Integrated RNA-seq and small RNA-seq profiling reveals tissue-associated transcriptional programs and candidate miRNA–mRNA relationships across tea floral tissues

Introduction Tea flowers comprise anatomically and functionally specialized tissues, yet the transcriptional and post-transcriptional programs that distinguish these tissues remain poorly characterized. Methods In this study, flower buds of Camellia sinensis cv. ‘Fudingdabaicha’ at the bud-white stage were dissected into seven tissues: ovary, outer green petal, inner white petal, filament, sepal, anther, and style. RNA-seq and small RNA-seq libraries were prepared from the same 21 biological samples, enabling direct comparison and integration of mRNA and miRNA expression profiles. Results Across the 21 pairwise tissue comparisons, 33,333 nonredundant differentially expressed genes were identified, revealing substantial variation in transcriptomic divergence among tissues. The outer green and inner white petals had the most similar transcriptomic profiles, and the genes that distinguished them were enriched mainly in plastid-associated and photosynthetic processes. By contrast, the anther and style showed the greatest transcriptomic divergence, involving metabolic, structural, and signaling functions. Weighted gene co-expression network analysis identified 24 modules, including three representative modules whose eigengenes showed positive correlations with the anther, sepal, and style traits. These modules were enriched in processes related to pollen germination and polarized growth, cell wall remodeling and organ growth, and RNA metabolism and chromatin-associated developmental regulation, respectively. RT-qPCR analysis of six representative genes showed tissue expression patterns broadly concordant with the RNA-seq data. Small RNA profiling identified 215 miRNAs, including 54 homologs of known plant miRNA families and 161 putative novel miRNAs; 175 miRNAs were differentially expressed among tissues. Intersecting psRNATarget predictions with candidate hub genes from the three representative modules yielded 45 candidate miRNA–mRNA relationships involving 39 miRNAs and 28 candidate hub genes. Six representative predicted pairs showed significant negative correlations across the matched samples. Discussion These findings characterize tissue-dependent mRNA and miRNA expression programs in tea flowers and prioritize candidate genes and miRNA–mRNA pairs for functional studies of floral tissue differentiation and reproductive development.

Authors

Institutions

Publication Details

Journal
Frontiers in Plant Science
Published
2026-09-14
DOI
https://doi.org/10.3389/fpls.2026.1941110
Primary Topic
Plant Molecular Biology Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Integrated RNA-seq and small RNA-seq profiling reveals tissue-associated transcriptional programs and candidate miRNA–mRNA relationships across tea floral tissues

Feipeng Sun, Shuangping Heng, Jinyan Yang, Guangzhi Mao et al.
Frontiers in Plant Science
Plant Molecular Biology Research
article

Integrated RNA-seq and small RNA-seq profiling reveals tissue-associated transcriptional programs and candidate miRNA–mRNA relationships across tea floral tissues

Feipeng Sun, Shuangping Heng, Jinyan Yang, Guangzhi Mao, Tianyu Li, Wei Zhang, Qingying Xie, Ling Xu, Feng Xing
article en

Abstract

Introduction Tea flowers comprise anatomically and functionally specialized tissues, yet the transcriptional and post-transcriptional programs that distinguish these tissues remain poorly characterized. Methods In this study, flower buds of Camellia sinensis cv. ‘Fudingdabaicha’ at the bud-white stage were dissected into seven tissues: ovary, outer green petal, inner white petal, filament, sepal, anther, and style. RNA-seq and small RNA-seq libraries were prepared from the same 21 biological samples, enabling direct comparison and integration of mRNA and miRNA expression profiles. Results Across the 21 pairwise tissue comparisons, 33,333 nonredundant differentially expressed genes were identified, revealing substantial variation in transcriptomic divergence among tissues. The outer green and inner white petals had the most similar transcriptomic profiles, and the genes that distinguished them were enriched mainly in plastid-associated and photosynthetic processes. By contrast, the anther and style showed the greatest transcriptomic divergence, involving metabolic, structural, and signaling functions. Weighted gene co-expression network analysis identified 24 modules, including three representative modules whose eigengenes showed positive correlations with the anther, sepal, and style traits. These modules were enriched in processes related to pollen germination and polarized growth, cell wall remodeling and organ growth, and RNA metabolism and chromatin-associated developmental regulation, respectively. RT-qPCR analysis of six representative genes showed tissue expression patterns broadly concordant with the RNA-seq data. Small RNA profiling identified 215 miRNAs, including 54 homologs of known plant miRNA families and 161 putative novel miRNAs; 175 miRNAs were differentially expressed among tissues. Intersecting psRNATarget predictions with candidate hub genes from the three representative modules yielded 45 candidate miRNA–mRNA relationships involving 39 miRNAs and 28 candidate hub genes. Six representative predicted pairs showed significant negative correlations across the matched samples. Discussion These findings characterize tissue-dependent mRNA and miRNA expression programs in tea flowers and prioritize candidate genes and miRNA–mRNA pairs for functional studies of floral tissue differentiation and reproductive development.

Frontiers in Plant ScienceVol. 17
Xinyang Normal University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking Union Medical College Hospital (CN)
Life in Land
Openalex Percentile: Top 13%
Plant Molecular Biology 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.