ArticleAntimicrobial agents and chemotherapy2020
Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community.
Article in Antimicrobial agents and chemotherapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Strengthen or Weaken: Evolutionary Directions of Cross-Feeding After Formation.Environmental microbiology reports · 2025Review
- Mutualism breakdown underpins evolutionary rescue in an obligate cross-feeding bacterial consortium.Nature communications · 2025Article
- Valnemulin restores colistin sensitivity against multidrug-resistant gram-negative pathogens.Communications biology · 2024Article
- The physiology and genetics of bacterial responses to antibiotic combinations.Nature reviews. Microbiology · 2022Review
- Extracellular Metabolism Sets the Table for Microbial Cross-Feeding.Microbiology and molecular biology reviews : MMBR · 2021Review
- Positive interactions within and between populations decrease the likelihood of evolutionary rescue.PLoS computational biology · 2021Article
- Ignoring social distancing: advances in understanding multi-species bacterial interactions.Faculty reviews · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.