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유전자 메커니즘 데이터셋

유전자 메커니즘 데이터셋

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● 데이터 상품명 유전자 메커니즘 데이터셋 ● 데이터 상품 부제 COVID-19의 유전자 메커니즘 관련 의학논문 데이터셋 ● 데이터 상품 요약 인용지수 상위 의학저널에 게재된 COVID-19의 유전자 메커니즘에 관련된 의학 학술논문 데이터 ● 키워드 데이터셋 상품 정보 ■ 상품 설명 및 특징 - 의학논문의 저자 키워드, 초록, 제목 등에서 추출한 키워드에 키워드 속성, 대역어, 키워드 출처, 논문 DOI, 저자, 발행연월, 논문URL, 저널명, 저널 ISSN, 발행기관, Impact Factor의 정보를 매핑한 데이터 ■ 컬럼(속성) 정보 - 키워드명: 키워드 - 키워드속성: 키워드 성격 - 키워드출처: 키워드 출현 위치 - 키워드대역어: 자체 보유 의학사전 및 구글번역기에 의한 대역어 - 논문DOI명: 키워드 출현 논문의 DOI - 논문제목: 키워드 출현 논문의 제목 - 논문저자: 키워드 출현 논문의 저자 - 논문발행연월: 키워드 출현 논문의 발행연월 - 논문초록: 키워드 출현 논문의 초록 - 논문URL: 키워드 출현 논문의 URL - 저널ISSN명: 키워드 출현 논문의 저널 ISSN - 저널제목: 키워드 출현 논문의 저널명 - 저널발행기관명: 키워드 출현 논문의 발행기관명 ● 연관 데이터셋 상품 정보 ■ 상품 설명 및 특징 - 특정 키워드의 연관 키워드를 co-occurrence기법과 Latent Semantic Algorithm에 의해 추출한 데이터셋 ■ 컬럼(속성) 정보 - 키워드명: 키워드 - 키워드속성: 키워드의 성격 - 연관키워드명: 키워드와 연관된 키워드 - 연관키워드 속성: 연관키워드의 속성 - 연관중요도: 동의여 여부와 동시출현수를 지표로 하는 중요도 ● 기간 및 범위 - 2014년 5월 ~ 2022년 12월 ● 활용 예제 - 특정 주제에 해당되는 키워드의 속성별, 저널별, 연도별, 저자별 추이 - 키워드의 연관어를 속성별, 저널별, 연도별, 저자별 분석

샘플정보
ID
카테고리ID
카테고리명
키워드명
키워드속성
대체키워드명
키워드출처
키워드대역어
논문ID
논문DOI명
논문제목
논문저자
논문발행연월
논문유형
논문출처
논문초록
논문URL
저널ID
저널ISSN명
저널제목
저널발행기관명
저널ImpactFactor명
224903 26 유전자 메커니즘 equity Term equity title 형평 37410 10.1038/s41576-020-0260-x How digital tools can advance quality and equity in genomic medicine 202006 Comments & Opinion Nature https://doi.org/10.1038/s41576-020-0260-x 1757 1471-0056 Nature reviews. Genetics London, UK : Nature Pub. Group 23.027
225410 26 유전자 메커니즘 circulating SARS-CoV-2 variant Term circulating sars-cov-2 variant title None 37434 10.1038/s41587-022-01388-x Wastewater is a robust proxy for monitoring circulating SARS-CoV-2 variants 202207 News & Views Nature https://doi.org/10.1038/s41587-022-01388-x 941 1087-0156 Nature biotechnology New York Ny : Nature America Publishing.
225412 26 유전자 메커니즘 wastewater Term wastewater title 폐수 37434 10.1038/s41587-022-01388-x Wastewater is a robust proxy for monitoring circulating SARS-CoV-2 variants 202207 News & Views Nature https://doi.org/10.1038/s41587-022-01388-x 941 1087-0156 Nature biotechnology New York Ny : Nature America Publishing.
226302 26 유전자 메커니즘 DBS sample Term DBS sample abstract None 37476 10.1038/s41370-022-00460-7 A state-of-the-science review and guide for measuring environmental exposure biomarkers in dried blood spots 202208 Reviews Nature Abstract!!Background!!Dried blood spot (DBS) sampling is a simple, cost-effective, and minimally invasive alternative to venipuncture for measuring exposure biomarkers in public health and epidemiological research. DBS sampling provides advantages in field-based studies conducted in low-resource settings and in studies involving infants and children. In addition, DBS samples are routinely collected from newborns after birth (i.e., newborn dried blood spots, NDBS), with many states in the United States permitting access to archived NDBS samples for research purposes.!!Objectives!!We review the state of the science for analyzing exposure biomarkers in DBS samples, both archived and newly collected, and provide guidance on sample collection, storage, and blood volume requirements associated with individual DBS assays. We discuss recent progress regarding analytical methods, analytical sensitivity, and specificity, sample volume requirements, contamination considerations, estimating extracted blood volumes, assessing stability and analyte recovery, and hematocrit effects.!!Methods!!A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted in March 2022. DBS method development and application studies were divided into three main chemical classes: environmental tobacco smoke, trace elements (including lead, mercury, cadmium, and arsenic), and industrial chemicals (including endocrine-disrupting chemicals and persistent organic pollutants). DBS method development and validation studies were scored on key quality-control and performance parameters by two members of the review team.!!Results!!Our search identified 47 published reports related to measuring environmental exposure biomarkers in human DBS samples. A total of 28 reports (37 total studies) were on methods development and validation and 19 reports were primarily the application of previously developed DBS assays. High-performing DBS methods have been developed, validated, and applied for detecting environmental exposures to tobacco smoke, trace elements, and several important endocrine-disrupting chemicals and persistent organic pollutants. Additional work is needed for measuring cadmium, arsenic, inorganic mercury, and bisphenol A in DBS and NDBS samples.!!Significance!!We present an inventory and critical review of available assays for measuring environmental exposure biomarkers in DBS and NDBS samples to help facilitate this sampling medium as an emerging tool for public health (e.g., screening programs, temporal biomonitoring) and environmental epidemiology (e.g., field-based studies). https://doi.org/10.1038/s41370-022-00460-7 2281 1559-0631 Journal of Exposure Science & Environmental Epidemiology New York, NY : Nature Pub. Group
226319 26 유전자 메커니즘 parameter Term parameter abstract None 37476 10.1038/s41370-022-00460-7 A state-of-the-science review and guide for measuring environmental exposure biomarkers in dried blood spots 202208 Reviews Nature Abstract!!Background!!Dried blood spot (DBS) sampling is a simple, cost-effective, and minimally invasive alternative to venipuncture for measuring exposure biomarkers in public health and epidemiological research. DBS sampling provides advantages in field-based studies conducted in low-resource settings and in studies involving infants and children. In addition, DBS samples are routinely collected from newborns after birth (i.e., newborn dried blood spots, NDBS), with many states in the United States permitting access to archived NDBS samples for research purposes.!!Objectives!!We review the state of the science for analyzing exposure biomarkers in DBS samples, both archived and newly collected, and provide guidance on sample collection, storage, and blood volume requirements associated with individual DBS assays. We discuss recent progress regarding analytical methods, analytical sensitivity, and specificity, sample volume