**2. Application of metagenomics analyses**

Studies carried out this far show that metagenomics can be used to identify novel pathogens as well as microbiota found on mucosal surfaces of cultured aquatic organisms.

#### **2.1. Application of metagenomics in diagnostics and discovery of novel pathogens**

The rapid expansion of aquaculture to become a leading source of proteins for human con‐ sumption in the world has brought with it a rapid increase in the number pathogens infecting farmed aquatic organisms [19]. To expedite the process of identifying emerging pathogens, there has been a shift in recent years from the use of traditional diagnostic tools based on isolation, culture and pathogen characterization to include metagenomics analyses in the identification of novel pathogens in aquaculture [10]. Metagenomics is a culture independent diagnostic tool that does not require prior knowledge of nucleic acids to be sequenced unlike conventional PCR that require prior knowledge of the nucleic acid to be sequenced for the design of primers [20]. Metagenomics analyses have the capacity to sequence all nucleic acids present in a sample at once thereby generating a vast array of data that requires computational analyses for interpretation [20, 21]. As pointed out in our previous studies [9, 10], it has the advantage of identifying co‐infections and in the case of viral pathogens, it has the capacity to generate all variable proteins that form complete virions thereby permitting comparative phylogenetic analyses with other viruses present in public databases. Moreover, it is a pro‐ active diagnostic tool able to identify novel pathogens before they cause outbreaks unlike the reactive traditional diagnostic tools in which etiological agents are only identified after causing disease outbreaks reaching epidemic proportions [21]. Using metagenomics, several novel pathogens have been identified at a much faster rate than traditional approaches in which the duration from first observation of clinical signs to identification of the pathogens is long [10]. For example, infectious pancreatic necrosis (IPN) was first reported as an acute infectious enteritis [22] in salmonids in the 1940s while the etiological was later characterized as IPN virus after 20 years in 1960 [23]. Similarly, viral haemorrhagic septicaemia (VHS) was first reported in the early 1950s in salmonids while the causative agent was characterized later after 10 years in 1962 [24]. This trend was observed for several other diseases such as infec‐ tious hematopoietic necrosis virus (IHNV), nervous necrosis virus (NNV), heart and skeletal muscle inflammation (HSMI) and cardiac myopathy syndrome (CMS) in which identification of the etiological agents took long after clinical signs were first reported [25–33]. However, the upcoming of metagenomics has accelerated our discovery of novel pathogens in which the duration from observation of first clinical signs to identification of the etiological agent has been reduced. In fish, viruses discovered using metagenomics include circoviruses from com‐ mon bream [34] and European eel [35], posavirus [36] and seadornavirus [37] from freshwater carp and totivirus from golden shiner. As shown in our recent study [9], more than 20 novel fish pathogenic viruses have been identified using metagenomics in the last 4 years, which is more than the number identified using traditional diagnostic tools in the last 5 decades, clearly showing the rapid rate at which metagenomics has accelerated our ability to identify novel pathogens compared with traditional diagnostic methods.

In crustaceans, mortalities due to white spot syndrome virus (WSSV) in shrimps were first reported in 1992 while the causative agent was identified in 2001 [38–40]. Mortalities due to taura syndrome virus (TSV) in shrimps were first reported in Ecuador in 1991 [41] and the virus was characterized in 1994 [42]. A similar trend was observed for Yellow heard disease virus (YTV) [43, 44], infectious hypodermal and hematopoietic necrosis virus (IHHNV) [45–47], shrimp infectious myonecrosis virus (SIMV) [48] and *Penaues vannamei* nodavirus (PvNV) [49, 50] in which the duration between the first report of the disease and identification of the etiological agent was long. Shrimps viruses discovered using metagenomics analyses include *Frafantepenaeus duorarum* nodavirus (FdNV) and shrimp hepatopancreas‐associated circular nodavirus (ShrimpCDV) [51].

#### **2.2. Monitoring of environmental microbiomes**

time PCRs (qRT‐PCRs) [3, 4], competitive PCRs [5] or nested PCRs [6] have been and are still widely used in biological sciences, they inherently lack the ability to provide a global overview of genomic transcripts found in living organisms. However, the recent advent of omics technologies such as metagenomics, nutrigenomics and epigenetics based on high throughput sequencing (HTS) is rapidly enhancing our ability to understand complex sys‐ tems underlying different biological functions. These omics technologies have not only accel‐ erated whole genome sequencing projects of different aquatic organisms [7, 8], but they also have the capacity to unravel the sequences of entire genomes without prior knowledge of the genes to be sequenced thereby enhancing the discovery and annotation of novel genes in non‐model species. And as shown from recent studies, their applications in aquaculture have accelerated our ability to identify emerging pathogens [9], monitor the microbiomes of different aquatic environments [10], develop nutritional diets with less side effects [11, 12] and understand the cellular networks that regulate different biological processes in aquatic organisms [13–15]. It is evident from studies carried out this far that an integrated use of dif‐ ferent omics technologies is bound to improve our production systems in aquaculture [10, 12, 16–18]. Hence, this chapter provides an overview of different omics technologies currently used in aquaculture mainly focusing on their overall contribution to transforming genomics

174 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

Studies carried out this far show that metagenomics can be used to identify novel pathogens

The rapid expansion of aquaculture to become a leading source of proteins for human con‐ sumption in the world has brought with it a rapid increase in the number pathogens infecting farmed aquatic organisms [19]. To expedite the process of identifying emerging pathogens, there has been a shift in recent years from the use of traditional diagnostic tools based on isolation, culture and pathogen characterization to include metagenomics analyses in the identification of novel pathogens in aquaculture [10]. Metagenomics is a culture independent diagnostic tool that does not require prior knowledge of nucleic acids to be sequenced unlike conventional PCR that require prior knowledge of the nucleic acid to be sequenced for the design of primers [20]. Metagenomics analyses have the capacity to sequence all nucleic acids present in a sample at once thereby generating a vast array of data that requires computational analyses for interpretation [20, 21]. As pointed out in our previous studies [9, 10], it has the advantage of identifying co‐infections and in the case of viral pathogens, it has the capacity to generate all variable proteins that form complete virions thereby permitting comparative phylogenetic analyses with other viruses present in public databases. Moreover, it is a pro‐ active diagnostic tool able to identify novel pathogens before they cause outbreaks unlike the reactive traditional diagnostic tools in which etiological agents are only identified after

as well as microbiota found on mucosal surfaces of cultured aquatic organisms.

