Department of Ecology and Complexity, Center of Advanced Studies of Blanes (CEAB), Spanish Research Council (CSIC), Blanes, Spain.
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, United States.
ISME J. 2024 Jan 8;18(1). doi: 10.1093/ismejo/wrae195.
Genomic information is now available for a broad diversity of bacteria, including uncultivated taxa. However, we have corresponding knowledge on environmental preferences (i.e. bacterial growth responses across gradients in oxygen, pH, temperature, salinity, and other environmental conditions) for a relatively narrow swath of bacterial diversity. These limits to our understanding of bacterial ecologies constrain our ability to predict how assemblages will shift in response to global change factors, design effective probiotics, or guide cultivation efforts. We need innovative approaches that take advantage of expanding genome databases to accurately infer the environmental preferences of bacteria and validate the accuracy of these inferences. By doing so, we can broaden our quantitative understanding of the environmental preferences of the majority of bacterial taxa that remain uncharacterized. With this perspective, we highlight why it is important to infer environmental preferences from genomic information and discuss the range of potential strategies for doing so. In particular, we highlight concrete examples of how both cultivation-independent and cultivation-dependent approaches can be integrated with genomic data to develop predictive models. We also emphasize the limitations and pitfalls of these approaches and the specific knowledge gaps that need to be addressed to successfully expand our understanding of the environmental preferences of bacteria.
现在已经可以获得广泛的细菌基因组信息，包括未培养的分类群。然而，我们对于环境偏好（即细菌在氧气、pH 值、温度、盐度和其他环境条件梯度上的生长反应）的了解相对狭窄，仅限于细菌多样性的一部分。这些对细菌生态学的理解限制了我们预测群落将如何响应全球变化因素而变化、设计有效的益生菌或指导培养工作的能力。我们需要创新的方法，利用不断扩大的基因组数据库来准确推断细菌的环境偏好，并验证这些推断的准确性。通过这样做，我们可以拓宽对大多数仍未被描述的细菌分类群的环境偏好的定量理解。从这个角度出发，我们强调了为什么从基因组信息推断环境偏好很重要，并讨论了实现这一目标的各种潜在策略。特别是，我们突出了如何将非培养和培养依赖的方法与基因组数据相结合，以开发预测模型的具体示例。我们还强调了这些方法的局限性和陷阱，以及为成功扩展对细菌环境偏好的理解而需要解决的具体知识差距。
