Evidence map›Paper›PMID 34728724›Full record

ArticleScientific reports2021

Functional disruption of cell wall invertase inhibitor by genome editing increases sugar content of tomato fruit without decrease fruit weight.

Kohei Kawaguchi, Rie Takei-Hoshi, Ikue Yoshikawa, Keiji Nishida, Makoto Kobayashi, Miyako Kusano, Yu Lu, Tohru Ariizumi, Hiroshi Ezura, Shungo Otagaki and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.5field-weighted citation impact, top 9% 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

22 citing papers in PubMed, 44 citations in OpenAlex.

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  6. ConstitutiveInternational journal of molecular sciences · 2025
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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

12 authors at 4 institutions in 1 country.

Kohei KawaguchiGraduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya, 464-8601, Japan.
Rie Takei-HoshiGraduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya, 464-8601, Japan.
Ikue YoshikawaGraduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya, 464-8601, Japan.
Keiji NishidaEngineering Biology Research Center, Kobe University, Chuo-ku, Kobe, 650-0047, Japan.
Makoto KobayashiRIKEN Center for Sustainable Resource Science, Yokohama, 230-0045, Japan.
Miyako KusanoRIKEN Center for Sustainable Resource Science, Yokohama, 230-0045, Japan.
Yu LuFaculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, 305-8572, Japan.
Tohru AriizumiTsukuba Plant Innovation Research Center, University of Tsukuba, Tsukuba, 305-8572, Japan.
Hiroshi EzuraTsukuba Plant Innovation Research Center, University of Tsukuba, Tsukuba, 305-8572, Japan.
Shungo OtagakiGraduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya, 464-8601, Japan.
Shogo MatsumotoGraduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya, 464-8601, Japan.
Katsuhiro ShiratakeGraduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya, 464-8601, Japan. shira@agr.nagoya-u.ac.jp.
Nagoya University · JPUniversity of Tsukuba · JPKobe University · JPRIKEN Center for Sustainable Resource Science · JP

Funding

Cross-ministerial Strategic Innovation Promotion Program (SIP) 14537460Grant-in-Aid for JSPS Research Fellow JP20J13580Grants-in-Aid for Scientific Research (KAKENHI) JP16K14850
6 · The paper itself

Abstract

Sugar content is one of the most important quality traits of tomato. Cell wall invertase promotes sucrose unloading in the fruit by maintaining a gradient of sucrose concentration between source leaves and fruits, while invertase inhibitor (INVINH) regulates this process. In this study, knock-out of cell wall INVINH in tomato (SlINVINH1) was performed by genome editing using, CRISPR/Cas9 and Target-AID technologies. Most of the genome-edited lines set higher soluble solid content (SSC) fruit than the original cultivar 'Suzukoma', while fruit weight was different among the genome-edited lines. From these genome-edited lines, three lines (193-3, 199-2, and 247-2), whose SSC was significantly higher than 'Suzukoma' and fruit weight were almost the same as the original cultivar, were selected. The fruit weight and overall plant growth of the two lines were comparable to those of the original cultivar. In contrast, the fructose and glucose contents in the mature fruits of the two lines were significantly higher than those of the original cultivar. The mature fruits of genome edited line 193-3 showed the highest sugar content, and the fructose and glucose contents were 29% and 36% higher than that of the original cultivar, respectively. Whole genome sequence data showed no off-target mutations in the genome-edited lines. Non-target metabolome analysis of mature fruits revealed that fructose was the highest loading factor in principal component analysis (PCA) between the genome-edited line and the original cultivar, and no unexpected metabolites appeared in the genome-edited line. In this study, we succeeded in producing tomato lines with high sugar content without a decrease in fruit weight and deterioration of plant growth by knock-out of SlINVINH1 using genome editing technology. This study showed that functional disruption of SlINVINH1 is an effective approach to produce tomato cultivars with high sugar content.

Indexed as

CRISPR-Cas SystemsGene Editingbeta-FructofuranosidaseCell WallFruitGene Expression Regulation, PlantPlant ProteinsPlants, Genetically ModifiedSolanum lycopersicumSugarsbeta-FructofuranosidasePlant ProteinsSugars

Identifiers

PMID34728724
PMCPMC8563804
OpenAlexW3209210081

What OpenQuestion holds

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LicenceCC BY
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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.