**2.1. Application of metagenomics in diagnostics and discovery of novel pathogens**

studies into functional applications.

**2. Application of metagenomics analyses**

A good understanding of microbial communities found in freshwater and marine environ‐ ment used for aquaculture is a prerequisite to designing effective disease control strategies tailored for each ecosystem. Metagenomics analyses provide a unique opportunity to study infectious agents in water samples outside their susceptible hosts [10]. Its ability to sequence all nucleic acids present in a sample at once enables it to profile microbial communities found in different ecosystems. For example, Angly et al. [52] showed that microbial composition varies with latitude gradient with highest diversity being in warm climates around the equa‐ tor and less diversity in the poles. After analysis of viromes from 32 different marine sites, Dinsdale et al. [53] noted that viral richness decreased from deep sea to surface waters and with distance from shore in surface waters and increased from winter to summer. Given that over 40% of the global human population live within 100 km of coastlines, anthropogenic activities have been shown to influence the composition of microbial communities in coastal areas where aquaculture activities are mostly carried out [54]. These anthropogenic activities include host species composition changes introduced by aquaculture [55, 56], waste disposal [57], agriculture [58], recreation [59] and industrial activities [59]. As a result, metagenomics is currently being used to monitor the impact of anthropogenic activities on coastal micro‐ bial composition. Port et al. [60] found an increase in antibiotic resistance genes caused by coastal effluent discharges, while Morán et al. [61] showed significant changes in bacterial community structures caused by coastal copper disposal in La Lancha and Chañaral bay in the Pacific Ocean. Overall, these studies show that metagenomics is not only used to identify novel pathogens, but it is also used to monitor the impact of human activities on microbial composition in different aquatic environments.

with particular fish species. Lokesh and Kiron [69] showed that the bacterial operational tax‐ onomy unit (OTU) composition on the skin of Atlantic salmon (*Salmo salar* L.) changed signifi‐ cantly as a result of transfer from fresh to seawater. Proteobacteria was the dominant phylum both in fresh and seawater while Bacteroidetes, Actinobacteria, Firmicutes, Cyanobacteria and Verrucomicrobia were the most abundant in freshwater. The genus *Oleispira* was the most abundant in seawater. Similarly, Wilson et al. [70] showed that bacterial communi‐ ties from the epidermal mucus of Atlantic cod (*Gadus morhua*) from the Baltic, Iceland and North seas collected over three seasons mainly comprised of Psychrobacter, Bacteroides and Photobacterium OTUs in all seasons although there were significant inter‐site and seasonal

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177

Boutin et al. [71] combined 16S RNA metagenomics and QTL analyses to show that host genotype can regulate the microbiota composition on the skin surface of brook charr (*Salvelinus fontinalis*). They found a strong negative correlation between Flavobacterium and Methylobacterium, pointing to a mutually competitive relationship between pathogenic and non‐pathogenic bacteria on the skin mucosa of brook charr. Flavobacterium is known to be pathogenic among different fish species, while Methylobacteria provide protection against pathogenic bacterial infections on skin surfaces suggesting that a shift from Methylobacteria to Flavobacterium dominance on the skin mucosal could point to increase in susceptibility to bacterial infection. Hence, by monitoring changes on mucosal bacteria composition, metage‐

As pointed out by Lyons et al. [72] that to better understand the gut microbiome and its impact on the health status of aquatic organisms, it is vital to determine its structure, diversity and potential functional capacity. Gajardo et al. [12] analysed the microbiota profile of the digesta and gut mucosal of Atlantic salmon (*S. salar* L.) fed commercial diets and showed that micro‐ biota richness and diversity differed significantly between the digesta and gut. The digesta had a higher and diverse richness than the gut mucosa. Proteobacteria was the dominant phyla in the mucosa whereas Proteobacteria and Firmicules were dominant in the digesta. In addition, there were significant differences in microbiota composition in different segments of the gut. Actinobacteria was dominant in the posterior intestinal (PI) than the mid‐intestinal (MI) mucosa. Moreover, the PI showed presence of Spirochaetes that were not found in the MI showing that metagenomics can be used to identify microbial communities that inhabit different segments of the gut. In another study, Gajardo et al. [11] identified bacterial groups associated with diet‐induced gut dysfunction that could serve as biological markers of the gut health status in Atlantic salmon. Mouchet et al. [73] compared the gut microbiota of 15 fish species from the Atlantic Ocean near Brazil and showed that the microbiota genetic diversity was highly influenced by the fish species, geographical location and diet. Put together, these studies show that metagenomics can be used to profile bacteria species on mucosal surfaces of different fish species and that different factors such as host species, geographical areas and

nomics can be used to determine the susceptibility of fish to microbial infections.

variations in community composition.

*2.4.2. Gut mucosal microbiota*

diet influence mucosal microbiota in fish.

