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Merchant JP, Zhu K, Henrion MYR, Zaidi SSA, Lau B, Moein S, Alamprese ML, Pearse RV, Bennett DA, Ertekin-Taner N, Young-Pearse TL, Chang R. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease. Commun Biol 2023; 6:503. [PMID: 37188718 PMCID: PMC10185548 DOI: 10.1038/s42003-023-04791-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2023] [Indexed: 05/17/2023] Open
Abstract
Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.
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Affiliation(s)
- Julie P Merchant
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Neuroscience Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kuixi Zhu
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Marc Y R Henrion
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, Pembroke Place, L3 5QA, UK
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, PO Box 30096, Blantyre, Malawi
| | - Syed S A Zaidi
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Branden Lau
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | - Sara Moein
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Melissa L Alamprese
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Tracy L Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Harvard Stem Cell Institute, Harvard University, Boston, MA, USA.
| | - Rui Chang
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA.
- Department of Neurology, University of Arizona, Tucson, AZ, USA.
- INTelico Therapeutics LLC, Tucson, AZ, USA.
- PATH Biotech LLC, Tucson, AZ, USA.
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Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression. Mol Psychiatry 2021; 26:3077-3092. [PMID: 33963278 DOI: 10.1038/s41380-021-01084-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 02/26/2021] [Accepted: 03/26/2021] [Indexed: 02/03/2023]
Abstract
Posttraumatic stress disorder (PTSD) is a debilitating syndrome with substantial morbidity and mortality that occurs in the aftermath of trauma. Symptoms of major depressive disorder (MDD) are also a frequent consequence of trauma exposure. Identifying novel risk markers in the immediate aftermath of trauma is a critical step for the identification of novel biological targets to understand mechanisms of pathophysiology and prevention, as well as the determination of patients most at risk who may benefit from immediate intervention. Our study utilizes a novel approach to computationally integrate blood-based transcriptomics, genomics, and interactomics to understand the development of risk vs. resilience in the months following trauma exposure. In a two-site longitudinal, observational prospective study, we assessed over 10,000 individuals and enrolled >700 subjects in the immediate aftermath of trauma (average 5.3 h post-trauma (range 0.5-12 h)) in the Grady Memorial Hospital (Atlanta) and Jackson Memorial Hospital (Miami) emergency departments. RNA expression data and 6-month follow-up data were available for 366 individuals, while genotype, transcriptome, and phenotype data were available for 297 patients. To maximize our power and understanding of genes and pathways that predict risk vs. resilience, we utilized a set-cover approach to capture fluctuations of gene expression of PTSD or depression-converting patients and non-converting trauma-exposed controls to find representative sets of disease-relevant dysregulated genes. We annotated such genes with their corresponding expression quantitative trait loci and applied a variant of a current flow algorithm to identify genes that potentially were causal for the observed dysregulation of disease genes involved in the development of depression and PTSD symptoms after trauma exposure. We obtained a final list of 11 driver causal genes related to MDD symptoms, 13 genes for PTSD symptoms, and 22 genes in PTSD and/or MDD. We observed that these individual or combined disorders shared ESR1, RUNX1, PPARA, and WWOX as driver causal genes, while other genes appeared to be causal driver in the PTSD only or MDD only cases. A number of these identified causal pathways have been previously implicated in the biology or genetics of PTSD and MDD, as well as in preclinical models of amygdala function and fear regulation. Our work provides a promising set of initial pathways that may underlie causal mechanisms in the development of PTSD or MDD in the aftermath of trauma.
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Petyuk VA, Chang R, Ramirez-Restrepo M, Beckmann ND, Henrion MYR, Piehowski PD, Zhu K, Wang S, Clarke J, Huentelman MJ, Xie F, Andreev V, Engel A, Guettoche T, Navarro L, De Jager P, Schneider JA, Morris CM, McKeith IG, Perry RH, Lovestone S, Woltjer RL, Beach TG, Sue LI, Serrano GE, Lieberman AP, Albin RL, Ferrer I, Mash DC, Hulette CM, Ervin JF, Reiman EM, Hardy JA, Bennett DA, Schadt E, Smith RD, Myers AJ. The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target. Brain 2018; 141:2721-2739. [PMID: 30137212 PMCID: PMC6136080 DOI: 10.1093/brain/awy215] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 04/20/2018] [Accepted: 06/22/2018] [Indexed: 11/24/2022] Open
Abstract
Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001.