requirements, contamination considerations, estimating extracted blood volumes, assessing stability and analyte recovery, and hematocrit effects.!!Methods!!A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted in March 2022. DBS method development and application studies were divided into three main chemical classes: environmental tobacco smoke, trace elements (including lead, mercury, cadmium, and arsenic), and industrial chemicals (including endocrine-disrupting chemicals and persistent organic pollutants). DBS method development and validation studies were scored on key quality-control and performance parameters by two members of the review team.!!Results!!Our search identified 47 published reports related to measuring environmental exposure biomarkers in human DBS samples. A total of 28 reports (37 total studies) were on methods development and validation and 19 reports were primarily the application of previously developed DBS assays. High-performing DBS methods have been developed, validated, and applied for detecting environmental exposures to tobacco smoke, trace elements, and several important endocrine-disrupting chemicals and persistent organic pollutants. Additional work is needed for measuring cadmium, arsenic, inorganic mercury, and bisphenol A in DBS and NDBS samples.!!Significance!!We present an inventory and critical review of available assays for measuring environmental exposure biomarkers in DBS and NDBS samples to help facilitate this sampling medium as an emerging tool for public health (e.g., screening programs, temporal biomonitoring) and environmental epidemiology (e.g., field-based studies). https://doi.org/10.1038/s41370-022-00460-7 2281 1559-0631 Journal of Exposure Science & Environmental Epidemiology New York, NY : Nature Pub. Group
226320 26 유전자 메커니즘 provide Action provide abstract None 37476 10.1038/s41370-022-00460-7 A state-of-the-science review and guide for measuring environmental exposure biomarkers in dried blood spots 202208 Reviews Nature Abstract!!Background!!Dried blood spot (DBS) sampling is a simple, cost-effective, and minimally invasive alternative to venipuncture for measuring exposure biomarkers in public health and epidemiological research. DBS sampling provides advantages in field-based studies conducted in low-resource settings and in studies involving infants and children. In addition, DBS samples are routinely collected from newborns after birth (i.e., newborn dried blood spots, NDBS), with many states in the United States permitting access to archived NDBS samples for research purposes.!!Objectives!!We review the state of the science for analyzing exposure biomarkers in DBS samples, both archived and newly collected, and provide guidance on sample collection, storage, and blood volume requirements associated with individual DBS assays. We discuss recent progress regarding analytical methods, analytical sensitivity, and specificity, sample volume requirements, contamination considerations, estimating extracted blood volumes, assessing stability and analyte recovery, and hematocrit effects.!!Methods!!A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted in March 2022. DBS method development and application studies were divided into three main chemical classes: environmental tobacco smoke, trace elements (including lead, mercury, cadmium, and arsenic), and industrial chemicals (including endocrine-disrupting chemicals and persistent organic pollutants). DBS method development and validation studies were scored on key quality-control and performance parameters by two members of the review team.!!Results!!Our search identified 47 published reports related to measuring environmental exposure biomarkers in human DBS samples. A total of 28 reports (37 total studies) were on methods development and validation and 19 reports were primarily the application of previously developed DBS assays. High-performing DBS methods have been developed, validated, and applied for detecting environmental exposures to tobacco smoke, trace elements, and several important endocrine-disrupting chemicals and persistent organic pollutants. Additional work is needed for measuring cadmium, arsenic, inorganic mercury, and bisphenol A in DBS and NDBS samples.!!Significance!!We present an inventory and critical review of available assays for measuring environmental exposure biomarkers in DBS and NDBS samples to help facilitate this sampling medium as an emerging tool for public health (e.g., screening programs, temporal biomonitoring) and environmental epidemiology (e.g., field-based studies). https://doi.org/10.1038/s41370-022-00460-7 2281 1559-0631 Journal of Exposure Science & Environmental Epidemiology New York, NY : Nature Pub. Group
226158 26 유전자 메커니즘 behavioural Term behavioural title 행동 적 37466 10.1038/s41562-020-00990-w Use caution when applying behavioural science to policy 202010 Comments & Opinion Nature https://doi.org/10.1038/s41562-020-00990-w 1738 2397-3374 Nature human behaviour [London] : Springer Nature Publishing
226243 26 유전자 메커니즘 Basic science Term basic science title 기초 과학 37474 10.1038/s41556-021-00803-w It’s time to incorporate diversity into our basic science and disease models 202111 Comments & Opinion Nature https://doi.org/10.1038/s41556-021-00803-w 1732 1465-7392 Nature cell biology London : Macmillan Magazines Ltd.
226519 26 유전자 메커니즘 European Term european title abnormality 37487 10.1038/s41431-020-00741-5 Abstracts from the 53rd European Society of Human Genetics (ESHG) Conference: e-Posters 202012 Article Nature https://doi.org/10.1038/s41431-020-00741-5 94 1018-4813 European Journal of Human Genetics London : Nature Publishing Group. 1.713
226521 26 유전자 메커니즘 Society Term society title None 37487 10.1038/s41431-020-00741-5 Abstracts from the 53rd European Society of Human Genetics (ESHG) Conference: e-Posters 202012 Article Nature https://doi.org/10.1038/s41431-020-00741-5 94 1018-4813 European Journal of Human Genetics London : Nature Publishing Group. 1.713
227166 26 유전자 메커니즘 Support Term support title None 37519 10.1038/s41593-022-01039-z Openness about animal research increases public support 202203 Comments & Opinion Nature https://doi.org/10.1038/s41593-022-01039-z 2267 1097-6256 Nature Neuroscience New York, NY : Nature Publishing Group.
