Transcriptome Analysis of Milkfish after Exposure to ...philjournalsci.dost.gov.ph/images/pdf/special... · negative bacterium Aeromonas hydrophila (Lio-po and Duremdez-Fernandez

Philippine Journal of Science148 (S1): 33-41, Special Issue on GenomicsISSN 0031 - 7683Date Received: 04 Apr 2019

Keywords: Aeromonas hydrophila, Chanos chanos, RNA-Seq, transcriptome

Transcriptome Analysis of Milkfish after Exposure to Aeromonas hydrophila using Next-generation Sequencing

1Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City 1101 Philippines

2Department of Biological Sciences, College of Arts Sciences and EducationTrinity University of Asia, Quezon City 1102 Philippines

Jaime Lorenzo N. Dinglasan1, Lorenz Rhuel P. Ragasa1, Anacleto M. Argayosa1,2, Zubaida U. Basiao1, and Michael C. Velarde1*

Milkfish is a major finfish product of the Philippines. But because the whole genome sequence of milkfish is still unknown, it is difficult to study the different molecular pathways involved in milkfish after bacterial exposure. Here, transcriptomic analyses by next-generation sequencing (NGS) were used to identify gene expression in milkfish liver after exposure to bacterial antigens from inactivated A. hydrophila. Obtained reads per individual were assembled de novo and fragments per kilobyte of exon per million mapped reads (FPKM) were measured to identify overall gene expression. Differential expression (DE) was analyzed by Cufflinks-Cuffdiff software. Gene ontology (GO) overrepresentation analysis revealed that milkfish exposed to A. hydrophila altered expression of genes involved in immune response pathways such as T cell and B cell signaling. The most differentially regulated genes include histamine n-methyltransferase (hnmt), nicotinamide phosphoribosyltransferase b (namptb), poliovirus receptor-related 2 like precursor (pvrl2), and the hepcidin antimicrobial peptide 1 – which are all involved in immunity. Overall, the study showed that milkfish liver contains immune-related genes that respond to bacterial antigens.

INTRODUCTIONSoutheast Asia’s total aquaculture production is about 9% of the world’s total aquaculture production. Chanos chanos (milkfish) is the top finfish product produced in this region, and the Philippines is one of the major milkfish-producing countries (Martinez et al. 2006). This particular finfish is the most commercially important aquaculture species in the Philippines, being produced at about 0.35 million metric tonnes per year – more than the next most produced finfish, Oreochromis niloticus (tilapia) (BAS 2013). Milkfish are suitable for production in the country due to their environmental adaptability and

their suitability to tropical conditions, as their reproductive patterns are reliant on warm temperature and plentiful sunlight (Martinez et al. 2006). However, milkfish are still susceptible to diseases (Chang 2006, Dequito et al. 2015, Virgula et al. 2017, Echem et al. 2018). One such disease that causes high mortality rate is due to the gram-negative bacterium Aeromonas hydrophila (Lio-po and Duremdez-Fernandez 1986, Emata 1994). In contrast to gram-positive bacteria, the cell wall of gram-negative bacteria contains lipopolysaccharides (LPS) as its major component (Anwar and Choi 2014) that contributes to structural integrity, as well as increased pathogenicity of gram-negative bacteria (Turska-Szewczuk et al. 2013). Exposure to inactivated bacteria will allow the stimulation of the host innate immunity by cell wall antigens such as LPS. *Corresponding Author: [email protected]

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In fact, inactivated bacteria have been used to investigate innate immunity in fish (Kamilya et al. 2015, Yan et al. 2016, Furlan et al. 2018). The adaptive immune system of fish is believed to be less specialized than mammals. Therefore, it can be considered that fish largely rely on their innate or non-specific immunity (Tort et al. 2003, Rauta et al. 2012). Hence, understanding the molecular targets in milkfish after bacterial exposure will provide insights into the mechanisms involved in its innate immune response. This type of information may help formulate effective interventions against these pathogens, benefit the commercial production of milkfish by the Philippine and global fish industries, increase the nation’s food security, and protect the health of milkfish consumers. But because the whole genome sequence of milkfish is not yet known, it is difficult to study molecular signaling in milkfish. Being the only living member of its Family that has yet to undergo speciation in over 100 million years (Near et al. 2014), the milkfish as a primitive species should be an interesting subject for molecular research.

