Skip to main content

A chromosome-level genome of Semiothisa cinerearia provides insights into its genome evolution and control

Abstract

Background

Semiothisa cinerearia belongs to Geometridae, which is one of the most species-rich families of lepidopteran insects. It is also one of the most economically significant pests of the Chinese scholar tree (Sophora japonica L.), which is an important urban greenbelt trees in China due to its high ornamental value. A genome assembly of S. cinerearia would facilitate study of the control and evolution of this species.

Results

We present a reference genome for S. cinerearia; the size of the genome was ~ 580.89 Mb, and it contained 31 chromosomes. Approximately 43.52% of the sequences in the genome were repeat sequences, and 21,377 protein-coding genes were predicted. Some important gene families involved in the detoxification of pesticides (P450) have expanded in S. cinerearia. Cytochrome P450 gene family members play key roles in mediating relationships between plants and insects, and they are important in plant secondary metabolite detoxification and host-plant selection. Using comparative analysis methods, we find positively selected gene, Sox15 and TipE, which may play important roles during the larval-pupal metamorphosis development of S. cinerearia.

Conclusion

This assembly provides a new genomic resource that will aid future comparative genomic studies of Geometridae species and facilitate future evolutionary studies on the S. cinerearia.

Peer Review reports

Background

Semiothisa cinerearia belongs to one of the most species-rich families in Lepidoptera, the Geometridae, which contains approximately 21,500 species [1]. Geometridae comprises 9 subfamilies [2] and have a global distribution (with the exception of the polar regions). In addition, many of them are considered as pests. Among the major groups of genes help Geometridae adapting to various environments, Cytochrome P450 and Hsp (heat shock protein) genes are most important. These genes involved in plant secondary metabolite detoxification and tolerance to heat stress might be related to the invasiveness of some insects [3,4,5]. Studying the P450 and HSP gene repertoire in Geometridae will help us understand pests and control them [6,7,8,9,10]. One of most economically significant in Geometridae is S. cinerearia, which is the pests of important urban greenbelt trees Chinese scholar tree (Sophora japonica L.) [11]. Many researchers are working on the control of S. cinerearia in recent years [12, 13]. Pesticides are key for the control of it; however, the use of pesticides has had deleterious effects on urban ecosystems and its natural enemies. Although the ecology of S. cinerearia is well studied, genomic data are still lacking, and this has impeded further improvements of control.

Here, we used a hybrid sequencing approach combining Illumina short reads, Nanopore long reads, and Hi-C scaffolding to generate a chromosome-level genome assembly for S. cinerearia. Using Hi-C scaffolding, we assigned 97.64% of the contigs to 31 chromosomes. This genome assembly fills a major taxonomic gap in Geometridae comparative genomics and provides valuable clues for pest control. In this study, we explore the evolution of the cytochrome P450 and HSP gene families in related species, as well as several important genes that might be involved in pupal development in Geometridae species. Two positively selected genes, Sox15 and TipE, may play important roles during the larval-pupal metamorphosis development of S. cinerearia. A complete genome will advance our understanding of the molecular mechanisms underlying processes of tolerance to insecticides and other abiotic stresses, and accelerate studies on S. cinerearia development, which will facilitate the control of S. cinerearia as a natural enemy.

Results and discussion

Genome sequencing and de novo assembly

A total of 48.34 Gb data of Illumina paired-end reads (coverage: 83.22 ×) were obtained for the genome survey, genome assembly, and other related analyses (Table S1). The size of the S. cinerearia genome estimated using 17-mer analyses was ~ 667.93 Mb (Figure S1). The primary genome assembly for S. cinerearia was performed using the 40.45 Gb Oxford Nanopore long reads (coverage: 69.64 × , Table S2) and further polished using Illumina paired-end reads. A draft genome assembly of 580.85 Mb was obtained, which yielded 450 contigs with a contig N50 of 4.15 Mb (Table S3). A total of 50.54 Gb Hi-C reads (coverage: 87.01 ×) were used to orient and anchor 450 contigs to 31 chromosomes (Fig. 1A, Tables S3 and S4). The Hi-C linking information indicated that more than 97.64% of the assembled bases were anchored to the chromosomes (Table S5). The N50 results suggest that the assembly is highly contiguous.

Fig. 1
figure 1

Chromosome-level assembly of Semiothisa cinerearia. A The genome‐wide Hi‐C interaction maps of 31 chromosomes in the S. cinerearia genome. Calculated interaction frequency distribution of Hi-C links between and within chromosomes. B Circos graph of characteristics of the S. cinerearia genome. From the outer ring to inner circle: marker distribution on 31 chromosomes at the megabase scale (I), gene distribution (II), tandem repeat (TRP) (III), long terminal repeat (LTR) (IV), long interspersed nuclear element (LINE) (V), short interspersed nuclear element (SINE) (VI), DNA elements (VII), and guanine-cytosine (GC) content (VIII)

For evaluating assembly quality, all RNA-seq reads were assembled into transcripts (Tables S6 and S7). A total of 98.40% of the assembled transcripts (58,742 of 59,695) could be mapped to the assembled genome (Table S8), indicating its high completeness. A BUSCO assessment showed that 94.70% of Lepidoptera core genes were successfully detected in the assembled genome (Table S9). Synteny analyses between S. cinerearia and Operophtera brumata revealed only 2 chromosomes (chromosome 12 and 14 in S. cinerearia) showing strong synteny, most of the chromosomes have no clear patterns of orthology (Figure S2), highlighting a high degree of genome structure evolution in Geometridae. The results of these approaches indicated that the genome assembly was complete and suitable for subsequent analysis. The assembled genome was also compared with the genomes of other related species (Table S10). This genome is the second chromosome-level genome assembly in Geometridae. Compared with other related species, the N50 of S. cinerearia (19.57 Mb) is in the same order of magnitude to that of other chromosome-level Lepidopteran assemblies (65.63 Kb-27.09 Mb). Additionally, the BUSCO complement of S. cinerearia (96.5%), shows comparable gene-completeness to that of other high quality Lepidopteran assemblies (from 78.8% to 99.2%) (Table S10). These results showed that the assembled S. cinerearia genome had a high level of continuity and completeness.

