兴义矮脚鸡肠道微生物与其体质量的关联性

2024-08-24 00:00:00龙小霞杨永鲜吕玉新吴古荣张欢杨金春汪潘杨胜宏王忠
南方农业学报 2024年2期
关键词:低体鸡体兴义

摘要:【目的】探究不同体质量分组兴义矮脚鸡肠道菌群及其网络结构特征,鉴定出与鸡体质量相关的潜在生物标志物,为养鸡业促生长益生菌的发掘提供理论依据。【方法】从同批次的110羽兴义矮脚母鸡群体(16周龄)中选取体质量最高的20羽组成HWC组(1.12±0.05 kg),体质量最低的20羽组成LWC组(0.74±0.05 kg),采集肛门粪便样品检测16S rRNA序列V3~V4可变区,利用Mothur进行肠道菌群Alpha多样性,采用基于未加权UniFrac距离的主坐标分析(PCoA)评估肠道菌群Beta多样性,通过SparCC算法构建肠道菌群互作网络,并应用LEfSe分析鉴定与兴义矮脚鸡体质量相关的肠道微生物。【结果】从40份兴义矮脚鸡粪便样品中共获得3145511条Clean reads,经DADA2聚类分析得到22297个扩增子序列变异体(ASVs)。在门分类水平上,兴义矮脚鸡肠道菌群中相对丰度排名前5的菌门包括厚壁菌门、变形菌门、拟杆菌门、放线菌门和热脱硫杆菌门,对应的相对丰度分别为67.54%、12.24%、11.28%、2.39%和1.26%;在属分类水平上,相对丰度排名前5的菌属分别为乳酸杆菌属、罗斯氏菌属、利吉拉杆菌属、拟杆菌属和志贺杆菌属,对应的相对丰度分别为23.35%、14.86%、5.44%、4.63%和2.77%。LWC组兴义矮脚鸡肠道菌群稳定性指数为3.95%,HWC组的为12.22%,且LWC组的网络复杂性(5.57%)也低于HWC组(7.70%),推测低体质量兴义矮脚鸡肠道菌群复杂性和稳定性的下降与其体质量较低有关。兴义矮脚鸡肠道菌群互作网络中重要性评分排名前5的枢纽菌群分别是植物乳杆菌、乳球菌、瘤胃球菌属扭链群、粪杆菌属和理研菌科_RC9_菌群。LEfSe分析发现,有18个ASVs的相对丰度在HWC组和LWC组间呈显著差异(LDAgt;2,Plt;0.05),其中,12个ASVs表现为在LWC组的相对丰度高于HWC组,6个ASVs表现为在HWC组的相对丰度高于LWC组。【结论】肠杆菌科、支原体属等有害菌相对丰度的上升会降低鸡肠道菌群互作网络稳定性和复杂性,且与兴义矮脚鸡的低体质量有关。乳酸杆菌、理研菌科_RC9_菌群、梭状芽孢杆菌等是影响兴义矮脚鸡体质量的潜在关键菌群,可作为兴义矮脚鸡体质量关联的候选生物标志物。

关键词:兴义矮脚鸡;肠道菌群;体质量;互作网络;16S rRNA

中图分类号:S831.89文献标志码:A文章编号:2095-1191(2024)02-0311-12

Correlation between intestinal microbiota and body weight of Xingyi bantam chickens

LONG Xiao-xia',YANG Yong-xian¹,LYU Yu-xin²,WU Gu-rong²,ZHANG Huan²,YANG Jin-chun²,WANG Pan³,YANG Sheng-hong',WANG Zhong1\"

('College of Animal Sciences,Guizhou University/Key Laboratory of Animal Genetics,Breeding and Reproduction in the Plateau Mountainous Region,Ministry of Education,Guiyang,Guizhou 550025,China;²Agriculture and Rural Affairs Bureau of Xingyi City,Xingyi,Guizhou 562400,China;Guizhou LaoheiniAgriculture and Animal Husbandry Co.,Ltd.,Xingyi,Guizhou 562400,China)

Abstract:[Objective】To explore the intestinal microbiota and network structure characteristics of Xingyi bantam chickens with different body weight groups,and identify potential biomarkers related to body weight,providing atheo- retical basis for the discovery of growth-promoting probiotics in the chicken industry.【Method]Selected 20 chickens each with the highest and lowest body weight from 110 hens(16 weeks old)to construct the high body weight group(HWC,1.12±0.05 kg)and the low body weight group(LWC,0.74±0.05 kg).Collected anal stool samples to detect the V3-V4 variable region of the 16S rRNA sequence.Mothur was used to measure the alpha diversity of the intestinal microbiota Principal coordinate analysis(PCoA)based on the unweighted UniFrac distance was used to evaluate the beta diversity of the intestinal microbiota.And microbial interaction network was constructed using the SparCC algorithm for the sequen- cing data.Finally,LEfSe analysis was used to identify intestinal microbes related to Xingyi bantam chicken body weight.【Result】The results showed that atotal of 3145511 Clean reads were obtained from 40 Xingyi bantam chicken stoo samples,and 22297 amplicon sequence variants(ASVs)wereobtained through DADA2 cluster analysis.The intestinal microbiota of Xingyi bantam chickens mainly included Firmicutes(relative abundance 67.54%),Proteobacteria(relative abundance 12.24%),Bacteroidota(relative abundance 11.28%),Actinobacteriota(relative abundance 2.39%),and De- sulfobacterota(relative abundance 1.26%)at the phylum level,At thegenus level,mainly included Lactobacillus(rela- tive abundance 23.35%),Rothia(relative abundance14.86%),Ligella(relative abundance 5.44%),Bacteroides(rela-tive abundance 4.63%),and Shigella(relative abundance 2.77%).The stability index of the intestinal microbiota of Xing-yi bantam chickens in the LWC group was 3.95%,and that of the HWC group was 12.22%.Interaction network analysis found that the complexity of gut microbial network in LWC(5.57%)was lower than thatof HWC(7.70%).It was specu-lated that the decrease of intestinal microbiota complexity and stability of low body weight Xingyi bantam chickens was related to the lower body weight.The top 5 hub microbiota with the highest importance scores in the interaction network of intestinal mircribuita of Xingyi bantam chickens were Lactobacillus plantarum,Lactococcus,Ruminococcus twisted chain group,Faecalibacterium,and Rikenellaceae_RC9_gut_group.In addition,LEfSe analysis found that relative abun-dance of 18 ASVs were significantly different between the two groups(LDAgt;2,Plt;0.05).Among them,12 ASVs showed higher relative abundance in the LWC group than HWC group.The relative abundance of 6 ASVs in the HWC group was higher than that in the LWC group.【Conclusion]The increase in the relative abundance of harmful bacteria such as Enterobacteriaceae and Mycoplasma can reduce the stability and complexity ofthe intestinal microbial interaction network in Xingyi bantam chickens,which may be thereason for the low body weight.Lactobacillus,Rikenellaceae RC9_gut_group,Clostridium_sensu_stricto_1 are the potential key microbiota affecting the body weight of Xingyi ban-tam chickens,they can beused as acandidate biomarker associated with body weight of Xingyi bantam chickens

Key words:Xingyi bantam chicken;intestinal microbiota;body weight;interaction network;16S rRNA