With the emergence of NGS technologies, gene expression profile analyses in non-model organisms like the milkfish have become more manageable. RNA sequencing (RNA-Seq) allows the identification and quantification of gene expression across different treatments while discovering novel transcripts (Wang et al. 2009). For example, de novo RNA-Seq was used to observe gene expression changes in teleost in response to environmental changes such as temperature and salinity (Barat et al. 2016, Hu et al. 2015). Another example is the comparative gene expression of Schistosoma mansoni egg, adult male, and adult female to identify novel transcripts among the three life stages (Anderson et al. 2015). RNA-Seq has also previously been used to study the immune response in fish (Kumar et al. 2017). In this study, the gene expression profile of juvenile milkfish in response to bacterial antigens from inactivated A. hydrophila was successfully determined using RNA-Seq. Quantitative comparisons between individual gene expression profiles of pathogen-treated versus untreated juvenile milkfish were performed. Gene expression profiles were estimated and compared at transcript-level resolution. Differentially regulated gene sequences from milkfish were annotated using the Danio rerio (zebrafish) genome as a reference to obtain insight on transcriptomic changes in the fish after exposure to A. hydrophila.

MATERIALS AND METHODS

RNA Sequencing, Processing, and AnnotationLiver samples from milkfish treated with or without A. hydrophila were processed previously for RNA-Seq and resulting sequences were uploaded to Genbank by Argayosa et al. (BioProject: PRJNA251672, SRA Study:

SRP042955, TSA accession no. GDQX01000001-GDQX01037546). Briefly, Milkfish culture and treatments were done in accordance with the Institutional Animal Care and Use Committee of the University of the Philippines Diliman. Milkfish fry were obtained from the Bureau of Fisheries and Aquatic Resources – National Integrated Fisheries Technology Development Center (BFAR-NIFTDC) in Bonuan Binloc, Dagupan, Pangasinan. The fish were transferred to the tanks (170 L storage containers) of dechlorinated water with salinity and temperature conditions at 20 ppt and 25 °C, respectively. Salinity levels in the tanks were reduced to zero within a week to acclimate the fish to fresh water. The fish were cultured and reared to juvenile stage (~6 mo old; ~20 g average weight) in tanks filled with filtered and aerated dechlorinated tap water. Dissolved oxygen (DO) content was maintained at 3–5 ppm and turbidity at 0.5 g. All fish were fed 2 g of feeding pellets per fry twice daily (early am and late pm) for one week prior to experimentation. Milkfish were separated into two different groups – a control group (n = 3) and a treated group (n = 3) – each with a separate tank. All fish were healthy prior to treatment. For the treated group, fish were fed with the gamma-irradiated inactivated pathogen A. hydrophila (Argayosa et al. 2015) twice daily (early AM and late PM) on the first and third weeks within an experimental feeding period of three weeks. All feeding pellets were autoclaved and fed to the fish at 5% the weight (g) of each fish. No morbidity or mortality was observed during the experimental period. Liver tissue (50 mg per fish) were collected following sedation of fish by adding 500 μL/L (lethal dose) 2-phenoxyethanol (Sigma-Aldrich Inc., St. Louis, MO, USA) to the tank. Total RNA was isolated using TRIzol reagent (Life Technologies, USA), then enriched for mRNA using DynaBeads (Life Technologies, USA) – following the manufacturer’s protocol. cDNA library preparation for each replicate of the treated and untreated groups was done with Ion Total RNA-Seq Kit v2 (Life Technologies, USA), following the manufacturer’s protocol. The samples were sent for RNA-Seq using Ion Torrent Proton (Life Technologies, USA) unpaired-end sequencing technology to obtain the raw reads used to perform the transcriptomic analyses.