#### **2.3. Application of metagenomics in recirculation systems**

In contrast to outdoor aquaculture systems that are dependent on natural water basins such as rivers and oceans, the recirculation aquaculture system (RAS) uses water that is filtered before it is recycled back into culture tanks in closed systems. Water used in RAS is subjected to several treatment processes such as biofiltration to reduce ammonium, removal of solids, oxygenation, pH control and pathogen denaturation using ozone and UV‐light. Although a well‐designed state‐of‐the‐art RAS has the potential to reduce the presence of waterborne microorganisms, some pathogens are able to resist RAS disinfection. Bacteria phyla detected from RAS biofilters include Actinobacteria [62], Firmicutes, Bacteroides [63–65], Protobacteria [63, 65], Verrucomicrobia [65] and Sphingobacteria [62, 65]. Hence, some microorganisms are being used as biosafety indicators whose dominance points to increase in the proliferation of pathogenic microorganisms [66]. As a result, metagenomics analyses are being used to moni‐ tor the increase in proliferation of pathogens in RAS [67].

#### **2.4. Metagenomics analyses of mucosa microbiota**

Given that mucosal surfaces are the major portals of microbial invasion, there has been a growing interest to study mucosal microbiota of cultured aquatic organisms. Metagenomics studies show that different environmental factors influence the composition of mucosal microbiota on different fish species.

#### *2.4.1. Skin mucosa microbiota*

Larsen et al. [68] compared the skin microbiota of six different fish species (*Mugil cephalus*, *Lutjanus campechanus*, *Cynoscion nebulosus*, *Cynoscion arenarius*, *Micropogonias undulatus* and *Lagodon rhomboides*) from the Gulf of Mexico and showed that Proteobacteria was the pre‐ dominant phylum followed by Firmicutes and Actinobacteria across all species. Although Aeribacillus was found in 19% of all fish species examined, genera such as Neorickettsia and Microbacterium were fish species‐specific pointing to existence of phyla and genera associated with particular fish species. Lokesh and Kiron [69] showed that the bacterial operational tax‐ onomy unit (OTU) composition on the skin of Atlantic salmon (*Salmo salar* L.) changed signifi‐ cantly as a result of transfer from fresh to seawater. Proteobacteria was the dominant phylum both in fresh and seawater while Bacteroidetes, Actinobacteria, Firmicutes, Cyanobacteria and Verrucomicrobia were the most abundant in freshwater. The genus *Oleispira* was the most abundant in seawater. Similarly, Wilson et al. [70] showed that bacterial communi‐ ties from the epidermal mucus of Atlantic cod (*Gadus morhua*) from the Baltic, Iceland and North seas collected over three seasons mainly comprised of Psychrobacter, Bacteroides and Photobacterium OTUs in all seasons although there were significant inter‐site and seasonal variations in community composition.

Boutin et al. [71] combined 16S RNA metagenomics and QTL analyses to show that host genotype can regulate the microbiota composition on the skin surface of brook charr (*Salvelinus fontinalis*). They found a strong negative correlation between Flavobacterium and Methylobacterium, pointing to a mutually competitive relationship between pathogenic and non‐pathogenic bacteria on the skin mucosa of brook charr. Flavobacterium is known to be pathogenic among different fish species, while Methylobacteria provide protection against pathogenic bacterial infections on skin surfaces suggesting that a shift from Methylobacteria to Flavobacterium dominance on the skin mucosal could point to increase in susceptibility to bacterial infection. Hence, by monitoring changes on mucosal bacteria composition, metage‐ nomics can be used to determine the susceptibility of fish to microbial infections.

#### *2.4.2. Gut mucosal microbiota*

over 40% of the global human population live within 100 km of coastlines, anthropogenic activities have been shown to influence the composition of microbial communities in coastal areas where aquaculture activities are mostly carried out [54]. These anthropogenic activities include host species composition changes introduced by aquaculture [55, 56], waste disposal [57], agriculture [58], recreation [59] and industrial activities [59]. As a result, metagenomics is currently being used to monitor the impact of anthropogenic activities on coastal micro‐ bial composition. Port et al. [60] found an increase in antibiotic resistance genes caused by coastal effluent discharges, while Morán et al. [61] showed significant changes in bacterial community structures caused by coastal copper disposal in La Lancha and Chañaral bay in the Pacific Ocean. Overall, these studies show that metagenomics is not only used to identify novel pathogens, but it is also used to monitor the impact of human activities on microbial

In contrast to outdoor aquaculture systems that are dependent on natural water basins such as rivers and oceans, the recirculation aquaculture system (RAS) uses water that is filtered before it is recycled back into culture tanks in closed systems. Water used in RAS is subjected to several treatment processes such as biofiltration to reduce ammonium, removal of solids, oxygenation, pH control and pathogen denaturation using ozone and UV‐light. Although a well‐designed state‐of‐the‐art RAS has the potential to reduce the presence of waterborne microorganisms, some pathogens are able to resist RAS disinfection. Bacteria phyla detected from RAS biofilters include Actinobacteria [62], Firmicutes, Bacteroides [63–65], Protobacteria [63, 65], Verrucomicrobia [65] and Sphingobacteria [62, 65]. Hence, some microorganisms are being used as biosafety indicators whose dominance points to increase in the proliferation of pathogenic microorganisms [66]. As a result, metagenomics analyses are being used to moni‐

Given that mucosal surfaces are the major portals of microbial invasion, there has been a growing interest to study mucosal microbiota of cultured aquatic organisms. Metagenomics studies show that different environmental factors influence the composition of mucosal

Larsen et al. [68] compared the skin microbiota of six different fish species (*Mugil cephalus*, *Lutjanus campechanus*, *Cynoscion nebulosus*, *Cynoscion arenarius*, *Micropogonias undulatus* and *Lagodon rhomboides*) from the Gulf of Mexico and showed that Proteobacteria was the pre‐ dominant phylum followed by Firmicutes and Actinobacteria across all species. Although Aeribacillus was found in 19% of all fish species examined, genera such as Neorickettsia and Microbacterium were fish species‐specific pointing to existence of phyla and genera associated

composition in different aquatic environments.

