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Balnis J, Madrid A, Drake LA, Vancavage R, Tiwari A, Patel VJ, Ramos RB, Schwarz JJ, Yucel R, Singer HA, Alisch RS, Jaitovich A. Blood DNA methylation in post-acute sequelae of COVID-19 (PASC): a prospective cohort study. EBioMedicine 2024; 106:105251. [PMID: 39024897 PMCID: PMC11286994 DOI: 10.1016/j.ebiom.2024.105251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND DNA methylation integrates environmental signals with transcriptional programs. COVID-19 infection induces changes in the host methylome. While post-acute sequelae of COVID-19 (PASC) is a long-term complication of acute illness, its association with DNA methylation is unknown. No universal blood marker of PASC, superseding single organ dysfunctions, has yet been identified. METHODS In this single centre prospective cohort study, PASC, post-COVID without PASC, and healthy participants were enrolled to investigate their symptoms association with peripheral blood DNA methylation data generated with state-of-the-art whole genome sequencing. PASC-induced quality-of-life deterioration was scored with a validated instrument, SF-36. Analyses were conducted to identify potential functional roles of differentially methylated loci, and machine learning algorithms were used to resolve PASC severity. FINDINGS 103 patients with PASC (22.3% male, 77.7% female), 15 patients with previous COVID-19 infection but no PASC (40.0% male, 60.0% female), and 27 healthy volunteers (48.1% male, 51.9% female) were enrolled. Whole genome methylation sequencing revealed 39 differentially methylated regions (DMRs) specific to PASC, each harbouring an average of 15 consecutive positions, that differentiate patients with PASC from the two control groups. Motif analyses of PASC-regulated DMRs identify binding domains for transcription factors regulating circadian rhythm and others. Some DMRs annotated to protein coding genes were associated with changes of RNA expression. Machine learning support vector algorithm and random forest hierarchical clustering reveal 28 unique differentially methylated positions (DMPs) in the genome discriminating patients with better and worse quality of life. INTERPRETATION Blood DNA methylation levels identify PASC, stratify PASC severity, and suggest that DNA motifs are targeted by circadian rhythm-regulating pathways in PASC. FUNDING This project has been funded by the following agencies: NIH-AI173035 (A. Jaitovich and R. Alisch); and NIH-AG066179 (R. Alisch).
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Affiliation(s)
- Joseph Balnis
- Division of Pulmonary and Critical Care Medicine, Albany Medical Center, Albany, NY, USA; Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA
| | - Andy Madrid
- Department of Neurological Surgery, University of Wisconsin, Madison, WI, USA
| | - Lisa A Drake
- Division of Pulmonary and Critical Care Medicine, Albany Medical Center, Albany, NY, USA; Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA
| | - Rachel Vancavage
- Division of Pulmonary and Critical Care Medicine, Albany Medical Center, Albany, NY, USA
| | - Anupama Tiwari
- Division of Pulmonary and Critical Care Medicine, Albany Medical Center, Albany, NY, USA
| | - Vraj J Patel
- Division of Pulmonary and Critical Care Medicine, Albany Medical Center, Albany, NY, USA
| | - Ramon Bossardi Ramos
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA
| | - John J Schwarz
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA
| | - Recai Yucel
- Department of Epidemiology and Biostatistics, Temple University, PA, USA
| | - Harold A Singer
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA
| | - Reid S Alisch
- Department of Neurological Surgery, University of Wisconsin, Madison, WI, USA
| | - Ariel Jaitovich
- Division of Pulmonary and Critical Care Medicine, Albany Medical Center, Albany, NY, USA; Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA.
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Yang S, Hu Y, Wang X, Deng M, Ma J, Hao Y, Ran Z, Luo T, Han G, Xiang X, Liu J, Shi H, Tan Y. Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing. J Cell Mol Med 2024; 28:e18296. [PMID: 38702954 PMCID: PMC11069052 DOI: 10.1111/jcmm.18296] [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: 11/04/2023] [Revised: 02/29/2024] [Accepted: 03/25/2024] [Indexed: 05/06/2024] Open
Abstract
We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.
