微生物学报

Item	Advantage	Disadvantage	Principle	Application	References
Microbiome sequencing technology
16S rRNA	Accurate identification of the phylogenetic relationships among different microorganisms	The resolution is limited	Nine hypervariable regions and 10 conserved regions	Analysis of influencing factors and correlation studies of the microbiota	[108-110]
Metagenomics	Identification of species functions and microbial interaction mechanisms	It is associated with relatively high costs and complex analysis	Large-scale sequencing of total DNA from all microorganisms	Analysis of composition, function, and traceability research	[111-112]
Microbial source tracking
SourceTracker	Accurate identification of microorganisms from multiple sources	There is a limitation in distinguishing similar bacterial community structures	The contribution of each source is evaluated based on the similarity between the community structure and the potential source environments	For microbial source tracking analysis in food manufacturing, medical and health care, and other fields	[113-117]
Meta-SourceTracker	It enables the simultaneous analysis of the sources of microorganisms across multiple domains	It exhibits a relatively high false positive rate and limited computational efficiency	Inherit the Bayesian framework of SourceTracker	It exhibits excellent performance in the traceability of marine ecosystems and human gut microbiota	[118]
FEAST	It features high precision and enables the quantification and tracking of microbial community formation	The operation is technically challenging	Fast expectation-maximization algorithm	In the medical and clinical fields, elucidate the process of maternal-infant microbiota vertical transmission	[119-122]
SNV-FEAST	Enhance resolution and improve accuracy	It requires complex calculations and has high demands on data quality	Based on single nucleotide variation	In microbial source tracking, achieving the transition from correlational inference to causal validation	[123-124]
SourceID-NMF	Its accuracy is higher than that of FEAST and SourceTracker	It is slightly complex in computation and exhibits poor stability	The NMF algorithm is employed to estimate the source proportions using the taxon abundance data	The transmission routes and sources of pathogens, as well as the tracing of the sources and migration of microorganisms in the environment	[125]
STENSL	Higher accuracy and broader applicability	There are requirements for the quality and quantity of data	Based on machine learning and statistical models, the most probable source environment is automatically selected, and effective source search is conducted with the aid of public repositories	Analysis of the digestive system microbiota and other related aspects in humans and mice	[126-127]

Item	Advantage	Disadvantage	Principle	Application	References
Microbiome sequencing technology
16S rRNA	Accurate identification of the phylogenetic relationships among different microorganisms	The resolution is limited	Nine hypervariable regions and 10 conserved regions	Analysis of influencing factors and correlation studies of the microbiota	[108-110]
Metagenomics	Identification of species functions and microbial interaction mechanisms	It is associated with relatively high costs and complex analysis	Large-scale sequencing of total DNA from all microorganisms	Analysis of composition, function, and traceability research	[111-112]
Microbial source tracking
SourceTracker	Accurate identification of microorganisms from multiple sources	There is a limitation in distinguishing similar bacterial community structures	The contribution of each source is evaluated based on the similarity between the community structure and the potential source environments	For microbial source tracking analysis in food manufacturing, medical and health care, and other fields	[113-117]
Meta-SourceTracker	It enables the simultaneous analysis of the sources of microorganisms across multiple domains	It exhibits a relatively high false positive rate and limited computational efficiency	Inherit the Bayesian framework of SourceTracker	It exhibits excellent performance in the traceability of marine ecosystems and human gut microbiota	[118]
FEAST	It features high precision and enables the quantification and tracking of microbial community formation	The operation is technically challenging	Fast expectation-maximization algorithm	In the medical and clinical fields, elucidate the process of maternal-infant microbiota vertical transmission	[119-122]
SNV-FEAST	Enhance resolution and improve accuracy	It requires complex calculations and has high demands on data quality	Based on single nucleotide variation	In microbial source tracking, achieving the transition from correlational inference to causal validation	[123-124]
SourceID-NMF	Its accuracy is higher than that of FEAST and SourceTracker	It is slightly complex in computation and exhibits poor stability	The NMF algorithm is employed to estimate the source proportions using the taxon abundance data	The transmission routes and sources of pathogens, as well as the tracing of the sources and migration of microorganisms in the environment	[125]
STENSL	Higher accuracy and broader applicability	There are requirements for the quality and quantity of data	Based on machine learning and statistical models, the most probable source environment is automatically selected, and effective source search is conducted with the aid of public repositories	Analysis of the digestive system microbiota and other related aspects in humans and mice	[126-127]

胃肠道微生物群落的定殖规律及其溯源技术研究进展

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赵安临 , 魏冰妮 , 韩诗豪 , 陈俊材 , 赵中权 , 赵永聚 , 张小丽 ^*

微生物学报 | 综述 2026,66(3): 1026-1044

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微生物学报 | 综述 2026, 66(3): 1026-1044

胃肠道微生物群落的定殖规律及其溯源技术研究进展

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赵安临, 魏冰妮, 韩诗豪, 陈俊材, 赵中权, 赵永聚, 张小丽^*

作者信息

西南大学动物科学技术学院，重庆

Assembly rules of the gut microbiota and source tracking technologies

Anlin ZHAO, Bingni WEI, Shihao HAN, Juncai CHEN, Zhongquan ZHAO, Yongju ZHAO, Xiaoli ZHANG^*

Affiliations

College of Animal Science and Technology, Southwest University, Chongqing, China

出版时间: 2026-03-04 doi: 10.13343/j.cnki.wsxb.20250755

文章导航

摘要

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胃肠道微生物在宿主营养代谢、免疫调节和肠道屏障功能等方面发挥关键作用，因此解析胃肠道微生物的来源、定殖规律及动态演变过程，对宿主健康调控具有重要意义。本文全面回顾了胃肠道微生物群落组成的多样性和功能的多样性，重点探讨其起源与传递途径，特别强调垂直传播中母婴传递和父系遗传对群落结构及功能的影响。此外，梳理了生命各阶段的定殖规律，并总结归纳了分娩方式、哺乳、抗生素暴露等关键因子在群落组装过程中的影响。最后，整合了微生物组测序技术和常见的微生物溯源技术，为未来精准调控微生物、实现以健康为导向的胃肠道微生态重建提供科学依据和新的思路。

关键词

胃肠道微生物 / 定殖 / 父系遗传 / 母婴传递 / 溯源技术

Abstract

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Understanding the source, colonization rules, and dynamic evolution process of the gut microbiota is of significant importance for regulating host health, given its crucial role in nutrient metabolism, immune regulation, and intestinal barrier function. This article comprehensively reviews the composition and functions of the gut microbiota and explores the origins and transmission pathways, with a particular focus on the effects of maternal-infant transmission and paternal inheritance on the structure and functions of the gut microbiota. We chart microbial assembly across pivotal life stages, distill the driving factors involved in the community assembly process, and critically appraise the advances in metagenomic and source-tracking toolkits. The review provides an integrated framework for microbiome-targeted strategies aimed at reconstructing a health-oriented gut ecosystem.

