Optimization of secretion and surface localization of heterologous OVA protein in mycobacteria by using LipY as a carrier
Bibliographic record
Abstract
BACKGROUND: Mycobacterium bovis Bacille Calmette-Guérin (BCG) is not only used as a vaccine against tuberculosis but also protects against leprosy and is used as part of bladder cancer treatment to induce a protective immune response. However, protection by BCG vaccination is not optimal. To improve vaccine efficacy, recombinant BCG expressing heterologous antigens has been put forward to elicit antigen-specific cellular and humoral responses. Cell surface localized or secreted antigens induce better immune responses than their cytosolic counterparts. Optimizing secretion of heterologous proteins or protein fragments holds therefore unexplored potential for improving the efficacy of recombinant BCG vaccine candidates. Secretion of heterologous antigens requires crossing the mycobacterial inner and outer membrane. Mycobacteria have specialized ESX or type VII secretion systems that enable translocation of proteins across both membranes. Probing this secretion system could therefore be a valid approach to surface localize heterologous antigens. RESULTS: We show that ESX-5 substrate LipY, a lipase, can be used as a carrier for heterologous secretion of an ovalbumin fragment (OVA). LipY contains a PE domain and a lipase domain, separated by a linker region. This linker domain is processed upon secretion. Fusion of the PE and linker domains of LipY to OVA enabled ESX-5-dependent secretion of the fusion construct LipY-OVA in M. marinum, albeit with low efficiency. Subsequent random mutagenesis of LipY-OVA and screening for increased secretion resulted in mutants with improved heterologous secretion. Detailed analysis identified two mutations in OVA that improved secretion, i.e. an L280P mutation and a protein-extending frameshift mutation. Finally, deletion of the linker domain of LipY enhanced secretion of LipY-OVA, although this mutation also reduced surface association. Further analysis in wild type LipY showed that the linker domain is required for surface association. CONCLUSION: We show that the ESX-5 system can be used for heterologous secretion. Furthermore, minor mutations in the substrate can enhance secretion. Especially the C-terminal region seems to be important for this. The linker domain of LipY is involved in surface association. These findings show that non-biased screening approaches aid in optimization of heterologous secretion, which can contribute to heterologous vaccine development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".