**2.3. Application of metagenomics in recirculation systems**

176 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

tor the increase in proliferation of pathogens in RAS [67].

**2.4. Metagenomics analyses of mucosa microbiota**

microbiota on different fish species.

*2.4.1. Skin mucosa microbiota*

As pointed out by Lyons et al. [72] that to better understand the gut microbiome and its impact on the health status of aquatic organisms, it is vital to determine its structure, diversity and potential functional capacity. Gajardo et al. [12] analysed the microbiota profile of the digesta and gut mucosal of Atlantic salmon (*S. salar* L.) fed commercial diets and showed that micro‐ biota richness and diversity differed significantly between the digesta and gut. The digesta had a higher and diverse richness than the gut mucosa. Proteobacteria was the dominant phyla in the mucosa whereas Proteobacteria and Firmicules were dominant in the digesta. In addition, there were significant differences in microbiota composition in different segments of the gut. Actinobacteria was dominant in the posterior intestinal (PI) than the mid‐intestinal (MI) mucosa. Moreover, the PI showed presence of Spirochaetes that were not found in the MI showing that metagenomics can be used to identify microbial communities that inhabit different segments of the gut. In another study, Gajardo et al. [11] identified bacterial groups associated with diet‐induced gut dysfunction that could serve as biological markers of the gut health status in Atlantic salmon. Mouchet et al. [73] compared the gut microbiota of 15 fish species from the Atlantic Ocean near Brazil and showed that the microbiota genetic diversity was highly influenced by the fish species, geographical location and diet. Put together, these studies show that metagenomics can be used to profile bacteria species on mucosal surfaces of different fish species and that different factors such as host species, geographical areas and diet influence mucosal microbiota in fish.

#### **2.5. Metagenomics technologies and their limitations**

Of the most widely used NGS technologies, both 454 pyrosequencing Roche and Illumina sequencers have been widely used in the metagenomics analyses of different aquatic organisms. For example, 454 pyrosequencing Roche has been used to study microbial communities of different fish species including rainbow trout (*Oncorhynchus mykiss*) [74], Atlantic cod (*G. morhua*) [75], Atlantic salmon [76], brook trout (*S. fontinalis*) [77], brown trout (*Salmo trutta*) [78], zebrafish (*Dario rerio*) [79], Gizzard shad (*Dorosoma cepedianum*) [80], silver carp (*Hypophthalmichthys molitrix*) [81], common carp (*Cyprinus carpio*) [82], grass carp (*Ctenopharyngodon idellus*) [83], orange spotted grouper (*Epinephelus coioides*) [84] and Senegalese sole (*Solea senegalensis*) [85]. On the other hand, Illumina sequencers have been used for the analyses of microbiota found in seabass (*Lates calcarifer*) [86], blunt snout bream (*Megalobrama amblycephala*) [87], grass carp (GC) [87], mandarin fish (*Siniperca chuatsi*) [87], topmouth culter (*Culter alburnus*), common carp [87] and Crucian carp (*Carassius auratus*) [87], silver carp [87] and bighead carp (*Hypophthalmichthys nobilis*) [87]. In terms of assembly, both whole genome shotgun and marker gene guided sequencing have been used on differ‐ ent aquatic organisms. The commonly used marker gene in metagenomics analyses is the 16S ribosomal RNA (16S rRNA), which has been widely used to characterize the microbiota of different aquatic organisms including rainbow trout [88, 89], Atlantic salmon [11, 12], tur‐ bot (*Scophthalmus maximus*) [90], lamprey (*Lampetra morii*) [91] and Baleen whale [92]. Whole genome shotgun sequencing has also been widely used in the study of environmental micro‐ bial communities and pathogens infecting different aquatic organisms. The major advantage with this approach is that it can be used to sequence whole genomes of known or unknown organisms using *de novo* assemblies unlike guided marker assemblies that are dependent on a reference gene [93–96].

molecular markers linked to nutrient digestion and absorption were high in the anterior (AI) and middle intestine (MI) while the posterior intestine (PI) predominantly expressed genes linked to immune defence mechanisms. These observations are in line with other scientists who showed that the AI and MI are mainly responsible for nutrient digestion and absorption [104, 105] while the PI is responsible for induction of innate immune responses linked to acti‐

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179

Different scientists have studied the genomic changes induced by various nutrients in the guts of different fish species. Krol et al. [110] compared the differential response of the Atlantic salmon gut to soybean meal (SBM) and fish meal (FM) as positive and negative controls for enteritis, respectively. They noted that SBM altered the gut histology and induced extensive transcriptomic changes linked to underlying mechanisms of SBM‐induced enteropathy. They found 18 enriched pathways linked to inflammation and immune responses induced by SBM enteropathy. Among these were the NF‐kB and IL‐8 signalling pathways known to induce the synthesis of various pro‐inflammatory cytokines. Phagocytic pathways such as the Fcγ recep‐ tor mediated phagocytosis and monocyte pathways were highly enriched. In another study, Torrecillas et al. [111] showed downregulation of TCRβ, COX‐2, TNFα, IL‐8, IL‐6, IL‐10, TGFβ and IgM when MHC‐II was upregulated in European seabass fed to Soya‐bean oil (SBO). Expression of these genes corresponded with reduced lengths of intestinal folds and mucus density in the gut. Conversely, mannan oligosaccharides (MOS) diets increased the length of intestinal folds and mucus density and upregulated MHC‐CD4, COX‐2, TNFα and IgM expression. Combined MOS and SBO diets reduced the harmful effects of SBO diets by mod‐ erating the downregulation of GALT‐related genes. Therefore, these observations show the importance of optimizing feed formulation in order to produce balanced diets able to pre‐