Raw reads from the two sequencing runs for each replicate were combined into a single fastq file by Welgene (Taiwan). FastQC was used to remove adapter sequences and trim poor quality sequences with Phred quality scores < 20. Per replicate, the filtered reads were assembled de novo into contiguous sequences (contigs) with Trinity version 13 Apr 2014 (Haas et al. 2013) so that a total of six assemblies were produced for each of the three treated and three untreated samples (i.e., one for each replicate). The

Dinglasan et al.: RNAseq in Bacteria-exposed MilkfishSpecial Issue on Genomics

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individual assemblies were subjected to contig sequence redundancy removal using CD-HIT-EST (Li and Godzik 2006, Huang et al. 2010), which identified sequences with greater than 95% identity and merged them into a single assembly or consensus sequence for downstream analyses. The contigs in the merged assembly were further annotated against the non-redundant protein (nr) database of the NCBI using Blast2GO (Conesa et al. 2005). Sequences without blast hits (blast hit = 0) in the nr database were blasted against the nucleotide (nt) and expressed sequence tags (est) databases of the NCBI.

Alignment, Expression Characterization, and DE AnalysisFiltered reads from FastQC were aligned to the indexed merged assembly using BOWTIE2 (Langmead et al. 2009). Gene expression was estimated using Cufflinks 2 (Trapnell et al. 2012). The obtained FPKM values of replicates for every gene were averaged and then used to characterize the expression pattern in each treatment. Genes commonly expressed by the control and treated groups were further analyzed for DE. Fold change was calculated with respect to untreated fish. Clustering and heatmap analyses was done using CummeRbund (Goff et al. 2013). A volcano plot was created for further visualization of differential gene expression.

GO AnalysisAfter the DE analysis, significantly down-regulated and up-regulated genes with blast hit > 0 were identified and were analyzed for GO by the Panther Classification System Ver. 11.0 (Mi et al. 2016). The PANTHER cellular pathways were of particular interest in this analysis. A PANTHER Overrepresentation Test (released 15 Jul 2016) determined the most enriched (padj-value < 0.05) pathways from both sets of significantly differentially expressed genes. The test employed a Bonferroni correction to adjust p-values for multiple testing.

RESULTS

Milkfish Transcript Sequence Alignment with Zebrafish and Mexican TetraTo examine relative gene expression of milkfish, sequences of milkfish gene transcripts were obtained from Argayosa et al. (BioProject: PRJNA251672). A total of 88,393,109 reads – corresponding to 5.6 Gb – were sequenced using the HTS Ion Torrent Proton platform. Almost 70% of base calls from sequencing reads had a Phred quality score greater than 20, which corresponds to a higher than 99% probability of accurately called

bases. After quality trimming, assembly, and merging of individual assemblies, 37,706 contigs were obtained from all six assemblies. Sequencing reads were further filtered by eliminating contigs shorter than 200. This resulted in 37,546 contigs with lengths ranging from 201 bp to 10,523 bp. Filtered reads were aligned with the merged assembly using BOWTIE2 with overall alignment rates ranging from 63.74% to 80.83%, of which 16,285 contigs (43.37%) gave zero blast hits. None of these contigs had a single blast hit when blasted against all est and nucleotide sequences in the NCBI database. Hence, these contigs were considered as novel transcripts. The 21,261 annotated (blast hit > 0) contigs were most similar to the Mexican tetra (Astyanax mexicanus) and zebrafish (Figure 1). The zebrafish genome was used as a reference for annotation as it is better characterized than Mexican tetra.

Figure 1. The BLAST hit genera distribution statistics for the top 15 most represented genera. BLAST hits were obtained from the BLASTx of milkfish transcripts against the nr protein database.

