Enhancing insight and prediction of Microcystis blooms through microbial community and exploring the high-throughput isolation technique of novel bacteria

국문 초록 (Abstract)

녹조는 담수 호소 및 하천에서 남조류가 급속하게 증식하고 독소를 생산함으로써 생태계 및 국민 보건에 심각한 문제를 일으키는 현상을 의미한다. 이러한 남조류 중 Microcystis는 전세계적으로 가장 빈번히 녹조를 일으키고 있으며, 남조류 대발생은 부영양화와 기후 변화의 복합적 영향으로 더욱 빈번해 지고 있다. 최근, 시퀀싱 기술의 발전으로 Microcystis와 미생물 군집 간의 상호작용을 더욱 명확하게 이해할 수 있게 되었다. 이를 바탕으로 본 연구는 기후 변화에 의한, 특히 강우 패턴의 차이에 의해 발생하는 미생물 군집과 Microcystis 녹조 간의 관계 변화를 비교 분석하고, 미생물 군집 데이터를 활용하여 Microcystis 발생 예측모델을 개발하였다. 2020년 한국에서는 이례적으로 길고 강한 장마가 발생하였다. 본 연구에서는 2019년의 평균적인 장마기간과 2020년의 긴 장마기간 동안 낙동강 중하류에서 Microcystis의 번성 정도와 미생물 군집 및 기능적 변화를 추적, 분석하였다. 2020년의 7월 중순부터 8월까지의 강한 비는 수온과 Microcystis의 세포수를 감소시켰으며, 이는 2019년과는 다른 녹조 패턴을 보여주었다. 여름 동안의 강한 비는 환경인자를 변화시키고, Limnohabitans와 Fluviicola 속(genus)의 증가와 더불어, 이들이 속한 Gammaproteobacteria가 증가하는 평균적인 가을철 post-bloom과 비슷한 미생물 군집을 형성하도록 하였다. 이러한 변화는 녹조 사멸 시기에 아미노산, 지방산과 같은 남조류 유래 유기물질의 분해에 특화된 미생물이 증가하는 기능상의 변화도 동반하였다. 따라서 여름철 이례적인 장마는 환경인자의 변화를 유도하여 미생물 군집과 기능을 변화시키며, 이는 가을철 Microcystis 녹조가 사멸하는 post-bloom 기간과 유사함을 증명하였다. 미생물 군집이 녹조와 밀접한 관계를 가지고 있음에도 불구하고, Microcystis 녹조를 예측하는 모델은 주로 물리화학적 환경인자를 기반으로 개발되어 왔다. 본 연구에서는 대청호와 낙동강에서 일주일 간격으로 확보한 미생물 군집 데이터를 활용하여 다층퍼셉트론(multilayer perceptron) 예측모델을 개발하였다. 환경인자만을 이용한 모델보다 미생물 군집 데이터를 이용한 모델의 예측력이 높았으며, 두 가지 자료를 모두 포함한 모델이 가장 높은 예측력을 보였다. 이는 미생물 군집이 Microcystis 녹조를 예측하는 데 더욱 중요한 변수임을 나타낸다. 이러한 예측모델을 통해 미생물과 Microcystis간의 상호작용이, 일반적인 수질 환경요인보다 녹조 발생에서 더 중요한 역할을 하고 있음을 확실히 알게 되었다. 앞서 제시한 미생물 군집 분석에 의한 상호작용 및 예측모델의 가장 큰 한계는, 데이터 분석상에서 Microcystis와 높은 상관관계를 보이는 미생물이 어떤 기능에 의해 이러한 패턴을 보였는지 실험적으로 증명하기가 어렵다는 점이다. 이를 실험적으로 입증하기 위해서는 상호작용 네트워크와 예측모델에서 핵심종으로 선정된 박테리아를 분리, 배양하는 것이 우선적으로 요구된다. 그러나 아직 지구상에 있는 미생물의 약 1% 내외만이 실험실에서 분리, 배양이 가능한 것으로 알려져 있다. 본 연구에서는 미세유체 시스템(microfluidic system)을 이용하여 소규모 고처리(high-throughput) 개념의 장기 액체배양 기법을 개발함으로써, 다수의 신종 미생물을 보다 높은 확률과 효율로 분리, 배양할 수 있게 되었다. 하나의 칩(chip)당 20,000개의 미세유체 액적(液滴, droplet)에 인근 하천 시료에 포함된 미생물을 하나의 액적당 0.5 세포에서 1, 2 세포까지 접종하여 4주간 배양하면서 액적당 미생물의 생장을 매주 모니터링하였다. 생장한 미생물의 액적은 주(week)별로 수확하여, 메타지놈 분석을 통하여 어떠한 박테리아들이 배양되었는지 확인하였다. 장기배양을 할수록 신종 미생물 발굴 확률이 높아질 것으로 예상하였고, 실제로 액적당 0.5개의 미생물이 들어간 미세유체 액적에서는 배양기간에 따라 신종 미생물 비율이 약 30% 까지 증가하였다. 이처럼 효율적인 미생물 분리배양 시스템의 개발은, 신종 분리의 가능성을 높임으로써 생태계에서 실제 작용하는 미생물의 생리, 생태적 기능에 대한 이해도를 증진시키는 데에 기여할 것이다. 주요단어(Key words): Microcystis 녹조, 미생물 군집, 기후변화, 다층퍼셉트론, 예측모델, 미세유체 시스템, 신종 박테리아

