Elucidating the mitogenomic blueprint of Pomadasys perotaei from the Eastern Atlantic: Characterization and matrilineal phylogenetic insights into haemulid grunts (Teleostei: Lutjaniformes)

doi:10.21203/rs.3.rs-5025579/v1

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Elucidating the mitogenomic blueprint of Pomadasys perotaei from the Eastern Atlantic: Characterization and matrilineal phylogenetic insights into haemulid grunts (Teleostei: Lutjaniformes)

https://doi.org/10.21203/rs.3.rs-5025579/v1

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published 25 Oct, 2024

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The parrot grunt fish, Pomadasys perotaei, has a limited distribution in the eastern Atlantic Ocean and is an important species in marine capture fisheries across several West African countries. Despite its ecological and economic significance, the mitogenomic information for this species is lacking. This study utilized next-generation sequencing to generate the de novo mitogenome of P. perotaei from eastern Atlantic Sea. The resulting mitogenome is 16,691 base pairs and includes 13 protein-coding genes (PCGs), 22 transfer RNAs, two ribosomal RNAs, and an AT-rich control region (CR). Most of the PCGs exhibit nonsynonymous (Ka) and synonymous (Ks) substitution rates of less than ‘1’, indicating strong negative selection across haemulid fishes. The control region of Pomadasys species contains four conserved domains, as seen in other teleost’s, with polymorphic nucleotides that can be used to study population structures through the amplification of short mitochondrial gene fragments. Additionally, Bayesian phylogenetic analysis based on PCGs revealed a non-monophyletic clustering pattern of Pomadasys within the haemulid matrilineal tree. Overall, the structural characterization and phylogenetic analysis enhance our understanding of the genetic composition and evolutionary history of Pomadasys species from the Indo-West Pacific and eastern Atlantic Oceans.

Marine fish

Endemism

Next-generation sequencing

Mitochondrial genome

Cladistics

Evolution

The Haemulidae family, within the suborder Percoidei of teleost fishes, represents one of the most taxonomically diverse groups among extant fish families (Nelson 2006). Although predominantly marine, some species occasionally venture into brackish and freshwater environments (Lindeman and Toxey 2002). Commonly known as grunter fishes, haemulids produce distinctive sounds by grinding their pharyngeal teeth (Burkenroad 1931). They are closely related to lutjanids, congregating on reefs during the day and foraging in nearby sand-flat, seagrass, and mangrove environments at night (Shantz et al. 2015). Morphologically, the Haemulidae family is divided into two subfamilies: Haemulinae and Plectorhynchinae, comprising 136 valid species across 21 genera (Fricke et al. 2024). These subfamilies differ significantly in their distribution patterns, habitat preferences, and diversity. The subfamily Plectorhynchinae, known as sweetlips, predominantly inhabits the coral and rocky reefs of the Indo-Pacific and eastern Atlantic Oceans. They are characterized by elongated bodies, rounded heads, and subterminal mouths (Allen et al. 2003). In contrast, the new world grunts of the Haemulinae subfamily exhibit elongated or stout bodies, greyish coloration, and occupy temperate reefs of the Atlantic, Indo-Pacific coral reefs, and sandy or muddy bottoms (Lindeman and Toxey 2002).

The genus Pomadasys, the largest group within the Haemulinae subfamily, is largely distributed across inshore bays and estuaries of the tropical Indo-West Pacific region (Hasan et al. 2022). Currently, 29 species of Pomadasys have been identified and are considered valid (Fricke et al. 2024). Typically, these species are characterized by the presence of two anterior pores and a median pit on the chin (McKay 2001). Ecologically, Pomadasys species are opportunistic feeders, serving as secondary consumers that prey on a variety of small benthic invertebrates (Diankha et al. 2018). Economically, several species within this genus are significant, forming an important component of marine capture fisheries (Campbell et al. 2018; Simier et al. 2019). Because they are a popular food source, the exploitation rate of grunt fishes has been increasing annually (Avşar et al. 2021). Among the known species, the parrot grunt, Pomadasys perotaei, is endemic to the eastern Atlantic and is a common catch in small-scale marine capture fisheries in several countries along the western coast of Africa (Camara et al. 2016). Although the IUCN Red List currently classifies this species as ‘Least Concern’, there is growing concern due to the high fishing pressure from multinational fishers in localized areas and the lack of co-management between countries (Polidoro et al. 2016). Furthermore, the classification of P. perotaei is challenging due to frequent taxonomic revisions and varying nomenclature practices across African countries (Parenti 2019). Identification of P. perotaei is further complicated by its sympatric coexistence with other congeners such as Pomadasys jubelini, Pomadasys incisus, and Pomadasys rogerii (Camara et al. 2016; Simier et al. 2019). Therefore, in addition to traditional taxonomy, the integration of molecular tools is urgently needed for accurate species identification, population estimation, and conservation genetics, which are essential for establishing effective fisheries management plans.

