, 喻嵘2, 陈华海1 1. 湖南科技学院 化学与生物工程学院湘南优势植物资源综合利用湖南省重点实验室，湖南 永州 425199;
2. 湖南中医药大学，湖南 长沙 410208
收稿日期：2021-07-23；接收日期：2021-09-24；网络出版时间：2021-10-05
基金项目：湖南省自然科学基金****基金(No. 2020JJ2016) 资助
作者简介：尹业师??博士、教授/研究员、湖南科技学院化学与生物工程学院副院长、湘南优势植物资源综合利用湖南省重点实验室主任。主持国家自然科学基金、国家863计划子课题和湖南省自然科学基金****基金等科研项目；以主要作者身份在ISME J、Appl Environ Microbiol、Mol Microbiol、Front Microbiol、FEMS Microbiol Ecol等期刊发表论文90余篇；以第一发明人获得中国发明专利授权5项。兼任湖南省微生物学会常务理事、湖南省生物化学与分子生物学学会理事、中华医学会消化病学分会微生态组委员；兼任多个专业期刊编委和审稿专家.
摘要：肠道微生物与人类健康的相关性研究仍然是当前生命科学研究领域的前沿热点之一。不依赖培养的16S rRNA基因高通量测序是当前的主要研究手段。但随着测序成本的降低和数据分析方法的日渐成熟，宏基因组鸟枪法测序因具有信息量更大、更全等优势，将逐渐成为今后一段时间内研究肠道微生物组的重要手段。美国在人类微生物组计划的资助下，对30 805份样品进行了肠道微生物宏基因测序分析。通过NCBI PubMed和SRA数据库检索，共发现72项研究收集了约10 000份中国人的肠道样品用于宏基因组测序。但到目前为止，仅56项研究进行了公开发表，其中与代谢性疾病相关的文献16篇，与感染和免疫性疾病相关的文献16篇，与心脑血管疾病相关的文献12篇。由于采样地点以北京、广州、上海等大城市为主，测序平台和测序分析方法均存在较大差异，且大部分研究仍以相关性分析为主，相关研究成果在临床疾病诊疗中所发挥的作用仍非常有限。规范采样方法、标准化测序平台和数据分析流程，开展多中心平行研究将有助于数据整合和比较分析。同时，结合使用转录组、蛋白质组和培养组学等多组学方法开展功能验证和分子作用机制研究，将有利于更好地将肠道微生物研究成果服务于临床疾病诊疗。
关键词：中国人肠道微生物鸟枪法测序宏基因组
Shotgun metagenome sequencing of Chinese gut microbiota: a review
Yeshi Yin1
, Rong Yu2, Huahai Chen1 1. Key Laboratory of Comprehensive Utilization of Advantage Plants Resources in Hunan South, College of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou 425199, Hunan, China;
2. Hunan University of Chinese Medicine, Changsha 410208, Hunan, China
Received: July 23, 2021; Accepted: September 24, 2021; Published: October 5, 2021
Supported by: Distinguished Young Scholars of Hunan Natural Science Foundation, China (No. 2020JJ2016)
Corresponding author: Yeshi Yin. Tel/Fax: +86-746-6381164; E-mail: yinyeshi@126.com;
Huahai Chen. Tel/Fax: +86-746-6381164; E-mail: Huahaichen2008@163.com.
Abstract: The research on the relationship between gut microbiota and human health continues to be a hot topic in the field of life science. Culture independent 16S rRNA gene high-throughput sequencing is the current main research method. However, with the reduction of sequencing cost and the maturity of data analysis methods, shotgun metagenome sequencing is gradually becoming an important method for the study of gut microbiome due to its advantages of obtaining more information. With the support from the human microbiome project, 30 805 metagenome samples were sequenced in the United States. By searching NCBI PubMed and SRA databases, it was found that 72 studies collecting about 10 000 Chinese intestinal samples were used for metagenome sequencing. To date, only 56 studies were published, including 16 related to metabolic diseases, 16 related to infectious and immune diseases, and 12 related to cardiovascular and cerebrovascular diseases. The samples were mainly collected in Beijing, Guangzhou, Shanghai and other cosmopolitan cities, where great differences exist in sequencing platforms and methods. The outcome of most studies are based on correlation analysis, which has little practical value in guiding the diagnosis and treatment of clinical diseases. Standardizing sampling methods, sequencing platform and data analysis process, and carrying out multi center parallel research will contribute to data integration and comparative analysis. Moreover, insights into the functional verification and molecular mechanism by using the combination of transcriptomics, proteomics and culturomics will enable the gut microbiota research to better serve the clinical diagnosis and treatment.
