Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2023 Jan 12;18(1):e0280250. doi: 10.1371/journal.pone.0280250

Signature selection forces and evolutionary divergence of immune-survival genes compared between two important shrimp species

Tze Chiew Christie Soo 1, Subha Bhassu 1,2,*
Editor: Sebastian D Fugmann3
PMCID: PMC9836293  PMID: 36634148

Abstract

In recent years, shrimp aquaculture industry had grown significantly to become the major source of global shrimp production. Despite that, shrimp aquaculture production was impeded by various shrimp diseases over the past decades. Interestingly, different shrimp species demonstrated variable levels of immune strength and survival (immune-survival) ability towards different diseases, especially the much stronger immune-survival ability shown by the ancient shrimp species, Macrobrachium rosenbergii compared to other shrimp species. In this study, two important shrimp species, M. rosenbergii and Penaeus monodon (disease tolerant strain) (uninfected control and VpAHPND-infected) were compared to uncover the potential underlying genetic factors. The shrimp species were sampled, followed by RNA extraction and cDNA conversion. Five important immune-survival genes (C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6) were selected for PCR, sequencing, and subsequent genetics analysis. The overall genetic analyses conducted, including Analysis of Molecular Variance (AMOVA) and population differentiation, showed significant genetic differentiation (p<0.05) between different genes of M. rosenbergii and P. monodon. There was greater genetic divergence identified between HMGB subgroups of P. monodon (uninfected control and VpAHPND-infected) compared to other genes. Besides that, based on neutrality tests conducted, purifying selection was determined to be the main evolutionary driving force of M. rosenbergii and P. monodon with stronger purifying selection exhibited in M. rosenbergii genes. Potential balancing selection was identified for VpAHPND-infected HMGB subgroup whereas directional selection was detected for HMGB (both species) and ATPase 8/6 (only P. monodon) genes. The divergence times between M. rosenbergii and P. monodon genes were estimated through Bayesian molecular clock analysis, which were 438.6 mya (C-type Lectin), 1885.4 mya (HMGB), 432.6 mya (STAT), 448.1 mya (ALF3), and 426.4 mya (ATPase 8/6) respectively. In conclusion, important selection forces and evolutionary divergence information of immune-survival genes between M. rosenbergii and P. monodon were successfully identified.

1 Introduction

The shrimp aquaculture industry is an essential global economic sector, especially for some low- and middle-level developing economies in Latin America and Asia [1, 2]. The main cultured shrimp species, including Litopenaeus vannamei, Penaeus monodon, and Macrobrachium rosenbergii had contributed to around 6.0 million tonnes of the annual global shrimp aquaculture production in the year 2018 [2]. However, shrimp aquaculture production had been heavily impeded by different shrimp diseases, which was also associated with high economic losses [3]. There was a previous reporting of an estimated annual production loss of 22% and corresponding value loss of US$ 1 billion due to shrimp diseases [4]. Common shrimp diseases include Acute Hepatopancreatic Necrosis Disease (AHPND), White Spot Disease (WSD), Hepatopancreatic Microsporidiosis, Yellow Head Disease, and Infectious Myonecrosis [5].

AHPND is a well-known shrimp bacterial disease that can lead to 40–100% mortality within the early 35 days after shrimp stocking [6]. This shrimp disease had originally emerged from China in the year 2009 and then spread to Southeast Asia and Mexico regions [7, 8]. AHPND disease is mainly caused by a pathogenic strain of Gram-negative Vibrio parahaemolyticus bacteria, known as VpAHPND [9, 10]. The VpAHPND bacteria contain a 70-kbp plasmid (pVA1), which can encode for Photorhabdus insect-related (Pir) toxins, PirA and PirB [9]. The detection of the AHPND disease can be done through the observation of gross clinical signs, which include pale and atrophied hepatopancreas, empty gut, empty stomach, lethargy, and slow growth [6].

Additionally, differing levels of disease resistance or tolerance were exhibited by different shrimp species towards a particular shrimp disease. The AHPND-susceptible shrimp species are P. monodon, Fenneropenaeus chinensis, and L. vannamei [11]. Intriguingly, despite being susceptible to Vibriosis disease caused by V. parahaemolyticus bacteria [12], M. rosenbergii was shown to be not susceptible to AHPND infection under salinity condition of 20 ppt or lower [13]. Another suitable example would be WSD which caused lethality in penaeid shrimp species, including P. monodon, F. chinensis, and L. vannamei [14]. On the other hand, M. rosenbergii showed resistance and viral clearance ability towards WSD [15].

Different strategies have been applied to reduce shrimp disease outbreaks, such as selective breeding [16, 17], usage of Specific Pathogen Free shrimps [16, 18], trained immunity [19], biological methods (prebiotics, probiotics, phytobiotics, synbiotics, bacteriophages, and postbiotics) [20], and farm biosecurity management [16]. Selective breeding is crucial for the development of disease resistant or tolerant shrimps, such as selectively bred Taura Syndrome Virus-resistant L. vannamei shrimps [21] and selectively bred Gill-Associated Virus-tolerant P. monodon shrimps [22]. Furthermore, selective breeding for disease resistance or tolerance leads to stronger shrimp immune response, as shown by increased immune parameters of selectively bred WSD-resistant L. vannamei shrimps [23].

The immune genetic differences or alterations that contribute to varying levels of disease resistance or tolerance between different shrimp species or between selectively bred and normal shrimp lines need to be better understood to further improve disease control efforts. Genetic divergence is often caused by selection forces, for example, positive selection or purifying selection, which then leads to genome evolution and enhanced immune response [24, 25]. A greater insight into the vital evolutionary events that happened can be obtained through immune gene divergence time estimations, particularly along the ancient ancestral lines [26, 27].

Shrimps mainly utilize their innate immunity for survival and elimination of invading pathogens. The shrimp innate immune system generally consists of pathogen recognition receptors (C-type Lectin) [28], damage-associated molecular patterns [high mobility group box (HMGB)] [29], signalling pathways [janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway] [30], antimicrobial peptides [antilipopolysaccharide factor (ALF)] [28], and other immune-related genes (ATPase 8/6). Other important shrimp innate immune responses include prophenoloxidase system, clotting cascade, nodule formation, phagocytosis, encapsulation, and apoptosis [28, 31].

Therefore, in this study, we aim to investigate the important immune genetic differences between disease tolerant P. monodon and M. rosenbergii shrimps. The experimental focus was given to five selected vital immune genes, including C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6. Each of these genes has crucial function in the shrimp innate immune response and survival. Genetic sequences were then obtained for these immune-survival genes and subsequently used for evolutionary genetics analyses between P. monodon and M. rosenbergii. The signature genetic divergence, selection forces, and divergence times were successfully determined.

2 Materials and methods

2.1 Shrimp sample preparations

Macrobrachium rosenbergii juvenile prawns (15–20 cm body length) were acquired from a local prawn farm. A total number of 50 prawns were dissected with the hepatopancreas samples collected and stored for subsequent downstream applications. On the other hand, similarly, disease-tolerant crossbred (13th generation Madagascar strain crossed with 5th generation local strain) Penaeus monodon juvenile shrimps (15–20 cm body length) were obtained from a local shrimp farm. The shrimps were then kept aseptically for seven days acclimatization (27 shrimps in each tank) in aerated artificial seawater (30 ppt) at 28 ± 1.0°C.

