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Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
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Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
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Gottschalk WK, Mahon S, Hodgson D, Barrera J, Hill D, Wei A, Kumar M, Dai K, Anderson L, Mihovilovic M, Lutz MW, Chiba-Falek O. The APOE-TOMM40 Humanized Mouse Model: Characterization of Age, Sex, and PolyT Variant Effects on Gene Expression. J Alzheimers Dis 2023; 94:1563-1576. [PMID: 37458041 PMCID: PMC10733864 DOI: 10.3233/jad-230451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND The human chromosome 19q13.32 is a gene rich region and has been associated with multiple phenotypes, including late onset Alzheimer's disease (LOAD) and other age-related conditions. OBJECTIVE Here we developed the first humanized mouse model that contains the entire TOMM40 and APOE genes with all intronic and intergenic sequences including the upstream and downstream regions. Thus, the mouse model carries the human TOMM40 and APOE genes and their intact regulatory sequences. METHODS We generated the APOE-TOMM40 humanized mouse model in which the entire mouse region was replaced with the human (h)APOE-TOMM40 loci including their upstream and downstream flanking regulatory sequences using recombineering technologies. We then measured the expression of the human TOMM40 and APOE genes in the mice brain, liver, and spleen tissues using TaqMan based mRNA expression assays. RESULTS We investigated the effects of the '523' polyT genotype (S/S or VL/VL), sex, and age on the human TOMM40- and APOE-mRNAs expression levels using our new humanized mouse model. The analysis revealed tissue specific and shared effects of the '523' polyT genotype, sex, and age on the regulation of the human TOMM40 and APOE genes. Noteworthy, the regulatory effect of the '523' polyT genotype was observed for all studied organs. CONCLUSION The model offers new opportunities for basic science, translational, and preclinical drug discovery studies focused on the APOE genomic region in relation to LOAD and other conditions in adulthood.
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Affiliation(s)
- William K. Gottschalk
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Scott Mahon
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Dellila Hodgson
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Julio Barrera
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Delaney Hill
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Angela Wei
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Manish Kumar
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Kathy Dai
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Lauren Anderson
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Mirta Mihovilovic
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Michael W. Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
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Transcript Variants of Genes Involved in Neurodegeneration Are Differentially Regulated by the APOE and MAPT Haplotypes. Genes (Basel) 2021; 12:genes12030423. [PMID: 33804213 PMCID: PMC7999745 DOI: 10.3390/genes12030423] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/01/2021] [Accepted: 03/10/2021] [Indexed: 12/17/2022] Open
Abstract
Genetic variations at the Apolipoprotein E (ApoE) and microtubule-associated protein tau (MAPT) loci have been implicated in multiple neurogenerative diseases, but their exact molecular mechanisms are unclear. In this study, we performed transcript level linear modelling using the blood whole transcriptome data and genotypes of the 570 subjects in the Parkinson’s Progression Markers Initiative (PPMI) cohort. ApoE, MAPT haplotypes and two SNPs at the SNCA locus (rs356181, rs3910105) were used to detect expression quantitative trait loci eQTLs associated with the transcriptome and differential usage of transcript isoforms. As a result, we identified 151 genes associated with the genotypic variations, 29 cis and 122 trans eQTL positions. Profound effect with genome-wide significance of ApoE e4 haplotype on the expression of TOMM40 transcripts was identified. This finding potentially explains in part the frequently established genetic association with the APOE e4 haplotypes in neurodegenerative diseases. Moreover, MAPT haplotypes had significant differential impact on 23 transcripts from the 17q21.31 and 17q24.1 loci. MAPT haplotypes had also the largest up-regulating (256) and the largest down-regulating (−178) effect sizes measured as β values on two different transcripts from the same gene (LRRC37A2). Intronic SNP in the SNCA gene, rs3910105, differentially induced expression of three SNCA isoforms. In conclusion, this study established clear association between well-known haplotypic variance and transcript specific regulation in the blood. APOE e4 and MAPT H1/H2 haplotypic variants are associated with the expression of several genes related to the neurodegeneration.
