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Pan X, Dai W, Wang Z, Li S, Sun T, Miao N. PIWI-Interacting RNAs: A Pivotal Regulator in Neurological Development and Disease. Genes (Basel) 2024; 15:653. [PMID: 38927589 PMCID: PMC11202748 DOI: 10.3390/genes15060653] [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: 04/13/2024] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
PIWI-interacting RNAs (piRNAs), a class of small non-coding RNAs (sncRNAs) with 24-32 nucleotides (nt), were initially identified in the reproductive system. Unlike microRNAs (miRNAs) or small interfering RNAs (siRNAs), piRNAs normally guide P-element-induced wimpy testis protein (PIWI) families to slice extensively complementary transposon transcripts without the seed pairing. Numerous studies have shown that piRNAs are abundantly expressed in the brain, and many of them are aberrantly regulated in central neural system (CNS) disorders. However, the role of piRNAs in the related developmental and pathological processes is unclear. The elucidation of piRNAs/PIWI would greatly improve the understanding of CNS development and ultimately lead to novel strategies to treat neural diseases. In this review, we summarized the relevant structure, properties, and databases of piRNAs and their functional roles in neural development and degenerative disorders. We hope that future studies of these piRNAs will facilitate the development of RNA-based therapeutics for CNS disorders.
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Affiliation(s)
| | | | | | | | | | - Nan Miao
- Center for Precision Medicine, School of Medicine and School of Biomedical Sciences, Huaqiao University, Xiamen 361021, China; (X.P.); (W.D.); (Z.W.); (S.L.); (T.S.)
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2
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Hoffmann M, Poschenrieder JM, Incudini M, Baier S, Fitz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl MV, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward M, Jankowski J, Lee HK, Tsoy O, Wenke N, Pedersen AG, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth PA, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal DB. Network medicine-based epistasis detection in complex diseases: ready for quantum computing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.07.23298205. [PMID: 38076997 PMCID: PMC10705612 DOI: 10.1101/2023.11.07.23298205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.
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Affiliation(s)
- Markus Hoffmann
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Julian M. Poschenrieder
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Massimiliano Incudini
- Dipartimento di Informatica, Universit’a di Verona, Strada le Grazie 15 - 34137, Verona, Italy
| | - Sylvie Baier
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Amelie Fitz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Christian Hoffmann
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nico Trummer
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Evelyn Scheibling
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Maximilian V. Harl
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Ingmar Lesch
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Hunor Frey
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Simon Kayser
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Paul Wissenberg
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Leon Schwartz
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Leon Hafner
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
| | - Aakriti Acharya
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Lena Hackl
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Sung-Gwon Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea
| | - Gyuhyeok Cho
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Matthew Cloward
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Jakub Jankowski
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nina Wenke
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Anders Gorm Pedersen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Mandarino
- International Centre for Theory of Quantum Technologies, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Federico Melograna
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Laura Schulz
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Luigi Iapichino
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Kristel Van Steen
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Michele Grossi
- European Organization for Nuclear Research (CERN), Geneva 1211, Switzerland
| | - Priscilla A. Furth
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Alessandra Di Pierro
- Dipartimento di Informatica, Universit’a di Verona, Strada le Grazie 15 - 34137, Verona, Italy
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - David B. Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
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3
