Back

Distribution of alternative routes for completing denitrification in microbial genomes and metagenomes of global biomes

Pold, G.; Saghai, A.; Jones, C. M.; Hallin, S.

2025-01-08 microbiology
10.1101/2025.01.07.631734 bioRxiv
Show abstract

Diverse microorgani sms can execute one or more steps in denitrification, during which nitrate or nitrite is successively reduced into nitric oxide, nitrous oxide, and ultimately dinitrogen. Many of the best-characterized denitrifiers are "complete" denitrifiers capable of executing all steps in the pathway, but whether they dominate in natural communities and what metabolic traits and environmental factors drive the global distribution of complete vs. partial denitrifiers remains to be determined. To address this, we conducted a comparative analysis of denitrification genes in 61,293 genomes, 3,991 metagenomes covering all major biomes, and 413 terrestrial and aquatic metatranscriptomes. We show that partial denitrifiers outnumber complete denitrifiers and the potential to initiate denitrification is more common than the potential to terminate it, both among genomes and at the community level across most biomes, particularly in nutrient rich environments. These patterns were also reflected in the metatranscriptomes. Our results further indicate that complete denitrifiers are more likely to be fast-growing organisms, favoring organic acid over sugar metabolism, and encoding the ability to oxidize and reduce a broader range of organic and inorganic compounds compared to partial denitrifiers. This suggests complete denitrifiers are metabolically flexible opportunists. Together, our results indicate an environmental footprint on the presence of denitrification genes which favors the genomic potential for partial over complete denitrification in most biomes and highlight that completion of the denitrification pathway is a community effort.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.