- Email: [email protected]
Transcriptome profiling of grass carp (Ctenopharyngodon idellus) infected with Aeromonas hydrophila
Accepted Manuscript Transcriptome profiling of Grass carp (Ctenopharyngodon idellus) infected with Aeromonas hydrophila Ying Yang, Hui Yu, Hua Li, Anli Wang PII:
S1050-4648(16)30079-1
DOI:
10.1016/j.fsi.2016.02.035
Reference:
YFSIM 3847
To appear in:
Fish and Shellfish Immunology
Received Date: 31 December 2015 Revised Date:
25 February 2016
Accepted Date: 29 February 2016
Please cite this article as: Yang Y, Yu H, Li H, Wang A, Transcriptome profiling of Grass carp (Ctenopharyngodon idellus) infected with Aeromonas hydrophila, Fish and Shellfish Immunology (2016), doi: 10.1016/j.fsi.2016.02.035. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Transcriptome profiling of Grass carp (Ctenopharyngodon idellus) infected with
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Aeromonas hydrophila
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Ying Yanga, b, Hui Yub**, Hua Lib, Anli Wanga*
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College of Life Sciences, South China Normal University, Guangzhou, Guangdong 510631, China
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b
College of Life Science, Foshan University, Foshan, Guangdong 528231, China
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*Correspondence to:
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Pro. Anli Wang
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College of Life Science
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South China Normal University
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No. 55 West Zhongshan Avenue
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Tianhe, Guangzhou, Guangdong, P.R.China
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Tel/fax +86 2085210141.
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E-mail: [email protected]
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**Co- Correspondence to:
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Pro. Hui Yu
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College of Life Science
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Foshan University
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No.1 Xianhu Road
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Nanhai, Foshan, Guangdong, P.R.China
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Tel/fax +86 757 86678587.
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E-mail: [email protected]
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ACCEPTED MANUSCRIPT Abstract: Aeromonas hydrophila is the causative pathogen of intestinal hemorrhage
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which has caused great economic loss in grass carp aquaculture. In order to
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understand the immunological response of grass carp to infection by A. hydrophila,
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the transcriptomic profiles of the spleens from infected and non-infected grass carp
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groups were obtained using HiSeq™ 2500 (Illumina). An average of 63 million clean
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reads per library was obtained, and approximately 80% of these genes were
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successfully mapped to the reference genome. A total of 1591 up-regulated and 530
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down-regulated genes were identified. Eight immune-related categories involving 105
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differently expressed genes were scrutinized. 16 of the differently expressed genes
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involving immune response were further validated by qRT-PCR. Our results provide
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valuable information for further analysis of the mechanisms of grass carp defense
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against A. hydrophila invasion.
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Key words: Ctenopharyngodon idellus; Aeromonas hydrophila; Transcriptome;
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Pathway analysis; Immune response
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1. Introduction
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Grass carp (Ctenopharygodon idella) is an economically important species and its
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global production is more than 4.5 million tones per year, which making it the most
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highly consumed freshwater fish worldwide[1]. However, infectious intestinal
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hemorrhage caused by Aeromonas hydrophila has been severe for years[2], resulting
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in great economic loss and threatening the development of grass carp aquaculture. A.
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hydrophila is facultative anaerobic, motile, and Gram-negative rods in the family
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Aeromonadaceae[3]. It is often found in association with hemorrhagic septicemia in
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cold-blooded animals including fish, reptiles and amphibians. However, this organism
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has also attracted attention as an emerging human pathogen, and has been considered
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to have a significant impact on public health[4]. Therefore, identification of host
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factors in response to A. hydrophila infection has a great significance for disease
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prevention and control in grass carp culture.
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Transcriptome analysis is a powerful tool for leading to a better understanding of
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the underlying pathways and mechanisms controlling cell fate, development, and
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disease progression[5]. Over the years, several technologies have been developed to
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(HTS) technologies permit genome-wide transcriptomic analysis at a higher resolution,
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and these technologies have been widely used to study pathogenic processes during
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bacterial infections[6]. Hegedus et al. firstly used Solexa/Illumina’s DGE system to
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study zebrafish transcriptome after Mycobacterium marinum infection, it showed the
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feasibility and superiority of high-throughput sequencing technology in the field of
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fish immune research[7]. Then RNA-Seq have been applied to immune-related gene
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and signaling pathway analysis of several fish species such as Mandarin fish, Large
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yellow croaker and Lates calcarijer[8-10].
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The results of sequencing of the transcriptome of juvenile grass carp after A.
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hydrophila infection and non-infection were presented in this study. High-quality
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cDNA sequences were obtained by using Illumina RNA-Seq method and the
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sequencing reads were mapped to the reference genome database of grass carp[11].
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Additionally, a great number of immune related genes that were differently expressed
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upon A. hydrophila infection were obtained and functionally annotated, and the gene
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expression patterns of some of these genes were verified by qRT-PCR. These results
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provide a valuable resource for further research into the mechanism of anti-infection
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immunity of grass carp.
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2. Materials and methods
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2.1 Fish and bacteria
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Healthy juvenile grass carps of 20g±2g body weight were kindly provided by the
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Hold-one aquatic breeding center in China and reared under pathogen-free conditions.
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Fish were maintained in aerated tap water at 28 °C in aquaria with Eheim biofilters
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until use. The bacterial strain A. hydrophila used for the experiments was isolated
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from diseased grass carp, and has been deposited in the China Center for Type Culture
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Collection under preservation number CCAM05068. The bacteria was cultured in
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Luria-Bertain (LB) at 28 °C for 24 h with constant shaking (150 rpm), then harvested
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by centrifugation at 6000 rpm for 5 min, washed once by PBS and centrifuged again.
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Bacterial pellets were then re-suspended in saline adjusted the concentration to 5×107
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colony forming units (CFU) ml-1.
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ACCEPTED MANUSCRIPT 2.2 Challenge experiments and RNA preparation
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Healthy fish were randomly divided into two groups (30 fish per group). For bacterial
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infection group, fish were injected intraperitoneally (i.p.) with 0.1ml A. hydrophila
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suspension above-mentioned. The fish treated with 0.1ml 0.65% physiological saline,
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were used as the control. After 6h of injection, the spleen were collected and
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immediately stored in liquid nitrogen until RNA extraction.
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Total RNA was extracted from each sample using TRIzol Reagent (Invitrogen,
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USA) according to the manufacture's protocol. The quality and quantity of the RNA
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samples were examined by use of the Agilent 2100 Bioanalyzer (Agilent Technologies)
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and the integrity was assessed by electrophoresis on 1% agarose gel. For each group,
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equal amount of RNA from the nine fish individuals per line were pooled to provide
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templates for RNA-Seq library construction.
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2.3 Library construction and Illumina sequencing
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After DNase I treatment, mRNA was purified using oligo (dT)25 magnetic beads
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(Dynabeadsoligo (dT)25, Invitrogen) and subsequently interrupted to short
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fragments of 200-250 nucleotides using RNA fragmentation reagent (Ambion, USA).
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Then sequencing libraries were generated using NEBNext® Ultra™ RNA Library
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Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations. The
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cDNAs were checked by Agilent 2100 Bioanalyzer (Agilent, USA) and ABI
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StepOnePlus Real-Time PCR System (ABI, USA). The mixed DNA libraries were
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diluted to 4-5 pM for sequencing by Illumina Hiseq 2500™ platform.
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2.4 Sequence annotation
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Raw reads were first cleaned by removing adaptor sequences, low quality sequences
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(Sanger base quality < 20) and reads with unknown nucleotides larger than 10%.
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Clean reads were mapped to the Ctenopharyngodon idellus reference genome
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(http://www.ncgr.ac.cn/grasscarp/) using HISAT (version 0.1.6) [12]. The HTSeq
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(version 0.6.1) [13] was utilized to calculate the number of aligned reads per exon
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through annotation of the grass carp genome. Subsequently, the transcripts were
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subjected to BLASTX similarity searching against NCBI non-redundant protein
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database (NR) with an E-value threshold of 10-5, and the unigenes were identified.
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ACCEPTED MANUSCRIPT 2.5 Differential expression analysis
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Expression levels were measured in reads per kilo base of exon per million mapped
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reads (RPKM) method. The distribution of gene expression was analyzed. Statistical
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comparison between two groups was conducted using DEGseq (version 1.18.0) [14].
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To assess the significance of differential gene expression, the threshold of FDR was
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set at ≤ 0.01 and the absolute value of log2 ratios (fold change between non-infected /
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infected samples) at ≥1. Differential expression genes (DEGs) were further annotated
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by Gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and
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Genomes (KEGG) pathway analysis. GO annotation was acquired using the GOseq
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(version 1.16.2) and WEGO [15, 16]. P-values generated from the enrichment
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analysis were subjected to multiple hypothesis testing, with p-values ≤ 0.05
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considered significant. Pathway analysis was performed using the Kyoto
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Encyclopedia
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(http://www.genome.jp/kegg)[17]. After multiple test correction, pathways with
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Q-values ≤ 0.05 were considered to be significantly enriched in DEGs.
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2.6 Confirmation using quantitative real-time RT-PCR (QPCR)
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To examine the reliability of the RNA-Seq results, a selected subset of differently
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expressed genes involved in immune responses were selected for validation using
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quantitative real-time RT-PCR (qRT-PCR). 16 genes differently expressed between
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the infected and non-infected groups, including Chemokine C motif receptor 1 (XCR
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1), interleukin-1 beta (IL-1β), tumor necrosis factorα (TNF-α), C-type lysozyme
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(LysC), interferon regulatory factor 4 (IRF4), B cell receptor CD22, coagulation
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factor 7 (F7), complement component 1 (C1), complement component 3 (C3),
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complement component 7 (C7), toll-like receptor 22 (TLR 22), MHC class I antigen
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(MHC-I Ag), MHC class II antigen (MHC-II Ag), macrophage expressed gene 1
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(MPEG 1), heat shock 70 kDa protein (HSP 70), and α-2-macroglobulin (α2MG)
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were selected for qRT-PCR assay. The housekeeping gene β-actin was used as the
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reference gene. Suitable primers were designed using Primer Express 3.0(Table.1) and
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synthesized by Sangon Bothech (Shanghai) Co., Ltd. QRT-PCR with SYBR Green
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dye (TaKaRa, Dalian, China) was performed on an ABI PRISM 7500 Fast Real-Time
Genes
and
Genomes
(KEGG)
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ACCEPTED MANUSCRIPT PCR System according to the manufacturer’s protocol. Each treatment group was
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created by combining equal amounts of RNA from three replicate pools (three
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individual fish per pool). All reactions were performed in triplicates. The qRT-PCR
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conditions were as follows: 30s at 95, followed 40 cycles of 5s at 95, 30s at 58,
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34s at 72. Dissociation curve analysis was performed to determine the target
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specificity. The relative expression ratio of the target genes versus β-actin gene was
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calculated using 2-△△CT method and all data were given in terms of relative mRNA
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expression.