Genome annotation

A combined structure- and homology-based analysis identified a total of 252.83 Mb repetitive sequences, accounting for 43.52% of the total S. cinerearia genome (Table S11). Among these repeats, DNA elements are the most abundant (7.62%), followed by long terminal repeats (LTRs) (6.46%), Short interspersed nuclear elements (SINEs) (2.46%), and long interspersed nuclear elements (LINEs) (2.26%) (Table S12). The GC content, gene density, and the distribution of all TEs of S. cinerearia are shown in Fig. 1B. We also assessed the types of TEs and the TEs insertion times of the 12 studied species (S. cinerearia, O. brumata, Papilio bianor, Cnaphalocrocis medinalis, Hyposmocoma kahamanoa, Spodoptera frugiperda, Galleria mellonella, Amyelois transitella, Bombyx mori, Manduca sexta, Trichoplusia ni, and Antheraea pernyi) and found that the TEs insertion events in Geometridae (S. cinerearia and O. brumata) occurred 5–10 Mya. The TEs insertion times of all Geometridae are much more recent than the divergence between them, which suggests that TEs insertion events might have occurred after their divergence (Fig. 2). The DNA elements proportion was much higher in S. cinerearia (19.76%) compared with that in other insects (from 5.51% to 17.24%) (Figure S3). However, the DNA elements proportion of another insect in Geometridae (O. brumata) was only 7.16%, which indicates that the DNA elements proportion in different species of Geometridae are quite different. In addition, the other TEs types in S. cinerearia and O. brumata are also different. The proportion of LINE, LTR, and SINE elements in S. cinerearia are 16.75%, 5.85%, and 6.38%. However, the proportion of LINE, LTR, and SINE elements in O. brumata are 25.39%, 0.92%, and 17.39%. These differences in the proportion of TEs types in the family suggest that changes are not conserved in Geometridae and might be specific to the S. cinerearia lineage.

Fig. 2
figure 2

Comparison of TEs insertion history among species. The x-axis indicates the inferred insertion time (unit: million years ago) of TEs. The y-axis indicates the length of different TEs elements

To annotate the genes in the draft S. cinerearia genome, we integrated ab initio, homology‐, and transcript‐based gene identification approaches. A total of 21,377 protein-coding genes were detected in the S. cinerearia genome. The mRNA length, CDS length, exon length, and exon number distribution of S. cinerearia are similar to other Lepidopteran species (Figure S4). The quality of the protein-coding gene annotations was comparable to that in previous studies of other species. Among all predicted genes, functional annotations based on the NCBI Nr databases [14], TrEMBL [15], InterProScan [16], GO (gene ontology: http://geneontology.org/), COG (https://www.ncbi.nlm.nih.gov/COG/), Swiss-Prot (www.uniprot.org), and Kyoto Encyclopedia of Genes and Genomes [17,18,19] could be assigned to 86.41% (18,472) of the 21,377 genes (Table S13). The functional annotation results implied that most of the protein-coding genes in S. cinerearia can find homolog genes in public database. Overall, these findings indicate the high accuracy and completeness of the predicted gene models.

Genome evolution

To evaluate the evolutionary conservation of the S. cinerearia genome relative to other insect species, we compared reported gene sets of 12 insect species. All the 1,149 single-copy orthologs were aligned to build a super-sequence and construct a phylogenetic tree. Phylogenetic inference confirmed that S. cinerearia and O. brumata were sister group to Noctuidae, which supported the grouping of Glossata. The Geometridae lineage was estimated to diverge from the Noctuidae lineage ~ 80.6 Mya (confidence intervals: 63.0–97.5 Mya) according to MCMCTree (Fig. 3A; Figure S5). A total of 17,884 gene family clusters were constructed, of which 2,772 belong to unique gene families (Fig. 3B).

Fig. 3
figure 3

Orthology and evolutionary analysis among species. A Species phylogenetic tree and gene family expansion/contraction. The phylogenetic relationships of S. cinerearia with other insects were characterized using a maximum likelihood analysis of a concatenation of single‐copy orthologous protein sequences and 100 bootstrap replicates. The numbers of gene families that underwent expansion (red) or contraction (green) are shown on branches with predicted species divergence times plotted at each node. B Orthologous genes distribution. The bars are subdivided to indicate different orthologous relationships. Single-copy and multiple-copy orthologs are families in which each species has only one or more than one copy, respectively. Other orthologs are genes with matches with other species that could not be placed into other ortholog categories. Unclustered genes are genes in each species that could not be associated with the gene predictions in any of the other lineages. Unique paralogs show species-specific families. C Relative evolutionary rates of these species. The analysis was performed using single-copy protein-coding genes with S. cinerearia as the reference species and P. bianor as the outgroup species. The x-axis indicates the relative evolutionary rates of the species

