow-up groups to calculate the Aβ change map. Aβ accumulation exhibited high levels in the frontoparietal region and default mode network at both baseline and follow-up time points. The absolute change in accumulation also included the visual cortex in addition to these previously identified regions, with the most significant gene, PSEN2, showing a prominent pattern in opposing regions (Figure 1A-B). Spatial correlation analysis between the amyloid-beta change map and gene expression revealed that eight genes were significant (p_FDR < 0.05; Figure 2). Among these, PSEN2 exhibited the strongest correlation and significance (r = -0.36, p_FDR < 0.001; Figure 1C).
·Figure 1
·Figure 2
Conclusions:
In this study, we investigated changes in Aβ levels during the AD conversion. By examining the correlation between Aβ changes and the expression levels of AD-related genes, we explored the relationship between gene expression in the brain and Aβ dynamics. The Aβ change map offers insights into the progression pathways of specific brain regions during the conversion period. Furthermore, correlations with specific gene expression levels highlight the potential role of these genes in either accelerating or mitigating Aβ-related processes, depending on the direction of the association.
ACKNOWLEDGMENTS
This study was supported by National Research Foundation (RS-2024-00408040), Institute for Basic Science (IBS-R015-D1), AI Graduate School Support Program (Sungkyunkwan University) (RS-2019-II190421), ICT Creative Consilience program (RS-2020-II201821), and the Artificial Intelligence Innovation Hub program (RS-2021-II212068).
ACKNOWLEDGMENTS
This study was supported by National Research Foundation (RS-2024-00408040), Institute for Basic Science (IBS-R015-D1), AI Graduate School Support Program (Sungkyunkwan University) (RS-2019-II190421), ICT Creative Consilience program (RS-2020-II201821), and the Artificial Intelligence Innovation Hub program (RS-2021-II212068).
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Transcriptomics 2
Keywords:
Degenerative Disease
Other - Alzheimer's disease, Gene expression
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
2. Hawrylycz, M. J. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391-399.
3. Schaefer, A. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.