Genome Survey Indicated Complex Evolutionary History of Garuga Roxb. Species

doi:10.21203/rs.3.rs-3905007/v1

Background

Garuga Roxb. is a genus endemic to southwest China and other tropical regions in Southeast Asia facing risk of extinction due to the loss of tropical forests and changes in land use. Conducting a genome survey of G. forrestii contribute to a deeper understanding and conservation of the genus.

Results

This study utilized genome survey of G. forrestii generated approximately 54.56 GB of sequence data, with approximately 112 × coverage. K-mer analysis indicated a genome size of approximately 0.48 GB, smaller than 0.52GB estimated by flow cytometry. The heterozygosity is of about 0.54%, and a repeat rate of around 51.54%. All the shotgun data were assembled into 339,729 scaffolds, with an N50 of 17,344 bp. The average content of guanine and cytosine was approximately 35.16%. A total of 330,999 SSRs were detected, with mononucleotide repeats being the most abundant at 70.16%, followed by dinucleotide repeats at 20.40%. A pseudo chromosome of G. forrestii and a gemone of Boswellia sacra were used as reference genome to perform a primer population resequencing analysis within three Garuga species. PCA indicated three distinct groups, but genome wide phylogenetics represented conflicting both between the dataset of different reference genomes and between maternal and nuclear genome.

Conclusion

In summary, the genome of G. forrestii is small, and the phylogenetic relationships within the Garuga genus are complex. The genetic data presented in this study holds significant value for comprehensive whole-genome analyses, the evaluation of population genetic diversity, investigations into adaptive evolution, the advancement of artificial breeding efforts, and the support of species conservation and restoration initiatives. Ultimately, this research contributes to reinforcing the conservation and management of natural ecosystems, promoting biodiversity conservation, and advancing sustainable development.

Genome survey

K-mer

Flow cytometry

SNP

Phylogenetic

Garuga forrestii

Nuclear-cytoplasmic conflict

Garuga Roxb. is an endemic genus to southwest China and other tropical regions in Southeast Asia. These deciduous trees bloom between March and April, prior to the onset of the rainy season, produce fruits maturing from May to November, with the main maturation phase occurring in July and August. There are approximately five species or varieties within this genus, including G. forrestii W. W. Smith, G. floribunda var. gamblei (King ex Smith) Kalkm., G. floribunda var. floribunda Decne., G. pierrei Guill., and G. pinnata Roxb.. Four of these species are found in China, except G. floribunda var. floribunda. G. forrestii is an endemic species of China.

Garuga forrestii, is a Chinese endemic species of this genus, distributed in the arid and warm river valleys of Yunnan, S.W. China, such as Jinshajiang River (JR), Lancangjiang River (LR), Red River (RR), Nanpanjiang River (NPR) and their tributaries. It is also the only species distributed beyond to the Tropic of Cancer, in the JR, extra north to the tropical regions. It extends to Leibo, Sichuan, making it the highest latitude distribution within the Burseraceae family. It distributed upper to the altitudes nearing 1600 meters at many points in the JR. It demonstrates an extraordinary ability to flourish under arid conditions. G. floribunda var. gamblei and G. pinnata grow in dense tropical forests of South Yunnan, further to Guangxi, Guangdong and Hainan. Our survey along the middle LR unveiled the coexistence of three species: G. forrestii, G. floribunda var. gamblei and G. pinnata. But individuals of this genus are all in danger of extinction due to the loss of tropical forests and changes of land uses.

The taxonomy of Garuga has experienced substantial reorganization, resulting in alterations to their taxonomic classifications. G. pierrei previously considered a variety of G. pinnata, exhibits leaf characteristics similar to G. pinnata. However, distinctions arise in the short and soft hairs present on the axis and leaflets of G. pierrei, contrasting with the shorter hairs found on the latter. G. pierrei bears spherical fruits, G. pinnata's fruits are nearly spherical and occasionally exhibit soft hairs. Nonetheless, typical G. pierrei specimens eluded discovery during our fieldwork. The fruiting attributes of G. floribunda var. gamblei share similarities with those of G. forrestii, yet notable differences manifest in terms of hair coverage, pedicel, fruit dimensions and shape. Within the Lancang-Mekong River region, these three species coexist, displaying a continuum of variations in tree structure and leaf morphology. Consequently, the current taxonomic classification needs further tests by phylogenetic and phylogenomic evidence.

The Sino-Himalaya region is in the southeast margin of the Qinghai-Tibetan Plateau (QTP) hosting several Asian rivers, including the Mekong, RRB and JRB, along with their tributaries. Presently, these rivers flow separately to the ocean [1, 2]. However, they once flowed southward into the Paleo-Red River (PRR), creating an extensive drainage network that ultimately emptied into the South China Sea millions of years before [3, 4]. The PRR system disassembled subsequently due to uplift of QTP and river capture events since the late Miocene [4]. This reorganization disrupted the previously continuous distribution pattern, resulting in distinctive genetic and biogeographical attributes, facilitating species' genetic differentiation giving rise to new taxa [5, 6]. The river captures also made separate ranges unique promoting lineage fusion and diversity accumulation [7].