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Insert Table 1 here.
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3. Results
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3.1 Transcriptome sequence and reads mapping
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Infected and non-infected groups were analyzed by RNA-Seq respectively. RNA
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sequencing resulted in about 65.5 million (infected) and 63.4 million (non-infected)
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raw reads of 125 bp (Table 2). After filtration, 64.6 million of infected groups and
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62.6 million of non-infected groups clean reads were obtained, and these sequences
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were carried forward for additional analysis. Average 80% of the clean reads were
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mapped to the reference genome, 24507 genes of infected group and 24383 genes of
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non-infected group were detected. These sequences were compared with the NCBI
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non-redundant (NR) protein for functional annotation. Raw sequencing reads data has
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been submitted to Sequence Read Archive in NCBI, the SRA accession numbers are
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SRR3045340 and SRR3045341.
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3.2 Differential expression analysis
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RNA-Seq reads mapped to the grass carp reference genome were aligned and their
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relative abundances were estimated using HTseq. According to the statistical analysis
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of unigene data with DEGseq, the genes significantly differentially expressed between
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infected and non-infected groups were identified. The results showed that 2121 genes
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were significantly differently expressed, including 1591 up-regulated genes and 530
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down-regulated genes. Differential expression genes and no differential expression
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genes distribution trends were presented clearly (Fig. 1).
ACCEPTED MANUSCRIPT Insert Fig. 1 here.
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3.3 GO and KEGG enrichment analysis of differently expressed genes
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The results of the GO enrichment analysis of differential expression genes were
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classified into three categories: biological process (121 subclasses), cellular
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component (31 subclasses) and molecular function (129 subclasses). The significant
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GO terms in these three categories and DEGs of them were showed in Fig. 2. The
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pathway enrichment analysis helped to further understand genes’ biological functions.
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In this study, 1631 of 2121 DEGs were annotated to 251 signaling pathways in KEGG.
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The top30 most enriched pathway terms and DEGs of them were showed in Fig. 3. Insert Fig. 2 here.
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Insert Fig. 3 here.
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3.4 Analysis of differently expressed genes related to immune responses
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In this report, we focused on the genes related to the immune responses. Based on
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gene clustering and pathway analysis, genes involved in both innate and adaptive
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immunity had been identified (Table 3), including 85 up-regulated and 20
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down-regulated genes. They were further grouped into 8 sub-classes as follows:
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phagocytosis, complement system, cytokine, antigen processing and presentation,
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pattern recognition receptor, cell adhesion molecules, apoptosis, antioxidant enzymes.
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Insert Table 3 here.
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3.5 Analysis of differently expressed genes by qRT-PCR
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Quantitative real-time RT-PCR was performed on 16 immune genes for validating the
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differently expressed genes identified by RNA-Seq. For the results, the analysis of
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qRT -PCR demonstrated a single product for all tested genes. Fold changes of qRT
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-PCR were compared with the RNA-Seq expression profiles. As shown in Fig. 4, all
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of qRT -PCR results were significantly correlated with the RNA-Seq results and they
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showed the identical up-regulated and down-regulated patterns of these genes in both
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assays.
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Insert Fig. 4 here.
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4. Discussion
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In the present study, transcriptome profiling of grass carp was undertaken to gain
ACCEPTED MANUSCRIPT insights into the molecular mechanisms underpinning host responses to A. hydrophila.
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There were a total of 2121 genes expressed differently between infected and
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non-infected group and these differently expressed genes were assigned multiple
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potential functions in molecular function and biological process. Here, we mainly
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focused on the immune-related genes of fish activated in the early stage of
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bacteria invasion. Taking the advantages of GO annotation and KEGG pathway
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classification, we categorized these immune related genes into eight categories and
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the most important parts of them were discussed.
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4.1 Antigen processing and presentation
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Apparently, innate immunity is the major approach to defense against invading
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virulent bacteria like A. hydrophila. But from the path-ways enriched in this present
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study, cellular immunity belonging to acquired immunity also plays a role in the
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disease. Based on analysis of gene differential expression, T-lymphoma invasion and
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metastasis-inducing protein 1, T cell receptor beta chain and MHC class I antigen
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were up-regulated significantly. CD2 family receptor and other CDs involved in
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lymphocyte recognition and signal transduction were found in DEGs. In addition, the
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discovery of novel immune-type receptors (NITRs) indicates that fish leukocytes
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express various types of Ig superfamily (IgSF) receptors for contact with other
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immune cells[18]. It is worth noting that MHC I and MHC II were both DEGs, but
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MHC I was up-regulated in this tissue while MHC II was down-regulated.
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We noticed that B-cell receptor CD22 was up-regulated and the expression of this
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membrane receptor is generally regarded as being restricted to B cells[19]. On the
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contrary, activated cytotoxic T lymphocytes (CTLs) released toxic particles such as
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perforin to kill host cells infected with pathogens[20].
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4.2 Phagocytosis
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Phagocytosis is an important, evolutionarily conserved mechanism that is integral
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to host defenses against invading microorganisms[21]. In professional phagocytes
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such as neutrophils and macrophages, phagocytosis is triggered by the recognition of
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pathogen
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immunoglobulins and complement components bound to the microbes, which are
associated
molecular
patterns
(PAMPs)
and
opsonins
such
as
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recognized
by
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differently expressed genes, 23 phagocytosis–related genes were found and all of
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them were up-regulated. MPEG 1 and neutrophil cytosolic factor were up-regulated
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indicating both macrophages and neutrophils were stimulated. Calreticulin and
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calmegin
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Phagocytosis is initiated by the interaction of receptors on the surface of the
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phagocyte with ligands on large particles such as bacterial. Subsequently integrins
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mediate particle internalization[25]. Once internalized, the phagosome that encloses
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the particle undergoes a series of maturation steps and culminates in phagolysosome
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fusion[26]. In this study, increasing expression of lysosome-related genes including
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C-type lysozyme, G-type lysozyme, Cathepsin K, Cathepsin B precursor, Cathepsin L
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precursor, and Cysteine proteinase promotes the lysis of bacteria[27-30].
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4.3 Complement system
receptors[22].
enhance the immune competence
of
Among
macrophages[23,
the
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phagocytosis-promoting
Complement system is a key component of innate immunity in teleosts and its
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activation leads to opsonization of pathogens, recruitment of inflammatory and
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immune competent cells, and the direct killing of pathogens[31, 32]. There are three
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different ways to activate the complement on the surface of invading pathogens:
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classical, lectin and alternative pathways[33]. In our results, C1
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up-regulated, indicating the activation of classical pathway. C3 was down-regulated
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and C3b, factor B were up-regulated, deducing C3 molecules were decomposed and
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activate the alternative pathway.
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The blood coagulation pathway controls the coagulation and fibrinolysis of blood
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through a series of sequentially activated serine kinases[34]. In our data,
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mannose-binding protein-associated serine protease, α-2-macroglobulin and several
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coagulation factors were significantly down-regulated, indicating the inhibition of
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blood coagulation[35, 36]. This may be related to the bleeding symptoms observed in
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grass carp caused by A. hydrophila[37].
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4.4 Cytokines
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Cytokines are a family of low molecular weight proteins secreted by activated
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immune-related cells upon induction by various pathogens such as parasitic, bacterial,
ACCEPTED MANUSCRIPT or viral components[38]. Cytokines could be divided into interferons (IFNs),
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interleukins (ILs), tumor necrosis factors (TNFs), colony stimulating factors, and
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chemokines[39]. In this report, a total of 20 immune related cytokine genes were
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found in DEGs, 17 were up-regulated and three were down-regulated. These
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differently expressed cytokine genes could be classified as interferons and signaling
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factors (IRF 4, IFN transmembrane protein 1, IRF 10 and IRG-47), interleukin family
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members and receptors (IL-1β, I IL -10, IL -21 receptor, IL -1 receptor I, IL -27
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subunit beta precursor and IL 13 receptor precursor), tumor necrosis factors (TNF-α
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induced protein 6 and TNF-α receptor 2) and chemokines (Chemokine-like receptor 1,
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XCR1, CXCR3, CXC motif chemokine 9-like and CXC motif chemokine 13
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precursor). Interleukin, tumor necrosis factors and chemokines play key roles in
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inflammation and host defense, besides that, activity of IFN and IRF genes expression
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also proved that interferons involved in bacterial immune responses[40-42]. The
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complicate coordinated approaches of the cytokine-related genes in the immune
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responses of grass carp during bacteria infection remains to be addressed in the future.
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4.5 Others
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The results also demonstrate that a number of other differentially expressed
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immune-related genes enriched in pattern recognition receptor (PRR), cell adhesion
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molecules
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previous reports suggested that toll-like receptor 22 (TLR22) exists exclusively in
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aquatic animals and recognizes double stranded RNA (dsRNA)[43, 44], but this study
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shows it can also be elevated by bacterial stimulation. The details of the regulation of
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these immune-related genes need to be elucidated in the future.
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5. Conclusion
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In conclusion, RNA-seq analysis is a highly valuable resource to address the issues of
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disease resistance at the genome and transcriptome levels. This study provided a first
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survey of host defense gene activities against bacterial challenge based on
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differentially
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significantly enriched gene clusters including phagocytosis, complement system,
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cytokines, antigen processing and presentation, PRR, CAM, apoptosis and antioxidant
apoptosis
and
antioxidant
enzymes.
Remarkably,
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in
grass
carp.
Additionally,
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enzymes were identified and discussed. This study will add new information that may
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help to prevent the A. hydrophila infection.
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Acknowledgments
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We would like to thank Yu-dong Du (Project manager of Hold-one aquatic breeding
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co., LTD) for providing juvenile grass carps. This work was jointly supported by
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National Spark Program Project of China (2015GA780004), Guangdong Natural
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Science Foundation (2013B020307017; 2013B020503070) and Foshan Science and
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Technology Innovation Platform Construction Program (2013AG10005).
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References
313
[1] Stephan M, Hobsdawn P. Australian fisheries and aquaculture statistics 2013.
314
Fisheries Research and 2014.
M AN U
312
[2] Liu L, Gong Y-X, Zhu B, Liu G-L, Wang G-X, Ling F. Effect of a new
316
recombinant Aeromonas hydrophila vaccine on the grass carp intestinal
317
microbiota and correlations with immunological responses. Fish Shellfish
318
immun. 2015; 45(1):175-83.
