Allelopathic effects of submerged macrophyte Myriophyllum spicatum on bloom-forming cyanobacteria, Microcystis aeruginosa
- 주제(키워드) Allelopathy , Ecometabolomics , Myriophyllum spicatum , Microcystis aeruginosa , GC-MS , Machine learning approach
- 주제(DDC) 570
- 발행기관 아주대학교
- 지도교수 박상규
- 발행년도 2021
- 학위수여년월 2021. 8
- 학위명 박사
- 학과 및 전공 일반대학원 생명과학과
- 실제URI http://www.dcollection.net/handler/ajou/000000031150
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
ABSTRACT Many studies have been performed to find measures for control of cyanobacterial harmful algal blooms (cyanoHABs). Due to ongoing need for development of species specific or selective control methods considering eco-safety, scientists are now attempting to apply allelopathic interactions between submerge vascular plants and phytoplankton. Especially, a submerged macrophyte Myriophyllum spicatum (Eurasian watermilfoil) have been intensively investigated for their allelopathic inhibition effects on bloom-forming cyanobacteria such as Microcystis aeruginosa. Despite the increasing knowledge through many studies along with technological advances, the mechanism of the suggested candidate allelochemicals have not been clearly concluded. To reveal the ecological relevance of M. spicatum and cyanobacteria, it is necessary to attempt additional studies considering sufficient experimental period and various experimental designs. The first study investigated allelopathic inhibition of M. spicatum on four phytoplankton species of two taxonomic groups: Chlorophyta Selenastrum capricornutum, Scenedesmus obliquus, and cyanobacteria M. aeruginosa (different strains for toxic, non-toxic, the North Han River originated (NHR) and colonies) and Anabaena circinalis. Inhibitions of unicellular cyanobacteria M. aeruginosa were over 50% for three consecutive days from the 3rd to the 5th day of the coexistence. Myriophyllum spicatum even inhibited M. aeruginosa at a high initial concentration (1.1 mg Chl L-1). Moreover, the growth of M. aeruginosa in a mixture of four phytoplankton species (S. capricornutum, S. obliquus, M. aeruginosa and A. circinalis) was selectively inhibited by M. spicatum. The inhibition of toxic, non-toxic and NHR of Microcystis by M. spicatum were not significantly different. Mucilaginous M. aeruginosa and filamentous A. circinalis were mostly not inhibited by M. spicatum. In the second study, metabolomes of M. spicatum from various sets of coexistence experiments were measured with gas chromatography quadrupole mass spectrometry (GC-Q/MS). This study proposed that the potential mechanism of the secretion of allelopathic substances by M. spicatum, might related to the ontogenetic development of the plant despite lack of direct proof. Applying un-targeted metabolomics approach, the five putative allelopathic compounds of M. spicatum, that significantly increased according to coexisted days, were selected using multivariate analysis. The third study reported the seasonal and regional metabolic differences of M. spicatum in South Korea. The selected important metabolites from the seasonal comparisons were significantly correlated with water temperature, while the important metabolites from the regional comparisons were significantly correlated with the parameters showing habitat features such as dissolved oxygen (DO). Considering the feasibility for application of M. spicatum as a measure to control the HABs, the results of machine learning approach suggested that the plant samples collected in January, August, November, and December were predicted to be more effective in cyanobacterial inhibition. And the M. spicatum collected from the site Gongreung-cheon, the Osan-cheon, the Seom-gang 1, and the Seom-gang 2 were predicted to be more effective in the inhibition. The subsequential neural network model training results suggested the clear prediction based on the multivariate data of the plant metabolomes. The prediction on the August samples were congruent with the previous results of the coexistence experiments; the inhibition effects in August began to appear earlier with high inhibition. However, to understand the metabolic features in autumn and winter more extended investigation on the metabolic changes of M. spicatum is necessary. Furthermore, environmental behaviors of the putative allelochemicals need to be investigated by more field or mesocosm experiments to reveal the ecological mechanisms.
more목차
Chapter 1. General Introduction 1
Chapter 2. Allelopathic Inhibition Effects of Myriophyllum spicatum on Growths of Bloom-Forming Cyanobacteria and Other Phytoplankton Species in Coexistence Experiments 6
2.1. Introduction 7
2.2. Materials and methods 9
2.2.1. Materials 9
2.2.2. Coexistence experiments 10
2.2.2.1. One-on-one trials of various phytoplankton species with M. spicatum 10
2.2.2.2. A mixture of cyanobacteria and green algae with M. spicatum 13
2.2.2.3. One-on-one trials of various strains and forms of M. aeruginosa with M. spicatum 14
2.2.2.4. Various concentration gradients of M. aeruginosa with M. spicatum 15
2.2.3. Statistical analysis 15
2.3. Results 17
2.3.1. One-on-one trials of various phytoplankton species with M. spicatum 17
2.3.2. A mixture of cyanobacteria and green algae with M. spicatum 19
2.3.3. One-on-one trials of various strains and forms of M. aeruginosa with M. spicatum 23
2.3.4. Various concentration gradients of M. aeruginosa with M. spicatum 27
2.4. Discussion 29
Chapter 3. Metabolic Changes of Myriophyllum spicatum in Coexistence Experiments with Microcystis aeruginosa 34
3.1. Introduction 34
3.2. Materials and methods 37
3.2.1. Coexistence experiments 37
3.2.2. Sampling 38
3.2.3. Sample extraction and derivatization 38
3.2.3.1. Chemicals and reagents 38
3.2.3.2. Metabolite extraction and sample derivatization 39
3.2.4. GC-Quadrupole/MS analysis and data acquisition 40
3.2.5. GC-MS data analysis using MS-DIAL 40
3.2.6. Statistical analysis 41
3.3. Results 44
3.3.1. Metabolic changes of M. spicatum depending on coexisted phytoplankton species 44
3.3.2. Metabolic changes of M. spicatum depending on concentration levels of coexisted cyanobacteria M. aeruginosa 46
3.3.3. Metabolic changes of M. spicatum depending on coexisted days with M. aeruginosa 48
3.4. Discussion 56
Chapter 4. Seasonal and Regional Variabilities in Metabolomes of Myriophyllum spicatum in Natural Systems 60
4.1. Introduction 61
4.2. Materials and methods 63
4.2.1. Sampling sites and collection 63
4.2.2. Sample extract and derivatization 68
4.2.3. Untargeted GC-Q/MS based metabolomics analysis 68
4.2.4. Raw GC-MS data preprocessing 69
4.2.5. Machine learning approach 70
4.2.6. Statistical analysis 73
4.3. Results 74
4.3.1. Seasonal comparison 74
4.3.2. Regional comparison 84
4.3.3. Machine learning approach 94
4.4. Discussion 95
Chapter 5. General Conclusions 99
References 103
Appendix 117
국문요약 122
