Predictive Blood Chemistry Parameters for Pansteatitis-Affected Mozambique Tilapia (Oreochromis mossambicus).

One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various "outbreaks" of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River.

Predicting growth and mortality of bivalve larvae using gene expression and supervised machine learning.

It is commonly known that the nature of the diet has diverse consequences on larval performance and longevity, however it is still unclear which genes have critical impacts on bivalve development and which pathways are of particular importance in their vulnerability or resistance. First we show that a diet deficient in essential fatty acid (EFA) produces higher larval mortality rates, a reduced shell growth, and lower postlarval performance, all of which are positively correlated with a decline in arachidonic and eicosapentaenoic acids levels, two EFAs known as eicosanoid precursors. Eicosanoids affect the cell inflammatory reactions and are synthesized from long-chain EFAs. Second, we show for the first time that a deficiency in eicosanoid precursors is associated with a network of 29 genes. Their differential regulation can lead to slower growth and higher mortality of Mytilus edulis larvae. Some of these genes are specific to bivalves and others are implicated at the same time in lipid metabolism and defense. Several genes are expressed only during pre-metamorphosis where they are essential for muscle or neurone development and biomineralization, but only in stress-induced larvae. Finally, we discuss how our networks of differentially expressed genes might dynamically alter the development of marine bivalves, especially under dietary influence.

Transcriptomics of mRNA and egg quality in farmed fish: Some recent developments and future directions.

Maternal mRNA transcripts deposited in growing oocytes regulate early development and are under intensive investigation as determinants of egg quality. The research has evolved from single gene studies to microarray and now RNA-Seq analyses in which mRNA expression by virtually every gene can be assessed and related to gamete quality. Such studies have mainly focused on genes changing two- to several-fold in expression between biological states, and have identified scores of candidate genes and a few gene networks whose functioning is related to successful development. However, ever-increasing yields of information from high throughput methods for detecting transcript abundance have far outpaced progress in methods for analyzing the massive quantities of gene expression data, and especially for meaningful relation of whole transcriptome profiles to gamete quality. We have developed a new approach to this problem employing artificial neural networks and supervised machine learning with other novel bioinformatics procedures to discover a previously unknown level of ovarian transcriptome function at which minute changes in expression of a few hundred genes is highly predictive of egg quality. In this paper, we briefly review the progress in transcriptomics of fish egg quality and discuss some future directions for this field of study.

Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

Inherited gene transcripts deposited in oocytes direct early embryonic development in all vertebrates, but transcript profiles indicative of embryo developmental competence have not previously been identified. We employed artificial intelligence to model profiles of maternal ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in the striped bass (Morone saxatilis), a farmed species with serious egg quality problems. In models developed using artificial neural networks (ANNs) and supervised machine learning, collective changes in the expression of a limited suite of genes (233) representing <2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in gene expression (<0.2-fold), with most individual transcripts making a small contribution (<1%) to the overall prediction of egg quality. These findings indicate that the predictive power of the transcriptome as regards egg quality resides not in levels of individual genes, but rather in the collective, coordinated expression of a suite of transcripts constituting a transcriptomic "fingerprint". Correlation analyses of the corresponding candidate genes indicated that dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence. The affected gene networks are centrally involved in regulation of early development in all vertebrates, including humans. By assessing collective levels of the relevant ovarian transcripts via ANNs we were able, for the first time in any vertebrate, to accurately predict the subsequent embryo developmental potential of eggs from individual females. Our results show that the transcriptomic fingerprint evidencing developmental dysfunction is highly predictive of, and therefore likely to regulate, egg quality, a biologically complex trait crucial to reproductive fitness.

The effect of pH on the toxicity of fatty acids and fatty acid amides to rainbow trout gill cells.

