Metagenomics and digital cell modeling facilitate targeted high-throughput sorting of anaerobic hydrogen-producing microorganisms

Jianfeng Liu; Wei Xing; Xingyang Zhang; Nengyao Xu; Ran Xu; Junsha Gong; Jia Zhang; Fengai Yang; Shuang Gao; Yanan Hou; Yongping Shan; Bin Liu; Qianqian Yuan; Aijie Wang; Nanqi Ren; Cong Huang

doi:10.1002/imt2.70082

iMeta ›› 2025, Vol. 4 ›› Issue (6) :e70082 DOI: 10.1002/imt2.70082

CORRESPONDENCE

Metagenomics and digital cell modeling facilitate targeted high-throughput sorting of anaerobic hydrogen-producing microorganisms

Author information +

¹. Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

². National Technology Innovation Center of Synthetic Biology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

³. School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin, China

⁴. College of Biotechnology, Tianjin University of Science and Technology, Tianjin, China

⁵. Key Laboratory of Environmental Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

⁶. State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

⁷. University of Chinese Academy of Sciences, Beijing, China

⁸. School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China

rennq@tib.cas.cn

huangc@tib.cas.cn

Show less

History +

PDF

Cite this article

Download citation ▾

Jianfeng Liu, Wei Xing, Xingyang Zhang, Nengyao Xu, Ran Xu, Junsha Gong, Jia Zhang, Fengai Yang, Shuang Gao, Yanan Hou, Yongping Shan, Bin Liu, Qianqian Yuan, Aijie Wang, Nanqi Ren, Cong Huang. Metagenomics and digital cell modeling facilitate targeted high-throughput sorting of anaerobic hydrogen-producing microorganisms. iMeta, 2025, 4(6): e70082 DOI:10.1002/imt2.70082

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]

Roopnarain, Ashira, Haripriya Rama, Busiswa Ndaba, Maryam Bello-Akinosho, Emomotimi Bamuza-Pemu, and Rasheed Adeleke. 2021. “Unravelling the Anaerobic Digestion ‘Black Box’: Biotechnological Approaches for Process Optimization.” Renewable & Sustainable Energy Reviews 152: 111717. https://doi.org/10.1016/j.rser.2021.111717

[2]

Lawson, Christopher E., William R. Harcombe, Roland Hatzenpichler, Stephen R. Lindemann, Frank E. Löffler, Michelle A. O'Malley, Héctor García Martín, et al. 2019. “Common Principles and Best Practices for Engineering Microbiomes.” Nature Reviews Microbiology 17: 725-741. https://doi.org/10.1038/s41579-019-0255-9

[3]

Jing, Xiaoyan, Yanhai Gong, Huihui Pan, Yu Meng, Yishang Ren, Zhidian Diao, Runzhi Mu, et al. 2022. “Single-Cell Raman-Activated Sorting and Cultivation (scRACS-Culture) for Assessing and Mining In Situ Phosphate-Solubilizing Microbes From Nature.” ISME Communications 2: 106. https://doi.org/10.1038/s43705-022-00188-3

[4]	Lewis, William H., Guillaume Tahon, Patricia Geesink, Diana Z. Sousa, and Thijs J. G. Ettema. 2021. “Innovations to Culturing the Uncultured Microbial Majority.” Nature Reviews Microbiology 19: 225-240. https://doi.org/10.1038/s41579-020-00458-8

[5]	Huang, Yiqun, Lingyu Wen, Lige Zhang, Jijun Xu, Weiwei Wang, Haiyang Hu, Ping Xu, Zhao Li, and Hongzhi Tang. 2023. “Community-Integrated Multi-Omics Facilitates the Isolation of an Organohalide Dehalogenation Microorganism.” The Innovation 4: 100355. https://doi.org/10.1016/j.xinn.2022.100355

[6]	Hidalgo, Dolores, and Jesús M. Martín-Marroquín. 2023. “Enhanced Production of Biohydrogen Through Combined Operational Strategies.” JOM 75: 718-726. https://doi.org/10.1007/s11837-022-05572-x

[7]	Jain, Rupal, Narayan Lal Panwar, Sanjay Kumar Jain, Trilok Gupta, Chitranjan Agarwal, and Sanwal Singh Meena. 2022. “Bio-Hydrogen Production Through Dark Fermentation: An Overview.” Biomass Conversion and Biorefinery 14: 12699-12724. https://doi.org/10.1007/s13399-022-03282-7

