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. 2010 Aug 13:11:472.
doi: 10.1186/1471-2164-11-472.

Deep sequencing-based transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus reveals insight into the immune-relevant genes in marine fish

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Deep sequencing-based transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus reveals insight into the immune-relevant genes in marine fish

Li-xin Xiang et al. BMC Genomics. .

Abstract

Background: Systematic research on fish immunogenetics is indispensable in understanding the origin and evolution of immune systems. This has long been a challenging task because of the limited number of deep sequencing technologies and genome backgrounds of non-model fish available. The newly developed Solexa/Illumina RNA-seq and Digital gene expression (DGE) are high-throughput sequencing approaches and are powerful tools for genomic studies at the transcriptome level. This study reports the transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus using RNA-seq and DGE in an attempt to gain insights into the immunogenetics of marine fish.

Results: RNA-seq analysis generated 169,950 non-redundant consensus sequences, among which 48,987 functional transcripts with complete or various length encoding regions were identified. More than 52% of these transcripts are possibly involved in approximately 219 known metabolic or signalling pathways, while 2,673 transcripts were associated with immune-relevant genes. In addition, approximately 8% of the transcripts appeared to be fish-specific genes that have never been described before. DGE analysis revealed that the host transcriptome profile of Vibrio harveyi-challenged L. japonicus is considerably altered, as indicated by the significant up- or down-regulation of 1,224 strong infection-responsive transcripts. Results indicated an overall conservation of the components and transcriptome alterations underlying innate and adaptive immunity in fish and other vertebrate models. Analysis suggested the acquisition of numerous fish-specific immune system components during early vertebrate evolution.

Conclusion: This study provided a global survey of host defence gene activities against bacterial challenge in a non-model marine fish. Results can contribute to the in-depth study of candidate genes in marine fish immunity, and help improve current understanding of host-pathogen interactions and evolutionary history of immunogenetics from fish to mammals.

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Figures

Figure 1
Figure 1
GO annotations of non-redundant consensus sequences. Best hits were aligned to the GO database, and 16,469 transcripts were assigned to at least one GO term. Most consensus sequences were grouped into three major functional categories, namely biological process, cellular component, and molecular function.
Figure 2
Figure 2
COG annotations of putative proteins. All putative proteins were aligned to the COG database and can be classified functionally into at least 25 molecular families.
Figure 3
Figure 3
KEGG categories of non-redundant consensus sequences. All non-redundant consensus sequences were annotated using KEGG Automatic Annotation Server for pathway information, and about 24,496 consensus sequences were annotated. The categories GIP and EIP stand for genetic information processing and environmental information processing, respectively.
Figure 4
Figure 4
Putative TLR signal pathway. Putative TLR signal pathway of L. japonicus was constructed based on knowledge of TLR signalling in mammalian species. However, most interactions have to be confirmed experimentally.
Figure 5
Figure 5
Putative TCR signal pathway. Putative TCR signal pathway of L. japonicus was constructed based on knowledge of TCR signalling in mammalian species. However, most interactions require experimental confirmation.
Figure 6
Figure 6
Distribution of tags and gene expression between experimental and control groups. The black and gray columns indicate the distribution of tags and gene expression in bacteria- and mock-challenged groups, respectively. The distribution of tags matches the distribution of genes for both groups. Furthermore, an increase in tags or gene expression is accompanied by a decrease in the frequencies of tags or genes.
Figure 7
Figure 7
Differential expression analyses of tags and consensus sequences by DGE. The expression level for each tag and consensus was included in the volcano plot (Figures A and B, respectively). 'Not DETs' and 'Not DEGs' indicate 'not detected expression tags' and 'not detected expression genes', respectively. For Figures A and B, the x-axis contains Log10 of transcript per million of the bacteria-challenged group and the y-axis indicates Log10 of transcript per million of the mock-challenged group. Limitations are based on P < 0.01, and the absolute value of Log2 (B/A) is greater than 1.
Figure 8
Figure 8
Volcano plot of differentially expressed consensus sequences. For every consensus, the ratio of expression levels in the bacteria-challenged group over that in the mock-challenged group was plotted against the -log error rate. The horizontal line indicates the significance threshold (0.01 FDR), and the vertical lines indicate the twofold change threshold.

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