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Record W2984480961 · doi:10.7554/elife.03125.030

Author response: Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics

2014· peer-review· en· W2984480961 on OpenAlexaffabout
Simon Roux, Alyse K. Hawley, Mónica Torres Beltrán, Melanie Scofield, Patrick Schwientek, Ramūnas Stepanauskas, Tanja Woyke, Steven Hallam, Matthew B. Sullivan

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyGenomeBacterial virusHost (biology)BacteriaMetagenomicsMicroorganismMicrobial population biologyBiogeochemical cycleViral replicationVirusEvolutionary biologyEcologyGeneticsGeneEscherichia coliBacteriophage

Abstract

fetched live from OpenAlex

Microorganisms help to drive a number of processes that recycle energy and nutrients, including elements such as carbon, nitrogen, and sulfur, around the Earth's ecosystems. Viruses that infect microbes can also affect these cycles by killing and breaking open microbial cells, or by reprogramming the cell's metabolism. However, as there are many different species of microbes and viruses —the vast majority of which cannot easily be grown in the laboratory— little is known about most virus–host interactions in natural ecosystems, especially in the oceans. In the world's oceans, the concentration of oxygen dissolved in the water changes in different regions and at different depths. ‘Oxygen minimum zones’ occur globally throughout the oceans at depths of 200–1000 meters, and climate change is causing these zones to expand and intensify. Although a lack of oxygen is sometimes considered detrimental to living organisms, oxygen minimum zones appear to be rich with microbial life that is adapted to thrive under oxygen-starved conditions. Sulfur-oxidizing bacteria are one of the most abundant groups of microbes in these oxygen minimum zones, and several of these bacteria are known to influence the recycling of chemical substances. Now, Roux et al. introduce a new method to identify viruses that infect the microbes in this environment, including those microbes that cannot be grown in the laboratory and which have previously remained largely unexplored. The genomes of 127 individual bacterial cells —collected from an oxygen minimum zone in western Canada— were examined. Roux et al. estimate that about a third of the sulfur-oxidizing bacterial cells are infected by at least one virus, but often multiple viruses infected the same bacterium. Five new genera (groups of one or more species) of viruses were also discovered and found to infect these bacteria. Looking for these new viral sequences in the DNA of this oxygen minimum zone's microbial community revealed that these newly discovered viruses persist in this region over several years. It also revealed that these viruses appear to only be found within the oxygen minimum zone. Roux et al. uncovered that these viruses carry genes that could manipulate how an infected bacterium processes sulfur-containing compounds; this is similar to previous observations showing that other viruses also influence cellular process (such as photosynthesis) in infected bacteria. As such, these newly discovered viruses might also influence the recycling of chemical elements within oxygen minimum zones. Together, Roux et al.'s findings provide an unprecedented look into a wild virus community using a method that can be generalized to uncover viruses in a data type that is quickly becoming more widespread: single cell genomes. This effort to understand virus–host interactions by looking in the genomes of individual cells now sets the stage for future efforts aimed to uncover the impact of viruses on bacteria in other environments across the globe.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0280.012

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.

Opus teacher head0.034
GPT teacher head0.292
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations3
Published2014
Admission routes2
Has abstractyes

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