Keywords: Chinesegut microbiotashotgun sequencingmetagenome
肠道居住着数量庞大的、能与人类宿主相互作用的微生物，包括细菌、真菌、古菌和病毒，是一个复杂动态的生态系统。所有肠道微生物组编码的基因数量比人类基因组高一个数量级[1]。在某种程度上，也被认为是人体的“功能器官”。正常情况下，肠道菌群与人类宿主和谐共生，具有帮助宿主获取营养和促进免疫系统发育等功能。但当菌群出现紊乱时，也会与多种疾病的发生发展密切相关，如帕金森病、阿尔茨海默病、高血压、动脉粥样硬化、肥胖、糖尿病、炎症性肠病、结肠癌等[2]。
自2008年美国启动人类微生物组计划(Human microbiome project，HMP，http://hmpdacc.org/)、欧盟启动人类肠道宏基因组计划(MetaHIT，http://www.metahit.eu/) 以来，各国相继启动或设立了人类肠道微生物组相关研究项目。但除了美国人类微生物组计划研究进展较快、成果突出、数据更新较及时外，其他研究进展均相对缓慢。
美国人类微生物组计划旨在探索人类宿主的微生物群落，并描述其在人类健康和疾病中的作用。HMP第一阶段(最初5年) 的主要目标是建立一个大样本、大数据的健康受试者的人体微生物组成基线。HMP第二阶段(为期3年)，主要研究肠道菌群在2型糖尿病、炎症性肠病和妊娠/早产发生发展和疾病防治中的作用。到目前为止，HMP计划已对30 805份样品进行了宏基因组分析，对3 687份样品进行了宏转录组分析，对2 699份样品进行宏蛋白质组分析[3]。
中国除了国家自然科学基金委员会和各省自然科学基金委员会立项支持人类肠道微生物组相关研究项目外，科技部于2013年立项973计划课题支持浙江大学牵头开展“肠道细菌微生态与感染及代谢的研究”；中国科学院于2017年部署了“人体与环境健康的微生物组共性技术研究”暨“中国科学院微生物组计划”项目；然而，到目前为止，针对中国人肠道样品进行的宏基因组测序还不到10 000份，其他组学相关的研究更是凤毛麟角。
2021年5月，中国科技部发布了国家重点研发计划“生物大分子与微生物组”重点专项申报指南，其中“标准微生物组及其与宿主/环境作用对生命活动影响的原理与机制研究”项目设置了一部分与肠道微生物研究相关的课题。如健康人微生物组库和特征解析；人体肠道微生物组稳态平衡及其失衡调控重大疾病的分子机制研究；微生物组与药物交互作用影响疗效及安全性的分子机制研究；微生物组学新技术及实验动物体系研究；病原微生物感染过程中的宿主免疫机制研究。该专项的实施将为建立健康中国人群肠道菌群基线、进一步研究肠道菌群在疾病发生发展中的作用及其分子机制奠定基础。相关研究成果也将为更好靶向肠道菌群开展新药研发和疾病防治提供理论依据和技术支撑。
截止到2021年7月1日，本研究通过NCBI (National Center for Biotechnology Information) PubMed文献检索和NCBI SRA数据库搜索，共找到72项研究对中国人肠道微生物菌群采用shotgun宏基因组测序方法进行了研究。现分析总结如下，希望能对下一步中国人肠道微生物宏基因组研究尤其是肠道微生物与疾病的相关性研究提供借鉴和指导。
1 中国人肠道微生物宏基因组测序进展1.1 总体进展概述在检索到的72项研究中，除1项研究使用了肠洗液样品外，其他研究均以粪便样品为研究对象。到目前为止，56项研究进行了公开发表，其中与代谢性疾病相关文献16篇，与感染和免疫性疾病相关文献16篇，与肿瘤和癌症相关文献5篇，与心脑血管疾病相关文献12篇，与双胞胎儿童、百岁老人和不同民族菌群特征相关文献7篇(表 1)。从发表时间来看，2017年以后相对较多；从样品采集城市来看，主要以北京(14篇) 和广州(8篇) 为主，占总数的40%，只有1篇进行了多医学中心采样分析，其他同一城市相关研究最多不超过4篇(上海、深圳各4篇，杭州、苏州、香港各3篇，海口、唐山、长沙、重庆、西安各2篇，聊城、呼和浩特、锡林郭勒、昆明、乌鲁木齐、岳阳各1篇)。从使用的测序平台来看，除7项研究使用了国产的BGISEQ-500外，其他49项均选择使用了Illumina测序平台，其中Hiseq2500和Hiseq2000使用较广泛。
表 1 中国人肠道微生物shotgun宏基因组测序研究统计Table 1 Statistical analyses of shotgun metagenome sequencing-based Chinese gut microbiota studies
| Classification | No. | Objective | Sample grouping | Data accession | References |
| Metagenomic analysis of the association between metabolic diseases and Chinese gut microbiota | 1 | Type 2 diabetes (T2D) | Probiotics + berberine group 106, probiotics group 102, berberine group 98, control group 103 | PRJNA643353 | [4] |
| 2 | T2D | Diabetes group 171, not-diabetes group 174 | PRJNA422434 | [5] | |
| 3 | T2D | T2D group 77, pre-T2D group 80, normal glucose tolerant group 97 | CNP0000175 | [6] | |
| 4 | T2D | Acarbose treatment group 51, glipizide treatment group 43 | PRJEB12124 | [7] | |
| 5 | T2D | T2D group 16, fiber intervened T2D group 27 | PRJEB15179 | [8] | |
| 6 | T2D | T2D group 30, pre-T2D group 33, control 66 | PRJEB30611 | [9] | |
| 7 | T2D | Metformin treatment group 22 | PRJNA486795 | [10] | |
| 8 | Gestational diabetes mellitus (GDM) | GDM group 43, healthy pregnant women 81 | PRJEB18755 | [11] | |
| 9 | GDM | GDM group 23, non-GDM pregnant women 26 | Unpublished | [12] | |
| 10 | Obesity | Obese group 95, lean controls 105 | PRJEB12123 | [13] | |
| 11 | Obesity | Prader-Willi syndrome 17, simple obesity 21 | PRJNA256106 | [14] | |
| 12 | Obesity | Obese 128, lean controls 101 | PRJNA648796 PRJNA648797 | [15] | |
| 13 | Polycystic ovary syndrome (PCOS) | PCOS group 14, control group 14 | PRJNA549764 | [16] | |
| 14 | PCOS | PCOS patients 14 | PRJNA513209 | [17] | |
| 15 | Renal failure | Renal disease group 223, control group 69 | PRJNA449784 | [18] | |
| 16 | Menopausal | Premenopausal 24, postmenopausal 24 | PRJNA530339 | [19] | |