2.2 VpAHPND experimental challenge

2.2.1 Pathogen preparations

P. monodon shrimps suspected with AHPND outbreaks were acquired and validated through gross clinical sign observation and AP3 PCR detection method [32]. The selective propagation of VpAHPND bacteria [33] was then done using digestive organs of VpAHPND-infected shrimps through a series of incubations in tryptic soy broth (TSB+), thiosulfate citrate bile salt (TCBS) agar, and tryptic soy agar (TSA+). The propagated bacteria were stored in cryovials (CRYOBANK™) at -80°C for usage in downstream experiments.

2.2.2 Pre-challenge preparations and experimental challenge

The acclimatized P. monodon shrimps were negatively screened using AP3 PCR method [32] for validation to be VpAHPND-free prior to the experimental challenge. During the VpAHPND experimental challenge, the P. monodon shrimps were infected with VpAHPND bacteria (KS17.S5-1 positive strain) (2 × 106 cfu/ml) [34] using a modified immersion method [10]. The negative control group shrimps were added with sterile TSB+ broth instead of VpAHPND bacteria. The shrimp hepatopancreas samples were obtained at 0, 3, 6, 12, 24, 36, and 48 hours post-infection (hpi) and stored at -80°C. The experimental challenge details were also described in a previous publication [33].

All shrimp tanks were equipped with water filters and aerators. The experimental challenge involved three biological replicates for each treatment and control group. The challenged P. monodon shrimps were positively screened using AP3 PCR method [32] to confirm the presence of VpAHPND bacteria. The ethical approval was given by University of Malaya for the study within its facilities (Ethical Application Ref: S/31012019/26112018-05/R).

2.3 Total RNA extraction and first strand cDNA synthesis

Total RNA samples were extracted from M. rosenbergii prawn and P. monodon shrimp hepatopancreas using TransZol Up Plus RNA Kit (TransGen Biotech, Beijing, China). The extracted RNA samples were then used for first-strand cDNA synthesis and DNA contaminant removal using TransScript® One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China). The manufacturer’s protocols were followed for the kits used. The cDNA samples acquired were stored and utilized in downstream experiments.

2.4 PCR and sequencing

Primer design was conducted using Primer Quest Tool software (https://sg.idtdna.com/Primerquest/home/Index) by referring to nucleotide sequences either downloaded from NCBI database or selected from previously acquired RNA-Seq analysis data (Differentially Expressed Genes sequences). The detailed information of designed and used primers were provided in S1A & S1B Table. The cDNA sequences of selected immune genes (C-type Lectin, HMGB, STAT, ALF3, ATPase 8/6) were determined through PCR amplification and Sanger Sequencing. A range of 30 to 50 individual sequences were obtained for each of the immune genes for both M. rosenbergii and P. monodon. Major regions of the gene sequences (>70%) particularly conserved domains were amplified using the designed primers to ensure validity and importance of the sequences obtained.

The PCR experiments involved the usage of GoTaq® Flexi DNA Polymerase (Promega, Wisconsin, USA) and Eppendorf Mastercycler EP Gradient S (Eppendorf, Hamburg, Germany)/Bio-Rad C1000 Thermal Cycler (Bio-Rad Laboratories, California, USA) instrument. Each PCR reaction had a total volume of 25 μL, including 5.0 μL 5 × GoTaq® Flexi Buffer, 1.5 μL 25 mM MgCl2 solution, 0.5 μL 10 mM dNTPs, 0.25 μL GoTaq® DNA Polymerase (5U/μL), 400 nM forward primer, 400 nM reverse primer, 1.2 μL template cDNA, and Nuclease-free water. The PCR amplification condition applied was initial denaturation of 95°C for 5 min, 40 cycles of 95°C for 45 s, TA °C for 45 s and 72°C for 1 min, and final extension of 72°C for 5 min.

Subsequently, the PCR product validation was done through agarose gel electrophoresis analysis, which involved Vivantis 100 bp DNA Ladder/100 bp Plus DNA Ladder (Vivantis Technologies, Selangor, Malaysia) and Major Science MP-300V/MBE-150 device (Major Science, California, USA). The validated PCR products were utilized for Sanger Sequencing analysis. The sequences obtained were then trimmed and compiled using MEGA7 [35] and Chromas (https://technelysium.com.au/wp/chromas/) software. The compiled sequences were used in subsequent genetics analyses.

2.5 Evolutionary genetics analysis

The acquired cDNA sequences were compared and analysed through different software, including Arlequin (Version 3.1) [36], DnaSP (Version 5.0) [37], MEGA7 [35], and R [38]. The sequence comparison was focused on different gene sequences of M. rosenbergii and P. monodon and different subgroup sequences of uninfected control and VpAHPND-infected P. monodon. The genetics analyses carried out using Arlequin software [36] are Analysis of Molecular Variance (AMOVA), Population Matrix Differentiation, Population Pairwise FSTs, and Neutrality tests (Ewens-Watterson, Tajima’s D, and Fu’s Fs).

Besides that, DNA divergence analysis was done through DnaSP software [37]. The number of conserved or diverged sites were also determined at both nucleotide and amino acid levels through MEGA7 software [35]. Subsequently, genetic differentiation display and PCA analysis were plotted using R software [38] for total cDNA sequences comparison (based on genetic distances). The software packages applied were ape, reshape2, and ggplot2 (genetic differentiation display) and ape, ggfortify, and ggplot2 (PCA analysis) respectively.

2.6 Phylogenetic and Bayesian molecular clock dating analyses

For further genetic divergence analysis, the acquired cDNA sequences were checked with JModelTest2 software [39, 40] to identify the optimal models of nucleotide substitution. The optimal nucleotide models were determined based on the best delta values of Akaike and Bayesian information criteria (AIC and BIC) for phylogenetic analysis and Bayesian analysis respectively. The total cDNA sequences were utilized for phylogenetic analysis using MEGAX software [41] through Maximum likelihood method (Number of bootstraps: 1000). Besides that, Bayesian Molecular Clock Dating analysis was also conducted using BEAST2 software [42] with relaxed clock model [43] (Chain length: 10000000 or before infinity and ESS>200) in this study. Different cDNA gene sequences including C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6 were involved. The genetic divergence time estimations were done by referring to a previously constructed time tree involving crabs, lobsters, and shrimps [44]. The estimated divergence time information between M. rosenbergii prawn family, Palaemonidae and P. monodon shrimp family, Penaeidae was utilized as the reference for the genetic divergence time estimation in this study. The prediction of divergence time, ancient divergence period, and evolutionary event was achieved through the Bayesian analysis.