Harmful algal blooms (HABs) expose aquatic organisms to multiple physical and chemical stressors during an acute time period. Algal toxins themselves may be altered by water chemistry parameters affecting their bioavailability and resultant toxicity. The purpose of this study was to determine the effects of two abiotic parameters (pH, inorganic metal salts) on the toxicity of fatty acid amides and fatty acids, two classes of lipids produced by harmful algae, including the golden alga, Prymnesium parvum, that are toxic to aquatic organisms. Rainbow trout gill cells were used as a model of the fish gill and exposed to single compounds and mixtures of compounds along with variations in pH level and concentration of inorganic metal salts. We employed artificial neural networks (ANNs) and standard ANOVA statistical analysis to examine and predict the effects of these abiotic parameters on the toxicity of fatty acid amides and fatty acids. Our results demonstrate that increasing pH levels increases the toxicity of fatty acid amides and inhibits the toxicity of fatty acids. This phenomenon is reversed at lower pH levels. Exposing gill cells to complex mixtures of chemical factors resulted in dramatic increases in toxicity compared to tests of single compounds for both the fatty acid amides and fatty acids. These findings highlight the potential of physicochemical factors to affect the toxicity of chemicals released during algal blooms and demonstrate drastic differences in the effect of pH on fatty acid amides and fatty acids.

Transcriptomic responses of juvenile Pacific whiteleg shrimp, Litopenaeus vannamei, to hypoxia and hypercapnic hypoxia.

Estuarine crustaceans are often exposed to low dissolved O2 (hypoxia) accompanied by elevated CO2 (hypercapnia), which lowers water pH. Acclimatory responses to hypoxia have been widely characterized; responses to hypercapnia in combination with hypoxia (hypercapnic hypoxia) are less well known. Here we used oligonucleotide microarrays to characterize changes in global gene expression in the hepatopancreas of Pacific whiteleg shrimp, Litopenaeus vannamei, exposed to hypoxia or hypercapnic hypoxia for 4 or 24 h, compared with time-matched animals held in air-saturated water (normoxia). Unigenes whose expressions were significantly impacted by treatment and/or time were used to build artificial neural networks (ANNs) to identify genes with the greatest sensitivity in pairwise discriminations between treatments at each time point and between times for each treatment. ANN gene sets that discriminated hypoxia or hypercapnic hypoxia from normoxia shared functions of translation, mitochondrial energetics, and cellular defense. GO terms protein modification/phosphorylation/cellular protein metabolism and RNA processing/apoptosis/cell cycling occurred at highest frequency in discriminating hypercapnic hypoxia from hypoxia at 4 and 24 h, respectively. For 75.4% of the annotated ANN genes, exposure to hypercapnic hypoxia for 24 h reduced or reversed the transcriptional response to hypoxia alone. These results suggest that high CO2/low pH may interfere with transcriptionally based acclimation to hypoxia or elicit physiological or biochemical responses that relieve internal hypoxia. Whether these data reflect resilience or sensitivity of L. vannamei in the face of expanding hypoxic zones and rising levels of atmospheric CO2 may be important to understanding the survival of this and other estuarine species.

Contaminants and impoSEX: transcriptomics of contaminant-induced sex change.

Reproduction is the goal of living organisms, and environmental conditions that influence sexual development are therefore critical to understanding adaptation in natural populations. It is not surprising that so much attention has been devoted to the impacts of the physical and chemical environment on this process (Vandenberg et al.2012). Chemicals of concern include a variety of endocrine disruptors (EDs) including oestrogen and oestrogen mimics that directly lead to malformation of the gonad. On the molecular side, the impact that EDs have on genes directly involved in the feminization or masculinization of the gonad such as Cyp 19A (or aromatase), foxl2,Sox9, Dmrt1 and NrOb1, has received considerable attention due to their direct involvement in the regulation of oestrogen and testosterone. In this issue of Molecular Ecology, Pascoal et al. (2013) examine the impact of a known endocrine disruptor (tributyltin or TBT) on the transcriptome of the dog whelk, Nucella lapillus (Fig. 1),in relation to the formation of imposex individuals (masculinized females). They conclude that TBT mimics the endogenous ligand of the nuclear retinoid X receptor (RXR) and/or peroxisome profilerator-activated receptor (PPAR) disrupting pathways.

Using machine learning tools to model complex toxic interactions with limited sampling regimes.

