Yudong Shen1, Wanwen Liang2, Yong Lin2, Huizan Yang2, Xiaohan Chen2, Pengfei Feng2, Bin Zhang2, Jiajie Zhu2, Yongde Zhang3, Honglin Luo4. 1. College of Fisheries, Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education/Key Lab of Freshwater Animal Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, Hubei, 430070, China. 2. Guangxi Key Laboratory for Aquatic Genetic Breeding and Healthy Aquaculture, Guangxi Academy of Fishery Sciences, Nanning, Guangxi, 530021, PR China. 3. Guangxi Key Laboratory for Aquatic Genetic Breeding and Healthy Aquaculture, Guangxi Academy of Fishery Sciences, Nanning, Guangxi, 530021, PR China. Electronic address: yondar@126.com. 4. Guangxi Key Laboratory for Aquatic Genetic Breeding and Healthy Aquaculture, Guangxi Academy of Fishery Sciences, Nanning, Guangxi, 530021, PR China. Electronic address: 541365548@qq.com.
Abstract
BACKGROUND: The tilapia aquaculture industry is facing heavy economic losses due to Streptococcus agalactiae (S. agalactiae) infections. While progress has been made in past years, the lack of a high-quality tilapia genome and transcript annotations makes systematic and comprehensive exploration for a non-coding RNA regulatory network associated with the infection process unfeasible, and it stunts further research focused on disease defense and treatment. Herein, single molecular real time sequencing (SMRT-Seq) and RNA-seq data were utilized to generate a high-quality transcript annotation. In addition, Changes in mRNA and non-coding RNA expression were also analyzed during a S. agalactiae infection in tilapia. FINDINGS: In total, 16.79 Gb of clean data were obtained by sequencing on six SMRT cells, with 712,294 inserts (326,645 full-length non-chimeric reads and 354,188 non-full-length reads). A total of 197,952 consensus transcripts were obtained. Additionally, 55,857 transcript sequences were acquired, with 12,297 previously annotated and 43,560 newly identified transcripts. To further examine the immune response in Oreochromis niloticus following a S. agalactiae infection, a total of 470.62 Gb of clean data was generated by sequencing a library containing 18 S. agalactiae infected tilapia samples. Of the identified genes, 9911 were newly exploited, of which 7102 were functional annotated. Furthermore, 7874 mRNAs, 1281 long non-coding RNAs (out of 21,860 long non-coding RNAs), and 61 circular RNAs (out of 1026 circular RNAs) were found to be differentially expressed during infection, with the 1026 circRNAs not previously identified in tilapia. Moreover, k-means clustering and WGCNA analyses revealed that the immune response of tilapia to a S. agalactiae infection can be divided into three stages: cytokines driven rapid immune response, energy metabolism promotion, and the production of lysosomes and phagosomes. During this response, the head kidney and spleen have synergistic effects, while maintaining independent characteristics. Finally, lncRNA-mRNA (trans and cis), lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks were constructed and revealed that non-coding RNA is involved in the regulation of immune-related genes. CONCLUSIONS: This study generated a greatly-improved transcript annotation for tilapia using long-read PacBio sequencing technology, and revealed the presence of a regulatory network comprised of non-coding RNAs in Nile tilapia infected with S. agalactiae.
BACKGROUND: The tilapia aquaculture industry is facing heavy economic losses due to Streptococcus agalactiae (S. agalactiae) infections. While progress has been made in past years, the lack of a high-quality tilapia genome and transcript annotations makes systematic and comprehensive exploration for a non-coding RNA regulatory network associated with the infection process unfeasible, and it stunts further research focused on disease defense and treatment. Herein, single molecular real time sequencing (SMRT-Seq) and RNA-seq data were utilized to generate a high-quality transcript annotation. In addition, Changes in mRNA and non-coding RNA expression were also analyzed during a S. agalactiaeinfection in tilapia. FINDINGS: In total, 16.79 Gb of clean data were obtained by sequencing on six SMRT cells, with 712,294 inserts (326,645 full-length non-chimeric reads and 354,188 non-full-length reads). A total of 197,952 consensus transcripts were obtained. Additionally, 55,857 transcript sequences were acquired, with 12,297 previously annotated and 43,560 newly identified transcripts. To further examine the immune response in Oreochromis niloticus following a S. agalactiaeinfection, a total of 470.62 Gb of clean data was generated by sequencing a library containing 18 S. agalactiaeinfected tilapia samples. Of the identified genes, 9911 were newly exploited, of which 7102 were functional annotated. Furthermore, 7874 mRNAs, 1281 long non-coding RNAs (out of 21,860 long non-coding RNAs), and 61 circular RNAs (out of 1026 circular RNAs) were found to be differentially expressed during infection, with the 1026 circRNAs not previously identified in tilapia. Moreover, k-means clustering and WGCNA analyses revealed that the immune response of tilapia to a S. agalactiaeinfection can be divided into three stages: cytokines driven rapid immune response, energy metabolism promotion, and the production of lysosomes and phagosomes. During this response, the head kidney and spleen have synergistic effects, while maintaining independent characteristics. Finally, lncRNA-mRNA (trans and cis), lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks were constructed and revealed that non-coding RNA is involved in the regulation of immune-related genes. CONCLUSIONS: This study generated a greatly-improved transcript annotation for tilapia using long-read PacBio sequencing technology, and revealed the presence of a regulatory network comprised of non-coding RNAs in Nile tilapia infected with S. agalactiae.