Several studies have focused on the species identification and classification of Pomadasys using body measurements, as well as morphometric and meristic analyses (Kulbicki et al. 2009; Sadio et al. 2021). Although sexual dimorphism has not been observed in several Pomadasys species, morphological attributes and color patterns can change throughout the lifespan of grunter and sweetlips fishes, potentially leading to misidentifications (Allen et al. 2003). Therefore, the addition of molecular approaches has become essential for the global investigation of haemulids, addressing taxonomic clarification and phylogenetic relationships (Tavera et al. 2012; Abidin et al. 2021), endemicity and cryptic speciation (Hasan et al. 2022), extended distribution due to climate change (Mos and Mos 2022), and host-parasite interactions (Wu et al. 2007). Furthermore, the molecular data of Pomadasys have been integrated with those of other haemulid species to infer evolutionary patterns and various higher classifications using both mitochondrial and nuclear partial gene sequences (Li et al. 2009; Sanciangco et al. 2011; Liang et al. 2012; Tavera et al. 2018). Molecular data have also been generated for species detection through environmental DNA metabarcoding and DNA barcoding analysis in estuarine and marine environments (Abidin et al. 2022). Nevertheless, our understanding of Pomadasys species through mitochondrial genomes is still incomplete. To date, only one mitogenome, that of Pomadasys kaakan, which is distributed in the Indo-West Pacific Ocean, is available to the scientific community (Chen et al. 2022). Consequently, the maternal evolutionary patterns of this large group remain unclear, and the current knowledge is biased towards the Indo-West Pacific Ocean. This highlights the need for additional mitogenome data of Pomadasys species from other marine realms to develop a more comprehensive understanding.

The complete mitochondrial genome plays a vital role in all eukaryotic organisms by providing insights into ancient evolutionary relationships (Boore 1999). Complete mitogenome sequences are crucial for various studies, including matrilineal phylogenetic tree reconstruction, gene structure and variation analysis, and the evolution of fishes from both freshwater and marine environments (Miya et al. 2003; Saitoh et al. 2006). Notably, the mitochondrial genome offers greater reliability in species grouping through phylogenetic reconstruction compared to partial mitochondrial genes, especially for large groups of reef-associated fishes, including those in the Haemulidae family (Miya et al. 2005; Yamanoue et al. 2007; Alvarenga et al. 2024). Therefore, this study aims to construct the complete mitogenome of the parrot grunt, P. perotaei, from the Eastern Atlantic Sea, off the coast of West Africa. The study integrates genomic characteristics, particularly the structure and variations of mitochondrial genes, along with phylogenetic analyses to elucidate the evolutionary and diversification patterns of this species in the Eastern Atlantic, in comparison to its Indo-West Pacific congener, P. kaakan. We anticipate that this study will provide molecular-based baseline information to enhance data monitoring, species-specific life history, and population structure analyses across localities in Western Africa, contributing to sustainable management in the future.

Sample collection and species identification

A deceased specimen of the parrot grunt was collected from the catch of commercial fishermen in the eastern Atlantic Ocean, West Africa (3.6322N, 9.8005E) (Fig. 1). The species was identified as P. perotaei based on established diagnostic characteristics documented in prior research (Parenti 2019; Fermon et al. 2022). Key distinguishing features included a second anal fin that is stronger and longer than the third, a dorsal fin with a series of spots at its base, and distinct dark spots on the operculum covering the upper two-thirds of the body (Parenti 2019). A muscle tissue sample was meticulously extracted from the dorsal region along the lateral line, preserved in a 2 ml tube containing 70% ethanol, and stored at 4°C in the Department of Marine Biology at Pukyong National University, Republic of Korea. Specimen was preserved in 10% formaldehyde and stored at the Ministry of Livestock, Fisheries, and Animal Industries (MINEPIA) in Yaoundé, Cameroon. The transfer of the deceased tissue sample was authorized by the Institutional Animal Care and Use Committee (IACUC) under the approval code PKNUIACUC-2022-72, ensuring adherence to ethical standards for the use of biological material in research.

DNA extraction and next-generation sequencing

Genomic DNA was extracted utilizing the AccuPrep® Genomic DNA extraction kit (Bioneer, Daejeon, Republic of Korea) by adhering to the manufacturer's guidelines. The integrity and concentration of the extracted DNA were determined through 1% agarose gel electrophoresis and a NanoDrop spectrophotometer (Thermo Fisher Scientific D1000, Waltham, MA, USA). Library preparation followed the TruSeq Nano DNA High-Throughput Library Preparation Kit (Illumina, Inc., San Diego, CA, USA) instructions, starting with the fragmentation of 100 ng of genomic DNA using Covaris' adaptive focused acoustic technology (Woburn, MA, USA). This produced double-stranded DNA fragments with blunt ends and 5’ phosphorylation. The fragments underwent end repair and size selection through a bead-based method. Subsequently, an 'A' base was added to the 3’ ends, and the fragments were ligated with TruSeq UD DNA Indexing adapters. The DNA library was then purified and enriched using PCR amplification. Quantification of the library was carried out with qPCR, based on the qPCR Quantification Protocol Guide (KAPA Library Quantification Kits for Illumina Sequencing Platforms), and its quality was assessed using an Agilent Technologies 4200 TapeStation D1000 screen tape (Agilent Technologies, Santa Clara, CA, USA). The complete mitogenome of P. perotaei was obtained by next-generation sequencing (2 × 150 bp) on the NovaSeq platform (Illumina, Inc., San Diego, CA, USA) at Macrogen (https://dna.macrogen.com/) in Daejeon, Republic of Korea.

