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Development of stable co‐culture of <i>P. aeruginosa</i> and <i>S. aureus</i> in Artificial Sputum Medium

2021· article· en· W3170377965 on OpenAlexaff
Stanislavs Vasiljevs, Arya Gupta, Deborah L. Baines

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsSputumPseudomonas aeruginosaMicrobiologyStaphylococcus aureusMucinCystic fibrosisBacteriaMedicineBiologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Staphylococcus aureus (SA) and Pseudomonas aeruginosa (PA) are both opportunistic pathogens that are known to cause severe respiratory infections in humans. While these pathogens don't commonly cause infections in healthy individuals, they are often found in patients with chronic lung diseases, such as Cystic Fibrosis (CF). Data from people with CF disease indicates that CF lungs are predominantly infected with SA at a younger age with PA appearing later (<10 years) when it then outcompetes SA. There is, however, significant overlap where both pathogens co‐exist in the patient's lungs. While clinical data on prevalence is abundant, little data exists on mechanisms of competition of two pathogens in CF lungs. Artificial sputum medium (ASM) is a culture medium that was designed to mimic sputum from CF patients. It contains components of CF sputum, such as amino acids, mucin and DNA. It was shown that PA growth in the ASM is similar to the growth in the lungs of CF patients. We hypothesised that the ASM model could be adapted to study the interaction between SA and PA in a controlled environment that is relevant to the in vivo environment of CF lungs. SA (ATCC29213) and PA (H174) were grown in the artificial sputum media (ASM). Approximately 3x10 5 bacteria were added to 15 mL ASM and grown at 37 o C with constant shaking. Conditioned media was prepared by growing SA in ASM for 24 hours and then removing SA. SA appeared to adapt quicker to the nutrient‐poor environment of ASM than PA and enters logarithmic growth immediately upon incubation, as opposed to PA, which exhibited an 8‐hour lag period. When both pathogens are added simultaneously to the ASM, SA outcompeted PA and dominated the culture restricting the growth of PA. To better mimic in vivo conditions, ASM was conditioned by adding SA for 24 hours. This allowed creation of an environment that is similar to that found in the CF lungs with established SA infections. When both pathogens were added into the conditioned media, PA did not exhibit the lag phase and outcompeted SA, suppressing its growth. This effect remained even if SA outnumbered PA 10 000‐fold. In an attempt to create media that is most similar to that of CF sputum, ASM was mixed 50:50 with conditioned media, which represents the constant renewal of secreted products and nutrients seen in the lungs. When both pathogens were added to the media, rapid growth was observed in both pathogens for 24 hours. After 24 hours the population of SA decreased from 1x10 9 CFU/ml to 3x10 6 CFU/ml, while the PA population remained stable at 1x10 8 CFU/ml. After 48 hours, the population of both pathogens remained constant. By modifying ASM with conditioned ASM, we have created a stable in vitro co‐culture of Staphylococcus aureus and Pseudomonas aeruginosa . Our model can be utilised to examine the competition and/or co‐existence between clinical strains and other bacterial species that reside within the lungs and identify factors which suppress pathogen growth.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.023
GPT teacher head0.299
Teacher spread0.276 · 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
GenreMethods

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 routes1
Has abstractyes

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