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Biofilm and Planktonic Bacteria Differentially Express a Small Molecule (<3kD), Heat‐Stable, Protease‐Resistant Factor that Affects Bovine Neutrophil Function in a NF‐Kappa β‐Independent Mechanism

2020· article· en· W3016853725 on OpenAlexaffabout
Joey Lockhart, André G. Buret, Douglas W. Morck

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiofilmMicrobiologyBacteriaChemotaxisBiologyNeutrophil extracellular trapsInflammationChemistryImmunologyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Introduction Chronic bacterial infections and the associated inflammation have a significant impact in both veterinary and human medicine. Bacterial biofilms are a major cause of the persistent inflammation observed in chronic infections and nearly 80% of all infections involve biofilms. Biofilms are typically polymicrobial in composition and many of these mixed‐infections involve anaerobic bacteria. Recent evidence demonstrates that immune cells often disregard the signals released from biofilm bacteria, and ineffectual clearance of biofilms may explain the chronicity of this sort of infection. A mixed‐species model of anaerobic biofilm growth was developed to evaluate medically relevant biofilms and their interactions with bovine neutrophils. Aims To generate mixed‐species bacterial biofilms composed of the two opportunistic pathogens Fusobacterium necrophorum and Porphyromonas levii , and to employ the in vitro system to investigate factors associated with biofilms that may limit neutrophil functional ability. Methods Mixed‐species biofilm formation was verified with SEM, CLSM and viable cell counts. Neutrophil oxidative burst was assessed with a fluorescent microplate assay for hydrogen peroxide. Chemotaxis was measured with a Transwell transmigration assay. Extracellular factors released from planktonic cultures and biofilms were assessed by size‐exclusion filtration, lipopolysaccharide (LPS) removal, protease and heat treatment. Mechanistic experiments to study neutrophil responses to these supernatants were conducted with specific inhibitors of function, such as NF‐κβ signaling (BAY‐11) and inflammasome activation (glyburide). Results Neutrophils exposed to planktonic bacterial supernatants showed significantly elevated oxidative (4‐fold increase in fluorescence) and chemotactic responses compared to neutrophils exposed to biofilm products from identical bacteria. LPS plays a significant role in the stimulation of neutrophils; in this experiment biofilms produced substantially more LPS than planktonic bacteria despite inducing less neutrophil response. Removal of LPS resulted in similar neutrophil responses to planktonic and biofilm supernatants. Size‐exclusion filtration of bacterial supernatants and subsequent exposure to neutrophils revealed that the active stimulatory molecule is below 3 kD and is released strictly from planktonic cultures. Intensive heat and protease treatment of the <3kD fractions did not alter neutrophil functional responses. Treatment with a NF‐κβ inhibitor demonstrated that neutrophils respond specifically to planktonic supernatants independently of NF‐κβ signaling. Conclusions We describe a heat‐stable, protease‐resistant, non‐LPS, small (<3kD) factor produced by in vitro planktonic cultures of F. necrophorum and P. levii that activates bovine neutrophils. Neutrophils did not exhibit the same oxidative and chemotactic responses upon exposure to in vitro generated biofilm supernatants and these differential responses are the result of a NF‐κβ independent mechanism, perhaps through an inflammasome‐mediated process. Support or Funding Information Funding provided by the University of Calgary Grant 10002461 to DW Morck.

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 score0.005

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 head0.029
GPT teacher head0.238
Teacher spread0.209 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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