Based on the previous results, the gene family expand analysis was performed by CAFÉ (v4.1) using default parameters. Gene family expansion often associated with the adaptive divergence of closely related species. To investigate the key genomic changes in S. cinerearia associated with adaptation, expanded gene families in S. cinerearia were identified. We detected 307 gene families that have undergone expansions, and GO enrichment analysis of these expanded gene families suggests that most of these genes are involved in metabolic process and biosynthetic process (Fig. 3A, Table S14). The results of KEGG enrichment analysis were consistent with these findings (Table S15). Interestingly, both GO and KEGG enrichment analysis indicated these genes involved in biosynthesis of cutin (map00073, p-value = 0.000441887) and structural constituent of cuticle (GO: 0,042,302, P-value = 1.18E-08) have expanded in S. cinerearia. These results suggests the cutin biosynthesis pathways in S. cinerearia is different from other inserts and more genes are involved in biosynthesis of cutin. It is generally known that the cuticle is the exoskeleton of insect, and also considered as an adaptable tissue that can determine the physiological development and defend against different environmental stresses, especially for dehydration, parasites, and pesticides [20, 21]. As the outer waterproofing materials, cuticular hydrocarbons and lipids are involved in contributing to the penetration insecticide resistance [22]. The insect cuticle composed primarily of chitin, proteins, catecholamines, lipids and its biosynthesis pathways is quite complex. The cutin biosynthesis genes in S. cinerearia were not reported before, however, the gene number variation in different insect were reported [20, 23,24,25,26]. The cutin biosynthesis genes in different insects may help them adapt to different environments stresses, the expanded cutin biosynthesis genes may also help S. cinerearia adapt to its environments stresses.

Rate of molecular evolution

We found that the branch length of S. cinerearia was longer than that of other insect species (Figure S6), suggesting that the rate of protein evolution is higher in S. cinerearia compared with other insect species analyzed in this study. Relative rate tests and two cluster analysis confirmed that the rate of protein evolution of S. cinerearia was significantly higher compared with that of the other insect species (Tables S16 and S17). The fastest evolutionary rate was observed in Geometridae (S. cinerearia and O. brumata); however, Noctuidae (S. frugiperda and T. ni) and Sphingidae (M. sexta) have the slowest evolution rate (Fig. 3C). The molecular mechanisms of the fast molecular evolutionary rate in Geometridae remain unclear. Variation in the rate of molecular evolution is manifest at different species due to variation mutation rate and substitution rate [27]. These two reasons may cause the fast molecular evolutionary rate of Geometridae species and more genome data are needed for further check.

Positively selected genes in Geometridae

We identified 34 positively selected genes (PSGs) in the Geometridae lineage, and these genes are most related to some biosynthesis and metabolic pathway (Table S18). Interestingly, TM-A2B (Larval cuticle protein A2B), a component of the cuticle, was undergoing positive selection. This result is consistent with expanded genes in S. cinerearia, the genes involved in biosynthesis of cutin not only expanded but also under positively selection in Geometridae. Chitinous structures are physiologically fundamental in insects, and are target sites for the development of new insect-pest-control strategies [28,29,30]. Further studying on this gene may help us develop new means of pest control. We also identified the two PSGs Sox15 (SoxF) and TipE, which each had two positively selected sites (position 229 and 452, Fig. 4A; position 57 and 150, Fig. 4B, respectively). The Sox15 gene has been shown to be involved in wing disc development in Drosophila melanogaster [31]. This gene might play an important role in wing disc development of larval-pupal metamorphosis. For TipE gene, the previous studies proved that TipE is functionally related to sodium channels [32], and sodium channels can affect spike shape and dendrite growth during postembryonic maturation [33]. Therefore, TipE gene might play a key role in pupal development of Geometridae species. S. cinerearia is a holometabolous insect, and these two genes may related to the larval-pupal metamorphosis development. More importantly, many studies showed that the larval-pupal metamorphosis is a very complex process, some key genes involved in larval-pupal metamorphosis could be used as targets for pest management [34,35,36,37]. These genes may become an important breakthrough for pest control of S. cinerearia.

Fig. 4
figure 4

Positively selected sites of sox15 and tipE in various insect species. A Positively selected sites in sox15 gene. The left panel is the phylogenetic tree of these species, and the right panel is the amino acids sequences of sox15 gene. B Positively selected sites in tipE gene. The left panel is the phylogenetic tree of these species, and the right panel is the amino acids sequences of tipE gene

Cytochrome P450

To facilitate studies of insecticide resistance, cytochrome P450 monooxygenase (P450s), which are major detoxification enzymes, were annotated in all these 12 species. The cytochrome P450 gene family is involved in host-plant adaptation. P450 enzymes, which are a large family in insect genomes, are involved in the detoxification of plant toxins and play a key role in insecticide resistance [38]. The number of P450 genes in S. cinerearia is 181, which is higher compared with the number of P450 genes in other studied Lepidopterans (81–179). The number of P450 genes of S. cinerearia (181) is higher than that in other Lepidopterans species, the number of P450 genes in O. brumata (117) is closed to other Lepidopterans species (Fig. 5), which showed the representative for a specific detoxification gene repertoire in S. cinerearia. The study of O. brumata genome data also showed the similar results [39].

Fig. 5
figure 5

Number of cytochrome P450 gene, HSP70, HSP90, and sHSP genes in various insect species. The left panel is the phylogenetic tree of these species, the right panel is the gene number of P450, HSP70, HSP90, and sHSP genes

Heat shock proteins

Hsps represent a supergene family and can be usually divided into several families, including Hsp90, Hsp70, Hsp60, Hsp40 and small heat shock proteins (sHsps, molecular weights ranging from 12 to 43 kDa) [40, 41]. They are important molecular chaperones that are involved in thermal adaptation and resistance to some proteotoxic stresses [42]. More importantly, the altered expression of Hsps was accompanied with larva-to-adult survival reduction [43]. The number of HSP70 gene in Geometridae are 13 (O. brumata) and 14 (S. cinerearia), which is close to other species (from 7 to 30). The gene number of HSP90 in O. brumata and S. cinerearia are 3 and 7, respectively. The gene number of sHSP in O. brumata and S. cinerearia are 14 and 12, respectively. Overall, the number of HSP genes was not higher in Geometridae compared with that in other insects (Fig. 5), suggesting that HSP genes have not expanded. The lack of expansion of HSP genes indicating that the P450 genes, not the HSP genes, may helpful for the invasiveness and development of Geometridae. These results are consistent with the previous study that P450 gene family expanded in another Geometridae species, O. brumata [39].

