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Record W4411624692 · doi:10.1128/msystems.00587-25

Multi-omics characterization of a lytic phage targeting <i>Listeria monocytogenes</i>

2025· article· en· W4411624692 on OpenAlexafffund
Stevan Cucić, Leena Putzeys, Maarten Boon, Dion Lepp, Rob Lavigne, Cezar M. Khursigara, Hany Anany

Bibliographic record

VenuemSystems · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersBijzonder Onderzoeksfonds UGentAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaHORIZON EUROPE Framework ProgrammeEuropean Commission
KeywordsLytic cycleBiologyListeria monocytogenesTranscriptomeListeriaBacteriophageGeneDNA microarrayTranscription (linguistics)Phage displayGene expressionGeneticsMicrobiologyBacteriaVirusEscherichia coliAntibody

Abstract

fetched live from OpenAlex

ABSTRACT Listeria monocytogenes is a foodborne pathogenic bacterium that can persist in food-processing environments. Strictly lytic Listeria phages have shown promise as biosanitation and biocontrol agents. However, little is known about the molecular progression of phage expression and the host gene expression profile it elicits in Listeria . In this work, the P100-like phage CKA15 was characterized using a proteogenomics-based approach to identify virion-associated proteins, Illumina-based RNA-seq to analyze time-resolved host and phage transcript abundance during infection, and ONT-cappable-seq to experimentally determine the operon structure of the phage genome. We detected 29 phage-encoded putative particle-associated proteins. During infection, a progressive decrease in host transcript abundance and an increase in phage transcript abundance are observed. The progression of phage gene expression indicates a switch in functions from hypothetical at 5 min; nucleic acid metabolism at 15; structural proteins at 25; and DNA packaging, tail assembly, and lysis at 40 min post-infection. Using ONT-cappable-seq, we identified 81 phage transcription start sites (TSS) and 66 transcription termination sites (TTS). We used motif analysis to identify two classes of promoters, corresponding to early and late infection stages. Profound changes in the host transcriptome became evident 5 min post-infection. GO enrichment and KEGG pathway analysis indicate a downregulation of host transcription factor expression and an upregulation of translation, cobalamin biosynthesis, and propanediol metabolism. This research contributes to our systems-level understanding of the infection process of a strictly lytic phage infecting an important foodborne pathogen. IMPORTANCE Listeria monocytogenes is an important foodborne pathogenic bacterium that contributes to significant mortality worldwide. Since bacteriophages have evolved diverse mechanisms to take over their host bacteria, studying phage interactions with pathogenic bacteria enables researchers to develop novel ways of controlling pathogenic bacteria and tools to study them. Detection of phage particle-associated proteins using mass spectrometry combined with transcriptomic techniques that determine the operon structure of the phage genome, time-resolved transcript abundance of phage, as well as host transcripts, comprises powerful approaches for phage characterization. Moreover, these analyses provide a starting point for hypothesis generation in relation to different aspects of the biology of phages infecting L. monocytogenes , including phage particle assembly, gene regulation, host takeover, and bacterial response to phage infection.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.249
Teacher spread0.237 · 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 designBench or experimental
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

Citations5
Published2025
Admission routes2
Has abstractyes

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