| Metagenomic analysis of the association between infectious-immune diseases and Chinese gut microbiota | 1 | Ankylosing spondylitis (AS) | AS group 127, control group 123 | Unpublished | [20] |
| 2 | AS | AS group 97, control group 114 | PRJNA353560 PRJNA375935 | [21] | |
| 3 | AS | AS group 113, control group 37 | PRJEB28545 | [22] | |
| 4 | AS | AS group 85, control group 62 | PRJEB29373 | [23] | |
| 5 | Rheumatoid arthritis (RA) | RA group 77, unrelated control 80, RA paired control 17, DMARD treated RA group 21 | PRJEB6997 | [24] | |
| 6 | Systemic lupus erythematosus (SLE) | Untreated SLE group 117, posttreatment SLE group 52, control group 115 | PRJNA532888 | [25] | |
| 7 | Ulcerative colitis (UC) | UC group 8, control group 8 | Unpublished | [26] | |
| 8 | Crohn’s disease (CD) | CD group 49, control group 54 | PRJEB15371 | [27] | |
| 9 | Immune thrombocytopenia (ITP) | ITP group 99, control group 52 | Unpublished | [28] | |
| 10 | Behcet’s disease (BD) | Active BD group 32, control group 74 | PRJNA431482- PRJNA356225 | [29] | |
| 11 | Vogt-Koyanagi-Harada disease (VKH) | Active VKH group 71, inactive VKH group 11, control group 67 | PRJNA356225 | [30] | |
| 12 | Segmented filamentous bacteria (SFB) | SFB positive group 7, SFB negative group 4 | PRJNA299342 | [31] | |
| 13 | Helicobacter pylori (HP) | C14 breath test 313, HP positive group 128, HP negative group 185 | Unpublished | [32] | |
| 14 | HIV-1 | HIV group 61, control group 30 | PRJNA391226 | [33] | |
| 15 | Tuberculosis | TB group 46, control group 31 | PRJNA401385 | [34] | |
| 16 | Clostridium difficile (CD) | CD-positive group 5, CD-negative group 4, control group 5 | PRJNA591064 | [35] | |
| Metagenomic analysis of the association between cancer and Chinese gut microbiota | 1 | Colorectal cancer (CRC) | Health subject 536 | PRJNA557323 | [36] |
| 2 | CRC | CRC group 74, control group 54 | PRJEB10878 | [37] | |
| 3 | Rectal cancer | Rectal cancer patients 37 | PRJNA484031 | [38] | |
| 4 | Liver cirrhosis | Liver cirrhosis group 98, control group 83 | PRJEB6337 | [39] | |
| 5 | Lung cancer | Chemotherapy responder group 33, chemotherapy nonresponder group 31 | Unpublished | [40] | |
| 1 | Hypertension | Pre-hypertension 56, hypertension 99, control 41 | PRJEB13870 | [41] | |
| 2 | Hypertension | Hypertensive patients 60, healthy controls 60 | ERP023883 | [42] | |
| 3 | Atherosclerotic cardiovascular disease (ACVD) | ACVD group 218, control group 187 | PRJEB21528 | [43] | |
| Metagenomic analysis of the association between cardiovascular and cerebrovascular diseases and Chinese gut microbiota | 4 | Coronary heart disease (CHD) | CHD group 59, control group 43 | PRJEB15111 | [44] |
| 5 | Atrial fibrillation | Atrial fibrillation group 50, control group 50 | PRJEB28384 | [45] | |
| 6 | Attention deficit hyperactivity disorder (ADHD) | ADHD group 17, control group 17 | Unpublished | [46] | |
| 7 | Autistic (ASD) | ASD group 39, control group 40 | CRA001746 | [47] | |
| 8 | Warfarin response | Warfarin low responder (LR) 5, high responder (HR) 5, normal responder (NR) 5 | PRJNA520777 | [48] | |
| 9 | Schizophrenia | Medication-free schizophrenia patients 90, control group 81 | PRJEB29127 | [49] | |
| 10 | Multiple system atrophy (MSA) | MSA group 15, control group 15 | PRJNA532538 | [50] | |
| 11 | Gastrointestinal complications | Thoracic aortic dissection patients 40，control group 10 | PRJNA379884 | [51] | |
| 12 | Pompe disease | Eight members including two pompe siblings both had cerebral infarction | CNP0000237 | [52] | |
