Impact of Inactivation Methods on Biosafety and Antigen Reactivity of <i>Brucella melitensis</i> from the Perspective of Astral-DIA Proteomics Based on Antibody Immunoprecipitation Mass Spectrometry
Bibliographic record
Abstract
Abstract Effective Brucella inactivation is imperative for safe vaccine development, diagnostics, and sample handling, particularly in resource-limited regions lacking high-level containment facilities. This study investigated inactivation methods using the Rev.1 vaccine strain and three Brucella melitensis field isolates from Gansu (GS-XG, GS-SN, GS-MQ). Heat (80°C/95°C, 10-20 min) and formaldehyde (0.4%/0.6%, 48-72 h) inactivation were evaluated for biosafety and antigenicity. Rev.1 was completely inactivated by 80°C/95°C for 20 min or 0.6% formaldehyde for 48-72 h. Superior antigenicity, compared to phenol inactivation, was confirmed by ELISA and Western Blot. However, field isolates demonstrated greater resistance, surviving 80°C for 20 min and 0.4% formaldehyde for 72 h, necessitating stricter conditions (95°C, 20 min; 0.6% formaldehyde, 72 h) for their complete inactivation. Astral-DIA proteomics analyzed approximately 60% of the proteome (∼2000/3300 proteins), revealing 256, 311, and 318 differentially expressed proteins between 80°C/95°C heat, 48-h/72-h formaldehyde, and heat/formaldehyde methods, respectively. Gene Ontology and KEGG analyses indicated heat inactivation upregulated cellular structure proteins but downregulated metabolic pathways, with 95°C potentially damaging conformational epitopes. Formaldehyde treatment (48 h) stabilized soluble antigens, preserving ribosomal and regulatory protein epitopes, while 72-h treatment induced organelle disintegration. Protein-Protein Interaction networks suggested heat inactivation enhanced immunogenicity of membrane and stress proteins, suitable for targeted studies, whereas formaldehyde preserved broader epitopes, beneficial for whole-cell vaccines and multi-epitope screening. Inactivation methods must be tailored to specific strain characteristics and applications. Astral-DIA provides molecular insights into antigenicity loss, guiding future research on protein functions and epitope dynamics for precise brucellosis control.
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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 | 0.000 | 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) | 0.001 | 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".