Evidence map›Paper›PMID 28558655›Full record

ArticleBMC plant biology2017

The common transcriptional subnetworks of the grape berry skin in the late stages of ripening.

Ryan Ghan, Juli Petereit, Richard L Tillett, Karen A Schlauch, David Toubiana, Aaron Fait, Grant R Cramer

Open access · goldAbstract read
In one paragraph

Article in BMC plant biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
16.2field-weighted citation impact, top 1% 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

29 citing papers in PubMed, 66 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Linking Autophagy to Potential Agronomic Trait Improvement in Crops.International journal of molecular sciences · 2022
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  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

7 authors at 2 institutions in 2 countries.

Ryan GhanDepartment of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, 89557, USA.
Juli PetereitNevada INBRE Bioinformatics Core, University of Nevada, Reno, NV, 89557, USA.
Richard L TillettNevada INBRE Bioinformatics Core, University of Nevada, Reno, NV, 89557, USA.
Karen A SchlauchDepartment of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, 89557, USA.
David ToubianaTelekom Innovation, Laboratories and Cyber Security Research Center, Department of Information, Systems Engineering, Ben Gurion University, Beer Sheva, Israel.
Aaron FaitBen-Gurion University of the Negev, Jacob Blaustein Institutes for Desert Research, 84990, Midreshet Ben-Gurion, Israel.
Grant R CramerDepartment of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, 89557, USA. cramer@unr.edu.ORCID 0000-0002-2374-4091
University of Nevada, Reno · USBen-Gurion University of the Negev · IL

Funding

Outreach/Diversity/Pipeline ProjectP20GM103440 · NIGMS · UNIVERSITY OF NEVADA RENO · PI Julia Petereit · 2012 to 2026
$58.0M
NIGMS NIH HHS P20 GM103440
6 · The paper itself

Abstract

backgroundWine grapes are important economically in many countries around the world. Defining the optimum time for grape harvest is a major challenge to the grower and winemaker. Berry skins are an important source of flavor, color and other quality traits in the ripening stage. Senescent-like processes such as chloroplast disorganization and cell death characterize the late ripening stage.

resultsTo better understand the molecular and physiological processes involved in the late stages of berry ripening, RNA-seq analysis of the skins of seven wine grape cultivars (Cabernet Franc, Cabernet Sauvignon, Merlot, Pinot Noir, Chardonnay, Sauvignon Blanc and Semillon) was performed. RNA-seq analysis identified approximately 2000 common differentially expressed genes for all seven cultivars across four different berry sugar levels (20 to 26 °Brix). Network analyses, both a posteriori (standard) and a priori (gene co-expression network analysis), were used to elucidate transcriptional subnetworks and hub genes associated with traits in the berry skins of the late stages of berry ripening. These independent approaches revealed genes involved in photosynthesis, catabolism, and nucleotide metabolism. The transcript abundance of most photosynthetic genes declined with increasing sugar levels in the berries. The transcript abundance of other processes increased such as nucleic acid metabolism, chromosome organization and lipid catabolism. Weighted gene co-expression network analysis (WGCNA) identified 64 gene modules that were organized into 12 subnetworks of three modules or more and six higher order gene subnetworks. Some gene subnetworks were highly correlated with sugar levels and some subnetworks were highly enriched in the chloroplast and nucleus. The petal R package was utilized independently to construct a true small-world and scale-free complex gene co-expression network model. A subnetwork of 216 genes with the highest connectivity was elucidated, consistent with the module results from WGCNA. Hub genes in these subnetworks were identified including numerous members of the core circadian clock, RNA splicing, proteolysis and chromosome organization. An integrated model was constructed linking light sensing with alternative splicing, chromosome remodeling and the circadian clock.

conclusionsA common set of differentially expressed genes and gene subnetworks from seven different cultivars were examined in the skin of the late stages of grapevine berry ripening. A densely connected gene subnetwork was elucidated involving a complex interaction of berry senescent processes (autophagy), catabolism, the circadian clock, RNA splicing, proteolysis and epigenetic regulation. Hypotheses were induced from these data sets involving sugar accumulation, light, autophagy, epigenetic regulation, and fruit development. This work provides a better understanding of berry development and the transcriptional processes involved in the late stages of ripening.

Indexed as

Gene Regulatory NetworksCircadian ClocksFruitGene Expression ProfilingGenes, PlantVitisChromosome organizationCircadian clockEpigenetic modificationFruit developmentGrape berryNetwork analysisRNA-seqTranscriptomicsVitis vinifera L

Identifiers

PMID28558655
PMCPMC5450095
OpenAlexW2617832706

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.