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Sun Q, Ni J, Wei M, Long S, Li T, Fan D, Lu T, Shi J, Tian J. Plasma β-amyloid, tau, neurodegeneration biomarkers and inflammatory factors of probable Alzheimer’s disease dementia in Chinese individuals. Front Aging Neurosci 2022; 14:963845. [PMID: 36062146 PMCID: PMC9433929 DOI: 10.3389/fnagi.2022.963845] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundPlasma-derived β-amyloid, tau, and neurodegeneration (ATN) biomarkers can accurately diagnose Alzheimer’s disease (AD) and predict its progression. Few studies have investigated the relationship between plasma biomarkers and changes in plasma inflammatory markers in clinically diagnosed AD.MethodsSeventy-four participants were recruited, including 30 mild-to-moderate AD dementia patients and 44 normal controls (NC). All participants underwent neuropsychological testing and blood sampling for biomarker testing. AD was clinically diagnosed according to the National Institute on Aging-Alzheimer’s Association (NIA-AA) core criteria and required age-mismatched hippocampal atrophy. We performed Single Molecule Array (Simoa), an ultra-sensitive enzyme-linked immunosorbent assay (ELISA), to examine plasma ATN markers, including β-amyloid (Aβ) 40, Aβ42, p-tau181, total (t)-tau, neurofilament protein light chain (NfL), and inflammatory factors (TNF-α, IL-1β, IL-6, and IL-8).ResultsThe level of the plasma Aβ42/Aβ40 ratio was significantly declined and the levels of the plasma p-tau181, NfL and TNF-α were significantly higher in the AD group than the NC group, but there was no significant difference in the levels of plasma t-tau, IL-1β, IL-6, and IL-8 between the AD and NC groups. The levels of plasma p-tau181, NfL, Aβ42/Aβ40 ratio, and TNF-α were all associated with impairments in multiple cognitive domains. Among them, the plasma Aβ42/Aβ40 ratio, and the p-tau181 and TNF-α levels were associated with impairments in global cognition, memory, and visuospatial abilities, but not with executive function, only plasma NfL level was associated with executive function. Plasma NfL showed higher diagnostic performance in AD than in NC individuals (AUC = 0.833). A combined diagnostic prediction model of plasma Aβ42/Aβ40 ratio, p-tau 181, and NfL had the highest value than each factor alone (AUC = 0.902),with a sensitivity and specificity of 0.867 and 0.886, respectively.ConclusionThe levels of plasma ATN biomarkers (Aβ42/Aβ40 ratio, p-tua181, and NfL) were significantly changed in clinically diagnosed AD patients and they all associated with different domains of cognitive impairment. Plasma ATN biomarkers better differentiate mild-to-moderate AD dementia from NC when they are incorporated into diagnostic models together rather than individually. Plasma ATN biomarkers have the potential to be a screening tool for AD. However, the expression of inflammatory factors in AD patients requires further research.
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Affiliation(s)
- Qingling Sun
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Jingnian Ni
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Mingqing Wei
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Siwei Long
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Ting Li
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, Beijing, China
| | - Tao Lu
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Jing Shi
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- *Correspondence: Jing Shi,
| | - Jinzhou Tian
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Jinzhou Tian,
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Zhang T, Chen L, Li R, Liu N, Huang X, Wong G. PIWI-interacting RNAs in human diseases: databases and computational models. Brief Bioinform 2022; 23:6603448. [PMID: 35667080 DOI: 10.1093/bib/bbac217] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/24/2022] [Accepted: 05/09/2022] [Indexed: 11/12/2022] Open
Abstract
PIWI-interacting RNAs (piRNAs) are short 21-35 nucleotide molecules that comprise the largest class of non-coding RNAs and found in a large diversity of species including yeast, worms, flies, plants and mammals including humans. The most well-understood function of piRNAs is to monitor and protect the genome from transposons particularly in germline cells. Recent data suggest that piRNAs may have additional functions in somatic cells although they are expressed there in far lower abundance. Compared with microRNAs (miRNAs), piRNAs have more limited bioinformatics resources available. This review collates 39 piRNA specific and non-specific databases and bioinformatics resources, describes and compares their utility and attributes and provides an overview of their place in the field. In addition, we review 33 computational models based upon function: piRNA prediction, transposon element and mRNA-related piRNA prediction, cluster prediction, signature detection, target prediction and disease association. Based on the collection of databases and computational models, we identify trends and potential gaps in tool development. We further analyze the breadth and depth of piRNA data available in public sources, their contribution to specific human diseases, particularly in cancer and neurodegenerative conditions, and highlight a few specific piRNAs that appear to be associated with these diseases. This briefing presents the most recent and comprehensive mapping of piRNA bioinformatics resources including databases, models and tools for disease associations to date. Such a mapping should facilitate and stimulate further research on piRNAs.
