Comprehensive chloroplast genome analysis of 73 genus Aconitum members of family Ranunculaceae reveals insights into genome structure, codon usage polymorphism, and phylogenetic relationships

Comprehensive chloroplast genome analysis of 73 genus Aconitum members of family Ranunculaceae reveals insights into genome structure, codon usage polymorphism, and phylogenetic relationships | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comprehensive chloroplast genome analysis of 73 genus Aconitum members of family Ranunculaceae reveals insights into genome structure, codon usage polymorphism, and phylogenetic relationships Richa Ashok Kakkar, Gaurav Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6954381/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Understanding the chloroplast genome is pivotal to unravel evolutionary relationships within plant species and facilitate accurate species identification by utilizing conserved yet diverse sequences. Genus Aconitum consists of around 300 traditional Indian and Chinese medicinal plant species, many native to mountainous regions. Despite their medicinal value, several species are known to be highly poisonous due to the presence of toxic diterpene alkaloids. Therefore, accurate identification and classification of these species is vital for traditional medicine systems especially for their safe usage. Results: Our investigation revealed a consistent quadripartite structure across all chloroplast genomes, comprising the typical large single copy (LSC), small single copy (SSC), and two inverted repeats (IR) regions. Using the available annotations, pangenome analysis unveiled 72 core and nine accessory genes, indicating an open pangenome characteristic. In-depth nucleotide-level homology analysis revealed that homologous genes of all accessory genes are present in all other genomes, implying the requisite for better chloroplast genome annotation tools that can identify all putative genes from such conserved genomes. Notably, the order of all core and accessory genes remained highly conserved across all analysed genomes, underscoring overall evolutionary stability with the diversity of accessory genes. Members of some core pathways are relatively absent on the chloroplast genome, suggesting its potential presence on the nuclear genome, which will be revealed after their nuclear genome sequencing. Furthermore, codon usage analysis demonstrated a preference for A/T ending codons over G/C ending codons, consistent with chloroplast genomes across species. Our phylogenetic results largely supported the morphological classification, with distinct Lycoctonum and Aconitum subgenera clustering. This validated the gross accuracy except for A. tanguticum and A. flavum , which clustered in wrong subgenus clades, suggesting discrepancy in morphological classification of the species or inaccurate classification. Conclusion: This comprehensive comparative analysis of 73 Aconitum chloroplast genomes elucidated their diversity at gene and genome architecture levels along with showcasing their evolutionary relationships with each other. Leveraging morphological classifications, we investigated the concordance between traditional taxonomy and molecular data through core gene-based and whole-genome phylogeny. The observed phylogenetic incongruences, such as non-monophyly of conspecific accessions and unexpected clustering patterns, likely reflect the combined effects of incomplete lineage sorting and historical hybridization events, both of which appear to be prominent evolutionary forces shaping the genomic architecture of Aconitum . Biological sciences/Evolution/Taxonomy Biological sciences/Computational biology and bioinformatics Biological sciences/Plant sciences/Plant evolution chloroplast genomics medicinal plants traditional medicinal plants pangenome analysis phylogenetics nucleotide diversity chloroplast evolution cellular organelle genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Medicinal plants have been widely used for the treatment of diverse diseases throughout the world among different cultures and societies based on their geographic location and availability of local medicinal plants. Around 53,000 species of plants have found applications as medicinal plants till date where different plants parts such as root, leaves, flower, seed, bark of tree or even whole plant are used as herbal medicine (Pan et al., 2014). Medicinal plants are rich in bioactive ingredients and effective in treatment of several diseases such as diabetes (Jacob & Narendhirakannan, 2019), arthritis (Gandhi et al., 2022), asthma (Usmani et al., 2023), and high blood pressure (Mishra & Tubaki, 2019), to name a few. One such Indian traditional medicinal plant genus Aconitum encompasses ~300 mountainous species of significant economic importance, albeit some being poisonous due to the presence of toxic diterpene alkaloids (Ameri, 1998). These plants have application in Ayurveda as well as Chinese traditional medicine addressing several ailments like neuralgia, sciatica, arthritis, gout, rheumatism, treatment of colds, sore throat, and inflammation of the respiratory tract (Ameri, 1998; Singh et al., 2020). As of now, their unique biodiversity is on the verge of extinction due to illegal human intervention triggered habitat loss, over-harvesting, and unrestricted trading, and based on this, several Acontium species have been identified as endangered, critically endangered or vulnerable by IUCN (Rafiq et al., 2021). The process of photosynthesis that transforms solar energy to chemical energy and sustains all life on earth is of utmost importance for the plants and this critical process is carried out inside a semiautonomous organelle called chloroplast. Plastids have evolved 1.5 to 1.6 billion years ago via endosymbiosis process (Yoon et al., 2004). In addition to photosynthesis, this semiautonomous organelle plays an important role in biosynthesis of lipids, amino acids, carotenoids among other important biomolecules (Hölzl & Dörmann, 2019), for which plastids have retained approximately 100 proteins synthesizing genes along with several others being encoded by nuclear genome (Ries et al., 2020). A lot of genes in the chloroplast genome have been have been functionally transferred to nuclear genome or lost during evolution (Cullis et al., 2009; Eckardt, 2006). The structure of chloroplast genome is generally quadripartite, having four regions namely LSC, SSC, IRa and IRb regions. Several structural rearrangements of the plastid genome have occurred throughout evolution resulting in contraction, expansion and loss of genetic content. According to a study conducted on 2,511 chloroplast genomes, approximately 10.31% of the examined species have lost the inverted repeats (IR) in the chloroplast genome spanning across all lineages (Mohanta et al., 2020). Phylogenetic studies of Ranunculaceae family based on diverse characteristics such as morphology, restriction site mapping, nuclear sequence and chloroplast sequence have complicated the classification of genus Aconitum . Several classification models of genus Aconitum have been proposed, based on different morphological characteristics such as inflorescence, branching of stem, shape of sepals and petals and structure of embryo sac among others. According to the chromosomal study by Schafer & La Cour (1934), Aconitum genus is classified into two subgenus: Lycoctonum and Aconitum based on ploidy levels of the two groups (Yuan & Yang, 2006). Based on several non-molecular characteristics such as phytochemical, cytological, anatomical and palynological (study of plant pollen and spores), Aconitum gymnandrum has been removed from genus Aconitum and converted into separate genus Gymnaconitum , with only one species Gymnaconitum gymnandrum (W. Wang et al., 2013). In this context, Gymnaconitum gymnandrum has been widely used as an outgroup for phylogenetic and various other analysis. Codon bias in all genes, all organisms, are important for studies of evolutionary adaptation and biotechnology applications. In recent years, researchers investigated the codon usage of several chloroplast genomes and found certain common patterns. It has been observed in green plant chloroplast genomes that it favours codon ending in base A or T and this pattern in consistent across species with pressure from both natural selection and mutational bias (W. Q. Kong & Yang, 2017; Z. Wang et al., 2020; Yengkhom et al., 2019). A study on codon usage bias of angiosperm chloroplast genes revealed that context dependent mutations explain codon usage bias of most chloroplast genes except the highly expressed psbA gene which is controlled by selection. (Morton, 2022). Although the codon usage in green plant chloroplasts is similar between species, differences between genes within the genomes has been identified. Methodology Data collection and summary: Chloroplast genome data of genus Aconitum members (including genome nucleotide sequences, translated CDS sequences, and GenBank files) was retrieved from the National Centre for Biotechnology Information (NCBI) via Entrez using command line interface. A total of 105 chloroplast genomes were obtained for which a summary file (Supplementary table 1) was generated using a custom script to extract relevant metadata from the GenBank files, including species name, accession number, genome size, GC content, and the counts of protein-coding genes, rRNA, tRNA, genes, pseudogenes, and voucher information. To eliminate redundancy, genomes were screened based on species names and genome sizes resulting in a refined dataset comprising 73 unique Aconitum chloroplast genomes. Gymnaconitum gymnandrum , a species closely related to Aconitum but belonging to a different subgenus, was included as an outgroup for the analysis, consequently, making the final dataset consisted of 74 chloroplast genomes. Genome annotation for differentiation into LSC, SSC and IR regions: PGA (Plastid Genome Annotator) tool (Qu et al., 2019) was used to perform annotation of all 74 genomes, and categorization into LSC, SSC and IR regions. Using the annotation data, the sequences of four regions were extracted using a self-written bash script followed by calculation of GC content in each of the regions. The genome with accession ID MW817090 of Aconitum scaposum was excluded from this analysis due to discrepancy in the annotation of four regions. Pangenome analysis and functional characterization of chloroplast genes: Pangenome analysis was performed with the tool Proteinortho 6.1.7 (Lechner et al., 2011), where translated CDS files of all 74 Aconitum genomes was provided as the input. This software detects orthologous genes within different species, comparing similarities of given gene sequences and clusters them to find significant groups. The tool was run with default parameters (percentage identity=25%, evalue=1e -5 ) along with two additional parameters –singles and –selfblast which potentially help in detecting the unique genes in the genomes if present. Calculation of beta value for Heap’s law and generation of graph was performed using R. Synteny analysis: Synteny studies were performed to visualize the genome architecture of 74 Aconitum genomes using web-based OrganellarGenomeDRAW v1.3.1 tool (Greiner et al., 2019) and Genbank (.gbk) files as input. The tool converts GenBank or EMBL/ENA format to graphical maps, either circular or linear. Two different set of linear maps were generated for each genome, one representing only the accessory genes and the other representing core and accessory genes. Configuration file was edited for each set to specify required genes in the map and their corresponding colour code. Each set resulted in 74 high-quality png files of linear maps which were merged into one and ‘convert’ command was used to crop, resize and merge the individual images. Analysis of Simple Sequence Repeats (SSRs): GMATA v2.01 tool was used to detect SSRs (X. Wang & Wang, 2016) using the genome file of 74 genomes. Self-written script was used to extract data from the ‘.ssr.sat2’ output file and graphs plotted using the extracted data. Minimum repeated times of motif was set to 10, 5, 4, 3, 3 and 3 for mono, di, tri, tetra, penta and hexa nucleotide repeats. Variable region identification: To identify the variable region in Aconitum chloroplast genomes with respect to the reference, BLAST alignment was performed using Circular genome viewer comparison tool (CG View CT) (Grant et al., 2012). This tool utilizes GenBank-formatted files as input and operates through a two-step process (project creation and map generation) facilitated by the wrapper script build_blast_atlas.sh. During the project creation step, a structured directory system is established, consisting of directories for input files, reference files, configuration files, and output files. In the subsequent map generation step, genome FASTA files are placed in the ‘comparison_genome’ folder, while the reference genome FASTA file (in this case, Gymnaconitum gymnandrum ) is placed in the reference folder. The build_blast_atlas.sh script then generates maps for both nucleotide (BLASTn) and translated coding sequence (BLASTp) comparisons. The output includes CGView XML files located in the cgview_xml folder, corresponding to the nucleotide (dna_vs_dna) and protein-coding sequence (cds_vs_cds) comparisons. The same methodology was applied using A. vilmorinianum as the reference genome for comparative analysis. Codon usage analysis : The total coding sequences for all 74 genomes (Table-1) were filtered according to the following criteria suggested by previous report (Y. Wang et al., 2023): The sequence length must exceed 300 base pairs. Each sequence must initiate with a start codon (ATG) and terminate with a stop codon (TAA/TAG/TGA). Intermediate stop codons must be absent within the sequence. The total number of nucleotides in the sequence must be divisible by three. The GC content of the first, second, and third codon positions of the 40 protein-coding sequences that passed filtration criteria was calculated using the CUSP program from the EMBOSS package (Rice et al., 2000) along with the overall codon usage frequency for all 74 chloroplast genomes. Codon usage analysis was conducted employing the CAI Calculator, which provided insights into nucleotide composition, relative synonymous codon usage (RSCU) values, codon usage frequency, and codon usage per thousand values (Puigbò et al., 2008) followed by heatmap generation using Heatmap Illustrator (HemI 1.0) (Deng et al., 2014) using the average linkage method with Euclidean distance as the clustering metric. The effective number of codons (Nc) and the expected Nc values were calculated using the software DAMBE7 (Xia & Xie, 2001). To test context dependent mutation, amino acids are categorised into eight distinct 4-fold degenerate families as follows: Arg, Leu and Ser were each divided into three 2-fold degenerate families (Arg2, Leu2, Ser2) and three 4-fold degenerate families (Arg4, Leu4, Ser4) along with five 4-fold degenerate families Pro4, Thr4, Ala4, Val4, Gly4. Considering mutation as an independent single-site event, the nucleotide frequencies of the third codon position in the 4-fold degenerate families will not be affected by the second and/or the first codon positions. Following the method used in analysis of codon usage in Quercus chloroplast genome (Shi et al., 2022), test of independence of the third codon position in the eight 4-fold degenerate families was tested. The chi-square test of independence was conducted for each of the six dataset as follows: Leu4/Pro/Arg4; Val/Ala/Gly; (Leu4 + Val)/(Ser4 + Pro + Thr + Ala)/(Arg4 + Gly); Leu4/Val; Arg4/Gly; Ser4/Pro/Thr/Ala. These analyses aimed to determine whether the nucleotide composition at the third codon position within each dataset is statistically independent of the first and second codon positions. Synonymous and non-synonymous substitution analysis: To analyse synonymous and non-synonymous substitutions, DnaSP v6.12.03 (Rozas et al., 2017) was employed using nucleotide FASTA files of 40 protein-coding sequences conserved across 74 species. The resulting output included pairwise estimates of Ka (non-synonymous substitution rate) and Ks (synonymous substitution rate), along with additional metrics. For each gene, the mean Ka and Ks values were subsequently calculated. To further understand overall nucleotide diversity, DnaSP v6.12.03 was further used (Rozas et al., 2017) where fasta sequences of coding genes, rRNA, tRNA genes and intergenic sequences were provided as input all at once using the Batch Mode and default parameters. Pi values for each sequence was used to generate a line graph using R. Phylogenetic analysis: The genus Aconitum has been classified into two subgenera, Aconitum and Lycoctonum , based on differences in morphology and ploidy levels. Therefore, to