| Metagenomic analysis of other gut microbiota related to Chinese | 1 | Centenarians | Centenarians 75 | Unpublished | [53] |
| 2 | Urbanization | Urban group 20, rural group 20 | PRJNA349463 | [54] | |
| 3 | Ethnic specificity | Han Chinese 48, kazaks 48, uyghurs 96 | NODEP00000053 | [55] | |
| 4 | Ethnic specificity | Mongolians 63, Inner Mongolia of China 47 | PRJNA328899 | [56] | |
| 5 | Infant twins | Monozygotic group 5, dizygotic group 5 | PRJEB12669 | [57] | |
| 6 | Bioregenerative life support system | Healthy Chinese subjects 4 | CNP0000408 | [58] | |
| 7 | Fecal sample storage | Volunteers 8 | PRJEB23662 | [59] | |
| Unpublished studies | PRJNA493884, PRJNA613947, PRJNA401977, PRJNA563508, PRJNA530971, PRJNA551354, PRJNA565546, PRJNA530620, PRJNA300602, PRJNA492158, PRJNA360177, /PRJNA638405, PRJNA565268, PRJNA674522, PRJNA474776, PRJNA597371 | ||||
表选项
由表 1可知，目前研究的疾病种类比较分散，很多疾病仅有1项相关研究报道。研究目的也存在较大差异，有些研究疾病与正常对照之间的菌群差异，有些研究疾病在发病过程中的菌群演变，有些研究药物治疗前后的菌群变化等。另外研究过程中样品的数量、收集的方法、测序的平台、数据处理的模型等均存在一定的差异。这些因素严重影响了相关研究结果的可比较性。本研究仅对在中国发病率较高、有较多公开发表文献的研究与国外相关研究进行比较分析。
1.2 2型糖尿病相关人肠道微生物宏基因组研究糖尿病是一种多器官、异质性、多因子疾病，临床上主要分为1型、2型和妊娠期糖尿病，其中2型糖尿病(Type 2 diabetes，T2D) 占发病率的90%以上。据国际糖尿病联合会统计，2019年，全世界有4.63亿年龄在20–79岁的成年人患有糖尿病，导致420万人死亡，相关医疗支出至少7 200亿美元；预计到2045年，糖尿病患病人数会上升到7亿[60]。糖尿病已经成为严重危害人民健康和带来巨大医疗开支的“万病之源”。
2020年Gurung等在Google scholar和PubMed搜索筛选到42项针对糖尿病与肠道菌群相关性进行的临床研究，其中大部分使用了16S rRNA基因高通量测序分析的方法，只有7篇研究选用了宏基因组测序方法[61]。综合这42篇研究，大多数研究报道双歧杆菌属Bifidobacterium、拟杆菌属Bacteroides、粪杆菌属Faecalibacterium、阿克曼菌属Akkermansia和罗斯氏菌属Roseburia的细菌丰度与T2D呈负相关，而瘤胃球菌属Ruminococcus、梭杆菌属Fusobacterium和布劳特氏菌属Blautia的细菌丰度与T2D呈正相关[61]。
由于宏基因组测序可以更好地在细菌种水平和功能基因水平进行比较分析，本研究对中国人、丹麦人、德国人、瑞典人的肠道宏基因组与糖尿病关联研究进行了比较。由于临床样品受到的影响因素很多，本研究只对T2D疾病组与正常对照组之间的研究结果进行了总结(表 2)。其中一项研究对阿拉伯联合酋长国的粪便样品进行了宏基因组测序分析，但由于只收集了12个T2D和6个正常对照，样品数较少，且没有发现与疾病相关的菌群差异[62]，所以没有列入表 2。尽管大部分研究认为，肠道产丁酸菌减少可能与T2D的发生发展密切相关，但从表 2可以看出，各研究结果之间的一致性相对较差，甚至有相互矛盾的地方。如Qin等研究发现嗜黏蛋白阿克曼菌Akkermansia muciniphila在T2D患者中相对丰度较高[5]，但Wang等发现Akkermansia muciniphila在正常对照组中相对丰度较高。在基因功能水平，各研究结果之间的一致性相对更好[9]。Reitmeier等筛选发现的区分T2D和正常对照的26个代谢通路中有23个与Qin等的研究结果一致[63]，且主要集中在与糖、脂、氨基酸和维生素代谢相关的信号通路。
表 2 2型糖尿病相关肠道微生物宏基因组分析结果比较Table 2 Comparison of metagenomic analysis of gut microbiota associated with type 2 diabetes mellitus
| Population | T2D/controls | Enriched in T2D group | Enriched in normal control group | References |
| Chinese | 171/174 | A. muciniphila, Bacteroides intestinalis, Clostridium bolteae, Clostridium hatheway, ramosum ramosum, symbiosum symbiosum, Eggerthella lenta, E. coli | Eubacterium rectale, Feacalibacterium prausnitzii, Haemophilus parainfluenzae, Roseburia intestinalis, Roseburia inulinivorans | [5] |
| Transport of sugars, branched-chain amino acid transport, methane metabolism, xenobiotics degradation and metabolism, oxidative stress resistance, sulphate reduction | Bacterial chemotaxis, flagellar assembly, butyrate biosynthesis, metabolism of cofactors and vitamins | |||
| Chinese | 77/97 | Bacteroides caccae, Bacteroides finegoldii, Collinsella intestinalis, Megasphaera elsdenii | A. muciniphila, Clostridium bartlettii, Dialister invisus, Roseburia hominis | |
| Sugar phosphotransferase systems (PTS), ATP-binding cassette transporters of amino acids, bacterial secretion systems, transport system for microcin C, transport system for autoinducer-2 | Modules of V-type ATPase, pyruvate: ferredoxin oxidoreductase, bacterial ribosomal proteins | [6] | ||
| Chinese | 30/30 | Paraprevotella unclassified, Porphyromonas bennonis, Roseburia hominis | Bifidobacterium longum, Coprobacillus unclassified, Veillonella dispar | [9] |
