Multi-year molecular quantification and ‘omics analysis of Planktothrix-specific cyanophage sequences from Sandusky Bay, Lake Erie
Bibliographic record
Abstract
Introduction Planktothrix agardhii is a microcystin-producing cyanobacterium found in Sandusky Bay, a shallow and turbid embayment of Lake Erie. Previous work in other systems has indicated that cyanophages are an important natural control factor of harmful algal blooms. Currently, there are few cyanophages that are known to infect P. agardhii, with the best-known being PaV-LD, a tail-less cyanophage isolated from Lake Donghu, China. Presented here is a molecular characterization of Planktothrix specific cyanophages in Sandusky Bay. Methods and Results Putative Planktothrix-specific viral sequences from metagenomic data from the bay in 2013, 2018, and 2019 were identified by two approaches: homology to known phage PaV-LD, or through matching CRISPR spacer sequences with Planktothrix host genomes. Several contigs were identified as having viral signatures, either related to PaV-LD or potentially novel sequences. Transcriptomic data from 2015, 2018, and 2019 were also employed for the further identification of cyanophages, as well as gene expression of select viral sequences. Finally, viral quantification was tested using qPCR in 2015–2019 for PaV-LD like cyanophages to identify the relationship between presence and gene expression of these cyanophages. Notably, while PaV-LD like cyanophages were in high abundance over the course of multiple years (qPCR), transcriptomic analysis revealed only low levels of viral gene expression. Discussion This work aims to provide a broader understanding of Planktothrix cyanophage diversity with the goals of teasing apart the role of cyanophages in the control and regulation of harmful algal blooms and designing monitoring methodology for potential toxin-releasing lysis events.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".