A major impediment to understanding the impact of environmental stress, including toxins and other pollutants, on organisms, is that organisms are rarely challenged by one or a few stressors in natural systems. Thus, linking laboratory experiments that are limited by practical considerations to a few stressors and a few levels of these stressors to real world conditions is constrained. In addition, while the existence of complex interactions among stressors can be identified by current statistical methods, these methods do not provide a means to construct mathematical models of these interactions. In this paper, we offer a two-step process by which complex interactions of stressors on biological systems can be modeled in an experimental design that is within the limits of practicality. We begin with the notion that environment conditions circumscribe an n-dimensional hyperspace within which biological processes or end points are embedded. We then randomly sample this hyperspace to establish experimental conditions that span the range of the relevant parameters and conduct the experiment(s) based upon these selected conditions. Models of the complex interactions of the parameters are then extracted using machine learning tools, specifically artificial neural networks. This approach can rapidly generate highly accurate models of biological responses to complex interactions among environmentally relevant toxins, identify critical subspaces where nonlinear responses exist, and provide an expedient means of designing traditional experiments to test the impact of complex mixtures on biological responses. Further, this can be accomplished with an astonishingly small sample size.

Dynamics of the striped bass (Morone saxatilis) ovary proteome reveal a complex network of the translasome.

We evaluated changes in the striped bass (Morone saxatilis) ovary proteome during the annual reproductive cycle using label-free quantitative mass spectrometry and a novel machine learning analysis based on K-means clustering and support vector machines. Modulated modularity clustering was used to group co-variable proteins into expression modules and Gene Ontology (GO) biological process and KEGG pathway enrichment analyses were conducted for proteins within those modules. We discovered that components of the ribosome along with translation initiation and elongation factors generally decrease as the annual ovarian cycle progresses toward ovulation, concomitant with a slight increase in components of the 26S-proteasome. Co-variation within more than one expression module of components from these two multi-protein complexes suggests that they are not only co-regulated, but that co-regulation occurs through more than one sub-network. These components also co-vary with subunits of the TCP-1 chaperonin system and enzymes of intermediary metabolic pathways, suggesting that protein folding and cellular bioenergetic state play important roles in protein synthesis and degradation. We provide further evidence to suggest that protein synthesis and degradation are intimately linked, and our results support function of a proteasome-ribosome supercomplex known as the translasome.

An ovary transcriptome for all maturational stages of the striped bass (Morone saxatilis), a highly advanced perciform fish.

BACKGROUND: The striped bass and its relatives (genus Morone) are important fisheries and aquaculture species native to estuaries and rivers of the Atlantic coast and Gulf of Mexico in North America. To open avenues of gene expression research on reproduction and breeding of striped bass, we generated a collection of expressed sequence tags (ESTs) from a complementary DNA (cDNA) library representative of their ovarian transcriptome. RESULTS: Sequences of a total of 230,151 ESTs (51,259,448 bp) were acquired by Roche 454 pyrosequencing of cDNA pooled from ovarian tissues obtained at all stages of oocyte growth, at ovulation (eggs), and during preovulatory atresia. Quality filtering of ESTs allowed assembly of 11,208 high-quality contigs ≥ 100 bp, including 2,984 contigs 500 bp or longer (average length 895 bp). Blastx comparisons revealed 5,482 gene orthologues (E-value < 10-3), of which 4,120 (36.7% of total contigs) were annotated with Gene Ontology terms (E-value < 10-6). There were 5,726 remaining unknown unique sequences (51.1% of total contigs). All of the high-quality EST sequences are available in the National Center for Biotechnology Information (NCBI) Short Read Archive (GenBank: SRX007394). Informative contigs were considered to be abundant if they were assembled from groups of ESTs comprising ≥ 0.15% of the total short read sequences (≥ 345 reads/contig). Approximately 52.5% of these abundant contigs were predicted to have predominant ovary expression through digital differential display in silico comparisons to zebrafish (Danio rerio) UniGene orthologues. Over 1,300 Gene Ontology terms from Biological Process classes of Reproduction, Reproductive process, and Developmental process were assigned to this collection of annotated contigs. CONCLUSIONS: This first large reference sequence database available for the ecologically and economically important temperate basses (genus Morone) provides a foundation for gene expression studies in these species. The predicted predominance of ovary gene expression and assignment of directly relevant Gene Ontology classes suggests a powerful utility of this dataset for analysis of ovarian gene expression related to fundamental questions of oogenesis. Additionally, a high definition Agilent 60-mer oligo ovary 'UniClone' microarray with 8 × 15,000 probe format has been designed based on this striped bass transcriptome (eArray Group: Striper Group, Design ID: 029004).

