Microbiome Big Data Research Towards Spatio-temporal Dynamic Modeling And Applications

Microorganisms have evolved alongside humans and environments,and form an integral part of life,carrying out a variety of vital functions.Such as human microbiota plays a crucial role in the health of human body and environmental microbiota plays a critical role in biogeochemical cycles.Microbial community is the aggregate of microbiota and 99%of them are unculturable.To fully explore and charactize the role of microbiota of microbial community,high throughput sequencing was applied to obtain the genetic information of microbial community.On the one hand,the settings of time points were mostly concentrated in one or a few time points in most of the current microbial studies,the temporal and spatial dynamics of microbial communities remain unclear.On the other hand,although many bioinformatic tools have been developed to analyze the microbiome data,these tools still have limitations in the analysis of the composition of microbial community and the visualization of results.Hence,to explore the temporal and spatial dynamic model of microbial community in this study,we established the spatio-temporal dynamic modeling methods of microbiome big data according to the data types commonly used in microbiome study.The established spatio-temporal dynamic modeling methods were applied to explore the spatio-temporal dynamic patterns of microbial community of human gut microbiome study and environmental microbiome studyAs to the spatio-temporal dynamic modeling methods of microbiome big data,we firstly established the pipelines for 16S rRNA amplicon analysis and metagenomic data analysis by integrating the current tools and commands.Using these pipelines,we can rapidly and effectively analyze the microbiome big data and profile the taxonomical and functional composition of microbial community.Secondly,we developed a virus analysis pipeline for data mining of the metagenomic data.Finally,we developed a pipeline based on phylogenetic method for identification of the horizontal gene transfer for both single species and microbial community.These pipelines adopt modular design and realize the modeling of microbiome big data and personalized analysis of microbiome researchIn the research of dynamic modeling of human gut microbiome,to explore the dynamic patterns of microbial community across a long time with multiple dietary shifts,we tracked a volunteer team who traveled from Beijing to Trinidad and Tobago,stayed there for half a year and then back.High density longitudinal sampling strategy has been applied to obtain their faecal samples and dietary habits,resulted in 287 faecal samples from 41 individuals and harvested 3.3 TB sequencing data.Taking advantage of the high-density longitudinal sampling and quantitative modeling strategies,we confirmed that the human gut microbial communities have a highly plastic and bidirectionally resilient pattern,and the direction of resilience is associated with enterotypes.In taxonomical level,we found that the bidirectional resilience was associated with the dynamic changes of species and subspecies of Prevotella,Bacteroides,Rzuminococcus,Bifidobacterium and Faecalibacterium.In functional level,we found that the bidirectional resilience was not only associated with the metabolic functions of Prevotella copri,Bacteroides dorei,Bacteroides pleteius,Bacteroides ovatus,Bacteroides uniformics and Faecalibacterizum prausnitzii,but also associated with the metabolic of carbohydrates.By integrating their dietary information,we confirmed that the bidirectional resilience was largely mediated by diets.The resilience of human gut microbial communities confirmed in this study can provide guidance for the clinical practice of treating the diseases related to gut microbiota.In the study of human gut microbiome related to hypertension,we selected hypertension as a disease model to explore the dynamic pattern of human gut virome and the association between gut virome and the development of hypertension.We collected 196 faecal metagenomic data related to the dynamic process of hypertension.We characterized the viral composition and bacterial composition of 196 samples,identified the viral-type of each sample and linked the alterations of the gut virome to the development of hypertension by using the viral analysis pipeline of the established spatio-temporal dynamic modeling methods.We stratified these 196 faecal samples into two viral-types and selected 32 viruses as the biomarkers for identifying samples of the different stages of hypertension.We found that viruses could have superior resolution and discrimination power than bacteria for differentiation of healthy samples as well as hypertension samples from different stages.Moreover,as to the co-occurrence networks linking virus and bacteria,we found increasingly pervasive virus-bacteria attachments in the development of hypertension.Overall,our results have shown strong indications of the link between alterations of gut virome and the development of hypertension,and might provide microbial solutions towards early diagnoses of hypertensionIn environmental microbiome study,to reveal the spatial variation pattern of lake microbial community under the influence of agricultural production activities,we selected Honghu lake to investigate the effects of agricultural activities on the ecosystem of Honghu lake.We performed a geospatial analysis of water and sediment associated microbial community structure,as well as physicochemical parameters and antibiotic pollution,across 18 sites in Honghu lake,which range from impacted to less-impacted by agricultural pollution.We confirmed that the structure of microbial community was influenced by the eutrophication and antibiotic pollution.Our results showed that the microbial communities of impacted and less-impacted samples of water were largely driven by the concentrations of total nitrogen(TN),total phosphorus(TP),nitrate nitrogen(NO3--N),and nitrite nitrogen(NO2--N),while those of sediment were affected by the concentrations of organic matter(Sed-OM)and total nitrogen(Sed-TN).Particularly,concentrations of oxytetracycline and tetracycline primarily reflected the variance in taxonomic diversity and predicted functional diversity between impacted and less-impacted sites in water and sediment samples,respectively.These results provide compelling evidence that the microbial community can be used as a sentinel of eutrophication and antibiotics pollution risk associated with agricultural activity;and that proper monitoring of this environment is vital to maintain a sustainable environment in Honghu lake.In a summary,we established the spatio-temporal dynamic modeling methods of microbiome big data.We used these methods to explore the spatio-temporal dynamic patterns of microbial community and its driving factors of human gut microbiome study and environmental microbiome study.