3 Results

3.1 Genetic conservation or divergence analysis

Generally, Penaeus monodon and Macrobrachium rosenbergii gene sequences, which comprise five immune genes (C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6), were used for the genetic conservation or divergence analysis. A range of 30 to 50 sequences were utilized for each of the immune genes from both P. monodon and M. rosenbergii. The genetic conservation or divergence was initiated with AMOVA analysis to identify the degree of genetic differentiation as shown in (Table 1A–1C). From the AMOVA results, significant genetic differentiations (p<0.05) were determined between different genes, species-based combined gene sequences, and inter-species comparison within individual genes. The AMOVA results were supported by subsequent Population Matrix Differentiation and Population Pairwise FSTs analyses conducted. Based on the Population Matrix Differentiation analysis conducted (S2A Table), there were significant genetic differentiations (p<0.05) between different gene sequences of P. monodon and M. rosenbergii. This finding was validated through Population Pairwise FSTs analysis (S3 Table). Intriguingly, a subgroup comparison between VpAHPND-infected and uninfected control P. monodon gene sequences (S2B Table) showed significant genetic differentiations (p<0.05) for the C-type Lectin, HMGB, and ALF3 genes. Among these genes, HMGB had the highest level of genetic differentiation between its subgroups. This finding was validated by the DNA divergence analysis as shown in S4 Table. The number of conserved and diverged sites (S5A & S5B Table) were counted at both nucleotide and translated amino acid levels in which major divergence was found at 5’ and 3’ regions. In addition, for individual gene sequence variations, M. rosenbergii C-type Lectin showed the highest divergence whereas P. monodon STAT showed the highest conservation. These significant genetic differentiations were supported and visualized by further analyses of genetic differentiation display (Fig 1) and PCA (Fig 2). Overall, from the evolutionary genetics analysis done, it can also be identified that the gene with the highest inter-species variance was STAT whereas the gene with the lowest inter-species variance was C-type Lectin.

Table 1. Measures of genetic differentiation based on analysis of molecular variance (AMOVA) approach using Arlequin software.

(A) Genes
d.f. Sum of Squares Variance Component Percentage of Variation (%) Fixation Index (FST) p a
Among populations 9 13348.094 38.19654 98.91 - 0.00000*
Within populations 380 160.060 0.42121 1.09 - 0.00000*
Total 389 13508.154 38.61775 100 0.98909 0.00000±0.00000*
(B) Species-based Combined Gene Sequences (M. rosenbergii and P. monodon)
d.f. Sum of Squares Variance Component Percentage of Variation (%) Fixation Index (FST) p a
Among populations 1 39363.397 1484.82728 98.05 - 0.00000*
Within populations 51 1503.603 29.48240 1.95 - 0.00000*
Total 52 40867.000 1514.30968 100 0.98053 0.00000±0.00000*
(C) Inter-species Comparison within Individual Genes
Variance Component (Among populations) Percentage of Variation (%) Fixation Index (FCT) p a
C-type Lectin 35.08103 95.87 0.95872 0.00000±0.00000*
HMGB 38.98945 99.97 0.99967 0.00000±0.00000*
STAT 38.00000 100 1.00000 0.00000±0.00000*
ALF3 33.47946 99.19 0.99189 0.00000±0.00000*
ATPase 8/6 42.22899 99.10 0.99101 0.00000±0.00000*
Open in a new tab

*Statistically significant (p<0.05)

aProbability of finding a more extreme variance component than the observed by chance alone after 1000 permutations.

Population 1: M. rosenbergii gene sequences

Population 2: P. monodon gene sequences

Fig 1. Display of genetic differentiation between different genes of total cDNA sequences.

Fig 1

Open in a new tab

Mr: M. rosenbergii; Pm: P. monodon. Genes: C-type Lectin (CTL), HMGB, STAT, ALF3 (ALF), ATPase 8/6 (ATP).

Fig 2. Principal component analysis (PCA) between different genes of total cDNA sequences.

Fig 2

Open in a new tab

Mr: M. rosenbergii; Pm: P. monodon. CMA: Mr ALF3 cDNA, CMC: Mr C-type Lectin cDNA, CMH: Mr HMGB cDNA, CMS: Mr STAT cDNA, CMT: Mr ATPase 8/6 cDNA; CPA: Pm ALF3 cDNA, CPC: Pm C-type Lectin cDNA, CPH: Pm HMGB cDNA, CPS: Pm STAT cDNA, CPT: Pm ATPase 8/6 cDNA. Out: Outgroup; Ref: Reference.

3.2 Selection forces

The same gene sequences from the genetic conservation or divergence analysis were further utilized for the analysis of selection forces. Neutrality tests, including Ewens-Watterson, Tajima’s D, and Fu’s Fs (Table 2), were successfully conducted to analyse the selection forces acting on different genes of P. monodon and M. rosenbergii together with different subgroups of P. monodon. The results revealed that strong purifying selection force was associated with immune genes of both shrimp species. Overall, M. rosenbergii had stronger purifying selection forces detected compared to P. monodon except for the P. monodon STAT gene which possessed the strongest purifying selection force. Balancing selection force was detected for P. monodon C-type Lectin gene uninfected control subgroup. Interestingly, P. monodon HMGB gene VpAHPND-infected subgroup showed balancing selection force as well whereas its uninfected control subgroup showed purifying selection force. Besides that, directional selection forces were identified in M. rosenbergii HMGB and ATPase 8/6 genes along with P. monodon HMGB and STAT genes.

Table 2. Neutrality test using Arlequin software.

Sample Groups Ewens-Watterson (FOE) Tajima’s D (D) Fu’s Fs (FS)
MrCTL_cDNA - 0.61978 -5.69760
MrHMGB_cDNA 0.10227* -2.00749** -15.23859***
MrSTAT_cDNA -0.00015 0.24862 -10.82544***
MrALF_cDNA 0.01642 -0.46800 -11.32571***
MrATP_cDNA 0.07382* -1.10030 -5.94322
PmCTL_cDNA -0.01593 1.10310 -2.66422
PmHMGB_cDNA 0.09438* 0.60207 2.49707
PmSTAT_cDNA 0* -0.34324 -25.26160***
PmALF_cDNA 0.03019 0.12411 -5.61908***
PmATP_cDNA -0.10251 0.40351 0.90836
PmCTL_N_cDNA -0.03267 2.07464** -1.58936
PmCTL_A_cDNA -0.07507 0.36036 -0.35407
PmHMGB_N_cDNA 0.16658* -2.23052** 2.14324
PmHMGB_A_cDNA 0.01840 2.08852** 2.00110
PmSTAT_N_cDNA 0.06574* -0.36072 -9.67408***
PmSTAT_A_cDNA - -0.18171 -9.47945***
PmALF_N_cDNA -0.01542 0.01260 -3.36184***
PmALF_A_cDNA 0.06957 -0.22667 -2.94485***
PmATP_N_cDNA -0.10527 1.32709 0.96614
PmATP_A_cDNA -0.09431 0.59123 0.61288
Open in a new tab

Mr: M. rosenbergii; Pm: P. monodon

Genes: C-type Lectin (CTL), HMGB, STAT, ALF3 (ALF), ATPase 8/6 (ATP)

N: Uninfected control subgroup; A: VpAHPND-infected subgroup

Ewens-Watterson:

Foe<0 and P<0.025: Balancing selection

Foe>0 and P>0.975: Directional selection

(Foe: Deviate of homozygosity; Statistical Significance: P<0.025, >0.975)

Tajima’s D:

D = 0: Population evolving as per mutation-drift equilibrium. There is no evidence of selection.