determine whether this morphological and ploidy level classification is supported by molecular data, two distinct phylogenetic analysis was conducted using core gene sequences and whole-genome data. For core genes-based phylogeny, 74 fasta files containing protein sequences of each core gene per genome was prepared using a self-written script with sequences extracted from translated CDS file. The extracted sequences were aligned using MUSCLE v3.8.1551(Edgar, 2004) followed by concatenating all 74 aligned blocks, which was further used for phylogenetic tree building using IQ-TREE multicore version 2.2.2.3 (Minh et al., 2020). The best model suggested by the tool is used to create the phylogenetic tree followed by visualization on the web-based tool iTOL v6 (Letunic & Bork, 2021). For whole genome-based phylogeny, 74 whole genome sequences were aligned using the tool MUSCLE v3.8.1551 (Edgar, 2004). The aligned sequences were given as input to IQ-TREE (multicore version 2.2.2.3) to generate a maximum likelihood phylogeny using the model suggested by IQ-TREE multicore version 2.2.2.3 (Minh et al., 2020). The generated tree is then visualized on the web-based tool iTOL v6 (Letunic & Bork, 2021). Calculation of intra-specific K2P distance: The Kimura 2-Parameter (Kimura, 1980) substitution model was used to find the intra-specific distance in R with the help of libraries named “ape” and “seqinr”. Input for calculating the distance was MUSCLE alignment file of all 73 genomes along with the outgroup as mentioned in the phylogenetic analysis methodology. Results Chloroplast genome statistics of 74 Aconitum species: Out of the 73 Aconitum genomes under study, the presence of 40 unique species depicts the diversity of the used dataset (Supplementary table 1). Out of the total available data, 30 and 43 genome files were sourced from RefSeq and GenBank databases, respectively. The genome size of these species ranges from 151,214 to 157,688 bp with Aconitum episcopale having the smallest and Aconitum brachypodum having the largest genome. Variation in number of genes is not very high and ranges from 123 in Aconitum austrokoreense, Aconitum coreanum and Aconitum volubile to 132 in twenty different Aconitum genomes (Supplementary table 1). Interestingly, one out of two reported Aconitum coreanum and Aconitum austrokoreense genomes has 123 genes annotated and the other has 132. The protein coding genes range from 82 to 87, and tRNA genes ranges from 36 to 38, however, 8 rRNA genes are present consistently across all genomes under study. Six pseudogenes are also annotated in Aconitum pseudolaeve, which is the highest among all the considered genomes. All 73 genomes of Aconitum had a quadripartite structure, i.e. had a large single copy region (LSC), small single copy region (SSC) and two inverted repeat regions (IRA and IRB) (Figure 1a). As far as the total GC content is considered, there is no significant difference in GC percentage among the chloroplast genomes, which ranges from 37.99% to 38.30%. However, the GC content between different regions of the same genome varies, with IR regions having high GC percentage and LSC region having lower GC percentage (Figure 1b, Supplementary table 2). The GC percentage of LSC region ranges from 36.01% to 36.39%, that of SSC ranges from 32.42% to 32.85%, whereas for IRA and IRB region the range lies from 42.94% to 43.10%. This indicates that although there is difference amongst the four regions, there is no significant difference among the different species for one particular region. Pangenome analysis revealed that Aconitum has an open pan-genome: Pangenome analysis revealed that the number of core genes ranged from 75 to 77 among the 74 species, whereas the accessory genes ranged from 6 to 10 (Supplementary Table 3). To understand if the pan-genome is open or closed, Heap’s law formula was utilized (Figure 1c), given by V = k*n^beta; where V is the vocabulary size (number of distinct genes), k is the scaling parameter, n is the size of sample (number of genomes observed), beta is the growth parameter. According to Heap’s law, if beta value is greater than 0, it is open pan-genome and if the beta value is less than 0, it is a close pan-genome. For the unique set of genes in the 74 genomes considered in this study, the beta value was calculated to be 0.0084, suggesting the pan-genome of Aconitum chloroplast genomes considered in this study to be an open pan-genome (Figure 1c). The gene order of both accessory and core genes was found to be well conserved across all species (Figure 2, Supplementary Figure 1). Functional characterization of genes (Table 1) classified all gene functions into three main categories: chloroplast envelope membrane protein genes, genes for photosynthesis and genes for transcription and translation, which were further classified into sub-categories. The Chloroplast envelope membrane protein genes include the Cytochrome b6f group of genes, whereas the photosynthesis genes include ATP synthase ( atp genes), NADH oxidoreductase ( ndh genes), photosystem I ( psa genes) and photosystem II genes ( psb genes). The sub-categories of genes for transcription and translation include the large ribosomal subunit ( rpl genes), RNA polymerase ( rpo genes), small ribosomal subunit ( rps genes) and translation initiation factor ( infA gene). All photosynthetic genes were assigned under core genes. Five ( rpl16, rpl2, rpl20, rps16 and infA ) out of nine accessory genes belonged to the category of genes related to transcription and translation. A group of researchers have performed the functional and structural analysis of rpl16 using bioinformatics tools, suggesting it to be a thermo-stable, acidic and hydrophilic protein. One of the predicted counterparts of RPL16 includes RPL2 which is also an accessory protein among the genomes considered in the study. The presence of its counterpart rpl2 in Aconitum reclinatum (MF186593) could be a possible reason behind the absence of rpl16 gene having no effect. The protein RPL20 along with other proteins initiates the 50s ribosomal subunit assembly which binds directly to the 5’ end of the 23s rRNA (Y. Yang et al., 2018) . It has been found that rps16 is lost in many taxa from ferns to angiosperms. However, its nuclear genome counterpart is present in such taxa as it is necessary for the survival of the organism (Schwarz et al., 2015). The investigation of nuclear genome will reveal presence or absence of such counterparts in the Aconitum genome. Analysis of SSRs exhibits lack of their conservation in Aconitum chloroplast genomes: This analysis revealed that mononucleotide simple sequence repeats (SSRs) exhibited the highest frequency, followed by di-, tri-, tetra-, penta-, and hexanucleotide repeats (Figure 3b-f, Supplementary Table 4a). The frequency of mononucleotide repeats ranged from 0.12 to 0.33 SSRs/kb, with A. finetianum displaying the highest abundance of mononucleotide repeats (Figure 3b). The number of mononucleotide SSRs varied from 19 in A. volubile to 52 in A. finetianum . In contrast, the number of dinucleotide SSRs showed minimal variation, ranging from 10 to 17 (Figure 3a), with a frequency distribution of 0.06 to 0.1 SSRs/kb. The frequency of trinucleotide, tetranucleotide, and pentanucleotide repeats ranged from 5 to 12, 5 to 9, and 1 to 5, respectively, with pentanucleotide SSRs absent in certain species. Hexanucleotide SSRs were identified exclusively in eight species: A. jaluense subsp. jaluense, A. austrokoreense, A. scaposum var. vaginatum, A. longecassidatum, A. tanguticum, A. ramulosum, A. stylosum , and A. delavayi . Variability in di-, tri-, and tetranucleotide SSRs was relatively low across species (Figure 3a). The frequency distributions of tri-, tetra-, penta-, and hexanucleotide repeats were 31.78-76.26, 31.71-57.8, 0–31.8, and 0–19.09 SSRs/Mb, respectively (Supplementary Table 4a). SSR analysis across the LSC, SSC, IRA, and IRB regions revealed that the LSC, followed by the SSC and IR regions, harboured the highest number of all six types of repeat sequences, which is expected given the larger genomic span of the LSC. Two genomes, MW817090 and MT584425, were excluded from this analysis due to discrepancies in IR region annotations. The frequency of total SSRs, expressed in SSRs/kb, was highest in the LSC region. Specifically, mononucleotide repeat frequencies ranged from 1.7 to 4.6, 0 to 3.8, and 0 to 1.2 SSRs/kb in the LSC, SSC, and IR regions, respectively (Figure 3b). Dinucleotide repeat frequencies varied between 0.3 and 0.9 SSRs/kb in the LSC, 0 to 0.8 SSRs/kb in the SSC, and 0 to 0.4 SSRs/kb in the IR regions (Figure 3c). Trinucleotide repeats exhibited frequency distributions of 0.04 to 0.3 SSRs/kb in the LSC, 0.2 to 0.6 SSRs/kb in the SSC, and 0 to 0.15 SSRs/kb in the IR regions (Figure 3d). Tetranucleotide repeats were identified in only one genome within the IR regions at a frequency of 0.1 SSRs/kb, whereas in the LSC and SSC regions, their frequencies ranged from 0.1 to 0.2 and 0.2 to 0.5 SSRs/kb, respectively (Figure 3e). Pentanucleotide repeats were observed at frequencies of 0 to 0.14 SSRs/kb in the LSC region and were detected in the SSC region in only one genome at a frequency of 0.2 SSRs/kb, while they were completely absent in the IR regions (Figure 3f). Hexanucleotide repeats were identified in six genomes within the LSC region, all exhibiting a frequency of approximately 0.03 SSRs/kb. These repeats were absent in the SSC region, whereas in the IR regions, they were found in only one genome at a frequency of 0.1 SSRs/kb (Supplementary Table 4b). Among mononucleotide repeats, A/T-rich repeats were more prevalent than C/G-rich repeats in both the LSC and SSC regions, whereas the IR regions contained only A/T repeats. The only dinucleotide repeats present across all four regions were of the AT/AT type. The LSC region contained three distinct trinucleotide repeat motifs: AAT/ATT, ATC/ATG, and CCG/CGG, while the SSC region exhibited only the AAT/ATT motif, and the IR regions exclusively harboured AAG/CTT trinucleotide repeats. The dominant tetranucleotide repeats in the LSC region were AAAG/CTTT and AAAT/ATTT, whereas AATG/ATTC was the predominant motif in the SSC region (Supplementary Table 4c). Variable region identification and nucleotide diversity analysis reveal some tRNA genes with diverse sequence: To accurately identify Aconitum at the species level, several methods have been explored and proposed (He Ka-Lok; Shaw, Pang-Chui; Wang, Hong; Li, De-Zhu, 2010; Park et al., 2017; Sun et al., 2024). The rbcL-matK phylogeny and ITS sequence phylogeny have been reported previously (Kakkar et al., 2023) that were not successful in clustering the sequences from same species together, and therefore, cannot be used for species level identification. Similarly, whole genome phylogeny of species with multiple samples considered in the study also did not yield the appropriate results (Supplementary Figure 2). Hence, other gene or intergenic sequences in the Aconitum genome need to be explored through variable region identification and nucleotide diversity analysis. Whole-genome BLASTN analysis was conducted for 74 chloroplast genomes, and comparative circular plots were generated using the CGView Comparison Tool against Gymnaconitum gymnandrum and A. vilmorinianum as reference genomes. The resulting plots illustrate the percentage similarity across genomes. Overall, the chloroplast genomes of the 74 Aconitum species exhibit high conservation, with more than 90% sequence identity. In the first plot, several regions display sequence identity below 96%, with a small stretch between matK and psbI showing less than 90% identity (Figure 4a). The second plot indicates variability in the matK to psbI region in only a subset of the genomes analysed (Figure 4b). Further examination revealed that these genomes belong to the subgenus Lycoctonum , whereas the reference genome used for the second plot belongs to the subgenus Aconitum . To gain deeper insights into genome-wide sequence variability at the nucleotide level, a nucleotide diversity analysis was performed. Nucleotide diversity (Pi) analysis provides a measure of sequence variation across different genomic regions. The Pi value represents the proportion of nucleotide sites expected to differ between any two randomly selected DNA sequences, with higher values indicating greater sequence variability. Regions exhibiting high Pi values are potential candidates for marker development. This analysis illustrates the Pi values of various genes (Figure 5a) and intergenic regions (Figure 5b), arranged in genome order. Notably, sequences with Pi values exceeding 0.2 predominantly correspond to tRNA genes, along with a single intergenic region between psbH and petB . The tRNA genes exhibiting high nucleotide diversity include trnL-CAA, trnN-GUU, trnV-GAC, trnR-ACG, trnI-GAU, trnA-UGC , and trnI-CAU , all of which are located within the IR regions. Additionally, in non-IR regions, tRNA genes such as trnG-GCC, trnG-UCC , and trnM-CAU show elevated Pi values (greater than 0.1). These highly diverse tRNA gene sequences hold potential for marker-based species identification. Within the matK – psbI region, rps16 is the only gene exhibiting relatively higher nucleotide diversity, with a Pi value exceeding 0.05. Codon usage analysis: Codon usage bias, the preferential use of certain synonymous codons over others, reveals fundamental evolutionary forces shaping genomic architecture. There are several aspects of codon usage that provide insights into crucial aspects of molecular evolution such as GC content of the codons, codon usage bias measured by Nc values (Effective number of codons) and codon usage preference based on RSCU values. Previous studies on chloroplast genomes have consistently shown that GC content decreases from the first (GC1) to the third (GC3) codon positions, favouring A/T-ending codons due to mutational pressures and compositional bias (Shi et al., 2022; Z.-K. Wang et al., 2023; Z. Wang et al., 2022), whereas chloroplast genomes shows consistent ENc values across species, reflecting weak overall codon bias and evolutionary conservation (Z.-K. Wang et al., 2023; Z. Wang et al., 2022; Wright, 1990). Relative Synonymous Codon Usage (RSCU) analysis has identified certain amino acids absent in specific genes across chloroplast genomes, emphasizing lineage-specific translational optimization and evolutionary constraints(F. Li et al., 2022; Shi et al., 2022). It has also been reported in previous studies through understanding the relationship between Nc and GC3 values that GC3 exerts some influence on the codon usage pattern although other factors like selection also play important role (He et al., 2016; Khandia et al., 2022). 1. GC content reduces from first position of codon to the third indicating preference of A/T ending codons over G/C ending codons: The base composition at the first (GC1), second (GC2), and third (GC3) codon positions was analysed for all 40 genes across 74 chloroplast genomes (Supplementary Table 5a). While GC content varied among genes, no significant variation was observed between genomes (Supplementary figure 3). However, a significant difference was noted among GC1, GC2, and GC3 values (Figure 6a, Supplementary figure 3). Across genomes, GC1 values ranged from 47.19% (n=74, SD=5.79) to 47.49% (n=74, SD=5.65), GC2 values from 39.27% (n=74, SD=5.24) to 39.73% (n=74, SD=5.36), and GC3 values from 28.58% (n=74, SD=3.39) to 28.90% (n=74, SD=4.02). In contrast, variation across genes was more pronounced, with GC1 values ranging from 36.14% (n=40, SD=0.24) to 58.55% (n=40, SD=0.14), GC2 values from 27.94% (n=40, SD=0.21) to 57.55% (n=40, SD=0.00), and GC3 values from 23.07% (n=40, SD=0.52) to 35.92% (n=40, SD=0.41). The heatmap presented in Supplementary figure 3 highlights the lower GC3 values, indicating a preference for A/T-ending codons over G/C-ending codons. This pattern has been commonly observed and reported across chloroplast genomes of various species, including Morus cathayana, Morus multicaulis , six Euphorbiaceae species, and three Camellia species, among others (Kong & Yang, 2017; Wang et al., 2020; Yengkhom et al., 2019). 2. Codon usage bias measured by effective number of codons reveals consistency of codon usage across genomes: The effective number of codons (Nc) metric was utilized to assess codon usage bias among different genes and across all 74 analysed genomes. Nc values range from 20 to 63, where lower values indicate a stronger codon bias, and higher values suggest a more uniform usage of synonymous codons. A lower Nc value implies that an organism preferentially utilizes a subset of synonymous codons, whereas a higher Nc value reflects a reduced bias in codon selection. As illustrated in Figure 6b, the rps18 and petD genes exhibit the highest codon bias among the 40 analyzed genes, whereas ycf4 and clpP display the least bias, clustering together in the heatmap. Additionally, genes such as rps14, psbA, ndhA, petB , and atpF exhibit a codon usage pattern similar to petD , forming a distinct cluster. The heatmap further highlights variations in codon bias among genes while demonstrating a largely consistent trend across the genomes under study. 