Foundation items:National Natural Science Foundation of China(32260829);Guizhou Science and Technology Plan Project(QKHPTRC[2019]5606,QKHZC〔2022〕Zhongdian 34);University Natural Science Research Project of Guizhou Department of Education(Qianjiaoji[2022]061)

0引言

【研究意义】体质量是肉鸡重要的复杂经济性状之一(Jin et al.,2015;李莹等,2022),已有研究报道许多与其相关的基因及数量性状基因座位(QTL)(Zhang et al.,2020),但现有的研究局限于基因组层面,并未完全解释体质量的表型变异机理。随着研究的不断深入,已证实鸡的体质量除了受宿主遗传因素影响外,还受性别、营养状况、疾病、饲养管理方式及肠道菌群等多种因素的影响(Ariza et al.,2021;Cui et al.,2021;Memon et al.,2021)。胃肠道是食物消化和营养吸收的主要场所,含有高度多样性和动态性的微生物区系(Du et al.,2020),且这些肠道菌群对鸡的生长具有重要作用(Zhang et al.,2022b;Wang et al.,2023)。因此,开展不同体质量分组的鸡肠道菌群特征及其差异微生物研究,对解析鸡体质量性状的形成机制具有重要意义。【前人研究进展】肠道菌群是指人类或动物胃肠道内广泛存在的微生物区系,可促进宿主对营养物质的消化吸收、提高免疫能力及预防病原菌侵入(Kogut,2022;Thiam et al.,2022;Zhang et al.,2022a),宿主的品种、性别、生长阶段、肠道部位等因素均会影响其组成和结构特征(Yan et al.,2017;Pandit et al.,2018;Cui et al.,2021;张兆杰等,2023)。Wei等(2013)研究表明,鸡不同胃肠道部位的菌群组成和多样性存在明显差异。Han等(2016)通过研究鸡嗦囊、回肠和盲肠菌群组成特征及其与体质量的关系,发现嗦囊和回肠的菌群多样性与鸡体质量相关,盲肠菌群多样性虽然最高,但与其体质量无相关性。Cui等(2021)研究发现,在相同的遗传背景和日粮条件下,不同性别的鸡盲肠菌群组成差异显著,公鸡盲肠富集了拟杆菌、巨单胞菌和乳酸乳球菌等特征菌群,而母鸡盲肠富集了瘤胃球菌和粪肠球菌等,这些性别依赖菌群可能通过聚糖和脂类代谢功能而影响鸡的生长。至今,已有研究鉴定出影响鸡生长性能的关键肠道微生物,并对其影响机制进行初步探究。Zhang等(2022b)通过比较高/低体质量组鸡盲肠菌群组成,发现乳微杆菌和鞘氨醇单胞菌属是高体质量鸡肠道的优势菌群,而斯莱克氏菌属是低体质量鸡肠道的优势菌群,这些微生物通过调控脂肪代谢而影响鸡的生长速度。Zhang等(2022a)研究表明,肠道中的志贺杆菌等有害菌相对丰度过高会诱导炎症细胞因子产生,通过TLR4介导的MyD88和NF-kB信号通路引起肠道炎症,进而影响鸡的生长性能;肠道中潜在有益菌的存在则促进抗炎因子释放而改善鸡的生长性能。Wang等(2023)研究发现,盲肠中存在的高丰度磷球菌属可促进血清共轭亚油酸类代谢物代谢,从而提高鸡的体质量。此外,肠道菌群可作为营养干预的靶点用于提高动物生产性能,而粪便微生物移植(FMT)是重塑受干扰肠道菌群的有效方法(Metzler-Zebeli et al.,2019)。有益菌群的植入可增加微生物多样性和优势菌群丰度(Glendinning et al.,2022),重塑肠道微生态区系,通过平衡Th17/Treg细胞而提高鸡的生长性能(Ma et al.,2023);尤其是早期移植乳杆菌、双歧杆菌及芽孢杆菌等有益菌群有利于促进饲料消化(Susanti et al.,2021;Zhang et al.,2022a),显著提高鸡的生长性能(Cui et al.2021)。由此可见,与鸡生长性状相关有益菌的发掘及通过肠道菌群干预提高鸡的生长性能具有良好应用前景。【本研究切入点】兴义矮脚鸡是我国贵州省重要的地方品种,因其胫短、躯体似匍匐地面而得名(叶涛等,2020),具有肉质鲜嫩、适应能力强等优良特性,深受当地消费者喜爱;但兴义矮脚鸡生长缓慢,上市周期长,影响养殖户的经济效益(张福平等,2011)。至今,有关兴义矮脚鸡体质量性状方面的研究相对较少,肠道菌群结构及多样性与其体质量是否存在关联也有待进一步探究。【拟解决的关键问题】从同批次的16周龄兴义矮脚鸡群体中筛选并构建高体质量(HWC)组和低体质量(LWC)组,基于微生物16S rRNA测序技术,通过微生物互作网络分析和线性判别分析(Linear discriminant analysis with effect sizeestimation,LEfSe)探究不同体质量分组兴义矮脚鸡肠道菌群及其网络结构特征,鉴定出与鸡体质量相关的潜在肠道菌群标志物,为养鸡业促生长益生菌的发掘提供理论依据。

1材料与方法

1.1试验动物及样品采集

试验所用兴义矮脚鸡于2022年6—12月饲养于贵州大学科研鸡场。为减少性别因素对研究结果的影响,试验鸡群选取生理状态良好且一致的健康兴义矮脚母鸡,共110羽。批次、饲料、栋舍及饲喂方法等条件均保持一致。饲养密度:0~4周16羽(公母混养);5~10周8羽(公母混养);11~16周1羽(单独饲养)。每日6:00和17:00各饲喂1次,自由饮水,每日采光时间大于16h;鸡舍采用自然通风降温,温度为15~25℃,湿度为60%~65%。饲料为定制商品饲料,具体配方及营养水平(代谢能12.8 MJ/kg)见表1。粪便样品采集前1个月内禁止服用抗生素类药物。饲养至16周龄时称重,选取体质量最高的20羽组成HWC组,体质量最低的20羽组成LWC组。采用无菌棉签拭子于鸡肛门采集粪便样品,样品采集完成后将棉签头置入灭菌EP管,立即置于干冰中运回实验室,-80℃冰箱保存备用。动物试验经贵州大学动物伦理委员会批准,批准号EAEGZU-2022-T050。

1.2肠道微生物16S rRNA测序及数据预处理

采用Magnetic Soil and Stool DNA Kit[天根生化科技(北京)有限公司]提取粪便微生物DNA,以NanoDrop 1000和0.8%琼脂糖凝胶电泳测定DNA浓度及纯度,合格样品送至上海百趣生物医学科技有限公司测序。使用引物338F(5'-ACTCCTACGG GAGGCAGCAG-3')和806R(5'-GGACTACHVGGG TWTCTAAT-3')对16S rRNA序列V3~V4可变区进行扩增。PCR反应体系10.0 μL:DNA模板50 ng,KOD FX Neo缓冲液5.0 μL,KOD FX Neo 0.2 μL,dNTP 2.0 μL,上、下游引物(10 μmol/L)各0.3 μL,ddH₂O补足至10.0 μL。扩增程序:95℃预变性5 min;95℃1 min,50℃1 min,72℃1 min,进行15个循环;72℃延伸7 min。使用VAHTS DNA清洁珠(南京诺唯赞生物科技股份有限公司)纯化第1轮PCR扩增产物,然后在40.0μL反应体系中进行第2轮PCR:第1轮PCR扩增产物10.0μL,2×Phu-sion HF 20.0μL,上、下游引物(10 μmol/L)各1.0μL,ddH₂O 8.0μL。扩增程序:98℃预变性30 s;98℃10s,65℃30s,72℃30s,进行100个循环;72℃延伸5 min。PCR扩增产物采用Quant-iT\"dsDNAHS试剂(赛默飞世尔科技公司)进行定量分析,在IIlumina HiSeq 2500平台(2×250 bp对末端)上完成16S rRNA高通量测序。

