Comparative metabolome and transcriptome analysis of foxtail millet cultivars with high and low grain quality

Consumer-driven demand for premium foxtail millet necessitates systematic identification of quality-determining metabolites. This investigation employed integrated metabolomic and transcriptomic analyses to compare two contrasting cultivars - the elite variety Jingu 21 (JG21) and traditional landrace Niumaobai (NMB) - during two critical grain-filling stages. 552 metabolites were identified in both cultivars, with 144 showing differential abundance. Comparative analysis of early (S2) to late (S4) grain-filling stage revealed 108 co-regulated metabolites alongside 78 JG21-exclusive and 72 NMB-specific metabolites, exhibiting differential accumulation patterns likely governing quality divergence. Differential metabolites were predominantly enriched in flavonoid and phenylpropanoid pathways between cultivars, suggesting potential roles in modulating color, nutritional quality, and grain texture. Co-expression network predictions revealed cultivar-specific regulatory associations, with 10 candidate genes potentially governing six pigmentation/nutrition-related flavonoid metabolites, while another four genes showed tentative correlations with five lignin pathway intermediates that may contribute to grain texture variations between JG21 and NMB cultivars. These findings provide mechanistic insights into metabolic determinants of millet quality while establishing a framework for targeted breeding strategies to enhance both nutritional value and sensory characteristics in foxtail millet.

Authors

Institutions

Publication Details

Journal
Food Chemistry Molecular Sciences
Published
2025-05-02
DOI
https://doi.org/10.1016/j.fochms.2025.100259
Primary Topic
GABA and Rice Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative metabolome and transcriptome analysis of foxtail millet cultivars with high and low grain quality

Xinjie Yao, P C Tian, Canran Zhang, Yuanhuai Han et al.
1 links
Food Chemistry Molecular Sciences
GABA and Rice Research
article

Comparative metabolome and transcriptome analysis of foxtail millet cultivars with high and low grain quality

Xinjie Yao, P C Tian, Canran Zhang, Yuanhuai Han, Xiaodong Liu, Hongying Li, Yiru Zhang
article en

Abstract

Consumer-driven demand for premium foxtail millet necessitates systematic identification of quality-determining metabolites. This investigation employed integrated metabolomic and transcriptomic analyses to compare two contrasting cultivars - the elite variety Jingu 21 (JG21) and traditional landrace Niumaobai (NMB) - during two critical grain-filling stages. 552 metabolites were identified in both cultivars, with 144 showing differential abundance. Comparative analysis of early (S2) to late (S4) grain-filling stage revealed 108 co-regulated metabolites alongside 78 JG21-exclusive and 72 NMB-specific metabolites, exhibiting differential accumulation patterns likely governing quality divergence. Differential metabolites were predominantly enriched in flavonoid and phenylpropanoid pathways between cultivars, suggesting potential roles in modulating color, nutritional quality, and grain texture. Co-expression network predictions revealed cultivar-specific regulatory associations, with 10 candidate genes potentially governing six pigmentation/nutrition-related flavonoid metabolites, while another four genes showed tentative correlations with five lignin pathway intermediates that may contribute to grain texture variations between JG21 and NMB cultivars. These findings provide mechanistic insights into metabolic determinants of millet quality while establishing a framework for targeted breeding strategies to enhance both nutritional value and sensory characteristics in foxtail millet.

Food Chemistry Molecular SciencesVol. 10
Shanxi Agricultural University (CN), Taiyuan Normal University (CN), Ningxia University (CN)
Zero hunger
Openalex Percentile: Top 94%
GABA and Rice Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.