Genome sequencing technology enables the thorough investigation of species evolution [8]. Next-generation sequencing, NGS, technique has greatly reduced the cost of DNA sequencing [9], and was widely used in most fields of genetics and genomics, providing researchers with a more convenient, refined, and comprehensive investigation method [10]. A genome survey is the most convenient strategy to provide a rough reference genome for those species without whole genome data [11–13].

This study employed genome survey to explore the genome size and characteristics of G. forrestii. The gathered genomic information from this research will serve to 1) learn deeper in our comprehension of the G. forrestii genome; 2) strive to construct a preliminary long contig or even a draft scaffold, offering a foundation for future evolutionary surveys of this genus.

2.1 Sample collection and DNA extractions

Fresh leaf from a mature G. forrestii individual were collected for genome survey during the summer of 2023 near to the Gangou Bridge in the Red River valley, Yunnan, China. Fresh leaves from other 25 individuals of three species (G. floribunda var. gamblei, G. pinnata, G. forrestii) in the Garuga genus. Among these, 6 individuals of G. floribunda var. gamblei, 8 of G. pinnata, and 11 of G. forrestii (Fig. 1; Table 1). No specific permissions were required for the collection of specimens for this study which were neither privately owned nor protected and the field study did not involve endangered or protected species. We complied with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. After the formal identification of the plant material was carried out by Liangliang Yue, voucher specimens were prepared and deposited at the herbarium of College of Wetlands, Southwest Forestry University (Yue, accession number Yue2023051032-1).

A total of 26 samples were sequenced. The genomic DNA was isolated using the CTAB method [14]. DNA was detected by 0.8% agarose gel electrophoresis, while DNA was quantified by UV spectrophotometer.

Table 1

Sampling Information
Species	Number of Individuals	Population	Province	Small Scale	Longitude	Latitude
G. pinnata	3	Pi_BB	Yunnan	Bubeng	101.585	21.60944
	3	Pi_YXG	Yunnan	Yexianggu	100.8648	22.18184
	1	Pi_SHZWY	Yunnan	Shanghaizhiwuyuan	121.4429	31.14649
	1	Pi_DTS	Yunnan	Datianshan	99.87337	24.7832
G. floribunda var. gamblei	2	Fl_SMC	Yunnan	Sanmaicun	100.6193	22.00044
	2	Fl_YXG	Yunnan	Yexianggu	100.8648	22.18184
	1	Fl_ML	Yunnan	Menglong	100.7445	21.7359
	1	Fl_DTBC	Yunnan	Datianbacun	99.88069	24.73952
G. forrestii	1	Fo_XP	Yunnan	Xinping	101.5044	24.14715
	2	Fo_DC	Yunnan	Dacun	100.4178	25.01894
	1	Fo_LJZ	Yunnan	Luzhijiang	101.9574	24.6514
	2	Fo_HN	Yunnan	Huaning	102.9734	24.0748
	2	Fo_KYC	Yunnan	Kaiyuancheng	103.2985	23.89117
	1	Fo_NXH	Yunnan	Nanxihe	101.8507	23.64461
	1	Fo_YJZ	Sichuan	Yinjiangzhen	101.7854	26.59825
	1	Fo_FF	Sichuan	Fenfang	101.9786	26.76728

2.2 Genome size estimation by flow cytometry

For the experimental material, we utilized tender leaves from tomato plants (genome size: 900 Mb) that were one month old following seed germination. The samples were carefully placed within 0.8 mL of pre-chilled MG^b dissociation solution, composed of 45 mM MgCl₂·6H₂O, 20 mM MOPS, 30 mM sodium citrate, 1% (W/V) PVP 40, 0.2% (V/V) Tritonx-100, 10 mM Na₂EDTA, 20 µL/mL β-mercaptoethanol, and adjusted to pH 7.5. The tissues were swiftly sectioned vertically using a sharp blade and allowed to rest in the dissociation solution on ice for a duration of 10 minutes. Following this, the mixture was filtered through a 40-micron mesh to obtain a nuclear cell suspension. This suspension was then combined with a suitable volume of pre-chilled propidium iodide (PI) solution, having a stock concentration of 1 mg/mL, along with a fitting amount of RNAase solution at a stock concentration of 1 mg/mL. The combined mixture was subsequently subjected to a dark ice-cold staining process for a period of 0.5-1 hour. The effective concentration of both the PI staining solution and the RNAase solution was maintained at 50 µg/mL [15, 16]. The stained suspension of nuclear cells underwent detection using a BD FACScalibur flow cytometer. This involved utilizing a 488 nm blue light excitation to measure the emitted fluorescence intensity of propidium iodide. Each detection cycle involved the collection of 10,000 particles. The coefficient of variation (CV%) was maintained at a level below 5%. Modifit3.0 software was employed to conduct graphing and analysis. The genome size was calculated using the subsequent formula [17]:

$$Sample genome size = standard genome size \times \frac{sample{G}_{0}/{G}_{1}peak mean}{standard{G}_{0}/{G}_{1}peak mean}$$