采用Trimmomatic v0.33对原始数据进行质量控制(Bolger et al.,2014),通过Cutadapt v1.9.1识别并去除adapter序列;用FLASHv1.2.11完成双端Reads拼接(Magoě and Salzberg,2011),并运用UCHIME v8.1去除嵌合体(Edgar et al.,2011)以获得Clean reads。利用QIIME2中的DADA2进行序列聚类分析以得到扩增子序列变异体(Ampliconsequencevari ants,ASVs)(Callahan et al.,2016),以0.005%为阈值对ASVs进行过滤(Bokulich et al.,2013),然后在SILVA数据库(http://www.arb-silva.de/)中对ASVs代表序列进行比对注释(Quast et al.,2013)。

1.3统计分析

1.3.1肠道菌群多样性分析利用Mothur计算ASVs相对丰度并进行菌群Alpha多样性(Schloss et al.,2009);采用基于未加权UniFrac距离的主坐标分析(PCoA)评估肠道菌群Beta多样性(Lozupone and Knight,2005),结果的可视化和统计分析通过R语言完成。

1.3.2肠道菌群互作网络构建相对丰度gt;0.05%的菌群类别,根据SparCC算法构建细菌互作网络(Friedman and Alm,2012)。通过PCIT算法计算菌属水平或分类单元ASVs间的相关性(Reverter and Chan,2008);细菌(节点)间的相互作用采用绝对稀疏相关系数表示,相关系数gt;0.65的成对菌属或分类单元ASVs纳入肠道菌群互作网络构建,然后以Cytoscape 3.7.1进行可视化处理及计算网络拓扑特征(Lopes et al.,2010;Smoot et al.,2011)。以互作网络中负相关(竞争性)数量占总相关数量的百分比作为衡量共生网络稳定性的指标(Coyte et al.,2015;Hernandez et al.,2021),互作网络复杂性则以每个点连接线的平均数作为衡量指标(Bader and Hogue, 2003)。在微生物生态网络中,枢纽菌群是指与其他微生物高度连接,单独或在菌群模块中对互作网络产生较大影响的菌群(Banerjee et al.,2018;Angulo et al.,2019)。本研究采用Cytoscape中cytoHubba插件的马修斯相关系数(Matthews correlation coefficient,MCC)算法进行枢纽菌群鉴别(Chin et al.,2014),并对各菌群在互作网络中的重要性进行评分。

1.3.3与兴义矮脚鸡体质量相关微生物类别鉴定通过在线分析工具(http:/huttenhower.sph.harvard.edu/galaxy)对LEfSe分析鉴别组间差异的菌群进行类别鉴定。

2结果与分析

2.1兴义矮脚鸡分组情况

本研究所用试验动物为同批次的16周龄兴义矮脚母鸡,其中,HWC组兴义矮脚鸡体质量平均值为1.12±0.05 kg,LWC组兴义矮脚鸡体质量平均值为0.74±0.05 kg,两组间差异显著(Wilcoxon检验,Plt;0.05)。

2.2兴义矮脚鸡肠道菌群轮廓

对肠道菌群的16S rRNA序列V3~V4可变区进行测序,结果从40个样品中共获得3145511条Clean reads,每个样品平均产生78638条Clean reads。对Clean reads进行聚类分析,共得到22297个ASVs,在SILVA数据库中注释到40个菌门,其中8个菌门在所有样品中均被检测到。有5个菌门的相对丰度gt;1.00%,平均相对丰度排名前5的菌门包括厚壁菌门(Firmicutes)、变形菌门(Proteobacteria)、拟杆菌门(Bacteroidota)、放线菌门(Actinobacteriota)和热脱硫杆菌门(Desulfobacterota),对应的相对丰度分别为67.54%、12.24%、11.28%、2.39%和1.26%。为探究分类学水平的组成结构,筛选出可注释到属分类水平的前50个菌属构建兴义矮脚鸡肠道菌群分布图,其中,相对丰度较高的5个菌属分别为乳酸杆菌属(Lactobacillus)、罗斯氏菌属(Romboutsia)、利吉拉杆菌属(Ligilactobacillus)、拟杆菌属(Bacteroides)和志贺杆菌属(Escherichia_Shigella),对应的相对丰度分别为23.35%、14.86%、5.44%、4.63%和2.77%。兴义矮脚鸡肠道微生物分类学水平组成轮廓如图1所示。

2.3不同体质量兴义矮脚鸡肠道菌群多样性比较

为探究不同体质量鸡肠道菌群多样性是否存在差异,对HWC组和LWC组兴义矮脚鸡肠道菌群多样性进行比较分析,结果发现,PD指数(图2-A)、Chaol指数(图2-B)及其他指数在两组间均无显著差异(Pgt;0.05,下同)。基于未加权UniFrac距离的PCoA分析结果显示,HWC组兴义矮脚鸡肠道菌群呈现一定的聚集效应,尤其是在第2坐标轴(PCoA2)上区分较明显(图2-C)。此外,从门分类水平上对HWC组和LWC组兴义矮脚鸡肠道菌群组成进行差异比较分析,但未鉴定到在2组兴义矮脚鸡肠道菌群相对丰度呈显著差异的菌门(图2-D)。

2.4不同体质量兴义矮脚鸡肠道菌群共生网络特性

为探究HWC组和LWC组兴义矮脚鸡肠道菌群网络结构是否存在差异,选取相对丰度gt;0.05%的ASVs分别构建HWC组和LWC组兴义矮脚鸡肠道菌群互作网络,并对网络拓扑结构特征(节点、边数、稳定性和复杂性)进行计算。结果表明,HWC组和LWC组兴义矮脚鸡肠道菌群中分别有233和213个ASVs可用于互作网络构建,互作网络指数见表2。对2组兴义矮脚鸡肠道菌群互作网络稳定性进行比较,结果发现,LWC组兴义矮脚鸡肠道菌群稳定性指数为3.95%,HWC组的为12.22%,表明低体质量的兴义矮脚鸡肠道菌群互作网络稳定性较低,对外界扰动干扰的抵抗力更差。互作网络复杂性可用每个节点所连接线的平均边数表示,LWC组兴义矮脚鸡肠道菌群互作网络复杂性(5.57%)低于HWC组兴义矮脚鸡(7.70%)。由此推测,低体质量兴义矮脚鸡肠道菌群复杂性和稳定性的下降与其体质量较低有关。