3. Codon usage preference based on RSCU values reveals the relative absence of some amino acids in several genes across 74 chloroplast genomes: Comparative analyses of transfer RNA (tRNA) across all kingdoms have demonstrated that no single organism possesses tRNAs with anticodons complementary to all 61 sense codons (Berg & Brandl, 2021; Grosjean et al., 2010). This is due to the wobble hypothesis, where a single tRNA can recognize multiple synonymous codons because the third position of the codon (wobble position) exhibits flexibility in base pairing. Relative Synonymous Codon Usage (RSCU) quantifies codon usage bias by comparing the observed frequency of synonymous codons for a given amino acid to the expected frequency under equal usage conditions. An RSCU value of 1 indicates no bias, values greater than 1 signify positive codon usage bias, while codons with RSCU values below 0.6 are considered underrepresented, and those above 1.6 are overrepresented. RSCU analysis revealed that atpE and rpoC2 genes lack both codons for tyrosine, rps18 lacks both codons for histidine, psbA lacks both codons for lysine, and ndhC, rps18 , and rps7 lack both codons for cysteine. As depicted in Figure 6c, G/C-ending and A/T-ending codons tend to form distinct clusters, with A/T-ending codons exhibiting relatively higher RSCU values. Notably, most G/C-ending codons clustered together, along with TTT, ATA, and CTA, whereas within the major A/T-ending cluster, only TTG was grouped with the G/C-ending codons. Codons were classified into six groups based on their RSCU values: (1) overrepresented codons (RSCU > 1.6), (2) positively biased codons but not overrepresented (1 < RSCU ≤ 1.6), (3) unbiased codons (RSCU = 1), (4) negatively biased codons but not underrepresented (0.6 ≤ RSCU < 1), (5) underrepresented codons (0 < RSCU < 0.6), and (6) unused codons (RSCU = 0). Our analysis revealed that 80.16% of thymine-ending codons and 71.25% of adenine-ending codons were positively biased, whereas only 12.19% of cytosine-ending and 14.23% of guanine-ending codons exhibited positive bias (Supplementary table 6). A comparison of the codon usage pattern of the psbA gene with the overall trends observed across 40 genes indicated that psbA follows a similar codon usage preference to the whole genome. However, fewer than half of the adenine-ending codons were positively biased (Supplementary table 6), suggesting that the psbA gene exhibits a stronger preference for A/T-ending codons. 4. GC3 values have low level correlation with the effective number of codons suggesting some influence of GC3 on codon usage: Correlation analysis revealed that neither GC1 nor GC2 values exhibited a significant correlation with the effective number of codons (Nc) (Figure 7a, b, c). Scatter plot of correlation analysis depicting the comparison between expected and observed Nc values against GC3 demonstrates a noticeable deviation of observed Nc values from expected trends (Figure 7c). The correlation coefficient was higher for expected Nc values against GC3 than for observed Nc values, indicating that while GC3 strongly influences theoretical Nc expectations, actual codon usage patterns deviate due to additional evolutionary factors. A low positive correlation was observed between Nc and GC3 values, with observed Nc values tending to be lower than expected at higher GC3 values. This suggests that an increased proportion of GC-ending codons corresponds to reduced codon bias. Notably, the ndhA gene clustered with psbA in Figure 6b, and its observed Nc value (50.2) closely matched the expected value (50.5) (Supplementary Table 5d). However, this trend did not hold true for psbA , implying that distinct selective pressures or mutational influences are shaping the codon usage of psbA differently from other genes. Additionally, regression analysis of average GC1 and GC2 values against GC3 yielded an insignificant R² value (0.02), reinforcing the notion that codon usage patterns arise from a complex interplay of mutational and selective forces (Figure 7d). 5. Analysis of RSCU values indicates absence of context dependent mutation: The analysis of context dependent mutation verifies the condition that mutation is a single-site event, meaning the nucleotide frequencies of the third codon position in the 4-fold degenerate families will not be affected by the second and/or the first codon positions. The result of this analysis showed that the variation of codon’s third base does not correlate with either second or first base (Supplementary table 7). It can thus be hypothesised that for the Aconitum chloroplast genomes, the likelihood of mutation occurring at a particular site in the codon is not influenced by the neighbouring nucleotides. Synonymous and non-synonymous substitution analysis reveal that the Aconitum chloroplast genes are under purifying selection: A nucleotide substitution that alters the encoded amino acid of a protein is termed a non-synonymous substitution (Ka), whereas a substitution that does not change the amino acid sequence is referred to as a synonymous substitution (Ks). The Ka/Ks ratio serves as an indicator of coding sequence evolution, where a value of 1 suggests neutral evolution, a value greater than 1 indicates positive or diversifying selection, and a value less than 1 implies negative or purifying selection. The Ka/Ks ratio was calculated for 40 conserved genes across 74 Aconitum species, revealing a predominant pattern of purifying selection. In this analysis, Ks values were generally lower than Ka values, with the exceptions of matK , clpP , and rpoC1 (Supplementary Figure 4). However, as the Ka/Ks ratio remained below 1 even for these genes, the results still suggest negative selection (Supplementary Table 8). This finding aligns with expectations, as all analysed genomes belong to the same genus, where strong evolutionary constraints act to preserve functional integrity and limit protein-coding sequence divergence. Non-monophyletic clustering of conspecific samples in phylogenetic analysis: The classification of genus Aconitum into subgenera Aconitum , Lycoctonum , and Gymnaconitum (as an outgroup) is based on differences in seed morphology and ploidy levels (H. H. Kong et al., 2013). To assess whether phylogenetic analysis aligns with this morphological classification, whole-genome and core-gene phylogenies were constructed using the best-fit models suggested by IQ-TREE. The whole-genome phylogeny was inferred using the TVM+F+I+R3 model, while the core-gene phylogeny was based on the Q.mammal+F+R2 model. In both phylogenies (Figure 8) all the accessions of subgenera Aconitum and Lycoctonum cluster together respectively, with one exception. Aconitum flavum (GenBank accession: MT982388) was observed to cluster phylogenetically with members of the subgenus Lycoctonum , rather than grouping with its taxonomic subgenus Aconitum , as would be expected based on current classification. This anomalous placement was consistently recovered in phylogenies constructed from both whole chloroplast genome sequences and core gene datasets of 73 species (Figure 8). To further investigate such incongruence, Kimura 2-Parameter (K2P) genetic distances were calculated among A. flavum accessions. The pairwise comparisons revealed that MT982388 displayed markedly higher intraspecific K2P distances (ranging from ~0.0093 to 0.0095) when compared with other A. flavum accessions (e.g., MW839579, MW839580, MW839582, and NC_056280), whose mutual distances were significantly lower (as low as 4.5×10⁻⁵) (Supplementary table 10). This elevated divergence, along with the unexpected phylogenetic placement, suggests that MT982388 may represent a misidentified sample. Alternatively, it could reflect deep genetic divergence within A. flavum , indicative of cryptic speciation or historic hybridization. However, given that the GenBank submission includes a voucher specimen, it is more likely that the anomaly stems from either incorrect species identification during sequencing or contamination of the submitted sample. Further, the phylogenetic tree constructed from complete chloroplast genome sequences of 73 accessions representing 40 Aconitum species revealed non-monophyly among several conspecific samples. While multiple accessions of some species clustered together as expected, others were placed in separate clades or grouped more closely with different species, suggesting complex evolutionary relationships. Notably, accessions of species such as A. pendulum , A. flavum and A. kusnezoffii exhibited such inconsistent clustering patterns. Discussion Chloroplast genomes exhibit a high degree of conservation, a pattern which this study also observed in the comparative analysis of 73 Aconitum chloroplast genomes, as evident in gene content, genomic architecture (quadripartite structure), synteny, GC percentage, base composition of codons and codon usage. Pangenome analysis suggested that there are 72 core and 9 accessory genes for this group of chloroplast genomes. Intriguingly, BLASTn analysis of nine accessory genes against the Aconitum chloroplast genomes revealed that the homologous sequences for all accessory genes were present in all chloroplast genomes with >96% similarity (Supplementary table 9). Upon re-examining the NCBI annotation files, we confirmed that the accessory genes were absent in the NCBI-annotated genome files despite their homology within the genome sequences with high sequence similarity. This discrepancy suggests that these genes were not called during annotation by the NCBI annotation pipeline, highlighting potential limitations in automated gene annotation pipelines. Annotation of these genomes was further performed using Plastid Genome Annotator (PGA) (Qu et al., 2019), which yielded additional insights. In the PGA-annotated datasets, the accessory genes infA and ycf15 still remained undetected in all genomes; however, other accessory genes, i.e., rpl16, rpl2, rpl20 rps16 and ycf1 , were detected. An intriguing pattern was observed for the accessory genes psbN and pbf1 : in genomes where psbN was present, pbf1 was absent, and vice versa, with pbf1 located at the same genomic position as psbN . In contrast, the PGA annotation files consistently included psbN in all genomes but failed to annotate pbf1 in any genome. The observed inconsistencies between the NCBI and PGA annotations in the relative presence/absence of genes in genome sequences despite their absence in annotation files, may stem from undetected mutations or sequencing errors in chloroplast genome assembly. These results strongly emphasize the need for rigorous manual validation and improved annotation algorithms to ensure thorough gene identification in chloroplast genome studies. The relative presence of several gene groups on chloroplast genome were accessed using KEGG pathway database. This study revealed that several members of different gene groups are absent from the chloroplast genome. It was noted that out of the 14 genes of Ndh group of oxidative phosphorylation, 11 are present on the chloroplast, the pet group of genes coding for cytochrome b6f have 6 out of 8 members on the chloroplast whereas none of the Pet genes coding for proteins of cytochrome b6f complex, a part of the photosynthetic electron transport chain, are present on the chloroplast genome. Further, the psa and psb group of genes coding for Photosystem I and Photosystem II respectively, have 5 out of 18 and 15 out of 28 genes respectively present on the chloroplast. As organellar (chloroplast) genomes have extremely reduced their genome size, the genes absent on the chloroplast genome might be encoded in the nuclear genome. The missing genes were looked up on the gene annotation file of A. thaliana nuclear genome. It was found that ndhL of the ndh group of genes, psaD, psaF, psaG, psaK and psaO of the psa group of genes, psbP, psbQ, psbR, psbY and psb27 of the psb group of genes are present in the nuclear genome. SSRs or Simple Sequence Repeats are also known as microsatellites and range from 1 to 6 nucleotides as repeating units. The frequency of mononucleotide repeats ranged from 0.7% to 2%, indicating a significant variability among the species. Notably, A. finetianum exhibited the highest number of mononucleotide repeats (52), while A. volubile had the lowest (19). This considerable range within the same genus suggests that certain species within Aconitum may have undergone different evolutionary pressures or replication dynamics that influenced their SSR accumulation. Both whole genome analysis of SSRs and region wise analysis points towards the fact that SSRs do not exhibit conservation in the chloroplast genomes. The distribution of repeats in the four regions of genome namely LSC, SSC and IR regions is inconsistent among the 74 genomes and in some case, inconsistencies are observed even among different strains of the same species. SSRs can also be used as identification markers at the genus level. In the case of Aconitum genus, some species of Aconitum show a consistent pattern in the occurrence of SSRs, such as A. scaposum, A. coreanum, A. kusnezoffii, A. barbatum and A. episcopale . The representation of numbers of SSRs as bar graph with the phylogenetic tree exhibits this observation (Supplementary Figure 5). Thus, SSRs can be further explored as molecular markers for identification at species level for those in which different strains of the same species show consistent pattern of presence of SSRs. Understanding codon usage is a key aspect in evolutionary study. The codon usage analysis in this study was performed gene wise, to reveal the inconsistencies in codon usage between genes if any. Studying them for the entire genome may result in masking of the differences of metrics between genes. In terms of nucleotide composition at the three codon positions, effective number of codons (Nc) and RSCU values, there was no significant difference between the Aconitum chloroplast genomes, but variation was observed between the genes. Codon usage analysis revealed that Aconitum chloroplast genes prefer A/T ending codons over G/C ending codons, consistent with the previous studies performed on Theaceae and Fagaceae chloroplast genomes(Z. Wang et al., 2022; S. Yang et al., 2018). The pattern of codon usage was consistent for the Aconitum chloroplast genomes, with higher RSCU values for A/T ending codons than G/C ending codons. It was also observed that more of the G/C ending codons were missing in several genes compared to the A/T ending codons. Previous studies on green plant chloroplasts have revealed a consistent pattern of codons preferring A/T ending codons over G/C ending codons and is under pressure from both, mutational bias and natural selection (W. Q. Kong & Yang, 2017; Z. Wang et al., 2020; Yengkhom et al., 2019). Study on angiosperm chloroplast revealed deviation from this pattern for codon bias in psbA gene. Although context dependent mutation explains codon bias in most of the genes, selection was proposed to be the main factor explaining codon bias in psbA gene (Morton, 2022). Similarly in the case of Aconitum , ndhA gene which clusters with psbA gene in Figure 6b has Nc value close to the expected Nc value; however, this is not the case for psbA gene indicating influence of selection on psbA gene whereas mutation is the main factor affecting other genes. The gene psbA was also the most biased gene when the Nc values are looked at, among the genes. Previous studies have reported that psbA gene in chloroplast genomes favours NNC codon over NNT for 2-fold degenerate amino acids of NNY type, and selection acts on this gene for high translational efficiency(Morton, 1993; Morton & Levin, 1997; Suzuki & Morton, 2016). However, for Aconitum species, this pattern was found for only phenylalanine and histidine amino acids. From the Pi values representing the nucleotide diversity among the 74 species, it is evident that significant nucleic acid divergence regions exist in the IR region, mainly tRNA genes. Other sequences outside of IR region with relatively higher Pi values are also tRNA genes. These sequences could serve as regions for designing DNA barcodes for species identification. Previously, tRNA sequences have been successfully used for identification of species that are difficult to identify. The region of matK-trnK-rps16 has been used for development of DNA barcode for identification of oak ( Quercus ) species (Pang et al., 2019). However, the results of nucleotide diversity revealed that intergenic regions were less divergent