TE D
315
[3] Seshadri R, Joseph SW, Chopra AK, Sha J, Shaw J, Graf J, et al. Genome
320
sequence of Aeromonas hydrophila ATCC 7966T: jack of all trades. J bacteriol.
321
2006; 188(23):8272-82.
323 324
[4] Agger WA, McCormick J, Gurwith MJ. Clinical and microbiological features of Aeromonas
hydrophila-associated
AC C
322
EP
319
diarrhea.
J
Clin
Microbiol.
1985;
21(6):909-13.
325
[5] Mutz KO, Heilkenbrinker A, Lonne M, Walter J-G, Stahl F. Transcriptome
326
analysis using next-generation sequencing. Curr opin biotech. 2013;
327 328 329
24(1):22-30. [6] Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol syst biol. 2013; 9(1):640.
330
[7] Hegedűs Z, Zakrzewska A, Ágoston VC, Ordas A, Rácz P, Mink M, et al. Deep
331
sequencing of the zebrafish transcriptome response to mycobacterium infection.
ACCEPTED MANUSCRIPT 332
Mol Immunol. 2009; 46(15):2918-30.
333
[8] Khoo CK, Abdul-Murad AM, Kua BC, Mohd-Adnan A. Cryptocaryon irritans
334
infection induces the acute phase response in Lates calcarifer: a transcriptomic
335
perspective. Fish Shellfish immun. 2012; 33(4):788-94. [9] Mu Y, Li M, Ding F, Ding Y, Ao J, Hu S, et al. De novo characterization of the
337
spleen transcriptome of the large yellow croaker (Pseudosciaena crocea) and
338
analysis of the immune relevant genes and pathways involved in the antiviral
339
response. Plos One. 2014; 9(5):e97471.
RI PT
336
[10] Zhou W, Zhang Y, Wen Y, Ji W, Zhou Y, Ji Y, et al. Analysis of the
341
transcriptomic profilings of Mandarin fish (Siniperca chuatsi) infected with
342
Flavobacterium columnare with an emphasis on immune responses. Fish
343
Shellfish immun. 2015; 43(1):111-9.
M AN U
SC
340
344
[11] Wang Y, Lu Y, Zhang Y, Ning Z, Li Y, Zhao Q, et al. The draft genome of the
345
grass carp (Ctenopharyngodon idellus) provides insights into its evolution and
346
vegetarian adaptation. Nat genet. 2015; 47(6):625-31.
349 350
TE D
348
[12] Kim D, Langmead B, Salzberg S L. HISAT: a fast spliced aligner with low memory requirements. Nat methods. 2015; 12(4):357-60. [13] Anders S, Pyl P T, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014:btu638.
EP
347
[14] Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for
352
identifying differentially expressed genes from RNA-seq data. Bioinformatics,
353
2010; 26(1):136-38.
354 355
AC C
351
[15] Jia Y, Lin F, Hongkun Z, Yong Z, Jie C, Zengjin Z, et al. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006; 34(2):: 293-7.
356
[16] Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, et al. The
357
Gene Ontology (GO) database and informatics resource. Nucleic Acids Res.
358
2004; 32(suppl 1):258-61.
359
[17] Minoru K, Susumu G, Miho F, Mao T, Mika H. KEGG for representation and
360
analysis of molecular networks involving diseases and drugs. Nucleic Acids Res.
361
2010; 38(1):355-60.
ACCEPTED MANUSCRIPT 362
[18] Litman GW, Yoder JA, Cannon JP, Haire RN. Novel Immune-type Receptor
363
Genes and the Origins of Adaptive and Innate Immune Recognition1. Integr
364
Comp Biol. 2009; 41(6):1507-8. [19] Sato S, Miller AS, Inaoki M, Bock CB, Jansen PJ, Tang MLK, et al. CD22 Is
366
Both a Positive and Negative Regulator of B Lymphocyte Antigen Receptor
367
Signal Transduction: Altered Signaling in CD22-Deficient Mice. Immunity.
368
1996; 5(6):551-62.
371 372 373 374 375 376
response. Central-Euro J Immun. 2014; 1(1):109-15.
SC
370
[20] Osińska I, Popko K, Demkow U. Perforin: an important player in immune
[21] Aderem A, Underhill DM. Mechanisms of phagocytosis in macrophages. Annu Rev Immunol. 1999; 17593-623.
M AN U
369
RI PT
365
[22] Jutras I, Desjardins M. Phagocytosis: at the crossroads of innate and adaptive immunity. Annu Rev Cell Dev Bio. 2005; 21(21):511-27. [23] Martinez FO, Sica AA, Locati M. Macrophage activation and polarization. Front Biosci. 2008; 13(4):453-61.
[24] Vandivier RW, Ogden CA, Fadok VA, Hoffmann PR, Brown KK, Botto M, et al.
378
Role of surfactant proteins A, D, and C1q in the clearance of apoptotic cells in
379
vivo and in vitro: calreticulin and CD91 as a common collectin receptor
380
complex. J Immunol. 2002; 169(7):3978-86.
383 384
EP
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[25] Hynes RO. Integrins: versatility, modulation, and signaling in cell adhesion. Cell. 1992; 69(1):11-25.
[26] Hampton MB, Kettle AJ, Winterbourn CC. Inside the neutrophil phagosome:
AC C
381
TE D
377
oxidants, myeloperoxidase, and bacterial killing. Blood. 1998; 92(9):3007-17.
385
[27] Zhi JX, Yuan L, yu MW. Research advance of the lysozyme. Lett Biotech. 2002.
386
[28] Repnik U, Stoka V, Turk V, Turk B. Lysosomes and lysosomal cathepsins in cell
387
death. BBA Proteins Proteomics. 2012; 1824(1):22-33.
388
[29] Turk V, Stoka V, Vasiljeva O, Renko M, Sun T, Turk B, et al. Cysteine
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cathepsins: From structure, function and regulation to new frontiers. Biochimica
390
Et Biophysica Acta. 2012; 1824(1):68-88.
391
[30] He J, Tohyama Y, Yamamoto KI, Kobayashi M, Shi Y, Takano T, et al.
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Lysosome is a primary organelle in B cell receptor-mediated apoptosis: an
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indispensable role of Syk in lysosomal function. Genes Cells. 2005;
394
10(1):23-35.
397 398 399 400
immun. 2002; 12(5):399-420.
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[31] Holland MC, Lambris JD. The complement system in teleosts. Fish Shellfish
[32] Boshra H, Li J, Sunyer J. Recent advances on the complement system of teleost fish. Fish Shellfish immun. 2006; 20(2):239-62.
[33] Ricklin D, Lambris JD. Complement in immune and inflammatory disorders: pathophysiological mechanisms. Journal of Immunology. 2013; 190(8):3831-8.
SC
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[34] Gando S, Nanzaki S, Sasaki S, Aoi K, Kemmotsu O. Activation of the extrinsic
402
coagulation pathway in patients with severe sepsis and septic shock. Crit Care
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Med. 1998; 26(12):2005-9.
404 405
M AN U
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[35] Lizbeth H. Serine protease mechanism and specificity. Chem Rev. 2002; 102(6):4501-23.
[36] Orfeo T, Brummel-Ziedins KE, Butenas S, Mann KG. Simulating blood
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coagulation: the contribution of α2-macroglobulin and α1-antitrypsin. Faseb J.
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2006; 20(4):A64.
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[37] Song X, Zhao J, Bo Y, Liu Z, Wu K, Gong C. Aeromonas hydrophila induces
410
intestinal inflammation in grass carp ( Ctenopharyngodon idella ): An
411
experimental model. Aquaculture. 2014; 434171-8.
413 414 415 416 417 418 419 420 421
[38] Salazar-Mather TP, Hokeness KL. Cytokine and Chemokine Networks: Pathways to Antiviral Defense. Curr Top Microbio. 2006; 303(1):29-46.
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[39] Savan R, Sakai M. Genomics of fish cytokines. Comp Biochem Phys D. 2006; 1(1):89-101.
[40] Robertsen B. The interferon system of teleost fish. Fish Shellfish immun. 2006; 20(2):172-91. [41] Secombes CJ, Wang T, Bird S. The interleukins of fish. Dev Comp Immunol. 2011; 35(12):1336-45. [42] Alejo A, Tafalla C. Chemokines in teleost fish species. Dev Comp Immunol. 2011; 35(12):1215-22.
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[43] Su J, Heng J, Teng H, Peng L, Yang C, Li Q. Identification, mRNA expression
423
and genomic structure of TLR22 and its association with GCRV
424
susceptibility/resistance in grass carp (Ctenopharyngodon idella). Dev Comp
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Immunol. 2011; 36(2):450-62. [44] Hu GB, Zhang SF, Xi Y, Liu DH, Liu QM, Zhang SC. Cloning and expression
427
analysis of a Toll-like receptor 22 ( tlr22 ) gene from turbot, Scophthalmus
428
maximus. Fish Shellfish immun. 2015; 44(2):399-409.
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ACCEPTED MANUSCRIPT Figure legends
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Fig. 1. (A. B) Overview of differently expressed genes distribution trends between
453
infected (YC_0909) and non-infected (YD_0909) group. (A) Gene expression
454
distribution. The x-axis is gene expression quantities (in logs) of non-infected group
455
and y-axis is gene expression quantities of infected group. (B) Volcano Plot. The log2
456
fold (infected/ non-infected) is indicated the mean expression level for each genes.
457
Each dot represents one gene. Red dots represent up-regulated genes and blue dots
458
represent down-regulated genes. Green dots represent no differential expression
459
genes.
460
Fig. 2. Gene ontology (GO) enrichment analysis of differently expressed genes. The
461
results of GO enrichment analysis of differently expressed genes were classified into
462
three categories: biological process, cellular component and molecular function. The
463
x-axis is gene functional classification of GO, y-axis is the corresponding number of
464
up-regulated and down-regulated genes.
465
Fig. 3. The top30 most enriched KEEG pathways. The x-axis is KEGG pathway
466
classification and y-axis is the corresponding number of up-regulated and
467
down-regulated genes.
468
Fig. 4. Comparison of the expressions of RNA-Seq and qRT-PCR results. The
469
transcript expression levels of the selected genes were each normalized to that of the
470
β-actin gene.