Mitogenome assembly and annotation

The mitochondrial genome was assembled using high-quality next-generation sequencing reads with Geneious Prime v2023.0.1. The assembly involved mapping the mitogenome of a sister species, P. kaakan (Accession No. MW421307), using standard mapping algorithms. Validation of the mitogenome was performed by aligning overlapping regions with MEGA v11 software (Tamura et al. 2021). Gene positions, boundaries, and orientations were determined using the Mitos Galaxy web server (https://mitos.bioinf.uni-leipzig.de/) (Bernt et al. 2013) and the Mitofish MitoAnnotator web server (https://mitofish.aori.u-tokyo.ac.jp/annotation/input/) (Iwasaki et al. 2013). To verify the protein-coding genes (PCGs), the putative amino acid sequences were examined using the Open Reading Frame Finder tool (https://www.ncbi.nlm.nih.gov/orffinder/) with the standard vertebrate mitochondrial genetic code. Both ribosomal RNA (rRNA) and transfer RNA (tRNA) gene structures and boundaries were confirmed using ARWEN 1.2 and the tRNA predictor tool within Mitos Galaxy (Laslett 2004; Laslett and Canbäck 2008). To verify the full-length of CR, a specific primer pair (5′-GGCTTCTATCCTCGTACTAATGG-3′ and 5′-GGCTTAATGTCTATCACTGC-3′) was designed for amplification. PCR was carried out using a TaKaRa Veriti Thermal Cycler with a standard reaction mixture that included 1X PCR buffer, 1 U Taq polymerase, 10 pmol primers, 2.5 mM dNTPs, and 1 µL template DNA. The resulting PCR product was purified using the AccuPrep® PCR/Gel Purification Kit (Bioneer, Daejeon, Republic of Korea) and then sequenced bidirectionally with the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA) on the ABI PRISM 3730XL DNA Analyzer at Macrogen. The generated sequence of CR was integrated into the complete mitogenome by aligning overlapping regions with MEGA v11, and noisy segments were removed using SeqScanner version 1.0 (Applied Biosystems Inc., Foster City, CA, USA). The finalized mitogenome of P. perotaei was submitted to the GenBank database and assigned a unique accession number.

Genomic characterization and comparative analyses

In this study, we utilized the MitoFish MitoAnnotator to generate a circular map representation of the novel complete mitogenome. We then conducted a comparative analysis to assess the mitogenomic structure and variation relative to P. kaakan, the only species of this genus with a complete mitogenome available in GenBank. The distances between adjacent genes and overlapping regions were calculated manually. The nucleotide composition of each PCG, rRNA, tRNA, and the CR was analyzed using MEGA v11. The skewness of A-T and G-C compositions was calculated with the formulas AT-skew = [A-T]/[A + T] and GC-skew = [G-C]/[G + C] (Perna and Kocher 1995). Start and stop codons for each PCG were identified using the Open Reading Frame Finder tool. Nucleotide diversity for Pomadasys species was analyzed using a sliding window approach with a step size of 25 bp and window size of 200 bp in DnaSP version 6.0 (Rozas et al. 2017). The saturation of transition versus transversion codons in mitochondrial PCGs was examined using DAMBE6 (Xia 2017). Pairwise comparisons of nonsynonymous (Ka) and synonymous (Ks) substitutions between Pomadasys and other haemulid species were performed with DnaSP6.0. The distribution of amino acids and Relative Synonymous Codon Usage (RSCU) were analyzed using MEGA v11. Conserved domains within the CRs were recognized through CLUSTAL X alignment, in comparison with other haemulid species (Thompson et al. 1997; Satoh et al. 2016).

Dataset construction and phylogenetic analyses

A total of 21 haemulid species, spanning the subfamilies Haemulinae and Plectorhinchinae, were retrieved from the GenBank database for phylogenetic analysis (Table S1). The mitogenome of the Mutton Snapper, Lutjanus analis (Accession Number PP032983) from the family Lutjanidae, was chosen as the outgroup. Two concatenated datasets were assembled using the Concatenator Tool version 0.2.1 within iTaxoTools software: one with 13 PCGs and another with 13 PCGs plus two rRNAs (Vences et al. 2021). These datasets were used to elucidate primary evolutionary relationships among haemulid fishes, particularly within the genus Pomadasys. The model with the lowest Bayesian Information Criterion (BIC) score, ‘GTR + G + I,’ was selected using PartitionFinder 2 and JModelTest v2 (Darriba et al. 2012; Lanfear et al. 2016). Phylogenetic reconstruction was performed using Bayesian analysis (BA) with MrBayes 3.1.2, employing one cold and three hot Metropolis-coupled Markov chain Monte Carlo (MCMC) chains (Ronquist and Huelsenbeck 2003). The analysis was conducted over 1,000,000 generations, with tree sampling occurring every 100 generations. Twenty-five percent of the samples were discarded as burn-in. The resulting Bayesian tree was visualized using the iTOL v4 web server (https://itol.embl.de/login.cgi) (Letunic and Bork 2007).

Mitogenome structure and organization

The assembled mitogenome of P. perotaei is 16,691 base pairs (bp) in length and has been deposited in the GenBank database (Accession No. OR487151). This mitogenome is shorter than that of P. kaakan (16,808 bp). The mitogenome of P. perotaei includes 13 PCGs, two rRNAs, 22 tRNAs, and one AT-rich CR. Of these, 28 genes (12 PCGs, two rRNAs, and 14 tRNAs) are encoded on the heavy strand, while the ND6 gene and eight tRNAs (tRNA-Glu, tRNA-Pro, tRNA-Gln, tRNA-Ala, tRNA-Asn, tRNA-Cys, tRNA-Tyr, and tRNA-Ser) are encoded on the light strand (Fig. 1, Table 1). The nucleotide composition of the P. perotaei mitogenome shows an AT-rich bias (51.31%), with 26.46% adenine (A), 24.85% thymine (T), 17.25% guanine (G), and 31.43% cytosine (C). The AT skew and GC skew values are 0.031 and − 0.291, respectively. Likewise, P. kaakan also exhibits an AT-rich bias (51.80%), with identical skew values of 0.031 and − 0.291 (Table 2). Additionally, the P. perotaei mitogenome contains 12 intergenic spacers and five overlapping regions, totaling 84 bp and 23 bp, respectively. The longest intergenic spacer (36 bp) occurs between tRNA-Asn and tRNA-Cys, while the longest overlap (10 bp) is between the ATP8 and ATP6 genes (Table 1). In comparison, P. kaakan has 10 intergenic spacers and five overlapping areas with total lengths of 76 bp and 35 bp, respectively. The largest gap (34 bp) in P. kaakan occurs between tRNA-Cys and tRNA-Asn, and the highest overlap (13 bp) is between COI and tRNA-Ser2 (Table S2).

