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Record W4360962726 · doi:10.26443/msurj.v18i1.195

Uncovering the Regulators of CRISPR-Cas Immunity

2023· article· en· W4360962726 on OpenAlexaff
Idia Boncheva

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsCRISPRBiologyGenome editingQuorum sensingComputational biologyCRISPR interferenceGeneGenomeArchaeaRegulatorTrans-activating crRNAOrganismAcquired immune systemGeneticsImmune systemBiofilmBacteria

Abstract

fetched live from OpenAlex

The clustered regularly interspaced short palindromic repeats (CRISPR) system and the CRISPR-associated proteins (Cas) make up an adaptive immune mechanism used by many bacteria and archaea to protect themselves from invading genetic material. Despite the immense evolutionary advantage of the CRISPR-Cas system, it must be meticulously regulated as it has the potential to target endogenous genes and damage the host organism. Identifying the main regulators involved in this process and how they are influenced by distinct conditions are of great clinical interest, since this prokaryotic defense system can be exploited for genome editing and therapy development. This review aims to elucidate the regulation of the CRISPR system in bacterial communities—upon quorum sensing and alginate production in biofilms—and in stressed conditions—upon antibiotic treatment or induction of the Rcs response. Despite the intrinsic contradictions of the results gathered in this review, growth rate is identified as a potential unifying regulator of CRISPR immunity. Overall, the regulation of the CRISPR-Cas system is shown to be multi-dimensional and cross-sectional, to greatly vary amongst lineages, and to be highly sensitive to conditional changes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.404
Teacher spread0.361 · 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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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