Open challenges for microbial network construction and analysis

Microbial network construction is a popular explorative data analysis technique in microbiome research. Although a large number of microbial network construction tools has been developed to date, there are several issues concerning the construction and interpretation of microbial networks that have received less attention. The purpose of this perspective is to draw attention to these underexplored challenges of microbial network construction and analysis.Subject terms: Microbiome, Microbial ecology     

Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.     

Progress of analytical tools and techniques for human gut microbiome research

Massive DNA sequencing studies have expanded our insights and understanding of the ecological and functional characteristics of the gut microbiome. Advanced sequencing technologies allow us to understand the close association of the gut microbiome with human health and critical illnesses. In the future, analyses of the gut microbiome will provide key information associating with human individual health, which will help provide personalized health care for diseases. Numerous molecular biological analysis tools have been rapidly developed and employed for the gut microbiome researches; however, methodological differences among researchers lead to inconsistent data, limiting extensive share of data. It is therefore very essential to standardize the current methodologies and establish appropriate pipelines for human gut microbiome research. Herein, we review the methods and procedures currently available for studying the human gut microbiome, including fecal sample collection, metagenomic DNA extraction, massive DNA sequencing, and data analyses with bioinformatics. We believe that this review will contribute to the progress of gut microbiome research in the clinical and practical aspects of human health.     

肠道微生物菌群共存网络的构建与分析

【目的】将网络分析应用到肠道微生物的分析之中,探究肠道微生物共存网络拓扑结构等相关网络系数的分析,从而展现肠道微生物共存网络的特性。【方法】将之前研究中的肠道微生物数据根据雌马酚代谢能力划分成雌马酚产生者和非产生者两组,计算两组微生物相对丰度,得出菌种之间的相关系数,构建肠道微生物的共存网络,分析两组间共存网络参数的差异;运用随机网络检验现实网络拓扑结构的特异性,分析两组网络中菌种间的差异。【结果】共存网络中两组节点数分别为45个和47个,即分别有45个和47个不同菌种。比较两组网络结构的差异,发现雌马酚产生者组中的共存网络菌群具有更复杂的连接,且两组之间的其他网络参数存在一定的差异。通过将现实网络与随机网络对比可知,现实网络的拓扑结构具有一定的特异性。将具有代谢雌马酚相关物质能力的菌种在两组网络中标出,发现它们在雌马酚产生者组共存网络中更趋向与来自不同门的菌种产生相互联系。【结论】将网络分析应用于肠道微生物分析之中,可以发掘菌种之间的相互作用和网络拓扑结构的复杂性与差异性,展现肠道菌群结构中之前较少被认识到的一些特征。因而,网络分析的方法可以为未来肠道微生物的研究提供新的视角。     

Osteoprotective effect of green tea polyphenols and annatto-extracted tocotrienol in obese mice is associated with enhanced microbiome vitamin K2 biosynthetic pathways

The role of the gut microbiome in bone health has received significant attention in the past decade. We investigated the effects of green tea polyphenols (GTP) and annatto-extracted tocotrienols (AT) on bone properties and gut microbiome in obese mice. Male mice were assigned to a two (no AT vs. 400 mg/kg diet AT) × two (no GTP vs. 0.5% w/v GTP) factorial design, namely control, G, T, and G+T group respectively, for 14 weeks. The 4th lumbar vertebra (LV-4) and femur were harvested for bone microstructural analysis using μ-CT. Microbiome analysis using 16S rRNA gene sequencing of cecal feces was performed. AT increased bone volume at distal femur. GTP increased serum procollagen type 1 N-terminal propeptide concentration, bone volume at the distal femur and the LV-4, and trabecular number at distal femur; whereas GTP decreased trabecular separation at distal femur. Interactions between GTP and AT were observed in serum C-terminal telopeptide of type I collagen level (control>G=T=G+T) as well as the cortical bone area (control<G=T=G+T) and thickness (T≥G+T≥G≥control) at femur mid-diaphysis. Redundancy analysis showed a significant difference in the gut microbiome profile among different groups and the relative abundance of Akkermansia muciniphila, Clostridum saccharogumia, and Subdoligranulum variabile was increased in the GTP- and AT-supplemented groups. Functional profiling of the gut microbiome showed the combination of GTP and AT induced biosynthetic pathways for vitamin K₂. Our results suggest that GTP and AT supplementation benefits bone properties in obese mice through modifying gut microbiome composition and function.     

Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species

Host-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted protein-coding genes, but on opposite strands or in different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species.     

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.     

Long-term treatment with green tea polyphenols modifies the gut microbiome of female sprague-dawley rats

Green tea polyphenols (GTP) have been shown to exert a spectrum of health benefits to animals and humans. It is plausible that the beneficial effects of GTP are a result of its interaction with the gut microbiota. This study evaluated the effect of long-term treatment with GTP on the gut microbiota of experimental rats and the potential linkage between changes of the gut microbiota with the beneficial effects of GTP. Six-month-old Sprague-Dawley rats were randomly allocated into three dosing regimens (0, 0.5%, and 1.5% of GTP) and followed for 6 months. At the end of month 3 or month 6, half of the animals from each group were sacrificed and their colon contents were collected for microbiome analysis using 16S ribosomal RNA and shotgun metagenomic community sequencing. GTP treatment significantly decreased the biodiversity and modified the microbial community in a dose-dependent manner; similar patterns were observed at both sampling times. Multiple operational taxonomic units and phylotypes were modified: the phylotypes Bacteroidetes and Oscillospira, previously linked to the lean phenotype in human and animal studies, were enriched; and Peptostreptococcaceae previously linked to colorectal cancer phenotype was depleted in GTP treated groups in a dose-dependent manner. Several microbial gene orthologs were modified, among which genes related to energy production and conversion were consistently enriched in samples from month 6 in a dose-dependent manner. This study showed that long-term treatment with GTP induced a dose-dependent modification of the gut microbiome in experimental rats, which might be linked to beneficial effects of GTP.     

Sex-dependent effects on the gut microbiota and host metabolome in type 1 diabetic mice

Sexual dimorphism exists in the onset and development of type 1 diabetes (T1D), but its potential pathological mechanism is poorly understood. In the present study, we examined sex-specific changes in the gut microbiome and host metabolome of T1D mice via 16S rRNA gene sequencing and nuclear magnetic resonance (NMR)-based metabolomics approach, and aimed to investigate potential mechanism of the gut microbiota-host metabolic interaction in the sexual dimorphism of T1D. Our results demonstrate that female mice had a greater shift in the gut microbiota than male mice during the development of T1D; however, host metabolome was more susceptible to T1D in male mice. The correlation network analysis indicates that T1D-induced host metabolic changes may be regulated by the gut microbiota in a sex-specific manner, mainly involving short-chain fatty acids (SCFAs) metabolism, energy metabolism, amino acid metabolism, and choline metabolism. Therefore, our study suggests that sex-dependent “gut microbiota-host metabolism axis” may be implicated in the sexual dimorphism of T1D, and the link between microbes and metabolites might contribute to the prevention and treatment of T1D.     

Towards Predictive Models of the Human Gut Microbiome

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the state of the art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model.     

