Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
BMC Microbiol. 2018 Nov 6;18(1):177. doi: 10.1186/s12866-018-1320-7.
The ambient temperature of all habitats is a key physical property that shapes the biology of microbes inhabiting them. The optimal growth temperature (OGT) of a microbe, is therefore a key piece of data needed to understand evolutionary adaptations manifested in their genome sequence. Unfortunately there is no growth temperature database or easily downloadable dataset encompassing the majority of cultured microorganisms. We are thus limited in interpreting genomic data to identify temperature adaptations in microbes.
In this work I significantly contribute to closing this gap by mining data from major culture collection centres to obtain growth temperature data for a nonredundant set of 21,498 microbes. The dataset ( https://doi.org/10.5281/zenodo.1175608 ) contains mainly bacteria and archaea and spans psychrophiles, mesophiles, thermophiles and hyperthermophiles. Using this data a full 43% of all protein entries in the UniProt database can be annotated with the growth temperature of the species from which they originate. I validate the dataset by showing a Pearson correlation of up to 0.89 between growth temperature and mean enzyme optima, a physiological property directly influenced by the growth temperature. Using the temperature dataset I correlate the genomic occurance of enzyme functional annotations with growth temperature. I identify 319 enzyme functions that either increase or decrease in occurrence with temperature. Eight metabolic pathways were statistically enriched for these enzyme functions. Furthermore, I establish a correlation between 33 domains of unknown function (DUFs) with growth temperature in microbes, four of which (DUF438, DUF1524, DUF1957 and DUF3458_C) were significant in both archaea and bacteria.
The growth temperature dataset enables large-scale correlation analysis with enzyme function- and domain-level annotations. Growth-temperature dependent changes in their occurrence highlight potential evolutionary adaptations. A few of the identified changes are previously known, such as the preference for menaquinone biosynthesis through the futalosine pathway in bacteria growing at high temperatures. Others represent important starting points for future studies, such as DUFs where their occurrence change with temperature. The growth temperature dataset should become a valuable community resource and will find additional, important, uses in correlating genomic, transcriptomic, proteomic, metabolomic, phenotypic or taxonomic properties with temperature in future studies.
所有栖息地的环境温度是塑造栖息其中微生物生物学的关键物理特性。因此，微生物的最适生长温度（OGT）是理解其基因组序列中表现出的进化适应的关键数据。不幸的是，没有涵盖大多数培养微生物的生长温度数据库或可下载数据集。因此，我们在解释基因组数据以识别微生物的温度适应方面受到限制。
在这项工作中，我通过从主要培养中心挖掘数据来显著促进缩小这一差距，从而获得了 21498 个非冗余微生物的生长温度数据。该数据集（https://doi.org/10.5281/zenodo.1175608）主要包含细菌和古菌，涵盖了嗜冷菌、中温菌、嗜热菌和超嗜热菌。使用该数据集，UniProt 数据库中多达 43%的所有蛋白质条目可以用其来源物种的生长温度进行注释。我通过显示生长温度与酶最适温度之间高达 0.89 的 Pearson 相关系数来验证数据集，酶最适温度是直接受生长温度影响的生理特性。使用温度数据集，我将酶功能注释的基因组出现与生长温度相关联。我确定了 319 种随着温度增加或减少的酶功能。这 8 条代谢途径在这些酶功能中具有统计学意义。此外，我确定了微生物中 33 个未知功能域（DUFs）与生长温度之间的相关性，其中 4 个（DUF438、DUF1524、DUF1957 和 DUF3458_C）在古菌和细菌中都具有统计学意义。
生长温度数据集可实现与酶功能和域级注释的大规模相关分析。其发生的生长温度依赖性变化突出了潜在的进化适应。一些已确定的变化是已知的，例如在高温下生长的细菌中通过 futalosine 途径偏好menaquinone 生物合成。其他变化则代表了未来研究的重要起点，例如 DUFs，其发生随着温度的变化而变化。生长温度数据集应该成为一个有价值的社区资源，并将在未来的研究中在将基因组、转录组、蛋白质组、代谢组、表型或分类属性与温度相关联方面找到更多重要的用途。