다국어 초록 (Multilingual Abstract)

Cyanobacterial harmful algal blooms (cyanoHABs) refer to the rapid proliferation of cyanobacteria in freshwater bodies and rivers, producing toxins that pose serious ecological and public health problems. Among these, Microcystis is the most frequent culprit of blooms globally, and the occurrence of cyanobacterial blooms is further exacerbated by the combined effects of eutrophication and climate change. With advancements in sequencing technology, the interactions between Microcystis and microbial communities have become clearer. Based on such previous reports, this study tried to compare and analyze the changes in the relationship between microbial communities and Microcystis blooms, particularly in response to different rainfall patterns due to climate change, and develop a predictive model for Microcystis occurrence using microbial community data. In 2020, an unusually prolonged and intense rainfall occurred in South Korea. This study analyzed the proliferation of Microcystis, as well as microbial community and functional transitions in the Nakdong River during the extended heavy rainfall in 2020 compared to the normal bloom patterns in 2019. Heavy rainfall altered environmental factors, leading to the formation of microbial communities similar to the post-bloom period in 2019, with an increase in genera such as Limnohabitans and Fluviicola, belonging to the Gammaproteobacteria. These changes were accompanied by functional shifts, including an increase in the degradation of organic compounds such as amino acids and fatty acids, which are assumed to have been released due to the significant collapse of cyanobacteria. Therefore, the heavy rainfall-induced changes in environmental factors, altered microbial communities and functions, which resembled the post- bloom period of Microcystis. Despite the close interaction between microbial communities and Microcystis, most models predicting Microcystis blooms have primarily been developed based only on physicochemical environmental factors. In this study, a multilayer perceptron model was developed using microbial community data obtained at weekly intervals from Daechung Reservoir and the Nakdong River. The predictability of the model using microbial community data was higher than that of the model using only environmental factors, and the model incorporating both datasets showed the highest accuracy. Through this model, interactions between microbial community and Microcystis, play a more significant role in cyanobacterial bloom occurrence than conventional environmental factors. The main limitation of the interaction and prediction models based on microbial community analysis is that the exact functions of the microbial species showing a high correlation with Microcystis in data analysis could not be easily verified experimentally in actual field conditions. To prove this, it is necessary to isolate and culture key bacteria identified as core species in interaction networks and prediction models. However, only 1% of microbes on Earth are currently capable of being isolated and cultured in the laboratory. Therefore, this study developed a small-scale, high-throughput long-term liquid culture technique based on microfluidic systems, allowing for the separation and cultivation of numerous novel microbes with higher probability and efficiency. Environmental freshwater was inoculated into microfluidic droplets, with each droplet containing 0.5 to 1 or 2 cells, and cultured for four weeks while monitoring microbial growth weekly per droplet. Harvested droplets were analyzed via 16S rRNA sequencing to identify the bacteria cultured. It was hypothesized that the probability of discovering novel microbes would increase with longer cultivation, and indeed, in droplets containing initially 0.5 microbe per droplet, the proportion of novel microbes increased by approximately 30% over the cultivation period. The development of an efficient microbial isolation and cultivation system is expected to contribute to an enhanced understanding of the physiology and ecological functions of microbes that operate in ecosystems by increasing the possibility of isolating novel species. Keywords: Microcystis blooms, Microbial community, Climate change, Multilayer perceptron, Prediction model, Microfluidic system, Novel bacteria

