Viral targeting of TFIIB impairs de novo polymerase II recruitment and affects antiviral immunity

Haas, Darya A.; Meiler, Arno; Geiger, Katharina; Vogt, Carola; Preuss, Ellen; Kochs, Georg; Pichlmair, Andreas

doi:10.1371/journal.ppat.1006980

Cited by 17 publications

(27 citation statements)

References 87 publications

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“…HCT intersections for nodes originally characterized as having a general role in RNA Pol II transcription, including TBP (q-values: SARS1, 2e-10; SARS2, 6e-23; MERS, 3e-16), GTF2B/TFIIB (q-values: SARS1, 7e-10; SARS2, 3e-23; MERS, 9e-14) and GTF2F1 (qvalues: SARS1, 2e-4; SARS2, 2e-13; MERS, 5e-5) were strong across all CoVs, and particularly noteworthy in the case of SARS2. In the case of GTF2B, these data are consistent with previous evidence identifying it as a specific target for orthomyxovirus 41 , and the herpes simplex 42 and hepatitis B 43 viruses. Moreover, a proteomic analysis that appeared in BioRXiv while this paper was under review identified a high confidence interaction between GTF2F2 and the SARS2 NSP9 replicase 32 .…”

Section: To Illuminate Human Signaling Pathways Orchestrating the Trasupporting

confidence: 90%

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Ochsner

Pillich

McKenna

2020

Preprint

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15Identifying transcriptional responses that are most consistently associated with 16 experimental coronavirus (CoV) infection can help illuminate human cellular signaling 17 pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from 18 publically archived CoV infection transcriptomic datasets into consensus regulatory 19 signatures, or consensomes, that rank genes based on their transcriptional 20 responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2 21 subtypes. We computed overlap between genes with elevated rankings in the CoV 22 consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly 23 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we 24 identified robust overlap between their highly ranked genes and high confidence targets 25 of signaling pathway nodes with known roles in CoV infection. We then developed a 26 series of use cases that illustrate the utility of the CoV consensomes for hypothesis 27 generation around mechanistic aspects of the cellular response to CoV infection. We 28 make the CoV infection consensomes and their universe of underlying data points freely 29 accessible through the Signaling Pathways Project web knowledgebase. 30 31 65 points in a series of use cases illuminates previously uncharacterized intersections 66 between CoV infection and human cellular signaling pathways. The CoV infection 67 consensome and its underlying datasets provide researchers with a unique and freely 68 accessible framework within which to generate and pressure test hypotheses around 69 human cellular signaling pathways impacted by CoV infection.70 71 72 73 5 Results 74Generation of the CoV consensomes 75 We first set out to generate a set of consensomes ranking human genes based on the 76 frequency of their significant differential expression in response to infection by MERS, 77 SARS1 and SARS2 CoVs. To do this we searched the Gene Expression Omnibus 78 (GEO) and ArrayExpress databases to identify datasets involving infection of human 79 cells by these species. From this initial collection of datasets, we next carried out a 80 three step quality control check as previously described (Ochsner et al., 2019), yielding 81 a total of 3,041,047 million data points in 111 experiments from 25 independent CoV 82 infection transcriptomic datasets (Supplementary information, Section 1). Using these 83 curated datasets, we next generated consensomes for each CoV species, as well as 84 one ranking genes across all CoV infection experiments (ALL CoV). As a reference 85 consensome for a virus whose transcriptional impact on human cells has been studied 86 in depth, we also generated a consensome for human influenza A virus (IAV) infection. 87The Supplementary information files contain the full human ALL CoV (Section 2), MERS 88 (Section 3), SARS1 (Section 4), SARS2 (Section 5) and IAV (Section 6) infection 89 transcriptomic consensomes. To assist researchers in inferring transcriptional regulation 90 of sig...

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Section: To Illuminate Human Signaling Pathways Orchestrating the Trasupporting

confidence: 90%

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Ochsner

Pillich

McKenna

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…HCT intersections for nodes originally characterized as having a general role in RNA Pol II transcription, including TBP ( q -values: SARS1, 2e-10; SARS2, 6e-23; MERS, 3e-16), GTF2B/TFIIB ( q -values: SARS1, 7e-10; SARS2, 3e-23; MERS, 9e-14) and GTF2F1 ( q -values: SARS1, 2e-4; SARS2, 2e-13; MERS, 5e-5) were strong across all CoVs, and particularly noteworthy in the case of SARS2. In the case of GTF2B, these data are consistent with previous evidence identifying it as a specific target for orthomyxovirus 43 , and the herpes simplex 44 and hepatitis B 45 viruses. Moreover, a recent preprint has identified a high confidence interaction between GTF2F2 and the SARS2 NSP9 replicase 34 .…”

Section: Resultssupporting

confidence: 90%

Consensus transcriptional regulatory networks of coronavirus-infected human cells

2020

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Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

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“…MS data from S. cerevisiae and from human cells identify RNAPII, TFIIB, and TFIIF amongst the strongest interactors of Mediator (75,86), and these factors are necessary for reinitiation (25). Notably, another recent study revealed that human genes which require de novo RNAPII recruitment for induction of transcription depend on TFIIB availability (87). It is unclear how transcription factors modulate reinitiation, and in vivo evidence of reinitiation and scaffold complexes is lacking (32).…”

Section: Discussionmentioning

confidence: 99%

PPARβ/δ recruits NCOR and regulates transcription reinitiation of ANGPTL4

Legrand

Bretscher

Zielke

et al. 2019

Nucleic Acids Research

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In the absence of ligands, the nuclear receptor PPARβ/δ recruits the NCOR and SMRT corepressors, which form complexes with HDAC3, to canonical target genes. Agonistic ligands cause dissociation of corepressors and enable enhanced transcription. Vice versa, synthetic inverse agonists augment corepressor recruitment and repression. Both basal repression of the target gene ANGPTL4 and reinforced repression elicited by inverse agonists are partially insensitive to HDAC inhibition. This raises the question how PPARβ/δ represses transcription mechanistically. We show that the PPARβ/δ inverse agonist PT-S264 impairs transcription initiation by decreasing recruitment of activating Mediator subunits, RNA polymerase II, and TFIIB, but not of TFIIA, to the ANGPTL4 promoter. Mass spectrometry identifies NCOR as the main PT-S264-dependent interactor of PPARβ/δ. Reconstitution of knockout cells with PPARβ/δ mutants deficient in basal repression results in diminished recruitment of NCOR, SMRT, and HDAC3 to PPAR target genes, while occupancy by RNA polymerase II is increased. PT-S264 restores binding of NCOR, SMRT, and HDAC3 to the mutants, resulting in reduced polymerase II occupancy. Our findings corroborate deacetylase-dependent and -independent repressive functions of HDAC3-containing complexes, which act in parallel to downregulate transcription.

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Viral targeting of TFIIB impairs de novo polymerase II recruitment and affects antiviral immunity

Cited by 17 publications

References 87 publications

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Consensus transcriptional regulatory networks of coronavirus-infected human cells

PPARβ/δ recruits NCOR and regulates transcription reinitiation of ANGPTL4

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