than the coding region, according to the average Pi values. Average nucleotide variability in the coding regions (0.035) is higher than the average Pi values in the non-coding/intergenic region (0.01). Since the genomes considered in this study belong to a single genus, the overall nucleotide diversity values are lower as is expected for highly conserved chloroplast genomes. The Ka/Ks ratio of the 40 common genes among 74 Aconitum species indicated they are under purifying selection as the ratio is significantly less than 1. Only three genes exhibited higher Ka values compared to Ks values, namely matK, rpoC1 and clpP . The gene matK is known to have high substitution rates compared to other chloroplast genes (Gül et al., 2005). The matK gene is nested in the group II intron between the 5’ and 3’ exons of the trnK in the LSC region of most of the green plants as well as in Aconitum genome (Figure 1a) (Barthet & Hilu, 2007). Maturase K or matK is a type of maturase enzyme (prokaryotic enzyme) that has a role in the crucial step of gene expression, i.e. intron removal. The enzyme aids excision of seven different chloroplast group IIA introns that lie within precursor RNAs for essential elements of chloroplast function (Barthet et al., 2020). The rpoC gene encodes the β subunit of chloroplast RNA polymerase and is split into rpoC1 and rpoC2 , which encode the β′ and β′′ subunits, respectively (Lee et al., 2012). Meanwhile, clpP plays a crucial role in cell viability by encoding a proteolytic subunit of the ATP-dependent protease complex (Shikanai et al., 2001). Despite the generally high substitution rate of matK , in Aconitum species, the highest non-synonymous substitution rate is observed in rpoC1 , followed by clpP , and then matK . The phylogenetic analysis validated seed morphology-based classification of subgenus Aconitum and Lycoctonum . However, the non-monophyletic clustering of multiple accessions belonging to the same subgenus Aconitum was observed in our phylogenetic tree which likely reflects complex evolutionary dynamics rather than methodological error. Several factors may contribute to these patterns. First, misidentification or labelling errors in public databases can lead to incorrect species assignments. Second, incomplete lineage sorting and chloroplast capture due to hybridization (Gonçalves et al., 2019; Sutkowska et al., 2017) can obscure phylogenetic signals, particularly when relying solely on chloroplast genomes. The clustering of A. pendulum and A. flavum accessions, despite their designation as distinct species, aligns with recent findings from population genetics and ecological niche modelling, which suggest these taxa may represent a single species complex with weak genetic differentiation, historical gene flow, and evidence of a demographic bottleneck during the Last Glacial Maximum(Q. Li et al., 2024). Similarly, the grouping of accessions from A. kusnezoffii (NC_031422), A. jaluense subsp. jaluense (KT820668) and A. japonicum subsp. napiforme (KT820670) supports earlier morphological and ecological studies from Mt. Sobaek in Korea, which indicate extensive hybridization and repeated introgression among these taxa (Lim & Park, 2001). Interestingly, the accessions mentioned above that form anomalous cluster in the core genes phylogeny, mainly belong to the Republic of Korea (Supplementary figure 6, Supplementary table 11). These observations underscore the need for integrative taxonomic approaches in Aconitum , combining molecular data from both nuclear and organellar genomes with detailed morphological and ecological analyses to resolve species boundaries and evolutionary histories more accurately. In conclusion, this study emphasizes the highly conserved yet subtly variable nature of chloroplast genomes within the Aconitum genus. The identification of accessory genes and annotation discrepancies underscores the limitations of current automated tools and the need for manual curation and better gene-calling and annotation tools. Codon bias patterns and Ka/Ks analyses reveal the interplay between mutational pressure and selection, particularly in genes like psbA. Phylogenetic incongruities suggest complex evolutionary histories shaped by hybridization and misidentification, calling for integrative taxonomic approaches. Declarations Ethics approval and consent to participate: Not applicable Availability of data and materials: Authors have used open-source tools in this analysis. All tool versions have been provided in the methodology. Competing interests: The authors declare no competing interests. Funding: GS acknowledges the Department of Science and Technology (DST)-INSPIRE and IIT Hyderabad for supporting his research. RAK is supported by the PhD fellowship from the University Grant Commission, Government of India. Authors' contributions: GS generated the idea. RAK performed the analysis and wrote the first draft of the manuscript. RAK and GS edited and finalized the manuscript. Consent to Publish declaration: not applicable References Ameri, A. (1998). The effects of Aconitum alkaloids on the central nervous system. Progress in Neurobiology , 56 (2), 211–235. https://doi.org/https://doi.org/10.1016/S0301-0082(98)00037-9 Barthet, M. M., & Hilu, K. W. (2007). 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Identification and analysis of up-regulated proteins in Lissorhoptrus oryzophilus adults for rapid cold hardening. Gene , 642 (October 2017), 9–15. https://doi.org/10.1016/j.gene.2017.11.002 Yang, Y., Zhu, J., Feng, L., Zhou, T., Bai, G., Yang, J., & Zhao, G. (2018). Plastid genome comparative and phylogenetic analyses of the key genera in fagaceae: Highlighting the effect of codon composition bias in phylogenetic inference. Frontiers in Plant Science , 9 (February), 1–13. https://doi.org/10.3389/fpls.2018.00082 Yengkhom, S., Uddin, A., & Chakraborty, S. (2019). Deciphering codon usage patterns and evolutionary forces in chloroplast genes of Camellia sinensis var. assamica and Camellia sinensis var. sinensis in comparison to Camellia pubicosta. Journal of Integrative Agriculture , 18 (12), 2771–2785. https://doi.org/https://doi.org/10.1016/S2095-3119(19)62716-4 Yoon, H. S., Hackett, J. D., Ciniglia, C., Pinto, G., & Bhattacharya, D. (2004). A Molecular Timeline for the Origin of Photosynthetic Eukaryotes. Molecular Biology and Evolution , 21 (5), 809–818. https://doi.org/10.1093/molbev/msh075 Yuan, Q., & Yang, Q. E. (2006). Polyploidy in Aconitum subgenus Lycoctonum (Ranunculaceae). Botanical Journal of the Linnean Society , 150 (3), 343–353. https://doi.org/10.1111/j.1095-8339.2006.00468.x Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations Competing interest reported. Gaurav Sharma is serving as an Editor for Scientific Reports. Supplementary Files table1.pdf Table 1. Pangenome analysis based functional characterization of genes within the broad functional category and subcategory. The table also shows their respective gene and their gene product. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6954381","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475010890,"identity":"512548ef-2047-4f55-8abb-dbd2915f600f","order_by":0,"name":"Richa Ashok Kakkar","email":"","orcid":"","institution":"Indian Institute of Technology Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Richa","middleName":"Ashok","lastName":"Kakkar","suffix":""},{"id":475010891,"identity":"3bcf2d27-a76e-490c-9803-bcf1b9b18064","order_by":1,"name":"Gaurav Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie2OMQrCQBBFJyykCtimSq6wIYWNeJYNAdNsQLC1CAixsxa8hGAh6QYGkiYHSGFjY68BsRLNgtptBBuLfcWfGZgHH8Bg+EdclWOPPxMFoDrxCyUOud0p4nsFo63dTaF/VvibxenSzDHZ+5mFx9sBBku0aKpR+KEMN7LEtMhVsRO4tQBa6xRXhEzaVbotlUIADQA5umLrpGXyTgl/KX6fAo0MWZqTeCu8T+GNnLF0FQdFHmUoJuQEdZT1FdsxeR37Q0Z0vo3I8yqiVlvsg5V16bwWg8FgMPzAA2riU/jlGhfLAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Technology Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-06-23 08:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6954381/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6954381/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-40105-5","type":"published","date":"2026-03-04T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85275846,"identity":"8d35d296-e446-4302-bf93-19f0afafe054","added_by":"auto","created_at":"2025-06-24 07:19:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3104464,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Circular map\u003cem\u003e \u003c/em\u003eof \u003cem\u003eAconitum barbatum\u003c/em\u003echloroplast genome. Genes represented on the outer side of the outermost track are transcribed clockwise and genes represented on the inner side are transcribed anti-clockwise. The second track represents GC content of chloroplast genome at different loci. LSC: Large Single Copy region; SSC: Small Single Copy region; IR: Inverted repeat region. (b) Jitter and box plot of GC content of four regions of chloroplast genome as well as the overall GC content of the 74 species. (c) Heap’s law model graph indicates that the pangenome of \u003cem\u003eAconitum\u003c/em\u003echloroplast is open. The Heap’s law formula is given by V = k*n^beta; beta\u0026gt;0 indicates open pangenome and beta\u0026lt;0 indicates closed pangenome. Here, beta value was 0.0084\u003c/p\u003e","description":"","filename":"figure1pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/5de8ca17254be4c687a98d78.png"},{"id":85275414,"identity":"2362b195-9334-44db-a433-c1b27e5ef07b","added_by":"auto","created_at":"2025-06-24 07:11:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":899961,"visible":true,"origin":"","legend":"\u003cp\u003eAccessory gene map representing synteny across 73 genomes of \u003cem\u003eAconitum\u003c/em\u003e genus and the outgroup \u003cem\u003eGymnaconitum gymnandrum\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"figure2pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/5fa15cfc0fc78c9738d3f44a.png"},{"id":85276818,"identity":"c360fb9e-7f7e-4a9a-8a8c-b230049a1c00","added_by":"auto","created_at":"2025-06-24 07:27:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1834373,"visible":true,"origin":"","legend":"\u003cp\u003eSSR analysis for 74 \u003cem\u003eAconitum\u003c/em\u003e genomes showcasing the lack of conservation of SSRs in the genomes. (a) Number of SSR loci per genome: here X-axis and Y-axis depicts accession ID of the genomes and number of SSRs, respectively. Region wise frequency (SSRs/kb) of (b) mononucleotide repeats (c) dinucleotide repeats (d) trinucleotide repeats (e) tetranucleotide repeats (f) pentanucleotide repeats. It should be noted that the scale for the graphs varies according to the range of frequencies for different types of SSR.\u003c/p\u003e","description":"","filename":"figure3pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/b5f3537f40c5b8b97157719d.png"},{"id":85274425,"identity":"4a2d7ccd-884c-49ac-81de-bc911867af76","added_by":"auto","created_at":"2025-06-24 07:03:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1083552,"visible":true,"origin":"","legend":"\u003cp\u003eCG View plot of 73 \u003cem\u003eAconitum\u003c/em\u003e genomes based on BLASTn analysis against (a) \u003cem\u003eG. gymnandrum\u003c/em\u003e as reference (b) \u003cem\u003eA. vilmorinianum\u003c/em\u003e as reference, showcase sequence identity across all studied genomes\u003c/p\u003e","description":"","filename":"figure4pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/0d630320a6bc9fe9fddf1345.png"},{"id":85275848,"identity":"55b530f3-f730-4cf0-9b8c-046631d2fab1","added_by":"auto","created_at":"2025-06-24 07:19:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1061247,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of Pi values representing nucleotide diversity of (a) genes (b) intergenic regions, depicting extent of variation in respective sequences amongst the 74 species\u003c/p\u003e","description":"","filename":"figure5pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/01e74fd40813d0a005a0f892.png"},{"id":85274431,"identity":"a03d05b5-61b8-4ad8-9f6a-23fcbd903fd8","added_by":"auto","created_at":"2025-06-24 07:03:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1114616,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Box plot of GC content of first, second and third position of codons (b) Gene-wise heatmap of Nc values (effective number of codons) and (c) RSCU values\u003c/p\u003e","description":"","filename":"figure6pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/4770e4724bc8ad0da570f4c5.png"},{"id":85274435,"identity":"abbafc3f-ab51-41c4-a843-56446980fe8e","added_by":"auto","created_at":"2025-06-24 07:03:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":644859,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of (a) Nc and ENc values against GC1; R\u003csup\u003e2\u003c/sup\u003e=0.06 and 0.14 (b) Nc and ENc values against GC2; R\u003csup\u003e2\u003c/sup\u003e=0.0 and 0.0009 (c) Nc and ENc values against GC3; R\u003csup\u003e2\u003c/sup\u003e=0.33 and 0.83 (d) GC1 and GC2 against GC3; R\u003csup\u003e2\u003c/sup\u003e=0.02; Nc is effective number of codons; ENc is expected effective number of codons.\u003c/p\u003e","description":"","filename":"figure7pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/c979ef27b7698371996e252a.png"},{"id":85274430,"identity":"440e7793-cf0f-40e2-8c8f-68a289263cfc","added_by":"auto","created_at":"2025-06-24 07:03:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2992372,"visible":true,"origin":"","legend":"\u003cp\u003eWhole genome phylogeny (left) and Core-gene phylogeny (right) for 73 \u003cem\u003eAconitum\u003c/em\u003e genomes with \u003cem\u003eG. gymnandrum\u003c/em\u003e as an outgroup. Both maximum likelihood phylogenies have been generated using IQTREE using its best model selection pipeline. ITol was used for visualization with subgenus mapped to the phylogenies for better understanding\u003c/p\u003e","description":"","filename":"figure8pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/3dc9acd66a57545a8ae5262d.png"},{"id":104251596,"identity":"146bd5ce-e439-4313-809e-b6395f84c242","added_by":"auto","created_at":"2026-03-09 16:14:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14695290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/028bf13d-22eb-472b-bbaa-c970af10e92c.pdf"},{"id":85274421,"identity":"f2f9dee1-c81e-4c6e-a5f8-0e4c3bd72acc","added_by":"auto","created_at":"2025-06-24 07:03:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":437222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. Pangenome analysis based functional characterization of genes within the broad functional category and subcategory\u003c/strong\u003e. The table also shows their respective gene and their gene product.\u003c/p\u003e","description":"","filename":"table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/c6e37769207aad5e94d0a608.pdf"},{"id":85275413,"identity":"edd9189f-2f3d-484a-8a9f-4accd5ac34da","added_by":"auto","created_at":"2025-06-24 07:11:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15440,"visible":true,"origin":"","legend":"","description":"","filename":"Listofsupplementarytablesfigureslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/e9f4e3c8f39f66eb5c4bac81.docx"},{"id":85274426,"identity":"222353e7-0be8-4377-ad85-8d3ff03dfef9","added_by":"auto","created_at":"2025-06-24 07:03:31","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1505206,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-6954381/v1/dbf1d5c75f7282dfda479436.zip"}],"financialInterests":"Competing interest reported. Gaurav Sharma is serving as an Editor for Scientific Reports.","formattedTitle":"\u003cp\u003eComprehensive chloroplast genome analysis of 73 genus \u003cem\u003eAconitum \u003c/em\u003emembers of family Ranunculaceae reveals insights into genome structure, codon usage polymorphism, and phylogenetic relationships\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMedicinal plants have been widely used for the treatment of diverse diseases throughout the world among different cultures and societies based on their geographic location and availability of local medicinal plants. Around 53,000 species of plants have found applications as medicinal plants till date where different plants parts such as root, leaves, flower, seed, bark of tree or even whole plant are used as herbal medicine (Pan et al., 2014). Medicinal plants are rich in bioactive ingredients and effective in treatment of several diseases such as diabetes (Jacob \u0026amp; Narendhirakannan, 2019), arthritis (Gandhi et al., 2022), asthma (Usmani et al., 2023), and high blood pressure (Mishra \u0026amp; Tubaki, 2019), to name a few.