D<0: A recent population expansion or purifying selection is suggested.

D>0: A recent population bottleneck or balancing selection is suggested.

(Statistical significance: D value of <-2 or >2; P<0.05)

Fu’s Fs:

FS< 0: A recent population expansion is suggested.

FS> 0: A recent population bottleneck is suggested.

(Statistical significance: P<0.02)

*Statistically significant (P<0.025 or >0.975)

**Statistically significant (P<0.05)

***Statistically significant (P<0.02)

3.3 Evolutionary divergence analyses

The total cDNA sequences used in the genetic and selection force analyses were analysed for evolutionary divergence through phylogenetic and Bayesian analyses. A maximum likelihood-based phylogenetic analysis was conducted using the total cDNA sequences as shown in Fig 3. From the phylogenetic analysis, it can be clearly observed that different gene sequences of P. monodon and M. rosenbergii were clustered accordingly. The gene sequences of C-type Lectin from the two shrimp species were grouped together in the closest distance followed by ALF3 gene sequences compared to other gene sequences. In addition, the genetic divergence times followed by event estimations of these immune genes between P. monodon and M. rosenbergii were determined through Bayesian Molecular Clock Dating method (Fig 4A–4E). The estimated divergence times obtained between M. rosenbergii and P. monodon immune genes were 438.6 million years ago (mya) (C-type Lectin), 1885.4 mya (HMGB), 432.6 mya (STAT), 448.1 mya (ALF3), and 426.4 mya (ATPase 8/6) respectively. The majority of the immune genes had divergence times in the Silurian period (443.7 mya to 416.0 mya) of the Paleozoic era (542 mya to 251 mya) except for the ALF3 gene that had time estimation in the Ordovician period (488.3 mya to 443.7 mya) and the HMGB gene that had time estimation in the Proterozoic era (2500 mya to 542 mya). Hence, a higher mutation rate was implied for the HMGB gene compared to the other genes which resulted in the very much earlier estimated divergence time in the Proterozoic era. The expected divergence time of the two shrimp species was indicated to be within the Paleozoic era as inferred from the estimated divergence times of the other genes obtained. The mentioned time periods were referred to a previously reported geological time scale [45, 46].

Fig 3. Maximum likelihood-based phylogenetic analysis generated using MEGA X software (Bootstrap: 1000) for total cDNA sequences.

Fig 3

Open in a new tab

Sample groups: M. rosenbergii and P. monodon. Genes: C-type Lectin, HMGB, STAT, ALF3 (ALF), and ATPase 8/6 (ATPase).

Fig 4. Bayesian molecular clock inference using BEAST2 software.

Fig 4

Open in a new tab

A: C-type Lectin, B: HMGB, C: STAT, D: ALF3, E: ATPase 8/6. (Gamma site model; Relaxed clock log normal; Chain length: 10000000 or before infinity and ESS>200). [Models used: A (HKY+G), B (TrN+G), C (HKY+G), D (HKY+G), E (HKY+G)].

4 Discussion

4.1 Evolutionary genetic variations and selection forces

Evolutionary genetic variations were demonstrated through AMOVA analysis results which showed that the compared immune genes are significantly different from each other in the same shrimp species or between Macrobrachium rosenbergii and Penaeus monodon. The significant inter-species genetic differentiations inferred that P. monodon and M. rosenbergii are distinctly separated in terms of genetic evolution. This finding is supported by a previously constructed prophenoloxidase phylogeny tree in which M. rosenbergii did not cluster together with P. monodon or other Penaeidae shrimps [47].

Notably, P. monodon HMGB possessed the highest subgroup genetic divergence (between uninfected control and VpAHPND-infected) followed by C-type Lectin and ALF3 compared to other genes. This observation was deduced to be caused by the occurrence of gene or transcript sequence alterations during AHPND infection for adaptation purpose due to the selection force exerted by the invading pathogen. A similar example would be the proposed host immune adaptation mechanisms triggered by HIV infection involving DNA methylation and alternative splicing, which then affected immune gene expression [48].

At individual species gene level, M. rosenbergii C-type Lectin had the highest level of variance whereas P. monodon STAT had the lowest level of variance. The individual species gene sequence variance is affected by the occurrence of Single Nucleotide Polymorphisms (SNPs) related to disease resistance [49]. The highest sequence variance shown in M. rosenbergii C-type Lectin gene was validated by the previously identified high sequence variations, additional domains, and diverse binding ability of the shrimp C-type Lectin due to its importance in immune recognition [50].

On the other hand, the lowest sequence variance shown in P. monodon STAT gene was postulated to be the effect of the highly conserved JAK/STAT pathway core components and functions in evolution [51, 52] as exemplified by the previously deduced grouping of the shrimp STAT into ancient STAT family [53]. These individual species gene variations were also associated with the reversed variance level observations at the inter-species level.

Genetic divergence is commonly found outside of conserved domains or translated regions. The highest degrees of divergence discovered in the 5’ and 3’ gene regions were inferred to be the effect of selection forces. This inference was supported by the identification of positive and negative selection forces that influenced the 5’ UTRs, 3’ UTRs, and cis-regulatory regions which then caused sequence variations in humans [54]. The vital function of 3’ UTRs in post-transcriptional regulation contributes to gene expression control and adaptive divergence [55].

The strong purifying selection force detected on the immune genes was postulated to be the main factor that caused the stronger immune response of M. rosenbergii in the recognition and elimination of invading pathogens. The P. monodon STAT gene with the strongest purifying selection force detected was also suggested to be crucial in the disease tolerance of the challenged P. monodon used in this study. Purifying selection is the most crucial evolutionary force of immune genes [25, 56–58] to achieve broad recognition of pathogens perhaps through conserved motifs [25]. Purifying selection was also inferred to be essential in the RNA sequence regulation for the maintenance of the correct RNA or protein functionalities in the innate immune system as shown by the previous discovery of its importance in the regulation of long dsRNA through RNA editing [59].

The P. monodon HMGB gene was postulated to be influenced by the balancing selection force formed during AHPND infection. Balancing selection plays vital role in the adaptive evolution across innate immune genes, particularly those involved in pathogen recognition. Immune genes showed molecular signatures of adaptation in response to various pathogens and associated selective pressures [60–62]. Besides that, directional selection was suggested to be an important evolutionary selection force acting on M. rosenbergii and P. monodon immune genes. The influence of directional selection on a Major Histocompatibility Class I marker in Rana temporaria was vital to achieve adaptation to Ranavirus [63]. A variable model of selection had also been determined for host-pathogen coevolution, which can be either balancing or directional selection for Daphnia pulex [61].

