Quantification of Endospore-Forming Firmicutes by Quantitative PCR with the Functional Gene<i>spo0A</i>
Bibliographic record
Abstract
Bacterial endospores are highly specialized cellular forms that allow endospore-forming Firmicutes (EFF) to tolerate harsh environmental conditions. EFF are considered ubiquitous in natural environments, in particular, those subjected to stress conditions. In addition to natural habitats, EFF are often the cause of contamination problems in anthropogenic environments, such as industrial production plants or hospitals. It is therefore desirable to assess their prevalence in environmental and industrial fields. To this end, a high-sensitivity detection method is still needed. The aim of this study was to develop and evaluate an approach based on quantitative PCR (qPCR). For this, the suitability of functional genes specific for and common to all EFF were evaluated. Seven genes were considered, but only spo0A was retained to identify conserved regions for qPCR primer design. An approach based on multivariate analysis was developed for primer design. Two primer sets were obtained and evaluated with 16 pure cultures, including representatives of the genera Bacillus, Paenibacillus, Brevibacillus, Geobacillus, Alicyclobacillus, Sulfobacillus, Clostridium, and Desulfotomaculum, as well as with environmental samples. The primer sets developed gave a reliable quantification when tested on laboratory strains, with the exception of Sulfobacillus and Desulfotomaculum. A test using sediment samples with a diverse EFF community also gave a reliable quantification compared to 16S rRNA gene pyrosequencing. A detection limit of about 10(4) cells (or spores) per gram of initial material was calculated, indicating this method has a promising potential for the detection of EFF over a wide range of applications.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".