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ACCEPTED MANUSCRIPT Table 1 Primers used for qRT-PCR verification of differently expressed genes
TGATGGCTTTCTCAC ACTGC CTTGTACCGAGTCGG IL-1β ATGGT GCAACTGGGCTCAA TNF-α GCTTAC TCCTCGTGTGAAAGC LysC AAGAC GTGGAGCCGAATGT F7 CCTAAA GTTTCATGCATCGTC C1 CAAAG GCCATGGCCAGTAA C3 CTTTGT GGCAAAGGGTCAGT C7 GTGTTT GGCTTCTATCAGCTG CD 22 CTGTG TCCTTACGAGGCATG TLR 22 AGCTT ATGACTTCGATGAG IRF 4 CTGGTG TTCAAGCTCCGTTAA MPEG 1 TGCTG ATCACCTGGCAGAA MHC-I Ag AAATGG TCTTCCCTCCTCCTG MHC-II Ag TCAGA AAATCCAGAAGCTG HSP 70 CTCCAA GGCCTTCTGTCTGTC α2MG CTCTG GTGCCCATCTACGA β-actin GGGTTA
GATATTTCTTCGCCGTT GGA GAACAGGAGGTTGGCA TGTT GGTCCTGGTTCACTCT CCAA ATCCCTCAAATCCATC AAGC GTCATGCTCACCTGCG ACTA TAAACAGCCAGTCCAC CAGA CTCCATGAACGACAGC TGAA ATTTGGTTTCTGGCCA ACTG CCACCGAAGATCCTTC AAAT CGACAAGAGGAGGGT GAGAG CGTGTGTCGTGTTTTCT TCC TTTCACTTGGTGGGTCT TCA CCTGATGTTCCACCAC ACAG CCCTCCACAGGTGTGA ACTT TCAATTCCAAGGGACA GAGG AACCATGATGCACTTG GACA TCTCAGCTGTGGTGGT GAAG
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XCR 1
Reverse primer (5′-3′)
Accession number KF937391.1 JQ692172.1
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Forward primer (5′-3′)
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Gene
JQ670915.1
AF402599.1
JX088706.1 JQ358795.1 AY374472.1 JN655169.1 NM_0010838 23.2 HQ676542.2 NM_0011227 10.1 NM_212737. 1 AB221535.1 EF140725.1 EU816595.1 AY425709.1 M25013.1
ACCEPTED MANUSCRIPT Table 2 Summary of sequencing and mapping Non-infected Group
Read Length (bp)
125
125
Total Raw Reads
65513172
63438956
Total Clean Reads
64627432
62568036
Mapped reads Ratio (%)
79.88
81.41
Detected Gene Number
24507
24383
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Infected Group
ACCEPTED MANUSCRIPT Table 3 List of the differently expressed genes (DEGs) involved in immune responses Category and Gene name
Log2Fold
Diff
Qvalue
1.807160033
Up
5.12E-276
Calmegin precursor
1.72725785
Up
8.66E-35
Vesicle-trafficking protein SEC22b-A
1.66135348
Up
8.89E-38
C-type lectin domain family 4 member
1.578698141
Up
8.97E-143
Calreticulin precursor
1.452254815
Up
0
C-type lysozyme
1.424010322
Up
1.65E-20
Up
0
Up
4.22E-12
Up
9.89E-23
Transferrin receptor 1a
Tubulin beta-1 chain
1.38832028
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Phagocytosis
1.344612409
Integrin beta 3a
1.330257571
Tubulin alpha-1C chain
1.283833515
Up
3.85E-08
Macrophage expressed gene 1 (MPEG 1)
1.237597456
Up
1.90E-43
Cysteine proteinase
1.217510291
Up
2.12E-40
1.198804756
Up
1.80E-97
1.13560686
Up
4.78E-40
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Macrophage mannose receptor 1
Cathepsin K Neutrophil cytosolic factor 1 Cathepsin L precursor
1.12855511
Up
0.000121212
1.088007207
Up
1.15E-68
1.074417256
Up
5.08E-233
1.073145635
Up
4.97E-180
1.06144093
Up
7.49E-08
1.026675458
Up
7.77E-07
Integrin alpha-V precursor
1.011633801
Up
2.12E-65
Neutrophil cytosolic factor 4
1.008973284
Up
0.000100343
Complement Component C3b
4.309367861
Up
8.24E-05
Factor B (Bf)
2.298309327
Up
0
Complement component C1
2.168355572
Up
5.50E-07
Complement component C7
1.438283815
Up
0
1.346203375
Up
0
Coagulation factor Ⅶ
-1.07107892
Down
1.77E-210
Coagulation factor Ⅸ precursor
-1.07927431
Down
2.53E-48
G-type lysozyme Tubulin alpha-1B chain Cathepsin B, precursor Coronin-2B isoform X2
EP
Complement system
TE D
Cystinosin precursor
AC C
Complement component C8β
precursor
Complement component 3 (C3)
-1.11192977
Down
4.05E-63
Mannose-binding serine protease
-1.17167819
Down
6.27E-28
Small inducible cytokine B14 precursor
-1.43605885
Down
0.000921317
α-2-macroglobulin
-1.49752246
Down
0
Complement factor H like 4 precursor
-3.11609747
Down
7.40E-60
Tumor necrosis factor-α induced protein 6
5.346842986
Up
5.49E-18
C-X-C motif chemokine 13 precursor
2.280377621
Up
3.44E-227
Interferon regulatory factor 4 (IRF4)
2.06144081
Up
1.91E-09
1.641029822
Up
1.41E-121
Cytokine
Chemokine-like receptor 1
ACCEPTED MANUSCRIPT Category and Gene name
Log2Fold
Diff
Qvalue
1.596217893
Up
5.32E-09
Perforin-like protein 2
1.415077953
Up
4.55E-05
Interferon-induced transmembrane protein 1
1.349685778
Up
4.56E-15
Matrix metalloproteinase 9
1.279235669
Up
0
Chemokine C motif receptor XCR1
1.204045397
Up
7.61E-09
Interleukin-21 receptor precursor
1.123368854
Up
6.71E-06
Interleukin-10
1.114121126
Up
2.99E-11
C-X-C motif chemokine 9-like
1.113733102
Up
3.67E-191
Granulocyte colony-stimulating factor receptor
1.064605775
Up
1.52E-53
Tumor necrosis factor receptor 2
1.058231015
Up
6.22E-18
Interferon regulatory factor 10
1.057900539
Up
2.48E-16
Interleukin-1 receptor I
1.055859594
Up
2.10E-20
CXCR3
1.001675808
Up
7.29E-24
Interleukin-27 subunit beta precursor
-1.35965669
Down
2.17E-100
Interleukin 13 receptor, alpha 1 precursor
-1.52185706
Down
1.42E-56
-2.02602217
Down
0.000352238
4.148903699
Up
0.00024541
3.391288769
Up
1.26E-192
3.061440646
Up
1.38E-13
2.338280491
Up
2.13E-08
1.769272742
Up
0
1.687275774
Up
1.06E-28
1.491680658
Up
3.26E-11
Novel immune-type receptor 5 (NITR5)
1.470246033
Up
8.15E-06
Perforin-like protein 2
1.415077953
Up
4.54746E-05
1.397043842
Up
3.00E-10
1.361001026
Up
2.42E-06
1.313827508
Up
3.06E-18
1.243644382
Up
0.000768153
CD180 antigen-like
1.208281846
Up
0.000747575
CD2 family receptor (CD2 f)
1.119543937
Up
3.11E-07
CD209 antigen
1.077742201
Up
7.19E-08
Lymphocyte activation gene 3 precursor
1.049805976
Up
2.13E-19
1.04181149
Up
1.07E-11
proteasome activator complex subunit 2
1.006913826
Up
2.92E-48
CD36 antigen
-1.01129673
Down
1.20E-90
MHC class II alpha antigen
-1.51012641
Down
2.54E-103
MHC class II DA-beta-2, partial
-1.60152434
Down
7.05E-51
3.203892677
Up
Log2Fold
Diff
SC
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Immunity-related GTPase family, LRG-47 Antigen processing and presentation MHC class I antigen Lymphocyte antigen 6F-like T cell receptor beta chain Novel immune-type receptor protein CD79a precursor B-cell receptor CD22
CD11
EP
Perforin 3
TE D
Heat shock protein 70 (HSP70)
Lymphocyte antigen 86 precursor
T-lymphoma invasion and metastasis-inducing
AC C
protein
CD9 antigen-like
RI PT
Interleukin-1 beta
Pattern recognition receptor Intelectin Category and Gene name
0 Qvalue
ACCEPTED MANUSCRIPT C-type lectin 4
2.39295431
Up
2.41E-19
Toll-like receptor 22
1.70110923
Up
9.10E-18
NALPL2
1.154550248
Up
1.10E-10
C-type lectin 10
1.118247028
Up
2.11E-22
NLRC3 protein
1.099242548
Up
2.90E-10
JAM-C-like protein, partial
3.231365769
Up
0.000613005
T cell receptor beta chain, partial
3.061440646
Up
1.38E-13
Claudin-1 precursor
2.563940973
Up
2.01E-05
Versican b precursor
1.831958763
Up
3.29E-31
Up
5.48E-05
Up
1.30E-13
Leukocyte immune-type receptor
1.38336861
RI PT
Cell adhesion molecules
Selectin P ligand precursor
1.119156551
Claudin b, partial
-1.46580617
Claudin 2
-1.65737772
Down
8.21E-24
Novel protein similar to E-cadherin (cdh1)
-2.23073992
Down
3.01E-235
Claudin-4
-3.63899852
Down
1.69E-05
1.604233798
Up
5.05E-35
1.440956114
Up
2.14E-36
1.365951445
Up
6.22E-15
1.240224556
Up
4.98E-51
Oxidoreductase-like domain-containing protein
1.616029493
Up
0.000228787
Peroxisomal membrane protein 4
1.588688079
Up
6.95E-08
Glutathione synthetase
1.567793403
Up
7.30E-36
1.146102839
Up
3.10E-09
1.13170309
Up
4.08E-49
1.012681682
Up
1.67E-23
-1.0320958
Down
3.73E-56
-1.30329752
Down
0
-1.37843725
Down
2.83E-83
Reticulocalbin-3 precursor Caspase recruitment domain protein Caspase-7
EF-hand calcium-binding domain-containing protein
Spermidine synthase
TE D
Antioxidant enzymes
Natural killer cell enhancing factor Arginase 2C precursor Catalase
EP
Glutathione S-transferase
Gamma-glutamylcyclotransferase precursor
AC C
SC
M AN U
Apoptosis
Down
0.000656937
AC C
EP
TE D
M AN U
SC
RI PT
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ACCEPTED MANUSCRIPT Highlights 1. Transcriptome analysis of the grass carp infected A. hydrophila was performed. 2. 2121 differential expression genes were identified. 3. 105 important immune related genes were categorized and discussed.
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4. For a better understanding of immune mechanism of grass carp infected bacteria.