Differential Gene Expression in Milkfish LiverHeatmap analysis of differentially expressed genes using CummeRbund showed that each biological replicate clustered together with its own treatment group for each gene (Figure 2). Significantly differentially expressed genes represented about 2% of the total expressed genes. Of the 361 downregulated genes, 164 (45.43%) had blast hits > 0 in the zebrafish reference genome, and 197 (54.57%) were identified as novel transcripts. Among the 458 up-regulated transcripts, 285 (62.22%) had blast hits while 173 (37.77%) were identified as novel.

To identify the highly significant differentially expressed genes in treated versus untreated milkfish, a volcano plot was performed (Figure 3). The top three most significantly downregulated genes are the hnmt gene, the namptb gene, and an uncharacterized zebrafish protein-coding gene (zgc:172053). On the other hand, the most significantly upregulated gene is the pvrl2, and the hepcidin antimicrobial peptide 1. Several other annotated and novel genes were significantly downregulated and upregulated following bacterial exposure (Tables 1–2).


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Figure 2. A CummeRbund heatmap analysis of differentially expressed data obtained from the Cuffdiff analysis. Replicates (n = 3 per treatment) clustered within treatments for all differentially expressed genes. Treated samples: 2T, 3T, and 4T. Untreated samples: 5U, 6U, and 7U.

Figure 3. Volcano plot illustrating total gene expression in the milkfish after bacterial exposure. Genes not significantly expressed (p < 0.05) are represented by the black dots. Significantly differentially expressed genes are represented either in green (with annotation) or in red (without blast hit). Labeled are the top expressing genes (p < 0.05, Log2FC > 2)

GO of Differentially Expressed Genes in Milkfish LiverGene transcripts encoding for proteins associated with immune response, carbohydrate metabolism, and phototransduction were increased after bacterial exposure. Pathway enrichment shows upregulation of immune response related pathways such as T cell activation (P00053), B cell activation (P00010), axon guidance mediated by semaphorins (P00007), angiotensin II-stimulated signaling through G proteins and beta-arrestin (P05911), integrin signaling pathway (P00034), and CCKR signaling map (P06959) (Table 3). Other upregulated pathways include fructose galactose metabolism (P02744), and heterotrimeric G-protein signaling pathway-rod outer segment phototransduction (P00028).

In contrast, expression of genes involved in oxidative stress and DNA damage response decreased (Table 3). This includes hypoxia response via HIF activation (P00030) and oxidative stress response (P00046), which are involved in oxidative stress, while Xanthine and guanine salvage pathway (P02788) is involved in DNA damage response.


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DISCUSSIONEffective solutions for disease prevention and resilience building in milkfish could be formulated through molecular signaling studies. However, the lack of available genomic information in milkfish has made it difficult to study the molecular systems of this commercially important aquaculture species. Because of the emergence

Table 2. The top twenty annotated upregulated genesa.

Sequence description log2-FC Primary GO

poliovirus receptor-related 2 like precursor 2.48 C:integral component of

plasma membrane monocarboxylate transporter 7-like isoform x2 2.43 P:ion transmembrane

transport hepcidin antimicrobial peptide 1 2.36 P:biological regulation

hhip-like protein 1 2.27 P:carbohydrate metabolic process

apolipoprotein tandem duplicate 1 isoform x1 2.26 P:lipid transport

monocarboxylate transporter 7-like isoform x2 2.13 P:ion transmembrane

transport

zgc:110410 protein 1.97 C:integral component of membrane

monocarboxylate transporter 7-like isoform x1 1.91 P:ion transmembrane

transport 26s protease regulatory subunit 8 1.91 F:ATP binding

sodium- and chloride- dependent taurine transporter 1.9 C:integral component of

plasma membrane sodium-dependent serotonin transporter isoform x1 1.83 P:norepinephrine

transport apolipoprotein tandem duplicate 1 precursor 1.82 P:cellular response to

xenobiotic stimulus novel protein human pre- mrna cleavage complex ii protein pcf11

