2024-12-31 2024, Volume 3 Issue 6

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  • RESEARCH ARTICLE
    Shen Fan, Peng Qin, Jie Lu, Shuaitao Wang, Jie Zhang, Yan Wang, Aifang Cheng, Yan Cao, Wei Ding, Weipeng Zhang

    Antimicrobial peptides (AMPs) have become a viable source of novel antibiotics that are effective against human pathogenic bacteria. In this study, we construct a bank of culturable marine biofilm bacteria constituting 713 strains and their nearly complete genomes and predict AMPs using ribosome profiling and deep learning. Compared with previous approaches, ribosome profiling has improved the identification and validation of small open reading frames (sORFs) for AMP prediction. Among the 80,430 expressed sORFs, 341 are identified as candidate AMPs with high probability. Most potential AMPs have less than 40% similarity in their amino acid sequence compared to those listed in public databases. Furthermore, these AMPs are associated with bacterial groups that are not previously known to produce AMPs. Therefore, our deep learning model has acquired characteristics of unfamiliar AMPs. Chemical synthesis of 60 potential AMP sequences yields 54 compounds with antimicrobial activity, including potent inhibitory effects on various drug-resistant human pathogens. This study extends the range of AMP compounds by investigating marine biofilm microbiomes using a novel approach, accelerating AMP discovery.

  • RESEARCH ARTICLE
    Na Zhou, Xue Han, Ning Hu, Shuo Han, Meng Yuan, Zhongfang Li, Sujuan Wang, Yingchun Li, Hongbo Li, Zed Rengel, Yuji Jiang, Yilai Lou

    Elevated CO2 (eCO2) stimulates productivity and nutrient demand of crops. Thus, comprehensively understanding the crop phosphorus (P) acquisition strategy is critical for sustaining agriculture to combat climate changes. Here, wheat (Triticum aestivum L) was planted in field in the eCO2 (550 µmol mol−1) and ambient CO2 (aCO2, 415 µmol mol−1) environments. We assessed the soil P fractions, root morphological and physiological traits and multitrophic microbiota [including arbuscular mycorrhizal fungi (AMF), alkaline phosphomonoesterase (ALP)-producing bacteria, protozoa, and bacterivorous and fungivorous nematodes] in the rhizosphere and their trophic interactions at jointing stage of wheat. Compared with aCO2, significant 20.2% higher shoot biomass and 26.8% total P accumulation of wheat occurred under eCO2. The eCO2 promoted wheat root length and AMF hyphal biomass, and increased the concentration of organic acid anions and the ALP activity, which was accompanied by significant decreases in calcium-bound inorganic P (Ca-Pi) (by 16.7%) and moderately labile organic P (by 26.5%) and an increase in available P (by 14.4%) in the rhizosphere soil. The eCO2 also increased the growth of ALP-producing bacteria, protozoa, and bacterivorous and fungivorous nematodes in the rhizosphere, governed their diversity and community composition. In addition, the eCO2 strengthened the trophic interactions of microbiota in rhizosphere; specifically, the eCO2 promoted the associations between protozoa and ALP-producing bacteria, between protozoa and AMF, whereas decreased the associations between ALP-producing bacteria and nematodes. Our findings highlighted the important role of root traits and multitrophic interactions among microbiota in modulating crop P-acquisition strategies, which could advance our understanding about optimal P management in agriculture systems under global climate changes.