\u003c/p\u003e\n\u003cp\u003eOne such Indian traditional medicinal plant genus \u003cem\u003eAconitum\u003c/em\u003e encompasses ~300 mountainous species of significant economic importance, albeit some being poisonous due to the presence of toxic diterpene alkaloids (Ameri, 1998). These plants have application in Ayurveda as well as Chinese traditional medicine addressing several ailments like neuralgia, sciatica, arthritis, gout, rheumatism, treatment of colds, sore throat, and inflammation of the respiratory tract (Ameri, 1998; Singh et al., 2020). As of now, their unique biodiversity is on the verge of extinction due to illegal human intervention triggered habitat loss, over-harvesting, and unrestricted trading, and based on this, several \u003cem\u003eAcontium\u003c/em\u003e species have been identified as endangered, critically endangered or vulnerable by IUCN (Rafiq et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe process of photosynthesis that transforms solar energy to chemical energy and sustains all life on earth is of utmost importance for the plants and this critical process is carried out inside a semiautonomous organelle called chloroplast. Plastids have evolved 1.5 to 1.6 billion years ago via endosymbiosis process (Yoon et al., 2004). In addition to photosynthesis, this semiautonomous organelle plays an important role in biosynthesis of lipids, amino acids, carotenoids among other important biomolecules (H\u0026ouml;lzl \u0026amp; D\u0026ouml;rmann, 2019), for which plastids have retained approximately 100 proteins synthesizing genes along with several others being encoded by nuclear genome (Ries et al., 2020). A lot of genes in the chloroplast genome have been have been functionally transferred to nuclear genome or lost during evolution (Cullis et al., 2009; Eckardt, 2006). The structure of chloroplast genome is generally quadripartite, having four regions namely LSC, SSC, IRa and IRb regions. Several structural rearrangements of the plastid genome have occurred throughout evolution resulting in contraction, expansion and loss of genetic content. According to a study conducted on 2,511 chloroplast genomes, approximately 10.31% of the examined species have lost the inverted repeats (IR) in the chloroplast genome spanning across all lineages (Mohanta et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhylogenetic studies of Ranunculaceae family based on diverse characteristics such as morphology, restriction site mapping, nuclear sequence and chloroplast sequence have complicated the classification of genus \u003cem\u003eAconitum\u003c/em\u003e. Several classification models of genus \u003cem\u003eAconitum\u003c/em\u003e have been proposed, based on different morphological characteristics such as inflorescence, branching of stem, shape of sepals and petals and structure of embryo sac among others. According to the chromosomal study by Schafer \u0026amp; La Cour (1934), \u003cem\u003eAconitum\u003c/em\u003e genus is classified into two subgenus: \u003cem\u003eLycoctonum\u003c/em\u003e and \u003cem\u003eAconitum\u003c/em\u003e based on ploidy levels of the two groups (Yuan \u0026amp; Yang, 2006). Based on several non-molecular characteristics such as phytochemical, cytological, anatomical and palynological (study of plant pollen and spores), \u003cem\u003eAconitum\u003c/em\u003e \u003cem\u003egymnandrum\u003c/em\u003e has been removed from genus \u003cem\u003eAconitum\u003c/em\u003e and converted into separate genus \u003cem\u003eGymnaconitum\u003c/em\u003e, with only one species \u003cem\u003eGymnaconitum gymnandrum\u0026nbsp;\u003c/em\u003e(W. Wang et al., 2013). In this context, \u003cem\u003eGymnaconitum gymnandrum\u003c/em\u003e has been widely used as an outgroup for phylogenetic and various other analysis.\u003c/p\u003e\n\u003cp\u003eCodon bias in all genes, all organisms, are important for studies of evolutionary adaptation and biotechnology applications. In recent years, researchers investigated the codon usage of several chloroplast genomes and found certain common patterns. It has been observed in green plant chloroplast genomes that it favours codon ending in base A or T and this pattern in consistent across species with pressure from both natural selection and mutational bias (W. Q. Kong \u0026amp; Yang, 2017; Z. Wang et al., 2020; Yengkhom et al., 2019). A study on codon usage bias of angiosperm chloroplast genes revealed that context dependent mutations explain codon usage bias of most chloroplast genes except the highly expressed \u003cem\u003epsbA\u003c/em\u003e gene which is controlled by selection. (Morton, 2022). Although the codon usage in green plant chloroplasts is similar between species, differences between genes within the genomes has been identified.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eData collection and summary:\u0026nbsp;\u003c/strong\u003eChloroplast genome data of genus \u003cem\u003eAconitum\u003c/em\u003e members (including genome nucleotide sequences, translated CDS sequences, and GenBank files) was retrieved from the National Centre for Biotechnology Information (NCBI) via Entrez using command line interface. A total of 105 chloroplast genomes were obtained for which a summary file (Supplementary table 1) was generated using a custom script to extract relevant metadata from the GenBank files, including species name, accession number, genome size, GC content, and the counts of protein-coding genes, rRNA, tRNA, genes, pseudogenes, and voucher information. To eliminate redundancy, genomes were screened based on species names and genome sizes resulting in a refined dataset comprising 73 unique \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes. \u003cem\u003eGymnaconitum gymnandrum\u003c/em\u003e, a species closely related to \u003cem\u003eAconitum\u003c/em\u003e but belonging to a different subgenus, was included as an outgroup for the analysis, consequently, making the final dataset consisted of 74 chloroplast genomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome annotation for differentiation into LSC, SSC and IR regions:\u0026nbsp;\u003c/strong\u003ePGA (Plastid Genome Annotator) tool (Qu et al., 2019) was used to perform annotation of all 74 genomes, and categorization into LSC, SSC and IR regions. Using the annotation data, the sequences of four regions were extracted\u0026nbsp;using a self-written bash script followed by calculation of GC content in each of the regions. The genome with accession ID MW817090 of \u003cem\u003eAconitum scaposum\u003c/em\u003e was excluded from this analysis due to discrepancy in the annotation of four regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePangenome analysis and functional characterization of chloroplast genes:\u0026nbsp;\u003c/strong\u003ePangenome analysis was performed with the tool Proteinortho 6.1.7 (Lechner et al., 2011), where translated CDS files of all 74 \u003cem\u003eAconitum\u003c/em\u003e genomes was provided as the input. This software detects orthologous genes within different species, comparing similarities of given gene sequences and clusters them to find significant groups. The tool was run with default parameters (percentage identity=25%, evalue=1e\u003csup\u003e-5\u003c/sup\u003e) along with two additional parameters \u0026ndash;singles and \u0026ndash;selfblast which potentially help in detecting the unique genes in the genomes if present. Calculation of beta value for Heap\u0026rsquo;s law and generation of graph was performed using R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynteny analysis:\u0026nbsp;\u003c/strong\u003eSynteny studies were performed to visualize the genome architecture of 74 \u003cem\u003eAconitum\u003c/em\u003e genomes using web-based OrganellarGenomeDRAW v1.3.1 tool (Greiner et al., 2019) and Genbank (.gbk) files as input. The tool converts GenBank or EMBL/ENA format to graphical maps, either circular or linear. Two different set of linear maps were generated for each genome, one representing only the accessory genes and the other representing core and accessory genes. Configuration file was edited for each set to specify required genes in the map and their corresponding colour code. Each set resulted in 74 high-quality png files of linear maps which were merged into one and \u0026lsquo;convert\u0026rsquo; command was used to crop, resize and merge the individual images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Simple Sequence Repeats (SSRs):\u0026nbsp;\u003c/strong\u003eGMATA v2.01 tool was used to detect SSRs (X. Wang \u0026amp; Wang, 2016) using the genome file of 74 genomes. Self-written script was used to extract data from the \u0026lsquo;.ssr.sat2\u0026rsquo; output file and graphs plotted using the extracted data. Minimum repeated times of motif was set to 10, 5, 4, 3, 3 and 3 for mono, di, tri, tetra, penta and hexa nucleotide repeats.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable region identification:\u0026nbsp;\u003c/strong\u003eTo identify the variable region in \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes with respect to the reference, BLAST alignment was performed using Circular genome viewer comparison tool (CG View CT) (Grant et al., 2012). This tool utilizes GenBank-formatted files as input and operates through a two-step process (project creation and map generation) facilitated by the wrapper script build_blast_atlas.sh. During the project creation step, a structured directory system is established, consisting of directories for input files, reference files, configuration files, and output files. In the subsequent map generation step, genome FASTA files are placed in the \u0026lsquo;comparison_genome\u0026rsquo; folder, while the reference genome FASTA file (in this case, \u003cem\u003eGymnaconitum gymnandrum\u003c/em\u003e) is placed in the reference folder. The build_blast_atlas.sh script then generates maps for both nucleotide (BLASTn) and translated coding sequence (BLASTp) comparisons. The output includes CGView XML files located in the cgview_xml folder, corresponding to the nucleotide (dna_vs_dna) and protein-coding sequence (cds_vs_cds) comparisons. The same methodology was applied using \u003cem\u003eA. vilmorinianum\u003c/em\u003e as the reference genome for comparative analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCodon usage analysis\u003c/strong\u003e: The total coding sequences for all 74 genomes (Table-1) were filtered according to the following criteria suggested by previous report (Y. Wang et al., 2023):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe sequence length must exceed 300 base pairs.\u003c/li\u003e\n \u003cli\u003eEach sequence must initiate with a start codon (ATG) and terminate with a stop codon (TAA/TAG/TGA).\u003c/li\u003e\n \u003cli\u003eIntermediate stop codons must be absent within the sequence.\u003c/li\u003e\n \u003cli\u003eThe total number of nucleotides in the sequence must be divisible by three.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe GC content of the first, second, and third codon positions of the 40 protein-coding sequences that passed filtration criteria was calculated using the CUSP program from the EMBOSS package (Rice et al., 2000) along with the overall codon usage frequency for all 74 chloroplast genomes. Codon usage analysis was conducted employing the CAI Calculator, which provided insights into nucleotide composition, relative synonymous codon usage (RSCU) values, codon usage frequency, and codon usage per thousand values (Puigb\u0026ograve; et al., 2008) followed by heatmap generation using Heatmap Illustrator (HemI 1.0) (Deng et al., 2014) using the average linkage method with Euclidean distance as the clustering metric. The effective number of codons (Nc) and the expected Nc values were calculated using the software DAMBE7 (Xia \u0026amp; Xie, 2001).\u003c/p\u003e\n\u003cp\u003eTo test context dependent mutation, amino acids are categorised into eight distinct 4-fold degenerate families as follows: Arg, Leu and Ser were each divided into three 2-fold degenerate families (Arg2, Leu2, Ser2) and three 4-fold degenerate families (Arg4, Leu4, Ser4) along with five 4-fold degenerate families Pro4, Thr4, Ala4, Val4, Gly4. Considering mutation as an independent single-site event, the nucleotide frequencies of the third codon position in the 4-fold degenerate families will not be affected by the second and/or the first codon positions. Following the method used in analysis of codon usage in \u003cem\u003eQuercus\u003c/em\u003e chloroplast genome (Shi et al., 2022), test of independence of the third codon position in the eight 4-fold degenerate families was tested. The chi-square test of independence was conducted for each of the six dataset as follows: Leu4/Pro/Arg4; Val/Ala/Gly; (Leu4 + Val)/(Ser4 + Pro + Thr + Ala)/(Arg4 + Gly); Leu4/Val; Arg4/Gly; Ser4/Pro/Thr/Ala. These analyses aimed to determine whether the nucleotide composition at the third codon position within each dataset is statistically independent of the first and second codon positions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynonymous and non-synonymous substitution analysis:\u0026nbsp;\u003c/strong\u003eTo analyse synonymous and non-synonymous substitutions, DnaSP v6.12.03 (Rozas et al., 2017) was employed using nucleotide FASTA files of 40 protein-coding sequences conserved across 74 species. The resulting output included pairwise estimates of Ka (non-synonymous substitution rate) and Ks (synonymous substitution rate), along with additional metrics. For each gene, the mean Ka and Ks values were subsequently calculated. To further understand overall nucleotide diversity, DnaSP v6.12.03 was further used (Rozas et al., 2017) where fasta sequences of coding genes, rRNA, tRNA genes and intergenic sequences were provided as input all at once using the Batch Mode and default parameters. Pi values for each sequence was used to generate a line graph using R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis:\u0026nbsp;\u003c/strong\u003eThe genus \u003cem\u003eAconitum\u003c/em\u003e has been classified into two subgenera, \u003cem\u003eAconitum\u003c/em\u003e and \u003cem\u003eLycoctonum\u003c/em\u003e, based on differences in morphology and ploidy levels. Therefore, to determine whether this morphological and ploidy level classification is supported by molecular data, two distinct phylogenetic analysis was conducted using core gene sequences and whole-genome data. For core genes-based phylogeny, 74\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efasta files containing protein sequences of each core gene per genome was prepared using a self-written script with sequences extracted from translated CDS file. The extracted sequences were aligned using MUSCLE v3.8.1551(Edgar, 2004) followed by concatenating all 74 aligned blocks, which was further used for phylogenetic tree building using IQ-TREE multicore version 2.2.2.3 (Minh et al., 2020). The best model suggested by the tool is used to create the phylogenetic tree followed by visualization on the web-based tool iTOL v6 (Letunic \u0026amp; Bork, 2021). For whole genome-based phylogeny, 74 whole genome sequences were aligned using the tool MUSCLE v3.8.1551 (Edgar, 2004). The aligned sequences were given as input to IQ-TREE (multicore version 2.2.2.3) to generate a maximum likelihood phylogeny using the model suggested by IQ-TREE multicore version 2.2.2.3 (Minh et al., 2020). The generated tree is then visualized on the web-based tool iTOL v6 (Letunic \u0026amp; Bork, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of intra-specific K2P distance:\u003c/strong\u003e The Kimura 2-Parameter (Kimura, 1980) substitution model was used to find the intra-specific distance in R with the help of libraries named \u0026ldquo;ape\u0026rdquo; and \u0026ldquo;seqinr\u0026rdquo;. Input for calculating the distance was MUSCLE alignment file of all 73 genomes along with the outgroup as mentioned in the phylogenetic analysis methodology.