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Pendergrass SA, Buyske S, Jeff JM, Frase A, Dudek S, Bradford Y, Ambite JL, Avery CL, Buzkova P, Deelman E, Fesinmeyer MD, Haiman C, Heiss G, Hindorff LA, Hsu CN, Jackson RD, Lin Y, Le Marchand L, Matise TC, Monroe KR, Moreland L, North KE, Park SL, Reiner A, Wallace R, Wilkens LR, Kooperberg C, Ritchie MD, Crawford DC. A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans. PLoS One 2019; 14:e0226771. [PMID: 31891604 PMCID: PMC6938343 DOI: 10.1371/journal.pone.0226771] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022] Open
Abstract
We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
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Affiliation(s)
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Janina M. Jeff
- Illumina, Inc., San Diego, California, United States of America
| | - Alex Frase
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Scott Dudek
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jose-Luis Ambite
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ewa Deelman
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | | | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lucia A. Hindorff
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | | | - Yi Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kristine R. Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Larry Moreland
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Sungshim L. Park
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Robert Wallace
- Departments of Epidemiology and Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America
- Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
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The Genetic Variability of APOE in Different Human Populations and Its Implications for Longevity. Genes (Basel) 2019; 10:genes10030222. [PMID: 30884759 PMCID: PMC6471373 DOI: 10.3390/genes10030222] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/01/2019] [Accepted: 03/12/2019] [Indexed: 12/11/2022] Open
Abstract
Human longevity is a complex phenotype resulting from the combinations of context-dependent gene-environment interactions that require analysis as a dynamic process in a cohesive ecological and evolutionary framework. Genome-wide association (GWAS) and whole-genome sequencing (WGS) studies on centenarians pointed toward the inclusion of the apolipoprotein E (APOE) polymorphisms ε2 and ε4, as implicated in the attainment of extreme longevity, which refers to their effect in age-related Alzheimer's disease (AD) and cardiovascular disease (CVD). In this case, the available literature on APOE and its involvement in longevity is described according to an anthropological and population genetics perspective. This aims to highlight the evolutionary history of this gene, how its participation in several biological pathways relates to human longevity, and which evolutionary dynamics may have shaped the distribution of APOE haplotypes across the globe. Its potential adaptive role will be described along with implications for the study of longevity in different human groups. This review also presents an updated overview of the worldwide distribution of APOE alleles based on modern day data from public databases and ancient DNA samples retrieved from literature in the attempt to understand the spatial and temporal frame in which present-day patterns of APOE variation evolved.
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Yashin AI, Fang F, Kovtun M, Wu D, Duan M, Arbeev K, Akushevich I, Kulminski A, Culminskaya I, Zhbannikov I, Yashkin A, Stallard E, Ukraintseva S. Hidden heterogeneity in Alzheimer's disease: Insights from genetic association studies and other analyses. Exp Gerontol 2018; 107:148-160. [PMID: 29107063 PMCID: PMC5920782 DOI: 10.1016/j.exger.2017.10.020] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 10/20/2017] [Accepted: 10/22/2017] [Indexed: 02/08/2023]
Abstract
Despite evident success in clarifying many important features of Alzheimer's disease (AD) the efficient methods of its prevention and treatment are not yet available. The reasons are likely to be the fact that AD is a multifactorial and heterogeneous health disorder with multiple alternative pathways of disease development and progression. The availability of genetic data on individuals participated in longitudinal studies of aging health and longevity, as well as on participants of cross-sectional case-control studies allow for investigating genetic and non-genetic connections with AD and to link the results of these analyses with research findings obtained in clinical, experimental, and molecular biological studies of this health disorder. The objective of this paper is to perform GWAS of AD in several study populations and investigate possible roles of detected genetic factors in developing AD hallmarks and in other health disorders. The data collected in the Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), Health and Retirement Study (HRS) and Late Onset Alzheimer's Disease Family Study (LOADFS) were used in these analyses. The logistic regression and Cox's regression were used as statistical models in GWAS. The results of analyses confirmed strong associations of genetic variants from well-known genes APOE, TOMM40, PVRL2 (NECTIN2), and APOC1 with AD. Possible roles of these genes in pathological mechanisms resulting in development of hallmarks of AD are described. Many genes whose connection with AD was detected in other studies showed nominally significant associations with this health disorder in our study. The evidence on genetic connections between AD and vulnerability to infection, as well as between AD and other health disorders, such as cancer and type 2 diabetes, were investigated. The progress in uncovering hidden heterogeneity in AD would be substantially facilitated if common mechanisms involved in development of AD, its hallmarks, and AD related chronic conditions were investigated in their mutual connection.
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Affiliation(s)
- Anatoliy I Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA.
| | - Fang Fang
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Mikhail Kovtun
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Deqing Wu
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Matt Duan
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Konstantin Arbeev
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Igor Akushevich
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Alexander Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Irina Culminskaya
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Ilya Zhbannikov
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Arseniy Yashkin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Eric Stallard
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA
| | - Svetlana Ukraintseva
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 W. Main Street, Durham, NC 27705, USA.