  • RESEARCH ARTICLE
    Gang Yu, Cuifang Xu, Xiaoyan Wang, Feng Ju, Junfen Fu, Yan Ni

    First introduced in 2021, MetOrigin has quickly established itself as a powerful web server to distinguish microbial metabolites and identify the bacteria responsible for specific metabolic processes. Building on the growing understanding of the interplay between the microbiome and metabolome, and in response to user feedback, MetOrigin has undergone a significant upgrade to version 2.0. This enhanced version incorporates three new modules: (1) Quick search module that facilitates the rapid identification of bacteria associated with a particular metabolite; (2) Orthology analysis module that links metabolic enzyme genes with their corresponding bacteria; (3) Mediation analysis module that investigates potential causal relationships among bacteria, metabolites, and phenotypes, highlighting the mediating role of metabolites. Additionally, the backend MetOrigin database has been updated with the latest data from seven public databases (KEGG, HMDB, BIGG, ChEBI, FoodDB, Drugbank, and T3DB), with expanded coverage of 210,732 metabolites, each linked to its source organism. MetOrigin 2.0 is freely accessible at http://metorigin.met-bioinformatics.cn.

  • COMMENTARY
    Usman Ali, Lei Tian, Ruihong Tang, Shunxi Wang, Weiwei Luo, Shanshan Liu, Jinghua Zhang, Liuji Wu
  • CORRESPONDENCE
    Yuzhao Jin, Songhua Cai, Yang Zhou, Dandan Guo, Yuzhen Zeng, Wangting Xu, Yiting Sun, Yueli Shi, Zhiyong Xu, Zaoqu Liu, Peng Luo, Zhao Huang, Bufu Tang
  • CORRESPONDENCE
    Zheng-Han Lian, Nimaichand Salam, Sha Tan, Yang Yuan, Meng-Meng Li, Yu-Xian Li, Ze-Tao Liu, Chao-Jian Hu, Ai-Ping Lv, Yu-Ting OuYang, Cai-Yu Lu, Jing-Yi Zhang, Ying Chen, Le-Bin Chen, Zhen-Hao Luo, Bin Ma, Zheng-Shuang Hua, Jian-Yu Jiao, Wen-Jun Li, Lan Liu
  • RESEARCH ARTICLE
    Lei Liu, Guoqiang Yi, Yilong Yao, Yuwen Liu, Jiang Li, Yalan Yang, Mei Liu, Lingzhao Fang, Delin Mo, Longchao Zhang, Yonggang Liu, Yongchao Niu, Liyuan Wang, Xiaolu Qu, Zhangyuan Pan, Lei Wang, Muya Chen, Xinhao Fan, Yun Chen, Yongsheng Zhang, Xingzheng Li, Zhen Wang, Yijie Tang, Hetian Huang, Pengxiang Yuan, Yuying Liao, Xinjian Li, Zongjun Yin, Di Liu, Dongjie Zhang, Quanyong Zhou, Wangjun Wu, Jicai Jiang, Yahui Gao, George E. Liu, Lixian Wang, Yaosheng Chen, Kui Li, Martien A. M. Groenen, Zhonglin Tang

    The genetic basis of complex traits and phenotypic differentiation remains unclear in pigs. Using nine genomes—seven of which were newly generated, high-quality de novo assembled genomes—and 1081 resequencing genomes, we built a pan-genome and identified 134.24 Mb nonredundant nonreference sequences, 1099 novel protein-coding genes, 187,927 structural variations (SVs) and 30,143,962 single-nucleotide polymorphisms (SNPs). Analysis of selective domestication revealed BRCA1 associated with enhanced adipocyte growth and fat deposition, and ABCA3 linked to an alleviated immune response and reduced lung injury. Integrating 162 transcriptomes and 162 methylomes of skeletal muscle across 27 developmental stages revealed the regulatory mechanism of phenotypic differentiation between Eastern and Western breeds. Artificial selection reshaped local DNA methylation status and imparted regulatory effects on the progression patterns of heterochronic genes such as GHSR and BDH1, particularly during embryonic development. Altogether, our work provides valuable resources for understanding molecular mechanisms behind phenotypic variations and enhancing the genetic improvement programs in pigs.

