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ABSTRACT Sideroxydans species are important chemolithoautotrophic Fe(II)-oxidizing bacteria in freshwater environments and play a role in biogeochemical cycling of multiple elements. Due to difficulties in laboratory cultivation and genetic intractability, the electron transport proteins required for the growth and survival of this organism remain understudied. In Sideroxydans lithotrophicus ES-1, it is proposed that the Mto pathway transfers electrons from extracellular Fe(II) oxidation across the periplasm to an inner membrane NapC/NirT family protein encoded by Slit_2495 to reduce the quinone pool. Based on sequence similarity, Slit_2495 has been putatively called CymA, a NapC/NirT family protein which in Shewanella oneidensis MR-1 oxidizes the quinol pool during anaerobic respiration of a wide range of substrates. However, our phylogenetic analysis using the alignment of different NapC/NirT family proteins shows that Slit_2495 clusters closer to NirT sequences than to CymA. We propose the name ImoA (inner membrane oxidoreductase) for Slit_2495. Our data demonstrate that ImoA can oxidize quinol pools in the inner membrane and is able to functionally replace CymA in S. oneidensis. The ability of ImoA to oxidize quinol in vivo as opposed to its proposed function of reducing quinone raises questions about the directionality and/or reversibility of electron flow through the Mto pathway in S. lithotrophicus. IMPORTANCE Fe(II)-oxidizing bacteria play an important role in biogeochemical cycles. At circumneutral pH, these organisms perform extracellular electron transfer, taking up electrons from Fe(II) outside the cell, potentially through a porin-cytochrome complex in the outer membrane encoded by the Mto pathway. Electrons from Fe(II) oxidation would then be transported to the quinone pool in the inner membrane via periplasmic and inner membrane electron transfer proteins. Directly demonstrating the functionality of genes in neutrophilic iron oxidizers is challenging due to the absence of robust genetic methods. Here, we heterologously expressed a NapC/NirT family tetraheme cytochrome ImoA, encoded by Slit_2495, an inner membrane protein from the Gram-negative Fe(II)-oxidizing bacterium Sideroxydans lithotrophicus ES-1, proposed to be involved in extracellular electron transfer to reduce the quinone pool. ImoA functionally replaced the inner membrane c-type cytochrome CymA in the Fe(III)-reducing bacterium Shewanella oneidensis. We suggest that ImoA may function primarily to oxidize quinol inS. lithotrophicus. 

ABSTRACT Pseudomonas aeruginosa is a human pathogen that relies on quorum sensing to establish infections. The PqsE quorum-sensing protein is required for P. aeruginosa virulence factor production and infection. PqsE has a reported enzymatic function in the biosynthesis of the quorum-sensing autoinducer called PQS. However, this activity is redundant because, in the absence of PqsE, this role is fulfilled by alternative thioesterases. Rather, PqsE drives P. aeruginosa pathogenic traits via a protein-protein interaction with the quorum-sensing receptor/transcription factor RhlR, an interaction that enhances the affinity of RhlR for target DNA sequences. PqsE catalytic activity is dispensable for interaction with RhlR. Thus, the virulence function of PqsE can be decoupled from its catalytic function. Here, we present an immunoprecipitation-mass spectrometry method employing enhanced green fluorescent protein-PqsE fusions to define the protein interactomes of wild-type PqsE and the catalytically inactive PqsE(D73A) variant in P. aeruginosa and their dependence on RhlR. Several proteins were identified to have specific interactions with wild-type PqsE while not forming associations with PqsE(D73A). In the Δ rhlR strain, an increased number of specific PqsE interactors were identified, including the partner autoinducer synthase for RhlR, called RhlI. Collectively, these results suggest that specific protein-protein interactions depend on PqsE catalytic activity and that RhlR may prevent proteins from interacting with PqsE, possibly due to competition between RhlR and other proteins for PqsE binding. Our results provide a foundation for the identification of the in vivo PqsE catalytic function and, potentially, new proteins involved in P. aeruginosa quorum sensing. IMPORTANCE Pseudomonas aeruginosa causes hospital-borne infections in vulnerable patients, including immunocompromised individuals, burn victims, and cancer patients undergoing chemotherapy. There are no effective treatments for P. aeruginosa infections, which are usually broadly resistant to antibiotics. Animal models show that, to establish infection and to cause illness, P. aeruginosa relies on an interaction between two proteins, namely, PqsE and RhlR. There could be additional protein-protein interactions involving PqsE, which, if defined, could be exploited for the design of new therapeutic strategies to combat P. aeruginosa . Here, we reveal previously unknown protein interactions in which PqsE participates, which will be investigated for potential roles in pathogenesis. 

It is often important to determine the source of a microbial strain. Examples include tracking a bacterium linked to a disease epidemic, contaminating the food supply, or used in bioterrorism. Strain identification and tracking are generally approached by using cultivation-based or relatively nonspecific gene fingerprinting methods. Genomic methods have the ability to distinguish strains, but this approach typically has been restricted to isolates or relatively low-complexity communities. We demonstrate that strain-resolved metagenomics can be applied to extremely complex soil samples. We genotypically defined a soil-associated bacterium and identified it as a contaminant. By linking together snapshots of the bacterial genome over time, it was possible to estimate how long the contaminant had been diverging from a likely source population. The results are congruent with the derivation of the bacterium from a strain isolated in Germany and sequenced a decade ago and highlight the utility of metagenomics in strain tracking.