Acute hepatopancreatic necrosis disease (AHPND), which has also been referred to as early mortality syndrome (EMS), initially emerged as a destructive disease of cultured shrimp species in Asia in 2009. The pathogen associated with the disease, Vibrio parahaemolyticus, subsequently spread to the Western Hemisphere and emerged in Mexico in early 2013. The spread to the Western Hemisphere is a major concern to shrimp producers in the region. To date, the only peer-reviewed published method for determining whether mortalities are due to AHPND is through histological examination. A novel PCR detection method was employed to assess samples from Mexico in order to confirm the presence of the pathogen in this country. This manuscript details the detection methods used to confirm the presence of AHPND in Mexico. Both immersion and per os challenge studies were used to expose the Penaeus vannamei to the bacteria in order to induce the disease. Histological analysis confirmed AHPND status following the challenge studies. Also provided are the details of the molecular test by PCR that was used for screening candidate V. parahaemolyticus isolates. A rapid PCR assay for detection of AHPND may help with early detection and help prevent the spread of AHPND to other countries.

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Systems biology approaches in traditional comparativebiology Major advances in high-throughput technologies for the detection and quantification of nucleic acids, proteins and metabolites have led to a paradigm shift in biological research. The new field of systems biology attempts to integrate the complex data sets generated by high-throughput approaches to develop a holistic understanding of complex biological structures, their dynamics and their responsiveness to external stimuli such as salinity stress. The level of complexity of structures modeled by systems biology ranges from molecular networks via cells and organs to whole organisms (Kitano, 2002). Systems biology organizes data obtained by genomic, transcriptomic, proteomic and metabolomic approaches to attempt to build descriptive and mechanistic models of integrative biological phenomena such as development or interactions of organisms with their environment.The ultimate goal of this approach is to generate a mathematical model that describes the biological system and has predictive power (Aggarwal and Lee, 2003). Achieving this tremendously ambitious goal depends on in-depth knowledge about each element constituting the system of interest. For instance, experimental data about the expression, regulation, function, compartmentation, interaction, modification and stability of individual RNAs and proteins have to be collected and integrated for each state of the system that is described by the model. Systems biology approaches have largely been applied to organisms whose genomes have been sequenced (socalled 'model' organisms) because many of the available bioinformatics tools are based on prior genome sequencing and annotation of the encoded transcriptome and proteome. However, most high-throughput technologies for analyzing the transcriptome, proteome and metabolome do not strictly All organisms are adapted to well-defined extracellular salinity ranges. Osmoregulatory mechanisms spanning all levels of biological organization, from molecules to behavior, are central to salinity adaptation. Functional genomics and proteomics approaches represent powerful tools for gaining insight into the molecular basis of salinity adaptation and euryhalinity in animals. In this review, we discuss our experience in applying such tools to so-called 'non-model' species, including euryhaline animals that are well-suited for studies of salinity adaptation. Suppression subtractive hybridization, RACE-PCR and mass spectrometry-driven proteomics can be used to identify genes and proteins involved in salinity adaptation or other environmental stress responses in tilapia, sharks and sponges. For protein identification in non-model species, algorithms based on sequence homology searches such as MSBLASTP2 are most powerful. Subsequent gene ontology and pathway analysis can then utilize sets of identified genes and proteins for modeling molecular mechanisms of environmental adaptation. Current limitations for proteomics in non-model species can be overcome by improving sequence co...

bThe bacteria that cause necrotizing hepatopancreatitis in Penaeus vannamei adversely affect penaeid shrimp cultured in the western hemisphere. 16S rRNA and gyrase B gene analyses determined the taxonomic position of these bacteria. The name "Candidatus Hepatobacter penaei" is proposed for these pathogenic bacteria, which are members of the Rickettsiales order.

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The high-quality draft genomes of two Vibrio parahaemolyticus strains, one that causes the acute hepatopancreatic necrosis disease (AHPND) in cultured shrimps (FIM-S1708+), and another that does not (FIM-S1392−) are reported. A chromosome-scale assembly for the FIM-S1392− genome is reported here. The analysis of the two genomes gives some clues regarding the genomic differences between the strains.

The e¡ect of arti¢cial substrates on the water quality, production parameters and nutritional condition was assessed in experimental intensive cultures of Litopenaeus vannamei grown in mesocosm units with zero water exchange. The initial stocking densities in triplicate 1000 L tanks were 600 and 800 g of juvenile shrimp (2.7 g) per unit, with (W) and without (WO) arti¢cial substrates. There were no signi¢cant di¡erences between the water quality parameters of the four treatments. In the tanks with the lower stocking biomass, the best survival (96.7% vs. 87.0%), growth rate (1.69 vs. 1.35 g week À 1 ), ¢nal biomass (1969.6 vs. 1516.0 g m 3 ) and feed conversion ratio (1.30 vs. 1.90) were obtained with arti¢cial substrates. Similar results were observed for the higher stocking biomass (90.9% vs. 74.5%, 1.50 vs. 1.13 g week À 1 , 2221.6 vs. 1560.8 g m 3 , and 1.50 vs. 2.60 respectively). The nutritional condition of shrimp was improved using arti¢cial substrates. The protein content in muscle was higher (21.6% and 20.4%) in ponds with substrates as compared with the control (21.2% and 15.9%).