1.8 C:cytoplasm

3-hydroxy-3-methylglutaryl- coenzyme a reductase a 1.8 C:peroxisomal membrane

kynurenine--oxoglutarate transaminase 3 1.78 F:pyridoxal phosphate

binding hydroxymethylglutaryl- cytoplasmic 1.78 P:oligodendrocyte

development ba1 protein 1.77 C:hemoglobin complex actin-binding lim protein 1 isoform x12 1.77 F:zinc ion binding

insulin-like growth factor- binding protein complex acid labile subunit

1.76 P:biological process

chromodomain-helicase-dna- binding protein 2 isoform x1 1.74

F:core promoter sequence-specific DNA binding

aThe corresponding top putative molecular function (F), biological process (P), and cellular component (C) associated with each gene are listed.

Table 1. The top twenty annotated downregulated genesa.

Sequence description log2-FC Primary GO

histamine n-methyltransferase –2.8 C:cell part

zgc:172053 protein –2.78 P:biological process

thymosin beta-11 –2.64 P:sequestering of actin monomers

nicotinamide phosphoribosyltransferase b –2.54

F:nicotinate-nucleotide diphosphorylase (carboxylating) activity

zgc:172053 protein –2.43 P:biological process

cysteine and histidine-rich protein 1 –2.4 F:zinc ion binding

sal-like protein 1 –2.31 F:nucleic acid binding

thymosin beta-11 –2.25 P:sequestering of actin monomers

gdp-man:man(3)glcnac(2)-pp- dol alpha-1,2-mannosyltrans- ferase isoform 2

–2.22 C:integral component of membrane

cyclin-g-associated kinase isoform x2 –2.18 F:nucleotide binding

actinin alpha 3b –2.18 F:actin filament binding

sal-like protein 1 –2.18 F:nucleic acid binding

dnaj homolog subfamily c member 22 –2.18 F:chaperone binding

atp synthase-coupling factor mitochondrial isoform x1 –2.14

C:mitochondrial proton-transporting ATP synthase complex

long-chain-fatty-acid-- ligase 4 –2.08P:positive regulation of BMP signaling pathway

aminopeptidase n-like –2.05 F:zinc ion binding

purine nucleoside phosphorylase 5a –2.03 C:cytoplasm

insulin receptor substrate 2 –1.98 F:phosphatidylinositol 3-kinase binding

neurobeachin-like protein partial –1.97 P:platelet formation

n-acetylgalactosamine-6-sulfatase precursor –1.96 F:sulfuric ester

hydrolase activityaThe corresponding top putative molecular function (F), biological process (P), and cellular component (C) associated with each gene are listed. Genes considered to be outliers (–log-p-value > 3 and log2-FC > 2 or log2-FC < –2) are bolded.

of RNA-Seq technology, the genome-wide expression of non-model organisms like milkfish can be understood. Moreover, novel transcripts unique to this species can easily be detected. The use of RNA-Seq in this paper identifies the molecular response of milkfish to bacterial exposure – providing information on upregulated or downregulated genes affected by bacterial exposure while recognizing response mechanisms that are potentially unique to this agriculturally important fish.


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Milkfish is a non-model organism and it may reflect a few differences with the zebrafish genome. Among the differentially regulated genes, only around 55% share similarities with the zebrafish genome, and the remaining unidentified genes were considered novel. This was expected as there is little information on milkfish genetics. Further analysis should be done to confirm the identity of the unidentified genes in milkfish.

Most of the overrepresented cellular pathways of significantly upregulated genes were associated with an immune or inflammatory response. One of the upregulated genes in this group is the precursor of poliovirus receptor-related 2 like protein, which is a ligand that binds to and stimulates the CD226 receptor of T cells in humans – enhancing T cell response (Bottino et al. 2003). The presence of B cell and T cell signaling in milkfish liver is further supported by previous studies describing the presence of immune cells in the liver

of other teleosts (Möller et al. 2014). Furthermore, antimicrobial peptide hepcidin was also significantly upregulated. This protein, originally found to be involved in iron metabolism, is also present in many teleost species as part of their antimicrobial response (Neves et al. 2015). Together, these results suggest that the milkfish liver contributes to immune response. Although current information on the immune function of the teleost liver is still relatively sparse, the differential gene expression data on milkfish presented here support previous studies that the teleost liver functions in immune response (De Santis et al. 2015, Wang et al. 2015, Tafalla et al. 2016).

