Back

A novel sulfatase for acesulfame degradation in wastewater treatment plants as evidenced from Shinella strains

Ding, C.; Rohwerder, T.; Liu, Y.; Bonatelli, M. L.; Von Postel, T.; Kleinsteuber, S.; Adrian, L.

2024-03-06 genomics
10.1101/2024.03.04.583314 bioRxiv
Show abstract

The artificial sweetener acesulfame is a persistent pollutant in wastewater worldwide. So far, only a few bacterial isolates were recently found to degrade acesulfame efficiently. In Bosea and Chelatococcus strains, a Mn2+-dependent metallo-{beta}-lactamase-type sulfatase and an amidase signature family enzyme catalyze acesulfame hydrolysis via acetoacetamide-N-sulfonate (ANSA) to acetoacetate. Here, we describe a new acesulfame sulfatase in Shinella strains isolated from German wastewater treatment plants. Their genomes do not encode the Mn2+-dependent sulfatase. Instead, a formylglycine-dependent sulfatase gene was found, together with the ANSA amidase gene on a plasmid shared by all known acesulfame-degrading Shinella strains. Heterologous expression, shotgun proteomics and size exclusion chromatography corroborated the physiological function of the Shinella enzyme as a Mn2+-independent acesulfame sulfatase. Since both the Bosea/Chelatococcus sulfatase and the novel Shinella sulfatase are absent in other bacterial genomes or metagenome assembled genomes, we surveyed 60 tera base pairs of wastewater-associated metagenome raw datasets. The Bosea/Chelatococcus sulfatase gene was regularly found from 2014 on, particularly in North America, Europe and East Asia, whereas the Shinella sulfatase gene was first detected in 2020. The complete Shinella pathway is only present in five datasets from China, Finland and Mexico, suggesting that it emerged quite recently in wastewater treatment facilities. SynopsisA novel sulfatase was identified that hydrolyzes the once recalcitrant xenobiotic acesulfame. Surveying metagenome datasets revealed the recent emergence of gene homologs encoding this sulfatase in wastewater treatment systems worldwide.

Matching journals

The top 7 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.