Global microbial DNA signatures of temperature and nutrient limitation across ecosystems.

Microbial genomes continuously adapt to environmental conditions, but identifying universal signatures of adaptation remains challenging. Here we show that environmental temperature can be accurately predicted across ecosystems from DNA composition alone (R2 = 0.75), using tetranucleotide frequencies from 1,235 marine and soil metagenomes and a machine learning approach. This predictive signal was also apparent within individual taxa, consistent with a fundamental temperature-associated signature. By contrast, GC content exhibited opposite correlations with temperature in soil (positive) and marine (negative) environments. This phenomenon was probably driven by differences in nutrient availability, as GC content increases with nutrients while nutrients decrease with temperature in marine samples. By integrating these observations, we identified specific tetranucleotides, with 50% GC, that displayed consistent and robust temperature correlations across environments and may have contributed to the stability of predictions. This work highlights metagenome-wide DNA-temperature associations, relevant for understanding microbial community responses to global changes.