Design of RNA-based condensates for enzymatic pathway engineering:Ph.D. Dissertation by Martin Vincent Gobry
Bibliographic record
Abstract
Engineering cell factories is central for the production of numerous pharmaceutical and food-related compounds. However, maintaining the host cell’s homeostasis while driving the expression of an exogenous metabolic pathway poses significant challenges. One promising strategy to enhance metabolic efficiency is the clustering and up-concentration of enzymes within microcompartments, which can improve yield and provide better control over metabolic rates. The scope of this PhD project was to develop artificial microcompartments in the form of modular RNA phase-separating scaffolds, capable of recruiting and clustering proteins to offer a new tool for enzymatic pathway control. Two distinct approaches to phase-separating RNA were explored: "RNA nanostars" and "Trinucleotide Repeat Sequences. RNA nanostars are three-way junction single-stranded RNAs equipped with kissing loops at the end of each arm, enabling them to phase-separate co-transcriptionally through internanostar kissing loop interactions. A key advancement in this work was the development of a switchable kissing loop, allowing for controlled condensate formation and dissolution via a strand-displacement mechanism targeting the kissing loop interactions. Additionally, the controlled clustering of proteins was investigated using a nanostar design with an extra arm carrying a protein-binding aptamer. We characterised the recruitment of cargo fluorescent proteins fused to RNA-binding domains to these RNA condensates, which involved optimisation of the nanostar designs, with particular focus on the arm length. Furthermore, trinucleotide repeat sequences (e.g., CAG ×47 repeats), known for their inherent ability to condensate, were optimized for cloning and expression in yeast. These trinucleotide repeat-based condensates were functionalised by incorporating protein-binding aptamer arrays to recruit fluorescent proteins and enzymes for pathway compartmentalisation. Overall, we characterised the RNA scaffolds in vitro before initiating preliminary work on the expression of nanostar- and trinucleotide repeat-based condensates in E. coli and S. cerevisiae, respectively. Bridging the gap between in vitro and cellular expression proved particularly challenging, but significantly advanced our understanding of RNA-based condensate behavior in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".