2.5不同体质量兴义矮脚鸡肠道菌群互作网络中的枢纽菌群

采用MCC算法鉴定兴义矮脚鸡肠道菌群互作网络中的枢纽菌群,并对每种菌群在肠道菌群互作网络中的重要性进行评分,结果表明,兴义矮脚鸡肠道菌群互作网络中重要性评分排名前5的枢纽菌群分别是植物乳杆菌(Lactiplantibacillus)、乳球菌(Lac-tococcus)、瘤胃球菌属扭链群(Ruminococcus_torques_group)、粪杆菌属(Faecalibacterium)和理研菌科_RC9_菌群(Rikenellaceae_RC9_gut_group)(图3)。同时,对HWC组和LWC组兴义矮脚鸡肠道菌群互作网络中的枢纽ASVs进行鉴定,重要性评分排名前20的枢纽ASVs见表3,发现2组间存在共同的ASVs,包括ASV344 g弓形杆菌属(ASV344 g Arco-bacter)、ASV29 g芽孢杆菌(ASV29 g Bacillus)、ASV45 s Helicobacter sp.UNSWMCSpl、ASV6 g 金氏乳杆菌(ASV 6 g Companilactobacillus)、ASV30 g_unclassified_Eubacterium_coprostanoligenes_group 和ASV36 g乳酸杆菌(ASV36 g Lacticaseibacillus)。

尽管有些不是相同的ASVs,但属于相同的属,如注释为植物乳植杆菌的菌群在HWC组和LWC组中均占据重要位置,对应的重要性评分排序分别为第1和第2,故推测这些肠道菌群是HWC组和LWC组兴义矮脚鸡肠道共同的枢纽菌群,与兴义矮脚鸡体质量无关,即不参与兴义矮脚鸡生长相关生理过程的调控。此外,不同体质量组兴义矮脚鸡肠道菌群中存在特定的枢纽菌群。在LWC组兴义矮脚鸡肠道菌群互作网络中发现的肠杆菌科(Enterobacteria-ceae)是极其重要的枢纽菌群,重要性评分排名前20的枢纽ASVs中ASV111、ASV35和ASV32均注释为肠杆菌科,其重要性评分排序分别为第1、4和6;在LWC组兴义矮脚鸡肠道菌群中还发现支原体属(Mycoplasma)。但这2种菌群在HWC组兴义矮脚鸡肠道菌群互作网络中重要性评分排名前20的枢纽菌群中均未发现,可能是对兴义矮脚鸡的生长具有负向抑制作用。阿克曼菌属(Akkermansia)是富集在HWC组兴义矮脚鸡肠道菌群互作网络中的重要枢纽菌群,而在LWC组兴义矮脚鸡肠道中未发现,提示阿克曼菌属可能对兴义矮脚鸡的生长具有促进作用。

2.6与兴义矮脚鸡体质量相关的特定肠道菌群鉴定结果

为鉴定与兴义矮脚鸡体质量相关的肠道菌群,采用LEfSe对质控后的ASVs进行组间差异分析,结果(图4)共鉴定出18个ASVs的相对丰度在HWC组与LWC组兴义矮脚鸡肠道菌群中呈显著差异(LDAgt;2,Plt;0.05,下同)。其中,在LWC组兴义矮脚鸡肠道菌群中鉴定到12个ASVs的相对丰度显著高于HWC组兴义矮脚鸡,且这些ASVs主要隶属于梭状芽孢杆菌纲(Clostridia)和芽孢杆菌纲(Bacilli),其中,5个ASVs注释为梭状芽孢杆菌纲,包括梭菌科(Clostridiaceae)的ASV25 g梭状芽孢杆菌(ASV25g_Clostridium_sensu_stricto_1)、ASV4243 g梭状芽孢杆菌(ASV4243 g Clostridium_sensu_stricto_1)和ASV 4117 g分节丝状菌(ASV4117 g Candidatus Arthromitus),以及ASV3348 g CHKCI001和ASV472_g_Faecalitalea;另有6个ASVs注释为芽孢杆菌纲,包括ASV6772 g芽孢杆菌(ASV6772 g Bacillus)、ASV6771 g芽孢杆菌(ASV6771 g Bacillus)、ASV 8960 g未分类杆菌(ASV8960 g unclassified Bacil-li)、ASV43 g未分类杆菌(ASV43 g unclassified_Bacilli)和ASV14683 g乳杆菌(ASV 14683 g Lacto-bacillus),以及ASV183 g未分类毛螺菌科(ASV183 g_unclassified_Lachnospiraceae)。在HWC组兴义矮脚鸡肠道菌群中有6个ASVs的相对丰度显著高于LWC组兴义矮脚鸡,包括注释为乳酸杆菌的ASV17和ASV8,以及ASV3299 g卡氏杆菌(ASV 329 g 9 Gallibacterium)、ASV1444 g理研菌科RC9_菌群(ASV1444 g Rikenellaceae RC9 gut group)、ASV38 g未分类毛螺菌科(ASV38_unclas-sified_Lachnospiraceae)和ASV314 g未分类颤螺旋菌科(ASV314 g unclassified Oscillospiraceae)。

3讨论

本研究通过探究与兴义矮脚鸡体质量相关的肠道菌群网络结构特征及特定菌群差异,发现乳酸杆菌、乳球菌、瘤胃球菌属扭链群、粪杆菌属、理研菌科_RC9_菌群等是兴义矮脚鸡肠道菌群互作网络中重要的枢纽菌群。LEfSe分析鉴定到ASV25 g梭状芽孢杆菌、ASV4243 g梭状芽孢杆菌及ASV4117 g_分节丝状菌等12个与低体质量有关的ASVs,以及ASV17 g乳杆菌、ASV3299 g卡氏杆菌和ASV1444 g理研菌科_RC9_菌群等6个与高体质量有关的ASVs。至今,在畜禽胃肠道菌群上的研究主要聚焦于单种细菌的鉴定及其功能研究(Wen et al.,2019;Fan et al.,2020),针对菌群互作网络的研究报道相对较少。与菌群多样性(Huttenhower et al.,2012)和肠型分析(Ramayo-Caldas et al.,2016)相比,互作网络分析能更好地展示微生物之间复杂的互作关系,更有利于揭示网络成员共享的生态位空间(马越等,2018;Angulo et al.,2019)。本研究通过比较低体质量和高体质量兴义矮脚鸡肠道菌群互作网络拓扑学特征,结果发现低体质量组兴义矮脚鸡肠道菌群互作网络的稳定性和复杂性更低,提示低体重兴义矮脚鸡菌群互作网络对外界扰动因素的抵抗力较差(Coyte et al.,2015;Hernandez et al.,2021)。外界扰动因素导致鸡肠道微生态系统紊乱,影响宿主的营养吸收及消化道微生物防御功能,进而导致兴义矮脚鸡生长发育受阻,与其体质量较低有密切关系。