2.3 Genome Sequencing and Data Quality Control

Dried leaves from the mature tree of G. forrestii was sent to Personalbio company for paired-end sequencing. We followed the standard protocol using Illumina's TruSeq DNA PCR-free prep kit reagents for sequencing library preparation. Extracted DNA sample underwent random shearing through ultrasonication, followed by end repair to eliminate overhanging bases at the 5' end and to add a phosphate group while filling in missing bases at the 3' end. To prevent self-ligation of DNA fragments and ensure compatibility with sequencing adaptors, an A base was added to the 3' end of the DNA sequence. Sequencing adaptors with library-specific tags were ligated to the 5' end of the DNA sequence, facilitating the immobilization of DNA molecules onto the Flow Cell. We used AMPureXP beads (Beckman Coulter, Brea, CA) for a selective removal of adaptor-ligated fragments and purification of the resulting library system. Subsequently, PCR amplification was performed on DNA fragments ligated with adaptors to enrich the sequencing library templates. A second purification step with BECKMAN AMPure XP Beads was carried out to purify the enriched library products. Finally, we conducted 2% agarose gel electrophoresis to select and purify the library's final fragments. The resulting library insert fragments were approximately 400 bp in size. We performed paired-end sequencing with 2 × 150 bp reads using an Illumina NovaSeq instrument (Table 2). Furthermore, we also subjected the remaining 25 samples to sequencing processing, including chloroplast genome sequencing.

Table 2

Sequencing Overview
Sample	Insert Size	Sequencing platform	Sequencing Mode
G. forrestii	400 bp	Illumina Novaseq	Paired-end, 2×150bp

2.4 High-quality data acquisition

Raw sequence was filtered using fastp [18] and using the sliding window method to generate high quality sequence (high quality data). The size of the slide window is set to 5 bp. Slide the window from the 3' end to the 5' end, calculate the average Q value of the bases in the window, if the Q value is < 20, delete the bases in the window; if the Q value is ≥ 20, stop sliding. Length filtering, if the length of any one reads in the bipartite end ≤ 50 bp, then remove the bipartite end reads. fuzzy base N filtering, if the number of N bases in the bipartite end ≥ 5, then remove the bipartite end reads.

2.5 Ploidy analysis and k-mer analysis

Smudgeplot software [19] was used to analyze the genome structure and to count the number of heterozygous k-mer pairs by comparing the total number of k-mer pair coverage (CovA + CovB) and relative coverage (CovB / (CovA + CovB)) and to obtain the genomic ploidy. The distribution of k-mer was calculated using jellyfish software [20], which gives information about the heterozygosity of G. forrestii, the proportion of repetitive sequences, and then genome size estimation based on the distribution of k-mer frequencies and the number of 19-mer.

2.6 De novo assembly and GC content analysis

The pair-end DNA sequencing data was de novo assembled with standard parameters using the MEGAHIT [21]. Contigs were further spliced into scaffold levels using SOAPdenovo [22] software. Sliding window calculations were performed using a window size of 10 kb. The average depth and GC content of each window were computed. The quality of genome assembly was assessed using the Assembly-stats utility, which calculated metrics such as N50, scaffold count, scaffold size, scaffold length, and genome length.

Before chloroplast assembly, low-quality sequences were filtered using SOAPnuke [23], and de novo assembly was performed following the pipeline of GetOrganelle [24].

2.7 Identification of microsatellite motifs

Microsatellite Identification Software (MISA) [25] was utilized to detect microsatellite patterns within the scaffolds generated above. The minimum number of repeats required for identifying mononucleotide, dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide repeat sequences is set as follows: mononucleotide repeats with less than 10 repetitions, dinucleotide repeats with less than 6 repetitions, and all other repeat types with less than 5 repetitions [26].

