Evidence map›Paper›PMID 40234583›Full record

ArticleScientific reports2025

Association analysis of maize stem vascular bundle micro-characteristics with yield components based on micro-CT and identification of related genes.

Yanru Wang, Ying Zhang, Guanmin Huang, Jinglu Wang, Lujia Lv, Shuaihao Zhao, Xianju Lu, Minggang Zhang, Minkun Guo, Changyu Zhang and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 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

13 authors.

Yanru Wang *Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Ying Zhang *Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. zhangying2507@163.com.
Guanmin HuangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Jinglu WangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Lujia LvBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Shuaihao ZhaoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Xianju LuBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Minggang ZhangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Minkun GuoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Changyu ZhangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Qingmei MenBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Xinyu GuoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. guoxy73@163.com.
Chunjiang ZhaoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. zhaocj@nercita.org.cn.

Funding

National Nature Science Foundation of China No. 32330075the Beijing Natural Science Foundation JQ24037the Construction of Collaborative Innovation Centre of Beijing Academy and Forestry Science KJCX20240406
6 · The paper itself

Abstract

The distribution pattern of vascular bundles and microstructure characteristics significantly impact crop yield. Previous studies have primarily focused on investigating the micro-phenotypic characteristics and genetic regulation of individual internode, neglecting the exploration of the relationship between different internodes. This study, for the first time, comprehensively analyzed multi-scale phenotypic information of stem cross-sections, zones, and vascular bundles in three different internodes (basal third internode, ear internode and highest internode) of 268 inbred maize lines using Micro-computed tomography scanning. Key findings revealed that basal third internode and ear internode exhibited more stable microscopic characteristics than highest internode. Inbred lines with higher numbers of vascular bundle and well-developed inner zone in ear internode exhibited better yield characteristics, particularly in the kernel number per row. Genome-wide association analysis respectively identified 15, 1 and 1 putative candidate genes in basal third internode, ear internode and highest internode. These genes encode a variety of enzymes, such as oxidases, synthetases, ligase enzyme and protein kinases. Notably, Zm00001d042490 may be an important putative candidate gene for The number of vascular bundles in the periphery zone and corn grain traits. This study provides an important theoretical basis and genetic resources for accurately identifying different internode phenotypes of maize stalks, potentially advancing the selection of high-yielding, high-quality maize varieties.

Indexed as

Genes, PlantPlant StemsPlant Vascular BundleZea maysGenome-Wide Association StudyPhenotypeQuantitative Trait LociX-Ray MicrotomographyCandidate geneDifferent internodesGrain yield componentsMaizeMicroscopic traits

Identifiers

PMID40234583
PMCPMC12000327

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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.