在微生物生态网络中,枢纽菌群是指与其他菌群高度连接的菌群,其单独或参与菌群模块对微生物生态网络结构产生较大影响,在菌群互作网络中发挥关键而独特的作用,移除后会导致微生物生态网络结构及其功能发生巨大变化(Banerjee et al.,2018;Angulo et al.,2019)。本研究在菌群互作网络基础上,鉴定了兴义矮脚鸡特征性的枢纽菌群,与贵州省其他几个品种地方鸡肠道枢纽菌群(Wang et al.,2023)相比,存在明显差异,包括乳酸杆菌和Mailhella仅在兴义矮脚鸡肠道菌群中发现,也证实了品种对网络枢纽菌群的重要影响。本研究还发现有几种菌群在低体质量兴义矮脚鸡肠道中占据重要位置,如重要性评分第1、4和6的ASVs均注释为肠杆菌科,主要包括克雷伯氏菌属(Klebsiella)、大肠杆菌一志贺菌属(Escherichia_Shigella)和肠杆菌属(Enterobacter),其中大肠杆菌一志贺菌属是引起鸡严重消化道疾病的主要病原(Olsen et al.,2016)。在低体质量兴义矮脚鸡中发现支原体属也是其肠道菌群互作网络中的特异菌群,而支原体属是引起鸡呼吸道及关节炎症的主要病原菌,感染后易导致机体消瘦、体质量下降等问题(Adeyemi et al.,2018)。因此,推测某些病原体或条件致病菌丰度升高,成为肠道菌群互作网络中的枢纽菌群时会导致机体肠道生态网络结构的改变,进而抑制其生长性能。此外,在高体质量兴义矮脚鸡肠道菌群中发现一些重要性评分排名靠前的枢纽菌群,如阿克曼菌属。越来越多的研究表明阿克曼菌是一种肠道有益菌,其中嗜黏蛋白阿克曼菌(A.muciniphila)具有促进肠道屏障完整性、调节免疫反应及抑制炎症功能(Cheng and Xie,2021),可缓解患者的肠炎症状(Yang et al.,2019),但阿克曼菌属促进兴义矮脚鸡生长的作用机制还有待进一步探究。

LEfSe分析是一种发现高纬度数据生物标志物(微生物、通路、基因)的重要工具(Segata et al.,2011)。本研究通过LEfSe分析对高体质量和低体质量兴义矮脚鸡的特定肠道菌群进行类别鉴定,结果发现,高体质量兴义矮脚鸡肠道富集的特定ASVs注释到乳酸杆菌属、卡氏杆菌属、理研菌科_RC9_菌群等,而低体质量兴义矮脚鸡肠道富集的特定ASVs注释到梭状芽孢杆菌属、分节丝状菌属、芽孢杆菌属、Fae-calitalea、乳酸杆菌属和福涅雷拉菌属(Fournierella)等。其中,乳酸杆菌属是目前广泛关注的潜在益生菌,如罗伊氏乳酸杆菌(L.reuteri)是研究相对较充分的益生菌,一般认为其有助于对抗感染、减少炎症及改善肠道健康。但值得注意的是,在低体质量兴义矮脚鸡肠道中也富集到ASV14683 g乳杆菌,可能是不同菌株具有不同的生理作用(Jones et al.,2013;Mehling and Busjahn,2013)。理研菌科_RC9_菌群被认为是一种益生菌,能以琥珀酸、乳酸或乙酸为底物,产生丙酸和丁酸而对宿主产生有益作用(Peterset al.,2018;Hosomi et al.,2022)。此外,在低体质量兴义矮脚鸡肠道中发现的梭状芽孢杆菌属于Clostridium cluster I,具有潜在的致病能力(Lop-etuso et al.,2013;Dohrmannet al.,2015)。分节丝状菌(Segmented filamentous bacteria,SFB)也称为Candidatus arthromitus,人体肠道的SFB与免疫调控和疾病症状等存在一定相关性(陈华海等,2019),也有研究发现SFB在自身免疫性关节炎小鼠肠道中的相对丰度升高(Zhao et al.,2023)。CHKCI001是厌氧芽孢梭菌(Clostridiales bacterium)中的一种,被认为可能具有促炎作用(Chen et al.,2022)。上述这些特征性菌群在鸡肠道中的相对丰度升高可能会通过诱发疾病、促进炎症等作用而致使鸡的生长速度减慢。但在高体质量兴义矮脚鸡肠道中也发现致病菌——卡氏杆菌(Krishnegowda et al.,2020),其富集情况与高体质量形成的关联性也有待进一步探究。

4结论

肠杆菌科、支原体属等有害菌相对丰度的上升会降低鸡肠道菌群互作网络稳定性和复杂性,且与兴义矮脚鸡的低体质量有关。乳酸杆菌、理研菌科_RC9_菌群、梭状芽孢杆菌等是影响兴义矮脚鸡体质量的潜在关键菌群,可作为兴义矮脚鸡体质量关联的候选生物标志物。

参考文献(References):

陈华海,吴柳,唐成,王欣,尹业师.2019.人体肠道分节丝状菌SFB研究进展[J].微生物学报,59(9):1778-1785.[Chen HH,Wu L,Tang C,Wang X,Yin YS.2019.Research progressof human intestinal segmented filamen-tous bacteria[J].Acta Microbiologica Sinica,59(9):1778-1785.]doi:10.13343/j.cnki.wsxb.20190159.

李莹,何静怡,陈鹏,张丽,许欣纯,计坚,罗成龙.2022.麻黄鸡体质量与体尺性状的相关分析和主成分分析[J].河南农业科学,51(5):126-132.[LiY,He JY,Chen P,Zhang L,Xu XC,Ji J,Luo CL.2022.Correlation and principal component analysis between body mass and body measure-

ment traitsin Mahuang chickens[J].Journal of Henan Agri-cultural Sciences,51(5):126-132.]doi:10.15933/j.cnki 1004-3268.2022.05.013.

马越,王军,胡永飞,陈亮,李晶,律娜,刘飞,王黎明,封雨晴,朱宝利.2018.肠道微生物菌群共存网络的构建与分析[J].微生物学报,58(11):2011-2019.[MaY,Wang J,Hu YF,Chen L,LiJ,LüN,Liu F,Wang LM,Feng YQ,Zhu BL.2018.Construction and analysis of co-0ccurrence network in the gut microbiome[J].Acta Microbiologica Sinica,58(11):2011-2019.]doi:10.13343/j.cnki.wsxb.20170614.

叶涛,吴古荣,李辉,王姣,罗韦,杨金春.2020.*兴义矮脚鸡’与‘贵州矮脚黄鸡’杂交试验[J].甘肃农业大学学报,55(6):34-39.[Ye T,WuGR,LiH,Wang J,Luo W,Yang JC.2020.Study on hybridization between Xingyi bantam and Guizhou yellow chicken[J].Journal of Gansu Agricul-tural University,55(6):34-9.]doi:10.13432/j.cnki.jgsau.

2020.06.005.

张福平,华时尚,傅筑荫,吴志国.2011.兴义矮脚鸡矮脚性状初步研究[J].江苏农业科学,39(2):316-317.[Zhang FP,Hua SS,FuZY,Wu ZG.2011.Study on character of short shank in Xingyi bantam chicken[J].Jiangsu Agricul-tural Sciences,39(2):316-317.]doi:10.3969/j.issn.1002-1302.2011.02.109.

张兆杰,李璐璐,赵祥民,薛淑贞,姚亚莉,张家玮,唐德富,郝生燕.2023.黄芪和党参茎叶粉对肉仔鸡机体抗氧化能力和肠道健康的影响[J].甘肃农业大学学报,58(4):9-20.[Zhang ZJ,Li LL,Zhao XM,Xue SZ,Yao YL,Zhang JW,Tang DF,Hao SY.2023.Effects of Astraga-lus and Codonopsis stemand leaf powder on antioxidant capacity and gut health in broilers[J].Journal of Gansu Agricultural University,58(4):9-20.]doi:10.13432/j.cnkijgsau.2023.04.002

Adeyemi M,Bwala DG,Abolnik C.2018.Comparative eva-luation of the pathogenicity of Mycoplasma gallinaceum in chickens[J].Avian Diseases,62(1):50-56.doi:10.1637/11743-081717-Reg.1.