2.8 Population resequencing variation detection and filtering of SNP data

The Picard [27], BWA [28], and Samtools [29] software is used to build the genome index and perform processes such as mapping, sorting, and deduplication, all aimed at subsequent variation detection. We used G. forrestii (we sampled, sequenced, and assembled the genome to ensure its usability as a reference genome.) and Boswellia sacra Flück. (downloaded from NCBI, accession: SNVD00000000) as reference genomes for mapping the 25 individuals. The GATK [30] software was employed to perform variant calling using HaplotypeCaller for each sample. This study involved the assembly of the genome of an individual of G. forrestii at the scaffold level, as well as the genomes of the remaining 25 individuals, which included 6 G. floribunda var. gamblei, 8 G. pinnata, and 11 G. forrestii. Each genome of the mentioned individuals was divided into 10 pseudo-chromosomes. The GATK software was applied to filter and extract SNPs and INDELs.

2.9 Phylogenetic reconstruction and PCA

We applied Plink [31] software to perform LD (Linkage Disequilibrium) filtering on the SNP data, resulting in a refined VCF file to generate an alignment for phylogenomic reconstruction and PCA analysis. The dataset encompassed nucleotide polymorphisms from the whole genomes of 25 individuals of the three species. We employed iqtree [32] to construct two Maximum Likelihood (ML) trees for the nuclear genome respectively using B. sacra Flück. and Garuga forrestii as reference genomes. Phylogenomic trees were visualized using Figtree [33]. The PCA based on the SNP data extracted from the 25 samples was conducted using Plink [31] and GCTA [34] software, followed by visualization of the results using an R script. We also constructed ML trees using iqtree [32] for the chloroplast genomes of 25 individuals.

3.1 Genome size estimation by flow cytometry

The utilization of flow cytometry analysis produced a detailed high-resolution histogram (Fig. 2; Table 3), revealing a fluorescence intensity of 17.04 for the sample and 29.67 for the internal reference. The ratio between these values is 0.57, leading to an estimated genome size of approximately 0.52 GB.

Table 3

Flow Cytometry Information
Sample	Reference Selection	Reference Fluorescence Intensity	Fluorescence Intensity of the Test Sample	Ratio	Genome Size (Gb)
G. forrestii	tomato	29.67	17.04	0.57	0.52

3.2 Sequence filtering and data quality control

Insertion of paired-end libraries with a size of 400 bp (Table 2) yielded approximately ~ 54.56 GB of raw sequence (Table 4). The percentage of bases with quality score ≥ Q20 was 97.46% and ≥ Q30 was 92.86% (Table 4). Approximately 1.1% of the low-quality data was filtered, and the remaining ~ 53.97 GB was used for downstream analysis (Table 5).

Table 4

Sequencing Data Statistics
Sample	Read Num	Total base	N_rate	GC_Content (%)	Q20_rate	Q30_rate
G. forrestii	361299934	54556290034	0	35.1	97.46	92.86

Table 5

High-Quality Data Statistics
Sample	HQ Reads	HQ Reads (%)	HQ Data (bp)	HQ Data (%)
G. forrestii	358261400	99.16	53967212246	98.92

The GC content is 35.10% (Table 4). The base distribution showed no separation of AT and GC either at the R1 end or the R2 end (Fig. 3), which laid the foundation for the subsequent quantitative analysis. The average sequencing error rates for single-base loci were all less than 0.1% (Fig. 4)

3.3 K-mer analysis and ploidy estimation

K-mer analysis provides an estimate of the genome size based on the substrings of length k contained in the biological sequence. The data volume was multiplied to achieve a coverage of 112x. Besides, this it also indicates low quality or contamination in the sequences. 19-mer frequency analysis identified the 90X depth as the dominant peak based on the k-mer count (Fig. 5). After dividing the k-mer number by 90, the genome size was predicted to be ~ 483 Mb (483,508,243 bp). The other peak at 1/2 the depth of the main peak (45X) is most likely due to heterozygosity (~ 0.54%), While the peak at twice the depth of the main peak (180X) is caused by the repetitive sequence (~ 51.54%).

The results of ploidy assessment yielded 83% AB, 12% AAB, and 5% AABB (Fig. 6), from which G. forrestii was predicted to be diploid, and this ploidy assessment is for reference only.

3.4 De-novo assembly and gene prediction

A total of 360 million paired-end reads were utilized to establish the initial genome assembly of G. forrestii. The genome, assembled using SOAPdenovo software, comprises 339,729 scaffolds (509.9 Mb) with scaffolds exceeding 200 bp chosen to exclude low-quality sequences. The genome size, excluding Ns, accounts for 499,986,768 bp, and the final assembly exhibits a low value of Ns content of roughly 0.01%. The largest scaffold spans 345,990 bp, with N50 = 17,344 bp, N90 = 293 bp (Table 6).