Health status, infection and disease in California sea lions (Zalophus californianus) studied using a canine microarray platform and machine-learning approaches.

Conservation biologists face many challenges in assessing health, immune status and infectious diseases in protected species. These challenges include unpredictable sample populations, diverse genetic and environmental backgrounds of the animals, as well as the practical, legal and ethical issues involved in experimentation. The use of whole genome scale transcriptomics with animal samples obtained in a minimally invasive manner is an approach that shows promise for health assessment. In this study we assessed the utility of a microarray to identify changes in gene expression predictive of health status by interrogating blood samples from California sea lions (Zalophus californianus) in rehabilitation. A custom microarray was developed from the commercially available dog microarray (Canis familiaris) by selecting probes that demonstrated reliable cross-hybridization with RNA in sea lion blood. This custom microarray was used for the analysis of RNA from 73 sea lion blood samples, from animals with a broad spectrum of health changes. Both traditional classifying techniques and newer artificial neural network approaches correctly classified sea lions with respect to health status, primarily distinguishing between leptospirosis infection and domoic acid exposure. Real time PCR validation for a small set of genes, followed by sequencing, showed good correlation with array results and high identity (96-98%) between the dog and sea lion sequences. This approach to health status classification shows promise for disease identification in a clinical setting, and assessment of health status of wildlife.

Arsenic exposure to killifish during embryogenesis alters muscle development.

Epidemiological studies have correlated arsenic exposure in drinking water with adverse developmental outcomes such as stillbirths, spontaneous abortions, neonatal mortality, low birth weight, delays in the use of musculature, and altered locomotor activity. Killifish (Fundulus heteroclitus) were used as a model to help to determine the mechanisms by which arsenic could impact development. Killifish embryos were exposed to three different sodium arsenite concentrations and were collected at 32 h post-fertilization (hpf), 42 hpf, 168 hpf, or < 24 h post-hatch. A killifish oligo microarray was developed and used to examine gene expression changes between control and 25-ppm arsenic-exposed hatchlings. With artificial neural network analysis of the transcriptomic data, accurate prediction of each group (control vs. arsenic-exposed embryos) was obtained using a small subset of only 332 genes. The genes differentially expressed include those involved in cell cycle, development, ubiquitination, and the musculature. Several of the genes involved in cell cycle regulation and muscle formation, such as fetuin B, cyclin D-binding protein 1, and CapZ, were differentially expressed in the embryos in a time- and dose-dependent manner. Examining muscle structure in the hatchlings showed that arsenic exposure during embryogenesis significantly reduces the average muscle fiber size, which is coupled with a significant 2.1- and 1.6-fold upregulation of skeletal myosin light and heavy chains, respectively. These findings collectively indicate that arsenic exposure during embryogenesis can initiate molecular changes that appear to lead to aberrant muscle formation.

The transcriptomic responses of the eastern oyster, Crassostrea virginica, to environmental conditions.

Understanding the mechanisms by which organisms adapt to environmental conditions is a fundamental question for ecology and evolution. In this study, we evaluate changes in gene expression of a marine mollusc, the eastern oyster Crassostrea virginica, associated with the physico-chemical conditions and the levels of metals and other contaminants in their environment. The results indicate that transcript signatures can effectively disentangle the complex interactive gene expression responses to the environment and are also capable of disentangling the complex dynamic effects of environmental factors on gene expression. In this context, the mapping of environment to gene and gene to environment is reciprocal and mutually reinforcing. In general, the response of transcripts to the environment is driven by major factors known to affect oyster physiology such as temperature, pH, salinity, and dissolved oxygen, with pollutant levels playing a relatively small role, at least within the range of concentrations found in the studied oyster habitats. Further, the two environmental factors that dominate these effects (temperature and pH) interact in a dynamic and nonlinear fashion to impact gene expression. Transcriptomic data obtained in our study provide insights into the mechanisms of physiological responses to temperature and pH in oysters that are consistent with the known effects of these factors on physiological functions of ectotherms and indicate important linkages between transcriptomics and physiological outcomes. Should these linkages hold in further studies and in other organisms, they may provide a novel integrated approach for assessing the impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms via relatively inexpensive microarray platforms.