Table 1

Organization of full-length annotated mitochondrial genes, location arrangement, size, and intergenic nucleotide of *P. perotaei* mitogenome. ‘H’ and ‘L’ indicates the position of each gene in the heavy and light strands. ‘-’ marks indicate an incomplete stop codon.
Gene	Position		Strand	Size (bp)	Codon			Intergenic nucleotide
Gene	Start (bp)	End (bp)	Strand	Size (bp)	Start	Stop	Anti Codon	Intergenic nucleotide
tRNA-Phe (F)	1	68	H	68	.	.	GAA	0
12S rRNA	69	1,019	H	951	.	.	.	0
tRNA-Val (V)	1,020	1,091	H	72	.	.	TAC	0
16S rRNA	1,092	2,926	H	1,835	.	.	.	0
tRNA-Leu (L2)	2,927	3,000	H	74	.	.	TAA	0
ND1	3,001	3,975	H	975	ATG	TAA	.	4
tRNA-Ile (I)	3,980	4,048	H	69	.	.	GAT	17
tRNA-Gln (Q)	4,066	4,136	L	71	.	.	TTG	8
tRNA-Met (M)	4,145	4,213	H	69	.	.	CAT	0
ND2	4,214	5,259	H	1,046	ATG	TA-	.	0
tRNA-Trp (W)	5,260	5,331	H	72	.	.	TCA	1
tRNA-Ala (A)	5,333	5,401	L	69	.	.	TGC	1
tRNA-Asn (N)	5,403	5,475	L	73	.	.	GTT	36
tRNA-Cys (C)	5,512	5,578	L	67	.	.	GCA	-1
tRNA-Tyr (Y)	5,578	5,645	L	68	.	.	GTA	1
COI	5,647	7,197	H	1,551	GTG	TAA	.	0
tRNA-Ser (S2)	7,198	7,268	L	71	.	.	TGA	3
tRNA-Asp (D)	7,272	7,343	H	72	.	.	GTC	4
COII	7,348	8,038	H	691	ATG	T--	.	0
tRNA-Lys (K)	8,039	8,113	H	75	.	.	TTT	1
ATP8	8,115	8,282	H	168	ATG	TAA	.	-10
ATP6	8,273	8,955	H	683	ATG	TA-	.	0
COIII	8,956	9,741	H	786	ATG	TAA	.	0
tRNA-Gly (G)	9,742	9,813	H	72	.	.	TCC	0
ND3	9,814	10,162	H	349	ATG	T--	.	0
tRNA-Arg (R)	10,163	10,231	H	69	.	.	TCG	0
ND4L	10,232	10,528	H	297	ATG	TAA	.	-7
ND4	10,522	11,902	H	1,381	ATG	T--	.	0
tRNA-His (H)	11,903	11,971	H	69	.	.	GTG	0
tRNA-Ser (S1)	11,972	12,040	H	69	.	.	GCT	4
tRNA-Leu (L1)	12,045	12,117	H	73	.	.	TAG	0
ND5	12,118	13,956	H	1,839	ATG	TAG	.	-4
ND6	13,953	14,474	L	522	ATG	TAG	.	0
tRNA-Glu (E)	14,475	14,543	L	69	.	.	TTC	4
CytB	14,548	15,688	H	1,141	ATG	T--	.	0
tRNA-Thr (T)	15,689	15,759	H	71	.	.	TGT	-1
tRNA-Pro (P)	15,759	15,828	L	70	.	.	TGG	0
Control region	15,829	16,691	H	863	.	.	.	.

Table 2

Nucleotide composition of mitochondrial genomes of *P. perotaei* and *P. kaakan.*
Species Name	Size (bp)	A%	T%	G%	C%	A + T%	G + C%	AT-Skew	GC-Skew
Complete Mitogenome
P. perotaei	16,691	26.46	24.85	17.25	31.43	51.31	48.69	0.031	-0.291
P. kaakan	16,808	27.12	24.67	16.55	31.65	51.80	48.20	0.047	-0.313
PCGs
P. perotaei	11,429	23.58	26.58	17.12	32.73	50.16	49.84	-0.060	-0.313
P. kaakan	11,440	24.27	26.47	16.17	33.09	50.74	49.26	-0.043	-0.343
rRNAs
P. perotaei	2,781	31.18	20.28	21.04	27.51	51.46	48.54	0.212	-0.133
P. kaakan	2,768	30.89	20.23	21.75	27.13	51.12	48.88	0.208	-0.110
tRNAs
P. perotaei	1,552	26.80	26.16	24.74	22.29	52.96	47.04	0.012	0.052
P. kaakan	1,557	25.63	26.59	24.66	23.12	52.22	47.78	-0.018	0.032
CRs
P. perotaei	863	32.21	32.10	13.21	22.48	64.31	35.69	0.002	-0.260
P. kaakan	1,002	33.63	31.94	12.67	21.76	65.57	34.43	0.026	-0.264

