Next Article in Journal
High Plasma Angiopoietin-2 Levels Predict the Need to Initiate Dialysis within Two Years in Patients with Chronic Kidney Disease
Previous Article in Journal
Evaluation of Radiosensitization and Cytokine Modulation by Differentially PEGylated Gold Nanoparticles in Glioblastoma Cells
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 12
10.3390/ijms241210034
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Characterization and Comparative Analysis of Chloroplast Genomes of Medicinal Herb Scrophularia ningpoensis and Its Common Adulterants (Scrophulariaceae)

by
Lei Guo
1,†,
Xia Wang
1,†,
Ruihong Wang
1,* and
Pan Li
2,*
1
Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
Laboratory of Systematic & Evolutionary Botany and Biodiversity, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2023, 24(12), 10034; https://doi.org/10.3390/ijms241210034
Submission received: 12 April 2023 / Revised: 7 June 2023 / Accepted: 10 June 2023 / Published: 12 June 2023
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Review Reports Versions Notes

Abstract

:
Scrophularia ningpoensis, a perennial medicinal plant from the Scrophulariaceae family, is the original species of Scrophulariae Radix (SR) in the Chinese Pharmacopoeia. This medicine is usually deliberately substituted or accidentally contaminated with other closely related species including S. kakudensis, S. buergeriana, and S. yoshimurae. Given the ambiguous identification of germplasm and complex evolutionary relationships within the genus, the complete chloroplast genomes of the four mentioned Scrophularia species were sequenced and characterized. Comparative genomic studies revealed a high degree of conservation in genomic structure, gene arrangement, and content within the species, with the entire chloroplast genome spanning 153,016–153,631 bp in full length, encoding 132 genes, including 80 protein-coding genes, 4 rRNA genes, 30 tRNA genes, and 18 duplicated genes. We identified 8 highly variable plastid regions and 39–44 SSRs as potential molecular markers for further species identification in the genus. The consistent and robust phylogenetic relationships of S. ningpoensis and its common adulterants were firstly established using a total of 28 plastid genomes from the Scrophulariaceae family. In the monophyletic group, S. kakudensis was determined to be the earliest diverging species, succeeded by S. ningpoensis. Meanwhile, S. yoshimurae and S. buergeriana were clustered together as sister clades. Our research manifestly illustrates the efficacy of plastid genomes in identifying S. ningpoensis and its counterfeits and will also contribute to a deeper understanding of the evolutionary processes within Scrophularia.

1. Introduction

Scrophularia Linnaeus is a genus of plants in the family Scrophulariaceae, which comprises over 250 extant species of plants that are mainly found in temperate regions of the Northern Hemisphere [1,2,3]. The dried roots of many Scrophularia plants have therapeutic properties and are widely used as herbal medicines around the world [4,5,6,7]. Scrophularia ningpoensis Hemsley, commonly known as “Xuan Shen” in Chinese, is a perennial medicinal herb endemic to China and the original species of Scrophulariae Radix (SR) in traditional Chinese medicine (TCM) with a domestication history of more than 1000 years [8]. The dried roots of S. ningpoensis are recorded as SR in the Chinese Pharmacopoeia and used to cure fever, constipation, swelling, rheumatism, and inflammatory affections [8,9]. It has been found in a previous survey that S. ningpoensis is commonly mixed and substituted with other adulterants in East Asia, such as S. buergeriana Miq., S. kakudensis Franch, and S. yoshimurae T. Yamazaki [10,11,12,13]. Studies conducted to identify the secondary metabolites in these Scrophularia plants have revealed marked differences in the contents and concentrations of the bioactive compounds they contain [4,14,15,16]. While they all contain iridoids, phenylethanoids, and flavonoids, there are some differences in the specific bioactive compounds [16,17,18]. The clinical safety and effectiveness of these species may be compromised due to the controversial identification caused by their similar and indistinguishable morphology, as well as the use of dried and sliced forms in medicinal material trade.
In more detail, S. ningpoensis is widely distributed and artificially cultivated throughout the central and eastern regions of China, and it is especially known as one of the famous authentic medicinal materials, “Zhe Ba Wei”, in Zhejiang Province [19,20,21]. S. yoshimurae is employed as a substitute for S. ningpoensis in Taiwan Province; S. buergeriana and S. kakudensis spread from northern China to Japan and Korea and have been prescribed and used as SR in Korea for a long time [13,14,15,16,17,18]. However, because of the resource scarcity of S. buergeriana, imported S. ningpoensis has also been used as a substitute in the Korean herbal market [10,11]. The development of molecular biology has made DNA barcoding a useful identification technique, which has gained popularity in herbal medicine research [22,23]. Systematic phylogenetic analysis and species identification of Scrophularia have also been studied using DNA barcoding, including ITS, ndhF, trnQ-rps16, psbA-trnH, and trnL-trnF, with some substitutes of S. ningpoensis not included [3,24,25,26]. Lee et al. used ITS especially to differentiate the three species (S. buergeriana, S. ningpoensis, S. kakudensis) utilized in Scrophulariae Radix [10]. Our preliminary phylogenetic analysis of the Scrophularia genus, which utilized universally applicable DNA barcodes (ITS, psbA-trnH, trnL-trnF), positioned S. ningpoensis, S. kakudensis, S. buergeriana, and S. yoshimurae as a monophyletic group situated within the East Asian clade but lacked phylogenetic resolution within the four closely related species [27,28]. Traditional plant classification methods based on morphological features are not always adequate for the delimitation of herbs that resemble each other. Furthermore, the use of DNA barcoding for analysis has limitations in terms of resolution, making it difficult to differentiate certain closely related or similar species. Therefore, the lack of an international industry standard or effective approach to distinguish these Scrophularia plants has already perplexed traders and consumers for a considerable time.
To address these methodological challenges, numerous molecular biology techniques that employ various genetic data have been developed for the purpose of species identification [29,30,31]. In comparison to conventional molecular markers, the complete plastid genome data matrix offers a greater number of genetic variation sites and can be applied in the fields of species delimitation and molecular marker development [32,33,34]. Hence, in the current study, we sequenced, assembled, and annotated the plastomes of four S. ningpoensis species. For the first time, the differences between S. ningpoensis and its common adulterants were meticulously examined and documented with the goals of (1) characterizing and contrasting the complete plastid genomes of Scrophularia; (2) understanding the evolutionary pattern of the plastid genome of Scrophularia species and resolving phylogenetic relationships within the four Scrophularia species; and (3) evaluating variations in high-variability sites and simple sequence repeats (SSRs) for accurate authentication of Scrophularia and its counterfeit herbs. Our study will contribute to the expansion of the genomic resources available for Scrophularia and provide critical information to support the identification and phylogenetic analysis of different Scrophularia species. Additionally, the research findings will also have practical applications for the safe use of Scrophulariae Radix (SR).

2. Results

2.1. Morphological Comparison

Our morphological study revealed the similar morphological characters and habitat preferences (forests along streams, thickets, and tall grasses) of the complex of four S. ningpoensis species, mainly differing in calyx lobes, inflorescences, and the serrate degree of leaf margin (Figure 1). S. buergeriana stands out with its yellow-to-green corolla color and subcapitate or terminal raceme/spike inflorescences, distinguishing it from the other three species. The calyx lobes of S. ningpoensis and S. buergeriana are obtuse to rounded, while S. kakudensis and S. yoshimurae have acute to acuminate lobes. Leaf characteristics also differ, with serrate margins in S. ningpoensis, S. buergeriana, and S. kakudensis, while S. yoshimurae has double-serrate margins. Leaf shapes and bases vary, ranging from ovate in S. ningpoensis to ovate–lanceolate in S. yoshimurae [35] (Figure 1, Supplementary Table S1).