Angulo MT,Moog CH,Liu YY.2019.A theoretical frame work for controlling complex microbial communities[J]Nature Communications,10:1045.doi:10.1038/s41467-019-08890-y.

Ariza AG,Arbulu AA,González FJN,Baena SN,Bermejo J"VD,Vallejo MEC.2021.The study of growth and perfor-mance in local chicken breeds and varieties:A review of methods and scientific transference[J].Animals,11(9):2492.doi:10.3390/ANI11092492.

Bader GD,Hogue CWV.2003.An automated method for finding molecular complexes in large protein interaction networks[J].BMC Bioinformatics,4:2.doi:10.1186/1471-2105-4-2.

Banerjee S,Schlaeppi K,van der Heijden MG A.2018.Key-stone taxa as drivers of microbiome structure and functio-ning[J].Nature Reviews Microbiology,16:567-576.doi:10.1038/s41579-018-0024-1.

Bokulich NA,Subramanian S,Faith JJ,Gevers D,Gordon JI,Knight R,Mills DA,Caporaso JG.2013.Quality-filtering vastly improves diversity estimates from Illumina ampli-con sequencing[J].Nature Methods,10(1):57-59.doi:10.1038/nmeth.2276.

Bolger AM,LohseM,Usadel B.2014.Trimmomatic:A flexi-ble trimmer for llumina sequencedata[J].Bioinformatics,30(15):2114-2120.doi:10.1093/bioinformatics/btu170.

Callahan BJ,McMurdieP J,Rosen MJ,Han AW,Johnson AJ,Holmes SP.2016.DADA2:High-resolution sample inference from Illuminaamplicon data[J].Nature Methods,13(7):581-583.doi:10.1038/nmeth.3869.

Chen SJ,Chen CC,Liao HY,Lin YT,Wu YW,Liou JM,Wu MS,Kuo CH,Lin CH.2022.Association of fecal and plasmalevels of short-chain fatty acids with gut micro-biota and clinical severity in patients with parkinson di-sease[J].Neurology,98(8):e848-e858.doi:10.1212/WNL.0000000000013225.

Cheng D,Xie MZ.2021.A review of apotential and pro-mising probiotic candidate—Akkermansia muciniphila[J].Journalof Applied Microbiology,130(6):1813-1822.doi:10.1111/jam.14911

Chin CH,Chen SH,Wu HH,Ho CW,Ko MT,Lin CY.2014.cytoHubba:Identifying hub objects and sub-networks from complex interactome[J].BMC Systems Biology,8(S4):S11.doi:10.1186/1752-0509-8-S4-S11.

Coyte KZ,Schluter J,Foster KR.2015.The ecology of the microbiome:Networks,competition,and stability[J].Scien-ce,350(6261):663-666.doi:10.1126/science.aad2602.

Cui L,Zhang XL,Cheng RR,Ansari AR,ElokilAA,HuYF,Chen Y,Nafady AA,LiuH Z.2021.Sex differences in growth performance are related to cecal microbiota in chicken[J].Microbial Pathogenesis,150:104710.doi:10.

1016/j.micpath.2020.104710.

Dohrmann AB,Walz M,Löwen A,Tebbe CC.2015.Clos-tridium cluster Iand their pathogenic members in afull-scale operating biogas plant[J].Applied Microbiology and Biotechnology,99:3585-3598.doi:10.1007/s00253-014-6261-y.

Du WY,Deng JX,Yang ZL,Zeng LH,Yang XR.2020.Metagenomic analysis reveals linkages between cecal mi-crobiota and feed efficiency in Xiayan chickens[J].Poultry Science,99(12):7066-7075.doi:10.1016/j.psj.2020.09076.

Edgar RC,HaasB J,ClementeJC,Quince C,Knight R.2011.UCHIME improves sensitivity and speed of chimera detec-tion[J].Bioinformatics,27(16):2194-2200.doi:10.1093/bioinformatics/btr381.

Fan PX,Bian BL,Teng L,Nelson CD,Driver J,Elzo MA,Jeong KC.2020.Host genetic effects upon the early gut microbiota in abovine modelwith graduated spectrum of genetic variation[J].The ISME Journal,14(1):302-317.doi:10.1038/s41396-019-0529-2.

FriedmanJ,AlmE J.2012.Inferring correlation networks from genomic survey data[J].PLoSComputational Biology,8(9):e1002687.doi:10.1371/journal.pcbi.1002687.

Glendinning L,Chintoan-Uta C,Stevens MP,Watson M.2022.Effect of cecal microbiota transplantation between different broiler breeds on the chick fora in the first week of life[J].Poultry Science,101(2):101624.doi:10.1016/j.psj.2021.101624.

Han GG,Kim EB,Lee J,Lee JY,Jin G,Park J,Huh CS,Kwon IK,Kil DY,ChoiYJ,Kong C.2016.Relationship between the microbiota in differentsecions of the gastroin-testinal tract,and the body weight of broiler chickens[J].Springerplus,5:911.doi:10.1186/s40064-016-2604-8.

Hernandez DJ,David AS,Menges ES,Searcy CA,Afkhami ME.2021.Environmental stress destabilizes microbial networks[J].The ISME Journal,15(6):1722-1734.doi:10.1038/s41396-020-00882-x.

Hosomi K,Saito M,Park J,Murakami H,Shibata N,Ando M,Nagatake T,Konishi K,Ohno H,TanisawaK,Mohsen A,Chen YA,Kawashima H,Natsume-KitataniY,Oka Y,Shi-mizu H,Furuta M,Tojima Y,Sawane K,Saika A,Kondo S,Yonejima Y,Takeyama H,Matsutani A,Mizuguchi K,Miyachi M,Kunisawa J.2022.Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota[J].Nature Communications,13(1):4477.doi:10.1038/s41467-022-32015-7.

