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Cruchaga C, Ali M, Shen Y, Do A, Wang L, Western D, Liu M, Beric A, Budde J, Gentsch J, Schindler S, Morris J, Holtzman D, Fernández M, Ruiz A, Alvarez I, Aguilar M, Pastor P, Rutledge J, Oh H, Wilson E, Le Guen Y, Khalid R, Robins C, Pulford D, Ibanez L, Wyss-Coray T, Ju Sung Y. Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease. RESEARCH SQUARE 2024:rs.3.rs-3631708. [PMID: 38410465 PMCID: PMC10896368 DOI: 10.21203/rs.3.rs-3631708/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Changes in Amyloid-β (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).
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Affiliation(s)
| | | | | | - Anh Do
- Washington University School of Medicine
| | - Lihua Wang
- Washington University School of Medicine
| | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | | | | | | | | | | | | | | | - Ignacio Alvarez
- Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona, Spain
| | | | - Pau Pastor
- University Hospital Germans Trias i Pujol
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3
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Phan BN, Ray MH, Xue X, Fu C, Fenster RJ, Kohut SJ, Bergman J, Haber SN, McCullough KM, Fish MK, Glausier JR, Su Q, Tipton AE, Lewis DA, Freyberg Z, Tseng GC, Russek SJ, Alekseyev Y, Ressler KJ, Seney ML, Pfenning AR, Logan RW. Single nuclei transcriptomics in human and non-human primate striatum in opioid use disorder. Nat Commun 2024; 15:878. [PMID: 38296993 PMCID: PMC10831093 DOI: 10.1038/s41467-024-45165-7] [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] [Received: 08/07/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024] Open
Abstract
In brain, the striatum is a heterogenous region involved in reward and goal-directed behaviors. Striatal dysfunction is linked to psychiatric disorders, including opioid use disorder (OUD). Striatal subregions are divided based on neuroanatomy, each with unique roles in OUD. In OUD, the dorsal striatum is involved in altered reward processing, formation of habits, and development of negative affect during withdrawal. Using single nuclei RNA-sequencing, we identified both canonical (e.g., dopamine receptor subtype) and less abundant cell populations (e.g., interneurons) in human dorsal striatum. Pathways related to neurodegeneration, interferon response, and DNA damage were significantly enriched in striatal neurons of individuals with OUD. DNA damage markers were also elevated in striatal neurons of opioid-exposed rhesus macaques. Sex-specific molecular differences in glial cell subtypes associated with chronic stress were found in OUD, particularly female individuals. Together, we describe different cell types in human dorsal striatum and identify cell type-specific alterations in OUD.
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Affiliation(s)
- BaDoi N Phan
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Medical Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - Madelyn H Ray
- Department of Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, 02118, USA
- Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Xiangning Xue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Chen Fu
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Robert J Fenster
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Division of Depression and Anxiety, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, 02478, USA
| | - Stephen J Kohut
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Behavioral Biology Program, McLean Hospital, Belmont, MA, 02478, USA
| | - Jack Bergman
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Behavioral Biology Program, McLean Hospital, Belmont, MA, 02478, USA
| | - Suzanne N Haber
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pharmacology and Physiology, University of Rochester, School of Medicine, Rochester, NY, 14642, USA
| | - Kenneth M McCullough
- Basic Neuroscience Division, Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, 02478, USA
| | - Madeline K Fish
- Center for Systems Neuroscience, Boston University, Boston, MA, 02118, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02118, USA
| | - Jill R Glausier
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15219, USA
| | - Qiao Su
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Allison E Tipton
- Center for Systems Neuroscience, Boston University, Boston, MA, 02118, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02118, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15219, USA
| | - Zachary Freyberg
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15219, USA
- Department of Cell Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15219, USA
| | - George C Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Shelley J Russek
- Department of Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02118, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02118, USA
| | - Yuriy Alekseyev
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Division of Depression and Anxiety, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, 02478, USA
| | - Marianne L Seney
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15219, USA
| | - Andreas R Pfenning
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
| | - Ryan W Logan
- Department of Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, 02118, USA.
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
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4
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Teaney NA, Cyr NE. FoxO1 as a tissue-specific therapeutic target for type 2 diabetes. Front Endocrinol (Lausanne) 2023; 14:1286838. [PMID: 37941908 PMCID: PMC10629996 DOI: 10.3389/fendo.2023.1286838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023] Open
Abstract
Forkhead box O (FoxO) proteins are transcription factors that mediate many aspects of physiology and thus have been targeted as therapeutics for several diseases including metabolic disorders such as type 2 diabetes mellitus (T2D). The role of FoxO1 in metabolism has been well studied, but recently FoxO1's potential for diabetes prevention and therapy has been debated. For example, studies have shown that increased FoxO1 activity in certain tissue types contributes to T2D pathology, symptoms, and comorbidities, yet in other tissue types elevated FoxO1 has been reported to alleviate symptoms associated with diabetes. Furthermore, studies have reported opposite effects of active FoxO1 in the same tissue type. For example, in the liver, FoxO1 contributes to T2D by increasing hepatic glucose production. However, FoxO1 has been shown to either increase or decrease hepatic lipogenesis as well as adipogenesis in white adipose tissue. In skeletal muscle, FoxO1 reduces glucose uptake and oxidation, promotes lipid uptake and oxidation, and increases muscle atrophy. While many studies show that FoxO1 lowers pancreatic insulin production and secretion, others show the opposite, especially in response to oxidative stress and inflammation. Elevated FoxO1 in the hypothalamus increases the risk of developing T2D. However, increased FoxO1 may mitigate Alzheimer's disease, a neurodegenerative disease strongly associated with T2D. Conversely, accumulating evidence implicates increased FoxO1 with Parkinson's disease pathogenesis. Here we review FoxO1's actions in T2D conditions in metabolic tissues that abundantly express FoxO1 and highlight some of the current studies targeting FoxO1 for T2D treatment.
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Affiliation(s)
- Nicole A. Teaney
- Stonehill College, Neuroscience Program, Easton, MA, United States
| | - Nicole E. Cyr
- Stonehill College, Neuroscience Program, Easton, MA, United States
- Stonehill College, Department of Biology, Easton, MA, United States
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5
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Zhang Y, Kiryu H. Identification of oxidative stress-related genes differentially expressed in Alzheimer's disease and construction of a hub gene-based diagnostic model. Sci Rep 2023; 13:6817. [PMID: 37100862 PMCID: PMC10133299 DOI: 10.1038/s41598-023-34021-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/22/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations "Alzheimer's disease pathway," "Parkinson's Disease," "Ribosome," and "Chronic myeloid leukemia." In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets.
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Affiliation(s)
- Yanting Zhang
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
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