2.2. Chloroplast Genome Structure and Characteristics Analyses

The chloroplast genomes of 5 individuals of the 4 Scrophularia species were sequenced using the Illumina HiSeq 2500 system, generating clean data ranging from 2.25 to 3.79 Gb. Integrating the previously published cp genome of S. ningpoensis (NC_053823, MN734369), S. buergeriana (NC_031437, KP718626), and S. kakudensis (MN255822), the length of the cp genomes of the S. ningpoensis complex were in the range of 153,016 bp (S. kakudensis) to 153,631 bp (S. buergeriana) (Table 1). The entire chloroplast genome has four standard structural regions common in angiosperms, which include a large single copy (LSC), a small single copy (SSC), and two reverse repeat regions (IRa and IRb) (Figure 2). The length of the IR region fluctuated between 25,482 bp (S. buergeriana, OQ633013) and 25,490 bp (S. ningpoensis, OQ633009); the LSC region ranged from 84,130 bp (S. kakudensis, OQ633011) to 84,274 bp (S. yoshimurae, OQ633010); and the SSC region extended from 17,922 bp (S. kakudensis, OQ633012 and OQ633011) to 17,938 bp (S. ningpoensis, OQ633009). Moreover, the GC content in the IR region was higher than in the LSC and SSC regions. The total GC content varied slightly, from 37.98% to 37.99%, the content of the LSC region varied from 36.07% to 36.08%, the content of the SSC region varied between 32.15% and 32.18%, and the content of the IR region was 43.16%~43.19%.
The 10 Scrophularia plastomes encoded 132 genes, namely, 80 protein-coding genes (CDS), 4 ribosomal RNA (rRNA) genes, 30 transfer RNA (tRNA) genes (totaling 114 unique genes), and 18 duplicated genes (Supplementary Table S2). Among the 114 unique genes, 10 protein-coding genes (petB, petD, atpF, ndhB(×2), rpl16, rpl2(×2), rps16, rpoC1) and 5 tRNA genes (trnA-UGC, trnG-GCC, trnI-GAU, trnL-UAA, trnV-UAC) contained a single intron, while 3 genes (clpP, rps12, and ycf3) had 2 introns. The ycf1 gene in this region has been identified as a pseudogene in nearly all currently sequenced plant chloroplast genomes [36], and its coding sequence contains internal stop codons, resulting in high variability. Additionally, the partial duplication of rps19 at the LSC/IRa junction region has lost its protein-coding capability as a result of the incomplete duplication. Our results confirmed that the ten Scrophularia chloroplast genomes have maintained a high degree of conservation in genomic characteristics, such as gene content and arrangement and GC content throughout the process of evolution.

2.3. Contraction and Expansion of Inverted Repeats

The comparison of overall sequence variation indicated that the IR region was highly conserved, but there were differences in the IR/SC boundary regions across various plant species. In the case of the rps19 gene, it spanned the LSC/IRb boundary and primarily occurred in the LSC region, covering a length of 238 bp, with the remaining 41 bp located in the IRb region, whereas in S. kakudensis (MN255822) it comprised 239 bp situated in the LSC and 40 bp in the IRb region. At the IRa/LSC boundary, the rps19 gene extended 2 bp to the LSC region which caused the rps19 pseudogene fragment to be positioned at the boundary, while in S. kakudensis (MN255822) it extended only 1 bp. However, in S. ningpoensis (OQ633009) the rps19 genes did not expand to the LSC region. The ycf1 gene located in the SSC/IRa boundary had 4485 bp in the SSC and 882 bp in the IRa region, whereas in S. kakudensis (OQ633012) it had 4467 bp situated in the LSC and 882 bp in the IRa region. Of these species, the distance from ycf1 to the IRb/SSC boundary was 1 bp in Scrophularia with GenBank accession numbers of MN734369, NC_053823, NC_031437, KP718626, and MN255822 and 5 bp in OQ633009~OQ633013 (Figure 3). In general, the differences in length of the cp genomes among the four Scrophularia species can be attributed to the expansion and contraction of the IR/SC boundary regions. By using Mauve software, collinearity analysis was conducted on the chloroplast genomes of the four Scrophularia species, which revealed that the genomes exhibited strong collinearity, characterized by a single-color block, conserved genes, and homologous regions arranged in a linear manner, with no instances of gene rearrangement or inversion (Supplementary Figure S1).

2.4. Codon Usage Analysis

The analysis of codon bias revealed that different Scrophularia species exhibit variation in the number of codons and the effective number of codons. The average number of codons for Scrophularia individuals was 51,058 (S. ningpoensis), 51,156 (S. buergeriana), 51,007 (S. kakudensis), and 51,054 (S. yoshimurae), while the average effective number of codons (ENC) was 55.83 (S. ningpoensis), 55.91 (S. buergeriana), 55.71 (S. kakudensis), and 56.13 (S. yoshimurae) (Supplementary Table S3). The relative synonymous codon usage (RSCU) value is the relationship between the actual and expected occurrences of a codon [37]. Among the 64 codons that encode amino acids, except for the 3 stop codons and the unbiased methionine (Met) and threonine (Thr) (RSCU = 1), 27 codons exhibited a preference with RSCU values greater than 1, indicating that these codons had higher priority. Among these, the AGA codon corresponding to arginine (Arg) had the highest recognition rate, with an average RSCU of 1.92. The remaining 32 codons showed low bias, with RSCU values less than 1 (Supplementary Figure S2). In addition, there was a strong correlation between the codon bias and the GC content of the third position of synonymous codons. Among the codon usage of all species, the total GC content was about 37.99% or 37.98%, and the average GC content of the third base of synonymous codons ranged from 37.58% (S. kakudensis) to 38.51% (S. buergeriana), indicating that the A/U-ending codons were more preferred. These results suggested that there was a slight bias in the codon usage among the four Scrophularia species cp genomes.

2.5. Repeat Sequence Analysis

Repeat sequence analysis in the chloroplast genome is a useful tool for understanding the structure, function, and evolution of this important organelle in plants. The analysis using REPuter demonstrated that the 10 genomes presented 4 types of dispersed repeat sequences, including forward (F), reverse (R), palindromic (P), and complementary (C) repeats. A total of 454 repeats were detected, consisting of 216 F, 227 P, 9 R, and 2 C repeats. F and R repeats were the most common types (Figure 4A). S. buergeriana (OQ633013, NC_031437, KP718626) possessed the highest count of repeats (47), while S. kakudensis (OQ633011, MN255822) had the lowest (44). We calculated the frequencies of different repeat sequence lengths in 10 individuals of Scrophularia, and the proportion of 30–39 bp repeat sequences reached 74.45% (Figure 4B). According to the principle that the identical-length fragments found in homologous DNA regions are deemed to share repetitive sequences [38,39], 35 repetitive sequences were identified in the 10 Scrophularia chloroplast genomes. In addition, S. buergeriana (OQ633013, NC_031437, KP718626) possessed the highest number of repetitive sequences (12), whereas S. kakudensis (OQ633011, MN255822) exhibited the lowest number of repetitive sequences (9) (Supplementary Table S4).

2.6. Simple Sequence Repeat Analysis

Simple sequence repeats (SSRs), also known as microsatellites, are molecular markers that consist of short, repetitive DNA sequences that are tandemly repeated [40,41]. The chloroplast genomes of 10 Scrophularia species were analyzed by Misa, and a total of 422 SSRs were detected across the 10 individuals. The highest number of SSRs was observed in S. ningpoensis (OQ633009) (44), while the lowest number was found in S. yoshimurae (39) (Supplementary Table S5). Both S. buergeriana and S. kakudensis (OQ633011, MN255822) had 42 SSRs, but the number of SSRs in S. kakudensis (OQ633012) was slightly lower at 41 (Supplementary Table S5). Most of these SSRs (94.55%) were composed of A/T base components. Out of the SSRs analyzed, a total of 339 single nucleotides were identified in 10 Scrophularia species, which accounted for 80.33% of the SSRs. All 37 dinucleotides detected were of the AT/TA type, making up 8.77% of the SSRs. Additionally, there were 10 trinucleotides of the AAT/ATT type, accounting for 2.37% of the SSRs, and 33 tetranucleotides with diverse patterns (AAAG/CTTT, AAAT/ATTT, AATC/ATTG, ACAG/CTGT), which represented 7.82% of the SSRs. Finally, there were only 3 pentanucleotides, all of which were AAAAT/ATTTT-type, making up 0.71% of the SSRs. The repeated sequences in the form of AT/TA were present in all four Scrophularia species, but only S. kakudensis had repeats in the form of TTTTA, whereas the other three species did not (Figure 5A). Through analysis, we also found that most SSR loci were located in IGS (45.97%), followed by introns (35%) and CDS (18.96%) (Figure 5B), which may be due to the high mutation rate of the intergenic spacers (IGS).

2.7. Selective Pressure Analyses

We used Scrophularia takesimensis (GenBank acc. no. KP718628) as the reference sequence. The nonsynonymous (Ka) and synonymous (Ks) substitutions of 10 chloroplast genomes were calculated using TBtools V1.113. The Ka/Ks (ω) values were used to analyze the selection pressure of 80 protein-encoding genes in Scrophularia. The highest Ka/Ks value was 1.4207 in S. buergeriana (OQ633013, NC_031437, KP718626) (Supplementary Table S6). The Ka/Ks of most genes in Scrophularia individuals were less than 1, indicating that the genome species had high evolutionary constraints, and most of the genes had undergone purification selection [34]. A few genes of Scrophularia had Ka/Ks greater than 1, such as the rps12 gene of S. ningpoensis (MN734369, NC_053823) and the ycf2 gene of 7 individuals of 3 species of S. ningpoensis (OQ633009, MN734369, NC_053823), S. buergeriana (OQ633013, KP718626, NC_031437), and S. yoshimurae (OQ633010) (Figure 6). The ndhF genes of five individuals from three species of S. ningpoensis, S. buergeriana (OQ633013), and S. yoshimurae (OQ633010) showed that these sites evolved rapidly and there may be positive selection sites.