  • EDITORIAL
    Yao Wang, Huiyu Hou, Hao Luo, Jiani Xun, Chuang Ma, Haifei Yang, Defeng Bai, Salsabeel Yousuf, Hujie Lyu, Tianyuan Zhang, Xiulin Wan, Xiaofang Yao, Tengfei Ma, Yuanping Zhou, Zhihao Zhu, Meiyin Zeng, Sanqi An, Qing Bai, Yao Bai, Guodong Cao, Tingting Cao, Yongkai Cao, Chihmin Chang, Lijia Chang, Bo Chen, Dai Chen, Dijun Chen, Hanqing Chen, Jiali Chen, Jinfeng Chen, Wei-Hua Chen, Xinhai Chen, Yue Chen, Zhangran Chen, Cheng Cheng, Quan Cheng, Xi-Jian Dai, Chaowen Deng, Feilong Deng, Jingwen Deng, Chang-Sheng Dong, Lei Dong, Lianhui Duan, Yi Duan, Qingjie Fan, Chao Fang, Tingyu Fang, Wensheng Fang, Zhencheng Fang, Min Fu, Minjie Fu, Cong Gao, Hao Gao, Weiwei Gao, Xinrui Gao, Yi-Zhou Gao, Yan Geng, Wenping Gong, Shaohua Gu, Xia Gu, Zhengquan Gu, Jian-Wei Guo, Junjie Guo, Qiuyan Guo, Xiang Guo, Xiaoqian Guo, Dongfei Han, Ziyi Han, Yanan Hao, Jiale He, Jianquan He, Jianyu He, Ruolin He, Guosen Hou, Bin Hu, Haibo Hu, Yi Hu, Yongfei Hu, Yucan Hu, Guanyin Huang, Haiyun Huang, Jiaomei Huang, Shenghui Huang, Baolei Jia, Xingxing Jian, Chao Jiang, Kun Jiang, Lanyan Jiang, Shuaiming Jiang, Jian-Yu Jiao, Hao Jin, Jiajia Jin, Siyuan Kong, Xinxing Lai, Yuxin Leng, Bang Li, Bing Li, Fang Li, Hao Li, Huanjie Li, Jing Li, Kai Li, Lanqi Li, Leyuan Li, Minghan Li, Pengsong Li, Wei Li, Wei Li, Xianyu Li, Li Xuemeng, Yafei Li, Yuantao Li, Zhi Li, Liqin Liang, Rong Liang, Zhuobin Liang, Qingya Liu, Dejian Liu, Huiheng Liu, Jinchao Liu, Li Liu, Lihui Liu, Moyang Liu, Ran Liu, Shuai Liu, Tianyang Liu, Wei Liu, Wenjuan Liu, Xiaomin Liu, Yang Liu, Yichen Liu, Yina Liu, Yuan Liu, Zhe Liu, Zhipeng Liu, Zhiquan Liu, Chunhao Long, Yun Long, Changying Lu, Chao Lu, Cheng Lu, Qi Lu, Yaning Luan, Peng Luo, Sheng Luo, Ning Ma, Xiao-Ya Ma, Yan Ma, Wenjun Mao, Yuanfa Meng, Yan Ni, Yawen Ni, Kang Ning, Dongze Niu, Kai Peng, Zhengwu Peng, Xubo Qian, Zhiguang Qiu, Hui Qu, Zepeng Qu, Yan Ren, Zhigang Ren, Youming Shen, Lin Shi, Linlin Shi, Wenxuan Shi, Yongpeng Shi, Tianyuan Song, Xiaohui Song, Xiaoming Song, Xiaowei Song, Qi Su, Yufan Su, Lifang Sun, Qiang Sun, Tiefeng Sun, Yunke Sun, Hua Tang, Wenjing