Key words

gut microbiota / colonization / paternal inheritance / maternal-infant transmission / source-tracking toolkits

引用本文

赵安临, 魏冰妮, 韩诗豪, 陈俊材, 赵中权, 赵永聚, 张小丽. 胃肠道微生物群落的定殖规律及其溯源技术研究进展. 微生物学报, 2026 , 66 (3) : 1026 -1044 . DOI: 10.13343/j.cnki.wsxb.20250755

Anlin ZHAO, Bingni WEI, Shihao HAN, Juncai CHEN, Zhongquan ZHAO, Yongju ZHAO, Xiaoli ZHANG. Assembly rules of the gut microbiota and source tracking technologies[J]. Acta Microbiologica Sinica, 2026 , 66 (3) : 1026 -1044 . DOI: 10.13343/j.cnki.wsxb.20250755

正文

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胃肠道微生物作为人体“第二基因组”，在宿主营养代谢(如蛋白质、脂肪的消化吸收)、肠道屏障稳态(如上皮细胞的增殖和修复)中发挥着不可替代的作用^[1-2]。其群落紊乱与肥胖、糖尿病、自身免疫病等多种代谢疾病密切相关^[3]，还可通过脑肠轴改变血脑屏障通透性，进而诱发神经炎症^[4]。因此，解析胃肠道微生物的来源、定殖规律及动态演变过程对宿主健康调控具有重要意义。依托微生物溯源等现代生物学技术能够追踪微生物的来源、定殖规律及动态变化，为个性化益生菌干预和疾病诊疗提供科学依据。本文系统综述了胃肠道微生物的多样性、来源、传递及定殖规律，并总结了现有的微生物溯源研究方法，旨在推动胃肠道微生物研究在精准医学和健康管理中的应用，为宿主营养调控与疾病预防提供科学基础。

1 胃肠道微生物及其多样性

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1.1 组成多样性：核心菌群与稀有物种

胃肠道微生物群落是定殖于宿主消化道内的复杂生态系统，其数量从几亿到上万亿不等，与宿主种类、消化系统类型和体型密切相关^[5]。该群落主要由细菌、原虫、真菌、古细菌和病毒等构成(图1)，其中细菌在数量和功能上占据绝对主导地位。以人类为例，其肠道内的微生物总量可达10¹³-10¹⁴个^[2]，细菌约占群落总数的98%-99%，该数量级与宿主自身的细胞总数相近，凸显了其在人体生理中的重要地位^[6]。从群落结构上看，肠道细菌在门水平上以芽孢杆菌门和拟杆菌门为主，二者合计约占总体丰度的90%，放线菌门与假单胞菌门次之；在属水平上，拟杆菌属丰度最高，其他主要菌属包括双歧杆菌属、真杆菌属、梭菌属、乳杆菌属等^[7]。这些细菌在胃肠道内通过参与短链脂肪酸(short-chain fatty acids, SCFAs)合成、蛋白质分解和维生素合成等途径参与机体代谢^[8-10]。此外，稀有菌群同样具有重要生理意义。例如在健康人群肠道中占比仅0.1%-3.0%的嗜黏蛋白阿克曼氏菌(Akkermansia muciniphila)可降解黏蛋白并促进黏液再生，增强肠道屏障功能，与肥胖、糖尿病等代谢疾病呈负相关^[11]。小鼠肠道内丰度仅为0.01%-1.00%的肠道罗斯拜瑞氏菌(Roseburia intestinalis)通过产生丁酸调节肠道免疫，抑制促炎因子释放，改善宿主动脉粥样硬化^[12]。类球布劳特氏菌(Blautia coccoides)是一种在胃肠道内因亮氨酸缺乏而增殖的新细菌，通过将色氨酸代谢成吲哚-3-乙酸改善胰岛素抵抗和脂肪堆积^[13]。拜氏梭菌(Clostridium beijerinckii) R8是从绵羊瘤胃中分离出的一种新型产丁酸盐菌，通过促进乳杆菌增殖增强羔羊肠道屏障和免疫功能，显著降低腹泻率^[14]。

原虫是一种单细胞真核生物，其在胃肠道微生态系统中的作用日益受到关注。例如，芽囊原虫在健康成人中的平均定殖率约为16%，且存在区域差异(10%-50%)^[15]。反刍动物瘤胃中的纤毛虫占到微生物总量的20%-50%，并参与纤维降解^[16]。此外，毛滴虫可激活上皮炎性小体，促进白细胞介素-18 (interleukin-18, IL-18)释放以增强宿主抗感染能力^[17]。隐孢子虫属(Cryptosporidium)和蓝氏贾第鞭毛虫(Giardia lamblia)可引发严重腹泻及肠易激综合征^[18]。此外，原虫与细菌之间存在互作。内阿米巴属和G. lamblia感染可导致儿童细菌群的α多样性(Chao1指数和Shannon指数)降低，结构趋于简单化，拟杆菌门和假单胞菌门丰度增加约20%-30%，而芽孢杆菌门和疣微菌门丰度减少，普氏栖粪杆菌(Faecalibacterium prausnitzii)和罗斯氏菌属(Rothia)等产丁酸菌的相对丰度降低40%-50%^[19]。这些研究揭示了原虫在胃肠道微生态中起着不可忽视的作用。

真菌在胃肠道微生物中占比相对较少(0.01%-0.10%)，但种类丰富、动态变化且功能重要，它们与细菌互作，共同影响宿主的营养、免疫和健康^[20-21]。通过ITS2测序分析317例粪便样本发现，酵母菌属是主要优势群体，马拉色菌属和念珠菌属次之^[22]。这些真菌可能会引起胃肠道感染，也可对宿主的生长、发育和健康产生正面作用^[21]。念珠菌属通过产生消化酶促进淀粉水解为单糖为机体供能^[23]。此外，肠道菌群通过激活真菌特异性病原体识别受体和邻近机制，形成病原体防御与共生体的耐受性，参与机体的免疫反应^[24]。

古菌是在形态上类似于细菌，但在遗传和代谢特征上更接近真核生物的原核生物^[25]。由于培养条件苛刻，目前对胃肠道古菌的研究相对有限。古菌在胃肠道微生物中占比较小(约占1%)，但其在宿主生理中发挥不可忽视的作用^[25]。其中，甲烷菌占比最大，可利用氢离子产生甲烷，从而参与宿主的能量代谢^[26]。在反刍动物的胃肠道中产甲烷菌有助于维持瘤胃内厌氧环境的稳定，为其他微生物的生长和发酵创造有利条件，从而间接促进宿主的能量代谢^[26]。值得注意的是，反刍动物甲烷排放量的90%源于产甲烷菌的代谢，约占畜牧业甲烷总排放量的80%^[27]。鉴于饲料效率与甲烷产量呈负相关，通过改进饲养策略和管理措施可在提升反刍动物饲料利用率的同时有效降低甲烷排放^[26-27]。

