Transcriptome analysis of the Japanese eel (Anguilla japonica) during larval metamorphosis.

BACKGROUND: Anguillid eels spend their larval period as leptocephalus larvae that have a unique and specialized body form with leaf-like and transparent features, and they undergo drastic metamorphosis to juvenile glass eels. Less is known about the transition of leptocephali to the glass eel stage, because it is difficult to catch the metamorphosing larvae in the open ocean. However, recent advances in rearing techniques for the Japanese eel have made it possible to study the larval metamorphosis of anguillid eels. In the present study, we investigated the dynamics of gene expression during the metamorphosis of Japanese eel leptocephali using RNA sequencing. RESULTS: During metamorphosis, Japanese eels were classified into 7 developmental stages according to their morphological characteristics, and RNA sequencing was used to collect gene expression data from each stage. A total of 354.8 million clean reads were generated from the body and 365.5 million from the head, after the processing of raw reads. For filtering of genes that characterize developmental stages, a classification model created by a Random Forest algorithm was built. Using the importance of explanatory variables feature obtained from the created model, we identified 46 genes selected in the body and 169 genes selected in the head that were defined as the "most characteristic genes" during eel metamorphosis. Next, network analysis and subsequently gene clustering were conducted using the most characteristic genes and their correlated genes, and then 6 clusters in the body and 5 clusters in the head were constructed. Then, the characteristics of the clusters were revealed by Gene Ontology (GO) enrichment analysis. The expression patterns and GO terms of each stage were consistent with previous observations and experiments during the larval metamorphosis of the Japanese eel. CONCLUSION: Genome and transcriptome resources have been generated for metamorphosing Japanese eels. Genes that characterized metamorphosis of the Japanese eel were identified through statistical modeling by a Random Forest algorithm. The functions of these genes were consistent with previous observations and experiments during the metamorphosis of anguillid eels.

Microbiome and Metabolome Analyses in Different Closed-Circulation Aquarium Systems and Their Network Visualization.

Understanding the causes of microbiome formation and its relationship to environmental conditions is important to properly maintain recirculating aquaculture systems (RASs). Although RAS has been applied to numerous fish types and environmental conditions (e.g., loading intensity), the effects of these environmental conditions (i.e., fish type and loading intensity) on microbiome composition are limitedly known. Therefore, we established three experimental aquarium tanks to explore the effects of fish type, loading intensity, filter pore size, and rearing day on microbiome compositions: (1) a tank for Acanthogobius flavimanus, (2) for Girella punctata, and (3) for G. punctata with higher loading intensity. Multivariate analysis showed that the microbial community composition differed among the tanks, indicating that the fish type and loading intensity significantly affected microbiome formation in rearing water. Some microbes, such as Sediminicola and Glaciecola, were detected at a higher loading intensity, indicating that these microbes might be an indicator of eutrophic conditions in the aquacultural systems. In addition, a partial correlation network revealed a connection between microbes and metabolites in the aquarium tanks. Such a microbe-metabolite network might be a clue to control the microbiome by adjusting the molecule abundance in the aquacultural environment.

Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling.

Efficient membrane filtration requires the understanding of the membrane foulants and the functional properties of different membrane types in water purification. In this study, dead-end filtration of aquaculture system effluents was performed and the membrane foulants were investigated via nuclear magnetic resonance (NMR) spectroscopy. Several machine learning models (Random Forest; RF, Extreme Gradient Boosting; XGBoost, Support Vector Machine; SVM, and Neural Network; NN) were constructed, one to predict the maximum transmembrane pressure, for revealing the chemical compounds causing fouling, and the other to classify the membrane materials based on chemometric analysis of NMR spectra, for determining their effect on the properties of the different membrane types tested. Especially, RF models exhibited high accuracy; the important chemical shifts observed in both the regression and classification models suggested that the proportional patterns of sugars and proteins are key factors in the fouling progress and the classification of membrane types. Therefore, the proposed strategy of chemometric analysis of NMR spectra is suitable for membrane research, which aims at investigating comprehensively the fouling phenomenon and how the foulants and environmental conditions vary according to the filtration systems.