목차 (Table of Contents)

• Chapter 1. Introduction 1
• 1. Microcystis blooms 1
• 1.1. Characteristics of Microcystis 1
• 1.2. Climate change and Microcystis blooms 3
• 1.3. Control of Microcystis through biotechnological approaches 5
• 1.4. Prediction of Microcystis 7
• 2. Interactions between microbial communities and Microcystis
• blooms 9
• 3. Isolation of microbes from the environment 11
• Chapter 2. Effect of rainfall in shaping microbial community
• during Microcystis bloom in Nakdong River, Korea 15
• 1. Introduction 15
• 2. Material and methods 18
• 2.1. Sampling sites and sample collection 18
• 2.2. Analysis of physicochemical parameters 20
• 2.3. DNA extraction, PCR amplification, and sequencing 20
• 2.4. Analysis of the microbial community 21
• 2.5. Construction of the microbial association network and
• calculation of topological features 23
• 2.6. Analysis of functional prediction 24
• 2.7. Data accessibility 25
• 3. Results 26
• 3.1. Changes in cyanobacterial abundances and physicochemical
• characteristics in response to heavy rainfall 26
• 3.2. Transition patterns of microbial community composition 34
• 3.3. Network analysis of rainfall-related microbial community 42
• 3.4. Functional prediction of microbial community 51
• 4. Discussion 56
• 4.1. Changes in cyanobacterial abundances and physicochemical
• characteristics in response to heavy rainfall 56
• 4.2. Transition patterns of microbial community composition 58
• 4.3. Network analysis of the impact of rainfall on the microbial
• community 60
• 4.4. Functional prediction associated with rainfall 62
• 5. Conclusion 64
• Chapter 3. Microcystis blooms are predictable through ambient
• microbial communities in freshwater: a data-oriented
• approach 65
• 1. Introduction 65
• 2. Materials and methods 68
• 2.1. Description of the study sites 68
• 2.2. Data acquisition 70
• 2.3. Modeling framework for predictions of Microcystis
• abundance 71
• 3. Results 78
• 3.1. Optimization of modeling conditions 78
• 3.2. Variable shrinkage based on influential bacterial ASVs 81
• 3.3. Comparison of prediction capabilities of environmental
• factors and bacterial ASVs 85
• 3.4. Comparison of significant ASVs with MLR 91
• 4. Discussion 94
• 4.1. Optimization of modeling conditions 94
• 4.2. Variable shrinkage based on influential bacterial ASVs 96
• 4.3. Important environmental variables for prediction 97
• 4.4. Important bacterial variables for prediction 100
• 4.5. Limitations and further suggestion for prediction 102
• 5. Conclusion 104
• Chapter 4. Isolation and culture of “difficult-to-culture”
• environmental microorganisms via droplet microfluidic
• systems 105
• 1. Introduction. 105
• 2. Materials and methods 108
• 2.1. Device design 108
• 2.2. Microfabrication 111
• 2.3. Operation of the screening platform 112
• 2.4. Environmental water screening 113
• 2.5. DNA extraction and sequencing 114
• 2.6. Data analysis and functional gene prediction 115
• 3. Results 117
• 3.1. Device characterization 117
• 3.2. Environmental water screening 126
• 3.3. Microbial communities in droplets with extended culture 130
• 3.4. Potential functions related to cultured bacteria 133
• 4. Discussion 136
• 5. Conclusion 139
• Bibliography. 140