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The effects of PPARγ on the regulation of the TOMM40-APOE-C1 genes cluster. Biochim Biophys Acta Mol Basis Dis 2017; 1863:810-816. [PMID: 28065845 DOI: 10.1016/j.bbadis.2017.01.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 12/12/2016] [Accepted: 01/04/2017] [Indexed: 11/24/2022]
Abstract
Chromosome 19q13.32 is a gene rich region, and has been implicated in multiple human phenotypes in adulthood including lipids traits, Alzheimer's disease, and longevity. Peroxisome Proliferator Activated Receptor Gamma (PPARγ) is a ligand-activated nuclear transcription factor that plays a role in human complex traits that are also genetically associated with the chromosome 19q13.32 region. Here, we study the effects of PPARγ on the regional expression regulation of the genes clustered within chromosome 19q13.32, specifically TOMM40, APOE, and APOC1, applying two complementary approaches. Using the short hairpin RNA (shRNA) method in the HepG2 cell-line we knocked down PPARγ expression and measured the effect on mRNA expression. We discovered PPARγ knock down increased the levels of TOMM40-, APOE-, and APOC1-mRNAs, with the highest increase in expression observed for APOE-mRNA. To complement the PPARγ knockdown findings we also examined the effects of low doses of PPARγ agonists (nM range) on mRNA expression of these genes. Low (nM) concentrations of pioglitazone (Pio) decreased transcription of TOMM40, APOE, and APOC1 genes, with the lowest mRNA levels for each gene observed at 1.5nM. Similar to the effect of PPARγ knockdown, the strongest response to pioglitazone was also observed for APOE-mRNA, and rosiglitazone (Rosi), another PPARγ agonist, produced results that were consistent with these. In conclusion, our results further established a role for PPARγ in regional transcriptional regulation of chr19q13.32, underpinning the association between PPARγ, the chr19q13.32 genes cluster, and human complex traits and disease.
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Younossi ZM, Stepanova M, Estep M, Negro F, Clark PJ, Hunt S, Song Q, Paulson M, Stamm LM, Brainard DM, Subramanian GM, McHutchison JG, Patel K. Dysregulation of distal cholesterol biosynthesis in association with relapse and advanced disease in CHC genotype 2 and 3 treated with sofosbuvir and ribavirin. J Hepatol 2016; 64:29-36. [PMID: 26341824 DOI: 10.1016/j.jhep.2015.08.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 08/21/2015] [Accepted: 08/24/2015] [Indexed: 02/06/2023]
Abstract
BACKGROUND & AIMS Hepatitis C virus (HCV) modulates host lipid metabolism for its replication and lifecycle. Our aims were to assess changes in the serum lipid and distal (post-squalene) cholesterol biosynthesis metabolite profile of HCV genotypes (GT) 2 and 3 patients treated with sofosbuvir+ribavirin. METHODS Serum samples [baseline, treatment week 12, 4weeks post-treatment] were analyzed for apolipoproteins B and E (apoB/E), total cholesterol, HDL, LDL, and 11 post-squalene sterol metabolites using a GC/MS platform. RESULTS We selected 127 patients (GT2 n=50, GT3 n=77), 50% cirrhotic patients, and 42% who experienced a virological relapse. At baseline, GT3 patients had lower level of serum lipids, apoB/E, 7-dehydrocholesterol, desmosterol, lathosterol, compared to GT2 (p<0.006). Baseline lathosterol was lower in relapsers with cirrhosis compared to cirrhotic patients with SVR (p=0.003). From baseline to treatment week 12, serum lipids, apoB/E, and key sterol pathway metabolites (7-dehydrocholesterol, desmosterol, lathosterol, lanosterol) increased in GT3. In contrast, in GT2 patients, apoB/E and dihydrolanosterol decreased with viral suppression (p<0.025). At follow-up week 4, cirrhotic SVR patients showed substantially greater increases in apoB and total sterols compared to cirrhotic relapsers regardless of HCV genotype. After adjustment for genotype and gender, baseline lathosterol was independently associated with virologic response (p=0.04). CONCLUSION HCV GT3 is associated with reduced circulation of lipids involved in the distal cholesterol biosynthesis pathway, resulting in relative hypocholesterolemia. HCV suppression during sofosbuvir+ribavirin restores distal sterol metabolites indicating viral interference with de novo lipogenesis or selective retention by hepatocytes.