Features of Protein-coding genes

The PCGs of P. perotaei spans a total length of 11,429 bp encompassing 13 genes (four cytochrome, two ATP synthase, and seven NADH dehydrogenase), which constitute 68.47% of the total mitogenome. Among these, ATP8 is the shortest (168 bp), while ND5 is the longest (1,839 bp). Comparative analysis reveals that the PCGs of P. perotaei are 11 bp shorter than those of P. kaakan, which measure 11,440 bp. Both species exhibit an AT bias, with P. perotaei showing 50.16% and P. kaakan 50.74%, resulting in AT skews ranging from − 0.060 to -0.043 and a consistent GC skew of -0.343 in both species (Table 2). The 13 PCGs of both Pomadasys species exhibit three intergenic overlaps of identical lengths: ATP8 and ATP6 overlap by 10 bp, ND4L and ND4 by 7 bp, and ND5 and ND6 by 4 bp. Nucleotide diversity investigation using a sliding window method across the 13 PCGs resulted in a mean nucleotide diversity (Pi) value of 0.19331 with ND6 observed as the highest diversity (0.37515), totalling 2,209 polymorphic nucleotides identified between the two Pomadasys species (Fig. 2A). Additionally, comparative saturation analyses indicated no saturation for either transitions or transversions with increasing Kimura 2-parameter divergence values across all haemulid species mitogenomes (Fig. 2B). The initiation codons are largely conserved across the two Pomadasys species, with most PCGs starting with ATG, except for COI, which starts with GTG. However, notable differences were observed in termination codon usage. In P. perotaei, seven PCGs terminate with either TAA (ND1, COI, ATP8, COIII, and ND4L) or TAG (ND5 and ND6), while the remaining PCGs end with incomplete codons TA- (ND2 and ATP6) and T– (COII, ND3, ND4, and Cytb) (Table 1). In contrast, P. kaakan has six PCGs ending with TAA, TAG, or AGG, and the rest terminate with incomplete codons TA- (ND2, ATP6, and COIII) or T– (COII, ND3, ND4, and Cytb) (Table S3).

Substitution pattern and codon usage in haemulids mitogenome

The calculation of the nonsynonymous (Ka) and synonymous (Ks) substitution ratios revealed that all PCGs of P. perotaei and its congeners within the family Haemulidae are subject to the same form of selection pressure. All PCGs revealed Ka/Ks ratio values less than ‘1’. The average pairwise Ka/Ks ratios ranged from a low of 0.0113 ± 0.0041 for COI to a high of 0.1426 ± 0.0243 for ND6, following the order: COI < COII < ND3 < COIII < Cytb < ND4 < ND5 < ND4L < ND1 < ATP6 < ND2 < ATP8 < ND6 (Fig. 2C, Table S4). The analysis of RSCU revealed that the codons in the 13 PCGs were conserved and can be specifically translated to amino acids. The total number of codon transcriptions for the 20 amino acids was 5,163 for P. perotaei and 5,209 for P. kaakan, excluding stop codons. The composition of amino acid was similar in P. perotaei and P. kaakan, with Leucine (13.54% and 13.55%), Serine (12.53% and 12.84%), Proline (12.36% and 12.25%), Threonine (8.21% and 8.64%), and Alanine (5.75% and 5.26%) were observed as the most frequently used. In contrary, Aspartic acid (2.17% and 2.02%), Cysteine (2.19% and 2.07%), Tryptophan (2.32% and 2.34%), Isoleucine (2.56% and 2.50%), and Methionine (2.54% and 2.63%) were less prevalent, respectively (Fig. 3A). Moreover, the Leucine and Serine in P. perotaei and P. kaakan were formed by six variations of codon, with codons of CCC, CCT, CCA (in Proline), TCC in Serine, and CAA in Glutamine were observed as the most frequent compared to the other codons (Fig. 3B, and Table S5).

Features of ribosomal RNA and transfer RNA

The mitogenome of P. perotaei contains two rRNA genes: the small subunit (12S) with 951 bp and the large subunit (16S) with 1,835 bp, collectively contributing 16.67% to the complete mitogenome. In comparison, the two rRNA genes of P. kaakan span 2,768 bp, accounting for 16.47% of its mitogenome. Both species exhibit a similar AT-rich bias in their rRNA genes, with P. perotaei at 51.46% and P. kaakan at 51.12%, resulting in AT skews of 0.212 and 0.208 and GC skews of -0.133 and − 0.110, respectively (Table 2). Additionally, the mitochondrial genomes of these two Pomadasys species contain 22 tRNA genes interspersed between the rRNA and PCGs. In P. perotaei, the tRNA sizes range from 67 bp (tRNA-Cys) to 75 bp (tRNA-Lys), while in P. kaakan, they range from 67 bp (tRNA-Ser1) to 75 bp (tRNA-Lys). The collective lengths of the 22 tRNAs are 1,552 bp for P. perotaei and 1,557 bp for P. kaakan, contributing 9.30% and 9.26% to the mitogenomes, respectively. The tRNA genes exhibit an AT bias of 52.96% in P. perotaei and 52.22% in P. kaakan, resulting in AT skews of 0.012 and − 0.018 and GC skews of 0.052 and 0.032, correspondingly (Table 2). Structurally, most of the 22 tRNAs in P. perotaei fold into the typical cloverleaf secondary structure, except for tRNA-Ser1, which lacks base pairing in the DHU arm. Fourteen tRNA genes (tRNA-Phe, tRNA-Leu2, tRNA-Ile, tRNA-Gln, tRNA-Trp, tRNA-Ala, tRNA-Asn, tRNA-Cys, tRNA-Tyr, tRNA-Ser2, tRNA-Asp, tRNA-Arg, tRNA-Glu, tRNA-Thr, and tRNA-Pro) form through a combination of formal Watson-Crick base pairs (A = T and G ≡ C) and wobble base pairs (G-T), whereas the other eight tRNA genes solely use Watson-Crick base pairs (Fig. 4). The anticodons of the 22 tRNAs are consistent between the two Pomadasys species, showing similar complete anticodons (Table S6).

