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研究生: 翁正軒
Weng, Cheng Hsuan
論文名稱: 探索斑腿樹蛙腸道菌以及其網絡關係
Exploring gut microbiota of Polypedates megacephalus and the inference of its microbial interactions
指導教授: 王達益
Wang, Dar-Yi
學位類別: 博士
Doctor
系所名稱: 生命科學系
Department of Life Science
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 132
中文關鍵詞: networkgut microbiotaartificial hibernationprobioticsPolypedates megacephalus
英文關鍵詞: network, gut microbiota, artificial hibernation, probiotics, Polypedates megacephalus
DOI URL: http://doi.org/10.6345/DIS.NTNU.SLS.006.2019.D01
論文種類: 學術論文
相關次數: 點閱:120下載:4
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    The concerted activity of intestinal microbes is crucial to the health and development of their host organisms. Studies have suggested that microbial assemblages in the intestine of animals are engines of globally important host physiological processes between hibernating and non-hibernating states. The advances in Next Generation Sequencing (NGS) technologies facilitate our understanding of gut microbiota with high resolutions in diversity and metabolic functioning between hibernating and non-hibernating seasons. Polypedates megacephalus is an invasive species in Taiwan since 2006. The approach of habitat usage and population dispersal of this invasive treefrogs across seasons have suggested a rapid expansion across counties in Taiwan. However, it still lacks an effective solution to control the expansion of invasive P. megacephalus. Due to the reciprocal interactions between gut microbiota and host physiology, I attempt to explore gut microbiota of P. megacephalus, decipher microbial interactions to understand the potential mechanisms of microbial ecosystem, and further manipulate host response by modulating gut microbiota according to the guidance by computational network analysis. This study not only delineated seasonal changes of gut microbiota in composition and metabolic functioning but demonstrated the potentials of computational network inference toward practical applications on animal systems.
    The compositional and predicted functional changes of gut microbiota across non-hibernating and artificial hibernating seasons were identified based on 16S rRNA amplicon analysis. The abundance profile and predicted functions of microbial community significantly change between artificial hibernating (AH) and non-hibernating (NH) treefrogs. Artificial hibernation significantly reduces microbial diversity and the level of Firmicutes and increases the level of Proteobacteria in the treefrog gut microbiota. In addition, AH treefrogs harbor core taxonomic units that are rarely abundant in NH treefrogs. Moreover, artificial hibernation significantly increased relative abundance of red-leg syndrome-related genera such as Citrobacter and Aeromonas. Functional predictions via PICRUSt and Tax4Fun suggested that artificial hibernation has effects on most pathways including metabolism and signal transduction. These results suggest that artificial hibernation restructure gut microbiota in treefrogs and significantly reduce microbial complexity of gut microbiome.
    The use of computational methods to decipher microbial interactions have been applied on microbiome data in a time-series fashion. A time-series microbiome data could monitor the population changes of each bacterium in the community over time. Due to the adjustable gut microbiome complexity of hibernating animals, the growth microbiome time-series (GMT) dataset is proposed to apply on the computational network inference methods. Among varieties of network inference tools, regression-based network model is selected and utilized due to its better performance tested by using in silico dataset. Lotka-Volterra models, also known as predator–prey equations, are the most currently used regression-based method, and predict both dynamics of microbial communities and how communities are structured and sustained. The interaction network of gut microbiota at the genus level in the treefrog was constructed using Metagenomic Microbial Interaction Simulator (MetaMIS) package. The interaction network contained 1,568 commensal, 1,737 amensal, 3,777 mutual, and 3,232 competitive relationships, e.g., Lactococcus garvieae has a commensal relationship with Corynebacterium variabile. To validate the interacting relationships, I took advantage of probiotic system to evaluate the responses of gut microbiota to the probiotic trials. The trials involved different groups including single strain (L. garvieae, C. variabile, and Bacillus coagulans, respectively) and a combination of L. garvieae, C. variabile, and B. coagulans, because of the cooperative relationship among their respective genera identified in the interaction network. After a two-week trial, the combination of cooperative microbes yielded significantly higher probiotic concentrations than single strains, and the immune response (interleukin-10 expression) also significantly changed in a manner consistent with improved probiotic effects.

