Selective breeding for tolerance to gill-associated virus in the black tiger shrimp, Penaeus monodon

Noble, Tansyn Honi (2018) Selective breeding for tolerance to gill-associated virus in the black tiger shrimp, Penaeus monodon. PhD thesis, James Cook University.

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Shrimp farming has expanded dramatically to now be the second most valuable aquaculture industry globally. However, with the expansion of shrimp farming has come the spectre of serious diseases that some estimate wipe out up to 40% of total production. With limited tools available for managing disease, the shrimp industry has employed three strategies to lower the disease risks, including use of specific pathogen free (SPF) shrimp, breeding disease resistant/tolerant shrimp, and using best management practices to reduce stress on animals. The use of selective breeding to improve disease resistance has been in some instances an effective strategy for reducing the risk of aquatic diseases. In the context of shrimp breeding programs, sib-selection is commonly practiced where progeny are evaluated for tolerance using controlled disease challenge tests, family breeding values are estimated, and siblings from the highest ranked families are selected to breed from. However, there is no standard protocol for disease challenge tests resulting in a number of methods and designs being used. Consequently, the accuracy of disease challenge tests for shrimp selective breeding programs are poorly understood.

In Australia, the shrimp farming industry is almost entirely based on the use of wild caught Penaeus monodon broodstock to supply seed. Therefore, domesticated and selectively bred stocks including SPF stocks are currently unavailable. Development of domesticated and selectively bred stocks are of huge interest to the local industry with several breeding programs under development. At the time of this PhD research, the most problematic disease affecting the local industry was caused by gill-associated virus (GAV), which is a highly prevalent virus in both wild and farmed stocks that can cause significant production loss. Selective breeding techniques may be useful in managing disease associated with GAV. This research aimed to address the current knowledge gaps specifically related to establishing a reliable method for measuring GAV disease responses (through survival and viral load) and to elucidate the underlying genetic basis of GAV disease tolerance traits and how they are linked to commercial production traits.

One of the difficulties of shrimp disease challenge tests is establishing a standardised and repeatable method that allows for accurate genetic parameter estimation. Various pathogen infection methods have been used to establish disease, including feeding, injection and waterborne methods. Each method has its own advantages and disadvantages. To investigate the utility of each of these challenge methods for establishing a reliable GAV challenge protocol, groups of juvenile P. monodon (2 – 10 g) were challenged using either intramuscular injection of a weight-standardised dose of GAV inoculum, feeding of both fresh and frozen GAV-infected shrimp meat, and immersion in water containing the GAV inoculum. The three groups were compared based on mortality and GAV infection load measured in the survivors using a reverse transcription qPCR (RT-qPCR) assay. Results demonstrated that immersion of shrimp for 2 h in GAV contaminated water resulted in no mortalities over the 11 day challenge period and furthermore RT-qPCR identified no evidence of these shrimp becoming infected. Intramuscular injection resulted in the quickest mortality rate, with cumulative mortality surpassing 50% on day 7 post-challenge (p.c.), while feeding of GAV infected shrimp meat resulted in cumulative mortality of ~50% on day 11 p.c. Results from the RT-qPCR analysis revealed the survivors of the injection group had approximately 100-fold higher and more consistent GAV infection loads compared to the group fed infected shrimp meat. Mean GAV infection loads (after log10 transformation) of the injection group survivors was 7.73 ± 0.49 GAV copies μg⁻¹ TNA and for the groups fed fresh and frozen infected shrimp meat, GAV infection loads were 5.71 ± 1.97 and 4.77 ± 1.90 GAV copies μg⁻¹ TNA, respectively. The coefficient of variation (CV) among individual GAV infection loads of shrimp injected with GAV was much lower (CV = 0.06) compared to the survivors of the feeding groups (CV = 0.41 & 0.33). These results suggest, of the three challenge methods assessed for their suitability in establishing a standardised GAV challenge protocol, injection provided the most uniform and reliable means of infecting shrimp and consequently was the preferred method of infection to use for subsequent GAV challenge tests.