Tang, Tao Yu, Simon Tian, Shuo Wang, Bowen Wang, Cheng Wang, Wang Jin, Leli Wang, Liangliang Wang, Lixiao Wang, Mingbang Wang, Ming-Ke Wang, Pingyi Wang, Shaolin Wang, Shaopu Wang, Xinxia Wang, Xueqiang Wang, Mi Wei, Yan Wei, Yanxia Wei, Yongjun Wei, Chaoliang Wen, Xin Wen, Linkun Wu, Shengru Wu, Yuting Wu, Shuting Xia, Xiaodong Xia, Yu Xia, Xionggen Xiang, Chuanxing Xiao, Weihua Xiao, Yingping Xiao, Ruohan Xie, Rui Xing, Hui Xu, Wei Xu, Zhimin Xu, Hongliang Xue, Chao Yan, Qiu-Long Yan, Shaofei Yan, Xiuchuan Yan, Mengli Yang, Yufan Yang, Zhipeng Yang, Ziyuan Yang, Guixiang Yao, Yanlai Yao, Xianfu Yi, Chong Yin, Mingliang Yin, Shicheng Yu, Ying Yu, Yongyao Yu, Fusong Yuan, Shao-Lun Zhai, Bo Zhang, Chen Zhang, Fang Zhang, Feng-Li Zhang, Hengguo Zhang, Jinping Zhang, Junya Zhang, Kun Zhang, Li Zhang, Lin Zhang, Lingxuan Zhang, Meng Zhang, Qian Zhang, Runan Zhang, Tongtong Zhang, Tongxue Zhang, Weipeng Zhang, Yong Zhang, Yuchao Zhang, Yujun Zhang, Zeng Zhang, Zhengxiao Zhang, Zhi-Feng Zhang, Boxi Zhao, Yanyan Zhao, Yibing Zhao, Ziwei Zhao, Diwei Zheng, Ying Zheng, Wenqiang Zhi, Jixin Zhong, Xiangjian Zhong, Wei Zhou, Xin Zhou, Zhemin Zhou, Zhichao Zhou, Congmin Zhu, Feiying Zhu, Xiaodie Zhu, Yutian Zou, Hongling Zhou, Lei Lei, Yanliang Bi, Hubing Shi, Hui-Zeng Sun, Shuangxia Jin, Wenkai Ren, Lei Dai, Xin Wang, Canhui Lan, Hongwei Liu, Shuang-Jiang Liu, Yulong Yin, Chun-Lin Shi, Ren-You Gan, Fangqing Zhao, Jun Yu, Tong Chen, Xin Hong, Hua Yang, Bangzhou Zhang, Shifu Chen, Xiaodong Li, Yunyun Gao, Yong-Xin Liu
  • CORRESPONDENCE
    Moyang Liu, Ming Yang, Heng Liang, Bote Luo, Junjie Deng, Lingyan Cao, Daojun Zheng, Cheng Chen
  • COMMENTARY
    Xin Shen, Hao Jin, Feiyan Zhao, Lai-Yu Kwok, Zhixin Zhao, Zhihong Sun
  • RESEARCH ARTICLE
    Deyin Zhang, Jiangbo Cheng, Xiaolong Li, Kai Huang, Lvfeng Yuan, Yuan Zhao, Dan Xu, Yukun Zhang, Liming Zhao, Xiaobin Yang, Zongwu Ma, Quanzhong Xu, Chong Li, Xiaojuan Wang, Chen Zheng, Defu Tang, Fang Nian, Xiangpeng Yue, Wanhong Li, Huibin Tian, Xiuxiu Weng, Peng Hu, Yuanqing Feng, Peter Kalds, Zhihua Jiang, Yunxia Zhao, Xiaoxue Zhang, Fadi Li, Weimin Wang

