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Record W3011628947 · doi:10.3389/fmicb.2020.00397

Binding of Phage-Encoded FlaGrab to Motile Campylobacter jejuni Flagella Inhibits Growth, Downregulates Energy Metabolism, and Requires Specific Flagellar Glycans

2020· article· en· W3011628947 on OpenAlexafffund
Jessica Sacher, Asif Shajahan, James Butcher, Robert T. Patry, Annika Flint, David R. Hendrixson, Alain Stintzi, Parastoo Azadi, Christine M. Szymanski

Bibliographic record

VenueFrontiers in Microbiology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of OttawaUniversity of Alberta
FundersNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCanadian Institutes of Health ResearchAlberta InnovatesU.S. Department of Energy
KeywordsCampylobacter jejuniFlagellumBiologyVirulenceGlycanMicrobiologyGeneCampylobacterPathogenCell biologyBacteriaGeneticsGlycoprotein

Abstract

fetched live from OpenAlex

Many bacterial pathogens display glycosylated surface structures that contribute to virulence, and targeting these structures is a viable strategy for pathogen control. The foodborne pathogen Campylobacter jejuni expresses a vast diversity of flagellar glycans, and flagellar glycosylation is essential for its virulence. Little is known about why C. jejuni encodes such a diverse set of flagellar glycans, but it has been hypothesized that evolutionary pressure from bacteriophages (phages) may have contributed to this diversity. However, interactions between Campylobacter phages and host flagellar glycans have not been characterized in detail. Previously, we observed that Gp047 (now renamed FlaGrab), a conserved Campylobacter phage protein, binds to C. jejuni flagella displaying the nine-carbon monosaccharide 7-acetamidino-pseudaminic acid, and that this binding partially inhibits cell growth. However, the mechanism of this growth inhibition, as well as how C. jejuni might resist this activity, is not well understood. Here we use RNA-Seq to show that FlaGrab exposure leads C. jejuni 11168 cells to downregulate expression of energy metabolism in a flagellar motor-dependent manner, which is consistent with a model whereby FlaGrab binding transmits a signal through flagella that leads to retarded cell growth. Using immunogold labeling and transmission electron microscopy, we identify two C. jejuni strains naturally resistant to FlaGrab binding. Through genetic analysis and mass spectrometric analysis of the flagellar glycans of these strains, we highlight possible roles for the acetamidino-pseudaminic acid transferase gene pseD (including possible phase-variable expression), for motility-associated (maf) and DUF2920-containing genes, and for display of as-yet uncharacterized flagellar glycans in C. jejuni resistance to Gp047 binding. Overall, we have further characterized the interaction between the phage-encoded flagellar glycan-binding protein and C. jejuni, both in terms of mechanism of action and mechanism of resistance. Our results suggest that C. jejuni encodes as-yet unidentified mechanisms for generating flagellar glycan diversity, and point to phage proteins as an exciting lens through which to study bacterial surface glycans.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.188
Teacher spread0.175 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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