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eChloroplast genome statistics of 74 \u003cem\u003eAconitum\u003c/em\u003e species:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of the 73 \u003cem\u003eAconitum\u003c/em\u003e genomes under study, the presence of 40 unique species depicts the diversity of the used dataset (Supplementary table 1). Out of the total available data, 30 and 43 genome files were sourced from RefSeq and GenBank databases, respectively. The genome size of these species ranges from 151,214 to 157,688 bp with \u003cem\u003eAconitum episcopale\u003c/em\u003e having the smallest and \u003cem\u003eAconitum brachypodum\u003c/em\u003e having the largest genome. Variation in number of genes is not very high and ranges from 123 in \u003cem\u003eAconitum austrokoreense, Aconitum coreanum\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Aconitum volubile\u003c/em\u003e to 132 in twenty different \u003cem\u003eAconitum\u0026nbsp;\u003c/em\u003egenomes (Supplementary table 1). Interestingly, one out of two reported \u003cem\u003eAconitum coreanum\u003c/em\u003e and \u003cem\u003eAconitum austrokoreense\u003c/em\u003e genomes has 123 genes annotated and the other has 132. The protein coding genes range from 82 to 87, and tRNA genes ranges from 36 to 38, however, 8 rRNA genes are present consistently across all genomes under study. Six pseudogenes are also annotated in \u003cem\u003eAconitum pseudolaeve,\u0026nbsp;\u003c/em\u003ewhich is the highest among all the considered genomes. All 73 genomes of \u003cem\u003eAconitum\u003c/em\u003e had a quadripartite structure, i.e. had a large single copy region (LSC), small single copy region (SSC) and two inverted repeat regions (IRA and IRB) (Figure 1a). As far as the total GC content is considered, there is no significant difference in GC percentage among the chloroplast genomes, which ranges from 37.99% to 38.30%. However, the GC content between different regions of the same genome varies, with IR regions having high GC percentage and LSC region having lower GC percentage (Figure 1b, Supplementary table 2). The GC percentage of LSC region ranges from 36.01% to 36.39%, that of SSC ranges from 32.42% to 32.85%, whereas for IRA and IRB region the range lies from 42.94% to 43.10%. This indicates that although there is difference amongst the four regions, there is no significant difference among the different species for one particular region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePangenome analysis revealed that \u003cem\u003eAconitum\u003c/em\u003e has an open pan-genome:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePangenome analysis revealed that the number of core genes ranged from 75 to 77 among the 74 species, whereas the accessory genes ranged from 6 to 10 (Supplementary Table 3). To understand if the pan-genome is open or closed, Heap’s law formula was utilized (Figure 1c), given by V = k*n^beta; where V is the vocabulary size (number of distinct genes), k is the scaling parameter, n is the size of sample (number of genomes observed), beta is the growth parameter. According to Heap’s law, if beta value is greater than 0, it is open pan-genome and if the beta value is less than 0, it is a close pan-genome. For the unique set of genes in the 74 genomes considered in this study, the beta value was calculated to be 0.0084, suggesting the pan-genome of \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes considered in this study to be an open pan-genome (Figure 1c). The gene order of both accessory and core genes was found to be well conserved across all species (Figure 2, Supplementary Figure 1). Functional characterization of genes (Table 1) classified all gene functions into three main categories: chloroplast envelope membrane protein genes, genes for photosynthesis and genes for transcription and translation, which were further classified into sub-categories. The Chloroplast envelope membrane protein genes include the Cytochrome b6f group of genes, whereas the photosynthesis genes include ATP synthase (\u003cem\u003eatp\u003c/em\u003e genes), NADH oxidoreductase (\u003cem\u003endh\u003c/em\u003e genes), photosystem I (\u003cem\u003epsa\u003c/em\u003e genes) and photosystem II genes (\u003cem\u003epsb\u003c/em\u003e genes). The sub-categories of genes for transcription and translation include the large ribosomal subunit (\u003cem\u003erpl\u003c/em\u003e genes), RNA polymerase (\u003cem\u003erpo\u003c/em\u003e genes), small ribosomal subunit (\u003cem\u003erps\u003c/em\u003e genes) and translation initiation factor (\u003cem\u003einfA\u003c/em\u003e gene). All photosynthetic genes were assigned under core genes. Five (\u003cem\u003erpl16, rpl2, rpl20, rps16\u003c/em\u003e and \u003cem\u003einfA\u003c/em\u003e) out of nine accessory genes belonged to the category of genes related to transcription and translation. A group of researchers have performed the functional and structural analysis of \u003cem\u003erpl16\u003c/em\u003e using bioinformatics tools, suggesting it to be a thermo-stable, acidic and hydrophilic protein. One of the predicted counterparts of RPL16 includes RPL2 which is also an accessory protein among the genomes considered in the study. The presence of its counterpart \u003cem\u003erpl2\u003c/em\u003e in \u003cem\u003eAconitum reclinatum\u003c/em\u003e (MF186593) could be a possible reason behind the absence of \u003cem\u003erpl16\u003c/em\u003e gene having no effect. The protein RPL20 along with other proteins initiates the 50s ribosomal subunit assembly which binds directly to the 5’ end of the 23s rRNA (Y. Yang et al., 2018) . It has been found that \u003cem\u003erps16\u003c/em\u003e is lost in many taxa from ferns to angiosperms. However, its nuclear genome counterpart is present in such taxa as it is necessary for the survival of the organism (Schwarz et al., 2015). The investigation of nuclear genome will reveal presence or absence of such counterparts in the \u003cem\u003eAconitum\u003c/em\u003e genome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of SSRs exhibits lack of their conservation in \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis revealed that mononucleotide simple sequence repeats (SSRs) exhibited the highest frequency, followed by di-, tri-, tetra-, penta-, and hexanucleotide repeats (Figure 3b-f, Supplementary Table 4a). The frequency of mononucleotide repeats ranged from 0.12 to 0.33 SSRs/kb, with \u003cem\u003eA. finetianum\u003c/em\u003e displaying the highest abundance of mononucleotide repeats (Figure 3b). The number of mononucleotide SSRs varied from 19 in \u003cem\u003eA. volubile\u003c/em\u003e to 52 in \u003cem\u003eA. finetianum\u003c/em\u003e. In contrast, the number of dinucleotide SSRs showed minimal variation, ranging from 10 to 17 (Figure 3a), with a frequency distribution of 0.06 to 0.1 SSRs/kb. The frequency of trinucleotide, tetranucleotide, and pentanucleotide repeats ranged from 5 to 12, 5 to 9, and 1 to 5, respectively, with pentanucleotide SSRs absent in certain species. Hexanucleotide SSRs were identified exclusively in eight species: \u003cem\u003eA. jaluense subsp. jaluense, A. austrokoreense, A. scaposum var. vaginatum, A. longecassidatum, A. tanguticum, A. ramulosum, A. stylosum\u003c/em\u003e, and \u003cem\u003eA. delavayi\u003c/em\u003e. Variability in di-, tri-, and tetranucleotide SSRs was relatively low across species (Figure 3a). The frequency distributions of tri-, tetra-, penta-, and hexanucleotide repeats were 31.78-76.26, 31.71-57.8, 0–31.8, and 0–19.09 SSRs/Mb, respectively (Supplementary Table 4a).\u003c/p\u003e\n\u003cp\u003eSSR analysis across the LSC, SSC, IRA, and IRB regions revealed that the LSC, followed by the SSC and IR regions, harboured the highest number of all six types of repeat sequences, which is expected given the larger genomic span of the LSC. Two genomes, MW817090 and MT584425, were excluded from this analysis due to discrepancies in IR region annotations. The frequency of total SSRs, expressed in SSRs/kb, was highest in the LSC region. Specifically, mononucleotide repeat frequencies ranged from 1.7 to 4.6, 0 to 3.8, and 0 to 1.2 SSRs/kb in the LSC, SSC, and IR regions, respectively (Figure 3b). Dinucleotide repeat frequencies varied between 0.3 and 0.9 SSRs/kb in the LSC, 0 to 0.8 SSRs/kb in the SSC, and 0 to 0.4 SSRs/kb in the IR regions (Figure 3c). Trinucleotide repeats exhibited frequency distributions of 0.04 to 0.3 SSRs/kb in the LSC, 0.2 to 0.6 SSRs/kb in the SSC, and 0 to 0.15 SSRs/kb in the IR regions (Figure 3d). Tetranucleotide repeats were identified in only one genome within the IR regions at a frequency of 0.1 SSRs/kb, whereas in the LSC and SSC regions, their frequencies ranged from 0.1 to 0.2 and 0.2 to 0.5 SSRs/kb, respectively (Figure 3e). Pentanucleotide repeats were observed at frequencies of 0 to 0.14 SSRs/kb in the LSC region and were detected in the SSC region in only one genome at a frequency of 0.2 SSRs/kb, while they were completely absent in the IR regions (Figure 3f). Hexanucleotide repeats were identified in six genomes within the LSC region, all exhibiting a frequency of approximately 0.03 SSRs/kb. These repeats were absent in the SSC region, whereas in the IR regions, they were found in only one genome at a frequency of 0.1 SSRs/kb (Supplementary Table 4b).\u003c/p\u003e\n\u003cp\u003eAmong mononucleotide repeats, A/T-rich repeats were more prevalent than C/G-rich repeats in both the LSC and SSC regions, whereas the IR regions contained only A/T repeats. The only dinucleotide repeats present across all four regions were of the AT/AT type. The LSC region contained three distinct trinucleotide repeat motifs: AAT/ATT, ATC/ATG, and CCG/CGG, while the SSC region exhibited only the AAT/ATT motif, and the IR regions exclusively harboured AAG/CTT trinucleotide repeats. The dominant tetranucleotide repeats in the LSC region were AAAG/CTTT and AAAT/ATTT, whereas AATG/ATTC was the predominant motif in the SSC region (Supplementary Table 4c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable region identification and nucleotide diversity analysis reveal some tRNA genes with diverse sequence:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo accurately identify \u003cem\u003eAconitum\u003c/em\u003e at the species level, several methods have been explored and proposed (He \u0026nbsp;Ka-Lok; Shaw, Pang-Chui; Wang, Hong; Li, De-Zhu, 2010; Park et al., 2017; Sun et al., 2024). The \u003cem\u003erbcL-matK\u003c/em\u003e phylogeny and ITS sequence phylogeny have been reported previously (Kakkar et al., 2023) that were not successful in clustering the sequences from same species together, and therefore, cannot be used for species level identification. Similarly, whole genome phylogeny of species with multiple samples considered in the study also did not yield the appropriate results (Supplementary Figure 2). Hence, other gene or intergenic sequences in the \u003cem\u003eAconitum\u003c/em\u003e genome need to be explored through variable region identification and nucleotide diversity analysis.\u003c/p\u003e\n\u003cp\u003eWhole-genome BLASTN analysis was conducted for 74 chloroplast genomes, and comparative circular plots were generated using the CGView Comparison Tool against \u003cem\u003eGymnaconitum gymnandrum\u003c/em\u003e and \u003cem\u003eA. vilmorinianum\u003c/em\u003e as reference genomes. The resulting plots illustrate the percentage similarity across genomes. Overall, the chloroplast genomes of the 74 \u003cem\u003eAconitum\u003c/em\u003e species exhibit high conservation, with more than 90% sequence identity. In the first plot, several regions display sequence identity below 96%, with a small stretch between \u003cem\u003ematK\u003c/em\u003e and \u003cem\u003epsbI\u003c/em\u003e showing less than 90% identity (Figure 4a). The second plot indicates variability in the \u003cem\u003ematK\u003c/em\u003e to \u003cem\u003epsbI\u003c/em\u003e region in only a subset of the genomes analysed (Figure 4b). Further examination revealed that these genomes belong to the subgenus \u003cem\u003eLycoctonum\u003c/em\u003e, whereas the reference genome used for the second plot belongs to the subgenus \u003cem\u003eAconitum\u003c/em\u003e. To gain deeper insights into genome-wide sequence variability at the nucleotide level, a nucleotide diversity analysis was performed.\u003c/p\u003e\n\u003cp\u003eNucleotide diversity (Pi) analysis provides a measure of sequence variation across different genomic regions. The Pi value represents the proportion of nucleotide sites expected to differ between any two randomly selected DNA sequences, with higher values indicating greater sequence variability. Regions exhibiting high Pi values are potential candidates for marker development. This analysis illustrates the Pi values of various genes (Figure 5a) and intergenic regions (Figure 5b), arranged in genome order. Notably, sequences with Pi values exceeding 0.2 predominantly correspond to tRNA genes, along with a single intergenic region between \u003cem\u003epsbH\u003c/em\u003e and \u003cem\u003epetB\u003c/em\u003e. The tRNA genes exhibiting high nucleotide diversity include \u003cem\u003etrnL-CAA, trnN-GUU, trnV-GAC, trnR-ACG, trnI-GAU, trnA-UGC\u003c/em\u003e, and \u003cem\u003etrnI-CAU\u003c/em\u003e, all of which are located within the IR regions. Additionally, in non-IR regions, tRNA genes such as \u003cem\u003etrnG-GCC, trnG-UCC\u003c/em\u003e, and \u003cem\u003etrnM-CAU\u003c/em\u003e show elevated Pi values (greater than 0.1). These highly diverse tRNA gene sequences hold potential for marker-based species identification. Within the \u003cem\u003ematK\u003c/em\u003e–\u003cem\u003epsbI\u003c/em\u003e region, \u003cem\u003erps16\u003c/em\u003e is the only gene exhibiting relatively higher nucleotide diversity, with a Pi value exceeding 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCodon usage analysis:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCodon usage bias, the preferential use of certain synonymous codons over others, reveals fundamental evolutionary forces shaping genomic architecture. There are several aspects of codon usage that provide insights into crucial aspects of molecular evolution such as GC content of the codons, codon usage bias measured by Nc values (Effective number of codons) and codon usage preference based on RSCU values. Previous studies on chloroplast genomes have consistently shown that GC content decreases from the first (GC1) to the third (GC3) codon positions, favouring A/T-ending codons due to mutational pressures and compositional bias (Shi et al., 2022; Z.-K. Wang et al., 2023; Z. Wang et al., 2022), whereas chloroplast genomes shows consistent ENc values across species, reflecting weak overall codon bias and evolutionary conservation (Z.-K. Wang et al., 2023; Z. Wang et al., 2022; Wright, 1990). Relative Synonymous Codon Usage (RSCU) analysis has identified certain amino acids absent in specific genes across chloroplast genomes, emphasizing lineage-specific translational optimization and evolutionary constraints(F. Li et al., 2022; Shi et al., 2022). It has also been reported in previous studies through understanding the relationship between Nc and GC3 values that GC3 exerts some influence on the codon usage pattern although other factors like selection also play important role (He et al., 2016; Khandia et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;GC content reduces from first position of codon to the third indicating preference of A/T ending codons over G/C ending codons:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe base composition at the first (GC1), second (GC2), and third (GC3) codon positions was analysed for all 40 genes across 74 chloroplast genomes (Supplementary Table 5a). While GC content varied among genes, no significant variation was observed between genomes (Supplementary figure 3). However, a significant difference was noted among GC1, GC2, and GC3 values (Figure 6a, Supplementary figure 3). Across genomes, GC1 values ranged from 47.19% (n=74, SD=5.79) to 47.49% (n=74, SD=5.65), GC2 values from 39.27% (n=74, SD=5.24) to 39.73% (n=74, SD=5.36), and GC3 values from 28.58% (n=74, SD=3.39) to 28.90% (n=74, SD=4.02). In contrast, variation across genes was more pronounced, with GC1 values ranging from 36.14% (n=40, SD=0.24) to 58.55% (n=40, SD=0.14), GC2 values from 27.94% (n=40, SD=0.21) to 57.55% (n=40, SD=0.00), and GC3 values from 23.07% (n=40, SD=0.52) to 35.92% (n=40, SD=0.41).\u003c/p\u003e\n\u003cp\u003eThe heatmap presented in Supplementary figure 3 highlights the lower GC3 values, indicating a preference for A/T-ending codons over G/C-ending codons. This pattern has been commonly observed and reported across chloroplast genomes of various species, including \u003cem\u003eMorus cathayana, Morus multicaulis\u003c/em\u003e, six \u003cem\u003eEuphorbiaceae\u003c/em\u003e species, and three \u003cem\u003eCamellia\u003c/em\u003e species, among others (Kong \u0026amp; Yang, 2017; Wang et al., 2020; Yengkhom et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;Codon usage bias measured by effective number of codons reveals consistency of codon usage across genomes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effective number of codons (Nc) metric was utilized to assess codon usage bias among different genes and across all 74 analysed genomes. Nc values range from 20 to 63, where lower values indicate a stronger codon bias, and higher values suggest a more uniform usage of synonymous codons. A lower Nc value implies that an organism preferentially utilizes a subset of synonymous codons, whereas a higher Nc value reflects a reduced bias in codon selection.