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Affiliation(s)
- Tianjiao Zhang
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R. 999078, China
| | - Liang Chen
- Department of Computer Science, School of Engineering, Shantou University, Shantou, China
| | - Rongzhen Li
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R. 999078, China
| | - Ning Liu
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R. 999078, China
| | - Xiaobing Huang
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R. 999078, China
| | - Garry Wong
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R. 999078, China
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5
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Lio CT, Kacprowski T, Klaedtke M, Jensen LR, Bouter Y, Bayer TA, Kuss AW. Small RNA Sequencing in the Tg4–42 Mouse Model Suggests the Involvement of snoRNAs in the Etiology of Alzheimer’s Disease. J Alzheimers Dis 2022; 87:1671-1681. [DOI: 10.3233/jad-220110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The Tg4-42 mouse model for sporadic Alzheimer’s disease (AD) has unique features, as the neuronal expression of wild type N-truncated Aβ4–42 induces an AD-typical neurological phenotype in the absence of plaques. It is one of the few models developing neuron death in the CA1 region of the hippocampus. As such, it could serve as a powerful tool for preclinical drug testing and identification of the underlying molecular pathways that drive the pathology of AD. Objective: The aim of this study was to use a differential co-expression analysis approach for analyzing a small RNA sequencing dataset from a well-established murine model in order to identify potentially new players in the etiology of AD. Methods: To investigate small nucleolar RNAs in the hippocampus of Tg4-42 mice, we used RNA-Seq data from this particular tissue and, instead of analyzing the data at single gene level, employed differential co-expression analysis, which takes the comparison to gene pair level and thus affords a new angle to the interpretation of these data. Results: We identified two clusters of differentially correlated small RNAs, including Snord55, Snord57, Snord49a, Snord12, Snord38a, Snord99, Snord87, Mir1981, Mir106b, Mir30d, Mir598, and Mir99b. Interestingly, some of them have been reported to be functionally relevant in AD pathogenesis, as AD biomarkers, regulating tau phosphorylation, TGF-β receptor function or Aβ metabolism. Conclusion: The majority of snoRNAs for which our results suggest a potential role in the etiology of AD were so far not conspicuously implicated in the context of AD pathogenesis and could thus point towards interesting new avenues of research in this field.
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Affiliation(s)
- Chit Tong Lio
- Chair of Experimental Bioinformatics, TechnicalUniversity of Munich, Freising, Germany
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Maik Klaedtke
- Department of Functional Genomics, Human Molecular Genetics Group, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Lars R. Jensen
- Department of Functional Genomics, Human Molecular Genetics Group, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Yvonne Bouter
- Department of Psychiatry and Psychotherapy, Division of Molecular Psychiatry, University Medical Center Goettingen (UMG), Georg-August-University, Goettingen, Germany
| | - Thomas A. Bayer
- Department of Psychiatry and Psychotherapy, Division of Molecular Psychiatry, University Medical Center Goettingen (UMG), Georg-August-University, Goettingen, Germany
| | - Andreas W. Kuss
- Department of Functional Genomics, Human Molecular Genetics Group, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
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Laudanski K, Hajj J, Restrepo M, Siddiq K, Okeke T, Rader DJ. Dynamic Changes in Central and Peripheral Neuro-Injury vs. Neuroprotective Serum Markers in COVID-19 Are Modulated by Different Types of Anti-Viral Treatments but Do Not Affect the Incidence of Late and Early Strokes. Biomedicines 2021; 9:1791. [PMID: 34944606 PMCID: PMC8698659 DOI: 10.3390/biomedicines9121791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 01/07/2023] Open
Abstract