Conclusions

In this study, we sequenced and assembled the chromosome-level genome of S. cinerearia using Illumina sequencing, Nanopore sequencing, and Hi-C technology. The high-quality genome assembly of S. cinerearia will facilitate future genome-level investigations of various aspects of the biology of this pest as well as comparative evolutionary studies of S. cinerearia and other Geometridae insects. This important genomic resource data thus makes a major contribution to invasion biology research and the study of geometrid moths.

The size of the S. cinerearia assembly was approximately 580.89 Mb; it contained 31 chromosomes, and the N50 was ~ 19.57 Mb. Various analyses indicated that the completeness of the genome assembly was high and thus that it was suitable for subsequent analysis. Based on this assembly, 252,828,304 bp repeat sequences (43.52%) and 21,377 protein-coding genes were identified. Divergence time analysis showed that S. cinerearia diverged from the common ancestor with O. brumata at 71.6 Mya (confidence intervals: 54.7–88.1 Mya). The structural constituents of cuticle gene families have undergone a significant expansion in the S. cinerearia genome. In addition, some important gene families involved in the detoxification of pesticides (P450) have expanded in S. cinerearia. Using comparative analysis methods, we found that two positively selected genes sox15 and TipE. Sox15 gene might play an important roles in wing disc development of larval-pupal metamorphosis development stage. TipE gene might play a key role in pupal development of Geometridae species. Due to the essential larval-pupal metamorphosis development stage in S. cinerearia, these 2 genes are important for its development and are critical for its control. We studied and identified the important genes in S. cinerearia and argue its link for its development, providing a reference for future studies in S. cinerearia for its control.

Methods

Sampling and sequencing

The pupae of S. cinerearia was collected in Haiyang County, Yantai City, Shandong Province, China, in June 2021 from soil under a Chinese scholar tree (Sophora japonica L.). To avoid genome contamination from other organisms, such as microbes, S. cinerearia was rinsed with distilled water for 2 min. Genome DNA extraction was performed using a Blood & Cell Culture DNA mini kit (Qiagen, Germany). Total RNA was extracted using a TRIzol kit (Life Technologies, USA). Extracted DNA that met quality and quantity standards was split into three aliquots, which were used to construct an Oxford Nanopore (PromethION) library, an Illumina NovaSeq 6000 library, and a Hi-C library (Illumina NovaSeq 6000 platform). The RNA sequencing library was constructed and sequenced on the Illumina NovaSeq 6000 platform.

Genome assembly and validation

For Illumina paired-end sequenced raw reads, including the genomic short-insert reads, Hi-C sequencing reads, and RNA-seq reads, adaptors and low-quality reads were removed by FASTX_Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/commandline.html#fastq_quality_filter_usage). For Nanopore reads, all reads with an average quality ≥ 7 were retained for genome raw assembly using a Perl script (https://ftp.cngb.org/pub/gigadb/pub/10.5524/102001_103000/102210/filter_ONT_data_for_7_with_auto_check_quality_position_wzk.pl). The genome size of the S. cinerearia genome was estimated by filtered Illumina reads, using 17-mer analysis. The genome size was estimated using the total number of 17-mers divided by G = K-num/K-depth (where K-num is the total number of 17-mers, K-depth denotes the k-mer depth and G represents the genome size) [44].

The filtered Nanopore long reads were used for de novo genome assembly with NextDenovo (V2.4, https://github.com/Nextomics/NextDenovo) with default parameters. To improve genome quality, the Illumina short-insert reads were used to polish the genome using NextPolish (v1.4.0) with default parameters. All Hi-C reads were used for chromosome construction. 3D DNA (v180419, https://github.com/aidenlab/3d-dna) was used to cluster, order, and orient the contigs into pseudo-chromosome sequences.

The quality of the reference genome sequence was also evaluated using BUSCO software (V5.2.2) [45] with the core gene set of the eukaryote, metazoan and lepidoptera databases, respectively. Illumina reads were mapped to the genome using BWA software (v0.7.12) [46] and the assembled transcripts were mapped to the genome using BLAT software [47]. Genome synteny between S. cinerearia and O. brumata was investigated using LAST software (version 802) with default parameters [48] and plotted using Circos (v0.69–6) software [49].

Repetitive elements annotation

A de novo repeat database was first built using RepeatModeler (v-1.0.11) (http://www.repeatmasker.org/RepeatModeler/), and RepeatMasker (v-4.1.0) was applied to produce a homolog-based repeat library with default parameters. After combining the de novo repeat database and homolog-based repeats in Repbase, comprehensive repeat and TE detection was conducted using RepeatMasker (v1.323) [50] and RepeatProteinMask (v1.36). Tandem repeats in the genome were analyzed using Tandem Repeat Finder (v4.09) (v4.09) [51] with default parameters. The divergence level (Kimura) of a repeat copy to its consensus sequence was estimated using RepeatMasker, and the insertion time (T) of repeats was estimated using the formula T = k/2r using custom Perl scripts (https://github.com/4ureliek/Parsing-RepeatMasker-Outputs) [52].