Huttenhower C,Gevers D,Knight R,Abubucker S,Badger JH,Chinwalla AT,Creasy HH,Earl AM,FitzGerald MG,Fulton RS,Giglio MG,Hallsworth-Pepin K,Lobos EA,Madupu R,Magrini V,Martin JC,Mitreva M,Muzny DM,Sodergren EJ,Versalovic J,Wollam AM,Worley KC,Wortman JR,Young SK,Zeng QD,Aagaard KM,Abolude O 0,Allen-Vercoe E,Alm EJ,Alvarado L,Andersen GL,Anderson S,Appelbaum E,Arachchi HM,Armitage G,Arze CA,Ayvaz T,Baker CC,Begg L,Belachew T,BhonagiriV,Bihan M,Blaser MJ,Bloom T,BonazziV,Brooks JP,Buck GA,Buhay CJ,Busam DA,Campbell JL,Canon SR,Cantarel BL,Chain PS G,Chen IMA,Chen L,Chhibba S,Chu K,Ciulla DM,Cle-mente JC,Clifton Sw,Conlan S,Crabtree J,Cutting MA,Davidovics NJ,Davis CC,DeSantis TZ,Deal C,Dele-haunty KD,Dewhirst FE,Deych E,Ding Y,Dooling DJ,Dugan SP,Dunne WM,DurkinAS,EdgarRC,Erlich RL,Farmer CN,Farrell RM,Faust K,Feldgarden M,Felix VM,Fisher S,Fodor AA,Forney LJ,Foster L,Di Fran-cesco V,Friedman J,Fredrich DC,Fronick CC,Fulton LL,Gao HY,Garcia N,Giannoukos G,Giblin C,Giovanni MY,GoldbergJM,Goll J,Gonzalez A,Griggs A,Gujja S,Haake SK,Has BJ,Hamilton HA,Harris EL,Hep-burn TA,Herter B,Hoffmann DE,Holder ME,Howarth C,HuangK H,Huse SM,Izard J,Jansson JK,Jiang HY,Jordan C,JoshiV,Katancik JA,Keitel WA,Kelley ST,KellsC,King NB,Knights D,Kong HH,Koren O,Koren S,Kota KC,Kovar CL,Kyrpides NC,La Rosa PS,Lee SL,Lemon KP,LennonN,Lewis CM,LewisL,Ley RE,LiK,Liolios K,Liu B,Liu Y,Lo CC,Lozupone CA,Lunsford RD,Madden T,Mahurkar AA,Mannon PJ,Mardis ER,Markowitz VM,Mavromatis K,McCorrison

JM,McDonald D,McEwen J,McGuire AL,McInnes P,Mehta T,Mihindukulasuriya KA,Miller JR,Minx PJ,Newsham I,Nusbaum C,O'Laughlin M,Orvis J,Pagani I,Palaniappan K,Patel SM,Pearson M,Peterson J,Podar"M,PohlC,Pollard KS,Pop M,Priest ME,Proctor LM,Qin X,Raes J,Ravel J,Reid JG,Rho M,Rhodes R,Rie-hle KP,Rivera MC,Rodriguez-Mueller B,Rogers YH,Ross MC,Russ C,Sanka RK,Sankar P,Sathirapongsa-suti JF,Schloss JA,Schloss PD,Schmidt TM,Scholz

M,Schriml L,Schubert AM,Segata N,Segre JA,Shan-non WD,Sharp RR,Sharpton TJ,Shenoy N,Sheth NU,Simone GA,SinghI,Smillie CS,SobelJ D,Sommer DD,Spicer P,SuttonG G,Sykes SM,Tabbaa DG,Thiaga-rajan M,Tomlinson CM,Torralba M,Treangen J,Truty RM,Vishnivetskaya TA,Walker J,Wang L,Wang ZY,Ward DV,Warren W,Watson MA,Wellington C,Wetter-strand KA,White JR,Wilczek-Boney K,WuYQ,Wylie KM,Wylie T,Yandava C,YeL,Ye YZ,Yooseph S,You-mans BP,Zhang L,ZhouYJ,Zhu YM,Zoloth L,Zucker JD,Biren BW,Gibbs RA,Highlander SK,Methé BA,Nelson KE,Petrosino JF,Weinstock GM,Wilson RK,

White O.2012.Structure,function and diversity of the healthy human microbiome[J].Nature,486(7402):207-214.doi:10.1016/bs.pmbts.2022.07.003.

Jin CF,Chen YJ,Yang ZQ,Shi K,Chen CK.2015.A genome-wide association study of growth trait-related single nucleotide polymorphisms in Chinese Yancheng chickens[J].Genetics and Molecular Research,14(4):15783-15792.doi:10.4238/2015.December.1.30.

Jones ML,MartoniCJ,Prakash S.2013.Oral supplementation withprobiotic L.reuteriNCIMB 30242 increases mean cir-culating 25-hydroxyvitamin D:A post hoc analysis of arandomized controlled trial[J].The Journal of Clinical En-docrinologyamp;Metabolism,98(7):2944-2951.doi:10.1210/jc.2012-4262

Kogut MH.2022.Role of diet-microbiota interactions in preci-sion nutrition of the chicken:Facts,gaps,and new concepts[J].Poultry Science,101(3):101673.doi:10.1016j.psj.2021.101673.

Krishnegowda ND,Dhama K,Mariappan AK,Munuswamy P,Iqbal YM,Tiwari R,Karthik K,Bhatt P,Reddy MR.2020.Etiology,epidemiology,pathology,and advances in diagnosis,vaccine development,and treatment of Gallibac-

terium anatis infection in poultry:A review[J].Veterinary Quarterly,40(1):16-34.doi:10.1080/01652176.2020.1712495.

Lopes CT,Franz M,Kazi F,Donaldson SL,Morris Q,Bader GD.2010.CytoscapeWeb:An interactive web-based net-work browser[J].Bioinformatics,26(18):2347-2348.doi:10.1093/bioinformatics/btq430.

Lopetuso LR,Scaldaferi F,Petito V,Gasbarrini A.2013.Commensal Clostridia:Leading players in the maintenance of guthomeostasis[J].Gut Pathogens,5:23.doi:10.1186/1757-4749-5-23.

Lozupone C,Knight R.2005.UniFrac:A new phylogenetic method for comparing microbial communities[J].Applied and Environmental Microbiology,71(12):8228-8235.doi:10.1128/AEM.71.12.8228-8235.2005.

MaZY,Akhtar M,Pan H,Liu QY,Chen Y,Zhou XX,YouY T,Shi DS,Liu HZ.2023.Fecal microbiota transplanta tion improves chicken growth performance by balancing jejunal Th17/Treg cells[J].Microbiome,11:137.doi:101186/s40168-023-01569-z.

Magoě T,Salzberg SL.2011.FLASH:Fast length adjustment"of short reads to improve genomeassemblies[J].Bioinfor-matics,27(21):2957-2963.doi:10.1093/bioinformatics/btr 507.

Mehling H,Busjahn A.2013.Non-viable Lactobacillus reuteri DSMZ 17648(PylopassTM)as anew approach to Helico-bacter pylori control in humans[J].Nutrients,5(8):3062-3073.doi:10.3390/nu5083062.

Memon FU,Yang Y,Lv F,Soliman AM,Chen Y,Sun J,Wang Y,Zhang G,Li Z,Xu B,Gadahi JA,Si H.2021.Effects of probiotic and Bidens pilosa on the performance and gut health of chicken during induced Eimeria tenella infection[J].Journal ofApplied Microbiology,131(1):425-434.doi:10.1111/jam.14928.

Metzler-Zebeli BU,Siegerstetter SC,Magowan E,Lawlor PG,O'Connell NE,Zebeli Q.2019.Fecal microbiota trans-plant from highlyfeed efficient donors affects cecalphysio-logy and microbiota in low-and high-feed efficient chic-kens[J].Frontiers in Microbiology,10:1576.doi:10.3389/fmicb.2019.01576.

OlsenR H,Bisgaard M,Christensen JP,Kabell S,Christensen H.2016.Pathology and molecular characterization of Escherichia coli associated with the avian salpingitis-peritonitis disease syndrome[J].Avian Diseases,60(1):1-7.doi:10.1637/11237-071715-Reg.1.