4.2 Evolutionary divergence and time estimation

The immune gene sequences of M. rosenbergii and P. monodon were compared and grouped accordingly in the phylogenetic analysis. Interestingly, M. rosenbergii and P. monodon C-type Lectin gene sequences were clustered closest to each other followed by ALF3 gene sequences compared to other gene sequences. This observation was postulated to be caused by the lowest level of divergence between M. rosenbergii and P. monodon C-type Lectin cDNA sequences among the compared gene sequences. C-type Lectin-like domains not only display recognition and functional versatility, but also possess highly conserved disulphide bridges associated with conserved hydrophobic and polar interactions [64]. The inter-species gene sequence conservation of C-type Lectin was supported by the previous demonstrations across different holometabolous insect species [65] and phylogenetically distinct viper species [66].

Besides that, similar inference of low ALF3 sequence divergence level between M. rosenbergii and P. monodon was also made to be accountable for the observed ALF3 gene sequences clustering. The inter-species gene sequence conservation of ALF3 was highlighted by the previously identified importance of conserved functional domain together with associated amino acid property and disulphide bond in maintaining the strong antibacterial activity of shrimp ALF [67]. Subsequently, the higher levels of inter-species evolutionary conservation shown in C-type Lectin and ALF3 genes compared to other immune genes were suggested to be the effect of their similar functional importance in the recognition and elimination of invading pathogens. This inference was validated by an example of the evolutionary conservation of a DExD/H-box helicase called DDX23 that plays vital role in the innate immune recognition of an RNA virus [68].

In addition, the estimated divergence times of C-type Lectin, STAT, ALF3, and ATPase 8/6 genes between M. rosenbergii and P. monodon were determined to be within the time range of 426.4 million years ago (mya) to 448.1 mya except for HMGB gene which was estimated at 1885.4 mya. These estimated divergence times were mostly grouped within the Paleozoic era (542 mya to 251 mya) [45, 46]. The main evolutionary event inferred to be responsible for these genetic divergences was the Cambrian Explosion. The Cambrian Explosion that happened around 520 mya to 515 mya caused the emergence of many different life forms on Earth, particularly those with hard body parts and eyes [69]. After the Cambrian animal diversification, the Ordovician and Silurian periods within the Paleozoic era were the crucial time periods of land colonization by arthropods and tetrapods [46].

The involvement of the oldest common ancestor of M. rosenbergii and P. monodon in the estimated genetic divergence was inferred as the oldest identified shrimp fossil can be traced back to the Devonian period (416 mya to 359.2 mya) [70]. Based on the fossil record, the occurrence of lineage divergence within the Penaeid shrimp superfamily, Penaeoidea was estimated to be around late Permian period (before 250 mya) [71]. This finding supported the estimated genetic divergence times between M. rosenbergii and P. monodon in the time periods before Permian period in this study. Furthermore, the deduced divergence time of the two shrimp species was shown to be within the Paleozoic era based on the estimated divergence times of the immune genes obtained. Intriguingly, a higher mutation rate was postulated for the HMGB gene evolution compared to the other genes which resulted in the very much earlier estimated divergence time in the Proterozoic era. The evolutionary mutation rates of different immune genes might be time dependent as exemplified by the previously demonstrated time dependency of human mitochondrial DNA mutation rate estimates [72]. Thus, the higher mutation rate of HMGB gene inferred might be caused by a unique evolutionary event that occurred in a particular time period.

5 Conclusion

In conclusion, significant genetic differentiations were identified among the immune-survival genes compared between Macrobrachium rosenbergii and Penaeus monodon, including C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6. For individual species genes, M. rosenbergii C-type Lectin showed the highest level of sequence divergence whereas P. monodon STAT showed the highest level of sequence conservation. For neutrality tests conducted, purifying selection was identified to be the main evolutionary force acting on the immune-survival genes of M. rosenbergii and P. monodon with overall stronger purifying selection force determined for M. rosenbergii immune-survival genes. The strongest purifying selection force was found on P. monodon STAT gene. Balancing selection was also determined to have occurred and acted on the P. monodon HMGB gene during AHPND infection. In addition, directional selection forces were identified for M. rosenbergii HMGB and ATPase 8/6 genes together with P. monodon HMGB and STAT genes. Based on the phylogenetic analysis, the C-type Lectin gene sequences were determined to be clustered closest between M. rosenbergii and P. monodon followed by ALF3 gene sequences compared to other genes. Furthermore, the Bayesian analysis successfully revealed estimated divergence times of C-type Lectin (438.6 mya), HMGB (1885.4 mya), STAT (432.6 mya), ALF3 (448.1 mya), and ATPase 8/6 (426.4 mya) genes between M. rosenbergii and P. monodon. The estimated divergence times were mostly within the Paleozoic era except for the unique estimated divergence time of HMGB in the Proterozoic era.

Evolutionary genetics analyses, including genetic differentiations, evolutionary selection forces, and estimation of divergence times were conducted for the immune-survival genes between M. rosenbergii and P. monodon. The important information uncovered from these evolutionary genetics analyses can bring deeper insight into shrimp genetic studies and potentially be referred by other researchers in similar field for experimental applications. Further evolutionary genetics analysis can also be conducted across many other shrimp species. The PCR primers designed in this study covering the conserved regions of immune-survival genes can be developed as shrimp health or immune activation diagnosis biomarkers. The developed biomarkers can then be utilized by shrimp farmers to minimize shrimp disease outbreaks. These biomarkers can assist in shrimp farm management and enhance precision aquaculture. The importance of biomarker application in shrimp aquaculture was emphasized by previous studies involving immune activation [73], temperature stress response [74], and environmental stress response [75]. The genetic information obtained can also assist in the development of stronger selective breeding programs to produce genetically enhanced shrimp lines with disease resistance or tolerance abilities.

Supporting information

S1 Table

List of primers designed and optimized for PCR amplification, (A) Primer information, (B) Primer sequences.

(DOCX)

Click here for additional data file. (19.2KB, docx)
S2 Table

Genetic differentiation matrix of populations (p values) under population differentiation using Arlequin software: (A) Gene comparison (B) P. monodon uninfected control versus VpAHPND-infected sub-group comparison.

(DOCX)

Click here for additional data file. (15.5KB, docx)
S3 Table. Genetic differentiation matrix of populations calculated by FST and p values (shown in parenthesis) under population pairwise FSTs using Arlequin software.

(DOCX)

Click here for additional data file. (13.9KB, docx)
S4 Table. Measures of nucleotide diversity (π) and net nucleotide divergence (DNA divergence between populations) using DNASP software.

(DOCX)

Click here for additional data file. (13.8KB, docx)
S5 Table

Comparison of conserved and diverged sites in M. rosenbergii and P. monodon gene sequences at (A) Nucleotide and (B) Amino acid levels.

(DOCX)

Click here for additional data file. (15.6KB, docx)

Acknowledgments

The authors would like to acknowledge all the help and support given by the AGAGEL lab members.

