Suppr超能文献

2024年刺胞动物节上刺胞动物生物学的发现与创新。

Discoveries and innovations in cnidarian biology at Cnidofest 2024.

作者信息

Leach Whitney B, Babonis Leslie, Juliano Celina E, Nakanishi Nagayasu, Schnitzler Christine E, Steinmetz Patrick R H, Layden Michael J

机构信息

Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA.

Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853, USA.

出版信息

Evodevo. 2025 Jun 16;16(1):9. doi: 10.1186/s13227-025-00247-5.

Abstract

The third iteration of the Cnidarian Model Systems Meeting (Cnidofest) was held August 14-17th, 2024 at Lehigh University in Bethlehem, PA. The meeting featured presentations from laboratories representing 11 countries, covering a broad range of topics related to cnidarian species. The research highlighted diverse topics, with sessions focused on regeneration, evo-devo, genomics, symbiosis, cell biology, physiology, neurobiology, and development. A notable shift at this meeting was the extent to which established cnidarian model systems have caught up with the classical laboratory models such as Drosophila and vertebrates, with modern genomic, genetic, and molecular tools now routinely applied. In addition, more cnidarian systems are now being developed for functional studies by the community, enhancing our ability to gain fundamental insights into animal biology that are otherwise difficult in the complex bilaterian model systems. Together, the integration of cnidarian and bilaterian model systems provides researchers with a broader toolkit for selecting animal models best suited to address their specific biological questions.

摘要

刺胞动物模型系统会议(Cnidofest)的第三次会议于2024年8月14日至17日在宾夕法尼亚州伯利恒的利哈伊大学举行。会议有来自代表11个国家的实验室的报告,涵盖了与刺胞动物物种相关的广泛主题。研究突出了各种不同的主题,会议场次聚焦于再生、进化发育生物学、基因组学、共生、细胞生物学、生理学、神经生物学和发育。此次会议的一个显著变化是,成熟的刺胞动物模型系统在多大程度上赶上了诸如果蝇和脊椎动物等经典实验室模型,现代基因组学、遗传学和分子工具现在已常规应用。此外,现在该领域正在开发更多用于功能研究的刺胞动物系统,增强了我们对动物生物学获得基本见解的能力,而这在复杂的两侧对称动物模型系统中原本是困难的。总之,刺胞动物和两侧对称动物模型系统的整合为研究人员提供了一个更广泛的工具包,以便选择最适合解决其特定生物学问题的动物模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4599/12172373/0e12e9fc4b97/13227_2025_247_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验