The balance between neurodegeneration, neuroinflammation, neuroprotection, and COVID-19-directed therapy may underly the heterogeneity of SARS-CoV-2's neurological outcomes. A total of 105 patients hospitalized with a diagnosis of COVID-19 had serum collected over a 6 month period to assess neuroinflammatory (MIF, CCL23, MCP-1), neuro-injury (NFL, NCAM-1), neurodegenerative (KLK6, τ, phospho τ, amyloids, TDP43, YKL40), and neuroprotective (clusterin, fetuin, TREM-2) proteins. These were compared to markers of nonspecific inflammatory responses (IL-6, D-dimer, CRP) and of the overall viral burden (spike protein). Data regarding treatment (steroids, convalescent plasma, remdasavir), pre-existing conditions, and incidences of strokes were collected. Amyloid β42, TDP43, NF-L, and KLK6 serum levels declined 2-3 days post-admission, yet recovered to admission baseline levels by 7 days. YKL-40 and NCAM-1 levels remained elevated over time, with clusters of differential responses identified among TREM-2, TDP43, and YKL40. Fetuin was elevated after the onset of COVID-19 while TREM-2 initially declined before significantly increasing over time. MIF serum level was increased 3-7 days after admission. Ferritin correlated with TDP-43 and KLK6. No treatment with remdesivir coincided with elevations in Amyloid-β40. A lack of convalescent plasma resulted in increased NCAM-1 and total tau, and steroidal treatments did not significantly affect any markers. A total of 11 incidences of stroke were registered up to six months after initial admission for COVID-19. Elevated D-dimer, platelet counts, IL-6, and leukopenia were observed. Variable MIF serum levels differentiated patients with CVA from those who did not have a stroke during the acute phase of COVID-19. This study demonstrated concomitant and opposite changes in neurodegenerative and neuroprotective markers persisting well into recovery.
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Affiliation(s)
- Krzysztof Laudanski
- The Department of Anesthesiology and Critical Care Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jihane Hajj
- School of Nursing, Widener University, Philadelphia, PA 19013, USA;
| | - Mariana Restrepo
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Kumal Siddiq
- College of Arts and Sciences, Drexel University, Philadelphia, PA 19104, USA;
| | - Tony Okeke
- School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, USA;
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA;
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7
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Simões-Pires EN, Ferreira ST, Linden R. Roles of glutamate receptors in a novel in vitro model of early, comorbid cerebrovascular, and Alzheimer's diseases. J Neurochem 2020; 156:539-552. [PMID: 32683713 DOI: 10.1111/jnc.15129] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 07/13/2020] [Accepted: 07/13/2020] [Indexed: 11/28/2022]
Abstract
Systemic multimorbidity is highly prevalent in the elderly and, remarkably, coexisting neuropathological markers of Alzheimer's (AD) and cerebrovascular (CVD) diseases are found at autopsy in most brains of patients clinically diagnosed as AD. Little is known on neurodegeneration peculiar to comorbidities, especially at early stages when pathogenesis may propagate at subclinical levels. We developed a novel in vitro model of comorbid CVD/AD in organotypic hippocampal cultures, by combining oxygen-glucose deprivation (OGD) and exposure to amyloid-Aβ oligomers (AβOs), both applied at levels subtoxic to neurons when used in isolation. We focused on synaptic proteins and the roles of glutamate receptors, which have been implicated in many basic and clinical approaches to either CVD or AD. Subtoxic insults by OGD and AβOs synergized to reduce levels of synaptophysin (SYP) and PSD-95 without cell death, while effects of antagonists of either metabotropic or ionotropic glutamate receptors were distinct from reports in models of isolated CVD or AD. In particular, modulation of glutamate receptors differentially impacted SYP and PSD-95, and antagonists of a single receptor subtype had distinct effects when either isolated or combined. Our findings highlight the complexity of CVD/AD comorbidity, help understand variable responses to glutamate receptor antagonists in patients diagnosed with AD and may contribute to future development of therapeutics based on investigation of the pattern of progressive comorbidity.
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Affiliation(s)
| | - Sergio T Ferreira
- Instituto de Biofísica Carlos Chagas Filho, UFRJ, Rio de Janeiro, Brazil.,Instituto de Bioquímica Médica Leopoldo de Meis, UFRJ, Rio de Janeiro, Brazil
| | - Rafael Linden
- Instituto de Biofísica Carlos Chagas Filho, UFRJ, Rio de Janeiro, Brazil
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8
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Yuan S, Li H, Xie J, Sun X. Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease. Int J Mol Sci 2019; 20:E5912. [PMID: 31775305 PMCID: PMC6928939 DOI: 10.3390/ijms20235912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/21/2019] [Accepted: 11/22/2019] [Indexed: 01/02/2023] Open
Abstract
The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.