| Fructose and mannose metabolism; starch and sucrose metabolism; amino sugar and nucleotide sugar metabolism, methane metabolism | Amino acid biosynthesis, lipopolysaccharide biosynthesis, fatty acid metabolism, bacterial secretion system, ABC transporters, xenobiotics biodegradation and metabolism | |||
| Swedish | 53/43 | Clostridium clostridioforme, Lactobacillus gasseri, Streptococcus mutans | B. intestinalis, Eubacterium eligens, unknown Clostridium species | [64] |
| Starch and glucose metabolism, fructose and mannose metabolism, ABC transporters for amino acids, ions and simple sugars, glycerolipid metabolism and fatty acid biosynthesis, cysteine and methionine metabolism | Flagellar assembly, riboflavin metabolism | |||
| Germany | 50/50 | No microbiota difference analysis was done | No microbiota difference analysis was done | |
| Bacterial invasion of epithelial cells, riboflavin metabolism, betaLactam resistance, chlorocyclohexane and chlorobenzene degradation, nitrotoluene degradation, phenylalanine metabolism, valine leucine and isoleucine degradation, retinol metabolism, drug metabolism cytochrome P450, metabolism of xenobiotics by cytochrome P450, fluorobenzoate degradation, penicillin and cephalosporin biosynthesis, alphaLinolenic acid metabolism, glycosaminoglycan degradation, nicotinate and nicotinamide metabolism, taurine and hypotaurine metabolism, toluene degradation, lipoic acid metabolism, ubiquinone and other terpenoidquinone biosynthesis | Photosynthesis, cysteine and methionine metabolism, RNA degradation, lysine biosynthesis, peptidoglycan biosynthesis, alanine aspartate and glutamate metabolism, novobiocin biosynthesis | [63] |
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1.3 高血压和动脉粥样硬化相关人肠道微生物宏基因组研究心血管疾病(Cardiovascular disease，CVD)近年来一直是世界范围内的主要死亡原因。据世界卫生组织预测，到2030年，将有2 300多万人死于心血管疾病。CVD是一类异质性复杂疾病，与CVD相关的因素包括基因、肠道微生物、生活环境、生活方式等。在过去10年中，微生物群与CVD的关联研究取得了许多重要进展。高血压和动脉粥样硬化是CVD发生发展的重要病理基础。本文重点对这2种疾病与肠道菌群的关联研究进行总结。
目前共统计到2项研究对中国高血压患者的肠道菌群使用宏基因组测序进行了分析。Li等研究发现[41]，与健康对照组相比，高血压患者微生物丰富度和多样性显著降低，且与健康状况相关的细菌减少，如粪杆菌属Faecalibacterium、颤螺旋菌属Oscillibacter、罗氏菌属Roseburia、双歧杆菌属Bifidobacterium、粪球菌属Coprococcus和丁酸弧菌属Butyrivibrio，但普氏杆菌Prevotella和克雷伯菌Klebsiella等细菌过度生长。在高血压患者中普雷沃菌肠型个体比例较高，而在健康对照组中拟杆菌肠型个体占比较高。此外，通过将高血压患者的粪便移植到无菌小鼠，观察到血压升高可通过微生物群传递，并证明肠道微生物群对宿主血压的直接影响。高血压患者中39个KEGG (Kyoto encyclopedia of genes and genomes) 模块减少，涉及支链氨基酸生物合成和运输、酮体生物合成、双组分调节系统以及蛋氨酸和嘌呤的降解等，17个模块在高血压患者中升高，包括LPS生物合成和输出、磷脂转运、磷酸转移酶系统、苯丙氨酸和磷脂酰乙醇胺的生物合成以及分泌。Yan等研究发现克雷伯氏杆菌Klebsiella spp.、链球菌Streptococcus spp.和粪副拟杆菌Parabacteroides merdae等条件致病菌在高血压患者中较常见，而短链脂肪酸产生菌，如罗氏菌Roseburia spp.和普拉梭菌Feacalibacterium prausnitzii，在对照组中含量较高[42]。在功能基因水平，高血压肠道微生物群表现出较高的膜转运、脂多糖生物合成和类固醇降解功能，而在对照组中，氨基酸、辅因子和维生素代谢活性较高[42]。
国外也有2项研究分别对日本人和芬兰人的肠道菌群与高血压的关系进行了微生物宏基因组测序分析。Stevens等研究发现，在日本高血压患者中，嗜黏蛋白阿克曼菌Akkermansia muciniphila、Alistipes senegalensis、马赛拟杆菌Bacteroides massiliensis、沃氏嗜胆菌Bilophila wadsworthia、内脏臭气杆菌Odoribacter splanchnicus丰度较高，在正常对照组中长双歧杆菌Bifidobacterium longum、灵巧粪球菌Coprococcus catus、大肠杆菌Escherichia coli、肠道罗斯拜瑞氏菌Roseburia intestinalis丰度较高[65]。Palmu等研究发现在芬兰人中高压指数与45个微生物属之间存在显著的、主要为正的相关性，其中27个属于厚壁菌门。有趣的是，他们发现19种不同的乳酸杆菌Lactobacillus与血压指数之间大多呈负相关。其中，公认的益生菌副干酪乳杆菌Lactobacillus paracasei的丰度越高，平均动脉压越低[66]。
动脉粥样硬化与肠道菌群的关联研究堪称研究疾病与肠道菌群的经典。肠道菌群依赖性代谢产物氧化三甲胺和短链脂肪酸以相反的模式调节动脉粥样硬化相关代谢过程，从而影响动脉粥样硬化的发生发展[67]。有2项研究分别对中国人和瑞典人使用宏基因组测序方法研究了肠道菌群与动脉粥样硬化的相关性[43, 68]。在瑞典患者和对照组样本之间共有17个物种的丰度存在显著差异，在中国患者和对照组样本之间共有162个物种的丰度存在显著差异。其中5个常见物种的丰度在2项研究的对照样本中均显著高于患者组。这些细菌包括木糖降解拟杆菌Bacteroides xylanisolvens、内脏臭气杆菌Odoribacter splanchnicus、挑剔真杆菌Eubacterium eligens、食葡糖罗斯拜瑞氏菌Roseburia inulinivorans、肠道罗斯拜瑞氏菌Roseburia intestinalis。由于瑞典总共只有25个样品，中国收集了385份样品，所以相关的比较研究还有待进一步加强。