中国土壤微生物多样性监测的现状和思考

土壤微生物多样性研究是整个生态系统研究中最薄弱的环节之一。高通量测序技术和生物信息学方法的快速发展极大地促进了土壤微生物多样性监测研究的深度和广度。目前世界范围内已经开展了一些综合的微生物多样性研究计划, 如地球微生物计划。这些计划存在的主要问题是缺少动态的监测、研究方法不统一、数据整合困难等。中国土壤微生物多样性监测网(Soil Microbial Observation Network, SMON)是中国生物多样性监测与研究网络(Chinese Biodiversity Monitoring and Research Network, Sino BON)的重要组成部分, 本文中我们对该监测网的建设提出了一些思考。在监测布局上建议选择我国南北水热梯度下的森林生态系统、东西降雨梯度下的草原生态系统、典型湿地生态系统及重要农田生态系统, 同时依托现已建成的生物多样性监测网络观测点或大样地, 布设监测样点, 利用现代环境基因组学和生物信息学技术, 重点围绕土壤微生物群落和功能基因组的组成与多样性, 开展长期定点的动态监测。监测的结果将以名录、数据集或图鉴的形式发布, 包括中国典型生态系统中土壤细菌、古菌、真菌与地衣、土壤宏基因组和重要功能基因的组成和多样性等数据, 同时建设土壤生物大数据平台, 达到监测数据的储存、查询、分析、下载、成图的功能。通过土壤微生物多样性监测, 将阐明我国重要森林、草地、湿地、农田生态系统中土壤微生物组成、多样性、功能基因的时空变化特征和驱动机制, 建立土壤微生物多样性变化与生态系统功能的关系及相关的模型, 预测全球环境条件变化下土壤微生物的演变规律, 为土壤微生物多样性资源的保护和利用提供科学依据。     

Gut microbial alterations in neonatal jaundice pre- and post-treatment

Neonatal jaundice is a common disease that affects up to 60% of newborns. Herein, we performed a comparative analysis of the gut microbiome in neonatal jaundice and non-neonatal jaundice infants (NJIs) and identified gut microbial alterations in neonatal jaundice pre- and post-treatment. We prospectively collected 232 fecal samples from 51 infants at five time points (0, 1, 3, 6, and 12 months). Finally, 114 samples from 6 NJIs and 19 non-NJI completed MiSeq sequencing and analysis. We characterized the gut microbiome and identified microbial differences and gene functions. Meconium microbial diversity from NJI was decreased compared with that from non-NJI. The genus Gemella was decreased in NJI versus non-NJI. Eleven predicted microbial functions, including fructose 1,6-bisphosphatase III and pyruvate carboxylase subunit B, decreased, while three functions, including acetyl-CoA acyltransferase, increased in NJI. After treatments, the microbial community presented significant alteration-based β diversity. The phyla Firmicutes and Actinobacteria were increased, while Proteobacteria and Fusobacteria were decreased. Microbial alterations were also analyzed between 6 recovered NJI and 19 non-NJI. The gut microbiota was unique in the meconium microbiome from NJI, implying that early gut microbiome intervention could be promising for the management of neonatal jaundice. Alterations of gut microbiota from NJI can be of great value to bolster evidence-based prevention against ‘bacterial dysbiosis’.     

Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity

The gut microbiota is involved in metabolic and immune disorders associated with obesity and type 2 diabetes. We previously demonstrated that prebiotic treatment may significantly improve host health by modulating bacterial species related to the improvement of gut endocrine, barrier and immune functions. An analysis of the gut metagenome is needed to determine which bacterial functions and taxa are responsible for beneficial microbiota–host interactions upon nutritional intervention. We subjected mice to prebiotic (Pre) treatment under physiological (control diet: CT) and pathological conditions (high-fat diet: HFD) for 8 weeks and investigated the production of intestinal antimicrobial peptides and the gut microbiome. HFD feeding significantly decreased the expression of regenerating islet-derived 3-gamma (Reg3g) and phospholipase A2 group-II (PLA2g2) in the jejunum. Prebiotic treatment increased Reg3g expression (by ∼50-fold) and improved intestinal homeostasis as suggested by the increase in the expression of intectin, a key protein involved in intestinal epithelial cell turnover. Deep metagenomic sequencing analysis revealed that HFD and prebiotic treatment significantly affected the gut microbiome at different taxonomic levels. Functional analyses based on the occurrence of clusters of orthologous groups (COGs) of proteins also revealed distinct profiles for the HFD, Pre, HFD-Pre and CT groups. Finally, the gut microbiota modulations induced by prebiotics counteracted HFD-induced inflammation and related metabolic disorders. Thus, we identified novel putative taxa and metabolic functions that may contribute to the development of or protection against the metabolic alterations observed during HFD feeding and HFD-Pre feeding.     