Virus detection and quantification of infection load are important measures for managing disease in shrimp farming, for understanding disease responses and potentially as a useful indirect measure of disease tolerance for selective breeding. Understanding how GAV infection loads vary between and within commonly sampled tissues (pleopod and gill filaments) is necessary in order to generate accurate phenotypic measures. Using RT-qPCR methods to quantify GAV infection loads, two groups of juvenile P. monodon naturally infected with GAV were examined. Gill-associated virus infection loads were found to vary considerably within the same tissue type (both within pleopod and gill tissue) collected from the same individual shrimp by up to ~3000-fold. However, there was no significant difference in the sensitivity of either pleopod or gill tissue in either group examined (P > 0.05), or similarly, there was no difference in the coefficient of variation (i.e. variability) in GAV loads among individual gill filaments or pleopods in either group (P > 0.05). The results from this research indicate no difference between gill or pleopod tissue as more or less suitable for generating data on GAV infections. What was found to be critical was sampling of more than one gill filament or pleopod given the large within-tissue variability observed to provide more accurate data on GAV presence and relative infection loads. Consequently, a minimum of three gill filaments were used in subsequent experiments when generating data on GAV infections.

The next steps in this research were to assess whether the GAV challenge methodologies established would be sensitive enough to differentiate shrimp families based on their survivorship and/or GAV infection loads. First, a suitable dose of the GAV inoculum needed to be identified using a series of titration experiments. In these experiments different dilutions of the inoculum were injected in groups of shrimp and their mortality was tracked over a defined period (~ 14 days). The dilutions assessed across three experiments ranged from undiluted to ~ 1:80,000 dilution. Based on the results of three titration experiments a dilution of 1:3000 was chosen as this dose consistently resulted in ~50% mortality by day 14 p.c., which should allow for differentiation of families. This dilution was then applied to a small number of shrimp families to determine whether the challenge methodology and dose were sensitive enough to differentiate family-based tolerances. Following traditional designs, where families are reared in separate tanks in order to easily track pedigrees, seven shrimp families were bred and kept in separate tanks throughout their rearing and subsequent challenge tests. Shrimp from each family were challenged via injection of the GAV inoculum diluted 1:3000. Survival of the seven families was tracked over 35 days and GAV infection loads were quantified from the survivors of each family. Overall survival at the termination of the challenge was 44%, with survival among families ranging from 22 – 72%. Using Cox proportional hazards mixed models, genetic (family) and non-genetic effects (rearing and challenge tanks) on survival were analysed. The results from these models revealed significant variability due to genetic effects (family), but also considerable variability due to separate rearing and challenge tanks. For example, survival among groups from shrimp from the same family and reared in the same tank varied from 0 – 100% between replicate challenge tanks. This level of non-genetic variability could easily mask genetic effects. Gill-associated virus infections were observed in 46% of the challenge survivors and prevalence varied between families from 0 to 100%. Mean GAV infection loads among families with GAV present ranged from 3.77 × 10² to 2.49 × 107 GAV copies μg⁻¹ TNA. It is important to note that shrimp used in this experiment were also heavily infected with another endemic virus IHHNV, which may have interfered with the GAV infection response. This study provides the first evidence of family differences in GAV induced mortality, but also highlights the importance of non-genetic factors such as separate rearing and challenge tanks that can greatly impact the observed performance of shrimp during disease challenge tests.

