bioRxiv Science⌕ Search

bioRxiv · 10.1101/646240

Species-specific InDel markers for authentication of the Korean herbs Zanthoxylum schinifolium and Zanthoxylum piperitum

Abstract

ObjectiveZanthoxylum schinifolium and Zanthoxylum piperitum are the sources of the well-known traditional Korean herbal medicines \"sancho\" (prickly ash) and \"chopi\" (Korean pepper), respectively. Sancho and chopi are often indiscriminately mixed due to the similar appearance of the herbal materials when used as spices and herbal medicines. Moreover, commercial sancho and chopi products often contain adulterants, which could compromise drug efficacy and safety.\n\nMethodsIn this study, we developed hypervariable InDel markers to distinguish between sancho and chopi products by comparing the complete chloroplast genome sequences of four Zanthoxylum species deposited in NCBI GenBank.\n\nResultsComparative analyses of the nucleotide diversity (Pi) of these Zanthoxylum genomes revealed four hypervariable divergent sites (trnH-psbA, psbZ-trnG, trnfM-rps14, and trnF-ndhK) with Pi > 0.025 among 520 windows. Of these four regions, including two genic and two intergenic regions, only psbZ-trnG yielded accurate PCR amplification results between commercial sancho and chopi products from the Korean herbal medicine market. We therefore selected psbZ-trnG, an InDel-variable locus with high discriminatory powers, as a candidate DNA barcode locus.\n\nConclusionThis InDel marker could be used as a valuable, simple, and efficient tool for identifying these medicinal herbs, thereby increasing the safety of these spices and herbal materials in the commercial market.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kim, Y., Shin, J., Cho, S.-S., Hwang, Y.-P., Choi, C.. 2019-05-24. Species-specific InDel markers for authentication of the Korean herbs Zanthoxylum schinifolium and Zanthoxylum piperitum. https://doi.org/10.1101/646240

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

scRM: A single-cell foundation model trained on scRNA-seq reads

To our knowledge, scRM is the first single-cell foundation model trained on sequencing reads, demonstrating that raw data pretraining is not only feasible but competitive at a fraction of the computational cost. We anticipate that scaling this framework and exploring alternative architectures will unlock biological information inaccessible to count models and extend the dataset-specific pretraining it already makes possible. The source code is available at https://github.com/altos-labs/scRM.

bioinformatics↗

Local fractal dimension as an indicator of functional reorganization in molecular interaction networks

Changes in protein abundance incompletely describe how molecular perturbations reshape cellular organization. Here we introduce DIFFRACT, a framework that integrates quantitative proteomics with protein-protein interaction networks to identify reorganization through changes in local fractal dimension. Molecular associations define effective distances between connected proteins, allowing the geometry of their surrounding network neighborhoods to be compared across conditions. Analyses across four organisms (E. coli, A. thaliana, C. elegans, and H. sapiens) and three interaction resources (STRING, BioGRID, and IntAct) establish heterogeneous local scaling, while controlled perturbations demonstrate sensitivity to regional changes and invariance to uniform distance rescaling. Applied to RAVER1-deficient A549 cells, DIFFRACT identifies components associated with established RNA-regulatory functions and candidate roles in tRNA biology, mitochondrial organization and ciliogenesis. Selected RNA-regulatory and ciliary themes are not significantly enriched by abundance-ranked gene set analysis, supporting complementary information from network geometry. Selected functional annotations also recur in HEK293T cells. Guided by the ciliary predictions, we show that RAVER1 depletion increases the proportion of ciliated A549 cells 2.57-fold and increases cilium length. DIFFRACT thus connects changes in molecular network geometry to experimentally testable biological responses, extending the interpretation of proteomic perturbations beyond differential abundance.

bioinformatics↗

Peripheral Exercise Transcriptomes Do Not Converge on Glutamatergic or Neuroinflammatory Signatures: A Cross-Platform Meta-Analysis

Executive dysfunction following physical overtraining has been linked to glutamatergic dysregulation in the lateral prefrontal cortex (LPFC), but whether this central mechanism is reflected in peripheral gene expression is unknown. This study tested whether peripheral transcriptomic responses to exercise converge on the glutamatergic or neuroinflammatory pathways implicated in LPFC dysfunction. Three publicly available GEO datasets spanning skeletal muscle, whole blood, and circulating neutrophils were analyzed across four exercise groups using limma, with a discovery threshold of nominal p < 0.05 and absolute log2 fold change above 0.5. Differential expression analysis identified 7,311 unique differentially expressed genes (DEGs) across the four contrasts, but overlap between datasets was limited: 7 genes were shared between skeletal muscle and blood, 38 between skeletal muscle and the GSE43856 contrasts, and 90 between blood and the GSE43856 contrasts, with none significant across all three studies. Gene Ontology and KEGG enrichment of the DEG union identified ribonucleoprotein complex biogenesis, ribosome biogenesis, protein folding, and T cell activation as the dominant biological processes; no glutamatergic or neuroinflammatory terms reached significance. Cross-tissue concordance between skeletal muscle and blood was absent (r = 0). Effect sizes among the genes driving the top enriched pathways were dominated by ribosomal proteins, heat shock proteins, and translation initiation factors rather than glutamate receptors or transporters. These findings indicate that peripheral transcriptomic responses to exercise are tissue-specific and dominated by proteostasis, translational, and immune programs rather than glutamatergic signaling. Direct assessment of central nervous system tissue or fluid-based glutamatergic biomarkers may be required to link overtraining-induced fatigue to LPFC dysfunction.

bioinformatics↗