참고문헌 (Reference)

1. Cyanobacterial blooms, Huisman J., Codd G. A., Paerl H. W., Ibelings B. W., Verspagen J. M. H., Visser P. M., 16, 471-483, , 2018

2. vegan community ecology package, Oksanen J., Simpson G., Blanchet F. G., Kindt R., Legendre P., Minchin P., et al, version 2.6-2, , 2022

3. vegan: Community Ecology Package, Blanchet F., Kindt R., Simpson G., Oksanen J., Minchin P. et al, Legendre P., R package version 2.6-4https://CRAN. R-project. org/package=vegan, , 2022

4. Metabolism of non-growing bacteria, Lubrano P., Bange G., Lempp M., Link H., 401, 1479-1485, , 2020

5. Deep learning for computational biology, Angermueller C., Pärnamaa T., Parts L., Stegle O, 12, 878, , 2016

6. Global lake responses to climate change, Lenters J. D., Woolway R. I., O’Reilly C. M., Kraemer B. M., Merchant C. J., Sharma S., 1, 388-403, , 2020

7. Recent advances in droplet microfluidics, Howes P. D., deMello A. J, Ding Y., 92, 132-149, , 2020

8. A review of neurotoxicity of microcystins, Hu Y., Chen J., Fan H., Xie P., He J, 23, 7211-7219, , 2016

9. PICRUSt2 for prediction of metagenome functions, Brown J. R., Zaneveld J. R., Yurgel S. N., Maffei V. J., Douglas G. M., Taylor C. M. et al, 38, 685-688, , 2020

10. Strategies for culture of unculturable’ bacteria, Vartoukian S. R., Palmer R. M., Wade W. G., 309, 1-7, , 2010

11. A global atlas of the dominant bacteria found in soil, Delgado-Baquerizo M., Brewer T. E., Eldridge D. J., Benavent-González A., Bardgett R. D. et al, Oliverio A. M., 359, 320-325, , 2018

12. The haber bosch–harmful algal bloom (HB–HAB) link, Glibert P. M., Maranger R., Sobota D. J., Bouwman L., 9, 105001, , 2014

13. The roles of cyanobacterial bloom in nitrogen removal, Peng Y., Liu L., Jiang L., Xiao L., 609, 297-303., , 2017

14. Eutrophication control of large shallow lakes in China, Qin B., Zhang Y., Zhu G., Gao G., 881, , 2023

15. Heme auxotrophy in abundant aquatic microbial lineages, Kim S.,, Kang I., Lee J.-W., Jeon C. O., Giovannoni S. J., Cho J.-C., 118, e2102750118, , 2021

16. R: A Language and Environment for Statistical Computing, Team R. C., https://www. R-project. org/, , 2023

17. The igraph software package for complex network research, Csardi G., Nepusz T., 1695, 1-9, , 2006

18. Culture-based approaches to studying microbial dark matter, He X, 120, e2219691120, , 2023

19. Innovations to culturing the uncultured microbial majority, Lewis W. H., Tahon G., Geesink P., Sousa D. Z., Ettema T. J. G., 19, 225-240, , 2021

20. Storm impacts on phytoplankton community dynamics in lakes, Adrian R., Anneville O., Doubek J. P., Carey C. C., Stockwell J. D., Carvalho L. et al, 26, 2756-2784, , 2020

21. Update on the adverse effects of microcystins on the liver, Liu H., Chen X., Wang R. et al, Ma Y., Shi L., Du X., 195, 110890, , 2021

22. Analysis of microbiome data in the presence of excess zeros, Kaul A., Mandal S., Davidov O., Peddada S. D., 8, 2114, , 2017

23. Reducing phosphorus to curb lake eutrophication is a success, Schindler, D. W., Carpenter, S. R., Chapra, S. C., Hecky, R. E., Orihel, D. M., 50, 8923-8929, , 2016

24. Standard methods for the examination of water and wastewater, APHA, Vol 24th ed, , 2023

25. Lake Erie fish safe to eat yet afflicted by algal hepatotoxins, Shahmohamadloo R. S., Bhavsar S. P., Ortiz Almirall X., Marklevitz S. A. C., Rudman S. M., Sibley P. K., 861, 160474, , 2023