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 6b, the \u003cem\u003erps18\u003c/em\u003e and \u003cem\u003epetD\u003c/em\u003e genes exhibit the highest codon bias among the 40 analyzed genes, whereas \u003cem\u003eycf4\u003c/em\u003e and \u003cem\u003eclpP\u003c/em\u003e display the least bias, clustering together in the heatmap. Additionally, genes such as \u003cem\u003erps14, psbA, ndhA, petB\u003c/em\u003e, and \u003cem\u003eatpF\u003c/em\u003e exhibit a codon usage pattern similar to \u003cem\u003epetD\u003c/em\u003e, forming a distinct cluster. The heatmap further highlights variations in codon bias among genes while demonstrating a largely consistent trend across the genomes under study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;Codon usage preference based on RSCU values reveals the relative absence of some amino acids in several genes across 74 chloroplast genomes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparative analyses of transfer RNA (tRNA) across all kingdoms have demonstrated that no single organism possesses tRNAs with anticodons complementary to all 61 sense codons (Berg \u0026amp; Brandl, 2021; Grosjean et al., 2010). This is due to the wobble hypothesis, where a single tRNA can recognize multiple synonymous codons because the third position of the codon (wobble position) exhibits flexibility in base pairing.\u003c/p\u003e\n\u003cp\u003eRelative Synonymous Codon Usage (RSCU) quantifies codon usage bias by comparing the observed frequency of synonymous codons for a given amino acid to the expected frequency under equal usage conditions. An RSCU value of 1 indicates no bias, values greater than 1 signify positive codon usage bias, while codons with RSCU values below 0.6 are considered underrepresented, and those above 1.6 are overrepresented.\u003c/p\u003e\n\u003cp\u003eRSCU analysis revealed that \u003cem\u003eatpE\u003c/em\u003e and \u003cem\u003erpoC2\u003c/em\u003e genes lack both codons for tyrosine, \u003cem\u003erps18\u003c/em\u003e lacks both codons for histidine, \u003cem\u003epsbA\u003c/em\u003e lacks both codons for lysine, and \u003cem\u003endhC, rps18\u003c/em\u003e, and \u003cem\u003erps7\u003c/em\u003e lack both codons for cysteine. As depicted in Figure 6c, G/C-ending and A/T-ending codons tend to form distinct clusters, with A/T-ending codons exhibiting relatively higher RSCU values. Notably, most G/C-ending codons clustered together, along with TTT, ATA, and CTA, whereas within the major A/T-ending cluster, only TTG was grouped with the G/C-ending codons.\u003c/p\u003e\n\u003cp\u003eCodons were classified into six groups based on their RSCU values: (1) overrepresented codons (RSCU \u0026gt; 1.6), (2) positively biased codons but not overrepresented (1 \u0026lt; RSCU ≤ 1.6), (3) unbiased codons (RSCU = 1), (4) negatively biased codons but not underrepresented (0.6 ≤ RSCU \u0026lt; 1), (5) underrepresented codons (0 \u0026lt; RSCU \u0026lt; 0.6), and (6) unused codons (RSCU = 0). Our analysis revealed that 80.16% of thymine-ending codons and 71.25% of adenine-ending codons were positively biased, whereas only 12.19% of cytosine-ending and 14.23% of guanine-ending codons exhibited positive bias (Supplementary table 6).\u003c/p\u003e\n\u003cp\u003eA comparison of the codon usage pattern of the \u003cem\u003epsbA\u003c/em\u003e gene with the overall trends observed across 40 genes indicated that \u003cem\u003epsbA\u003c/em\u003e follows a similar codon usage preference to the whole genome. However, fewer than half of the adenine-ending codons were positively biased (Supplementary table 6), suggesting that the \u003cem\u003epsbA\u003c/em\u003e gene exhibits a stronger preference for A/T-ending codons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;GC3 values have low level correlation with the effective number of codons suggesting some influence of GC3 on codon usage:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed that neither GC1 nor GC2 values exhibited a significant correlation with the effective number of codons (Nc) (Figure 7a, b, c). Scatter plot of correlation analysis depicting the comparison between expected and observed Nc values against GC3 demonstrates a noticeable deviation of observed Nc values from expected trends (Figure 7c). The correlation coefficient was higher for expected Nc values against GC3 than for observed Nc values, indicating that while GC3 strongly influences theoretical Nc expectations, actual codon usage patterns deviate due to additional evolutionary factors.\u003c/p\u003e\n\u003cp\u003eA low positive correlation was observed between Nc and GC3 values, with observed Nc values tending to be lower than expected at higher GC3 values. This suggests that an increased proportion of GC-ending codons corresponds to reduced codon bias. Notably, the \u003cem\u003endhA\u003c/em\u003e gene clustered with \u003cem\u003epsbA\u003c/em\u003e in Figure 6b, and its observed Nc value (50.2) closely matched the expected value (50.5) (Supplementary Table 5d). However, this trend did not hold true for \u003cem\u003epsbA\u003c/em\u003e, implying that distinct selective pressures or mutational influences are shaping the codon usage of \u003cem\u003epsbA\u003c/em\u003e differently from other genes.\u003c/p\u003e\n\u003cp\u003eAdditionally, regression analysis of average GC1 and GC2 values against GC3 yielded an insignificant R² value (0.02), reinforcing the notion that codon usage patterns arise from a complex interplay of mutational and selective forces (Figure 7d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;Analysis of RSCU values indicates absence of context dependent mutation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of context dependent mutation verifies the condition that mutation is a single-site event, meaning the nucleotide frequencies of the third codon position in the 4-fold degenerate families will not be affected by the second and/or the first codon positions. The result of this analysis showed that the variation of codon’s third base does not correlate with either second or first base (Supplementary table 7). It can thus be hypothesised that for the \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes, the likelihood of mutation occurring at a particular site in the codon is not influenced by the neighbouring nucleotides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynonymous and non-synonymous substitution analysis reveal that the \u003cem\u003eAconitum\u003c/em\u003e chloroplast genes are under purifying selection:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nucleotide substitution that alters the encoded amino acid of a protein is termed a non-synonymous substitution (Ka), whereas a substitution that does not change the amino acid sequence is referred to as a synonymous substitution (Ks). The Ka/Ks ratio serves as an indicator of coding sequence evolution, where a value of 1 suggests neutral evolution, a value greater than 1 indicates positive or diversifying selection, and a value less than 1 implies negative or purifying selection.\u003c/p\u003e\n\u003cp\u003eThe Ka/Ks ratio was calculated for 40 conserved genes across 74 \u003cem\u003eAconitum\u003c/em\u003e species, revealing a predominant pattern of purifying selection. In this analysis, Ks values were generally lower than Ka values, with the exceptions of \u003cem\u003ematK\u003c/em\u003e, \u003cem\u003eclpP\u003c/em\u003e, and \u003cem\u003erpoC1\u003c/em\u003e (Supplementary Figure 4). However, as the Ka/Ks ratio remained below 1 even for these genes, the results still suggest negative selection (Supplementary Table 8). This finding aligns with expectations, as all analysed genomes belong to the same genus, where strong evolutionary constraints act to preserve functional integrity and limit protein-coding sequence divergence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-monophyletic clustering of conspecific samples in phylogenetic analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe classification of genus \u003cem\u003eAconitum\u003c/em\u003e into subgenera \u003cem\u003eAconitum\u003c/em\u003e, \u003cem\u003eLycoctonum\u003c/em\u003e, and \u003cem\u003eGymnaconitum\u003c/em\u003e (as an outgroup) is based on differences in seed morphology and ploidy levels (H. H. Kong et al., 2013). To assess whether phylogenetic analysis aligns with this morphological classification, whole-genome and core-gene phylogenies were constructed using the best-fit models suggested by IQ-TREE. The whole-genome phylogeny was inferred using the TVM+F+I+R3 model, while the core-gene phylogeny was based on the Q.mammal+F+R2 model. In both phylogenies (Figure 8) all the accessions of subgenera \u003cem\u003eAconitum\u003c/em\u003e and \u003cem\u003eLycoctonum\u003c/em\u003e cluster together respectively, with one exception. \u003cem\u003eAconitum flavum\u003c/em\u003e (GenBank accession: MT982388) was observed to cluster phylogenetically with members of the subgenus \u003cem\u003eLycoctonum\u003c/em\u003e, rather than grouping with its taxonomic subgenus \u003cem\u003eAconitum\u003c/em\u003e, as would be expected based on current classification. This anomalous placement was consistently recovered in phylogenies constructed from both whole chloroplast genome sequences and core gene datasets of 73 species (Figure 8).\u003c/p\u003e\n\u003cp\u003eTo further investigate such incongruence, Kimura 2-Parameter (K2P) genetic distances were calculated among \u003cem\u003eA. flavum\u003c/em\u003e accessions. The pairwise comparisons revealed that MT982388 displayed markedly higher intraspecific K2P distances (ranging from ~0.0093 to 0.0095) when compared with other \u003cem\u003eA. flavum\u003c/em\u003e accessions (e.g., MW839579, MW839580, MW839582, and NC_056280), whose mutual distances were significantly lower (as low as 4.5×10⁻⁵) (Supplementary table 10). This elevated divergence, along with the unexpected phylogenetic placement, suggests that MT982388 may represent a misidentified sample. Alternatively, it could reflect deep genetic divergence within \u003cem\u003eA. flavum\u003c/em\u003e, indicative of cryptic speciation or historic hybridization. However, given that the GenBank submission includes a voucher specimen, it is more likely that the anomaly stems from either incorrect species identification during sequencing or contamination of the submitted sample. Further, the phylogenetic tree constructed from complete chloroplast genome sequences of 73 accessions representing 40 \u003cem\u003eAconitum\u003c/em\u003e species revealed non-monophyly among several conspecific samples. While multiple accessions of some species clustered together as expected, others were placed in separate clades or grouped more closely with different species, suggesting complex evolutionary relationships. Notably, accessions of species such as \u003cem\u003eA. pendulum\u003c/em\u003e, \u003cem\u003eA. flavum\u003c/em\u003e and \u003cem\u003eA. kusnezoffii\u003c/em\u003e exhibited such inconsistent clustering patterns.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChloroplast genomes exhibit a high degree of conservation, a pattern which this study also observed in the comparative analysis of 73 \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes, as evident in gene content, genomic architecture (quadripartite structure), synteny, GC percentage, base composition of codons and codon usage. Pangenome analysis suggested that there are 72 core and 9 accessory genes for this group of chloroplast genomes. Intriguingly, BLASTn analysis of nine accessory genes against the \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes revealed that the homologous sequences for all accessory genes were present in all chloroplast genomes with \u0026gt;96% similarity (Supplementary table 9). Upon re-examining the NCBI annotation files, we confirmed that the accessory genes were absent in the NCBI-annotated genome files despite their homology within the genome sequences with high sequence similarity. This discrepancy suggests that these genes were not called during annotation by the NCBI annotation pipeline, highlighting potential limitations in automated gene annotation pipelines. Annotation of these genomes was further performed using Plastid Genome Annotator (PGA) (Qu et al., 2019), which yielded additional insights. In the PGA-annotated datasets, the accessory genes infA and ycf15 still remained undetected in all genomes; however, other accessory genes, i.e., \u003cem\u003erpl16, rpl2, rpl20 rps16\u003c/em\u003e and \u003cem\u003eycf1\u003c/em\u003e, were detected. An intriguing pattern was observed for the accessory genes \u003cem\u003epsbN\u003c/em\u003e and \u003cem\u003epbf1\u003c/em\u003e: in genomes where \u003cem\u003epsbN\u003c/em\u003e was present, \u003cem\u003epbf1\u003c/em\u003e was absent, and vice versa, with \u003cem\u003epbf1\u003c/em\u003e located at the same genomic position as \u003cem\u003epsbN\u003c/em\u003e. In contrast, the PGA annotation files consistently included \u003cem\u003epsbN\u003c/em\u003e in all genomes but failed to annotate \u003cem\u003epbf1\u003c/em\u003e in any genome. The observed inconsistencies between the NCBI and PGA annotations in the relative presence/absence of genes in genome sequences despite their absence in annotation files, may stem from undetected mutations or sequencing errors in chloroplast genome assembly. These results strongly emphasize the need for rigorous manual validation and improved annotation algorithms to ensure thorough gene identification in chloroplast genome studies.\u003c/p\u003e\n\u003cp\u003eThe relative presence of several gene groups on chloroplast genome were accessed using KEGG pathway database. This study revealed that several members of different gene groups are absent from the chloroplast genome. It was noted that out of the 14 genes of \u003cem\u003eNdh\u003c/em\u003e group of oxidative phosphorylation, 11 are present on the chloroplast, the \u003cem\u003epet\u003c/em\u003e group of genes coding for \u003cem\u003ecytochrome b6f\u003c/em\u003e have 6 out of 8 members on the chloroplast whereas none of the \u003cem\u003ePet\u003c/em\u003e genes coding for proteins of cytochrome b6f complex, a part of the photosynthetic electron transport chain, are present on the chloroplast genome. Further, the\u003cem\u003e\u0026nbsp;psa\u003c/em\u003e and \u003cem\u003epsb\u003c/em\u003e group of genes coding for Photosystem I and Photosystem II respectively, have 5 out of 18 and 15 out of 28 genes respectively present on the chloroplast. As organellar (chloroplast) genomes have extremely reduced their genome size, the genes absent on the chloroplast genome might be encoded in the nuclear genome. The missing genes were looked up on the gene annotation file of \u003cem\u003eA. thaliana\u003c/em\u003e nuclear genome. It was found that \u003cem\u003endhL\u003c/em\u003e of the \u003cem\u003endh\u003c/em\u003e group of genes, \u003cem\u003epsaD, psaF, psaG, psaK\u003c/em\u003e and \u003cem\u003epsaO\u003c/em\u003e of the \u003cem\u003epsa\u003c/em\u003e group of genes, \u003cem\u003epsbP, psbQ, psbR, psbY\u003c/em\u003e and \u003cem\u003epsb27\u003c/em\u003e of the\u003cem\u003e\u0026nbsp;psb\u003c/em\u003e group of genes are present in the nuclear genome.