Protein-coding gene annotation

Protein-coding genes of S. cinerearia were predicted using ab initio prediction, as well as homology‐ and transcript‐based approaches. For ab initio prediction, Augustus (v3.3), SNAP (release: 2006–07-28), and Genescan were used for ab initio prediction. For homology-based prediction, the protein sequences of Operophtera brumata (https://www.bioinformatics.nl/wintermoth/portal/data), Papilio bianor (http://gigadb.org/dataset/view/id/100653/File_page), Cnaphalocrocis medinalis (http://www.insect-genome.com/Cmed/), Hyposmocoma kahamanoa (GCA_003589595.1), Spodoptera frugiperda (GCF_011064685.1), Galleria mellonella (GCF_003640425.2), Amyelois transitella (GCF_001186105.1), Bombyx mori (GCF_014905235.1), Manduca sexta (GCF_014839805.1), Trichoplusia ni (GCF_003590095.1), and Antheraea pernyi (https://ngdc.cncb.ac.cn/search/?dbId=gwh&q=Antheraea+pernyi) were downloaded from NCBI and other related public databases. Protein sequences were searched in the S. cinerearia genome using blastp in BLAST (v2.2.28) [53], and then identified using GeneWise (v2.2.0) [54]. For transcript-based annotation, the assembled transcripts were mapped to the genome for gene structure prediction using PASA (v2.1). Lastly, EVM (v1.1.1) was used to integrate the predicted genes and generate a consensus gene set.

To assign functions to the newly annotated genes in the S. cinerearia genome, all gene sequences were searched using BLAST (v2.2.28) [53] with an e-value threshold of 1e−5 against the SwissProt, NCBI nonredundant amino acid sequences (NR), Gene Ontology (GO) [55], Translated EMBL-Bank (Trembl), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases [17,18,19]. We also annotated motifs and domains using Interproscan (version 5.27) [56] with publicly available databases, including Gene3D, PRINTS, Pfam, CDD, SMART, MobiDBLite, and PROSITE.

Phylogenetic tree construction and divergence time estimation

Protein sequence data sets from 12 species (H. kahamanoa (GCA_003589595.1), C. medinalis (http://www.insect-genome.com/Cmed/), A. transitella, B. mori, P. bianor, O. brumata, A. pernyi, T. ni, G. mellonella, S. frugiperda, S. cinerearia, and M. sexta) were downloaded from NCBI and other databases. Redundant alternative splicing events were filtered to generate a single transcript for each protein set and aligned pair-wise to identify conserved orthologues using Blastp (E-value ≤ 1 × 10 − 5). The Blastp results were used in Orthmcl (v2.0.9) [57] to cluster gene families; orthologous single-copy genes were aligned using MUSCLE (v3.8.31) [58] with default parameters. Phylogenetic reconstruction of the 12 species was performed with RAxML (v8.2.10) [59] using this super-sequence and P. bianor as an outgroup species. Species divergence times were estimated using the MCMCTree program in PAML (v4.9) [60] based on 4d sites extracted using in-house scripts. The fossil records of these species were downloaded from TIMETREE website (http://www.timetree.org) for calibration in this step.

Analysis of gene family expansion and contraction

Following gene family clustering and divergence estimation, the expansion and contraction of gene families were analyzed using CAFÉ (v4.1) with a probabilistic graphical model (PGM) to calculate the probability of transition in each gene family from parent to child nodes in the phylogeny. The expanded genes families in S. cinerearia were subjected to GO and KEGG enrichment analysis. GO enrichment analysis was conducted using the EnrichGO package in R (3.2.5). An R script was used for KEGG enrichment analysis [61, 62].

Analysis on molecular evolution rate

The aligned protein sequences of the single-copy genes were used to calculate relative evolutionary rates via two methods: Tajima’s relative rate test and two cluster analysis. MEGA software (v10) [63] was used for Tajima’s relative rate test. P. bianor was used as the outgroup species, and the relative evolutionary rates between S. cinerearia and other species were calculated. A Chi-square test was used to identify species with faster evolutionary rates. Two cluster analysis was conducted in LINTRE software [64] using the TPCV model. We also specified P. bianor as the outgroup species and determined the relative evolutionary rates between S. cinerearia and other species.

Positive selection analysis

Single-copy genes identified by Orthomcl software among the 12 species were extracted and aligned using MUSCLE software (v3.8.31) [58] with default parameters to identify potential positively selected genes (PSGs), using branch model in the Codeml tool of the PAML package (v4.8) [65]. At first, the rate ratio (ω) of nonsynonymous to synonymous nucleotide substitutions was estimated. The one-ratio model was used to detect average ω across the species tree (ω0). For each gene, the two-ratio branch model was used to detect the ω of the appointed branch (Geometridae) to test the (ω1) and ω of all other branches (ω_background). A likelihood ratio test was performed to compare the fit of the two-ratio models with the one ratio model to determine whether the gene was positively selected in the appointed branch (ω1 > ω0; ω1 > ω_background; ω1 > 1; P-value < 0.05).

Availability of data and materials

The raw genome, transcriptome and Hi-C data in this study have been deposited with links to BioProject accession number PRJNA830997 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA830997). The assembled genome was deposited in the Genome Warehouse in National Genomics Data Center, under accession number GWHBJXE00000000 that is publicly accessible at https://ngdc.cncb.ac.cn/gwh/Assembly/26019/show.

Abbreviations

BUSCO:

Benchmarking Universal Single-Copy Orthologs

TEs:

Transposable elements

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

Mya:

Million years ago

NR:

Non-redundant protein

BWA:

Burrows-Wheeler aligner

PSGs:

Positively selected genes

References

  1. Zahiri R, Kitching IJ, Lafontaine JD, Mutanen M, Kaila L, Holloway JD, Wahlberg N. A new molecular phylogeny offers hope for a stable family level classification of the Noctuoidea (Lepidoptera). Zoolog Scr. 2011;40(2):158–73.

