Background: Siamese fighting fish Betta splendens are notorious for their aggressiveness and accordingly have been widely used to study aggression. However, the lack of a reference genome has, to date, limited the understanding of the genetic basis of aggression in this species. Here, we present the first reference genome assembly of the Siamese fighting fish. Findings: Frist, we sequenced and de novo assembled a 465.24-Mb genome for the B. splendens variety Giant, with a weighted average (N50) scaffold size of 949.03 Kb and an N50 contig size of 19.01 Kb, covering 99.93% of the estimated genome size. To obtain a chromosome-level genome assembly, we constructed one Hi-C library and sequenced 75.24 Gb reads using the BGISEQ-500 platform. We anchored approximately 93% of the scaffold sequences into 21 chromosomes and evaluated the quality of our assembly using the high-contact frequency heat map and Benchmarking Universal Single-Copy Orthologs. We also performed comparative chromosome analyses between Oryzias latipes and B. splendens, revealing a chromosome conservation evolution in B. splendens. We predicted  23,981 genes assisted by RNA-sequencing data generated from brain, liver, muscle, and heart tissues of Giant and annotated 15% repetitive sequences in the genome. Additionally, we resequenced five other B. splendens varieties and detected ∼3.4 M single-nucleotide variations and  27,305 insertions and deletions. Conclusions: We provide the first chromosome-level genome for the Siamese fighting fish. The genome will lay a valuable foundation for future research on aggression in B. splendens.

Guangyi Fan    Chan Judy    Ma Kailong    Yang Binrui    Zhang He    Yang Xianwei    Chengcheng Shi    Chun-Hin Law Henry    Ren Zhitao    Xu Qiwu    Liu Qun    Wang Jiahao    Wenbin Chen    Shao Libin    Gonçalves David    Ramos Andreia    Cardoso Sara D.    Guo Min    Cai Jing    徐迅    Wang Jian    杨焕明     Liu Xin    Wang Yitao   

GigaScience

2018

The Chinese Taihu pig breeds are an invaluable component of the world's pig genetic resources, and they are the most prolific breeds of swine in the world. In this study, the genomes of 252 pigs of the six indigenous breeds in the Taihu Lake region were sequenced using the genotyping by genome reducing and sequencing approach. A total of 950 million good reads were obtained using an Illumina Hiseq2000 at an average depth of 13× (for SNP calling) and an average coverage of 2.3%. In total, 122 632 indels, 31 444 insertions, 44 056 deletions and 455 CNVs (copy number variants) were identified in the genomes of the pigs. Approximately 2.3% of these genetic markers were mapped to gene exon regions, and 25% were in QTL regions related to economically important traits. The KEGG pathway or GO enrichment analyses revealed that genetic variants assumed to be large-effect mutations were significantly overrepresented in 22 SNP, 56 indel, 26 insertion, 28 deletion and three CNV gene sets. A total of 343 breed-specific SNPs were also identified in the six Chinese indigenous pigs. The findings from this study can contribute to future investigations of the genetic diversity, population structure, positive selection signals and molecular evolutionary history of these pigs at the genome level and can serve as a valuable reference for improving the breeding and cultivation of these pigs.

Zhen Wang    Qiang Chen    Rongrong Liao    Zhe Zhang    张向喆    Liu    Zhu    Zhang    Xue    杨焕明     Zheng Youmin    王起山    Yuchun Pan   