随着高通量测序等生命科学技术的进步，产生了大量的科学数据，如基因组数据、蛋白质组数据、转录组数据和微生物组数据等，从原始数据到科学知识引导的个性化应用，标准化数据的共享和集成至关重要。在心血管疾病研究过程中总结发展的以数据驱动指导疾病相关的微生物群生物标记物发现方法，将对其他疾病的相关研究具有很强的借鉴和指导作用(表 3)。
表 3 数据驱动的心血管疾病生物标志物筛选数据库和生物信息学工具[69]Table 3 Data-driven cardiovascular biomarker screening database and bioinformatics tools [69]
| Classifications | Names | Descriptions | Websites |
| Microbial reference genome | SILVA | Ribosomal RNA sequence data | https://www.arb-silva.de |
| RDP | 16S rRNA sequences, and Fugal 28S rRNA sequences | http://rdp.cme.msu.edu | |
| NCBI-Refseq | RefSeq microbial genomes database | http://www.ncbi.nlm.nih.gov/genome | |
| MG-RAST | Metagenomics database and portal | http://metagenomics.anl.gov | |
| Mgnify | Comprehensive microbial analysis, archiving platform | http://www.ebi.ac.uk/metagenomics | |
| IGC | Human gut microbiome reference genes that includes 9 879 796 genes | https://db.cngb.org/microbiome | |
| UHGG | The most comprehensive reference genome for human gastrointestinal microbes to date | http://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/ | |
| Microbial and disease relationships | HMDAD | Data sets on microbe and human disease associations | http://www.cuilab.cn/hmdad |
| Disbiome | Database for microbiot a disease information | https://disbiome.ugent.be/home | |
| gutMDisorder | Database for gut microbiota in disorders and interventions | http://bio-annotation.cn/gutMDisorder | |
| Cardiovascular disease related databases | MorCVD | A database for host-pathogen PPIs involved in CVD | http://morcvd.sblab-nsit.net/About |
| CVDHD | A herbal database in CVD | http://pkuxxj.pku.edu.cn/CVDHD | |
| MIRKB | A myocardial infarction risk knowledge-base | http://www.sysbio.org.cn/mirkb | |
| CARDIO-LNCRNAS | The database of the lncRNA transcriptome in human cardiovascular system | http://bio-bigdata.hrbmu.edu.cn/CARDIO-LNCRNAS | |
| CVDncR | Non-coding RNA related to cardiovascular diseases | http://sysbio.org.cn/cvdncr | |
| CardioGenBase | Multi-omics database for major CVD | http://www.CardioGenBase.com | |
| C/VDdb | Multi-omic studies of cardiovascular-related traits | http://www.padb.org/cvd | |
| CADgene | Database for coronary artery disease genes | http://www.bioguo.org/CADgene | |
| In-Cardiome | Knowledgebase for coronary artery disease | http://www.tri-incardiome.org | |
| CHD@ZJU | Knowledgebase research platform on CHD | http://tcm.zju.edu.cn/chd | |
| Microbe-disease associations prediction | KATZHMDA | A computational model of KATZ measure for predict the human microbe-disease association | http://dwz.cn/4oX5mS |
| BMCMDA | Prediction microbe-disease association based on binary matrix completion | NA | |
| LGRSH | Learning graph representations and a modified scoring mechanism on the heterogeneous network | NA | |
| Risk-prediction model in CVD | The Framingham Heart Study | Multi-omics and multivariable risk-prediction algorithms | NA |
| SPoRT | A stroke risk prediction model based on health behaviors | NA | |
| QRISK3 | A comprehensive cardiovascular disease prediction algorithm that includes physical signs, lifestyle, other diseases, drugs, etc. | NA | |
| HHS | A lifestyle-based tool that estimates ASCVD events | https://healthyheartscore.sph.harvard.edu/. | |
| PREDICT | Cardiovascular disease cohort in New Zealand | NA | |
| CVDPoRT | A cardiovascular disease risk-prediction model using population health surveys | https://github.com/Ottawa-mHealth/predictive-algorithms. | |