18S rRNA gene sequences of leptocephalus gut contents, particulate organic matter, and biological oceanographic conditions in the western North Pacific.

Eel larvae apparently feed on marine snow, but many aspects of their feeding ecology remain unknown. The eukaryotic 18S rRNA gene sequence compositions in the gut contents of four taxa of anguilliform eel larvae were compared with the sequence compositions of vertically sampled seawater particulate organic matter (POM) in the oligotrophic western North Pacific Ocean. Both gut contents and POM were mainly composed of dinoflagellates as well as other phytoplankton (cryptophytes and diatoms) and zooplankton (ciliophoran and copepod) sequences. Gut contents also contained cryptophyte and ciliophoran genera and a few other taxa. Dinoflagellates (family Gymnodiniaceae) may be an important food source and these phytoplankton were predominant in gut contents and POM as evidenced by DNA analysis and phytoplankton cell counting. The compositions of the gut contents were not specific to the species of eel larvae or the different sampling areas, and they were most similar to POM at the chlorophyll maximum in the upper part of the thermocline (mean depth: 112 m). Our results are consistent with eel larvae feeding on marine snow at a low trophic level, and feeding may frequently occur in the chlorophyll maximum in the western North Pacific.

Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties.

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3-95.4%. Markov blanket-based feature selection method with an ecological-chemical-physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database.

Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1H-NMR databases of other complex gelatinous mixtures in foods.

Molecular diet analysis of Anguilliformes leptocephalus larvae collected in the western North Pacific.

Natural diets of leptocephalus larvae have been enigmatic. In this study, we collected DNA samples from the gut contents and body surface of leptocephali belonging to the five Anguilliform families (Anguillidae, Chlopsidae, Congridae, Muraenidae, and Serrivomeridae) from the northwest Pacific and performed next-generation 18S rDNA sequencing. Wide variety of eukaryotes was detected in both samples, from which eight eukaryotic groups (jellyfish, conoid parasite, tunicate, copepod, krill, segmented worm, fungi, and dinoflagellate) were selected on the basis of abundance. All groups except conoid parasites were common in both the samples. Cnidarian 18S rDNA reads were the most abundant in both the samples; however, the number of samples having cnidarian reads and the read counts were significantly higher in the body surface scraping samples than in the gut content samples, regardless of careful rinsing of the body surface. These results indicate that the cnidarian DNAs are most likely found because of cross contamination from the body surface and/or environment. 18S rDNA read counts of copepod and tunicate in the gut contents were greater than or comparable with those in the body surface scraping samples, which may correspond to the previous observations of fecal pellets and larvacean houses in the leptocephali gut. Thus, the present study supports previous implications that leptocephali utilize detritus materials, so called marine snow.

Application of ensemble deep neural network to metabolomics studies.

Deep neural network (DNN) is a useful machine learning approach, although its applicability to metabolomics studies has rarely been explored. Here we describe the development of an ensemble DNN (EDNN) algorithm and its applicability to metabolomics studies. As a model case, the developed EDNN approach was applied to metabolomics data of various fish species collected from Japan coastal and estuarine environments for evaluation of a regression performance compared with conventional DNN, random forest, and support vector machine algorithms. This study also revealed that the metabolic profiles of fish muscles were correlated with fish size (growth) in a species-dependent manner. The performance of EDNN regression for fish size based on metabolic profiles was superior to that of DNN, random forest, and support vector machine algorithms. The EDNN approach, therefore, should be helpful for analyses of regression and concerns pertaining to classification in metabolomics studies.

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (Plectropomus leopardus).