Apart from soyabean, nutrigenomics have also been used to evaluate the impact of other nutrients in fish diets. Azeredo et al. [112] showed that the immune status of the European seabass was impaired by arginine dietary supplements. They observed that different cell‐ mediated immune markers were downregulated in fish fed 1–2% arginine diets. Leukocytes obtained from fish fed arginine diets showed low respiratory burst compared to control fish. After challenge with *Vibrio aguillarum*, fish fed arginine diet supplements showed higher mortality than control fish. Interestingly, reducing arginine levels to 0.5% in the diet supplements significantly increased respiratory burst to levels comparable with control fish. In another study, Estensoro et al. [113] showed that butyrate (BP‐70 ®NOREL) helped to restore the intestinal status of marine gilthead sea bream (*Sparus aurata*) fed extremely low diets of fish meal (FM) and fish oil (FO). They observed that extremely low FO and FM diet levels significantly altered the transcriptomic profiles linked to nutrient absorption in the AI and increased expression of inflammatory, antioxidant, permeability and mucus production genes that coincided with increased granulocyte and lymphocyte presence in the PI submucosa. Interestingly, expression of these genes was restored to control values by adding butyrate (BP‐70) to the feed. As pointed out by Krol et al. [110], gut transcriptomic profiling is a useful tool for testing the adverse impacts of different feeds and that under‐ standing gut‐diet interactions is a prerequisite to designing diets able to prevent induction

vation of adaptive immunity in teleosts fish [106–109].

serve the GALT‐immune homeostasis.

of diet‐related diseases in the gut.

Despite its positive contribution to the discovery of novel pathogens and environmental mon‐ itoring of microbial communities, metagenomics has significant limitations that require the support of other tools [95]. The immense metagenome data generated using NGS technolo‐ gies require the support of other tools for clustering, classification and annotation of individ‐ ual sequences [95]. For *de novo* assembled sequences, the most reliable annotation approach is by homology search using reference sequences available in public databases. However, the number of existing public databases for aquatic organisms is limited, which makes it difficult to identify novel pathogens [97]. In general, functional annotation lags behind the rate at which metagenome data is generated. Alternative methods used to identify novel pathogens include motif or pattern‐based identification [98, 99], phylogenetic profiling [100] and neigh‐ bourhood tree alignments [101, 102].

#### **3. Nutrigenomics in aquaculture**

Nutrigenomics is the study of the role of nutrition on gene expression. Galduch‐Giner et al. [103] showed that there was specialization in the functional properties of different compo‐ nents of the intestinal tract of the European seabass (*Dicentarchus labrax*). They observed that molecular markers linked to nutrient digestion and absorption were high in the anterior (AI) and middle intestine (MI) while the posterior intestine (PI) predominantly expressed genes linked to immune defence mechanisms. These observations are in line with other scientists who showed that the AI and MI are mainly responsible for nutrient digestion and absorption [104, 105] while the PI is responsible for induction of innate immune responses linked to acti‐ vation of adaptive immunity in teleosts fish [106–109].

**2.5. Metagenomics technologies and their limitations**

178 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

a reference gene [93–96].

bourhood tree alignments [101, 102].

**3. Nutrigenomics in aquaculture**

Of the most widely used NGS technologies, both 454 pyrosequencing Roche and Illumina sequencers have been widely used in the metagenomics analyses of different aquatic organisms. For example, 454 pyrosequencing Roche has been used to study microbial communities of different fish species including rainbow trout (*Oncorhynchus mykiss*) [74], Atlantic cod (*G. morhua*) [75], Atlantic salmon [76], brook trout (*S. fontinalis*) [77], brown trout (*Salmo trutta*) [78], zebrafish (*Dario rerio*) [79], Gizzard shad (*Dorosoma cepedianum*) [80], silver carp (*Hypophthalmichthys molitrix*) [81], common carp (*Cyprinus carpio*) [82], grass carp (*Ctenopharyngodon idellus*) [83], orange spotted grouper (*Epinephelus coioides*) [84] and Senegalese sole (*Solea senegalensis*) [85]. On the other hand, Illumina sequencers have been used for the analyses of microbiota found in seabass (*Lates calcarifer*) [86], blunt snout bream (*Megalobrama amblycephala*) [87], grass carp (GC) [87], mandarin fish (*Siniperca chuatsi*) [87], topmouth culter (*Culter alburnus*), common carp [87] and Crucian carp (*Carassius auratus*) [87], silver carp [87] and bighead carp (*Hypophthalmichthys nobilis*) [87]. In terms of assembly, both whole genome shotgun and marker gene guided sequencing have been used on differ‐ ent aquatic organisms. The commonly used marker gene in metagenomics analyses is the 16S ribosomal RNA (16S rRNA), which has been widely used to characterize the microbiota of different aquatic organisms including rainbow trout [88, 89], Atlantic salmon [11, 12], tur‐ bot (*Scophthalmus maximus*) [90], lamprey (*Lampetra morii*) [91] and Baleen whale [92]. Whole genome shotgun sequencing has also been widely used in the study of environmental micro‐ bial communities and pathogens infecting different aquatic organisms. The major advantage with this approach is that it can be used to sequence whole genomes of known or unknown organisms using *de novo* assemblies unlike guided marker assemblies that are dependent on