胃肠道中的病毒以DNA和RNA病毒为主，其中噬菌体占总体的97.7%^[28]，其蛋白质外壳兼具保护遗传物质和介导对宿主细菌识别与吸附的功能^[29]。研究发现，噬菌体在宿主免疫调节和维持胃肠道微环境稳态中起着重要作用^[30]。噬菌体可通过裂解优势细菌、提供生态位促进群落多样性，且参与免疫调节与微环境稳态维持，但其侵染能力及生态功能依赖环境因素^[31]。

1.2 时空动态下的多样性：定殖与演替规律

胃肠道微生物群落的组成和功能受日龄、饮食、环境等因素调控，呈现动态演变特征，在不同生长阶段，其组成及功能差异显著^[5]。近期，运用内标质粒法对新生儿胎粪和粪便样本进行绝对定量，结果发现胎粪中的细菌丰度达到1.14×10⁷ copies/g，而出生后72 h内粪便细菌丰度则显著升高至1.59×10⁹ CFU/g，并且细菌的绝对丰度不受分娩方式的影响，揭示了新生儿肠道菌群在出生后短时间内迅速建立，且胎粪中的细菌数量处于无菌与成人肠道菌群之间的过渡状态^[32]。人类新生儿在1月龄时胃部微生物以帮助分解母乳中寡糖的双歧杆菌属和乳杆菌属为主，小肠内以双歧杆菌属和乳球菌属为主；3-6月龄添加辅食后菌群多样性增加，聚糖降解相关菌群拟杆菌门逐渐增多^[33]。1岁龄引入固体食物后，胃中双歧杆菌属相对丰度降低，而芽孢杆菌门菌群增加，小肠中乳球菌属减少，菌群趋近成人^[33]。肠型从以双歧杆菌属为主的不成熟状态向拟杆菌门为主导的成熟肠型转变^[34]。幼年期时，双歧杆菌属丰度继续下降，而发酵纤维产生SCFAs的专性厌氧菌迅速增殖^[35]。成年期大肠菌群多样性和功能趋于稳定并达到峰值，主要优势菌为拟杆菌门和芽孢杆菌门，参与复杂多糖与蛋白质的降解和维生素合成；进入衰老期，双歧杆菌属类有益菌丰度减少，而促炎性肠球菌增加，这可能是衰老期消化功能减退，更易感染肠道疾病的原因^[36]。动物模型中也观察到类似的演替规律，有研究采用转录组和扩增子等技术并构建互作网络等分析手段，解析从1日龄到90日龄山羊瘤胃黏膜微生物的定殖规律，发现其黏膜菌群呈现“病原相关-黏液降解-纤维分解”的演替路径^[9]。1日龄时以曼海姆氏菌属(Mannheimia)等条件致病菌为主，占比39.7%；到10日龄，A. muciniphila等黏液降解菌显著富集，占比达21.4%；而90日龄时则形成了以瘤胃解琥珀酸菌(Succiniclasticum ruminis)等纤维分解菌为特征的成熟群落，其占比为58.3%^[9]。

胃肠道不同区域细菌群落组成和功能也存在显著空间异质性(图2)。总体而言，消化道前端以假单胞菌门和芽孢杆菌门为主，后端则以芽孢杆菌门和拟杆菌门为主^[8,37]。具体地，小肠内主要由兼性和专性厌氧菌如拟杆菌属、梭菌属、肠杆菌属、肠球菌属、乳杆菌属和韦荣氏球菌属等组成，其功能聚焦营养消化吸收、免疫调节和胆汁酸平衡^[37]。大肠内主要以厌氧菌为主，如拟杆菌属、普雷沃氏菌属、瘤胃球菌属等，功能侧重发酵未消化的碳水化合物以产生SCFAs和维生素合成等^[8]。除此之外，同一区域食糜与黏膜微生物也存在显著差异。研究发现，小肠食糜中微生物的拷贝数是黏膜的3.3-24.0倍，且食糜的优势菌为乳杆菌属、瘤胃球菌属、真杆菌属和梭菌属等，而黏膜主要优势菌为拟杆菌属和未分类细菌等^[38]。相似地，在后肠道的研究中同样发现在微生物的绝对数量上食糜是细菌的主要载体，食糜细菌密度比黏膜高出10-100倍^[39]。在组成上，后肠道食糜的优势菌为芽孢杆菌门和假单胞菌门，而黏膜中的主要优势菌有拟杆菌属、粪杆菌属、埃希氏菌属、志贺氏菌属、沙门氏菌属、阿克曼氏菌属、副拟杆菌属等^[8,40]。此外，食糜中的细菌富集与营养物质降解相关的功能基因簇，包括纤维素、半纤维素、果胶等复杂碳水化合物的分解酶，以及SCFAs合成通路，核心作用是将食糜中的大分子营养转化为宿主可利用的小分子代谢物^[8]。黏膜中的细菌更擅长利用宿主衍生的营养物质，聚焦肠道屏障互作与宿主免疫调控相关功能，如黏蛋白降解酶、炎症调控因子和抗氧化应激相关基因等适应黏膜环境的同时起到宿主免疫监视作用^[38-39]。此外，食糜微生物代谢产生的丁酸可通过弥散作用影响黏膜微生物的群落结构，促进抗炎性黏膜菌的定殖^[41]。黏膜微生物降解黏液产生的单糖可作为食糜微生物的补充碳源，形成“食糜-黏膜”微生物的功能互补网络^[42]。这种空间分布差异可能与不同区域氧气浓度、营养可用性及宿主消化液分泌等微环境因子的梯度变化密切相关^[38]。

1.3 功能多样性：异质性与可塑性

胃肠道微生物的功能异质性是指群落功能在宿主之间、消化道不同区域以及时间维度上存在的差异^[1,43]。例如，双歧杆菌在人类肠道中能够抑制幽门螺旋杆菌等病原体，减少肠上皮细胞的凋亡并调节免疫应答，在小鼠中则可拮抗高脂肪饮食诱导的代谢疾病^[44-45]。在小肠中双歧杆菌通过糖酵解产生乳酸来降低pH，从而抑制有害菌生长、促进营养吸收；而在大肠中其发酵膳食纤维产SCFAs以维持黏膜健康^[9]。此外，双歧杆菌在母乳喂养婴儿肠道中占主导地位，可高效利用母乳寡糖；配方奶喂养婴儿肠道中则呈现双歧杆菌和拟杆菌各占约40%的格局^[46]。至成年期，双歧杆菌丰度约为15%，主要通过发酵膳食纤维产生SCFAs和ATP^[47]。相似地，拟杆菌在婴儿肠道中可促进神经发育，而在成人肠道中则主要降解难消化多糖以供能^[48]。另外，拟杆菌属在胃和小肠中主要降解蛋白质、脂肪和部分碳水化合物，而在大肠中则转向纤维发酵及SCFAs生成^[8]。

