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A Candidate RNAi Screen Reveals Diverse RNA-Binding Protein Phenotypes in Drosophila Flight Muscle

2021· preprint· en· W3189003104 on OpenAlexfundno aff
Shao-Yen Kao, Elena Nikonova, Sabrina Chaabane, Albiona Sabani, Alexandra Martitz, Anja Wittner, Jakob Heemken, Tobias Straub, Maria L. Spletter

Bibliographic record

VenuePreprints.org · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersInstitute of GeneticsDeutsche Gesellschaft für MuskelkrankeDeutsche ForschungsgemeinschaftInternational Max Planck Research School for Environmental, Cellular and Molecular Microbiology
KeywordsGene knockdownBiologySarcomerePhenotypeAlternative splicingRNA splicingRNA interferenceMyogenesisCell biologyRNA-binding proteinSpliceosomeGene isoformGeneticsMyocyteGeneRNA

Abstract

fetched live from OpenAlex

The proper regulation of RNA processing is critical for muscle development and the fine-tuning of contractile ability between muscle fiber-types. RNA binding proteins (RBPs) regulate the diverse steps in RNA processing including alternative splicing, which generates fiber-type specific isoforms of structural proteins that confer contractile sarcomeres with distinct biomechanical properties. Alternative splicing is disrupted in muscle diseases such as myotonic dystrophy and dilated cardiomyopathy, and is altered after intense exercise as well as with aging. It is therefore important to understand splicing and RBP function, but currently only a small fraction of the hundreds of annotated RBPs expressed in muscle have been characterized. Here we demonstrate the utility of Drosophila as a genetic model system to investigate basic developmental mechanisms of RBP function in myogenesis. We find that RBPs exhibit dynamic temporal and fiber-type specific expression patterns in mRNA-Seq data and display muscle-specific phenotypes. We performed knockdown with 105 RNAi hairpins targeting 35 RBPs and report associated lethality, flight, myofiber and sarcomere defects, including flight muscle phenotypes for Doa, Rm62, mub, mbl, sbr, and clu. Interestingly, knockdown phenotypes of spliceosome components, as highlighted by phenotypes for A-complex components SF1 and Hrb87F (hnRNPA1), revealed level- and temporal-dependent myofibril defects. We further show that splicing mediated by SF1 and Hrb87F is necessary for Z-disc stability and proper myofibril development, and strong knockdown of either gene results in impaired localization of Kettin to the Z-disc. Our results expand the number of RBPs with a described phenotype in muscle and underscore the diversity in myofibril and transcriptomic phenotypes associated with splicing defects. Drosophila is thus a useful model to gain disease-relevant insight into cellular and molecular phenotypes observed when expression levels of splicing factors, spliceosome components and splicing dynamics are altered.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.328
Teacher spread0.269 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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