Integrated metabolomic, transcriptomic and computational analyses reveal geographical variation and potential anti-osteoarthritis bioactive compounds in Arenga pinnata pith
5 affiliations
- 1Faculty of Information Engineering, Nantong Institute of Technology
- 2Faculty of Data Science and Information Technology, INTI International University
- 3Department of Information Engineering, Heilongjiang Communications Polytechnic
- 4Faculty of Artificial Intelligence, Dongguan City University
- 5Faculty of Intelligent Engineering, Guangxi City Vocational University
Abstract
Arenga pinnata pith is an underexplored plant resource whose chemical and transcriptional characteristics may vary substantially among geographical populations. Here, we performed a comparative multi-omics analysis of pith samples collected from Hainan, Jiangxi, and Taiwan in China, as well as Thailand and Indonesia, using untargeted metabolomics and transcriptome sequencing. Metabolomic profiling detected 1,707 metabolite features spanning major classes including amino acids and derivatives, flavonoids, phenolic acids, alkaloids, and lipids. Clear geographical differences were observed in both metabolite composition and accumulation patterns. Taiwan samples showed the greatest overall metabolite diversity and abundance, whereas pathway enrichment highlighted recurrent differences in flavonoid and phenylpropanoid biosynthesis, amino-acid metabolism, lipid metabolism, central carbon metabolism, and glutathione metabolism. Transcriptomic profiling likewise revealed pronounced population-level differentiation. Hainan and Jiangxi exhibited comparatively similar expression patterns, while the Thailand population showed the strongest transcriptional divergence, including 10,720 and 10,284 differentially expressed genes relative to Hainan and Taiwan, respectively. Flavonoid-related variation observed in the metabolomic dataset further motivated a secondary exploratory analysis in which quercetin and naringenin were used as representative flavonoid scaffolds to investigate possible osteoarthritis (OA)-related molecular associations. Network analysis identified AKT1, MMP9, PTGS2, PPARG, MMP2, and MMP13 as highly connected candidate targets. Independent analysis of the human OA dataset GSE114007 identified DDIT3, KLF10, CFI, and MAFF as candidate OA-associated biomarkers, and molecular docking predicted favorable interactions of quercetin and naringenin with AKT1 and MMP9. These OA-related results are computational and hypothesis-generating and should not be interpreted as evidence of anti-OA activity of A. pinnata pith. Collectively, this study defines substantial geographical variation in the metabolomic and transcriptomic profiles of A. pinnata pith and provides a comparative framework for resource evaluation, chemical characterization, and future experimental investigation of selected flavonoid-related constituents.
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BibTeX
@article{xie2026integrated,
title = {{Integrated metabolomic, transcriptomic and computational analyses reveal geographical variation and potential anti-osteoarthritis bioactive compounds in Arenga pinnata pith}},
author = {XiangXin Xie and Marini Othman and HongYun Chen and XiMing Li and JunYan Li and JinMei Xie and ZhiHao Liu},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1974434},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1974434/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated metabolomic, transcriptomic and computational analyses reveal geographical variation and potential anti-osteoarthritis bioactive compounds in Arenga pinnata pith
AU - XiangXin Xie
AU - Marini Othman
AU - HongYun Chen
AU - XiMing Li
AU - JunYan Li
AU - JinMei Xie
AU - ZhiHao Liu
PY - 2026
DA - 2026/10/21
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1974434
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1974434/full
AB - Arenga pinnata pith is an underexplored plant resource whose chemical and transcriptional characteristics may vary substantially among geographical populations. Here, we performed a comparative multi-omics analysis of pith samples collected from Hainan, Jiangxi, and Taiwan in China, as well as Thailand and Indonesia, using untargeted metabolomics and transcriptome sequencing. Metabolomic profiling detected 1,707 metabolite features spanning major classes including amino acids and derivatives, flavonoids, phenolic acids, alkaloids, and lipids. Clear geographical differences were observed in both metabolite composition and accumulation patterns. Taiwan samples showed the greatest overall metabolite diversity and abundance, whereas pathway enrichment highlighted recurrent differences in flavonoid and phenylpropanoid biosynthesis, amino-acid metabolism, lipid metabolism, central carbon metabolism, and glutathione metabolism. Transcriptomic profiling likewise revealed pronounced population-level differentiation. Hainan and Jiangxi exhibited comparatively similar expression patterns, while the Thailand population showed the strongest transcriptional divergence, including 10,720 and 10,284 differentially expressed genes relative to Hainan and Taiwan, respectively. Flavonoid-related variation observed in the metabolomic dataset further motivated a secondary exploratory analysis in which quercetin and naringenin were used as representative flavonoid scaffolds to investigate possible osteoarthritis (OA)-related molecular associations. Network analysis identified AKT1, MMP9, PTGS2, PPARG, MMP2, and MMP13 as highly connected candidate targets. Independent analysis of the human OA dataset GSE114007 identified DDIT3, KLF10, CFI, and MAFF as candidate OA-associated biomarkers, and molecular docking predicted favorable interactions of quercetin and naringenin with AKT1 and MMP9. These OA-related results are computational and hypothesis-generating and should not be interpreted as evidence of anti-OA activity of A. pinnata pith. Collectively, this study defines substantial geographical variation in the metabolomic and transcriptomic profiles of A. pinnata pith and provides a comparative framework for resource evaluation, chemical characterization, and future experimental investigation of selected flavonoid-related constituents.
SN - 1664-462X
ER -