Epigenetic Landscape during Coronavirus Infection
Identifieur interne : 000866 ( Pmc/Checkpoint ); précédent : 000865; suivant : 000867Epigenetic Landscape during Coronavirus Infection
Auteurs : Alexandra Sch Fer ; Ralph S. BaricSource :
- Pathogens [ 2076-0817 ] ; 2017.
Abstract
Coronaviruses (CoV) comprise a large group of emerging human and animal pathogens, including the highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) strains. The molecular mechanisms regulating emerging coronavirus pathogenesis are complex and include virus–host interactions associated with entry, replication, egress and innate immune control. Epigenetics research investigates the genetic and non-genetic factors that regulate phenotypic variation, usually caused by external and environmental factors that alter host expression patterns and performance without any change in the underlying genotype. Epigenetic modifications, such as histone modifications, DNA methylation, chromatin remodeling, and non-coding RNAs, function as important regulators that remodel host chromatin, altering host expression patterns and networks in a highly flexible manner. For most of the past two and a half decades, research has focused on the molecular mechanisms by which RNA viruses antagonize the signaling and sensing components that regulate induction of the host innate immune and antiviral defense programs upon infection. More recently, a growing body of evidence supports the hypothesis that viruses, even lytic RNA viruses that replicate in the cytoplasm, have developed intricate, highly evolved, and well-coordinated processes that are designed to regulate the host epigenome, and control host innate immune antiviral defense processes, thereby promoting robust virus replication and pathogenesis. In this article, we discuss the strategies that are used to evaluate the mechanisms by which viruses regulate the host epigenome, especially focusing on highly pathogenic respiratory RNA virus infections as a model. By combining measures of epigenome reorganization with RNA and proteomic datasets, we articulate a spatial-temporal data integration approach to identify regulatory genomic clusters and regions that play a crucial role in the host’s innate immune response, thereby defining a new viral antagonism mechanism following emerging coronavirus infection.
Url:
DOI: 10.3390/pathogens6010008
PubMed: 28212305
PubMed Central: 5371896
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Epigenetic Landscape during Coronavirus Infection</title><author><name sortKey="Sch Fer, Alexandra" sort="Sch Fer, Alexandra" uniqKey="Sch Fer A" first="Alexandra" last="Sch Fer">Alexandra Sch Fer</name></author><author><name sortKey="Baric, Ralph S" sort="Baric, Ralph S" uniqKey="Baric R" first="Ralph S." last="Baric">Ralph S. Baric</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28212305</idno><idno type="pmc">5371896</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5371896</idno><idno type="RBID">PMC:5371896</idno><idno type="doi">10.3390/pathogens6010008</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">001400</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001400</idno><idno type="wicri:Area/Pmc/Curation">001400</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001400</idno><idno type="wicri:Area/Pmc/Checkpoint">000866</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000866</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Epigenetic Landscape during Coronavirus Infection</title><author><name sortKey="Sch Fer, Alexandra" sort="Sch Fer, Alexandra" uniqKey="Sch Fer A" first="Alexandra" last="Sch Fer">Alexandra Sch Fer</name></author><author><name sortKey="Baric, Ralph S" sort="Baric, Ralph S" uniqKey="Baric R" first="Ralph S." last="Baric">Ralph S. Baric</name></author></analytic><series><title level="j">Pathogens</title><idno type="eISSN">2076-0817</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Coronaviruses (CoV) comprise a large group of emerging human and animal pathogens, including the highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) strains. The molecular mechanisms regulating emerging coronavirus pathogenesis are complex and include virus–host interactions associated with entry, replication, egress and innate immune control. Epigenetics research investigates the genetic and non-genetic factors that regulate phenotypic variation, usually caused by external and environmental factors that alter host expression patterns and performance without any change in the underlying genotype. Epigenetic modifications, such as histone modifications, DNA methylation, chromatin remodeling, and non-coding RNAs, function as important regulators that remodel host chromatin, altering host expression patterns and networks in a highly flexible manner. For most of the past two and a half decades, research has focused on the molecular mechanisms by which RNA viruses antagonize the signaling and sensing components that regulate induction of the host innate immune and antiviral defense programs upon infection. More recently, a growing body of evidence supports the hypothesis that viruses, even lytic RNA viruses that replicate in the cytoplasm, have developed intricate, highly evolved, and well-coordinated processes that are designed to regulate the host epigenome, and control host innate immune antiviral defense processes, thereby promoting robust virus replication and pathogenesis. In this article, we discuss the strategies that are used to evaluate the mechanisms by which viruses regulate the host epigenome, especially focusing on highly pathogenic respiratory RNA virus infections as a model. By combining measures of epigenome reorganization with RNA and proteomic datasets, we articulate a spatial-temporal data integration approach to identify regulatory genomic clusters and regions that play a crucial role in the host’s innate immune response, thereby defining a new viral antagonism mechanism following emerging coronavirus infection.