    Comprehensive functional genome annotation is crucial to elucidate the molecular mechanisms of agronomic traits in livestock, yet systematic functional annotation of the sheep genome is lacking. Here, we generated 92 transcriptomic and epigenomic data sets from nine major tissues, along with whole-genome data from 2357 individuals across 29 breeds worldwide, and 4006 phenotypic data related to tail fat weight. We constructed the first multi-tissue epigenome atlas in terms of functional elements, chromatin states, and their functions and explored the utility of the functional elements in interpreting phenotypic variation during sheep domestication and improvement. Particularly, we identified a total of 753,723 nonredundant functional elements, with over 60% being novel. We found tissue-specific promoters and enhancers related to sensory abilities and immune response that were highly enriched in genomic regions influenced by domestication, while longissimus dorsi tissue-specific active enhancers and tail fat tissue-specific active promoters were highly enriched in genomic regions influenced by breeding and improvement. Notably, a variant, Chr13:51760995A>C, located in an enhancer region, was identified as a causal variant for tail fat deposition based on multi-layered data sets. Overall, this research provides foundational resources and a successful case for future investigations of complex traits in sheep through the integration of multi-omics data sets.

  • EXPRESSION OF CONCERN

    EXPRESSION OF CONCERN: Y. Li, D. Zhang, M. Wang, H. Jiang, C. Feng, and Y.–X. Li, “Intratumoral Microbiota is Associated with Prognosis in Patients with Adrenocortical Carcinoma,” iMeta 2, no. 2 (2023): e102, https://doi.org/10.1002/imt2.102.

    This Expression of Concern is for the above article, published online on 05 April 2023 in Wiley Online Library (wileyonlinelibrary.com), and has been published by agreement between the journal Editors-in-Chief, Shuang-Jiang Liu and Jingyuan Fu; iMeta Science; and John Wiley & Sons Australia, Ltd.

    The above article utilized data partially derived from Poore et al. [1], which was later retracted due to data analysis errors [2]. In light of this retraction and the resulting uncertainty regarding the data, readers are advised to interpret the results of the present study with caution.

    The journal team and the publisher are currently investigating the concerns to determine whether it affects the conclusions of the article. The authors have agreed to re-analyze their data. In the meantime, the journal has decided to issue an Expression of Concern to inform and alert the readers.

  • COMMENTARY
    Guang-Xu Ren, Liang He, Yong-Xin Liu, Yu-Ke Fei, Xiao-Fan Liu, Qiu-Yi Lu, Xin Chen, Zhi-Da Song, Jia-Qi Wang
  • CORRESPONDENCE
    Dong Li, Yulong Wang, Tiantian Yuan, Minghao Cao, Yulin He, Lin Zhang, Xiang Li, Yifan Jiang, Ke Li, Jingchun Sun, Guangquan Lv, Guosheng Su, Qishan Wang, Yuchun Pan, Xinjian Li, Yu Jiang, Gongshe Yang, Martien A. M. Groenen, Martijn F. L. Derks, Rongrong Ding, Xiangdong Ding, Taiyong Yu
  • RESEARCH ARTICLE
    Jingquan Li, Fei Huang, Yunyan Zhou, Tao Huang, Xinkai Tong, Mingpeng Zhang, Jiaqi Chen, Zhou Zhang, Huipeng Du, Zifeng Liu, Meng Zhou, Yiwen Xiahou, Huashui Ai, Congying Chen, Lusheng Huang

    Understanding the community structure of the lower respiratory tract microbiome is crucial for elucidating its roles in respiratory tract diseases. However, there are few studies about this topic due to the difficulty in obtaining microbial samples from both healthy and disease individuals. Here, using 744 high-depth metagenomic sequencing data of lower respiratory tract microbial samples from 675 well-phenotyped pigs, we constructed a lung microbial gene catalog containing the largest scale of 10,031,593 nonredundant genes to date, 44.8% of which are novel. We obtained 356 metagenome-assembled genomes (MAGs) which were further clustered into 256 species-level genome bins with 41.8% being first reported in the current databases. Based on these data sets and through integrated analysis of the isolation of the related bacterial strains, in vitro infection, and RNA sequencing, we identified and confirmed that Mesomycoplasma hyopneumoniae (M. hyopneumoniae) MAG_47 and its adhesion-related virulence factors (VFs) were associated with lung lesions in pigs. Differential expression levels of adhesion- and immunomodulation-related VFs likely determined the heterogenicity of adhesion and pathogenicity among M. hyopneumoniae strains. M. hyopneumoniae adhesion activated several pathways, including nuclear factor kappa-light-chain-enhancer of activated B, mitogen-activated protein kinase, cell apoptosis, T helper 1 and T helper 2 cell differentiation, tumor necrosis factor signaling, interleukin-6/janus kinase 2/signal transducer and activator of transcription signaling, and response to reactive oxygen species, leading to cilium loss, epithelial cell‒cell barrier disruption, and lung tissue lesions. Finally, we observed the similar phylogenetic compositions of the lung microbiome between humans with Mycoplasma pneumoniae and pigs infected with M. hyopneumoniae. The results provided important insights into pig lower respiratory tract microbiome and its relationship with lung health.