胃肠道微生物的功能可塑性是指其在宿主生理状态和外界扰动(如饮食、抗生素等)下进行动态调整和适应的特性，涵盖短期响应与长期进化^[49]。其中，饮食调控主要表现为：高纤维饮食可促进肠道中拟杆菌门和芽孢杆菌门等微生物快速增殖，并上调糖苷水解酶基因的表达以分解膳食纤维生成SCFAs^[8]；长期高糖饮食会导致假单胞菌门丰度增加、拟杆菌门丰度降低^[50]；低碳水饮食会降低双歧杆菌的丰度，但恢复适度的碳水和纤维可逆转此变化^[51]；高脂饮食可使拟杆菌门和放线菌门的比例升高，芽孢杆菌门和假单胞菌门的比例降低，诱导促炎型菌群定殖并增加代谢综合征风险，而低脂饮食则有助于抗炎菌的生长^[49]。婴儿肠道菌群会随着母乳成分和肠道氧环境的变化从需氧菌向厌氧菌群演替^[46]；老年人肠道菌群多样性下降，但部分有益菌通过功能可塑性维持代谢活性以补偿宿主消化功能的衰退^[52]。此外，益生菌或添加剂也可改变胃肠道微生物的多样性及结构组成^[53]。

胃肠道微生物的功能可塑性增强了菌群应对饮食变更、抗生素冲击等外界扰动的能力，通过代谢重组与菌群演替维持宿主与微生物的共生稳态是抵御代谢疾病及炎症的关键机制，为胃肠道功能的稳态奠定了基础。胃肠道微生物的功能异质性是微生物适应消化道复杂微环境的必然结果，可为宿主不同生理阶段及区域提供精准功能支持。异质性与可塑性为理解胃肠道微生物的功能提供了新的研究维度，也为相关疾病的干预策略奠定了理论基础。

2 胃肠道微生物的起源与传递

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2.1 微生物的起源：获取与共生

胃肠道微生物起源于生命演化早期，部分微生物随环境变迁逐渐适应了生物体内环境并在胃肠道定殖^[54]。与大猩猩相比，人类肠道微生物进化速度更快，这与烹饪的出现、农业的发展等人类进化事件相关^[55]。伴随社会文明发展，饮食结构发生巨大变化，胃肠道微生物通过适应性演变匹配新的食物资源，草食动物的胃肠道中富集擅长分解植物多糖的微生物^[8]，而肉食性动物胃肠道则富集协助消化蛋白质和脂肪的菌群^[1]。东方人高纤维、低糖低脂的饮食可提高肠道菌群多样性，增加乳杆菌属与双歧杆菌属的丰度^[56]；而西式饮食因碳水摄入不足，对肠道菌群代谢功能产生不利影响^[57]。卫生条件、医学水平，以及防腐剂、添加剂等的出现也会对宿主胃肠道菌群产生显著影响^[53,58-59]。研究发现，食品添加剂中的甜味剂(如糖精、三氯蔗糖等)可通过调节肠道微生物群的组成和功能导致人类和小鼠的葡萄糖耐受不良；2种天然的高强度甜味剂(甜菊糖苷、新橙皮苷二氢查尔酮)可以促进肠道中乳杆菌属的生长，甜菊糖苷还可以增强双歧杆菌的生长，抑制肉鸡肠道中大肠埃希氏菌的浓度^[59]。

2.2 微生物的传递：饮食与环境驱动

环境中外源性的微生物可通过食物、水源、宠物及空气等介质，经口鼻呼吸道传递至人类的胃肠道^[60]。在种植、加工、储存或烹饪等环节均可引入梭菌属、霉菌孢子或一些原虫包囊^[61]。大肠埃希氏菌O157:H7通过污染食物和水源可导致溶血性尿毒症综合征暴发^[62]。冷藏不当也可导致嗜冷菌增殖，肉蛋乳类食品携带的沙门氏菌属、弯曲杆菌属或单核增生李斯特氏菌可引发食物源性腹泻^[63]。霍乱弧菌、隐孢子虫等病原体通过受到污染的水体直接或间接传递到生鲜果蔬中，或通过饮水或嬉水导致感染暴发^[64-65]。宠物源人畜共患病菌可经手-口途径传播，如儿童接触宠物后感染隐孢子虫的风险显著增加，且宠物口腔中的巴斯德氏菌属、产气荚膜梭菌等可引发腹泻^[66]。此外，空气中的微生物借助尘埃与飞沫扩散，经呼吸道黏附并伴随吞咽进入胃肠道^[67]。因此，在人员密集、通风不良的医院等密闭环境中，免疫力较低的个体感染病原微生物的风险增加^[67-68]。综上所述，加强食品加工卫生、饮用水消毒及培养良好的个人卫生习惯是阻断外源致病菌输入的关键，也是维护肠道菌群平衡的重要措施。

2.3 垂直传播：母婴传递与父系遗传

垂直传播是微生物经妊娠、分娩和哺乳等过程由亲代直接传递给子代的过程^[69]。该途径被认为是肠道菌群建立的主要途径，且母源菌株在婴儿肠道中定殖更持久，适应性更强^[70]。研究表明，婴儿肠道中50.7%的微生物来源于母亲的肠道、阴道、口腔或皮肤，该比例在出生后4个月内相对稳定^[70]。阴道分娩的新生儿肠道菌群与母体匹配度高达58.5%，出生2-5 d内同源比例高达72%^[71]。相较于母体其他部位来源的菌群，母源肠道菌群影响更持久，是婴儿肠道的“核心菌群”，其生态适应性可能与婴儿肠道中具有选择性优势的特定基因有关^[70,72]。研究显示，出生最初3 d内新生儿与其母亲共享的微生物物种数量明显高于与其他无亲缘关系个体^[70]。此外，阴道分娩被认为是“微生物-免疫协同发育”的黄金路径，阴道分娩新生儿获得母体阴道菌群(乳杆菌属)，通过Toll样受体2 (Toll-like receptor, TLR2)信号诱导肠道上皮细胞分泌抗菌肽，奠定黏膜屏障基础^[73]。一般情况下，阴道分娩婴儿出生后72 h内其胃肠道以母体阴道来源的乳杆菌科为主，该菌通过细胞壁肽聚糖激活TLR2-髓样分化因子88 (myeloid differentiation factor 88, MyD88)通路，诱导树突状细胞分泌白细胞介素-10 (interleukin-10, IL-10)，影响肠道上皮细胞的白细胞介素-22 (interleukin-22, IL-22)表达，协同调控肠道屏障完整性与免疫耐受^[74]。