Table 6

Statistics of Contigs and Scaffolds
Assembly Level	Number	Sum (bp)	Longest Seq	N50	N90	N count	Gaps	Software
contig	514298	516660543	179938	6972	280			MEGAHIT
scaffold	339729	509934743	345990	17344	293	6439439	146457	SOAPdenovo

3.5 Analysis of GC content

The resultant scaffolds longer than 200 bases in length were chosen. A window size of 10 kb was used for non-repetitive advancement in the sequence and calculation of the mean depth and GC content of every window to generate a GC depth plot. Most windows displayed GC content ranging from 25–50%, resulting in an estimated GC ratio of ~ 35.16% (Fig. 7). We did not identify any significant regions with abnormal accumulation. This suggests that the DNA samples used for sequencing were not mixed with DNA from other species.

3.6 Identification of SSRs

The assembled scaffolds were searched for the presence of SSR markers by using the MISA software. It yielded 330,999 SSR motifs (Table 7). Among these, mononucleotides were the largest in number (232,225; 70.16%), dinucleotides were the second in number (67,518; 20.40%), which was followed by trinucleotide (23,506; 7.10%), tetranucleotide (5,103; 1.54%), pentanucleotide (1,472; 0.44%), and hexanucleotide (1,175; 0.35%) SSR markers (Fig. 8a).

Table 7

SSR Information
Total number of sequences examined:	339729
Total size of examined sequences (bp):	506426207
Total number of identified SSRs:	330999
Number of SSR containing sequences:	6914
Number of sequences containing more than 1 SSR:	34647
Number of SSRs present in compound formation:	51666

Within mononucleotide repeat sequences, the highest content is represented by A/T (230,708; 99.35%) (Fig. 8b). Among dinucleotide repeat sequences, the predominant composition is AT/AT (48,604; 71.99%), followed by AG/CT (12,133; 17.97%), AC/GT (6,723; 9.96%), and CG/CG (58; 0.09%) (Fig. 8c). Regarding trinucleotide repeat sequences, the principal repetitive motifs include AAT/ATT, AAG/CTT, ATC/ATG, and AAC/GTT, accounting for contents of 16,427 (69.88%), 3,005 (12.78%), 1,337 (5.69%), and 1,062 (4.52%), respectively (Fig. 8d).

3.7 Phylogenetic reconstruction and PCA

Both two nuclear genomic reconstruction suggested that the G. forrestii located at the basal position (Fig. 9ab). However, plastid phylogenomic reconstruction indicated conflict structure. G. floribunda var. gamblei and G. pinnata are clustered at basal position, (Fig. 9c). The Maximum Likelihood (ML) reconstruction of the nuclear genome with G. forrestii as the reference genome, reveals that G. forrestii constitutes a monophyletic lineage in the Clade-A. G. pinata and G. floribunda var. gamblei individuals clustered into mixed clades. In the Clade-B, 2 individuals of G. floribunda var. gamblei (from populations of FL_DTBC and FL_YXG) were found mixed with 6 individuals of G. pinata (from Pi_YXG, Pi_SHZWY and Pi_DTS). In the Clade-C, 2 individuals of G. pinata (from of Pi_YXG) were mixed with 4 individuals of G. floribunda var. gamblei (from Fl_YXG, Fl_ML and Fl_SMC) (Fig. 9a). Phylogenomic reconstruction based on dataset using B. sacra as the reference genome revealed similar patterns mentioned above, with differences lie in the internal topology of the branches (Fig. 9ab). But G. forrestii does not form a monophyletic lineage (Clade-F, Clade-G and Clade-H), mixing free with the other two species (Fig. 9b). The plastid ML tree shows that G. forrestii and G. pinata are mixed with each other in the Clade-D ((Fig. 9c), G. pinata and G. floribunda var. gamblei are mixed in Clade-E (Fig. 9c).

25 individuals clustered into three distinct groups in the PCA (Fig. 10). In the first group, two individuals of G. floribunda var. gamblei (from Fl_DTBC and Fl_YXG) were found mixed within the G. pinata group, whereas within the predominantly G. floribunda var. gamblei group, two individuals of G. pinata (from Pi_YXG) were also mixed.

4.1 Genome Characterization

We observed variations in genome size when measuring it using both k-mer analysis and flow cytometry for the same individual. In this study, for the first time, we conducted genome survey sequencing on G. forrestii and obtained ~ 53.97 GB of clean data. The 19 K-mer analyses showed that the G. forrestii genome was approximately 483.51 Mb, slightly smaller than that by flow cytometry (~ 520.00 Mb) (Fig. 2, 5). We observed similar variations in other published studies and statistically analyzed the differences among these 15 species (Table 8). The genomic size differences obtained by these two methods ranged from 16.00 Mb to 138.09 Mb, with G. forrestii showing a difference of 37 Mb, falling within the range of differences we calculated and being lower than the average difference of 82.93 Mb. Different principles of the two methods determined the differences. Flow cytometry is a technique based on staining undamaged nuclei with a fluorescent dye that adheres quantitatively to the DNA to calculate the amount of DNA. Processes are varying in different laboratories in sample preparation, staining/staining strategies. Random drift of the instruments may lead to significant deviation in genome size estimation [16, 35]. K-mer approach emerged as a computational technique to generate a k-mer frequency distribution (similar to a Poisson distribution) by plotting the coverage distribution of all k-mers in a sequence, where the peak of the distribution would be centered on the average sequencing depth of the genome. The genome size would be better inferred by directly sequencing reads and analyzing the frequency of k-mers [36].