Data Availability

The sequence data has been deposited on NCBI GenBank database with the accession numbers of OP744026-OP744425. All other relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was completed under the funding of ST052-2022 UM International Collaboration Grant awarded to SB under Centre for Research in Biotechnology for Agriculture (CEBAR), IPPP, University of Malaya. URL: https://cebar.um.edu.my/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Dastidar PG, Mallik A, Mandal N. Contribution of shrimp disease research to the development of the shrimp aquaculture industry: an analysis of the research and innovation structure across the countries. Scientometrics. 2013;97(3): 659–674. [Google Scholar]
  • 2.Food and Agriculture Organization of the United Nations. The State of World Fisheries and Aquaculture 2020. Sustainability in action. Rome, Italy: The Organization; 2020. [Google Scholar]
  • 3.Asche F, Anderson JL, Botta R, Kumar G, Abrahamsen EB, Nguyen LT, et al. The economics of shrimp disease. J Invertebr Pathol. 2020;186: 107397. doi: 10.1016/j.jip.2020.107397 [DOI] [PubMed] [Google Scholar]
  • 4.Flegel TW, Lightner DV, Lo CF, Owens L. Shrimp disease control: past, present and future. Diseases in Asian Aquaculture VI. Fish Health Section, Asian Fisheries Society, Manila, Philippines. 2008;505: 355–378. [Google Scholar]
  • 5.Thitamadee S, Prachumwat A, Srisala J, Jaroenlak P, Salachan PV, Sritunyalucksana K, et al. Review of current disease threats for cultivated penaeid shrimp in Asia. Aquaculture. 2016;452: 69–87. [Google Scholar]
  • 6.Hong X, Lu L, Xu D. Progress in research on acute hepatopancreatic necrosis disease (AHPND). Aquac Int. 2016;24(2): 577–593. [Google Scholar]
  • 7.Food and Agriculture Organization of the United Nations. Report of the FAO/MARD technical workshop on early mortality syndrome (EMS) or acute hepatopancreatic necrosis syndrome (AHPNS) of cultured shrimp (UNDER TCP/VIE/3304). Fisheries and Aquaculture Report. 2013: 1053. [Google Scholar]
  • 8.Nunan L, Lightner D, Pantoja C, Gomez-Jimenez S. Detection of acute hepatopancreatic necrosis disease (AHPND) in Mexico. Dis Aquat Organ. 2014;111(1): 81–86. doi: 10.3354/dao02776 [DOI] [PubMed] [Google Scholar]
  • 9.Lee CT, Chen IT, Yang YT, Ko TP, Huang YT, Huang JY, et al. The opportunistic marine pathogen Vibrio parahaemolyticus becomes virulent by acquiring a plasmid that expresses a deadly toxin. Proc Natl Acad Sci. 2015;112(34): 10798–10803. doi: 10.1073/pnas.1503129112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Tran L, Nunan L, Redman RM, Mohney LL, Pantoja CR, Fitzsimmons K, et al. Determination of the infectious nature of the agent of acute hepatopancreatic necrosis syndrome affecting penaeid shrimp. Dis Aquat Organ. 2013;105(1): 45–55. doi: 10.3354/dao02621 [DOI] [PubMed] [Google Scholar]
  • 11.Zorriehzahra MJ, Banaederakhshan R. Early mortality syndrome (EMS) as new emerging threat in shrimp industry. Adv Anim Vet Sci. 2015;3(2S): 64–72. [Google Scholar]
  • 12.Khuntia CP, Das BK, Samantaray BR, Samal SK, Mishra BK. Characterization and pathogenicity studies of Vibrio parahaemolyticus isolated from diseased freshwater prawn, Macrobrachium rosenbergii (de Man). Aquac Res. 2008;39(3): 301–310. [Google Scholar]
  • 13.Schofield PJ, Noble BL, Caro LFA, Mai HN, Padilla TJ, Millabas J, et al. Pathogenicity of Acute Hepatopancreatic Necrosis Disease (AHPND) on the freshwater prawn, Macrobrachium rosenbergii, and Pacific White Shrimp, Penaeus vannamei, at various salinities. Aquac Res. 2021;52(4): 1480–1489. [Google Scholar]
  • 14.Pradeep B, Rai P, Mohan SA, Shekhar MS, Karunasagar I. Biology, host range, pathogenesis and diagnosis of white spot syndrome virus. Indian J Virol. 2012;23(2): 161–174. doi: 10.1007/s13337-012-0079-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sarathi M, Basha AN, Ravi M, Venkatesan C, Kumar BS, Hameed AS. Clearance of white spot syndrome virus (WSSV) and immunological changes in experimentally WSSV-injected Macrobrachium rosenbergii. Fish Shellfish Immunol. 2008;25(3): 222–230. doi: 10.1016/j.fsi.2008.04.011 [DOI] [PubMed] [Google Scholar]
  • 16.Moss SM, Moss DR, Arce SM, Lightner DV, Lotz JM. The role of selective breeding and biosecurity in the prevention of disease in penaeid shrimp aquaculture. J Invertebr Pathol. 2012;110(2): 247–250. doi: 10.1016/j.jip.2012.01.013 [DOI] [PubMed] [Google Scholar]
  • 17.Houston RD. Future directions in breeding for disease resistance in aquaculture species. Rev Bras Zootec. 2017;46: 545–551. [Google Scholar]
  • 18.Sanz VA. Specific pathogen free (SPF), specific pathogen resistant (SPR) and specific pathogen tolerant (SPT) as part of the biosecurity strategy for whiteleg shrimp (Penaeus vannamei Boone 1931). Asian Fish Sci. 2018;31S: 112–120. [Google Scholar]
  • 19.Flegel TW. A future vision for disease control in shrimp aquaculture. J World Aquac Soc. 2019;50(2): 249–266. [Google Scholar]
  • 20.Pérez-Sánchez T, Mora-Sánchez B, Balcázar JL. Biological approaches for disease control in aquaculture: advantages, limitations and challenges. Trends Microbiol. 2018;26(11): 896–903. doi: 10.1016/j.tim.2018.05.002 [DOI] [PubMed] [Google Scholar]
  • 21.Argue BJ, Arce SM, Lotz JM, Moss SM. Selective breeding of Pacific white shrimp (Litopenaeus vannamei) for growth and resistance to Taura Syndrome Virus. Aquaculture. 2002;204(3–4): 447–460. [Google Scholar]
  • 22.Noble TH. Selective breeding for tolerance to gill-associated virus in the black tiger shrimp, Penaeus monodon. Doctoral dissertation, James Cook University. 2018.
  • 23.Huang Y, Yin Z, Weng S, He J, Li S. Selective breeding and preliminary commercial performance of Penaeus vannamei for resistance to white spot syndrome virus (WSSV). Aquaculture. 2012;364: 111–117. [Google Scholar]
  • 24.Ellegren H. Comparative genomics and the study of evolution by natural selection. Mol Ecol. 2008;17(21): 4586–4596. doi: 10.1111/j.1365-294X.2008.03954.x [DOI] [PubMed] [Google Scholar]
  • 25.Mukherjee S, Sarkar-Roy N, Wagener DK, Majumder PP. Signatures of natural selection are not uniform across genes of innate immune system, but purifying selection is the dominant signature. Proc Natl Acad Sci. 2009;106(17): 7073–7078. doi: 10.1073/pnas.0811357106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Hellgren O, Ekblom R. Evolution of a cluster of innate immune genes (β-defensins) along the ancestral lines of chicken and zebra finch. Immunome Res. 2010;6(1): 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Montaño AM, Tsujino F, Takahata N, Satta Y. Evolutionary origin of peptidoglycan recognition proteins in vertebrate innate immune system. BMC Evol Biol. 2011;11(1): 1–10. doi: 10.1186/1471-2148-11-79 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Li F, Xiang J. Recent advances in researches on the innate immunity of shrimp in China. Dev Comp Immunol. 2013;39(1–2): 11–26. doi: 10.1016/j.dci.2012.03.016 [DOI] [PubMed] [Google Scholar]
  • 29.Chen YY, Chen JC, Lin YC, Kitikiew S, Li HF, Bai JC, et al. Endogenous molecules induced by a Pathogen-Associated Molecular Pattern (PAMP) elicit innate immunity in shrimp. PLoS One. 2014;9(12): e115232. doi: 10.1371/journal.pone.0115232 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Kulkarni A, Krishnan S, Anand D, Kokkattunivarthil Uthaman S, Otta SK, Karunasagar I, et al. Immune responses and immunoprotection in crustaceans with special reference to shrimp. Rev Aquac. 2021;13(1): 431–459. [Google Scholar]
  • 31.Aguirre-Guzman G, Sanchez-Martinez JG, Campa-Cordova AI, Luna-Gonzalez A, Ascencio F. Penaeid shrimp immune system. Wetchasan Sattawaphaet. 2009;39(3): 205–215. [Google Scholar]
  • 32.Sirikharin R, Taengchaiyaphum S, Sanguanrut P, Chi TD, Mavichak R, Proespraiwong P, et al. Characterization and PCR detection of binary, Pir-like toxins from Vibrio parahaemolyticus isolates that cause acute hepatopancreatic necrosis disease (AHPND) in shrimp. PLoS One. 2015;10(5): e0126987. doi: 10.1371/journal.pone.0126987 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Soo TCC, Devadas S, Din MSM, Bhassu S. Differential transcriptome analysis of the disease tolerant Madagascar–Malaysia crossbred black tiger shrimp, Penaeus monodon hepatopancreas in response to acute hepatopancreatic necrosis disease (AHPND) infection: inference on immune gene response and interaction. Gut Pathog. 2019;11(1): 1–13. doi: 10.1186/s13099-019-0319-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Devadas S, Bhassu S, Soo TCC, Mohamed Iqbal SN, Yusoff FM, Shariff M. Draft genome sequence of a Vibrio parahaemolyticus strain, KS17. S5-1, with multiple antibiotic resistance genes, which causes acute hepatopancreatic necrosis disease in Penaeus monodon in the West Coast of Peninsular Malaysia. Microbiol Resour Announc. 2018;7(2): e00829–18. doi: 10.1128/MRA.00829-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33(7): 1870–1874. doi: 10.1093/molbev/msw054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Excoffier L, Laval G, Schneider S. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform. 2005;1: 117693430500100003. [PMC free article] [PubMed] [Google Scholar]