[8]	Liu, Sijia, Christina D. Moon, Nan Zheng, Sharon Huws, Shengguo Zhao, and Jiaqi Wang. 2022. “Opportunities and Challenges of Using Metagenomic Data to Bring Uncultured Microbes into Cultivation.” Microbiome 10: 76. https://doi.org/10.1186/s40168-022-01272-5

[9]	Chen, Yang, Yanan Yin, and Jianlong Wang. 2021. “Recent Advance in Inhibition of Dark Fermentative Hydrogen Production.” International Journal of Hydrogen Energy 46: 5053-5073. https://doi.org/10.1016/j.ijhydene.2020.11.096

[10]	Greening, Chris, Princess R. Cabotaje, Luis E. Valentin Alvarado, Pok Man Leung, Henrik Land, Thiago Rodrigues-Oliveira, Rafael I. Ponce-Toledo, et al. 2024. “Minimal and Hybrid Hydrogenases Are Active From Archaea.” Cell 187: 3357-3372.e19. https://doi.org/10.1016/j.cell.2024.05.032

[11]	Yang, Zi-Wen, Zheng-Han Lian, Lan Liu, Bao-Zhu Fang, Wen-Jun Li, and Jian-Yu Jiao. 2023. “Cultivation Strategies for Prokaryotes From Extreme Environments.” iMeta 2: e123. https://doi.org/10.1002/imt2.123

[12]	Bordbar, Aarash, Jonathan M. Monk, Zachary A. King, and Bernhard O. Palsson. 2014. “Constraint-Based Models Predict Metabolic and Associated Cellular Functions.” Nature Reviews Genetics 15: 107-120. https://doi.org/10.1038/nrg3643

[13]	Schäfer, Martin, Alan R. Pacheco, Rahel Künzler, Miriam Bortfeld-Miller, Christopher M. Field, Evangelia Vayena, Vassily Hatzimanikatis, and Julia A. Vorholt. 2023. “Metabolic Interaction Models Recapitulate Leaf Microbiota Ecology.” Science 381: eadf5121. https://doi.org/10.1126/science.adf5121

[14]

Suthers, Patrick F., Charles J. Foster, Debolina Sarkar, Lin Wang, and Costas D. Maranas. 2021. “Recent Advances in Constraint and Machine Learning-Based Metabolic Modeling by Leveraging Stoichiometric Balances, Thermodynamic Feasibility and Kinetic Law Formalisms.” Metabolic Engineering 63: 13-33. https://doi.org/10.1016/j.ymben.2020.11.013

[15]	Qiao, YiZhu, Qiwei Huang, Hanyue Guo, Meijie Qi, He Zhang, Qicheng Xu, Qirong Shen, and Ning Ling. 2024. “Nutrient Status Changes Bacterial Interactions in a Synthetic Community.” Applied and Environmental Microbiology 90: e01566-23. https://doi.org/10.1128/aem.01566-23

[16]	Machado, Daniel, Oleksandr M. Maistrenko, Sergej Andrejev, Yongkyu Kim, Peer Bork, Kaustubh R. Patil, and Kiran R. Patil. 2021. “Polarization of Microbial Communities Between Competitive and Cooperative Metabolism.” Nature Ecology & Evolution 5: 195-203. https://doi.org/10.1038/s41559-020-01353-4

[17]	Ho, PoYi, Taylor H. Nguyen, Juan M. Sanchez, Brian C. DeFelice, and Kerwyn Casey Huang. 2024. “Resource Competition Predicts Assembly of Gut Bacterial Communities In Vitro.” Nature Microbiology 9: 1036-1048. https://doi.org/10.1038/s41564-024-01625-w

[18]	Mataigne, Victor, Nathan Vannier, Philippe Vandenkoornhuyse, and Stéphane Hacquard. 2021. “Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes.” Frontiers in Microbiology 12: 780469. https://doi.org/10.3389/fmicb.2021.780469

[19]	Lagus, Todd P., and Jon F. Edd. 2013. “High-Throughput Co-Encapsulation of Self-Ordered Cell Trains: Cell Pair Interactions in Microdroplets.” RSC advances 3: 20512-20522. https://doi.org/10.1039/c3ra43624a

[20]	Chung, Meng Ting, Daniel Nunez, Dawen Cai, and Katsuo Kurabayashi. 2018. “Sort ‘N Merge: A Deterministic Microfluidic Platform for Co-Encapsulating Distinct Particles in Microdroplets.” 2018 IEEE Micro Electro Mechanical Systems (MEMS), 265-268. https://doi.org/10.1109/MEMSYS.2018.8346535