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Affiliation(s)
- Sha Yang
- Department of NeurosurgeryThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
- Guizhou University Medical CollegeGuiyangChina
| | - Yunjia Hu
- Department of NeurosurgeryThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Xiang Wang
- Department of NeurosurgeryThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Mei Deng
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Jun Ma
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Yin Hao
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Zhongying Ran
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Tao Luo
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Guoqiang Han
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Xin Xiang
- Department of NeurosurgeryThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Jian Liu
- Department of NeurosurgeryThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
- Guizhou University Medical CollegeGuiyangChina
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
| | - Hui Shi
- Department of NeurosurgeryYongchuan Hospital affiliated to Chongqing Medical UniversityChongqingChina
| | - Ying Tan
- Department of NeurosurgeryGuizhou Provincial People's HospitalGuiyangChina
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Rubio K, Molina-Herrera A, Pérez-González A, Hernández-Galdámez HV, Piña-Vázquez C, Araujo-Ramos T, Singh I. EP300 as a Molecular Integrator of Fibrotic Transcriptional Programs. Int J Mol Sci 2023; 24:12302. [PMID: 37569677 PMCID: PMC10418647 DOI: 10.3390/ijms241512302] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
Fibrosis is a condition characterized by the excessive accumulation of extracellular matrix proteins in tissues, leading to organ dysfunction and failure. Recent studies have identified EP300, a histone acetyltransferase, as a crucial regulator of the epigenetic changes that contribute to fibrosis. In fact, EP300-mediated acetylation of histones alters global chromatin structure and gene expression, promoting the development and progression of fibrosis. Here, we review the role of EP300-mediated epigenetic regulation in multi-organ fibrosis and its potential as a therapeutic target. We discuss the preclinical evidence that suggests that EP300 inhibition can attenuate fibrosis-related molecular processes, including extracellular matrix deposition, inflammation, and epithelial-to-mesenchymal transition. We also highlight the contributions of small molecule inhibitors and gene therapy approaches targeting EP300 as novel therapies against fibrosis.
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Affiliation(s)
- Karla Rubio
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Instituto de Ciencias, Ecocampus Valsequillo, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico
- Laboratoire IMoPA, Université de Lorraine, CNRS, UMR 7365, F-54000 Nancy, France
| | - Alejandro Molina-Herrera
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Instituto de Ciencias, Ecocampus Valsequillo, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico
| | - Andrea Pérez-González
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Instituto de Ciencias, Ecocampus Valsequillo, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico
| | - Hury Viridiana Hernández-Galdámez
- Departamento de Biología Celular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Ciudad de México 07360, Mexico
| | - Carolina Piña-Vázquez
- Departamento de Biología Celular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Ciudad de México 07360, Mexico
| | - Tania Araujo-Ramos
- Emmy Noether Research Group Epigenetic Machineries and Cancer, Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Indrabahadur Singh
- Emmy Noether Research Group Epigenetic Machineries and Cancer, Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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Urday P, Gayen nee’ Betal S, Sequeira Gomes R, Al-Kouatly HB, Solarin K, Chan JSY, Li D, Rahman I, Addya S, Boelig RC, Aghai ZH. SARS-CoV-2 Covid-19 Infection During Pregnancy and Differential DNA Methylation in Human Cord Blood Cells From Term Neonates. Epigenet Insights 2023; 16:25168657231184665. [PMID: 37425024 PMCID: PMC10328022 DOI: 10.1177/25168657231184665] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/08/2023] [Indexed: 07/11/2023] Open
Abstract