| Human microbe-drug associations prediction | MDAD | Database for microbe-drug association | http://chengroup.cumt.edu.cn/MDAD |
| RapidAIM | Microbiome responded to drugs based on culture and metaproteomics | NA | |
| GCNMDA | Predicting human microbe-drug associations via graph convolutional network | https://github.com/longyahui/GCNMDA |
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1.4 慢性肠炎和肠癌相关人肠道微生物宏基因组研究2021年发布的中国人健康大数据显示，不管是发病率还是死亡率，肠癌均名列中国常见癌症的前5位，对人类健康造成严重威胁。炎性肠病(Inflammatory bowel disease，IBD) 是一种病因不明的慢性肠道炎症性疾病，因发病率逐渐升高，已成为我国常见的消化系统疾病，临床上分为溃疡性结肠炎(Ulcerative colitis，UC) 和克罗恩病(Crohn’s disease，CD)。众所周知，肠道微生物群组成的改变与IBD的发生相关。此外，IBD患者在晚年发生大肠癌的风险增加，对渗透性共生微生物的异常免疫反应可能在促进疾病进展中发挥关键作用。
我国****分别针对中国UC、CD、结肠癌和直肠癌患者各开展了1项肠道微生物宏基因组测序研究(表 1)，并通过生物信息学与欧美多国的肠道微生物宏基因组进行了大数据比较分析[36, 70-72]。比较研究发现，普拉梭菌Faecalibacterium prausnitzii、直肠真杆菌Eubacterium rectale、布氏瘤胃球菌Ruminococcus Bromii、青春双歧杆菌Bifidobacterium adolescentis、长双歧杆菌Bifidobacterium Longum和产气柯林斯菌Collinsella aerofaciens在健康人群中相对丰度较高，单形拟杆菌Bacteroides uniformis、普通拟杆菌Bacteroides vulgatus、Blautia stercoris、肠道罗斯拜瑞氏菌Roseburia intestinalis、脆弱类杆菌群Bacteroides fragilis、卵形拟杆菌Bacteroides ovatus和粪拟杆菌Bacteroides caccae在IBD患者中相对丰度较高，嗜黏蛋白阿克曼菌Akkermansia muciniphila、大肠杆菌Escherichia coli、Prevotella copri、Alistipes putredinis和扭链瘤胃球菌Ruminococcus torques在结直肠癌(Colorectal cancer，CRC) 患者中相对丰度较高。尽管不同人群队列研究结果存在一定的差异，但基本公认具核梭杆菌Fusobacterium nucleatum在CRC发病过程中发挥重要作用。
1.5 强直性脊柱炎(Ankylosing spondylitis，AS) 和类风湿性关节炎(Rheumatoid arthritis，RA) 相关人肠道微生物宏基因组研究AS和RA是2种临床上较常见的由于慢性炎症引起的关节功能障碍性疾病。目前有5项研究采用宏基因测序方法分析了肠道菌群与AS发生的相关性，其中4项针对中国人群，1项针对美国人群。有2项研究采用宏基因测序方法分析了肠道菌群与RA发生的相关性，其中针对中国人群和日本人群的研究各1项。从表 4可以看出，各项研究结果之间差异较大，不管是在细菌种水平，还是在基因功能水平，国内研究之间及国内与国外研究之间均没有相对一致的研究发现。
表 4 强直性脊柱炎和类风湿性关节炎相关肠道微生物宏基因组分析结果比较Table 4 Comparison of metagenomic analysis of gut microbiota associated with ankylosing spondylitis and rheumatoid arthritis
| Diseases | Patients/Controls | Enriched in patients | Enriched in normal controls | References |
| Ankylosing spondylitis | 127/123 Chinese | Clostridiales bacterium 1747FAA, C. bolteae, C. hatheway | Bifidobacterium adolescentis, Coprococcus comes, Lachnospiraceae bacterium 5163FAA, Roseburia inulinivorans | [20] |
| Superoxide proteinase | ATP-dependent serine phosphatase, ATP phosphoribosyltransferase, histidinol-phosphate transaminase, polyphosphate kinase, pyridoxal 5′-phosphate synthase | |||
| 97/114 Chinese | Prevotella melaninogenica, Prevotella copri, Prevotella sp. C561, Bifidobacterium bifidum, Bifidobacterium longum, Bifidobacterium pseudocatenulatum | Bacteroides spp. | [21] | |
| Cell motility, membrane transport, metabolism of cofactors and vitamins, proteasome functions, signal transduction | Glycosaminoglycan metabolism, secondary metabolites biosynthesis, and symbiosis | |||
| 113/37 Chinese | Bacteroides nordii, Flavonifractor plautii, Oscillibacter unclassified, Parabacteroides distasonis | [22] | ||
| Carbohydrate metabolism, glycan biosynthesis and metabolism, glycosaminoglycan degradation, starch and sucrose metabolism | Degrade dioxin, energy metabolism, folding, replication and repair, sorting and degradation, translation, vitamin B12 transport system, xenobiotics biodegradation and metabolism | |||
| 85/62 Chinese | Acidaminococcus fermentans, Bacteroides coprophilus, Eubacterium siraeum, Prevotella copri, Parabacteroides distasonis | [23] | ||
| Glycosaminoglycan degradation, lipopolysaccharide (LPS) biosynthesis, oxidative phosphorylation | ATP-binding cassette (ABC) transporters, butanoate metabolism, gluconeogenesis, glycolysis, phosphoenolpyruvate-dependent phosphotransferase (PTS) system | |||