    Article  Google Scholar 

  2. Holloway JD. The moths of Borneo: Family geometridae, subfamilies sterrhinae and larentiinae. Malaynatj. 1997;51:1–242.

    Google Scholar 

  3. Chen X, Xiao D, Du X, Guo X, Zhang F, Desneux N, Zang L, Wang S. The Role of the Dopamine Melanin Pathway in the Ontogeny of Elytral Melanization in Harmonia axyridis. Front Physiol. 2019;10:1066.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Zhang L, Li S, Luo J, Du P, Wu L, Li Y, Zhu X, Wang L, Zhang S, Cui J. Chromosome-level genome assembly of the predator Propylea japonica to understand its tolerance to insecticides and high temperatures. Mol Ecol Resour. 2020;20(1):292–307.

    Article  CAS  PubMed  Google Scholar 

  5. Chen M, Mei Y, Chen X, Chen X, Xiao D, He K, Li Q, Wu M, Wang S, Zhang F, et al. A chromosome-level assembly of the harlequin ladybird Harmonia axyridis as a genomic resource to study beetle and invasion biology. Mol Ecol Resour. 2021;21(4):1318–32.

    Article  CAS  PubMed  Google Scholar 

  6. Yang T, Li T, Feng X, Li M, Liu S, Liu N. Multiple cytochrome P450 genes: conferring high levels of permethrin resistance in mosquitoes, Culex quinquefasciatus. Sci Rep. 2021;11(1):9041.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lu K, Song Y, Zeng R. The role of cytochrome P450-mediated detoxification in insect adaptation to xenobiotics. Curr Opin Insect Sci. 2021;43:103–7.

    Article  PubMed  Google Scholar 

  8. Fotoukkiaii SM, Wybouw N, Kurlovs AH, Tsakireli D, Pergantis SA, Clark RM, Vontas J, Leeuwen TV. High-resolution genetic mapping reveals cis-regulatory and copy number variation in loci associated with cytochrome P450-mediated detoxification in a generalist arthropod pest. Cold Spring Harbor Lab. 2021;17(6):e1009422.

    CAS  Google Scholar 

  9. Xu Y, Shi F, Li Y, Zong S, Tao J. Genome-wide identification and expression analysis of the Hsp gene superfamily in Asian long-horned beetle (Anoplophora glabripennis). Int J Biol Macromol. 2022;200:583–92.

    Article  CAS  PubMed  Google Scholar 

  10. Jiang F, Chang G, Li Z, Abouzaid M, Du X, Hull JJ, Ma W, Lin Y. The HSP/co-chaperone network in environmental cold adaptation of Chilo suppressalis. Int J Biol Macromol. 2021;187:780–8.

    Article  CAS  PubMed  Google Scholar 

  11. Fan XL, Liang YM, Ma R, Tian CM. Morphological and phylogenetic studies of Cytospora (Valsaceae, Diaporthales) isolates from Chinese scholar tree, with description of a new species. Mycoence. 2013;55(4):252–9.

    Google Scholar 

  12. Zhu X-Y, Xu J-W, Li L-L, Wang D-Y, Zhang M-L, Yu N-N, Purba ER, Zhang F, Li X-M, Zhang Y-N, et al. Analysis of chemosensory genes in Semiothisa cinerearia reveals sex-specific contributions for type-II sex pheromone chemosensation. Genomics. 2020;112(6):3846–55.

    Article  CAS  PubMed  Google Scholar 

  13. Liu P, Zhang X, Meng R, Liu C, Li M, Zhang T. Identification of chemosensory genes from the antennal transcriptome of Semiothisa cinerearia. PLoS One. 2020;15(8):e0237134.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007;35(Database Issue):D61-65.

    Article  CAS  PubMed  Google Scholar 

  15. Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28(1):45–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009;37(Database Issue):D211-215.

    Article  CAS  PubMed  Google Scholar 

  17. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545-d551.

    Article  CAS  PubMed  Google Scholar 

  20. Dittmer NT, Tetreau G, Cao X, Jiang H, Wang P, Kanost MR. Annotation and expression analysis of cuticular proteins from the tobacco hornworm, Manduca sexta. Insect Biochem Mol Biol. 2015;62:100–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lu JB, Luo XM, Zhang XY, Pan PL, Zhang CX. An ungrouped cuticular protein is essential for normal endocuticle formation in the brown planthopper. Insect Biochem Mol Biol. 2018;100:1–9.

    Article  CAS  PubMed  Google Scholar 

  22. Wu L, Zhang Z-F, Yu Z, Yu R, Ma E, Fan Y-L, Liu T-X, Feyereisen R, Zhu KY, Zhang J. Both LmCYP4G genes function in decreasing cuticular penetration of insecticides in Locusta migratoria. Pest Manag Sci. 2020;76(11):3541–50.

    Article  CAS  PubMed  Google Scholar 

  23. Yang CH, Yang PC, Zhang SF, Shi ZY, Kang L, Zhang AB. Identification, expression pattern, and feature analysis of cuticular protein genes in the pine moth Dendrolimus punctatus (Lepidoptera: Lasiocampidae). Insect Biochem Mol Biol. 2017;83:94–106.

    Article  PubMed  Google Scholar 

  24. Chen EH, Hou QL. Identification and expression analysis of cuticular protein genes in the diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae). Pestic Biochem Physiol. 2021;178:104943.

