Browsing by Author "Cheng, Haoxiang"

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    Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes
    (Springer Nature, 2021-03-12) Novikova, Gloriia; Kapoor, Manav; TCW, Julia; Abud, Edsel M.; Efthymiou, Anastasia G.; Chen, Steven X.; Cheng, Haoxiang; Fullard, John F.; Bendl, Jaroslav; Liu, Yiyuan; Roussos, Panos; Björkegren, Johan LM; Liu, Yunlong; Poon, Wayne W.; Hao, Ke; Marcora, Edoardo; Goate, Alison M.; Medical and Molecular Genetics, School of Medicine
    Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer’s disease (AD), but the causal variants, regulatory elements, genes and pathways remain largely unknown, impeding a mechanistic understanding of AD pathogenesis. Previously, we showed that AD risk alleles are enriched in myeloid-specific epigenomic annotations. Here, we show that they are specifically enriched in active enhancers of monocytes, macrophages and microglia. We integrated AD GWAS with myeloid epigenomic and transcriptomic datasets using analytical approaches to link myeloid enhancer activity to target gene expression regulation and AD risk modification. We identify AD risk enhancers and nominate candidate causal genes among their likely targets (including AP4E1, AP4M1, APBB3, BIN1, MS4A4A, MS4A6A, PILRA, RABEP1, SPI1, TP53INP1, and ZYX) in twenty loci. Fine-mapping of these enhancers nominates candidate functional variants that likely modify AD risk by regulating gene expression in myeloid cells. In the MS4A locus we identified a single candidate functional variant and validated it in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Taken together, this study integrates AD GWAS with multiple myeloid genomic datasets to investigate the mechanisms of AD risk alleles and nominates candidate functional variants, regulatory elements and genes that likely modulate disease susceptibility.
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    Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease
    (Wiley, 2022) Horgusluoglu, Emrin; Neff, Ryan; Song, Won-Min; Wang, Minghui; Wang, Qian; Arnold, Matthias; Krumsiek, Jan; Galindo-Prieto, Beatriz; Ming, Chen; Nho, Kwangsik; Kastenmüller, Gabi; Han, Xianlin; Baillie, Rebecca; Zeng, Qi; Andrews, Shea; Cheng, Haoxiang; Hao, Ke; Goate, Alison; Bennett, David A.; Saykin, Andrew J.; Kaddurah-Daouk, Rima; Zhang, Bin; Alzheimer's Disease Neuroimaging Initiative (ADNI); Alzheimer Disease Metabolomics Consortium; Radiology and Imaging Sciences, School of Medicine
    Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD‐specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co‐expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short‐chain acylcarnitines/amino acids and medium/long‐chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP‐AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co‐expression network analysis of the AMP‐AD brain RNA‐seq data suggests the CPT1A‐ and ABCA1‐centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short‐chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large‐scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.