Animal Genetics

2017

The inflammatory intestinal disorder Crohn's disease (CD) has become a health challenge worldwide. The gut microbiota closely interacts with the host immune system, but its functional impact in CD is unclear. Except for studies on a small number of CD patients, analyses of the gut microbiota in CD have used 16S rDNA amplicon sequencing. Here we employed metagenomic shotgun sequencing to provide a detailed characterization of the compositional and functional features of the CD microbiota, comprising also unannotated bacteria, and investigated its modulation by exclusive enteral nutrition. Based on signature taxa, CD microbiotas clustered into 2 distinct metacommunities, indicating individual variability in CD microbiome structure. Metacommunity-specific functional shifts in CD showed enrichment in producers of the pro-inflammatory hexa-acylated lipopolysaccharide variant and a reduction in the potential to synthesize short-chain fatty acids. Disruption of ecological networks was evident in CD, coupled with reduction in growth rates of many bacterial species. Short-term exclusive enteral nutrition elicited limited impact on the overall composition of the CD microbiota, although functional changes occurred following treatment. The microbiotas in CD patients can be stratified into 2 distinct metacommunities, with the most severely perturbed metacommunity exhibiting functional potentials that deviate markedly from that of the healthy individuals, with possible implication in relation to CD pathogenesis.

He Qing    Gao Yuan    Jie Zhuye    Yu Xinlei    Laursen Janne M.    Xiao Liang    Li Ying    Li Lingling    Faming Zhang    Feng Qiang    Li Xiaoping    Yu Jinghong    Liu Chuan    兰平    Yan Ting    Liu Xin    徐迅    杨焕明     Wang Jian    Lise Madsen    Brix Susanne    Jianping Wang    Karsten Kristiansen    Huijue Jia   

GigaScience

2017

Hypopharyngeal cancer (HPC) frequently presents at an advanced stage, resulting in poor prognosis. Although combined surgical therapy and chemoradiotherapy have improved the survival for patients with HPC over the past 3 decades, the mortality rate in late-stage diagnosis of HPC is unsatisfactory. In this study, we performed whole-exome sequencing (WES) of 23 hypopharyngeal tumor and paired adjacent normal tissue to identify novel candidate driver genes associated with hypopharyngeal carcinoma. We identified several copy number variants (CNVs) and 15 somatic mutation genes that were associated with hypopharyngeal carcinoma. Mutations in nine new genes (PRB4, NSD1, REC8, ZNF772, ZNF69, EI24, CYFIP2, NEFH, KRTAP4-5) were also indentified. PRB4 and NSD1 expression were significantly upregulated in hypopharyngeal carcinoma, which was confirmed in an independent cohort using IHC. There was a positive relationship between PRB4 and NSD1. Downregulation of PRB4 by siRNA could inhibit cell growth, colony formation and cell invasion. Notably, we here demonstrate that NSD1 could bind to the promoter regions of PRB4 and activate promoter activity by reducing the binding of H3K27me2 and increasing the binding of H3K36me2 on PRB4 promoter. In summary, we pinpoint the predominant mutations in hypopharyngeal carcinoma by WES, highlighting the substantial genetic alterations contributing to hypopharyngeal carcinoma tumorigenesis. We also indentify a novel epigenetically regulatory between PRB4 and NSD1 that contribute to hypopharyngeal carcinoma tumorigenesis. They may become potential prognostic biomarkers and therapeutic target for hypopharyngeal carcinoma treatment.

Wu Ping    Wu Honglong    Tang Yaoyun    Luo Shi    Fang Xing    Xie Chubo    He Jian    Zhao Su Ping    Wang Xiaofeng    Xu Jiajia    Chen Xi    Dongfang Li    杨焕明     王健   

Oncotarget

2017

In the final publication of "Genome sequence of a rice pest, the white-backed planthopper (Sogatella furcifera)," by Lin Wang et al., the listed accepted date was incorrect, and the article did not include the revised date. The accepted date has been corrected and the revised date added. The corrected manuscript can be found online at https://academic.oup.com/gigascience/article/6/1/1/2865203/Genome-sequence-of-a-rice-pest-thewhite-backed?searchresult = 1. The Publisher regrets these errors.