S1050-4648(16)30079-1
DOI:
10.1016/j.fsi.2016.02.035
Reference:
YFSIM 3847
To appear in:
Fish and Shellfish Immunology
Received Date: 31 December 2015 Revised Date:
25 February 2016
Accepted Date: 29 February 2016
Please cite this article as: Yang Y, Yu H, Li H, Wang A, Transcriptome profiling of Grass carp (Ctenopharyngodon idellus) infected with Aeromonas hydrophila, Fish and Shellfish Immunology (2016), doi: 10.1016/j.fsi.2016.02.035. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT 1
Transcriptome profiling of Grass carp (Ctenopharyngodon idellus) infected with
2
Aeromonas hydrophila
3
Ying Yanga, b, Hui Yub**, Hua Lib, Anli Wanga*
4 a
College of Life Sciences, South China Normal University, Guangzhou, Guangdong 510631, China
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b
College of Life Science, Foshan University, Foshan, Guangdong 528231, China
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*Correspondence to:
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Pro. Anli Wang
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College of Life Science
15
South China Normal University
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No. 55 West Zhongshan Avenue
17
Tianhe, Guangzhou, Guangdong, P.R.China
18
Tel/fax +86 2085210141.
19
E-mail: [email protected]
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**Co- Correspondence to:
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Pro. Hui Yu
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College of Life Science
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Foshan University
25
No.1 Xianhu Road
26
Nanhai, Foshan, Guangdong, P.R.China
27
Tel/fax +86 757 86678587.
28
E-mail: [email protected]
30 31
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ACCEPTED MANUSCRIPT Abstract: Aeromonas hydrophila is the causative pathogen of intestinal hemorrhage
33
which has caused great economic loss in grass carp aquaculture. In order to
34
understand the immunological response of grass carp to infection by A. hydrophila,
35
the transcriptomic profiles of the spleens from infected and non-infected grass carp
36
groups were obtained using HiSeq™ 2500 (Illumina). An average of 63 million clean
37
reads per library was obtained, and approximately 80% of these genes were
38
successfully mapped to the reference genome. A total of 1591 up-regulated and 530
39
down-regulated genes were identified. Eight immune-related categories involving 105
40
differently expressed genes were scrutinized. 16 of the differently expressed genes
41
involving immune response were further validated by qRT-PCR. Our results provide
42
valuable information for further analysis of the mechanisms of grass carp defense
43
against A. hydrophila invasion.
44
Key words: Ctenopharyngodon idellus; Aeromonas hydrophila; Transcriptome;
45
Pathway analysis; Immune response
46
1. Introduction
47
Grass carp (Ctenopharygodon idella) is an economically important species and its
48
global production is more than 4.5 million tones per year, which making it the most
49
highly consumed freshwater fish worldwide[1]. However, infectious intestinal
50
hemorrhage caused by Aeromonas hydrophila has been severe for years[2], resulting
51
in great economic loss and threatening the development of grass carp aquaculture. A.
52
hydrophila is facultative anaerobic, motile, and Gram-negative rods in the family
53
Aeromonadaceae[3]. It is often found in association with hemorrhagic septicemia in
54
cold-blooded animals including fish, reptiles and amphibians. However, this organism
55
has also attracted attention as an emerging human pathogen, and has been considered
56
to have a significant impact on public health[4]. Therefore, identification of host
57
factors in response to A. hydrophila infection has a great significance for disease
58
prevention and control in grass carp culture.
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Transcriptome analysis is a powerful tool for leading to a better understanding of
60
the underlying pathways and mechanisms controlling cell fate, development, and
61
disease progression[5]. Over the years, several technologies have been developed to
ACCEPTED MANUSCRIPT survey transcriptomes in a high-throughput manner. High-throughput sequencing
63
(HTS) technologies permit genome-wide transcriptomic analysis at a higher resolution,
64
and these technologies have been widely used to study pathogenic processes during
65
bacterial infections[6]. Hegedus et al. firstly used Solexa/Illumina’s DGE system to
66
study zebrafish transcriptome after Mycobacterium marinum infection, it showed the
67
feasibility and superiority of high-throughput sequencing technology in the field of
68
fish immune research[7]. Then RNA-Seq have been applied to immune-related gene
69
and signaling pathway analysis of several fish species such as Mandarin fish, Large
70
yellow croaker and Lates calcarijer[8-10].
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The results of sequencing of the transcriptome of juvenile grass carp after A.
72
hydrophila infection and non-infection were presented in this study. High-quality
73
cDNA sequences were obtained by using Illumina RNA-Seq method and the
74
sequencing reads were mapped to the reference genome database of grass carp[11].
75
Additionally, a great number of immune related genes that were differently expressed
76
upon A. hydrophila infection were obtained and functionally annotated, and the gene
77
expression patterns of some of these genes were verified by qRT-PCR. These results
78
provide a valuable resource for further research into the mechanism of anti-infection
79
immunity of grass carp.
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2. Materials and methods
81
2.1 Fish and bacteria
82
Healthy juvenile grass carps of 20g±2g body weight were kindly provided by the
83
Hold-one aquatic breeding center in China and reared under pathogen-free conditions.
84
Fish were maintained in aerated tap water at 28 °C in aquaria with Eheim biofilters
85
until use. The bacterial strain A. hydrophila used for the experiments was isolated
86
from diseased grass carp, and has been deposited in the China Center for Type Culture
87
Collection under preservation number CCAM05068. The bacteria was cultured in
88
Luria-Bertain (LB) at 28 °C for 24 h with constant shaking (150 rpm), then harvested
89
by centrifugation at 6000 rpm for 5 min, washed once by PBS and centrifuged again.
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Bacterial pellets were then re-suspended in saline adjusted the concentration to 5×107
91
colony forming units (CFU) ml-1.
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ACCEPTED MANUSCRIPT 2.2 Challenge experiments and RNA preparation
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Healthy fish were randomly divided into two groups (30 fish per group). For bacterial
94
infection group, fish were injected intraperitoneally (i.p.) with 0.1ml A. hydrophila
95
suspension above-mentioned. The fish treated with 0.1ml 0.65% physiological saline,
96
were used as the control. After 6h of injection, the spleen were collected and
97
immediately stored in liquid nitrogen until RNA extraction.
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Total RNA was extracted from each sample using TRIzol Reagent (Invitrogen,
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USA) according to the manufacture's protocol. The quality and quantity of the RNA
100
samples were examined by use of the Agilent 2100 Bioanalyzer (Agilent Technologies)
101
and the integrity was assessed by electrophoresis on 1% agarose gel. For each group,
102
equal amount of RNA from the nine fish individuals per line were pooled to provide
103
templates for RNA-Seq library construction.
104
2.3 Library construction and Illumina sequencing
105
After DNase I treatment, mRNA was purified using oligo (dT)25 magnetic beads
106
(Dynabeadsoligo (dT)25, Invitrogen) and subsequently interrupted to short
107
fragments of 200-250 nucleotides using RNA fragmentation reagent (Ambion, USA).
108
Then sequencing libraries were generated using NEBNext® Ultra™ RNA Library
109
Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations. The
110
cDNAs were checked by Agilent 2100 Bioanalyzer (Agilent, USA) and ABI
111
StepOnePlus Real-Time PCR System (ABI, USA). The mixed DNA libraries were
112
diluted to 4-5 pM for sequencing by Illumina Hiseq 2500™ platform.
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2.4 Sequence annotation
114
Raw reads were first cleaned by removing adaptor sequences, low quality sequences
115
(Sanger base quality < 20) and reads with unknown nucleotides larger than 10%.
116
Clean reads were mapped to the Ctenopharyngodon idellus reference genome
117
(http://www.ncgr.ac.cn/grasscarp/) using HISAT (version 0.1.6) [12]. The HTSeq
118
(version 0.6.1) [13] was utilized to calculate the number of aligned reads per exon
119
through annotation of the grass carp genome. Subsequently, the transcripts were
120
subjected to BLASTX similarity searching against NCBI non-redundant protein
121
database (NR) with an E-value threshold of 10-5, and the unigenes were identified.
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Expression levels were measured in reads per kilo base of exon per million mapped
124
reads (RPKM) method. The distribution of gene expression was analyzed. Statistical
125
comparison between two groups was conducted using DEGseq (version 1.18.0) [14].
126
To assess the significance of differential gene expression, the threshold of FDR was
127
set at ≤ 0.01 and the absolute value of log2 ratios (fold change between non-infected /
128
infected samples) at ≥1. Differential expression genes (DEGs) were further annotated
129
by Gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and
130
Genomes (KEGG) pathway analysis. GO annotation was acquired using the GOseq
131
(version 1.16.2) and WEGO [15, 16]. P-values generated from the enrichment
132
analysis were subjected to multiple hypothesis testing, with p-values ≤ 0.05
133
considered significant. Pathway analysis was performed using the Kyoto
134
Encyclopedia
135
(http://www.genome.jp/kegg)[17]. After multiple test correction, pathways with
136
Q-values ≤ 0.05 were considered to be significantly enriched in DEGs.
137
2.6 Confirmation using quantitative real-time RT-PCR (QPCR)
138
To examine the reliability of the RNA-Seq results, a selected subset of differently
139
expressed genes involved in immune responses were selected for validation using
140
quantitative real-time RT-PCR (qRT-PCR). 16 genes differently expressed between
141
the infected and non-infected groups, including Chemokine C motif receptor 1 (XCR
142
1), interleukin-1 beta (IL-1β), tumor necrosis factorα (TNF-α), C-type lysozyme
143
(LysC), interferon regulatory factor 4 (IRF4), B cell receptor CD22, coagulation
144
factor 7 (F7), complement component 1 (C1), complement component 3 (C3),
145
complement component 7 (C7), toll-like receptor 22 (TLR 22), MHC class I antigen
146
(MHC-I Ag), MHC class II antigen (MHC-II Ag), macrophage expressed gene 1
147
(MPEG 1), heat shock 70 kDa protein (HSP 70), and α-2-macroglobulin (α2MG)
148
were selected for qRT-PCR assay. The housekeeping gene β-actin was used as the
149
reference gene. Suitable primers were designed using Primer Express 3.0(Table.1) and
150
synthesized by Sangon Bothech (Shanghai) Co., Ltd. QRT-PCR with SYBR Green
151
dye (TaKaRa, Dalian, China) was performed on an ABI PRISM 7500 Fast Real-Time
Genes
and
Genomes
(KEGG)
database
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ACCEPTED MANUSCRIPT PCR System according to the manufacturer’s protocol. Each treatment group was
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created by combining equal amounts of RNA from three replicate pools (three
154
individual fish per pool). All reactions were performed in triplicates. The qRT-PCR
155
conditions were as follows: 30s at 95, followed 40 cycles of 5s at 95, 30s at 58,
156
34s at 72. Dissociation curve analysis was performed to determine the target
157
specificity. The relative expression ratio of the target genes versus β-actin gene was
158
calculated using 2-△△CT method and all data were given in terms of relative mRNA
159
expression.
