Identification of bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei reveals distinct temperature-driven patterns

Change in temperature is often a major environmental factor in triggering waterborne disease outbreaks. Previous research has revealed temporal and spatial patterns of bacterial population in several aquatic ecosystems. To date, very little information is available on aquaculture environment. Here, we assessed environmental temperature effects on bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei (FASFL). Water samples were collected over a one-year period, and aquatic bacteria were characterized by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and 16S rDNA pyrosequencing. Resulting DGGE fingerprints revealed a specific and dynamic bacterial population structure with considerable variation over the seasonal change, suggesting that environmental temperature was a key driver of bacterial population in the FASFL. Pyrosequencing data further demonstrated substantial difference in bacterial community composition between the water at higher (WHT) and at lower (WLT) temperatures in the FASFL. Actinobacteria, Proteobacteria and Bacteroidetes were the highest abundant phyla in the FASFL, however, a large number of unclassified bacteria contributed the most to the observed variation in phylogenetic diversity. The WHT harbored remarkably higher diversity and richness in bacterial composition at genus and species levels when compared to the WLT. Some potential pathogenenic species were identified in both WHT and WLT, providing data in support of aquatic animal health management in the aquaculture industry.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.

Characterization of the carbonic anhydrase gene family and other key osmoregulatory genes in Australian freshwater crayfish (genus Cherax)

Cherax quadricarinatus (Redclaw), C. destructor (Yabby) and C. cainii (Marron) are a group of economically important freshwater crayfish and have been developed for aquaculture production in many countries. As crayfish are farmed in a wide range of culture conditions, optimisation of water quality parameters, are crucial for their maximum growth performance. Previous reports have shown that fluctuations in water quality can negatively impact on growth of crayfish. Therefore, this project aims to identify and characterize the major genes that enable freshwater crayfish to persist in different water chemistries and evaluate their patterns of expression under different water parameters. Overall, this project found a number of candidate genes in all three species and determined that water chemistry had a strong influence on the expression of candidate genes. This information is important in the optimization of water quality parameters in freshwater crayfish aquaculture production.

Transcriptome analysis and characterisation of gill-expressed carbonic anhydrase and other key osmoregulatory genes in freshwater crayfish Cherax quadricarinatus

The pH and salinity balance mechanisms of crayfish are controlled by a set of transport-related genes. We identified a set of the genes from the gill transcriptome from a freshwater crayfish Cherax quadricarinatus using the Illumina NGS-sequencing technology. We identified and characterized carbonic anhydrase (CA) genes and some other key genes involved in systematic acid-base balance and osmotic/ionic regulation. We also examined expression patterns of some of these genes across different sublethal pH levels [1]. A total of 72,382,710 paired-end Illumina reads were assembled into 36,128 contigs with an average length of 800 bp. About 37% of the contigs received significant BLAST hits and 22% were assigned gene ontology terms. These data will assist in further physiological-genomic studies in crayfish.