231548 26 유전자 메커니즘 viral infections Symptom viral infection abstract 바이러스 감염 78284 10.1128/mbio.02543-22 The Integral Membrane Protein ZMPSTE24 Protects Cells from SARS-CoV-2 Spike-Mediated Pseudovirus Infection and Syncytia Formation Khurts Shilagardi@@@Eric D Spear@@@Rachy Abraham@@@Diane E Griffin@@@Susan Michaelis 202210 Article PMC {{{ Abstract }}} !! COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a devastating impact on global public health, emphasizing the importance of understanding innate immune mechanisms and cellular restriction factors that cells can harness to fight viral infections. The multimembrane-spanning zinc metalloprotease ZMPSTE24 is one such restriction factor. ZMPSTE24 has a well-characterized proteolytic role in the maturation of prelamin A, precursor of the nuclear scaffold protein lamin A. An apparently unrelated role for ZMPSTE24 in viral defense involves its interaction with the interferon-inducible membrane proteins (IFITMs), which block virus-host cell fusion by rigidifying cellular membranes and thereby prevent viral infection. ZMPSTE24, like the IFITMs, defends cells against a broad spectrum of enveloped viruses. However, its ability to protect against coronaviruses has never been examined. Here, we show that overexpression of ZMPSTE24 reduces the efficiency of cellular infection by SARS-CoV-2 Spike-pseudotyped lentivirus and that genetic knockout or small interfering RNA-mediated knockdown of endogenous ZMPSTE24 enhances infectivity. We further demonstrate a protective role for ZMPSTE24 in a Spike-ACE2-dependent cell-cell fusion assay. In both assays, a catalytic dead version of ZMPSTE24 is equally as protective as the wild-type protein, indicating that ZMPSTE24's proteolytic activity is not required for defense against SARS-CoV-2. Finally, we demonstrate by plaque assays that Zmpste24 -/- mouse cells show enhanced infection by a genuine coronavirus, mouse hepatitis virus (MHV). This study extends the range of viral protection afforded by ZMPSTE24 to include coronaviruses and suggests that targeting ZMPSTE24's mechanism of viral defense could have therapeutic benefit. IMPORTANCE The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has underscored the importance of understanding intrinsic cellular components that can be harnessed as the cell's first line of defense to fight against viral infection. Our paper focuses on one such protein, the integral membrane protease ZMPSTE24, which interacts with interferon-inducible transmembrane proteins (IFITMs). IFITMs interfere with virus entry by inhibiting fusion between viral and host cell membranes, and ZMPSTE24 appears to contribute to this inhibitory activity. ZMPSTE24 has been shown to defend cells against several, but not all, enveloped viruses. In this study, we extend ZMPSTE24's reach to include coronaviruses, by showing that ZMPSTE24 protects cells from SARS-CoV-2 pseudovirus infection, Spike protein-mediated cell-cell fusion, and infection by the mouse coronavirus MHV. This work lays the groundwork for further studies to decipher the mechanistic role of ZMPSTE24 in blocking the entry of SARS-CoV-2 and other viruses into cells. !!{{ Keywords: }} COVID-19; SARS-CoV-2; ZMPSTE24; coronavirus; innate immune response; prelamin A; proteases; restriction factor. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601121/ 160 2161-2129 mBio Washington, D.C. : American Society for Microbiology
227159 26 유전자 메커니즘 diagnostic Term diagnostic title 특징적인 37518 10.1038/s41587-021-00845-3 Could mutations of SARS-CoV-2 suppress diagnostic detection? 202102 Correspondence Nature https://doi.org/10.1038/s41587-021-00845-3 941 1087-0156 Nature biotechnology New York Ny : Nature America Publishing.
227306 26 유전자 메커니즘 catch Term catch title None 37529 10.1038/s41580-022-00470-1 Transposons: catch them if you can 202203 Article Nature https://doi.org/10.1038/s41580-022-00470-1 2798 1471-0072 Nature Reviews Molecular Cell Biology London, UK : Nature Pub. Group
228433 26 유전자 메커니즘 spike glycoprotein Protein spike glycoprotein abstract 스파이크 당단백질 77664 10.3389/fcimb.2022.990875 Engineering recombinantly expressed lectin-based antiviral agents Irene Maier 202209 Article PMC {{{ Abstract }}} !! Cyanovirin-N (CV-N), a lectin from Nostoc ellipsosporum was found an infusion inhibitory protein for human immunodeficiency virus (HIV)-1. A tandem-repeat of the engineered domain-swapped dimer bound specific sites at hemagglutinin (HA), Ebola and HIV spike glycoproteins as well as dimannosylated HA peptide, N-acetyl-D-glucosamine and high-mannose containing oligosaccharides. Among these, CV-N bound the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein at a dissociation constant (K D ) of 18.6 ?M (and K D =260 ?M to RBD), which was low-affinity carbohydrate-binding as compared with the recognition of the other viral spikes. Binding of dimannosylated peptide to homo-dimeric CVN2 and variants of CVN2 that were pairing Glu-Arg residues sterically located close to its high-affinity carbohydrate binding sites, was measured using surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC). Binding affinity increased with polar interactions, when the mutated residues were used to substitute a single, or two disulfide bonds, in CVN2. Site-specific N-linked glycans on spikes were mediating the infection with influenza virus by broadly neutralizing antibodies to HA and lectin binding to HA was further investigated via modes of saturation transfer difference (STD)-NMR. Our findings showed that stoichiometry and the lectin's binding affinity were revealed by an interaction of CVN2 with dimannose units and either the high- or low-affinity binding site. To understand how these binding mechanisms add to viral membrane fusion we compare our tested HA-derived peptides in affinity with SARS-CoV-2 glycoprotein and review lectins and their mechanisms of binding to enveloped viruses for a potential use to simulate neutralization ability. !!{{ Keywords: }} SARS-CoV-2; carbohydrate-binding agent; cyanovirin-N; lectin; virus. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539805/ 206 2235-2988 Frontiers in Cellular and Infection Microbiology Lausanne : Frontiers Media SA.
228416 26 유전자 메커니즘 ITC Term ITC abstract None 77664 10.3389/fcimb.2022.990875 Engineering recombinantly expressed lectin-based antiviral agents Irene Maier 202209 Article PMC {{{ Abstract }}} !! Cyanovirin-N (CV-N), a lectin from Nostoc ellipsosporum was found an infusion inhibitory protein for human immunodeficiency virus (HIV)-1. A tandem-repeat of the engineered domain-swapped dimer bound specific sites at hemagglutinin (HA), Ebola and HIV spike glycoproteins as well as dimannosylated HA peptide, N-acetyl-D-glucosamine and high-mannose containing oligosaccharides. Among these, CV-N bound the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein at a dissociation constant (K D ) of 18.6 ?M (and K D =260 ?M to RBD), which was low-affinity carbohydrate-binding as compared with the recognition of the other viral spikes. Binding of dimannosylated peptide to homo-dimeric CVN2 and variants of CVN2 that were pairing Glu-Arg residues sterically located close to its high-affinity carbohydrate binding sites, was measured using surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC). Binding affinity increased with polar interactions, when the mutated residues were used to substitute a single, or two disulfide bonds, in CVN2. Site-specific N-linked glycans on spikes were mediating the infection with influenza virus by broadly neutralizing antibodies to HA and lectin binding to HA was further investigated via modes of saturation transfer difference (STD)-NMR. Our findings showed that stoichiometry and the lectin's binding affinity were revealed by an interaction of CVN2 with dimannose units and either the high- or low-affinity binding site. To understand how these binding mechanisms add to viral membrane fusion we compare our tested HA-derived peptides in affinity with SARS-CoV-2 glycoprotein and review lectins and their mechanisms of binding to enveloped viruses for a potential use to simulate neutralization ability. !!{{ Keywords: }} SARS-CoV-2; carbohydrate-binding agent; cyanovirin-N; lectin; virus. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539805/ 206 2235-2988 Frontiers in Cellular and Infection Microbiology Lausanne : Frontiers Media SA.
