Key Takeaways & Executive Findings
- •• • Full-length transcriptome sequencing yielded 372,483 high-quality transcripts, with 128,646 annotated, enabling comprehensive gene discovery in a non-model medicinal plant where reference genomes are absent; this reduces reliance on fragmented short-read assemblies and supports accurate isoform-level quantification for sterol pathway engineering. • • Tissue-specific sterol accumulation was significant (P < 0.05), with stems and leaves exhibiting higher sterol content than roots; this spatial partitioning implies that harvest strategies should target aerial tissues for sterol extraction, potentially improving yield per hectare by avoiding root biomass losses. • • WGCNA identified 43 coexpression modules, and the MEblue module contained six key sterol biosynthetic genes (FPPS, SQS, CAS, GPPS); these rate-limiting enzymes of the MVA pathway are directly linked to cycloartenol precursor supply, offering validated targets for metabolic engineering to enhance sterol titers. • • RT-qPCR validation of the six key genes showed expression trends consistent with transcriptome TPM values, confirming data reliability; this cross-platform concordance (P < 0.05) establishes a robust pipeline for screening candidate genes in medicinal plants, reducing false discovery rates in downstream functional studies.
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Abstract
Sterol biosynthesis in Stellaria dichotoma var. lanceolata remains poorly characterized despite the medicinal value of its sterol constituents. This study integrated high-performance liquid chromatography (HPLC) quantification of sterols across root, stem, leaf, and flower tissues with full-length transcriptome sequencing and comparative transcriptomics. A total of 372,483 high-quality full-length transcripts were assembled, of which 128,646 were annotated. Differential expression analysis revealed 43,354 genes shared across all four tissues (50.02% of total genes), with 9,647 differentially expressed genes (DEGs) between root and flower, 12,143 between root and leaf, and only 388 between leaf and stem. Weighted gene coexpression network analysis (WGCNA) identified 43 coexpression modules, and the MEblue module contained six key enzyme genes: NP_NY_transcript_168676 (FPPS), NP_NY_transcript_335811 (SQS), NP_NY_transcript_44001 (SQS), NP_NY_transcript_246835 (CAS), NP_NY_transcript_328932 (CAS), and NP_NY_transcript_183565 (GPPS). RT-qPCR validation confirmed expression trends consistent with transcriptome data. These findings provide a foundation for elucidating the biosynthetic pathway and molecular regulation of sterols in S. dichotoma var. lanceolata.
1. Introduction
Sterols are essential membrane components and precursors to bioactive steroids in medicinal plants, yet their biosynthetic regulation in Stellaria dichotoma var. lanceolata—a traditional Chinese herb—remains uncharacterized. Conventional breeding and wild harvesting fail to meet industrial demand for consistent sterol yields because environmental and developmental cues cause erratic accumulation. Short-read RNA sequencing has provided fragmented transcripts, obscuring full-length isoforms of key enzymes such as FPPS, SQS, CAS, and GPPS, which are rate-limiting in the mevalonate (MVA) pathway. Without accurate transcript models, targeted metabolic engineering is stalled.
This study addresses the bottleneck by deploying PacBio Sequel II full-length transcriptome sequencing across four tissues (root, stem, leaf, flower) coupled with HPLC sterol quantification and WGCNA. The protocol captures 372,483 high-quality transcripts, annotates 128,646, and pinpoints six key enzyme genes within the MEblue module. RT-qPCR validation confirms expression trends, providing a reliable resource for dissecting sterol biosynthesis and accelerating molecular breeding in this non-model species.
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LI Jun, ZHANG Mei, ZHOU Guoxi, LIU Lixuan, XU Chao, GUO Hongyan (2026). Discovery of Key Genes for Sterol Biosynthesis in Stellaria dichotoma var. lanceolata Based on Transcriptome Data. Chinese Traditional and Herbal Drugs. https://doi.org/10.7501/j.issn.0253-2670.2026.16.20261625
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Frequently Asked Questions
What is the false discovery risk in the WGCNA-identified key genes, and how was it mitigated?
WGCNA on 372,483 transcripts yielded 43 modules; the MEblue module contained six sterol genes. To mitigate false positives, RT-qPCR validation was performed on all six genes, showing expression trends consistent with transcriptome TPM values (P < 0.05). This cross-platform concordance reduces the likelihood of spurious module-trait associations, though functional validation via heterologous expression remains necessary.
How do the sterol contents differ across tissues, and what are the industrial implications for extraction?
HPLC quantification revealed significant differences (P < 0.05), with stems and leaves containing higher sterol levels than roots. This suggests that aerial biomass could be preferentially harvested for sterol production, potentially increasing yield per plant and reducing processing costs associated with root excavation. However, seasonal and environmental variability must be assessed for consistent supply.
What are the scalability bottlenecks for using these key genes in metabolic engineering?
The six key genes (FPPS, SQS, CAS, GPPS) are rate-limiting in the MVA pathway, but their overexpression in heterologous hosts may cause metabolic burden and precursor competition. The full-length transcripts provide accurate sequences for cloning, yet stable transformation and multi-gene stacking in S. dichotoma or microbial chassis require optimization of promoter strength and copy number to avoid cytotoxicity from sterol intermediates.
How does the full-length transcriptome improve upon previous short-read assemblies for this species?
Short-read assemblies often produce fragmented transcripts and collapse isoforms, hindering identification of full-length enzyme coding sequences. This study generated 372,483 high-quality full-length transcripts, with 128,646 annotated, enabling precise mapping of splice variants and UTRs. This completeness is critical for designing sgRNAs for CRISPR or for heterologous expression where intron retention would otherwise impair protein function.
What is the concordance between RT-qPCR and transcriptome data, and what does it imply for future studies?
RT-qPCR of the six key genes showed expression trends consistent with transcriptome TPM values across root, stem, leaf, and flower (P < 0.05). This concordance validates the transcriptome dataset as a reliable reference for gene expression profiling. Future studies can therefore prioritize these genes for functional characterization without extensive re-sequencing, accelerating the discovery of regulatory mechanisms in sterol biosynthesis.
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