Enciso Garcia Juan Sebastian, Chignola Marta, Ragionieri Lapo, Rey Filippo, Fluch Maja, Borruso Luigimaria, Corretto Erika, Schuler Hannes
Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
Research Centre Laimburg, Vadena, Italy.
Methods Mol Biol. 2025;2935:237-258. doi: 10.1007/978-1-0716-4583-3_10.
Insects represent more than 80% of all described species on the planet. This diversity is a result of millions of years of evolution, during which insects have colonized nearly every habitat. Their success is partly due to their ability to form symbiotic relationships with a wide variety of other organisms, especially microorganisms. Identifying and characterizing associated microorganisms are crucial to understanding the complexity and dynamics of these symbiotic relationships. To date, advancements in sequencing technologies that provide large sequence data sets have become ideal tools for characterizing insect microbiomes, including information about non-cultivable microorganisms commonly found in insects. Despite the growing number of studies focused on insect microbiome characterization, there are few protocols detailing methodological procedures for fieldwork, DNA extraction, and data processing. Here, we present an overview of the characterization of insect-associated bacterial communities. We cover best practices for data interpretation and visualization, including alpha and beta diversity analyses, community composition profiling, and statistical testing to identify microbial associations of insects.
昆虫占地球上所有已描述物种的80%以上。这种多样性是数百万年进化的结果,在此期间昆虫几乎占据了每一个栖息地。它们的成功部分归功于它们与各种各样其他生物,尤其是微生物形成共生关系的能力。识别和表征相关微生物对于理解这些共生关系的复杂性和动态至关重要。迄今为止,能够提供大量序列数据集的测序技术进步已成为表征昆虫微生物组的理想工具,包括有关昆虫中常见的不可培养微生物的信息。尽管专注于昆虫微生物组表征的研究数量不断增加,但很少有协议详细说明实地调查、DNA提取和数据处理的方法程序。在这里,我们概述了昆虫相关细菌群落的表征。我们涵盖了数据解释和可视化的最佳实践,包括α和β多样性分析、群落组成分析以及识别昆虫微生物关联的统计测试。