Evidence map›Paper›PMID 40249859›Full record

ArticlePlant biotechnology journal2025

Identification of HSSP1 as a regulator of soybean protein content through QTL analysis and Soy-SPCC network.

Huilin Tian, Yanbin Yin, Xin Li, Zhanguo Zhang, Shaowei Feng, Song Jin, Xue Han, Mingliang Yang, Chang Xu, Limin Hu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

14 authors.

Huilin Tian *National Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.ORCID https://orcid.org/0009-0002-9448-2233
Yanbin Yin *National Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Xin LiNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Zhanguo ZhangCollege of Arts and Sciences, Northeast Agricultural University, Harbin, China.
Shaowei FengNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Song JinNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Xue HanNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Mingliang YangNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Chang XuNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Limin HuNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Chunyan LiuNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Fanjiang KongGuangdong Provincial Key Laboratory of Plant Adaptation and Molecular Design, Guangzhou Key Laboratory of Crop Gene Editing, Innovative Center of Molecular Genetics and Evolution, School of Life Sciences, Guangzhou University, Guangzhou, China.ORCID https://orcid.org/0000-0001-7138-1478
Qingshan ChenNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.
Zhaoming QiNational Key Laboratory of Smart Farm Technology and System, National Research Center of Soybean Engineering and Technology, Northeast Agricultural University, Harbin, Heilongjiang, China.ORCID https://orcid.org/0000-0002-0657-9127

Funding

Heilongjiang Cooperation and Innovation Breeding Foundation LJGXCG2022-035Heilongjiang Provincial Postdoctoral Science Foundation LBH-Z23011Heilongjiang Provincial Postdoctoral Science Foundation LBH-Z24091National Key Research and Development Program of China 2021YFD1201103National Key Research and Development Program of China 2023ZD0403201-03National Natural Science Foundation of China 32201755National Natural Science Foundation of China 32301900National Natural Science Foundation of China 32472108Natural Science Foundation of Heilongjiang Province of China LH2021C026Natural Science Foundation of Heilongjiang Province of China ZL2024C007the China Agriculture Research System CARS-04-PS15the China Postdoctoral Science Foundation 2023MD744203the China Postdoctoral Science Foundation 2024MD753910The Hainan Seed Industry Laboratory and China National Seed Group B23YQ1503
6 · The paper itself

Abstract

Soybeans (Glycine max L. Merr.) are a major source of plant-based protein for human nutrition and livestock feed. Enhancing the protein content of soybean seeds is vital for meeting growing dietary needs and promoting sustainable agricultural practices. In this study, we first performed QTL (Quantitative Trait Loci) mapping analysis and constructed a Soybean Seed Protein Content Co-expression (Soy-SPCC) network to identify key genes associated with soybean seed protein accumulation. Next, we investigated the role of High Seed Storage Protein1 (HSSP1) in regulating soybean seed protein content through a comprehensive analysis. Functional validation through overexpression and gene knockout experiments demonstrated that HSSP1, a key component of the Soy-SPCC network, significantly influences seed storage protein levels. Particularly, HSSP1 enhances the expression of GmCG1 by binding directly to its cis-acting element, leading to increased protein content in soybean seeds. Furthermore, we performed a molecular module stacking breeding analysis of 120 candidate genes identified from the Soy-SPCC network, including HSSP1, to identify genetic variations associated with protein content. This study provides a novel perspective on soybean protein regulation. The identification of HSSP1 as a critical regulator offers valuable insights for developing high-protein soybean varieties and advancing breeding strategies aimed at improving soybean seed quality.

Indexed as

Glycine maxQuantitative Trait LociSeed Storage ProteinsSoybean ProteinsChromosome MappingGene Expression Regulation, PlantPlant ProteinsSeedsPlant ProteinsSeed Storage ProteinsSoybean Proteinsco‐regulation networkmolecular module stacking breedingseed protein contentsoybean

Identifiers

PMID40249859
PMCPMC12205858

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.