Aquaculture is currently a major source of fish and has the potential to become a major source of protein in the future. These demands require efficient aquaculture. The intestinal microbiota plays an integral role that benefits the host, providing nutrition and modulating the immune system. Although our understanding of microbiota in fish gut has increased, comprehensive studies examining fish microbiota and host metabolism remain limited. Here, we investigated the microbiota and host metabolism in the coral leopard grouper, which is traded in Asian markets as a superior fish and has begun to be produced via aquaculture. We initially examined the structural changes of the gut microbiota using next-generation sequencing and found that the composition of microbiota changed between fasting and feeding conditions. The dominant phyla were Proteobacteria in fasting and Firmicutes in feeding; interchanging the dominant bacteria required 12 hours. Moreover, microbiota diversity was higher under feeding conditions than under fasting conditions. Multivariate analysis revealed that Proteobacteria are the key bacteria in fasting and Firmicutes and Fusobacteria are the key bacteria in feeding. Subsequently, we estimated microbiota functional capacity. Microbiota functional structure was relatively stable throughout the experiment; however, individual function activity changed according to feeding conditions. Taken together, these findings indicate that the gut microbiota could be a key factor to understanding fish feeding conditions and play a role in interactions with host metabolism. In addition, the composition of microbiota in ambient seawater directly affects the fish; therefore, it is important to monitor the microbiota in rearing tanks and seawater circulating systems.

Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem.

Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing. Hidden interactions in the integrated datasets such as the potential role of intestinal bacteria in the control of spawning migration, essential amino acids and fatty acids synthesis in wild yellowfin goby were further extracted using correlation clustering and market basket analysis-generated networks. Importantly, our systematic analysis of both the seasonal and latitudinal variations in metabolome, ionome and microbiome of wild yellowfin goby pointed out that the environmental factors such as the temperature play important roles in regulating the body homeostasis of wild fish.

Noninvasive analysis of metabolic changes following nutrient input into diverse fish species, as investigated by metabolic and microbial profiling approaches.

An NMR-based metabolomic approach in aquatic ecosystems is valuable for studying the environmental effects of pharmaceuticals and other chemicals on fish. This technique has also contributed to new information in numerous research areas, such as basic physiology and development, disease, and water pollution. We evaluated the microbial diversity in various fish species collected from Japan's coastal waters using next-generation sequencing, followed by evaluation of the effects of feed type on co-metabolic modulations in fish-microbial symbiotic ecosystems in laboratory-scale experiments. Intestinal bacteria of fish in their natural environment were characterized (using 16S rRNA genes) for trophic level using pyrosequencing and noninvasive sampling procedures developed to study the metabolism of intestinal symbiotic ecosystems in fish reared in their environment. Metabolites in feces were compared, and intestinal contents and feed were annotated based on HSQC and TOCSY using SpinAssign and network analysis. Feces were characterized by species and varied greatly depending on the feeding types. In addition, feces samples demonstrated a response to changes in the time series of feeding. The potential of this approach as a non-invasive inspection technique in aquaculture is suggested.

Comparative analysis of chemical and microbial profiles in estuarine sediments sampled from Kanto and Tohoku regions in Japan.

Estuarine environments accumulate large quantities of organic matter from land masses adjoining the sea, and this is consumed as part of the detritus cycle. These environments are rich in biodiversity, and their ecosystem services greatly benefit humans. However, the estuarine environments have complicated aqueous ecosystems, thus the comprehensive evaluation of biotic interactions and stability is difficult using conventional hypothesis-driven approaches. In this study, we describe the advancement of an evaluation strategy for characterizing and visualizing the interactions and relationships among the microorganisms and chemicals in sediment ecosystems of estuarine environments by a combination of organic matter and elemental profiling as well as microbial profiling. We also report our findings from a comparative analysis of estuarine and coastal environmental samples collected from the Kanto and Tsunami-affected Tohoku regions in Japan. The microbial-gated correlation deployed from the coefficient of microbiota from the correlation matrix and network analysis was able to visualize and summarize the different relationships among the microbial communities, sediment organic matter, and element profiles based on geographical differences in Kanto and Tohoku regions. We demonstrated remarkable estuarine eutrophication in the Kanto region based on abundant sediment polypeptide signals and water nitrogen ions catabolized by microbiota. Therefore, we propose that this data-driven approach is a powerful method for analyzing, visualizing, and evaluating complex metabolic dynamics and networks in sediment microbial ecosystems and can be applied to other environmental ecosystems, such as deep sea sediments and agronomic and forest soils.