XanthoMoClo─A Robust Modular Cloning Genetic Toolkit for the Genera <i>Xanthobacter</i> and <i>Roseixanthobacter</i>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Interest in Xanthobacter species is increasing due to their unique metabolic capabilities. They can grow in both heterotrophic and fully autotrophic environments, including carbon dioxide, dinitrogen gas, and hydrogen as the sole carbon, nitrogen, and energy sources, respectively. Academic and industrial groups looking to leverage these metabolic properties are already using Xanthobacter strains for the sustainable production of food and commodities. However, only a handful of genetic parts and protocols exist in scattered genetic backgrounds, and there is an unmet need for reliable genetic engineering tools to manipulate Xanthobacter species. Here, we developed XanthoMoClo, a robust modular cloning genetic toolkit for Xanthobacter and Roseixanthobacter species and strains, providing extensive tools to transform them, manipulate their metabolism, and express genes of interest. The toolkit contains plasmid parts, such as replication origins, antibiotic selection markers, fluorescent proteins, constitutive and inducible promoters, a standardized framework to incorporate novel components into the toolkit, and a conjugation donor to transform Xanthobacter and Roseixanthobacter strains easily with no or minimal optimization. We validated these plasmid components in depth in three of the most commonly studied Xanthobacter strains: X. versatilis Py2, X. autotrophicus GZ29, and X. flavus GJ10, as well as in R. finlandensis VTT E-85241. Finally, we demonstrate robust toolkit functionality across 21 different species of Xanthobacter and Roseixanthobacter, comprising 23 strains in total. The XanthoMoClo genetic toolkit is available to the research community (through AddGene) and will help accelerate the genetic engineering of Xanthobacter to further their applications in sustainability and bioremediation efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".