230632 26 유전자 메커니즘 similarity Term similarity abstract 유사증 78027 10.3389/fimmu.2022.975848 Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach Lu Lu@@@Le-Ping Liu@@@Rong Gui@@@Hang Dong@@@Yan-Rong Su@@@Xiong-Hui Zhou@@@Feng-Xia Liu 202208 Article PMC {{{ Abstract }}} !! Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis. !!{{ Keywords: }} COVID-19; differentially expressed gene (DEG); drug molecule; functional enrichment; gene ontology; hub gene; protein?protein interaction (PPI); sepsis. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471316/ 109 1664-3224 Frontiers in Immunology [Lausanne : Frontiers Research Foundation].
230750 26 유전자 메커니즘 Compound Term compound abstract None 78099 10.1016/j.talanta.2022.123824 An integrated metabolomic and proteomic approach for the identification of covalent inhibitors of the main protease (Mpro) of SARS-COV-2 from crude natural extracts Giovanna Baron@@@Sofia Borella@@@Larissa Della Vedova@@@Serena Vittorio@@@Giulio Vistoli@@@Marina Carini@@@Giancarlo Aldini@@@Alessandra Altomare 202301 Article PMC {{{ Abstract }}} !! M pro represents one of the most promising drug targets for SARS-Cov-2, as it plays a crucial role in the maturation of viral polyproteins into functional proteins. HTS methods are currently used to screen M pro inhibitors, and rely on searching chemical databases and compound libraries, meaning that they only consider previously structurally clarified and isolated molecules. A great advancement in the hit identification strategy would be to set-up an approach aimed at exploring un-deconvoluted mixtures of compounds such as plant extracts. Hence, the aim of the present study is to set-up an analytical platform able to fish-out bioactive molecules from complex natural matrices even where there is no knowledge on the constituents. The proposed approach begins with a metabolomic step aimed at annotating the MW of the matrix constituents. A further metabolomic step is based on identifying those natural electrophilic compounds able to form a Michael adduct with thiols, a peculiar chemical feature of many M pro inhibitors that covalently bind the catalytic Cys145 in the active site, thus stabilizing the complex. A final step consists of incubating recombinant M pro with natural extracts and identifying compounds adducted to the residues within the M pro active site by bottom-up proteomic analysis (nano-LC-HRMS). Data analysis is based on two complementary strategies: (i) a targeted search applied by setting the adducted moieties identified as Michael acceptors of Cys as variable modifications; (ii) an untargeted approach aimed at identifying the whole range of adducted peptides containing Cys145 on the basis of the characteristic b and y fragment ions independent of the adduct. The method was set-up and then successfully tested to fish-out bioactive compounds from the crude extract of Scutellaria baicalensis, a Chinese plant containing the catechol-like flavonoid baicalin and its corresponding aglycone baicalein which are well-established inhibitors of M pro . Molecular dynamics (MD) simulations were carried out in order to explore the binding mode of baicalin and baicalein, within the SARS-CoV-2 M pro active site, allowing a better understanding of the role of the nucleophilic residues (i.e. His41, Cys145, His163 and His164) in the protein-ligand recognition process. !!{{ Keywords: }} Covalent binder; M(pro); Mass spectrometry; Metabolomics; Proteomics; SARS-CoV-2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371774/ 971 0039-9140 Talanta Amsterdam : Elsevier.
230752 26 유전자 메커니즘 Cys145 Term cys145 abstract None 78099 10.1016/j.talanta.2022.123824 An integrated metabolomic and proteomic approach for the identification of covalent inhibitors of the main protease (Mpro) of SARS-COV-2 from crude natural extracts Giovanna Baron@@@Sofia Borella@@@Larissa Della Vedova@@@Serena Vittorio@@@Giulio Vistoli@@@Marina Carini@@@Giancarlo Aldini@@@Alessandra Altomare 202301 Article PMC {{{ Abstract }}} !! M pro represents one of the most promising drug targets for SARS-Cov-2, as it plays a crucial role in the maturation of viral polyproteins into functional proteins. HTS methods are currently used to screen M pro inhibitors, and rely on searching chemical databases and compound libraries, meaning that they only consider previously structurally clarified and isolated molecules. A great advancement in the hit identification strategy would be to set-up an approach aimed at exploring un-deconvoluted mixtures of compounds such as plant extracts. Hence, the aim of the present study is to set-up an analytical platform able to fish-out bioactive molecules from complex natural matrices even where there is no knowledge on the constituents. The proposed approach begins with a metabolomic step aimed at annotating the MW of the matrix constituents. A further metabolomic step is based on identifying those natural electrophilic compounds able to form a Michael adduct with thiols, a peculiar chemical feature of many M pro inhibitors that covalently bind the catalytic Cys145 in the active site, thus stabilizing the complex. A final step consists of incubating recombinant M pro with natural extracts and identifying compounds adducted to the residues within the M pro active site by bottom-up proteomic analysis (nano-LC-HRMS). Data analysis is based on two complementary strategies: (i) a targeted search applied by setting the adducted moieties identified as Michael acceptors of Cys as variable modifications; (ii) an untargeted approach aimed at identifying the whole range of adducted peptides containing Cys145 on the basis of the characteristic b and y fragment ions independent of the adduct. The method was set-up and then successfully tested to fish-out bioactive compounds from the crude extract of Scutellaria baicalensis, a Chinese plant containing the catechol-like flavonoid baicalin and its corresponding aglycone baicalein which are well-established inhibitors of M pro . Molecular dynamics (MD) simulations were carried out in order to explore the binding mode of baicalin and baicalein, within the SARS-CoV-2 M pro active site, allowing a better understanding of the role of the nucleophilic residues (i.e. His41, Cys145, His163 and His164) in the protein-ligand recognition process. !!{{ Keywords: }} Covalent binder; M(pro); Mass spectrometry; Metabolomics; Proteomics; SARS-CoV-2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371774/ 971 0039-9140 Talanta Amsterdam : Elsevier.
