Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment

Yuan, Shaoxun; Li, Haitao; Gu, Yu; Sun, Xiao

doi:10.3390/biomedicines9060658

Cited by 3 publications

(5 citation statements)

References 48 publications

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“… 67 Interestingly, PPP4R2 has also been identified as a top hypomethylated gene of interest in a methylome-wide association study of Parkinson’s disease, 68 an illness which is also often accompanied by cognitive decline. 69 Other top contributors to the three-modal subtypes, such as PSMD11, 70 APBB2 71 and TMEM253, 72 are also known to be involved in the development of Alzheimer’s disease pathology. TMEM253 is also linked with mild cognitive impairment via predicted gene expression based on genetic variation (transcriptome-wide association study [TWAS]).…”

Section: Discussionmentioning

confidence: 99%

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Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Yang

Matan-Lithwick

Wang

et al. 2023

Brain Communications

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Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer’s disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive aging trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18,629 genes), DNA methylation (53,932 CpG sites), histone acetylation (26,384 peaks), proteomics (7,737 proteins), and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (nS1 = 53, nS2 = 58) which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both 3- and 5-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both 3- and 5-subtype solutions (pBonf = 5.9 × 10−3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of aging derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of postmortem brain.

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Section: Discussionmentioning

confidence: 99%

“…TMEM253 is also linked with mild cognitive impairment via predicted gene expression based on genetic variation (transcriptome-wide association study [TWAS]). 72 However, some top genes (e.g. ZNF219 , a Kruppel-like zinc finger gene, has been associated with a-synucleinopathy 73 and has binding sites in the MAPT gene).…”

Section: Discussionmentioning

confidence: 99%

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Yang

Matan-Lithwick

Wang

et al. 2023

Brain Communications

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“…Interestingly, PPP4R2 has also been identified as a top hypomethylated gene of interest in a methylome-wide association study of Parkinson’s disease [67], an illness which is also often accompanied by cognitive decline [68]. Other top contributors to the three-modal subtypes, such as PSMD11 [69], APBB2 [70], and TMEM253 [71] are also known to be involved in the development of AD pathology. TMEM253 is also linked with mild cognitive impairment (MCI) via predicted gene expression based on genetic variation (TWAS) [71].…”

Section: Discussionmentioning

confidence: 99%

“…Other top contributors to the three-modal subtypes, such as PSMD11 [69], APBB2 [70], and TMEM253 [71] are also known to be involved in the development of AD pathology. TMEM253 is also linked with mild cognitive impairment (MCI) via predicted gene expression based on genetic variation (TWAS) [71]. However, some top genes (e.g.…”

Section: Discussionmentioning

confidence: 99%

Multi-‘Omic Integration via Similarity Network Fusion to Detect Molecular Subtypes of Aging

Yang

Matan-Lithwick

Wang

et al. 2022

Preprint

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Background: Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease (AD). However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied Similarity Network Fusion (SNF), a method capable of integrating multiple high-dimensional multi-'omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive aging trajectories. Methods: We analyzed human frontal cortex brain samples characterized by five 'omic modalities: bulk RNA sequencing (18,629 genes), DNA methylation (53,932 cpg sites), histone H3K9 acetylation (26,384 peaks), proteomics (7,737 proteins), and metabolomics (654 metabolites). SNF followed by spectral clustering was used for subtype detection, and subtype numbers were determined by eigen-gap and rotation cost statistics. Normalized Mutual Information (NMI) determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Results: Fusion of all five data modalities (n=111) yielded two subtypes (nS1=53, nS2=58) which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (NMI=0.38), DNA methylation (NMI=0.18) and RNA abundance (NMI=0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n=513) indicated support for both 3- and 5-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both 3- and 5-subtype solutions (pBonf=5.9x10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. Conclusion: We identified highly integrative molecular subtypes of aging derived from multiple high dimensional, multi-'omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and H3K9 acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-'omic integration in unsupervised subtyping of postmortem brain.

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“…Sestrins seem able to also act in other commonly disrupted pathways in AD, such as, for example, by improving antioxidation and adjusting autophagy. Several other genes involved in oxidative stress and mitochondrial dysfunction have been associated with mild cognitive impairment and AD [7], representing targets of interest for future investigations. Another typical aspect of AD is characterized by neuroinflammation, especially in AD's later stages [8].…”

mentioning

confidence: 99%

Molecular Research on Alzheimer’s Disease

Falsetti¹

2023

Biomedicines

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Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment

Cited by 3 publications

References 48 publications

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Multi-‘Omic Integration via Similarity Network Fusion to Detect Molecular Subtypes of Aging

Molecular Research on Alzheimer’s Disease

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