Wang Lin    Tang Nan    Gao Xinlei    Chang Zhaoxia    Zhang Liqin    Guohui Zhou    Guo Dongyang    Zeng Zhen    Li Wenjie    Akinyemi Ibukun A.    杨焕明     吴清发   

GigaScience

2017

In Table 2: Family 6 should be c.643-13_662delCTATCTTTTCTAGGGTCCCATGGGTCCCCGAGG instead of c.643-13_662delCTATCTTTTCTAGGGTCCCATGGGTCCCC. Family 33 should be c.271_279dupGCCCTCTCG instead of c.271_279dupGCCCTCT. In the 2nd para. of the Molecular diagnosis, section t(5;8)(q32;q21) should be t(5;7)(q32;q21).

Liu Yi    Asan    Ma    Lv Fang    Xu Xiao-Jie    Wang Jian-yi    Weibo Xia    Yan Jiang    Ou Wang    Xiaoping Xing    Yu Wei    Wang J.    Sun J.    Song L.    Zhu Y.    杨焕明     Jianliu Wang    Mei Li   

Osteoporosis International

2017

Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles.

Liu Geng    Li Dongli    Li Zhang    Qiu Si    Li Wenhui    Chao Cheng Chi    Yang Naibo    Li Handong    Cheng Zhen    Song Xin    Cheng Le    Xiuqing Zhang    Wang Jian    杨焕明     Ma Kun    Yong Hou    Li Bo   

GigaScience

2017

Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides, thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella, those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment.

Yanyun Gu    Wang Xiaokai    Junhua Li    Yifei Zhang    Zhong Huanzi    Ruixin Liu    Zhang Dongya    Feng Qiang    Xie Xiaoyan    洪洁    Ren Huahui    Liu Wei    Ma Jing    苏青    Hongmei Zhang    Yang Jialin    Wang Xiaoling    Xinjie Zhao    Weiqiong Gu    毕宇芳    彭永德    Xu Xiaoqiang    Xia Huihua    Li Fang    徐迅    杨焕明     Guowang Xu    Lise Madsen    Karsten Kristiansen    Ning Guang    王卫庆   

Nature Communications

2017

The root microbes play pivotal roles in plant productivity, nutrient uptakes, and disease resistance. The root microbial community structure has been extensively investigated by 16S/18S/ITS amplicons and metagenomic sequencing in crops and model plants. However, the functional associations between root microbes and host plant growth are poorly understood. This work investigates the root bacterial community of foxtail millet (Setaria italica) and its potential effects on host plant productivity. We determined the bacterial composition of 2882 samples from foxtail millet rhizoplane, rhizosphere and corresponding bulk soils from 2 well-separated geographic locations by 16S rRNA gene amplicon sequencing. We identified 16 109 operational taxonomic units (OTUs), and defined 187 OTUs as shared rhizoplane core OTUs. The β-diversity analysis revealed that microhabitat was the major factor shaping foxtail millet root bacterial community, followed by geographic locations. Large-scale association analysis identified the potential beneficial bacteria correlated with plant high productivity. Besides, the functional prediction revealed specific pathways enriched in foxtail millet rhizoplane bacterial community. We systematically described the root bacterial community structure of foxtail millet and found its core rhizoplane bacterial members. Our results demonstrated that host plants enrich specific bacteria and functions in the rhizoplane. The potentially beneficial bacteria may serve as a valuable knowledge foundation for bio-fertilizer development in agriculture.

Tao Jin    Wang Ya-Yu    Huang Yueying    Xu Jin    Zhang Pengfan    Nian Wang    Liu Xin    Haiyan Chu    Liu Guang    Jiang Honggang    Li Yu-Zhen    Xu Jing    Karsten Kristiansen    Xiao Liang    Zhang Yunzeng    Gengyun Zhang    Du Guohua    Zhang Houbao    Zou Hongfeng    Zhang Haifeng    Jie Zhuye    Suisha Liang    Huijue Jia    Wan Jingwang    Lin Dechun    Li Jinying    Fan Guangyi    杨焕明     Wang Jian    Bai Yang    徐迅   

GigaScience

2017

戴俊彪    Cai Yizhi    Yuan Yinjing    杨焕明     Boeke Jef D.   

Quantitative Biology

2017