Despite its positive contribution to the discovery of novel pathogens and environmental mon‐ itoring of microbial communities, metagenomics has significant limitations that require the support of other tools [95]. The immense metagenome data generated using NGS technolo‐ gies require the support of other tools for clustering, classification and annotation of individ‐ ual sequences [95]. For *de novo* assembled sequences, the most reliable annotation approach is by homology search using reference sequences available in public databases. However, the number of existing public databases for aquatic organisms is limited, which makes it difficult to identify novel pathogens [97]. In general, functional annotation lags behind the rate at which metagenome data is generated. Alternative methods used to identify novel pathogens include motif or pattern‐based identification [98, 99], phylogenetic profiling [100] and neigh‐

Nutrigenomics is the study of the role of nutrition on gene expression. Galduch‐Giner et al. [103] showed that there was specialization in the functional properties of different compo‐ nents of the intestinal tract of the European seabass (*Dicentarchus labrax*). They observed that Different scientists have studied the genomic changes induced by various nutrients in the guts of different fish species. Krol et al. [110] compared the differential response of the Atlantic salmon gut to soybean meal (SBM) and fish meal (FM) as positive and negative controls for enteritis, respectively. They noted that SBM altered the gut histology and induced extensive transcriptomic changes linked to underlying mechanisms of SBM‐induced enteropathy. They found 18 enriched pathways linked to inflammation and immune responses induced by SBM enteropathy. Among these were the NF‐kB and IL‐8 signalling pathways known to induce the synthesis of various pro‐inflammatory cytokines. Phagocytic pathways such as the Fcγ recep‐ tor mediated phagocytosis and monocyte pathways were highly enriched. In another study, Torrecillas et al. [111] showed downregulation of TCRβ, COX‐2, TNFα, IL‐8, IL‐6, IL‐10, TGFβ and IgM when MHC‐II was upregulated in European seabass fed to Soya‐bean oil (SBO). Expression of these genes corresponded with reduced lengths of intestinal folds and mucus density in the gut. Conversely, mannan oligosaccharides (MOS) diets increased the length of intestinal folds and mucus density and upregulated MHC‐CD4, COX‐2, TNFα and IgM expression. Combined MOS and SBO diets reduced the harmful effects of SBO diets by mod‐ erating the downregulation of GALT‐related genes. Therefore, these observations show the importance of optimizing feed formulation in order to produce balanced diets able to pre‐ serve the GALT‐immune homeostasis.

Apart from soyabean, nutrigenomics have also been used to evaluate the impact of other nutrients in fish diets. Azeredo et al. [112] showed that the immune status of the European seabass was impaired by arginine dietary supplements. They observed that different cell‐ mediated immune markers were downregulated in fish fed 1–2% arginine diets. Leukocytes obtained from fish fed arginine diets showed low respiratory burst compared to control fish. After challenge with *Vibrio aguillarum*, fish fed arginine diet supplements showed higher mortality than control fish. Interestingly, reducing arginine levels to 0.5% in the diet supplements significantly increased respiratory burst to levels comparable with control fish. In another study, Estensoro et al. [113] showed that butyrate (BP‐70 ®NOREL) helped to restore the intestinal status of marine gilthead sea bream (*Sparus aurata*) fed extremely low diets of fish meal (FM) and fish oil (FO). They observed that extremely low FO and FM diet levels significantly altered the transcriptomic profiles linked to nutrient absorption in the AI and increased expression of inflammatory, antioxidant, permeability and mucus production genes that coincided with increased granulocyte and lymphocyte presence in the PI submucosa. Interestingly, expression of these genes was restored to control values by adding butyrate (BP‐70) to the feed. As pointed out by Krol et al. [110], gut transcriptomic profiling is a useful tool for testing the adverse impacts of different feeds and that under‐ standing gut‐diet interactions is a prerequisite to designing diets able to prevent induction of diet‐related diseases in the gut.

Omics technologies commonly used for nutrigenomics analyses in aquaculture mainly comprise of microarray and RNA‐seq. RNA‐seq has been widely used to study the impact of different diets in various fish species including Atlantic salmon [114], rainbow trout [115], channel catfish (*Iactalurus punctatus*) [116], blue catfish (*Ictalurus furcatus*) [117] and zebrafish [118]. On the other hand, microarray has also been widely used to study nutrigenomics in different fish species that include Atlantic salmon, rainbow trout, Atlantic cod (*G. morhua*) and Gilthead sea bream (*S. aurata*). However, the use of RNA‐seq and microarray leads to several challenges that include the need for large data processing softwares as well as the need of bioinformatics tools required for differential gene expression, network pathway, alternative splicing and gene duplication analyses. To cope with these challenges, different bioinformatics tools have been developed and new innovations are being invented to cover different aspects of quality assessment of mapped genes, mapping for *de novo* assembled genes, expression quantification, differential expression analyses, alternative splicing and network pathway analyses [119–122]. Different reviews have been published provid‐ ing in‐depth comparative analyses of existing tools highlighting their strengths and weakness that could serve as a guide for end users to select the most appropriate tool suitable for nutrigenomics studies in different aquatic organisms [119, 123, 124].