Oxidative stress is upregulated in teleosts like Pimephales promales (fathead minnow), Perca flavescens (yellow perch), and Oncorhynchus mykiss (rainbow trout) when in an infected state (Marcogliese et al. 2005, Stumbo et al. 2012, Pacitti et al. 2014, Tkachenko et al. 2014). In contrast to these findings, results presented here showed a downregulation of genes associated with oxidative stress and DNA damage in the bacteria-exposed milkfish. One of the most significantly downregulated genes is namptb. NAMPT plays a role in NAD+ synthesis, maintaining the levels of NAD+ to protect cells against oxidative stress (Rongvaux et al. 2008). It is targeted directly by HIF-2α, a component of the HIF signaling pathway (Yang et al. 2010), which was also downregulated in this study. The association between lowered oxidative stress response and immunity in the liver is not yet clear. Therefore, a deeper look into this pathway and its components could shed light into its role in milkfish immunity.

Another downregulated gene is the hnmt. The encoded enzyme is responsible for the conversion of histamine to 1-methylhistamine (Shiozaki et al, 2003). This indicates that when exposed to bacterial antigens, milkfish histamine is not being converted to 1-methylhistamine, therefore possibly accumulating histamine. Increase in histamine is associated with increased immune response in fish via regulation of inflammation (Galindo-Villegas et al, 2016). This further supports the ability of the milkfish liver to elicit an immune response when exposed to bacterial antigens.

Due to the complexity of these pathways and a limited pool of related information, their exact roles in the milkfish liver infected with live bacteria, instead of inactivated bacteria, should still be further explored. Furthermore, these findings should be validated through quantitative real-time PCR assays. Tapping into and enhancing these mechanisms, which seem to make the milkfish a relatively resilient species, could contribute to security in the aquaculture industry later on, especially when the molecular machinery of the fish is better understood.

Table 3. List of overrepresented pathways of differentially expressed genes with annotationa.

PANTHER pathways Fold enrichment p-value

Pathways involving downregulated genes

Hypoxia response via HIF activation (P00030)

36.74 3.08E–06

Oxidative stress response (P00046)

19.44 1.07E–03

Xanthine and guanine salvage pathway (P02788)

81.64 4.48E–02

Pathways involving upregulated genes

Axon guidance mediated by semaphorins (P00007)

21.02 8.39E–05

Heterotrimeric G-protein signaling pathway-rod outer segment phototransduction (P00028)

14.25 7.72E–04

Angiotensin II-stimulated signaling through G proteins and beta-arrestin (P05911)

13.47 6.31E–03

B cell activation (P00010) 9.04 9.70E–03

Fructose galactose metabolism (P02744)

30.03 2.33E–02

T cell activation (P00053) 7.31 3.05E–02

Integrin signaling pathway (P00034)

5.12 3.10E–02

CCKR signaling map (P06959)

5.07 3.29E–02

aThe PANTHER Overrepresentation Test was used with the Bonferroni correction for multiple testing to predict these pathways.


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ACKNOWLEDGMENTSWe would like to thank A.M. Argayosa, J.R. Daclan, R.J. Pascual, and P.R. Mejia for performing the initial work on RNA-Seq and uploading the sequences on GenBank. This paper was funded by the Department of Science and Technology – Philippine Council for Agriculture, Aquatic, and Natural Resources Research and Development (DOST-PCAARRD, ZUB) and, in part, by the University of the Philippines Diliman Office of the Vice Chancellor for Research and Development (UPD OVCRD) Outright Research Grant (171730 PNSE, MCV).

NO CONFLICT OF INTERESTThe authors have no conflict of interest to declare.

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