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Affiliation(s)
| | | | | | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; (S.Y.)
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Mao Q, Fan L, Wang X, Lin X, Cao Y, Zheng C, Zhang Y, Zhang H, Garcia-Milian R, Kang L, Shi J, Yu T, Wang K, Zuo L, Li CSR, Guo X, Luo X. Transcriptome-wide piRNA profiling in human brains for aging genetic factors. JACOBS JOURNAL OF GENETICS 2019; 4:014. [PMID: 32149191 PMCID: PMC7059831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Piwi-interacting RNAs (piRNAs) represent a molecular feature shared by all nonaging biological systems, including the germline and somatic cancer stem cells, which display an indefinite renewal capacity and lifespan-stable genomic integrity and are potentially immortal. Here, we tested the hypothesis that piRNA is a critical genetic determinant of aging in humans. METHODS Expression of transcriptome-wide piRNAs (n=24k) was profiled in the human prefrontal cortex of 12 subjects (84.9±9.5, range 68-100, years of age) using microarray technology. We examined the correlation between these piRNAs' expression levels and age, adjusting for covariates including disease status. RESULTS A total of 9,453 piRNAs were detected in brain. Including seven intergenic and three intronic piRNAs, ten piRNAs were significantly associated with age after correction for multiple testing (|r|=0.9; 1.9×10-5≤p≤9.9×10-5). CONCLUSION We conclude that piRNAs might play a potential role in determining the years of survival of humans. The underlying mechanisms might involve the suppression of transposable elements (TEs) and expression regulation of aging-associated genes.
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Affiliation(s)
- Qiao Mao
- Department of Psychosomatic Medicine, People’s Hospital of Deyang City, Deyang, Sichuan 618000, China
| | - Longhua Fan
- Department of Vascular Surgery, Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai 201700, China
| | - Xiaoping Wang
- Department of Neurology, Shanghai Tongren Hospital, Shanghai Jiaotong University, Shanghai 200080, China
| | - Xiandong Lin
- Laboratory of Radiation Oncology and Radiobiology, Fujian Provincial Cancer Hospital, the Teaching Hospital of Fujian Medical University, Fuzhou, Fujian 350014, China
| | - Yuping Cao
- Department of Psychiatry, Second Xiangya Hospital, Central South University, Changsha 410012, China
| | | | - Yong Zhang
- Tianjin Mental Health Center, Tianjin 300222, China
| | - Huihao Zhang
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350001, China
| | - Rolando Garcia-Milian
- Curriculum & Research Support Department, Cushing/Whitney Medical Library, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of Tibet Autonomous Region, Xizang Minzu University School of Medicine, Xiangyang, Shaanxi 712082, China
| | - Jing Shi
- Biological Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Ting Yu
- Biological Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Kesheng Wang
- Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA
| | - Lingjun Zuo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Chiang-Shan R. Li
- Biological Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Xiaoyun Guo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA
- Shanghai Mental Health Center, Shanghai 200030, China
| | - Xingguang Luo
- Biological Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA
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10
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Watson CN, Belli A, Di Pietro V. Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease. Front Genet 2019; 10:364. [PMID: 31080456 PMCID: PMC6497742 DOI: 10.3389/fgene.2019.00364] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 04/05/2019] [Indexed: 12/11/2022] Open
Abstract
Neurodegenerative diseases (NDs) are becoming increasingly prevalent in the world, with an aging population. In the last few decades, due to the devastating nature of these diseases, the research of biomarkers has become crucial to enable adequate treatments and to monitor the progress of disease. Currently, gene mutations, CSF and blood protein markers together with the neuroimaging techniques are the most used diagnostic approaches. However, despite the efforts in the research, conflicting data still exist, highlighting the need to explore new classes of biomarkers, particularly at early stages. Small non-coding RNAs (MicroRNA, Small nuclear RNA, Small nucleolar RNA, tRNA derived small RNA and Piwi-interacting RNA) can be considered a "relatively" new class of molecule that have already proved to be differentially regulated in many NDs, hence they represent a new potential class of biomarkers to be explored. In addition, understanding their involvement in disease development could depict the underlying pathogenesis of particular NDs, so novel treatment methods that act earlier in disease progression can be developed. This review aims to describe the involvement of small non-coding RNAs as biomarkers of NDs and their potential role in future clinical applications.