2.8. Sequence Divergence Analysis

mVISTA was used to compare the differences among 10 cp genomes, with S. buergeriana (NC_031437) serving as the reference genome and DnaSP used to analyze the nucleic acid diversity and identify divergence hotspot regions of the 10 Scrophularia chloroplast genomes. According to the results obtained from the mVISTA analysis, these species exhibited a high degree of sequence similarity (Figure 7). The results showed that the Pi values of nucleotide diversity of these 10 species ranged from 0 to 0.01115 (Supplementary Table S7). We detected 8 intergenic spacers (IGSs), with high divergence values (Pi > 0.004), namely, trnH-GUG-psbA, psbZ-trnG-GCC, trnT-UGU-trnL-UAA, trnP-GAA-ndhJ, ndhJ-ndhk, rbcL-accD, psbE-petL, and psaC-ndhE. The sequence of the ndhF gene had high nucleotide diversity with a Pi value of 0.422. In the chloroplast genome, the sequence divergence observed in the IR regions was comparatively lower than that of the LSC and SSC regions, and the coding region was found to be more conserved than the non-coding regions. The outcomes obtained from the mVISTA and DnaSP analysis were consistent with each other. By analyzing the divergence hotspots, we were able to identify the dissimilarities between related species (Figure 8). The divergence hotspot regions identified in the LSC and SSC regions can be utilized to discern and estimate the divergence among closely related species.

2.9. Phylogenetic Analysis

The phylogenetic reconstruction based on 28 complete cp genomes of Scrophulariaceae was performed using the maximum likelihood method (ML) and Bayesian inference analysis (BI). The phylogenetic trees of the two methods produced almost the same tree topology and were integrated together with high support values of the BI posterior probability (PP) and bootstrap support values of the ML labeling in each branch. Almost all of the nodes in the phylogenetic tree received strong support (PP/BS = 1/100). The phylogenetic tree had three monophyletic groups, including tribe Scrophularieae, tribe Budlejeae, and tribe (Myoporeae + Leucophylleae) as outgroups. Buddleja was the sister of Scrophularia and Verbascum. The genus Scrophularia was divided into two parts: S. sect. Caninae and S. sect. Scrophularia. In this part of S. sect. Scrophularia, we analyzed 10 chloroplast genomes of the monophyletic S. ningpoensis complex. S. kakudensis was the earliest branch to be differentiated, followed by S. ningpoensis, S. buergeriana, and S. yoshimurae clustered into a sister branch (Figure 9).

3. Discussion

The chloroplast genome serves as a valuable resource for investigating plant evolution, phylogenetics, and genetic diversity [42,43,44]. Scrophularia comprises more than 250 species; despite the diverse range of species in this genus, only a limited number of chloroplast genome sequences have been deposited into GenBank [32,45]. In particular, the S. ningpoensis complex is a group of perennial herbs with pharmaceutical properties that have been applied in traditional Asian medicine for hundreds of years [4,8]. This study reports the complete chloroplast genomes of four Scrophularia species, including S. ningpoensis, S. buergeriana, S. kakudensis, and S. yoshimurae, among which the S. yoshimurae cp genome is reported for the first time, while the cp genomes of S. ningpoensis, S. buergeriana, and S. kakudensis have been reported before, but this is the first systematic comparison and analysis of the cp genomes of all four species. The comparative analysis revealed that the chloroplast genome structure and features were highly conserved relative to nuclear and mitochondrial genomes within the four species. The determined nucleotide sequences of the 4 species’ chloroplast genomes ranged narrowly from 153,016 bp to 153,631 bp, with a similar gene pattern of 132 genes, which has been observed in other Scrophularia plants (Figure 2, Table 1) [32,45]. The LSC region contains most of the genes required for chloroplast function, including those involved in photosynthesis, carbon fixation, and amino acid synthesis; the SSC region contains a smaller number of genes, including those involved in RNA processing and protein synthesis; the IR regions contain genes involved in photosynthesis, as well as rRNA genes and tRNA genes [46,47,48]. The observed conservation of the S. ningpoensis complex’s chloroplast genomes can be attributed to the similarities in the morphological characters and habitat preferences, as well as the low rate of chloroplast substitution [27,45].
Inverted repeats (IRs) are regions of the chloroplast genome where the DNA sequence is duplicated and inverted, resulting in two copies of the same sequence in opposite orientations [43]. The contraction and expansion of IR regions can have significant impacts on the evolution of the chloroplast genome and the genes that are located within them [49,50]. While expansion can lead to functional divergence or even the evolution of new genes, contraction can cause the pseudogenization of genes and a lack of genetic information [50,51,52]. Pseudogenization has been observed in various organisms and can have significant implications for their evolutionary trajectories and adaptation to changing environments [53,54,55]. The ycf gene is an essential open reading frame present in the cp genome of all photosynthetic organisms and highly conserved across different species; it plays a fundamental role in the functioning of the chloroplast, though the exact function and regulation of the gene are still not fully understood [56,57,58]. Comparative genomic studies have revealed that the ycf1 pseudogene is present in nearly all plant cp genomes sequenced to date and exhibits significant variability [59,60]. The IR/LSC junctions of Scrophularia extended into the rps19 gene, which was consistent with the chloroplast genome characteristics of many typical angiosperms [36]. The rps gene is an important gene in the chloroplast genome, coding for the ribosomal protein S which is critical for protein synthesis. Its conservation across photosynthetic organisms indicates its importance, while its evolution highlights the dynamic nature of the chloroplast genome [61]. Further research on the rps gene and its function could provide valuable insights into the evolution of photosynthetic organisms and their ability to adapt to changing environments.
SSR markers have been widely utilized in genetic research due to their high level of polymorphism, co-dominant inheritance, and ease of detection [62,63]. They are useful for genetic diversity studies, population genetics, and linkage mapping and are relatively inexpensive and accessible to researchers with limited resources [64]. We detected 39 to 44 cp SSR markers (Supplementary Table S5) in the 10 individuals, which can be employed for further population genetic research in Scrophularia. Codon usage bias is the phenomenon of the unequal usage of synonymous codons in the genetic code of an organism [65,66]. In other words, certain synonymous codons are utilized more frequently than others to code for the same amino acid in the protein-coding genes. Leucine (Leu) and Tryptophane (Trp) were the most prevalent amino acids in Scrophularia, with an average count of 5137 and 705, respectively, which accords with previous studies on Scrophularia species. Meanwhile, the codons that terminate with A and/or T (U) typically exhibited high RSCU scores across the 10 cp genomes, e.g., AGA (1.97) for Arginine, GAU (1.47) for Aspartate, and CUU (1.31) for Leucine, which is largely due to a pronounced preference for the synonymous codons ending in A and T codons. Understanding codon usage bias can be important for optimizing gene expression in genetic engineering and synthetic biology, as well as for understanding the evolutionary history and biology of organisms [67].
Understanding the selective pressures acting on genes can provide insights into their function, adaptation, and diversity [68]. One of the ways to measure the selective pressures acting on genes is to compare the frequencies of synonymous and nonsynonymous substitutions [69]. In this study, the selective pressure analysis performed on the genes revealed that the majority of the genes exhibited a Ka/Ks ratio of less than 1 and had undergone purifying selection, indicating their functional and evolutionary importance as the target of natural selection. The finding that most genes had undergone purifying selection is not surprising as it is essential for maintaining the function and stability of genes. Nucleotide variability of different individuals or populations is typically measured using a statistic called the Pi value, which is calculated by comparing DNA sequences in the sample and counting the number of nucleotide differences between them, and then dividing this number by the total number of sites compared [39,70]. In the present investigation, we detected 8 intergenic spacers (IGSs) with elevated divergence values (Pi > 0.004) that could be used to infer evolutionary relationships among different groups of Scrophularia.
Phylogenetic analysis is the study of the evolutionary relationships among organisms based on their genetic, morphological, or other characteristics. It can be performed using diverse methods, such as ML, BI, and maximum parsimony (MP) [71,72,73]. Phylogenetic analysis has broad applications in fields such as evolutionary biology, systematics, biogeography, conservation biology, and comparative genomics, and it has enabled scientists to gain a better understanding of the history of life on earth and the diversity of organisms that inhabit our planet [74]. Our study presents the first robust and consistent phylogenetic relationships of the S. ningpoensis complex, utilizing a substantial sampling of 28 complete chloroplast genomes from 19 species of Scrophulariaceae based on ML and BI. Three individuals of S. ningpoensis, S. buergeriana, and S. kakudensis as biological duplicates were taken to improve the reliability of the data, excepting S. yoshimurae with a narrow distribution in Taiwan Province. Both the ML and BI analyses indicated that Scrophularia can be separated into two monophyletic lineages: S. sect. Caninae and S. sect. Scrophularia (Figure 9). The S. ningpoensis complex clustered into a strongly supported monophyletic clade within S. sect. Scrophularia. The species of S. kakudensis from Korea and Liaoning Province in northern China clustered together and was determined as the earliest diverging lineage at the basal position, followed by the S. ningpoensis lineage from eastern China. The S. buergeriana lineage from Korea and Hebei Province in northern China established sister relationships with S. yoshimurae from Taiwan Province. This research not only provides a clearer picture of Scrophularia’s evolutionary history but also offers important genetic information on the chloroplasts that will be useful in investigating the origins and biodiversity patterns within the Scrophulariaceae plant family. In conclusion, by conducting a comprehensive phylogenetic analysis using chloroplast genomic data, we have made significant strides in understanding the intricate relationships within the Scrophularia genus, ultimately contributing valuable insights into the evolutionary history of Scrophulariaceae as a whole.

