The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
The Mercury Resistance Operon: From an Origin in a Geothermal Environment to an Efficient Detoxification Machine
Open accessAbstract
Mercuric mercury (Hg[II]) is a highly toxic and mobile element that is likely to have had a pronounced and adverse effect on biology since Earth's oxygenation ∼2.4 billion years ago due to its high affinity for protein sulfhydryl groups, which upon binding destabilize protein structure and decrease enzyme activity, resulting in a decreased organismal fitness. The central enzyme in the microbial mercury detoxification system is the mercuric reductase (MerA) protein, which catalyzes the reduction of Hg(II) to volatile Hg(0). In addition to MerA, mer operons encode for proteins involved in regulation, Hg binding, and organomercury degradation. Mer-mediated approaches have had broad applications in the bioremediation of mercury-contaminated environments and industrial waste streams. Here, we examine the composition of 272 individual mer operons and quantitatively map the distribution of mer-encoded functions on both taxonomic SSU rRNA gene and MerA phylogenies. The results indicate an origin and early evolution of MerA among thermophilic bacteria and an overall increase in the complexity of mer operons through evolutionary time, suggesting continual gene recruitment and evolution leading to an improved efficiency and functional potential of the Mer detoxification system. Consistent with a positive relationship between the evolutionary history and topology of MerA and SSU rRNA gene phylogenies (Mantel R = 0.81, p < 0.01), the distribution of the majority of mer functions, when mapped on these phylograms, indicates an overall tendency to inherit mer-encoded functions through vertical descent. However, individual mer functions display evidence of a variable degree of vertical inheritance, with several genes exhibiting strong evidence for acquisition via lateral gene transfer and/or gene loss. Collectively, these data suggest that (i) mer has evolved from a simple system in geothermal environments to a widely distributed and more complex and efficient detoxification system, and (ii) merA is a suitable biomarker for examining the functional diversity of Hg detoxification and for predicting the composition of mer operons in natural environments.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M., & Kumar, S. (2011) · Molecular Biology and Evolution · doi:10.1093/molbev/msr121 · Cited by 39,969
Clustal W and Clustal X version 2.0
Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., Lopez, R., et al. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm404 · Cited by 28,358
APE: analyses of phylogenetics and evolution in R language
Paradis, E., Claude, J., & Strimmer, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg412 · Cited by 11,335
jModelTest: Phylogenetic Model Averaging
Posada, D. (2008) · Molecular Biology and Evolution · doi:10.1093/molbev/msn083 · Cited by 10,499
Picante: R tools for integrating phylogenies and ecology
Kembel, S. W., Cowan, P. D., Helmus, M. R., Cornwell, W. K., Morlon, H., Ackerly, D. D., Blomberg, S. P., & Webb, C. O. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq166 · Cited by 6,243
TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE
Blomberg, S. P., Garland, T., & Ives, A. R. (2003) · Evolution · doi:10.1111/j.0014-3820.2003.tb00285.x · Cited by 4,678
Lateral gene transfer and the nature of bacterial innovation
Ochman, H., Lawrence, J. G., & Groisman, E. A. (2000) · Nature · doi:10.1038/35012500 · Cited by 3,816
ProtTest: selection of best-fit models of protein evolution
Abascal, F., Zardoya, R., & Posada, D. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti263 · Cited by 3,063
The Toxicology of Mercury and Its Chemical Compounds
Clarkson, T. W., & Magos, L. (2006) · Critical Reviews in Toxicology · doi:10.1080/10408440600845619 · Cited by 2,371
Cited by
Metal Resistance and Its Association With Antibiotic Resistance
Pal, C., Asiani, K., Arya, S., Rensing, C., Stekel, D. J., Larsson, D. G. J., & Hobman, J. L. (2017) · Advances in Microbial Physiology · doi:10.1016/bs.ampbs.2017.02.001 · Cited by 438
Genome-Wide Signatures of Adaptation to Extreme Environments in Red Algae
Cho, C. H., Park, S. I., Huang, T.-Y., Lee, Y., Ciniglia, C., Yadavalli, H. C., Yang, S. W., Bhattacharya, D., & Yoon, H. S. (2023) · Nature Communications · doi:10.1038/s41467-022-35566-x · Cited by 83
Bravo, G., Vega-Celedón, P., Gentina, J. C., & Seeger, M. (2020) · Microorganisms · doi:10.3390/microorganisms8121952 · Cited by 48
Kamathewatta, K., Bushell, R., Rafa, F., Browning, G., Billman-Jacobe, H., & Marenda, M. (2020) · Antimicrobial Resistance & Infection Control · doi:10.1186/s13756-020-00828-0 · Cited by 27
Alviz-Gazitua, P., Durán, R. E., Millacura, F. A., Cárdenas, F., Rojas, L. A., & Seeger, M. (2022) · Microorganisms · doi:10.3390/microorganisms10020484 · Cited by 22
Metadata
- Authors
- Boyd, E. S., & Barkay, T.
- Venue
- Frontiers in Microbiology
- Publisher
- Frontiers Media SA
- Published
- 2012
- Volume
- 3
- Pages
- 349-349
- ISSN
- 1664-302X (ISSN-L)
- OpenAlex
- W2109342913
- Cited by
- 286
- References
- 53
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Mercury Resistance Operon: From an Origin in a Geothermal Environment to an Efficient Detoxification Machine. https://app.cochat.ai/papers/10.3389/fmicb.2012.00349
- The paper
- Boyd, E. S., & Barkay, T. (2012). The Mercury Resistance Operon: From an Origin in a Geothermal Environment to an Efficient Detoxification Machine. Frontiers in Microbiology. https://doi.org/10.3389/fmicb.2012.00349
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.