Features of control region

The CR of P. perotaei spans 863 bp, constituting 5.17% of the complete mitogenome, which is shorter than the CR of its congener P. kaakan, which measures 1,002 bp. The control region of both species showed an AT bias of 64.31% and 65.57%, with the AT-skew varies from 0.002 in P. perotaei to 0.026 in P. kaakan, whereas the GC-skew ranges from − 0.264 in P. kaakan to -0.260 in P. perotaei (Table 2). Furthermore, the CR of Pomadasys species consists of four conserved domains (CSB-D, CSB-1, CSB-2, and CSB-3). Among these conserved domains, CSB-I is the longest at 12 bp, compared to others which range from 7 bp (CSB-D and CSB-III) to 11 bp (CSB-II) (Fig. 5). Comparative analyses of the CRs between the two Pomadasys species reveal that CSB-I and CSB-III have a high number of variable sites, 61.12% and 63.16%, respectively. Notably, tandem repeats were observed exclusively in both Pomadasys species within the extended termination-associated sequences (ETAS) region. This included 3.3 copies of a 28 bp consensus sequence ‘ATTAAAACAATTAAATGATATTTACATT’ in P. perotaei, and two copies of a 132 bp consensus sequence as well as 1.9 copies of a 25 bp consensus sequence ‘TTATAATACATAAATGTATTAGCGA’ in P. kaakan.

Matrilineal phylogenetic relationship of haemulid grunts

The mitogenome-based topology was constructed using the concatenation of 13 PCGs and two rRNAs from two Pomadasys species and other related species within the Haemulidae family. Both the PCGs-only and PCGs + rRNAs-based BA phylogenies exhibited similar clade patterns for all the studied species (Fig. 6, Fig. S1). The BA cladistic analysis distinctly separated all species into their respective classifications based on two subfamilies, Plectorhinchinae and Haemulinae, with high posterior probability support. Notably, within the subfamily Plectorhinchinae, all species exhibited a monophyletic cladistic pattern. Interestingly, within the subfamily Haemulinae, most species demonstrated a monophyletic clustering pattern, except for the two Pomadasys species, which revealed either paraphyletic or polyphyletic lineages (Fig. 6).

Mitogenomic insights into the grunts

Despite the differences in gene intergenic, the mitochondrial genome of two Pomadasys species comprises 37 genes, with 28 genes located on the heavy strand and nine on the light strand, which is consistent with other teleost mitogenomes (Miya et al. 2005; Kundu et al. 2023a). Since mitochondrial genomes are generally conserved, gene arrangement is the key area of study in organismal systematics (Gong et al. 2020; Zhang et al. 2020). The arrangement of genes in the mitochondria can impact various facets of fish physiology, molecular mechanisms, life cycles, and the processes involved in genomic evolution (Montaña-Lozano et al. 2022). Therefore, our results indicate that the mitochondrial genome of two Pomadasys species are conserved compared to other species in the Haemulidae family. Furthermore, the mitogenome's nucleotide composition of two Pomadasys species also shows an A-T preference, which are typical of vertebrate and haemulid mitogenomes (He et al. 2022). It is also aligning with the hydrophobic characteristics of mitochondrial proteins (Naylor et al. 1995).

In PCGs, the majority of genes of two Pomadasys species employ ATG as the start codon, except for COI (GTG), and the stop codon of both species also exhibits similarity with terminated either with complete or incomplete codons. The configuration of initiation and termination codon usage in PCGs are consistent with that examined in other haemulids species (He et al. 2022; Liang et al. 2019). Our findings showed various codon combinations can be contributed to the translating of specific amino acids. Codon usage was further assessed using RSCU, where different RSCU values represent different codon usage scenarios. Overall, the RSCU value was determined to be unequaled to ‘1’, suggesting that there were different degrees of bias in each codon utilization. The codon usage bias is correlated to the intensity of gene expression, where the codons used by the genes with high efficiency expression have significantly different gene use frequencies compared with the genes with low expression (Liao et al. 2024; Parvathy et al. 2022). Compared to non-efficient genes, genes exhibiting efficient expression display a greater bias in their codon usage, and they usually utilize a set of preferential synonymous codons. The results of this study suggest that PCGs in the Haemulidae family may conserve and have comparable functions. In several organisms, codon use bias has developed as a result of mutation, natural selection, and genetic drift. Codon bias is influenced by several parameters, including tRNA quantity and interactions, recombination rates, mRNA folding, codon position and context in the gene, GC content, expression level and length of the gene, and genome makeup (Parvathy et al. 2022).