4. Materials and Methods

4.1. Phenotype Observations and Plant Samples

The four Scrophularia species were subjected to observation of their morphological features in accordance with the general plant classification methods and the taxonomic characteristics of Scrophularia spp. in Flora of China [35]. All the morphologic records were based on our field observations. Leaves of the five individuals of main clades of the S. ningpoensis complex, including two individuals of S. kakudensis from Liao Ning Province, S. ningpoensis from Zhejiang Province, S. buergeriana from Hebei Province, and S. yoshimurae from Taiwan Province, were collected. Voucher specimens were deposited in the herbarium of Zhejiang University (HTU, Hangzhou, China). Complete cp genomes sequences of S. ningpoensis (NC_053823, MN734369), S. buergeriana (NC_031437, KP718626), and S. kakudensis (MN255822) were obtained from NCBI for the purpose of comparison with the S. ningpoensis complex.

4.2. Genomic DNA Extraction, Sequencing, Assembly, Annotation, and Submission to GenBank

A DNA library, including original reads, was constructed by randomly interrupting total DNA with ultrasound. These original reads were assembled into complete plastid genomes by de novo assembly and reference-guided combination [75]. First, the modified CTAB method was used to extract DNA from plant leaves. Genomic DNA was extracted from silica-dried leaf samples using the DNA Plantzol kit (Invitrogen, Carlsbad, CA, USA). Chloroplast sequences were then produced using the platform Illumina HiSeq2500 at the Beijing Genomics Institute (BGI, Shenzhen, China). All raw readings were processed using Trimmomatic v0.39 software (Julich, Germany) to remove adapter sequences, short reads (length: 75 bp), and low-quality bases (q value: 20). Subsequently, homologous plasmid sequences were chosen from complete cellular DNA reads, and a de novo assembly pipeline was used to assemble these sequences into numerous contigs. Because of the organelles and nuclear DNA included in the sequencing data [32], GetOrganelle was utilized to assemble the complete chloroplast of Scrophularia to filter contigs. Finally, the filtered contigs were ordered and aligned to the chloroplast genomes of related species that have been previously published, using the reference chloroplast genome (MN734369) as a guide. The chloroplast genome was annotated by Geneious software (Geneious Biologics 2023 (https://www.geneious.com/biopharma, accessed on 10 September 2022)), and the annotation of rps12 gene was examined with CPGview [76]. Ultimately, the five new sequencing columns were submitted to the GenBank database.

4.3. Contraction and Expansion of Inverted Repeats

The length variation of plant chloroplast genomes is mainly caused by the expansion and contraction of the IR region and single copy boundary [77,78]. The IRSCOPE online website (Helsinki, Finland) (https://irscope.shinyapps.io/irapp/, accessed on 7 January 2023) was used to compare IR boundary amplification and contraction of the ten sequences [79]. The Java environment was configured, and the chloroplast genome sequence in fasta format was uploaded to the Mauve [80] to analyze the collinearity of 10 Scrophularia chloroplast genomes.

4.4. Codon Usage Bias Analysis

Different organisms have different frequencies of using degenerate codons in the process of translation, and a set of common codons corresponding to them have come into being in the course of evolution [81]. The CUSP and CHIPS plugins in EMBOSS [82] (EMBOSS Explorer (inra.fr)) and CodonW V1.4.2 (https://codonw.sourceforge.net/, accessed on 9 February 2023) were used to calculate codon usage bias parameters, including the values of the effective number of codons (ENC) and relative synonymous codon usage (RSCU) and the GC content relevant to the first, second, and third codon positions in the target coding sequence (CDS) and the entire plastid [83,84].

4.5. Repeat Sequence Analysis

The importance of repeat sequences in phylogenetic studies and genome rearrangement is well known [85]. We used the online tool REPuter (https://bibiserv.cebitec.uni-bielefeld.de/reputer, accessed on 14 February 2023) to identify scattered repeats in the chloroplast genome, including forward repeats, reverse repeats, palindromic repeats, and supplementary repeats [86]. The parameters were set to a Minimal Repeat size of 30 bp, a Hamming Distance of 3, and a Maximum Computed Repeats of 80 times.

4.6. Simple Sequence Repeat Analyses

MISA [87] (http://pgrc.ipk-gatersleben.de/misa, accessed on 18 February 2023) was used to analyze simple sequence repeats (SSRs) in the chloroplast genome of Scrophularia. The parameters were set to single nucleotide, dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide SSRs with more than 10, 6, 4, 3, 3, and 3 repeat units.

4.7. Selective Pressure Analyses

In genetics, the ratio of nonsynonymous (Ka) to synonymous (Ks) substitution rates (ω) is frequently used to evaluate the presence of selection pressure on protein-coding genes [88]. In this study, we used a Perl script to extract protein-coding genes from the chloroplast genome of Scrophularia, used Geneious to extract CDS, and calculated Ka, Ks, and Ka/Ks values for each plastid gene with TBtools V1.113 [89]. The evolutionary process of genes, i.e., positive selection, purified selection, or neutral selection, is Ka > Ks (ω > 1), Ka < Ks (ω < 1), or Ka = Ks (ω = 1), respectively.

4.8. Sequence Divergence Analysis and Nucleotide Diversity Analysis

To compare the chloroplast genomes among the four Scrophularia species, the mVISTA program (Berkeley, CA, USA) (https://genome.lbl.gov/vista/mvista/submit.shtml, accessed on 20 July 2022) was used for genome comparison. The LAGAN mode was employed with default parameters to align the ten chloroplast genomes [90]. The gene, exon, UTR, CNS, and mRNA of these sequences were compared using the mVISTA for complete sequence alignment with settings of 70% and window length of 100 bp.
In order to distinguish the hypervariable regions of the chloroplast genome of Scrophularia, all plastid sequences were aligned and DnaSP v6.0 [91] was used for diversity analysis. S. buergeriana (NC_031437) was taken as a reference, and DnaSP was used to identify the divergence hotspots of 10 Scrophularia chloroplast genomes. The window length was set to 600 bp and the step size was 200 bp. The total number of mutations (Eta) and nucleotide diversity (Pi) values of the chloroplast genome of Scrophularia were evaluated.

4.9. Phylogenetic Analysis

A total of 28 chloroplast genomes of 7 genera in Scrophulariaceae were selected for phylogenetic tree construction (Table S8). Among them, there were 19 species in Scrophularia, including 2 Verbascum; 2 Buddleja; 2 Myoporum; 1 each for Diocirea, Eremophila, and Leucophyllum; and a total of 9 individuals as outgroup. We used MAFFT V7 [92] to compare these 28 plastid sequences under default parameters. The maximum likelihood method was used to construct the phylogenetic tree using IQ-TREE V1.6.8 [93], and the model was TVM+F+R2. In jModelTest v2.1.10 [94], the TVM+I+G substitution model was determined according to the Bayesian Information Criterion (BIC), and the phylogenetic tree was constructed by the Bayesian method in MrBayes V3.2.7 [95]. Monte Carlo Simulation and Markov Chain (MCMC) algorithm were used for 2 million generations. The tree was sampled every 500 generations, and the checkfreq was 5000 times. The first quarter of the calculated tree was discarded, and the Bayesian phylogenetic tree was constructed from the remaining trees [32].

5. Conclusions

In this study, 10 chloroplast genomes of Scrophularia from 4 species were analyzed. The results showed that the chloroplast genomes of Scrophularia were relatively conservative, but there were eight (trnH-GUG-psbA, psbZ-trnG-GCC, trnT-UGU-trnL-UAA, trnP-GAA-ndhJ, ndhJ-ndhk, rbcL-accD, psbE-petL, and psaC-ndhE) intergenic spacers and one (ndhF) protein-coding gene that had higher variation. Morphological analysis and evolutionary analysis of the 10 Scrophularia individuals distinguished S. ningpoensis from S. kakudensis, S. buergeriana, and S. yoshimurae, which provides support for the species identification and differentiation of Chinese herbal medicine Scrophulariae Radix. This study expands the genomic resources of Scrophularia species, presents key information for ensuring the safety and efficacy of clinical drugs, and contributes to the bioprospecting and conservation research of Scrophularia.

Supplementary Materials

The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms241210034/s1.

Author Contributions

Methodology, software, validation, formal analysis, data curation, and writing—original draft preparation, L.G. and X.W.; conceptualization, writing—review and editing, visualization, and funding acquisition, R.W.; investigation, resources, supervision, project administration, and funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Zhejiang Province (LGH22H280005), National Nature Science Foundation of China (31600183).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The chloroplast genome sequences of this study are openly available at the NCBI database (https://www.ncbi.nlm.nih.gov).