Background The global pandemic of coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). About 18.4% of total Covid-19 cases were reported in children. Even though vertical transmission from mother to infant is likely to occur at a low rate, exposure to COVID-19 during fetal life may alter DNA methylation patterns with potential long-term effects. Objective To determine if COVID-19 infection during pregnancy alters the DNA methylation patterns in umbilical cord blood cells from term infants and to identify potential pathways and genes affected by exposure to COVID-19 infection. Methods Umbilical cord blood was collected from 8 infants exposed to COVID-19 during pregnancy and 8 control infants with no COVID-19 exposure. Genomic DNA was isolated from umbilical cord blood cells and genome-wide DNA methylation was performed using Illumina Methylation EPIC Array. Results 119 differentially methylated loci were identified at the FDR level of 0.20 (64 hypermethylated loci and 55 hypomethylated loci) in umbilical cord blood cells of COVID-19 exposed neonates compared to the control group. Important canonical pathways identified by Ingenuity Pathway Analysis (IPA) were related to stress response (corticotropin releasing hormone signaling, glucocorticoid receptor signaling, and oxytocin in brain signaling pathway), and cardiovascular disease and development (nitric oxide signaling in the cardiovascular system, apelin cardiomyocyte signaling pathways, factors promoting cardiogenesis, and renin-angiotensin signaling). The genes affected by the differential methylations were associated with cardiac, renal, hepatic, neurological diseases, developmental and immunological disorders. Conclusions COVID-19 induces differential DNA methylation in umbilical cord blood cells. The differentially methylated genes may contribute to hepatic, renal, cardiac, developmental and immunological disorders in offspring born to mothers with COVID-19 infection during pregnancy, and their developmental regulation.
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Affiliation(s)
- Pedro Urday
- Neonatology, Thomas Jefferson University/Nemours, Philadelphia, PA, USA
| | | | | | - Huda B Al-Kouatly
- Division of Maternal Fetal Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Kolawole Solarin
- Neonatology, Thomas Jefferson University/Nemours, Philadelphia, PA, USA
| | - Joanna SY Chan
- Department of Pathology and Genomic Medicine, Thomas Jefferson University, Philadelphia, PA, USA
| | - Dongmei Li
- Department of Clinical and Translational Research, University of Rochester Medical Center, Rochester, NY, USA
| | - Irfan Rahman
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Sankar Addya
- Laboratory of Cancer Genomics, Thomas Jefferson University, Philadelphia, PA, USA
| | - Rupsa C Boelig
- Division of Maternal Fetal Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Zubair H Aghai
- Neonatology, Thomas Jefferson University/Nemours, Philadelphia, PA, USA
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5
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Fang C, Ma Y. Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis. Int J Mol Sci 2023; 24:ijms24032591. [PMID: 36768914 PMCID: PMC9916586 DOI: 10.3390/ijms24032591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/21/2023] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
Severe coronavirus disease 2019 (COVID-19) has led to a rapid increase in death rates all over the world. Sepsis is a life-threatening disease associated with a dysregulated host immune response. It has been shown that COVID-19 shares many similarities with sepsis in many aspects. However, the molecular mechanisms underlying sepsis and COVID-19 are not well understood. The aim of this study was to identify common transcriptional signatures, regulators, and pathways between COVID-19 and sepsis, which may provide a new direction for the treatment of COVID-19 and sepsis. First, COVID-19 blood gene expression profile (GSE179850) data and sepsis blood expression profile (GSE134347) data were obtained from GEO. Then, we intersected the differentially expressed genes (DEG) from these two datasets to obtain common DEGs. Finally, the common DEGs were used for functional enrichment analysis, transcription factor and miRNA prediction, pathway analysis, and candidate drug analysis. A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets. Protein-protein interactions (PPIs) were constructed using the STRING database. Subsequently, hub genes were identified based on PPI networks. In addition, we performed GO functional analysis and KEGG pathway analysis of common DEGs, and found a common association between sepsis and COVID-19. Finally, we identified transcription factor-gene interaction, DEGs-miRNA co-regulatory networks, and protein-drug interaction, respectively. Through ROC analysis, we identified 10 central hub genes as potential biomarkers. In this study, we identified SARS-CoV-2 infection as a high risk factor for sepsis. Our study may provide a potential therapeutic direction for the treatment of COVID-19 patients suffering from sepsis.
