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Record W4322626205 · doi:10.21203/rs.3.rs-2472749/v1

Lower viral evolutionary pressure under stable versus fluctuating conditions in subzero Arctic brines

2023· preprint· en· W4322626205 on OpenAlexaff
Zhi-Ping Zhong, Dean Vik, Josephine Z. Rapp, Olivier Zablocki, Heather Maughan, Ben Temperton, Jody W. Deming, Matthew B. Sullivan

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité Laval
FundersBiological and Environmental ResearchByrd Polar and Climate Research Center, Ohio State UniversityJoint Genome InstituteGordon and Betty Moore FoundationU.S. Department of EnergyNational Science Foundation
KeywordsBrineSeawaterArcticSea iceBiologyExtreme environmentEcologyArctic ice packOceanographyGeologyChemistryPaleontologyBacteria

Abstract

fetched live from OpenAlex

Abstract Background: Climate change threatens Earth’s ice-based ecosystems which currently offer archives and eco-evolutionary experiments in the extreme. Arctic cryopeg brine (marine-derived, within permafrost) and sea-ice brine, similar in subzero temperature and high salinity but different in temporal stability, are inhabited by microbes adapted to these extreme conditions. However, little is known about their viruses (community composition, diversity, interaction with hosts, or evolution) or how they might respond to geologically stable cryopeg versus fluctuating sea-ice conditions. Results: We used long- and short-read viromics and metatranscriptomics to study viruses in Arctic cryopeg brine, sea-ice brine, and underlying seawater, recovering 11,088 vOTUs (~species-level taxonomic unit), a 4.4-fold increase of known viruses in these brines. More specifically, the long-read-powered viromes doubled the number of longer (≥25 kb) vOTUs generated and recovered more hypervariable regions by >5-fold compared to short-read viromes. Distribution assessment, by comparing to known viruses in public databases, supported that cryopeg-brine viruses were of marine origin yet distinct from either sea-ice-brine or seawater viruses, while 94% of sea-ice-brine viruses also presented in seawater. A virus-encoded, ecologically important exopolysaccharide biosynthesis gene was identified, and many viruses (~half of metatranscriptome-inferred ‘active’ vOTUs) were predicted as actively infecting the dominant microbial genera Marinobacter and Polaribacterin cryopeg and sea-ice brines, respectively. Evolutionarily, microdiversity (intra-species genetic variations) analyses suggested that viruses within the stable cryopeg brine were under significantly lower evolutionary pressures than those in the fluctuating sea-ice environment, while many sea-ice-brine virus-tail genes were under positive selection, indicating virus-host co-evolutionary arms races. Conclusions: Our results confirmed the benefits of long-read-powered viromics in understanding the environmental virosphere through significantly improved genomic recovery, expanding viral discovery and the potential for biological inference. Evidence of viruses actively infecting the dominant microbes in subzero brines and modulating host metabolism underscored the potential impact of viruses on these remote and underexplored extreme ecosystems. Microdiversity results shed light on the different strategies viruses use to evolve and adapt when extreme conditions are stable versus fluctuating. Together, these findings verify the value of long-read-powered viromics and provide foundational data on viral evolution and virus-microbe interactions in Earth’s destabilized and rapidly disappearing cryosphere.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.075
GPT teacher head0.386
Teacher spread0.311 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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