Acknowledgments

We thank Z.C. Qi for the server data calculation support and J. Gao and J.Y. Feng for help with software operation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hong, D.Y. The Distribution of Scrophulariaceae in the Holarctic with Special Reference to the Floristic Relationships between Eastern Asia and Eastern North America. Ann. Mo. Bot. Gard. 1983, 70, 701–712. [Google Scholar] [CrossRef]
  2. Fischer, E. Scrophulariaceae. In The Families and Genera of Vascular Plants; International Association for Plant Taxonomy; Springer: Berlin/Heidelberg, Germany, 2004; Volume 7. [Google Scholar]
  3. Scheunert, A.; Heubl, G. Against all odds: Reconstructing the evolutionary history of Scrophularia (Scrophulariaceae) despite high levels of incongruence and reticulate evolution. Org. Divers Evol. 2017, 17, 323–349. [Google Scholar] [CrossRef]
  4. Lee, H.J.; Kim, H.L.; Lee, D.R.; Choi, B.K.; Yang, S.H. Scrophulariae Radix: An Overview of Its Biological Activities and Nutraceutical and Pharmaceutical Applications. Molecules 2021, 26, 5250. [Google Scholar] [CrossRef]
  5. Lalabadi, M.A.; Ali, N.; Jelodar, B.; Bagheri, N. Effect of Plant Hormones on Micro-propagation of Tashnedari (Scrophularia striata) Medicinal plant. Bull. Environ. Pharmacol. Life Sci. 2014, 3, 250–253. [Google Scholar]
  6. Pirbalouti, A.G.; Azizi, S.; Koohpayeh, A. Healing potential of Iranian traditional medicinal plants on burn wounds in alloxan-induced diabetic rats. Rev. Bras. Farmacogn. 2012, 22, 397–403. [Google Scholar] [CrossRef] [Green Version]
  7. Ozbilgin, A.; Durmuskahya, C.; Kayalar, H.; Ostan, I. Assessment of in vivo antimalarial activities of some selected medicinal plants from Turkey. Parasitol. Res. 2014, 113, 165–173. [Google Scholar] [CrossRef]
  8. Zhang, Q.; Liu, A.; Wang, Y.S. Scrophularia ningpoensis Hemsl: A review of its phytochemistry, pharmacology, quality control and pharmacokinetics. J. Pharm. Pharmacol. 2021, 73, 573–600. [Google Scholar] [CrossRef]
  9. Ren, D.; Shen, Z.Y.; Qin, L.P.; Zhu, B. Pharmacology, phytochemistry, and traditional uses of Scrophularia ningpoensis Hemsl. J. Ethnopharmacol. 2021, 269, 113688. [Google Scholar] [CrossRef]
  10. Lee, G.J.; Doh, E.J.; Lee, M.Y.; Ko, B.S.; Oh, S.E. Discrimination of three Scrophularia plants utilizing ‘Scrophularia Radix’ by DNA markers based on internal transcribed spacer (ITS) sequences. Genes Genom. 2010, 32, 181–189. [Google Scholar] [CrossRef]
  11. Song, H.J.; Shin, M.K.; Park, S.B.; Kim, T.H. A herbalogical study on the plants of Scrophulariaceae in Korea. Kor. J. Herb. 1998, 13, 241–270. [Google Scholar] [CrossRef]
  12. Chiu, N.Y.; Chang, K.H. The Illustrated Medicinal Plants of Taiwan; SMC Publishing: Taibei, Taiwan, 1986; Volume 1, p. 129. [Google Scholar]
  13. Manivannan, A.; Soundararajan, P.; Park, Y.G.; Jeong, B.R. Physiological and Proteomic Insights into Red and Blue Light-Mediated Enhancement of in vitro Growth in Scrophularia kakudensis-A Potential Medicinal Plant. Front. Plant Sci. 2020, 11, 607007. [Google Scholar] [CrossRef] [PubMed]
  14. Nam, H.-H.; Lee, A.Y.; Seo, Y.-S.; Park, I.; Yang, S.; Chun, J.M.; Moon, B.C.; Song, J.-H.; Kim, J.-S. Three Scrophularia Species (Scrophularia buergeriana, S. koraiensis, and S. takesimensis) Inhibit RANKL-Induced Osteoclast Differentiation in Bone Marrow-Derived Macrophages. Plants 2020, 9, 1656. [Google Scholar] [CrossRef] [PubMed]
  15. Lee, H.J.; Spandidos, D.A.; Tsatsakis, A.; Margina, D.; Izotov, B.N.; Yang, S.H. Neuroprotective effects of Scrophularia buergeriana extract against glutamate-induced toxicity in SH-SY5Y cells. Int. J. Mol. Med. 2019, 43, 2144–2152. [Google Scholar] [CrossRef] [Green Version]
  16. Sagare, A.P.; Kuo, C.L.; Chueh, F.S.; Tsay, H.S. De novo regeneration of Scrophularia yoshimurae Yamazaki (Scrophulariaceae) and quantitative analysis of harpagoside, an iridoid glucoside, formed in aerial and underground parts of in vitro propagated and wild plants by HPLC. Biol. Pharm. Bull. 2001, 24, 1311–1315. [Google Scholar] [CrossRef] [Green Version]
  17. Jeong, E.J.; Ma, C.J.; Lee, K.Y.; Kim, S.H.; Sung, S.H.; Kim, Y.C. KD-501, a standardized extract of Scrophularia buergeriana has both cognitive-enhancing and antioxidant activities in mice given scopolamine. J. Ethnopharmacol. 2009, 121, 98–105. [Google Scholar] [CrossRef]
  18. Manivannan, A.; Soundararajan, P.; Park, Y.G.; Jeong, B.R. Chemical Elicitor-Induced Modulation of Antioxidant Metabolism and Enhancement of Secondary Metabolite Accumulation in Cell Suspension Cultures of Scrophularia kakudensis Franch. Int. J. Mol. Sci. 2016, 17, 399. [Google Scholar] [CrossRef] [Green Version]
  19. Sun, J.C.; Li, X.J.; Qu, Z.; Wang, H.R.; Cheng, Y.; Dong, S.J.; Zhao, H. Comparative proteomic analysis reveals novel insights into the continuous cropping induced response in Scrophularia ningpoensis. J. Sci. Food Agric. 2023, 103, 1832–1845. [Google Scholar] [CrossRef] [PubMed]
  20. Yang, S.T.; Chen, C.; Zhao, Y.P.; Xi, W.; Zhou, X.L.; Chen, B.L.; Fu, C.X. Association between Chemical and Genetic Variation of Wild and Cultivated Populations of Scrophularia ningpoensis Hemsl. Planta Med. 2011, 77, 865–871. [Google Scholar] [CrossRef]
  21. Chen, C.; Li, P.; Wang, R.H.; Schaal, B.A.; Fu, C.X. The Population Genetics of Cultivation: Domestication of a Traditional Chinese Medicine, Scrophularia ningpoensis Hemsl. (Scrophulariaceae). PLoS ONE 2014, 9, e105064. [Google Scholar] [CrossRef] [Green Version]
  22. Xiong, C.; Sun, W.; Li, J.; Yao, H.; Shi, Y.; Wang, P.; Huang, B.; Shi, L.; Liu, D.; Hu, Z.; et al. Identifying the Species of Seeds in Traditional Chinese Medicine Using DNA Barcoding. Front. Pharmacol. 2018, 9, 701. [Google Scholar] [CrossRef]
  23. Jiao, J.; Huang, W.; Bai, Z.; Liu, F.; Ma, C.; Liang, Z. DNA barcoding for the efficient and accurate identification of medicinal polygonati rhizoma in China. PLoS ONE 2018, 13, e0201015. [Google Scholar] [CrossRef]
  24. Navarro-Perez, M.L.; Lopez, J.; Fernandez-Mazuecos, M.; Rodriguez-Riano, T.; Vargas, P.; Ortega-Olivencia, A. The role of birds and insects in pollination shifts of Scrophularia (Scrophulariaceae). Mol. Phylogenet. Evol. 2013, 69, 239–254. [Google Scholar] [CrossRef] [PubMed]
  25. Scheunert, A.; Heubl, G. Diversification of Scrophularia (Scrophulariaceae) in the Western Mediterranean and Macaronesia--phylogenetic relationships, reticulate evolution and biogeographic patterns. Mol. Phylogenet. Evol. 2014, 70, 296–313. [Google Scholar] [CrossRef] [PubMed]
  26. Scheunert, A.; Heubl, G. Phylogenetic relationships among New World Scrophularia L. (Scrophulariaceae): New insights inferred from DNA sequence data. Plant Syst. Evol. 2010, 291, 69–89. [Google Scholar] [CrossRef]
  27. Chen, C.; LEE, P.; Lee, J.; Schaal, B.A.; Fu, C.X. Studies on Domestication & Phylogeography of Scrophularia ningpoensis Hemsley and Phylogeny of Eastern Asia Scrophularia. Ph.D. Dissertation, Zhejiang University, Hangzhou, China, 2011. [Google Scholar]
  28. Wang, R.H. Phylogeny and Biogeography of Scrophularia and Phylogeography of S. incisa Complex. Ph.D. Thesis, Zhejiang University, Hangzhou, China, 2015. [Google Scholar]