Before GAV disease tolerance can be incorporated as a trait in a selective breeding program, knowledge of the underlying genetic basis needs to be established. Large numbers of families are needed to accurately estimate key genetic parameters such as heritability and genetic correlations between traits in order to predict genetic gains and optimise the breeding program design. Given the significant variability observed due to non-genetic factors like separate family rearing and challenge tanks from previous experiments, a new approach to shrimp disease challenge tests needed to be implemented. Therefore, in this experiment pools of families that were spawned and reared under a common environment were used to estimate genetic parameters of GAV disease tolerance in P. monodon. Shrimp were challenged via injection of the same GAV inoculum used previously diluted at 1:3000 and individual dosage (inoculum volume) was standardised for body weight. Mortality and genetic pedigree data were collected from 1717 shrimp made up of full (n = 72) and half-sib (maternal n = 42, paternal n = 30) families. Gill-associated virus load was measured on the challenge survivors (n = 963) to determine its utility as an indirect measure of tolerance. Variance components were estimated for mortality using a binomial animal model (mortality as a binary trait) and Cox's proportional hazards animal model (mortality as a longitudinal trait incorporating time until death data), while GAV load was analysed using a linear animal model. Overall mortality at the end of the challenge test was 35.5%, but ranged from 0 to 71% among families with 10 or more offspring. Heritability (h²) estimates for mortality were h² = 0.11 ± 0.03 using the binomial model and h² = 0.14 using the Cox's model. In addition, family rankings using estimated breeding values (EBV) did not differ between the two models (rEBV = 0.99). Heritability for viral load was h² = 0.21 ± 0.07, however, genetic correlations and correlations of family EBVs between mortality and GAV load were weak (rg = 0.30 ± 0.23 and rEBV = 0.17ns), suggesting GAV load may not be a good indirect measure of GAV induced mortality, at least in the way the data was collected in this experiment (i.e. on survivors only). Overall, the results from this experiment demonstrate for the first time that a pooled family design can be used to estimate significant genetic variation of GAV disease tolerance among P. monodon families and that this trait could be improved through targeted selective breeding.

When incorporating a trait for selection it is important to understand how the trait is genetically correlated with other traits of economic importance. Therefore, this study utilised siblings of those evaluated for GAV tolerance measured using controlled challenge tests to estimate genetic parameters for three commercial production traits, GAV infection prevalence (GAV infection status), GAV infection load (GAV load) and body weight (BW), and to assess the correlation between the commercial production traits and disease tolerance traits measured under controlled challenge conditions. To do this, 1835 shrimp were sampled from two replicate commercial ponds and their pedigrees determined via genotyping and parentage analyses. The total number of full-sib families identified among those sampled was 80, which included 55 maternal half-sib and 30 paternal half-sib families. Data on GAV infection traits were determined using RT-qPCR from 913 shrimp tested. The overall prevalence of GAV infection was 45.5% and the mean GAV load of those that were positive (n = 415) was 3.11 ± 1.42 log₁₀ GAV copies μg⁻¹ TNA. The mean BW of shrimp sampled from the two ponds was 15.88 ± 3.68 g, but differed significantly between the two ponds and between male and female shrimp (i.e. males were smaller). Heritability estimates for each of the three traits were assessed using mixed animal models with GAV infection status (infected versus not infected) analysed as a binary trait using a binomial model and GAV load (after log10 transformation) and BW analysed using linear models. Heritability estimates for each trait were; GAV infection status h² = 0.06 ± 0.03, GAV load h² = 0.21 ± 0.10 and BW h² = 0.38 ± 0.07. Genetic correlations (using bivariate models and Pearson's correlations of family EBVs) among the commercial production traits were all positive but ranged from low to high. Correlations between the GAV infection status and GAV load were moderate to high (rg = 0.90 ± 0.24 and rEBV = 0.36), meaning selection for low GAV load would likely lead to lower GAV infection prevalence as well. Correlations between the two GAV infection traits and BW were also positive but were weak, with GAV infection vs BW rg = 0.36 ± 0.26 and rEBV 0.10 and GAV load vs BW rg = 0.26 ± 0.25 and rEBV 0.13. Given the low correlations and high standard errors between these traits, suggests that selecting for increased body weight would unlikely have a significant impact on GAV infections. Of critical importance, however, were correlations between GAV tolerance traits measured under controlled challenge conditions and the commercial productions traits evaluated in this study. Using Pearson's correlations of family EBVs for each trait, GAV induced mortality and GAV infection load from challenged shrimp survivors were not significantly correlated with either GAV infection status or GAV load from pond reared shrimp (- 0.06 ≤ rEBV ≥ -0.27). These results suggest that viral infection data measured from pond reared shrimp may not be a good indicator of GAV tolerance measured as mortality under controlled challenge tests. However, there was no GAV related disease outbreak that occurred in the ponds evaluated. Correlations between the challenge test traits and body weight were also not significant (0.06 ≤ rEBV ≥ 0.10., The lack of significant correlations between GAV tolerance traits measured under controlled challenge conditions and commercial productions traits would suggest that selecting for GAV tolerant shrimp would not lead to correlated responses in the commercial production traits assessed here.

