Abstract
Relationships between microbial genes and performance are often evaluated in the laboratory in pure cultures, with little validation in nature. Here, we show that genomic traits related to laboratory measurements of maximum growth potential failed to predict the growth rates of bacteria in unamended soil, but successfully predicted growth responses to resource pulses: growth increased with 16S rRNA gene copy number and declined with genome size after substrate addition to soils, responses that were repeated in four different ecosystems. Genome size best predicted growth rate in response to addition of glucose alone; adding ammonium with glucose weakened the relationship, and the relationship was absent in nutrient-replete pure cultures, consistent with the idea that reduced genome size is a mechanism of nutrient conservation. Our findings demonstrate that genomic traits of soil bacteria can map to their ecological performance in nature, but the mapping is poor under native soil conditions, where genomic traits related to stress tolerance may prove more predictive. These results remind that phenotype depends on environmental context, underscoring the importance of verifying proposed schemes of trait-based strategies through direct measurement of performance in nature, an important and currently missing foundation for translating microbial processes from genes to ecosystems.
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Acknowledgements
This work was supported by the US National Science Foundation (Dimensions of Biodiversity, DEB-1321792) and the US Department of Energy, Program in Genomic Sciences (DE-SC0016207). We thank Jingrun Sun, Zacchaeus Compson, Bri Finley, and Jeff Propster for assistance in the laboratory, and Alicia Purcell for intellectual contributions. Work at Lawrence Livermore National Laboratory (LLNL) was funded by the Department of Energy through the Genome Sciences Program under contracts SCW1024 and SCW1590, and performed under the auspices of LLNL under Contract DE-AC52-07NA27344.
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All authors contributed to the conceptual development of this work. BAH, XJAL, PD, ES, and RLM designed the incubation study. XJAL and RLM performed the incubation study. RLM and MH conducted the molecular analyses. RLM, BJK, ES, BAH, BWS, and EMM analyzed the qSIP data. JL developed and performed the bioinformatics analysis, the meta-analysis of growth estimates of bacteria in the laboratory, and statistical inference tests. EMM, SJB, JPR, and BWS provided additional intellectual input. BAH and JL wrote the manuscript, with editorial contributions from all authors, and particularly substantive contributions from PD, JP, EMM, and SJB.
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41396_2019_422_MOESM1_ESM.docx
Table S1 Sensitivity analyses of Table 1 comparing results across identity thresholds as described in the legend to Supplemental Figure 3.
41396_2019_422_MOESM2_ESM.docx
Table S2 Model selection for predicting bacterial growth rates based on genomic traits. All models have the genetic form: excess atom fraction 18O = copy number * slope1 + genome size * slope2 + intercept. For cases where at least one model was statistically significant, the best model is indicated in bold, underline.
41396_2019_422_MOESM3_ESM.docx
Table S3 Sensitivity analyses of Table 3 comparing results across identity thresholds as described in the legend to Supplemental Figure 3.
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Figure S1 Comparison of estimated 16S rRNA copy number to that from the rrnDB database (https://rrndb.umms.med.umich.edu/). Source data for Supplemental Figure 1 are available at https://bitbucket.org/junhuilinau/manuscript-supplementary/src/master/.
41396_2019_422_MOESM5_ESM.pdf
Figure S2 The distribution of 16S rRNA gene copy number of each OTU used in the analysis at different percent match at the 16S locus. Grey circles represent the 16S rRNA gene copy number of each individual sequence assigned to the respective OTU; Colored circles represent the average 16S rRNA gene copy number of all sequences assigned to the respective OTU, and error bars are the 95% confidence intervals.
41396_2019_422_MOESM6_ESM.pdf
Figure S3 Sensitivity analyses of Figure 1, comparing relationships between growth and copy number, and growth and genome size, for each of the sequence identity thresholds we considered, using thresholds suggested by Yarza et al. (2014), where 94.5% corresponds to genus and 98.7% to species, and 99.5% and 100% are also recommended as match thresholds indicating closer relatedness.
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Li, J., Mau, R.L., Dijkstra, P. et al. Predictive genomic traits for bacterial growth in culture versus actual growth in soil. ISME J 13, 2162–2172 (2019). https://doi.org/10.1038/s41396-019-0422-z
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DOI: https://doi.org/10.1038/s41396-019-0422-z
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