Genomic Discovery of Taxon-Specific Molecular Markers for <em>Lactobacillaceae</em> Genera
Bibliographic record
Abstract
Background: Members of the family Lactobacillaceae, comprising 36 genera, play vital roles in food fermentation (e.g., wine, yogurt, and cheese production) and contribute significantly to human health through their probiotic properties. Despite their importance, species from different genera are primarily distinguished by phylogenetic clustering and genomic similarity matrices, and no consistent molecular, biochemical, or physiological traits are known that are uniquely found in species from different genera. Methods: To address this limitation, we conducted comprehensive phylogenomic and comparative analyses of protein sequences from 410 publicly available Lactobacillaceae genomes. Results: Based on these analyses, we identified 167 novel conserved signature indels (CSIs) in proteins involved in diverse cellular functions, each specific to a particular genus within the Lactobacillaceae family. These taxon-specific CSIs serve as robust molecular markers for genus-level differentiation and have potential applications in functional and diagnostic studies. Using these markers and the AppIndels.com server, we successfully predicted the genus-level affiliation of 111 uncharacterized Lactobacillus isolates. Structural analysis of representative CSIs from four genera revealed their consistent location in surface-exposed protein loops, suggesting possible roles in genus-specific protein–protein or protein–ligand interactions. Conclusions: The identified CSIs provide novel molecular markers for the robust differentiation of species from different Lactobacillaceae genera, offering new tools for exploring the functional traits unique to each genus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".