Diversity of Viral Communities in a Northern Temperate Lake through Metagenomics
Bibliographic record
Abstract
Viruses in oceanic environments play pivotal roles in regulating nutrient transfer and increasing rates of carbon transport. They have also been implicated in the regulation of micro-organism community dynamics through infection of successful hosts, the stimulation of primary productivity, and the termination of algal blooms. Through auxiliary metabolic genes stolen from host cells, viruses are also thought to influence biogeochemical cycles through gene expression during infection. Viruses have been comparatively well studied in oceanic environments, but there is a dearth of knowledge in their roles in freshwater environments. As toxins produced by harmful algal blooms (HABs) can challenge conventional drinking water operations, it is critical to understand the role viruses may play in their proliferation and potentially their management. Examining viral communities using metagenomics will allow for the characterization of the diversity of viruses and their potential roles in freshwater environments. To accomplish this, metagenomic sequencing was carried out on water samples from Big Turkey Lake (Ontario) over a seasonal period from 2018 to 2020. Multiple viral classifiers were first combined with a viral binning approach, and subsequently generated viral metagenome-assembled genomes (vMAGs) were then evaluated for quality. All contigs identified as viral were assigned taxonomy based on amino acid alignment through BLAST, and the vMAGs were further analyzed through the IMG/VR database to determine if there was notable alignment with viruses that have known hosts. Auxiliary metabolic gene analysis was performed using the KEGG orthology database as a reference. The majority of viral contigs across all samples were unable to be assigned taxonomy, indicating that there is a large amount of unknown diversity of viruses within Big Turkey Lake. Identified contigs were either assigned as bacteriophages from the order Caudovirales or eukaryotic algal viruses from the family Phycodnaviridae, with the presence of virophages observed in low abundance. Bacteriophage abundance remained relatively stable throughout the year with a spike in winter, while eukaryotic algal viruses were most abundant in the summer. Auxiliary metabolic genes related to folate biosynthesis, carbon metabolism, nitrogen fixation, and photosynthesis were identified, and genes related to lipid metabolism were also found during winter. Findings from this study will provide a baseline foundation of viral communities in Big Turkey Lake, allowing for further research into potential hosts for these viruses, and how they could influence the formation and termination of harmful algal blooms in freshwater.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".