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Record W4403660979 · doi:10.1101/2024.10.21.619399

The <i>Streptococcus pyogenes</i> mannose phosphotransferase system (Man-PTS) influences antimicrobial activity and niche-specific nasopharyngeal infection

2024· preprint· en· W4403660979 on OpenAlexaff
Amanda C. Marple, Blake A. Shannon, Aanchal Rishi, Lana Estafanos, Brent D. Armstrong, Verónica Guariglia-Oropeza, Stephen W. Tuffs, John K. McCormick

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsStreptococcus pyogenesPEP group translocationAntimicrobialMicrobiologyMannosePhosphotransferaseNicheBiologyPhosphorylationGeneticsBiochemistryBacteriaEscherichia coliGeneStaphylococcus aureus

Abstract

fetched live from OpenAlex

ABSTRACT Streptococcus pyogenes is a human-adapted pathogen that causes a variety of infections including pharyngitis and skin infections, and although this bacterium produces many virulence and host colonization factors, how S. pyogenes competes with the host microbiota is not well understood. Here we detected antimicrobial activity produced from S. pyogenes MGAS8232 that was able to prevent the growth of Micrococcus luteus . This activity was produced when cells were grown in 5% CO 2 and in M17 media supplemented with galactose; however, evaluation of the phenotype with the addition of alternative sugars coupled with genome sequencing experiments revealed the antimicrobial phenotype was not related to classical bacteriocins. To further determine genes involved in the production of this activity, a transposon mutant library in S. pyogenes MGAS8232 was generated. The transposon screen identified the mannose phosphotransferase system (Man-PTS), a major sugar transporter in S. pyogenes , as important for the antimicrobial phenotype. Additional loss-of-function transposon mutants linked to the antimicrobial activity were identified to also be involved in alternative sugar utilization and additionally, the Man-PTS was also further identified from a secondary mutation in a bacteriocin operon mutant. Sugar utilization profiles in all the Man-PTS mutants demonstrated that galactose, mannose, and N-acetylglucosamine utilization was impaired in different Man-PTS mutants. In vitro RNA-seq experiments in high and low glucose concentrations further identified the Man-PTS as a glucose transporter; however, there was no transcriptional regulators or virulence factors affected with the loss of the Man-PTS. A clean deletion in the Man-PTS demonstrated defects in a mouse model of nasopharyngeal infection. Overall, the ability of S. pyogenes to utilize alternative sugars presented by glycans seems to play a role in acute infection and interactions with the endogenous microbial population existing in the nasopharynx. IMPORTANCE Streptococcus pyogenes causes a wide range of infections and is responsible for over 500,000 deaths per year due to invasive infections and post-infection sequelae. The most common clinical manifestation of S. pyogenes however are acute infections such as pharyngitis or impetigo. S. pyogenes can adapt to its environment through alternative sugar metabolism and in this study, we identified an antimicrobial phenotype that was not bacteriocin-related but a by-product of alternative sugar metabolism. Evidently, the mannose phosphotransferase system, a well-studied sugar transporter, was involved in production of the antimicrobial, and was also important for S. pyogenes to utilize alternative sugars and establish nasopharyngeal infection, but not skin infection. Overall, this study identified potential strategies used by S. pyogenes for interactions with the endogenous microbiota and further elucidated the importance of sugar metabolism in acute 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 score0.002
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)0.0020.001

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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
Published2024
Admission routes1
Has abstractyes

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