The widely prescribed pharmaceutical metformin and its main metabolite guanylurea are currently two of the most common contaminants in surface and wastewater. Guanylurea often accumulates and is poorly, if at all, biodegraded in wastewater treatment plants. This study describes Pseudomonas mendocina strain GU isolated from a municipal wastewater treatment plant using guanylurea as its sole nitrogen source. The genome was sequenced with 36-fold coverage and mined to identify guanylurea degradation genes. The gene encoding the enzyme initiating guanylurea metabolism was expressed, the enzyme purified and characterized. Guanylurea hydrolase, a newly described enzyme, was shown to transform guanylurea to one equivalent of ammonia and guanidine. Guanidine also supports growth as a sole nitrogen source. Cell yields from growth on limiting concentrations of guanylurea revealed that metabolism releases all four nitrogen atoms. Genes encoding complete metabolic transformation were identified bioinformatically, defining the pathway as follows: guanylurea to guanidine to carboxyguanidine to allophanate to ammonia and carbon dioxide. The first enzyme, guanylurea hydrolase, is a member of the isochorismatase-like hydrolase protein family that includes biuret hydrolase and triuret hydrolase. Although homologs, the three enzymes show distinct substrate specificities. Pairwise sequence comparisons and the use of sequence similarity networks allowed fine structure discrimination between the three homologous enzymes and provided insights into the evolutionary origins of guanylurea hydrolase. IMPORTANCE Metformin is a pharmaceutical most prescribed for type 2 diabetes and is now being examined for potential benefits to COVID-19 patients. People taking the drug pass it largely unchanged and it subsequently enters wastewater treatment plants. Metformin has been known to be metabolized to guanylurea. The levels of guanylurea often exceed that of metformin, leading to the former being considered a “dead end” metabolite. Metformin and guanylurea are water pollutants of emerging concern as they persist to reach non-target aquatic life and humans, the latter if it remains in treated water. The present study has identified a Pseudomonas mendocina strain that completely degrades guanylurea. The genome was sequenced and the genes involved in guanylurea metabolism were identified in three widely separated genomic regions. This knowledge advances the idea that guanylurea is not a dead end product and will allow for bioinformatic identification of the relevant genes in wastewater treatment plant microbiomes and other environments subjected to metagenomic sequencing.

Metformin is used globally to treat type II diabetes, has demonstrated anti-ageing and COVID mitigation effects and is a major anthropogenic pollutant to be bioremediated by wastewater treatment plants (WWTPs). Metformin is not adsorbed well by activated carbon and toxic N-chloro derivatives can form in chlorinated water. Most earlier studies on metformin biodegradation have used wastewater consortia and details of the genomes, relevant genes, metabolic products, and potential for horizontal gene transfer are lacking. Here, two metformin-biodegrading bacteria from a WWTP were isolated and their biodegradation characterized. Aminobacter sp. MET metabolized metformin stoichiometrically to guanylurea, an intermediate known to accumulate in some environments including WWTPs. Pseudomonasmendocina MET completely metabolized metformin and utilized all the nitrogen atoms for growth. Pseudomonas mendocina MET also metabolized metformin breakdown products sometimes observed in WWTPs: 1-N-methylbiguanide, biguanide, guanylurea, and guanidine. The genome of each bacterium was obtained. Genes involved in the transport of guanylurea in Aminobacter sp. MET were expressed heterologously and shown to serve as an antiporter to expel the toxic guanidinium compound. A novel guanylurea hydrolase enzyme was identified in Pseudomonas mendocina MET, purified, and characterized. The Aminobacter and Pseudomonas each contained one plasmid of 160 kb and 90 kb, respectively. In total, these studies are significant for the bioremediation of a major pollutant in WWTPs today.

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Metformin is the most prescribed type 2 diabetes medication in the United States and in many countries worldwide. In diabetes patients, this drug has been shown to alter the gut microbiome resulting in improved glucose metabolism. More recently, metformin has been proposed to have anti‐aging and antiviral properties making the drug a potential candidate to treat other health conditions. Metformin and its proposed “dead‐end” product, guanylurea, are not fully metabolized by humans and enter municipal wastewater where they cannot be removed through conventional water treatment processes. These compounds have been detected in surface waters around the world and are currently considered emerging pollutants. This study examined a bacterial consortium enriched from activated sludge which demonstrated the ability to utilize metformin as a sole source of nitrogen, as well as to degrade metformin to its transformation product, guanylurea. Metagenomic sequencing yielded an 18 Mb assembly distributed over 7,440 contigs with an average GC content of 64%. 16S rRNA analysis suggested the presence of Sphingopyxis, Pseudomonas mendocina, Microbacterium, and Mesorhizhobium species within the consortia. Bioinformatic analysis led to the identification of three relevant enzymes involved in metformin metabolism: guanylurea hydrolase, carboxyguanidine deiminase, and allophanate hydrolase. Biochemical studies revealed that these proteins catalyze the degradation of guanylurea to ammonia and carbon dioxide. Protein sequence analyses and structural modeling studies are currently in progress to identify a candidate gene(s) encoding the enzyme initiating the metabolism of metformin. This research presents the first evidence for a biochemical pathway associated with the microbial degradation of guanylurea. Significantly, it also advances understanding of the microbial capacity for metformin biodegradation. These findings could lead to the development of practical biotechnological applications to improve water treatment processes and provide insight into the effects of metformin on human microbiome metabolism.

Aminoglycosides are broad‐spectrum antibiotics common in clinical, veterinary, and agricultural settings and are often reserved for treating severe bacterial infections. With antibiotic resistance becoming a global crisis, understanding the mechanisms by which bacteria attain such resistance is more urgent than ever. One such mechanism specific to aminoglycosides is ribosomal modification by methyltransferases (RMTases). RMTases pose a significant threat as they grant simultaneous resistance to various aminoglycosides and are transmitted between species. The purpose of this project was to develop a 3D physical model of NpmA, an RMTase that confers blanket resistance to aminoglycoside antibiotics by transferring a methyl group to the A1408 nucleotide in helix 44 of the 30S ribosomal subunit. Methylation of A1408 makes helix 44 unrecognizable to these antibiotics. Database searches and sequence alignments were performed to identify conserved amino acids and structural features important in the catalytic mechanism of NpmA. Details of the protein structure and its interaction with helix 44 were obtained by analyzing the Protein Databank File 4OX9. To construct the physical model, the structure file (4OX9) was imported into Jmol and modified into a format suitable for 3D printing using scripts created by undergraduate researchers. The 3D model features NpmA interacting with rRNA and S‐adenosyl‐L‐methionine, the methyl group donor for the methylation reaction. The physical model also highlights key amino acids such as Arg207, E146, Trp107 and Trp197, which are critical to flip A1408 from helix 44 and position it into the enzyme's active site prior to methylation. A Jmol tutorial was created to complement the 3D model and assess students' learning of the structure and function of NpmA. Initial assessment of this activity showed improvement of students' protein visualization abilities, and computational skills. Future work will focus on field‐testing the complete exercise in a microbiology course to evaluate the impact of the 3D model on students' understanding of the mechanisms of antimicrobial resistance.Support or Funding InformationThis project was supported by NSF‐DUE 1725940 awarded to the CREST (Connecting Researchers, Educators, and Students) ProjectThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.