\u003c/p\u003e\n\u003cp\u003eSSRs or Simple Sequence Repeats are also known as microsatellites and range from 1 to 6 nucleotides as repeating units. The frequency of mononucleotide repeats ranged from 0.7% to 2%, indicating a significant variability among the species. Notably, \u003cem\u003eA. finetianum\u003c/em\u003e exhibited the highest number of mononucleotide repeats (52), while \u003cem\u003eA. volubile\u003c/em\u003e had the lowest (19). This considerable range within the same genus suggests that certain species within \u003cem\u003eAconitum\u003c/em\u003e may have undergone different evolutionary pressures or replication dynamics that influenced their SSR accumulation. Both whole genome analysis of SSRs and region wise analysis points towards the fact that SSRs do not exhibit conservation in the chloroplast genomes. The distribution of repeats in the four regions of genome namely LSC, SSC and IR regions is inconsistent among the 74 genomes and in some case, inconsistencies are observed even among different strains of the same species. SSRs can also be used as identification markers at the genus level. In the case of \u003cem\u003eAconitum\u003c/em\u003e genus, some species of \u003cem\u003eAconitum\u003c/em\u003e show a consistent pattern in the occurrence of SSRs, such as \u003cem\u003eA. scaposum, A. coreanum, A. kusnezoffii, A. barbatum\u003c/em\u003e and \u003cem\u003eA. episcopale\u003c/em\u003e. The representation of numbers of SSRs as bar graph with the phylogenetic tree exhibits this observation (Supplementary Figure 5). Thus, SSRs can be further explored as molecular markers for identification at species level for those in which different strains of the same species show consistent pattern of presence of SSRs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnderstanding codon usage is a key aspect in evolutionary study. The codon usage analysis in this study was performed gene wise, to reveal the inconsistencies in codon usage between genes if any. Studying them for the entire genome may result in masking of the differences of metrics between genes. In terms of nucleotide composition at the three codon positions, effective number of codons (Nc) and RSCU values, there was no significant difference between the \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes, but variation was observed between the genes. Codon usage analysis revealed that \u003cem\u003eAconitum\u003c/em\u003e chloroplast genes prefer A/T ending codons over G/C ending codons, consistent with the previous studies performed on \u003cem\u003eTheaceae\u003c/em\u003e and \u003cem\u003eFagaceae\u003c/em\u003e chloroplast genomes(Z. Wang et al., 2022; S. Yang et al., 2018). The pattern of codon usage was consistent for the \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes, with higher RSCU values for A/T ending codons than G/C ending codons. It was also observed that more of the G/C ending codons were missing in several genes compared to the A/T ending codons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies on green plant chloroplasts have revealed a consistent pattern of codons preferring A/T ending codons over G/C ending codons and is under pressure from both, mutational bias and natural selection (W. Q. Kong \u0026amp; Yang, 2017; Z. Wang et al., 2020; Yengkhom et al., 2019). Study on angiosperm chloroplast revealed deviation from this pattern for codon bias in \u003cem\u003epsbA\u003c/em\u003e gene. Although context dependent mutation explains codon bias in most of the genes, selection was proposed to be the main factor explaining codon bias in \u003cem\u003epsbA\u003c/em\u003e gene (Morton, 2022). Similarly in the case of \u003cem\u003eAconitum\u003c/em\u003e, \u003cem\u003endhA\u003c/em\u003e gene which clusters with \u003cem\u003epsbA\u003c/em\u003e gene in Figure 6b has Nc value close to the expected Nc value; however, this is not the case for\u003cem\u003e\u0026nbsp;psbA\u003c/em\u003e gene indicating influence of selection on \u003cem\u003epsbA\u003c/em\u003e gene whereas mutation is the main factor affecting other genes. The gene \u003cem\u003epsbA\u003c/em\u003e was also the most biased gene when the Nc values are looked at, among the genes. Previous studies have reported that \u003cem\u003epsbA\u003c/em\u003e gene in chloroplast genomes favours NNC codon over NNT for 2-fold degenerate amino acids of NNY type, and selection acts on this gene for high translational efficiency(Morton, 1993; Morton \u0026amp; Levin, 1997; Suzuki \u0026amp; Morton, 2016). However, for \u003cem\u003eAconitum\u003c/em\u003e species, this pattern was found for only phenylalanine and histidine amino acids.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the Pi values representing the nucleotide diversity among the 74 species, it is evident that significant nucleic acid divergence regions exist in the IR region, mainly tRNA genes. Other sequences outside of IR region with relatively higher Pi values are also tRNA genes. These sequences could serve as regions for designing DNA barcodes for species identification. Previously, tRNA sequences have been successfully used for identification of species that are difficult to identify. The region of matK-trnK-rps16 has been used for development of DNA barcode for identification of oak (\u003cem\u003eQuercus\u003c/em\u003e) species (Pang et al., 2019). However, the results of nucleotide diversity revealed that intergenic regions were less divergent than the coding region, according to the average Pi values. Average nucleotide variability in the coding regions (0.035) is higher than the average Pi values in the non-coding/intergenic region (0.01). Since the genomes considered in this study belong to a single genus, the overall nucleotide diversity values are lower as is expected for highly conserved chloroplast genomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Ka/Ks ratio of the 40 common genes among 74 \u003cem\u003eAconitum\u003c/em\u003e species indicated they are under purifying selection as the ratio is significantly less than 1. Only three genes exhibited higher Ka values compared to Ks values, namely \u003cem\u003ematK, rpoC1\u003c/em\u003e and \u003cem\u003eclpP\u003c/em\u003e. The gene \u003cem\u003ematK\u003c/em\u003e is known to have high substitution rates compared to other chloroplast genes (G\u0026uuml;l et al., 2005). The \u003cem\u003ematK\u003c/em\u003e gene is nested in the group II intron between the 5\u0026rsquo; and 3\u0026rsquo; exons of the \u003cem\u003etrnK\u003c/em\u003e in the LSC region of most of the green plants as well as in \u003cem\u003eAconitum\u003c/em\u003e genome (Figure 1a) (Barthet \u0026amp; Hilu, 2007). Maturase K or \u003cem\u003ematK\u003c/em\u003e is a type of maturase enzyme (prokaryotic enzyme) that has a role in the crucial step of gene expression, i.e. intron removal. The enzyme aids excision of seven different chloroplast group IIA introns that lie within precursor RNAs for essential elements of chloroplast function (Barthet et al., 2020). The \u003cem\u003erpoC\u003c/em\u003e gene encodes the \u0026beta; subunit of chloroplast RNA polymerase and is split into \u003cem\u003erpoC1\u003c/em\u003e and \u003cem\u003erpoC2\u003c/em\u003e, which encode the \u0026beta;\u0026prime; and \u0026beta;\u0026prime;\u0026prime; subunits, respectively (Lee et al., 2012). Meanwhile, \u003cem\u003eclpP\u003c/em\u003e plays a crucial role in cell viability by encoding a proteolytic subunit of the ATP-dependent protease complex (Shikanai et al., 2001). Despite the generally high substitution rate of \u003cem\u003ematK\u003c/em\u003e, in \u003cem\u003eAconitum\u003c/em\u003e species, the highest non-synonymous substitution rate is observed in \u003cem\u003erpoC1\u003c/em\u003e, followed by \u003cem\u003eclpP\u003c/em\u003e, and then \u003cem\u003ematK\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe phylogenetic analysis validated seed morphology-based classification of subgenus \u003cem\u003eAconitum\u003c/em\u003e and \u003cem\u003eLycoctonum\u003c/em\u003e. However, the non-monophyletic clustering of multiple accessions belonging to the same subgenus \u003cem\u003eAconitum\u003c/em\u003e was observed in our phylogenetic tree which likely reflects complex evolutionary dynamics rather than methodological error. Several factors may contribute to these patterns. First, misidentification or labelling errors in public databases can lead to incorrect species assignments. Second, incomplete lineage sorting and chloroplast capture due to hybridization (Gon\u0026ccedil;alves et al., 2019; Sutkowska et al., 2017) can obscure phylogenetic signals, particularly when relying solely on chloroplast genomes.\u003c/p\u003e\n\u003cp\u003eThe clustering of \u003cem\u003eA. pendulum\u003c/em\u003e and \u003cem\u003eA. flavum\u003c/em\u003e accessions, despite their designation as distinct species, aligns with recent findings from population genetics and ecological niche modelling, which suggest these taxa may represent a single species complex with weak genetic differentiation, historical gene flow, and evidence of a demographic bottleneck during the Last Glacial Maximum(Q. Li et al., 2024). Similarly, the grouping of accessions from \u003cem\u003eA. kusnezoffii\u0026nbsp;\u003c/em\u003e(NC_031422), \u003cem\u003eA. jaluense\u0026nbsp;\u003c/em\u003esubsp. jaluense (KT820668) and \u003cem\u003eA. japonicum\u003c/em\u003e subsp. napiforme (KT820670) supports earlier morphological and ecological studies from Mt. Sobaek in Korea, which indicate extensive hybridization and repeated introgression among these taxa (Lim \u0026amp; Park, 2001). Interestingly, the accessions mentioned above that form anomalous cluster in the core genes phylogeny, mainly belong to the Republic of Korea (Supplementary figure 6, Supplementary table 11). These observations underscore the need for integrative taxonomic approaches in \u003cem\u003eAconitum\u003c/em\u003e, combining molecular data from both nuclear and organellar genomes with detailed morphological and ecological analyses to resolve species boundaries and evolutionary histories more accurately.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study emphasizes the highly conserved yet subtly variable nature of chloroplast genomes within the Aconitum genus. The identification of accessory genes and annotation discrepancies underscores the limitations of current automated tools and the need for manual curation and better gene-calling and annotation tools. Codon bias patterns and Ka/Ks analyses reveal the interplay between mutational pressure and selection, particularly in genes like psbA. Phylogenetic incongruities suggest complex evolutionary histories shaped by hybridization and misidentification, calling for integrative taxonomic approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAuthors have used open-source tools in this analysis. All tool versions have been provided in the methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eGS acknowledges the Department of Science and Technology (DST)-INSPIRE and IIT Hyderabad for supporting his research. RAK is supported by the PhD fellowship from the University Grant Commission, Government of India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eGS generated the idea. RAK\u003csup\u003e\u0026nbsp;\u003c/sup\u003eperformed the analysis and wrote the first draft of the manuscript. RAK and GS edited and finalized the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmeri, A. (1998). The effects of Aconitum alkaloids on the central nervous system. \u003cem\u003eProgress in Neurobiology\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(2), 211\u0026ndash;235. https://doi.org/https://doi.org/10.1016/S0301-0082(98)00037-9\u003c/li\u003e\n\u003cli\u003eBarthet, M. M., \u0026amp; Hilu, K. W. (2007). 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Plastid genome comparative and phylogenetic analyses of the key genera in fagaceae: Highlighting the effect of codon composition bias in phylogenetic inference. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(February), 1\u0026ndash;13. https://doi.org/10.3389/fpls.2018.00082\u003c/li\u003e\n\u003cli\u003eYengkhom, S., Uddin, A., \u0026amp; Chakraborty, S. (2019). Deciphering codon usage patterns and evolutionary forces in chloroplast genes of Camellia sinensis var. assamica and Camellia sinensis var. sinensis in comparison to Camellia pubicosta. \u003cem\u003eJournal of Integrative Agriculture\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(12), 2771\u0026ndash;2785. https://doi.org/https://doi.org/10.1016/S2095-3119(19)62716-4\u003c/li\u003e\n\u003cli\u003eYoon, H. S., Hackett, J. D., Ciniglia, C., Pinto, G., \u0026amp; Bhattacharya, D. (2004). A Molecular Timeline for the Origin of Photosynthetic Eukaryotes. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(5), 809\u0026ndash;818. https://doi.org/10.1093/molbev/msh075\u003c/li\u003e\n\u003cli\u003eYuan, Q., \u0026amp; Yang, Q. E. (2006). Polyploidy in Aconitum subgenus Lycoctonum (Ranunculaceae). \u003cem\u003eBotanical Journal of the Linnean Society\u003c/em\u003e, \u003cem\u003e150\u003c/em\u003e(3), 343\u0026ndash;353. https://doi.org/10.1111/j.1095-8339.2006.00468.x\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chloroplast genomics, medicinal plants, traditional medicinal plants, pangenome analysis, phylogenetics, nucleotide diversity, chloroplast evolution, cellular organelle genomics","lastPublishedDoi":"10.21203/rs.3.rs-6954381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6954381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the chloroplast genome is pivotal to unravel evolutionary relationships within plant species and facilitate accurate species identification by utilizing conserved yet diverse sequences. Genus \u003cem\u003eAconitum\u003c/em\u003e consists of around 300 traditional Indian and Chinese medicinal plant species, many native to mountainous regions. Despite their medicinal value, several species are known to be highly poisonous due to the presence of toxic diterpene alkaloids. Therefore, accurate identification and classification of these species is vital for traditional medicine systems especially for their safe usage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur investigation revealed a consistent quadripartite structure across all chloroplast genomes, comprising the typical large single copy (LSC), small single copy (SSC), and two inverted repeats (IR) regions. Using the available annotations, pangenome analysis unveiled 72 core and nine accessory genes, indicating an open pangenome characteristic. In-depth nucleotide-level homology analysis revealed that homologous genes of all accessory genes are present in all other genomes, implying the requisite for better chloroplast genome annotation tools that can identify all putative genes from such conserved genomes. Notably, the order of all core and accessory genes remained highly conserved across all analysed genomes, underscoring overall evolutionary stability with the diversity of accessory genes. Members of some core pathways are relatively absent on the chloroplast genome, suggesting its potential presence on the nuclear genome, which will be revealed after their nuclear genome sequencing. Furthermore, codon usage analysis demonstrated a preference for A/T ending codons over G/C ending codons, consistent with chloroplast genomes across species. Our phylogenetic results largely supported the morphological classification, with distinct \u003cem\u003eLycoctonum\u003c/em\u003e and \u003cem\u003eAconitum\u003c/em\u003e subgenera clustering. This validated the gross accuracy except for \u003cem\u003eA. tanguticum\u003c/em\u003e and \u003cem\u003eA. flavum\u003c/em\u003e, which clustered in wrong subgenus clades, suggesting discrepancy in morphological classification of the species or inaccurate classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis comprehensive comparative analysis of 73 \u003cem\u003eAconitum\u003c/em\u003e chloroplast genomes elucidated their diversity at gene and genome architecture levels along with showcasing their evolutionary relationships with each other. Leveraging morphological classifications, we investigated the concordance between traditional taxonomy and molecular data through core gene-based and whole-genome phylogeny. The observed phylogenetic incongruences, such as non-monophyly of conspecific accessions and unexpected clustering patterns, likely reflect the combined effects of incomplete lineage sorting and historical hybridization events, both of which appear to be prominent evolutionary forces shaping the genomic architecture of \u003cem\u003eAconitum\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Comprehensive chloroplast genome analysis of 73 genus Aconitum members of family Ranunculaceae reveals insights into genome structure, codon usage polymorphism, and phylogenetic relationships","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 07:03:26","doi":"10.21203/rs.3.rs-6954381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-24T15:02:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T09:56:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T05:18:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120241835777195720363398750627011072227","date":"2025-10-15T08:34:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54266359378190641546177112295848828045","date":"2025-10-08T10:40:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T13:22:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136507608410216814810107448444010333732","date":"2025-09-24T07:54:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55503723143675036794246080694961230331","date":"2025-07-21T08:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T07:46:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-27T06:58:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-26T05:21:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T06:43:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-23T08:11:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7eae9e17-7935-49c1-ad95-a99f03f21bb6","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50497126,"name":"Biological sciences/Evolution/Taxonomy"},{"id":50497127,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":50497128,"name":"Biological sciences/Plant sciences/Plant evolution"}],"tags":[],"updatedAt":"2026-03-09T16:09:28+00:00","versionOfRecord":{"articleIdentity":"rs-6954381","link":"https://doi.org/10.1038/s41598-026-40105-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-04 15:57:03","publishedOnDateReadable":"March 4th, 2026"},"versionCreatedAt":"2025-06-24 07:03:26","video":"","vorDoi":"10.1038/s41598-026-40105-5","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40105-5","workflowStages":[]},"version":"v1","identity":"rs-6954381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6954381","identity":"rs-6954381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}