207529 26 유전자 메커니즘 pandemic Term pandemic author 범유행_전염병 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
231527 26 유전자 메커니즘 prelamin A Term prelamin A author None 78284 10.1128/mbio.02543-22 The Integral Membrane Protein ZMPSTE24 Protects Cells from SARS-CoV-2 Spike-Mediated Pseudovirus Infection and Syncytia Formation Khurts Shilagardi@@@Eric D Spear@@@Rachy Abraham@@@Diane E Griffin@@@Susan Michaelis 202210 Article PMC {{{ Abstract }}} !! COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a devastating impact on global public health, emphasizing the importance of understanding innate immune mechanisms and cellular restriction factors that cells can harness to fight viral infections. The multimembrane-spanning zinc metalloprotease ZMPSTE24 is one such restriction factor. ZMPSTE24 has a well-characterized proteolytic role in the maturation of prelamin A, precursor of the nuclear scaffold protein lamin A. An apparently unrelated role for ZMPSTE24 in viral defense involves its interaction with the interferon-inducible membrane proteins (IFITMs), which block virus-host cell fusion by rigidifying cellular membranes and thereby prevent viral infection. ZMPSTE24, like the IFITMs, defends cells against a broad spectrum of enveloped viruses. However, its ability to protect against coronaviruses has never been examined. Here, we show that overexpression of ZMPSTE24 reduces the efficiency of cellular infection by SARS-CoV-2 Spike-pseudotyped lentivirus and that genetic knockout or small interfering RNA-mediated knockdown of endogenous ZMPSTE24 enhances infectivity. We further demonstrate a protective role for ZMPSTE24 in a Spike-ACE2-dependent cell-cell fusion assay. In both assays, a catalytic dead version of ZMPSTE24 is equally as protective as the wild-type protein, indicating that ZMPSTE24's proteolytic activity is not required for defense against SARS-CoV-2. Finally, we demonstrate by plaque assays that Zmpste24 -/- mouse cells show enhanced infection by a genuine coronavirus, mouse hepatitis virus (MHV). This study extends the range of viral protection afforded by ZMPSTE24 to include coronaviruses and suggests that targeting ZMPSTE24's mechanism of viral defense could have therapeutic benefit. IMPORTANCE The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has underscored the importance of understanding intrinsic cellular components that can be harnessed as the cell's first line of defense to fight against viral infection. Our paper focuses on one such protein, the integral membrane protease ZMPSTE24, which interacts with interferon-inducible transmembrane proteins (IFITMs). IFITMs interfere with virus entry by inhibiting fusion between viral and host cell membranes, and ZMPSTE24 appears to contribute to this inhibitory activity. ZMPSTE24 has been shown to defend cells against several, but not all, enveloped viruses. In this study, we extend ZMPSTE24's reach to include coronaviruses, by showing that ZMPSTE24 protects cells from SARS-CoV-2 pseudovirus infection, Spike protein-mediated cell-cell fusion, and infection by the mouse coronavirus MHV. This work lays the groundwork for further studies to decipher the mechanistic role of ZMPSTE24 in blocking the entry of SARS-CoV-2 and other viruses into cells. !!{{ Keywords: }} COVID-19; SARS-CoV-2; ZMPSTE24; coronavirus; innate immune response; prelamin A; proteases; restriction factor. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601121/ 160 2161-2129 mBio Washington, D.C. : American Society for Microbiology
232533 26 유전자 메커니즘 affecting Action affecting abstract None 78748 10.3390/v14102197 Association of Midgut Bacteria and Their Metabolic Pathways with Zika Infection and Insecticide Resistance in Colombian Aedes aegypti Populations Andrea Ar?valo-Cort?s@@@Ashish Damania@@@Yurany Granada@@@Sara Zuluaga@@@Rojelio Mejia@@@Omar Triana-Chavez 202210 Article PMC {{{ Abstract }}} !!{{ Introduction: }} Aedes aegypti is the vector of several arboviruses such as dengue, Zika, and chikungunya. In 2015-16, Zika virus (ZIKV) had an outbreak in South America associated with prenatal microcephaly and Guillain-Barr? syndrome. This mosquito's viral transmission is influenced by microbiota abundance and diversity and its interactions with the vector. The conditions of cocirculation of these three arboviruses, failure in vector control due to insecticide resistance, limitations in dengue management during the COVID-19 pandemic, and lack of effective treatment or vaccines make it necessary to identify changes in mosquito midgut bacterial composition and predict its functions through the infection. Its study is fundamental because it generates knowledge for surveillance of transmission and the risk of outbreaks of these diseases at the local level. !!{{ Methods: }} Midgut bacterial compositions of females of Colombian Ae. aegypti populations were analyzed using DADA2 Pipeline, and their functions were predicted with PICRUSt2 analysis. These analyses were done under the condition of natural ZIKV infection and resistance to lambda-cyhalothrin, alone and in combination. One-step RT-PCR determined the percentage of ZIKV-infected females. We also measured the susceptibility to the pyrethroid lambda-cyhalothrin and evaluated the presence of the V1016I mutation in the sodium channel gene. !!{{ Results: }} We found high ZIKV infection rates in Ae. aegypti females from Colombian rural municipalities with deficient water supply, such as Honda with 63.6%. In the face of natural infection with an arbovirus such as Zika, the diversity between an infective and non-infective form was significantly different. Bacteria associated with a state of infection with ZIKV and lambda-cyhalothrin resistance were detected, such as the genus Bacteroides , which was related to functions of pathogenicity, antimicrobial resistance, and bioremediation of insecticides. We hypothesize that it is a vehicle for virus entry, as it is in human intestinal infections. On the other hand, Bello, the only mosquito population classified as susceptible to lambda-cyhalothrin, was associated with bacteria related to mucin degradation functions in the intestine, belonging to the Lachnospiraceae family, with the genus Dorea being increased in ZIKV-infected females. The Serratia genus presented significantly decreased functions related to phenazine production, potentially associated with infection control, and control mechanism functions for host defense and quorum sensing. Additionally, Pseudomonas was the genus principally associated with functions of the degradation of insecticides related to tryptophan metabolism, ABC transporters with a two-component system, efflux pumps, and alginate synthesis. !!{{ Conclusions: }} Microbiota composition may be modulated by ZIKV infection and insecticide resistance in Ae. aegypti Colombian populations. The condition of resistance to lambda-cyhalothrin could be inducing a phenome of dysbiosis in field Ae. aegypti affecting the transmission of arboviruses. !!{{ Keywords: }} Aedes aegypti; Zika; bacteria; dysbiosis; functions; lambda-cyhalothrin; midgut. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609292/ 45 1999-4915 Viruses Basel, Switzerland : MDPI.
