Evidence map›Paper›PMID 37418376›Full record

ArticlePloS one2023

Gene regulatory network inference in soybean upon infection by Phytophthora sojae.

Brett Hale, Sandaruwan Ratnayake, Ashley Flory, Ravindu Wijeratne, Clarice Schmidt, Alison E Robertson, Asela J Wijeratne

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Review
  4. Article
  5. Review
  6. 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

7 authors.

Brett HaleMolecular Biosciences Graduate Program, Arkansas State University, State University, AR, United States of America.
Sandaruwan RatnayakeArkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America.
Ashley FloryArkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America.
Ravindu WijeratneHouston High School, Germantown, TN, United States of America.
Clarice SchmidtDepartment of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America.
Alison E RobertsonDepartment of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America.
Asela J WijeratneArkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America.ORCID 0000-0002-7817-6351

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.

Indexed as

ArabidopsisPhytophthoraDisease ResistanceGene Regulatory NetworksGlycine maxHumansPlant Diseases

Identifiers

PMID37418376
PMCPMC10328377

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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.