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zhong, N" uniqKey="Zhong N">N. Zhong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peiris, S J" uniqKey="Peiris S">S.J. Peiris</name></author><author><name sortKey="Guan, Y" uniqKey="Guan Y">Y. Guan</name></author><author><name sortKey="Yuen, K Y" uniqKey="Yuen K">K.Y. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Pathogens</journal-id><journal-id journal-id-type="iso-abbrev">Pathogens</journal-id><journal-id journal-id-type="publisher-id">pathogens</journal-id><journal-title-group><journal-title>Pathogens</journal-title></journal-title-group><issn pub-type="epub">2076-0817</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28212305</article-id><article-id pub-id-type="pmc">5371896</article-id><article-id pub-id-type="doi">10.3390/pathogens6010008</article-id><article-id pub-id-type="publisher-id">pathogens-06-00008</article-id><article-categories><subj-group subj-group-type="heading"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Epigenetic Landscape during Coronavirus Infection</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Schäfer</surname><given-names>Alexandra</given-names></name></contrib><contrib contrib-type="author"><name><surname>Baric</surname><given-names>Ralph S.</given-names></name><xref rid="c1-pathogens-06-00008" ref-type="corresp">*</xref></contrib></contrib-group><contrib-group><contrib contrib-type="editor"><name><surname>Young</surname><given-names>Lawrence S.</given-names></name><role>Academic Editor</role></contrib></contrib-group><aff id="af1-pathogens-06-00008">Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA;<email>aschaefe@email.unc.edu</email></aff><author-notes><corresp id="c1-pathogens-06-00008"><label>*</label>Correspondence: <email>rbaric@email.unc.edu</email></corresp></author-notes><pub-date pub-type="epub"><day>15</day><month>2</month><year>2017</year></pub-date><pub-date pub-type="collection"><month>3</month><year>2017</year></pub-date><volume>6</volume><issue>1</issue><elocation-id>8</elocation-id><history><date date-type="received"><day>23</day><month>11</month><year>2016</year></date><date date-type="accepted"><day>07</day><month>2</month><year>2017</year></date></history><permissions><copyright-statement>© 2017 by the authors.</copyright-statement><copyright-year>2017</copyright-year><license license-type="open-access"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Coronaviruses (CoV) comprise a large group of emerging human and animal pathogens, including the highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) strains. The molecular mechanisms regulating emerging coronavirus pathogenesis are complex and include virus–host interactions associated with entry, replication, egress and innate immune control. Epigenetics research investigates the genetic and non-genetic factors that regulate phenotypic variation, usually caused by external and environmental factors that alter host expression patterns and performance without any change in the underlying genotype. Epigenetic modifications, such as histone modifications, DNA methylation, chromatin remodeling, and non-coding RNAs, function as important regulators that remodel host chromatin, altering host expression patterns and networks in a highly flexible manner. For most of the past two and a half decades, research has focused on the molecular mechanisms by which RNA viruses antagonize the signaling and sensing components that regulate induction of the host innate immune and antiviral defense programs upon infection. More recently, a growing body of evidence supports the hypothesis that viruses, even lytic RNA viruses that replicate in the cytoplasm, have developed intricate, highly evolved, and well-coordinated processes that are designed to regulate the host epigenome, and control host innate immune antiviral defense processes, thereby promoting robust virus replication and pathogenesis. In this article, we discuss the strategies that are used to evaluate the mechanisms by which viruses regulate the host epigenome, especially focusing on highly pathogenic respiratory RNA virus infections as a model. By combining measures of epigenome reorganization with RNA and proteomic datasets, we articulate a spatial-temporal data integration approach to identify regulatory genomic clusters and regions that play a crucial role in the host’s innate immune response, thereby defining a new viral antagonism mechanism following emerging coronavirus infection.</p></abstract><kwd-group><kwd>coronaviruses</kwd><kwd>epigenetics</kwd><kwd>systems biology</kwd></kwd-group></article-meta></front><floats-group><fig id="pathogens-06-00008-f001" position="float"><label>Figure 1</label><caption><p>Genome organization of severe acute respiratory syndrome coronavirus (SARS-CoV). The SARS-CoV genome is approximately 29.7 kb and encodes for 14 open reading frames (ORFs). The 5′ end is capped and contains a leader sequence (L). SARS-CoV encodes for 14 ORFs, including ORF1, which is processed into nsp1 to nsp16, 4 structural ORFs (S, E, M, and N) in grey, and luxury downstream ORFs (3a, 3b, 6, 7a, 7b, 8a, 8b and 9b). nsp: nonstructural protein; S: spike; E: envelope; M: matrix; and N: nucleoprotein.