    1 Introduction 13 1.1 Strong links between animal gut microbiome and its physiology and behaviour 13 1.1.1 The amount and importance of microorganisms in the gastrointestinal tract (gut microbiota) 13 1.1.2 Phylogenetic diversity and structural assembly of gut microbiota correlates to host physiology and behaviour 13 1.2 Seasonal behavioural observations of Polypedates megacephalus 15 1.2.1 Population distribution of P. megacephalus in Taiwan 15 1.2.2 Seasonal dispersal and habitat usage of P. megacephalus in Taiwan 15 1.2.3 Invasive P. megacephalus, potential threat, and current solutions for invasion 16 1.3 Seasonal alterations on gut microbiota in hibernators 17 1.3.1 Compositional changes of gut microbiota during hibernation 17 1.3.2 Seasonal shifts of dominant bacterial populations in hibernators identified by conventional methods 18 1.3.3 Seasonal restructure of gut bacterial assembly in hibernators characterized by advanced sequencing technology 19 1.4 Network inference provides insight into the mechanisms of gut bacterial assembly 21 1.4.1 The potentials of top-down approach on microbial community 21 1.4.2 Deciphering microbial interaction provides insights into causal relationships 22 1.4.3 Network inference theory 24 1.4.4 From static network to dynamic network: time-series data 26 1.4.5 Current pitfalls of network inference 28 1.5 Well-developed intestinal probiotic system 29 1.5.1 Lactic acid bacteria (LAB) – the wild used probiotics 29 1.5.2 The interactions between probiotic strains and other bacteria 30 1.5.3 Host immune responses by probiotics 31 1.6 Probiotics used in real applications 32 1.6.1 Probiotic strains used in raniculture 32 1.6.2 The growing process of probiotics development 33 1.7 From network theory to practical probiotic system 33 1.7.1 Network inference by the growth microbiome time-series (GMT) datasets 33 1.7.2 Network validation by probiotic system 34 1.7.3 Research objectives 34 2 Materials and Methods 37 2.1 Animal individuals 37 2.1.1 Treefrogs from season fall, winter, and spring 37 2.1.2 AH treefrogs in the laboratory 38 2.2 Microbiome dataset of seasonal changes 39 2.2.1 Feces collection and DNA extraction 39 2.2.2 16S rRNA sequencing 40 2.2.3 16S rRNA amplicon analysis 41 2.2.4 Functional predictions 41 2.3 The growth microbiome time-series dataset 42 2.3.1 Animal individuals 42 2.3.2 16S rRNA sequencing 43 2.3.3 16S rRNA amplicon analysis 44 2.4 Network inference tools 44 2.4.1 Correlation-based network inference tool 44 2.4.2 Regression-based network inference tool 46 2.4.3 Training dataset and validation 47 2.5 Network analysis 49 2.5.1 The probiotic sub-network for validation and application 49 2.5.2 Network visualization 49 2.6 Microbiome dataset of probiotic trials 50 2.6.1 Probiotic selection and culturing 50 2.6.2 Animal individuals 51 2.6.3 16S rRNA sequencing and 16S rRNA amplicon analysis 51 2.6.4 Validation of network inference 51 2.6.5 Level of IL-10 52 2.7 Statistical analysis 53 2.7.1 Alpha and beta diversity 53 3 Results – compositional and metabolic functional changes of gut microbiota over AH treefrogs 55 3.1 Sampling information in fall, winter, spring, and artificial hibernation 55 3.1.1 Environmental parameters 55 3.2 Microbiome in fall, winter, spring, and artificial hibernation 56 3.2.1 Sequencing quality and throughput 56 3.2.2 Microbial diversity 56 3.2.3 Microbial compositions 57 3.3 Functional prediction 59 4 Results – inference, validation, and potential application of microbial interactions 63 4.1 Model selection 64 4.1.1 Performance of correlation-based and regression-based model 64 4.2 The growth microbiota time-series (GMT) dataset 64 4.2.1 Preliminary test for the GMT dataset 64 4.2.2 Sequencing throughput 65 4.2.3 Diversity changes in the GMT dataset 65 4.2.4 Compositional changes in the GMT dataset 66 4.3 The inferred interaction relations 66 4.4 Probiotic selection 67 4.5 The inferred positive relation facilitates the growth of probiotics, L. garvieae 68 4.6 The up-regulated IL-10 caused by the increased L. garvieae 69 4.7 Validation on probiotic system 71 5 Discussion 73 5.1 Seasonal impacts on gut microbiome of hibernators 74 5.1.1 Hibernation decreases microbial diversity among all hibernating animals 74 5.1.2 Hibernating animals shared similar phyla of gut microbiota 76 5.1.3 Seasonal changes in composition of gut microbiota among hibernators 77 5.1.4 Seasonal changes in functions of gut microbiota among