尽管它们对环境和人类产生了巨大的影响,最大的天然次生代谢产物生产者的分布和种类,链霉菌属,没有得到充分的调查。我们从公共EMP16SrRNA扩增子序列微生物数据中开发了代表性图谱。从EMP整体细菌群落中提取ASV链霉菌,展示链霉菌的多样性并确定关键的多样性模式。我们的发现表明,虽然EMP主要将细菌群落区分为宿主相关或自由生活(EMPO水平1),链霉菌群落没有显着差异,但在EMPO2级中表现出类别之间的区别(动物,植物,非盐水,和盐水)。多元线性回归分析表明,温度,盐度显著预测链霉菌的丰富度,随着这些因素的增加,丰富度降低。然而,纬度和经度不能预测链霉菌的丰富度。我们的链霉菌地图显示,需要在非洲和东南亚进行额外的采样。此外,我们的研究结果表明,更多的样本并不总是导致更多的链霉菌丰富度;未来的调查可能不需要从单个地点进行广泛的采样.更广泛的采样,而不是本地/区域抽样,在回答微生物生物地形图问题时可能更关键。最后,使用16SrRNA基因测序数据有一些局限性,对此应谨慎解释。
Despite their enormous impact on the environment and humans, the distribution and variety of the biggest natural secondary metabolite producers, the genus Streptomyces, have not been adequately investigated. We developed representative maps from public EMP 16S rRNA amplicon sequences microbiomics data. Streptomyces ASVs were extracted from the EMP overall bacterial community, demonstrating Streptomyces diversity and identifying crucial diversity patterns. Our findings revealed that while the EMP primarily distinguished bacterial communities as host-associated or free-living (EMPO level 1), the Streptomyces community showed no significant difference but exhibited distinctions between categories in EMPO level 2 (animal, plant, non-saline, and saline). Multiple linear regression analysis demonstrated that pH, temperature, and salinity significantly predicted Streptomyces richness, with richness decreasing as these factors increased. However, latitude and longitude do not predict Streptomyces richness. Our Streptomyces maps revealed that additional samplings in Africa and Southeast Asia are needed. Additionally, our findings indicated that a greater number of samples did not always result in greater Streptomyces richness; future surveys may not necessitate extensive sampling from a single location. Broader sampling, rather than local/regional sampling, may be more critical in answering microbial biogeograph questions. Lastly, using 16S rRNA gene sequencing data has some limitations, which should be interpreted cautiously.