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Affiliation(s)
- Vladislav A Petyuk
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rui Chang
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manuel Ramirez-Restrepo
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Noam D Beckmann
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marc Y R Henrion
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul D Piehowski
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kuixi Zhu
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sven Wang
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer Clarke
- Food Science and Technology Department, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Matthew J Huentelman
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Fang Xie
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Victor Andreev
- Arbor Research Collaborative for Health, 340 E Huron St # 300, Ann Arbor, MI, USA
| | - Anzhelika Engel
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Loida Navarro
- Roche Sequencing, 4300 Hacienda Drive, Pleasanton, CA, USA
| | - Philip De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
- New York Genome Center, New York NY, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Julie A Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Christopher M Morris
- Newcastle Brain Tissue Resource, Institute of Neuroscience, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK
| | - Ian G McKeith
- NIHR Biomedical Research Centre, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Robert H Perry
- Neuropathology and Cellular Pathology, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne, UK
| | - Simon Lovestone
- University of Oxford, Medical Sciences Division, Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Randall L Woltjer
- Neuropathology Core of the Layton Aging and Alzheimer’s Disease Center, Oregon Health and Science University, Portland, OR, USA
| | | | - Lucia I Sue
- Banner Sun Health Research Institute, Sun City, AZ, USA
| | | | | | - Roger L Albin
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
- Geriatrics Research, Education, and Clinical Center, VAAAHS, Ann Arbor, MI, USA
| | - Isidre Ferrer
- Department of Pathology and Experimental Therapeutics, University of Barcelona; CIBERNED; Hospitalet de Llobregat, Spain
| | - Deborah C Mash
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Christine M Hulette
- Department of Pathology, Division of Neuropathology, Duke University Medical Center, Durham, NC, USA
| | - John F Ervin
- Kathleen Price Bryan Brain Bank, Department of Medicine, Division of Neurology, Duke University, Durham, NC, USA
| | - Eric M Reiman
- The Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA
- Banner Alzheimer’s Institute, Phoenix, Arizona, USA
| | - John A Hardy
- Department of Molecular Neuroscience and Reta Lila Research Laboratories, University College London Institute of Neurology, London, UK
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Eric Schadt
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Richard D Smith
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Amanda J Myers
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Interdepartmental Program in Neuroscience, University of Miami Miller School of Medicine, Miami, FL, USA
- Interdepartmental Program in Human Genetics and Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
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Perna G, Grassi M, Caldirola D, Nemeroff CB. The revolution of personalized psychiatry: will technology make it happen sooner? Psychol Med 2018; 48:705-713. [PMID: 28967349 DOI: 10.1017/s0033291717002859] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.
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Affiliation(s)
- G Perna
- Department of Clinical Neurosciences,Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano,Como 22032,Italy
| | - M Grassi
- Department of Clinical Neurosciences,Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano,Como 22032,Italy
| | - D Caldirola
- Department of Clinical Neurosciences,Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano,Como 22032,Italy
| | - C B Nemeroff
- Department of Psychiatry and Behavioral Sciences,Leonard Miller School of Medicine, University of Miami,Miami, FL,USA
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A quantitative transcriptome reference map of the normal human brain. Neurogenetics 2014; 15:267-87. [PMID: 25185649 DOI: 10.1007/s10048-014-0419-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Accepted: 08/08/2014] [Indexed: 10/24/2022]
Abstract
We performed an innovative systematic meta-analysis of 60 gene expression profiles of whole normal human brain, to provide a quantitative transcriptome reference map of it, i.e. a reference typical value of expression for each of the 39,250 known, mapped and 26,026 uncharacterized (unmapped) transcripts. To this aim, we used the software named Transcriptome Mapper (TRAM), which is able to generate transcriptome maps based on gene expression data from multiple sources. We also analyzed differential expression by comparing the brain transcriptome with those derived from human foetal brain gene expression, from a pool of human tissues (except the brain) and from the two normal human brain regions cerebellum and cerebral cortex, which are two of the main regions severely affected when cognitive impairment occurs, as happens in the case of trisomy 21. Data were downloaded from microarray databases, processed and analyzed using TRAM software and validated in vitro by assaying gene expression through several magnitude orders by 'real-time' reverse transcription polymerase chain reaction (RT-PCR). The excellent agreement between in silico and experimental data suggested that our transcriptome maps may be a useful quantitative reference benchmark for gene expression studies related to the human brain. Furthermore, our analysis yielded biological insights about those genes which have an intrinsic over-/under-expression in the brain, in addition offering a basis for the regional analysis of gene expression. This could be useful for the study of chromosomal alterations associated to cognitive impairment, such as trisomy 21, the most common genetic cause of intellectual disability.
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Kamath-Rayne BD, Smith HC, Muglia LJ, Morrow AL. Amniotic fluid: the use of high-dimensional biology to understand fetal well-being. Reprod Sci 2013; 21:6-19. [PMID: 23599373 DOI: 10.1177/1933719113485292] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Our aim was to review the use of high-dimensional biology techniques, specifically transcriptomics, proteomics, and metabolomics, in amniotic fluid to elucidate the mechanisms behind preterm birth or assessment of fetal development. We performed a comprehensive MEDLINE literature search on the use of transcriptomic, proteomic, and metabolomic technologies for amniotic fluid analysis. All abstracts were reviewed for pertinence to preterm birth or fetal maturation in human subjects. Nineteen articles qualified for inclusion. Most articles described the discovery of biomarker candidates, but few larger, multicenter replication or validation studies have been done. We conclude that the use of high-dimensional systems biology techniques to analyze amniotic fluid has significant potential to elucidate the mechanisms of preterm birth and fetal maturation. However, further multicenter collaborative efforts are needed to replicate and validate candidate biomarkers before they can become useful tools for clinical practice. Ideally, amniotic fluid biomarkers should be translated to a noninvasive test performed in maternal serum or urine.
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Affiliation(s)
- Beena D Kamath-Rayne
- 1Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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