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.

中文翻译：

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利用基因组信息预测细菌的环境偏好


现在，基因组信息可用于多种多样的细菌，包括未培养的分类群。然而，对于相对狭窄的细菌多样性，我们对环境偏好（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）有相应的了解。我们对细菌生态学理解的这些限制限制了我们预测组合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表征的细菌分类群的环境偏好的定量理解。从这个角度来看，我们强调了为什么从基因组信息中推断环境偏好很重要，并讨论了这样做的潜在策略范围。特别是，我们强调了如何将不依赖培养和依赖培养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，以及需要解决的具体知识差距，以成功扩大我们对细菌环境偏好的理解。