Evidence map›Paper›PMID 27725824›Full record

ReviewFrontiers in plant science2016

Recent Developments in Systems Biology and Metabolic Engineering of Plant-Microbe Interactions.

Vishal Kumar, Mehak Baweja, Puneet K Singh, Pratyoosh Shukla

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
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

32 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Investigating the Ocular Surface Microbiome: What Can It Tell Us?Clinical ophthalmology (Auckland, N.Z.) · 2023
    Review
  9. Article
  10. Review
  11. Review
  12. Metagenomic profiling of ocular surface microbiome changes inFrontiers in cellular and infection microbiology · 2022
    Article
  13. Review
  14. Review
  15. Emerging Molecular Tools for Engineering Phytomicrobiome.Indian journal of microbiology · 2021
    Review
  16. Article
  17. Metabolic modelling approaches for describing and engineering microbial communities.Computational and structural biotechnology journal · 2021
    Review
  18. Review
  19. Review
  20. Review
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

4 authors.

Vishal KumarEnzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University Rohtak, India.
Mehak BawejaEnzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University Rohtak, India.
Puneet K SinghEnzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University Rohtak, India.
Pratyoosh ShuklaEnzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University Rohtak, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microorganisms play a crucial role in the sustainability of the various ecosystems. The characterization of various interactions between microorganisms and other biotic factors is a necessary footstep to understand the association and functions of microbial communities. Among the different microbial interactions in an ecosystem, plant-microbe interaction plays an important role to balance the ecosystem. The present review explores plant-microbe interactions using gene editing and system biology tools toward the comprehension in improvement of plant traits. Further, system biology tools like FBA (flux balance analysis), OptKnock, and constraint-based modeling helps in understanding such interactions as a whole. In addition, various gene editing tools have been summarized and a strategy has been hypothesized for the development of disease free plants. Furthermore, we have tried to summarize the predictions through data retrieved from various types of sources such as high throughput sequencing data (e.g., single nucleotide polymorphism detection, RNA-seq, proteomics) and metabolic models have been reconstructed from such sequences for species communities. It is well known fact that systems biology approaches and modeling of biological networks will enable us to learn the insight of such network and will also help further in understanding these interactions.

Indexed as

CRISPR-Casgene editingplant–microbe interactionssignalingsystems biology

Identifiers

PMID27725824
PMCPMC5035732

What OpenQuestion holds

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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.