| 21/24 American | B. adolescentis, P. bennonis Tryptophan synthesis | Bacteroides dorei, Streptococcus anginosus | [73] | |
| Rheumatoid arthritis | 77/17 Chinese | Bifidobacterium dentium, Clostridium asparagiforme, E. lenta, Gordonibacter pamelaeae, Lactobacillus sp., Ruminococcus lactaris | B. bifidum, Klebsiella pneumoniae, Megamonas hypermegale, Sutterella wadsworthensis | [24] |
| Converting acetate to methane, reductive acetyl-CoA | Lipopolysaccharide biosynthesis, lipopolysaccharide transport, secretion systems (type Ⅱ, type Ⅳ and type Ⅵ) | |||
| 82/42 Japanese | Bacteroides sartorii, Gardnerella vaginalis, Prevotella amnii, Prevotella corporis, Prevotella denticola, Prevotella disiens, Prevotella marshii, Prevotella somerae | [74] | ||
| Adipocytokine signalling pathway, fatty acid biosynthesis, folate biosynthesis, glycosaminoglycan degradation, MAPK signalling pathwayplant, nitrotoluene degradation, ubiquinone and other terpenoid-quinone biosynthesis | Transport and catabolism |
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2 总结与展望除了以上详细总结的疾病外，如表 1所示，其他疾病与中国人肠道微生物宏基因组之间的关联还有零散少数报道，如肥胖、多囊卵巢综合征、肾衰竭、更年期综合征、冠心病、心房颤动、自闭症、注意缺陷多动障碍、精神分裂症、系统性红斑狼疮、幽门螺杆菌感染、HIV感染、肺结核感染、肝硬化和肺癌等。另外，也有****对肠道菌群在发育与长寿、民族特性和城镇化等方面的作用进行了研究。
尽管新一代测序技术的发展使得研究人员能够从更广泛、更深入的角度探索和了解肠道微生物群。然而，即使在同一种疾病中，肠道菌群的不同研究结果也存在较大差异，难以直接指导临床诊断和治疗[75]。这可能是由于：(1) 肠道菌群会受到地理位置、年龄、性别、饮食、抗菌药物、心理状况、运动等因素的影响[76-77]，健康微生物组仍难以定义。如中国人群研究发现的阿克曼菌Akkermansia可以用于T2D的鉴别诊断，不适用于德国人群，德国人群研究发现可以用于T2D鉴别诊断的乳杆菌Lactobacillus不适用于中国人群[63]。(2) 临床疾病非常复杂，同一种疾病分为不同的亚型和不同的疾病发展阶段。如Wang等研究发现单纯收缩型高血压和单纯舒张压其菌群变化存在一定的差异[78]。同时，不同疾病之间存在着临床症状相似和菌群变化交叉重叠等现象，增加了利用菌群进行鉴别诊断和靶向治疗的难度。如IBD与CRC等疾病，在与正常对照组相比后存在一些相似的菌群变化特征[70]。(3) 微生物组样品收集与储存、测序平台和分析流程等特别容易出现重大差异[79-80]，导致研究结果的高度可变性[81]。(4) 目前大部分研究使用粪便样本替代肠道微生物群，但小肠和大肠的长度不同，包括化学和营养梯度的生理变化以及宿主免疫活性的划分也不同，所有这些都会影响微生物的组成。另外，粪便样本采集前的均质化会扰乱粪便的生物结构，但如果不均质化处理，样本的代表性又可能不够。另外，有些细菌虽然能被测序检测到，但可能是死细菌[82]，因而影响了测序结果的可靠性。
目前，大多数研究仍集中在基因组水平，很少涉及转录组、蛋白质组或代谢组。即使在基因组水平，shotgun宏基因组测序由于成本较高、数据分析门槛较高，使得基于标记的扩增子测序如16S rRNA基因测序盛行。尽管16S rRNA基因测序已经成为揭示微生物群变化与疾病相关性的有力工具，但在揭示微生物群如何改变哺乳动物生理学的机制研究方面仍受到很多限制[83]：(1) 许多疾病关联是在细菌门、纲、目或属等比较高级的细菌分类水平上分析。考虑到同一物种不同菌株的功能不同，如Gálvez等研究发现Prevotella copri不同菌株对多糖的代谢利用能力存在较大差异[84]，鉴定与疾病相关的微生物菌株是一个挑战。(2) 由于90%以上肠道细菌属于难培养或未培养微生物，即使鉴定到了疾病相关菌株，要成功获得靶标菌株仍然存在较大困难。(3) 基于PCR扩增的测序在DNA扩增过程中会存在一定偏倚。(4) 忽视了真菌[85]和病毒[86]等在维护人类健康中发挥的重要作用。此外，尽管人体肠道被成百上千种不同细菌所占据，但它们生物合成产生的代谢物可能存在较多冗余[87]。鉴于人体肠道菌群是一个动态的受到多种因素影响、微生物群落内部以及微生物群落与宿主之间相互作用关系非常复杂的生态系统，采用多组学整合策略，研发新的实验模型和数据算法等仍然是更好认识肠道菌群组成和生理功能的必要条件。
鉴于肠道菌群的重要生理功能及其与疾病发生发展的密切关系，肠道菌群已经成为很多药物研发的新靶标。尤其在肿瘤免疫治疗中，肠道菌群具有影响宿主免疫反应的功能，已经被列为肿瘤免疫治疗药物药效评价的重要指标之一[88]。同时越来越多研究表明肠道菌群广泛参与药物代谢，从而影响药物的有效利用度和疗效[89-90]。系统地理解药物-微生物-宿主三者之间的复杂关系将有利于新药的研发和新型治疗方法的选择，从而提高药物疗效，减少不良反应。我国传统中医药已经有几千年的临床疾病治疗经验积累，然而，由于缺乏系统的科学验证，缺乏有关中药作用机制的详细阐述限制了其应用。中药以口服给药为主，中药成分往往不被宿主直接吸收，而是进入肠道被肠道菌群转化。近年来的研究表明，肠道微生物群参与了食物和营养物质的代谢，在中药成分转化为功能性代谢产物过程中发挥着重要作用，这可能影响了中药的治疗活性。在各种中草药与肠道菌群相互作用研究不断增加的同时，善于使用高通量测序和代谢组学平台等前沿多组学研究工具以及先进的生物信息学分析、数据库和算法，将为新的功能代谢物识别和未来中医药的研究奠定基础[91]。
近年来，尽管关于肠道微生物的研究开展得如火如荼，但关于肠道微生物与疾病的关系仍有许多未解之谜。(1) 关于什么是“好的”肠道微生物群仍没有形成共识。如何定义临床上肠道微生物紊乱和发展可靠准确的微生物诊断方法仍然具有困难。(2) 很多疾病与肠道微生物紊乱之间的因果关系仍不清楚。其发病原因是由于某一种细菌或菌株数量的改变引起，还是整个菌群紊乱导致仍有待进一步研究。肠道微生物在疾病发生发展中的作用及其作用机制仍不清楚。(3) 大数据分析建立了一套204 938个基因组和1.71亿个蛋白质序列的人体肠道微生物组数据集[92]。但在4 644个物种中，71%的细菌仍属于未培养菌，微生物的分离培养和功能研究仍有待进一步加强。(4) 除粪微生态移植外，是否可以使用单一或少数几种菌株组合用于疾病的有效防治仍需不断探索。(5) 疾病不仅是复杂的，而且是高度动态的，在整个疾病治疗过程中可能需要采用不同的靶向肠道菌群的治疗策略。
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