Evidence map›Paper›PMID 32455748›Full record

ArticleCells2020

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions.

Tahila Andrighetti, Balazs Bohar, Ney Lemke, Padhmanand Sudhakar, Tamas Korcsmaros

Open access · goldAbstract read
In one paragraph

Article in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

23 citing papers in PubMed, 39 citations in OpenAlex.

  1. Review
  2. Microbial Omics.Progress in molecular and subcellular biology · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024
    Review
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Computational prediction and experimental validation ofFrontiers in cellular and infection microbiology · 2022
    Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. 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

5 authors at 3 institutions in 4 countries.

Tahila AndrighettiEarlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
Balazs BoharEarlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
Ney LemkeInstitute of Biosciences, São Paulo University (UNESP), Botucatu 18618-689 SP, Brazil.
Padhmanand SudhakarEarlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.ORCID 0000-0003-1907-4491
Tamas KorcsmarosEarlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
Norwich Research Park · GBEötvös Loránd University · HUUniversidade Estadual Paulista (Unesp) · BR

Funding

Biotechnology and Biological Sciences Research Council BB/CSP17270/1Biotechnology and Biological Sciences Research Council BB/J004529/1Biotechnology and Biological Sciences Research Council BB/P016774/1Biotechnology and Biological Sciences Research Council BB/R012490/1Biotechnology and Biological Sciences Research Council BBS/E/F/000PR10353Biotechnology and Biological Sciences Research Council BBS/E/F/000PR10355
6 · The paper itself

Abstract

Microbiome-host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe-host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions-as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn's disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

Indexed as

Gastrointestinal MicrobiomeHost-Pathogen InteractionsComputational BiologyGene OntologyHumansSignal Transductioncomputational pipelinemicrobiota–host interactionsnetwork diffusionnetworksprotein–protein interactionssystems biology

Identifiers

PMID32455748
PMCPMC7291277
OpenAlexW3027812246

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.