Pandit RJ,Hinsu AT,Patel NV,Koringa PG,Jakhesara SJ,Thakkar JR,Shah TM,Limon G,Psifidi A,Guitian J,Hume DA,Tomley FM,Rank DN,Raman M,Tirumuru-gaan KG,Blake DP,JoshiCG.2018.Microbial diversity and communitycomposition of caecal microbiota in com-mercial and indigenous Indian chickens determined using 16S rDNA amplicon sequencing[J].Microbiome,6(1):115.doi:10.1186/s40168-018-0501-9.

Peters BA,Shapiro JA,Church TR,MillerG,Trinh-Shevrin"C,Yuen E,Friedlander C,HayesR B,AhnJ.2018.A taxo-nomic signature of obesity in alarge study of American adults[J].Scientific Reports,8(1):9749.doi:10.1038/s41598-018-28126-1.

Quast C,Pruesse E,Yilmaz P,Gerken J,Schweer T,Yarza P,Peplies J,Glöckner F0.2013.The SILVAribosomal RNA gene database project:Improved data processing and web-based tools[J].Nucleic Acids Research,41(D1):D590-D596.doi:10.1093/nar/gks1219.

Ramayo-Caldas Y,Mach N,Lepage P,Levenez F,Denis C,Lemonnier G,Leplat JJ,BillonY,Berri M,Doré J,Rogel-Gaillard C,Estelle J.2016.Phylogenetic network analysis appliedto pig gut microbiota identifies an ecosystem struc-ture linked with growth traits[J].The ISME Journal,10(12):2973-2977.doi:10.1038/ismej.2016.77

Reverter A,Chan EK F.2008.Combining partial correlation and an information theory approach to the reversed engi-neering of gene co-expression networks[J].Bioinforma-tics,24(21):2491-2497.doi:10.1093/bioinformatics/btn482.

Schloss PD,Westcott SL,Ryabin T,Hall JR,Hartmann M,Hollister EB,Lesniewski RA,Oakley BB,Parks DH,Robinson CJ,Sahl JW,Stres B,Thallinger GG,van Horn DJ,Weber CF.2009.Introducing mothur:Open-source,platform-independent,community-supported soft-ware for describing and comparing microbial communities[J].Applied and Environmental Microbiology,75(23):7537-7541.doi:10.1128/AEM.01541-09.

Segata N,Izard J,Waldron L,Gevers D,Miropolsky L,Garrett WS,Huttenhower C.2011.Metagenomic biomarker dis-covery and explanation[J].Genome Biology,12(6):R60.doi:10.1186/gb-2011-12-6-r60.

Smoot ME,Ono K,Ruscheinski J,Wang PL,Ideker T.2011 Cytoscape 2.8:New features for data integration and net-workvisualization[J].Bioinformatics,27(3):431-432.doi:10.1093/bioinformatics/btq675.

Susanti D,Volland A,Tawari N,Baxter N,Gangaiah D,Plata G,Nagireddy A,HawkinsT,Mane SP,Kumar A.2021.Multi-omics characterization of host-derived Bacillus spp probiotics for improved growth performance in poultry[J]Frontiers inMicrobiology,12:747845.doi:10.3389/fmicb.2021.747845.

Thiam M,Wang Q,Sánchez ALB,Zhang J,Ding JQ,Wang H

L,ZhangQ,Zhang N,WangJ,LiQH,Wen J,Zhao GP 2022.Heterophillymphocyte ratio level modulates salmo-nella resistance,cecal microbiotacomposition and func-tional capacity in infected chicken[J].Frontiers in Immu-nology,13:816689.doi:10.3389/fimmu.2022.816689.

Wang LQ,Zhang FP,Li H,Yang SL,ChenX,Long SH,Yang SH,YangYX,Wang Z.2023.Metabolic and inflam-matory linkage of the chicken cecal microbiome to growth performance[J].Frontiers in Microbiology,14:1060458.doi:10.3389/fmicb.2023.1060458.

Wei S,Morrison M,Yu Z.2013.Bacterial census of poultry intestinal microbiome[J].PoultryScience,92(3):671-683.doi:10.3382/ps.2012-02822.

Wen CL,Yan W,Sun CJ,JiC L,Zhou QQ,Zhang DX,Zheng JX,Yang N.2019.The gut microbiota is largely independent ofhost genetics in regulating fat deposition in chickens[J].The ISME Journal,13(6):1422-1436.doi:101038/s41396-019-0367-2.

Yan W,Sun CJ,Yuan JW,Yang N.2017.Gut metagenomic analysis reveals prominent roles of Lactobacillus and cecal microbiota in chicken feed efficiency[J].Scientific Re-ports,7:45308.doi:10.1038/srep45308.

Yang Y,Zhong ZQ,Wang BJ,Xia XW,Yao WY,Huang L,Wang YL,Ding WJ.2019.Early-life high-fat diet-induced obesity programs hippocampal development and cognitive functions via regulation of gut commensal Akkermansia muciniphila[J].Neuropsychopharmacology,44(12):2054-2064.doi:10.1038/s41386-019-0437-1.

Zhang H,ShenLY,XuZC,Kramer LM,YuJQ,Zhang XY,Na W,Yang LL,Cao ZP,Luan P,Reecy JM,LiH.2020.Haplotype-based genome-wide association studies for car-cass and growth traits in chicken[J].Poultry Science,99(5):2349-2361.doi:10.1016j.psj.2020.01.009.

Zhang XL,Akhtar M,Chen Y,Ma ZY,Liang YY,Shi DS,Cheng RR,Cui L,Hu YF,Nafady AA,Ansari AR,Abdel-Kafy ES M,Liu HZ.2022a.Chicken jejunal

microbiota improves growth performance by mitigating intestinal inflammation[J].Microbiome,10(1):107.doi:10.1186/s40168-022-01299-8.

Zhang XL,Hu YF,Ansari AR,Akhtar M,Chen Y,Cheng RR,Cui L,NafadyAA,Elokil AA,Abdel-Kafy EM,Liu HZ.2022b.Caecal microbiota could effectively increase chicken growth performance by regulating fat metabolism[J].Microbial Biotechnology,15(3):844-861.doi:10.111/1751-7915.13841.

Zhao QX,Yu JD,Zhou H,Wang XY,Zhang C,Hu J,Hu YW,Zheng HP,Zeng FL,YueCC,Gu LN,Wang Z,Zhao FL,Zhou P,Zhang HZ,Huang NY,Wu WL,Zhou YF,Li J.2023.Intestinal dysbiosis exacerbates the pathoge nesis of psoriasis-like phenotype through changes in fatty acid metabolism[J].Signal Transduction and TargetedThe-rapy,8(1):40.doi:10.1038/S41392-022-01219-0.

(责任编辑 兰宗宝)

猜你喜欢
低体鸡体兴义
蛋鸡羽毛覆盖度计算及其与体温关系研究
首发抑郁症住院患者低体质量率及其影响因素
经络干预对早低体质量儿神经发育的影响
青海省大学生BMI与体质健康的关系研究①
重返三叠纪——兴义国家地质公园
乡村地理(2019年2期)2019-11-16 08:49:50
兴义万峰林
乡村地理(2018年1期)2018-07-06 10:29:08
鸡日粮中为啥要加食盐
骑行在兴义
乡村地理(2017年4期)2017-09-18 02:54:22
赏花——兴义万峰林
乡村地理(2017年4期)2017-09-18 02:53:54
鸡日粮中为啥要加食盐
今日农业(2017年12期)2017-02-01 05:21:32