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Record W3152693069 · doi:10.1101/2021.04.11.439338

Emergent RNA-RNA interactions can promote stability in a nascent phototrophic endosymbiosis

2021· preprint· en· W3152693069 on OpenAlexaff
Benjamin H. Jenkins, Finlay Maguire, Guy Leonard, Joshua D Eaton, Steven West, Benjamin E. Housden, Studholme David, Thomas A. Richards

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsDalhousie University
FundersLister Institute of Preventive MedicineUniversity of ExeterWellcome Trust
KeywordsEndosymbiosisEukaryoteBiologyArgonauteRNARNA interferenceCell biologyGeneGeneticsPlastidGenomeChloroplast

Abstract

fetched live from OpenAlex

ABSTRACT Eukaryote-eukaryote endosymbiosis was responsible for the spread of chloroplast (plastid) organelles. Stability is required for the metabolic and genetic integration that drives the establishment of new organelles, yet the mechanisms which act to stabilise nascent endosymbioses – between two fundamentally selfish biological organisms – are unclear. Theory suggests that enforcement mechanisms, which punish misbehaviour, may act to stabilise such interactions by resolving conflict. However, how such mechanisms can emerge in a nascent endosymbiosis has yet to be explored. Here, we propose that endosymbiont-host RNA-RNA interactions, arising from digestion of the endosymbiont population, can result in a cost to host growth for breakdown of the endosymbiosis. Using the model nascent endosymbiosis, Paramecium bursaria – Chlorella spp., we demonstrate that this mechanism is dependent on the host RNA-interference (RNAi) system. We reveal through small RNA (sRNA) sequencing that endosymbiont-derived mRNA released upon endosymbiont digestion can be processed by the host RNAi system into 23-nt sRNA. We predict multiple regions of shared sequence identity between endosymbiont and host mRNA, and demonstrate through delivery of synthetic endosymbiont sRNA that exposure to these regions can knock-down expression of complementary host genes, resulting in a cost to host growth. This process of host gene knock-down in response to endosymbiont-derived RNA processing by host RNAi factors, which we term ‘RNAi-collisions’, represents a mechanism which can promote stability in a nascent eukaryote-eukaryote endosymbiosis. By imposing a cost for breakdown of the endosymbiosis, endosymbiont-host RNA-RNA interactions may drive maintenance of the symbiosis across fluctuating ecological conditions and symbiotic status. SIGNIFICANCE STATEMENT Stable endosymbiosis between eukaryotic microbes has driven the evolution of further cellular complexity. Yet the mechanisms which can act to stabilise a nascent eukaryote-eukaryote endosymbiosis are unclear. Using the model nascent endosymbiotic system, Paramecium bursaria–Chlorella , we demonstrate that endosymbiont-host RNA-RNA interactions can drive a cost to host growth upon endosymbiont digestion, punishing the host for misbehaviour. These RNA-RNA interactions are facilitated by the host RNA-interference system. For endosymbiont mRNA sharing a high-level of sequence identity with host transcripts, this process can result in host gene knock-down. We propose that these endosymbiont-host RNA-RNA interactions–‘RNAi collisions’–represent a viable enforcement mechanism to sanction the host for breakdown of the endosymbiosis, promoting the stability of a nascent endosymbiotic interaction.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.016
GPT teacher head0.222
Teacher spread0.206 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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