In summary, this PhD research has unveiled several potential inaccuracies in the way disease tolerance has been evaluated using traditional challenge designs. It also fills several knowledge gaps of genetic parameter estimates of GAV tolerance in P. monodon that are necessary to known prior to incorporating this trait in a selective breeding program. This research has shown for the first time that a communal pooled family rearing approach to shrimp disease challenge protocols can be used successfully and may improve the accuracy of genetic estimates by eliminating non-genetic effects caused by separate family tanks. Furthermore, this research has clearly demonstrated significant genetic variation exists for GAV tolerance and that selection of this trait should have little to no impact on other important traits such as body weight at harvest. Still remaining, however, is to develop a better understanding of the genetic correlation between disease tolerance measured under controlled challenge conditions and tolerance measured under field conditions when exposed to significant levels of GAV.

Item ID: 64435
Item Type: Thesis (PhD)
Keywords: Black Tiger shrimp, Challenge test, Detection variability, Disease resistance, Disease tolerance, Disease, Gill-associated virus (GAV), Heritability, Penaeid shrimp, Penaeus monodon, RT-qPCR, Selective breeding, Shrimp disease, Viral load
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Copyright Information: Copyright © 2018 Tansyn Honi Noble.
Additional Information:

For this thesis, Tansyn Noble received the Dean's Award for Excellence 2020, which recognises excellence in Higher Degree by Research and recipients of this award are commended by independent expert examiners as having made a substantial contribution to their field of research.

Four publications arising from this thesis are stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publications are:

Chapter 2: Noble, T.H., Coman, G.J., Cowley, J.A., Wade, N., Sellars, M., and Jerry, D.R. (2017) Comparison of methods for uniformly challenging Black Tiger shrimp (Penaeus monodon) with gill-associated virus. Aquaculture, 473. pp. 191-196.

Chapter 3: Noble, T.H., Stratford, C.N., Wade, N., Cowley, J.A., Sellars, M.J., Coman, G.J., and Jerry, D.R. (2018) PCR testing of single tissue samples can result in misleading data on gill-associated virus infection loads in shrimp. Aquaculture, 492. pp. 91-96.

Chapter 5: Noble, Tansyn N., Coman, Gregory J., Wade, Nicholas M., Thomson, Peter C., Raadsma, Herman W., Khatkar, Mehar S., Guppy, Jarrod L., and Jerry, Dean R. (2020) Genetic parameters for tolerance to gill-associated virus under challenge-test conditions in the black tiger shrimp (Penaeus monodon). Aquaculture, 516. 734428.

Chapter 6: Noble, Tansyn H., Coman, Gregory J., Wade, Nicholas M., Thomson, Peter, Raadsma, Herman W., Khatkar, Mehar S., Guppy, Jarrod L., and Jerry, Dean R. (2020) Genetic parameters of Gill-associated virus infection and body weight under commercial conditions in black tiger shrimp, Penaeus monodon. Aquaculture, 528. 735580.

Date Deposited: 22 Sep 2020 23:34
FoR Codes: 07 AGRICULTURAL AND VETERINARY SCIENCES > 0704 Fisheries Sciences > 070401 Aquaculture @ 30%
07 AGRICULTURAL AND VETERINARY SCIENCES > 0704 Fisheries Sciences > 070404 Fish Pests and Diseases @ 35%
07 AGRICULTURAL AND VETERINARY SCIENCES > 0704 Fisheries Sciences > 070405 Fish Physiology and Genetics @ 35%
SEO Codes: 83 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 8301 Fisheries - Aquaculture > 830105 Aquaculture Prawns @ 100%
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