233447 26 유전자 메커니즘 upregulated Action upregulated abstract None 78897 10.1155/2022/2148627 The Variation of Transcriptomic Perturbations is Associated with the Development and Progression of Various Diseases Zehua Dong@@@Qiyu Yan@@@Xiaosheng Wang 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Although transcriptomic data have been widely applied to explore various diseases, few studies have investigated the association between transcriptomic perturbations and disease development in a wide variety of diseases. !!{{ Methods: }} Based on a previously developed algorithm for quantifying intratumor heterogeneity at the transcriptomic level, we defined the variation of transcriptomic perturbations (VTP) of a disease relative to the health status. Based on publicly available transcriptome datasets, we compared VTP values between the disease and health status and analyzed correlations between VTP values and disease progression or severity in various diseases, including neurological disorders, infectious diseases, cardiovascular diseases, respiratory diseases, liver diseases, kidney diseases, digestive diseases, and endocrine diseases. We also identified the genes and pathways whose expression perturbations correlated positively with VTP across diverse diseases. !!{{ Results: }} VTP values were upregulated in various diseases relative to their normal controls. VTP values were significantly greater in define than in possible or probable Alzheimer's disease. VTP values were significantly larger in intensive care unit (ICU) COVID-19 patients than in non-ICU patients, and in COVID-19 patients requiring mechanical ventilatory support (MVS) than in those not requiring MVS. VTP correlated positively with viral loads in acquired immune deficiency syndrome (AIDS) patients. Moreover, the AIDS patients treated with abacavir or zidovudine had lower VTP values than those without such therapies. In pulmonary tuberculosis (TB) patients, VTP values followed the pattern: active TB > latent TB > normal controls. VTP values were greater in clinically apparent than in presymptomatic malaria. VTP correlated negatively with the cardiac index of left ventricular ejection fraction (LVEF). In chronic obstructive pulmonary disease (COPD), VTP showed a negative correlation with forced expiratory volume in the first second (FEV1). VTP values increased with H. pylori infection and were upregulated in atrophic gastritis caused by H. pylori infection. The genes and pathways whose expression perturbations correlated positively with VTP scores across diseases were mainly involved in the regulation of immune, metabolic, and cellular activities. !!{{ Conclusions: }} VTP is upregulated in the disease versus health status, and its upregulation is associated with disease progression and severity in various diseases. Thus, VTP has potential clinical implications for disease diagnosis and prognosis. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530920/ 779 0278-0240 Disease Markers New York, NY : Hindawi Pub. Corp.
207498 26 유전자 메커니즘 challenges Term challenge author 시험감염 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
207510 26 유전자 메커니즘 COVID-19 prognosis Term covid-19 prognosis title,abstract None 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
234379 26 유전자 메커니즘 influenza A Virus influenza a abstract 인플루엔자 A 78917 10.1097/MD.0000000000029554 Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods Peter Natesan Pushparaj@@@Laila Abdullah Damiati@@@Iuliana Denetiu@@@Sherin Bakhashab@@@Muhammad Asif@@@Abrar Hussain@@@Sagheer Ahmed@@@Mohammad Hamid Hamdard@@@Mahmood Rasool 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. !!{{ Methods: }} Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. !!{{ Results: }} Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. !!{{ Conclusions: }} In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439635/ 82 0025-7974 Medicine Hagerstown, Md : Lippincott Williams & Wilkins.
207511 26 유전자 메커니즘 curtail Action curtail abstract None 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
234380 26 유전자 메커니즘 in silico Term in silico abstract 인 실리코 78917 10.1097/MD.0000000000029554 Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods Peter Natesan Pushparaj@@@Laila Abdullah Damiati@@@Iuliana Denetiu@@@Sherin Bakhashab@@@Muhammad Asif@@@Abrar Hussain@@@Sagheer Ahmed@@@Mohammad Hamid Hamdard@@@Mahmood Rasool 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. !!{{ Methods: }} Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. !!{{ Results: }} Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. !!{{ Conclusions: }} In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439635/ 82 0025-7974 Medicine Hagerstown, Md : Lippincott Williams & Wilkins.
207512 26 유전자 메커니즘 deaths Disease death abstract 사망 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
234381 26 유전자 메커니즘 In silico method Term in silico method title None 78917 10.1097/MD.0000000000029554 Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods Peter Natesan Pushparaj@@@Laila Abdullah Damiati@@@Iuliana Denetiu@@@Sherin Bakhashab@@@Muhammad Asif@@@Abrar Hussain@@@Sagheer Ahmed@@@Mohammad Hamid Hamdard@@@Mahmood Rasool 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. !!{{ Methods: }} Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. !!{{ Results: }} Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. !!{{ Conclusions: }} In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439635/ 82 0025-7974 Medicine Hagerstown, Md : Lippincott Williams & Wilkins.
207516 26 유전자 메커니즘 effective Term effective abstract abnormality 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
234385 26 유전자 메커니즘 IRF3 Gene irf3 abstract IRF3 78917 10.1097/MD.0000000000029554 Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods Peter Natesan Pushparaj@@@Laila Abdullah Damiati@@@Iuliana Denetiu@@@Sherin Bakhashab@@@Muhammad Asif@@@Abrar Hussain@@@Sagheer Ahmed@@@Mohammad Hamid Hamdard@@@Mahmood Rasool 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. !!{{ Methods: }} Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. !!{{ Results: }} Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. !!{{ Conclusions: }} In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439635/ 82 0025-7974 Medicine Hagerstown, Md : Lippincott Williams & Wilkins.
207517 26 유전자 메커니즘 Epidemics Term epidemic abstract 유행병 32436 10.1016/j.bspc.2021.102814 Deep insight: Convolutional neural network and its applications for COVID-19 prognosis Nadeem Yousuf Khanday@@@Shabir Ahmad Sofi 202108 Review article Sciencedirect Abstract!!Background and objective!!SARS-CoV-2, a novel strain of coronavirus’ also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a “pandemic”. Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.!!Method!!In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.!!Result!!This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.!!Conclusion!!The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. https://doi.org/10.1016/j.bspc.2021.102814 2514 1746-8094 Biomedical Signal Processing and Control Elsevier Ltd.
234386 26 유전자 메커니즘 IRF9 Gene IRF9 abstract None 78917 10.1097/MD.0000000000029554 Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods Peter Natesan Pushparaj@@@Laila Abdullah Damiati@@@Iuliana Denetiu@@@Sherin Bakhashab@@@Muhammad Asif@@@Abrar Hussain@@@Sagheer Ahmed@@@Mohammad Hamid Hamdard@@@Mahmood Rasool 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. !!{{ Methods: }} Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. !!{{ Results: }} Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. !!{{ Conclusions: }} In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439635/ 82 0025-7974 Medicine Hagerstown, Md : Lippincott Williams & Wilkins.
234405 26 유전자 메커니즘 STAT2 Gene STAT2 abstract None 78917 10.1097/MD.0000000000029554 Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods Peter Natesan Pushparaj@@@Laila Abdullah Damiati@@@Iuliana Denetiu@@@Sherin Bakhashab@@@Muhammad Asif@@@Abrar Hussain@@@Sagheer Ahmed@@@Mohammad Hamid Hamdard@@@Mahmood Rasool 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. !!{{ Methods: }} Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. !!{{ Results: }} Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. !!{{ Conclusions: }} In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439635/ 82 0025-7974 Medicine Hagerstown, Md : Lippincott Williams & Wilkins.
