1.
KO enrichment in Polar viral genomes and pathways
“Polar”, “Nonpolar”, or “Unknown” biome niche was assigned to each
viral genome based on presence/absence and overrepresentation
(“Biome and size niche” section). For individual lineages at four taxonomic levels (root, main group, family, and genus), the enrichment of a
given KO in Polar genomes assessed using Fisher’s exact test in SciPy
v.1.7.190. Adjustments for multiple testing were performed using the
Benjamini-Hochberg (BH). The significance threshold was set to a
corrected P value of 0.05.
Polar-specific KOs were defined as those with a temperature
optimum below 10 °C and a latitude optimum above 50°. For pathways
with at least half of the detected KOs as polar-specific KOs, we compared the fraction of components (i.e., enzymes) defined as polarspecific KOs with the fraction of all other pathways. This fraction was
tested by the Fisher’s exact test and adjusted by the BenjaminiHochberg (BH). This analysis excluded rare KOs (observed in fewer
than five genomes). To avoid the enrichment of pathways led by sparse
KOs, the pathways were exhibited only if the number of detected viral
KOs in a given pathway constituted more than 10% of the total number
of KOs in the pathway.
Phylogenetic signal of functions
We hypothesized that the phylogenetic distributions of some polar
specific functions (i.e., “trait distribution”) could be better explained in
part by environment selection rather than only by speciation history.
We therefore compared two models, (i) the Brownian motion model
(Pagel’s lambda = 1, used as the null hypothesis in which the distribution of a trait is simply explained by species tree) and (ii) the Lambda
model (0 ≤ Pagel’s lambda ≤ 1; lambda = 0 corresponds to the lack of
phylogenetic signal in the distribution of a trait), by the likelihood ratio
test using the function “fitContinuous” in an R package “geiger”91. The
p values to reject the null hypothesis were calculated by assuming chisquared distribution with 1 d.f. for the likelihood-ratio test statistic and
adjusted using the BH procedure. The threshold was set to a corrected
p value of 0.05
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Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
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Acknowledgements
This work was supported by JSPS/KAKENHI (18H02279 and 22H00384,
to H.O.), and the Collaborative International Joint Research Program of
the Institute for Chemical Research, Kyoto University (No. 2021-29, 202226 to T.O.D.; No. 2022-27, to S.C.), and the H2020 European Commission
project AtlantECO (award number 862923, to S.C.). Computational time
was provided by the SuperComputer System, Institute for Chemical
Research, Kyoto University. We further thank the Tara Oceans consortium, and the people and sponsors who supported Tara Oceans. Tara
Oceans (including both the Tara Oceans and Tara Oceans Polar Circle
expeditions) would not exist without the leadership of the Tara Expeditions Foundation and the continuous support of 23 institutes (https://
oceans.taraexpeditions.org). This article is contribution number 147 of
Tara Oceans. We thank Gabe Yedid, PhD, from Edanz (http://jp.edanz.
com) for editing a draft of this manuscript.
Author contributions
L.M. and H.O. designed the study. L.M. performed the primary biogeographical analysis. T.O.D completed the genome-resolved metagenomic analysis. M.G. performed phylogenomic analyses. E.P. generated
the reads mapping data. A.F.-G. provided de novo clusters of viral genes.
S.C., R.Y.N., J.W., H.K., H.E. contributed to the bioinformatics analysis. All
the authors contributed to interpreting the data and writing the
manuscript.
Competing interests
The authors declare no competing interests.
Additional information
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