研究表明，母乳微生物群的贡献度仅次于阴道^[73]。剖宫产婴儿虽缺乏阴道菌群接种，但可经母乳将双歧杆菌属、乳杆菌属等微生物通过“乳腺-口腔-肠道轴”起到部分补偿作用^[70]。例如，围生期母体补充的鼠李糖乳酪杆菌(Lacticaseibacillus rhamnosus)GG可以传播给婴儿^[75]。母乳代谢物(神经递质前体、免疫调节肽)同步调控婴儿菌群功能^[76]。此外，近期研究表明，母体在怀孕期间接触到的一些微量元素不仅影响母体菌群的结构及组成，还深入影响其代谢功能和抗生素耐药性，进而会对新生儿胃肠道微生物产生持久影响^[77]。新生儿的免疫系统发育深受母体微生物群的影响，例如母体肠道菌群代谢物(SCFAs、次级胆汁酸)经胎盘进入胎儿循环，上调肠道GPR43受体表达，为出生后菌群定殖作准备^[78]。乳杆菌科产生乳酸，调节pH，抑制病原菌生长，促进免疫耐受，维持肠道稳态^[74]。双歧杆菌通过代谢母乳寡糖产生乙酸激活肠道上皮细胞GPR43受体，促进紧密连接蛋白表达，增强肠屏障完整性，降低新生儿坏死性小肠结肠炎风险^[79-80]。

传统研究多聚焦于母体，近年来研究发现父源微生物也可通过直接或间接传播影响子代菌群建立^[81]。研究表明，婴儿1岁时父源菌株贡献度与母亲相当，且不受分娩方式的影响，父母源菌群互补性强，重叠率低^[82]。在出生后2 h内接受1次母体粪菌移植干预的剖宫产婴儿，其肠道中母源菌株的比例在移植后3周时高达61%，但随婴儿生长该比例逐渐下降，12月龄时降至约30%，而父源菌株在第1年内稳定维持在20%-25%^[82]。婴儿肠道15%的长双歧杆菌(Bifidobacterium longum)源自父亲，揭示皮肤接触及家庭环境是父婴传递的关键路径。此外，2-10岁儿童与父亲的微生物共享程度不低于母婴传递，且父亲作为家庭中新菌株引入者的角色更为突出^[82]。父亲肠道微生物及其代谢产物经饮食-行为-环境链间接塑造子代胃肠道微生态^[83]。与母婴传递相比，父婴传递程度虽较低，但仍具有重要的意义和影响。当前对父婴传播的研究主要是探究其在传统母系遗传下的补偿和特殊作用，为新生儿肠道的发育及生命健康发展提供理论依据。

综上所述，垂直传播是微生物代际传递的核心机制，母亲通过胎盘、产道和哺乳发挥主导作用，父亲则提供补充性菌源，二者共同塑造新生儿胃肠道微生态。

3 胃肠道微生物的定殖过程

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3.1 胎儿阶段的定殖

关于“微生物何时开始定殖”，学术界存在“胎盘无菌”与“胎盘有菌”两大假说。传统观点认为胎儿在子宫内是无菌的，微生物的定殖始于分娩过程^[71]。支持“胎盘无菌”观点的研究，联合运用细菌培养、实时荧光定量聚合酶链反应、16S rRNA基因测序和宏基因组学等研究技术，未在胎盘中检测到细菌^[84]。尽管在少数胎盘样本中检测到了与孕妇感染有关的无乳链球菌(Streptococcus agalactiae)，但这并不否定健康妊娠子宫内是无菌状态；严格设置阴性对照的研究显示，胎儿肠道中存在有活力细菌的比例极低，且由于缺乏统一的污染防控与判别标准，所观察到的细菌也难以确认其活性^[85]。此外，早产与足月胎儿的肠道样本中均未检测到微生物^[86]。Kennedy等^[87]通过16S rRNA基因测序检测剖宫产婴儿的胎粪样本，结果表明足月健康婴儿的胎儿肠道内在出生前不存在细菌。揭示此前部分阳性结果可能源于污染或检测方法的局限^[88]。

然而，随着检测技术的发展，胎粪、羊水、胎膜、胎盘中陆续检测到微生物，使“胎盘无菌”假说受到挑战^[32,89]。Aagaard等^[90]在严格无菌条件下收集了大量胎盘组织，运用多组学研究技术在胎盘中检测到了微生物，并且发现胎盘与口腔微生物相似度高，确定了胎盘微生物独特的生态位。此外，研究人员收集了各种生产方式下的胎盘样本，以一例临床证实为绒毛膜羊膜炎的胎盘作为阳性对照，借助高稳定性的原位杂交技术在其余无感染症状的足月及早产胎盘中同样检测到低丰度微生物的存在，揭示胎盘普遍存在低丰度微生物的模式且不受胎龄、分娩方式影响^[91]。Mishra等^[92]采用16S rRNA基因测序和细菌分离培养的方法进一步证实胎儿肠道、皮肤和胎盘中存在活菌。以羔羊为动物模型的研究发现，足月无菌子宫切除术分娩的羔羊肠道中存在低生物量菌群，为胎儿早期于宫内定殖微生物提供了依据^[93]。特别地，近期研究人员运用内标质粒法对新生儿胎粪样本进行绝对定量时发现胎粪中的细菌丰度达到1.14×10⁷ copies/g，这个丰度处于无菌和成人(10¹⁰-10¹¹ CFU/g)之间的中间状态^[32]。综上所述，随着技术提升与污染控制加强，“胎盘有菌”假说逐渐获得支持，意味着胎儿在宫内即开始接触微生物，这将对出生后肠道菌群的建立、免疫发育乃至长期健康产生深远影响，为生命早期菌群与健康关联机制提供了新的理论视角。