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6
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Avila-Ponce de León U, Vazquez-Jimenez A, Cervera A, Resendis-González G, Neri-Rosario D, Resendis-Antonio O. Machine Learning and COVID-19: Lessons from SARS-CoV-2. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1412:311-335. [PMID: 37378775 DOI: 10.1007/978-3-031-28012-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.
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Affiliation(s)
- Ugo Avila-Ponce de León
- Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Aarón Vazquez-Jimenez
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Alejandra Cervera
- Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Galilea Resendis-González
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico.
- Coordinación de la Investigación Científica - Red de Apoyo a la Investigación - Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico.
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7
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Jahanimoghadam A, Abdolahzadeh H, Rad NK, Zahiri J. Discovering Common Pathogenic Mechanisms of COVID-19 and Parkinson Disease: An Integrated Bioinformatics Analysis. J Mol Neurosci 2022; 72:2326-2337. [PMID: 36301487 PMCID: PMC9607846 DOI: 10.1007/s12031-022-02068-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 09/13/2022] [Indexed: 12/14/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has emerged since December 2019 and was later characterized as a pandemic by WHO, imposing a major public health threat globally. Our study aimed to identify common signatures from different biological levels to enlighten the current unclear association between COVID-19 and Parkinson's disease (PD) as a number of possible links, and hypotheses were reported in the literature. We have analyzed transcriptome data from peripheral blood mononuclear cells (PBMCs) of both COVID-19 and PD patients, resulting in a total of 81 common differentially expressed genes (DEGs). The functional enrichment analysis of common DEGs are mostly involved in the complement system, type II interferon gamma (IFNG) signaling pathway, oxidative damage, microglia pathogen phagocytosis pathway, and GABAergic synapse. The protein-protein interaction network (PPIN) construction was carried out followed by hub detection, revealing 10 hub genes (MX1, IFI27, C1QC, C1QA, IFI6, NFIX, C1S, XAF1, IFI35, and ELANE). Some of the hub genes were associated with molecular mechanisms such as Lewy bodies-induced inflammation, microglia activation, and cytokine storm. We investigated regulatory elements of hub genes at transcription factor and miRNA levels. The major transcription factors regulating hub genes are SOX2, XAF1, RUNX1, MITF, and SPI1. We propose that these events may have important roles in the onset or progression of PD. To sum up, our analysis describes possible mechanisms linking COVID-19 and PD, elucidating some unknown clues in between.
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Affiliation(s)
- Aria Jahanimoghadam
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
- Biocenter, Julius-Maximilians-Universität Würzburg, Am Hubland, Würzburg, Germany
| | - Hadis Abdolahzadeh
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Niloofar Khoshdel Rad
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Javad Zahiri
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA.
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de León UAP, Resendis-Antonio O. Macrophage Boolean networks in the time of SARS-CoV-2. Front Immunol 2022; 13:997434. [DOI: 10.3389/fimmu.2022.997434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
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Single-Cell Gene Expression Analysis Revealed Immune Cell Signatures of Delta COVID-19. Cells 2022; 11:cells11192950. [PMID: 36230912 PMCID: PMC9563974 DOI: 10.3390/cells11192950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/04/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked with high mortality risk, has become dominant in many countries. Understanding the immune responses associated with COVID-19 lineages may therefore aid the development of therapeutic and diagnostic strategies. Multiple single-cell gene expression studies revealed innate and adaptive immunological factors and pathways correlated with COVID-19 severity. Additional investigations covering host–pathogen response characteristics for infection caused by different lineages are required. Here, we performed single-cell transcriptome profiling of blood mononuclear cells from the individuals with different severity of the COVID-19 and virus lineages to uncover variant specific molecular factors associated with immunity. We identified significant changes in lymphoid and myeloid cells. Our study highlights that an abundant population of monocytes with specific gene expression signatures accompanies Delta lineage of SARS-CoV-2 and contributes to COVID-19 pathogenesis inferring immune components for targeted therapy.
