Characterisation of Amino Acid Biosynthetic Pathways in Synechocystis sp. PCC 6803 via Analysis of Auxotrophic Mutants
Bibliographic record
Abstract
Cyanobacteria are an important phylum which play a key role in ecological processes such as nitrogen and carbon fixation, in addition to being a potential renewable platform for production of high value chemical precursors. Many stains of cyanobacteria also produce notable natural products such as herbicides, anti-microbial, and anti-fungal compounds. To utilise cyanobacteria more effectively, greater understanding of central metabolism would aid engineering strategies for redirection of metabolic flux to secondary metabolites. Even in well studied cyanobacteria such as Synechocystis sp. PCC 6803 (Synechocystis), many genes involved in the biosynthesis of amino acids, precursors for a range of chemicals and natural products, remain uncharacterised. In this study I attempted to characterise genes encoding for amino acid biosynthetic enzymes via generation of auxotrophic mutants, a process whereby a target gene is deleted while mutants are cultivated on media containing the metabolite synthesised by the deleted target pathway. An initial approach at generating auxotrophic mutants on BG11 media supplemented exclusively with a single amino acid yielded only partially segregated mutants. This suggests that native Synechocystis amino acid transporters were either absent, or inefficient at importing amino acids to compensate for loss of gene function. A second approach successfully obtained auxotrophic mutants in multiple amino acid biosynthesis pathways by using BG11 media supplemented with an oligopeptide mix. This is the first study in which an auxotrophic mutant was generated in Synechocystis using these methods. This thesis establishes and discusses methods of generating auxotrophic mutants to provide insight for further characterisation of proteins involved in amino acid biosynthesis.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".