Table 8

Genome Size Difference between Flow Cytometry and K-mer
Species	Genome Size of K-mer	Genome Size of Flow Cytometry	Absolute Value (Low Flow Cytometry - K-mer)	Absolute Value (High Flow Cytometry - K-mer)	Average	Article
Carex cristatella	317.3	255.2 ± 13.1	75.2	49	62.1	Genome Survey Sequencing for the Characterization of the Genetic Background of Rosa roxburghii Tratt and Leaf Ascorbate Metabolism Genes
Carex scoparia	294.5	268.8 ± 9.7	35.4	16	25.7
Juncus effusus	225.5	198.9 ± 4.6	31.2	22	26.6
Juncus inflexus	196	286.4 ± 4.7	85.7	95.1	90.4
Raddia distichophylla	608	589	19		19	The draft genome sequence of herbaceous diploid bamboo Raddia distichophylla
Psammochloa villosa	1564	1503.27 ± 3.41	64.14	57.32	60.73	Estimation of genome size for Psammochloa villosa by flow cytometry and K-mer analysis
Reseda lutea	934	867.7	66.3		66.3	Estimation of Genome Size in the Endemic Species Reseda pentagyna and the Locally Rare Species Reseda lutea Using comparative Analyses of Flow Cytometry and K-Mer Approaches
R. pentagyna	1022	896	126		126
Aspalathus linearis	1070	1.24 ± 0.01	180	160	170	Rooibos (Aspalathus linearis) Genome Size Estimation Using Flow Cytometry and K-Mer Analyses
Acer henryi	561.72	691.12 ± 8.69	120.71	138.09	129.4	Estimation of genome sizes of six Acer species by flow cytometry and K-mer analysis
A. buergerianum	743	863.90 ± 8.69	112.21	129.59	120.9
A. elegantulum	777.87	896.50 ± 4.35	114.28	122.98	118.63
A. griseum	771.51	893.24 ± 8.69	113.04	130.42	121.73
A. pentaphyllum	650.64	766.10 ± 8.69	106.77	124.15	115.46
A. tegmentosum	1103.46	1 154.04 ± 13.04	37.9	63.98	50.94

GC content of G. forrestii is ~ 35.16% (Fig. 7), based on most of the plant genetic data summarized by previous authors, it was found that most of the GC content ranged from 30–47% [37]. This value can serve as an indicator of genome stability, as DNA sequences with higher GC content tend to exhibit tolerance to extreme temperatures and drought [38, 39]. We assessed the genomic GC content of three species at shallow sequencing depths, assembled to the contig level. It was observed that G. forrestii has the lowest GC content (~ 33.60%), G. floribunda var. gamblei has the highest (~ 33.94%), while G. pinnata falls in between (~ 33.81%) (Table 9). This GC content may be due to the species' adaptation to environmental stress [40], and it may be similar. As a report found that the highest GC content was found in gramineous plants [41]. During the Tertiary period's global cooling, Poaceae plants underwent differentiation and adaptation to this stress, consequently establishing their dominance in thriving under today's extreme climatic conditions [42–44]. Two species of G. floribunda var. gamblei and G. pinnata did suffer more interspecific competition from other higher tree species from rainforests and from longer, hotter and drier situations in South Yunnan area.

Table 9

Three Species GC Content
Species	Individual	GC Content (%)
G. pinnata	Pi_BB	33.7
	Pi_BB	33.67
	Pi_BB	33.87
	Pi_YXG	33.59
	Pi_YXG	33.63
	Pi_YXG	34.29
	Pi_SHZWY	33.97
	Pi_DTS	33.5
Average	33.7775
G. floribunda var. gamblei	Fl_SMC	34.52
	Fl_SMC	34.51
	Fl_YXG	33.63
	Fl_ML	33.56
	Fl_DTBC	33.64
Average	33.972
G. forrestii	Fo_XP	33.59
	Fo_DC	33.69
	Fo_DC	33.59
	Fo_LJZ	33.62
	Fo_HN	33.81
	Fo_HN	33.6
	Fo_KYC	33.91
	Fo_KYC	33.7
	Fo_NXH	33.7
	Fo_YJZ	33.52
	Fo_FF	33.57
Average	33.66363636

The individual Fl_YXG of G. floribunda var. gamblei was not included in the calculation.