26. Nanopore sequencing technology, bioinformatics and applications, Wang Y., Zhao Y., Bollas A., Wang Y., Au K. F., 39, 1348-1365, , 2021

27. A few Ascomycota taxa dominate soil fungal communities worldwide, Egidi E., Delgado-Baquerizo M., Plett J. M., Wang J., Eldridge D. J., Bardgett R. D. et al, 10, 2369, , 2019

28. How does Microcystis aeruginosa respond to elevated temperature?, Guo Y., Meng H., Zhao S., Wang Z., Zhu L., Deng D., et al, 889, 164277, , 2023

29. Isolation of an archaeon at the prokaryote–eukaryote interface, Ogawara M., Morono Y., Imachi H., Nobu M. K., Nakahara N., Takaki Y. et al, 577, 519-525, , 2020

30. STAMP: Statistical analysis of taxonomic and functional profiles, Parks D. H., Tyson G. W., Hugenholtz P., Beiko R. G., 30, 3123-3124, , 2014

31. Recent advances in droplet microfluidics for single-cell analysis, Jiang Z., Shi H., Tang X., Qin J, 159, , 2023

32. DADA2: high-resolution sample inference from Illumina amplicon data, Callahan BJ McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP, 13, 581-583, , 2016

33. Influence of rainfall on cyanobacterial bloom in Daechung Reservoir, Ahn C. Y., Kim H. S., Yoon B. D., Oh H. M., 36, 413-419., , 2003

34. Short peptide induces an uncultivable microorganism to grow in vitro, Nichols, D., Lewis, K., Orjala, J., Mo, S., Ortenberg, R., O'connor, P., et al, 74, 4889-4897, , 2008

35. High-throughput single-cell cultivation on microfluidic streak plates, Jiang C.-Y., Dong L., Zhao J.-K., Hu X., Shen C., Qiao Y., et al, 82, 2210-2218., , 2016

36. A capillary racetrack’ method for isolation of magnetotactic bacteria, Wolfe R. S., Thauer R. K., Pfennig N., 3, 31-35, , 1987

37. Cyanobacterial blooms in China: diversity, distribution, and cyanotoxins, Huo D., Geng R., Gan N., Cao Q., Song L., Yu G. et al, 109, 102106, , 2021

38. The Arctic has warmed nearly four times faster than the globe since 1979, Ruosteenoja K. et al, Nordling K., Karpechko A. Y., Lipponen A., Rantanen M., Hyvärinen O., 3, 168, , 2022

39. A critical assessment of extreme events trends in times of global warming, Alimonti G., Mariani L., Prodi F., Ricci R. A, 137, 112, , 2022

40. Toxic cyanobacteria and drinking water: Impacts, detection, and treatment, He X., Liu Y.-L., Conklin A., Westrick J., Weavers L. K., Dionysiou D. D., et al, 54, 174-193, , 2016

41. Microbial diversity and influencing factors in a small watershed in winter, Guo R. L. et al, Gao R. X., Wang B. Z., Sun Q. Y., Qin H. L., Zhu J. S., Huan. Jing. Ke. Xue. 41, 5016-5026, , 2020

42. Phylogenetically novel uncultured microbial cells dominate earth microbiomes, Lloyd K. G., Steen A. D., Ladau J., Yin J., Crosby L., 3, e00055-18, , 2018

43. Fluorescence in situ hybridization (FISH) and cell sorting of living bacteria, Batani G., Bayer K., Böge J., Hentschel U., Thomas T., 9, , 2019

44. In situ cultivation of previously uncultivable microorganisms using the ichip, Berdy B., Spoering A. L., Ling L. L., Epstein S. S., 12, 2232-2242, , 2017

45. The lethal effects and determinants of microcystin-LR on heart: a mini review, Alosman M., Yang F., Massey I. Y., Cao L., 40, 517-526, , 2021

