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<i>Giardia</i> releases extracellular vesicles which can modulate growth and behavior of commensal bacteria

2021· article· en· W3171927008 on OpenAlexafffund
Affan Siddiq, Thibault Allain, George Dong, Martin Olivier, André G. Buret

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsMcGill UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrobiologyBacteriaGiardiaBiofilmBiologyExtracellularPopulationVesicleCell biologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION Extracellular vesicles (EVs) are a heterogeneous population of secreted vesicles that have been shown to play important roles in the pathophysiology of various parasitic diseases. The protozoan parasite Giardia duodenalis , which causes diarrheal disease, also produces EVs. However, their exact role in the pathogenesis of giardiasis remains to be fully understood. In this study, we examined whether Giardia EVs could mediate interactions with the commensal bacteria. This represents a novel role of Giardia EVs that has not been studied before. AIMS The aim of this research is to characterize Giardia extracellular vesicles and examine their effects on commensal bacterial. METHODS Extracellular vesicles (EVs) of G. duodenalis (isolate NF) were isolated using Qiagen Exo‐Easy Maxi Kit . The concentration and size of EVs were assessed using Nanosight track analysis (NTA). EVs were also characterized using transmission electron microscopy (TEM). Furthermore, we conducted a proteomic analysis of EVs using liquid chromatography with tandem mass spectrometry. To examine the effects of Giardia EVs on commensal bacteria, we used E. coli HB101 (lab strain) and Enterobacter cloacae (human isolate). The bacteria were incubated with Giardia EVs and their growth kinetics were examined. The swimming motility of EVs treated bacteria was assessed on a 0.3% agar. The ability of EVs treated bacteria to adhere to epithelial cells was examined using an adhesion assay. Finally, the biofilm forming ability of EVs treated bacteria was investigated using a crystal violet for biofilm quantification. RESULTS Our findings show that Giardia trophozoites release EVs that modulate the growth and behavior of commensal bacteria. Giardia EVs exerted bacteriostatic effects on E.coli HB101and E. cloacae . Additionally, Giardia EVs significantly increased the swimming motility of both E.coli HB101 and E. cloacae as well as their adhesion to the intestinal epithelial cells. Finally, Giardia EVs decreased the ability of E.coli HB101 to form biofilms. Proteomic analysis revealed that well characterized virulence factors were packaged in EVs. CONCLUSION This study indicates that Giardia EVscan mediate interactions with the commensal bacteria. EVs were shown to alter the behavior of bacteriaand increase their virulence.The presence of virulence factors in EVs shows thatthey have significant implications in pathophysiology .

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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