Plant genomes present the most challenging task for sequencing and assembly due to their high levels of heterozygosity, complex polyploidy, and abundant repeat content [45]. A genome with a heterozygosity exceeding 1% becomes highly challenging for de novo assembly [46]. However, the K-mer analysis in this study indicated a relatively low heterozygosity of approximately 0.54% in the G. forrestii genome (Fig. 6), making de novo assembly in this aspect more accurate. The repeat rate of the genome is 51.54%, This high repeat rate likely due to gene family expansion [47], gene transposition activity [48] and genome recombination [49]. Chromosome-level assembly for G. forrestii is needed to gain deeper insights into its genome and facilitate a comprehensive understanding of speciation and population dynamics of this species.

SSR has consistently been one of the most preferred molecular markers for plant genotyping due to its high level of polymorphism, wide distribution in the majority of plant genomes, user-friendly, and anticipated significance as a valuable tool for numerous species in the future [50–52]. Mononucleotide repeats are the predominant type, in accordance with previous reports [53]. The high abundance of AT/AT (48,604; 71.99%) motifs (Fig. 8c) in our study was consistent with some earlier genomic surveys. As an illustration, Akebia trifoliata exhibits an AT/AT content of 50.21% [54], while Cunninghamia lanceolata (Lamb.) Hook. showcases a content of 59% [55], and Acer truncatum Bunge boasts a significantly higher AT/AT content at 71.31% [56], confirming their representation as the most typical dinucleotide motifs in higher plants.

We investigated several species exited also in warm and hot valleys, Terminalia franchetii Gagnep. [57], Osteomeles schwerinae Schneid. [58], Nouelia insignis Franch. [59], Buddleja crispa Benth. [60] and Excoecaria acerifolia Didr. [61] for a comparison analysis. No raw reads of four species mentioned can be found in the GenBank for genome survey. Our data would provide a first view for evolutionary pattern of this area.

4.2 Nuclear-Cytoplasmic Conflict

In this study, the topography of the basal clades is different among phylogenomic trees. The G. forrestii referred tree indicates that G. forrestii is monophyletic, while the B. sacra referred tree suggests polyphyletic. For if we used a reference genome from other genus could have overestimated more ancient characters in the dataset. Consequently, a significantly higher number of SNP loci were generated through the alignment, three times more than those produced in the latter (Table 10). The disparity in SNP loci counts results in the observed differences in the topological structures of the two nuclear gene phylogenetic trees [62].

Table 10

SNP Calling Information
Reference	Number of Individual	Number of SNP Sites	VCF Size (Mb)
G. forrestii	26	244936	43
B. scara	26	79691	15.5

The topological structure distinctly shows a significant increase in the branch length of Fo_HN relative to other taxonomic units, designating it as the earliest diverging species. This could indicate a result of long-branch attraction, explaining the anomalous position in the phylogenetic tree [63, 64]. The basal clade of the chloroplast genome phylogenetic tree is composed of G. pinnata and G. floribunda var. gamblei, showing a nuclear-cytoplasmic conflict with the two nuclear gene trees. Hybridization could properly explain [65, 66]. The rapid elevation of the QTP at the beginning of the Paleogene, and the formation and intensification of the monsoon climate zone brought abundant precipitation leading to river erosion, and that the rapid downcutting and uptake events led to changes in the spatial pattern of the PRR drainage system, which in turn affected the geographic distribution pattern of plants [1, 2, 67, 68]. Their sympatric distribution in river valleys may produce interspecific hybridization as otherwise disconnected river systems are linked together, making it easier for species to migrate and spread along river valleys. A previous report on the study of three river valley species of the genus Ostryopsis Decne indicates that the inconsistent interspecific relationship of Ostryopsis intermedia B. Tian & J. Q. Liu with the other two species (Ostryopsis davidiana Decaisne and Ostryopsis nobilis I. B. Balfour et W. W. Smith) recovered from two different sets of molecular markers strongly indicates its origin through hybrid speciation in the southeast Qinghai-Tibet Plateau through hybrid speciation [69]. To address this issue in the Garuga genus, we plan to conduct further Hi-C or Hi-Fi sequencing in the future, studying at the chromosomal level to acquire additional genetic information. By employing sophisticated models, we aim to consider this situation comprehensively and reconstruct the evolutionary history between species more accurately.

