Mapping ars gene clusters in arsenic-resistant bacteria
An operon by any other name
Almost any operon (a unit of DNA that contains a number of genes controlled by a single messenger molecule) that acts against arsenic tends to have three major genes – arsR, arsB, and arsC. These code for a transcriptional regulator, a transmembrane efflux pump, and an arsenate reductase, respectively. The first protein, ArsR, oversees the activation and repression of the operon by preventing its initiation unless arsenic is present. As a result, the operon will not function when arsenic is not present. The second protein, ArsB, sits in the membrane of the bacterial cell. This pump-type protein transports arsenic in its toxic form – arsenite – from inside to outside the cell. The last protein, ArsC, is a reductase enzyme – it reduces the mildly toxic form of arsenic (arsenate) to its highly toxic arsenite ion form. This promotes the expulsion of arsenic from the cell.
Most operons have these three proteins, but they may also have additional proteins that supplement and enhance the function of the operon based on the microbe’s needs and habitat. In a particular bacterium discovered in the Dadri wetlands in India, the ars operon seems to show extreme resistance to arsenic. This bacterium, Deinococcus indicus DR1, belongs to a family of bacteria known to thrive in extreme environments, Deincoccaceae. The operon codes for six proteins – ArsR1 and ArsR2 (the transcriptional regulators) whose genes flank the operon, ArsC2 and ArsC3 (which are Arsenate Reductases), ArsB (the transmembrane efflux pump), and MPase (a metallophosphatase family protein). D. indicus DR1 also has an additional Arsenate Reductase (ArsC1) enzyme located outside the operon.
D. indicus DR1 in the wild
The six proteins found in D. indicus DR1 had not previously been studied, and so their structures and functions were relatively unknown. Previous studies could help inform educated guesses as to their function, but the real reason for their extreme efficiency could not be adequately explained. To understand the respective functions of each protein, it was important to discern their structures. A recent study set out to fully understand the structure and behaviour of these proteins, using deep-learning approaches to predict the structure.
Once the structures of the proteins were predicted through the use of deep-learning techniques, the study focused on the natural behaviour of the proteins. To answer this question of how the proteins would behave ‘in the wild’, they were simulated as close to the situations that would be found in nature. The study then looked at how different the starting and final structures of the simulation were to see if the predicted structure performed well. A final structure that differs very little from the initial structure is considered well-modelled and stable. Fortunately, all six structures performed really well – indicating that they had been predicted accurately by the deep-learning method. And more importantly, they had maintained their predicted conformations, more or less, throughout the simulations, which indicated that those conformations are the most preferred conformations.
To hypothesise how these proteins work together to render the bacterium D. indicus DR1 highly resistant to arsenic, the study focused on previous literature and phylogeny (the history of a species’ evolution, particularly when taken in context of line of descent or relationships with other similar organisms). The structural sequences of these proteins were compared to similar looking proteins to investigate similarities – or dissimilarities – between these proteins and their counterparts in other organisms. The study noted that these proteins did not differ greatly from their homologs – many of the active site residues, which are the ones that carry out the main function of the protein, and the structural fold were highly similar to the homologous proteins’ as well.
Furthermore, the study observed the strong possibility of these proteins existing as dimers or tetramers, instead of the predicted monomers. This shows that these proteins are related to other high-functioning proteins, but ones that are usually associated with effusing different heavy metals – not arsenic. Such a model, where the bacterium evolves towards extreme performance by moving towards efficient systems seen in other operons, seems to provide a plausible explanation.
In conclusion, the structural and behavioural predictions of these six proteins coded by the ars operon in D. indicus DR1 are accurate and stable. It is also possible that the proteins have a mechanism of action that may be skeletally similar to that observed in already studied models such as that seen in E. coli or S. aureus, but may differ in the way they interact and bring about an effect much higher in efficiency, due to the selective pressure of arsenic in the environment.
References1. Ranganathan, S, et al (2023) Structural and functional mapping of ars gene cluster in Deinococcus indicus DR1. Computational and Structural Biotechnology, 21: 519-534.
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