46. FACS-iChip: a high-efficiency iChip system for microbial dark matter’ mining, Wang Y., Xu J. et al, Xue R., Stirling E., Liu H., Ye S., 3, 162-168, , 2021

47. Creating, generating and comparing random network models with NetworkRandomizer, Tosadori G., Bestvina I., Spoto F., Laudanna C., Scardoni G., 5, 1-16, , 2017

48. Models predict planned phosphorus load reduction will make Lake Erie more toxic, Smith D. J., Dick G. J., Eigemann F., Wilhelm S. W., Martin R. M., Hellweger F. L., 376, 1001-1005, , 2022

49. Principles of enrichment, isolation, cultivation and preservation of prokaryotes, Overmann J, 1, 80-136., , 2006

50. Rainfall-driven resuspension of pathogenic Leptospira in a leptospirosis hotspot, Thibeaux R., Genthon P., Govan R., Selmaoui-Folcher N., Tramier C., Kainiu M., et al, 911, 168700, , 2024

51. Extreme slow growth as alternative strategy to survive deep starvation in bacteria, Gray D. A., Dugar G., Gamba P., Strahl H., Jonker M. J., Hamoen L. W., 10, 890, , 2019

52. Field evaluation of seven products to control cyanobacterial blooms in aquaculture, Gladfelter M. F., Belfiore A. P., Garner B. et al, Fernandez-Figueroa E. G., Buley R. P., Adams C., 28, 29971-29983, , 2021

53. Dynamic response of bacterial communities to Microcystis blooms: A three-year study, Le V. V., Kang M., Ko S.-R., Jeong S., Park C.-Y., Lee J. J., et al, 902, 165888, , 2023

54. Eutrophication and recovery in experimental lakes: Implications for lake management, Schindler D. W., 184, 897-899, , 1974

55. Future precipitation increase constrained by climatological pattern of cloud effect, Leung L. R., Zhou W., Lu J, Siler N., 14, 6363, , 2023

56. Lake eutrophication: Control of phytoplankton overgrowth and invasive aquatic weeds, Geletu T. T., 28, e12425, , 2023

57. Potential use of aquatic vascular plants to control cyanobacterial blooms: A review, Nezbrytska I., Usenko O., Abramiuk I., Leontieva T., Konovets I., Goncharova M. et al, 14, 1727, , 2022

58. Short-term Lake Erie algal bloom prediction by classification and regression models, Zhang H., Sun J., Ai H., Zhang K., 232, 119710, , 2023

59. Temperature effects explain continental scale distribution of cyanobacterial toxins, De Senerpont Domis L., Mantzouki E., Koreivienė J. et al, Fastner J., Wilk-Woźniak E., Lürling M., 10, 156, , 2018

60. The social network of microorganisms — how auxotrophies shape complex communities, Zengler K., Zaramela L. S., 16, 383-390, , 2018

61. Widespread changes in Southern Ocean phytoplankton blooms linked to climate drivers, Thomalla S. J., Nicholson S.-A., Ryan-Keogh T. J., Smith M. E., 13, 975-984, , 2023

62. Recent frontiers of climate changes in East Asia at global warming of 1.5°C and 2°C, Cao J. et al, Yue X., Jiang Z., Guo W., You Q., Liu Y., 5, 80, , 2022

63. Metabolomic analysis of Microcystis aeruginosa after exposure to the algicide L-lysine, Wang Z., Wang G., Zhao F., Yan X., Zhou M., Zhang L. et al, 110, 12, , 2022

64. Seasonal dynamics and diversity of cyanobacteria in a eutrophied Urban River in Brazil, Mânica A. N., de Lima Isaac R., 23, 3868-3880, , 2023

65. Use of ichip for high-throughput in situ cultivation of uncultivable microbial species, Nichols D., Cahoon N., Trakhtenberg E. M., Pham L., Mehta A., Belanger A., et al, 76, 2445-2450, , 2010

66. Rapid DNA sequencing technology based on the Sanger method for bacterial identification, Furutani S., Furutani N., Kawai Y., Nakayama A., Nagai H., 22, 2130, , 2022