  29. Hajibabaei, M.; Singer, G.A.C.; Hebert, P.D.N.; Hickey, D.A. DNA barcoding: How it complements taxonomy, molecular phylogenetics and population genetics. Trends Genet. 2007, 23, 167–172. [Google Scholar] [CrossRef]
  30. Feng, J.Y.; Jin, X.J.; Zhang, S.L.; Yang, J.W.; Fei, S.P.; Huang, Y.S.; Liu, Y.; Qi, Z.C.; Li, P. Smilax weniae, a New Species of Smilacaceae from Limestone Areas Bordering Guizhou and Guangxi, China. Plants 2022, 11, 1032. [Google Scholar] [CrossRef]
  31. Garrido-Cardenas, J.A.; Mesa-Valle, C.; Manzano-Agugliaro, F. Trends in plant research using molecular markers. Planta 2018, 247, 543–557. [Google Scholar] [CrossRef]
  32. Wang, R.H.; Gao, J.; Feng, J.Y.; Yang, Z.P.; Qi, Z.C.; Li, P.; Fu, C.X. Comparative and Phylogenetic Analyses of Complete Chloroplast Genomes of Scrophularia incisa Complex (Scrophulariaceae). Genes 2022, 13, 1691. [Google Scholar] [CrossRef]
  33. Lu, Q.X.; Chang, X.; Gao, J.; Wu, X.; Wu, J.; Qi, Z.C.; Wang, R.H.; Yan, X.L.; Li, P. Evolutionary Comparison of the Complete Chloroplast Genomes in Convallaria Species and Phylogenetic Study of Asparagaceae. Genes 2022, 13, 1724. [Google Scholar] [CrossRef] [PubMed]
  34. Chen, M.M.; Zhang, M.; Liang, Z.S.; He, Q.L. Characterization and Comparative Analysis of Chloroplast Genomes in Five Uncaria Species Endemic to China. Int. J. Mol. Sci. 2022, 23, 11617. [Google Scholar] [CrossRef] [PubMed]
  35. Hong, D.Y.; Yang, H.B.; Jin, C.L.; Holmgren, N.H. Scrophulariaceae. In Flora of China; Wu, Z.-Y., Ed.; Missouri Botanical Garden: St. Louis, MI, USA, 1998; Volume 18, 212p. [Google Scholar]
  36. Yang, J.B.; Yang, S.X.; Li, H.T.; Yang, J.; Li, D.Z. Comparative Chloroplast Genomes of Camellia Species. PLoS ONE 2013, 8, e73053. [Google Scholar] [CrossRef] [Green Version]
  37. Zhou, J.H.; Zhang, J.; Chen, H.T.; Ma, L.N.; Liu, Y.S. Analysis of synonymous codon usage in foot-and-mouth disease virus. Vet. Res. Commun. 2010, 34, 393–404. [Google Scholar] [CrossRef]
  38. Lu, R.S.; Li, P.; Qiu, Y.X. The Complete Chloroplast Genomes of Three Cardiocrinum (Liliaceae) Species:Comparative Genomic and Phylogenetic Analyses. Front. Plant Sci. 2017, 7, 2054. [Google Scholar] [CrossRef] [Green Version]
  39. Li, P.; Lu, R.S.; Xu, W.Q.; Ohi-Toma, T.; Cai, M.Q.; Qiu, Y.X.; Cameron, K.M.; Fu, C.X. Comparative Genomics and Phylogenomics of East Asian Tulips (Amana, Liliaceae). Front. Plant Sci. 2017, 8, 451. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Bodin, S.S.; Kim, J.S.; Kim, J.-H. Complete Chloroplast Genome of Chionographis japonica (Willd.) Maxim. (Melanthiaceae): Comparative Genomics and Evaluation of Universal Primers for Liliales. Plant Mol. Biol. Report. 2013, 31, 1407–1421. [Google Scholar] [CrossRef]
  41. Zhao, Y.; Yin, J.; Guo, H.; Zhang, Y.; Xiao, W.; Sun, C.; Wu, J.; Qu, X.; Yu, J.; Wang, X.; et al. The complete chloroplast genome provides insight into the evolution and polymorphism of Panax ginseng. Front. Plant Sci. 2014, 5, 696. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Daniell, H.; Lin, C.S.; Yu, M.; Chang, W.J. Chloroplast genomes: Diversity, evolution, and applications in genetic engineering. Genome Biol. 2016, 17, 134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Ruhlman, T.A.; Jansen, R.K. The plastid genomes of flowering plants. In Chloroplast Biotechnology; Humana Press: Totowa, NJ, USA, 2014; Volume 1132, pp. 3–38. [Google Scholar]
  44. Xiao, S.; Xu, P.; Deng, Y.; Dai, X.; Cao, Q. Comparative analysis of chloroplast genomes of cultivars and wild species of sweetpotato (Ipomoea batatas [L.] Lam). BMC Genom. 2021, 22, 368. [Google Scholar] [CrossRef]
  45. Xu, W.Q.; Losh, J.; Chen, C.; Li, P.; Wang, R.H.; Zhao, Y.P.; Qiu, Y.X.; Fu, C.X. Comparative genomics of figworts (Scrophularia, Scrophulariaceae), with implications for the evolution of Scrophularia and Lamiales. J. Syst. Evol. 2019, 57, 55–65. [Google Scholar] [CrossRef] [Green Version]
  46. Wu, C.; Tang, G.; Wang, X.; Zhang, J.; Chen, S.; Lu, C.; Zhang, D.; Li, Y. Micro-RNA-21 rs1292037 A>G polymorphism can predict hepatocellular carcinoma prognosis (HCC), and plays a key role in cell proliferation and ischemia-reperfusion injury (IRI) in HCC cell model of IRI. Saudi Med. J. 2020, 41, 383–392. [Google Scholar] [CrossRef]
  47. Guo, Y.Y.; Yang, J.X.; Bai, M.Z.; Zhang, G.Q.; Liu, Z.J. The chloroplast genome evolution of Venus slipper (Paphiopedilum): IR expansion, SSC contraction, and highly rearranged SSC regions. BMC Plant Biol. 2021, 21, 248. [Google Scholar] [CrossRef]
  48. Gastineau, R.; Davidovich, N.A.; Davidovich, O.I.; Lemieux, C.; Turmel, M.; Wrobel, R.J.; Witkowski, A. Extreme Enlargement of the Inverted Repeat Region in the Plastid Genomes of Diatoms from the Genus Climaconeis. Int. J. Mol. Sci. 2021, 22, 7155. [Google Scholar] [CrossRef] [PubMed]
  49. Zhang, M.; Chen, M.-M.; Zhang, X.-M.; Chen, S.-N.; Liang, Z.-S. The complete chloroplast genome sequence of traditional Chinese medicine Uncaria macrophylla (Rubiaceae). Mitochondrial DNA Part B 2022, 7, 694–695. [Google Scholar] [CrossRef]
  50. Zhu, A.D.; Guo, W.H.; Gupta, S.; Fan, W.S.; Mower, J.P. Evolutionary dynamics of the plastid inverted repeat: The effects of expansion, contraction, and loss on substitution rates. New Phytol. 2016, 209, 1747–1756. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Bock, R. Structure, function, and inheritance of plastid genomes. In Cell and Molecular Biology of Plastids; Bock, R., Ed.; Springer: Berlin/Heidelberg, Germany, 2007; pp. 29–63. [Google Scholar] [CrossRef]
  52. Ruhlman, T.A.; Jansen, R.K.; Kuehl, J.V. Evolution and function of plastid genomes in higher plants. Annu. Rev. Plant Biol. 2019, 70, 395–420. [Google Scholar]
  53. Folk, R.A.; Sewnath, N.; Xiang, C.L.; Sinn, B.T.; Guralnick, R.P. Degradation of key photosynthetic genes in the critically endangered semi-aquatic flowering plant Saniculiphyllum guangxiense (Saxifragaceae). BMC Plant Biol. 2020, 20, 324. [Google Scholar] [CrossRef] [PubMed]
  54. Barrett, C.F.; Freudenstein, J.V.; Li, J.; Mayfield-Jones, D.R.; Perez, L.; Pires, J.C.; Santos, C. Investigating the path of plastid genome degradation in an early-transitional clade of heterotrophic orchids, and implications for heterotrophic angiosperms. Mol. Biol. Evol. 2014, 31, 3095–3112. [Google Scholar] [CrossRef] [Green Version]
  55. Lin, C.S.; Chen, J.J.W.; Chiu, C.C.; Hsiao, H.C.W.; Yang, C.J.; Jin, X.H.; Leebens-Mack, J.; de Pamphilis, C.W.; Huang, Y.T.; Yang, L.H.; et al. Concomitant loss of NDH complex-related genes within chloroplast and nuclear genomes in some orchids. Plant J. 2017, 90, 994–1006. [Google Scholar] [CrossRef] [Green Version]
  56. Szmidt-Małyska, K.; Gabryś, H. The ycf gene family in Populus trichocarpa. Plant Mol. Biol. Report. 2016, 34, 561–572. [Google Scholar]
  57. Zhang, X.; Wessler, S.R. The roles of ycf15 and ycf68 in chloroplasts of Yucca filamentosa (Agavaceae). Curr. Genet. 2004, 45, 44–50. [Google Scholar]
  58. Wicke, S.; Naumann, J. Molecular Evolution of Plastid Genomes in Parasitic Flowering Plants. Adv. Bot. Res. 2018, 85, 315–347. [Google Scholar] [CrossRef]