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Liu W, Jia J, Dai Y, Chen W, Pei G, Yan Q, Zhao Z. Delineating COVID-19 immunological features using single-cell RNA sequencing. Innovation (N Y) 2022; 3:100289. [PMID: 35879967 PMCID: PMC9299978 DOI: 10.1016/j.xinn.2022.100289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/16/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance.
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Affiliation(s)
- Wendao Liu
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Johnathan Jia
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Wenhao Chen
- Immunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute and Institute for Academic Medicine, Houston Methodist Hospital, Houston, TX 77030, USA
- Department of Surgery, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Qiheng Yan
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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11
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Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis. Cancers (Basel) 2021; 13:cancers13236024. [PMID: 34885134 PMCID: PMC8656778 DOI: 10.3390/cancers13236024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 11/23/2022] Open
Abstract
Simple Summary Fibrosis is a major player and contributor in the tumor microenvironment. Profibrotic changes precede the early development and establishment of a variety of human diseases, such as fibrosis and cancer. Being able to measure such early signals at the single cell level is critically useful for identifying new mechanisms and potential drug targets for a wide range of diseases. This study was designed to computationally identify profibrotic cell populations using single-cell transcriptomic data and to identify gene signatures that could predict cancer prognosis. Abstract Fibrosis is a major cause of mortality. Key profibrotic mechanisms are common pathways involved in tumorigenesis. Characterizing the profibrotic phenotype will help reveal the underlying mechanisms of early development and progression of a variety of human diseases, such as fibrosis and cancer. Fibroblasts have been center stage in response to various stimuli, such as viral infections. However, a comprehensive catalog of cell types involved in this process is currently lacking. Here, we deployed single-cell transcriptomic data across multi-organ systems (i.e., heart, kidney, liver, and lung) to identify novel profibrotic cell populations based on ECM pathway activity at single-cell resolution. In addition to fibroblasts, we also reported that epithelial, endothelial, myeloid, natural killer T, and secretory cells, as well as proximal convoluted tubule cells of the nephron, were significantly actively involved. Cell-type-specific gene signatures were enriched in viral infection pathways, enhanced glycolysis, and carcinogenesis, among others; they were validated using independent datasets in this study. By projecting the signatures into bulk TCGA tumor samples, we could predict prognosis in the patients using profibrotic scores. Our profibrotic cellular phenotype is useful for identifying new mechanisms and potential drug targets at the cell-type level for a wide range of diseases involved in ECM pathway activation.
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Saha C, Laha S, Chatterjee R, Bhattacharyya NP. Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis. Noncoding RNA 2021; 7:74. [PMID: 34940755 PMCID: PMC8708613 DOI: 10.3390/ncrna7040074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/14/2022] Open
Abstract
Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.
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Affiliation(s)
- Chinmay Saha
- Department of Genome Science, School of Interdisciplinary Studies, University of Kalyani, Nadia 741235, India;
| | - Sayantan Laha
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India; (S.L.); (R.C.)
| | - Raghunath Chatterjee
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India; (S.L.); (R.C.)
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and Metabolism, Institute of Post Graduate Medical Education & Research and Seth Sukhlal Karnani Memorial Hospital, Kolkata 700020, India
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Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation. Hum Genet 2021; 140:1313-1328. [PMID: 34155559 PMCID: PMC8216591 DOI: 10.1007/s00439-021-02305-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/13/2021] [Indexed: 12/12/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8+ T (TRM) cells, we found a 2.24-fold decrease of cell proportion among CD8+ T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the TRM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis.
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