This study conducted a preliminary investigation into the genome size and characteristics of G. forrestii for the first time and reconstructed the phylogenetic relationships of three species within the Garuga genus, namely G. floribunda var. gamblei, G. pinnata, and G. forrestii. It provided valuable genomic resources for the further exploration and utilization of G. forrestii and offered initial insights into the phylogenetic relationships within the Garuga genus. However, there is further work to be done as the construction of two nuclear gene phylogenetic trees for these three species resulted in differences in the topological structure at the base clade (G. forrestii) with one being monophyletic and the other polyphyletic. Additionally, conflicts arose between the two nuclear gene phylogenetic trees and the chloroplast genome phylogenetic tree (where the base clade in the nuclear gene trees is G. forrestii, while in the chloroplast genome tree, it includes G. floribunda var. gamblei, G. pinnata, and G. forrestii). To clearly explain the differences in the topology of nuclear trees and the nuclear-cytoplasmic conflict with the chloroplast tree, we plan to supplement the study with Hi-C or Hi-Fi sequencing at the chromosomal level in future whole-genome sequencing. This will lead to better assembly results, and we will use complex models to study the aforementioned issues.

The genetic data presented in this study holds significant value for comprehensive whole-genome analyses, the evaluation of population genetic diversity, investigations into adaptive evolution, the advancement of artificial breeding efforts, and the support of species conservation and restoration initiatives. Ultimately, this research contributes to reinforcing the conservation and management of natural ecosystems, promoting biodiversity conservation, and advancing sustainable development.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

A raw sequence data of Garuga floribunda var. gamblei were deposited in NCBI under the BioProject ID: PRJNA783803. The other raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA014668, CRA014684, CRA014694, CRA014677) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. The whole genome sequence data reported in this paper have been deposited in the Genome Warehouse in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, under accession number GWHERCJ00000000 that is publicly accessible at https://ngdc.cncb.ac.cn/gwh. The chloroplast assembly and annotation data for this study are deposited in NCBI with accession numbers PP337695-PP337718.

Competing interests

The authors declare that they have no competing interests

Funding

This research was funded by the National Natural Science Foundation of China (grant No. 42161015).

Authors’ contribution

LLY and HS conceived the study. CMM collected plant materials and drew the figure. RC organized the plant materials. RR performed data analysis for SNP calling and generated plots. DBZ carried out genome assembly, data analysis, figure generation, and contributed to the paper writing. DBZ and RR contribute equally in this study. All authors read and approved the final manuscript.

Authors’ information

(1) Authors and Affiliations

Southwest Forestry University, Kunming, PR, 650224, China

Dongbo Zhu ([email protected])

Southwest Forestry University, Kunming, PR, 650224, China

Rui Rao ([email protected])

Technology Center of Kunming Customs, Kunming, PR, 650228, China

Yu Du ([email protected])

Southwest Forestry University, Kunming, PR, 650224, China

Chunmin Mao ([email protected])

Southwest Forestry University, Kunming, PR, 650224, China

Rong Chen ([email protected])

Kunming Institute of Botony, Chinese Academy of Science, Kunming, PR, 650201, China

Hang Sun ([email protected])

Yunnan Key Laboratory of Plateau Wetland Conservation, Restoration and Ecological Services, Southwest Forestry University, Kunming, PR, 650224, China. National Plateau Wetland Research Center, Kunming, PR, 650224, China

Liang-Liang Yue ([email protected])

(2) Contributions

LLY and HS conceived the study. CMM collected plant materials and drew the figure. RC organized the plant materials. RR performed data analysis for SNP calling and generated plots. DBZ carried out genome assembly, data analysis, figure generation, and contributed to the paper writing. DBZ and RR contribute equally in this study. All authors read and approved the final manuscript.

(3) Corresponding authors

Correspondence to Liangliang Yue and Hang Sun

(4) Acknowledgements

We appreciate the assistance of Yanchun Liu from the Shanghai Botanical Garden and Jinlong Dong from the Xishuangbanna Tropical Botanical Garden for their support in sample collection for this study.

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No competing interests reported.

Genome Survey Indicated Complex Evolutionary History of Garuga Roxb. Species

Status:

Journal Publication

Version 1

Abstract

Background

Results

Conclusion

Figures

1 Background

2 Materials and methods

2.1 Sample collection and DNA extractions

2.2 Genome size estimation by flow cytometry

2.3 Genome Sequencing and Data Quality Control

2.4 High-quality data acquisition

2.5 Ploidy analysis and k-mer analysis

2.6 De novo assembly and GC content analysis

2.7 Identification of microsatellite motifs

2.8 Population resequencing variation detection and filtering of SNP data

2.9 Phylogenetic reconstruction and PCA

3 Results

3.1 Genome size estimation by flow cytometry

3.2 Sequence filtering and data quality control

3.3 K-mer analysis and ploidy estimation

3.4 De-novo assembly and gene prediction

3.5 Analysis of GC content

3.6 Identification of SSRs

3.7 Phylogenetic reconstruction and PCA

4 Discussion

4.1 Genome Characterization

4.2 Nuclear-Cytoplasmic Conflict

5 Conclusion

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors’ contribution

Authors’ information

References

Additional Declarations

Status:

Journal Publication

Version 1