Distinct composition and metabolic functions of human gut microbiota are associated with cachexia in lung cancer patients

Cachexia is associated with decreased survival in cancer patients and has a prevalence of up to 80%. The etiology of cachexia is poorly understood, and limited treatment options exist. Here, we investigated the role of the human gut microbiome in cachexia by integrating shotgun metagenomics and plasma metabolomics of 31 lung cancer patients. The cachexia group showed significant differences in the gut microbial composition, functional pathways of the metagenome, and the related plasma metabolites compared to non-cachectic patients. Branched-chain amino acids (BCAAs), methylhistamine, and vitamins were significantly depleted in the plasma of cachexia patients, which was also reflected in the depletion of relevant gut microbiota functional pathways. The enrichment of BCAAs and 3-oxocholic acid in non-cachectic patients were positively correlated with gut microbial species Prevotella copri and Lactobacillus gasseri, respectively. Furthermore, the gut microbiota capacity for lipopolysaccharides biosynthesis was significantly enriched in cachectic patients. The involvement of the gut microbiome in cachexia was further observed in a high-performance machine learning model using solely gut microbial features. Our study demonstrates the links between cachectic host metabolism and specific gut microbial species and functions in a clinical setting, suggesting that the gut microbiota could have an influence on cachexia with possible therapeutic applications.Subject terms: Microbiome, Metagenomics, Next-generation sequencing, Metabolomics     

Alterations in the gut microbiota and metabolite profiles of patients with Kashin-Beck disease,an endemic osteoarthritis in China

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.Subject terms: Metagenomics, Metabolomics     

肠道微生物功能宏基因组学与代谢组学关联分析方法研究进展

近年来基于高通量基因测序的微生物组学研究极大加深了人们对微生物与健康和疾病关系的认识。然而基因测序方法不能直接测定微生物的功能活性,难以鉴定微生物中的关键功能分子,单独使用无法回答肠道微生物何种成员通过何种方式影响宿主等关键科学问题。单一组学研究弊端尽显,多组学联用势在必行。肠道微生物代谢组学以微生物群落所有小分子代谢物为研究对象,可发现肠道微生物随宿主病理生理变化的关键代谢物,为微生物组-宿主互作机制研究提供线索,成为微生物组学研究的重要补充。肠道微生物功能基因组学与代谢组学关联分析在宿主生理、疾病病理、药物药理等方面取得众多进展,展现良好应用前景。然而目前肠道微生物功能基因组学与代谢组学关联分析存在方法滥用、相关性结论与生物学知识相悖等突出问题。为帮助正确应用肠道微生物功能宏基因组学与代谢组学关联分析,本文综述了各种多组学数据整合分析方法的原理、优缺点与适用范围,并给出了应用建议。     

肠道微生物菌株资源库的构建与应用开发

肠道微生物组在组成和功能上都具有极高的复杂性,大量基于免培养的微生物组学研究表明肠道菌群失调与多种疾病都存在密切关联,肠道菌群的稳态与宿主健康密切相关已是共识。同时,越来越多的研究者认识到,可培养微生物菌株资源是肠道微生物组研究从关联分析向功能验证、机理解析和应用开发方向深入发展的基础和保障。本文主要对近年来完成的一些具有代表性的人肠道微生物大规模分离培养和菌株资源库构建工作,进行整理,总结回顾肠道微生物分离培养技术和方法的进展;并通过几个有代表性的基于可培养微生物菌株资源开展的肠道微生物数据挖掘、宿主–微生物互作机理研究和应用开发的成果,展示肠道微生物菌株资源库的应用价值和开发潜力。