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Abstract B002: A CRISPR/Cas9 screening strategy for identifying modifiers of the viral mimicry response

2022· article· en· W4311194927 on OpenAlexaff
Raymond Chen, Ilias Ettayebi, Daniel D. De Carvalho

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsBiologyMolecular mimicryCell biologyTrans-activating crRNAInterferonCRISPRGeneticsImmune systemCas9Gene

Abstract

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Abstract Viral mimicry is the induction of a cellular antiviral response triggered by endogenous nucleic acids rather than exogenous viral infection. Such nucleic acids are sensed by cytosolic pattern recognition receptors and interpreted by the cell as an infection, leading to activation of an antiviral signaling cascade that results in interferon (IFN) signaling and subsequent upregulation of interferon-stimulated genes (ISGs). While downstream effects of ISGs are diverse and vary from direct induction of apoptosis to adaptive immune system engagement, the end result of viral mimicry is the culling of the ‘infected’ cancer cell. The viral mimicry response was first described as a mechanism of the FDA-approved DNA demethylating agents azacytidine (AZA) and decitabine (DAC). By removing transcriptionally repressive DNA methylation, these compounds allow normally-silenced repetitive elements of the genome to be expressed as double-stranded RNA (dsRNA) species that trigger viral mimicry. Since then, a body of literature has emerged implicating viral mimicry as a common mechanism underlying many different therapies and drug targets, including histone modifiers (e.g., LSD1, EZH2) as well as non-epigenetic targets such as cell cycle regulators (e.g., CDK4/6). The diversity of viral mimicry-inducing targets highlights the need for a systematic and discovery-based method to identify novel drug targets. Here, we accomplish this through the development of a viral mimicry cell reporter system composed of a green fluorescent protein (GFP) reporter coupled to the interferon-stimulated response element (ISRE) for CRISPR/Cas9-based pooled library screening. Using ISRE stimulation and subsequent GFP expression as a readout for viral mimicry activation, flow cytometry-based methods can be used to quantify the population of cells undergoing viral mimicry. Exogenous treatments of type I IFN or the synthetic dsRNA analogue polyinosinic-polycytidylic acid in the absence or presence of the JAK/STAT inhibitor ruxolitinib were used in conjunction with flow cytometry to validate the robustness and specificity of the reporter. To date, two preliminary screens in colorectal and lung cancer cell lines (LIM1215 and A549, respectively) have been performed using this system with a pooled single guide RNA library targeting epigenetic modifiers. These proof-of-principle experiments revealed two candidate hits – the histone methyltransferase SETDB1 and the lysine demethylase KDM5C. Follow-up validation experiments confirm GFP expression and viral mimicry activation in cells with single-gene knockouts for SETDB1 and KDM5C and demonstrate enhanced activation in combination with decitabine, as well as provide rationale for further study of these hits as therapeutic targets. Our work thus far demonstrates the feasibility of our approach and indicates the utility of our system as a novel tool for studying viral mimicry biology. Citation Format: Raymond Chen, Ilias Ettayebi, Daniel D. De Carvalho. A CRISPR/Cas9 screening strategy for identifying modifiers of the viral mimicry response. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr B002.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.114
GPT teacher head0.466
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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