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Insert Table 1 here.
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3. Results
162
3.1 Transcriptome sequence and reads mapping
163
Infected and non-infected groups were analyzed by RNA-Seq respectively. RNA
164
sequencing resulted in about 65.5 million (infected) and 63.4 million (non-infected)
165
raw reads of 125 bp (Table 2). After filtration, 64.6 million of infected groups and
166
62.6 million of non-infected groups clean reads were obtained, and these sequences
167
were carried forward for additional analysis. Average 80% of the clean reads were
168
mapped to the reference genome, 24507 genes of infected group and 24383 genes of
169
non-infected group were detected. These sequences were compared with the NCBI
170
non-redundant (NR) protein for functional annotation. Raw sequencing reads data has
171
been submitted to Sequence Read Archive in NCBI, the SRA accession numbers are
172
SRR3045340 and SRR3045341.
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3.2 Differential expression analysis
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RNA-Seq reads mapped to the grass carp reference genome were aligned and their
176
relative abundances were estimated using HTseq. According to the statistical analysis
177
of unigene data with DEGseq, the genes significantly differentially expressed between
178
infected and non-infected groups were identified. The results showed that 2121 genes
179
were significantly differently expressed, including 1591 up-regulated genes and 530
180
down-regulated genes. Differential expression genes and no differential expression
181
genes distribution trends were presented clearly (Fig. 1).
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182
3.3 GO and KEGG enrichment analysis of differently expressed genes
184
The results of the GO enrichment analysis of differential expression genes were
185
classified into three categories: biological process (121 subclasses), cellular
186
component (31 subclasses) and molecular function (129 subclasses). The significant
187
GO terms in these three categories and DEGs of them were showed in Fig. 2. The
188
pathway enrichment analysis helped to further understand genes’ biological functions.
189
In this study, 1631 of 2121 DEGs were annotated to 251 signaling pathways in KEGG.
190
The top30 most enriched pathway terms and DEGs of them were showed in Fig. 3. Insert Fig. 2 here.
192
Insert Fig. 3 here.
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3.4 Analysis of differently expressed genes related to immune responses
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In this report, we focused on the genes related to the immune responses. Based on
195
gene clustering and pathway analysis, genes involved in both innate and adaptive
196
immunity had been identified (Table 3), including 85 up-regulated and 20
197
down-regulated genes. They were further grouped into 8 sub-classes as follows:
198
phagocytosis, complement system, cytokine, antigen processing and presentation,
199
pattern recognition receptor, cell adhesion molecules, apoptosis, antioxidant enzymes.
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Insert Table 3 here.
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3.5 Analysis of differently expressed genes by qRT-PCR
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Quantitative real-time RT-PCR was performed on 16 immune genes for validating the
203
differently expressed genes identified by RNA-Seq. For the results, the analysis of
204
qRT -PCR demonstrated a single product for all tested genes. Fold changes of qRT
205
-PCR were compared with the RNA-Seq expression profiles. As shown in Fig. 4, all
206
of qRT -PCR results were significantly correlated with the RNA-Seq results and they
207
showed the identical up-regulated and down-regulated patterns of these genes in both
208
assays.
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Insert Fig. 4 here.
209 210
4. Discussion
211
In the present study, transcriptome profiling of grass carp was undertaken to gain
ACCEPTED MANUSCRIPT insights into the molecular mechanisms underpinning host responses to A. hydrophila.
213
There were a total of 2121 genes expressed differently between infected and
214
non-infected group and these differently expressed genes were assigned multiple
215
potential functions in molecular function and biological process. Here, we mainly
216
focused on the immune-related genes of fish activated in the early stage of
217
bacteria invasion. Taking the advantages of GO annotation and KEGG pathway
218
classification, we categorized these immune related genes into eight categories and
219
the most important parts of them were discussed.
220
4.1 Antigen processing and presentation
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Apparently, innate immunity is the major approach to defense against invading
222
virulent bacteria like A. hydrophila. But from the path-ways enriched in this present
223
study, cellular immunity belonging to acquired immunity also plays a role in the
224
disease. Based on analysis of gene differential expression, T-lymphoma invasion and
225
metastasis-inducing protein 1, T cell receptor beta chain and MHC class I antigen
226
were up-regulated significantly. CD2 family receptor and other CDs involved in
227
lymphocyte recognition and signal transduction were found in DEGs. In addition, the
228
discovery of novel immune-type receptors (NITRs) indicates that fish leukocytes
229
express various types of Ig superfamily (IgSF) receptors for contact with other
230
immune cells[18]. It is worth noting that MHC I and MHC II were both DEGs, but
231
MHC I was up-regulated in this tissue while MHC II was down-regulated.
232
We noticed that B-cell receptor CD22 was up-regulated and the expression of this
233
membrane receptor is generally regarded as being restricted to B cells[19]. On the
234
contrary, activated cytotoxic T lymphocytes (CTLs) released toxic particles such as
235
perforin to kill host cells infected with pathogens[20].
236
4.2 Phagocytosis
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Phagocytosis is an important, evolutionarily conserved mechanism that is integral
238
to host defenses against invading microorganisms[21]. In professional phagocytes
239
such as neutrophils and macrophages, phagocytosis is triggered by the recognition of
240
pathogen
241
immunoglobulins and complement components bound to the microbes, which are
associated
molecular
patterns
(PAMPs)
and
opsonins
such
as
ACCEPTED MANUSCRIPT 242
recognized
by
243
differently expressed genes, 23 phagocytosis–related genes were found and all of
244
them were up-regulated. MPEG 1 and neutrophil cytosolic factor were up-regulated
245
indicating both macrophages and neutrophils were stimulated. Calreticulin and
246
calmegin
247
Phagocytosis is initiated by the interaction of receptors on the surface of the
248
phagocyte with ligands on large particles such as bacterial. Subsequently integrins
249
mediate particle internalization[25]. Once internalized, the phagosome that encloses
250
the particle undergoes a series of maturation steps and culminates in phagolysosome
251
fusion[26]. In this study, increasing expression of lysosome-related genes including
252
C-type lysozyme, G-type lysozyme, Cathepsin K, Cathepsin B precursor, Cathepsin L
253
precursor, and Cysteine proteinase promotes the lysis of bacteria[27-30].
254
4.3 Complement system
receptors[22].
enhance the immune competence
of
Among
macrophages[23,
the
24].
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phagocytosis-promoting
Complement system is a key component of innate immunity in teleosts and its
256
activation leads to opsonization of pathogens, recruitment of inflammatory and
257
immune competent cells, and the direct killing of pathogens[31, 32]. There are three
258
different ways to activate the complement on the surface of invading pathogens:
259
classical, lectin and alternative pathways[33]. In our results, C1
260
up-regulated, indicating the activation of classical pathway. C3 was down-regulated
261
and C3b, factor B were up-regulated, deducing C3 molecules were decomposed and
262
activate the alternative pathway.
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The blood coagulation pathway controls the coagulation and fibrinolysis of blood
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through a series of sequentially activated serine kinases[34]. In our data,
265
mannose-binding protein-associated serine protease, α-2-macroglobulin and several
266
coagulation factors were significantly down-regulated, indicating the inhibition of
267
blood coagulation[35, 36]. This may be related to the bleeding symptoms observed in
268
grass carp caused by A. hydrophila[37].
269
4.4 Cytokines
270
Cytokines are a family of low molecular weight proteins secreted by activated
271
immune-related cells upon induction by various pathogens such as parasitic, bacterial,
ACCEPTED MANUSCRIPT or viral components[38]. Cytokines could be divided into interferons (IFNs),
273
interleukins (ILs), tumor necrosis factors (TNFs), colony stimulating factors, and
274
chemokines[39]. In this report, a total of 20 immune related cytokine genes were
275
found in DEGs, 17 were up-regulated and three were down-regulated. These
276
differently expressed cytokine genes could be classified as interferons and signaling
277
factors (IRF 4, IFN transmembrane protein 1, IRF 10 and IRG-47), interleukin family
278
members and receptors (IL-1β, I IL -10, IL -21 receptor, IL -1 receptor I, IL -27
279
subunit beta precursor and IL 13 receptor precursor), tumor necrosis factors (TNF-α
280
induced protein 6 and TNF-α receptor 2) and chemokines (Chemokine-like receptor 1,
281
XCR1, CXCR3, CXC motif chemokine 9-like and CXC motif chemokine 13
282
precursor). Interleukin, tumor necrosis factors and chemokines play key roles in
283
inflammation and host defense, besides that, activity of IFN and IRF genes expression
284
also proved that interferons involved in bacterial immune responses[40-42]. The
285
complicate coordinated approaches of the cytokine-related genes in the immune
286
responses of grass carp during bacteria infection remains to be addressed in the future.
287
4.5 Others
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The results also demonstrate that a number of other differentially expressed
289
immune-related genes enriched in pattern recognition receptor (PRR), cell adhesion
290
molecules
291
previous reports suggested that toll-like receptor 22 (TLR22) exists exclusively in
292
aquatic animals and recognizes double stranded RNA (dsRNA)[43, 44], but this study
293
shows it can also be elevated by bacterial stimulation. The details of the regulation of
294
these immune-related genes need to be elucidated in the future.
295
5. Conclusion
296
In conclusion, RNA-seq analysis is a highly valuable resource to address the issues of
297
disease resistance at the genome and transcriptome levels. This study provided a first
298
survey of host defense gene activities against bacterial challenge based on
299
differentially
300
significantly enriched gene clusters including phagocytosis, complement system,
301
cytokines, antigen processing and presentation, PRR, CAM, apoptosis and antioxidant
apoptosis
and
antioxidant
enzymes.
Remarkably,
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transcriptomic
profiling
in
grass
carp.
Additionally,
ACCEPTED MANUSCRIPT 302
enzymes were identified and discussed. This study will add new information that may
303
help to prevent the A. hydrophila infection.
304
Acknowledgments
306
We would like to thank Yu-dong Du (Project manager of Hold-one aquatic breeding
307
co., LTD) for providing juvenile grass carps. This work was jointly supported by
308
National Spark Program Project of China (2015GA780004), Guangdong Natural
309
Science Foundation (2013B020307017; 2013B020503070) and Foshan Science and
310
Technology Innovation Platform Construction Program (2013AG10005).
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References
313
[1] Stephan M, Hobsdawn P. Australian fisheries and aquaculture statistics 2013.
314
Fisheries Research and 2014.
M AN U
312
[2] Liu L, Gong Y-X, Zhu B, Liu G-L, Wang G-X, Ling F. Effect of a new
316
recombinant Aeromonas hydrophila vaccine on the grass carp intestinal
317
microbiota and correlations with immunological responses. Fish Shellfish
318
immun. 2015; 45(1):175-83.
