Comparative genome-scale analysis of niche-based stress-responsive genes in<i>Lactobacillus helveticus</i>strains
Bibliographic record
Abstract
Next generation sequencing technologies with advanced bioinformatic tools present a unique opportunity to compare genomes from diverse niches. The identification of niche-specific stress-responsive genes can help in characterizing robust strains for multiple applications. In this study, we attempted to compare the stress-responsive genes of a potential probiotic strain, Lactobacillus helveticus MTCC 5463, and a cheese starter strain, Lactobacillus helveticus DPC 4571, from a gut and dairy niche, respectively. Sequencing of MTCC 5463 was done using 454 GS FLX, and contigs were assembled using GS Assembler software. Genome analysis was done using BLAST hits and the prokaryotic annotation server RAST. The MTCC 5463 genome carried multiple orthologs of genes governing stress responses, whereas the DPC 4571 genome lacked in the number of major stress-response proteins. The absence of the bile salt hydrolase gene in DPC 4571 and its presence in MTCC 5463 clearly indicated niche adaptation. Further, MTCC 5463 carried higher copy numbers of genes contributing towards heat, cold, osmotic, and oxidative stress resistance as compared with DPC 4571. Through comparative genomics, we could thus identify stress-responsive gene sets required to adapt to gut and dairy niches.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".