Metagenomic Insights Unveil the Dominance of Undescribed Actinobacteria in Pond Ecosystem of an Indian Shrine
Bibliographic record
Abstract
Abstract Metagenomic analysis holds immense potential for identifying rare and uncharacterized microorganisms from many ecological habitats. Actinobacteria have been proved to be an excellent source of novel antibiotics for several decades. The present study was designed to delineate and understand the bacterial diversity with special focus on Actinobacteria from pond sediment collected from Sanjeeviraya Hanuman Temple, Ayyangarkulam, Kanchipuram, Tamil Nadu, India. The sediment had an average temperature (25.32%), pH (7.13), salinity (0.960 mmhos/cm) and high organic content (10.7%) posing minimal stress on growth condition of the microbial community. Subsequent molecular manipulations, sequencing and bioinformatics analysis of V3 and V4 region of 16S rRNA metagenomics analysis confirmed the presence of 40 phyla, 100 classes, 223 orders, 319 families and 308 genera in the sediment sample dominated by Acidobacteria (18.14%), Proteobacteria (15.13%), Chloroflexi (12.34), Actinobacteria (10.84%), Cyanobacteria (5.58%), Verrucomicrobia (3.37%), Firmicutes (2.28%), and, Gemmatimonadetes (1.63%). Among the Actinobacteria phylum, Acidothermus (29.68%) was the predominant genus followed by Actinospica (17.65%), Streptomyces (14.64%), Nocardia (4.55%) and Sinomonas (2.9%). Culture-dependent isolation of Actinobacteria yielded all strains of similar morphology to that of Streptomyces genus which clearly indicating that the traditional based technique is incapable of isolating majority of the non-Streptomyces or the so called rare Actinobacteria. Although Actinobacteria were among the dominant phylum, a close look at the species level indicated that only 15.2% within the Actinobacterial phylum could be assigned to cultured species. This leaves a vast majority of the Actinobacterial species yet to be explored with possible novel metabolites have special pharmaceutical and industrial application. It also indicates that the microbial ecology of pond sediment is neglected fields which need attention.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".