67. An automated Raman-based platform for the sorting of live cells by functional properties, Pereira F. C., Fernandez V. I., Lee K. S., Palatinszky M., Nguyen J., Mueller A. J. et al, 4, 1035-1048, , 2019

68. Impacts of cyanobacterial biomass and nitrate nitrogen on methanogens in eutrophic lakes, Zhu Y., Chen X., Yang Y., Xie S., 848, 157570, , 2022

69. Predicting bacterial interaction outcomes from monoculture growth and supernatant assays, Schmitz D. A., Wechsler T., Mignot I., Kümmerli R., 2023.07.03.547502, , 2023

70. Separation of bacteria, protozoa and carbon nanotubes by density gradient centrifugation, Mortimer, M., Petersen, E. J. Buchholz, B. A. Holden, P. A, 6(10), 181, , 2016

71. Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback, Gutiérrez Mena J., Kumar S., Khammash M., 13, 4808, , 2022

72. Eutrophication will increase during the 21st century as a result of precipitation changes, Michalak A. M., Sinha E., Balaji V., 357, 405-408, , 2017

73. Long-Term Transcriptional Activity at Zero Growth of a Cosmopolitan Rare Biosphere Member, Hausmann B., Pelikan C., Rattei T., Loy A., Pester M., 10, 10.1128/mbio.02189-18, , 2019

74. Phenotypic plasticity of carbon fixation stimulates cyanobacterial blooms at elevated CO2, Rost B., Ji X., Van de Waal D. B., Huisman J, Verspagen J. M. H., 6, eaax2926, , 2020

75. Co-occurring microorganisms regulate the succession of cyanobacterial harmful algal blooms, Wang K., Mou X., Cao H., Struewing I., Allen J., Lu J, 288, 117682, , 2021

76. Comment on Models predict planned phosphorus load reduction will make Lake Erie more toxic, Van de Waal D. B. et al, Fastner J., Huisman J., Scott J. T., Schuurmans J. M., Dittmann E., 378, eadd9959, , 2022

77. Cyanobacterial dominance and succession: Factors, mechanisms, predictions, and managements, Sun Y., Wang Z., Gu L., Lyu K. et al, Akbar S., Zhang L., 297, 113281, , 2021

78. Nitrogen and phosphorus inputs control phytoplankton growth in eutrophic Lake Taihu, China, Xu H., Paerl H. W., Qin B., Zhu G., Gaoa G., 55, 420-432, , 2010

79. The microbial community spatially varies during a Microcystis bloom event in Lake Kinneret, Schweitzer-Natan O., Ofek-Lalzar M., Sher D., Sukenik A, 68, 349-363, , 2023

80. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea, Herlemann D. P. R., Labrenz M., Jürgens K., Bertilsson S., Waniek J. J., Andersson A. F., 5, 1571-1579, , 2011

81. Alternative alert system for cyanobacterial bloom, using phycocyanin as a level determinant, Ahn C. Y., Joung S. H., Yoon S. K., Oh H. M., 45, 98-104, , 2007

82. Impact of warming and nitrogen addition on soil greenhouse gas fluxes: A global perspective, Jia X., Zhang X. et al, Yang J., Chen W., Ma H., Liu J., 151, 88-99, , 2025

83. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools, Quast C., Pruesse E., Yilmaz P., Gerken J., Schweer T., Yarza P., et al, 41, D590-D596, , 2013

84. Wastewater microbial community structure and functional traits change over short timescales, Guo B., Liu C., Gibson C., Frigon D., 662, 779-785, , 2019

85. An agarose-in-plug bridge method to study chemotaxis in the Archaeon Halobacterium salinarum, Yu H. S., Alam M., 156, 265-269, , 1997

86. Cytoscape: A software environment for integrated models of biomolecular interaction networks, Shannon P., Markiel A., Ozier O., Baliga N. S., Wang J. T., Ramage D., et al, 13, 2498-2504, , 2003

87. Individually addressable arrays of replica microbial cultures enabled by splitting SlipChips, Ma L., Datta S. S., Karymov M. A., Pan Q., Begolo S., Ismagilov R. F., 6, 796-805, , 2014