    Article  CAS  PubMed  Google Scholar 

  25. Chen EH, Hou QL, Dou W, Wei DD, Yue Y, Yang RL, Yang PJ, Yu SF, De Schutter K, Smagghe G, et al. Genome-wide annotation of cuticular proteins in the oriental fruit fly (Bactrocera dorsalis), changes during pupariation and expression analysis of CPAP3 protein genes in response to environmental stresses. Insect Biochem Mol Biol. 2018;97:53–70.

    Article  CAS  PubMed  Google Scholar 

  26. Liu J, Li S, Li W, Peng L, Chen Z, Xiao Y, Guo H, Zhang J, Cheng T, Goldsmith MR, et al. Genome-wide annotation and comparative analysis of cuticular protein genes in the noctuid pest Spodoptera litura. Insect Biochem Mol Biol. 2019;110:90–7.

    Article  CAS  PubMed  Google Scholar 

  27. Bromham L. Why do species vary in their rate of molecular evolution? Biol Let. 2009;5(3):401–4.

    Article  Google Scholar 

  28. Tetreau G, Wang P. Chitinous Structures as Potential Targets for Insect Pest Control. Adv Exp Med Biol. 2019;1142:273–92.

    Article  CAS  PubMed  Google Scholar 

  29. Lovett B, St Leger RJ. Genetically engineering better fungal biopesticides. Pest Manag Sci. 2018;74(4):781–9.

    Article  CAS  PubMed  Google Scholar 

  30. Perkin LC, Oppert B. Gene expression in Tribolium castaneum life stages: Identifying a species-specific target for pest control applications. PeerJ. 2019;7:e6946.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dichtel-Danjoy ML, Caldeira J, Casares F. SoxF is part of a novel negative-feedback loop in the wingless pathway that controls proliferation in the Drosophila wing disc. Development. 2009;136(5):761–9.

    Article  CAS  PubMed  Google Scholar 

  32. Feng G, Deák P, Chopra M, Hall LM. Cloning and functional analysis of TipE, a novel membrane protein that enhances Drosophila para sodium channel function. Cell. 1995;82(6):1001–11.

    Article  CAS  PubMed  Google Scholar 

  33. Ryglewski S, Kilo L, Duch C. Sequential acquisition of cacophony calcium currents, sodium channels and voltage-dependent potassium currents affects spike shape and dendrite growth during postembryonic maturation of an identified Drosophila motoneuron. Eur J Neurosci. 2014;39(10):1572–85.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Shen Z-J, Zhu F, Liu Y-J, Li Z, Moural TW, Liu X-M, Liu X. MicroRNAs miR-14 and miR-2766 regulate tyrosine hydroxylase to control larval–pupal metamorphosis in Helicoverpa armigera. Pest Manag Sci. 2022;78(8):3540–50.

    Article  CAS  PubMed  Google Scholar 

  35. Xu QY, Du JL, Mu LL, Guo WC, Li GQ. Importance of Taiman in Larval-Pupal Transition in Leptinotarsa decemlineata. Front Physiol. 2019;10:724.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hou QL, Chen EH. RNA-seq analysis of gene expression changes in cuticles during the larval-pupal metamorphosis of Plutella xylostella. Comp Biochem Physiol D: Genomics Proteomics. 2021;39:100869.

    CAS  PubMed  Google Scholar 

  37. Shen ZJ, Liu YJ, Zhu F, Cai LM, Liu XM, Tian ZQ, Cheng J, Li Z, Liu XX. MicroRNA-277 regulates dopa decarboxylase to control larval-pupal and pupal-adult metamorphosis of Helicoverpa armigera. Insect Biochem Mol Biol. 2020;122:103391.

    Article  CAS  PubMed  Google Scholar 

  38. Feyereisen R. Insect P450 enzymes. Annu Rev Entomol. 1999;44:507–33.

    Article  CAS  PubMed  Google Scholar 

  39. Derks MFL, Smit S, Salis L, Schijlen E, Bossers A, Mateman C, Pijl AS, de Ridder D, Groenen MAM, Visser ME, et al. The Genome of Winter Moth (Operophtera brumata) Provides a Genomic Perspective on Sexual Dimorphism and Phenology. Genome Biol Evol. 2015;7(8):2321–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kim KK, Kim R, Kim S-H. Crystal structure of a small heat-shock protein. Nature. 1998;394(6693):595–9.

    Article  CAS  PubMed  Google Scholar 

  41. Huang LH, Wang HS, Kang L. Different evolutionary lineages of large and small heat shock proteins in eukaryotes. Cell Res. 2008;18(10):1074–6.

    Article  CAS  PubMed  Google Scholar 

  42. Colinet H, Siaussat D, Bozzolan F, Bowler K. Rapid decline of cold tolerance at young age is associated with expression of stress genes in Drosophila melanogaster. J Exp Biol. 2013;216(Pt 2):253–9.

    CAS  PubMed  Google Scholar 

  43. Krebs RA, Feder ME. Deleterious consequences of Hsp70 overexpression in Drosophila melanogaster larvae. Cell Stress Chaperones. 1997;2(1):60–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Liu B, Shi Y, Yuan J, Hu X, Zhang H, Li N, Li Z, Chen Y, Mu D, Fa NW. Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects. Quantitative Biology. 2013;35(s13):62–7.

    Google Scholar 

  45. Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210–2.

    Article  CAS  PubMed  Google Scholar 

  46. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kent WJ. BLAT - The BLAST-like alignment tool. Genome Res. 2002;12(4):656–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Kielbasa SM, Wan R, Sato K, Horton P, Frith MC. Adaptive seeds tame genomic sequence comparison. Genome Res. 2011;21(3):487–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19(9):1639–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bedell JA, Korf I, Gish W. MaskerAid : a performance enhancement to RepeatMasker. Bioinformatics. 2000;16(11):1040.