TE D
315
[3] Seshadri R, Joseph SW, Chopra AK, Sha J, Shaw J, Graf J, et al. Genome
320
sequence of Aeromonas hydrophila ATCC 7966T: jack of all trades. J bacteriol.
321
2006; 188(23):8272-82.
323 324
[4] Agger WA, McCormick J, Gurwith MJ. Clinical and microbiological features of Aeromonas
hydrophila-associated
AC C
322
EP
319
diarrhea.
J
Clin
Microbiol.
1985;
21(6):909-13.
325
[5] Mutz KO, Heilkenbrinker A, Lonne M, Walter J-G, Stahl F. Transcriptome
326
analysis using next-generation sequencing. Curr opin biotech. 2013;
327 328 329
24(1):22-30. [6] Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol syst biol. 2013; 9(1):640.
330
[7] Hegedűs Z, Zakrzewska A, Ágoston VC, Ordas A, Rácz P, Mink M, et al. Deep
331
sequencing of the zebrafish transcriptome response to mycobacterium infection.
ACCEPTED MANUSCRIPT 332
Mol Immunol. 2009; 46(15):2918-30.
333
[8] Khoo CK, Abdul-Murad AM, Kua BC, Mohd-Adnan A. Cryptocaryon irritans
334
infection induces the acute phase response in Lates calcarifer: a transcriptomic
335
perspective. Fish Shellfish immun. 2012; 33(4):788-94. [9] Mu Y, Li M, Ding F, Ding Y, Ao J, Hu S, et al. De novo characterization of the
337
spleen transcriptome of the large yellow croaker (Pseudosciaena crocea) and
338
analysis of the immune relevant genes and pathways involved in the antiviral
339
response. Plos One. 2014; 9(5):e97471.
RI PT
336
[10] Zhou W, Zhang Y, Wen Y, Ji W, Zhou Y, Ji Y, et al. Analysis of the
341
transcriptomic profilings of Mandarin fish (Siniperca chuatsi) infected with
342
Flavobacterium columnare with an emphasis on immune responses. Fish
343
Shellfish immun. 2015; 43(1):111-9.
M AN U
SC
340
344
[11] Wang Y, Lu Y, Zhang Y, Ning Z, Li Y, Zhao Q, et al. The draft genome of the
345
grass carp (Ctenopharyngodon idellus) provides insights into its evolution and
346
vegetarian adaptation. Nat genet. 2015; 47(6):625-31.
349 350
TE D
348
[12] Kim D, Langmead B, Salzberg S L. HISAT: a fast spliced aligner with low memory requirements. Nat methods. 2015; 12(4):357-60. [13] Anders S, Pyl P T, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014:btu638.
EP
347
[14] Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for
352
identifying differentially expressed genes from RNA-seq data. Bioinformatics,
353
2010; 26(1):136-38.
354 355
AC C
351
[15] Jia Y, Lin F, Hongkun Z, Yong Z, Jie C, Zengjin Z, et al. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006; 34(2):: 293-7.
356
[16] Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, et al. The
357
Gene Ontology (GO) database and informatics resource. Nucleic Acids Res.
358
2004; 32(suppl 1):258-61.
359
[17] Minoru K, Susumu G, Miho F, Mao T, Mika H. KEGG for representation and
360
analysis of molecular networks involving diseases and drugs. Nucleic Acids Res.
361
2010; 38(1):355-60.
ACCEPTED MANUSCRIPT 362
[18] Litman GW, Yoder JA, Cannon JP, Haire RN. Novel Immune-type Receptor
363
Genes and the Origins of Adaptive and Innate Immune Recognition1. Integr
364
Comp Biol. 2009; 41(6):1507-8. [19] Sato S, Miller AS, Inaoki M, Bock CB, Jansen PJ, Tang MLK, et al. CD22 Is
366
Both a Positive and Negative Regulator of B Lymphocyte Antigen Receptor
367
Signal Transduction: Altered Signaling in CD22-Deficient Mice. Immunity.
368
1996; 5(6):551-62.
371 372 373 374 375 376
response. Central-Euro J Immun. 2014; 1(1):109-15.
SC
370
[20] Osińska I, Popko K, Demkow U. Perforin: an important player in immune
[21] Aderem A, Underhill DM. Mechanisms of phagocytosis in macrophages. Annu Rev Immunol. 1999; 17593-623.
M AN U
369
RI PT
365
[22] Jutras I, Desjardins M. Phagocytosis: at the crossroads of innate and adaptive immunity. Annu Rev Cell Dev Bio. 2005; 21(21):511-27. [23] Martinez FO, Sica AA, Locati M. Macrophage activation and polarization. Front Biosci. 2008; 13(4):453-61.
[24] Vandivier RW, Ogden CA, Fadok VA, Hoffmann PR, Brown KK, Botto M, et al.
378
Role of surfactant proteins A, D, and C1q in the clearance of apoptotic cells in
379
vivo and in vitro: calreticulin and CD91 as a common collectin receptor
380
complex. J Immunol. 2002; 169(7):3978-86.
383 384
EP
382
[25] Hynes RO. Integrins: versatility, modulation, and signaling in cell adhesion. Cell. 1992; 69(1):11-25.
[26] Hampton MB, Kettle AJ, Winterbourn CC. Inside the neutrophil phagosome:
AC C
381
TE D
377
oxidants, myeloperoxidase, and bacterial killing. Blood. 1998; 92(9):3007-17.
385
[27] Zhi JX, Yuan L, yu MW. Research advance of the lysozyme. Lett Biotech. 2002.
386
[28] Repnik U, Stoka V, Turk V, Turk B. Lysosomes and lysosomal cathepsins in cell
387
death. BBA Proteins Proteomics. 2012; 1824(1):22-33.
388
[29] Turk V, Stoka V, Vasiljeva O, Renko M, Sun T, Turk B, et al. Cysteine
389
cathepsins: From structure, function and regulation to new frontiers. Biochimica
390
Et Biophysica Acta. 2012; 1824(1):68-88.
391
[30] He J, Tohyama Y, Yamamoto KI, Kobayashi M, Shi Y, Takano T, et al.
ACCEPTED MANUSCRIPT 392
Lysosome is a primary organelle in B cell receptor-mediated apoptosis: an
393
indispensable role of Syk in lysosomal function. Genes Cells. 2005;
394
10(1):23-35.
397 398 399 400
immun. 2002; 12(5):399-420.
RI PT
396
[31] Holland MC, Lambris JD. The complement system in teleosts. Fish Shellfish
[32] Boshra H, Li J, Sunyer J. Recent advances on the complement system of teleost fish. Fish Shellfish immun. 2006; 20(2):239-62.
[33] Ricklin D, Lambris JD. Complement in immune and inflammatory disorders: pathophysiological mechanisms. Journal of Immunology. 2013; 190(8):3831-8.
SC
395
[34] Gando S, Nanzaki S, Sasaki S, Aoi K, Kemmotsu O. Activation of the extrinsic
402
coagulation pathway in patients with severe sepsis and septic shock. Crit Care
403
Med. 1998; 26(12):2005-9.
404 405
M AN U
401
[35] Lizbeth H. Serine protease mechanism and specificity. Chem Rev. 2002; 102(6):4501-23.
[36] Orfeo T, Brummel-Ziedins KE, Butenas S, Mann KG. Simulating blood
407
coagulation: the contribution of α2-macroglobulin and α1-antitrypsin. Faseb J.
408
2006; 20(4):A64.
TE D
406
[37] Song X, Zhao J, Bo Y, Liu Z, Wu K, Gong C. Aeromonas hydrophila induces
410
intestinal inflammation in grass carp ( Ctenopharyngodon idella ): An
411
experimental model. Aquaculture. 2014; 434171-8.
413 414 415 416 417 418 419 420 421
[38] Salazar-Mather TP, Hokeness KL. Cytokine and Chemokine Networks: Pathways to Antiviral Defense. Curr Top Microbio. 2006; 303(1):29-46.
AC C
412
EP
409
[39] Savan R, Sakai M. Genomics of fish cytokines. Comp Biochem Phys D. 2006; 1(1):89-101.
[40] Robertsen B. The interferon system of teleost fish. Fish Shellfish immun. 2006; 20(2):172-91. [41] Secombes CJ, Wang T, Bird S. The interleukins of fish. Dev Comp Immunol. 2011; 35(12):1336-45. [42] Alejo A, Tafalla C. Chemokines in teleost fish species. Dev Comp Immunol. 2011; 35(12):1215-22.
ACCEPTED MANUSCRIPT 422
[43] Su J, Heng J, Teng H, Peng L, Yang C, Li Q. Identification, mRNA expression
423
and genomic structure of TLR22 and its association with GCRV
424
susceptibility/resistance in grass carp (Ctenopharyngodon idella). Dev Comp
425
Immunol. 2011; 36(2):450-62. [44] Hu GB, Zhang SF, Xi Y, Liu DH, Liu QM, Zhang SC. Cloning and expression
427
analysis of a Toll-like receptor 22 ( tlr22 ) gene from turbot, Scophthalmus
428
maximus. Fish Shellfish immun. 2015; 44(2):399-409.
429
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Fig. 1. (A. B) Overview of differently expressed genes distribution trends between
453
infected (YC_0909) and non-infected (YD_0909) group. (A) Gene expression
454
distribution. The x-axis is gene expression quantities (in logs) of non-infected group
455
and y-axis is gene expression quantities of infected group. (B) Volcano Plot. The log2
456
fold (infected/ non-infected) is indicated the mean expression level for each genes.
457
Each dot represents one gene. Red dots represent up-regulated genes and blue dots
458
represent down-regulated genes. Green dots represent no differential expression
459
genes.
460
Fig. 2. Gene ontology (GO) enrichment analysis of differently expressed genes. The
461
results of GO enrichment analysis of differently expressed genes were classified into
462
three categories: biological process, cellular component and molecular function. The
463
x-axis is gene functional classification of GO, y-axis is the corresponding number of
464
up-regulated and down-regulated genes.
465
Fig. 3. The top30 most enriched KEEG pathways. The x-axis is KEGG pathway
466
classification and y-axis is the corresponding number of up-regulated and
467
down-regulated genes.
468
Fig. 4. Comparison of the expressions of RNA-Seq and qRT-PCR results. The
469
transcript expression levels of the selected genes were each normalized to that of the
470
β-actin gene.