The Ka/Ks ratio is a well-established measure of selective pressure according to Darwinian theory and reflects evolutionary kinships at the genetic level, pertinent to both uniform and diverse species (Yang and Nielsen 2000; Zhao et al. 2022). The selection pressure is crucial for explaining the evolutionary dynamics of any genes under selection pressure and is key to understanding species divergence (Foote et al. 2011). A Ka/Ks ratio greater than ‘1’ indicates positive selection, a ratio of ‘1’ signifies neutrality, and a ratio less than ‘1’ reflects negative selection (Nei and Kumar 2000). Our results showed that all PCGs had Ka/Ks ratios less than ‘1’, signifying strong negative selection between the two Pomadasys species and other haemulids, with mutations largely replaced by synonymous substitutions. This remark mirrors the effect of natural selection in mitigating deleterious nucleotide mutations with negative selective factors, aligning with universal patterns detected in other vertebrates, including teleost’s (Kundu et al. 2024). Thus, the analysis of Ka/Ks ratios from the mitogenomes of Pomadasys and related haemulid species provides a platform for earning new perspectives on the distinctions of natural selection. This incorporates the evolutionary path and distribution among the species, aiding in explaining the complex interaction between mutations and selective pressures and illuminating their collective role in navigation protein evolution, which was represented by the abundance and composition of amino acids.

The 12S and 16S rRNA were located on heavy strand and separated by tRNA-Val as similar with another species under Pomadasys group (Chen et al. 2019). These ribosomal genes, which are generally conserved to ribonucleoproteins, play a critical role in translating genetic information from mRNAs into proteins, offering worthful insights into the fundamental catalytic actions of protein synthesis (Satoh et al. 2016). In addition, the secondary structure of tRNAs in P. perotaei exhibited a traditional canonical cloverleaf pattern, except the tRNA-Ser1 (GCT) displayed a simplified loop due to lack of base pairing in the dihydrouridine arm (DHU arm). This characteristic is commonly observed in the mitogenomes of many teleosts, specifically haemulids fish (He et al. 2022; Liang et al. 2019; Chen et al. 2022). For bony fish, the missing DHU arm could be a typical characteristic and potential to be recognized. The tRNA molecules act as adaptors, facilitating the translation of genetic evidence into proteins by rendering amino acids throughout the translation process. The high-level arrangement of tRNA and heteroplasmic genes in the WANCY region is crucial for mitochondrial gene expression (Cantatore et al. 1987; Ponce et al. 2008).

The biasness towards AT-rich in the control region (CR) of P. perotaei was consistent with findings in many other teleost fish, including P. kaakan and haemulids species (Chen et al., 2022; Miya et al. 2003). Moreover, the CR of Pomadasys species consists of four conserved domains (CSB-D, CSB-1, CSB-2, and CSB-3), as observed in other fish mitogenomes, including other Haemulidae species such as Diagramma picta (Accession No. AP009167) (Satoh et al. 2016). The CR is particularly substantial due to its typical in dynamic character, as it is the most variable region in the mitochondrial genome. Notably, the repeat-rich within ETAS region of the CR is characterized by its high variability and specific motifs that likely form stable hairpin loops, which are thought to serve as sequence-based indicators for the end of mitochondrial DNA replication. The complex mechanisms affecting the CR, such as gene rearrangements through dual replications, dimer-mitogenomes, non-random and random loss, play a crucial role in explaining the structural assortment of mitogenomes and the intricate processes involved in mitochondrial genome evolution (Satoh et al. 2016; Kundu et al. 2024). Thus, generating more mitogenomes of Pomadasys species and screening these variable sequences, along with polymorphic nucleotides, can be utilized for species-level identification and elucidating population structures within this group.

The mitochondrial genome-based phylogenetic analysis in this study has proven effective in explaining higher teleostean phylogenies based on maternal lineage, particularly for grunts and sweetlips fishes belonging to the family Haemulidae. The cladistic pattern derived from mitogenome data of the two Pomadasys species in this study is consistent with previous results on their evolutionary hypotheses (Tavera et al. 2012, 2018). However, to better understand their non-monophyletic clustering pattern, more mitogenomes data alongside with morphometrical information from diverse geographical locations are needed for Pomadasys species in the further studies. Nevertheless, phylogenetic analyses often help elucidate the evolutionary and diversification patterns of taxa through cladistic patterns (Tavera et al. 2012, 2018). Understanding the diversification pattern of these two grunt species based on recent matrilineal phylogeny is crucial, particularly in relation to their ecology, as a mix of biotic (such as distribution range in tropical and temperate environments) and abiotic factors likely affect the speciation of teleost fish. The parrot grunt, P. perotaei, inhabits the eastern Atlantic Ocean off West Africa, while P. kaakan is distributed across the Indo-West Pacific Ocean, indicating that allopatric speciation might have occurred between these two species in ancient times. The separation of their native distributions is potentially maintained by the cold-water barrier formed by the Benguela and Agulhas currents off the southern coast of South Africa, which separates the Atlantic and Indian Oceans. This barrier has also facilitated allopatric speciation in other marine teleosts such as flatfishes (Pleuronectiformes: Psettodidae) and trevallies (Carangiformes: Carangidae) (Glass et al. 2023; Kundu et al. 2023b). Generating molecular data from other Pomadasys species and estimating divergence times will help identify the ancestral and descendant lineages of Pomadasys and their evolutionary patterns. Additionally, gaining in-depth knowledge of oceanographic factors and their correlation with evolutionary patterns will be essential in understanding the triggers for speciation, diversification, and adaptation of Pomadasys species in diverse marine environments.

