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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：	network 、gut microbiota 、artificial hibernation 、probiotics 、Polypedates 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：118 下載：4
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無中文摘要

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.

  Introduction    13
1    Strong links between animal gut microbiome and its physiology and behaviour    13
1.1    The amount and importance of microorganisms in the gastrointestinal tract (gut microbiota)    13
1.2    Phylogenetic diversity and structural assembly of gut microbiota correlates to host physiology and behaviour    13
2    Seasonal behavioural observations of Polypedates megacephalus    15
2.1    Population distribution of P. megacephalus in Taiwan    15
2.2    Seasonal dispersal and habitat usage of P. megacephalus in Taiwan    15
2.3    Invasive P. megacephalus, potential threat, and current solutions for invasion    16
3    Seasonal alterations on gut microbiota in hibernators    17
3.1    Compositional changes of gut microbiota during hibernation    17
3.2    Seasonal shifts of dominant bacterial populations in hibernators identified by conventional methods    18
3.3    Seasonal restructure of gut bacterial assembly in hibernators characterized by advanced sequencing technology    19
4    Network inference provides insight into the mechanisms of gut bacterial assembly    21
4.1    The potentials of top-down approach on microbial community    21
4.2    Deciphering microbial interaction provides insights into causal relationships    22
4.3    Network inference theory    24
4.4    From static network to dynamic network: time-series data    26
4.5    Current pitfalls of network inference    28
5    Well-developed intestinal probiotic system    29
5.1    Lactic acid bacteria (LAB) – the wild used probiotics    29
5.2    The interactions between probiotic strains and other bacteria    30
5.3    Host immune responses by probiotics    31
6    Probiotics used in real applications    32
6.1    Probiotic strains used in raniculture    32
6.2    The growing process of probiotics development    33
7    From network theory to practical probiotic system    33
7.1    Network inference by the growth microbiome time-series (GMT) datasets    33
7.2    Network validation by probiotic system    34
7.3    Research objectives    34
  Materials and Methods    37
1    Animal individuals    37
1.1    Treefrogs from season fall, winter, and spring    37
1.2    AH treefrogs in the laboratory    38
2    Microbiome dataset of seasonal changes    39
2.1    Feces collection and DNA extraction    39
2.2    16S rRNA sequencing    40
2.3    16S rRNA amplicon analysis    41
2.4    Functional predictions    41
3    The growth microbiome time-series dataset    42
3.1    Animal individuals    42
3.2    16S rRNA sequencing    43
3.3    16S rRNA amplicon analysis    44
4    Network inference tools    44
4.1    Correlation-based network inference tool    44
4.2    Regression-based network inference tool    46
4.3    Training dataset and validation    47
5    Network analysis    49
5.1    The probiotic sub-network for validation and application    49
5.2    Network visualization    49
6    Microbiome dataset of probiotic trials    50
6.1    Probiotic selection and culturing    50
6.2    Animal individuals    51
6.3    16S rRNA sequencing and 16S rRNA amplicon analysis    51
6.4    Validation of network inference    51
6.5    Level of IL-10    52
7    Statistical analysis    53
7.1    Alpha and beta diversity    53
  Results – compositional and metabolic functional changes of gut microbiota over AH treefrogs    55
1    Sampling information in fall, winter, spring, and artificial hibernation    55
1.1    Environmental parameters    55
2    Microbiome in fall, winter, spring, and artificial hibernation    56
2.1    Sequencing quality and throughput    56
2.2    Microbial diversity    56
2.3    Microbial compositions    57
3    Functional prediction    59
  Results – inference, validation, and potential application of microbial interactions    63
1    Model selection    64
1.1    Performance of correlation-based and regression-based model    64
2    The growth microbiota time-series (GMT) dataset    64
2.1    Preliminary test for the GMT dataset    64
2.2    Sequencing throughput    65
2.3    Diversity changes in the GMT dataset    65
2.4    Compositional changes in the GMT dataset    66
3    The inferred interaction relations    66
4    Probiotic selection    67
5    The inferred positive relation facilitates the growth of probiotics, L. garvieae    68
6    The up-regulated IL-10 caused by the increased L. garvieae    69
7    Validation on probiotic system    71
  Discussion    73
1    Seasonal impacts on gut microbiome of hibernators    74
1.1    Hibernation decreases microbial diversity among all hibernating animals    74
1.2    Hibernating animals shared similar phyla of gut microbiota    76
1.3    Seasonal changes in composition of gut microbiota among hibernators    77
1.4    Seasonal changes in functions of gut microbiota among hibernators    77
2    Habitat impacts on gut microbiome of amphibian    78
2.1    Gut microbiota of adult amphibian harbor similar composition in aquatic animals than terrestrial animals    79
3    Artificial hibernation increases the risk of pathogenic infection in treefrogs    81
3.1    The increase of pathogenic bacteria in artificial hibernation    81
3.2    High risk of pathogenic infection in artificial hibernation    81
4    The potential control agent for invasive treefrog: a perspective from gut microbiota    83
5    Regression-based models provide better performance than correlation-based methods    84
5.1    Performance comparison between regression-based and correlation-based methods    85
6    The breakthrough of gut microbiota in amphibians – microbial interactions    86
6.1    The potentials of network inference in amphibian studies    87
6.2    Inference of microbial interactions based on the GMT datasets    88
7    The interactions between probiotics and other gut microbes in amphibians    90
7.1    Consistency of microbial interactions between bottom-up and top-down research and more insights from network inference    90
8    The probiotic consortia    91
8.1    Potential probiotic consortia determined by network inference    91
9    Validation of network inference using probiotics    92
9.1    Network validation according to real abundance of bacterial changes in the community    92
10    The bridge between network theory and applications    94
  Conclusions and future directions    95
1    Future directions    96
1.1    Incorporate species network with metabolic network    96
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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