Epigenetic modifications help algae to adapt to low CO2 environments

http://english.cas.cn/newsroom/research_news/life/202510/t20251010_1089023.shtml

https://www.cell.com/plant-communications/pdf/S2590-3462(25)00296-2.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2590346225002962%3Fshowall%3Dtrue

A research team from the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of the Chinese Academy of Sciences has identified a specific histone modification as the key regulator governing microalgae’s adaptation to low-CO2environments.

The study focused on Nannochloropsis oceanica, tracking its epigenomic dynamics as it transitioned from an environment with 5% CO2 to one with just 0.01% CO2. Using multi-dimensional epigenomic sequencing techniques, the researchers discovered that global DNA methylation in the alga remained stable at 0.13%, effectively ruling out DNA methylation as a major driver of its low-CO2response. By contrast, H3K4me2 methylationwas found to be closely associated with 43.1% of the genes that respond to low-CO2 conditions. These genes include those critical to photosynthesis and ribosome biogenesis, two processes essential for the alga’s survival under carbon-limited stress. Further analysis revealed that H3K4me2 appears to regulate gene transcription by altering chromatin accessibility, a mechanism that aligns with its role as a central regulator of low-CO2 adaptation.

To validate their findings, the team used CRISPR/Cas9 gene-editing technology. They knocked out NO24G02310—a gene that encodes an H3K4 methyltransferase, the enzyme responsible for adding methyl groups to H3K4. The modified algae showed a roughly 22% reduction in growth rate and a 15% decrease in biomass. Additionally, the levels of another histone modification (H3K4me1) dropped, and the genome-wide localization of H3K4me2 shifted—providing direct evidence of H3K4me2’s role in low-CO2 adaptation. Further experiments uncovered that H3K4 modification may act via two pathways: by regulating enzyme networks and by modulating chloroplast transmembrane pH gradients. Both mechanisms work to optimize the alga’s use of available CO2, enhancing its survival under low-carbon conditions.

more insights

https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

http://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml

https://link.springer.com/article/10.1186/s40168-026-02339-3

Scientists from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics.

RamEx streamlines the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. An Iterative Convolutional Outlier Detection (ICOD) algorithm tackles spectral noise in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform’s performance was validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in genetically identical yeast cells by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

https://japanese.cri.cn/2026/02/21/ARTI1771656131487329

The “Zhifei,” China’s first commercially operational smart container ship, accurately docked at a berth at the automated wharf in Qingdao Port, using unmanned autonomous navigation mode. This marks the first time that China has achieved an unmanned operation of a container ship, including navigation, berthing, and operation.

After the “Zhifei” container ship arrived at its designated location, the vacuum suction cups installed in its vacuum automatic mooring system powerfully attracted the hull, firmly securing it to the berth in less than 30 seconds, without any manual mooring work. The terminal’s fully automated loading and unloading equipment then operated simultaneously, and China’s A-TOS (Atelier Terminal Smart Management System) and A-ECS (Analytical Equipment Control System) worked together to precisely coordinate equipment such as automated cranes and automated guided vehicles at millisecond speeds, completing the container loading and unloading process.

The Zhifei is equipped with smart navigation core system, and as China’s first smart cargo ship for commercial operation, it features three navigation modes: manned, remotely controlled, and unmanned autonomous navigation.

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