3.2 新生儿微生物定殖

早期微生物群的定殖对宿主健康至关重要，分娩方式和抗生素使用是影响定殖的关键因素。阴道分娩新生儿首先接触母体阴道和肠道的微生物，其胃肠道优势菌为肠杆菌科、肠球菌属、乳杆菌属、拟杆菌属等^[94]。然而，产程延长可能增加医院环境微生物暴露及感染的风险^[95]，且母体应激会影响母乳中免疫球蛋白等活性物质的含量，间接调控肠道菌群建立^[70]。研究表明，母源性的乳杆菌属和大肠埃希氏菌等作为先锋菌在出生后72 h内定殖于新生儿胃肠道中，为后续严格厌氧菌的定殖创造了厌氧环境，随后拟杆菌通过其代谢产物多糖直接诱导调节性T细胞的分化，而双歧杆菌和梭菌属则通过产生SCFAs进一步强化肠道屏障并促进免疫耐受^[74]。剖宫产新生儿主要通过接触环境微生物以及母体皮肤微生物，使其初始菌群以葡萄球菌属、棒杆菌属等为主，这可能影响新生儿肠道的正常发育和免疫功能^[71]。拟杆菌和双歧杆菌在剖宫产婴儿肠道中的定殖延迟，导致调节性T细胞数量减少、自然杀伤细胞数升高、IL-10降低，可能引发儿童期的哮喘、肠易激综合征等疾病^[74]。随着年龄的增长，2种分娩方式的菌群差异逐渐减小^[71]，揭示其他母婴微生物传播途径(如母乳)可起到辅助补偿作用。例如，乳杆菌属、双歧杆菌属、普雷沃氏菌属等早期主要存在于阴道分娩新生儿胃肠道中，葡萄球菌属、肠球菌属等主要存在于剖宫产新生儿胃肠道中，但随着时间推移，肠球菌属、双歧杆菌属等会在所有阴道分娩与剖宫产新生儿中普遍存在^[96]。值得注意的是，剖宫产手术中预防性使用抗生素可干扰肠道菌群的正常演替，增加菌群紊乱和疾病风险^[94]。母乳喂养、饮食结构以及家庭环境等出生后因素进一步调控新生儿菌群^[94]，垂直传播、环境因素和生活方式共同参与菌群演替^[97]。除此之外，早产儿的肠道菌群也因住院和抗生素治疗等因素导致菌群失调^[86,94]，使其定殖更加复杂。目前，针对剖宫产与早产儿的菌群干预策略不断涌现，如用母亲的阴道拭子涂抹新生儿、补充益生菌和益生元、粪便微生物移植等可改善其肠道菌群的平衡，促进肠道健康^[98-101]。

3.3 早期生命阶段的微生物演替

早期生命阶段的微生物演替是指宿主从出生后到成年过程中微生物群落结构与功能的动态变化^[102]。研究表明，早期生命阶段的微生物定殖于出生后的72 h内，尽管不同的分娩方式对这个阶段的微生物绝对丰度无影响^[32]，但在组成上阴道分娩和剖宫产的优势菌有显著差异，阴道分娩新生儿初始菌群以母体乳杆菌属为主，而剖宫产婴儿肠道以皮肤来源的葡萄球菌属和棒杆菌属为主，这种差异可持续至出生后6个月^[74]。出生后的0-6个月，婴儿以母乳为主要食物，该阶段菌群定殖以双歧杆菌为主，该菌通过分解母乳寡糖成为优势菌，其代谢产生的乙酸可促进肠道黏膜完整性^[79]。随着饮食方式从母乳转化为奶粉再到固体食物，胃肠道菌群的多样性增加^[9,33]。相应地，胃肠道内菌群的比例也会发生变化，总体呈现从演变到稳定的态势^[9,36]。同时，随着宿主免疫系统逐渐成熟，菌群从相对简单、非特异性的状态逐渐转变为具有部位特异性的稳定群落^[9,38]。因此，某些微生物的丰度与年龄相关，如F. prausnitzii和宠大厌氧棒状菌(Anaerostipes hadrus)是预测年龄重要性得分最高的分类群，与年龄呈正相关，短双歧杆菌(Bifidobacterium breve)和B. longum与年龄呈负相关，此外活泼瘤胃球菌(Ruminococcus gnavus)和韦氏布劳特氏菌(Blautia wexlerae)也是一组高度重要的年龄预测因子^[103]。总体而言，肠道微生物从婴儿期以双歧杆菌为主的简单结构，逐渐发展为成年期以拟杆菌与芽孢杆菌门为主的稳定复杂群落^[33-35]。

3.4 生命中后期对胃肠道微生物定殖的关键事件

成年人的肠道菌群相对稳定，但仍受饮食、运动和压力水平等因素影响。例如，长期高脂高糖饮食会促进与肥胖及代谢性疾病相关的微生物增殖，而运动则有助于维持有益微生物的丰度，促进微生物菌群的健康平衡^[104]。老年期菌群多样性通常会下降，双歧杆菌等有益微生物的丰度减少，而与炎症相关的微生物，如假单胞菌门、肠杆菌科的丰度增加^[102]，可能诱发阿尔茨海默病、帕金森病等疾病^[105]。反之，疾病状态也反作用于菌群，如炎症性肠病患者的肠道菌群多样性降低，且机会性致病菌的定殖增加^[106]。此外，长期使用抗生素、所处环境、生活方式及宿主的基因等均直接或间接影响胃肠道菌群的组成^[107]。

4 微生物溯源方法与技术进展

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4.1 微生物组测序技术

微生物组测序技术是解析群落结构、功能及互作的核心技术，其中16S rRNA基因测序和宏基因组测序应用最为广泛。16S rRNA基因序列包含9个用于区分物种的高变区和10个反映物种亲缘关系的保守区^[108] (表1)。通过PCR扩增高变区(V3-V4)并测序，可精准识别不同微生物的亲缘关系，进而构建系统发育树，适用于属水平群落结构分析，但其物种分辨率有限，难以精确到种或菌株，也无法提供更详细的功能信息^[109]。该技术常用于慢性病与微生物关联的探索，但其结果受DNA提取方法和靶向区域选择的影响^[110]。宏基因组学测序技术直接对环境中所有微生物的总DNA进行大规模测序，无需扩增从而减少偏差，不仅可更准确地鉴定物种，还可解析功能基因和微生物互作机制^[8-9]。尽管成本较高，数据分析较复杂且需要强大的计算资源，但在解析物种组成、代谢潜能及溯源研究中具有一定优势^[111-112]。

4.2 溯源分析方法

微生物溯源分析(microbial source tracking, MST)是借助生物信息学工具追踪微生物来源和演化路径的关键技术，广泛应用于食品安全、环境监测、医学等领域^[128]。它主要分为库相关方法和库无关方法，为追踪食物链中粪便细菌和食源性病原体提供支撑^[129-130]。库相关方法通过比对已知微生物数据库实现快速、标准化溯源，适用于已知污染源场景，但其识别能力受数据库完整性限制^[129]。库无关方法则不依赖数据库，直接基于群落结构、功能基因或代谢特征进行统计推断以实现溯源，适用于未知或复杂污染源的识别，但对低丰度微生物敏感性较低，且需大量样本以优化模型^[130]。常用工具有SourceTracker和Fast Expectation-Maximization for Microbial Source Tracking (FEAST)。