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ACCEPTED MANUSCRIPT Table 1 Primers used for qRT-PCR verification of differently expressed genes
TGATGGCTTTCTCAC ACTGC CTTGTACCGAGTCGG IL-1β ATGGT GCAACTGGGCTCAA TNF-α GCTTAC TCCTCGTGTGAAAGC LysC AAGAC GTGGAGCCGAATGT F7 CCTAAA GTTTCATGCATCGTC C1 CAAAG GCCATGGCCAGTAA C3 CTTTGT GGCAAAGGGTCAGT C7 GTGTTT GGCTTCTATCAGCTG CD 22 CTGTG TCCTTACGAGGCATG TLR 22 AGCTT ATGACTTCGATGAG IRF 4 CTGGTG TTCAAGCTCCGTTAA MPEG 1 TGCTG ATCACCTGGCAGAA MHC-I Ag AAATGG TCTTCCCTCCTCCTG MHC-II Ag TCAGA AAATCCAGAAGCTG HSP 70 CTCCAA GGCCTTCTGTCTGTC α2MG CTCTG GTGCCCATCTACGA β-actin GGGTTA
GATATTTCTTCGCCGTT GGA GAACAGGAGGTTGGCA TGTT GGTCCTGGTTCACTCT CCAA ATCCCTCAAATCCATC AAGC GTCATGCTCACCTGCG ACTA TAAACAGCCAGTCCAC CAGA CTCCATGAACGACAGC TGAA ATTTGGTTTCTGGCCA ACTG CCACCGAAGATCCTTC AAAT CGACAAGAGGAGGGT GAGAG CGTGTGTCGTGTTTTCT TCC TTTCACTTGGTGGGTCT TCA CCTGATGTTCCACCAC ACAG CCCTCCACAGGTGTGA ACTT TCAATTCCAAGGGACA GAGG AACCATGATGCACTTG GACA TCTCAGCTGTGGTGGT GAAG
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Reverse primer (5′-3′)
Accession number KF937391.1 JQ692172.1
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Forward primer (5′-3′)
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Gene
JQ670915.1
AF402599.1
JX088706.1 JQ358795.1 AY374472.1 JN655169.1 NM_0010838 23.2 HQ676542.2 NM_0011227 10.1 NM_212737. 1 AB221535.1 EF140725.1 EU816595.1 AY425709.1 M25013.1
ACCEPTED MANUSCRIPT Table 2 Summary of sequencing and mapping Non-infected Group
Read Length (bp)
125
125
Total Raw Reads
65513172
63438956
Total Clean Reads
64627432
62568036
Mapped reads Ratio (%)
79.88
81.41
Detected Gene Number
24507
24383
AC C
EP
TE D
M AN U
SC
RI PT
Infected Group
ACCEPTED MANUSCRIPT Table 3 List of the differently expressed genes (DEGs) involved in immune responses Category and Gene name
Log2Fold
Diff
Qvalue
1.807160033
Up
5.12E-276
Calmegin precursor
1.72725785
Up
8.66E-35
Vesicle-trafficking protein SEC22b-A
1.66135348
Up
8.89E-38
C-type lectin domain family 4 member
1.578698141
Up
8.97E-143
Calreticulin precursor
1.452254815
Up
0
C-type lysozyme
1.424010322
Up
1.65E-20
Up
0
Up
4.22E-12
Up
9.89E-23
Transferrin receptor 1a
Tubulin beta-1 chain
1.38832028
RI PT
Phagocytosis
1.344612409
Integrin beta 3a
1.330257571
Tubulin alpha-1C chain
1.283833515
Up
3.85E-08
Macrophage expressed gene 1 (MPEG 1)
1.237597456
Up
1.90E-43
Cysteine proteinase
1.217510291
Up
2.12E-40
1.198804756
Up
1.80E-97
1.13560686
Up
4.78E-40
M AN U
SC
Macrophage mannose receptor 1
Cathepsin K Neutrophil cytosolic factor 1 Cathepsin L precursor
1.12855511
Up
0.000121212
1.088007207
Up
1.15E-68
1.074417256
Up
5.08E-233
1.073145635
Up
4.97E-180
1.06144093
Up
7.49E-08
1.026675458
Up
7.77E-07
Integrin alpha-V precursor
1.011633801
Up
2.12E-65
Neutrophil cytosolic factor 4
1.008973284
Up
0.000100343
Complement Component C3b
4.309367861
Up
8.24E-05
Factor B (Bf)
2.298309327
Up
0
Complement component C1
2.168355572
Up
5.50E-07
Complement component C7
1.438283815
Up
0
1.346203375
Up
0
Coagulation factor Ⅶ
-1.07107892
Down
1.77E-210
Coagulation factor Ⅸ precursor
-1.07927431
Down
2.53E-48
G-type lysozyme Tubulin alpha-1B chain Cathepsin B, precursor Coronin-2B isoform X2
EP
Complement system
TE D
Cystinosin precursor
AC C
Complement component C8β
precursor
Complement component 3 (C3)
-1.11192977
Down
4.05E-63
Mannose-binding serine protease
-1.17167819
Down
6.27E-28
Small inducible cytokine B14 precursor
-1.43605885
Down
0.000921317
α-2-macroglobulin
-1.49752246
Down
0
Complement factor H like 4 precursor
-3.11609747
Down
7.40E-60
Tumor necrosis factor-α induced protein 6
5.346842986
Up
5.49E-18
C-X-C motif chemokine 13 precursor
2.280377621
Up
3.44E-227
Interferon regulatory factor 4 (IRF4)
2.06144081
Up
1.91E-09
1.641029822
Up
1.41E-121
Cytokine
Chemokine-like receptor 1
ACCEPTED MANUSCRIPT Category and Gene name
Log2Fold
Diff
Qvalue
1.596217893
Up
5.32E-09
Perforin-like protein 2
1.415077953
Up
4.55E-05
Interferon-induced transmembrane protein 1
1.349685778
Up
4.56E-15
Matrix metalloproteinase 9
1.279235669
Up
0
Chemokine C motif receptor XCR1
1.204045397
Up
7.61E-09
Interleukin-21 receptor precursor
1.123368854
Up
6.71E-06
Interleukin-10
1.114121126
Up
2.99E-11
C-X-C motif chemokine 9-like
1.113733102
Up
3.67E-191
Granulocyte colony-stimulating factor receptor
1.064605775
Up
1.52E-53
Tumor necrosis factor receptor 2
1.058231015
Up
6.22E-18
Interferon regulatory factor 10
1.057900539
Up
2.48E-16
Interleukin-1 receptor I
1.055859594
Up
2.10E-20
CXCR3
1.001675808
Up
7.29E-24
Interleukin-27 subunit beta precursor
-1.35965669
Down
2.17E-100
Interleukin 13 receptor, alpha 1 precursor
-1.52185706
Down
1.42E-56
-2.02602217
Down
0.000352238
4.148903699
Up
0.00024541
3.391288769
Up
1.26E-192
3.061440646
Up
1.38E-13
2.338280491
Up
2.13E-08
1.769272742
Up
0
1.687275774
Up
1.06E-28
1.491680658
Up
3.26E-11
Novel immune-type receptor 5 (NITR5)
1.470246033
Up
8.15E-06
Perforin-like protein 2
1.415077953
Up
4.54746E-05
1.397043842
Up
3.00E-10
1.361001026
Up
2.42E-06
1.313827508
Up
3.06E-18
1.243644382
Up
0.000768153
CD180 antigen-like
1.208281846
Up
0.000747575
CD2 family receptor (CD2 f)
1.119543937
Up
3.11E-07
CD209 antigen
1.077742201
Up
7.19E-08
Lymphocyte activation gene 3 precursor
1.049805976
Up
2.13E-19
1.04181149
Up
1.07E-11
proteasome activator complex subunit 2
1.006913826
Up
2.92E-48
CD36 antigen
-1.01129673
Down
1.20E-90
MHC class II alpha antigen
-1.51012641
Down
2.54E-103
MHC class II DA-beta-2, partial
-1.60152434
Down
7.05E-51
3.203892677
Up
Log2Fold
Diff
SC
M AN U
Immunity-related GTPase family, LRG-47 Antigen processing and presentation MHC class I antigen Lymphocyte antigen 6F-like T cell receptor beta chain Novel immune-type receptor protein CD79a precursor B-cell receptor CD22
CD11
EP
Perforin 3
TE D
Heat shock protein 70 (HSP70)
Lymphocyte antigen 86 precursor
T-lymphoma invasion and metastasis-inducing
AC C
protein
CD9 antigen-like
RI PT
Interleukin-1 beta
Pattern recognition receptor Intelectin Category and Gene name
0 Qvalue
ACCEPTED MANUSCRIPT C-type lectin 4
2.39295431
Up
2.41E-19
Toll-like receptor 22
1.70110923
Up
9.10E-18
NALPL2
1.154550248
Up
1.10E-10
C-type lectin 10
1.118247028
Up
2.11E-22
NLRC3 protein
1.099242548
Up
2.90E-10
JAM-C-like protein, partial
3.231365769
Up
0.000613005
T cell receptor beta chain, partial
3.061440646
Up
1.38E-13
Claudin-1 precursor
2.563940973
Up
2.01E-05
Versican b precursor
1.831958763
Up
3.29E-31
Up
5.48E-05
Up
1.30E-13
Leukocyte immune-type receptor
1.38336861
RI PT
Cell adhesion molecules
Selectin P ligand precursor
1.119156551
Claudin b, partial
-1.46580617
Claudin 2
-1.65737772
Down
8.21E-24
Novel protein similar to E-cadherin (cdh1)
-2.23073992
Down
3.01E-235
Claudin-4
-3.63899852
Down
1.69E-05
1.604233798
Up
5.05E-35
1.440956114
Up
2.14E-36
1.365951445
Up
6.22E-15
1.240224556
Up
4.98E-51
Oxidoreductase-like domain-containing protein
1.616029493
Up
0.000228787
Peroxisomal membrane protein 4
1.588688079
Up
6.95E-08
Glutathione synthetase
1.567793403
Up
7.30E-36
1.146102839
Up
3.10E-09
1.13170309
Up
4.08E-49
1.012681682
Up
1.67E-23
-1.0320958
Down
3.73E-56
-1.30329752
Down
0
-1.37843725
Down
2.83E-83
Reticulocalbin-3 precursor Caspase recruitment domain protein Caspase-7
EF-hand calcium-binding domain-containing protein
Spermidine synthase
TE D
Antioxidant enzymes
Natural killer cell enhancing factor Arginase 2C precursor Catalase
EP
Glutathione S-transferase
Gamma-glutamylcyclotransferase precursor
AC C
SC
M AN U
Apoptosis
Down
0.000656937
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Highlights 1. Transcriptome analysis of the grass carp infected A. hydrophila was performed. 2. 2121 differential expression genes were identified. 3. 105 important immune related genes were categorized and discussed.
AC C
EP
TE D
M AN U
SC
RI PT
4. For a better understanding of immune mechanism of grass carp infected bacteria.