    Article  CAS  PubMed  Google Scholar 

  51. Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27(2):573–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Chalopin D, Naville M, Plard F, Galiana D, Volff JN. Comparative analysis of transposable elements highlights mobilome diversity and evolution in vertebrates. Genome Biol Evol. 2015;7(2):567–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Altschul SF. Basic local alignment search tool (BLAST). J Mol Biol. 2012;215(3):403–10.

    Article  Google Scholar 

  54. Birney E, Durbin R. Dynamite: a flexible code generating language for dynamic programming methods used in sequence comparison. Pro Int Conf Intell Syst Mol Biol. 1997;5:56–64.

    CAS  Google Scholar 

  55. Ashburner M, Ball CA, Blake JA, Botstein D, Cherry JM. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sarah H, Rolf A, Attwood TK, Amos B, Alex B, David B, Peer B, Ujjwal D, Louise D, Lauranne D. InterPro: the integrative protein signature database. Nucleic Acids Research. 2009;37(Database):D211–5.

    Article  Google Scholar 

  57. Li L. OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes. Genome Res. 2003;13(9):2178–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Edgar RC. MUSCLE: Multiple Sequence Alignment with High Accuracy and High Throughput. Nucleic Acids Res. 2004;32(5):1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yang Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13(5):555.

    CAS  PubMed  Google Scholar 

  61. Beissbarth T, Speed TP. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics. 2004;20(9):1464–5.

    Article  CAS  PubMed  Google Scholar 

  62. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13.

    Article  Google Scholar 

  63. Kumar S, Tamura K, Nei M. Mega - Molecular Evolutionary Genetics Analysis Software for Microcomputers. Comput Appl Biosci. 1994;10(2):189–91.

    CAS  PubMed  Google Scholar 

  64. Takezaki N, Rzhetsky A, Nei M. Phylogenetic Test of the Molecular Clock and Linearized Trees. Mol Biol Evol. 1995;12(5):823–33.

    CAS  PubMed  Google Scholar 

  65. Yang ZH. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13(5):555–6.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Dr. Hui Xiang (South China Normal University, Guangzhou, China) for providing a bioinformatics analysis platform.

Funding

This research was supported by Modern Agriculture Industrial Technology System Funding of Shandong Province (Grant No. SDAIT-16–06) and National Natural Science Foundation of China (31672002, 32000383).

Author information

Authors and Affiliations

Authors

Contributions

Y.R. and S.C. conceived and designed the investigation. Y.W. and S.G. performed field and laboratory work. Z. W. assembled the genome. H. L. performed the Hi-C scaffold. Y. R., S.C., and Y. W. analyzed the data. S.C. contributed materials and reagents. S.C. and Y. W. wrote the paper. Y.R. revised the manuscript. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shengqi Chi or Yandong Ren.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Addition file 1: Table S1.

The statistics of sequencing reads on Illumina platform. These data are produced by short insert library, and the results were shown by the raw sequencing reads. The sequencing depth was calculated by the assembled genome size, the genome is 580,885,107 bp. Table S2. The statistics of sequencing reads on Nanopore platform. The reads with quality value Q > 7 were considered. The sequencing depth was calculated by the assembled genome size, the genome is 580,885,107 bp. Table S3. The statistics of the contig-level genome and chromosome-level genome. These data are produced by short insert library, and the results were shown by the raw sequencing reads. The contig-level genome was assembled by Nexedenovo and polished by Nextpolish. The chromosome-level genome is constructed by 3D DNA. Table S4. The statistics of Hi-C sequencing reads. The sequencing depth was calculated by the assembled genome size, the genome is 580,885,107 bp. Table S5. Statistics of the assembled chromosome-level genome via 3D de novo assembly software. Table S6. The statistics of RNA sequencing reads on Illumina platform. These data are produced by short insert library, and the results were shown by the filtered reads. Table S7. The statistics of the assembled transcripts by Bridger of 5 organs/tissues. Table S8.The statistics of the transcripts mapping ratio on the assembled genome. Table S9. The quality evaluation of assembled genome by BUSCO software. Table S10. Comparison of related species genomes with our chromosome-level genome. Table S11. The statistics of the annotated repeat sequences in our assembled genome. The type represents that the way or software used in this study. Table 12. The statistics of the annotated repeat sequences in our assembled genome by de novo prediction. Table S13. The functional annotation of the predicted protein-coding genes. Table S14. GO enrichment of the expanded gene families in S. cinerearia analyzed by CAFÉ (v4.1). Table S15. KEGG enrichment of the expanded gene families in S. cinerearia analyzed by CAFÉ (v4.1). Table S16. Relative evolution rate among these species by LINTRE software. Table S17. Relative evolution rate among these species by MEGA software. Table S18. Statistics of positively selected genes in Geometridae. Figure S1. 17-mer analysis of S. cinerearia genome. Figure S2. Whole genome synteny analyses between S. cinerearia and O. brumata. Figure S3. TEs ratio in these species. Figure S4. Distribution of gene parameters in these species. Figure S5. Divergence time of these species. Figure S6. Phylogenetic relationship among the 12 species inferred by the nucleotide acid sequences of the single-copy genes. Number in the node represents the corresponding bootstrap value.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chi, S., Wang, Y., Wang, Z. et al. A chromosome-level genome of Semiothisa cinerearia provides insights into its genome evolution and control. BMC Genomics 23, 718 (2022). https://doi.org/10.1186/s12864-022-08949-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-022-08949-z

Keywords

  • Semiothisa cinerearia
  • Geometridae
  • tipE
  • Cytochrome P450