The transcriptomic response to viral infection of two strains of shrimp (Litopenaeus vannamei).

The extent to which data-intensive studies of the transcriptome can provide insight into biological responses is not well defined, especially in the case of species (such as shrimp) where much physiological and biochemical knowledge is missing. In this study we took a transcriptomic approach to gain insight into the response to viral infection of two strains of the Pacific whiteleg shrimp (Litopenaeus vannamei) that differ in their resistance to Taura Syndrome Virus (TSV). Changes in gene expression in the hepatopancreas following infection with TSV and Yellow Head Virus (YHV) were assessed using a cDNA microarray containing 2469 putative unigenes. The null hypothesis tested was that significant differences between the transcriptomic responses to viral infection of resistant and sensitive strains would not be detected. This hypothesis was broadly rejected, with the most surprising observation being that the baseline (control, unchallenged) sensitive and resistant strains expressed distinguishable transcriptomic signatures. The resistant line was pre-disposed to lower expression of genes encoding viral (and host) proteins. Many of the genes differentiating resistant and sensitive lines are involved in protein metabolism, cellular trafficking, immune defense and stress response, although it was not possible to clearly identify candidate genes responsible for TSV resistance. In contrast to TSV challenge, YSV either failed to perturb the host transcriptome or created a "confused" response that was difficult to interpret.

Non-specific activation of antiviral immunity and induction of RNA interference may engage the same pathway in the Pacific white leg shrimp Litopenaeus vannamei.

Many questions remain unanswered regarding RNAi-based mechanisms and dsRNA-induced antiviral immune responses in penaeid shrimp. In this study, we report the characterization in the white leg shrimp Litopenaeus vannamei of RNAi pathway associated proteins Lv-Ago 1 and Lv-Ago 2, two members of the Argonaute family of proteins, as well as Lv-sid 1, the first shrimp homologue of Sid-1, a membrane channel-forming protein implicated in the cellular import of dsRNA. To decipher their functional implication in RNAi-related phenomena, we monitored their relative expression following stimulation by specific and non-specific RNA duplexes of diverse length. The findings show that the length of small RNA duplexes plays a critical role in the activation of both RNAi-related and innate antiviral responses. They also suggest that these two mechanisms of antiviral response may activate the same pathway, requiring Lv-Sid 1 and Lv-Ago 2 induction.

Modelling interactions of acid-base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica.

Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001-0.400 microM), Zn (0.001-3.059 microM) or Cu (0.002-0.787 microM), either alone or in combination for 1 to 27 days. We measured indicators of acid-base balance (hemolymph pH and total CO(2)), gas exchange (Po(2)), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid-base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid-base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of sub-acute exposure to contaminant mixtures.

Lack of evidence for Litopenaeus vannamei Toll receptor (lToll) involvement in activation of sequence-independent antiviral immunity in shrimp.

Injection of non-specific dsRNA initiates a broad-spectrum innate antiviral immune response in the Pacific white shrimp, Litopenaeus vannamei, however, the receptor involved in recognition of this by-product of viral infections remains unknown. In vertebrates, dsRNA sensing is mediated by a class of Toll-like receptors (TLRs) and results in activation of the interferon system. Because a TLR (lToll) was recently characterized in L. vannamei, we investigated its potential role in dsRNA recognition. We showed that injection of non-specific RNA duplexes did not modify lToll gene expression. A reverse genetic approach was therefore implemented to study its role in vivo. Silencing of lToll did not impair the ability of non-specific dsRNA to trigger protection from white spot syndrome virus and did not increase the shrimp susceptibility to viral infection, when compared to controls. In contrast, gene-specific dsRNA injected to specifically silence lToll expression activated an antiviral response. These data strongly suggest that shrimp lToll plays no role in dsRNA-induced antiviral immunity.

A transcriptomic analysis of land-use impacts on the oyster, Crassostrea virginica, in the South Atlantic bight.

Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part, this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems, and perhaps more important, is the insensitivity of the biological end points that we have used to assess these impacts. In this study, we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land-use practices in the surrounding watershed using advanced machine-learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data show that gill tissues are far more responsive and provide superior discrimination of land-use classes than hepatopancreas and that transcripts encoding proteins involved in energy production, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P-450.