Evidence map›Paper›PMID 38029174›Full record

ArticleFrontiers in microbiology2023

Competition-cooperation mechanism between

Caifeng Li, Lixin Yin, Xiaoqing He, Yi Jin, Xuli Zhu, Rongling Wu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 19% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Preparing a Dual-SpeciesMolecular pharmaceutics · 2025
    Article
  3. Mechanisms of host-bacterial interactions duringFrontiers in veterinary science · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Caifeng LiCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Lixin YinCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Xiaoqing HeCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Yi JinCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Xuli ZhuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Rongling WuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Beijing Forestry University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging. Methods: In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions. Results: By designing and implementing a co-culture experiment for 100 pairs of Discussion: By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.

Indexed as

competition-cooperation mechanismEscherichia coliinterspecific interactionStaphylococcus aureussystems mapping

Identifiers

PMID38029174
PMCPMC10657823
OpenAlexW4388413717

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.