Green Carbon co-organizes 17th International Clostridium Conference

http://qibebt.cas.cn/news/zyxw/202409/t20240922_7378447.html

From September 19th to 22nd, the 17th International Clostridium Conference was held in Qingdao, hosted by the International Clostridium Conference Organizing Committee, supported by the Bioenergy Research Laboratory of Qingdao Institute of Energy, the Key Laboratory of Solar Photovoltaic Conversion and Utilization, Shandong Energy Research Institute, and Qingdao New Energy Shandong Provincial Laboratory, and co-organized by the One Carbon Biotechnology Research Center and Green Carbon Editorial Department.

Since 1990, the International Clostridium Conference has been held every two years, and this Clostridium Conference is the second time to be held in China. On the afternoon of the 19th, the executive chairman of the conference, Researcher Li Fuli, director of the One Carbon Biotechnology Research Center, announced the opening of the conference. Director Lv Xuefeng delivered a speech on behalf of the Qingdao Institute of Energy and introduced the construction and development of the institute to the delegates.

This conference invited about 150 scholars and guests from domestic and foreign academic and business circles to attend the conference, including more than 50 foreign experts from Germany, the United States, France, South Korea, the United Kingdom, Italy and other countries. The conference was divided into four parts according to the topic direction: physiology and systems biology, genetics and synthetic biology, metabolic engineering and raw material utilization, industry and new applications. More than 40 oral speakers shared the latest research results with the participants, discussed future research directions, and exchanged problems and challenges encountered in Clostridium research and industrialization engineering.

As a valuable platform for scientific exchange and cooperation, this conference will further promote the development of Clostridium research. At the same time, the successful holding of this conference is of great significance to enhancing the influence of the institute in the field of Clostridium research. (Text/Photo by Ma Xiaoqing)

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http://english.cas.cn/newsroom/research-news/202604/t20260428_1158214.shtml

https://www.pnas.org/doi/10.1073/pnas.2530496123

Researchers from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) have identified a conserved ubiquitin-mediated regulatory mechanism that coordinates metabolic flux among multiple biosynthetic pathways in yeast.

Eukaryotic cells operate under constant resource constraints, requiring them to allocate limited carbon supplies among multiple biosynthetic processes. Pathways responsible for producing carotenoids, sterols, and lipids are particularly interconnected, as they rely on shared metabolic precursors. Yet how cells dynamically balance these competing demands has remained unclear.

Using astaxanthin-producing Xanthophyllomyces dendrorhous yeast, the researchers identified an E3 ubiquitin ligase, PTR1, as a central regulatory hub that links carotenoid, sterol, and lipid metabolism. Further analysis revealed a PTR1-centered regulatory network that integrates these pathways. PTR1 modulates carotenoid biosynthesis through a reciprocal regulatory loop with the White Collar Complex (WCC), which is a key transcriptional regulator associated with carotenoid production. In addition, several PTR1-interacting proteins were identified, suggesting broader roles in fine-tuning sterol and lipid metabolism. Importantly, PTR1 homologs are conserved across diverse eukaryotic lineages, indicating that ubiquitin-mediated regulation represents an evolutionarily conserved strategy for coordinating metabolic networks.

http://english.cas.cn/newsroom/research-news/202604/t20260423_1157877.shtml

https://doi.org/10.1016/j.tibtech.2026.03.017

A team led by the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) has developed a new “process ramanomics” platform. This technology enables real-time, data-driven control of biomanufacturing.

The researchers validated their approach in polyhydroxyalkanoate (PHA) fermentation, a key route for biodegradable polyesters used in packaging and medical materials. Powered by machine learning, the platform achieved 99.75% accuracy in distinguishing PHB-producing cells from P34HB-producing ones, and quantified total PHA content and monomer composition at the single-cell level with a median absolute deviation below 3.8%, comparable to traditional gas chromatography.

In a pivotal 5,000-liter industrial fermenter trial, traditional offline testing pointed to harvesting at 28 hours when PHA content registered 66.32%. Process ramanomics, however, revealed a compositional shift invisible to conventional methods: the 4HB monomer ratio was 8.67% at 26 hours (within specification) but climbed to 11.28% by 28 hours, exceeding the compliance limit, demonstrating that earlier termination could safeguard product quality.

The platform’s single-cell resolution also showed that the content of intracellular PHA can vary by more than threefold among individual cells. At 26 hours, population heterogeneity was lowest, with 91.54% of cells producing at high levels and a 4HB composition that was within specification. This confirmed that 26 hours was the optimal harvest window.

The scientists further showed that process ramanomics can be applied to different chassis organisms and products. For example, it can be used for protein synthesis in yeast and lipid synthesis in Rhodococcus. This suggests that process ramanomics could serve as a general-purpose analytics engine for next-generation intelligent bioreactors.

https://en.people.cn/n3/2026/0324/c90000-20439477.html

At the Qingdao Humanoid Robot Data Training Center in Laoshan District of Qingdao, humanoid robots are trained for jobs such as intelligent industrial manufacturing, smart home, and commercial services. Data collectors here control robots to complete specific tasks like logistics sorting, supermarket restocking, kitchen operations, and component assembly. Through thousands of repetitions and trials, massive amounts of motion data are generated, endowing robots with a smarter “intelligent brain,” and helping humanoid robots enter all walks of life to serve thousands of households.

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