restriction enzymes and cloned into plasmid vectors. Induced colonies of the expression library are probed using pooled sera from bacterial infected individuals as shown in **Figure 1**. Reactive clones are purified and used as vaccine candidates [133]. This technology has been widely used to identify antigenic proteins for different bacteria species such as *Streptococcus iniae* [134], *Vibrio anguillarum* [135], *Aeromonas salmonicida* [136, 137], *Edwardsiella tarda* [138] and *Streptococcus parauberis* [139]. Jia et al. [138] used the IVIAT to identify a 510 aa peptidase protein, which they used to produce a subunit vaccine against *E. tarda* in Japanese flounder. Sun et al. [134] used the IVIAT technique to identify a secretory antigen, which they designated as Sia10, and cloned it to produce a DNA vaccine against *S. iniae*. In vaccinated turbot, the Sia10 protein was detected in the muscle, liver, kidney and spleen by 7 days post‐vaccination (dpv) lasting until 49 dpv. Post‐challenge RPS showed 73.9 and 92.3% in fish challenged with high‐ and low‐challenge dose, respectively. In addition, the Sia10 protein produced protective antibodies in passively vaccinated fish. In another study, Sun et al. [140] used the IVIAT method to identify a surface

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**Figure 1.** Schematic layout of the IVIAT technique for the identification of bacterial antigenic proteins essential for the production of fish vaccines: A: bacteria culture. B: bacteria infection in fish and the sera from infected fish is pooled. C: library construction using chromosomal DNA fragments of the bacteria cultured in (A). D: bacteria eliminate absorbed antibodies from sera while IVIAT unbound antibodies are used to probe the library constructed in (C). E: clones from fragments of bacterial chromosomal DNA are probed with IVIAT pooled sera. F: after probing with pooled sera from infected fish, clones depicting binding capacity to IVIAT sera are sub‐cultured. G: the identified clones are purified,

sequenced and used for subunit or DNA vaccine production followed by vaccination and challenge trials.

#### **4. Functional genomics in vaccine development**

Given that most pathogens exist as multiple strains having different antigenic proteins, the challenge in vaccine design has been to find cross protective antigens against variant strains of the same pathogen. In the case of viruses, different approaches have been used aiming at finding the most neutralizing epitopes using methods such as epitope mapping, peptide‐ scan and reverse genetics [125–128]. However, the upcoming of next generation sequencing (NGS) supported with current advances of bioinformatics tools is expected to expedite our ability to identify the most immunogenic proteins for vaccine production against viral dis‐ eases. For example, Ou‐yang et al. [129] used bioinformatics to identify the antigenic proteins for Singapore grouper iridovirus. They used the 162 open reading frames (ORFs) of SGIV for sequence similarity searches to identify motifs, cellular locations and other prediction domains to identify the most immunogenic epitopes required for vaccine production. They identified 13 genes that were cloned to produce DNA vaccines of which three vaccines pro‐ duced relative percent survival (RPS) ranging from 58.3 to 66.7% in vaccinated grouper.

In the case of bacterial vaccines, identification of protective antigens can be a challenge given that they contain several antigenic proteins such as capsular antigens, fimbriae, pili and outer membrane proteins [130–132]. Some of these proteins lead to serotype, biovar or strain differ‐ ences leading to antigenic diversity within bacterial species. Hence, the challenge is to identify broad neutralizing antigens able to confer cross protection against variant bacterial strains can be a difficult task. To overcome this problem, Handfield et al. [133] developed an *in vivo* induced antigen technology (IVIAT) that uses antibodies generated from individuals infected by the bacterial strain homologous to the vaccine strain to probe for immunogenic proteins using an *in vitro* expression system. To do this, a genomic library is generated using DNA fragments from the bacteria strain to be used for vaccine production. The DNA fragments are digested using restriction enzymes and cloned into plasmid vectors. Induced colonies of the expression library are probed using pooled sera from bacterial infected individuals as shown in **Figure 1**. Reactive clones are purified and used as vaccine candidates [133]. This technology has been widely used to identify antigenic proteins for different bacteria species such as *Streptococcus iniae* [134], *Vibrio anguillarum* [135], *Aeromonas salmonicida* [136, 137], *Edwardsiella tarda* [138] and *Streptococcus parauberis* [139]. Jia et al. [138] used the IVIAT to identify a 510 aa peptidase protein, which they used to produce a subunit vaccine against *E. tarda* in Japanese flounder. Sun et al. [134] used the IVIAT technique to identify a secretory antigen, which they designated as Sia10, and cloned it to produce a DNA vaccine against *S. iniae*. In vaccinated turbot, the Sia10 protein was detected in the muscle, liver, kidney and spleen by 7 days post‐vaccination (dpv) lasting until 49 dpv. Post‐challenge RPS showed 73.9 and 92.3% in fish challenged with high‐ and low‐challenge dose, respectively. In addition, the Sia10 protein produced protective antibodies in passively vaccinated fish. In another study, Sun et al. [140] used the IVIAT method to identify a surface

Omics technologies commonly used for nutrigenomics analyses in aquaculture mainly comprise of microarray and RNA‐seq. RNA‐seq has been widely used to study the impact of different diets in various fish species including Atlantic salmon [114], rainbow trout [115], channel catfish (*Iactalurus punctatus*) [116], blue catfish (*Ictalurus furcatus*) [117] and zebrafish [118]. On the other hand, microarray has also been widely used to study nutrigenomics in different fish species that include Atlantic salmon, rainbow trout, Atlantic cod (*G. morhua*) and Gilthead sea bream (*S. aurata*). However, the use of RNA‐seq and microarray leads to several challenges that include the need for large data processing softwares as well as the need of bioinformatics tools required for differential gene expression, network pathway, alternative splicing and gene duplication analyses. To cope with these challenges, different bioinformatics tools have been developed and new innovations are being invented to cover different aspects of quality assessment of mapped genes, mapping for *de novo* assembled genes, expression quantification, differential expression analyses, alternative splicing and network pathway analyses [119–122]. Different reviews have been published provid‐ ing in‐depth comparative analyses of existing tools highlighting their strengths and weakness that could serve as a guide for end users to select the most appropriate tool suitable for nutrigenomics