  59. Szczecinska, M.; Sawicki, J. Genomic Resources of Three Pulsatilla Species Reveal Evolutionary Hotspots, Species-Specific Sites and Variable Plastid Structure in the Family Ranunculaceae. Int. J. Mol. Sci. 2015, 16, 22258–22279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Ma, J.; Yang, B.X.; Zhu, W.; Sun, L.L.; Tian, J.K.; Wang, X.M. The complete chloroplast genome sequence of Mahonia bealei (Berberidaceae) reveals a significant expansion of the inverted repeat and phylogenetic relationship with other angiosperms. Gene 2013, 528, 120–131. [Google Scholar] [CrossRef] [PubMed]
  61. Okada, K.; Fujiwara, S.; Tsuzuki, M. Energy conservation in photosynthetic microorganisms. J. Gen. Appl. Microbiol. 2020, 66, 59–65. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Lu, Q.X.; Gao, J.; Wu, J.J.; Zhou, X.; Wu, X.; Li, M.D.; Wei, Y.K.; Wang, R.H.; Qi, Z.C.; Li, P. Development of 19 novel microsatellite markers of lily-of-the-valley (Convallaria, Asparagaceae) from transcriptome sequencing. Mol. Biol. Rep. 2020, 47, 3041–3047. [Google Scholar] [CrossRef]
  63. Yang, Q.W.; Jiang, Y.J.; Wang, Y.P.; Han, R.L.; Liang, Z.S.; He, Q.L.; Jia, Q.J. SSR Loci Analysis in Transcriptome and Molecular Marker Development in Polygonatum sibiricum. Biomed Res. Int. 2022, 2022, 4237913. [Google Scholar] [CrossRef]
  64. Zalapa, J.E.; Cuevas, H.; Zhu, H.Y.; Steffan, S.; Senalik, D.; Zeldin, E.; McCown, B.; Harbut, R.; Simon, P. Using Next-Generation Sequencing Approaches to Isolate Simple Sequence Repeat (SSR) Loci in the Plant Sciences. Am. J. Bot. 2012, 99, 193–208. [Google Scholar] [CrossRef] [Green Version]
  65. Mazumdar, P.; Othman, R.B.; Mebus, K.; Ramakrishnan, N.; Harikrishna, J.A. Codon usage and codon pair patterns in non-grass monocot genomes. Ann. Bot. 2017, 120, 893–909. [Google Scholar] [CrossRef] [Green Version]
  66. Ingvarsson, P.K. Molecular evolution of synonymous codon usage in Populus. BMC Evol. Biol. 2008, 8, 307. [Google Scholar] [CrossRef] [Green Version]
  67. Parvathy, S.T.; Udayasuriyan, V.; Bhadana, V. Codon usage bias. Mol. Biol. Rep. 2022, 49, 539–565. [Google Scholar] [CrossRef]
  68. Llaurens, V.; Whibley, A.; Joron, M. Genetic architecture and balancing selection: The life and death of differentiated variants. Mol. Ecol. 2017, 26, 2430–2448. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Herdegen-Radwan, M.; Phillips, K.P.; Babik, W.; Mohammed, R.S.; Radwan, J. Balancing selection versus allele and supertype turnover in MHC class II genes in guppies. Heredity 2021, 126, 548–560. [Google Scholar] [CrossRef] [PubMed]
  70. Xu, C.; Dong, W.P.; Li, W.Q.; Lu, Y.Z.; Xie, X.M.; Jin, X.B.; Shi, J.P.; He, K.H.; Suo, Z.L. Comparative Analysis of Six Lagerstroemia Complete Chloroplast Genomes. Front. Plant Sci. 2017, 8, 15. [Google Scholar] [CrossRef] [Green Version]
  71. Yang, Z.H. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 2007, 24, 1586–1591. [Google Scholar] [CrossRef] [Green Version]
  72. Huelsenbeck, J.P.; Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 2001, 17, 754–755. [Google Scholar] [CrossRef] [Green Version]
  73. Felsenstein, J. Parsimony in systematics: Biological and Statistical issues. Ann. Rev. Ecol. Syst. 1983, 14, 313–333. [Google Scholar] [CrossRef]
  74. Delsuc, F.; Brinkmann, H.; Philippe, H. Phylogenomics and the reconstruction of the tree of life. Nat. Rev. Genet. 2005, 6, 361–375. [Google Scholar] [CrossRef] [Green Version]
  75. Cronn, R.; Liston, A.; Parks, M.; Gernandt, D.S.; Shen, R.; Mockler, T. Multiplex sequencing of plant chloroplast genomes using Solexa sequencing-by-synthesis technology. Nucleic Acids Res. 2008, 36, e122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Liu, S.; Ni, Y.; Li, J.; Zhang, X.; Yang, H.; Chen, H.; Liu, C. CPGView: A package for visualizing detailed chloroplast genome structures. Mol. Ecol. Resour. 2023, 23, 694–704. [Google Scholar] [CrossRef] [PubMed]
  77. Downie, S.R.; Jansen, R.K. A Comparative Analysis of Whole Plastid Genomes from the Apiales: Expansion and Contraction of the Inverted Repeat, Mitochondrial to Plastid Transfer of DNA, and Identification of Highly Divergent Noncoding Regions. Syst. Bot. 2015, 40, 336–351. [Google Scholar] [CrossRef]
  78. Lu, Q.; Ye, W.; Lu, R.; Xu, W.; Qiu, Y. Phylogenomic and Comparative Analyses of Complete Plastomes of Croomia and Stemona (Stemonaceae). Int. J. Mol. Sci. 2018, 19, 2383. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Amiryousefi, A.; Hyvönen, J.; Poczai, P. IRscope: An online program to visualize the junction sites of chloroplast genomes. Bioinformatics 2018, 34, 3030–3031. [Google Scholar] [CrossRef] [Green Version]
  80. Darling, A.C.; Mau, B.; Blattner, F.R.; Perna, N.T. Mauve: Multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 2004, 14, 1394–1403. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  81. Bahiri-Elitzur, S.; Tuller, T. Codon-based indices for modeling gene expression and transcript evolution. Comput. Struct. Biotechnol. 2021, 19, 2646–2663. [Google Scholar] [CrossRef] [PubMed]
  82. Rice, P.; Longden, I.; Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000, 16, 276–277. [Google Scholar] [CrossRef] [PubMed]
  83. Sharp, P.M.; Li, W.H. An evolutionary perspective on synonymous codon usage in unicellular organisms. J. Mol. Evol. 1986, 24, 28–38. [Google Scholar] [CrossRef]
  84. Liu, X.S.; Zhang, Y.G.; Fang, Y.Z.; Wang, Y.L. Patterns and influencing factor of synonymous codon usage in porcine circovirus. Virol. J. 2012, 9, 68. [Google Scholar] [CrossRef] [Green Version]
  85. Nie, X.; Lv, S.; Zhang, Y.; Du, X.; Wang, L.; Biradar, S.S.; Tan, X.; Wan, F.; Weining, S. Complete chloroplast genome sequence of a major invasive species, crofton weed (Ageratina adenophora). PLoS ONE 2012, 7, e36869. [Google Scholar] [CrossRef] [Green Version]
  86. Kurtz, S.; Schleiermacher, C. REPuter: Fast computation of maximal repeats in complete genomes. Bioinformatics 1999, 15, 426–427. [Google Scholar] [CrossRef] [Green Version]
  87. Beier, S.; Thiel, T.; Munch, T.; Scholz, U.; Mascher, M. MISA-web: A web server for microsatellite prediction. Bioinformatics 2017, 33, 2583–2585. [Google Scholar] [CrossRef] [Green Version]
  88. Wang, D.; Zhang, S.; He, F.; Zhu, J.; Hu, S.; Yu, J. How do variable substitution rates influence Ka and Ks calculations? Genom. Proteom. Bioinform. 2009, 7, 116–127. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Chen, C.J.; Chen, H.; Zhang, Y.; Thomas, H.R.; Frank, M.H.; He, Y.H.; Xia, R. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol. Plant 2020, 13, 1194–1202. [Google Scholar] [CrossRef] [PubMed]
  90. Frazer, K.A.; Pachter, L.; Poliakov, A.; Rubin, E.M.; Dubchak, I. VISTA: Computational tools for comparative genomics. Nucleic Acids Res. 2004, 32, W273–W279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Rozas, J.; Ferrer-Mata, A.; Sanchez-DelBarrio, J.C.; Guirao-Rico, S.; Librado, P.; Ramos-Onsins, S.E.; Sanchez-Gracia, A. DnaSP 6: DNA Sequence Polymorphism Analysis of Large Data Sets. Mol. Biol. Evol. 2017, 34, 3299–3302. [Google Scholar] [CrossRef]
  92. Katoh, K.; Standley, D.M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 2013, 30, 772–780. [Google Scholar] [CrossRef] [Green Version]
  93. Nguyen, L.T.; Schmidt, H.A.; von Haeseler, A.; Minh, B.Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef]
  94. Darriba, D.; Taboada, G.L.; Doallo, R.; Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 2012, 9, 772. [Google Scholar] [CrossRef] [Green Version]