Genetic insights into haemulid conservation in the Eastern Atlantic

The sustainable management of natural resources, including fisheries, is increasingly critical to mitigating adverse effects on ecosystems, such as biodiversity loss, genetic erosion, and ecological disruption, all of which are crucial for economic stability and food security (FAO 2020a; Takyi et al. 2023). Consequently, adhering to international guidelines and agreements, such as the FAO’s Code of Conduct for Responsible Fisheries (CCRF) and the Sustainable Development Goals (SDGs), underscores the significance of these efforts (Durussel et al. 2018). Integrating multidimensional research is essential to promote the conservation and sustainable use of fisheries at local, national, and regional levels (FAO 2020b). In the East Atlantic Ocean, illegal, unreported, and unregulated (IUU) fishing results in an annual economic loss exceeding USD 2.3 billion for West African nations (Doumbouya et al. 2017). These countries rely heavily on marine fisheries, including haemulids, for livelihood, revenue generation, and nutritional needs. This marine region is also a crucial spawning ground and migratory route for various fish species, which are co-managed regionally by the International Commission for the Conservation of Atlantic Tunas (ICCAT) (FAO 2024). However, numerous marine fish species endemic to this ecosystem faces significant threats and declining stock status, necessitating enhanced monitoring and surveillance through integrated management approaches. Reef-associated species, such as haemulids and lutjanids, are frequently captured by multinational artisanal and industrial fishing fleets, often without coordinated management among West African countries (Polidoro et al. 2016).

Globally, marine teleosts and their genetic diversity are increasingly threatened by overexploitation and habitat loss (Kenchington et al. 2003; Blasiak et al. 2020). This threat is further exacerbated by rising sea temperatures and other environmental changes, which affect species responses, adaptation, reproduction, and physiology, potentially influencing species-specific genetic information (Hoban et al. 2020). Consequently, enhancing the availability of genetic data, such as mitogenome sequences of indigenous fish species in global databases, is crucial for genomic characterization and understanding evolutionary significance and adaptive capabilities (Setiamarga et al. 2008; Alvarenga et al. 2024). Molecular characterization can also identify current exploitation levels and potential local overfishing through genetic diversity and population genetic analyses (Nielsen et al. 2009). Marine ecosystems are traditionally viewed as highly interconnected due to gene flow and population dynamics that transcend national boundaries, sharing biodiversity resources among neighboring countries (Conover et al. 2006; Cano et al. 2008). Thus, data on population genetics and genetic diversity serve as critical baselines for establishing co-management strategies and assessing the impacts of fishing pressure in distant maritime regions (Binashikhbubkr et al. 2024). By generating the mitogenome for the endemic P. perotaei from the eastern Atlantic Ocean, this study recommends for the development of additional mitogenomes for haemulid species to support sustainable fisheries management in West African countries.

In this study, mitochondrial genome sequence analysis was performed to characterize the P. perotaei, which is distributed exclusively in the eastern Atlantic. The complete mitochondrial genome provided detailed insights into the genetic configuration and matrilineal evolutionary patterns of P. perotaei, confirming its evolutionary relationships with other haemulids fish. Given the potential conservation implications for haemulids in the eastern Atlantic, this research highlights the necessity of a thorough understanding of both mitogenomic and demographic information in the future. Overall, the analysis of the mitochondrial genome and the evolutionary history of the parrot grunt yields significant insights into its genetic characteristics and possible environmental factor which may trigger the distribution of non-monophyletic pattern under Pomadasys in the marine ecosystem.

Supplementary Information

The online version contains Supplementary Material available at

Acknowledgements

The authors wish to express their gratitude to Fantong Zealous Gietbong from the Fisheries, and Animal Industries (MINEPIA) in Yaoundé, Ministry of Livestock, Cameroon for his invaluable support in conducting the sampling.

Author contributions

Conceptualization: SK and H-WK; methodology: AW, GB, and YJ; software: AW and GB; validation: SK and MHFA; formal analysis: AW, GB, and YJ; investigation: MHFA, H-WK; resources: H-WK and SK; data curation: AW and GB; writing—original draft: AW and SK; writing—review and editing: H-WK and SK; visualization: MHFA and H-WK; supervision: H-WK and SK; project administration: H-WK and SK; funding acquisition: H-WK; All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A1A03039211).

Data availability

The mitochondrial genome sequence data that support the findings of this study are openly available in GenBank of NCBI at https://www.ncbi.nlm.nih.gov, under the accession no. OR487151.

Competing interests

The authors declare no conflict of interest.

Ethical approval

Not applicable.

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Elucidating the mitogenomic blueprint of Pomadasys perotaei from the Eastern Atlantic: Characterization and matrilineal phylogenetic insights into haemulid grunts (Teleostei: Lutjaniformes)

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Journal Publication

Version 1

Abstract

Figures

Introduction

Materials and Methods

Sample collection and species identification

DNA extraction and next-generation sequencing

Mitogenome assembly and annotation

Genomic characterization and comparative analyses

Dataset construction and phylogenetic analyses

Results

Mitogenome structure and organization

Features of Protein-coding genes

Substitution pattern and codon usage in haemulids mitogenome

Features of ribosomal RNA and transfer RNA

Features of control region

Matrilineal phylogenetic relationship of haemulid grunts

Discussion

Mitogenomic insights into the grunts

Genetic insights into haemulid conservation in the Eastern Atlantic

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

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