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Affiliation(s)
- Callum N. Watson
- Neuroscience and Ophthalmology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- National Institute for Health Research Surgical Reconstruction and Microbiology Research Centre, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom
| | - Antonio Belli
- Neuroscience and Ophthalmology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- National Institute for Health Research Surgical Reconstruction and Microbiology Research Centre, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom
| | - Valentina Di Pietro
- Neuroscience and Ophthalmology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- National Institute for Health Research Surgical Reconstruction and Microbiology Research Centre, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, United States
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11
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Che D, Huang W, Fang Z, Li L, Wu H, Pi L, Zhou H, Xu Y, Fu L, Tan Y, Lu Z, Li Q, Gu X. The lncRNA CCAT2 rs6983267 G allele is associated with decreased susceptibility to recurrent miscarriage. J Cell Physiol 2019; 234:20577-20583. [PMID: 30982978 DOI: 10.1002/jcp.28661] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/25/2019] [Accepted: 01/28/2019] [Indexed: 12/13/2022]
Abstract
Genetics might play various roles in susceptibility to recurrent miscarriage, and previous studies suggest that some gene polymorphisms might be associated with abortion and breast cancer onset. Colon cancer-associated transcript 2 (CCAT2) is a novel long noncoding RNA (lncRNA) transcript that might be correlated with susceptibility to multiple cancers, including breast cancer. However, whether lncRNA CCAT2 polymorphisms are related to susceptibility to recurrent miscarriage is unclear. We genotyped two lncRNA CCAT2 polymorphisms (rs6983267 and rs3843549) in 248 patients with recurrent miscarriage and 392 controls through a TaqMan real-time polymerase chain reaction assay, and the strength of each association was evaluated via 95% confidence intervals (CIs) and odds ratios (ORs). Our results showed that the rs6983267 G allele in lncRNA CCAT2 was associated with decreased susceptibility to recurrent miscarriage (TG vs. TT: adjusted OR = 0.603; 95% CI = 0.420-0.866; p = 0.0062; GG/TG vs. TT: adjusted OR = 0.620; 95% CI = 0.441-0.873; p = 0.0061). The combined analysis of the two protective polymorphisms (rs3843549 AA and rs6983267 TG/GG) revealed that individuals with two unfavorable alleles exhibited a lower risk of recurrent miscarriage than those with no or only one unfavorable allele (adjusted OR = 0.531; 95% CI = 0.382-0.739). Moreover, the decreased risk associated with the two protective alleles was most obvious in women aged less than 35 years (OR = 0.551; 95% CI = 0.378-0.8803; p = 0.0019) and in women with two to three miscarriages (adjusted OR = 0.466; 95% CI = 0.318-0.683; p < 0.0001). In conclusion, our study indicates that the rs6983267G allele might contribute to a decreased risk of recurrent miscarriage in the South Chinese population.
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Affiliation(s)
- Di Che
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Wendong Huang
- Department of Pharmacy, Maoming People's Hospital, Maoming, China
| | - Zhenzhen Fang
- Program of Molecular Medicine, Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Li Li
- Department of Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Haiying Wu
- Department of Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Lei Pi
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Huazhong Zhou
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yufen Xu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - LanYan Fu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yaqian Tan
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Zhaoliang Lu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Qingfeng Li
- Department of Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Xiaoqiong Gu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Department of Blood Transfusion, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Department of Clinical Laboratory, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
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