  95. Ronquist, F.; Huelsenbeck, J.P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 2003, 19, 1572–1574. [Google Scholar] [CrossRef] [Green Version]
Ijms 24 10034 g001 550
Figure 1. Morphological characters of four Scrophularia species. (A,E) Flowers and leaves of S. ningpoensis, (B,F) flowers and leaves of S. buergeriana, (C,G) flowers and leaves of S. kakudensis, and (D,H) flowers and leaves of S. yoshimurae, respectively.
Figure 1. Morphological characters of four Scrophularia species. (A,E) Flowers and leaves of S. ningpoensis, (B,F) flowers and leaves of S. buergeriana, (C,G) flowers and leaves of S. kakudensis, and (D,H) flowers and leaves of S. yoshimurae, respectively.
Ijms 24 10034 g001
Ijms 24 10034 g002 550
Figure 2. The genetic physical maps of four species, S. kakudensis, S.ningpoensis, S. buergeriana, and S. yoshimurae. Genes on the outside of the circle are transcribed clockwise, while those in the inner circle are transcribed counterclockwise. The dark gray inside represents GC content and the light gray represents AT content. The function of genes is color-coded.
Figure 2. The genetic physical maps of four species, S. kakudensis, S.ningpoensis, S. buergeriana, and S. yoshimurae. Genes on the outside of the circle are transcribed clockwise, while those in the inner circle are transcribed counterclockwise. The dark gray inside represents GC content and the light gray represents AT content. The function of genes is color-coded.
Ijms 24 10034 g002
Ijms 24 10034 g003 550
Figure 3. Comparison of LSC, IR, and SSC junction position among ten individuals from four Scrophularia species in cp genomes. JLB means the LSC/IRb junction, JSB means the SSC/IRb junction, JLA means the LSC/IRa junction, and JSA means the SSC/IRa junction.
Figure 3. Comparison of LSC, IR, and SSC junction position among ten individuals from four Scrophularia species in cp genomes. JLB means the LSC/IRb junction, JSB means the SSC/IRb junction, JLA means the LSC/IRa junction, and JSA means the SSC/IRa junction.
Ijms 24 10034 g003
Ijms 24 10034 g004 550
Figure 4. Repeat analyses in ten chloroplast genomes of Scrophularia. (A) Frequency of repeat types. (B) Frequency of repeats by length.
Figure 4. Repeat analyses in ten chloroplast genomes of Scrophularia. (A) Frequency of repeat types. (B) Frequency of repeats by length.
Ijms 24 10034 g004
Ijms 24 10034 g005 550
Figure 5. Simple sequence repeats (SSRs) in the ten Scrophularia chloroplast genomes. (A) Number of SSRs by length. (B) Distribution of SSR loci. CDS, coding DNA sequence; IGS, intergenic spacer region.
Figure 5. Simple sequence repeats (SSRs) in the ten Scrophularia chloroplast genomes. (A) Number of SSRs by length. (B) Distribution of SSR loci. CDS, coding DNA sequence; IGS, intergenic spacer region.
Ijms 24 10034 g005
Ijms 24 10034 g006 550
Figure 6. The 80 CDS regions of 10 individuals of Scrophularia Ka/Ks analysis.
Figure 6. The 80 CDS regions of 10 individuals of Scrophularia Ka/Ks analysis.
Ijms 24 10034 g006
Ijms 24 10034 g007 550
Figure 7. Sequence identity plots among the 10 Scrophularia chloroplast genomes, with S. buergeriana (NC_031437) as a reference. Annotated genes are displayed along the top. The vertical scale represents the percent identity between 50 and 100%. Window length of 100 bp and 70% as criteria. Genome regions are color-coded as exon, intron, and conserved non-coding sequences (CNS).
Figure 7. Sequence identity plots among the 10 Scrophularia chloroplast genomes, with S. buergeriana (NC_031437) as a reference. Annotated genes are displayed along the top. The vertical scale represents the percent identity between 50 and 100%. Window length of 100 bp and 70% as criteria. Genome regions are color-coded as exon, intron, and conserved non-coding sequences (CNS).
Ijms 24 10034 g007
Ijms 24 10034 g008 550
Figure 8. The nucleotide variation (Pi) values of 10 Scrophularia genomes were compared, with nucleotide diversity at the midpoint of the window on the x axis and within each window on the y axis (window length: 600 bp, step length: 200 bp).
Figure 8. The nucleotide variation (Pi) values of 10 Scrophularia genomes were compared, with nucleotide diversity at the midpoint of the window on the x axis and within each window on the y axis (window length: 600 bp, step length: 200 bp).
Ijms 24 10034 g008
Ijms 24 10034 g009 550
Figure 9. Phylogenetic relationships of ten Scrophularia species inferred from Bayesian inference (BI) and maximum likelihood (ML) based on the complete cp genome sequence dataset. Support values marked above the branches follow the order PP (posterior probability)/BS (bootstrap support).
Figure 9. Phylogenetic relationships of ten Scrophularia species inferred from Bayesian inference (BI) and maximum likelihood (ML) based on the complete cp genome sequence dataset. Support values marked above the branches follow the order PP (posterior probability)/BS (bootstrap support).
Ijms 24 10034 g009
Table
Table 1. The basic characteristics of Scrophularia chloroplast genome.
Table 1. The basic characteristics of Scrophularia chloroplast genome.
CharacteristicsS. ningpoensisS. buergerianaS. kakudensisS. yoshimurae
GenBank
Acc. No.		OQ633009NC_053823MN734369OQ633013NC_031437KP718626OQ633012OQ633011MN255822OQ633010
Total cpDNA size (bp)153,173153,175153,175153,148153,631153,631153,032153,024153,016153,173
LSC length84,25584,25784,25784,25984,45484,45484,13884,13084,12484,274
SSC length17,93817,93817,93817,92517,92917,92917,92217,92217,92217,925
IR length25,49025,49025,49025,48225,62425,62425,48625,48625,48525,487
Total GC content (%)37.9937.9937.9937.9837.9937.9937.9837.9837.9837.98
LSC36.0836.0836.0836.0836.0736.0736.0736.0836.0836.07
SSC32.1832.1832.1832.1732.1732.1732.1532.1632.1732.18
IR43.1943.1943.1943.1843.1843.1843.1643.1743.1743.17
Total number of genes132132132132132132132132132132
Protein-coding genes80808080808080808080
rRNA genes4444444444
tRNA genes30303030303030303030
Duplicated genes18181818181818181818
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, L.; Wang, X.; Wang, R.; Li, P. Characterization and Comparative Analysis of Chloroplast Genomes of Medicinal Herb Scrophularia ningpoensis and Its Common Adulterants (Scrophulariaceae). Int. J. Mol. Sci. 2023, 24, 10034. https://doi.org/10.3390/ijms241210034

AMA Style

Guo L, Wang X, Wang R, Li P. Characterization and Comparative Analysis of Chloroplast Genomes of Medicinal Herb Scrophularia ningpoensis and Its Common Adulterants (Scrophulariaceae). International Journal of Molecular Sciences. 2023; 24(12):10034. https://doi.org/10.3390/ijms241210034

Chicago/Turabian Style

Guo, Lei, Xia Wang, Ruihong Wang, and Pan Li. 2023. "Characterization and Comparative Analysis of Chloroplast Genomes of Medicinal Herb Scrophularia ningpoensis and Its Common Adulterants (Scrophulariaceae)" International Journal of Molecular Sciences 24, no. 12: 10034. https://doi.org/10.3390/ijms241210034

APA Style

Guo, L., Wang, X., Wang, R., & Li, P. (2023). Characterization and Comparative Analysis of Chloroplast Genomes of Medicinal Herb Scrophularia ningpoensis and Its Common Adulterants (Scrophulariaceae). International Journal of Molecular Sciences, 24(12), 10034. https://doi.org/10.3390/ijms241210034

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop