The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli
Open accessAbstract
The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability. The ferric uptake regulator, Fur, is involved in the transcriptional regulation of iron metabolism. Here the authors show that Fur exhibits genome-wide regulatory effects in Escherichia colithat control many fundamental cellular processes linked to iron metabolism.
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
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Andrews, S. C., Robinson, A. K., & Rodríguez-Quiñones, F. (2003) · FEMS Microbiology Reviews · doi:10.1016/s0168-6445(03)00055-x · Cited by 2,702 · cited 1×
“To identify the metabolic pathways regulated by Fur, the members of Fur regulon were mapped to the iron uptake/utilization pathways 2,8,9,29 (Fig.”
Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471
Self-citationRecognition of DNA by Fur: a Reinterpretation of the Fur Box Consensus Sequence
Baichoo, N., & Helmann, J. D. (2002) · Journal of Bacteriology · doi:10.1128/jb.184.21.5826-5832.2002 · Cited by 282 · cited 1×
“The motif search for holo -Fur repressed genes yielded a consensus sequence (TRAKAAYSATTATYA) with canonical Fur boxes containing an internal palindromic 7-1-7 sequence (TGATAATnATTATCA) 25 (Fig.”
Staphylococcus aureus Redirects Central Metabolism to Increase Iron Availability
Friedman, D. B., Stauff, D. L., Pishchany, G., Whitwell, C. W., Torres, V. J., & Skaar, E. P. (2006) · PLoS Pathogens · doi:10.1371/journal.ppat.0020087 · Cited by 222
This Is Not Your Mother's Repressor: the Complex Role of Fur in Pathogenesis
Carpenter, B. M., Whitmire, J. M., & Merrell, D. S. (2009) · Infection and Immunity · doi:10.1128/iai.00116-09 · Cited by 215
Combinatorics of feedback in cellular uptake and metabolism of small molecules
Krishna, S., Semsey, S., & Sneppen, K. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706231105 · Cited by 27
Cited by
Engineering Cellular Metabolism
Nielsen, J., & Keasling, J. D. (2016) · Cell · doi:10.1016/j.cell.2016.02.004 · Cited by 1,378
Seo, S. W., Kim, D., Szubin, R., & Palsson, B. O. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.07.043 · Cited by 237
Self-citationCopper tolerance in bacteria requires the activation of multiple accessory pathways
Giachino, A., & Waldron, K. J. (2020) · Molecular Microbiology · doi:10.1111/mmi.14522 · Cited by 223
Siderophore-mediated iron acquisition and modulation of host-bacterial interactions
Ellermann, M., & Arthur, J. C. (2017) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.10.489 · Cited by 186
Next-generation genome-scale models for metabolic engineering
King, Z. A., Lloyd, C. J., Feist, A. M., & Palsson, B. O. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.12.016 · Cited by 157
Self-citation
Metadata
- Authors
- Seo, S. W., Kim, D., Latif, H., O’Brien, E. J., Szubin, R., & Palsson, B. O.
- Venue
- Nature Communications
- Published
- 2014-09-15
- Volume
- 5
- Issue
- 1
- Pages
- 4910-4910
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2075632982
- Cited by
- 321
- References
- 8
- FWCI
- 10.50
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli. https://app.cochat.ai/papers/10.1038/ncomms5910
- The paper
- Seo, S. W., Kim, D., Latif, H., O’Brien, E. J., Szubin, R., & Palsson, B. O. (2014). Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli. Nature Communications. https://doi.org/10.1038/ncomms5910
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.