hibernators 77 5.2 Habitat impacts on gut microbiome of amphibian 78 5.2.1 Gut microbiota of adult amphibian harbor similar composition in aquatic animals than terrestrial animals 79 5.3 Artificial hibernation increases the risk of pathogenic infection in treefrogs 81 5.3.1 The increase of pathogenic bacteria in artificial hibernation 81 5.3.2 High risk of pathogenic infection in artificial hibernation 81 5.4 The potential control agent for invasive treefrog: a perspective from gut microbiota 83 5.5 Regression-based models provide better performance than correlation-based methods 84 5.5.1 Performance comparison between regression-based and correlation-based methods 85 5.6 The breakthrough of gut microbiota in amphibians – microbial interactions 86 5.6.1 The potentials of network inference in amphibian studies 87 5.6.2 Inference of microbial interactions based on the GMT datasets 88 5.7 The interactions between probiotics and other gut microbes in amphibians 90 5.7.1 Consistency of microbial interactions between bottom-up and top-down research and more insights from network inference 90 5.8 The probiotic consortia 91 5.8.1 Potential probiotic consortia determined by network inference 91 5.9 Validation of network inference using probiotics 92 5.9.1 Network validation according to real abundance of bacterial changes in the community 92 5.10 The bridge between network theory and applications 94 6 Conclusions and future directions 95 6.1 Future directions 96 6.1.1 Incorporate species network with metabolic network 96 6.1.2 Pathogen resist gut microbiota 97 Figures and Tables 99 Figures 99 Figure 1 – The diagram of correlation-based method. 99 Figure 2 – The flowchart of a null model. 100 Figure 3 – Simulated time series dataset of 50 operational taxonomic units spending 10 time points 101 Figure 4 – Weekly records of temperature and humidity in October, November, December, January, February, and March. 101 Figure 5 – Alpha-diversity rarefaction plot of fecal microbioas between AH and NH treefrogs. 102 Figure 6 – Heatmap. 103 Figure 7 – Compositional variation in microbial communities between AH and NH treefrogs. 104 Figure 8 – Core genera of AH and NH treefrogs. 104 Figure 9 – Rarefaction analyses for the observed number of genera from 12 time points. 105 Figure 10 – Time dependent taxonomic composition spanning 15 days over AH. 106 Figure 11 – Inferred interaction partners of Bacillus, Corynebacterium, and Lactococcus. 107 Figure 12 – Relative abundance of IIPs of Bacillus, Corynebacterium, and Lactococcus after two-week oral trials. 108 Tables 109 Table 1 – Summary of sample information for AH and NH treefrogs. 109 Table 2 – Sample information of preliminary time-series. 109 Table 3 – Summary of sample information of the GMT dataset. 110 Table 4 – The intrinsic growth rate and initial abundance of 50 OTUs from simulation dataset 111 Table 5 – Summary of sample information of oral administration for bacterial composition. 112 Table 6 – Summary of sample information of oral administration for quantitative PCR. 112 Table 7 – Phylogenetic diversity indices of AH and NH treefrogs. 113 Table 8 – Relative abundance of dominant phyla in AH and NH treefrogs. 113 Table 9 – PICRUSt showing predicted relative abundance of KEGG ortholog groups (Level 2 KOs). 114 Table 10 – The performance of correlation-based and regression-based methods 115 Table 11 – Time-dependent diversity spanning 15 days over AH 115 Table 12 – Expression analysis of IL-10 and level of Lactococcus. 116 Table 13 – Validation of IIPs that correlate with Lactococcus, Corynebacterium, or Bacillus. 116 Supplementary Tables 117 Supplementary Table 1 – Interacting matrix between 50 OTUs. 117 Supplementary Table 2 – Tax4Fun showing predicted relative abundance of KEGG ortholog groups. 117 Supplementary Table 3 – Microbial interactions inferred by Pearson’s correlation coefficient based on simulated datasets. 117 Supplementary Table 4 – Ten thousand and three hundred fourteen significant inferred interacting pairs (IIPs) identified by MetaMIS. 117 Supplementary Table 5 – The raw data for the interaction network at the species level. 117 Supplementary Table 6 – Fold change of the inferred relationships with Lactococcus, Corynebacterium, and Bacillus. 117 Supplementary Table 7 – Relative abundance of IIPs of Bacillus, Corynebacterium, and Lactococcus after two-week oral trials. 118 References 119

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