88. Periphyton reduces cyanobacterial blooms by promoting potentially cyanobactericidal bacteria, Kang M., Shin Y., Lim B., Le V. V., Ko S.-R., Kang Y.-H. et al, 35, 1285–1299, , 2023

89. Temperature-dependent growth characteristics and competition of Pseudanabaena and Microcystis, Hu L., Wang H., Cui J., Zou W., Li J., Shan K., 15, 2404, , 2023

90. Bacterioplankton community indicators for seasonal variation in a fragmented subtropical river, Song P., Zhao B., Liu Q. et al, Yang W., Mai Y., Li H., 194, 458, , 2022

91. Leaching risk of antibiotic resistance contamination from organic waste compost in rural areas, Huang J., He P., Duan H., Yang Z., Zhang H., Lü F., 320, 121108, , 2023

92. Nitrate as a predictor of cyanobacteria biomass in eutrophic lakes in a climate change context, Nõges P., Öglü B., Newell S. E., McCarthy M. J., Cremona F., Nõges T., 818, 151807, , 2022

93. Comparative analysis of Microcystis buoyancy in western Lake Erie and Saginaw Bay of Lake Huron, Strickler J. R., Vanderploeg H. A, Harrison S. B., Rowe M. D., Godwin C. M., Den Uyl P. A., 108, 102102, , 2021

94. Inertiandash;Acoustophoresis Hybrid Microfluidic Device for Rapid and Efficient Cell Separation, Kim U., Oh B., Ahn J., Lee S., Cho Y, 22, 4709, , 2022

95. Optimizing feature selection methods by removing irrelevant features using sparse least squares, Afshar M., Usefi H., 200, 116928, , 2022

96. Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course, Tromas N., Fortin N., Bedrani L., Terrat Y., Cardoso P., Bird D., et al, 11, 1746-1763, , 2017

97. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy, Wang Q., Garrity G. M., Tiedje J. M., Cole J. R., 73, 5261-5267, , 2007

98. Assessing the toxicity of cell-bound microcystins on freshwater pelagic and benthic invertebrates, Bhavsar S. P., Shahmohamadloo R. S., Ortiz Almirall X., Sibley P. K., Poirier D. G., 188, 109945, , 2020

99. Characterization of distinct cyanoHABs-related modules in microbial recurrent association network, Baek K., Cui Y., Chun S.-J., Choi A. et al, Cho A. R., Lee C. S., 10, 1637, , 2019

100. Climate change impacts on harmful algal blooms in U. S. freshwaters: A screening-level assessment, Chapra S. C., Boehlert B., Fant C., Bierman V. J., Henderson J., Mills D., et al, 51, 8933-8943, , 2017

101. Global co-occurrence of methanogenic archaea and methanotrophic bacteria in Microcystis aggregates, Burford M. A. et al, Grossart H. P., Trammell M. A., Li C., Hambright K. D., Bowen H. G., 23, 6503-6519, , 2021

102. Effects of weir construction on phytoplankton assemblages and water quality in a large river system, Lee H. J., Park H. K., Cheon S. U, 15, 2348, , 2018

103. Isolation of phages infecting the abundant freshwater Actinobacteriota order Ca. Nanopelagicales’, Kavagutti V. S., Chiriac M.-C., Ghai R., Salcher M. M., Haber M., 17, 943-946, , 2023

104. The microbiota of recreational freshwaters and the implications for environmental and public health, Lee C. S., Kim M., Lee C., Yu Z., Lee J, 7, 1826, , 2016

105. Water quality seasonal variation assessment of the Gongji and Yaksa streams, Chuncheon, South Korea, Jang T., Hong E., Kim J.-H., Ha S.-J., Choi J.-W., Park J.-A, 195, 1465, , 2023

106. Residential urban stormwater runoff: A comprehensive profile of microbiome and antibiotic resistance, Lee S., Suits M., Wituszynski D., Winston R., Martin J., Lee J, 723, 138033, , 2020

107. Toxic cyanobacteria in water: a guide to their public health consequences, monitoring and management, Welker M., Chorus I., Taylor Francis, , 2021