Given that most pathogens exist as multiple strains having different antigenic proteins, the challenge in vaccine design has been to find cross protective antigens against variant strains of the same pathogen. In the case of viruses, different approaches have been used aiming at finding the most neutralizing epitopes using methods such as epitope mapping, peptide‐ scan and reverse genetics [125–128]. However, the upcoming of next generation sequencing (NGS) supported with current advances of bioinformatics tools is expected to expedite our ability to identify the most immunogenic proteins for vaccine production against viral dis‐ eases. For example, Ou‐yang et al. [129] used bioinformatics to identify the antigenic proteins for Singapore grouper iridovirus. They used the 162 open reading frames (ORFs) of SGIV for sequence similarity searches to identify motifs, cellular locations and other prediction domains to identify the most immunogenic epitopes required for vaccine production. They identified 13 genes that were cloned to produce DNA vaccines of which three vaccines pro‐ duced relative percent survival (RPS) ranging from 58.3 to 66.7% in vaccinated grouper.

In the case of bacterial vaccines, identification of protective antigens can be a challenge given that they contain several antigenic proteins such as capsular antigens, fimbriae, pili and outer membrane proteins [130–132]. Some of these proteins lead to serotype, biovar or strain differ‐ ences leading to antigenic diversity within bacterial species. Hence, the challenge is to identify broad neutralizing antigens able to confer cross protection against variant bacterial strains can be a difficult task. To overcome this problem, Handfield et al. [133] developed an *in vivo* induced antigen technology (IVIAT) that uses antibodies generated from individuals infected by the bacterial strain homologous to the vaccine strain to probe for immunogenic proteins using an *in vitro* expression system. To do this, a genomic library is generated using DNA fragments from the bacteria strain to be used for vaccine production. The DNA fragments are digested using

studies in different aquatic organisms [119, 123, 124].

**4. Functional genomics in vaccine development**

180 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

**Figure 1.** Schematic layout of the IVIAT technique for the identification of bacterial antigenic proteins essential for the production of fish vaccines: A: bacteria culture. B: bacteria infection in fish and the sera from infected fish is pooled. C: library construction using chromosomal DNA fragments of the bacteria cultured in (A). D: bacteria eliminate absorbed antibodies from sera while IVIAT unbound antibodies are used to probe the library constructed in (C). E: clones from fragments of bacterial chromosomal DNA are probed with IVIAT pooled sera. F: after probing with pooled sera from infected fish, clones depicting binding capacity to IVIAT sera are sub‐cultured. G: the identified clones are purified, sequenced and used for subunit or DNA vaccine production followed by vaccination and challenge trials.

antigen designated as Esa1, which they used to produce a DNA vaccine against *E. tarda* in Japanese flounder. They showed that the pCEsa1 vaccine enhanced respiratory burst, acid phosphatase activity and bactericidal activity of headkidney macrophages. In addition, it produced RPS = 57% in passively vaccinated fish. Overall, these studies show that genomics approaches can be used to identify the most immunogenic proteins for different bacterial strains in order to produce the most protective vaccines for use in aquaculture.

**Fish species Trait Method References**

Turbot (*Scophthalmus maximus*) Growth trait Transcriptome [243]

Mandarin fish (*Siniperca chuatsi*) Growth traits Microsatellite [245] Atlantic salmon (*Salmo salar* L.) Growth traits SNP/GWAS [149] Rainbow trout (*Oncorhynchus mykiss*) Robustness Transcriptome [173] Nile tilapia (*Oreochromis niloticus*) Growth traits Transcriptome [154] Nile tilapia (*Oreochromis niloticus*) Skeletal muscle quality Transcriptome [154] gilthead sea bream (*Sparus aurata*) Skeletal muscle quality Transcriptome [246] Rainbow trout (*Oncorhynchus mykiss*) Growth traits SNP [150] Rainbow trout (*Oncorhynchus mykiss*) Stress factor traits Transcriptome [247] Atlantic cod (*Gadus morhua*) Growth/reproduction Transcriptome [248]

Atlantic salmon (*Salmo salar* L.) Smoltification Transcriptome [177] Common carp (*Cyprinus carpio*) Cold tolerance QTL [163] Arctic char (*Salnelinus alpinus*) Temperature tolerance QTL [176] Arctic char (*Salnelinus alpinus*) Growth rate SNP [251] Tilapia (*Oreochromis niloticus*) Cold tolerance QTL [175] Tilapia (*Oreochromis niloticus*) Fish size QTL [175] Coho salmon (*Oncorhynchus kisutch*) Flesh colour QTL [167] Rainbow trout (*Oncorhynchus mykiss*) Spawning time QTL [178] Rainbow trout (*Oncorhynchus mykiss*) Albinism QTL [170]

Thyroid hormones Transcriptome [241]

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Growth trait Transcriptome [242]

Superiority in growth Transcriptome [244]

Reproduction Transcriptome [249]

Adaptation QTL [250]

QTL [252]

Blue bream (*Ballerus ballerus*)

Blunt snout bream (*Megalobrama* 

Grouper hybrids (*Epinephelus* 

Lake whitefish pairs (*Coregonus* spp.

Lake whitefish pairs (*Coregonus* spp.

Rainbow trout (*Oncorhynchus mykiss*) High temperature

**Table 1.** Growth and performance traits for different fish species.

tolerance

(Cyprinidae

*amblycephala*)

*fuscogutatus*)

*Salmonidae*)

*Salmonidae*)