  • 37.Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25(11): 1451–1452. doi: 10.1093/bioinformatics/btp187 [DOI] [PubMed] [Google Scholar]
  • 38.Team RC. R: A language and environment for statistical computing. 2013. [cited 2022 Jan 31]. Available from: https://www.R-project.org/. [Google Scholar]
  • 39.Darriba D, Taboada GL, Doallo R, Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat Methods. 2012;9(8): 772–772. doi: 10.1038/nmeth.2109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Guindon S, Gascuel O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003;52(5): 696–704. doi: 10.1080/10635150390235520 [DOI] [PubMed] [Google Scholar]
  • 41.Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35(6): 1547. doi: 10.1093/molbev/msy096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 2019;15(4): e1006650. doi: 10.1371/journal.pcbi.1006650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 2006;4(5): e88. doi: 10.1371/journal.pbio.0040088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Crandall KA, Porter ML, Pérez-Losada M. Crabs, shrimps, and lobsters (decapoda). In: Hedges SB, Kumar S, editors. The Timetree of Life. Oxford, United Kingdom: Oxford University Press; 2009. pp. 293–297. [Google Scholar]
  • 45.Gradstein FM, Ogg JG, Smith AG, Bleeker W, Lourens LJ. A new geologic time scale, with special reference to Precambrian and Neogene. Episodes. 2004;27(2): 83–100. [Google Scholar]
  • 46.Knoll AH, Nowak MA. The timetable of evolution. Sci Adv. 2017;3(5): e1603076. doi: 10.1126/sciadv.1603076 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Padhi A, Verghese B, Vaid A, Otta SK. Pattern of nucleotide substitution and divergence of prophenoloxidase in decapods. Fish Shellfish Immunol. 2007;22(6): 628–640. doi: 10.1016/j.fsi.2006.08.010 [DOI] [PubMed] [Google Scholar]
  • 48.Zeng X, Tsui JCC, Shi M, Peng J, Cao CY, Kan LLY, et al. Genome-Wide Characterization of Host Transcriptional and Epigenetic Alterations During HIV Infection of T Lymphocytes. Front Immunol. 2020;11: 2131. doi: 10.3389/fimmu.2020.02131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Liu J, Yu Y, Li F, Zhang X, Xiang J. A new anti-lipopolysaccharide factor (ALF) gene with its SNP polymorphisms related to WSSV-resistance of Litopenaeus vannamei. Fish Shellfish Immunol. 2014;39(1): 24–33. doi: 10.1016/j.fsi.2014.04.009 [DOI] [PubMed] [Google Scholar]
  • 50.Wang XW, Vasta GR, Wang JX. The functional relevance of shrimp C-type lectins in host-pathogen interactions. Dev Comp Immunol. 2020;109: 103708. doi: 10.1016/j.dci.2020.103708 [DOI] [PubMed] [Google Scholar]
  • 51.Arbouzova NI, Zeidler MP. JAK/STAT signalling in Drosophila: insights into conserved regulatory and cellular functions. Development. 2006;133: 2605–2616. doi: 10.1242/dev.02411 [DOI] [PubMed] [Google Scholar]
  • 52.Song X, Zhang Z, Wang S, Li H, Zuo H, Xu X, et al. A Janus Kinase in the JAK/STAT signaling pathway from Litopenaeus vannamei is involved in antiviral immune response. Fish Shellfish Immunol. 2015;44(2): 662–673. doi: 10.1016/j.fsi.2015.03.031 [DOI] [PubMed] [Google Scholar]
  • 53.Chen WY, Ho KC, Leu JH, Liu KF, Wang HC, Kou GH, et al. WSSV infection activates STAT in shrimp. Dev Comp Immunol. 2008;32(10): 1142–1150. doi: 10.1016/j.dci.2008.03.003 [DOI] [PubMed] [Google Scholar]
  • 54.Torgerson DG, Boyko AR, Hernandez RD, Indap A, Hu X, White TJ, et al. Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence. PLoS Genet. 2009;5(8): e1000592. doi: 10.1371/journal.pgen.1000592 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Xiong P, Hulsey CD, Meyer A, Franchini P. Evolutionary divergence of 3’UTRs in cichlid fishes. BMC Genomics. 2018;19(1): 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Jiggins FM, Hurst GD. The evolution of parasite recognition genes in the innate immune system: purifying selection on Drosophila melanogaster peptidoglycan recognition proteins. J Mol Evol. 2003;57(5): 598–605. doi: 10.1007/s00239-003-2506-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Jiggins FM, Kim KW. The evolution of antifungal peptides in Drosophila. Genetics. 2005;171(4): 1847–1859. doi: 10.1534/genetics.105.045435 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Lehmann T, Hume JC, Licht M, Burns CS, Wollenberg K, Simard F, et al. Molecular evolution of immune genes in the malaria mosquito Anopheles gambiae. PLoS One. 2009;4(2): e4549. doi: 10.1371/journal.pone.0004549 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Barak M, Porath HT, Finkelstein G, Knisbacher BA, Buchumenski I, Roth SH, et al. Purifying selection of long dsRNA is the first line of defense against false activation of innate immunity. Genome Biol. 2020;21(1): 1–10. doi: 10.1186/s13059-020-1937-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Ferrer-Admetlla A, Bosch E, Sikora M, Marquès-Bonet T, Ramírez-Soriano A, Muntasell A, et al. Balancing selection is the main force shaping the evolution of innate immunity genes. J Immunol. 2008;181(2): 1315–1322. doi: 10.4049/jimmunol.181.2.1315 [DOI] [PubMed] [Google Scholar]
  • 61.McTaggart SJ, Obbard DJ, Conlon C, Little TJ. Immune genes undergo more adaptive evolution than non-immune system genes in Daphnia pulex. BMC Evol Biol. 2012;12(1): 1–11. doi: 10.1186/1471-2148-12-63 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Wilson JN, Rockett K, Keating B, Jallow M, Pinder M, Sisay-Joof F, et al. A hallmark of balancing selection is present at the promoter region of interleukin 10. Genes Immun. 2006;7(8): 680–683. doi: 10.1038/sj.gene.6364336 [DOI] [PubMed] [Google Scholar]
  • 63.Teacher AG, Garner TW, Nichols RA. Evidence for directional selection at a novel major histocompatibility class I marker in wild common frogs (Rana temporaria) exposed to a viral pathogen (Ranavirus). PLoS One. 2009;4(2): e4616. doi: 10.1371/journal.pone.0004616 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Zelensky AN, Gready JE. The C‐type lectin‐like domain superfamily. FEBS J. 2005;272(24): 6179–6217. doi: 10.1111/j.1742-4658.2005.05031.x [DOI] [PubMed] [Google Scholar]
  • 65.Lu Y, Su F, Zhu K, Zhu M, Li Q, Hu Q, et al. Comparative genomic analysis of C-type lectin-domain genes in seven holometabolous insect species. Insect Biochem Mol Biol. 2020;126: 103451. doi: 10.1016/j.ibmb.2020.103451 [DOI] [PubMed] [Google Scholar]
  • 66.Harrison RA, Oliver J, Hasson SS, Bharati K, Theakston RDG, et al. Novel sequences encoding venom C-type lectins are conserved in phylogenetically and geographically distinct Echis and Bitis viper species. Gene. 2003;315: 95–102. doi: 10.1016/s0378-1119(03)00716-9 [DOI] [PubMed] [Google Scholar]
  • 67.Li S, Lv X, Li F, Xiang J. Characterization of a lymphoid organ specific anti-lipopolysaccharide factor from shrimp reveals structure-activity relationship of the LPS-binding domain. Front Immunol. 2019;10: 872. doi: 10.3389/fimmu.2019.00872 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Ruan J, Cao Y, Ling T, Li P, Wu S, Peng D, et al. DDX23, an evolutionary conserved dsRNA sensor, participates in innate antiviral responses by pairing with TRIF or MAVS. Front Immunol. 2019;10: 2202. doi: 10.3389/fimmu.2019.02202 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Parker AR. On the origin of optics. Opt Laser Technol. 2011;43(2): 323–329. [Google Scholar]
  • 70.Feldmann RM, Schweitzer CE. The oldest shrimp (Devonian: Famennian) and remarkable preservation of soft tissue. J Crust Biol. 2010;30(4): 629–635. [Google Scholar]
  • 71.Ma KY, Chan TY, Chu KH. Phylogeny of penaeoid shrimps (Decapoda: Penaeoidea) inferred from nuclear protein-coding genes. Mol Phylogenet Evol. 2009;53(1): 45–55. doi: 10.1016/j.ympev.2009.05.019 [DOI] [PubMed] [Google Scholar]
  • 72.Henn BM, Gignoux CR, Feldman MW, Mountain JL. Characterizing the time dependency of human mitochondrial DNA mutation rate estimates. Mol Biol Evol. 2009;26(1): 217–230. doi: 10.1093/molbev/msn244 [DOI] [PubMed] [Google Scholar]
  • 73.Zeng S, Zhou R, Bao S, Li X, Deng Z, Hou D, et al. Identification of multigene biomarker for shrimp white feces syndrome by full-length transcriptome sequencing. Front Genet. 2020;11: 71. doi: 10.3389/fgene.2020.00071 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Visudtiphole V, Watthanasurorot A, Klinbunga S, Menasveta P, Kirtikara K. Molecular characterization of Calreticulin: a biomarker for temperature stress responses of the giant tiger shrimp Penaeus monodon. Aquaculture. 2010;308: S100–S108. [Google Scholar]
  • 75.Schock TB, Duke J, Goodson A, Weldon D, Brunson J, Leffler JW, et al. Evaluation of Pacific white shrimp (Litopenaeus vannamei) health during a superintensive aquaculture growout using NMR-based metabolomics. PLoS One. 2013;8(3): e59521. doi: 10.1371/journal.pone.0059521 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table