  • CORRECTION

    In Hong et al. [1], following details should have been corrected.

    1. In page 6, “We also investigated the effect of Maca-EVs on microbial diversity and found that microbial richness as indicated by the Faith-pd and Observed features index was higher in the Maca-EVs groups (Control+Maca-EVs and UCMS+Maca-EVs).” was incorrect.

    This should be: “We also investigated the effect of Maca-EVs on microbial diversity and found that microbial richness as indicated by the Faith-pd and Observed features index was lower in the Maca-EVs groups (Control+Maca-EVs and UCMS+Maca-EVs).”

    2. In page 6, the sentence “Only the Faith pd index in Control + Maca EVs group was significantly lower than that in the Control group (p < 0.05, figure 3D).” should be deleted.

    3. In Figure 4, page 8 of the article, “(F) Reciprocal interactions between altered gut bacteria and serum metabolites identified by a co-occurrence network based on Spearman correlation analysis in pos mode.” was incorrect.

    This should be: “(F) Reciprocal interactions between altered gut bacteria and metabolites identified by a co-occurrence network based on Spearman correlation analysis in pos mode.”

    We apologize for these errors.

  • COMMENTARY
    Lijun Chen, Guofan Zhu, Alberto Pascual-Garcia, Francisco Dini-Andreote, Jie zheng, Xiaoyue Wang, Shungui Zhou, Yuji Jiang
  • RESEARCH ARTICLE
    Andong Zha, Ming Qi, Yuankun Deng, Hao Li, Nan Wang, Chengming Wang, Simeng Liao, Dan Wan, Xia Xiong, Peng Liao, Jing Wang, Yulong Yin, Bi'e Tan

    Gut microbiome is crucial for lipid metabolism in humans and animals. However, how specific gut microbiota and their associated metabolites impact fat deposition remains unclear. In this study, we demonstrated that the colonic microbiome of lean and obese pigs differentially contributes to fat deposition, as evidenced by colonic microbiota transplantation experiments. Notably, the higher abundance of Bifidobacterium pseudocatenulatum was significantly associated with lower backfat thickness in lean pigs. Microbial-derived lithocholic acid (LCA) species were also significantly enriched in lean pigs and positively correlated with the abundance of B. pseudocatenulatum. In a high-fat diet (HFD)-fed mice model, administration of live B. pseudocatenulatum decreased fat deposition and enhances colonic secondary bile acid biosynthesis. Importantly, pharmacological inhibition of the bile salt hydrolase (BSH), which mediates secondary bile acid biosynthesis, impaired the anti-fat deposition effect of B. pseudocatenulatum in antibiotic-pretreated, HFD-fed mice. Furthermore, dietary LCA also decreased fat deposition in HFD-fed rats and obese pig models. These findings provide mechanistic insights into the anti-fat deposition role of B. pseudocatenulatum and identify BSH as a potential target for preventing excessive fat deposition in humans and animals.

  • CORRESPONDENCE
    Shu Lou, Guirong Zhu, Changyue Xing, Shushu Hao, Junyan Lin, Jiayi Xu, Dandan Li, Yifei Du, Congbo Mi, Lian Sun, Lin Wang, Meilin Wang, Mulong Du, Yongchu Pan