SourceTracker基于贝叶斯算法，通过评估微生物群落结构与潜在源环境的相似性来评估各来源对目标样本的贡献度，是截至目前应用最广泛的MST工具之一^[128]。该方法的核心目标是在无培养条件下，从复杂微生物群落中定量推断各来源对目标样本的贡献比例，它将每个源头的微生物群落看作一个独特的特征分布，然后计算目标样本中的每个扩增子序列变体(amplicon sequence variants, ASVs)/操作分类单元(operational taxonomic units, OTUs)最可能来自哪个源头，优点在于模型稳健，能处理稀疏数据，并且能给出一个未知来源的比例^[113]。尽管它在区分具有相似细菌群落结构的来源时存在局限，但它能够准确地识别出多种来源微生物的可靠来源。例如，Knights等^[113]使用SourceTracker对婴儿肠道菌群进行溯源分析，成功识别出婴儿肠道中来自母亲阴道、皮肤、肠道的菌群比例，发现阴道分娩婴儿更多继承母亲阴道菌群，剖宫产婴儿则以皮肤和环境菌群为主。Hewitt等^[114]运用SourceTracker对2家新生儿重症监护病房婴儿相关微生物与所在环境中微生物进行溯源，发现婴儿肠道和皮肤菌群中30%以上的OTU在环境中检测到，特别是早产儿的菌群与环境相似度极高，提示当胃肠道菌群尚未稳定时，环境菌群是影响婴儿菌群的主要因素。随后，Staley等^[115]将SourceTracker应用于接受粪便微生物移植后的复发性艰难拟梭菌(Clostridioides difficile)感染患者的菌群溯源分析，得出粪便微生物移植治疗后即使供体菌群仅占受体肠道15%-20%，仍可达到85%以上的临床治愈率。Moutsoglou等^[116]对接受粪便微生物移植治疗的一些溃疡性结肠炎患者，使用SourceTracker来量化患者受到健康供体所供给的健康菌群的数量，发现植入数量与临床疗效成正比，为评估微生物移植的治疗效果提供了依据。此外，Peng等^[117]使用SourceTracker成功揭示中国黄酒酿造过程中原料、发酵醪和酿造环境之间的相关性。其扩展版Meta-SourceTracker适用于宏基因组数据，在海洋生态系统和人体肠道菌群溯源中稳定性与准确性较高^[118]。

FEAST采用快速期望最大化算法，通过统计检验判断目标样本中的某个特征ASV/OTU是否在某个源头中显著富集，可高效地处理数千个潜在源环境，其计算效率约是SourceTracker的30-300倍，且具有较强的精确度，可量化跟踪微生物群落的形成^[119]。研究表明FEAST溯源结果与实际污染源调查结果的相关性达0.85以上，而SourceTracker仅为0.6-0.7，随机森林不足0.6；FEAST运行时间更优，其计算效率约是SourceTracker的30倍，是随机森林的6-7倍^[119]。此外，在混合DNA和混合粪便样本的测试中FEAST也表现出更高的敏感性与合理性，虽存在一定的局限性，但在现有微生物溯源技术基础上实现了突破，有望成为环境管理领域实用的技术新手段^[120]。此外，FEAST在医学和临床领域应用甚广，如用于比较不同生产方式下婴儿的肠道菌群，发现剖宫产婴儿的肠道菌群与其母亲的相似度显著低于阴道分娩婴儿^[119]。Qi等^[121]将FEAST用于揭示分泌型免疫球蛋白A (secretory immunoglobulin A, sIgA)包裹的“微量细菌变异体”如何通过母乳垂直传递至婴儿肠道，并评估其定殖动态，证实了母乳中sIgA包裹的微量细菌变异体虽占比极低，但通过高效垂直传递成为婴儿肠道早期菌群的核心组分，并持续塑造婴儿免疫稳态。Meng等^[122]运用FEAST解析母婴微生物垂直传递的动态过程，发现阴道分娩的新生儿出生当天肠道中的微生物有54.7%由母亲肠道微生物贡献，阴道的贡献度为21.3%，而剖宫产新生儿母亲肠道贡献31.4%，阴道贡献仅7.8%，“未知”来源高达46%。此外，产前益生菌显著增强母体肠道菌群向新生儿的垂直传递效率，尤其在剖宫产中可部分补偿传递不足，从而加速新生儿肠道微生态的早期成熟^[75-78]。

除此之外，新型MST工具不断涌现。基于单核苷酸变异(single nucleotide variation, SNV)作为溯源遗传标记的SNV-FEAST，通过从所有SNV中筛选出对区分不同来源具有高度信息量的“特征SNV”，并与成熟的FEAST溯源算法相结合，极大地降低了计算负担，同时仍具有更高的准确性^[123]。SNV-FEAST的核心思想是追踪菌株水平上的遗传变异。对于广泛存在的物种，SNV-FEAST通过菌株特异性SNV可以更准确地指向真正的来源，减少“模糊”贡献。研究者将宏基因组SNV分析与机器学习结合，对数例艰难拟梭菌感染患者接受粪便微生物移植前后的粪便样本进行深度宏基因组测序，通过核心基因的SNV位点区分供体与受体菌株^[124]。目前，SNV-FEAST在母婴菌群的传递、医院环境的监测、海洋微生物地理学上展示了其强大的应用潜力，对微生物溯源技术的发展作出贡献^[123]。将SNV-FEAST的结果与分离培养的菌株基因组数据进行验证和整合，可以实现从“相关性推断”到“因果性证实”的飞跃^[123]。该方法目前存在的挑战在于对数据质量和计算资源的要求高，未来的发展应将集中于优化算法效率、开发更高效的SNV调用流程，以及将其应用于更复杂的微生物群落(如真菌、病毒)的溯源。

基于非负矩阵分解(non-negative matrix factorization, NMF)的SourceID-NMF，利用NMF的非负性特性和其擅于识别关键且具代表性的微生物来源的特点^[125]，通过类群丰度数据估计来源比例，它能够更准确地识别不相关来源和未知来源，区分高度相似的来源，降低低丰度来源和噪声的干扰，无需预设源环境组成，减少计算复杂度。其准确性优于FEAST与SourceTracker，为未来整合更多维度的微生物数据和适应更广泛的应用需求奠定了基础^[125]。

基于机器学习和统计模型的Source Tracking with Environment Selection (STENSL)，直接从地球微生物组计划这类庞大的公共数据库纳入成百上千个潜在的环境样本作为候选源，自动选择最可能的源环境，使用快速期望最大化算法从大量非贡献源的环境中准确地估计出真实源的贡献比例^[126]。该方法主要解决候选来源极多但真正贡献者极少场景下的高维噪声与估计偏差问题。该方法未来可以与超大规模的公共数据库对接，无需预先筛选来源即可直接运行，实现“以库为源”的全球尺度溯源；研究证实，使用STENSL分析人类和小鼠消化系统微生物比FEAST、SourceTracker等方法的准确度更高^[126]。Flores Ventura等^[127]运用STENSL系统评价了双歧杆菌菌株母婴垂直传递率，并在菌株水平上验证了母亲肠道是垂直传递的核心来源，为剖宫产婴儿提供了可量化的“传递缺口”指标，可用于早期微生态干预的精准评估。

