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NLR singletons, pairs, and networks: evolution, assembly, and regulation of the intracellular immunoreceptor circuitry of plants

Current Opinion in Plant Biology(2019)10.1016/j.pbi.2019.04.007Source: DataRank Database

NLR singletons, pairs, and networks: evolution, assembly, and regulation of the intracellular immunoreceptor circuitry of plants is a research paper published in Current Opinion in Plant Biology (2019). On theSindex it has a DataRank of 0.850. It has been cited 288 times.

N/A
0.850DataRank · unranked
0.850
Open Access288 citations · base score 5.7
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

NLRs are modular plant and animal proteins that are intracellular sensors of pathogen-associated molecules. Upon pathogen perception, NLRs trigger a potent broad-spectrum immune reaction known as the hypersensitive response. An emerging paradigm is that plant NLR immune receptors form networks with varying degrees of complexity. NLRs may have evolved from multifunctional singleton receptors, which combine pathogen detection (sensor activity) and immune signalling (helper or executor activity) into a single protein, to functionally specialized interconnected receptor pairs and networks. In this article, we highlight some of the recent advances in plant NLR biology by discussing models of NLR evolution, NLR complex formation, and how NLR (mis)regulation modulates immunity and autoimmunity. Multidisciplinary approaches are required to dissect the evolution, assembly, and regulation of the immune receptor circuitry of plants. With the new conceptual framework provided by the elucidation of the structure and activation mechanism of a plant NLR resistosome, this field is entering an exciting era of research.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      0.850

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      Base score B(p)
      log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
      Network N(p)
      Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
      Damping factor d = 0.85
      DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
      Self-citations excluded
      Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

      Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

      Read the full methodology →

      Authors (3)

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