234533 26 유전자 메커니즘 COVID-19 pandemic Term covid-19 pandemic abstract COVID-19 팬데믹 78920 10.3389/fimmu.2022.952987 Bioinformatics and systems biology approach to identify the pathogenetic link of Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Yongbiao Lv@@@Tian Zhang@@@Junxiang Cai@@@Chushuan Huang@@@Shaofeng Zhan@@@Jianbo Liu 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global crisis. Although many people recover from COVID-19 infection, they are likely to develop persistent symptoms similar to those of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) after discharge. Those constellations of symptoms persist for months after infection, called Long COVID, which may lead to considerable financial burden and healthcare challenges. However, the mechanisms underlying Long COVID and ME/CFS remain unclear. !!{{ Methods: }} We collected the genes associated with Long COVID and ME/CFS in databases by restricted screening conditions and clinical sample datasets with limited filters. The common genes for Long COVID and ME/CFS were finally obtained by taking the intersection. We performed several advanced bioinformatics analyses based on common genes, including gene ontology and pathway enrichment analyses, protein-protein interaction (PPI) analysis, transcription factor (TF)-gene interaction network analysis, transcription factor-miRNA co-regulatory network analysis, and candidate drug analysis prediction. !!{{ Results: }} We found nine common genes between Long COVID and ME/CFS and gained a piece of detailed information on their biological functions and signaling pathways through enrichment analysis. Five hub proteins (IL-6, IL-1B, CD8A, TP53, and CXCL8) were collected by the PPI network. The TF-gene and TF-miRNA coregulatory networks were demonstrated by NetworkAnalyst. In the end, 10 potential chemical compounds were predicted. !!{{ Conclusion: }} This study revealed common gene interaction networks of Long COVID and ME/CFS and predicted potential therapeutic drugs for clinical practice. Our findings help to identify the potential biological mechanism between Long COVID and ME/CFS. However, more laboratory and multicenter evidence is required to explore greater mechanistic insight before clinical application in the future. !!{{ Keywords: }} Long COVID; ME/CFS; bioinformatics analyses; myalgic encephalomyelitis/chronic fatigue syndrome; protein?protein interaction network; systems biology. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524193/ 109 1664-3224 Frontiers in Immunology [Lausanne : Frontiers Research Foundation].
234532 26 유전자 메커니즘 COVID-19 infection Disease covid-19 infection abstract 코로나19 감염 78920 10.3389/fimmu.2022.952987 Bioinformatics and systems biology approach to identify the pathogenetic link of Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Yongbiao Lv@@@Tian Zhang@@@Junxiang Cai@@@Chushuan Huang@@@Shaofeng Zhan@@@Jianbo Liu 202209 Article PMC {{{ Abstract }}} !!{{ Background: }} The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global crisis. Although many people recover from COVID-19 infection, they are likely to develop persistent symptoms similar to those of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) after discharge. Those constellations of symptoms persist for months after infection, called Long COVID, which may lead to considerable financial burden and healthcare challenges. However, the mechanisms underlying Long COVID and ME/CFS remain unclear. !!{{ Methods: }} We collected the genes associated with Long COVID and ME/CFS in databases by restricted screening conditions and clinical sample datasets with limited filters. The common genes for Long COVID and ME/CFS were finally obtained by taking the intersection. We performed several advanced bioinformatics analyses based on common genes, including gene ontology and pathway enrichment analyses, protein-protein interaction (PPI) analysis, transcription factor (TF)-gene interaction network analysis, transcription factor-miRNA co-regulatory network analysis, and candidate drug analysis prediction. !!{{ Results: }} We found nine common genes between Long COVID and ME/CFS and gained a piece of detailed information on their biological functions and signaling pathways through enrichment analysis. Five hub proteins (IL-6, IL-1B, CD8A, TP53, and CXCL8) were collected by the PPI network. The TF-gene and TF-miRNA coregulatory networks were demonstrated by NetworkAnalyst. In the end, 10 potential chemical compounds were predicted. !!{{ Conclusion: }} This study revealed common gene interaction networks of Long COVID and ME/CFS and predicted potential therapeutic drugs for clinical practice. Our findings help to identify the potential biological mechanism between Long COVID and ME/CFS. However, more laboratory and multicenter evidence is required to explore greater mechanistic insight before clinical application in the future. !!{{ Keywords: }} Long COVID; ME/CFS; bioinformatics analyses; myalgic encephalomyelitis/chronic fatigue syndrome; protein?protein interaction network; systems biology. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524193/ 109 1664-3224 Frontiers in Immunology [Lausanne : Frontiers Research Foundation].
234631 26 유전자 메커니즘 change Term change abstract 변화 78922 10.1371/journal.ppat.1010809 Lipocalin-2 is an essential component of the innate immune response to Acinetobacter baumannii infection Jessica R Sheldon@@@Lauren E Himmel@@@Dillon E Kunkle@@@Andrew J Monteith@@@K Nichole Maloney@@@Eric P Skaar 202209 Article PMC {{{ Abstract }}} !! Acinetobacter baumannii is an opportunistic pathogen and an emerging global health threat. Within healthcare settings, major presentations of A. baumannii include bloodstream infections and ventilator-associated pneumonia. The increased prevalence of ventilated patients during the COVID-19 pandemic has led to a rise in secondary bacterial pneumonia caused by multidrug resistant (MDR) A. baumannii. Additionally, due to its MDR status and the lack of antimicrobial drugs in the development pipeline, the World Health Organization has designated carbapenem-resistant A. baumannii to be its priority critical pathogen for the development of novel therapeutics. To better inform the design of new treatment options, a comprehensive understanding of how the host contains A. baumannii infection is required. Here, we investigate the innate immune response to A. baumannii by assessing the impact of infection on host gene expression using NanoString technology. The transcriptional profile observed in the A. baumannii infected host is characteristic of Gram-negative bacteremia and reveals expression patterns consistent with the induction of nutritional immunity, a process by which the host exploits the availability of essential nutrient metals to curtail bacterial proliferation. The gene encoding for lipocalin-2 (Lcn2), a siderophore sequestering protein, was the most highly upregulated during A. baumannii bacteremia, of the targets assessed, and corresponds to robust LCN2 expression in tissues. Lcn2-/- mice exhibited distinct organ-specific gene expression changes including increased transcription of genes involved in metal sequestration, such as S100A8 and S100A9, suggesting a potential compensatory mechanism to perturbed metal homeostasis. In vitro, LCN2 inhibits the iron-dependent growth of A. baumannii and induces iron-regulated gene expression. To elucidate the role of LCN2 in infection, WT and Lcn2-/- mice were infected with A. baumannii using both bacteremia and pneumonia models. LCN2 was not required to control bacterial growth during bacteremia but was protective against mortality. In contrast, during pneumonia Lcn2-/- mice had increased bacterial burdens in all organs evaluated, suggesting that LCN2 plays an important role in inhibiting the survival and dissemination of A. baumannii. The control of A. baumannii infection by LCN2 is likely multifactorial, and our results suggest that impairment of iron acquisition by the pathogen is a contributing factor. Modulation of LCN2 expression or modifying the structure of LCN2 to expand upon its ability to sequester siderophores may thus represent feasible avenues for therapeutic development against this pathogen. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477428/ 62 1553-7366 PLoS Pathogens San Francisco, CA : Public Library of Science
54584 26 유전자 메커니즘 genetic defect Term genetic defect abstract None 244 10.1126/science.abd4570 Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 202010 Research Article PMC The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570, p. eabd4585 ; see also p. 404 A large immunological and genomics study of COVID-19 patients reveals excess mutations in the type I IFN pathway. INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. RATIONALE We established the COVID Human Genetic Effort ( www.covidhge.com ) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3 ?/? , TLR3 +/? , IRF7 ?/? , and IFNAR1 ?/? fibroblasts were susceptible to SARS-CoV-2 infection in vitro. CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857407/ 47 0036-8075 Science (New York, N.y.) Washington, DC : American Association for the Advancement of Science.