5 总结与展望

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胃肠道微生物的演进是一个动态过程，其群落结构与功能受饮食、药物、压力等内外部环境因素的持续影响。这一过程起始于新生儿期，新生儿通过分娩、哺乳等母源途径获得微生物，并随着宿主生长发育与环境变化不断适应。目前，胎盘是否存在微生物仍因存在污染风险和检测瓶颈而争议未决，这制约了对宫内定殖机制的解析。

未来研究应结合无菌动物模型、高分辨率空间转录组与代谢组学技术，严格控制低生物量样本，完善样本处理流程以科学、确凿的证据证实胎盘微生物组是否存在、其丰度如何变化以及具有何种功能意义。

此外，新近证据表明，父系肠道微生物可通过垂直传递影响子代免疫与代谢，这为传统母婴传播框架补充了潜在的父源维度^[82-83]。然而，父源微生物传递的具体路径及其与母系微生物的互作机制尚不明确。未来可利用宏基因组学分析，并对家庭队列开展纵向研究，力求精确量化父母双方在不同时间窗口对子代菌群的贡献度。

目前，微生物溯源技术的应用尚处于发展阶段。未来亟需构建更全面、高质量的微生物组参考数据库，开发适用于临床样本的快速、低成本且高精度的溯源技术，并结合高灵敏、高特异技术规范采样。如此，才能厘清胎盘菌群的真伪，阐明父婴传递路径及其与母系的互作机制，为生殖与发育健康提供理论支撑。

基金

收起

国家自然科学基金(32202686)
国家重点研发计划(2023YFD1300900)
中央高校基本科研业务费专项资金(SWU-KQ031)

参考文献

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文献

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2026年第66卷第3期

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doi: 10.13343/j.cnki.wsxb.20250755

接收时间：2025-10-09
首发时间：2026-03-12
出版时间：2026-03-04

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收稿日期：2025-10-09
录用日期：2025-12-17

基金

National Natural Science Foundation of China(32202686)

国家自然科学基金(32202686)

National Key Research and Development Program of China(2023YFD1300900)

国家重点研发计划(2023YFD1300900)

Fundamental Research Funds for the Central Universities(SWU-KQ031)

中央高校基本科研业务费专项资金(SWU-KQ031)

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西南大学动物科学技术学院，重庆

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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TxT

Item	Advantage	Disadvantage	Principle	Application	References
Microbiome sequencing technology
16S rRNA	Accurate identification of the phylogenetic relationships among different microorganisms	The resolution is limited	Nine hypervariable regions and 10 conserved regions	Analysis of influencing factors and correlation studies of the microbiota	[108-110]
Metagenomics	Identification of species functions and microbial interaction mechanisms	It is associated with relatively high costs and complex analysis	Large-scale sequencing of total DNA from all microorganisms	Analysis of composition, function, and traceability research	[111-112]
Microbial source tracking
SourceTracker	Accurate identification of microorganisms from multiple sources	There is a limitation in distinguishing similar bacterial community structures	The contribution of each source is evaluated based on the similarity between the community structure and the potential source environments	For microbial source tracking analysis in food manufacturing, medical and health care, and other fields	[113-117]
Meta-SourceTracker	It enables the simultaneous analysis of the sources of microorganisms across multiple domains	It exhibits a relatively high false positive rate and limited computational efficiency	Inherit the Bayesian framework of SourceTracker	It exhibits excellent performance in the traceability of marine ecosystems and human gut microbiota	[118]
FEAST	It features high precision and enables the quantification and tracking of microbial community formation	The operation is technically challenging	Fast expectation-maximization algorithm	In the medical and clinical fields, elucidate the process of maternal-infant microbiota vertical transmission	[119-122]
SNV-FEAST	Enhance resolution and improve accuracy	It requires complex calculations and has high demands on data quality	Based on single nucleotide variation	In microbial source tracking, achieving the transition from correlational inference to causal validation	[123-124]
SourceID-NMF	Its accuracy is higher than that of FEAST and SourceTracker	It is slightly complex in computation and exhibits poor stability	The NMF algorithm is employed to estimate the source proportions using the taxon abundance data	The transmission routes and sources of pathogens, as well as the tracing of the sources and migration of microorganisms in the environment	[125]
STENSL	Higher accuracy and broader applicability	There are requirements for the quality and quantity of data	Based on machine learning and statistical models, the most probable source environment is automatically selected, and effective source search is conducted with the aid of public repositories	Analysis of the digestive system microbiota and other related aspects in humans and mice	[126-127]

Item

Advantage

Disadvantage

Principle

Application

References

Microbiome sequencing

technology

16S rRNA

Accurate identification of the phylogenetic relationships among different microorganisms

The resolution is limited

Nine hypervariable regions and 10 conserved regions

Analysis of influencing factors and correlation studies of the microbiota

[108-110]

Metagenomics

Identification of species functions and microbial interaction mechanisms

It is associated with relatively high costs and complex analysis

Large-scale sequencing of total DNA from all microorganisms

Analysis of composition, function, and traceability research

[111-112]

Microbial source

tracking

SourceTracker

Accurate identification of microorganisms from multiple sources

There is a limitation in distinguishing similar bacterial community structures

The contribution of each source is evaluated based on the similarity between the community structure and the potential source environments

For microbial source tracking analysis in food manufacturing, medical and health care, and other fields

[113-117]

Meta-SourceTracker

It enables the simultaneous analysis of the sources of microorganisms across multiple domains

It exhibits a relatively high false positive rate and limited computational efficiency

Inherit the Bayesian framework of SourceTracker

It exhibits excellent performance in the traceability of marine ecosystems and human gut microbiota

[118]

FEAST

It features high precision and enables the quantification and tracking of microbial community formation

The operation is technically challenging

Fast expectation-maximization algorithm

In the medical and clinical fields, elucidate the process of maternal-infant microbiota vertical transmission

[119-122]

SNV-FEAST

Enhance resolution and improve accuracy

It requires complex calculations and has high demands on data quality

Based on single nucleotide variation

In microbial source tracking, achieving the transition from correlational inference to causal validation

[123-124]

SourceID-NMF

Its accuracy is higher than that of FEAST and SourceTracker

It is slightly complex in computation and exhibits poor stability

The NMF algorithm is employed to estimate the source proportions using the taxon abundance data

The transmission routes and sources of pathogens, as well as the tracing of the sources and migration of microorganisms in the environment

[125]

STENSL

Higher accuracy and broader applicability

There are requirements for the quality and quantity of data

Based on machine learning and statistical models, the most probable source environment is automatically selected, and effective source search is conducted with the aid of public repositories

Analysis of the digestive system microbiota and other related aspects in humans and mice

[126-127]