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Genomic Discovery of Robust Molecular Markers Differentiating Lactobacillaceae Genera and Providing Novel Tools for Functional Insights

2025· preprint· en· W4411855571 on OpenAlexaff
Sarah Bello, Radhey S. Gupta

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputational biologyBiologyEvolutionary biology

Abstract

fetched live from OpenAlex

Background: Members of the family Lactobacillaceae, encompassing 23 distinct genera, play essential roles in food fermentation processes such as wine, yogurt, and cheese production and contribute significantly to human health through their probiotic properties. Despite their importance, accurately distinguishing between Lactobacillaceae genera has been challenging due to the absence of reliable biochemical or molecular markers. Currently, these genera are primarily differentiated based on phylogenetic relationships. Methods: To address this limitation, we have performed comprehensive phylogenomic and comparative analyses of protein sequences from 411 publicly available Lactobacillaceae genomes. Results: The results of these analyses have identified 171 novel conserved signature indels (CSIs), within proteins involved in diverse cellular functions, which are specific for the species from different Lactobacillaceae genera. The taxon-specificities of these CSIs make them robust molecular markers for differentiation of Lactobacillaceae genera and for functional insights. Using these taxon-specific CSIs and the AppIndels.com server, we were able to successfully predict the taxonomic affiliation of 112 uncharacterized genomes of Lactobacillus isolates, demonstrating the practical utility of these CSIs for genus-level identification and classification. Structural analyses on representative CSIs specific for Lactobacillaceae genera reported here show that all examined CSIs are located in surface-exposed loops of proteins, suggesting their potential roles in genus-specific functional traits, such as interaction with specific proteins and ligands, host interactions, or environmental adaptations. Conclusions: The CSIs identified here not only provide reliable tools for diagnostic and taxonomic studies but also open new avenues for exploring the functional diversity and biotechnological potential of species from different Lactobacillaceae genera.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.126
GPT teacher head0.272
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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