</p></caption><graphic xlink:href="pathogens-06-00008-g001"></graphic></fig><fig id="pathogens-06-00008-f002" position="float"><label>Figure 2</label><caption><p>Epigenetic modifications. (<bold>A</bold>) Histone tails are targets for post-translational covalent modification. Particular lysine residues (K) can be methylated, acetylated, phosphorylated, and ubiquitinated, and particular serine residues (S) can be also phosphorylated. (<bold>B</bold>) DNA is being methylated by transferring a methyl group to the C-5 position on cytosine bases. (<bold>C</bold>) miRNA, usually 22nt long, bind the 3′ end of their target mRNA with their seed region and mediate the degradation of the mRNA by incorporation into RISC. DNMT: DNA methyltransferase; mRNA: messenger RNA; miRNA: microRNA; RISC: RNA-induced silencing complex; K: lysine; S: serine; T: threonine.</p></caption><graphic xlink:href="pathogens-06-00008-g002"></graphic></fig><fig id="pathogens-06-00008-f003" position="float"><label>Figure 3</label><caption><p>Methods of epigenetic analysis. Schematic representation of the major epigenetic methods that are being used to detect epigenetic modifications (solid arrow <inline-graphic xlink:href="pathogens-06-00008-i001.jpg"></inline-graphic>) that are associated with functional gene components (dashed arrow <inline-graphic xlink:href="pathogens-06-00008-i002.jpg"></inline-graphic>), like promoters, transcriptional start sites, enhancers, gene bodies, slicing sites, and transcriptional end sites [<xref rid="B43-pathogens-06-00008" ref-type="bibr">43</xref>]. TSS: transcription start site; TF: transcription factor; TFBS: transcription factor binding site; FAIRE: formaldehyde-assisted isolation of regulatory elements; ChIP: chromatin immuno-precipitation; MeDIP: methylated DNA immunoprecipitation.</p></caption><graphic xlink:href="pathogens-06-00008-g003"></graphic></fig><fig id="pathogens-06-00008-f004" position="float"><label>Figure 4</label><caption><p>Epigenetic modification infection with highly virulent respiratory viruses. Infection with pathogenic influenza viruses and coronaviruses induces changes in the basal state of host chromatin. Infection with H1N1-09 (blue) and SARS-CoV (red) results in enrichment of H3K4me3 incorporation (green ovals) and depletion of H3K27 (red diamonds), and therefore in open, transcription-active chromatin. In contrast, H5N1-VN1203 and Middle East respiratory syndrome coronavirus (MERS-CoV) infection drives H3K27me3 enrichment and depletes H3K4me3 for a subset of genes, favoring a closed chromatin conformation that inhibits interferon-stimulated gene (ISG) expression [<xref rid="B18-pathogens-06-00008" ref-type="bibr">18</xref>].</p></caption><graphic xlink:href="pathogens-06-00008-g004"></graphic></fig><fig id="pathogens-06-00008-f005" position="float"><label>Figure 5</label><caption><p>Interferon-stimulated gene (ISG) profile during MERS-CoV. Global ISG transcriptional response to MERS-CoV infection. Genes are ordered by 18 h post infection (hpi) expression levels [<xref rid="B18-pathogens-06-00008" ref-type="bibr">18</xref>]. Columns labelled HM (histone modification) indicate occupancy with H3K27me3 (K27, blue) and H3K4me3 (K4, yellow), respectively.</p></caption><graphic xlink:href="pathogens-06-00008-g005"></graphic></fig><fig id="pathogens-06-00008-f006" position="float"><label>Figure 6</label><caption><p>Model system for the study of coronaviruses and other respiratory viruses. Human respiratory cell lines (Calu3) or primary cells (HAE, AT2, LF, MEV, and AM) are used as a minimalistic model system to study the response to perturbations like cytokine treatment and virus infection. IFN: interferon; TLR: Toll-like receptors; IL: interleukin; IAV: Influenza Type A virus; CoV: coronavirus.</p></caption><graphic xlink:href="pathogens-06-00008-g006"></graphic></fig><fig id="pathogens-06-00008-f007" position="float"><label>Figure 7</label><caption><p>Integration across data types. Epigenetic approaches combined with transcriptomics and proteomics datasets provide a spatial-temporal data integration approach to identify regulatory genomic clusters and regions playing a role in viral infection.</p></caption><graphic xlink:href="pathogens-06-00008-g007"></graphic></fig><table-wrap id="pathogens-06-00008-t001" position="float"><object-id pub-id-type="pii">pathogens-06-00008-t001_Table 1</object-id><label>Table 1</label><caption><p>Histone (H) modification and their role in transcription (list is not complete, only well-established motifs in the literature are listed) [<xref rid="B49-pathogens-06-00008" ref-type="bibr">49</xref>,<xref rid="B51-pathogens-06-00008" ref-type="bibr">51</xref>].</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Modification</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Role in Transcription</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Modification Site</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">Acetylation</td><td align="center" valign="middle" rowspan="1" colspan="1">Activation</td><td align="center" valign="middle" rowspan="1" colspan="1">H3ac, H3K9ac, H3K14ac, H3K27ac</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Methylation</td><td align="center" valign="middle" rowspan="1" colspan="1">Activation</td><td align="center" valign="middle" rowspan="1" colspan="1">H3K4me1, H3K4me2, H3K4me3, H3K36me3, H3K79me2</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Methylation</td><td align="center" valign="middle" rowspan="1" colspan="1">Repression</td><td align="center" valign="middle" rowspan="1" colspan="1">H3K9me3, H3K27me3</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Phosphorylation</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Activation</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">H3S10</td></tr></tbody></table></table-wrap></floats-group></pmc><affiliations><list></list><tree><noCountry><name sortKey="Baric, Ralph S" sort="Baric, Ralph S" uniqKey="Baric R" first="Ralph S." last="Baric">Ralph S. 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