Screening and Application of DNA Markers for Novel Quality Consistency Evaluation in Panax ginseng

Abstract

Quality control remains a challenge in traditional Chinese medicine (TCM). This study introduced a novel genetic-based quality control method for TCM. Genetic variations in ginseng were evaluated across whole-genome, chloroplast genome, and ITS2 DNA barcode dimensions. Significant genetic variations were found in whole-genome comparison, leading to the use of inter-simple sequence repeat markers to assess the genetic diversity of ginseng decoction pieces (PG), garden ginseng (GG), and ginseng under forest (FG). Fingerprints of ginseng samples revealed instability within some batches. These evaluations were transformed into information entropy to calculate the size of Hardy-Weinberg equilibrium population (HWEP). FG had significantly higher genetic and chemical minimum HWEP than GG (p < 0.05). Notably, a significant positive correlation was observed between the minimum HWEP for genetics and for chemistry (r = 0.857, p = 0.014). Genetic polymorphism analysis of ginseng has the potential to evaluate chemical quality consistency, offering a new method to ensure quality consistency in TCM.

Keywords: HWEP; Panax ginseng; genetic polymorphism; quality consistency.

Figure 1

Genetic variation and SNP (single nucleotide polymorphism) density in P. ginseng. (A) ITS2 sequence comparison between P. ginseng and P. quinquefolius. ITS2 region shows high conservation among 35 ginseng samples and the interspecific nucleotide diversity in the ITS2 of P. ginseng and P. quinquefolius is represented by two SNPs (32 bp and 43 bp). (B) SNP density between the FG (ginseng under forest) and CG (garden ginseng) genome among 24 chromosomes. (C) Assembly of FG chloroplast genome (156,272 bp) and SNP density between FG and CG chloroplast genome, with 97.76% of SNPs concentrated in the LSC (large single copy) region. The outermost circle indicates SNP density, and SNP density increases progressively from brown to green, with green regions indicating higher SNP concentrations.

Figure 2

Genetic and chemical analysis of ginseng samples. (A) UPGMA cluster analysis of ginseng samples. (B) Comparison of genetic similarity coefficient among ginseng samples. (C) UPLC fingerprints after peak alignment, with six common peaks identified. (D) Heatmap of fingerprint similarity. * p < 0.05.

Figure 3

Comparison of HWEP (Hardy–Weinberg equilibrium population) of different ginseng samples. For both genetic data (means ± SEs, n = 8) and chemical data (means ± SEs, n = 6), different letters (a, b, c) indicate significant differences (p < 0.05, LSD-t test).

Figure 4

Comparison between genetic and chemical MQS within same batch of ginseng. t-test was used to test difference between genetic MQS data (means ± SEs, n = 8) and chemical MQS data (means ± SEs, n = 6). NS: not significant, * p < 0.05.

Figure 5

Correlation analysis between chemical compositions fluctuation and genetic diversity characterized by sic common peaks (marked in red) and three identified ginsenosides (marked in green). Spearman’s correlation analysis was conducted and significant positive correlation was found, with correlation coefficient of 0.857.