Tri-Functional CRISPR Screen Reveals Overexpression of <i>QDR2</i> and <i>QDR3</i> Transporters Increase Fumaric Acid Production in <i>Kluyveromyces marxianus</i>
Bibliographic record
Abstract
Organic acids such as fumaric acid are widely used in the food and beverage industry as acidulants and preservatives, while also serving as versatile precursors for industrially relevant compounds. Fumaric acid is still predominantly produced through petroleum-derived processes. To enhance production efficiency and diversify supply, we are engineering Kluyveromyces marxianus as a biosynthetic platform from renewable feedstocks. In previous work, we have established K. marxianus Y-1190 as a host for lactose valorization based on its high growth rate on lactose and its tolerance for acid conditions. Here, we establish a trifunctional genome-wide library for K. marxianus using CRISPR activation, interference, and deletion to allow identification of gene expression perturbations that enhance tolerance to fumaric acid. We determined that deletion of ATP7, encoding a subunit of the mitochondrial F 1 F 0 ATP synthase, and overexpression of QDR2 and QDR3, two previously uncharacterized members of the 12-spanner H + antiporter (DHA1) family in K. marxianus, can enhance fumaric acid tolerance. We also found that integrated overexpression of both QDR2 and QDR3 in a Δ FUM1 background strain improved titers of fumaric acid production from 0.26 to 2.16 g L –1 . Together, these results highlight roles for membrane transport and mitochondrial function in enabling fumaric acid tolerance and production in K. marxianus .
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 | 0.000 |
| 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) | 0.002 | 0.001 |
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.
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".