List of primers designed and optimized for PCR amplification, (A) Primer information, (B) Primer sequences.

(DOCX)

Click here for additional data file. (19.2KB, docx)
S2 Table

Genetic differentiation matrix of populations (p values) under population differentiation using Arlequin software: (A) Gene comparison (B) P. monodon uninfected control versus VpAHPND-infected sub-group comparison.

(DOCX)

Click here for additional data file. (15.5KB, docx)
S3 Table. Genetic differentiation matrix of populations calculated by FST and p values (shown in parenthesis) under population pairwise FSTs using Arlequin software.

(DOCX)

Click here for additional data file. (13.9KB, docx)
S4 Table. Measures of nucleotide diversity (π) and net nucleotide divergence (DNA divergence between populations) using DNASP software.

(DOCX)

Click here for additional data file. (13.8KB, docx)
S5 Table

Comparison of conserved and diverged sites in M. rosenbergii and P. monodon gene sequences at (A) Nucleotide and (B) Amino acid levels.

(DOCX)

Click here for additional data file. (15.6KB, docx)

Data Availability Statement

The sequence data has been deposited on NCBI GenBank database with the accession numbers of OP744026-OP744425. All other relevant data are within the manuscript and its Supporting Information files.

Articles from PLOS ONE are provided here courtesy of PLOS

RESOURCES