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Affiliation(s)
- Zobair M Younossi
- Center for Liver Diseases, Department of Medicine, Inova Fairfax Hospital, Falls Church, VA, United States; Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, United States.
| | - Maria Stepanova
- Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, United States
| | - Michael Estep
- Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, United States
| | | | | | - Sharon Hunt
- Center for Liver Diseases, Department of Medicine, Inova Fairfax Hospital, Falls Church, VA, United States; Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, United States
| | | | | | | | | | | | | | - Keyur Patel
- Division of Gastroenterology, Duke University Medical Center, Durham, NC, United States
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Monastero R, Caruso C, Vasto S. Alzheimer's disease and infections, where we stand and where we go. IMMUNITY & AGEING 2014; 11:26. [PMID: 25535510 PMCID: PMC4273443 DOI: 10.1186/s12979-014-0026-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 12/08/2014] [Indexed: 11/29/2022]
Affiliation(s)
- Roberto Monastero
- Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, Palermo, Italy
| | - Calogero Caruso
- Department of Pathobiology and Medical and Forensic Biotechnologies, University of Palermo, Palermo, Italy
| | - Sonya Vasto
- National Center for Research, Institute of Biomedicine and Molecular Immunology, Palermo, Italy ; Department of Science and Biological, Chemical and Pharmaceutical Technologies, Institute of Biomedicine and Molecular Immunology, Palermo, Italy
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Licastro F, Carbone I, Raschi E, Porcellini E. The 21st century epidemic: infections as inductors of neuro-degeneration associated with Alzheimer's Disease. Immun Ageing 2014; 11:22. [PMID: 25516763 PMCID: PMC4266955 DOI: 10.1186/s12979-014-0022-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 11/22/2014] [Indexed: 01/18/2023]
Abstract
Alzheimer's disease (AD) is a complex disease resulting in neurodegeneration and cognitive impairment. Investigations on environmental factors implicated in AD are scarce and the etiology of the disease remains up to now obscure. The disease's pathogenesis may be multi-factorial and different etiological factors may converge during aging and induce an activation of brain microglia and macrophages. This microglia priming will result in chronic neuro-inflammation under chronic antigen activation. Infective agents may prime and drive iper-activation of microglia and be partially responsible of the induction of brain inflammation and decline of cognitive performances. Age-associated immune dis-functions induced by chronic sub-clinical infections appear to substantially contribute to the appearance of neuro-inflammation in the elderly. Individual predisposition to less efficient immune responses is another relevant factor contributing to impaired regulation of inflammatory responses and accelerated cognitive decline. Life-long virus infection may play a pivotal role in activating peripheral and central inflammatory responses and in turn contributing to increased cognitive impairment in preclinical and clinical AD.
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Affiliation(s)
- Federico Licastro
- />Department of Experimental, Diagnostic and Specialty Medicine, School of Medicine, University of Bologna, Bologna, 40100 Italy
- />Laboratory of Immunopathology and Immunogenetics, Department of Experimental, Diagnostic and Specialty Medicine, School of Medicine, University of Bologna, Via S. Giacomo 14, 40126 Bologna, Italy
| | - Ilaria Carbone
- />Department of Experimental, Diagnostic and Specialty Medicine, School of Medicine, University of Bologna, Bologna, 40100 Italy
| | - Elena Raschi
- />Department of Experimental, Diagnostic and Specialty Medicine, School of Medicine, University of Bologna, Bologna, 40100 Italy
| | - Elisa Porcellini
- />Department of Experimental, Diagnostic and Specialty Medicine, School of Medicine, University of Bologna, Bologna, 40100 Italy
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Salakhov RR, Goncharova IA, Makeeva OA, Golubenko MV, Kulish EV, Kashtalap VV, Barbarash OL, Puzyrev VP. TOMM40 gene polymorphisms association with lipid profile. RUSS J GENET+ 2014. [DOI: 10.1134/s1022795413120090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Eslam M, Booth DR, George J, Ahlenstiel G. Interaction of IFNL3 with insulin resistance, steatosis and lipid metabolism in chronic hepatitis C virus infection. World J Gastroenterol 2013; 19:7055-61. [PMID: 24222948 PMCID: PMC3819540 DOI: 10.3748/wjg.v19.i41.7055] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2013] [Revised: 09/14/2013] [Accepted: 09/29/2013] [Indexed: 02/06/2023] Open
Abstract
Metabolic changes are inextricably linked to chronic hepatitis C (CHC). Recently polymorphisms in the IFNL3 (IL28B) region have been shown to be strongly associated with spontaneous and treatment induced recovery from hepatitis C virus (HCV) infection. Further, circumstantial evidence suggests a link between IFNL3 single nucleotide polymorphisms and lipid metabolism, steatosis and insulin resistance in CHC. The emerging picture suggests that the responder genotypes of IFNL3 polymorphisms are associated with a higher serum lipid profile, and less frequent steatosis and insulin resistance. This review analyzes the current data regarding this interaction and its meaning for HCV pathogenesis and disease progression.
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