54585 26 유전자 메커니즘 genetic defects Disease genetic defect abstract None 244 10.1126/science.abd4570 Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 202010 Research Article PMC The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570, p. eabd4585 ; see also p. 404 A large immunological and genomics study of COVID-19 patients reveals excess mutations in the type I IFN pathway. INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. RATIONALE We established the COVID Human Genetic Effort ( www.covidhge.com ) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3 ?/? , TLR3 +/? , IRF7 ?/? , and IFNAR1 ?/? fibroblasts were susceptible to SARS-CoV-2 infection in vitro. CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857407/ 47 0036-8075 Science (New York, N.y.) Washington, DC : American Association for the Advancement of Science.
54586 26 유전자 메커니즘 genetics Term genetic abstract 유전학 244 10.1126/science.abd4570 Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 202010 Research Article PMC The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570, p. eabd4585 ; see also p. 404 A large immunological and genomics study of COVID-19 patients reveals excess mutations in the type I IFN pathway. INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. RATIONALE We established the COVID Human Genetic Effort ( www.covidhge.com ) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3 ?/? , TLR3 +/? , IRF7 ?/? , and IFNAR1 ?/? fibroblasts were susceptible to SARS-CoV-2 infection in vitro. CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857407/ 47 0036-8075 Science (New York, N.y.) Washington, DC : American Association for the Advancement of Science.
54587 26 유전자 메커니즘 Genome Term genome abstract 게놈 244 10.1126/science.abd4570 Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 202010 Research Article PMC The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570, p. eabd4585 ; see also p. 404 A large immunological and genomics study of COVID-19 patients reveals excess mutations in the type I IFN pathway. INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. RATIONALE We established the COVID Human Genetic Effort ( www.covidhge.com ) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3 ?/? , TLR3 +/? , IRF7 ?/? , and IFNAR1 ?/? fibroblasts were susceptible to SARS-CoV-2 infection in vitro. CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857407/ 47 0036-8075 Science (New York, N.y.) Washington, DC : American Association for the Advancement of Science.
54588 26 유전자 메커니즘 genomics study Term genomics study abstract None 244 10.1126/science.abd4570 Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 202010 Research Article PMC The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570, p. eabd4585 ; see also p. 404 A large immunological and genomics study of COVID-19 patients reveals excess mutations in the type I IFN pathway. INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. RATIONALE We established the COVID Human Genetic Effort ( www.covidhge.com ) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3 ?/? , TLR3 +/? , IRF7 ?/? , and IFNAR1 ?/? fibroblasts were susceptible to SARS-CoV-2 infection in vitro. CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857407/ 47 0036-8075 Science (New York, N.y.) Washington, DC : American Association for the Advancement of Science.
63642 26 유전자 메커니즘 CRISPR Term crispr abstract CRISPR-CAS9 2058 10.3389/fcimb.2020.560616 An Update on Molecular Diagnostics for COVID-19 Khursheed Ul Islam@@@Jawed Iqbal 202011 Review PMC A novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been recently identified as an infectious disease affecting the respiratory system of humans. This disease is caused by SARS-CoV-2 that was identified in Chinese patients having severe pneumonia and flu-like symptoms. COVID-19 is a contagious disease that spreads rapidly via droplet particles arising through sneezing and coughing action of an infected person. The reports of asymptomatic carriers changed the scenario of symptom based-diagnosis in COVID-19 and intensified the need for proper diagnosis of the majority of the population to combat the rapid transmission of virus. The diagnosis of positive cases is necessary to ensure prompt care to affected people and also to curb further spread of infection in the population. Collecting samples at the right time and from the exact anatomical site is crucial for proper molecular diagnosis. After the complete genome sequence was available, China formulated RT-PCR as a primary diagnostic procedure for detecting SARS-CoV-2. Many in-house and commercial diagnostic kits have been developed or are under development that have a potential to lower the burden of diagnosis on the primary diagnostic techniques like RT-PCR. Serological based diagnosis is another broad category of testing that can detect different serum antibodies like IgG, IgM, and IgA in an infected patient. PCR-based diagnostic procedures that are commonly used for pathogen detection need sophisticated machines and assistance of a technical expert. Despite their reliable accuracy, they are not cost-effective tests, which a common man can afford, so it becomes imperative to look for other diagnostic approaches, which could be cost effective, rapid, and sensitive with consistent accuracy. To make such diagnostics available to the common man, many techniques can be exploited among, which are Point of Care (POC), also known as bed side testing, which is developing as a portable and promising tool in pathogen diagnosis. Other lateral flow assay (LFA)-based techniques like SHERLOCK, CRISPR-Cas12a (AIOD-CRISPR), and FNCAS9 editor-limited uniform detection assay (FELUDA), etc. have shown promising results in rapid detection of pathogens. Diagnosis holds a critical importance in the pandemic situation when there is no potential drug for the pathogen available in the market. This review sums up the different diagnostic approaches designed or proposed to combat the crisis of widespread diagnosis due to the sudden outbreak of a novel pathogen, SARS-CoV-2 in 2019. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683783/ 206 2235-2988 Frontiers in Cellular and Infection Microbiology Lausanne : Frontiers Media SA.
54525 26 유전자 메커니즘 affecting Action affecting abstract None 244 10.1126/science.abd4570 Inborn errors of type I IFN immunity in patients with life-threatening COVID-19 202010 Research Article PMC The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570, p. eabd4585 ; see also p. 404 A large immunological and genomics study of COVID-19 patients reveals excess mutations in the type I IFN pathway. INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. RATIONALE We established the COVID Human Genetic Effort ( www.covidhge.com ) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)? and interferon regulatory factor 7 (IRF7)?dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3 ?/? , TLR3 +/? , IRF7 ?/? , and IFNAR1 ?/? fibroblasts were susceptible to SARS-CoV-2 infection in vitro. CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influen
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